Quantcast
Channel: Biosciences Biotechnology Research Asia
Viewing all 1376 articles
Browse latest View live

A Correlational Study of Hepatic Steatosis (Fatty Liver Disease) and Liver Enzymes (ALT, AST, GGT) In The Scenario of Insulin Resistance Among Young Medicos

$
0
0

Introduction

Nowadays Nonalcoholic fatty liver disease (NAFLD) has become an emerging health issue in adultsand children. It is a condition with similar histology with alcoholic liver disease but without any history of alcohol intake and the disease,the spectrum includes nonalcoholic fatty liver, nonalcoholic steatohepatitis, liver cirrhosis, and hepatocellular carcinoma1-3. In western countries, at least one-quarter of the general population is being affected by NAFLD. Adaptation of sedentary lifestyle like west and increasing frequency of obesity contributes to increased prevalence of NAFLD in the Asia-pacific region over the past two decades4,5and India is no exception to this scenario.6,7. Though it is more common in adults, now it is affecting children and young adults, especially obese ones1.

The pathogenesis of NAFLD has been proposed as a two-hit model by Day and James. The “first hit” takes place with the accumulation of lipids in the hepatocytes and insulin resistance is the main contributor to hepatic steatosis8-10.The “second hit” leads to hepatocyte injury, inflammation, and fibrosis. Oxidative stress and subsequent lipid peroxidation, pro-inflammatory cytokines, adipokines, and mitochondrial dysfunction are behind the “second hit”8.

NAFLD is often diagnosed after the finding of mildly abnormal LFTs. It happens to be the most common cause of elevated Transaminases other than viral and alcoholic hepatitis. However, liver Transaminase levels rarely reach beyond 3 or 4 times the upper normal limit. Serum Transaminases elevation is linked with higher body mass index, waist circumference, serum triglycerides, and fasting insulin, and lower HDL cholesterol1,11.The Alanine Transaminases (ALT) levels are higher than the Aspartate Transaminases (AST) levels in most instances1,12. It has also been found that Gamma-Glutamyl-Transferase (GGT) is elevated in persons having NAFLD13,14 and it is frequently associated with insulin resistance and higher BMI15,16.

In healthy individuals, some amount of cytoplasmic enzyme, like ALT, AST (within the reference range) are present in the circulation mainly due to cell leakage. In pathological conditions, Cell injury allows more leakage of cytoplasmic enzymes from cells, but a minimal release of other types of enzymes. Synthesis of GGT also increases in diseased human liver16.The mechanism of release of membrane-bound enzymes such as GGT into the circulation is less well understood. Elevated Serum GGT level probably reflects ongoing chronic inflammation; often associated with low levels of anti-inflammatory hormones e.g. adiponectin or with reduced effectiveness of insulin as a modulator of cytokine action.17,18 GGT is often found to be high in hepatic steatosis associated with insulin resistance presenting with chronic inflammation due to oxidative stress.17

The medicos who are nowadays habituated toa sedentary lifestyle are the population at risk because of the intense stress they go through before and after joining the curriculum. So they easily fall prey to metabolic derangement which has long-term adverse effects. But these harms can be prevented early, simply by changing lifestyle, regular exercise, weight reduction19,20. Justification for selecting young medicos lies in the fact that; as they are representing the medical fraternity as well as the youth, through their awareness about the risk factors of NAFLD, can be spread to society so that more severe complications developing from this can be avoided.

In this context, the present study is aiming to correlate hepatic steatosis with IR, ALT, AST, and GGT and to find out the Enzyme better correlating with hepatic steatosis than others in the Scenario of Insulin Resistance among young medicos of North Bengal Medical College and Hospital.

Materials and Methods

STUDY DESIGN: The present study is an institution-based observational study with a mixed design for a one-year duration.The study was conducted after approval from the Ethics committee of North Bengal Medical College and Hospital from April-2015 to March-2016 in the department of Biochemistry of North Bengal Medical College and hospital.

132 apparently healthy young medicos of North Bengal Medical College aged between 18-25 years were included in the study after detailed history taking. Informed consent was taken from all the subjects under the study.

Inclusion Criteria

AGE: 18-30 years

GENDER: both male and female.

Both under- graduate& post-graduate of North Bengal Medical College.

Apparently healthy individuals

Exclusion Criteria

Exclusion criteria for selection are the following Persons with a history of Habitual alcohol intake (Daily ingestion of less than 20 g ethanol has been suggested as the maximum alcohol intake compatible with a diagnosis of NAFLD) 16. Smoking, any other addiction or drug abuse like Cannabis, Opioids, Sedatives, etc. Any medication like lipid-lowering drugs, Paracetamol, Methotrexate, Barbiturates, Oral Contraceptives, Persons with any kind of liver, renal, or thyroid disorders, cardiovascular diseases (e.g. h/o Acute myocardial infarction, ischemic heart disease etc.), chronic obstructive pulmonary disease, pancreatic pathologies, gall bladder pathologies, febrile illness, Acute inflammatory conditions like Juvenile Rheumatoid Arthritis, Systemic Lupus Erythematosus, etc.

For all the subjects, thorough physical examinations including anthropometry were performed. Among laboratory investigations, fasting blood glucose and fasting insulin, liver enzymes were of paramount importance as per the study was concerned. 5-6 ml fasting blood sample was collected. Fasting blood glucose, ALT, AST were measured in an automated analyzer(Transasia- XL-600) using ERBA XL- system packs. GGT was measured in Transasia-CHEM 5X Semi-automated analyzer using reagents from (CORAL: Clinical systems).Insulin was assayed by the ELISA method using kits from Accubind in Robonik ELISA Washer and Reader. Quality assurance of the parameters under study was maintained by internal quality assurance. Insulin resistance was calculated with the help of the HOMA IR calculator. Ultrasonography was done for every case. Both subcostal and inter-costal scanning were done. Normal liver parenchyma is seen as solid homogenous echotexture which is midway between the renal cortex and pancreatic echogenicity. The findings of hepatic steatosis at sonography include increased echogenicity and sound attenuation.

Analysis of Data

The data obtained were analyzed by SPSS 20 software for Windows. Results obtained were arranged in tabular and graphical forms.

Cut off value of Insulin resistance in this study population is determined by ROC (receiver operating characteristics) curve using the same software. (State variables were: normal denoted by 0 and NAFLD group denoted by 1). The area under the ROC curve ranges from 0.5 to1.

The area under the curve 1 suggests a perfect test, 0.9-0.99 is an excellent one, 0.8-0.89 suggests a good test, 0.7-0.79 is fair test, 0.51-0.69 suggests poor one and <0.5 is a worthless test. Cut off value of IR was determined by fitting the highest value of sensitivity with the lowest value of 1- specificity. (Vide figure-1)

Figure 1: ROC curve for determination of cut-off value of Insulin Resistance Figure 1: ROC curve for determination of cut-off value of Insulin Resistance.

Click here to View figure

Results And Analysis

The study population consisted of 132 medicos aged between 18 to 25 years of North Bengal Medical College. A sample size of 122 was estimated taking prevalence as 10 percent, with 95 percent level of confidence using modified Cochran formula.  We have screened all the medicos of the age group 18-25, of our teaching institution, in the study period and included 132 individuals into our study fitting with study criteria.

From the total number of 132 subjects, 89 (67.4%) were male and 43 (32.6%) were female. Among all the subjects 92 subjects (67.4%) were normal, 30 (25%) had grade 1 fatty change, 10 (7.6%) had grade 2 fatty change. Furthermore,the frequency distribution of the hepatic sonographic findings among male & female subjects was done which is shown in tabular format (vide Table-1). Anthropometric details of the study population is given in a table-2(vide table-2)

Table1: Distribution of gender variable in case of normal findings, Grade 1 fatty change, Grade 2 fatty change

Normal Male Female Total
63

(71%)

29

(67.4%)

92

(69.7%)

Grade 1 fatty grade 19

(20%)

11

(25.6%)

30

(22.7%)

Grade 2 fatty grade 7

(9%)

3

(7 %)

10

(7.6%)

Total 89

(100%)

43

(100%)

132

(100%)

Table 2: Distribution (Mean ± SD) of the following Anthropometric variables in case of normal findings,Grade 1 fatty change, Grade 2 fatty change.

Anthropo-metric Variable

Normal

Mean ± S.D.

Grade1fatty change

Mean ± S.D.

Grade 2 fatty change

Mean ± S.D.

Height (cm) 162.144±8.392 167.054±9.375 169.625±7.227
Weight (kg) 62.430±10.719 74.070±9.327 73.750±6.702

BMI

(kg/ m2)

23.616±2.865 26.357±2.530 25.628±0.940

Using Chi-Square (χ2) test for independence, it was found that there is no significant association between hepatic steatosis and sex distribution p-value-0.238).

For further ease of analysis, both grade -1 and grade-2 fatty change are combined under NAFLD. As subjects having grade 2 fatty liver is less innumber, we have merged both grade 1 and grade 2 fatty liver under the broad heading NAFLD group.

It is observed that in the NAFLD group AST: ALT is 0.9 which is less than 1. This finding is more in favor of a diagnosis of NAFLD than alcoholic liver disease and other advanced liver disease21. In alcoholic liver disease synthesis of a mitochondrial isoform of AST is increased which has longer half-life than the cytoplasmic isoform of AST. So, in alcoholic liver disease, AST to ALT ratio is usually greater than 114.

Cut off value of Insulin resistance in this study population is determined by ROC (Receiver Operating Characteristic) curve. The area under the curve is 0.937 which is nearer to 1. So, the accuracy of the test is very good.The cut-off value of IR in the present study population is determined to be 1.525 by matching the highest value of sensitivity with the lowest value of 1- sensitivity (p-value < 0.001). (Vide figure-1)

Using unpaired student’s t-test it has been found that there are significant differences between the mean values of ALT, AST, GGT, IR in normal subjects and subjects having NAFLD. Difference is significant at the (p < 0.001) level (Vide table-3).

Table 3: Comparison of the mean values of Liver enzymes (ALT, AST, GGT) & IR (HOMA-IR) between Normal and NAFLD group (unpaired Student’s t test)

 

Parameters

Normal (92)

Mean ±SD

NAFLD (40)

Mean ± SD

 

t value

 

P value

ALT (IU/ lt) 28.599 ±15.491 51.450 ±18.134 -7.389 <0.001**
AST (IU/ lt) 27.323 ±15.626 43.675 ±18.148 -5.257 <0.001**
GGT (IU/ lt) 12.349 ± 4.878 23.168 ± 11.179 -7.763 <0.001**
IR (HOMA-IR) 1.164 ± 0.548 2.057± 0.595 -8.389 <0.001**

Another grouping was done using the cutoff value of Insulin Resistance (1.525) among study subjects and significant differences are found between the mean values of ALT, AST, and GGT among the two groups. (I.e. without Insulin Resistance and Those who have Insulin Resistance more than the Cut-Off). Difference is significant at the (p < 0.001) level (Vide table-4).

Table 4: Comparison of the mean values of Liver enzymes (ALT, AST, GGT) between groups (IR ≥ 1.525) & (IR<1.525) (unpaired Student’s t test).

Parameters IR ≥ 1.525 (42) Mean ±SD IR<1.525 (90) Mean ± SD t value P value
ALT (IU/ lt) 51.024 ±18.130 28.291 ±15.340 7.476 <0.001**
AST(IU/ lt) 43.905 ±18.319 26.852 ±15.152 5.627 <0.001**
GGT(IU/ lt) 22.725± 11.012 12.316±5.002 7.485 <0.001**

By both Kendall’s tau_b and Spearman’s rho correlation test, it is found that there is significant positive concordance between liver enzymes ALT, GGT, and hepatic steatosis in subjects having insulin resistance ≥ 1.525 (Vide table-5).

Table5: Showing correlation between Hepatic Steatosis& Liver enzymes (ALT, AST, GGT) in subjects having insulin resistance ≥ 1.525

  Hepatic

  Steatosis

  Kendall’stau_b   Correlation coefficient (r)

ALT AST GGT

0. 350**

 

0.140 0.426**

Significance

(p) 2- tailed

0.007 0.281 0.001
Spearman’s rhoCorrelation coefficient (r) 0.421** 0.169 0.514**
Significance

(p) 2- tailed

0.005 0.286 0.001

Binary logistic regression analysis with stepwise adjustment added more strength to the findings of the correlation study. It was found that the odds of developing hepatic steatosis with One-unit higher ALT values in the total study population (n = 132) was 1.072 whereas for GGT it was 1.3, with the predictability 78.8 % vs 85.6 % for ALT to GGT. When the statistical model further narrowed that down to the population with Insulin Resistance (i.e. n = 42), the Odds for GGT increases significantly to 1.532 (p = 0.03) with the predictability of developing hepatic fatty changes 92.29% which is better than the same adjustment for ALT i.e. 90.2%. Hence it can be said that within our study population, in presence of insulin resistance, subjects having higher GGT values, rather ALT, can possess a greater risk of developing Steatotic changes, comparing the Odds (GGT to ALT 1.532 vs 1.067) (vide table-6).

Table 6: Showing Odds ratio for development of fatty changes in different statistical models using Binary Logistic regression.

Parameters Odds for Developing Hepatic Fatty changes Significance

(P value)

Nagelkerke Rfor Model Summary Predictability Percentage

ALT (n= 132)

1.072 < 0.001 0.376

78.8%

GGT(n= 132)

1.3 < 0.001 0.469

85.6%

ALT in presence of IR

( > 1.525)

(n= 42)

1.067 0.19 0.525 90.2%
GGT in presence of IR

( > 1.525)

(n= 42)

1.532 0.03* 0.572 92.29%

Discussion

Prevalence of NAFLD is increasing in adolescents and the young population nowadays. Previous studies have been shown that NAFLD may advance to more severe hepatic conditions like cirrhosis, liver failure, and hepatocellular carcinoma. A strong association of NAFLD and metabolic syndrome has been found1-3so that often NAFLD is addressed as a hepatic presentation of metabolic syndrome.

NAFLD also represent as an important self-governing risk for the development of Cardiovascular disease (CVD). Several recent longitudinal studies have shown that CVD and atherosclerosis are important causes of morbidity and mortality in patients with NAFLD14. The liver is the center forthe production of classical biomarkers of inflammation and endothelial dysfunction. It has been shown that fibrinogen and CRP levels, which, known CVD risk factors, are increased in NAFLD patients, particularly in those with NASH 22.

Insulin resistance plays a major role in both NAFLD and metabolic syndrome. Both peripheral and hepatic insulin resistance is present in patients with NAFLD, irrespective of the coexistence of impaired glucose tolerance or obesity. Insulin resistance contributes to increased blood glucose level which in turn produces free fatty acids (FFA). Excess FFAs are not taken up by peripheral adipocytes and myocytes, instead of stored as diacyl and triacyl-glycerol in hepatocytes leading to the development of steatosis 23. Such Insulin resistance is regulated by both genetic and acquired factors which in turn influence the complications developed from insulin resistance19. It is associated with many serious medical conditions such as type 2 diabetes mellitus, cerebrovascular and coronary artery diseases, neurodegenerative disorders, etc. The association between insulin resistance and increased cardiovascular disease is mediated mainly at the genetic level. Insulin resistance leading to impaired nitric oxide-mediated vasorelaxation may contribute to hypertension and increased risk of atherosclerosis24.

The study population includes 132 medicos aged between 18 to 25 years. Details of the demographic distribution of the study population are discussed in the results and analysis section.

Though Ludwig in the definition of NAFLD stated that it is more common in women than in men25and in older studies also NAFLD was more frequent in women, the present study is not in agreement with this finding. In the present study, it has been seen that there is no significant association between hepatic steatosis and sex distribution.“TheDionysos study” also states that gender is not a risk factor for NAFLD in the general population1,4.

In the present study, it has been observed that healthy individuals had lower values of AST, ALT, GGT& IR than subjects having hepatic steatosis.  [ (AST- 27.323 ± 15.626 vs 43.675 ± 18.148 IU/lt), (ALT- 28.599 ± 15.491 vs 51.450 ± 18.134 IU/lt), (GGT- 12.349 ± 4.878 vs 23.168 ± 11.179 IU/lt), (IR- 1.164± 0.548 vs 2.057 ± 0.595). The difference between the mean values of AST, ALT, GGT & IR among the two groups is significant at the level of P-value <0.01.

The further grouping was done using the cutoff value of Insulin Resistance (1.525) and a significant difference was found between the means values of ALT, AST, GGT among two groups (i.e. with Insulin Resistance ≥ 1.525 and those having Insulin Resistance < 1.525). [ (AST- 43.905 ± 18.319 vs 26.852 ± 115.152 IU/lt), (ALT- 51.024 ± 18.130vs 28.291± 15.340 IU/lt), (GGT- 22.725 ± 11.012 vs 12.316 ± 5.002 IU/lt)] The difference between the mean values of AST, ALT&GGT among two groups are significant at the level of P-value <0.01.

A good number of studies were conducted in western countries as well as in India which established that NAFLD is frequently associated with higher values of liver enzymes. R. Haring, H. Wallaschofski et al. in 2009 found in their study that GGT is frequently elevated in NAFLD and may also be a marker of increased mortality13. S. Akila, R. Deepti et al also concluded in their study in 2014 that NAFLD with MetS had increased serum GGT level 26. Both studies support the finding of the present study. Iqbal MurshedKabir et al also found in their study that the mean values of ALT and AST were much higher than the reference range in patients with NAFLD12. But the study of A. Wieckowska, A.E. Feldstein et al contradict the finding of the present study. In their study, more than two-thirds of NAFLD patients were found to have normal aminotransferase levels27. In another study, it was found that the entire histological spectrum of NAFLD can be observed in patients with normal ALT values28.In many previous studies, it was reported that MetS which is also known as Insulin Resistance syndrome is associated with abnormal liver function tests.  AST, ALT, and GGT levels are high in patients of MetS, especially with high BMI15.Results from the cross-sectional study of S. Perera, V. Lohsoonthorn is also unison29.

Further statistical analysis has been done to find out is there any Enzyme better correlating with hepatic steatosis than others in the Scenario of Insulin Resistance and an interesting finding came into the light. Let’s explore that in further discussion.

When both Kendall’s tau_b and Spearman’s rho correlation test performed it was found that there is significant concordance (p value<0.01) between liver enzymes ALT and GGT and hepatic steatosis in subjects having insulin Resistance (>1.525) but not with AST (p-value = 0.286). The correlation coefficient (i.e. r) is found to be higher incase of GGT than ALT in both the test (vide Table-5).The finding implies that GGT has more positive concordance with NAFLD than ALT in the scenario of insulin resistance.

Having ALT and GGT positively correlating with hepatic steatosis in presence of insulin resistance, further Binary Logistic Regression with stepwise adjustment has been performed to ascertain which one of them predicts better about the development of fatty changes in this scenario. It has been observed that with Higher GGT values stronger predictability was found with hepatic steatosis than ALT among subjects having insulin resistance > 1.525 (p-value 0.03). A very few studies are found to directly or indirectly support the finding of the present study. R. Haring, H. Wallaschofski et al. in 2009 found in their study that GGT is frequently elevated in NAFLD and may also be a marker of increased mortality13. S. Akila, R. Deepti et al also concluded in their study in 2014 that NAFLD with MetS had increased serum GGT level26. Oxidative stress is pretty high in hepatic steatosis mediated by fat accumulation inside hepatocytes and been associated with hepatic insulin resistance. GGT is a cell-surface enzyme which primarily maintains intracellular defense against oxidative stress. So GGT is often found to be chronically elevated in NAFLD. Increased GGT level not only reflects the hepatic oxidative stress but also its association with insulin resistance. In studies of Koushik GG, Sharm S et al. (2009), it was concluded there is a significant positive correlation between GGT and Insulin resistance among all the liver enzymes, and monitoring GGT and fasting insulin levels might help to prevent the development of diabetes in obese children30. In a few previous studies elevated GGT level has been reported to be of prognostic significance of coronary artery disease which is an important complication of long-standing NAFLD. A positive correlation between elevated GGT level and Framingham cardiovascular risk scoring system has also been observed.Tara M. WallaceKristina M. Utzschneide et al also found in their study, that GGT was positively correlated with hepatic steatosis and associated with insulin sensitivity and glucose tolerance in both men and women. They also concluded that although GGT has been widely used as a specific biomarker of alcoholic liver disease, it has recently been found to be related to an increased risk of development of type 2diabetes,  irrespective of alcohol intake as well as an increased risk of hypertension and cardiovascular mortality31.

Conclusion

Not only a good positive correlation found between Liver enzymes ALT and GGT with hepatic steatosis but also an interesting finding was established that subjects having higher GGT values rather than ALT possessa greater risk of developing steatohepatic changes in insulin-resistant background. Such observations add an extra edge to the pathophysiological understanding of NAFLD.

Although GGT has been widely used as a marker of severity of alcoholic liver disease, the present study observes that it has a potential to be used as a novel marker for assessing the severity of NAFLD in context with the insulin-resistant condition. On the other-hand, these observations also raise a question that can GGT be considered as the specific marker only for Alcoholic liver disease or it should be generalized as a marker for assessing the severity of fatty liver disease irrespective of the etiopathogenesis.

Considering the present study as a pilot one, further studies can be done on the general population to establish ranges and changes in GGT levels in fatty liver diseases of different etiologies and correlating them with previous observations. Moreover, studies can also be conducted in favor of GGT as a marker for monitoring the risks of cardiovascular, cerebrovascular, and severe hepatic complications in patients of NAFLD along with Insulin Resistance.

Acknowledgement

We take the opportunity to express our sincere gratitude to Dr. Asit Chandra Roy, Associate Professor, Department of Radio-diagnosis, North Bengal Medical College & Hospitalfor his advice and assistance in the radiological data. We also extend our extreme gratefulness to all the medicos who volunteered themselves for our study. This study would not have been possible without their kind co-operations.

Financial Support and Sponsorhip

There are no Financial support

Conflict of Interest

Authors declare there is no conflict of interest

References

  1. Paschos P, Paletas K. Nonalcoholic fatty liver disease and metabolic syndrome. Hippokratia. 2009;13(1):9.
  2. Bugianesi E, Leone N, Vanni E, Marchesini G, Brunello F, Carucci P, Musso A, De Paolis P, Capussotti L, Salizzoni M, Rizzetto M. Expanding the natural history of nonalcoholic steatohepatitis: from cryptogenic cirrhosis to hepatocellular carcinoma. Gastroenterology. 2002;123(1):134-40.
    CrossRef
  3. Adams LA, Lymp JF, Sauver JS, Sanderson SO, Lindor KD, Feldstein A, Angulo P. The natural history of nonalcoholic fatty liver disease: a population-based cohort study. Gastroenterology. 2005;129(1):113-21.
    CrossRef
  4. Bedogni G, Miglioli L, Masutti F, Tiribelli C, Marchesini G, Bellentani S. Prevalence of and risk factors for nonalcoholic fatty liver disease: the Dionysos nutrition and liver study. Hepatology. 2005;42(1):44-52.
    CrossRef
  5. Singh SP, Singh A, Misra D, Misra B, Pati GK, Panigrahi MK, Kar SK, Bhuyan P, Pattnaik K, Meher C, Agrawal O. Risk factors associated with non-alcoholic fatty liver disease in Indians: a case–control study. Journal of clinical and experimental hepatology. 2015;5(4):295-302.
    CrossRef
  6. Singh SP, Nayak S, Swain M, Rout N, Mallik RN, Agrawal O, Meher C, Rao M. Prevalence of nonalcoholic fatty liver disease in coastal eastern India: a preliminary ultrasonographic survey. Tropical gastroenterology: official journal of the Digestive Diseases Foundation. 2004;25(2):76-9.
  7. Madan K, Batra Y, Gupta SD, Chander B, AnandRajan KD, Tewatia MS, Panda SK, Acharya SK. Non-alcoholic fatty liver disease may not be a severe disease at presentation among Asian Indians. World journal of gastroenterology. 2006;12(21):3400.
    CrossRef
  8. Day CP. james OF. Steatohepatitis: a tale of two “hits. 1998; 842-5.
    CrossRef
  9. Bajaj S, Nigam P, Luthra A, Pandey RM, Kondal D, Bhatt SP, Wasir JS, Misra A. A case-control study on insulin resistance, metabolic co-variates & prediction score in non-alcoholic fatty liver disease. Indian Journal of Medical Research. 2009;129(3):285.
  10. Sanyal AJ, Campbell–Sargent C, Mirshahi F, Rizzo WB, Contos MJ, Sterling RK, Luketic VA, Shiffman ML, Clore JN. Nonalcoholic steatohepatitis: association of insulin resistance and mitochondrial abnormalities. Gastroenterology. 2001;120(5):1183-92
    CrossRef
  11. Patell R, Dosi R, Joshi H, Sheth S, Shah P, Jasdanwala S. Non-alcoholic fatty liver disease (NAFLD) in obesity. Journal of clinical and diagnostic research: JCDR. 2014;8(1):62.
    CrossRef
  12. Kabir IM, Alam M, Mahmuduzzaman M, Al Mamoon A, Ahmed MU, Safwath SA. Correlation between bright echogenic liver, elevated liver enzymes and liver histology. Journal of Dhaka National Medical College & Hospital. 2011;17(1):8-13.
    CrossRef
  13. Haring R, Wallaschofski H, Nauck M, Dörr M, Baumeister SE, Völzke H. Ultrasonographic hepatic steatosis increases prediction of mortality risk from elevated serum gamma‐glutamyltranspeptidase levels. Hepatology. 2009;50(5):1403-11.
    CrossRef
  14. Ghouri N, Preiss D, Sattar N. Liver enzymes, nonalcoholic fatty liver disease, and incident cardiovascular disease: a narrative review and clinical perspective of prospective data. Hepatology. 2010;52(3):1156-61.
    CrossRef
  15. Hsieh MH, Ho CK, Hou NJ, Hsieh MY, Lin WY, Yang JF, Chiu CC, Huang JF, Chang NC, Wang CL, Dai CY. Abnormal liver function test results are related to metabolic syndrome and BMI in Taiwanese adults without chronic hepatitis B or C. International journal of obesity. 2009;33(11):1309.
    CrossRef
  16. Burtis CA, Ashwood ER, Bruns DE. Tietz textbook of clinical chemistry and molecular diagnostics-e-book. Elsevier Health Sciences; 2012.
  17. Kaushik GG, Sharm S, Sharma R, Mittal P. Association between gamma glutamyl transferase and insulin resistance markers in healthy obese children. J Assoc Physicians India. 2009;57;695-8.
  18. Campos SP, Baumann H. Insulin is a prominent modulator of the cytokine-stimulated expression of acute-phase plasma protein genes. Molecular and cellular biology. 1992;12(4):1789-97.
    CrossRef
  19. Sivapackianathan R, Asivatham AJ, Al-Mahtab M, Chowdhury TA. Association between non-alcoholic fatty liver disease and metabolic syndrome. International Journal of Hepatology. 2010;1(4):17-24.
  20. Bae JC, Suh S, Park SE, Rhee EJ, Park CY, Oh KW, Park SW, Kim SW, Hur KY, Kim JH, Lee MS. Regular exercise is associated with a reduction in the risk of NAFLD and decreased liver enzymes in individuals with NAFLD independent of obesity in Korean adults. PloS one. 2012;7(10)e46819.
    CrossRef
  21. Angulo P, Keach JC, Batts KP, Lindor KD. Independent predictors of liver fibrosis in patients with nonalcoholic steatohepatitis. Hepatology. 1999;30(6):1356-62.
    CrossRef
  22. Yoneda M, Mawatari H, Fujita K, Iida H, Yonemitsu K, Kato S, Takahashi H, Kirikoshi H, Inamori M, Nozaki Y, Abe Y. High-sensitivity C-reactive protein is an independent clinical feature of nonalcoholic steatohepatitis (NASH) and also of the severity of fibrosis in NASH. Journal of gastroenterology. 2007;42(7):573-82.
    CrossRef
  23. Day C. Non-alcoholic steatohepatitis (NASH): where are we now and where are we going?Gut 2002; 50: 585-88.
    CrossRef
  24. Abel ED, O’Shea KM, Ramasamy R. Insulin resistance: metabolic mechanisms and consequences in the heart. Arteriosclerosis, thrombosis, and vascular biology. 2012;32(9):2068-76.
    CrossRef
  25. Ludwig J, Viggiano TR, Mcgill DB, Oh BJ. Nonalcoholic steatohepatitis: Mayo Clinic experiences with a hitherto unnamed disease. Mayo Clinic Proceedings 1980; (Vol. 55, No. 7, pp. 434-438).
  26. Akila S, Deepti R, Ramesh L, Saritha B. The impact of increased serum GGT levels and NAFLD on the components of Metabolic Syndrome. Journal of current trends in clinical medicine & laboratory Biochemistry. 2014;2(4):34-38.
  27. Wieckowska A, Feldstein AE. Diagnosis of nonalcoholic fatty liver disease: invasive versus noninvasive. InSeminars in liver disease 2008; (Vol. 28, No. 04, pp. 386-395).
    CrossRef
  28. Mofrad P, Contos MJ, Haque M, Sargeant C, Fisher RA, Luketic VA, Sterling RK, Shiffman ML, Stravitz RT, Sanyal AJ. Clinical and histologic spectrum of nonalcoholic fatty liver disease associated with normal ALT values. Hepatology. 2003;37(6):1286-92.
    CrossRef
  29. Perera S, Lohsoonthorn V, Jiamjarasrangsi W, Lertmaharit S, Williams MA. Association between elevated liver enzymes and metabolic syndrome among Thai adults. Diabetes & Metabolic Syndrome: Clinical Research & Reviews. 2008;2(3):171-8.
    CrossRef
  30. Kaushik GG, Sharm S, Sharma R, Mittal P. Association between gamma glutamyl transferase and insulin resistance markers in healthy obese children. J Assoc Physicians India. 2009;57;695-8.
  31. Wallace TM, Utzschneider KM, Tong J, Carr DB, Zraika S, Bankson DD, Knopp RH, Kahn SE. Relationship of liver enzymes to insulin sensitivity and intra-abdominal fat. Diabetes care. 2007;30(10):2673-8.
    CrossRef

 


Bio-degradation of Azo Dye Acid Orange-10 by a New Isolate of Bacillus subtilis Isolated from Soil Sample around Textile Industry in South Karnataka

$
0
0

Introduction

Synthetic dyes are important industrial coloring agents. Dyes are characterized by chromophore bunches in their compound structures and are primarily grouped as azo colors, anthraquinone colors, phthalocyanine dyes etc.1 They are used as scatter colors for polyester and reactive dyes for cotton. The absorbance spectrum of azo dyes lies in the visible region,which can be attributed to theircommon structure, consisting of at least one azo bond (- N=N-).2

Globally, 2.8×105 tons of colored chemical compounds are allowed to drain into the ecological water system each year.3 Azo compounds are the most widely useddyes, on account of their simpler synthesis, concoction dependability and the decent variety of hues accessible when contrasted with normal dyes.4 They are generally utilized intextiles, leather,pharmaceutical,paper, food, makeup, and pharmaceutical industries. Analyses indicate that 10-15% of the coloring agents utilized in coloring process do not bind with the textile strands and get released into effluents.5

Discharge of the dye-containing wastes from different industrial practices into surrounding water bodies is of significant concern, and has many unfavorable consequences, including diminished aquatic photosynthesis, exhaustion of available DO and harmful impact on different life forms. These colored effluents not only cause a disagreeable appearance of water bodies, but alsorelease poisonous colorless amines by breakdown of the dyes, which aremutagenic, carcinogenic and capable of causing other health hazards.6, 7

Different research activities uncovered the utilization of physico-chemical methodology strategies for removal of dyes from the effluents.8,9 These techniques incorporate use of adsorption agents, particle pair extractions, coagulation, and chemical processes, which also posesignificant connected issues, for example, high expense and production of a lot of slime after treatment, disposal of which is cumbersome and hazardous.

Various studies available on utilization of microorganisms for dye degradation in effluents, recommend it to be an eco-friendly and economically viable technique.10, 11 The advantages of microbial/enzymatic methodology also include less slime production and hence more practical.12,13 With this backdrop, the present study was aimed at studying the effect of various physical parameters on azo dye Acid orange-10 decolorization by a new isolate of Bacillus subtilis.

Materials and Methods

Chemicals required

The Acid orange-10 dye was obtained from Shailaja textile industry, Sholapur Maharashtra, India. Dehydrated culture medium obtained from Hi-media and other chemicalsand reagents used are of analytical grade.

Acid Orange-10 Decolorizing Bacteria-Isolation, Screening and Identification

The dye decolorizing bacteria were isolated from the soil samples in and around textile industrial area in South Karnataka. 10 gm of soil sample was suspended in 100 ml of complete medium broth supplemented with Acid orange-10 (100mg/L) individually and acclimatized for 5 days at 30°C at 150 rpm. Bushnell and Haas medium (BHM) containingMgSO4-0.2, K2HPO4-1.0, CaCl2– 0.02, FeCl3-0.05, NH4NO3-1.0 (g l-1) supplemented with glucose and yeast extract (0.1% and 0.05% w/v) respectively,at pH 7.0.

The dye decolorizing bacteria were isolated on BHM agar (pH 7.0) containing Acid orange-10 (100mg/L.) from acclimatized soil sample using serial dilution. All the bacterial isolates were studied by inoculating them in complete medium broth supplemented with dye. The inoculated liquid broth medium was incubated at 30°C /37°C under shaking condition at 150 rpm for 1-5 days. The decolorization was visually observed. The isolates showing considerable decolorization of the dyes were selected for further investigation.  Morphological and biochemical tests for identification of the selected bacterial isolates were based on the Bergey’s Manual of Systematic Bacteriology.14

Decolorization Assay

The decolorized supernatant (aliquots of 2 ml each) at regular intervals of time were collected and subjected to centrifugation at 10000 rpm for 10 min to remove any cells to avoid interference with the spectroscopic measurement. The supernatant was used for spectrophotometric analysis (Shimadzu 1900) at 300-700 nm. The absorbance maximum of Acid orange10 is at 480nm (Fig.1). The efficiency of decolorization was determined using the given formula

D= [A0-A1)/A0 A1] x 100

Where,

D – Decolorization (%)

A0 – Initialabsorbance before decolorization

A1 – Final absorbance after decolorization

Vol17No4_Bio_Hem_fig1 Figure 1: UV-Visible Spectrum of Acid orange-10.

Click here to View figure

Identification of Metabolites

The culture medium after complete decolorization of Acid orange-10 was subjected to centrifugation at 10,000 rpm for 15 min. The 200 ml of the supernatant was taken after bringing pH to 7 and extracted twice with diethylether (500 ml). The extraction was repeated after adjusting the pH of the remaining aqueous layer was brought to 2, using 1N HCl. The acidic and alkaline extracts were combined and evaporated using anhydrous Na2SO4 under a reduced pressure at 30oC. Then the residue was suspended in 0.5ml of methanol and then subjected to TLC.

Thin Layer Chromatography (TLC)

TLC of extracted compounds was performed to identify the components of the decolorized medium. An aqueous slurry of silica gel- G (40% w/v) with binder was coated over a glass plate (200×100×2 mm) dimension for TLC. 10 µl each of standard Acid orange-10 dye and of the extracted fractions was loaded on the glass plate coated with silica gel.The solvent system used for developing the TLC plateconsisted of propanol: water: acetic acid (80:19:01).  The chromatogram was developed by exposing the plate to Iodine vapors. The diazotization and carbylamines test were used for the identification of metabolites.

Standardization of Various Operational and Environment Conditions

Different operational and environmental conditions were standardized, by varying only oneparameter, maintaining the other constant at a time. Following parameters were standardized and their effects were observed on decolorization of Acid orange10. Effect of temperature on decolorization was studied between 20-500C, different pH levels ranging from 4-10 at an interval of 0.5, different inoculum sizes ranging from 1-10% in 100 ml of culture media containing 30 ppm dye and different dye concentration 50-1000 mg/L and shaking rpm in the range of 50-200 rpm.

Bushnell and Haas medium as mentioned earlier was used for all the studies

Results

Isolation, Screening and Identification of Acid Orange-10 Decolorizing Bacteria

Bacterial species capable of decolorizing Acid Orange-10 were isolated from different sources using Bushnell-Hass medium containing 100 ppm dye at 37oC. The isolates efficient in decolorizing Acid orange-10 were selected through visual observation up to 72hrs of incubation. The isolate which took shortest period of 16 hrs for complete decolorization was used for further studies. A series of tests was performed according to the Bergey’s manual to identify the microorganism, the results of which are presented in Table 1. According to the results obtained in these tests, the isolate was identified as Bacillis subtilis.

Table 1: Morphological and Biochemical Characteristics shown by the new isolate of Bacillus subtilis.

Test Result
Gram’s Staining +
Shape Rod
Motility Motile
Indole Production
Methyl Red
Voges-Prausker +
Citrate utilization +
Catalase +
Oxidase

Identification of Metabolic Intermediates: 

The products of dye degradation were separated on TLC plates using solvent system propanol: water: acetic acid (80:19:01). The separated products of TLC plates when exposed to iodine vapors showed two degraded products with 0.75 and 0.56 Rf values, whereas control Rf value is 0.50 (Figure 2). Each degraded product was analyzed by diazotization and carbolamine test, both the degraded compounds with Rf values of 0.75, 0.56 gave positive results, identified and confirmed as aromatic amines.

Figure 2: TLC experiments showing the different Rf values of control dye solution and decolorized samples. Figure 2: TLC experiments showing the different Rf valuesof control dye solution and decolorized samples.

Click here to View figure

Optimization of Different Parameters for Acid orange-10 Decolorization

Effect of Incubation Time

The samples were removed at different intervals of time during incubation viz. 6, 12, 18 and 24 hrs and scanned for λmax of Acid orange-10. The results indicated a gradual disappearance of λmax peak(at 480nm) to complete disappearance in 24 hrs. These observations indicate the disappearance of the Acid orange-10dye in its original form and could be due to modification or breakdown of dye by the bacterium.

Effect of shaking

Aeration may be either favorable or inhibitive towards microbial decolorization of dyes. To study the effect of aeration on decolorization of Acid orange10, Bacillus subtilis cells were cultured in nutrient broth containing dye under static and shaking cultural conditions; while the time taken for complete decolorization at static conditions was 24 hrs, at a shaking speed of 50 rpm, it took 36 hrs for complete decolorization;  further increase in speed of shaking to 100, 150 and 200 rpm, the time taken for 100% decolorization was increased to 48 hrs, 72 hrs and 96 hrs respectively, indicating that the  decolorization process was inhibited upon increase of aeration (Figure 3).

Figure3: Effect of shaking on complete decolorization of Acid orange-10 by the new isolate of Bacillis subtilis Figure3: Effect of shaking on complete decolorization of Acid orange-10  by the new isolate of Bacillis subtilis

Click here to View figure

Effect of Temperature

The temperature between 35-450C was found to be an optimum range for the complete decolorization of Acid orange10. At 400C the complete decolorization (i.e.100%) was found with in the shortest time of 22hrs, hence 40oC could be taken as the optimum. At 200C the decolorization of Acid Orange10 was very slow and it took almost 72 hrs for 100%decolorization. At 600C the time required increased to 84hrs while above 600C complete decolorization did not occur (Figure 4).

Figure 4: Effect of temperature on complete decolorization of Acid orange-10 by the new isolate of Bacillis subtilis Figure 4: Effect of temperature on complete decolorization of Acid orange-10 by the new isolate of Bacillis subtilis

Click here to View figure

Effect of pH

The results of the present study depict that this isolate was capable of 100% decolorization of Acid orange10 at a wide range of pH i.e .6.00 to 9.50. The optimum decolorization occurred at pH 8.50 wherein 300 ppm of the dye was decolorized within 24 hours. At pH 5.5 and below and also at pH 10, 100% decolorization could not be achieved (Fig.5).

Figure 5: Effect of pH on complete decolorization of Acid orange-10 by the new isolate of Bacillis subtilis Figure 5: Effect of pH on complete decolorization of Acid orange-10 by the new isolate of Bacillis subtilis.

Click here to View figure

Effect of Dye Concentration

As the concentration of Acid orange-10 increases the time taken for the complete decolorization also increases. The bacterium decolorized 100 ppm of dye completely (i.e.100%) in 12hrs of incubation whereas 100% decolorization of 200 ppm of Acid orange-10 dye was found at 24 hrs. Further increase in dye concentration to 300, 400, 500, 600 and 700 ppm, the time required for complete decolorization of the azo dye increased to 32, 48, 56, 62 and 72hrs respectively. The isolate decolorized the Acid orange-10 completely up to a maximum concentration of 700 ppm, but the time taken was 72 hrs. A dye concentration above 700 ppm was not completely decolorized even after extended incubation period (Fig.6).

Figure 6: Effect of dye concentration on complete decolorization of Acid orange-10 by the new isolate of Bacillis subtilis Figure 6: Effect of dye concentration on complete decolorization of Acid orange-10 by the new isolate of Bacillis subtilis

Click here to View figure

Effect of Inoculum size

The study of effect of the inoculum size of the newly isolated Bacillus subtilis on the decolorization of Acid Orange-10 (300 ppm) indicated that increase in the inoculum size from 1-10% progressively, decreased the time required for the complete decolorization. The minimum inoculum size of 1% required 37hrs for 100% decolorization of Acid orange-10, and as the inoculum size increased the time taken for complete decolorization of Acid orange-10 dye was decreased from 37hrs at 1% to 17hrs at 10% of inoculum size (Fig.7).

Figure 7: Effect of inoculum size on complete decolorization of Acid orange-10 by the new isolate of Bacillis subtilis Figure 7: Effect of inoculum size on complete decolorization of Acid orange-10 by the new isolate of Bacillis subtilis

Click here to View figure

The isolate showed the characteristics of Bacillis subtillis in the assays performed for identification of the organism as shown in the Table 1. Similar kind of observations were reported by Ponraj et al.15

Decolorization and degradation of azo dyes can be either due to biosorption (adsorption on the microbial biomass) or enzymatic biodegradation. Decolorization of Acid orange10 by Bacillus subtilis in our study was not due to adsorption, because when bacterial cell mass was collected and treated with solvents methanol or chloroform, there was no release of color from the cells. Appearance of aromatic amines in the TLC of decolorized culture supernatant indicates degradation of the dye by the bacterium. Biodegradation of textile azo dyes has been suggested as the best model because of removal of practically all of the dye with minimal disadvantages presented by the physico-chemical procedures. Various studies also emphasize on the utilization of microorganisms for dye containing wastewater treatment as an eco-friendly and cost-effective technique.10, 11

Study of available literature study shows that bacterial degradation of azo dyes involves azo-reductase mediated disruption of azo bond (- N=N-) under anaerobic conditions, leading to formation of colorless, hazardous aromatic amines, which could be removed further.16, 17 Formation of colorless aromatic amines by reductive cleavage of -N=N- (azo) bond is suggested to be the initial step in bacterial degradation of dyes.

Earlier studies have reported Bacillus subtilis18, Aeromonas hydrophilia 19 and Bacillus cereus20 culture capable of degrading azo dyes. Lalnunhlimi and Krishnaswamy report a consortium of different Bacillus species fit for decolorization of direct blue 151 and 31 up to 95%.21 Presence of aromatic amines in TLC in the current study is an indication that decolorization was due to degradation of the Acid orange-10 dye into intermediate products.

Time taken by the bacterial isolatein the present study was much less than previous reports. Jothimani and Prabhakaran report that Pseudomonas and Bacillus species can remove 59% dye from industrial effluents in a period of 14 days.22 Bacterial decolorization of direct blue dye by Bacillus sp. ETL-1979, with an incubation period of 168 hr was reported by Shah et al. 23 96.4% decolorization of black WNN (100mg/L) was obtained in 48 hr by Paenibacillus alvei MTCC 10625.24 Decolorization of direct blue 151 at a concentration of 200 mg/L was reported to be 95.25% in 5 days.21

Inhibition of dye decolorization at higher speed of shaking indicates that decolorization process was inhibited in presence of higher concentration of oxygen whereas lower rpm ort static condition i.e. lower concentration of oxygen was required to enhance decolorization.The reports of current study are in concurrence with results reported by Verma and Madamwar wherein the authors have reported that under static conditions decolorization was high and under agitation, decolorization was negligable.25

Azoreductase is a cytoplasmic enzyme with low specificity, and is responsible for reduction of azo bonds in the azo dyes, degrading them to their corresponding amines. The reaction is slowed down or is inhibited by the presence of O2, since it is a strong terminal electron acceptor compared to the azo groups during the oxidation of reduced electron carriers like NADH.26,27 Under static conditions, electrons required for azo bond cleavage are readily accessible to the enzyme from NADH; while under shaking, the availability of O2 prevents the azoreductase from receiving the electrons.28,29

Kumar and Sawhney reported that Bacillus subtilis (RA 29) caused decolorization of Congo red to the extent of 95.67% at 37°C.30 Bayoumi et al., had reported maximum bacterial decolourization of Acid orange 7 and Direct blue 75occurs at an optimum incubation temperature of 35°C.31

The findings of our study with respect to the effect of pH on decolorization of acid orange-10 are in agreement with other studies in which decolorization of Methyl Red was maximal in pH range of 6-8 by a strain of Micrococcus32. Removal of Acid red 2 and Acid orange 7 by Bacillus 33 and of Acid Orange by a strain of Staphylococcus hominis was also found  to be maximum in the range of 6-8 pH.34 Bayoumi et al., reported that neutral to moderately alkaline pH enhance the removal of azo dyes.31

Karunya et al., reported that the dye concentration at 200mg/l was completely decolorized in 48 hrs by Pseudomonas aeruginosa, but beyond this, the decolorization was not effective.35

The percentage decolorization of azo dye has linear relationship with inoculum size. The results of the current study are in corroboration with the reports of Kumar et al.36 Gurulakshmi et al., reported that maximal decolorization by B.subtilis strain was achieved at 20% inoculum size.37

Conclusion

Under static condition the new isolate of Bacillus subtilis from soil sample contaminated with textile effluent could completely decolorize the Acid orange-10 mono azo dye. Optimization of various parameters like temperature, pH, inoculum size, aeration and incubation period were done to enhance the decolorization process of Acid orange-10. The currentlyisolated Bacillus subtilis could efficiently decolorize the dye even at higher concentrations up to 700 ppm. Hence the new isolate of the microorganism Bacillus subtilis is found to be potential candidate for Acid orange-10 azo dye decolorization in textile effluents. Further studies can help to successfully employ this bacterium for treating the textile effluents before they are released into the surroundings.

Acknowledgement

The authors’acknowledge the support of all people who have helped to carry out this research work. We also thank Maharani’s Science College for Women, palace Road, Bangalore, for providing infrastructure.

Funding source

This is a self-funded research work.

Conflict of Interest

The authors declare no conflict of interest.

References

  1. Singh P, Iyengar L and Pandey A. Bacterial decolorization and degradation of azo dyes. In: Singh, S.N., (ed.) Microbial Degradation of Xenobiotics. Springer, Heidelberg Dordrecht London New York, 2012; pp.101-131.
    CrossRef
  2. Chang JS, Chou C, Lin YC, Lin PJ, Ho JY, Hu TL. Kinetic characteristics of bacterial azo-dye decolorization by Pseudomonas luteola. Water Research. 2001;35(12):2841-50.
    CrossRef
  3. Jin XC, Liu GQ, Xu ZH, Tao WY. Decolorization of a dye industry effluent by Aspergillus fumigatus Applied Microbiology and Biotechnology. 2007;74(1):239-43.
    CrossRef
  4. Chang JS, Chen BY, Lin YS. Stimulation of bacterial decolorization of an azo dye by extracellular metabolites from Escherichia coli strain NO3. Bioresource Technology. 2004;91(3):243-8.
    CrossRef
  5. Asad S, Amoozegar MA, Pourbabaee A, Sarbolouki MN, Dastgheib SM. Decolorization of textile azo dyes by newly isolated halophilic and halotolerant bacteria. Bioresource Technology. 2007;98(11):2082-8.
    CrossRef
  6. Mugdha A, Usha M. Enzymatic treatment of wastewater containing dyestuffs using different delivery systems. Sci Rev Chem Commun. 2012;2(1):31-40.
  7. Xu M, Guo J, Cen Y, Zhong X, Cao W, Sun G. Shewanelladecolorationis sp. nov., a dye-decolorizing bacterium isolated from activated sludge of a waste-water treatment plant. J. Systematic and Evolutionary Microbiology. 2005;55(1):363-8.
    CrossRef
  8. Golob V, Vinder A, Simonič M. Efficiency of the coagulation/flocculation method for the treatment of dyebath effluents. Dyes and Pigments. 2005;67(2):93-7.
    CrossRef
  9. López-Grimau V, Gutierrez MC. Decolorization of simulated reactive dyebath effluents by electrochemical oxidation assisted by UV light. Chemosphere. 2006;62(1):106-12.
    CrossRef
  10. Vitor V, Corso CR. Decolorization of textile dye by Candida albicans isolated from industrial effluents. Industrial Microbiology &Biotechnology. 2008;35(11):1353-7.
    CrossRef
  11. Pajot HF, Delgado OD, de Figueroa LI, Farina JI. Unraveling the decolorizing ability of yeast isolates from dye-polluted and virgin environments: an ecological and taxonomical overview. Antonie van Leeuwenhoek. 2011;99(3):443-56.
    CrossRef
  12. Singh S, Chatterji S, Nandini PT, Prasad AS, Rao KV. Biodegradation of azo dye Direct Orange 16 by Micrococcusluteus strain Inter. J of Environmental Science and Technology. 2015; 12(7):2161-8.
    CrossRef
  13. Sari IP and Simarani K. Decorization of selected azo dye by Lysinibacillus fusiformis W1B6: Biodegradation optimization, isotherm, and kinetic study biosorption mechanism. Adsorption Science and Technology.2019; 37(5-6):492-508.
    CrossRef
  14. Sneha J, Gomashe AV, Sonal A. Decolorization potential of Bacillus sp. for removal of synthetic textile dyes. J of Current Microbiology and Applied Sciences. 2014; 3(12):83-8.
  15. Ponraj M, Gokila K, Zambare V. Bacterial decolorization of textile dye-Orange 3R. J Advanced Biotechnology and Research. 2011; 2(1):168-77.
  16. Van der Zee FP, Villaverde S. Combined anaerobic–aerobic treatment of azo dyes—a short review of bioreactor studies. Water Research. 2005; 39(8):1425-40.
    CrossRef
  17. Joshi T, Iyengar L, Singh K, Garg S. Isolation, identification and application of novel bacterial consortium TJ-1 for the decolorization of structurally different azo dyes. Bioresource Technology. 2008; 99(15):7115-21.
    CrossRef
  18. Horitsu H, Takada M, Idaka E, Tomoyeda M, Ogawa T. Degradation of p-Aminoazobenzene by Bacillus subtilis. Eur J Applied Microbiology and Biotechnology. 1977; 4(3):217-24.
    CrossRef
  19. Idaka E, Ogawa T, Horitsu H, Tomoyeda M. Degradation of azo compounds by Aeromonas hydrophila var. 24B. J the Society of Dyers and Colorists. 1978; 94(3):91-4.
    CrossRef
  20. Wuhrmann K, Mechsner KL, Kappeler TH. Investigation on rate—Determining factors in the microbial reduction of azo dyes. Eur J Applied Microbiology and Biotechnology. 1980; 9(4):325-38.
    CrossRef
  21. Lalnunhlimi S, Krishnaswamy V. Decolorization of azo dyes (Direct Blue 151 and Direct Red 31) by moderately alkaliphilic bacterial consortium. Brazilian JMicrobiology. 2016; 47(1):39-46.
    CrossRef
  22. Jothimani P, Prabakaran J. Influence of bacterial system on the decolorization of dye effluent under enrichment techniques. In State Level Seminar in Recent Developments in Applied Microbiology, Tamil Nadu Agricultural University. Coimbatore, pp-25-26 1998.
  23. Sha MP, Patel KA, Nair SS, Darji AM. An application of response surface methodology in microbial degradation of azo dye by Bacillus subtillis ETL-1979. Am J Microbiol Res. 2014; 2:24-34.
    CrossRef
  24. Pokharia A, Ahluwalia SS. Decolorization of Xenobiotic Azo Dye-Black WNN by Immobilized Paenibacillus alvei MTCC 10625. Int J Environ BioremedBiodegrad. 2016; 4:35-46.
  25. Verma P, Madamwar D. Decolourization of synthetic dyes by a newly isolated strain of Serratia marcescens. World J Microbiology and Biotechnology. 2003; 19(6):615-8.
    CrossRef
  26. Chang JS, Lin YC. Fed-batch bioreactor strategies for microbial decolorization of azo dye using a Pseudomonas luteola strain. Biotechnol Progress. 2000; 16:979–85.
    CrossRef
  27. Misal SA, Gawai KR. Azoreductase: a key player of xenobiotic metabolism. Bioprocess. 2018. 5:17.
    CrossRef
  28. Stolz A. Basic and applied aspects in the microbial degradation of azo dyes. Appl Microbiol Biotechnol. 2001; 56:69–80.
    CrossRef
  29. Mahmood S, Khalid A, Arshad MG, Mahmood T and Crowley DE. Detoxification of azo dyes by bacterial oxidoreductase enzymes. Critical Reviews in Biotechnology. 2015; 36(4): 1-13.
    CrossRef
  30. Kumar A, Sawhney R. Identification of Bacillus subtilis subsp. subtilis “RA-29”, a congo red decolorizer using 16S rDNA sequencing. Researcher. 2011; 3:18-22.
  31. Bayoumi RA, Musa SM, Bahobil AS, Louboudy SS, El-Sakawey TA. Bio decolorization and biodegradation of azo dyes by some bacterial isolates. J Appl Environ Biol Sci. 2010; 1(1):1-25.
  32. Olukanni DO, Osuntoki AA, Gbenle GO. Decolourization of azo dyes by a strain of Micrococcus isolated from a refuse dump soil. Biotechnology. 2009; 8(4):442-8.
    CrossRef
  33. Sneha SJ, Gomashe AV. Bioremediation of textile azo dyes by newly isolated Bacillus sp. from dye contaminated soil. International Journal of Biotechnology and Biochemistry. 2017; 13(2):147-153.
  34. Singh RP, Singh PK, Singh RL. Bacterial decolorization of textile azo dye acid orange by Staphylococcus hominis Toxicology Int. 2014; 21(2):160.
    CrossRef
  35. Karunya A, Rose C. Studies on degradation of textile azo dye, Mordant Black 17 using Pseudomonas aeruginosa SUB 7, isolated from textile effluent. Int J Applied Bioengineering. 2013; 7(1).
    CrossRef
  36. Kumar K, Dastidar MG, Sreekrishnan TR. Effect of process parameters on aerobic decolorization of reactive azo dye using mixed culture. World Acad SciEng Technol. 2009; 58:952-5.
  37. Gurulakshmi M, Sudarmani DN, Venba R. Biodegradation of leather acid dye by Bacillus subtilis.Adv Biotech. 2008; 7:12-9.

Characterization of hypothalamic Nuclei in Indian Fresh Water Spiny Eel Mastacembelusarmatus (Lacepede)

$
0
0

Introduction

Management and conservation of fish together with its breeding biology areessential for successful culture and mobilization of seed resources. Bothenvironmental and hormonal factors are extremely important in regulatingreproductive behavior and spawning in fishes. Various central mechanismstranslate environmental cues into chemical messengers which function to activateand maintain the reproductive organs. In this regard the functional relationshipbetween the hypothalamus and pituitary gland is important, and the pineal glandplays a positive role in regulating sexual maturation. Therefore environment,hypothalamus, pituitary and gonad are the four principle factors which areinterrelated and behave together (Malhotra and Gupta, 1985; Lal and Pandey,1998). The function of pituitary is mostly controlled by the hypothalamus through the synthesis and release of gonadotropin-releasing hormone(GnRH), therefore,acting as a major initiator of the hormonal cascade controlling the reproductiveaxis. Pituitary gonadotrophic hormones and GnRH are important in implicatingthese hormones in gonadal maturation and sex steroid production which plays avery important role in gametogenesis, final maturation of oocytes and spermiation(Parharet al., 2003; Lethimonieret al., 2004). Gonadal activities in teleost fishesprimarily depend on the function of pituitary gonadotrophs and that the pituitary and the gonads exist in a mutual state of excitation and inhibition (Farbridgeetal.,1985; Kaneko et al., 1986). The hypothalamo-hypophyseal complex invertebrates with their neurosecretory nuclei and long axons, is a coordination pointin the vertebrate brain and is known to involve in a complex interaction of a varietyof neurotransmitters which modulate the influence of several trophic hormones bycontrolling their active secretion by releasing or inhibiting hormones within thehypophysis itself (Peter et al., 1991).

Materials and methods

Adult male (average length 15.2 to 15.8 cm) and mean body weight (50g to 75g)and female (average length 17.5 to 17.7 cm) and mean body weight (55g to 70g) ofM. armatuswere procured fortnightly throughout the consecutive years fromparticular pond of Asansol in order to avoid ecological variations than can affectdevelopment of hypothalamus, pituitary and gonads. The fishes were collectedduring the second week of every month from January 2019 to December 2019.As the pituitary gland of M. armatuslodged inside sella turcica, it is difficultto dissect out the pituitary intact along with the brain. The entire brain was exposedby dissection from the dorsal aspect and subsequently immersed in 10% neutralformalin for hardening at the fish collection site. After 45 minutes, the brain including the hypothalamusand the pituitary gland were carefully dissected out from the cranium andsubsequently fixed in Bouin’s fixative, Zenker’s fluid and Eltman fixatives.After proper fixation, pituitary gland throughout the year were placed in 70%ethanol for overnight and subsequently dehydrated through ascending ethanolseries followed by acetone and then cleared in benzene. Tissues were thenembedded in paraffin wax (560C-580C melting point). Mid sagittal section andfrontal section of pituitary gland along with hypothalamus were cut at 4 μmthickness using a Leica RM 2125 RT microtome. Deparaffinized sections ofpituitary and hypothalamus were stained by techniques which areas follows: a)Chrome alum haematoxylinPhloxin (CAHP) (Gomori 1941). b)Aldehyde Fuchsin (AF) (Gabe, 1953).

Results

The cells of the NPO are situated above the optic chiasma in an oblique planeand lie on either side of the ventricle. The cells of the NPO in M. armatusshowconsiderable variation in morphological features and staining reactions (Fig.1).They may be divided into two groups viz., the pars magnocellularis (PMC) andpars parvocellularis (PPC). The PMC occupies the dorsal part of the nucleus and isgenerally composed of relatively larger cells measuring 16.2 μm to 20.5 μm indiameter. The nuclei are 7.5 μm to 9.2 μm. The nuclei of the cells of PMC take up deeper stain probably due to the presence of large amount of intranuclear granulesaround the nucleus (Fig.2). The PPC constitutes ventral part of the NPO. Itcomprises generally smaller cells measuring 12.5 μm to 14.2 μm in diameter(Fig.2). The cells of PMC and PPC are oval. The cytoplasm and nuclei of PMCand PPC varying in abundance and tinctorial intensity during different months ofthe year. The nuclei and surrounding areas of the PMC and PPC cells take up bluish purple colour in chrome alum haematoxylinphloxin stain (Fig.1) and deepaldehyde fuchsin stain (Fig.2) probably due to the presence of large amounts ofintranuclear granules around the nucleus.

Figure 1: NPO showing arrangement of nuclei on both side of the ventricle. (CAHP)x 150. Figure 1: NPO showing arrangement of nuclei on both side of the ventricle. (CAHP)x 150.

Click here to View figure

Figure 2: Enlargedview of NPO showing ventrally arranged pars parvocellularis (PPC) (solid arrows) and dorsally arranged pars magnocellularis (PMC) (brokenarrows). (AF) x 600. Figure 2: Enlargedview of NPO showing ventrally arranged pars parvocellularis (PPC) (solid arrows) and dorsally arranged pars magnocellularis (PMC) (brokenarrows). (AF) x 600.

Click here to View figure

The NLT extends longitudinally as far as plane corresponding to the pituitarygland (Fig.3). The cells of the NLT may be of two types viz., the larger cells or α– cells and the smaller or β – cells. This region is highly vascular. The cells of theNLT are paired and occupy nearer to the pituitary gland. The cells of the NLT areconnected by axonal pathway with the pituitary (Fig.3). The α – cells havedistinct nuclei with abundant cytoplasm and generally vary in size from 11.8 μm to14.6 μm. The nuclei generally range from 5.6 μm to 7.8 μm in diameter. The comparatively smaller cells or β – cells occupying a position lateral to α – cellswith scanty cytoplasm. The size varies from 9.2 μm to 11.6 μm and the nucleigenerally range from 3.8 μm to 5.0 μm. The cells of the NLT take reddish purplecolour in aldehyde fuchsin stain (Fig.4).

Figure 3: Showing the position of NLT above the pituitary and showing axonalpathway (broken arrows) from NLT. (CAHP) x 100. Figure 3: Showing the position of NLT above the pituitary andshowing axonalpathway (broken arrows) from NLT. (CAHP) x 100.

Click here to View figure

Figure 4: Enlarged view of NLT showing aggregation α – cells (broken arrows)and dispersed β – cells (solid arrows). (AF) x 600. Figure 4: Enlarged view of NLT showing aggregation α – cells (broken arrows)and dispersed β – cells (solid arrows). (AF) x 600.

Click here to View figure

Discussion

In the present investigation the nucleus preopticus (NPO) are paired, eachnuclear area being situated on either side of the third ventricle. The NPO iselongated in structure and the differentiated zones, the pars magnocellularis andpars parvocellularis. The shape of NPO in fishes has been reported to vary.Chandrasekhar and Khosa (1972) reported that in Ophiocephalus punctatus theNPO is located anteriorly at the point of emergence of the optic nerve while inClariasbatrachusand Heteropneustesfossilisthey occupy a position posterior to it. In the present study the cells of magnocellularis and parvocellularis are AF andCAHP positive.Anterior parvocelular preoptic (PPa) neurons exhibit very staining than neurons from magnocelular preoptic (PM) neurons (Laura Rincón et al., 2017), thus exhibits a close agreement with the author. A similar observation has also been identified in the preopticnuclei of certain teleosts (Sathyanesan and Haider, 1970; Sathyanesan, 1973;Rizkalla, 1976; Bose and Chakrabarti, 2018). Belsare (1967) opined that inOphiocephalus punctatus the occurrence of vacuoles in the cytoplasm and colloiddroplets in the vicinity of blood vessels indicate the state of secretory activity ofthe nucleus preopticus.At posterior diencephalic area, neurons form ventral hypothalamic area, located around diencephalic ventricle do show round and strongly stained nuclei, with scarce cytoplasm (Camilo R. Q et. al., 2019), also superimposed with present findings.

In the present observation, the neurosecretory nuclei of NLT are veryprominent and occupy a position nearer to the pituitary gland. The cells of the NLTas observed in the present study, may be divided into two subgroups. The comparatively larger α – cells are located anterior end of lateral wall of thehypothalamus and the β – cells are located above the pituitary gland. The nuclei ofα – cells and β – cells respond to CAHP and AF staining. Samuelsson et al., (1968)suggested that the groups of nerve fibre cells situated in the infundibular region ofthe teleost hypothalamus constitute the paired nucleus lateralis tuberis (NLT). Thedivision of NLT cells into two subgroups have been suggested by Desai andAkhunji (1971) in Pampus argenteus and Sathyanesan (1973) in Catlacatla. Desaiand Akhunji further reported AF negative and CAHP positive NLT cells in twospecies of Hilsa and Pampusrespectively. On the contrary, Jose and Sathyanesan(1977) reported that in Labeorohitathe ventromedian component of the NLT is AF positive whereas the anterolateral neurons are AF negative. This studyindicates that the cells of NLT vary in their staining reactions in fishes. In M.armatusaxons arising from NLT cells are traceable during the maturation andspawning periods when they come in close contact with blood capillaries. Theaccumulation of neurosecretory materials (nsm) occurs in the subterminal area andnsm are found to accumulate around the blood capillaries. The nsm play pivotal role in maintaining the hypothalamo – pituitary – gonadal cascade. Up-regulated transcription of brain FSHβ and LHβ along with ovarian ERαFSHR and LHR suggested positive feedback regulation in the HPGL-axis (Jie Hou,2016).Kasuga and Takahashi(1971), Sathyanesan and Jose (1975) have also made similar observations in otherteleosts. There is some relation between secretory phenomena in the NLT and thematuration of gonocytes (Belsare, 1967). In M. armatusit has been observed thatthe probable passages of neurosecretory materials from the NLT cells are along theaxonal routes as well as blood capillaries. The cells of the NLT undergo seasonalcyclical changes which appear to correspond with quantitative variations inpituitary gonadotrophin.Existence of a hypothalamic neurosecretory control over pituitary function that occurs in teleost fish was histologically demonstrated by Adina Popescu et.al. (2020).

Conclusion

In MastacembelusarmatusNPO are paired, each nuclear area being situated on either side of the third ventricle. The NPO is elongated in structure and the differentiated zones, the pars magnocellularis and pars parvocellularis. Both nuclei are CAHP and AF positive. The NLT are very prominent and occupy a position nearer to the pituitary gland. The cells of the NLT as observed in the present study, may be divided into two subgroups. The comparatively larger α – cells are located anterior end of lateral wall of the hypothalamus and the β – cells are located above the pituitary gland. The nuclei of α – cells and β – cells respond to CAHP and AF staining.Understanding the pituitary architecture and cell types for this fish species is of immense importance to save this indigenous variety by artificial breeding.

Acknowledgement

I would like to acknowledge Prof. (Retd.) P. Chakrabarti, Department of Zoology, The University of Burdwan and Mainak Banerjee, research scholar Department of Zoology, The University of Burdwan, for guiding me during the time of preparation of the manuscript.

Conflict of interest

Author does not have any conflict of interest.

References

  1. Adina Popescu, Daniela Cristina Ibanescu, FanicaBalanescu, ‘’Pituitary – lobulation and seasonal changes of the basophil pituitary in cyprinids’’. Animal Science. LXIII, (1), pp. 247-252, 2020.
    CrossRef
  2. Bose S. and P. Chakrabarti,‘‘Changes in the hypothalamus in relation totesticular cells during growth, maturation and spawning phases in thebrackishwater teleost Liza parsia(Ham.)’’. Indian J.Fish,65(1) , pp. 40-46, 2018.
  3. BelsareD.K. ‘‘Periodic activity of the pituitary gland and nucleuspreopticus in Channa punctatus’’. Zool. Polonica, 17, pp. 273-285, 1967.
  4. Camilo Riaño-Quintero, Edwin Gómez-Ramírez and Hernán Hurtado-Giraldo.Glyphosate commercial formulation effects on preoptic area and hypothalamus of Cardinal Neon Paracheirodonaxelrodi (Characiformes: Characidae). Neotropical Ichthyology, 17(4), e190025, 2019.
    CrossRef
  5. Chandrasekhar, K. and D. Khosa, ‘‘Histomorphological studies on theneurosecretory system of three genera of freshwater teleotean fishes’’. Proc. Ind.Acad. Sci., Sec. B, springer, 75(6),pp. 257 – 265. 1972.
    CrossRef
  6. Desai K. and U. U. Akhunji. ‘‘Histological studies on the hypothalamoneurohypophyseal complex of Pampus argenteus’’. Zool. Jap, 44 (3),pp. 161-169, 1971.
  7. M.G. Burke and J.K. Leatherland. ‘‘Seasonal changes in thestructure of the adenohypophysis of the brown bull head, Ictalurus nebulosus(Leusuer)’’. Cytobios, 44,pp. 49-66, 1985.
  8. Gabe M. ‘‘Sur quelques applications de la coloration par la fuchsine paraldehyde’’.Bull. Micr. Appl, Paris, 3,pp. 153-162, 1953.
  9. ‘‘Observations with differential stains on human islets of Langerhans’’. Amer. J. Path, 17(3), pp. 395, 1941.
  10. Jie Hou, Li Li , Ning Wu, YujingSu, Wang Lin, Guangyu Li, “Reproduction impairment and endocrine disruption in female zebrafish after long – term exposure to MC – LR : A life cycle assessment’’ Environmental Pollution, 208 (B), pp 477 – 485, 2016.
    CrossRef
  11. 11.  Jose T.M. and A.G. Sathyanesan. ‘‘Pituitary cytology of the Indian carp Labeorohita(Ham.)’’. Anat. Anz, 142(4), pp. 410-423,1977.
    CrossRef
  12. Kaneko T. K. Aida and J. Haryu. ‘‘Ultrastructural changes in thepituitary gonadotrophs during the annual reproductive cycle of the female chichibugoby, Triclenfiger obscurus’’. Cell Tissue Res, 246, pp. 137-144, 1986.
    CrossRef
  13. Kasuga, S. and H. Takahashi. ‘‘The preoptico-hypophysial neurosecretory system of the medaka, Oryziaslatipesand its changes in relation to the annual reproductive cycle under natural conditions’’. Bull. Fac. Hakkaido Univ,21(4),pp.259-268, 1971.
    CrossRef
  14. Lal K.K. and A.K. Pandey. ‘‘Hypothalamo-neurosecretory system of thefemale seabass, Lates calcarifer (Bloch), with special reference to gonadalmaturation’’. Indian J. Fish, 45, pp. 51-60, 1998.
    CrossRef
  15. Laura Rincón1, Martha J. Obando2, Mario O. Tovar, MatíasPandolfi and Hernan Hurtado, “Topological and histological description of preoptic area and hypothalamus in cardinal tetra Paracheirodonaxelrodi” (Characiformes: Characidae), Neotropical Ichthyology, 15(1), e160145, 2017.
    CrossRef
  16. Lethimonier, C., T. Madigou, J.A. plunoz-cueto, J.J. Lareyre and O. Kah. ‘‘Evolutionary aspects of GnRHs, GnRH neuronal systems and GnRHreceptors in teleost fish’’. Gen. comp. endocrinol, 135 (1), pp. 1-16, 2004.
    CrossRef
  17. Malhotra, Y.R. and K.Gupta, ‘‘Histophysiology of hypothalamohypophysealsystem in relation with reproductive cycle in Puntius sophore(Ham.)’’Zool. Orient, 2, pp. 59-65, 1985.
    CrossRef
  18. Parhar, I.S., T. Soga, S. Ogawa and Y. Sakuma. ‘‘FSH and LH-β subunitsin the preoptic nucleus: Ontogenic expression in teleost’’. Gen. Comp. Endocrinol,132(3), pp. 369-378, 2003.
    CrossRef
  19. Peter, R.E., V.L. Trudeau and B.D. Sloley.‘‘Brainregulation of reproduction in teleosts’’. Bull. Inst. Zool., Acad. Sinica (Monograph),16, pp. 89-118, 1991.
  20. Rizkalla. W. ‘The hypothalamic neurosecretory system of the marine teleost fish, Mugil auratus’. Risso. Acta Biol. Acad. Sci., Hung, 27(2-3),pp.163-170,1976.
  21. Samuelsson, B., B. Ernholm and G. Fridberg. ‘‘Light microscopic studies on the Nucleus Lateralis Tuberis and the pituitary of the perch, Leuciscus rutilus with reference to the nucleus-pituitary relationship’’. Acta Zoologica, 49(1-2),pp. 141- 153, 1968.
    CrossRef
  22. Sathyanesan, A.G. and S. Haider. ‘‘Hypothalamo-neurohypophysialcomplex of the teleost, Heteropneustesfossilis(Bl.) with some experimentalevidence on the regeneration of the neurosecretory tract’’. Ind. J. Exp. Biol, 8(3),pp. 174-178,1970.
  23. Sathyanesan, A.G. ‘‘Hypothalamo-hypophyseal neurosecretory system ofthe freshwater teleost, Catlacatla(Ham.)’’. Zool. Beitr, 19(2),pp. 163-178, 1973.
  24. Sathyanesan, A.G. and T.N. Jose. ‘‘Hypothalamo-hypophysial vascular and neurosecretory link in the teleost, Bagariusbagarius(Ham.)’’.Cell Tissue Organs, 93(3), pp. 387-398, 1975.
    CrossRef

Gut Microbiome of Two Different Honeybee Workers Subspecies In Saudi Arabia

$
0
0

Introduction

Honeybees belong to the genus Apis, which is known for its tremendous role in pollination. Unfortunately, honeybee population is recently declining with a potential risk on the agricultural service and subsequently the food supply, not only locally in Saudi but also globally1. There is a known mutually beneficial relationship between honeybee gut microbiome and its host. The host provides the optimum environment for bacterial growth, while the bacterial community in honeybee guts aids in efficacy of nutrients absorption, optimum growth and development of the host and its ability to defend pathogens, and its adaptation to surrounding environment2.Honeybee gut represents a simple model system to study the relationshipbetween gut microbiome with honeybeehosts3,4.The bacterial community in adult honeybee workers is diverse and estimated to reach one billion bacterial cells in each worker’s gut5,6. Such a diversity in bacterial community is dependent on the type of flower that hosts the insect, as well as many other environmental factors7.Gut microbiome of honeybee (Apis mellifera) workers is composed ofeight to nine core species8,9, e.g., Bartonella apis10, Acetobacteraceae11,Parasaccharibacter11, Snodgrassella alvi12, Bifidobacterium asteroids13, Lactobacillus sp.14, Frischella perrara15and Gilliamella apicola12.

The two most common bee species that are widely distributed throughout the kingdom of Saudi Arabia are the indigenous Apis mellifera jementica, which is a native species, and Apis mellifera carnica, which is imported from Egypt16 as honey production of domestic bees does not meet the growing demands in Saudi Arabia. Moreover, the production cost is relatively high. Exotic bee colonies have been imported over time, reaching 200,000 bee packages annually16. It is well known for local beekeepers that the indigenous bees A.m. jementicahighly tolerates local stressful conditions when compared with exogenous races A.m. carnica, particularly during summer when the air temperature becomes extremely high. It is also noticed that at high temperatures, indigenous bees continue to forage for pollen and collect nectar, whereas imported bees will stop foraging16. Initial reports revealed that the subspecies of exotic honeybees have lower heat tolerance, shorter foraging durations and are more susceptible to Varroa mites when compared with indigenous bees16.

In the present study, we compered the gut microbiomecomposition and diversity of the adult honeybees of Apis mellifera jementica and Apis mellifera carnica in Saudi Arabia using high-throughput 16S rRNA gene sequencing technology.

Material and Methods

Sample collection,isolation of guts microbiota and DNA extraction

Five samples each from honeybee workers of A.m. jemenitica and A.m. carnicawere collected in November 2019 from a single hive of Beekeeper Cooperative Association at Al Baha, Saudi Arabia. The collected samples were immediately stored at −80°C.Forwhole gut dissection of honeybee workers,surface disinfection was done using 1 ml aqueous ethanol (70%, v/v) for 45 sec. Dissected guts were, then,placed in a pre-frozen mortar and 700μl S1 lysis buffer (Invitrogen, Thermo Fischer Scientific, USA) were added and guts were transferred to bead tube for extraction process.DNA of gut samples was extracted by the genomic DNA extraction kit (Invitrogen, Thermo Fischer Scientific, USA), and stored at -20°C for further molecular analysis.

PCR amplification

PCR was run to amplify bacterial 16S rRNA gene of the variable regions V3-V4. The two universal primers used for PCR are 341F 5′-ACTCCTACGGGAGGCAGCAG-3′ (forward primer) and 806R 5′- GGACTACHVGGGTWTCTAAT-3′ (reveres primer). The PCR conditions were set as the following: one cycle for initial denaturation at 95°C for 5 min; 25 cycles of denaturation at 95°C for 30sec followed by annealing at 56°C for 30 sec andprimer extension at 72°C for 40 sec; and a one cycle for final extension at 72°C for 10 min. The generated PCR products were checked for quality and selected products were utilized in preparing Illumina DNA libraries. DNA sequencing was run using Illumina Miseq platform (Illumina, San Diego, CA) at Beijing Genome Institute (BGI), China to generate high-quality pair-ends of ~300 bp.

Statistical analysis

The high quality paired reads produced in fasta files as raw data were de-multiplexed, quality-filtered and trimmed by trimmomatic package (Version 0.33) through Quantitative Insights Into Microbial Ecology 2 pipeline (QIIME2, v1.80). Obtained reads were merged into single sequence files by the Fast Length Adjustment of SHort reads (FLASH, Version 1.2.11). In order to assign generated unique sequences into operational taxonomic units (OTUs), reads were tagged and clustered into OTUs with similarity cut off of 97% using the de novo OTU piking procedure. Usearch (Version 7.0.1090)19 was, then, used to remove Chimeric sequences. Taxonomies were plotted against the gut Microbiome Database (HOMD RefSeq, Version 13.2) through the RDP classifier (Version 2.2)17 and the Green-genes database (version 2013051816S rDNA database, http://qiime.org/home_static/dataFiles.html) with a cut off of 70%. Alpha diversity indeceswere measured in order to assess the intra-species variations within a given sample using Mothur (v1.31.2). Alpha diversity and rarefaction curve boxplots were constructed using software R (v3.1.1). To investigate the inter-species variations within samples, the beta diversity matrices were conducted and visualized using principal coordinate analysis (PCoA) by package ‘ade4’ of software R (v3.1.1). Also, heat maps were generated using the package ‘gplots’ of software R (v3.1.1), and, then, sequence alignments were searched against the Silva core set (Silva_108_core_aligned_seqs) by using PyNAST ‘align_seqs.py’. The obtained OTU phylogenetic tree was, then, plotted by software R (v3.1.1), and visualized through QIIME2 (v1.80).

Annotation of generated OTUs was done in order to detect the relative abundance at different taxonomical levels (phylum, genus and species). Finally, Metastats, PERMANOVA and Benjamini–Hochberg false discovery rate (FDR) correction were also used to correct for multiple hypothesis. The Linear Discriminant Analysis (LDA) Effect Size (LEfSe) was applied using software LEfSewith the online interface Galaxy (version 1.0.0; http://huttenhower.sph.harvard.edu/galaxy/root),to discriminate the two taxonomic races determining highly presented bacterial taxon within each race depending on statistical significance.

Results

Statistics of 16S rRNA Sequence data

The five gut microbiome samples of A.m. carnicawere identified asC1 to C5, while the five gut microbiome samples of A.m. jemeniticawereidentified as J1 to J5. Illumina MiSeq was used in sequencing thepartial 16S rRNA gene and statistics details are tabulated in Table 1. A total of 2,279,519 sequence reads were obtained with an average length of 297 bp across different samples ranging from 293 to 300 bp. The average clean reads per subject were 218,741 and 237,162 for A.m. carnicaand A.m. jemeniticaworkers, respectively. A total of 2,269,665 tag-linked sequences were obtained across samples from both taxonomic groups with an average read of 236,125 and 217,807 per subject, for both A.m. carnicaand A.m. jemeniticaworkers, respectively (Figure S1). Furthermore, a total of 601,485 tags were obtained across samples with an average reads of 63,313 and 56,983 per subject, in both A.m. carnicaand A.m. jemeniticaworkers,respectively (Figure S1). The tagged sequences were assigned to a total of 171 OTUsacross samples ranging from 45 (J3) to 154 (C5) OTUs (Table S1). The sum of OTUs in A.m. carnica (A.M.C) is 373 with an average number of 74 OTUs, while 241 in A.m. jemenitica(A.M.J)with an average number of 48 OTUs.

Table 1: Statistics of deep sequencing data generated for gut microbiomes of 10 adult honeybee workers from subspecies A.m.carnica(C1-C5) andA.m.jemenitica (J1-J5).

Sample ID Reads length (bp) Raw Data (Mbp) N Base (%) Low quality (%) Clean Data (Mbp) Data utilization (%) Raw reads Clean reads Read utilization (%)
C1 300:300 157.62 0.035 3.863 143.01 90.73 2626982 2434712 92.68
C2 299:300 160.13 0.028 4.042 144.97 90.53 2673272 2472472 92.49
C3 298:300 145.38 0.024 4.128 131.32 90.33 2431182 2244372 92.32
C4 297:300 154.8 0.022 4.032 140.04 90.47 2592892 2397372 92.46
C5 296:300 90.52 0.025 4.629 80.83 89.29 1518842 1388132 91.39
J1 297:293 149.45 0.021 3.73 136.59 91.4 2533052 2356712 93.04
J2 296:293 162.1 0.026 3.963 147.33 90.89 2752092 2547162 92.55
J3 294:293 145.85 0.02 3.664 133.58 91.59 2484682 2316072 93.21
J4 293:293 135.37 0.021 3.797 123.52 91.25 2310112 2148432 93
J5 300:293 158.64 0.032 3.702 145.15 91.5 2675232 2489772 93.07

Alpha diversity and principle coordinate analyses and rarefaction curve measurement

Alpha diversity indices were used to analyse the complexity of the included species. These indices are observed species (Sobs), Chao1, Ace, Shannon and Simpson. The Sobs and Chao1 indices indicated significance differences between A.M.C and A.M.Jgroups with higher diversity in A.M.C group. The P-values determined were 0.03175 for Sob and0.01587 for Chao1 indices (Figure 1 and Table 2). On theother hand, Shannon and Simpson indices revealed no significant difference between A.M.C and A.M.Jgroups. Chao1 and Shannon indicesreflect the species diversity in terms of richness, while Simpson index isindicative of evenness20.The Simpson values in A.M.C and A.M.J groups were 0.1377 and 0.12313, respectively, while, Shannon index values were 2.4658 and 2.4769 in A.M.C and A.M.J groups, respectively(Figure 1 and Table 2).

Vol14No1_Gut_Rai_Mar1 Figure 1: Boxplots of alpha diversity indices illustrates richness and evenness at the group level of gut microbiomes of adult honeybee  workers of A.m. carnica (red) and A.m. jemenitica (blue)

Click here to view figure

Table 2: Alpha diversity comparison results among groups of gut microbiomes of honeybee workers from subspeciesA.m.carnica(A.M.C) andA.m.jemenitica (A.M.J).

Alpha diversity measure Mean (A.M.C) SD (A.M.C) Mean (A.M.J) SD (A.M.J) p-value
Sobs 74.6 44.59036 48.2 3.11448 0.03175
Chao 79.1 42.37209 52.01667 3.76128 0.05556
Ace 83.69118 40.78638 53.23999 3.78296 0.01587
Shannon 2.4658 0.22593 2.4769 0.21404 0.84127
Simpson 0.1377 0.0347 0.12313 0.0389 0.30952

Principal coordinate analysis (PCoA) was used to display the diversity as well as the differences in OTU composition.Diversity of A.M.C subjects was higher towards positive and negative PCA 1 directions (PC1), whereasthat of A.M.J subjects was higher towards positive and negative PCA 2 directions (PC2). As an overall picture, the diagram shows that the mean value of A.M.C group was localized in positive portion of PC1 and negative portion of PC2, whereasA.M.J group was mainly localized in the positive portion of PC2(Figure 2). The principal coordinate analysis (PCoA) plots were created using a Bray-Curtis distance matrix and the samples were plotted to represent the microbial community compositional differences between samples. The plots are dimensionally scattered in accordance to their gut microbiome compositional relationships. The results of the present study indicate that the differences ingut microbiomes between these two groups arepossibly due to the different origins of worker honeybeesof the two subspecies.

Vol14No1_Gut_Rai_Mar2 Figure 2: PCoA based on OTU abundance of samples. Red boxes represent A.M.C (A.m.carnica) samples. Blue boxes represent A.M.J (A.m.jemenitica) samples. Each dot denotes one sample. X-axis is the first principal component and Y-axis is the second. Number in brackets denotes contributions of PCAs to differences among samples.

Click here to view figure

The stacked number of OTUs and the number of observed species for different samples as rarefaction measures are shown in Figure S2. When the refraction curves inclines (Figure S2a) or stops climbing (Figure S2b), the produced data would be enough for further analysis. However, as long as the curve is still climbing, the complexity of the data in samples become higher; since more species being detected throughout sequencing analysis. The two rarefaction curve measures refer to the maximum number of sequences attained for all samples that allows to study taxonomic relative abundance and to assess eligibility of such data to represent all species of any microbial community. The findings from both rarefaction measures show that 54,000 is the maximumnumber of sequence reads that can be used further instudyingtaxonomic abundance (Figure S2).

Structure of gut microbiomes across the two honeybee workers

Two taxonomic ranks (phylum and species) were used in the comparison of gut microbiomesbetween adult honeybee workers A.M.CandA.M.J at the phylogenetic level (Figure 3). The results indicate that phylumFirmicutesharbours 24 genera,while Proteobacteria,Actinobacteria, Bacteroidetes andThermiharbour 23, 8, 6 and 2 genera, respectively (Figure 3).

Vol14No1_Gut_Rai_Mar3 Figure 3: Phylogenetic tree at genus level of gut microbiomes of adult honeybee workers of A.m.carnica and A.m.jemenitica.        Genera having the same color belong to the same phylum.

Click here to view figure

Differential abundance of microbes due to different origin of worker

The observed microbial taxa along with their redundancies across different samples identified after OTU annotation are described in Table S2. The taxa refer to phylum, class, order, family, genus, and species. Eight phyla of the gut bacteria were identified according to relative abundance. They are Actinobacteria, Bacteroidetes, Cyanobacteria, Firmicutes, Protobacteria, TM7, Tenericutes and Thermi(Figure 4). Aligning with the number of genera of each phylum shown in Figure 3, the most abundant phylum were Firmicutes (57%), Protobacteria (31%) and Actinobacteria (10%) inA.M.C group (Figure 4).

Vol14No1_Gut_Rai_Mar4 Figure 4: Metastatsmeasures for relative abundance of gut microbiomes at the phylum level forthe group levels of A.m.carnica andA.m.jemenitica.

Click here to view figure

Meanwhile, Firmicutes (48%), Protobacteria (44%) and Actinobacteria (6%) were the most abundant in A.M.J group (Figure 4). The comparison at phylum level revealed a significant increase in Cyanobacteria in the A.M.C group (P-value = 0.031746), while a significant increaseof Protobacteria in the A.M.J group (P-value = 0.037724)(Table S3). Interestingly, Table S3 also indicates the existence of the three phyla TM7, Tenericutes and Thermi only in A.M.C group. The previous results align with those ofthe heat map at phylum level asFirmicutes, Protobacteria and Actinobacteria were shown to be the most abundant phyla across samples and groups (Figure S3).

In terms of species relative abundance in the gut microbiomes of two groups A.M.C and A.M.J Bacteroides_fragilis, Bacteroides_ovatus, Commensalibacter_intestini, Blautia_producta, Melissococcus_plutonius, Ruminococcus_gnavus,Saccharibacter_floricola and Snodgrassella_alviwere shown to be the most abundant (Figure 5).

Vol14No1_Gut_Rai_Mar5 Figure 5: Relative abundance of gut microbiomes of adult honeybee workers of A.m.carnica and A.m.jemeniticaamong(a and b, respectively) and across species (c) as measured by Metastats.**High significant difference between microbiomes of A.m.carnicaand A.m.jemenitica.

Click here to view figure

The figure also indicates that a large proportion of the OTUs were not assigned to a certain species (93.80% for A.M.C and 86.20% for A.M.J). We have no explanation for these results except that a large number of species in workers of honeybee was not identified or classified before. The results in Table S4 indicates asignificant increase of Melissococcus_plutoniusin the gut microbiome of A.M.C (P-value = 0.034454), while Snodgrassella_alvi in theA.M.J group (P-value =0.008948). Results for the latter species Snodgrassella_alvi align with that presented in Figure 5c. TheRuminococcus_gnavusandSaccharibacter_floricolawere not existed in theA.M.J group. The heat map at species level indicates that Snodgrassella_alviharbours the highest relative abundanceacross all samples (Figures S4).

Linear discriminant analysis effect size (LEfSe) and its LDA scores (˃ 3) wereused to identify possible biomarkers in gut microbiota that refer to the origin of the host (Figure 6). The results in cladogram indicate that the possible marker in gut microbiome of A.M.C is Enterococcaceae family, while Neisseriaceaea, Neisseriales and Betaproteobacteria taxa of A.M.J (Figure 6a). Biomarkers in A.M.C based on LDA score includeEnterococcaceae, Saccharibactersp.,Saccharibacterflorcola, Firmicutes (Melissococcussp. andMelissococcusplutonius) and Cyanobacteria while Betaproteobacteria (Neisseriales, Neisseriaceae,Snodgrassella sp. andSnodgrassella_alvi)in A.M.J (Figure 6b).

Vol14No1_Gut_Rai_Mar6 Figure 6: The main different bacterial taxa (biomarkers) for gut microbiomes of adult honeybee workers of A.m. carnica (A.M.C) and A.m. jemenitica (A.M.J). a) Cladogram-based LEfSe analysis representing main different microbiota taxa between groups. b) taxa with LDA score > 3. Colour codes: Yellow (a) denotes no significant difference in taxa; Green denotes significantly different taxa (biomarkers) with their relative maximum abundance in (A.M.J); Red denotes significantly different taxa (biomarkers) with their relative maximum abundance in (A.M.C).

Click here to view figure 

Discussion

The gut microbiomestructure of honeybee workersis dependent upon monophyletic origin of the host9, social interactions8and the type of diet consumed, whether workers are beebread, pollen or nectar1.In the present study, high-throughput sequencing was carried out for samples taken from the two honeybee subspecies A.m. carnicaandA.m. jemenitica and statistical analysis proved that the diversity of the bacterial community composition ofA.m. carnicaandA.m. jemenitica was statistically significant.

Four major bacterial phyla (Firmicutes, Proteobacteria, Actinobacteria and Bacteroidetes) were recognized in the guts of honeybee workers of the two subspecies A.m. carnicaand A.m. jemenitica. The dominant phylum ingut microbiomes of the two subspecies wasFirmicutes with values of 57.2 and 48.5%, respectively. This conclusion was also drawn in several previous reports9,21,22,23.Genus Lactobacillus, gram-positive bacteria belongingto the family Lactobacillaceae (Firmicutes), was found to have a high relative abundance in adult workers of both A.m. carnicaandA.m. jemeniticawith values of 52% and 80%, respectively. It is a core gut bacterium that is dominant in the rectum of honeybee workers. Within this context, Ahnet al.24 concluded thatLactobacillaceaedominates in both of A. cerana and A. mellifera species.This genus produces several compounds in honeybee gut with known antimicrobial activities such as organic acids, hydrogen peroxide, bacteriocin, reutericyclin and reuterin that mostly inhibit decaying and protects againstpathogenic bacteria, as well as some fungi25,26.Therefore,Honeybeeslikely use lactobacilli as probiotic27.In the present study, the dominance of Lactobacilli in both A.m. carnicaandA.m. jemeniticaadult workers is supported by the presence of low pH (3.9) of honey and nectar28. This is concluded because of the ability of lactobacilli to ferment sugar in the gut of honeybee workers and, hence,to generate acidic environment29, which inhibits the growth of many other bacteria. The low abundance in Lactobacillaceaewas reported to be associated with the presence of pathogenic bacteria30.

GenusBifidobacterium,gram-positive bacteria belonging to the Actinobacteria phylum, was also identified in gut of both A.m. carnicaandA.m. jemenitica adult workers. Again, it is dominant in rectum, and a core gut bacteria of honeybee workers.Bifidobacterium strains carry large surface proteins, which have a role in adhesion or degradation of plant materials7,31,32. Additionally, Bifidobacteriumcarriesgene clusters that are responsible for the production and utilization of trehalose, which is a disaccharide molecule used by insects as an energy reservoir, in comparison to glycogen, which is the energy storage form in mammals33.

Family Neisseriaceaeand its descendentSnodgrassella_alvi(S. alvi), gram-negative bacteria belonging toBetaproteobacteria phylum, significantly increased in A.m. jemenitica.These bacteria participate in oxidation of carbohydrates. However, the pathway for the uptake and glycolytic breakdown of carbohydrates does not exist in S. alvi, thus,this bacteriumis located consistently within the periphery of the insect’s gut lumen. This area has high oxygen concentrations and this environment is preferable for S. alvidue to its dependence on aerobic respiration34,35. Insects depend on the aerobic oxidation of carboxylates rather than breaking down carbohydrates resulting in various products such as citrate, malate, acetate and lactic acid that serve as energy sources12,27. The steady co-exits of S. alvi with other fermentative bacterial taxa in the same gastrointestinal environment can result from utilizing separate sets of resources leading to metabolic variations suggesting a syntrophic interaction. For example, S. alvican utilize some of the substrates such as lactic acid, acetate and formate, which are produced from carbohydrate fermentation36,37. Furthermore, S. alvi and G. apicola38are enriched with genes encoding biofilm formation. The two species inhabit the host’s ileum, indicating that the biofilm can provide a protective layer against pathogens.

The bacteria of the family Acetobacteraceaeand its descendent genusCommensalibacter(also referred to as Alpha 2.1), gram-negative bacteria belonging to phylum Proteobacteria,wereidentifiedas a core member of the gut microbiota in honeybees and bumble bees9,31. It was observed mainly in the midgut and hindgut of honeybee workers. In our study, Commensalibacterpresents in A.m. carnicaandA.m. jemenitica. However, Saccharibacterflorica (Alpha-2.2) presents only in A.m. carnica. Furthermore, Saccharibacterflorica is isolated from pollen, suggesting that this phylotype is associated with flowers39.The role of these phylotypes (Alpha 2.1 andAlpha-2.2) is associatedwith their abilities to adaptwith fast growing metabolic processes, with two distinctive mechanisms. Alpha2.1 bacteria harvest energy througha wide range of substrates linkedand utilizedthrough a flexible oxidative and biosynthetic metabolism.Whereas, Alpha2.2 bacteria, that lack alternative oxidative pathways, determine metabolic processes through oxidative fermentation after harvesting glucose for rapid energy40.

The bacteria of the familyEnterococccaceaeand its descendent species Melissococcusplutonius, gram-positive bacteria of phylum Firmicutes, present in low abundance (3%) in gut microbiome ofA.m. carnica honeybee workers. This conclusion was also noted in previous reports41.M. plutoniusis known to cause the European foulbrood (EFB) in earlystage of honeybee larvae, with assistance from secondary invaders (Enterococcus faecalis, Paenibacillus alvei and Bacillus pumilus). M. plutonius wasshown to have 30 different sequence types clustered under three clonal complexes (CC 3, CC12, and CC13)42,44, where CC13is the least virulent complex43,45. Honeybee workers transmit M. plutonius between colonies via robbing and drifting46,47.Erban et al.45 compared control samples from the EFB zone with samples from EFB zone without clinical symptoms,and bees from colonies from EFB zone with clinical symptoms. The study identified a 100-fold higher prevalence of M. plutonius in colonies with EFB symptoms, while it only presents in 3 of 16 control colonies that are distant from the EFB zone. This suggests that M. plutonius has lower abundance in healthy honeybee colonies, which is consistent with the results of the present study.

Conclusion

The present findings indicative that differences in gut microbiome structuresof honeybee workers of the two subspecies A.m. carnicaand A.m. jemeniticaare due to varied monophyletic origin of the host. These findings support previous results suggesting that honeybee workers have a mutual coevolving relationship with specific group of bacteria. This group of bacteria co-exists and is maintained throughout the descending generations of the host.Inclusion of more subspecies inhabited in Saudi Arabia along with ones of this study can further support our findings.

Supplementary Information

Additional file: Additional Table  S1, S3 and S4 Additional Figure S1 to S4

Additional file: Additional Table S2

Acknowledgements

This study was supported by Beekeeper Cooperative Association-Al Baha. The authors would like to thank Prof. Dr.Ahmad Al-Ghamdi, Dept. Plant Protection, College of Food and Agricultural Sciences, KSU, Riyadh, for his support and cooperation during this study.

Conflict of Interest

The authors declare no conflict of interest.

References

  1. Powell, J. E., Martinson, V. G., Urban-Mead, K. & Moran, N. A. Routes of acquisition of the gut microbiota of the honeybeeApis mellifera. Environ. Microbiol.80, 7378–7387 (2014).
    CrossRef
  2. Jones, J. C. et al. Gut microbiota composition is associated with environmental landscape in honeybees. Ecology and evolution8, 441–451 (2018).
    CrossRef
  3. Engel, P. & Moran, N. A. The gut microbiota of insects–diversity in structure and function. FEMS microbiology reviews37, 699–735 (2013).
    CrossRef
  4. Krishnan, M. et al. Insect gut microbiome–An unexploited reserve for biotechnological application. Asian Pacific journal of tropical biomedicine4, S16–S21 (2014).
    CrossRef
  5. Martinson, V. G., Moy, J. & Moran, N. A. Establishment of characteristic gut bacteria during development of the honeybee worker. Environ. Microbiol.78, 2830–2840 (2012).
    CrossRef
  6. Moran, N. A. Genomics of the honeybee microbiome. Current opinion in insect science10, 22–28 (2015).
    CrossRef
  7. Jones, J. C. et al. The gut microbiome is associated with behavioural task in honeybees. Insectessociaux65, 419–429 (2018).
    CrossRef
  8. Kapheim, K. M. et al. Caste-specific differences in hindgut microbial communities of honeybees (Apis mellifera). PloS one10, e0123911 (2015).
    CrossRef
  9. Martinson, V. G. et al. A simple and distinctive microbiota associated with honeybees and bumble bees. Molecular Ecology20, 619–628 (2011).
    CrossRef
  10. Kešnerová, L., Moritz, R. & Engel, P. Bartonella apis sp. nov., a honeybee gut symbiont of the class Alphaproteobacteria. International journal of systematic and evolutionary microbiology66, 414–421 (2016).
    CrossRef
  11. Corby-Harris, V. et al. Origin and Effect of Alpha 2.2 Acetobacteraceae in Honeybee Larvae and Description of Parasaccharibacterapium gen. nov., sp. nov. Environ. Microbiol.80, 7460–7472 (2014).
    CrossRef
  12. Kwong, W. K. & Moran, N. A. Cultivation and characterization of the gut symbionts of honeybees and bumble bees: description of Snodgrassellaalvi gen. nov., sp. nov., a member of the family Neisseriaceae of the Betaproteobacteria, and Gilliamellaapicola gen. nov., sp. nov., a memb. International journal of systematic and evolutionary microbiology63, 2008–2018 (2013).
    CrossRef
  13. Bottacini, F. et al. Bifidobacterium asteroides PRL2011 genome analysis reveals clues for colonization of the insect gut. PLoS One7, e44229 (2012).
    CrossRef
  14. Olofsson, T. C., Alsterfjord, M., Nilson, B., Butler, È. & Vásquez, A. Lactobacillus apinorum sp. nov., Lactobacillus mellifer sp. nov., Lactobacillus mellis sp. nov., Lactobacillus melliventris sp. nov., Lactobacillus kimbladii sp. nov., Lactobacillus helsingborgensis sp. nov. and Lactobacillus kullabergensis sp. nov., isol. International journal of systematic and evolutionary microbiology64, 3109 (2014).
    CrossRef
  15. Engel, P., Kwong, W. K. & Moran, N. A. Frischellaperrara gen. nov., sp. nov., a gammaproteobacterium isolated from the gut of the honeybee, Apis mellifera. International journal of systematic and evolutionary microbiology63, 3646–3651 (2013).
    CrossRef
  16. Zhu, X., Wang, J., Reyes-Gibby, C. &Shete, S. Processing and Analyzing Human Microbiome Data. in Statistical Human Genetics 649–677 (Springer, 2017).
    CrossRef
  17. Cole, J. R. et al. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic acids research42, D633–D642 (2014).
    CrossRef
  18. DeSantis, T. Z. et al.Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Applied and environmental microbiology72, 5069–5072 (2006).
    CrossRef
  19. Edgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature methods10, 996–998 (2013).
    CrossRef
  20. Lombogia, C. A., Tulung, M., Posangi, J. &Tallei, T. E. Bacterial Composition, Community Structure, and Diversity in Apisnigrocincta Gut. International Journal of Microbiology2020, (2020).
    CrossRef
  21. Jeyaprakash, A., Hoy, M. A. & Allsopp, M. H. Bacterial diversity in worker adults of Apis mellifera capensis and Apis mellifera scutellata (Insecta: Hymenoptera) assessed using 16S rRNA sequences. Journal of invertebrate pathology84, 96–103 (2003).
    CrossRef
  22. Mohr, K. I. &Tebbe, C. C. Diversity and phylotype consistency of bacteria in the guts of three bee species (Apoidea) at an oilseed rape field. Environmental Microbiology8, 258–272 (2006).
    CrossRef
  23. Yoshiyama, M. & Kimura, K. Bacteria in the gut of Japanese honeybee, Apiscerana japonica, and their antagonistic effect against Paenibacillus larvae, the causal agent of American foulbrood. Journal of Invertebrate Pathology102, 91–96 (2009).
    CrossRef
  24. Ahn, J.-H. et al. Pyrosequencing analysis of the bacterial communities in the guts of honeybees Apiscerana and Apis mellifera in Korea. Journal of Microbiology50, 735–745 (2012).
    CrossRef
  25. Lahtinen, S., Ouwehand, A. C., Salminen, S. & von Wright, A. Lactic acid bacteria: microbiological and functional aspects. (Crc Press, 2011).
    CrossRef
  26. Mokoena, M. P. Lactic acid bacteria and their bacteriocins: classification, biosynthesis and applications against uropathogens: a mini-review. Molecules22, 1255 (2017).
    CrossRef
  27. Kwong, W. K., Mancenido, A. L. & Moran, N. A. Genome sequences of Lactobacillus sp. strains wkB8 and wkB10, members of the Firm-5 clade, from honeybee guts. Genome Announc.2, e01176-14 (2014).
    CrossRef
  28. JA, S. Cliver DO. Microorganisms in honey. J. Food Microbiol31, 1–26 (1996).
    CrossRef
  29. Bignell, D. E. & Heath, L. A. F. Electropositive redox state of the fifth-instar larval gut of Apis mellifera. Journal of apicultural research24, 211–213 (1985).
    CrossRef
  30. Koch, H. & Schmid-Hempel, P. Socially transmitted gut microbiota protect bumble bees against an intestinal parasite. Proceedings of the National Academy of Sciences108, 19288–19292 (2011).
    CrossRef
  31. Kwong, W. K. & Moran, N. A. Gut microbial communities of social bees. Nature Reviews Microbiology14, 374–384 (2016).
    CrossRef
  32. Bonilla-Rosso, G. & Engel, P. Functional roles and metabolic niches in the honeybee gut microbiota. Current opinion in microbiology43, 69–76 (2018).
    CrossRef
  33. Alatawy, M. et al. Gut Microbial Communities of Adult Honeybee Workers (Apis Mellifera). Biosciences Biotechnology Research Asia17, (2020).
    CrossRef
  34. Brune, A. Symbiotic digestion of lignocellulose in termite guts. Nature Reviews Microbiology12, 168 (2014).
    CrossRef
  35. Egert, M. et al. Structure and topology of microbial communities in the major gut compartments of Melolonthamelolontha larvae (Coleoptera: Scarabaeidae). Environ. Microbiol.71, 4556–4566 (2005).
    CrossRef
  36. Anderson, K. E. et al. Hive‐stored pollen of honeybees: many lines of evidence are consistent with pollen preservation, not nutrient conversion. Molecular ecology23, 5904–5917 (2014).
    CrossRef
  37. Corby-Harris, V., Maes, P. & Anderson, K. E. The bacterial communities associated with honeybee (Apis mellifera) foragers. PloS one9, e95056 (2014).
    CrossRef
  38. Engel, P., Martinson, V. G. & Moran, N. A. Functional diversity within the simple gut microbiota of the honeybee. Proceedings of the National Academy of Sciences109, 11002–11007 (2012).
    CrossRef
  39. Jojima, Y. et al.Saccharibacterfloricola gen. nov., sp. nov., a novel osmophilic acetic acid bacterium isolated from pollen. International Journal of Systematic and Evolutionary Microbiology54, 2263–2267 (2004).
    CrossRef
  40. Bonilla-Rosso, G. et al.Acetobacteraceae in the honeybee gut comprise two distant clades with diverging metabolism and ecological niches. bioRxiv 861260 (2019).
    CrossRef
  41. Sopko, B. et al. Detection and quantification of Melissococcusplutonius in honeybee workers exposed to European foulbrood in Czechia through conventional PCR, qPCR, and barcode sequencing. Journal of Apicultural Research 1–12 (2019).
  42. Budge, G. E. et al. The occurrence of Melissococcusplutonius in healthy colonies of Apis mellifera and the efficacy of European foulbrood control measures. Journal of invertebrate pathology105, 164–170 (2010).
    CrossRef
  43. Budge, G. E. et al. Molecular epidemiology and population structure of the honeybee brood pathogen Melissococcusplutonius. The ISME journal8, 1588–1597 (2014).
    CrossRef
  44. Takamatsu, D. et al. Typing of Melissococcusplutonius isolated from European and Japanese honeybees suggests spread of sequence types across borders and between different Apis species. Veterinary microbiology171, 221–226 (2014).
    CrossRef
  45. Erban, T. et al. European foulbrood in Czechia after 40 years: application of next-generation sequencing to analyzeMelissococcusplutonius transmission and influence on the bacteriome of Apis mellifera. PeerJ Preprints4, e2618v1 (2017).
    CrossRef
  46. Hunt, M. G. Index to Department Bulletins. (US Government Printing Office, 1936).
  47. Belloy, L. et al. Spatial distribution of Melissococcusplutonius in adult honeybees collected from apiaries and colonies with and without symptoms of European foulbrood. Apidologie38, 136–140 (2007).
    CrossRef

The Dynamic Fluctuation of Red Palm Weevil Rhynchophorus Ferrugineus (Olivier) in Makkah Al-Mukarramah city

$
0
0

Introduction

The palm trees have economic importance at the global level as they  are one of main food sources in many countries, as indicators confirm that palm tree cultivation is constantly growing. The number of palm trees in the world is 120 million date palms, 70% of them present in the Arab world, with a production rate of 67%  and  28%  of it in the Gulf countries, and that is from the global production of dates, which is estimated at about 6.7 million tons (1;2;3 &4)

Kingdom of Saudi Arabia is second largest producer of dates in the world with an estimated rate of 14.96% of the global production of dates(5&6), as there it has more than 400 cultivars of palm varieties in the world and 25of them are important (7;8&9) .The number of palm trees planted in the Kingdom of Saudi Arabia is estimated at about (28570884) palm trees, and the Makkah region contains about (1237568) palm trees (10) .

On the other hand, palm trees are affected by many insect and non-insects pests, but the RPWRhynchophorus ferrugineus (Olivier) (Coleoptera: Dryophthoridae) is one of the most dangerous invasive pests that threaten date palm trees and cause them a lot of damage and economic losses(11).The annual losses in the Middle Eastern countries are estimated by millions of dollars, with a loss of 30% of the total date production (12)

The first infection with RPW in the KSA was recorded in   of Hofuf city in 1987 AD (13).Although the first recording  by red palm weevil infestation in the world was on coconut palms, it was able to expand the range of its host from palm trees to (40) species, the most important of which are date palms(14;15;16&17). According to FAO It is classified as category-1 pest of date palm in the Middle-East. (5;9;18;19&20) .

Red  palm weevil larvae are the most destructive stage to the palm, as they feed on the internal tissues of palm and complete their growth and development internally (20&21).This cryptic habitat of RPW affects the early detection process of infestation with it, and thus makes controlling it a difficult task, in addition this behavior provides protection from harsh climatic conditions, which enhances its presence in a wide and varied environmental range, and this is the biggest challenge facing RPW control (22).(23&24)

considered that the use of pheromone traps as part of an Integrated Management Strategy (IPM) to control RPW is an environmentally friendly tool, easy to handle, long lasting and  does not make resistances .pheromone traps have been widely used in  many countries of the world, including Saudi Arabia, as part of the integrated RPW control strategies (25&26) .

In view of the scarcity of recent and integrated environmental studies on the RPW in the Makkah Al Mukarramah region and based on Saudi Arabia’s vision 2030 in diversification of the Kingdom’s production base, achieving food security, by increasing the contribution of the agricultural sector to the local product, advancing economic development, and reducing the infestation of RPW from currently estimated 10% to 1%,(27).The aim  of this research is to study the dynamic fluctuation and  determine the seasonal variations of abundance of adults RPW  and effect of temperature and relative humidity on weevils’ activity during the year in Makkah city, and  the effectiveness color of  pheromone traps  on monitoring  RPW populations.

Materials and Methods

Ecological study area

Environmental studies were carried out in the farms that are located in Makkah Al-Mukarramah Governorate in the western part of KSA  Fig.(1) from January through December (2019), with the agreement of  farm owners the control operations which were provide by the Ministry of Environment, Water and Agriculture to  the farmers were stopped.The sites were selected on the basis  of the large number of farms that were infested with theRPW, to monitoring the dynamic fluctuation of the red palm weevil during the four seasons (spring, summer, autumn and winter) and determining the relationship between the numerical density of the red palm weevil and the climatic conditions (temperature and relative humidity), by using food bait pheromone traps (FBPTs).A total of 40 traps were randomly installed  according to the method describe by (28), the distance between each trap  not less than 12 meters. The traps were inspected once every fortnightlyin order to collect RPWand replace freshfood baits and water.

Vol18No1_The_Waf_fig1 Figure 1: Study site of dynamic fluctuation for RPW.

click here to view figure

Sex ratio

The sex of adult RPW was distinguished, depending on the morphological characteristics, where the  males contain bristles on the rostrum while it was absent in the females (Fig. 2)

Vol18No1_The_Waf_fig2 Figure 2: The adult male and female of RPW

click here to view figure

Color preference experiment

Three colors were tested (black, red and white with the natural color of burlap), where the traps were painted with the tested colors and each color had four replicates.

Statistical analysis

The completely randomized design was used, and the results were  analysis using the general linear models procedure method.  The statistical software, 2001 (SAS) program was used to analyze the field results and compare the averages using the least significant difference (LSD) test at the level of significance. (0.05), and the Pearson Correlation Coefficient was used to determine the relationship between the RPW population density and the climatic conditions (temperature, relative humidity).

Results

The results showed the presence of RPW in traps throughout the year, with significant differences in numerical densities according to different collection times. Atotal of 1179 RPW were collected from all the traps during the study period started at January to  December. The  highest population density was recorded  during the month ofApril and March with mean population density (43.2 and 41.7) insect / trap respectively. It was the highest significantly compared to that were collected  during  July, October, August  and September, but it does not differ significantly from the numerical densities which  collected during  May, January, December, February, June and November, as the results showed the presence of numerical differences, but they are not significant (Table 1).

The study recorded two peaks of RPW activity throughout the year. A major peakin April with theaverage density (13.0, 30.2, and 43.2) insect / trap, and a lowest peakin December, where the monthly average was about (6.5, 22.0, 28.5) insect / trap for both males, females and total adults, respectively, Table (1) and Fig.(3).

Table 1: Monthly average of red palm weevil insects in Makkah Al-Mukarramah city during 2019.

MEAN ±S.E Months
Humidity Temperature Total Female Male
57.0 A±2.0 29.0EF ± 2.0 34.00 AB± 10.4 21.2ABC± 5.9 12.75 AB± 4.4 Jan.
54.0 AB±2.0 31.0 EF± 1.0 25.50ABCD ±6.2 18.7ABCD±5.1 6.75ABC±1.3 Feb.
48..0 BC±2.0 33.0DEF± 3.0 41.75 A±10.5 30.0A±6.5 11.75 AB±4.4 Mar.
42.0 CD±2.0 36.0BCDE±2.0 43.25 A± 11.1 30.2A±7.9 13.0 A±3.3 Apr.
34.0 EFG±2.0 39.0ABC±1.0 37.25  AB ± 10.8 27.0AB±8.2 10.25 AB±3.3 May.
32.0 GF±2.0 41.0AB±1.0 24.25  ABCD ±6.2 18.0ABCD±4.6 6.25 ABC±1.6 Jun.
31.0 G±2.0 41.0AB±1.0 16.75 BCD±1.6 11.0BCD±2.5 5.75BC±1.4 Jul.
38.0 DEF±2.0 41.0AB±1.0 5.50 D±3.2 4.0 D±2.4 1.5 C±0.8 Aug.
39.0 DE±2.0 43.0A±1.0 3.50 D ±1.9 3.2D±1.7 0.25 C±0.2 Sep.
48.0 BC±2.0 38.0 ABCD±2.0 10.00 CD ±4.7 8.5 DC±3.5 1.5 C±1.1 Oct.
56.0 A±2.0 34.0 CDEF±2.0 22.75 ABCD ±8.2 17.0ABCD±6.6 5.75 BC±1.6 Nov.
57.0 A±2.0 31.0 EF±1.0 28.50 ABC ±10.8 22.0ABC±9.8 6.50 ABC ±1.1 Dec.
6.1626 5.0318 22.94 17.082 7.1584 LSD
0.0001 0.0005 0.0090 0.0214 0.0051 P

*The averages which followed by similar letters in the same column, have no significant differences between them at  level of significance (0.05)

Vol18No1_The_Waf_fig3 Figure 3: Activity peaks of the red palm weevil during 2019 in Makkah Al-Mukarramah city.

click here to view figure

Statistical analysis results showed that the RPW has significant activity at spring season compared to the other seasons, where the mean number of insects collected (163.0) insects / trap; while the least active for RPW  was at autumn season with mean numerical density (63.0 and 48.3 ) insects / trap.Table (2) and Fig.(4).

Table 2: Seasonal fluctuation of the red palm weevil insects in Makkah Al-Mukarramah city during 2019.

Mean ±SE Seasons

 

Total Female Male
115.33 AB ±11.5 82.6 A±3.9 34.6  AB ± 8.1 Winter
163.0 A ±7.2 116.3 A±4.1 46.6 A ± 3.1 Spring
63.0 BC±21.9 44.0 B±16.1 18.0 BC ±6.0 Summer
48.33 C ±22.6 38.33 B±16.0 10.0 C ± 6.6 Autumn
55.97 38.276 20.467 LSD
0.0055 0.0049 0.0131 P

*The averages which followed by similar letters in the same column, have no significant differences between them at  level of significance (0.05).

Vol18No1_The_Waf_fig4 Figure 4 :Seasonal fluctuation of red palm weevil insects  in Makkah Al-Mukarramah city.

click here to view figure 

The results also showed a negative significant correlation between the mean population density of RPW and the temperature; where the correlation strength was (r = -0.318) and the correlation significant (P = 0.0027). The highest density was recorded  in April at an average temperature of (36.0) ° C, after that decrease the number associated  with an increase in the average temperature during  May – August, where the lowest population density of the weevil was recorded at September at (43.0) C. With the gradual decrease in temperatures, a gradual increase in the average population density of the weevil was recorded, reaching its highest average by the end of the fourth quarter of the year (2019) during December at (31.0) ° C (Fig.5).

Vol18No1_The_Waf_fig5 Figure 5 : The effect of temperature on the monthly and dynamic fluctuation of red palm weevil during 2019 in Makkah Al-Mukarramah city.

click here to view figure

Also the result showed a positive non-significant correlation between seasonal abundance and relative humidity where the  correlation strength (r = + 0.0715) and correlation significance (P = 0.629).  Then, at the beginning of the study, a relatively high average density of RPW was recorded in January at an average high relative humidity of 57%. The positive correlation between  relative humidity and the seasonal abundance of the weevil appeared during the fourth quarter of the year during October, November, and December, as the traps recorded a gradual increase in the numbers of weevils with a gradual increase.

Vol18No1_The_Waf_fig6 Figure 6 : The effect of humidity on the monthly and dynamic fluctuation of red palm weevil during  2019 in Makkah Al-Mukarramah city.

click here to view figure 

The male to female ratio was(1:3). During the study 72% of specimens collected were  identified as Females and 28 % were identified as males Table (3) and Fig.(7).

Table (3): The  average of male and female red palm weevil  that collected by all traps in Makkah Al-Mukarramah city.

MEAN ±S.E Sex
13.6 B± 2.0 Male
35.1 A± 4.0 Female
9.07 LSD
0.0001 P

*The averages which followed by similar letters in the same column, have no significant differences between them at  level of significance (0.05)

Vol18No1_The_Waf_fig7 Figure 7: The average percentage for males and females of the red palm weevil which collected by all traps  in Makkah Al-Mukarramah city.

click here to view  figure 

The color preference for RPW was tested, The result revealed that the  black traps were more effective and significantly in attracting RPW than that of other tested colors, where the mean collection of one black trap was (20.3) weevil.  Followed by red and white with burlap traps, which didn’t differ significantly from each other, as the average that collected by one trap was (13.5 and 10.2) weevil respectively.While the Saudi trap was the least significant in attracting the adults of the red palm weevil, with an average of 4.7 weevil per trap.Table (4) and Fig.(8).

Table 4: Red palm weevil numerical density under effect of trap color.

Mean ±SE Trap color

 

Total Female Male
13.5  B ± 1.9 10.0  B ± 1.3 3.4  B ± 0.6 Red
10.2 B ± 3.1 7.2 B ± 1.0 3.0 BC ± 0.5 White
20.3 A ± 2.3 14.7 A ± 1.5 5.5 A ± 0.9 Black
4.7 C± 0.8 3.0 C ± 0.5 1.6 C ± 0.4 Saudi
4.8409 3.3969 1.8331 LSD
0.0001 0.0001 0.0007 P

*The averages which followed by similar letters in the same column, have no significant differences between them at  level of significance (0.05)

Vol18No1_The_Waf_fig8 Figure 8: Temporal changes for red palm weevil under effect of trap color.

click here to view figure

Discussion

In this study, pheromone traps were used to monitor the dynamic fluctuation of RPW during all seasons (spring; summer; autumn and winter), and determine the relationship between the population density of RPW and the climatic conditions (temperature and relative humidity) ; this is consistent with in that since the beginning of the 1990s, pheromone traps have been used all over the world either to monitor and control RPW infestation in palm farms or to control it (29).

The results showed the presence of RPW throughout the four seasons, with the difference in numerical densities between months in different seasons as well as between months within a single season. This result is consistent with that found by (30&31) , who recorded the presence of the red palm weevil throughout the year.(20)reported that the population density of RPW is affected by the different climatic conditions, where the captured RPW adults differed significantly between months and during the same month; and the population density was higher in warmer seasons (32),  indicated that the weevil was more active in the spring and autumn season, specifically in April and November respectively in Egypt.

The results of this study were also identical to that reported by (33), which the highest number of adults of RPW in spring season in  March, April and May,  in KSA and Egypt was. He also added that RPW  had two activity peaks, the first one was in May and the second was in November, and the lowest density of weevil was in August. In the Middle East, the high seasonal activity of RPW was recorded during the months of March, May, September and October (34&35).

These seasonal changes in temperature and relative humidity reflect the temporal variation in RPW numbers collected by pheromone traps (36).

This difference in seasonal abundance is due to the influence of climatic factors on the activity of RPW, as many environmental studies have indicated that both of environmental parameters (temperature and humidity) affect on the activity ofRPW. This is confirmed by the results of the study that conducted by (24)which agreed with the results of the present study, where  the seasonal variations of RPW abundance due to climatic conditions and added that the red palm weevil tends to rise during the spring season.

The results of their study also concluded that there was a positive relationship with high statistical significance between the number of RPW that was captured and the temperature, while a negative relationship was observed between the numbers of the weevils and the relative humidity, which is consistent with the results of the current study, as well as with what (37) that there is an effect of environmental factors on the population density of red palm weevil, as they found a direct correlation between the numbers of the weevil and the temperature and an opposite relationship with relative humidity.

The importance of environmental factors in general and temperature and relative humidity in particular on the living organisms is not hidden, as they are considered among the limiting factors for the distribution and spread of living organisms in different environments due to their joint effect on the vitality and activity of living organisms.

Each species of insect has a temperature range in which it can live, and within this range there is an optimum temperature at which the insect activity is at its peak; also within this range there are hot and cold dormancy zone , in which the temperature increases or decreases than optimum temperature, so insect’s activity begins to decline until it stops as a result of  beyond the extent temperature that the insect can tolerate, which leads to its death. The same is true for humidity, as it affects the biological processes of the insect through a water imbalance, and the effect of humidity is more clear with high temperatures (38).

The insect loses water in response to the increase of temperature in the surrounding environment. The loss process done through the spiracles; cuticular and by excretory system, but 90% of transpiration is via the cuticle, this layer consists of three layers one of them is epicuticle and the lipid layers that present in its structure are one of the main substrates that hinder water loss.

The results of (39)study shown the physiological basis to explaining the effect of drought on RPW; they isolated and identified ten hydrocarbons compounds the weevil cuticle. Seven of them were un-branched saturated n-kinases (n-Heptadecane; n-Nonadecane; n-Heneicosane; n-Tricosane; n-Pentacosane; n-Nonacosane and n-hexatriacontane) and  they represent 75%, while the fatty alcoholic compound (3- (E) Eicosanol) ; the ester compound (1-Henicosyl formate) and the unsaturated alkene compound (Tricosene) represented 17-25% of the total hydrocarbons.(40)added that at a rather high environmental temperature, most of the saturated un-branched hydrocarbons take a solid crystalline form and thus prevent water loss,also the study results that carried out by (41)showed the temperatures which  required to dissolve these compounds, where the temperature for most saturated un-branched hydrocarbons ranged between (41 – 76)  C°, which explains the ability of red palm weevil to prevent the water loss from its bodies under high temperature and low humidity.  As for the rest of the unsaturated compounds, their melting point is less than 21C ° .(39) added that, based on previous studies, 25% of the hydrocarbons in RPW cuticle are in liquid form at temperatures below 20C ° , where they act as large pores that precipitate through the wax cover. Thus, the large rate deposition of these compounds in the cuticle of RPW may be responsible for the high water loss rates that are attributed to insect death under  high temperature and low relative humidity.

Furthermore, the temperature and relative humidity have an effect on the growth and development of RPW, starting from eggs laying; hatching success; larval development and ability to emerge as an adult insect, as mentioned by a lot of scientific research (42; 43; 44; 45; 46 &47).This explains the relationship of tested climatic factors to the dynamic fluctuation and seasonal abundance of red palm weevil.

In addition, the present study aimed to determine the sexual ratio of adults red palm weevil that captured by pheromone traps during the study period.The results showed significant differences between the mean number of males and females, where females were the most significant, this agree with many scientific studies (16; 33; 48; 49; 50; 51; 52; 53; 54 &55).

In a study conducted during 2018 and 2019 to compare the effectiveness of two types of traps used to control RPW in Egypt, the results showed that there was no significant difference between the total number of RPW collected by the two tested traps. While there was a significant difference between the number of males and females, where the sexual ratio between males and females was (1: 1.43 and 1: 1.94) during 2018 and 2019, respectively, (56).In another experiment by (37). to follow up the red palm weevil, it was found that the number of females collected exceeds the number of males, with ratio of males to females (3.1: 1).

In the current study, the increased number of females may be due to they may have a higher sensitivity  to aggregation pheromone than males and this has been confirmed by previous studies, as mentioned by (57)where they found that females of RPW respond to aggregation pheromone more than males.

(58)also indicated that females of RPW have more basioconic sensillae on antenna than males, as it is known that these basioconic sensillae are sensitive to aggregation  pheromone (59).

Also, as a result of feeding the larvae and crowding the palm with the different stages of RPW , the host becomes of poor quality. Thus, it is necessary to leave the this host and search for another host for laying eggs to ensure continuity of survival; perhaps the reason is that the females search for males for mating, so the females are sensitive to the aggregation  pheromone, which explains the increase number of females over males in pheromone traps.

Or perhaps reason of more females over males is due to their higher levels of activity. This is confirmed by previous studies and current result in that the majority of captured females are young and fertile(49&60), which enhances the use of pheromone traps as a tool of monitoring and controlling red palm weevil infestation, because it reduces the numerous densities as a result of captured fertile females thus, the infestation decreases through lack of laying eggs, which serves integrated management to control red palm weevil.

In many scientific studies and researches, focus has been on the design and color of the trap; the pheromone and the kermon which  used in it and the duration of their change as variables affecting the success of RPW control process using food bait pheromone traps  (55&61).

In the ourstudy, the color preference of RPW was tested, and the results showed that black-colored traps were more effective and more significant in attracting red palm weevil adults compared to the tested colors, and this is in agreement with many scientific studies (36;  54; 62;63 64 &65 ).

The main reason for red palm weevil’s attraction to black trap is that the black color wavelength spectrum is very similar to the palm tree fibers when analyzing the spectral reflectance of the tested colors and some plant tissues, which leads to the attraction of the weevil adults to black traps. More compared to other colors (66); or due to the black color absorbs more sunlight compared to other colors which leads to a high temperature in black traps, that might cause greater pheromone release, which  result in increase insect captures (67).

Conclusions

Environmental studies concluded that the RPW was present in traps throughout the year, The study recorded the highest population density during April and March, and two peaks of weevil activity in April and  in December. The spring season witnessed significant activity of weevil; with a negative significant correlation between the mean population density of weevil and the temperature; and a positive non-significant correlation with relative humidity. Also the females were the highest density significant Compared with males. The black traps were more effective and more significant in attracting weevils adults than the tested colors.

Acknowledgments

We are very grateful to the farms owners  for allowing us to work throughout the study period;  and  Ministry of Environment, Water and Agriculture branch in Makkah Al-Mukarramah, KSA andDr. Khalid Ali Asiri, the head of  Arid Land Agriculture Department, King Abdulaziz University  for help .

Conflict of Interest

There is no conflict of interest.

Funding Source

none

References

  1. Erskine, W.; Moustafa, A. T.; Osman, A. E.;  Lashine, Z.;  Nejatian, A. and  Badawi, T. (2004)  Date palm in the GCC countries of the Arabian Peninsula, Regional Workshop on Date Palm development in the Abu Dhabi, Ministry of Education and scientific research, UAE.
  2. Kader, A. A. and Hussein, A. (2009) Harvesting and postharvesting handling ofdates, International Center for Agricultural Research in the Dry Areas (ICARDA), Aleppo, Syria.
  3. El-Juhany, L. I. (2010) Degradation of date palm trees and date production in Arab countries: Causes and potential rehabilitation, Australian Journal of Basic and Applied Sciences , 4(8): 3998-4010.
  4. Sewify, G.H. (2013) Red palm weevil problem and solution, Scientific publishing center, KAU.
  5. Sallam, A.A.; El-Shafie, H.A.F. and Al-Abdan, S. (2012) Influence of farming practices on infestation by red palm weevil Rhynchophorus ferrugineus (Olivier) in date palm: a case study, International Research Journal of Agricultural Science and Soil Science, Vol. 2(8) pp. 370-376.
  6. Siddiq, M. and Greiby, I. (2014) Overview of date fruit production, postharvest handling, processing and nutrition dates, Postharvest Science, Processing Technology and Health Benefits, First Edition.
    CrossRef
  7. Anonymous (2009) Annual statistical data (Ed. Department of studies, planning and statistics, Ministry of Agriculture, Kingdom of Saudi Arabia). 269p.
  8. Ashraf, Z. and Hamidi-Esfahani, Z. (2011) Date and date processing: a review, Food Reviews International, 27: 101-133.
    CrossRef
  9. Al-Hudaib, K.; Ajlan, A. and Faleiro, (2017) Genetic Diversity among Rhynchophorus ferrugineus Populations from Saudi Arabia and India, Scientific Journal of King Faisal University (Basic and Applied Sciences) 19:(1).
  10. National Palm and Dates Center (2018) Semi-annual report,Riyadh.
  11. Milosavljevic, I.; El‑Shafie, H.A.F.;  Faleiro, J. R.; Hoddle, C.D.;  Lewis, M. and Hoddle,  S. (2019) Palmageddon: the wasting of ornamental palms by invasive palm weevils, Rhynchophorusspp,  Journal of Pest Science, 92: 143-156.
    CrossRef
  12. Al-Ayedh, H. (2016) Global research review of red palm weevil (RPW) Rhynchophorus ferrugineus, Life science and environment research institute king Abdulaziz city for science and technology.
  13. Al-Abdulmohsin, A. M.H. (1987) The First Record of the Red Palm Weevil in the Kingdom of Saudi Arabia, Agriculture in the Arab World, 3 (9): 15-16.
  14. Ojeu, (2008) Commission decision of 6 October 2008 amending decision of 2007/365/EC on emergency measures to prevent the introduction into and the spread within the community of Rhynchophorus ferrugineus (Olivier) 2008/5550/C, Official J. European Union L 266: 14.
  15. Anonymous (2013) Save Algarve palms.http://www.savealgarvepalms.com/en/weevilfacts/ host-palm-trees (accessed on 1/8/2020).
  16. AL- Saroj, S.; AL-Abdallah, E.; AL-Shawaf, A.; AL-Dandan, A.; AL-Abdullah, I.; AL-Shagag, A.; AL-Fehaid, Y.; Abdallah, A.B. and Faleiro, J.R.(2017) Efficacy of bait free pheromone trap (ElectrapTM) for management of red palm weevil, Rhynchophorus ferrugineus (Olivier) (Coleoptera: Curculionidae, Pest Management in Horticultural Ecosystems, Vol. 23, No. 1 pp 55-59 .
  17. (2020) Rhynchophorus ferrugineus, EPPO datasheets on pests recommended for regulation.  Available from: https://gd.eppo.int [Accessed: 15 May 2020]
  18. Zaid, A, De Wet PF, Djerbi, M , Oihabi A (2002) Date palm cultivation, The semi-annual report of National Center for Palms and dates.
  19. Anonymous (2004) Proceedings of the date palm regional workshop on ecosystem based IPM for date palm in the Gulf Countries, Al-Ain, United Arab Emirates. UAE .
  20. Faleiro, J. R. (2006) A review on the issues and management of red palm weevil Rhynchophorus ferrugineus (Coleoptera: Rhynchophoridae) in coconut and date palm during the last one hundred years, International Journal of Tropical Insect Science, 26(3): 135-154.
  21. Faleiro, J. R. (2004) Pheromone based strategy for the management of red palm weevil in date palm and coconut agro-ecosystems: Implications, protocols and impact. Pages 44-52 in Proceedings of The Date Paper presented at Date Palm Regional Workshop on Ecosystem based IPM for Date Palm in the Gulf Countries UAE University, Al-Ain, UAE; 28-30 .
  22. Faleiro, J.R.; Ferry, M.; Yaseen, T. and Al-Dobai,  (2019b) Overview of the gaps, challenges and prospects of red palm weevil management. In: Presented at the International Scientific Meeting on ‘Innovative and Sustainable Approaches to Control the Red Palm Weevil, Arab Journal of Plant Protection. 37(2):170-177.
    CrossRef
  23. Kaakeh, W. (2000) The use of synthetic pheromones in integrated pest management program (Review), Emirates J. Agri. Sci., 12: 1-32.
  24. El-Bokl, M. M.; Sallam, A. M.; Abdallah, G.    and Gabr, B. M.(2015) Efficacy of aggregation pheromone in trapping red palm weevil (Rhynchophorus ferrugineus Olivier) infested Date palms in Damietta, Egypt, Egyptian Academic Journal of Biological Sciences C. Physiology & Molecular Biology.  7(1): 51 – 59.
    CrossRef
  25. Abbas, M. S. T. Hanounik, S. B.; Shahdad, S. and Aibagham, S. A.  (2006) Aggregation pheromone traps, a major component of IPM strategy for the red palm weevil, Rhynchophorus ferrugineus in date palms (Coleoptera: Curculionidae), J. Pest Sci. 79: 69-73.
    CrossRef
  26. Faleiro, J. R.; Ben Abdullah, A.; Kumar, J.A.; Shagagh, A. and Al Abdan, S. (2010) Sequential sampling plan for areawide management of Rhynchophorusferrugineus(Olivier) in date palm plantations of Saudi Arabia, J. Trop.Insect Sci., 30: 145-153.
    CrossRef
  27. Bob, M. A. (2019) Management of the red palm weevil Rhynchophorus ferrugineus (Olivier) using sustainable options in Saudi Arabia, Arab J. Pl. Prot. Vol. 37, No. 2.
    CrossRef
  28. Oehlschlager, ( 2006) Mass trapping as a strategy for management of Rhynchophorus palm weevils, In: I jornada internacionalsobre el picudorojo de las palmeras.- Fundación agroalimed, Valencia, Spain. , pp. 143-180.
  29. Faleiro, J.R.; Al-Shawaf, A.-M.; El-Shafie, H.A.F. and Pai Raikar. S. (2019a) Studies on service free semiochemical mediated technologies to control red palm weevil Rhynchophorus ferrugineus Olivier based on trials in Saudi Arabia and India, Arab Journal of Plant Protection, 37(2): 136-142.
    CrossRef
  30. Abdallah, F. F. and Al-Khatri, S. A. (2003) Seasonal fluctuation of Rhynchophorusferrugineus(Oliv.) Coleoptera – Curculionidae) in the Sultanate of Oman, International conference on date palm (2003) kingdom of Saudi Arabia king Saud University.
  31. Maruthadurai, R. and Ramesh, R. (2020) Mass trapping of red palm weevil and rhinoceros beetle in coconut with aggregation pheromone, Indian Journal of Entomology, 82(3), 439-441.
    CrossRef
  32. El-Sebaey, Y. (2003) Ecological studies on the red palm weevil, Rhynchophorusferrugineus (Coleoptera: Curculionidae) in Egypt, Egypt. J. Agric. Res., 81 (2):523-529.
  33. Faleiro, J. R .(2005) Insight into the management of red palm weevil Rhynchophorus ferrugineus Olivier: based on experiences on coconut in India and date palm in Saudi Arabia,FundacionAgroalimed. I Jornada Internacionalsobre el PicudoRojo de las Palmeras, November 27-29, pp. 35-57.
  34. Dembilio, O.; Riba, J.M.; Gamon, M. and Jacas, J.A. (.2015) Mobility and efficacy of abamectin and imidacloprid against Rhynchophorus ferrugineus in Phoenix canariensisby different application methods, Pest Management Science, 71(8): 1091-1098.
    CrossRef
  35. Milosavljevic, I.; El-Shafie,  A.F.;  Faleiro, J.R.;  Hoddle, C.D.; Lewis , M.; Hoddle, M.S. and  Palmageddon (2018)  The wasting of ornamental palms by invasive palm weevils, Rhynchophorus spp, Journal of Pest Science. 2018;92:143-156
    CrossRef
  36. Al Saoud , A.H.; AL-Deeb, M.A. and Murchie, A.K. (2010) Effect of color on the trapping effectiveness of red palm weevil pheromone traps, Journal of Entomology,7(1):54-59.
    CrossRef
  37. Firdaus, M. M.; Chuah, T. S. and Wahizatul, A. A. (2020) Synergistic Effect of Synthetic Pheromone and Kairomone-Releasing Food Baits in Mass Trapping System of Red Palm Weevil, Rhynchophorus ferrugineus. In IOP Conference Series: Earth and Environmental Science (Vol. 494, No. 1, p. 012015). IOP Publishing .
    CrossRef
  38. Al-Mahmadi, Ruqayya. M. A. (2011) General Entomology, Scientific Publishing Center, King Abdulaziz University.
  39. Monzer, M.A. and Srour, A. (2009)  Desiccation intolerance of the red palm weevil, Rhynchophorus ferrugineus(oliv) adults in relation to their cuticular hydrocarbons, Egypt. Acad. J. biolog. Sci., 2(1): 47-53 .
    CrossRef
  40. Woodrow, R. J.; Grace, J. K.; Nelson, L. J. and Haverty, M. I. (2000) Modification of cuticular hydrocarbons of cryptotermes brevis (isopteran: Kalotermitidae) inresponse to temperature and relative humidity, Environ. Entomol. 29(6):1100-1107.
    CrossRef
  41. Gibbs, A.G. (2002).Lipid melting and cuticular permeability: new insights into an old Problem, Insect Physiol. 48: 391- 400
    CrossRef
  42. Martin, M. and Garcia, T.C.( 2006) Manejo de la cría del picudorojo de la palmera,” Rhynchophorus ferrugineus (olivier, 1790) (coleoptera, dryophthoridae), endietaartificialyefectosensubiometríaybiologia, Boletin de sanidad vegetal. Plagas. 32:631-642.
  43. Al‐Ayedh, H. and Rasool, K. (2010) Sex ratio and the role of mild relative humidity in mating behaviour of red date palm weevil Rhynchophorus ferrugineus. (coleoptera:Curculionidae) gamma‐irradiated adults, J. App. Entomol. 134:157-162.
    CrossRef
  44. Li, L.Q.; Wei-Quan, M.; Zi-long,Y.; Wei,H.; Shan-chun and Pen,Z.Q.(2010) Effect of temperature on the population growth of Rhynchophorus ferrugineus (Coleoptera: Curculionidae), on sugarcane, Environmental Entomology, 39(3): 999-1003.
    CrossRef
  45. Dembilio,O. and Jacas, J. A. (2011)Basic bio-ecological parameters of the invasive red palm weevil, weevil Rhynchophorus ferrugineus (Coleoptera: Curculionidae) in Phoenix canariensis under Mediteranean climate, Bulletin of Entomological Research, 101 (2): 153-163.
    CrossRef
  46. Dembilio, O.; Tapia, G.V.;  Tellez, M.M.  and Jacas, J. A. (2012) Lower temperature thresholds for oviposition and egg hatching of the Red Palm Weevil, Rhynchophorus ferrugineus (Coleoptera: Curculionidae), in a Mediterranean climate,Bulletin of Entomological Research, 97-102.
    CrossRef
  47. El-Shafie, H.A.F.; Faleiro, J.R.; Abo-El-Saad, M.M. and Aleid, S.M.(2013) A meridic diet for laboratory rearing of Red Palm Weevil, Rhynchophorus ferrugineus (Coleoptera: Curculionidae), academic Journals, Vol.8(39),pp. 1924-1932.
  48. Vidyasagar, P.; Mohammed, H.; Abozuhairah, R.; ALMohanna. O. and AL-Saihati, A.  (2000) Impact of mass pheromone trapping on red palm weevil adult population and infestation level in date palm gardens of Saudi Arabia, Planter, 76 (891): 347-355.
  49. Abraham, V. A.; Faleiro, J. R.; AL Shuaibi, M. A. and AL Abdans, S. (2001) Status of pheromone trap captured female red palm weevils from date gardens in Saudi Arabia J. Trop. Agric. 39: 197-199.
  50. Faleiro, J.; Kumar J.A. and Rangnekar, P. (2002) Spatial distribution of red palm weevil Rhynchophorus ferrugineus oliv (coleoptera: Curculionidae) in coconut plantations, Crop Prot. 21:171-176.
    CrossRef
  51. Abdallah, F. F. and AL-Khatri, S. A. (2005) The effect of pheromone, kairomone and food bait on attracting adults of red palm weevil Rhynchophorus ferrugineus in the Sultanate of Oman in Date palm plantations,  Egyptian Journal of Agricultural Research, 83: 169-177.
  52. Al Saoud , A.H. (2007) Importance of Date Fruit in the Red Palm Weevil, Rhynchophorus Ferrugineus Olivier (Coleoptera: Curculionidae), Aggregation Pheromone Traps, Rsearchgat, 405-411.
    CrossRef
  53. Jayanth, K. P.; Mathew, M. T.; Narabenchi, G. B. and Bhanu, K. R. M. (2007) Impact of large scale mass trapping of red palm weevil Rhynchophorus ferrugineus Olivier in coconut plantations in Kerala using indigenously synthesized aggregation pheromone lures, Indian Coconut J. 38: 2-9.
  54. Vacas, S.; Primo, J. and Navarro-Llopis, V. (2013) Advances in the use of trapping systems for Rhynchophorus ferrugineus (Coleoptera: Curculionidae): Traps and attractants, Journal of Economic Entomology 106: 1739-1746.
    CrossRef
  55. Vacas, S.; Abad-Paya, M.; Primo, J. and Navarro-Llopis, V. (2014) Identification of pheromone synergists for Rhynchophorus ferrugineus trapping systems from Phoenix canariensispalm volatiles, Agric. Food Chem. 62: 6053-6064.
    CrossRef
  56. Abd El-Wahaba, A. S.; Abd El-Fattaha, A. Y.;  El-Shafeib, W. K. M. and El-Helalya, A. A. (2020) Efficacy of aggregation nano gel pheromone traps on the catchability of Rhynchophorus ferrugineus (Olivier) in Egypt, Brazilian Journal of Biology.
    CrossRef
  57. Poorjavad, N.; . Goldansaz, S. H and Avand-faghih,  (2009) Response of the red  palm weevil Rhynchophorus ferrugineus to its aggregation pheromone under laboratory conditions, Bull. insectol., 62:257-260.
  58. Avand-Faghih, A. (2004) Identification et application agronomique de synergistesvegetaux de la pheromone du charançonRhynchophorus ferrugineus (Olivier) 1790, These pour obtenir le titre de docteur de l’INA-PG, Institut National Agronomique Paris-Grignon et Institut National de la Recherche Agronomique, France.
  59. Said, I.; Tauban, D.; Rrnou, M.; Mori, K. and  Rochat, D. (2003) Structure and function of the antennal sensilla of the palm weevil Rhynchophorus palmarum (Coleoptera, Curculionidae), Journal of Insect Physiology, 49: 857-872.
    CrossRef
  60. Faleiro, R.l; Rangnekar, P. A. and Satarkar, V. R. (2003) Age and fecundity of female red palm weevils Rhynchophorus ferrugineus (Olivier) (Coleoptera: Rhynchophoridae) captured by pheromone traps in coconut plantations of India, Crop Prot. 22: 999- 1002.
    CrossRef
  61. Jaques, A.  (2020) Guidelines on visual inspection for early detection of red palm weevil in Canary Island palm (Phoenix canariensis), In: Elkakhy M, Faleiro JR, editors. Red Palm Weevil: Guidelines on Management Practices. Rome: FAO
  62. Abuagla, A. and Al- Deeb, M. (2012 )   Effect of bait quantity and trap color on the trapping efficacy of the pheromone trap for the red palm weevil, Rhynchophorus ferrugineus,  Journal of Insect Science, 12 (120): 1-6.
    CrossRef
  63. Al Saoud , A.H. (2013) Effect of ethyl acetate and trap colour on weevil captures in red palm weevil Rhynchophorus ferrugineus (Coleoptera: Curculionidae) pheromone traps, International Journal of Tropical Insect Science, 33 (3): 202- 206.
    CrossRef
  64. Abdel-Azim, M.M.; Khan, R.; AL-Dosari, S. Vidyasagar, P.; Ibrahim, S. and Shukl, P. (2014) Studies for colour-selection of Rhynchophorus ferrugineus pheromone trap, Journal of Plantation Crops, 42 (3): 386-391.
  65. Avalos, J.A. & Soto, A. (2015) Study of chromatic attraction of the red palm weevil,Rhynchophorus ferrugineus using bucket traps,Bulletin of Insectology 68 (1): 83-90.
  66. Maso, J.A.A. (2015) Factors influencing the mobility of Red palm weevil Rhynchophorus ferrugineus (Coleoptera: Dryophthoridae) adults, Ph.D. Thesis, UniversitatPolitecnica De Valencia
  67. Hallett, R.H.; Oehlschlager A.C. and Borden, J.H. (1999) Pheromone trapping protocols for the asian palm weevil, Rhynchophorus ferrugineus (coleoptera: Curculionidae), Int. J. Pest Manage. 45:231-237.
    CrossRef

Phytochemical Properties and Pharmacological Role of Plants: Secondary Metabolites

$
0
0

Introduction

Today’s lifestyle is a leading cause to many human diseases. Allopathic medicines often work effectively against the disease but may show extreme side effects in certain cases. Commonly manifested side effects of allopathic medicines are face swelling, rashes on the body, itching, headache, inflammation, and drug resistance. A safer alternative to treat diseases is herbal or plant derived medicines that have been used since the ancient period (Kaberaet.al 2014). India and China provide the best example of the early use of medicinal plants. Both countries enlist countless plant-derived medicines (Tang et.al 1992).The diversity of medicinal plants depends on many factors such as climate, altitude, seasonal fluctuations etc. While many plants are perennial and live for many years contributing as a consistent source of medicinal compounds, other plants have shorter life span ranging from seasonal to annual or biennial. There is a huge variety of seasonal plants that show medicinal properties, some plants grow in summer, some in winters, and some plants occur only in the spring season. Some examples of medicinal plants that grow in different seasonsare Achilleafilipendulina, Santolinachamecyparissus,and Menthalongifolia grows in summer, Cistusmonspeliensi, Ocimumgratissimum grows in the spring season (Soniet.al 2015).

The versatile and vast pharmacological effects of medicinal plants are completely dependent on their phytochemical constituents. Various phytochemicals of plants have been isolated for drug discovery and development.Modern analytical techniques such as electrophoresis, chromatography, enzymology, and isotope techniques have been used to characterize phytochemicals, elucidate their structural formulas and decipher their biosynthetic pathways (Hussein et.al 2018, Okada et.al 2010). To explore the therapeutic use of plants, it is pertinent to have deep understanding of phytochemistry and have detailed knowledge of phytochemical composition of plant extracts which further can be used to develop different medicines.

Generally, the phytochemicals are divided into two categoriesi.e. primary and secondary metabolites based on their role in different metabolic processes.Primary metabolites are involved in primary processes such as respiration, growth, cell division, photosynthesis and food storage. The biomolecules such as carbohydrates, amino acids and lipids are categorized as primary metabolites as they are fundamental reactants and intermediates in carbon metabolism, nitrogen metabolism and associated pathways (Seigleret.al 1995). On the other hand, secondary metabolites are derived from primary metabolites in a very small amount, usually at a certain growth stage or to serve a specific function. Secondary metabolites provide the ability to defend against biotic and abiotic stress in plants. The mechanism of defence in plants varies according to the specific requirements of plants and is affected by physiological conditions, climate variations and environmental factors (Ballhornet.al 2009, Blank et.al 2012).

Plant secondary metabolites are broadly divided into three categories: Terpenoids, Alkaloids, and Phenolics (Savithramma et.al 2011). Each of these classes of secondary metabolites includes a huge array of compounds that have been found effective to treat different diseases, some of these compounds are- atropine, codeine, morphine, and nicotine, coming under alkaloids; linalool comes under terpenoids, while flavonoids, lignans and proanthocyanidins are categorized asphenolics. In the present review, secondary metabolites are studied thoroughly that include properties of secondary metabolites, biosynthetic pathways of secondary metabolites, structures and classification of secondary metabolites, and their pharmacological activities.Pharmacological activities of some secondary metabolites that have been used to treat various diseases are enlisted in the given table (Table 1).

Table 1: Pharmacological Activities of Secondary Metabolites.

Name of secondary metabolites Pharmacological activities References
Linalool Antibacterial, exert an effect on CNS Taniguchi et.al 2014,

Zhang et.al 1987

Codeine Antitussive, antidepressant, analgesic, sedative, and hypnotic properties Smith et.al 2006,

Vreeet.al 2000

Morphine Acute pulmonary edam and reduce the shortness of breath Takitaet.al 2000
Quinine Antipyretic. Antimalarial, analgesic EI-Tawilet.al 2010,

Mwitaet.al 2012

Atropine Anti-cholinergic, ant myopia, effects competitive antagonist of muscarine acetylcholine receptors Guet.al 2011
Nicotine Insecticide, anti-inflammatory, antiherbivore Melton et.al 2006,

Rhoades et.al 1976

Berberine Antiviral, antibacterial, anticancer, antidiabetic, and anti-inflammatory Zhaet.al 2010,

Zhang et.al 2010

 

Gallic acid antibacterial, antiviral, antifungal, anti-inflammatory, antitumor, ant anaphylactic, antimutagenic, choleretic, and bronchodilator actions and promote muscle relaxation Harborneet.al 1993
Hydroquinone Antimicrobial and used as antiseptics Pelczaret.al 1988
hydrolyzable tannins Anti-diarrhoeal, antidotes in poisoning by heavy metals, antiangiogenic, also treat urinary tract infections Jepson et.al 2008
Coumarins Anti-inflammatory, anticoagulant, anticancer, and anti-Alzheimer’s Xuet.al 2015

Description of classes of secondary metabolites

Secondary metabolites can be classified based on their chemical composition. These phytochemicals are divided into three broad categories-Alkaloids,Phenolics and Terpenoids, as already mentioned above. A brief description of each of these categories is given further.

Alkaloids

Alkaloids are nitrogen-containing compounds which are widely distributed among large number of plant families. These compounds can be found in the whole plant or sometimes in a specific part of the plant. It is a highly diverse and large group consisting of more than 1800 alkaloids, all of which are different from each other and have different chemical structures. Alkaloids contain one or more nitrogen groups in their chemical structures. A number of researches have shown potential pharmacological effects and curative properties of alkaloids against many human diseases and disorders. There isa huge list of alkaloids that are used in pharmacological activities. Some of the alkaloids are enlisted in the Table 2 given below (Egamberdie va et.al 2017).

Table 2: List of plants that contain pharmacologically important alkaloids.

S.No. Name of the plant Alkaloids References
1 Liriodendron tulipifera L. Aporphine, liriodenine, lysicamine, lanuginosine Ziyaevet.al 1987
2 Nitraiaschoberi L. Schoberine, nitraraine, nitraramine, sibiridine, vasicinone Tulyaganov and Kozimova 2005
3 Convolvulus subhirsutus Convolvine, convolamine, convolidine, phyllabine, phyllalbine, nortropine, conpropine Gapparovet.al 2007
4 Convolvulus pseudocanthabricaschrenk Convolvine, convolamine, convolvidine, convolicine Gapparov and Aripovaet.al 2011
5 Arundodonax L. Deoxyvasicinone, ardine, donine, donaxarine, arundamine Khuzhaevet.al 2004
6 Crambekotschyana Goitrin and goiridin Okhunovet.al 2011

Phenolic compounds

Phenolic compounds encompass a large number of phytochemicals consisting of one or more phenol groups. Phenols are responsible for the color, flavor, and taste of many herbs that are used in drinks and food. These secondary metabolites are highly valued for their pharmacological activities. Phenols are also used in many drugs due to their important pharmacological properties such as antioxidant, anti-microbial, anti-inflammatory, anti-cancer etc.  Phenols are classified on the basis of their different chemical structures, enlisted in Table3 given below, along with their respective pharmacological activities(Puneetet.al 2013, Montanheret.al 2007, Serafiniet.al 2010).

Table 3: Classification of phenolic compounds with their pharmacological activities.

Types of phenolic compounds Pharmacological activities
Simple phenols Treat urinary tract infections, antimicrobial, anti-inflammation and used as antiseptic in surgeries
Tannins Used to convert raw animal hides into leather, anti-diarrhoeal, antidotes in poisoning
Coumarins Anticoagulant and anti-Alzheimer
Flavonoids Antithrombotic, anti-allergic, vasoproptective, inhibit tumour to grow and protect gastric mucosa
Xanthos Antifungal
Stilbenes Helps in the production of Estrogen
Lignans Antimicrobial, antifungal activities

Terpenes

Terpenes also form a diverse group of plant secondary metabolites that mainly consist of a five-carbon isoprene unit. Terpenes are classified according to the number of isoprene units in the molecule,the classes are summarized in Table4 (Hoffmann et.al 2003).

Table 4: Classification of terpenes.

Name of Terpenes Name of terpenoids Location of terpenoids References
Hemiterpene (C5) Isoprenenol

Isovalenic acid

Synthesis

Essential oils

Eadieet.al 2004. Araet.al 2006, Elson et.al 1988
Monoterpene (C10) Limonene Essential oil Espinaet.al 2013
Sesquiterpene  ( C15) ABA (Abscisic acid) Zhang et.al 1987
Diterpene

(C20)

Gibberellin Gibberellafujikuroi Hakoshimaet.al 2011
Triterpene

(C30)

Brassinosteroids Lychinsviscaria, Brassica napus Coelho et.al 2013, Krishna et.al 2003
Tetraterpene

(C40)

Carotenoids Carrot, chloroplast, and chromoplasts of plants M.M et.al 2014

Some secondary metabolites recognized for their pharmacological activities along with their general chemical structures and examples are enlisted in Table 5.

Vol18No1_Phy_Bhu_tab5 Table 5: Secondary metabolites and their examples.

Click here to view table

Properties of phytochemicals and Pharmacological activities of plants

Plants survived on the planet for more than 400 million years. Plants cannot move from one place to another so they have to face lots of biotic and abiotic stress that are represented in Figure1. Plants neither have any active weapon to attack plant-eating animals or herbivores and microbes,nor do they have any shield to protect themselves from environmental stress. Secondary metabolites serve as the defense system of plants as they protect them from all the biotic and abiotic stresses (Asif 2015). Owing to their bio activity, secondary metabolites have been historically used not only in Indian medicines (Ayurveda) but also used traditionally in Kampo medicines, European medicines, American, Australian, and traditional medicine system of Africa.There is extensive research that has been carried out in search of novel and safe plant derived medicine. For example, Alorkpaet.al 2016 extracted out bio active compounds from Carica papaya leaves and investigated their antimicrobial activity. They identified the presence of many secondary metabolites such as alkaloids, flavonoids, saponins and glycosides and found that the extracts showed antimicrobial activity against human pathogenic bacteria and fungi. Plant derived extracts and compounds have many beneficial uses due to their biochemical, pharmaceutical and therapeutic properties. Some of the uses and beneficial properties of phytochemicals are enlisted in Table 6 given below.

Vol18No1_Phy_Bhu_fig1 Figure 1: Representation of plant stresses.

Click here to view figure 

Table 6: Example of plant molecules that used for human health.

Phytochemicals Properties References
Menthol, benzyl acetate, linalool, limonene, 2-phenylthel alcohol, vanillin Flavors Altemimiet.al 2017
Vitamins, Taxol, quinine, minerals, amino-acids, enzymes, morphine, polysaccharides Health Fridlenderet.al 2015
Stevioside, rebaudioside Sweeteners Soejartoet.al 2019
Vitamins, non-dairy milk, genistein, daidzein, lycopene, genistein, daidzein, resveratrol food and nutrition Rahalet.al 2014

Secondary metabolites work alone or in combination with other compounds/ metabolites to cure diseases. Such combinations can enhance the efficacy of treatment of a disease which have been proven in many studies (Wink et.al 2015). Many phytochemicals have shown great success to defeat the dreadful disease like cancers (Secaet.al 2018, Rainaet.al 2014).The medicinal plant Hypericumper for atum is used for it anti-depressant, anti-inflammatory, antiviral, anticancer, and antibacterial properties. This plant contains fluoxetine and sertraline that cures depression, and other metabolites like hypericin, hyperforin, flavonoids and xanthones,which further enhance its medicinal value (Shakyaet.al 2017).Badgujaret.al in 2014 studied the use of Ficuscarica to treat many disorders that are related to the digestive, endocrine, reproductive, and respiratory system. Ficuscaricabelongs to angiosperm genera and consists of more than 800 different species. Phaleriamacrocarpa belongs to Thymelaeaceae family and has been traditionally used in Malaysia and Indonesia.Many diseases such as rheumatism, high blood pressure, diabetes mellitus, cancer, skin diseases, allergies, stroke, migraine, and hemorrhoids have been treated using this plant(Or et.al 2016). Echinacea purpurea, a medicinal herb with many secondary metabolites, has been used to cure anxiety, depression, cytotoxicity, and mutagenic disorders. However the use of this plant has been controversial as apart from its beneficial effects,it has potential side effects that are revealed by many studies, such as abdominal pain, nausea, angioedema, rash, and pruritus were reported in many patients after treatment (Manayiet.al 2015).Ziziphora species comprises a large number of flowering plants that belong to Lamiaceae family and further have been classified into 236 genera and 6900-7200 species. These plants are rich in essential oils or many secondary metabolites used in the field of pharmaceutical, medicinal, traditional, and folk medicines. This species is used to treat cold, fever, inflammation, intestinal disorders, insomnia, and cardiovascular malfunction for centuries (Mohammad hosseini et.al 2017). Secondary metabolites investigation on Thymus alternates showed that this species contains terpenoids, pentacyclic, and betulinic acid. Thephytochemicals of this plant have been used as flavoring agents while for medicinal purpose it has been found effective against cancer cell lines (Acquaet.al 2017).  PhyllanthusUrinarica L. genus belonging to Phyllantaceae family has been investigated asa rich source of lignans, tannins, flavonoids, phenolics, terpenoids, and other secondary metabolites. These secondary metabolites cure jaundice, diabetes, malaria, and liver disease. This plant also shows activity against cancer, microbial infections, and cardiovascular effects (Geethangili et.al 2018).

Some more investigations are there that represent the pharmacological activity of medicinal plants. Ipomoea batata L., commonly known as sweet potato, is widely consumed all over the world. It has many beneficial effects on human health as it contains many vitamins and phytochemicals. These phytochemicals also reveal activity against cancer, diabetes, inflammation, and antioxidants. Sweet potato also contains beta-carotene and a precursor of vitamin A that helps to cure night blindness and overcome the deficiency of vitamin A (Ghasemzadeh et.al 2016). South Indian grass that belongs to Cyperaceae species possesses large number of secondary metabolites that belong to classes alkaloids, flavonoids, steroids, phenols, and quinones. Out of all the phytochemicals, this grass contains alkaloids in a large amount and also shows many pharmaceutical activities that cure microbial infections and inflammation (Babuet.al2014). Capparisspinosa has lots of secondary metabolites that help to improve biomarkers of cardiovascular diseases and diabetes (Zhang et.al  2018). Glycyrrhizaglabra root revealed the presence of many phytochemicals. These phytochemicals are very beneficial for human health in the enhancement of memory, cures depression, helps to maintain the glucose level in the body, and shows many other pharmacological effects (Ali Esmail AL-Snafi 2018).Ocimum sanctum L. commonly known as Holy basil or Tulsi, is used in India as medicine sinceancient times as it helps to improve stress, inflammation, and cancer (Sing et.al 2018, Siva et.al 2016).

Genus Macaranga Thou.Belongs to Euphorbiaceae that comprises 300 species of plants. These species are mainly found in the tropics of Africa, Australia, Pacific regions, and Asia. This genus is traditionally used to treat cuts, sore, bruises, boils, and swelling (Magadulaet.al 2014). Pleurotussajorcajuis commonly known as mushrooms, are great source of primary and secondary metabolites and contain about 40-49% of protein. Apart from this mushrooms have anticancer, antidiabetic, antibacterial, and anti-inflammation activities. Mushrooms also play an important role in healing (Devi et.al 2015). Cymbopogancitratusstapf, Eugenia unifloraleaves and Citrullus vulgaris schard also contain many primary and secondary metabolites that are reported by Geethaet.al 2014, Daniel et.al 2014 and Hannah et.al 2015. Calophyllum Inophyllum belongs to clusiaceae family and it occurs above the high tide mark along the sea coast of Northern Australia and expanding throughout South India and S south-East Asia. This plant species contains lots of secondary metabolites in their root, stem, and leaves that help to fight against microbial infections, inflammation, and used in cosmetics (Sunduret.al 2014).Morusalba belongs to Moraceae family and contains many medicinal plants, and has numerous applications in various fields such as agriculture, food, cosmetic and pharmaceutical industries. Pharmacological activities of these plants help in the treatment of an inflammatory condition, gastrointestinal disorder, cancer, and microbial infections with the help of many secondary metabolites (Hussainet.al 2017). Another study was performed by B. J. Divya and other scientists in 2017 that worked on Allium sativum. Allium sativum belongs to the family Amaryllidaceae and commonly known as garlic. In this study, to extract secondary metabolites from garlic cloves different chemicals and techniques were used. Hexane, ethyl acetate, methanol and water revealed steroids, alkaloids, flavonoids and other bioactive compounds. These phytochemicals of garlic cloves have been used to treat several infections from ancient period.

Momordicadiocca commonly known as Kakrol or spiny gourd is mainly found in India and Bangladesh, and is not only used as medicinal plant but also consumed as vegetable on a large scale. This plant consist of many minerals compositions, preventive, protective and curative agents in their root, stem and fruit. This plant includes many pharmacological activities such as anti-oxidant, analgesic, nephron protective, neuro protective, antiallergic, antimalarial, hepato protective and antihe patotoxic activity  (Talukdar and Hossain 2014). Genitinais an important genus of Gentianaceae family that comprises 400 species and distributed all over the world. Based on investigation, this plant is used traditionally in Iran. This plant species consist lots of phytochemicals such as gentipicroside, xanthones, monoterpenes, alkaloids and flavonoids. This plant species has lots of promising bioactive agents are present that cure menstrual over bleeding, animal venom poisoning, infected wounds, injuries, vitiligo and swelling of liver, spleen, stomach and sprain of muscles (Mirzaeeet.al 2017). Some more medicinal plants with their pharmacological activities are summarized in Table 7 given below.

Table 7: Pharmalogical activities of medicinal plants with their common names.

S. No. Name of plant species Common name of plant Phytochemical name Pharmacological activity of plant References
1 Curcuma longa Haldi Flavonoid Anti-inflammatory, anticancer, hepato-protective Sharma et.al 2013
2 Withaniasomnifera Ashwagandha Withanolides, steroidal lactones Helps to treat Alzheimer’s and Parkinson’s disorders, helps to enhance memory and immunomodulatory, anti-cancerous and chemo preventive Rathinamoorthyet.al 2014
3 Catharanthusroseus Sadabahar Alkaloids Anticancer Priyadarshiniet.al 2012
4 AzadirachtaIndica Neem Di and Tri terpenoids, limonoids Blood purifier that prevents skin disease, anti-diabetic, inhibit colon cancer, anti-allergic Gupta et.al 2014
5 Piper nigrum Kali mirch Dehydro-pipernonaline, piperidine Helps to remove cough, purify lungs, used in weight loss with turmeric, epilepsy, anti-carcinogenic, anti-hyperlipidaemic Kaushiket.al 2002
6 Tinosporacordifolia Geloy Tinosporin, isoquinoline alkaloids Cardioprotective, anti-diabetic, immunomodulator, chemo preventive Nisaret.al 2012
7 Aloe vera GhritKumari ß-sitosterol, compesterol, emodin and aloin Helps to nourish skin and hairs, anti-diabetic, has healing properties, shows antiseptic effects, anti-viral and antitumor Mittal et.al 2014
8 Phyllanthusemblica Amla Emblicanin B, punigluconin and pedunculagin Good for skin, eyes and hairs, antiviral, anticancer, antidiabetic, anticancer and hepatoprotective Paarakhet.al 2010
9 Cinchona robusta Quina Quinine Antiparasitic and helps to treat malaria Paarakhet.al 2010
10 Swertiachirata Chirayita Amarogenitine, ophellic acid, sawertiamarine and mangeferin Antiviral, hepato-renal protective and shows anti-diabetic effect Krishnaaet.al 2004
11 Allium sativum Lahsun Allicin Anti-inflammatory, cardioprotective (helps to maintain hypertension) Joshi et.al 2005
12 Bergenia ciliate Pakhenbhed IS-01246 Anti-arthritis (helps to treat Rheumatoid) Seyyedet.al 2012

By using advance research technologies, scientists are working hard to produce rich variety of phytochemicals under laboratory conditionsusing plant cell cultures (Yueet.al 2014). Guerrieroet.al 2018,culturedArtemisia, Coffeaarabica L. and Urticadioica L. to produce large amount of secondary metabolites terpenoids, alkaloids and phenolic compounds respectively. Many trans genes such as rol ABC genes are also used by Kianiet. al. 2015, to increase the production of phytochemicals. Secondary metabolites are extracted from many plant species and used to make many drugs that cures different disorders. There are number of drugs that are composed of heterogenous phytochemicals and are available in market. Some of the drugs are enlisted in Table 8 (Garnatjee.al 2017). These drugs not only cure the diseases but also solve the problem of drug resistance and provide a new path for scientist to discover more drugs to fight against dreadful diseases (Anandet.al 2019).

Table 8: Commercially available plant derived medicines.

Plant Name Name of the drug
Colchicum autumnale L. Colchicine
Filipendulaulmaria (L.) Maxim Aspirin
Artemisia annua L. Artemisinin
Camptotheca acuminate Decne Camptothecin
Taxusbrevifolia Nutt. Paclitaxel
Artemisia annua L. Artemisinin
Catharanthusroseus (L.) G. Don Vinblastine and vincristine
Papaversomniferum L. Codeine
Papaversomniferum L. Papaverine
Cannabis sativa L. Cannabidiol

Conclusion

Plants are a valuable resource that yields numerous phytochemicals which can be used as potential drugs to treat and prevent many human ailments and diseases. These drugs also provide a safer alternative to allopathic medicines overcoming the problems of drug resistance, toxicity and side effects. The bioactivity of plant extracts and their component phytochemicals have been studied extensively and put to use since ancient times. Novel approaches are now being explored for enhanced production and efficient yielding of secondary metabolites through cell and tissue cultures. Advances in cell line culture allowing in-vitro bioactivity testing also opens avenues for faster drug development.

Acknowledgment

We sincerely acknowledge the contribution of Dr. Shilpa S. Chapadgaonkar, Associate Professor, Department of Biotechnology, MRIIRS, Faridabad, India, in form of her valuable inputs to the co-authors in preparing the manuscript.

Conflicts of Interest

The authors declare that there is no conflicts of interest.

Funding Source

There is no funding source

References

  1. Ahmad, N., Fazal, H., Abbasi, B.H., Farooq, S., Ali, M. and Khan, M.A., 2012. Biological role of Piper nigrum L.(Black pepper): A review. Asian Pacific Journal of Tropical Biomedicine2(3), pp.S1945-S1953.
    CrossRef
  2. Alara, O.R., Alara, J.A. and Olalere, O.A., 2016. Review on Phaleriamacrocarpa pharmacological and phytochemical properties. Drug Des5(134), pp.2169-0138.
  3. Alorkpa, E.J., Boadi, N.O., Badu, M. and Saah, S.A., 2016. Phytochemical screening, antimicrobial and antioxidant properties of assorted Carica papaya leaves in Ghana.
  4. Al-Snafi, A.E., 2018. Glycyrrhizaglabra: A phytochemical and pharmacological review. IOSR Journal of Pharmacy8(6), pp.1-17.
  5. Altemimi, A., Lakhssassi, N., Baharlouei, A., Watson, D.G. and Lightfoot, D.A., 2017. Phytochemicals: Extraction, isolation, and identification of bioactive compounds from plant extracts. Plants6(4), p.42.
    CrossRef
  6. Anand, U., Jacobo-Herrera, N., Altemimi, A. and Lakhssassi, N., 2019. A comprehensive review on medicinal plants as antimicrobial therapeutics: potential avenues of biocompatible drug discovery. Metabolites9(11), p.258.
    CrossRef
  7. Ara, K., Hama, M., Akiba, S., Koike, K., Okisaka, K., Hagura, T., Kamiya, T. and Tomita, F., 2006. Foot odor due to microbial metabolism and its control. Canadian journal of microbiology52(4), pp.357-364.
    CrossRef
  8. Asif, M., 2015. Pharmacological activities and phytochemistry of various plant containing coumarin derivatives. Current Science Perspectives1(3), pp.77-90.
  9. Aziz, R.K., Breitbart, M. and Edwards, R.A., 2010. Transposases are the most abundant, most ubiquitous genes in nature. Nucleic acids research38(13), pp.4207-4217.
    CrossRef
  10. Babu, H.R. and Savithramma, N., 2014. Screening of secondary metabolites of underutilized species of Cyperaceae. Int J Pharm Sci Rev Res24, pp.182-187.
  11. Badgujar, S.B., Patel, V.V., Bandivdekar, A.H. and Mahajan, R.T., 2014. Traditional uses, phytochemistry and pharmacology of Ficuscarica: A review. Pharmaceutical biology52(11), pp.1487-1503.
    CrossRef
  12. Ballhorn, D.J., Kautz, S., Heil, M. and Hegeman, A.D., 2009. Analyzing plant defenses in nature. Plant signaling&behavior4(8), pp.743-745.
    CrossRef
  13. Biswas, K., Chattopadhyay, I., Banerjee, R.K. and Bandyopadhyay, U., 2002. Biological activities and medicinal properties of neem (Azadirachtaindica). CURRENT SCIENCE-BANGALORE-82(11), pp.1336-1345.
  14. Clifford, M., Leah, M. and Charles, N., 2012. Antiepileptic properties of Quinine: A systematic review. Annals of neurosciences19(1), p.14.
    CrossRef
  15. Coelho, V., Mazzardo-Martins, L., Martins, D.F., Santos, A.R.S., da Silva Brum, L.F., Picada, J.N. and Pereira, P., 2013. Neurobehavioral and genotoxic evaluation of (−)-linalool in mice. Journal of natural medicines67(4), pp.876-880.
    CrossRef
  16. Dall’Acqua, S., Peron, G., Ferrari, S., Gandin, V., Bramucci, M., Quassinti, L., Mártonfi, P. and Maggi, F., 2017. Phytochemical investigations and antiproliferative secondary metabolites from Thymus alternans growing in Slovakia. Pharmaceutical biology55(1), pp.1162-1170.
    CrossRef
  17. Daniel, G. and Krishnakumari, S., 2015. Quantitative analysis of primary and secondary metabolites in aqueous hot extract of Eugenia uniflora (L) leaves. Asian Journal of Pharmaceutical and Clinical Research8(1), pp.334-338.
  18. Devi, M.R. and Krishnakumari, S., 2015. Quantitative estimation of primary and secondary metabolites in hot aqueous extract of Pleurotussajorcaju. Journal of Pharmacognosy and Phytochemistry4(3), p.198.
  19. Eadie, M.J., 2004. Could valerian have been the first anticonvulsant?. Epilepsia45(11), pp.1338-1343.
    CrossRef
  20. Egamberdieva, D., Mamedov, N., Ovidi, E., Tiezzi, A. and Craker, L., 2017. Phytochemical and pharmacological properties of medicinal plants from Uzbekistan: A review. Journal of Medicinally Active Plants5(2), pp.59-75.
  21. Elson, C.E., Maltzman, T.H., Boston, J.L., Tanner, M.A. and Gould, M.N., 1988. Anti-carcinogenic activity of d-limonene during the initiation and promotion/progression stages of DMBA-induced rat mammary carcinogenesis. Carcinogenesis9(2), pp.331-332.
    CrossRef
  22. ElTawil, S., Al Musa, T., Valli, H., Lunn, M.P., Brassington, R., ElTawil, T. and Weber, M., 2015. Quinine for muscle cramps. Cochrane database of systematic reviews, (4).
    CrossRef
  23. Espina, L., Gelaw, T.K., de Lamo-Castellví, S., Pagán, R. and García-Gonzalo, D., 2013. Mechanism of bacterial inactivation by (+)-limonene and its potential use in food preservation combined processes. PloS one8(2), p.e56769.
    CrossRef
  24. Fridlender, M., Kapulnik, Y. and Koltai, H., 2015. Plant derived substances with anti-cancer activity: from folklore to practice. Frontiers in plant science6, p.799.
    CrossRef
  25. Gapparov, A.M. and Aripova, S.F., 2011. Alkaloids from the aerial part and roots of Convolvulus pseudocanthabrica indigenous to Uzbekistan. Chemistry of Natural Compounds47(4), p.673.
    CrossRef
  26. Gapparov, A.M., Razzakov, N.A. and Aripova, S.F., 2007. Alkaloids of Convolvulus subhirsutus from Uzbekistan. Chemistry of Natural Compounds43(3), pp.291-292.
    CrossRef
  27. Garnatje, T., Peñuelas, J. and Vallès, J., 2017. Ethnobotany, phylogeny, and ‘omics’ for human health and food security. Trends in plant science22(3), pp.187-191.
    CrossRef
  28. Geetha, T.S. and Geetha, N., 2014. Phytochemical screening, quantitative analysis of primary and secondary metabolites of Cymbopogancitratus (DC) Stapf. leaves from Kodaikanal hills, Tamilnadu. International Journal of pharmtech research6(2), pp.521-529.
  29. Ghasemzadeh, A., Talei, D., Jaafar, H.Z., Juraimi, A.S., Mohamed, M.T.M., Puteh, A. and Halim, M.R.A., 2016. Plant-growth regulators alter phytochemical constituents and pharmaceutical quality in Sweet potato (Ipomoea batatas L.). BMC complementary and alternative medicine16(1), p.152.
    CrossRef
  30. Gu, L., Li, N., Gong, J., Li, Q., Zhu, W. and Li, J., 2011. Berberine ameliorates intestinal epithelial tight-junction damage and down-regulates myosin light chain kinase pathways in a mouse model of endotoxinemia. Journal of Infectious Diseases203(11), pp.1602-1612.
    CrossRef
  31. Guerriero, G., Berni, R., Muñoz-Sanchez, J.A., Apone, F., Abdel-Salam, E.M., Qahtan, A.A., Alatar, A.A., Cantini, C., Cai, G., Hausman, J.F. and Siddiqui, K.S., 2018. Production of plant secondary metabolites: Examples, tips and suggestions for biotechnologists. Genes9(6), p.309.
    CrossRef
  32. Hakoshima, T., Murase, K., Hirano, Y. and Sun, T.P., 2011. Gibberellin Perception by the Gibberellin Receptor and its Effector Recognition. NKG52(1), pp.37-41.
  33. Hannah, M.A.C. and Krishnakumari, S., 2015. Quantitative estimation of plant metabolites in the hot aqueous seed extract of watermelon (Citrullus vulgaris Schrad.). Journal of Medicinal Plants3(5), pp.107-111.
  34. Harborne, J.B., Baxter, H. and Moss, G.P.A., 1993. A handbook of bioactive compounds from plants. Phytochemical dictionary.
  35. Hoffmann, D., 2003. Medical herbalism: the science and practice of herbal medicine. Simon and Schuster.
  36. Hussain, F., Rana, Z., Shafique, H., Malik, A. and Hussain, Z., 2017. Phytopharmacological potential of different species of Morusalba and their bioactive phytochemicals: A review. Asian Pacific journal of tropical biomedicine7(10), pp.950-956.A study on phytochemicals, functions, groups and mineral composition of Allium sativum (Garlic) cloves, 20 march 2017, B. J. Divya, B. suman, M venkataswamy, K. Thyagaraju.
    CrossRef
  37. Hussein, R.A. and El-Anssary, A.A., 2018. Plants secondary metabolites: the key drivers of the pharmacological actions of medicinal plants. Herbal Medicine.
    CrossRef
  38. Hussein, R.A. and El-Anssary, A.A., 2018. Plants secondary metabolites: the key drivers of the pharmacological actions of medicinal plants. Herbal Medicine.
    CrossRef
  39. Hussein, R.A. and El-Anssary, A.A., 2018. Plants secondary metabolites: the key drivers of the pharmacological actions of medicinal plants. Herbal Medicine.
    CrossRef
  40. Javadzadeh, S.M. and Fallah, S.R., 2012. Therapeutic application of different parts Berberis vulgaris.
  41. Jepson, R.G., Williams, G. and Craig, J.C., 2012. Cranberries for preventing urinary tract infections. Cochrane database of systematic reviews, (10).
    CrossRef
  42. Joshi, P. and Dhawan, V., 2005. Swertiachirayita–an overview. Current science, pp.635-640.
  43. Kabera, J.N., Semana, E., Mussa, A.R. and He, X., 2014. Plant secondary metabolites: biosynthesis, classification, function and pharmacological properties. J Pharm Pharmacol2, pp.377-392.
  44. Kar, A., 2007. Pharmaocgnosy and Pharmacobiotechnology (Revised-Expanded Second Edition). New Age International LimtedPublishres New Delhi, pp.332-600.
  45. Kar, A., 2007. Pharmaocgnosy and Pharmacobiotechnology (Revised-Expanded Second Edition). New Age International LimtedPublishres New Delhi, pp.332-600.
  46. Khuzhaev, V.U., 2004. Alkaloids of Arundodonax. XVIII. Nitrogenous bases in flowers of cultivars. Chemistry of natural compounds5(40), pp.516-517.
    CrossRef
  47. Kiani, B.H., Ullah, N., Haq, I.U. and Mirza, B., 2019. Transgenic Artemisia dubia WALL showed altered phytochemistry and pharmacology. Arabian Journal of Chemistry12(8), pp.2644-2654.
    CrossRef
  48. Krishna, P., 2003. Brassinosteroid-mediated stress responses. Journal of Plant Growth Regulation22(4), pp.289-297.
    CrossRef
  49. Krishna, S., Uhlemann, A.C. and Haynes, R.K., 2004. Artemisinins: mechanisms of action and potential for resistance. Drug Resistance Updates7(4-5), pp.233-244.
    CrossRef
  50. Kumar Gupta, S. and Sharma, A., 2014. Medicinal properties of Zingiberofficinale Roscoe-A review.  Pharm. Biol. Sci9, pp.124-129.
    CrossRef
  51. Magadula, J.J., 2014. Phytochemistry and pharmacology of the genus Macaranga: a review. Journal of Medicinal Plants Research8(12), pp.489-503.
    CrossRef
  52. Manayi, A., Vazirian, M. and Saeidnia, S., 2015. Echinacea purpurea: Pharmacology, phytochemistry and analysis methods. Pharmacognosy reviews9(17), p.63.
    CrossRef
  53. Melton, L., 2006. Body blazes. Scientific American294(6), pp.24-24.
    CrossRef
  54. Mirzaee, F., Hosseini, A., Jouybari, H.B., Davoodi, A. and Azadbakht, M., 2017. Medicinal, biological and phytochemical properties of Gentiana species. Journal of Traditional and Complementary Medicine7(4), pp.400-408.
    CrossRef
  55. Mittal, J., Sharma, M.M. and Batra, A., 2014. Tinosporacordifolia: a multipurpose medicinal plant-A. Journal of Medicinal Plants2(2).
  56. Mohammadhosseini, M., 2017. The ethnobotanical, phytochemical and pharmacological properties and medicinal applications of essential oils and extracts of different Ziziphora species. Industrial crops and products105, pp.164-192.
    CrossRef
  57. Montanher, A.B., Zucolotto, S.M., Schenkel, E.P. and Fröde, T.S., 2007. Evidence of anti-inflammatory effects of Passifloraedulis in an inflammation model. Journal of Ethnopharmacology109(2), pp.281-288.
    CrossRef
  58. Okada, T., MochamadAfendi, F., Altaf-Ul-Amin, M., Takahashi, H., Nakamura, K. and Kanaya, S., 2010. Metabolomics of medicinal plants: the importance of multivariate analysis of analytical chemistry data. Current computer-aided drug design6(3), pp.179-196.
    CrossRef
  59. Okhunov, I.I., Levkovich, M.G., Abdullaev, N.D., Khuzhaev, V.U. and Aripova, S.F., 2011. Alkaloids from Crambekotschyana endemic to Uzbekistan. Chemistry of Natural Compounds47(3), p.487.
    CrossRef
  60. Paarakh, P.M., 2010. Terminaliaarjuna (Roxb.) Wt. and Arn.: a review. IJP-International Journal of Pharmacology6(5), pp.515-534.
    CrossRef
  61. Pelczar, M.J., Chan, E.C.S. and Krieg, N.R., 1988. Control of microorganisms, the control of microorganisms by physical agents. Microbiology469, p.509.
  62. Priyadarshini, K. and Keerthi, A.U., 2012. Paclitaxel against cancer: a short review. Med chem2(7), pp.139-141.
  63. Raina, H., Soni, G., Jauhari, N., Sharma, N. and Bharadvaja, N., 2014. Phytochemical importance of medicinal plants as potential sources of anticancer agents. Turkish Journal of Botany38(6), pp.1027-1035.
    CrossRef
  64. Rathinamoorthy, R. and Thilagavathi, G., 2014. Terminaliachebula-review on pharmacological and biochemical studies. Int J PharmTech Res6(1), pp.97-116.
  65. Rhoades, D.F. and Cates, R.G., 1976. Toward a general theory of plant antiherbivore chemistry. In Biochemical interaction between plants and insects(pp. 168-213). Springer, Boston, MA.
    CrossRef
  66. Samuni-Blank, M., Izhaki, I., Dearing, M.D., Gerchman, Y., Trabelcy, B., Lotan, A., Karasov, W.H. and Arad, Z., 2012. Intraspecific directed deterrence by the mustard oil bomb in a desert plant. Current biology22(13), pp.1218-1220.
    CrossRef
  67. Sarker, S.D. and Nahar, L., 2007. Chemistry for pharmacy students. John Willey & Sons Ltd. UK, pp.322-4.
    CrossRef
  68. Savithramma, N., Rao, M.L. and Ankanna, S., 2011. Screening of traditional medicinal plants for secondary metabolites. International Journal of Research in Pharmaceutical Sciences2(4), pp.643-647.
  69. Scalbert, A., Johnson, I.T. and Saltmarsh, M., 2005. Polyphenols: antioxidants and beyond. The American journal of clinical nutrition81(1), pp.215S-217S.
    CrossRef
  70. Seca, A.M. and Pinto, D.C., 2018. Plant secondary metabolites as anticancer agents: successes in clinical trials and therapeutic application. International journal of molecular sciences19(1), p.263.
    CrossRef
  71. Seigler, D.S., 2012. Plant secondary metabolism. Springer Science & Business Media.
  72. Serafini, M., Peluso, I. and Raguzzini, A., 2010. Flavonoids as anti-inflammatory agents. Proceedings of the Nutrition Society69(3), pp.273-278.
    CrossRef
  73. Shakya, A.K., 2016. Medicinal plants: future source of new drugs. International Journal of Herbal Medicine4(4), pp.59-64.
  74. Shakya, A.K., 2016. Medicinal plants: future source of new drugs. International Journal of Herbal Medicine4(4), pp.59-64.
  75. Shakya, P., Marslin, G., Siram, K., Beerhues, L. and Franklin, G., 2019. Elicitation as a tool to improve the profiles of high‐value secondary metabolites and pharmacological properties of Hypericumperforatum. Journal of Pharmacy and Pharmacology71(1), pp.70-82.
    CrossRef
  76. Sharma, V., 2013. Part based HPLC-PDA quantification of podophyllotoxin in populations of PodophyllumhexandrumRoyle “Indian Mayapple” from higher altitude Himalayas. Journal of Medicinal Plants Studies1(3), pp.176-183.
    CrossRef
  77. Singh, D. and Chaudhuri, P.K., 2018. A review on phytochemical and pharmacological properties of Holy basil (Ocimum sanctum L.). Industrial Crops and Products118, pp.367-382.
    CrossRef
  78. Siva, M., Shanmugam, K.R., Shanmugam, B., Venkata, S.G., Ravi, S., Sathyavelu, R.K. and Mallikarjuna, K., 2016. Ocimum sanctum: a review on the pharmacological properties. International Journal of Basic & Clinical Pharmacology5(3), pp.558-565.
    CrossRef
  79. Smith, J., Owen, E., Earis, J. and Woodcock, A., 2006. Effect of codeine on objective measurement of cough in chronic obstructive pulmonary disease. Journal of Allergy and Clinical Immunology117(4), pp.831-835.
    CrossRef
  80. Soejarto, D.D., Addo, E.M. and Kinghorn, A.D., 2019. Highly sweet compounds of plant origin: From ethnobotanical observations to wide utilization. Journal of ethnopharmacology243, p.112056.
    CrossRef
  81. Soni, U., Brar, S. and Gauttam, V.K., 2015. Effect of seasonal variation on secondary metabolites of medicinal plants. Int J Pharm Sci Res6(9), pp.3654-62.
  82. Sundur, S., Shrivastava, B., Sharma, P., Raj, S.S. and Jayasekhar, V.L., 2014. A review article of pharmacological activities and biological importance of Calophylluminophyllum. International Journal of Advanced Research2(12), pp.599-603.
  83. Takita, K., Herlenius, E., Yamamoto, Y. and Lindahl, S.G., 2000. Effects of neuroactive substances on the morphine-induced respiratory depression; an in vitro study. Brain research884(1-2), pp.201-205.
    CrossRef
  84. Talukdar, S.N. and Hossain, M.N., 2014. Phytochemical, phytotherapeutical and pharmacological study of Momordicadioica. Evidence-Based Complementary and Alternative Medicine2014.
    CrossRef
  85. Tang, W. and Eisenbrand, G., 2013. Chinese drugs of plant origin: chemistry, pharmacology, and use in traditional and modern medicine. Springer Science & Business Media.
  86. Taniguchi, S., Hosokawa Shinonaga, Y.U.M.I., Tamaoki, D., Yamada, S., Akimitsu, K. and Gomi, K., 2014. Jasmonate induction of the monoterpene linalool confers resistance to rice bacterial blight and its biosynthesis is regulated by JAZ protein in rice. Plant, cell & environment37(2), pp.451-461..
    CrossRef
  87. Tulyaganov, T.S. and Kozimova, N.M., 2005. Alkaloids from Nitrariaschoberi. O-acetylnitraraine. Chemistry of natural compounds41(5).
    CrossRef
  88. Vree, T.B., Van Dongen, R.T. and Koopman-Kimenai, P.M., 2000. Codeine analgesia is due to codeine-6-glucuronide, not morphine. International Journal of Clinical Practice54(6), p.395.
  89. Wink, M., 2015. Modes of action of herbal medicines and plant secondary metabolites. Medicines2(3), pp.251-286.
    CrossRef
  90. Yue, W., Ming, Q.L., Lin, B., Rahman, K., Zheng, C.J., Han, T. and Qin, L.P., 2016. Medicinal plant cell suspension cultures: pharmaceutical applications and high-yielding strategies for the desired secondary metabolites. Critical reviews in biotechnology36(2), pp.215-232.
    CrossRef
  91. Zha, W., Liang, G., Xiao, J., Studer, E.J., Hylemon, P.B., PandakJr, W.M., Wang, G., Li, X. and Zhou, H., 2010. Berberine inhibits HIV protease inhibitor-induced inflammatory response by modulating ER stress signaling pathways in murine macrophages. PLoS One5(2), p.e9069.
    CrossRef
  92. Zhang, H. and Ma, Z.F., 2018. Phytochemical and pharmacological properties of Capparisspinosa as a medicinal plant. Nutrients10(2), p.116.
    CrossRef
  93. Zhang, J., Schurr, U. and Davies, W.J., 1987. Control of stomatal behaviour by abscisic acid which apparently originates in the roots. Journal of experimental botany38(7), pp.1174-1181.
    CrossRef
  94. Zhang, J., Schurr, U. and Davies, W.J., 1987. Control of stomatal behaviour by abscisic acid which apparently originates in the roots. Journal of experimental botany38(7), pp.1174-1181.
    CrossRef
  95. Zhang, Q., Cai, L., Zhong, G. and Luo, W., 2010. Simultaneous determination of jatrorrhizine, palmatine, berberine, and obacunone in PhellodendriAmurensis Cortex by RP-HPLC. ZhongguoZhongyaozazhi= Zhongguozhongyaozazhi= China journal of Chinese materiamedica35(16), pp.2061-2064.
  96. Ziyaev, R., Abdusamatov, A. and Yunusov, S.Y., 1987. Alkaloids ofLiriodendron tulipifera. Chemistry of Natural Compounds23(5), pp.521-528.
    CrossRef

Diversity and Distribution of Thermophiles and Thermo-Tolerant Bacteria in the Soil Samples Obtained from Different Regions in Saudi Arabia

$
0
0

Introduction

Growth and adaptation of organisms in the hosting environment are affected by many factors, temperature in particular (Brooks et al.,2011). For organisms classified as thermo-tolerant, the mesophilic range (30−37 °C) is the most optimal; however, they are also able to grow in high-temperature environments in the philic range. On the other hand, the optimal growth temperature of organisms classified as thermophiles is much higher (60 °C), even though they were found to thrive in much hotter environments, such as terrestrial volcanic sites (Nazina et al., 2008). Microorganisms, specifically bacteria, are increasinglyfound to thrive in extreme conditions, such as those characterizing deserts, namely low nutrient status, extreme temperature fluctuations, high levels of UV radiation, and strong winds (Chamizo et al., 2012; Lester et al., 2007; Stomeo et al., 2013). Temperature is the dominant factor controlling the growth ofmicrobial species in the desert soil (Brooks et al., 2011). In the pertinent literature, a variety of thermophilic bacteria are described, which were extracted from various regions in theworld, including China (Lau et al., 2009), Turkey (Gul-Guven et al., 2008), Bulgaria (Derekova et al., 2008), India (Sharma et al., 2008), Greece (Sievert et al., 2000), Italy(Maugeri et al., 2001), Iceland (Takacs et al., 2001), and Saudi Arabia (SarhanandAlamrri 2014).  Desert soils, irrespective of the locationfrom which they are obtained, typically comprise a number of universal phyla,including Proteobacteria, Bacteroidetes, and Actinobacteria (Chanal et al., 2006; Connon et al., 2007; Fierer et al., 2009; Lester et al., 2007).On theother hand, Cyanobacteria, Gemmatimonadetes, and Firmicutes (Bahl et al., 2011; Lacap et al., 2011; Makhalanyane et al., 2013; Richer et al., 2015) may be relatively more abundant in desert soils than in other biomes (Fierer et al., 2012). A wide range of molecular biology techniques can be employed in microorganism identification, such as 16S rRNA sequencing, rep-PCR profiling, and fatty acid methyl ester, which can be used in microorganism characterization at both species and subspecies levels (Adiguzel, 2006; Nazina et al., 2008; Zaliha et al., 2007). These techniques are also valuable for studying ecosystem diversity, in particular for analyzing the phylogenetic relation between strains, and discriminatingmicroorganisms that are genetically close to each other (Adiguzel, 2006).

The majority of research in microorganisms of the desert ecosystem was limited to few desert sites, particularly, in America and Australia. Consequently, significant effort is needed to expand to these studies to other regions, e.g., Asian and Africa. The more diverse data about microbial communities we have, the batter we are able to predict their impact on climate and land-use change (Makhalanyane et al., 2015). Subsequently, this study seeks to investigate microbial communities in hot deserts in Saudi Arabia.

The aim of this study was to identify and characterize thermophiles and thermo-tolerant bacteria isolated from soil in Saudi Arabia. In order to ensure diversity in soil samples, these were collected from Riyadh (central region), Dammam (eastern region), Hail (northern region), Abha (southern region), and AlmadinaAlmonawara (western region) via phenotypic and genotypic methods.

Materials and Methods

Sample Collection

Soil samples were obtained from natural ecosystems by collecting specimens of 20–40 g wet weight from the top 50 cm layer aseptically, which wereplaced in sterile glass containers and boxes kept at about 4 oC during transport. To ensure diversity, samples were collected from several sites, is illustrated in Figure 1,located in northern, southern, eastern, western, and central regions of Saudi Arabia.

Vol18No1_DIV_Kaw_fig1 Figure 1: Map of regions of Saudi Arabia.

Click here to view figure

Chemical Analysis of Soil

pH of the soils was determined by using an electronic pH meter (EckertandSims, 2011), and total salt concentration was determined by using Mehlich 3 method (Wolf and Beegle, 2011). Particle Size Analysis was conducted using Hydrometer Method (Gavlak,et al., 2005).The obtained results were presented as mg/l of dry soil.

Microbial Analysis

Each soil samplewas grown in 250ml flasks using 10g / 90ml of liquid media for enrichment,namely nutrient broth (v/v) for bacteria.The flasks were incubated at 45oC for 6 days. The Bacteria in the samples was enriched and isolated using the solid medium. Inoculum with adequate turbidity was transferred to three agar media, namely blood, nutrient, and MacConkey agar for bacteria. Each bacterial sample was incubated at four different temperatures (45°C, 50°C, 55°C, and 60°C) for 48−72 h. For all species, purification was carried out by applying the streaking plate method. Bacterial colonies were identified using microscopic examination, morphological analyses, and a biochemical kit.

DNA Extraction and 16S Ribosomal RNA-PCR Analysis

Bacterial DNA isolates was taken from 5 ml bacterial cultures grown overnight using DNeasy Blood & Tissue Kit (Qiagen, cat. #69504) for DNA isolation. Samples were processed as per the instructions in the kit.DNA amplification reactions were carried in Veriti® Thermal Cycler (AB, Applied Biosystems). The small-subunit rRNA (16S rDNA) were amplified by primers targeted to universal regions. The primers had the following sequences: universal forward primer Bac27F (5′-AGAGTTTGGATCMTGGCTCAG-3′) and universal reverse primer Bac1492R (5′- CGGTTACCTTGTTACGACTT-3′), used to amplify bacterial 16S rRNA. PCR amplifications were put according to the protocol described earlier (Flanagan et al., 2007).The PCR product was analyzed on 2.0% agarose gel with 0.5 μg/ml ethidium bromide and was imaged using Bio-Rad Gel Documentation System 2000.

16S rRNA Sequence Analysis

The 16S rRNA gene of the isolates was sequenced using ABI 3700 DNA Analyzer (Applied Biosystems, USA).BLAST algorithm in Gen Bank was used for analyzed homology of the 16S rRNA gene sequence of the isolates based on the available reference 16S rRNA sequences. MEGA version 7.0 software (Kumar et al., 2016) was employed when conducting phylogenetic and molecular evolutionary analyses.

Results

Physical and Chemical Characteristics of Soil from Different Regions

Five locations were prospected in Saudi Arabia, whereby the soil samples were obtained from Riyadh (central region), Dammam (eastern region), Hail (northern region), Abha (southern region) and AlmadinaAlmonawara (western region).The results of physical and chemical analyses performed on the fivesoil types are summarized in Table 1. During September 2014, temperaturesin Saudi Arabia were in the 31−43 °C range.The analyses revealed that all soil samplescontained slightly alkaline water (pH = 7.9–8.5), as well as high potassium concentrations.In addition, soil samples collected from the Dammam and Almadina locations had high concentrations of sodium,phosphate,and calcium chloride.

Table 1: Physiochemical Characteristics of Soil Samples.

Characteristics Soil Sample Location
East Centre North South West
Texture L LS LS LS LiS
Clay % 16.8 6.8 4.3 6.8 24.3
Silt % 35 5 1.25 7.5 60
Sand % 48.2 88.2 94.45 85.7 15.7
pH 8.12 8.2 8.45 7.94 8.38
EC ms/cm 3.83 0.38 1.21 2.02 1.99
Na (ppm) 178 49 63 35 840
P (ppm) 8.4 7 9.8 30 8.4
K (ppm) 156 96 103 90 305
CaCl2 % 10.2 2.29 2.9 2.6 13.03

Isolation, Selection, and Identification of Isolates

Among the 57 isolates of thermophilic and thermo-tolerant bacteria that grew on different agar media at 45−60°C, 16(28.1%)were obtained from the eastern region, 11(19.3%) from the central region, 7 (12.2%) from the northern region, 11 (19.3%) from the southern region, and 12 (21.1%) from the western region.

Further analyses confirmed that nine bacterial genera were identified across the soil sampling sites, with Enterobacter genera being the most dominant, andClostridium and Cedeceabeing the least prevalent, as indicated in Table 2. Other bacterial genera included Pseudomonas, Staphylococcus, Bacillus, Paenibacillus, andBrevibacillus. Moreover, three species were found to be thermophiles, Clostridiumsporognes, Paenibacillusdendritiformis, and Paenibacillus lactis, as they only grow under temperature condition of 60 °C. Whereas, all other isolates were found to be thermo-tolerant, as they grow under temperature conditions of 45-60 °C.

Table 2: Nine Bacterial Genera Identified in the Soil Samples.

Temp. Location

55 °C

East Centre North South West
Clostridium sporogenes Enterobacter sp. Paenibacillusdendritiformis Paenibacillussp Paenibacillus lactis
Enterobacter ludwigii Bacillus sp. Paenibacillusdendritiformis Pseudomonas aeruginosa
Enterobacter ludwigii Paenibacillusdendritiformis Enterobacter sp. Paenibacillus lactis
Enterobacter ludwigii Paenibacillussp.
Brevibacillusborstelensis
Paenibacillus sp.
Enterobacter sp.
Paenibacillussp Paenibacillussp. Bacillus subtilis Enterobacter ludwigii Bacillus subtilis

50 °C

Enterobacter sp. Staphylococcus sp. Staphylococcus sp. Cedeceadavisae Bacillus licheniformis
Bacillus cereus Pseudomonas aeruginosa   Pseudomonas aeruginosa
Pseudomonas aeruginosa Enterobacter sp.
Enterobacter ludwigii Bacillus subtilis Enterobacter ludwigii Enterobacter ludwigii Enterobacter ludwigii
Bacillus licheniformis   Enterobacter ludwigii Pseudoalteromonassp.

45 °C

Enterobacter hormaechei Enterobacter ludwigii Enterobacter sp. Escherichia sp. Pseudomonas aeruginosa
Enterobacter ludwigii Enterobacter ludwigii   Enterobacter ludwigii Enterobacter sp.
Enterobacter ludwigii Bacillus subtilis subsp.   Pseudomonas aeruginosa Bacillus sp.
Brevibacillusborstelensis Enterobacter ludwigii

16S rRNA Sequence Analysis

Species level confirmations of 27 isolates were performed by 16S rRNA sequencing. Based on the findings yielded by the BLAST search analysis of the sequences, the isolates showed maximum identity (99%). The isolate sequences have been deposited in GenBank, as follows:Enterobacter ludwigiiwith accession numbers MF682065, MF682066, MF682067, MF682068, MF682071, MF682073, MF682074, MF682077, MF682078, MF682079, MF682080, MF682083, MF682084, MF682085, MF682086, MF682088, and MF682091;Enterobacter sp. with accession numbers MF682069, MF682070, MF682075, MF682081, MF682082, and MF682089;Enterobacter hormaecheiwith the accession number MF682072;Bacillus sp. with accession numbers MF682076 and MF682090; and Paenibacillus sp. with the accession number MF682087 (Table 3).

Table 3: Identity of the 27 Thermophilic and Thermo-tolerant Bacterial Isolates Based on BLAST Searches.

Code (accession number) Identity Based on BLAST Searches Max Identity (%) GenBank Accession No. E-value (Query Coverage %)
KF1 (MF682065) Enterobacter ludwigii 99% KM077046.1 0.0 (100)
KF2 (MF682066) Enterobacter ludwigii 99% KM077046.1 0.0 (100)
KF3 (MF682067) Enterobacter ludwigii 99% KM077046.1 0.0 (100)
KF4 (MF682068) Enterobacter ludwigii 97% KM077046.1 0.0 (99)
KF5 (MF682069) Enterobacter sp. 99% KR856429.1 0.0 (100)
KF6 (MF682070) Enterobacter sp. 99% KC342873.1 0.0 (100)
KF7 (MF682071) Enterobacter ludwigii 99% KM077046.1 0.0 (100)
KF8 (MF682072) Enterobacter hormaechei 96% KU312822.1 0.0 (100)
KF9 (MF682073) Enterobacter ludwigii 96% KM077046.1 0.0 (98)
KF10(MF682074) Enterobacter ludwigii 98% KM077046.1 0.0 (99)
KF11(MF682075) Enterobacter sp. 99% KR856429.1 0.0 (100)
KF12(MF682076) Bacillus sp. 99% HM566879.1 0.0 (95)
KF13(MF682077) Enterobacter ludwigii 99% KX024731.1 0.0 (100)
KF14(MF682078) Enterobacter ludwigii 99% KM077046.1 0.0 (99)
KF15 MF682079 Enterobacter ludwigii 98% KM077046.1 0.0 (99)
KF17(MF682080) Enterobacter ludwigii 99% KM077046.1 0.0 (100)
KF18(MF682081) Enterobacter sp. 98% KC342873.1 0.0 (100)
KF19(MF682082) Enterobacter sp. 98% MF125281.1 0.0 (99)
KF20 (MF682083) Enterobacter ludwigii 99% KM077046.1 0.0 (100)
KF22 (MF682084) Enterobacter ludwigii 99% KM077046.1 0.0 (100)
KF23 (MF682085) Enterobacter ludwigii 99% KM077046.1 0.0 (100)
KF24 (MF682086) Enterobacter ludwigii 99% KM077046.1 0.0 (100)
KF25 (MF682087) Paenibacillus sp. 99% KR364781.1 0.0 (96)
KF26 (MF682088) Enterobacter ludwigii 99% KM077046.1 0.0 (100)
KF27 (MF682089) Enterobacter sp. 98% MF125281.1 0.0 (100)
KF28 (MF682090) Bacillus sp. 99% KF217252.1 0.0 (100)
KF29 (MF682091) Enterobacter ludwigii 98% KM077046.1 0.0 (100)

The phylogenetic analyses of the 27 thermophilic and thermo-tolerantbacterial isolates and closely related species were conducted using the neighbor-joining tree method, as shown in Figure2. The generated dendrogram revealed two clades supported by high bootstrap values. These clades are represented by two major lineages, namely Proteobacteria (89%) consisting mainly of the generaEnterobacter, and Firmicutes consisting of the genusBacillus (11%).

Vol18No1_DIV_Kaw_fig2 Figure 2: Phylogenetic analysis of the two clades based on the 16S rRNA gene quences and neighbor-joining tree method analysis results.

Click here to view figure

We found that the Enterobacterludwigiiwas the most dominant species of the identified genera Enterobacter (71%). The phylogenetic tree of the 17Enterobacterludwigiistrains and the closest NCBI (BLASTn) strains—KM077046.1 and KX024731.1—based on the 16S rRNA gene sequences (neighbor-joining tree method) is illustrated in Figure3. A high similarity (~ 99%) with the reference strains available in the GenBank databases was identified (Table 3). Thirty-five percent of the 17 Enterobacterludwigii strains were found in the Eastern region of the country. To the best of the authors’ knowledge, this is the first report of the Enterobacterludwigiiisolate from the soil samples collected in Saudi Arabia.

Vol18No1_DIV_Kaw_fig3 Figure 3: Phylogenetic tree of the Enterobacterludwigii strain and the closest NCBI (BLASTn) strains based on the 16S rRNA gene sequences (neighbor-joining tree method).

Click here to view figure 

Discussion

In this study we have isolated and identified different bacteria that grow and survive at high temperatures from 5 five different regions in the Kingdom of Saudi Arabia. We identified four genera of thermophilic and thermo-tolerantbacteria isolated from soil samples.This study shows that Proteobacteria and Firmicutes were the dominant phyla in the microbiota of the Soil Samples. Interestingly, these two phyla were also found to be dominant in hot springs (Lee et al., 2018). Analyses also revealed the presence of 57 thermophilic isolates pertaining toEnterobacter, Bacillus, Paenibacillus, and Pseudomonas.Other studies have identified Bacillus genus, specifically the Bacillus licheniformis, in hot springs, deserts and salt marshes in Morocco and in hot spring in Jorden (Aanniz et al., 2015; Mohammad et al., 2017; Al-Shammary et al., 2017;Bahkaliand Khiyami, 2008; Khalil, 2011). In addition, a study in the Northern region of the Kingdom of Saudi Arabia, Hail, identified Bacillus and Staphylococcusin soil isolates similar to our findings of the northern soil samples (Al-Shammary et al., 2017). A nation-wide study on thermophilic organism soil isolates has also identified all the reported genus in this study (Bahkaliand Khiyami, 2008). Bacillus and Brevibacillusgenus were identified in hot springs in the Kingdom of Saudi Arabia (Khalil, 2011). In another investigation, Alotaibi et al. 2020 have proven a wide variety of microbial communities in various areas that varied in physiochemical soil features in Saudi Arabia. Also, they have proven that higher fungal diversity than bacterial was isolated from desert areas and Sabkha. Also,Murgia et al. 2019 have showed significant fungal biodiversity in the Middle East desert soil.

The highest and lowestpercentages of bacterial species wasnoted in the samples collected from in the eastern and northern region, respectively. On the other hand, we observed similar percentages were found in the central and southern regions of Saudi Arabia.Enterobacter ludwigii was the most common bacterial species, followed by Enterobacter sp. and Bacillussp.To our knowledge, we are the first to identify this species of Enterobacter in the Kingdom of Saudi Arabia.

Conclusion

The resultsyielded by the present study indicate that numerous thermophilic and thermo-tolerant bacteria species thrive in different regions of Saudi Arabia.Moreover, although samples were collected from different regions to ensure diversity and comprehensive geographic coverage, the isolated bacteria species were generally similar. The abundance of bacteria in this study was typical of the environment with functional diversity and high species richness.Consequently, the findings of this study will provide invaluable information to microbial ecologists, as a diverse set of microbial communities of hot deserts in Saudi Arabia was identified. In addition, identification of thermophilic bacteria could be later used for biotechnological industry. In our future studies, the aim will be to determine the genetic variance among isolated thermophilic and thermo-tolerant bacteria and affected downstream proteins.

Data Availability

The sequencing data generated in this paper have been deposited in the GenBank repository with accession codes provided in Table 3.

Acknowledgements

We would like to express our gratitude to Prof. Mohammad Al ahdaal, Dr. Ahmed AlQahtani, and MashaelAlanazi at King Faisal Specialist Hospital & Research Center, Department of Infection and Immunity, for providing us access to their facilities.

Conflicts of Interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

References

  1. Brooks, A. N.,Turkarslan, S., Beer, K. D., Lo, F. Y., and Baliga,N. S. 2011. Adaptation of cells to new environments. Wiley Interdisciplinary Reviews Systems Biology and Medicine 3(5), 544-561.
    CrossRef
  2. Nazina, T. N., Sokolova, D. S., Grigoryan, A. A., Shestakova, N. M., Mikhailova, E. M., Oshima T., Moriya T. 2008. A preliminary analysis of microbial and biochemical properties of high-temperature compost. Annals of the New York Academy of Sciences 1125, 338-344.
    CrossRef
  3. Chamizo, S., Canto ́n, Y., Miralles, I. 2012. Biological soil crust development affects physicochemical characteristics of soil surface in semiarid ecosystems. Soil Biology and Biochemistry 49, 96-105.
    CrossRef
  4. Lester, E. D., Satomi, M., Ponce, A. 2007. Micro ora of extreme arid Atacama Desert soils. Soil biology and biochemistry 39, 704-708.
    CrossRef
  5. Stomeo, F., Valverde, A., Pointing, S. B. 2013. Hypolithic and soil microbial community assembly along an aridity gradient in the Namib Desert. Extremophiles17, 329-337.
    CrossRef
  6. Lau, M. C., Aitchison, J. C., Pointing, S. B. 2009. Bacterial community composition in thermophilic microbial mats from five hot springs in central Tibet. Extremophiles13, 139-149.
    CrossRef
  7. Gul-Guven, R., Guven, K., Poli, A., Nicolaus, B.2008; Anoxybacilluskamchatkensis subsp. asaccharedens subsp. nov., a thermophilic bacterium isolated from a hot spring in Batman. Journal of General and Applied Microbiology 54, 327-334.
    CrossRef
  8. Derekova, A., Mandeva, R.,Kambourova, M. 2008. Phylogenetic diversity of thermophilic carbohydrate degrading bacilli from Bulgarian hot springs. World Journal of Microbiology and Biotechnology24, 1697-1702.
    CrossRef
  9. Sharma, A., Pandey, A., Shouche, Y. S., Kumar, B.,Kulkarni, G. 2008.Characterization and identification of Geobacillus spp. isolated from Soldharhot spring site of Garhwal Himalaya, India. Journal of Basic Microbiology48, 1-8.
  10. Sievert, S. M., Ziebis, W., Kuever, J., Sahm, K. 2000. Relative abundance of Archaea and Bacteria along a thermal gradient of a shallow water hydrothermal vent quantified by rRNA slot-blot hybridization. Microbiology146, 1287-1293.
    CrossRef
  11. Maugeri, T. L., Gugliandolo, C., Caccamo, D., Stackebrandt, E. 2001. A polyphasic taxonomic study of thermophilic bacilli from shallow, marine vents. Systematic and Applied Microbiology24, 572-587.
    CrossRef
  12. Takacs, C. D., Ehringer, M., Favre, R., Cermola, M., Eggertsson, G., Palsdottir, A., Reysenbach, A. 2001. Phylogenetic characterization of the blue filamentous bacterial community from an Icelandic geothermal spring. Federation of European Microbiological Societies Microbiology Ecology35, 123-128.
    CrossRef
  13. Sarhan, M. A., Alamrri, S. 2014. Characterization and Identification of Moderately Thermophilic Bacteria Isolated from Jazan Hot Springs in Saudi Arabia, Egypt. Academic Journal of Biological Sciences, G, Microbiology6(1), 67-75.
    CrossRef
  14. Chanal, A., Chapon, V., Benzerara, K. 2006. The desert of Tataouine: an extreme environment that hosts a wide diversity of microorganisms and radiotolerant bacteria. Environmental Microbiology 8, 514-525.
    CrossRef
  15. Connon, S. A., Lester, E. D., Shafaat, H. S. 2007. Bacterial diversity in hyperarid Atacama Desert soils. Journal of Geophysical Research 112, G04S17.
    CrossRef
  16. Fierer, N., Strickland, M. S., Liptzin, D. 2009. Global patterns in below-ground communities. Ecology Letters 12, 1238-1249.
    CrossRef
  17. Bahl, J., Lau, M. C., Smith, G. J. 2011. Ancient origins determine global biogeography of hot and cold desert cyanobacteria. Nature Communications2: 163-168.
    CrossRef
  18. Lacap, D. C., Warren-Rhodes, K. A., McKay, C. P. 2011. Cyanobacteria and chloroexi-dominated hypolithic colonization of quartz at the hyper-arid core of the Atacama Desert, Chile. Extremophiles15, 31-38.
    CrossRef
  19. Makhalanyane, T. P., Valverde, A., Lacap, D.C. 2013. Evidence of species recruitment and development of hot desert hypolithic communities. Environmental Microbiology Reports,5, 219-224.
    CrossRef
  20. Richer, R., Banack, S. A., Metcalf, J. S. 2015. The persistence of cyanobacterial toxins in desert soils. Journal of Arid Environments, Part B112, 134-139.
    CrossRef
  21. Fierer, N., Leff, J. W., Adams, B. J. 2012. Cross-biome metagenomic analyses of soil microbial communities and their functional attributes. Proceedings of the National Academy of Sciences109, 21390-21395.
    CrossRef
  22. Adiguzel, A. Molecular characterization of thermophilic bacteria isolated from water samples taken from various thermal plants Ph.D. Thesis,Atatürk University, Graduate School at Natural and Applied Sciences, Erzurum, Turkey2006.
  23. Zaliha, R. N., Rahman, R. A., Leow, T. C., Salleh, A. B., Basri, M. 2007. Geobacilluszalihae sp. nov., a thermophilic lipolytic bacterium isolated from palm oil mill effluent in Malaysia. BMC Microbiology7, 77-87.
    CrossRef
  24. Makhalanyane, T. P., Valverde, A., Gunnigle, E., Frossard, A., Ramond, J. Cowan, A. P.2015. Microbial ecology of hot desert edaphic systems. FEMS Microbiology Reviews39, 203–221.
    CrossRef
  25. Eckert, D. Sims, J.T. Recommended soil pH and lime requirement tests(ed). Recommended soil testing procedures for the northeastern United States. Northeast regional bulletin # 493. 3rd edn. Agricultural Experiment Station, University of Delaware, NewYark, DE. 2011, pp 19 -25.
  26. Wolf, A.M. Beegle, D.B. Recommended soil pH and lime requirement tests (ed). Recommended soil testing procedures for the northeastern United States. Northeast regional bulletin # 493. 3rd edn. Agricultural Experiment Station, University of Delaware, Newark, DE. 2011; 19 -25.
  27. Gavlak, R., D. Horneck, and R. Miller. Plant, soil and water reference methods for the Western Region. Western Regional Extension Publication (WREP), 2005; 125, WERA-103 Technical Committee.
  28. Flanagan J.L., Brodie E.L., Weng L., Lynch S.V., Garcia O., Brown R. et al. 2007. Loss of bacterial diversity during antibiotic treatment of intubated patients colonized with Pseudomonas aeruginosa. Journal of Bacteriology45, 1954–1962.
    CrossRef
  29. Kumar, S., Stecher, G., Tamura, K. 2016. MEGA7: Molecular Evolutionary Genetics Analysis version 7.0 for bigger datasets. Molecular Biology and Evolution33, 1870-1874.
    CrossRef
  30. Lee Li., Goh Ki., Chan Ch., Tan Ge., Yin Wa., Chong Ch., Chan Ko. 2018. Microbial diversity of thermophiles with biomass deconstruction potential in a foliage-rich hot spring. MicrobiologyOpen 7, 615.
    CrossRef
  31. Aanniz, T., Ouadghiri, M., Melloul, M., Swings, J., Elfahime, E., Ibijbijen, J., Amar, M. 2015. Thermophilic bacteria in Moroccan hot springs, salt marshes and desert soils. Brazilian Journal of Microbiology46(2), 443-453.
    CrossRef
  32. Mohammad B., Al Daghistani H., Jaouani A, Abdel-Latif S., Kennes C. 2017. Isolation and Characterization of Thermophilic Bacteria from Jordanian Hot Springs: Bacillus licheniformis and Thermomonashydrothermalis Isolates as Potential Producers of Thermostable Enzymes. International Journal of Microbiology 12.
    CrossRef
  33. Al-Shammary, A., Sulieman, A., Abdelmageed, A., Veettil, V. Microbiological Study of the Soil in Hail industrial Zone, Kingdom of Saudi Arabia. Journal of Microbiology Research7(1), 8-13.
  34. Bahkali, A.,Khiyami, M. Isolation of thermophiles (>45 oC) and hyperthermophiles (>80 oC) bacteria and evaluation their enzymes and biosurfactants. Kingdom of Saudi Arabia, King Abdulaziz City for Science and Technology General Directorate of Research Grants Programs, 2008.
  35. Khalil, A. 2011. Isolation and characterization of three thermophilic bacterial strains (lipase, cellulose and amylase producers) from hot springs in Saudi Arabia. African Journal of Biotechnology10(44): 8834-8839.
    CrossRef
  36. Alotaibia, H. Sonbola, H. Alwakeela, S. Suliman R. Fod, R. S.AbuJaffal, A. AlOthman, N. Mohammeda, A. 2020. Microbial Diversity of Some Sabkha and Desert Sites in Saudi Arabia. Saudi Journal of Biological Sciences2778-2789.
    CrossRef
  37. Murgia, M. Fiamma, M.  Barac, A.  Deligios, M.  Mazzarello, V.  Paglietti, B.  Cappuccinelli, P.  Al‐Qahtani, A. Squartini, A.  Rubino, S.   Al‐Ahdal, M. 2019. Biodiversity of fungi in hot desert sands, MicrobiologyOpen8:1-10.
    CrossRef

Balantidiasis a Potential Neglected Zoonotic Disease and the Liar Paradox

$
0
0

A neglected zoonotic disease (NZD)could be defined as a disease transmitted from animals to humans,commonly associated with poverty thatimpacts the lives and livelihoods of neglected populations. The socioeconomic impacts of NZDs are expanding in the developing world, there is a major burden for poor rural communities1. Balantidiasisis not listed among the well-known NZDs, however, it shows all the key characteristics of NZDs.In fact, Balantidium coli,the etiological agent of balantidiasis,the sole ciliated protozoan that affect the gastrointestinal tract of humans, is transmitted by fecal-oral route in which contaminated drinking water or food are the mainroute of transmission. In balantidiasis exists a kind of “liar paradox”, in fact, on one hand, based on the literature,B. coli could not be considered a public health problem because infections are usually asymptomatic and in humans, the overall prevalence is estimated to be 0.02 to 1%2,3,4.On the other hand,some authors report that the parasite could invade the intestinal wall causing diarrhea, abdominal pain, nausea, vomiting, and in severe cases the death of the host 5; with an overall prevalence in endemic areas that reaches 30%6,7. What is the truth? The truth is that this parasitosis, due to the low pathogenic relevance, is often underestimated, both in human and animal populations.Considering the high prevalence in livestock and the pathogenic potential of this disease, it is mandatory to predict the appearance of new balantidiasis outbreak, especially in the poor rural scenario.Remarkably, the outbreaks of balantidiasis are strongly related to its presence in animals hosts, being particularly exposed to people working with animals (i.e., veterinary, farmer and slaughterhouse worker), especially those in contact with pigs that,together with rodents, are the main reservoir of the disease 2,8.Worldwide,the B. coli prevalence in domestic pigs ranges from 50 to 100% and the breeding system and related management practices are the main factors influencing the infection rates 4,9,10,11. In livestock, sanitation management of environment and animals ensure effective control of parasitic infection 9.

In developing countries, this parasitosis could be a serious threat and its spread is related to the contamination of water and food sources with swine faeces, while in developed countries recreational water represents a further source of infection (i.e., in swimming pools, human-to-human transmission)12.

The presence of B. colicysts, revealed in faeces of slaughtered animals, raises concerns for food safety and public health due to the possible contamination of the car casses 13. During slaughtering, since B. coli inhabits the last intestinal tracts of animals, inadequate evisceration could determine meat contamination as well as poor hygiene practices could lead to cross-contamination among the carcasses.Under certain favourable conditions (i.e., cold chain failure), B. colicysts could persist on the carcass surface along with the production chain exposing humans to infection by consumption of raw or under cooked meat and meat products 14

Furthermore, more attention should be paid to the hygiene condition during the domestic slaughtering of pigs frequently practiced in both developing and developed countries.

Overall,good manufacturing and hygiene practices would ensure effective management of B. coli risk along with the production chain, as well as the cooking of meat in the households.

Nowadays, despite the high prevalence reported in farmed animals and its zoonotic relevance, only a few and dated studies investigated the occurrence of B.coli in foods which may represent a new and important transmission pathway, especially in the developed countries.Considering that the pathogenic relevance (i.e., from asymptomatic to severe clinical disease)of B. coli infection in humans is still poorly understood, it is mandatory to monitor the presence of this parasitosis and to update the information on B. coli prevalence in farm animals in both developed and developing countries.

Reference

  1. Welburn, S.C., Beange, I., Ducrotoy, M.J., Okello, A.L. The neglected zoonoses—the case for integrated control and advocacy. Microbiol. Infec. 2015;21(5),433-443.https://doi.org/10.1016/j.cmi.2015.04.011.
    CrossRef
  2. Ponce-Gordo, F.,Jirků-Pomajbíková, K.:Balantidium coli. In: Global Water Pathogens(Rose JB, Jiménez-Cisneros B, eds). Michigan State University, E. Lansing, MI, UNESCO. 2017.http://www.waterpathogens.org /book/balantidium-coli.
    CrossRef
  3. Boonjaraspinyo, S., Boonmars, T., Kaewsamut, B., Ekobol, N., Laummaunwai, P., Aukkanimart, R., Wonkchalee, Juasook A.,Sriraj, P. A cross-sectional study on intestinal parasitic infections in rural communities, northeast Thailand. Korean J.Parasitol.,2013;51(6),727.https: //doi.org  /10.3347 /kjp.2013. 51.6.727
    CrossRef
  4. Schuster, F.L., Ramirez-Avila, L. Current world status of Balantidium coli. Microbiol. Rev. 2008;21(4),626-638.https://doi.org/10.1128/CMR.00021-08.
    CrossRef
  5. Neafie, R.C., Andersen, E.M, Klassen-Fischer, M.K. Balantidiasis. Meyers, W.M., Firpo, A., Wear, D.J. (eds): Topics on the Pathology of protozoan and invasive arthropod diseases. Washington (DC): Armed Forces Institute of Pathology. Available from http://www.dtic.mil/docs/citations/ADA547528.
    CrossRef
  6. Devera, R., Requena, I., Velasquez, V., Castillo, H., Guevara, R., De Sousa, M., Marin, C., Silva, M. Balantidiasis in a rural community from Bolivar State, Venezuela. Chil. Parasitol. 1999;54(1-2),7-12.https://europepmc .org /article/med/10488584
  7. Kline, K., McCarthy, J.S., Pearson, M., Loukas, A., Hotez, P.J. Neglected tropical diseases of Oceania: review of their prevalence, distribution, and opportunities for control. PLoS Negl. Trop. Dis. 2013;7(1),e1755. https://doi.org/10.1371/journal.pntd.0001755
  8. Ferry, T., Bouhour, D., De Monbrison, F., Laurent, F., Dumouchel-Champagne, H., Picot, S., Piens, M.A., Granier, P. Severe peritonitis due to Balantidium coli acquired in France. J. Clin. Microbiol. Infect. Dis. 2004;23(5),393-395.https://doi.org/10.1007/s10096-004-1126-4
    CrossRef
  9. Giarratana, F., Muscolino, D., Taviano, G. and Ziino, G. Balantidium coli in pigs regularly slaughtered at abattoirs of the province of Messina: hygienic observations. J. Vet. Med.2012;02,77-80. https://doi.org/ 10.4236/ojvm.2012.22013
    CrossRef
  10. Ismail, H.A.H.A., Jeon, H.K., Yu, Y.M.,Do, C.,Lee, Y.H. Intestinal parasite infections in pigs and beef cattle in rural areas of Chungcheongnam-do, Korea. J. Parasitol.2010;48(4),347. https://doi.org/ 10.3347/kjp.2010 .48.4.347
    CrossRef
  11. Yin, D.M., Lv, C.C., Tan, L., Zhang, T.N., Yang, C.Z., Liu, Y., Liu, W. Prevalence of Balantidium coli infection in sows in Hunan province, subtropical China. Trop. Anim. Health. Prod. 2015;47(8),1637-1640. https://doi.org /10.1007/s11250-015-0904-6
    CrossRef
  12. Bellanger, A.P., Scherer, E., Cazorla, A., Grenouillet, F. Dysenteric syndrome due to Balantidium coli: a case report. Microbiol. 2013;36(2),203-05.http://www.newmicrobiologica.org/PUB/allegati_pdf/2013/2/203.pdf
  13. Ahmed, A., Ijaz, M., Ayyub, R.M., Ghaffar, A., Ghauri, H.N., Aziz, M.U., Alia, S., Altaf, M., Awais, M., Naveed, M., Nawab, Y.,Javed, M. U. Balantidium coli in domestic animals: An emerging protozoan pathogen of zoonotic significance.Acta trop. 2020;203,105298.https://doi.org/10.1016/j.actatropica.2019.105298
    CrossRef
  14. Panebianco, F. Igiene delle Carni e Balantidium coli (Malmsten, 1857). Annali della Facoltà di Medicina Veterinaria di Messina. 1967;4:49-64.

Long term Impacts of Effluents on Quality of the Kosi River Water at District Rampur, Uttar Pradesh, India

$
0
0

The transition metals with the 5 g/cm3 density are the elements which are essential components of soil, water and air. Several researchers have proved that the concentration of heavy metal higher than the maximum permissible value are lethal and can cause considerable harm to the ecosystem plants, animals and human health1,2.The elevated levels of these metals not only disturb the aquatic ecosystem but also cause toxic effects to living organisms1. The rapid industrialization in India is also an important factor of releasing heavy metals and other harmful pollutants into the soil and water bodies through their untreated waste. Therefore the rules and regulation of environmental protection are implemented more strictly which instructs manufacturers to treat the effluent before their drainage to decrease the levels of certain metal ions. The measurement of extent of contamination is therefore essential to get the information of the quality of soil, water and air for society3. Studies conducted on surface water soil revealed that agricultural runoff, industrial and urban waste are source of heavy metals accumulation 4,5. Along with industrial and agricultural runoff the leaching of heavy metals from sea waters to estuaries and rivers are also a cause of heavy metals contamination in the surface water .It was found that coastal lagoons usually do not allow fast water exchange 7, resulting the gathering of heavy metals in the ecosystem. Several among them are vital for living organisms, like Cu and Zn, whereas, some other heavy metals such as Pb, Cd, Hg, etc. are highly toxic for all living beings 8,9. They are responsible for severe harm to physiological and metabolic processes of organisms when environments possess high concentrations of these elements than desired permissible limit. These heavy metals not only directly affect the organisms by accumulating in the body but also they indirectly enter through food chain to the higher tropic level 10. The gravest consequence of this shift is biological magnification by the food chain11. The dissolution of the heavy metals inflowing water bodies can be inhibited by a balanced set of physicochemical parameters like pH, conductivity, type of metal species, turbidity, the hardness, total alkalinity and the redox atmosphere of the marine system12,28. When these metals reached to the aquatic bodies through numerous sources, they get adsorbed onto inorganic and organic systems and settled as residueensuing in higher concentration of heavy metals in bed sediment13,14. The Physicochemical studies provide important information to determine the water quality. Many external factors like pH, dissolved oxygen, electrical conductivity and the available surface area for adsorption caused by the variation in grain size distribution are factors which determine the solubility of heavy metals15. Though, it is also a fact that metals cannot always be removed by sediments only. Some sediment bounded elements may reach back to water by means of the many disturbed environmental factors as low pH, imbalanced redox potential, the organic ligand levelsetc. and enforceundesirable effects on living organisms12. The Kkosi river water in district Rampur UP India was found to possess higher concentration of lead, mercury and pesticides at some selected sites16. Therefore the present paper is an attempt to analyze and monitor the effect of industrial and agricultural waste on the heavy metals content like Pb, Zn, Cu,Hg and As and physico-chemical balance of the river Kosi at district Rampur, UP, India because such accumulations of heavy metal screatelatenthazard to entire ecosystems including wildlife as well as human welfare.

Materials and Methods

Description of the study area

The basin of river Kosi is the area under study, passed through district Rampur, Uttar Pradesh. It is positioned among longitudes 78o54” to 69o28” E and latitude 28o25”to 29o10”N and on coordinate it 28.8N to 79.0oE. The river Kosi under the basin of Rampur has 3,429 Km² total areas. The citizens of this district labor mostly in farming and industries in neighboring areas. The Kosistream water is mainly utilized for agricultural, domiciliary practice and as well as for drinking purpose. Kosi is one of the chief branches of river Ramganga and is one of the important tributary of northern part of Uttar Pradesh and Uttrakhand. Now days, waterway contamination is a severe and evolving problem in most of the developing countries. Because of speedy industrial development, the disposal of effluent to natural water bodies has been enhanced. Before entering Rampur, the river Kosi crosses Kashipur (UdhamSingh Nagar) Uttarkhand, India, known for its rice-belt and several industries.These industries releasetheir treated/untreatedwaste into it and then it arrives into the district Rampur (Figure 01 and Table 01).

Vol18No1_Lon_Gul_fig1 Figure 01: Descriptive representation route of Kosi River17

Click here to view  figure

Table 01: Description of sampling sites and their geographical locations.

Site of each sample for collection Geographical location
S1 DadiyalTanda 28.974°N 78.942°E.
S2 Swar 29.027°N 79.057°E
S3 Lalpur barrage 27.4060N 77.6110E
S4 Pranpur  Said Nagar 28.840 N 79.0050 E
S5 Shahbad 28.34°N 79.10°E
S6 NH24 Rampur kosi bridge 29.027°N 79.057°E
S7 Industrial drainage from Kashipur 29.220N 78.950E

Water Sampling

The samples from each site under study were taken in the decontaminated plastic bottles. Replica samples for heavy metals and physicochemical parameters measurement were collected together from all sample sites. The process of collection of samples was carried out each season in a year i.e. in summer, rainy season and in the mid-winter season for one year (2018-2019), the period of sampling was from June 2018 to February 2019. Five different locations were identified from each site for sampling as SB, NB, CP, 20-CP (Table 01).Standard procedures were acquired for the investigation of different water quality assessments15. All flasks were cleaned with dilute acid followed by distilled water and then dried. The sampling bottles were carefully filled completely and sealed, devoid of letting airbubble during sampling. The samples taken were kept airtight in an icebox and preceded to the laboratory immediately for analysis. In the laboratory, samples were kept at -20°C and removalof impurities was carried out within 48 h. Each 100 ml of water sample is acidified with concentrated HNO3 for heavy metal analysis.

Vol18No1_Lon_Gul_fig2 Figure 02: Descriptive representation of District Rampur showing the route of Kosi River18

Click here to view figure 

Route of flow of Kosi River through selected sampling sites.

Analyses of physicochemical parameters

All the analyses were carried out thrice in a year i.e. in the summer, spring and winter season systematically. The studies of the several physicochemical parameters were conducted as per the standard methods and protocol19.The estimation of Temperature, hydrogen potential (pH) and electrical conductivity were carried out at the same time of sample collection.

pH determination

The pH was recorded with the digital pH-meter20. First pH meter was calibrated with the buffer solutions, the electrode(s) and glassware were rinsed with distilled water.  100 ml of samples were measured and kept in a 150 ml beaker for the pH determination. Then rinsed electrodes were immersed in the test sample. All samples are kept at room temperature in the tightly sealed bottle before estimation. pH was recorded within 5 minutes of opening of the sample bottle.

Turbidity determination

Turbidity of collected water samples was determined by Naphelo-turbidity meter21. Since turbidity is an optical property of water, hence it is very important parameter to measure. It gives the information of suspended impurities or suspended particles in the water. When light passes through suspended particles in water it gets reflected by suspended particles present. This property of scattering of light due to suspended Particle is called turbidity.

Total Hardness determination

The total hardness of water samples was determined by the volumetric method with EDTA22.

Electrical Conductivity determination

It was measured by the dissolved matter in the water, the charge of ions produced, the ionization potential, the frequency and the temperature of the water. Thus, the measurement of the conductivity indicates the total dissolved salts in the water and therefore its mineral content2450 ml of water sample of each site was taken in the conical flask and stirred for 30 minutes and analyzed through conductivity meter its mineral content.

BOD determination

BOD was observed as per standard method. Biochemical oxygen demand is a measure of the quantity of oxygen used by microorganisms (e.g., aerobic bacteria) in the oxidation of organic matter. BOD is conducted over a five day period. To determine five-day biochemical oxygen demand (BOD5), the samples with different dilutions are measured for dissolved oxygen before and after a five-day incubation period at 20 °C in the dark.

COD determination

COD was determined by potassium dichromate open reflex method23.The chemical oxygen demand measurement is conducted to find out the toxic atmosphere of the river and occurrence of biologically immune organic matters.

Chloride determination

The Chlorides content were determined by Mohr’s argentometric method. In this titration the silver nitrate solution is gradually added drop-wise and silver chloride precipitate is obtained. The precipitation of all chloride ions is represented as end point.

Heavy metals determination

Atomic absorption spectroscopy is a technique that records the concentration of metals qualitatively and quantitatively. The examination of heavy metals in water samples was carried out by atomic absorption spectrophotometer each season11.

Statistical analyses

The data of the present study was analyzed by the following statistical approaches.

Percent enrichment

In this study the heavy metal pollution at various sites of Kosi River were ascertained by the calculation of percent enrichment.  The percent enrichment24 was calculated by using following formula

Vol18No1_Lon_Gul_eq1

Where,

Cmin and Cmax are the minimum and maximum concentrations(mg/L) obtained in this study

C is the mean concentration (mg/L) in the water sample.

Standard Deviation

The standard deviation of all the observed values was calculated by following formula. Where xi is the observed value, is the average value of all the observed readings and n is the number of observations.

Vol18No1_Lon_Gul_eq2

Results

Physicochemical parameters of Kosi

The Kosi river water samples were analyzed for physicochemical characteristics of the water from February 2018 to February 2019.The results showed that temperature range was from 28.12 to 30.49 °C and the minimum value obtained in January and maximum value obtained in May and June. The standard pH values of the water samples obtained between 7.2 and 6.3, which confirm the almost neutral to slightly acidic state of water. At S2 the water pH-value was 7.4 in winter season whileat S5it was 6.3. The results obtained for turbidity of each sample indicated the range from 0.4 to 7.067 NTU. The electrical conductivity values of all the samples were obtained from 129.42to 399.36μs/cm. The highest value obtained at S5. The total solid content value of Kosi river water samples was observed from 15.05 (mg/L) to 2009.166 (mg/L). The highest value obtained at S5 and lowest value at S2.  The TDS values ranged from 11.08 (mg/L) to 174.746 (mg/L). The highest value obtained at S5 and lowest value at S1 (Table 02). The maximum permissible limit of each analysis has been enlisted in the Table 03.

Table 02: Physico-chemical parameters of River Kosi and industrial drainage location.

Sample

Code

season Turbidity

NTU

pH Conductivity

μs/cm

Tem Total solid

(mg/L)

TDS

(mg/L)

Total hardness

(mg/L)

Chloride

(mg/L)

S1 Summer 0.40 6.85 221.8 36 44.75 12.093 103.0 111.0
Spring 2,80 6.98 176.9 29 20.08 11.08 98.60 94.50
Winter 6.35 6.35 232.2 23 29.15 12.10 109.5 81.00
S2 Summer 1.65 7.20 261.7 36 53.55 23.75 144.0 88.00
Spring 1.43 7.10 234.0 29 34.91 38.09 142.0 78.00
Winter 1.30 7.40 255.6 23 15.05 47.50 134.0 68.00
S3 Summer 3.30 7.10 250.1 36 29.67 12.67 94.33 149.0
Spring 2.05 7.10 247.1 29 20.14 11.90 102.0 136.0
Winter 1.16 7.00 234.2 23 29.66 12.50 108.6 97.33
S4 Summer 2.43 6.83 491.7 36 63.00 23.36 73.00 51.33
Spring 2.50 6.90 257.5 29 46.1 23.567 78.00 50.21
Winter 5.23 6.46 241.9 23 6 25.06 66.60 57.33
S5 Summer 7.06 6.33 309.55 36 2009.0 174.74 446.3 244.66
Spring 20.3 6.89 399.36 29 1800.0 152.43 366.0 165.90
Winter 25.2 6.20 244.83 23 809.26 117.57 218.6 125.88
S6 Summer 3.30 6.95 129.42 36 950.8 363 160.0 148.00
Spring 2.90 6.78 150.32 29 850.6 360 146.0 137.00
Winter 2.65 6.90 457.4 23 435.9 363 89.60 105.80
S7

 

Summer 2.03 6.86 297.4 36 83.46 104.4 506.3 110.66
Spring 1.89 6.90 270.5 29 68.09 102.8 380.0 102.82
Winter 2.26 6.43 213.5 23 26.20 104.43 226.3 74.00

Table 03: Permissible limits of drinking water quality.

Parameters USEPA WHO ISI ICMR CPCB
pH (mg/L) 6.5-8.5 6.5-8.5 6.5-8.5 6.5-9.2 6.5-8.5
Turbidity (NTU) 5 10.00 10
Electrical conductivity (μs/cm) 500 400
DO (mg/L) 4.5 6.0
BOD (mg/L) 2.0
Total hardness(mg/L) 500 300 600 600
Chloride (mg/L) 250 200 250 1000 1000
Lead (mg/L) 0.05 0.10 0.05 No relaxation
Mercury (mg/L) 0.02 0.001 0.00 0.00 No relaxation
Zinc (mg/L) 5.0 5.00 0.10 15.0
Arsenic (mg/L) 0.05 0.05 0.05 0.05 No relaxation
Copper (mg/L) 1.30 1.0 0.05 1.50 1.5

Total hardness of the selected sites was observed from 73 (mg/L) at S4 to 506.33 (mg/L) at S7. The chloride content was observed from 88(mg/L) at S2 and 244.667(mg/L) at S5. The chloride contents of the analyzed water samples (Table 03) results show that the chloride concentration is less than 1000 mg/L as per WHO standards of Drinking water. The Dissolved oxygen of Kosi river samples showed the range of 0 mgL-1at S4 & S5 to 2 (mg/L) at S7. Generally when the value of Biochemical oxygen demand exceeds there is decline in the dissolved oxygen level. The Biochemical oxygen demand ranged from 10.5 (mg/L) at S4 to 137.4 (mg/L) at S5. The higher value of COD is indicating the extent of pollution in the Kosi River. The minimum value of chemical oxygen demand observed at S3 32.60 (mg/L) and maximum value was observed at S5 168.65 (mg/L) (Figure 03).

Vol18No1_Lon_Gul_fig3 Figure 3: DO, BOD and COD content of various samples collected from different samples .

Click here to view figure

Samples were collected from seven sampling sites i.e. S1. S2. S3. S4. S5. S6. S7 during summer, winter and spring seasons for the period from June 2018 to Feb 2019. These samples were analyzed for the estimation of DO, BOD and COD contents.

Heavy metals contamination of water of Kosi River

In the present paper, the effect of industrial and agricultural discharge have studied to estimate the quality of Kosi river water by determining the levels of heavy metals of the Kosi River in the basin of district Rampur U.P. When collected samples of at Kosi River examined the value of As was found to be 0.04 at S2, 0.08 at S3, 0.85 at S5, 0.72 at S6 and 0.71 at S7. These values are clearly indicating the exceeding value of As as per WHO at all the sampling sites except S4 and S1.The contents of zinc were found to be 0.04 at S1, 1.28 at S2, 0.19 at S3, 0.05 at S4, 7.76 at S5, 0.685 at S6 and 0.04 at S7 in summer season. The highest value of Zn was recorded at S5 (Shahbad near sewage disposal point). The concentration of Cu was 0.01, 0.06 and 0.01 at S4, S5 and S6 respectively. While at S1,S2, S3 and S7 the Cu was not detected. The results obtained reveal that the concentration of lead in the analyzed waters is very high, varying between 0.14 to 3.86 mg/L. The values recorded in the different sites are very high than the maximum acceptable value (Table 02). The data obtained reveal that the total mercury concentrations in the analyzed waters samples are low, varying from nil to 0.001 mg /L, the values recorded in the different sites are lower than the maximum value as mentioned in Table 02. The results obtained by the analysis of the zinc, copper, arsenic, mercury and lead levels in the water samples are shown in Table 04, Figure 04.

Vol18No1_Lon_Gul_fig4 Table 04: Heavy metals contents in the water samples of the Kosiriver, Rampur. India.

Click here to view figure 

Table 04: Heavy metals contents in the water samples of the Kosiriver, Rampur. India

Sample code Seasons Pb Zn Cu Hg As
S1 Mean 0.538 0.05 NIL 0.00 NIL
PE 33.69 42.85 51.85
SD 0.88 0.03 0.00
S2 Mean 0.06 0.78 0.00 0.01
PE 39.47 58.89 30.00 33.25
SD 0.06 0.62 0.00 0.01

S3

Mean 0.55 0.41 0.00 0.04
PE 33.29 38.24 66.66 54.12
SD 0.88 0.51 0.00 0.02

S4

Mean 0.03 0.06 0.02 0.00 NIL
PE 51.20 50.00 41.73 65.00
SD 0.03 0.01 0.02 0.00
S5 Mean 1.30 3.26 0.03 0.00 0.30
PE 33.40 37.84 55.50 67.00 34.91
SD 2.21 3.92 0.023 0.018 0.47
S6 Mean 1.25 0.97 0.026 0.012 0.24
PE 33.51 46.60 41.75 66.68 34.74
SD 2.12 0.72 0.024 0.00 0.37
S7 Mean 1.20 0.53 0.37 0.00 0.24
PE 33.54 52.12 33.68 33.33 34.26
SD 2.025 0.47 0.545 0.00 0.36

PE: Percent Enrichment, SD: standard deviation.

Samples were collected from seven sampling sites during summer, winter and spring seasons for the period from June 2018 to Feb 2019 and analyzed were subjected to the estimation of various heavy metal ions.

Discussion

Contamination of water bodies is one of the major rising environmental concerns in India. Urban discharge sources, industrial effluents, and agricultural runoff increase heavy metal levels in receptive rivers. In the present study the impact of prolong industrial and agricultural drainage have been taken into consideration for the assessment of quality of Kosi river water in terms of levels of heavy metals and physicochemical parameters of the Kosi river in the basin of district Rampur U.P. The results showed that temperature range was from 28.12 to 30.49 °C and the minimum value obtained in January and maximum value obtained in May and June. It is quite obvious that the pH of the water is the quantity of the concentration of the H+ ions present which indicates the balance between miscellaneous forms of carbonic acid and formation of buffer system by carbonates and bicarbonates24. The standard pH values of the water samples obtained between 7.2 and 6.3, which is indicative of more or less neutral to some extent acidic state of water. At S2 7.4 water pH-value was obtained in winter season while at S5 it was 6.3. Both the values are suggesting the slight alkaline to slight acidic state of water respectively. The highest value of turbidity was observed at S5 in winters. Turbidity is a parameter to measure thetransparency and clarity of water. It directly alters the color of the water. The amount of suspended impurities in water reduces the channel of light in the water. Soil particles, microbes, algae and other materials are included in suspended substances. Generally in the size range of these substances may be 0.004 mm (clay) to 1.0 mm (sand). As per WHO (World Health Organization), the turbidity of drinking water should be less than 5 NTU, and ideally it should be below 1 NTU.The study of electrical conductivity the capacity of the water to conduct an electrical current is measured. The conductivity increases as concentration of ions increases.    All the values of the conductivities found within the maximum permissible value 200-800μs/cm (Table 02), which revealed that water is feebly mineralized. As per (McGowan, 2000) the range of soft water should be 60-12 mg/L, for moderately hard 120-180mg/L and for hard water it remains more than 180 mg/L. therefore S5 and S7 are indicating values higher than moderately hard. The total solid content value of Kosi river water samples was observed from 15.05 (mg/L) to 2009.166 (mg/L). As per EPA its max permissible limits is 500mg/L. The highest value obtained at S5 and lowest value at S2. According to WHO TDS are inorganic salts and small amount of organic matter present in the water. The TDS values ranged from 11.08 (mg/L) to 174.746(mg/L). The highest value obtained at S5 and lowest value at S1 (Table 03) (Figure 02, 04). The maximum permissible limit of TDS is 500 ppm (WHO).

The Dissolved oxygen of Kosi river samples showed the range of 0 mg/L at S4 and S5 to 2 (mg/L) at S7.Generally when the value of Biochemical oxygen demand exceeds there is decline in the dissolved oxygen level. The Biochemical oxygen demand ranged from 10.5 (mg/L) at S4 to 137.4 (mg/L) at S5. At all the sites of Kosi River the BOD value was higher than normal value which is clearly signifying that the surface water is significantly contaminated. The chemical oxygen demand measurement is conducted to find out the poisonous environment of the river and occurrence of biologically immune organic matters. The higher value of COD is indicating the extent of pollution in the Kosi River.The results of the analyses of heavy metals in the collected water samples of Kosiriver at district Rampur UP show a significant increase in the concentration of Pb, Zn, Cu, Hg & As at S5, and the concentration of all the metals at other sites are relatively low. As per the present study lead concentration was highest at S6 and S7 in winter season. Although it was also detected that the Pb concentration was high in winter season as compared to other seasons in all waste water samples like S1, S2, S3,S4, S5, S6 and S7. The level of Pb found at S1 was from (0.0197-0.0131), at S2 (0.0222-0.0125) at S3 (0.0275-0.0183) at S4 (0.0252-0.0148), at S5 (0.027-0.0147), at S6 (0.0217-0.0102), at S7 (0.02075-0.0138).

The higher Hg concentration is obtained at S1 in summer season as compared to other sites. The maximum Hg level was detected at S1 0.00675 μg/L in summer season, while the concentration at other sites were S2 0.001 μg/L in Summer season, S3 0.0009 μg/L in Summer season, S4 0.0002 μg/L in Summer season, S5 0.001 μg/L in Summer season and 0.00012 μg/L in winter season, S6 0.0009 μg/L in Summer season and 0.019 μg/L in winter season and at S7 0.00006 μg/L in winter season. In spring season the Mercury was not recorded at any site it may be due to the dilution effect of the rivers due to rain fall. The maximum concentration for Zn was 7.76 mg/L at S5 in summer season while minimum at  0.02mg/L at S1 in spring season. The concentration of Zinc found at S1 was from (0.02-0.09), at S2 (0.08-1.28) at S3 (0.05-1) at S4 (0.05-0.07), at S5 (7.76-1.5), at S6 (0.685-1.3), at S7 (0.04-0.98).The maximum concentration of Cu was detected at S7 in summer season. While at S1, S2 and S3 Cu level is not observed. The concentration of Cu found at S4 (0.01-0.05), at S5 (0.04-0.06), at S6 (0.01-0.05), at S7 (0.05-1). However the higher concentration of As was obtained at S7 in winter season however at S1 it was not detected. The concentration of As found at S2 (0-0.04) at S3 (0.05-0.08) at S5 (0.01-0.08), at S6 (0.0-0.72), at S7 (0.71-2).

The concentration of Arsenic in drinking water has been investigated at various sites of the Kosi River. WHO has set a provisional guideline value of As 0.01 mg/L in drinking water and in India standard drinking water specification 1991, the maximum limit is 0.05 mg/L and there is no relaxation for maximum permissible level26. The anomalies in the physical and chemical parameters variability of the sub-lagoon Aby (Ebrié Lagoon, Ivory Coast) were monitored. Statistics, recorded from 2007-2009 and at 18 different locations, abnormalities in physical and chemical parameters were observed 27. As per WHO guidelines the highest desirable limit of Zn in drinking water is 5.0 mg/L. Therefore only at S5 the zinc content is exceeding the normal value. The copper content in the KosiRiver was found to lower than permissible value according to WHO (1.0 mg/L) .Such concentration may be either due to the leaching of the heavy metals rich soil or industrial discharges directly to the Kosi. Their contents at S5 are too high and the maximum permissible value has been crossed.

Conclusion

This research paper aimed to through light upon the present state of extent of pollution in the river Kosi due to the presence of heavy metals and alteration in the values of physicochemical parameters of the River. Present research conducted on heavy metal pollution on this river showed that the concentration of heavy metals in these selected sites are exceeding the permissible concentrations, which penetrate the stream, through straight discharges of municipal, industrial and mining effluents as discussed in the paper.  The toxic heavy metals and imbalance in physicochemical parameters are not only badly affecting the human health by causing severe diseases but also creating the imbalance of the aquatic ecosystem of river. Since protection and management plan of other rivers is going on a large scale by Government of India but still there is a need of attention towards Kosi River. Therefore the conservation and supervision strategies are suggested for the contaminated sites of Kosi River and to implement the preservation and awareness plan of river Kosi at all the mentioned sites.

Acknowledgement

The authors are highly thankful to council of Science & Technology Uttar Pradesh for providing financial assistance for this work under study.

Conflict of Interest

All authors declare that there is no conflict of interest in this work.

Funding Source

This study was funded by Council of Science & Technology, Uttar Pradesh, India.

References

  1. Akpoveta, O.V., Okoh B.E.,Osakwe, S.A. Quality assessment of borehole water used in the vicinities of Benin, Edo State and Agbor, Delta State of Nigeria. CurrRes in Chem,2011,3: 62-69.
    CrossRef
  2. Coulibaly A.S., Monde S., Wognin A.V., Aka K., Analyse des éléments traces métalliques (ETM) dans les baiesestuariennesd’Abidjanen Côte d’Ivoire. AfriqueSci, 2009, 5; 77 – 96
  3. Voorde, L. Pinoy, E. Courtijn, F. Verpoort, Influence of acetate ions and the role of the diluents on the extraction of copper (II), nickel (II), cobalt (II), magnesium (II) and iron (II, III) with different types of extractants, Hydrometallurgy, 2005, 78(1-2);92-106.
    CrossRef
  4. Shrivastava P., Saxena A., SwarupA.,Heavy metal pollution in sewage fed Lake of Bhopal, (M.P.) India. Lake ReservResMgmt, 2001,8: 1–4.
    CrossRef
  5. Kambole M.S., Managing the water quality of the Kafue River. In: Water demand management for sustainable development. 3rd water net werfsa symposium, Dare s Salaam,2002, 1–6.
  6. Coulibaly A.S., Monde S., Wognin A.V., Aka K.,.Dynamique des elements tracesmétalliquesdans les sédiments des baiesd’Abidjan (baie du Banco et radePortuaire). European J of Sciti. Res, 2012, 46: 204-215.
  7. Akpetou K.L., Kouassi A.M., Goula B.T.A., Assémian S., Aka K.Nutrients induction on lead, cadmium, manganese, zinc and cobalt speciation in the sediments of Abylagoon (Côte d’Ivoire). Intl JofEnggSciand Technol.2010,2: 3894-3900
  8. Heidary S.,ImanpourNamin J., Monsefrad F, Bioaccumulation of heavy metals Cu, Zn, and Hg in muscles and liver of the stellate sturgeon (Acipenserstellatus) in the Caspian Sea and their correlation with growth parameters. Iranian JofFishSci. 2012, 11(2): 325-337.
  9. Virha R., Biswas A.K., Kakaria V.K., Qureshi T.A., BoranaK.Malik N., Seasonal Variation in Physicochemical Parameters and Heavy Metals in Water of Upper Lake of Bhopal. Bulletin of EnvContaand Toxicol,2011,86 (2): 168-174.
    CrossRef
  10. Lalah J.O., Ochieng E.Z., Wandiga S.O., Sources of heavy metal input into Winam Gulf, Kennya. Bulletin of EnvContaandToxicol, 2008, 81: 27-284.
    CrossRef
  11. Ochieng E.Z., Lalah J.O., Wandiga S.O., Analysis of heavy metals in water and surface sediment in five Rift Valley Lakes in Kenya for assessment of recent increase in anthropogenic activities. Bulletin of EnvContaand Toxicol,2007,79: 570–576
    CrossRef
  12. Liu C., Xu J., Liu C., Zhang P., Dai M., Heavy Metals in the Surface Sediments in Lanzhou Reach of Yellow River, China. Bulletin of EnvContaand Toxicol,2009,82: 26–30
    CrossRef
  13. Davies O.A., Allison M.E., Uyi H.S., Bioaccumulation of heavy metals in water, sediment and periwinkle (Tympanoonusfuccatusvar radula) from the Elechi creek, Niger Delta. African J Biotechnol,2006,5: 968-973.
    CrossRef
  14. Canli M. &Atli G., The relationships between heavy metal (Cd, Cr, Cu, Fe, Pb, Zn) levels and the size of six Mediterranean fish species. Environm Pollution,2003,121:129-136.
    CrossRef
  15. APHA Standard methods for the examination of water and waste water. 21st ed. American Public Health 2005.
  16. Nizami G, Rehman S. Assessment of contamination of heavy metals and pesticides in the Kosi River in District Rampur U.P., India. J EnvironChemToxicol. 2018;2(2):60-64.
  17. Kumar A, Bahadur Y. Water Quality of kosi river and Rajera systemat Rampur India: Impact Assessment, J Chemistry Hindwai 2013, 4 pages
    CrossRef
  18. Yadav S.S., Kumar Rajesh, “Monitoring water quality of Kosi River in Rampur district, Uttar Pradesh Rampur”, Advances in Applied Research, 2011, 2(2): 197-201
  19. Ezzaouaq, Hydrodynamic, physico-chemical and bacteriological characterization of the surface waters of the Bouregreg estuary (Morocco) subjected to discharges from the cities of Rabat-Salé. Thesis D.E.S. Fac.Sci. Rabat, 2005, 140p.
  20. Yadav S.S., Kumar Rajesh, “Assessment of ground water pollution due to fluoride content and water quality in and around TandaTaluka of Rampur district, Uttar Pradesh, India. ChemandPharma Res, 2010, 2(4), 564-568.
  21. ParasharCharu, Verma N., Dixit S., Shrivastava R., “Multivariate analysis of drinking water quality parameters in Bhopal, India” Environ Moinit Assess, 2008,140,119-122.
    CrossRef
  22. APHA, Standard methods for the examination of water and wastewater, 18th edition, APHA AWWA. WPCF. (Eds) Washington DC,1998.
  23. NEERI, Mannual on water and waste water analysis, Nation Environmental Engg. Research, Nagpur, IN NEERI, 1987,86, xvi p.
  24. Zonta R, Zaggia L, Argrse E, Heavy metal and grain size distributions in estuarine shallow water sediments of the Cona Marsh (Venice Lagoon Italy). Sci Total Environ, 1994 151:19–28
    CrossRef
  25. Himmi, M,Fekhaoui, A. Foutlane, H. Bourchic, M. El Maroufy, T. Benazzout, M. Hasnaoui, Ratio ofphysicochemical parameters in a plankton basin dimaturazione (mixed lagoon BeniSlimane – Moroccohydrobiology plays Universitadegli de Perugia, Department of animal biology and ecology hydrobiologylaboratory “G.B. Fats”, 2003, 110.
  26. Umeobika, U.C., Ajiwe, V.I.E, Iloamaeke, M.I., Alisa, C.O.. Physicochemical analysis of rain water collected from 10 selected areas in Awka South, Anambra State, Nigeria. Intl JSciInnovand Dis, 2013, 3(1), 56-73.
  27. Saha K.C, Dikshit A. K, Bandyopadhyay M.A. A review of arsenic poisoning and its effect onhuman health. CritRev Environ Sci Technol. 1999;29:281–313.
    CrossRef 
  28. KouaméAkpétou,Hermann1 YapiYapo,Ted-Edgar Wango,SylvieAssémian,AlexKouaJérémie , Marcel KouassiAka,Anomalistics of physical and chemical parameters variability under anthropogenic and natural conditions in the four sectors of Aby lagoon (Ebrié lagoon system, CÔte d′Ivoire),2020, 10 (1); 34-40.

Molecular Mechanisms Underlying Salt Stress Tolerance in Jojoba (Simmondsia Chinensis)

$
0
0

Introduction

Molecular analysis of a plant’s response to extreme salt stress conditions paved the way for the improvement of agricultural productivity worldwide (Wang et al., 2009; Sailaja et al., 2014; Cao et al., 2018). Next-generation sequencing (NGS) technology using RNA-Seq promotes the study of organism transcriptomes, including transcriptomes of Jojoba(Simmondsia chinensis) (link) Schneider, an organism for which genome sequencing data is scarce (Martin and Wang, 2011; Garber, 2011; Jain, 2012; Nejat et al., 2018). Understanding thephysiological, biochemical, and molecular dynamics of salinity tolerance can help in the development of new genotypes with enhanced salinity tolerance via molecular breeding and plant genetic transformation (Bafeelet al., 2016). Salt stress is one of the abiotic stresses challenging or burdening cultivated plants (Liang et al., 2018). Jojoba is a candidate plant for biodiesel production and is also identified as a potential candidate for several other applications, including medicinal projects, cosmetics, and personal care formulations (Passerini and Lombardo, 2000; Al-Obaidi et al., 2017). Unfortunately, there is a paucity of reports investigating the mechanisms by which this important plant species tolerates abiotic stresses. Such mechanisms and responses to salt stress were previously studied via RNA-Seq in other plant species, including Rhazya stricta (Hajrah et al., 2017), Arabidopsis (Kawa and Testerink, 2017), barley, and rice (Ueda et al., 2006).

Abiotic stresses, including salinity, drought, and heavy metals, are major barriers that adversely impact plant growth, development, and productivity (Mittler, 2006;Assahaet al., 2016; Wangsawanget al., 2018). Under such conditions, reactive oxygen species (ROS) accumulate, thereby resulting in the manifestation of a secondary stress termed oxidative stress (Assahaet al., 2017a,b;Abdelaziz et al., 2018; Yassin et al., 2019). Recent reports indicate that the overproduction of ROS is extremely toxic, leading to the oxidation of biomolecules with different chemical structures, such as lipids, proteins, and nucleic acids. Oxidative stress results in peroxidation of lipids, membrane injury, and inactivation of enzymes. The consequence of these reactions impacts various processes, such as membrane transport mechanisms and metabolic pathways (Zhang et al., 2007). Major defensive mechanisms to mitigate the deleterious effects of ROS in plants include the production of ROS-detoxifying enzymes, such as superoxide dismutase (SOD), and the production of antioxidants with a low molecular mass, such as metallothioneins (MTs) (Mittler, 2002; Jinet al., 2010; 2017).

The present study seeks to investigate the expression patterns of salt-related genes in the leaf transcriptome of jojoba to enhance understanding of the molecular mechanisms underlying salt stress tolerance in this plant species.

Materials and Methods

Salt Stress Experiment

A salt stress experiment was conducted at the laboratories of the Department of Biological Sciences, King Abdulaziz University, Jeddah, Saudi Arabia. jojoba seeds were grown in pots (9 cm, 3 seeds/pot) filled with a soil mix (1 soil: 1 vermiculite) and watered with deionized double distilled water under standard growth conditions. Specifically, they were grown at 21 ± 2°C (day/night), with a light intensity of approximately175 µmoles m-2sec-1, and a 16 h-light/8 h-dark cycle. The description of the experiment is detailed in Figure 1. Generated seedlings were watered with distilled water until day 19, then plantlets were morphologically screened for homozygosity and the number of plantlets per pot was narrowed from three to one as previously described (Bahieldin et al., 2015). The pots were divided into two groups. The first group continued to be irrigated every five days with deionized double distilled water (control), while the second group was exposed to salt stress. The plantlets were irrigated three times at intervals of five days. Salt stressed plantlets were first irrigated with 50 mM NaCl (on day 24), followed by 50mM NaCl (on day 29), and 100 mM NaCl (on day 34).Incremental increases in salt stress were applied as recommended by Munns (2002). Leaf samples from the control and salt-stressed plantlets were harvested on the second day of salt treatments. For example, on days 25, 30 and 35, respectively, where salt concentrations reached 50, 100, and 200 mM NaCl, respectively.

Vol18No1_Mol_Bud_fig1 Figure 1: Schematic description of the salt stress experiment of Jojoba (Simmondsia chinensis)  conducted starting day 24 at three salt concentrations increased incrementally with five-day intervals(50, 100, and 200 mMNaCl) in Jojoba plant.

Click here to view figure 

Application of salt was done at days 24, 29 and 34, while leaf samples were harvested the second day of applying salt treatment (days 25, 30 and 35, respectively) and total RNAs  were isolated and samples shipped to Beijing Genome Institute (BGI),China for RNA-Seq analysis.

RNA-Seq analysis

Total RNA was extracted from three similar-sized (10 mm2) flash-frozen plantlets. Subsequently, the leaf material (approximately 50 mg tissue) was crushed using Trizol (Invitrogen) and then treated with RNase-free DNase (Promega Inc.) to remove contaminating DNA. The yield and quality of RNA were determined using a Nanodrop-8000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA). 30 µg (400 ng/µl) of the extracted RNA was then shipped to the Beijing Genome Institute (BGI), China, for deep sequencing using an Illumina MiSeq. The raw read data on thejojobaleaves were provided in FASTQ format. The reads were submitted to the National Center for Biotechnology Information (NCBI) for reviewing and allocating accession numbers. Next, the raw transcriptomic data was filtered and trimmed to remove the adaptor and primer sequences. Reads of less than 40 bp were removed using Trimmomatic v0.30 (Bolger et al., 2014), and sequencing data with a Phred quality score ofQ≥20 was used in the assembly. Generated read counts were used to detect differential expression via EdgeR (version 3.0.0, R version 2.1.5). Approximately 5 million reads per sample were recovered using RNA-Seq, and de novo assembly was performed using the Trinity RNA-Seq Assembly package (r2013-02-25) with optimized parameters and a K-mer size set to 25 (Zhang et al., 2015). Validation of assembled transcript contigs was performed using the CLC Genomics workbench (CLC Bio, Boston, MA 02108 USA). All high-quality reads were subsequently mapped back to the assembled transcript contigs. Coding DNA sequences (CDSs) from the assembled transcript contigs were identified using the online tool ORF-Predictor (Min et al. 2005) (http://proteomics. ysu.edu/ tools/ OrfPredictor. html) using the default parameters. Blastx was then performed (with an E-value cut off of 1e-5) and the fold change values of differentially expressed transcripts were measured via EdgeR (version 3.0.0, R version 2.1.5) and by usingactin as the housekeeping gene. Significant Pearson correlation was determined during permutation analysis. Assembled transcripts in different clusters were annotated, and gene ontology (GO) terms were detected using Blast2GO (http://www.blast2go.org/). Next, the CDSs were categorized by WEGO analysis, which involved sketching a WEGO plot based on the GO hits. To retrieve GO terms for the annotated CDS, the GO mapping used the following parameters: (i) Blastx result accession IDs to retrieve gene names or symbols, (ii) UniProt IDs, and (iii) a direct search of the dbxref table of the GO database. UniProt IDs were retrieved using the Protein Information  Resource (PIR). PIR includes the protein sequence database (PSD), UniProt, SwissProt, TrEMBL, RefSeq, GenPept, and PDB databases. Accordingly, all predicted CDSs were annotated against the protein database to help assign the putative function of the unmapped transcriptome following its translation into protein.

Validation of RNA-Seq data

RNA-Seq datasets of jojoba were validated using qRT-PCR of four randomly selected genes that were highly upregulated (≥5-fold change or FC)at the three time points (days 25, 30, and 35). Expression levels of the transcripts were detected by real-time PCR using the Agilent Mx3000P qPCR Systems (Agilent Technology, USA). The MaximaTM SYBR Green/ROX qPCR procedure was performed as detailed in Bahieldin et al. (2015). First-strand cDNA was synthesized using reverse primers of each gene, and all synthesized cDNA was amplified using the forward and reverse primers of different genes listed in Table S1. Data were collected and amplification plots of DRn versus the cycle number were generated for analysis. The actin gene (Table S1) was used as the housekeeping gene. The data for the housekeeping gene was used in the established calculations to detect the expression level of each gene.

Results

Data Statistics and Quality

The results of the jojoba RNA-Seq data indicate that over 500 transcripts were detected, of which the number of differentially expressed (DE) transcripts were 164 (Table 1). However, the number of highly DE (≥5 FC) transcripts was 156 (Table S2). Hierarchical cluster analysis of gene expression of jojobausing the Pearson correlation as the distance metric based on the log ratio RPKM data and a multi-dimensional scaling plot showed a close relationship for transcriptomes at day 25 regardless of salt stress (50 mM NaCl) (Figure 2). This indicates that this concentration induced a few salt-related genes. The heat map also indicated no significant DE transcripts between days 30 and 35 under salt stress (100 and 200 mM NaCl, respectively), on the one hand, or alternatively, between the same two days under control conditions. Accordingly, these findings indicate that neither prolonged exposure to salt stress beyond six days (day 30) nor use of salt concentrations higher than 100 mM NaCl can result in more DE transcripts. The RNA-Seq datasets were validated by performing qRT-PCR on two randomly selected highly (≥5 FC) upregulated transcripts (cluster 12) and two downregulated (cluster 19) transcripts under salt stress conditions. Theoutcomes of this analysis perfectly aligned with those of the RNA-Seq data for the transcripts used in the validation (Figure S2).

Vol18No1_Mol_Bud_fig2 Figure 2: Heat map describing the differences among genes between control and salt-treated samples in Jojoba (Simmondsia chinensis). 

Click here to view figure

C25 = watered (control)plant at day 24 with distilled water and leaf sample harvested at day 25, C30 = watered (control) plant at day 29 with distilled water and leaf sample harvested at day 30, C35 = watered (control) plant at day 34 with distilled water and leaf sample harvested at day 35, T25_50mM = treated plant at day 24 with 50mM salt and leaf sample harvested at day 25, T30_100mM = treated plant at day 29 with 100mM salt and leaf sample harvested at day 30, T35_200mM = treated day 34 watered with 200mM salt and leaf sample harvested at day 35.

GO classification

Some of the annotated jojobatranscripts were not assigned to any of the three main categories of gene expression,of which 37 were upregulated and 42 were downregulated genes (Table 1). However, the numbers of upregulated genes assigned to the three main categories, namely,  “biological process”, “cellular component” and “molecular function”, were as little as 10, 8, and 9, respectively (Table 1). The numbers of downregulated genes assigned to the three main categories were 20, 18, and 20, respectively (Table 1). In the three categories, highly expressed genes under salt stress were found in the subcategories of “cell part”, “cell”, “organelle part”, and “organelle” for the “cellular component” category, and the subcategories “binding” and “catalytic activity” for the “molecular function” category. The subcategories of “cellular process”, “metabolic process” and “response to stimulus” were established for the “biological process” category (Figure 3). In contrast, highly suppressed genes under salt stress were found in the subcategories of “cell part”, “cell”, “organelle part”, “organelle” and “membrane” for the “cellular component” category, and the subcategories of “binding”, and “catalytic activity” for the “molecular function” category.While the “cellular process”, “metabolic process” and “response to stimulus” subcategories were noted for the “biological process” category (Figure 4).

Vol18No1_Mol_Bud_fig3 Figure 3: GO classification of upregulated genes across salt stress treatments in the transcriptome of Jojoba (Simmondsia chinensis).

Click here to view figure

 

Vol18No1_Mol_Bud_fig4 Figure 4: GO classification of downpregulated genes across salt stress treatments in the transcriptome of Jojoba (Simmondsia chinensis).

Click here to view figure

Table 1: Number of differentially expressed genes of Jojoba (Simmondsia chinensis)across the three GO categories due to salt stress.

Expression upregulated downregulated Total
Annotated genes 37 42 79
GO Terms Biological process 10 20 30
Cellular component 8 18 26
Molecular function 9 20 29
Total 69 95 164

Cluster analysis

Cluster analysis indicated the occurrence of 26 clusters, of which only two clusters refer to downregulation with nine (cluster 3), six (cluster 19) and one (cluster 20) transcripts (Figure S1 and Table S2). The results of cluster analysis barely indicate a significant increase in the expression levels of transcripts at 200 mM NaCl compared with those at 100 mM NaCl. As little as three out of the 26 clusters were downregulated, and most of the other clusters showed upregulation in one or more time points (days 25, 30, and 35) of salt stress. The three clusters with downregulated genes include numbers of 10 (cluster 6), two (cluster 8), and two (cluster 13) transcripts, of which either one of the two transcripts in cluster 8 or cluster 13 refers to the transcripts of the same gene. For example, glutathione S-transferase (for cluster 8) and GDSL esterase/lipase (for cluster 13). These results support our previous claim that prolonged exposure to salt stress does not necessarily result in higher expression levels of salt-related genes. These results confirm that this plant species moderately tolerates salt stress.Thus, it harbors a high number of genes expressed under moderate levels of salt stress and a low number of genes expressed specifically under high levels of salt stress (for example 200 mM NaCl).

As previously indicated, 26 clusters harboring 156 transcripts were detected following analysis of the read data (Table S2). Among this set, six clusters (nos. 4, 5, 12, 15, 16, and 21) were selected for further analysis (Table 2). Criteria for selecting clusters were based on the annotation data and the feasibility of gene expression patterns. Clusters 4 and 16 indicate upregulation of transcripts under 100 and 200 mM NaCl at days 30 and 35, respectively.Clusters 5 and 15 indicate upregulation under 100 mM NaCl at day 30, and clusters 12 and 21 indicate upregulation under 50, 100 and 200 mM NaCl at each of the three time points (days 25, 30 and 35), respectively(Figure S1). Accordingly, the genes in clusters 5 and 15 seemed necessary for a short time under salt stress, while those in the other four clusters were indicated as necessary for a longer period or until the stress conditions had ended. The total number of transcripts selected in the six clusters (nos. 4, 5, 12, 15, 16, and 21) was 15 (Table 2). These transcripts refer to 13 genes, as three transcripts were detected in cluster 5 for only one gene encoding metallothionein-like protein type 3. The number of transcripts studied in these clusters were three, four, four, two, one, and one, respectively. A description of these transcripts is detailed in Tables 2 and S2, while the expression patterns of these transcripts are illustrated in Figures 5-10, respectively.

Table 2: List of regulated salt-related gene-encoded proteinsof Jojoba (Simmondsia chinensis)and gene codes.

Cluster Protein encoded Gene code
Cluster 4 Miraculin G1
Protein ROS1A G2
Germin-like protein subfamily 3 member 3 G3
Cluster 5 Metallothionein-like protein type 3 isoform1 G4
Metallothionein-like protein type 3 isoform 7 G5
Ankyrin repeat domain-containing protein 2B G6
Metallothionein-like protein type 3 isoform 21 G7
Cluster 12 Gibberellin-regulated protein 2 G8
Sucrose-phosphate synthase G9
1-aminocyclopropane-1-carboxylate oxidase homolog 1 G10
Callose synthase 3 G11
Cluster 15 Cysteine proteinase inhibitor 12 G12
ubiquitin-conjugating enzyme E2 24 G13
Cluster 16 xyloglucan endotransglucosylase/hydrolase protein 7 G14
Cluster 21 BURP domain protein RD22 G15

Discussion

Differentially expressed genes in jojobaunder salt stress were upregulated in six clusters. The roles of a set of these genes in conferring salt stress tolerance are discussed below:

Miraculin and cysteine proteinase inhibitor 12

The miraculin-encoding gene is upregulated in the present study under 100 and 200 mM NaCl on days 30 and 35, while the cysteine proteinase inhibitor 12-encoding gene is upregulated under 100 mM NaCl only on day 30 (Figures 5 and 8, respectively). Miraculin was discovered in the red berries of the miracle fruit (Richadelladulcifica) as a taste modifier that can change sour tastes to sweet (Masuda et al., 1995). Under salt stress, the gene encoding this protein was highly expressed in tomatoes and, essentially, this was a consequence of fruit miniaturization (Hirai et al., 2011). Moreover, this increase resulted in anelevationin the level of the encoded protein and the content of all other soluble proteins. Although the functions of miraculin proteins under salt stress remains unclear, this protein was proposed to play a defensive role against biotic and abiotic stresses by limiting cell damage (Tsukudaet al., 2006). The latter process might be explained by this protein’s high degree of amino acid sequence homology and that of the Kunitz family of proteinase inhibitors. Studies have reported that proteinase inhibitors, such as cysteine proteinase inhibitor 12, are generally involved in hypersensitive cell death execution in tobacco leaves (Montilletet al., 2005; Xu et al., 2012). The latter action leads to the regulation of ROS production, thereby leading to the production of a strong antioxidant defense system (Grant and Loake, 2000; Zhang et al., 2010). Dombrowski (2003) indicated that salt stress-induced accumulation of proteinase inhibitors in tomato plants was jasmonic acid-dependent. Interestingly, the higher the expression of the gene encoding miraculin in citrus, the higher the level of tolerance to salt stress (Mouhayaet al., 2010).

Protein ROS1A

The gene encoding the REPRESSOR OF SILENCING protein (ROS1A) was shown to be upregulated in jojobaleaves under salt stress (100 and 200 mM NaCl) (Figure 5). ROS1A was reported to participate in the demethylation of vegetative cells in plants, rice (Ono et al., 2012; Lanciano and Mirouze 2017; Kim et al., 2019b; Parrilla-Doblaset al., 2019).The plant methylation/demethylation process is involved in many physiological processes, including seed development and germination, fruit ripening and plant responses to biotic and abiotic stresses (Parrilla-Doblaset al., 2019). Environmental stresses are known to impact genome stability and epigenetic mechanisms. Moreover, DNA methylation was reported to be involved in both adaptation and agronomic performances (Kim et al., 2019b). In rice, active DNA demethylation has been proven to assist in salt tolerance and responses to salt stress. Salt-tolerant rice showed decreased DNA methylation levels following exposure to salt stress. Accordingly, reduced levels of DNA methylationareconsidered to be an adaptive mechanism to salt stress. Furthermore, a recent study also proposed that DNA demethylation during environmental stresses is involved in the intergenerational transmission of a “stress memory”. It is believed that the process of intergenerational transmission facilitates rapid adaptation to short-term environmental fluctuations, a phenomenon known as ‘priming’ (Serrano et al., 2019). Thus, this memory is remembered upon subsequent exposure to the same stress. Stress memory is thought to resemble the “memory cell” in our immune system that allows our bodies to remember the pathogen, thereby enabling the launching of a more rapid response. Through studies on Arabidopsis, researchers established that stress memory in plants exposed to salt stress is associated with changes in DNA methylation and that this memory was transmitted to subsequent generations (Wibowo et al., 2016). Interestingly, salt stress tolerance and changes in DNA methylation levels are preferentially transmitted through the female germline. Active DNA demethylation is also involved in the plant response to abscisic acid (ABA). The latter phytohormone accumulates as a result of biotic and abiotic stresses. ABA is known to play a crucial role in coordinating cascades of signal transduction during abiotic stresses (Parrilla-Doblaset al., 2019). Thus, ArabidopsisROS1 mutants are hypersensitive to ABA during early seedling development (Kim et al., 2019a). Moreover, ROS1 is required to demethylate and activate a subset of ABA-inducible genes (Kim et al., 2019a).Generally speaking, a better understanding of epigenetic regulations in plants is required to enhance understanding of how demethylation governed by genes encoding ROS1 contributes to improving plant adaptation to a changing environment.

Germin-like protein subfamily 3 member 3

The gene encoding germin-like protein subfamily 3 member 3(GLP3) was shown to be upregulated in jojobaleaves under salt stress (100 and 200 mM NaCl) (Figure 5). Members of the germin-like proteins (GLPs)contribute to the defense mechanisms launched following a pathogenic attack. Moreover, they can produce proteins with superoxide dismutase (SOD) activity in cereals and soybeans, among others (Schweizer et al. 1999; Christensen et al. 2004; Godfrey et al. 2007; Davidson et al. 2009, Lu et al., 2010a). Under normal conditions, plants express the natural formation/removal process of O2. Under stress conditions, the defense system is accompanied by increased ROS formation and is eventually overwhelmed. Nevertheless, SOD is considered to be the first line of defense against ROS (Davidson et al., 2010). In the sunflower (Helianthus annuus), GLPs were reported to prevent oxidative damage due to salt stress in plantsthrough the pathways of protein oxidation and lipid peroxidation (Davenport et al., 2003).

Vol18No1_Mol_Bud_fig5 Figure 5: Expression pattern of selected genes in cluster 4 in leaves of non-salinized (C) and salinized (T) Jojoba (Simmondsia chinensis) plants at three NaCl concentrations (50, 100 and 200 mM) increased incrementally with five-day intervals and harvested at the second day (days 25, 30 and 35, respectively).G1 = Miraculin, G2 = Protein ROS1A, G3 = Germin-like protein subfamily 3 member 3.

Click here to view figure

Metallothionein-like protein type 3

Metallothioneins (MTs) are proteins that contribute to metal chelation in plants. In the metal-thiolate cluster, MTs bind to metal ions and detoxify them, thus resulting in the buffering of the cytosolic metal concentration (Mekawyet al., 2020). Several studies have reported the positive effects of MTs on plants subjected to various abiotic stresses (Lee et al., 2004; Jinet al., 2017; Patankaret al., 2019). The MT family of proteins is a low molecular weight (7–10 kDa), cysteine- (Cys)-rich metal-chelating protein. The family comprises four types: MT1, MT2, MT3, and MT4 (Cobbett and Goldsbrough, 2002). The four types differ by the arrangement of their preserved cysteine residue. Moreover, their tissue-specific expression demonstrates the differences in their functions (Freisinger, 2011; Leszczyszyn et al., 2013). In the present study, the upregulation of three MT transcripts under salt stress (50, 100, and 200 mM NaCl) proves that three typesof MTsare expressed in the leaves of jojobaand that they likely promote the plant’s ability to tolerate salt stress (Figure 6). A recent study indicated that the overproduction of MTs resulted in an increased activity of antioxidant enzymes, such as catalase and ascorbate peroxidase (Mekawyet al., 2020). Thus, MTs act as potential antioxidants that reduce cellular injury due to ROS overproduction (Chiaverini and De Ley, 2010). Specifically, the thio group (–SH) of the MT’s cysteine residues acts to eliminate the overproduced ROS, thereby reducing the cellular level of ROS to the required level and maintainingthecell’s equilibrium (Hassinen, et al., 2011). Earlier reports indicate that MTs can promote homeostasis,the tolerance of metal ions (Cobbett and Goldsbrough, 2002; Zimeriet al., 2005), and oxidative stress mitigation (Akashi et al. 2004).

Ankyrin Repeat Domain-Containing Protein 2B

Gene encoding ankyrin (ANK) repeat domain-containing protein 2B was shown to be upregulated in jojobaleaves under salt stress (50, 100, and 200 mM NaCl) (Figure 6). ANK is a repeat domainand is one of the most commonly conserved protein domains in all organisms (Sedgwick and Smerdon, 1999; Lopez-Ortiz et al., 2020). Study findings have reported ANKrepeats as mediators of protein–protein interactions (Michaely and Bennett, 1992; Li et al., 2006). Furthermore, they highlighted their function as molecular chaperones that work to maintain other protein structures under abiotic stresses (Chen et al., 2002).ANK domain-containing proteins are frequently found participating in crucial physiological and developmental processes such as signaling (Yuan et al., 2013), chloroplast biogenesis (Bae et al., 2008), and leaf morphogenesis (Ha et al., 2004). These studies supported earlier reports that indicated how ANK repeat-containing proteins are involved in diverse cellular functions, including cell cycle regulation, signal transduction, and ion transport (Sedgwick and Smerdon, 1999; Yan et al., 2002).Additionally, the ANK repeat-containing proteins play important roles in the cell’s response to both biotic and abiotic stresses (Zhanget al., 2016). Other reports indicated the role of these proteins indrought tolerance (Sakamoto et al., 2013) and ABA-mediated regulation of ROS levels under saltstress (Sakamoto et al., 2008).Nodzonet al. (2004) proved that the expression of certain ANK genes in plants is induced by auxin (IAA), abscisic acid (ABA), and jasmonic acid (SA), which mediate the responses to biotic and abiotic stresses. Other reports indicate that the promoter elements of ANK genes are also induced by gibberellic acid (GA) (Batlang, 2008) and ethylene (Seonget al., 2007). The proteins interacting with the ANK domain were predicted to exist in the membrane and in chloroplasts at the intracellular level.

Vol18No1_Mol_Bud_fig6 Figure 6: Expression pattern of selected genes in cluster 5 in leaves of non-salinized (C) and salinized (T) Jojoba (Simmondsia chinensis) plants at three NaCl concentrations (50, 100 and 200 mM) increased incrementally with five-day intervals and harvested at the second day (days 25, 30 and 35, respectively).

Click here to view figure

G4 = Metallothionein-like protein type 3 isoform 1, G5 = Metallothionein-like protein type 3 isoform 7, G6 = Ankyrin repeat  domain-containing protein 2B, G7 = Metallothionein-like protein type 3 isoform 21.

Gibberellin-regulated protein 2

The gene encoding gibberellin-regulated protein 2 was shown to be upregulated in jojobaleaves under the three salt stress concentrations (50, 100, and 200 mM NaCl) (Figure 7). Gibberellin-regulated protein 2 is among the gibberellic acid-stimulated Arabidopsis protein family (GASAs). They areidentified as a class of cysteine-rich small peptides or CRPs (cysteine-rich peptides) (Silverstein et al., 2007). Numerous GASA genes have been isolated and identified from several plant species (Wigodaet al., 2006; Sun et al., 2013; Zhang and Wang, 2017). GASA proteins play an important role in the regulation of plant growth and development, including participation in the biotic and abiotic stress responses and in hormonal signal transduction (Wigodaet al., 2006; Zhang and Wang, 2008; Sun et al., 2013). One study reported the interaction of GASA proteins with other proteins in their regulation of plant hormone signal transduction (Zhang and Wang, 2017). For example, GASA5 in Arabidopsis participates in the heat stress response by regulating SA signaling toward the accumulation of heat shock protein (Zhang and Wang, 2008). Additionally, GASA4 over-expression was proven to inhibit ROS accumulation, thereby contributing to the antioxidant process and promoting tolerance to high salt stress (Zhang and Wang, 2017). The latter conclusion was supported by the loss-of-function mutant gasa14,which resulted in sensitivity to high salt stress in Arabidopsis.

Sucrose-Phosphate Synthase

Gene encoding sucrose-phosphate synthase (SPS) was shown to be upregulated in jojobaleaves under the three salt stress concentrations (50, 100, and 200 mM NaCl) (Figure 7). The functions of sucrose-metabolizing enzymes provide an alternative avenue to the plant’s salt stress response (Lu et al., 2010b). Examples of these enzymes include sucrose synthase (SS), sucrose-phosphate synthase (SPS),and invertase (INV). SS plays a vital role in regulating the phloem loading process, while SPS catalyzes the synthesis of sucrose phosphate during the last step of CO2 fixation in plants (Azevedo-Neto et al., 2004).

Methyl jasmonate (MeJA) is identified as an important phytohormone that participates in diverse processes, including plant response to salinity and water stresses (Kang et al., 2005). MeJA triggers the reprogramming of gene expression to help plant cells adapt to environmental stresses such as drought, low temperature, and salinity (Wolucka and Goossens, 2005).Moreover, MeJA participates in alleviating the damage caused by salt stress through sucrose synthesis regulation and its metabolism, which contribute toward maintaining normal growth under such harsh conditions (Yu et al., 2019).One study reported that this hormone promoted thegene expression of the APS protein and that this appeared to constitute the rate-limiting step in sucrose synthesis, thus providing the greatest effect on the sucrose accumulation rate (Daiet al., 2020).

Interestingly, the activation of SPS appears to concurrently result in the elimination of the sucrose-degrading enzyme INV’s activity. Collectively, these responses result in a high rate of sucrose accumulation. Such effects are specific responses to salt stress and constitute a strategy for maintaining balanced sucrose content under stress conditions (Yu et al., 2019). Additionally, under conditions of salt stress, the osmotic balance between the cell’s interior and its exterior is achieved by the regulation of the accumulation rate of compatible solutes. Such regulation promotes the reduction of intracellular inorganic ions, and this decreased intracellular ion pool is responsible for the reduced levels of sucrose accumulation as a result of lowered SPS activity and increased INV activity(Kirsch et al., 2018).

1-aminocyclopropane-1-carboxylate oxidase

The gene encoding the enzyme 1-aminocyclopropane-1-carboxylate (ACC) oxidase was shown to be upregulated in jojobaleaves under the three salt stress concentrations (50, 100, and 200 mM NaCl) (Figure 7). ACC oxidase is an ethylene-forming enzyme that mediates the second step of the two-step reaction of ethylene biosynthesis,while ACC synthase mediates the first step (Kende, 1993; Steffens, 2014). A plant’s adaptation to abiotic and biotic stresses is regulated by small endogenous signaling molecules, of which the gaseous phytohormone ethylene and ROS play important roles in mediating responses to numerous aspects of growth or cell death (Kende, 1993; Li et al., 2014; Trinh et al., 2014).

Upregulation of the ACC oxidase transcripts has been found under high salt stress in Saccharum arundinaceumand Oryza brachyantha. Conversely, they have been reported to be down regulated in the homolog of Arabidopsis thaliana. One study explained these conflicting results by potentially attributing them to the differential perception of emitted ethylene under salt stress (Steffens, 2014). Downstream signaling cascades due to ethylene perception include the ion transporter EIN2 (Ethylene-insensitive protein 2), the transcription factor EIN3, and members of the APETALA2/Ethylene Response Factor (AP2/ERF) multi gene family (Chen et al., 2002). Generally, ethylene represents an internal signal that promotes the plants’ ability to coordinate other downstream signals and that acts as an adaptive survival response to salt stress (Wang et al., 2002). Ethylene signaling is also necessary for ROS accumulation inconditions of salt stress (Li et al., 2014). In addition to ethylene signaling, ethylene homeostasis is required for conferring salinity tolerance upon plants (Dong et al., 2011). Occasionally, a plant may die as a result of abiotic stresses, but such death may be prevented by the triggering of processes by ethylene and ROS (Dong et al., 2011).

Callose synthase 3

The gene encoding callose synthase (CalS) was found to be upregulated in jojobaleaves under the three salt stress concentrations (50, 100, and 200 mM NaCl) (Figure 7). Callose synthase (CalS) is an enzyme that regulates the accumulation of callose at theplasmodesmal channels and their subsequent disposition in the plasmodesmata (Cui and Lee, 2016). The processing of callose in this manner is commonly used to alter plasmodesmal permeability under biotic and abiotic stresses. Several callose synthase genes have been proven to be involved in this process in Arabidopsis. There are numerous cell communication strategies. However, molecular trafficking between adjacent cells through plasma membrane channels represents a potentially universal signaling mechanism (Abounit and Zurzolo, 2012; Gerdes et al., 2013; Lee, 2014). Plasmodesmata-mediated trafficking of nutrients among adjacent cells is critical for normal plant growth and development and for plant tolerance to abiotic stresses (Lucas et al., 2009; Benitez-Alfonso et al., 2011; Burch-Smith and Zambryski, 2012; Sevilemet al., 2013; Sager and Lee, 2014). The modulation of plasmodesmal permeability under stress occurs by hyper- or hypo-accumulation of a β-1,3 glucan termed callose, with the changes in callose levels often correlating negatively with the plasmodesmal permeability (Zavalievet al., 2011; De Storme and Geelen, 2014). The latter process is dependent on the callose synthase expression level or of the β-1,3-glucanase genes that are presumed to be implicated in plasmodesmal permeability (Vaténet al., 2011; Benitez-Alfonso et al., 2013). During this process, cellular signals are transduced to the molecular machinery that regulates plasmodesmal callose deposition, thus impacting the plasmodesmal permeability (Cui and Lee, 2016). Moreover, the accumulation of callose at the plasmodesmata by CalS is a key mechanism in regulating the molecular movement taking place between adjacent cells through the plasmodesmata. Recent reports connect specific abiotic and biotic signals to callose-dependent plasmodesmal regulation (De Storme and Geelen, 2014; Sager and Lee, 2014; Cui and Lee, 2016).

Vol18No1_Mol_Bud_fig7 Figure 7: Expression pattern of selected genes in cluster 12 in leaves of non-salinized (C) and salinized (T) Jojoba (Simmondsia chinensis) plants at three NaCl concentrations (50, 100 and 200 mM) increased incrementally with five-day intervals and harvested at the second day (days 25, 30 and 35, respectively).G8 = Gibberellin-regulated protein 2, G9 = Sucrose-phosphate synthase, G10 = 1-aminocyclopropane-1-carboxylate oxidase homolog 1,  G11 = Callose synthase 3.

Click here to view figure

Ubiquitin-Conjugating Enzyme E2 24

The gene encoding ubiquitin-conjugating enzyme E2 24 was shown to be upregulated in jojobaleaves under a salt stress concentration of 100 mM NaCl (Figure 8). The ubiquitin-conjugating enzyme E2 is found in the second step of ubiquitination. The first step involves the ubiquitin-activating enzyme E1, which catalyzes the activation of ubiquitin. In the second step, ubiquitin is transferred to the ubiquitin-conjugating enzyme (UBC) E2. The final ubiquitination step involves the ligation of ubiquitin to the protein substrate by the direct transfer of ubiquitin from E2 or from the protein ligase E3 (Chung et al., 2013). The ubiquitin/proteasome system represents the original pathway for selective protein degradation in plants (Kornitzer and Ciechanover, 2000). Protein degradationcontributes to the signal transduction pathways during plant responses to abiotic stresses (Hellmann and Estelle, 2002). Ubiquitination has important functions in numerous biological processes in plants, including light signaling, embryogenesis, leaf senescence, and plant stress tolerance (Chung et al., 2013).All forms of theubiquitin-conjugating enzyme E2 contain a conserved protein domain of approximately 16 kDa with a 150-amino-acid catalytic core termed theubiquitin-conjugating enzyme (UBC) domain (Sung et al., 1990). Upregulation of the gene encoding UBC E2 results in high expression levels of stress-responsive genes such as P5CS,which encodes the proline biosynthetic key enzyme (Zhou et al., 2010), and the genes encoding brassinosteroid-mediated signaling (Cui et al., 2012), the ion antiporter (NHX1 and CLCa), and the copper chaperone (superoxide dismutase gene) (Zhou et al., 2010). The activity of UBCE2 also results in a higher sensitivity to ABA and to the upstream ABA-responsive bZIP transcription factors (Chung et al., 2013).

Vol18No1_Mol_Bud_fig8 Figure 8: Expression pattern of selected genes in cluster 15 in leaves of non-salinized (C) and salinized (T) Jojoba (Simmondsia chinensis) plants at three NaCl concentrations (50, 100 and 200 mM) increased incrementally with five-day intervals and harvested at the cond day (days 25, 30 and 35, respectively). G12 = Cysteine proteinase inhibitor 12, G13 = ubiquitin-conjugating enzyme E2 24.

Click here to view figure

Xyloglucan endotransglucosylase/hydrolase protein 7

The gene encoding xyloglucan endotransglucosylase/hydrolase (XTH) protein 7 was also found to be upregulated in jojobaleaves under two salt stress concentrations (100 and 200 mM NaCl) (Figure 9). Xyloglucan (XyG) is a hemicellulose polymer involved in the formation of the primary cell wall of plants (Yan et al., 2019). XyGbinds to cellulose microfibrils by hydrogen bonding during cell elongation, thus causing the loosening of the cell wall (Hayashi and Kaida, 2011; Park and Cosgrove, 2015; Pauly and Keegstra, 2016). Simultaneously, the gene encoding XTH acts to modulate the production of tracheary elements in leaves (Matsui et al., 2005) andcellulose deposition inplant roots (Liu et al., 2007). The gene is involved in plant salt tolerance and in a subset of physiological responses to counteract dehydration (Cho et al., 2006; Choi et al., 2011). Furthermore, XTH also participates in cell wall loosening to increase the walls’ flexibility and to enable its rapid remodeling when cells are subjected to abiotic stresses (Yan et al., 2019). It also strengthens the cell wall layers, thereby enhancing the protection of the mesophyll cells from salt stress. Overall, XTH governs the primary cell wall architecture by cleaving and rejoining the XyG chains to alter the size and shape of XyG, thereby acting as an adaptive response to salt and dehydration stresses (Park and Cosgrove, 2015).

Vol18No1_Mol_Bud_fig9 Figure 9: Expression pattern of selected genes incluster 16in leaves of non-salinized (C) and salinized (T) Jojoba (Simmondsia chinensis) plants at three NaCl concentrations (50, 100 and 200 mM) increased incrementally with five-day intervals and harvested at the second day (days 25, 30 and 35, respectively). G14 = xyloglucan endotransglucosylase/hydrolase protein 7.

Click here to view figure

BURP domain protein RD22

The gene encoding the BURP domain protein RD22 was shown to be upregulated in jojobaleaves under the three salt stress concentrations (50, 100, and 200 mM NaCl) (Figure 10). The RD22 protein belongs to the BURP domain protein family (Hattori et al., 1998). The BURP domain consists of approximately 230 amino acids and is named for the first four identified members of the group, namely, BNM2, USP, RD22, and PG1beta. Under conditions of salt stress, the overexpression of the gene encoding RD22 results in enhanced growth and a higher chlorophyll content (Jamoussiet al., 2014). Simultaneously, the plant was able to accumulate lower levels of sodium (Na+) in leaves and chloride (Cl-) in all organs due to the action of theRD22 gene. Ion analysis of plants overexpressing the RD22 gene demonstrated the occurrence of osmotic adjustment due to the overproduction of total soluble sugars (Jamoussiet al., 2014). Other reports also showed that expression of the RD22 gene is induced by water deficit, salinity stress, and abscisic acid application (Shunwuet al., 2004). The RD22 gene helps in the production of lignin in plant leaves, which acts as another protective mechanism (Wang et al., 2012). Expression of the RD22 gene is induced by the two stress-responsive transcription factors MYC and MYB, acting as cis-elements in the RD22 promoter (Abe et al., 2003). The latter action reduces the adverse effects of stress on overall plant growth (Roxaset al., 2000). In conclusion, the jojoba plant initiates numerous physiological and molecular mechanisms to combat salt stress. These include mechanisms that limit cellular damage by regulating ROS production, methylation/demethylation mechanisms followed by an intergenerational  transmission of a “stress memory,” and the production of proteins with superoxide dismutase (SOD) activity. Additional mechanisms include detoxification of metal ions and the production of molecular chaperones, the regulation of signal transductionand ion transport processes, the reprogramming of selective gene expression,andthe maintenance ofa balanced sucrose content. Furthermore, ethylene signaling and homeostasis, the regulation of plasmodesmal permeability, ubiquitination,and selective protein degradation are all mechanisms that also work toward  reducing the impact of salt stress. The jojoba plant also employs cell wall remodeling, alleviating the chlorophyll content, reducing the levels of sodium (Na+) and chloride (Cl-) accumulation and lignin production as means of combatting salt stress. These findings will aid the utilization of jojoba in various ways, including its use in breeding programs and/or its mass production. Knowledge of these mechanisms will secure the quality and yield of its economically important chemical compounds.

Vol18No1_Mol_Bud_fig10 Figure 10: Expression pattern of selected genes in cluster 21 in leaves of non-salinized (C) and salinized (T) Jojoba (Simmondsiachinensis) plants at three NaCl concentrations (50, 100 and 200 mM) increased incrementally with five-day intervals and harvested at the second day (days 25, 30 and 35, respectively). G15 = BURP domain protein RD22.2.

Click here to view Figure

Acknowledgment

The authors are grateful to King Abdulaziz University and Subol Almarefah Trading Company for funding this project.

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

References

  1. Abdelaziz, M. N., Xuan, T. D., Mekawy, A. M. M., Wang, H., &Khanh, T. D. (2018). Relationship of salinity tolerance to Na+ exclusion, proline accumulation, and antioxidant enzyme activity in rice seedlings. Agriculture8(11), 166.
    CrossRef
  2. Abe, H., Urao, T., Ito, T., Seki, M., Shinozaki, K., & Yamaguchi-Shinozaki, K. (2003). Arabidopsis AtMYC2 (bHLH) and AtMYB2 (MYB) function as transcriptional activators in abscisic acid signaling. The Plant Cell15(1), 63-78.
    CrossRef
  3. Abounit, S., &Zurzolo, C. (2012). Wiring through tunneling nanotubes–from electrical signals to organelle transfer. Journal of cell science125(5), 1089-1098.
    CrossRef
  4. Akashi, K., Nishimura, N., Ishida, Y., & Yokota, A. (2004). Potent hydroxyl radical-scavenging activity of drought-induced type-2 metallothionein in wild watermelon. Biochemical and Biophysical Research Communications323(1), 72-78.
    CrossRef
  5. Al-Obaidi, J. R., Halabi, M. F., AlKhalifah, N. S., Asanar, S., Al-Soqeer, A. A., & Attia, M. F. (2017). A review on plant importance, biotechnological aspects, and cultivation challenges of jojoba plant. Biological research,
    CrossRef
  6. Assaha, D. V. M., Liu, L., Ueda, A., Nagaoka, T. &Saneoka, H. (2016). Effects of drought stress on growth, solute accumulation and membrane stability of leafy vegetable, huckleberry (Solanum scabrum). J. Environ. Biol., 37, 107-114
  7. Assaha, D. V., Ueda, A., Saneoka, H., Al-Yahyai, R., &Yaish, M. W. (2017a). The role of Na+ and K+ transporters in salt stress adaptation in glycophytes. Frontiers in physiology8, 509.
    CrossRef
  8. Assaha, D. V. M., Mekawy, A. M. M., Liu, L., Noori, M. S., Kokulan, K. S., Ueda, A., … &Saneoka, H. (2017b). Na+ retention in the root is a key adaptive mechanism to low and high salinity in the glycophyte, Talinum paniculatum (Jacq.) Gaertn.(Portulacaceae). Journal of Agronomy and Crop Science203(1), 56-67.
    CrossRef
  9. Azevedo Neto, A. D. D., Prisco, J. T., Enéas-Filho, J., Lacerda, C. F. D., Silva, J. V., Costa, P. H. A. D., & Gomes-Filho, E. (2004). Effects of salt stress on plant growth, stomatal response and solute accumulation of different maize genotypes. Brazilian Journal of Plant Physiology16(1), 31-38.
    CrossRef
  10. Bae, W., Lee, Y. J., Kim, D. H., Lee, J., Kim, S., Sohn, E. J., & Hwang, I. (2008). AKR2A-mediated import of chloroplast outer membrane proteins is essential for chloroplast biogenesis. Nature cell biology10(2), 220-227.
    CrossRef
  11. Bafeel, S. O., Galal, H. K., & Basha, A. Z. (2016). Effect of seawater irrigation on growth and some metabolites of jojoba plants.-Eurasian J. Agric. Environ. Sci.16, 49-59.‏
  12. Bahieldin, A., Atef, A., Sabir, J. S., Gadalla, N. O., Edris, S., Alzohairy, A. M., … & Hassan, S. M. (2015). RNA-Seq analysis of the wild barley ( spontaneum) leaf transcriptome under salt stress. Comptesrendusbiologies, 338(5), 285-297.
    CrossRef
  13. Batlang, U. (2008). Benzyladenine plus gibberellins (GA4+ 7) increase fruit size and yield in greenhouse-grown hot pepper (Capsicum annuum). J. Biol. Sci8(3), 659-662.
    CrossRef
  14. Benitez-Alfonso, Y., Faulkner, C., Pendle, A., Miyashima, S., Helariutta, Y., & Maule, A. (2013). Symplastic intercellular connectivity regulates lateral root patterning. Developmental cell26(2), 136-147.
    CrossRef
  15. Benitez-Alfonso, Y., Jackson, D., & Maule, A. (2011). Redox regulation of intercellular transport. Protoplasma248(1), 131-140.
    CrossRef
  16. Bolger, A. M., Lohse, M., &Usadel, B. (2014). Trimmomatic: a flexible trimmer for Illumina sequence data.Bioinformatics30(15), 2114-2120.
    CrossRef
  17. Burch-Smith, T. M., &Zambryski, P. C. (2012). Plasmodesmata paradigm shift: regulation from without versus within. Annual review of plant biology63, 239-260.
    CrossRef
  18. Cao, D., Liu, Y., Ma, L., Jin, X., Guo, G., Tan, R., … & Liu, W. (2018). Transcriptome analysis of differentially expressed genes involved in selenium accumulation in tea plant (Camellia sinensis).PloS one13(6), e0197506.
    CrossRef
  19. Chen, Y. F., Randlett, M. D., Findell, J. L., & Schaller, G. E. (2002). Localization of the ethylene receptor ETR1 to the endoplasmic reticulum of Arabidopsis. Journal of Biological Chemistry277(22), 19861-19866.
    CrossRef
  20. Chiaverini, N., & De Ley, M. (2010). Protective effect of metallothionein on oxidative stress-induced DNA damage. Free radical research44(6), 605-613.
    CrossRef
  21. Christensen, A. B., Thordal-Christensen, H., Zimmermann, G., Gjetting, T., Lyngkjær, M. F., Dudler, R., & Schweizer, P. (2004). The germin-like protein GLP4 exhibits superoxide dismutase activity and is an important component of quantitative resistance in wheat and barley. Molecular Plant-Microbe Interactions17(1), 109-117.
    CrossRef
  22. Chung, E., Cho, C. W., So, H. A., Kang, J. S., Chung, Y. S., & Lee, J. H. (2013). Overexpression of VrUBC1, a mung bean E2 ubiquitin-conjugating enzyme, enhances osmotic stress tolerance in Arabidopsis. PloS one8(6), e66056.
    CrossRef
  23. Cho, S. K., Kim, J. E., Park, J. A., Eom, T. J., & Kim, W. T. (2006). Constitutive expression of abiotic stress-inducible hot pepper CaXTH3, which encodes a xyloglucan endotransglucosylase/hydrolase homolog, improves drought and salt tolerance in transgenic Arabidopsis plants. FEBS letters580(13), 3136-3144.
    CrossRef
  24. Choi, J. Y., Seo, Y. S., Kim, S. J., Kim, W. T., & Shin, J. S. (2011). Constitutive expression of CaXTH3, a hot pepper xyloglucan endotransglucosylase/hydrolase, enhanced tolerance to salt and drought stresses without phenotypic defects in tomato plants (Solanum lycopersicum cv. Dotaerang). Plant Cell
    CrossRef
  25. Cobbett, C., &Goldsbrough, P. (2002). Phytochelatins and metallothioneins: roles in heavy metal detoxification and homeostasis. Annual review of plant biology53(1), 159-182.
    CrossRef
  26. Cui, F., Liu, L., Zhao, Q., Zhang, Z., Li, Q., Lin, B., … &Xie, Q. (2012). Arabidopsis ubiquitin conjugase UBC32 is an ERAD component that functions in brassinosteroid-mediated salt stress tolerance. The Plant Cell24(1), 233-244.
    CrossRef
  27. Cui, W., & Lee, J. Y. (2016). Arabidopsis callose synthases CalS1/8 regulate plasmodesmal permeability during stress. Nature plants2(5), 1-9.
    CrossRef
  28. Dai, Z., Yuan, Y., Huang, H., Hossain, M. M., Xiong, S., Cao, M., … & Tu, S. (2020). Methyl jasmonate mitigates high selenium damage of rice via altering antioxidant capacity, selenium transportation and gene expression. Science of The Total Environment, 143848.
    CrossRef
  29. Davenport, S. B., Gallego, S. M., Benavides, M. P., &Tomaro, M. L. (2003). Behaviour of antioxidant defense system in the adaptive response to salt stress in Helianthus annuus cells. Plant Growth Regulation40(1), 81-88.
    CrossRef
  30. Davidson, R. M., Manosalva, P. M., Snelling, J., Bruce, M., Leung, H., & Leach, J. E. (2010). Rice germin-like proteins: allelic diversity and relationships to early stress responses. Rice3(1), 43-55.
    CrossRef
  31. Davidson, R. M., Reeves, P. A., Manosalva, P. M., & Leach, J. E. (2009). Germins: A diverse protein family important for crop improvement. Plant Science177(6), 499-510.
    CrossRef
  32. De Storme, N., &Geelen, D. (2014). Callose homeostasis at plasmodesmata: molecular regulators and developmental relevance. Frontiers in plant science5, 138.
    CrossRef
  33. Dombrowski, J. E. (2003). Salt stress activation of wound-related genes in tomato plants. Plant Physiology132(4), 2098-2107.
    CrossRef
  34. Dong, H., Zhen, Z., Peng, J., Chang, L., Gong, Q., & Wang, N. N. (2011). Loss of ACS7 confers abiotic stress tolerance by modulating ABA sensitivity and accumulation in Arabidopsis. Journal of Experimental Botany62(14), 4875-4887.
    CrossRef
  35. Freisinger, E. (2011). Structural features specific to plant metallothioneins. JBIC Journal of Biological Inorganic Chemistry, 16(7), 1035-1045.‏Leszczyszyn, O. I., Imam, H. T., &Blindauer, C. A. (2013). Diversity and distribution of plant metallothioneins: a review of structure, properties and functions. Metallomics, 5(9), 1146-1169.
    CrossRef
  36. Garber, M., Grabherr, M. G., Guttman, M., &Trapnell, C. (2011). Computational methods for transcriptome annotation and quantification using RNA-seq.Nature methods8(6), 469-477.
    CrossRef
  37. Gerdes, H. H., Rustom, A., & Wang, X. (2013). Tunneling nanotubes, an emerging intercellular communication route in development. Mechanisms of development130(6-8), 381-387.
    CrossRef
  38. Godfrey, D., Able, A. J., & Dry, I. B. (2007). Induction of a grapevine germin-like protein (VvGLP3) gene is closely linked to the site of Erysiphe necator infection: a possible role in defense?. Molecular Plant-Microbe Interactions20(9), 1112-1125.
    CrossRef
  39. Grant, J. J., &Loake, G. J. (2000). Role of reactive oxygen intermediates and cognate redox signaling in disease resistance. Plant physiology124(1), 21-30.
    CrossRef
  40. Ha, C. M., Jun, J. H., Nam, H. G., & Fletcher, J. C. (2004). BLADE-ON-PETIOLE1 encodes a BTB/POZ domain protein required for leaf morphogenesis in Arabidopsis thalianaPlant and Cell Physiology45(10), 1361-1370.
    CrossRef
  41. Hajrah, N. H., Obaid, A. Y., Atef, A., Ramadan, A. M., Arasappan, D., Nelson, C. A., … &Makki, R. M. (2017). Transcriptomic analysis of salt stress responsive genes in Rhazya stricta.PloS one12(5), e0177589.
    CrossRef
  42. Hattori, J., Boutilier, K. A., Campagne, M. L., & Miki, B. L. (1998). A conserved BURP domain defines a novel group of plant proteins with unusual primary structures. Molecular and General Genetics MGG259(4), 424-428.
    CrossRef
  43. Hassinen, V. H., Tervahauta, A. I., Schat, H., &Kärenlampi, S. O. (2011). Plant metallothioneins–metal chelators with ROS scavenging activity?. Plant Biology13(2), 225-232.
    CrossRef
  44. Hayashi, T., &Kaida, R. (2011). Functions of xyloglucan in plant cells. Molecular Plant, 4(1), 17-24.
    CrossRef
  45. Hellmann, H., & Estelle, M. (2002). Plant development: regulation by protein degradation. Science, 297(5582), 793-797.
    CrossRef
  46. Hirai, T., Duhita, N., Hiwasa-Tanase, K., &Ezura, H. (2011). Cultivation under salt stress increases the concentration of recombinant miraculin in transgenic tomato fruit, resulting in an increase in purification efficiency. Plant Biotechnology, 28, 387–392.
    CrossRef
  47. Jamoussi, R. J., Elabbassi, M. M., JOUIRA, H. B., Hanana, M., Zoghlami, N., Ghorbel, A., &Mliki, A. (2014). Physiological responses of transgenic tobacco plants expressing the dehydration-responsive RD22 gene of Vitis vinifera to salt stress. Turkish Journal of Botany38(2), 268-280.
    CrossRef
  48. Jain, M. (2012). Next-generation sequencing technologies for gene expression profiling in plants.Briefings in functional genomics11(1), 63-70.
    CrossRef‏
  49. Jin, H., Sun, Y., Yang, Q., Chao, Y., Kang, J., Jin, H., … & Margaret, G. (2010). Screening of genes induced by salt stress from Alfalfa. Molecular biology reports37(2), 745-753.
    CrossRef
  50. Jin, S., Xu, C., Li, G., Sun, D., Li, Y., Wang, X., & Liu, S. (2017). Functional characterization of a type 2 metallothionein gene, SsMT2, from alkaline-tolerant Suaeda salsaScientific reports7(1), 1-11.
    CrossRef
  51. Kang, D. J., Seo, Y. J., Lee, J. D., Ishii, R., Kim, K. U., Shin, D. H., … & Lee, I. J. (2005). Jasmonic acid differentially affects growth, ion uptake and abscisic acid concentration in salt‐tolerant and salt‐sensitive rice cultivars. Journal of Agronomy and Crop Science191(4), 273-282.
    CrossRef
  52. Kawa, D., &Testerink, C. (2017). Regulation of mRNA decay in plant responses to salt and osmotic stress.Cellular and Molecular Life Sciences74(7), 1165-1176.
    CrossRef
  53. Kende, H. (1993). Ethylene biosynthesis. Annual review of plant biology44(1), 283-307.
    CrossRef
  54. Kim, J. S., Lim, J. Y., Shin, H., Kim, B. G., Yoo, S. D., Kim, W. T., & Huh, J. H. (2019a). ROS1-dependent DNA demethylation is required for ABA-inducible NIC3 expression. Plant physiology179(4), 1810-1821.
    CrossRef
  55. Kim, M. Y., Ono, A., Scholten, S., Kinoshita, T., Zilberman, D., Okamoto, T., & Fischer, R. L. (2019b). DNA demethylation by ROS1a in rice vegetative cells promotes methylation in sperm. Proceedings of the National Academy of Sciences116(19), 9652-9657.
    CrossRef
  56. Kirsch, F., Luo, Q., Lu, X., & Hagemann, M. (2018). Inactivation of invertase enhances sucrose production in the cyanobacterium Synechocystis PCC 6803. Microbiology164(10), 1220-1228.
    CrossRef
  57. Kornitzer, D., & Ciechanover, A. (2000). Modes of regulation of ubiquitin‐mediated protein degradation. Journal of cellular physiology182(1), 1-11.
    CrossRef
  58. Lanciano, S., &Mirouze, M. (2017). DNA methylation in rice and relevance for breeding. Epigenomes1(2), 10.
    CrossRef
  59. Lee, J., Donghwan, S., Won-yong, S., Inhwan, H., &Youngsook, L. (2004). Arabidopsis metallothioneins 2a and 3 enhance resistance to cadmium when expressed in Vicia faba guard cells. Plant Molecular Biology54(6), 805-815.
    CrossRef
  60. Lee, J. Y. (2014). New and old roles of plasmodesmata in immunity and parallels to tunneling nanotubes. Plant Science221, 13-20.
    CrossRef
  61. Li, J., Mahajan, Mahajan, A. & Tsai, M.-D. (2006). Ankyrin repeat: A unique motif mediating protein–protein interactions. Biochemistry, 45, 15168–15178.
    CrossRef
  62. Li, C. H., Wang, G., Zhao, J. L., Zhang, L. Q., Ai, L. F., Han, Y. F., … & Sun, Y. (2014). The receptor-like kinase SIT1 mediates salt sensitivity by activating MAPK3/6 and regulating ethylene homeostasis in rice. The Plant Cell26(6), 2538-2553.
    CrossRef
  63. Liang, W., Ma, X., Wan, P., & Liu, L. (2018). Plant salt-tolerance mechanism: A review.Biochemical and biophysical research communications495(1), 286-291.
    CrossRef
  64. Liu, Y. B., Lu, S. M., Zhang, J. F., Liu, S., & Lu, Y. T. (2007). A xyloglucan endotransglucosylase/hydrolase involves in growth of primary root and alters the deposition of cellulose in Arabidopsis. Planta, 226(6), 1547-1560.
    CrossRef
  65. Lopez-Ortiz, C., Peña-Garcia, Y., Natarajan, P., Bhandari, M., Abburi, V., Dutta, S. K., … & Reddy, U. K. (2020). The ankyrin repeat gene family in Capsicum spp: Genome-wide survey, characterization and gene expression profile. Scientific reports10(1), 1-16.
    CrossRef
  66. Lu, M., Han, Y. P., Gao, J. G., Wang, X. J., & Li, W. B. (2010a). Identification and analysis of the germin-like gene family in soybean. Bmc Genomics11(1), 620.
    CrossRef
  67. Lu, S., Li, T., & Jiang, J. (2010b). Effects of salinity on sucrose metabolism during tomato fruit development. African Journal of Biotechnology9(6), 842-849.
    CrossRef
  68. Lucas, W. J., Ham, B. K., & Kim, J. Y. (2009). Plasmodesmata–bridging the gap between neighboring plant cells. Trends in cell biology19(10), 495-503.
    CrossRef
  69. Martin, J. A., & Wang, Z. (2011). Next-generation transcriptome assembly.Nature Reviews Genetics12(10), 671-682.
    CrossRef
  70. Martin, J. A., & Wang, Z. (2011). Next-generation transcriptome assembly.Nature Reviews Genetics12(10), 671-682.
    CrossRef
  71. Masuda, Y., Nirasawa, S., Nakaya, K., &Kurihara, Y. (1995). Cloning and sequencing of a cDNA encoding a taste-modifying protein, miraculin. Gene161(2), 175-177.
    CrossRef
  72. Matsui, A., Yokoyama, R., Seki, M., Ito, T., Shinozaki, K., Takahashi, T., … &Nishitani, K. (2005). AtXTH27 plays an essential role in cell wall modification during the development of tracheary elements. The Plant Journal, 42(4), 525-534.
    CrossRef
  73. Mekawy, A. M. M., Assaha, D. V., & Ueda, A. (2020). Constitutive overexpression of rice metallothionein-like gene OsMT-3a enhances growth and tolerance of Arabidopsis plants to a combination of various abiotic stresses. Journal of plant research133(3), 429-440.
    CrossRef
  74. Michaely, P., & Bennett, V. (1992). The ANK repeat: a ubiquitous motif involved in macromolecular recognition. Trends in cell biology2(5), 127-129.
    CrossRef
  75. Min, X. J., Butler, G., Storms, R., & Tsang, A. (2005). OrfPredictor: predicting protein-coding regions in EST-derived sequences. Nucleic acids research33(suppl_2), W677-W680.
    CrossRef
  76. Mittler, R. (2002). Oxidative stress, antioxidants and stress tolerance. Trends in plant science7(9), 405-410.
    CrossRef
  77. Mittler, R. (2006). Abiotic stress, the field environment and stress combination. Trends in plant science11(1), 15-19.
    CrossRef
  78. Montillet, J. L., Chamnongpol, S., Rustérucci, C., Dat, J., Van De Cotte, B., Agnel, J. P., … &Triantaphylidès, C. (2005). Fatty acid hydroperoxides and H2O2 in the execution of hypersensitive cell death in tobacco leaves. Plant physiology138(3), 1516-1526.
    CrossRef
  79. Mouhaya, W., Allario, T., Brumos, J., Andrés, F., Froelicher, Y., Luro, F., … &Morillon, R. (2010). Sensitivity to high salinity in tetraploid citrus seedlings increases with water availability and correlates with expression of candidate genes. Functional plant biology37(7), 674-685.
    CrossRef
  80. Munns, R. (2002). Comparative physiology of salt and water stress.Plant, cell & environment25(2), 239-250.
    CrossRef
  81. Nejat, N., Ramalingam, A., & Mantri, N. (2018). Advances in transcriptomics of plants. InPlant Genetics and Molecular Biology (pp. 161-185), Springer, Cham.
    CrossRef
  82. Nodzon, L. A., Xu, W. H., Wang, Y., Pi, L. Y., Chakrabarty, P. K., & Song, W. Y. (2004). The ubiquitin ligase XBAT32 regulates lateral root development in Arabidopsis. The Plant Journal40(6), 996-1006.
    CrossRef
  83. Ono, A., Yamaguchi, K., Fukada‐Tanaka, S., Terada, R., Mitsui, T., & Iida, S. (2012). A null mutation of ROS1a for DNA demethylation in rice is not transmittable to progeny. The Plant Journal71(4), 564-574.
    CrossRef
  84. Park, Y. B., & Cosgrove, D. J. (2015). Xyloglucan and its interactions with other components of the growing cell wall. Plant and Cell Physiology56(2), 180-194.
    CrossRef
  85. Parrilla-Doblas, J. T., Roldán-Arjona T., Ariza R. R. & Córdoba-Cañero, D. (2019). Active DNA demethylation in plants. International Journal of Molecular Sciences, 20, 4683.
    CrossRef
  86. Passerini, E., & Lombardo, P. (2000). Cosmetics.Cosmet News22
  87. Patankar, H. V., Al-Harrasi, I., Al Kharusi, L., Jana, G. A., Al-Yahyai, R., Sunkar, R., &Yaish, M. W. (2019). Overexpression of a Metallothionein 2A gene from date palm confers abiotic stress tolerance to yeast and Arabidopsis thaliana. International journal of molecular sciences20(12), 2871.
    CrossRef
  88. Pauly, M., &Keegstra, K. (2016). Biosynthesis of the plant cell wall matrix polysaccharide xyloglucan. Annual review of plant biology, 67, 235-259.
    CrossRef
  89. Roxas, V. P., Lodhi, S. A., Garrett, D. K., Mahan, J. R., & Allen, R. D. (2000). Stress tolerance in transgenic tobacco seedlings that overexpress glutathione S-transferase/glutathione peroxidase. Plant and Cell Physiology41(11), 1229-1234.
    CrossRef
  90. Sager, R., & Lee, J. Y. (2014). Plasmodesmata in integrated cell signalling: insights from development and environmental signals and stresses. Journal of experimental botany65(22), 6337-6358.
    CrossRef
  91. Sailaja, B., Mangrauthia, S. K., Sarla, N., &Voleti, S. R. (2014). Transcriptomics of heat stress in plants. InImprovement of crops in the era of climatic changes (pp. 49-89). Springer, New York, NY.
    CrossRef
  92. Sakamoto, H., Matsuda, O., &Iba, K. (2008). ITN1, a novel gene encoding an ankyrin‐repeat protein that affects the ABA‐mediated production of reactive oxygen species and is involved in salt‐stress tolerance in Arabidopsis thalianaThe Plant Journal56(3), 411-422.
    CrossRef
  93. Sakamoto, H., Nakagawara, Y., &Oguri, S. (2013). The expression of a novel gene encoding an ankyrin-repeat protein, DRA1, is regulated by drought-responsive alternative splicing.  J. Biol. Veterinary Agr. Food Engin7, 12.
  94. Schweizer, P., Christoffel, A., &Dudler, R. (1999). Transient expression of members of the germin‐like gene family in epidermal cells of wheat confers disease resistance. The Plant Journal20(5), 541-552.
    CrossRef
  95. Sedgwick, S. G., &Smerdon, S. J. (1999). The ankyrin repeat: a diversity of interactions on a common structural framework. Trends in biochemical sciences24(8), 311-316.
    CrossRef
  96. Seong, E. S., Choi, D., Cho, H. S., Lim, C. K., Cho, H. J., & Wang, M. H. (2007). Characterization of a stress-responsive ankyrin repeat-containing zinc finger protein of Capsicum annuum (CaKR1). Journal of biochemistry and molecular biology40(6), 952.
    CrossRef
  97. Serrano, N., Ling, Y., Bahieldin, A., & Mahfouz, M. M. (2019). Thermopriming reprograms metabolic homeostasis to confer heat tolerance. Scientific reports9(1), 1-14.
    CrossRef
  98. Sevilem, I., Miyashima, S., &Helariutta, Y. (2013). Cell-to-cell communication via plasmodesmata in vascular plants. Cell adhesion & migration7(1), 27-32.
    CrossRef
  99. Shen, G., Kuppu, S., Venkataramani, S., Wang, J., Yan, J., Qiu, X., & Zhang, H. (2010). ANKYRIN REPEAT-CONTAINING PROTEIN 2A is an essential molecular chaperone for peroxisomal membrane-bound ASCORBATE PEROXIDASE3 in Arabidopsis. The Plant Cell22(3), 811-831.
    CrossRef
  100. Shunwu, Y., Zhang, L., Zuo, K., Li, Z., & Tang, K. (2004). Isolation and characterization of a BURP domain‐containing gene BnBDC1 from Brassica napus involved in abiotic and biotic stress. Physiologia Plantarum122(2), 210-218.
    CrossRef
  101. Silverstein, K. A., Moskal Jr, W. A., Wu, H. C., Underwood, B. A., Graham, M. A., Town, C. D., &VandenBosch, K. A. (2007). Small cysteine‐rich peptides resembling antimicrobial peptides have been under‐predicted in plants. The Plant Journal51(2), 262-280.
    CrossRef
  102. Steffens, B. (2014). The role of ethylene and ROS in salinity, heavy metal, and flooding responses in rice. Frontiers in plant science5, 685.
    CrossRef
  103. Sun, S., Wang, H., Yu, H., Zhong, C., Zhang, X., Peng, J., & Wang, X. (2013). GASA14 regulates leaf expansion and abiotic stress resistance by modulating reactive oxygen species accumulation. Journal of experimental botany64(6), 1637-1647.
    CrossRef
  104. Sung, P., Prakash, S., & Prakash, L. (1990). Mutation of cysteine-88 in the Saccharomyces cerevisiae RAD6 protein abolishes its ubiquitin-conjugating activity and its various biological functions. Proceedings of the National Academy of Sciences87(7), 2695-2699.
    CrossRef
  105. Tsukuda, S., Gomi, K., Yamamoto, H., & Akimitsu, K. (2006). Characterization of cDNAs encoding two distinct miraculin-like proteins and stress-related modulation of the corresponding mRNAs in Citrus jambhiriPlant molecular biology60(1), 125-136.
    CrossRef
  106. Trinh, N. N., Huang, T. L., Chi, W. C., Fu, S. F., Chen, C. C., & Huang, H. J. (2014). Chromium stress response effect on signal transduction and expression of signaling genes in rice. Physiologia plantarum150(2), 205-224.
    CrossRef
  107. Ueda, A., Kathiresan, A., Bennett, J., &Takabe, T. (2006). Comparative transcriptome analyses of barley and rice under salt stress.Theoretical and Applied Genetics112(7), 1286-1294.
    CrossRef
  108. Vatén, A., Dettmer, J., Wu, S., Stierhof, Y. D., Miyashima, S., Yadav, S. R., … &Lehesranta, S. (2011). Callose biosynthesis regulates symplastic trafficking during root development. Developmental cell21(6), 1144-1155.
    CrossRef
  109. Wang, H., Zhou, L., Fu, Y., CHEUNG, M. Y., WONG, F. L., PHANG, T. H., … & LAM, H. M. (2012). Expression of an apoplast‐localized BURP‐domain protein from soybean (GmRD22) enhances tolerance towards abiotic stress. Plant, Cell & Environment35(11), 1932-1947.
    CrossRef
  110. Wang, K. L. C., Li, H., & Ecker, J. R. (2002). Ethylene biosynthesis and signaling networks. The plant cell14(suppl 1), S131-S151.
    CrossRef
  111. Wang, Z., Gerstein, M., & Snyder, M. (2009). RNA-Seq: a revolutionary tool for transcriptomics.Nature reviews genetics10(1), 57-63.
    CrossRef
  112. Wangsawang, T., Chuamnakthong, S., Kohnishi, E., Sripichitt, P., Sreewongchai, T., & Ueda, A. (2018). A salinity‐tolerant japonica cultivar has Na+ exclusion mechanism at leaf sheaths through the function of a Na+ transporter Os HKT 1; 4 under salinity stress. Journal of Agronomy and Crop Science204(3), 274-284.
    CrossRef
  113. Wibowo, A., Becker, C., Marconi, G., Durr, J., Price, J., Hagmann, J., … & Weigel, D. (2016). Hyperosmotic stress memory in Arabidopsis is mediated by distinct epigenetically labile sites in the genome and is restricted in the male germline by DNA glycosylase activity. Elife5, e13546.
    CrossRef
  114. Wigoda, N., Ben‐Nissan, G., Granot, D., Schwartz, A., & Weiss, D. (2006). The gibberellin‐induced, cysteine‐rich protein GIP2 from Petunia hybrida exhibits in planta antioxidant activity. The Plant Journal48(5), 796-805.
    CrossRef
  115. Wolucka, B. A., Goossens, A., &Inzé, D. (2005). Methyl jasmonate stimulates the de novo biosynthesis of vitamin C in plant cell suspensions. Journal of experimental Botany56(419), 2527-2538.
    CrossRef
  116. Xu, Q. F., Cheng, W. S., Li, S. S., Li, W., Zhang, Z. X., Xu, Y. P., … & Cai, X. Z. (2012). Identification of genes required for Cf-dependent hypersensitive cell death by combined proteomic and RNA interfering analyses. Journal of experimental botany63(7), 2421-2435.
    CrossRef
  117. Yan, J., Huang, Y., He, H., Han, T., Di, P., Sechet, J., … & Ni, L. (2019). Xyloglucan endotransglucosylase-hydrolase30 negatively affects salt tolerance in Arabidopsis. Journal of experimental botany70(19), 5495-5506.
    CrossRef
  118. Yan, J., Wang, J., & Zhang, H. (2002). An ankyrin repeat‐containing protein plays a role in both disease resistance and antioxidation metabolism. The Plant Journal29(2), 193-202.
    CrossRef
  119. Yassin, M., El Sabagh, A., Mekawy, A. M. M., Islam, M. S., Hossain, A., Barutcular, C., … &Saneoka, H. (2019). Comparative performance of two bread wheat (Triticum aestivum) genotypes under salinity stress. Applied Ecology and Environmental Research17(2), 5029-5041.
    CrossRef
  120. Yu, X., Fei, P., Xie, Z., Zhang, W., Zhao, Q., & Zhang, X. (2019). Effects of methyl jasmonate on growth, antioxidants, and carbon and nitrogen metabolism of Glycyrrhiza uralensis under salt stress. Biologia plantarum63(1), 89-96.
    CrossRef
  121. Yuan, X., Zhang, S., Liu, S., Yu, M., Su, H., Shu, H., & Li, X. (2013). Global analysis of ankyrin repeat domain C3HC4-type RING finger gene family in plants. PloS one8(3), e58003.
    CrossRef
  122. Zavaliev, R., Ueki, S., Epel, B. L., &Citovsky, V. (2011). Biology of callose (β-1, 3-glucan) turnover at plasmodesmata. Protoplasma248(1), 117-130.
    CrossRef
  123. Zhang, D., Wan, Q., He, X., Ning, L., Huang, Y., Xu, Z., … & Shao, H. (2016). Genome-wide characterization of the ankyrin repeats gene family under salt stress in soybean. Science of the Total Environment568, 899-909.
    CrossRef
  124. Zhang, F. Q., Wang, Y. S., Lou, Z. P., & Dong, J. D. (2007). Effect of heavy metal stress on antioxidative enzymes and lipid peroxidation in leaves and roots of two mangrove plant seedlings (Kandeliacandel and Bruguieragymnorrhiza). Chemosphere67(1), 44-50.
    CrossRef
  125. Zhang, S., & Wang, X. (2008). Expression pattern of GASA, downstream genes of DELLA, in Arabidopsis. Chinese Science Bulletin53(24), 3839-3846.
    CrossRef
  126. Zhang, S., & Wang, X. (2017). One new kind of phytohormonal signaling integrator: Up-and-coming GASA family genes. Plant Signaling & Behavior12(2), e1226453.
    CrossRef
  127. Zhang, X., Allan, A. C., Li, C., Wang, Y., & Yao, Q. (2015). De novo assembly and characterization of the transcriptome of the Chinese medicinal herb, Gentiana rigescensInternational journal of molecular sciences16(5), 11550-11573.
    CrossRef
  128. Zhang, X. Y., Hu, C. G., & Yao, J. L. (2010). Tetraploidization of diploid Dioscorea results in activation of the antioxidant defense system and increased heat tolerance. Journal of plant physiology167(2), 88-94.
    CrossRef
  129. Zimeri, A. M., Dhankher, O. P., McCaig, B., & Meagher, R. B. (2005). The plant MT1 metallothioneins are stabilized by binding cadmiums and are required for cadmium tolerance and accumulation. Plant molecular biology58(6), 839-855.
    CrossRef
  130. Zhou, G. A., Chang, R. Z., &Qiu, L. J. (2010). Overexpression of soybean ubiquitin-conjugating enzyme gene GmUBC2 confers enhanced drought and salt tolerance through modulating abiotic stress-responsive gene expression in Arabidopsis. Plant molecular biology72(4-5), 357-367.
    CrossRef

Natural Extracts as Eco-Friendly Larvicides Against Aedes Aegypti Mosquito, Vector of Dengue Fever Virus in Jeddah Governorate

$
0
0

Introduction

Mosquitoes belong to (Diptera: Culicidae) are a category of insects that pose the greatest threat to human and veterinary health as vectors of diseases, more than any other insect group. The mosquito Aedes aegypti is one of this category that shares a similar ecological niche with human.Globally, mosquitoes represent a major public health problem. They are estimated to spread diseases to more than 700 million individuals annually and are currently expected to be responsible for the deaths of a round one person in 17 people1.

Aedes. aegypti is the principal transmitter of Dengue, Chikungunya,Yellow fever and Zikaviruses. This mosquito species is well suited to humankind. Their females get blood meals through biting the mammals and digesting it inside their bodies to obtain the eggs. The control of A. aegypti is difficult task because they able to lay their eggs in many places even those of low quantity of water.The eggs have the ability to survive months in the dry conditions and they hatch as soon as water is available. Furthermore, they have been developed resistance against commonly used insecticides2,3,4.

Extensive application of pyrethroid and organophosphate insecticides to monitor all types of mosquito has resulted in an acceleration of the level of resistance created. In addition to resistance, other adverse effects may be associated with certain mosquiticides, including harmful effects against non-targeet species, environmental problems and human health concerns5.

Surveys of appropriate alternatives to conventional insecticides have shown that phytochemicals are a good choice in terms of relative protection, global availability and low cost. Therefore, screening for locally available medicinal plants for mosquito control may be an option that is cheaper than costly imported products to improve local jobs and public health6.

Chemicals extracted from natural sources have recently been predicted to be weapons of potential mosquito control programs as they are shown to be eco-friendly and largely non-toxic to humans and other mammals and biodegradable7.

These natural metabolites exhibit significant bioactivities including antidiabetic, antioxidant, antibacterial,anticancer and anti inflammatory activities 8,9 and several of these natural products can be used for derive a unique drug agents 10.

The marine ecosystem is an enormous and incredibly rich source of biological and chemicals products of natural origin.Many of these substances have beneficial medical and pharmaceutical properties.A large number of marine bio active metabolites with specific properties have recently been isolated and characterized 11. Natural biologically active constituents such as steroids, terpenoids, alkaloids, sterols and other metabolites are formed by the marine biota 12. Recently, bioassay work was designed to evaluate the larvicidal effect of methanol, chloroform, ethyl acetate and aqueous extracts of macro algae Codium edule against Aedes aegypti larvae.The findings indicated that Chloroform fraction exhibited the most larvicidal activity with LC50 value of 19.54 ppm13. In this manner the present work aimed to study the possibility of control of Aedes aegypti by extracts of Holothuriascabra as a marine animal and of Acalypha fruticosaas a terrestrial plantunder the laboratory conditions.

Materials and Methods

Mosquito colony

The study requires a sufficient number of larvae to carry out the biological evaluation experiments and for this purpose, The mosquito Aedes aegypti strain was brought from the Jeddah Municipality laboratory to combat public health pests, Kingdom of Saudi Arabia. The mosquito colony was established under laboratory controlled conditions (temperature 27±2 °C, Relative humidity 75± 5 % and 10-14 light-dark periods) in the dengue research unit at King Abdulaziz University, where adult insects were kept in Canvas cages and daily provided with 10% sucrose solution. The females mosquitoes were fed with adequate blood meals from a pigeon and then mosquito eggs were obtained, which were immersed in ceramic dishes with sizes of 20-30 cm half-filled with dechlorinated water.Mosquitoes raring continued for a number of generations according to the method of (Mahyoub, 2013)14. The hatching larvae were fed daily by mixture of yeast powder: dry bread powder: skimmed milk powder in 1:1:1 proportions15.

Sea cucumber and plant materials

Sea cucumber Holothuriascabra was collected from AL-kharrar lagoon, Red Sea (west of Saudi Arabia) during July 2019. The animal was identified by marine biology department-King Abdulaziz university. The fresh leaves of plant Acalypha fruticosawere collected from AL-Baha region (south of Saudi Arabia) during August 2019 and the plant was classified by taxonomist, department of biology, faculty of Science, King Abdulaziz university. The H.scabra body wall and the leaves of A. fruticosawere washed by water and left in shade area at room temperature until become dry, then grinded using electrical blender to obtain fine powder.

Preparation of extracts

The extraction was performed by soaking100 gm fine powder of both organismsin sufficient amount of 70 % ethanol in shaking machine for overnight. The supernatant was filtered using what man filter paper, the extraction was repeated three times and the combined solvent was subjected to rotary evaporator at 45 °C. The dry extract (6.45 gm of H. scabra and 8.67 gm of A. fruticosa) were kept under -4°C until start the experiments16.

The bio assay experiments

The mosquito fourth instars larvae were treated with a series of concentrations of both plant and sea cucumber extracts under laboratory conditions at a temperature of 27 ± 2 ºC and a relative humidity of 75 ± 5%. Standard method of immersion was used 1. The tests were carried out in a beaker containing 100 ml of water and five replicates were used for each concentration where each single contains 20 larvae in addition to five replicates for control. Taking into account the supply of larvae with food to avoid starvation factor. Mortality rate were recorded during the period from the start of the experiment until pupation and adult emergence.

Statistical analysis

The mortality percentages of larvae were calculated for each concentration. The results were analyzed using LDP line softwareto derive statistical values and constants 17 at 95% confidence intervals and a significant level of 0.05 based on the degree of probability Probit analysis and calculating LC50 and LC90 together with the lower and upper confident limits and the inclination of toxicity line and Chi square according to Finney 197118.

Record of deformities

The morphological features of treated larvae have been compared to control media during the course of lethal experiments. Any noticeable difference in appearance between control and treated was reported as deformity. The deformities were described according to their resemblanceto those previously seen in the literature19.

Results and Discussion

Bioactive products from natural origin with insecticidal properties have been used in the recent pest control of different insect pests and vectors20. The mosquito-human relationship is related to many diseases. Serious diseases such as malaria, arboviral encephalitis, dengue fever, chikungunia fever, west Nile virus and yellow fever are transmitted by mosquitoes. These diseases cause significant mortality in human and livestock around the globe16.

Results of toxicity test of H. scabra and A.  fruticosaextracts onthe 4th larval instar ofA. aegyptiwere listed in Table (1 & 2) and Figure (1, 2 and 3). It is clear that the mortality percent increased with increasing extracts concentrations.H.scabraextract was found to be more bioactive than A.  fruticosa extract where LC50 values were (79.31, 152.89) ppm and LC90 values (314.12, 782.34) ppm, respectively.

Table 1: Effect of sea cucumber H. scabraand plant A. fruticosaon Aedes aegypti4th instar larvae.

Conc. (ppm) Mortality

(%)

LC50 (ppm)

(LCL-UCL)

LC90 (ppm)

(LCL-UCL)

(Chi)2 slope
H. scabra

 

50 35 79.31

(65.4 -92.6)

314.12

(260.4 -402.6)

2.2 2.144
100 58
200 79
300 87
500 98
A.     fruticosa

 

50 18 152.86

(130.3-177.7)

782.34

(588.3-1174.2)

0.367 1.807
100 39
200 57
300 71
500 82

Table 2:  Index compared Holothuriascabra with Acalypha fruticosa.

Line name LC50 Lower limit Upper limit RR
Holothuriascabra 79.31 65.49 92.61 1.927
Acalypha fruticosa 152.86 130.30 177.73

 

Vol18No1_Nat_Han_fig1 Figure 1: Relationship between H. scabra extract and larval mortality percent of A. aegypti.

Click here to view figure 

 

Vol18No1_Nat_Han_fig2 Figure 2: Relationship between A.fruticosaextract and larval mortalit  percent of A. aegypti.

Click here to view figure

 

Vol18No1_Nat_Han_fig3 Figure 3: Relationship between H. scabraand A.fruticosaextracts and larval mortality percent of A. aegypti.

Click here to view figure

Both extracts caused reduction in the pupation percentage.The pupation decreased as the extract concentration increased. Moreover, the toxic effect of H. scabra had been extended to the pupae and adults and this effect also observed with A.  fruticosa but to less extent.These effects of both extracts manifested in deformities noticed under microscope (Figure 4&5).In addition, the extracts caused decline in the emergence of adults and this reduction depended on the extract concentration.Such findings are consistent to some extent with previous outcomes of (Sharma et al., 2006)21.

Vol18No1_Nat_Han_fig4 Figure 4: (a) larvae shows dark melanization (b) dwarf of larval body (c) Normal larvae (larvae treated with A. fruticosa).

Click here to view figure

 

Vol18No1_Nat_Han_fig5 Figure 5: (a, b) albino case (c) normal pupae (d, e) partly emerged adult with attached pupal case (larvae treated with H. scabra).

Click here to view figure 

Strong activity of H. scabra may be attributable to their high saponin content. High larvicidal activity of saponin extract has been proven in earlier study with LC50 11.7 ppm against Culex pipiens 20.Also dose of 35 ppm of saponin extract of Balanites aegyptiaca fruit mesocarp inhibitedfifty percent of the population of treated larvae, this would definitely help to significantly decrease the mosquito density22.Earlier reports suggested the bioactivity of saponins as a naturally mosquito larvicidal agent; however, no investigation has been done on saponins regarding their effect on mosquito adult emergence 23.

These observed and  reported deformations on the larvae treated with the sea cucumber extract may be due to the fact that it contains many valuable valid active chemical constituents such as saponins (mentioned above) and terpenes, this results are in line with previous study on Sea cucumber Holothuriaatra extract against A. aegypti24. On the other hand phytochemicals found in A.fruticosacan contribute collectively or independently in larvicidal action. In general, the death of treated larvae couldbe attributable to the failure of the moulting bodies to swallow adequate volume of air during ecdysis to break the old cuticle and extend the new one, or to the effect of the plant extract to inhibit larvae body metamorphosis which may be based on the hormone control disturbanceand lead to an imbalance of the growth processes and larval deaths19,25.

Acknowledgement

This work was funded by Mawakeb Alajer Association, Jeddah, Saudi Arabia through the Science Research and Innovation Unit at the Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia under research number (scigriu41/11).The authors also are thankful to the staff of Dengue fever unit, King Abdulaziz University for their valuable technical cooperation in achievement the experimental part.

Conflict of interest

The authors declare no conflict of interest

References

  1. World Health Organization WHO (2005) Prevention and control of dengue and dengue hemorrhagic fever. WHO, Regional Publication, searl No.29, 134.
  2. Lima, E.P., Paiva, M.H.S., Paula de Araújo, A., da Saliva, U. M. da Saliva, de Oliveira, L. N., et al. (2011)Insecticide resistant in Aedes aegypti populations from ceara, Brazil. Parasites & Vectors, 4(1), 5.
    CrossRef
  3. Marcombe, S., Mathieu, R.B., Pocquet, N., Riaz, M.-A, Poupardin, R., Selior, S., et al. (2012) Insecticide resistant in the dengue vector Aedes aegypti from Matrinique: Distribution, mechanisms and relations with environmental factors, PLoS One, 7 (2), Article e30989.
    CrossRef
  4. Alkuriji, M. A., Al-Fageeh, M. B., Shaher, F. M. and Almutairi B. F. (2020) Dengue Vector Control: A Review for Wolbachia-based Strategies, BIOSCIENCES BIOTECHNOLOGY RESEARCH ASIA, 17(3): 507-515.
    CrossRef
  5. Rawani, A., Haldar, K.M., Ghosh, A., Chandra, G.(2009). Larvicidal activities of three plants against filarial vector Culex quinquefasciatus Say (Diptera: Culicidae). Parasitol. Res. 105(5) 1411-1417.
    CrossRef
  6. Bowers, W. S., Sener, B., Evans, P. H., Bingol, F., Erdogan, I. (1995). Activity of Turkish medicinal plants against mosquitoes Aedes aegypti and Anopheles gambiae. International Journal of Tropical Insect Science16, 339-341
    CrossRef
  7. Rahuman AA, Bagavan A, Kamaraj C, Saravanan E, Zahir AA, Elango G. (2009) Efficacy of larvicidal botanical extracts against Culex quinquefasciatusSay (Diptera: Culicidae). Res., 104(6), 1365-1372.
    CrossRef
  8. Fernando I.S., Kim M., Son K.T., Jeong Y. and Jeon, Y. J. (2016) Antioxidant activity of marine algal polyphenolic compounds: a mechanistic J. Med. Food. 19 (7) 615-628.
    CrossRef
  9. Wang H. D., Li X. C., Lee D. J. and Chang J. S. (2017) Potential biomedical applications of marine algae. Bioresource technology, 244, 1407-1415.
    CrossRef
  10. Benelli G., Pavela, R., Maggi F., Petrelli, R., Nicoletti (2017) Commentary: making green pesticides greener? The potential of plant products for nanosynthesis and pest control, J. Clust. Sci. 28, 3-10
    CrossRef
  11. Rania A. E. , Amany I., Eman H., Amir w., Haidy K., Manal A., Hashim H. and Safwat A. (2017) review of natural products from marine organisms in the Red Sea, IJPSR, (3): 940-974.
  12. Firn RD and Jones CG. (2003) Natural products: a simple model to explain chemical diversity. Natural Product Reports; 20: 382-391.
    CrossRef
  13. Alkuriji, M. A., Al-Fageeh, M. B., Shaher, F. M., Alorf M. S and Almazyad, H. F. (2020) Larvicidal Effect of Seaweed Codium Edule extracts on Aedes Aegypti mosquito. Research Journal of Pharmaceutical, Biological and Chemical Sciences (RJPBCS), 11(5): 82-89.
  14. Mahyoub J. A. (2013) Evaluation of the IGRs alsystin and pyriproxyfen as well as the plant extract jojoba oil against the mosquito Aedes aegypti . J. of Pure and Applied Microbiology I ; 7(04).
  15. Mahyoub Jazem A. (2019) USE OF METHOPRENE AND DIFLUBENZURON FOR LONG-TERM CONTROL OF AEDES AEGYPTI, THE VECTOR OF DENGUE FEVER IN JEDDAH GOVERNORATE, January–February, RJPBCS 10(1) Page No. 1
  16. El-Sheikh, T. M., Al-Fifi, Z. I., Alabboud, M. A. (2016) Larvicidal and repellent effect of some Tribulus terrestris L.,(Zygophyllaceae) extracts against the dengue fever mosquito, Aedes aegypti (Diptera: Culicidae). Journal of Saudi Chemical Society20, 13-19.
    CrossRef
  17. Hanan AS, Jazem MA, Hamed GA, Alhag SK. (2018) Larvicidal Activity of Synthesized Silver Nanoparticles using Rhazya stricta Leaf Extract against Mosquito Vectors Aedes Aegypti. Res J Biotechnol., 13(10).
  18. Finney, D. J. (1971). Statistical Method in Biological Assay, 3 rdedn. Griffn, London.
  19. Saranya M., Mohanraj R. S. and Dhanakkodi B. (2013) Larvicidal, pupicidal activities and morphological deformities of Spathodeacampanulata aqueous leaf extract against the dengue vector Aedes aegypti, European Journal of Experimental Biology 3(2):205-213.
  20. Djeghader, N. E.-H., Aïssaoui, L., Amira, K., Boudjelida, H. (2018)Toxicity evaluation and effects on the development of a plant extract, the Saponin, on the domestic mosquito, Culex pipiens. International Journal of Mosquito Research5, 01-05.
  21. Sharma P., Mohan L. and Srivastava C. N. (2006) Growth inhibitory nature of Artemisia annuaextract against Culex autnauetesctetus (Say), J. Asia-Pacific Entomol., 9 (4), pp. 389-395.
    CrossRef
  22. Chapagain B. P. and Wiesman Z. (2005) Larvicidal Activity of the Fruit Mesocarp Extract of Balanites aegyptiaca and its Saponin Fractions against Aedes aegypti, Dengue Bulletin, Vol 29, 203-207
  23. Wiesman Z and Chapagain B. (2003) Laboratory evaluation of natural saponin as a bioactive agent against Aedes aegypti and Culex pipiens. Dengue Bull. 27: 168-173.
  24. Mahyoub, J. A., Hawas U. W., Al-Ghamdi K. M., Aljameeli, M. M., Shaher F. M., Bamakhrama M. A. and Alkenani N. A. (2016) The Biological Effects of Some Marine Extracts Against Aedes aegypti (L.) Mosquito Vector of the Dengue Fever in Jeddah Governorate, Saudi Arabia, JOURNAL OF PURE AND APPLIED MICROBIOLOGY, 10(3): 1-8.
  25. Grzybowski, A., Tiboni, M., Silva, M. A., Chitolina, R. F., Passos, M., & Fontana, J. D. (2013) Synergistic larvicidal effect and morphological alterations induced by ethanolic extracts of Annona muricata and Piper nigrum against the dengue fever vector Aedes aegypti., Pest management science,2013; 69(5): 589-601.þ
    CrossRef

Bioclimatic Impact on the Carob Seeds Morphological Diversity in Morocco

$
0
0

Introduction

The carob, Ceratonia siliqua L., is a xerophytic plant that only accommodates hot and arid climates of the subtropical countries 1 . It develops as a sclerophyllous tree and  up to 10 m 2. It is one of the most useful indigenous Mediterranean trees 3. Although its origins were once attached to the East and to the Arabian Peninsula 1,3, the distribution of autochthonous carob trees in Mediterranean agro-ecosystems has widely been debated. It occupies spontaneously the different countries of the Mediterranean basin and then spread in America, Asia, Australia and South Africa in regions with a Mediterranean climate3-8. The female or hermaphroditic flower types are the most widely grown and expanded by man through their production9.

It is a multipurpose tree (agronomic, silvicultural and pastoral). Virtually all parts of the tree are useful for humans (leaves, woods, pods, seeds) and have important pharmaceutical and industrial food10. It has been widely exploited since ancient times for food and fodder 3. Extracts of carob tree leaves are rich in active antioxidants that significantly inhibit tumor growth 11. The seeds are of less importance. In fact, their extract revealed medicinal virtues of great importance 12. The foods traditionally used from this fruit, carob flour and carob syrup contain elements of vital importance in the human metabolism necessary for healthier growth and the prevention and cure of diseases. Indeed, they are very rich in protein, crude fiber, mineral elements (Ca, K, Mg, Na, P) and sugars; and they contain low-fat levels compared to some other oilseeds and nuts 13. In addition, carob tree pods showed levels of phenolic compound positively correlated with antioxidant activity 14. The leaves and stem bark of this tree have significant antioxidant activity and higher phenolic content 11

The high content of sugar pulp (sucrose, fructose and glucose) is an economical alternative for the production of lactic acid, which is highly valued and demanded on the industrial market 15. Moreover, given its geographical distribution and the richness of locust beans in carbohydrates, carob trees represent an important potential for the production of lactic acid 16.  Thus, the carob tree has aroused in recent years, a very particular interest for manufacturers for its very high content of D-pinitol sugar. Wild trees contain a higher content than cultivated trees 17. The carob tree is also favored by its cultural management, which remains traditional without chemical fertilizers or phytosanitary treatments.

The seed is the richest organ of the tree. All its components (integument, endosperm and cotyledon) play an important industrial and medical role. Gum (endosperm) remains the most important because it is used as a stabilizer, gelling agent, fixer in various fields. Such as food products (cheese, mayonnaise, salads …), cosmetics (creams, toothpastes …), pharmaceuticals (medicines, syrups …), tannins, textiles 18,19. For example, carob pods are considered to be of secondary importance in the carob processing process, whereas seeds are considered the most valuable part of the fruit, containing polysaccharides, which are widely used in the processing of locust beans. Food industry. They therefore constitute an inexpensive source of natural phytochemicals, especially polyphenolic products20.

In Morocco, the carob tree stretches over the plains and the low mountains on a long bio climatic fringe from the arid to the per-humid in the different geographical regions from the Anti-Atlas in the south to the Rif in the north of the country. Its distribution is limited to the thermomediterranean vegetation stage with warm and temperate thermal variants 21. It plays an important socioeconomic role. It generates a total revenue that would be at least 7.94% of the total value of crop production of farmers in the north of the country 22. Because of its adaptability to soil and climate stress, it could contribute to the development of marginal areas with an arid or semi-arid climate, rugged terrain, poor soil, and where specific erosion is high and the regression of the vegetation cover limits any development of these zones. In view of the importance of the carob tree seed, on the one hand, and the diversity of the natural distribution of the carob tree in Morocco, spreading from the arid in the south of the country to the humid North, the present work has set itself the objective of studying the morphological diversity of the seeds of five natural populations of carob trees distributed along a latitudinal and rainfall gradient: from the arid bio climatic in the south of the country, to the wet at north of the country.

Materials and methods

Plant material and its geographical distribution

The present work concerned five ecotypes, carob trees in Morocco distributed according to an aridity gradient based on average annual rainfall and bio climatic stages. These provenances are spread over bioclimatic ranging from the arid south of the Western High Atlas to the humid western Rif 23. The ecotypes studied are:

P1 : Taroudante on the southern slope of the high western atlas (arid bioclimaticwith a warm winter);

P2 : Essaouira on the west coast of the high western atlas (semi-arid bioclimaticwith a warm winter);

P3 : Tighdouine on the northern slope of the high central atlas (semi-arid bioclimatic inferior with a cool winter);

P4 :Demnate in the center of the country (semi-arid bioclimatic superior with a cool winter);

P5 : Maggo at the Western Rif (wet bioclimatic with a cool winter).

Table 1: Climatic characteristics of the provenances studied:

Ecotypes

rainfall (mm/year) Bioclimate Tranche altitudinale (m)

Localisation GPS

Taroudant 226 arid with a warm winter 200 – 300 30,470°N 8,877°W
Essaouira 285 semi-arid with a warm winter 0 – 50 31,513°N 9,770° W
Tighdouine 380 semi-arid inferior with a cool winter 1000 – 1100 31.428°N 7.525W
Demnate 475 semi-arid superior with a cool winter 1000 – 1100 31,734°N 7,005°W
Maggo 880 wet with a cool winter 600 – 700 35.186N 5.285W

The collected pods were thoroughly mixed and then crushed and sieved to obtain a perfect mix of seeds. For each ecotype, shape parameter measurements were performed on one thousand (1000) seeds selected in a completely random fashion.

Parameters studied

The seeds of the five provenances were a subject of different measurements to study the morphological variation using the different shape parameters, namely:

Length (mm) ;

Width (mm) ;

thickness (mm);

volume (ml),

weight (mg) ;

Seed density (mg / ml).

Statistical data processing tools

Descriptive analysis of the variables studied.

Development of the distribution curves (frequencies) of the observations.

Analysis of the variance (ANOVA1) (effect of origin on the measured parameters); and comparison of averages (Tukey test, etc.)

Software used: IBM SPSS statistics 20 & sigma Plot 11.0

Methods of statistical processing of data

The samples were extracted from totally independent populations

Measured seeds were chosen at each source in a completely random manner

After checking the normality test and the equality test for variances at (P <0.050), the measured data were statistically processed by ANOVA 1 (effect of origin on the different shape parameters measured).

The verification of the significance of the F. test in the ANOVA results table1, allows us to conclude whether there is a relationship between the measured parameters and their geographical origin.

In the case of confirmation of an effect relationship, we determine at which level the statistically significant differences between these provenances are found by using the average comparison test of all the provenances studied.

The method used in the comparison test of averages is the Tukey test given the large size of our samples and that they are the same size for all provenances.

Results

Table 2 below summarizes the statistical descriptions (mean and coefficient of variation) of the seed shape parameters of the five carob tree provenances studied (length, width, thickness, weight, volume and density of seeds).

The reading of this table shows that globally the seeds of the five provenances studied are not all homogeneous from the morphological point of view. Indeed, we note that morphological parameters, seeds from each source, show a certain trend (expressed on average) that seems to vary from one source to another. Similarly, for all the measured parameters, there is variability within each source. This variability is expressed by the values of the coefficients of variation (CV).

Vol18No1_Bio_Fou_tab2 Table 2: Statistical results of the shape parameters of the five provenances of carob.

Click here to view table 

To better understand the variability observed between and within provenances, we carried out the various statistical treatments of the measured data of the various seed shape parameters.

Table 3: One-Way ANOVA Analysis of the effect of provenance on the Morphological Parameters.

  ddl F Signification
Seed length 4 545,812 ,000
Seed width 4 415,581 ,000
seed thickness 4 54,180 ,000
seed volume 4 1005,248 ,000
seed weight 4 3146,555 ,000

seed density

4 405,636

,000

Seed length

Figure 1.a shows that the seeds of the five-carob ecotypes studied are characterized by a length that ranges from 7 to 12 mm. This distribution curve illustrates an almost marked differentiation between the five ecotypes.

As illustrated by the figure 1.b, the arithmetic means of seed length was very differentiated depending on the ecotypes.The seeds of Maggo had the greatest length; followed respectively by those of Tighdouine, Demnate and Essaouira; while those of Taroudante are the shortest although they are characterized by the widest spectrum of variability with the highest CV (Table 2).

Vol18No1_Bio_Fou_fig1 Figure 1: Seeds length of the five ecotypes from different ecological provenance (Traoudante; Essaouira; Tighidouine; Demnate and Maggo), distribution of seeds length (a) and average means of seedlength (b).

Click here to view figure

Statistical analysis (table 3) show that provenance had a very high significance (p<0.001) effect on the seeds length. This allows us to conclude that there is a relationship between seed length and geographical origin. This means that seed length differs from at least one ecotype compared to other ecotypes.

Means comparison of seeds length using the Tukey test was apllicated to all the provenances to determine at what level the statistically significant differences between these provenances are. Reading the results illustrated in figure 1.b, it can be deduced that the five ecotypes studied stand out from each other at a threshold of (P <0.010). This means that each source is a separate group and they are completely separate from each other.

Width of the seeds

Reading the figure 2.a and 2.b below shows that from the point of view of the width, the seeds of Taroudante are of the smallest width, and they are relatively similar to those of Demnate

and Essaouira. The seeds of Maggo present the big width followed by those of Tighdouine. Considering CV values ​​(Table 2), seed width has a wide range of variability (higher CVs) compared to seed length variability.

Vol18No1_Bio_Fou_fig2 Figure 2: Seeds width of the five ecotypes from different ecological provenance (Traoudante; Essaouira; Tighidouine; Demnate and Maggo), distribution of seeds length (a) and average means of seeds width (b).

Click here to view figure 

The results of the statistical analysis (table 3) showed that the effect of provenance is very highly significant (P <0.001) on seed width. It is deduced that at least one group is different from the others in terms of seed width.

In addition, the Tukey test confirmed that the width of the seeds of Taroudante is similar to that of Demnate and therefore constitute a single homogeneous group. Similarly, Essaouira seed width is similar to that of Demnate at the P <0.010 level. While the Tighdouine and Maggo ecotypes are significantly distinct from one another, and they are different from the other groups.

Thickness of the Seeds

Table 2 shows that carob seeds are highly heterogeneous from the point of view of the thickness since the CV exceeds 20% for all ecotypes.The distribution curve of the seeds according to their thickness (figure 3.a) as well as the diagram of the average width (figure 3.b) show that the seeds of Maggo and Taroudante have a great thickness, whereas the seeds of Essaouira, Tighdouine and Demnate are less thick.

Vol18No1_Bio_Fou_fig3 Figure 3: Seeds thickness of the five ecotypes from different ecological provenance (Traoudante; Essaouira; Tighidouine; Demnate and Maggo), distribution of seeds thickness (a) and average means of seed thickness (b).

Click here to view figure

Statistical analysis (table 2) showed a very highly significant (p<0.001) effect of seeds origin on their thickness. This says that at least one group is different from the others in terms of seed thickness.The Tukey comparison test of the average P <0.010 thresholds showed two distinct groups of provenances. The first group consists of Tighdouine, Essaouira and Demnate, and the second group contains Maggo and Taroudante (figure 3.b)

Seed Volume

From the point of view of seed volume, we can see in Table 2 that the carob seeds show heterogeneity between the five provenances studied. Similarly, within each provenance there is some variability.The distribution curve of the seeds according to their volume (figure 4.a) as well as the diagram of the average width (figure 4.b) show that the seeds of Maggo and Tighdouine are larger, whereas the seeds of Tarourante Essaouira and Demnate are relatively less volume.

Vol18No1_Bio_Fou_fig4 Figure 4: Seeds volume of the five ecotypes from different ecological provenance (Traoudante; Essaouira; Tighidouine; Demnate and Maggo), distribution of seeds volume (a) and average means of seed volume (b).

Click here to view figure

Statistical analysis showed a very highly significant (P <0.001) effect of seed origin on their volume (Table 3).To better understand the results of the above-mentioned, we performed Tukey’s comparison of volume averages at the P <0.010 threshold. The results obtained (figure 4.b) show that in terms of volume, the seeds of Essaouira and Taroudante are homogeneous and therefore constitute a single group, while the other three ecotypes (Maggo, Demnate and Tighdouine) are distinguished from each other and therefore each constitute a separate group.

Seed Weight

From Table 2 and Figure 5, seed weight appears to have the same trend as seed volume. In fact, the heaviest seeds are from provenances of Tighdouine and Maggo whereas the seeds of the provenances of Taroudante, Essaouira and Demnate shows the lowest weight. terms of weight. The results obtained (figure 5.b) show that all provenances differ from one another in terms of seed weight at the P <0.010 threshold.

Vol18No1_Bio_Fou_fig5 Figure 5: Seeds weight of the five ecotypes from different ecological provenance (Traoudante; Essaouira; Tighidouine; Demnate and Maggo), distribution of seeds weight(a) and average means of seeds weight (b).

Click here to view figure 

Density of Seeds

Figure 6 shows that the average seed density of the ecotypes studied differs from one source to another. Seed density seems to follow the same trends in seed weight. Indeed, the seeds of the provenances of Maggo and Tighdouine are of greater density whereas the seeds of Taroudante, Demnate and Essaouira are relatively less dense.

Vol18No1_Bio_Fou_fig6 Figure 6: Seeds density of the five ecotypes from different ecological provenance (Traoudante; Essaouira; Tighidouine; Demnate and Maggo), distribution  of seeds density (a) and average means of seed density (b).

Click here to view figure

The results of ANOVA 1 (Table 3) showed that the effect of provenance is very highly significant (P <0.001), on the seed density.Tukey’s comparison of seed density averages showed that the density of Taroudante and Demnate seeds are similar and therefore constitute a single homogeneous group. While, the averages of the density of the seeds of Essaouira, Tighdouine and Maggo are significantly distinct from each other.They therefore constitute different groups.

Correlation and ACP Analysis

Table 4 shows different correlations between studied parameters and geographical parameters, with the exception of the seed density, which has no significant correlation with other seed shape parameters (length, width, thickness, volume and weight) or geographic (altitude, rainfall) and bioclimatic parameters.All other morphological parameters show a very high significance (p<0.001) within them and with the geographical parameters. However, the thickness shows a lower significance (p<0.01) with the geographical parameters, and a negative correlation with the altitude. Therefore, as the rainfall and altitude increased the all the morphological paramthères will be increased except the thickness.

Rainfall and bioclimatic, mutually and naturally correlated in a very highly significant (p<0.001) manner, have a highly significant correlation to the length, width, weight and volume of the carob seed studied.

Table 4: Pearson correlation coefficient of morphological and geographical parameters of seeds of fivegenotype of carob tree.

Parameters Length Width thickness weight Volume Density Rainfall Altitude Bioclimat
Length 1 0,878** 0,696** 0,474** 0,316** 0,038 ns 0,454** 0,287** 0,414**
Width 1 0,758** 0,422** 0,278** 0,036ns 0,391** 0,204** 0,319**
thichness 1 0,006ns 0,003ns 0,093ns 0,096* -0,068* 0,041*
weight 1 0,721** 0,063ns 0,730** 0,266* 0,604**
Volume 1 -0,033ns 0,595** 0,237* 0,489**
Density 1 0,038 ns 0,139ns 0,032ns
Rainfall 1 0,364** 0,941***
Altitude 1 0,421**
Bioclimat 1

The correlation is very highly significant at the 0.001 level.

The correlation is highly significant at the 0.01 level.

The correlation is significant at the 0.05 level.

ns. No significance

The principal component analysis of the data revealed 9 components including the 3 main components that express about 80% of the data variability (figure 7). These two diagrams as well present the components extracted by PCA and the matrix of components show that the first component alone accounts for almost half of the variability (47.13%)of the data. This factor is composed mainly of the rainfall, the weight of the seeds, the bioclimatic, the length, the width and the volume of the seeds, which contributes positively to the constitution of this component. While component 2 (21.28%) consists mainly of shape parameters (thickness, length and width of seeds). The third component constructed mainly by the density of seeds and present just 11.63% of variability.

Vol18No1_Bio_Fou_fig7 Figure 7: Graphical representations of the 3 components extracted by the Principal Components Analysis (PCA) of all the variables.

Click here to view figure

Discussion

The results obtained show that the seeds from the most arid zone (from Taroudante with an average annual rainfall of 226 mm / year) located on the southern slope of the high western atlas, have the lowest form parameters compared to the others. For provenances from relatively more watered areas. Indeed, the seeds of Taroudante are of the smallest size with the average of length, width, weight and volume respectively of the order of 8,52 ± 1,14 mm, 6,59 ± 1,00 ml, 0,156 ± 0.250g and 1.31 ± 0.15ml. These results are similar to the results of the work carried out for the provenance of Ait Berrehil, also located on the southern slope of the high western atlas but also for the provenances of Issafen and Tafraoute, located in the anti atlas, in an arid bioclimatic reported by Sidina, et al. 5.

The observed effect of aridity on the length of carob seeds was also reported in the results of the work carried out in Tunisia, for example the population of Chbika 24. In addition, the worksdone by Konaté, et al. 4and by Turnbull, et al. 25, on the weight of carob seeds showed similar results.

On the other hand, the seeds coming from humid bioclimatic provenances of the north of the country, case of Maggo in particular where the highaverage annual precipitation is of the order of 880 mm /year, are of the largest size. Indeed, the average size of the seeds of Maggo is of the order of 10.40 ± 1.00mm, 8.03 ± 1.00ml, 0.236 ± 0.019g and 1.65 ± 0.14ml respectively for the length, the width, weight and volume of the seeds. These results were also observed for carob seeds from other sources in humid regions of Morocco5,26. The results obtained for these parameters (length, width and weight of the seeds) are comparable to the results observed in Spain 27.

In this sense, we find that the two ecotypes of the arid region and the humid region are always opposed in the different groups released by the comparison of the averages of the set of parameters (variables) of shape of the seeds.

This finding was confirmed for all the ecotypes studied by calculating the Pearson correlation coefficient of the morphological parameters of the seeds studied and the geographical parameters of the origin of their ecotypes (Table 3). In fact, this table shows that with the exception of the density of the seeds, which has no correlation with the other seed shape parameters (length, width, thickness, volume and weight) nor with the parameters of the geographical origin (altitude rainfall and bioclimatic), rainfall and bioclimatic, mutually and naturally correlated in a very highly significant way, have a highly significant correlation to the length, width, weight and volume of the carob seed studied. That said, the size of the natural carob tree seeds follows the annual rainfall gradient or their size depends on the degree of aridity of the climate of the site of their origin. Seed thickness shows indifference to environmental factors. This result was also reportedin work done in Spain 27.

If the genetic heterogeneity is assumed ascertained by Sidina, et al. 5, we can deduce that this is a kind of adaptation of the species to severe rainfall conditions. This is explained by the independence of the density (ratio of weight to seed volume) to the other geographical and shape parameters, on the one hand, and by the low effect of altitude on these parameters.On the other hand, the three seed shape parameters used in the mathematical formulas for measuring the volume of carob seeds 28, namely: length, width and thickness, both determine the volume and the weight of the seeds of all the ecotypes studied. Similarly, the length and width of seeds that determine their weight and volume since the thickness is not correlated with them. Thus, the contribution of the thickness in the volume and the weight of the seeds are minimal.

According to the PCA analysis, we can once again confirm that the size of the carob seeds (length, width, weight and volume) depends on their geographical origin (rainfall and bioclimatic). In fact, these highly positively correlated variables are distinguished together in component 1. As for component 2, it groups together the three shape parameters (length, width and thickness) that mathematically determine both the volume and the weight of the seeds. While component 3 mainly represents seed density which is independent of other parameters morphological and geographical site.

Conclusion

In terms of conclusion, it is deduced that in Morocco the seeds of the carob tree are characterized by a great morphological diversity. This variability is found in all the measured parameters.If this variability was also observed within each ecotype, with the apparent existence of a general trend in the data around a given average (unimodal frequency distribution by class of the variable), these distributions show highly contrasting variability between the different ecotypes practically for all the variables.

Statistical treatments by ANOVA1 have shown the existence of an effect of provenance on the different variables. The multiple comparison of averages made it possible to identify the different homogeneous groups for each variable. In all the cases found, the seeds of moist bio climatic (Maggo from the north of the country) provenances, which are larger and heavier, are distinguished from those of the arid and semi-arid bio climatic zones of the south of the country (Taroudante and Demnate).The weight and volume of carob seeds are based on the main shape parameters, length and width. These are closely related to rainfall and bio climatic.It is thus deduced that the size of the natural carob tree seeds depends on the rainfall and the bio climatic of their original site

Acknowledgment

Authors thanks the universities.

Conflict of interest 

Authors declare no conflict of interest.

Funding source

This ressearch was funded by the loboratorys source.

References

  1. Evreinoff, V. A. Le Caroubier ou Ceratonia siliqua L. Revue internationale de botanique appliquée et d’agriculture tropicale  (1947); 27(299): 389-401.
    CrossRef
  2. Neville-Rolfe, E. Carob Tree. (Ceratonia Siliqua, L.). Bulletin of Miscellaneous Information (Royal Gardens, Kew)  (1898); 1898(140).
    CrossRef
  3. Battle, I. & Tous, J. Carob tree: Ceratonia siliqua L.-Promoting the conservation and use of underutilized and neglected crops. 17. Bioversity International, 1997.
  4. Konaté, I., Filali-Maltouf, A. & Berraho, E. B. Diversity analysis of Moroccan carob (” Ceratonia siliqua” L.) accessions using phenotypic traits and RAPD markers.  (2007).
    CrossRef
  5. Sidina, M. M. et al. Fruit and seed diversity of domesticated carob (Ceratonia siliqua L.) in Morocco. Scientia Horticulturae  (2009); 123(1): 110-116.
    CrossRef
  6. Gharnit, N. & Ennabili, A. Categories of Carob Tree (Ceratonia siliquaL.) from Morocco. International Journal of Fruit Science  (2015); 16(3): 259-274.
    CrossRef
  7. Gharnit, N. & Ennabili, A. J. B. E. Essais préliminaires de culture in vitro du caroubier (Ceratonia siliqua L.) originaire du Nord ouest du Maroc.  (2009); 3(6): 18-25.
  8. Winer, N. The Potential of the Carob(Ceratonia Siliqua). International Tree Crops Journal  (1980); 1(1): 15-26.
    CrossRef
  9. Hills, L. D. The Cultivation of the Carob Tree(Ceratonia Siliqua). International Tree Crops Journal  (1980); 1(1): 27-36.
    CrossRef
  10. El Kahkahi, R. et al. Technical sheet on the culture carob tree (Ceratonia Siliqua L.) in Morocco.  (2016).
  11. Custódio, L. et al. Antioxidant activity andin vitroinhibition of tumor cell growth by leaf extracts from the carob tree (Ceratonia siliqua). Biol.  (2009); 47(8): 721-728.
    CrossRef
  12. Teillet, B., Colin, M., Armengol, J. & Prigent, A. J. P. d. è. j. s. l. r. c. Effet d’un extrait de graines de caroube partiellement décortiquées sur les performances de viabilité et de croissance chez le lapin.  (2011): 22-23.
  13. Ozcan, M. M., Arslan, D. & Gokcalik, H. Some compositional properties and mineral contents of carob (Ceratonia siliqua) fruit, flour and syrup. J. Food Sci. Nutr.  (2007); 58(8): 652-658.
    CrossRef
  14. Benchikh, Y. & Louailèche, H. Effects of extraction conditions on the recovery of phenolic compounds andin vitroantioxidant activity of carob (Ceratonia siliquaL.) pulp. Acta Bot. Gallica  (2014); 161(2): 175-181.
    CrossRef
  15. Hariri, A., Ouis, N. & Bouhadi, D. Effect of the Alternative Addition of Sodium Acetate and Tween 80 on the Production Curve of Lactic Acid by Lactobacillus Casei Subsp Rhamnosus from date variety Hmira and carob pods syrups. J SciFed Journal of Chemical Research  (2017); 1(1).
  16. OUIS, N. & HARIRI, A. Improving of lactic acid production by Lactobacillus plantarum from carob pods syrup. wulfenia journal  (2018); 25( 7): 12-25.
    CrossRef
  17. Turhan, I. Relationship Between Sugar Profile and D-Pinitol Content of Pods of Wild and Cultivated Types of Carob Bean (Ceratonia siliqua L.). J. Food Prop.  (2013); 17(2): 363-370.
    CrossRef
  18. Dakia, P. A., Wathelet, B. & Paquot, M. Isolation and chemical evaluation of carob (Ceratonia siliqua L.) seed germ. Food Chem.  (2007); 102(4): 1368-1374.
    CrossRef
  19. Biner, B., Gubbuk, H., Karhan, M., Aksu, M. & Pekmezci, M. Sugar profiles of the pods of cultivated and wild types of carob bean (Ceratonia siliqua L.) in Turkey. Food Chem.  (2007); 100(4): 1453-1455.
    CrossRef
  20. Makris, D. P. & Kefalas, P. Carob pods (Ceratonia siliqua L.) as a source of polyphenolic antioxidants. J Food Technology  (2004); 42(2): 105-108.
  21. Benabid, A. Flore et écosystèmes du Maroc: Evaluation et préservation de la biodiversité.  (2000).
  22. Gharnit, N., El Mtili, N., Ennabili, A. & Sayah, F. J. T. B. B. d. D. N. d. l. F. d. F. B. importance socioéconomique du caroubier (Ceratonia siliqua L.) dans la Province de Chefchaouen (nord-ouest du Maroc), Rev.  (2006); 4(33).
  23. Mokhtari, N., MRABET, R., LEBAILLY, P. & Laurent, B. J. R. M. d. S. A. e. V. Spatialisation des bioclimats, de l’aridité et des étages de végétation du Maroc.  (2014); 2(1): 50-66.
  24. Naghmouchi, S., Khouja, M. L., Romero, A., Tous, J. & Boussaid, M. Tunisian carob (Ceratonia siliqua L.) populations: Morphological variability of pods and kernel. Scientia Horticulturae  (2009); 121(2): 125-130.
    CrossRef
  25. Turnbull, L. A., Santamaria, L., Martorell, T., Rallo, J. & Hector, A. Seed size variability: from carob to carats. Lett.  (2006); 2(3): 397-400.
    CrossRef
  26. El Kahkahi, R., Zouhair, R., Ait Chitt, M. & Errakhi, R. J. I. J. P. A. B. Morocco carob (Ceratonia siliqua L.) populations: Morphological variability of Pods and Kernel.  (2014); 2(4): 38-47.
  27. Albanell, E., Caja, G. & Plaixats, J. CHARACTERIZATION OF CAROB FRUITS (Ceratonia siliqua L.), CULTIVATED IN SPAIN FOR AGROINDUSTRIAL USE. International Tree Crops Journal  (1996); 9(1): 1-9.
    CrossRef
  28. Benkovic, M. et al. Assessment of Drying Characteristics and Texture in Relation with Micromorphological Traits of Carob (Ceratonia silliqua L.) Pods and Seeds. Food Technol. Biotechnol.  (2016); 54(4): 432-440.
    CrossRef

Phylogenetic analysis of Indian freshwater pond mussels Lamellidenscorrianusand L. phenchooganjensis(Bivalvia: Unionidae) from the upper Brahmaputra Basin of Assam, India

$
0
0

Introduction

The members of class Bivalvia shows unique evolutionary radiation as it displays parental care through brooding of eggs and larva1 and they (Glocidial larva) depend on the species-specific fish host for completion of their life cycle[2]. Taxonomically, the freshwater mussels are placed in the order Unionoida which is the largest group among the five orders of class Bivalvia represented by 6 families, 181 genera and about 840 species. On the other hand, the family Unionidae shows the largest radiation among the 6 families of class Bivalvia that comprises of 142 genera and 620 species worldwide. Though the families of Unionoida are ubiquitously distributed throughout the globe, except Antarctica, this is considered as one of the most threatened groups of freshwater animals alive today 3–5. Being an ecosystem engineer, these freshwater bivalves of order Unionida and family Unionidae play many significant roles to maintain the balance of aquatic ecosystem health[3, 6–14]and need utmost conservation priorities. However, due to the lack of detailed taxonomic and ecological study, application of management and conservation planning is still found to be very difficult. This highlights the urgent need for detailed study in the areas of phylogenetic and evolutionary relationships within the order, family or at the genus level15. Due to their interesting biological characters, for example, reproductive dependence on a host-specific fish for completion of the life cycle, double uniparental inheritance (a particular form of mitochondrial inheritance)16–19 and ecological as well as economic importance, there is a development on phylogenetic studies on freshwater bivalve in recent years [20, 21]. However, conservation efforts focused on species-based and habitat-based unionids are impeded by uncertainties like taxonomic, morphological, ecological, phylogenetic22, 23 and most of the species around the globe have been assigned as Data Deficient (DD) or Least Concerned (LC) species of IUCN Redlist, especially those outside of North America and Western Europe 2, 14, 15, 24 where the majority of phylogenetic study on freshwater unionids have been going on [25, 26]. Recently, several systematic and phylogenetic studies have been carried out on comparatively diverse tropical Asian lineages that have improved our understanding of the classification, morphological evolution, and geographic distribution of many tropical freshwater bivalves 15, 26–28. But still, the south-east Asian and Indo-tropical freshwater unionids have received less attention from the systematic perspective and most of the freshwater unionids taxa remain poorly understood phylogenetically.

India harbours 263 freshwater mollusc species found in two mega biodiversity hotspots, viz. The Western Ghats and Eastern Himalayas of which 95 species are belonging to class Bivalvia14, 29. Out of these total species richness, most of the freshwater bivalves are under DD or LC category of IUCN Redlist. These high numbers of species under DD and LC category are mainly due to unknown species distribution pattern and population trends, lack of information on potential threats and phylogenetic lineages. On the other hand, most of these freshwater mollusc species known only from morphological descriptions of the late 19th and early 20th century. Since then no vast amount of knowledge has been added on the freshwater mollusc species and many morphologically described species seems to be doubtful14, 24, 30, 31. The study on systematics and evolutionary lineages of freshwater unionids in the mainland of India is very limited with the aid of molecular and modern bioinformatics tools32, 33. However, with a single exception of the report by Jadhav and Jamkhedkar 200934 studied the phylogeny of freshwater bivalve, L. corrianus collected from Maharashtra, India, there is absolutely no other report available on DNA sequencing of freshwater mussels based on 18S rRNA sequences from the Assam as well as north-east India. The 18S rRNA is a structural component of the small eukaryotic ribosomal subunit and popularly used in biodiversity research and phylogenetic studies35–38. Compared to conserved cytochrome oxidase subunit I (COI) gene, the 18S rRNA gene is more conserved and its evolutionary progress is much slower making it a suitable molecular marker for phylogenetic studies and for differentiating between taxon at each taxonomic levels 39. Recently, a good number of new species, new information on their biology, bionomics and distribution have been added. A good number of taxonomic account was given by different authors, but at the same time, much confusion was created by the addition of several isolated and inadequate description of species40. So these work needs revision and up to date the present knowledge of freshwater molluscs based phylogenetic studies using sophisticated molecular markers and modern bioinformatics tools. Considering this fact, an attempt was made to a phylogenetic study of two Lamellidens spp. using the amplified sequence of the 18S rRNA gene and to study evolutionary lineage of different Lamellidensspp. concerning available 18S rRNA sequences of other unionids in the global database.

Materials and Methods

Collection and morphological identification Lamellidens specimens

The specimens of the Lamellidens spp. were collected from the upper Brahmaputra basin of Assam covering a total geographical area of approximately 3900 km2 between latitude (27°16’20”–27°47’77”) N and longitude (94°35’30”–95°22’42.16″) E (Figure 1). The large specimens were handpicked and the smaller ones were collected from the bottom substrata by using a metal sieve of mesh size 2mm2. Specimens were then washed, sorted into morpho-species and representatives were brought to the laboratory for future reference. Identification of the recorded specimens was done according to Subba Rao (1989)[41], Ramakrishnan and Dey (2007)[40] and by tallying with authentic voucher specimens deposited at Zoological Survey of India (ZSI), Kolkata.

Vol18No1_Phy_Jyo_fig1 Figure 1: Map of the study area between latitude 27o16’20”–27o47’77” N and longitude 94o35’30”–95°22’42.16″ E. Coloured dots are different sampling stations of the study area.

Click here to view figure

DNA extraction and sequencing of the 18S rRNA gene.

The DNA was extracted from mantle using the Qiagen Blood and Tissue Extraction Kit (Cat No./ ID: 69504). The quality of the extracted DNA from gills and mantle tissue was evaluated in 1% agarose gel. A single band of high molecular weight DNA was observed. Its quantity was determined by measuring the optical density at λ260/230 and λ260/280. After that, the fragment of the 18S rDNA region was amplified by the polymerized chain reaction (PCR) using forward NS1 and reverse NS4 universal primers. A single discrete PCR amplicon band of ~1300 base pairs (bp) was observed when resolved on the agarose gel. The PCR amplicon was purified to remove contaminants. Forward and reverse DNA sequencing reaction of PCR amplicon was carried out with the NS1 and NS4 universal primers using BDT v3.1 Cycle sequencing kit on ABI 3730xl Genetic Analyzer.

Phylogenetic analysis of the sequenced Lamellidens spp.

The consensus sequence was generated from the forward and the reverse sequences, and it was subjected to BLAST-n against non-redundant nucleotide collection. Twenty similar sequences were taken from BLAST result, and the phylogenetic tree was constructed using MEGA 7 software and based on BLAST similarity and phylogenetic affinity of the sequence, the species were identified as L. corrianus and L. phenchooganjensis (Figure 2). The sequences were then deposited to NCBI Genbank and accession numbers MT11611 and MT112199 were obtained for L. corrianus and L. phenchooganjensis respectively.

Vol18No1_Phy_Jyo_fig2 Figure 2: Photographs of Lamelliden sspp. Lc – Lamellidens corrianus(Lea, 1834), Lp – Lamellidensphen chooganjensis Preston, 1912.

Click here to view figure 

Comparison of Lamellidens sequence with other available fresh water unionid bivalves

The partial sequence of the 18S rRNA gene was analysed by Clustal W software to find out the pairwise distance of 18S rRNA gene between Lamellidens spp. and other unionid bivalve sequences present in the nucleotide database of NCBI. A distantly related 18S rRNA sequence was selected as an outgroup to find out the phylogenetic lineages of Lamellidens spp. All the sequences including the outgroup sequence were loaded as FASTA format and multiple sequence alignment (MSA) was performed following Kumar et al. 201642 using MEGA 7 software. After trimming off the flanking ends of the aligned sequences, a phylogenetic tree was constructed using the maximum likelihood method with a bootstrap replication value of 1000.

Result and Discussion

After blast analysis and construction of the phylogenetic tree, it was observed that L. corrianus generated a sequence of about 903 bases and the first 10 hits comprised of partial sequences of 18S rRNA gene of freshwater mussel Lamprotulaleai (Accession No. MF072524), Potomidalittoralis (Accession No. KU763287), Lampsiliscardium (Accession No.KX713305), Elliptiocomplanata (Accession No. JF899209), Aculamprotulatientsinensis (Accession No. MF072525), Thyasira sp. (Accession No. LC187037), Uniopictorum (Accession No. KC429349), Anodontacygnea (Accession No. AM774476), Psiluniolittoralis (Accession No. AF120536) , Elliptiocomplanata (Accession  No. AF117738) and Anodonta sp. (Accession No. AY579090). The phylogenetic tree generated showed four major clusters (Figure 3a). The L. corrianus(Accession No. MT111611) forms a cluster with Lamprotulaleai and Potomidalittoralisat the bootstrap confidence level of 77. The three Neotrigoniaspp. (Accession No.s AF 120538, AM774478, and AF411690) forms the second cluster at the bootstrap confidence level of 100. Triplodoncorrugatus (Accession No. KX713352), Hyridellaaustralis (Accession No. KX713301) and Velesunio ambiguous (Accession No. KC429346) formed the third cluster at the bootstrap confidence level of 99. The fourth major cluster was formed by the sequences of freshwater mussels Psiluniolittoralis, Elliptiocomplanata, Aculamprotulatientsinensis, Uniopictorum, Anodontacygneaand Lampsiliscardium.

Vol18No1_Phy_Jyo_fig3a Figure 3a: The evolutionary history of L. corrianuswas inferred by using the Maximum Likelihood method based on the Kimura 2-parameter model (Kimura 1980). The tree with the highest log likelihood (-4755.98) is shown.

Click here to view figure 

The percentage of trees in which the associated taxa clustered together is shown next to the branches. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach and then selecting the topology with superior log likelihood value. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 16 nucleotide sequences and one outgroup sequence of Patella rustica. There were a total of 896 positions in the final dataset.

The L. phenchooganjensis generated a sequence of about 458 bases and after blast analysis and, it was observed that the first 10 hits comprised of partial of 18S rRNA gene of freshwater mussel Aculamprotulatientsinensis, Lamprotulaleai, Lampsilissiliquoidea (Accession No. KY978476), Lampsiliscardium, Fusconaiaflava(Accession No. KX342024), Uniopictorum, Anodontacygnea, Elliptiocomplanata, Psiluniolittoralis etc. The phylogenetic tree generated showed three major clusters (Figure 3b). The three Neotrigoniaspp. forms the first cluster at a bootstrap confidence level of 96. The second cluster comprises Triplodoncorrugatus, Velesunio ambiguous and Hyridellaaustralis at a bootstrap confidence level of 87. The partial 18S rRNA gene sequence of L. phenchooganjensisforms cluster comprises Aculamprotulatientsinensis, Lamprotulaleai, Lampsiliscardium, Uniopictorum, Anodontacygnea, Psiluniolittoralisand Elliptiocomplanata (Figure 3b).

Vol18No1_Phy_Jyo_fig3b Figure 3b: The evolutionary history of L. phenchooganjensiswas inferred by using the Maximum Likelihood method based on the Kimura 2-parameter model (Kimura 1980). The tree with the highest log likelihood (-755.98) is shown.

Click here to view figure 

The percentage of trees in which the associated taxa clustered together is shown next to the branches. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach and then selecting the topology with superior log likelihood value. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 16 nucleotide sequences and one outgroup sequence of Patella rustica. There were a total of 453 positions in the final dataset.

Identification of species is the basis of studies like biological, ecological, genetic, conservation as well as different culture operations. Almost all freshwater mollusc species of Indo-tropical region were identified by the pioneer researchers based on external morphological characters of shells, glocidial larva and internal morphological characters such as the structure of marsupial gills 40, 41 and still, morphological identification has taken as the major tool for the identification of freshwater mussels in India.  Although, morphological, anatomical and reproductive keys are found to be important features of basic classification. But, in many instances due to overlapping characters, it is very difficult to make species distinction. Simpson (1900)[43], Modell (1942)44, and Starobogatov (1970)45 had largely used on external shell characters for the establishment of the non-phylogenetic classifications of various new taxa at genus and family level. Since numerous classification and reclassification has been made for unionid bivalve, different and inconsistent classification schemes create many taxonomic confusions and uncertainties in the classification of these freshwater bivalves46.

Some members of genus Lamellidensis more or less widespread in India and some are confined to a limited geographic region within the Indian subcontinent. Due to allopatric distribution pattern and parapatric speciation, the genus display wide range of shell structural variability and colour pattern which remains a challenging task to identify accurately based on morphological, anatomical and reproductive taxonomic keys. Taxonomic keys are still a major problem in establishing a correct database for freshwater mollusc especially the freshwater mussel of India14, 24, 31. With the lake of correct database and inconsistence classification therefore, it is difficult to implement conservation strategies and management measures that result from commercial, municipal and residential development.

From the phylogenetic study of 18S rRNA gene sequencing, it was found that the L. corrianusis closely related to 18S rRNA gene sequences of Lamprotulaleai and Potomidalittoralisat an indent 99.89% and Lampsiliscardium, Elliptiocomplanata, Aculamprotulatientsinensis, Uniopictorum and Anodontacygneaat an indent of around 99.23%. Like L. corrianus, all these species are freshwater mussels belonging to the family Unionidae of order Unionida. After generation of the phylogenetic tree using maximum likelihood method, it was observed that the L. corrianusis phylogenetically neighbour/ very close to Lamprotulaleai and Potomidalittoralis at 77% bootstrap confidence of trees in which the associated taxa are clustered together (Figure 3a). It was also observed that the L. corrianusshowed a high degree of association with the other freshwater mussel from the family Unionidae as branch lengths measured in the number of substitutions per site was very minimum. Therefore, the present study indicates that the L. corrianus showed a single evolutionary lineage with the other considered freshwater mussel species as they showed a common ancestor which was separate from that marine gastropod lineage. The species Patella rustica commonly called a true limpet, a marine gastropod belonging to the family Patellidae was taken as an out group and from the analysis, it was observed that the freshwater mussels and marine gastropods were evolved separately in due course of time from different ancestors as this marine gastropod species group from the rest in the phylogenetic tree (Figure 3a). Similarly, after sequencing analysis of 18S rRNA gene, it was observed that L. phenchooganjensis was also closely related to 18S rRNA gene sequences of Lamprotulaleai, Aculamprotulatientsinensis, Lampsiliscardium, Uniopictorum, Potomidalittoralis and Anodontacygnea with an indent of 99.78% and Elliptiocomplanata and Triplodon corrugates with 99.56% indent. From the analysis of the phylogenetic tree, it was observed that the L. phenchooganjensis was grouped with the above mentioned freshwater mussel species at a bootstrap confidence level of 87% except for Triplodoncorrugatus. The Triplodon corrugates formed a separate group, although the branch length and substitution per site were very less than 0.02 (Figure 3b). Therefore, like other freshwater mussel species, L. phenchooganjensiswas also evolved from a common ancestor which was separate from that of marine lineage as the considered outgroup Patella rusticaformed a separate lineage in the phylogenetic tree (Figure 3b). Though L. corrianusand L. phnechooganjensiswere morphologically distinct species[40, 41], their evolutionary relationship was found to be similar and showed monophyletic origin. Like other freshwater mussels species, the members of Lamellidensspp. shared a common ancestor and showed linear evolutionary lineage (Figure 4).

Vol18No1_Phy_Jyo_fig4 Figure 4: The evolutionary history of L. corrianusand L. phenchooganjensis was inferred by using the ML method based on the Kimura 2-parameter model (Kimura 1980). The tree with the highest log likelihood (-1755.98) is shown.

Click here to view figure 

The percentage of trees in which the associated taxa clustered together is shown next to the branches. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the MCL approach and then selecting the topology with superior log likelihood value. The analysis involved 16 nucleotide sequences and one outgroup sequence of Patella rustica. There were a total of 427 positions in the final dataset.

The result obtained from the phylogenetic analysis of Lamellidens spp. based on 18S rRNA sequencing was found to be significant, because there is only one report of 18S rRNA gene sequence of L. corrianusby Jadhav and Jamkhedkar, 200934 from Maharastra, India. After that, there is no other report about 18S rRNA sequences available/ submitted to the global database except for the present report. On the other hand, 18S rRNA gene sequences of L. phenchooganjensis was submitted for the first time in the Global nucleotide Database (Genebank, NCBI). No previous record on molecular data for this species is available.

According to IUCN, there are 10 globally recognized species under the genus Lamellidens and found to be endemic to Indo-tropical ecoregion. But recent assessment reports suggested that there are only seven species of Lamellidens that are found in mainland of India and some species are still doubtful [40, 47, 48, 49]. For instance, Lamellidensnarain porensisis known only from a single location Kamdai Nadi of Nepal and thought to be possibly a synonym of L. corrianus. Detailed taxonomic study at the molecular level is required to confirm the status of the species47. Similarly, the status as a separate species for Lamellidensunioidesis still doubtful and more taxonomic review is required to confirm it 50.  The Lamellidenslamellatusis known only from early 20th century records by Preston (1915)51 and detailed survey and taxonomic study are required to confirm its status as a species, geographical distribution and IUCN Redlist status [48]. The information about the Lamellidensscutumis only based on museum specimens collected only from some localities of Myanmar and India[49]. On the other hand, information on some Lamellidensspp. in global mollusc databases like Molluscabase, Malacolog, UnitasMalacologica is not available or whatever the information available is seem to outdated (Worldwide mollusc species database, Catalogue of Life).

Conclusion

Due to the difficulties in developing a database on population trend, distribution pattern, current geographic range, species coverage and lack of information on potential threats, a comprehensive phylogenetic study on Lamellidens spp. had not been attempted in the past. Therefore the outcome of the present phylogenetic study will help to establish an accurate database and add new information to the existing database especially from those regions where information on freshwater mollusc is considerably lacking.

Acknowledgement

Authors are thankful to Eurofins Scientific, Bengaluru and ZSI, Kolkata for their technical support; DST, Govt. of India for financial support and DST-FIST, Department of Life Sciences for providing necessary facilities for carrying out this work.

References

  1. Wächtler K., Dreher-Mansur M. C., Richter T.: Larval types and early post larval biology in naiads (Unionoida). In: Ecology and evolution of the freshwater mussels Unionoida. Springer, Berlin, Heidelberg. 2001; pp 93–125.
    CrossRef
  2. Bogan A. E., Roe K. J. Freshwater bivalve (Unioniformes) diversity, systematics, and evolution: status and future directions. J. N. Am. Benthol. Soc. 2008; 27: 349-369.
    CrossRef
  3. Lydeard C., Cowie R. H., Ponder W. F., Bogan A. E., Bouchet P., Clark S. A., Cummings K. S., Frest T. J., Gargominy O., Herbert D. G., Hershler R., Perez K. E., Roth B., Seddon M., Strong E. E., Thompson F. G. The global decline of nonmarine mollusks. BioSciences 2004; 54: 321-330.
    CrossRef
  4. Graf D. L., Cummings K. S. Palaeoheterodont diversity (Mollusca: Trigonoida: Unionoida): What we know and what we wish we knew about freshwater mussel evolution. In:  Bivalvia – A Look at the Branches (Bieler R. ed). Zool. J. Linnean. Soc. 2006; 148 pp 343–394.
    CrossRef
  5. Bogan A. E. Global diversity of freshwater bivalves (Mollusca:Bivalvia) in freshwater. Hydrobiologia2008; 595: 139-147.
    CrossRef
  6. Fenchel T., Kofoed L. H. Evidence for exploitative inter-specific competition in mud snails (Hydrobiidae). Oikos1976; 27: 367-376.
    CrossRef
  7. Bertness M. D. 1984 Habitat and community modification by an introduced herbivorous snail. Ecology 1984; 65: 370-381.
    CrossRef
  8. Peterson C. H., Black R. Resource depletion by active suspension feeders on tidal fiats: influence of local density and tidal elevation. Limnol. Oceanogr. 1987; 32: 143-166.
    CrossRef
  9. Kay E. A. The Conservation Biology of Molluscs.  Proceedings of a Symposium Held at the 9th International Malacological Congress, Edinburgh, Scotland, 1995 (No. 9), IUCN.
  10. Stewart T. W., Miner J. G., Lowe R. L. Quantifying mechanisms for zebra mussel effects on benthic macroinvertebrates: organic matter production and shell–generated habitat. J. N. Am. Benthol. Soc. 1998; 17: 81-94.
    CrossRef
  11. Strayer D. L., Caraco N. F., Cole J. J., Findlay S., Pace M. L. Transformation of freshwater ecosystems by bivalves: a case study of zebra mussels in the Hudson River. BioScience 1999; 49: 19-27.
    CrossRef
  12. Gutierrez J. L., Jones C. G., Strayer D. L. Iribarne O. O. Mollusks as ecosystem engineers: the role of shell production in aquatic habitats. Oikos 2003; 101: 79-90.
    CrossRef
  13. Vaughn C. C., Gido K. B., Spooner D. E. Ecosystem processes performed by unionid mussels in stream mesocosms: species roles and effects of abundance. Hydrobiologia2004; 527: 35-47.
    CrossRef
  14. Budha P.B., Aravind N.A., Daniel B.A.: The status and distribution of freshwater molluscs of the eastern Himalaya. In: The Status and Distribution of Freshwater Biodiversity in the Eastern Himalaya, India (Allen DJ, Molur S, Daniel BA, eds). IUCN, 2010; pp 42–53.
  15. Lopes-Lima M., Froufe E., Ghamizi M., Mock K. E., Kebapçı Ü., Klishko O., Kovitvadhi S., Kovitvadhi U., Paulo O. S., Pfeiffer III J. M., Raley M. Phylogeny of the most species–rich freshwater bivalve family (Bivalvia: Unionida: Unionidae): Defining modern subfamilies and tribes. Mol. Phylogenetics Evol. 2017; 106: 174-191.
    CrossRef
  16. Hoeh W. R., Black M. B., Gustafson R., Bogan A. E., Lutz R., Vrijenhoek R. C. Testing alternative hypotheses of Neotrigonia (Bivalvia: Trigonioida) phylogenetic relationships using cytochrome c oxidase subunit I DNA sequences. Malacologia 1998; 40: 267–278.
  17. Hoeh W. R., Stewart D. T., Guttman S. I. High fidelity of mitochondrial genome transmission under the doubly uniparental mode of inheritance in freshwater mussels (Bivalvia: Unionoidea). Evolution 2002; 56: 2252-2261.
    CrossRef
  18. Barnhart M.C., Haag W.R., Roston W.N. Adaptations to host infection and larval parasitism in Unionoida. J. N. Am. Benthol. Soc. 2008; 27: 370–394.
    CrossRef
  19. Breton S., Beaupre H.D., Stewart D.T., Hoeh W.R., Blier P.U. The unusual system of doubly uniparental inheritance of mtDNA: isn’t one enough?. Trends Genet. 2007; 23: 465–474.
    CrossRef
  20. Haag W.R. (ed): North American freshwater mussels: natural history, ecology, and conservation. Cambridge University Press. 2012
    CrossRef
  21. Lopes-Lima M., Teixeira A., Froufe E., Lopes A., Varandas S., Sousa R. Biology and conservation of freshwater bivalves: past, present and future perspectives. Hydrobiologia 2014; 735: 1–13.
    CrossRef
  22. Inoue K., McQueen A.L., Harris J.L., Berg D.J. Molecular phylogenetics and morphological variation reveal recent speciation in freshwater mussels of the genera Arcidens and Arkansia (Bivalvia: Unionidae). Biol. J. Linn. Soc. 2014; 112, 535–545.
    CrossRef
  23. Pfeiffer J.M., Graf D.L. Evolution of bilaterally asymmetrical larvae in freshwater mussels (Bivalvia: Unionoida: Unionidae). Zool. J. Linnean Soc.2015; 175: 307–318.
    CrossRef
  24. Köhler F., Seddon M., Bogan A.E., Tu D.V., Aroon P.S., Allen D. The status and distribution of freshwater molluscs of the Indo-Burma region. In: The Status and Distribution of Freshwater Biodiversity in Indo–Burma (Allen DJ, Smith KG, Darwall WRT, eds.). IUCN: Cambridge, UK and Gland, Switzerland; Zoo Outreach Organization, Coimbatore, India. 2012; pp 67–85.
  25. Lopes-Lima M., Burlakova, L.E., Karatayev, A.Y., Mehler, K., Seddon, M. & Sousa, R. (2018) Conservation of freshwater bivalves at the global scale: diversity, threats and research needs. Hydrobiologia 810, 1–14.
    CrossRef
  26. Bolotov I.N., Pfeiffer J.M., Konopleva E.S., Vikhrev I.V., Kondakov A.V., Aksenova O.V., Gofarov M.Y., Tumpeesuwan S., Win T. A new genus and tribe of freshwater mussel (Unionidae) from Southeast Asia. Scientific reports 2018; 8: 1–12.
    CrossRef
  27. Zieritz A., Bogan A.E., Froufe E., Klishko O., Kondo T., Kovitvadhi U., Kovitvadhi S., Lee J.H., Lopes–Lima M., Pfeiffer J.M., Sousa R. Diversity, biogeography and conservation of freshwater mussels (Bivalvia: Unionida) in East and Southeast Asia. Hydrobiologia 2018; 810: 29–44.
    CrossRef
  28. Konopleva E.S., Bolotov I.N., Vikhrev I.V., Gofarov M.Y., Kondakov A.V. An integrative approach underscores the taxonomic status of Lamellidensexolescens, a freshwater mussel from the Oriental tropics (Bivalvia: Unionidae). Systematics and Biodiversity 2017; 15: 204–217.
    CrossRef
  29. Aravind N.A., Madhyastha N.A., Rajendra G.M., Dey A. The status and distribution of freshwater molluscs of the Western Ghats. In: The Status and Distribution of Freshwater Biodiversity in the Western Ghats, India (Allen DJ, Molur S, Daniel BA, eds). IUCN, 2011; pp 49–62.
  30. Kumar A., Vyas V. Diversity of Molluscan community in River Narmada, India. J. Chem. Biol. Phys. Sci. 2012; 2: 1407–1412.
  31. Ramesha M.M., Sophia S., Muralidhar, M. Freshwater bivalve fauna in the Western Ghats Rivers of Karnataka, India: Diversity, distribution patterns, threats and conservation needs. Int. J. Cur. Res. 2013; 5: 2500–2505.
  32. Upadhye M.V. Patil R.C. Manohar S.M., Jadhav U. Phylogenetic Study of Freshwater Bivalve ParreysiaCorrugata from Maharashtra State, India by 18S rRNA Sequences. J. Life Sci. 2011; 5: 733–738.
  33. Magare V.N., Kulkarni C.P., Maurya C.B., Patil R.C., Upadhye M.V. Phylogenetic analysis of freshwater mussel Corbicula regularisby 18S rRNA gene sequencing. J. Exp. Biol. Agri. Sci. 2015; 3: 213–219.
  34. Jadhav B.L., JamkhedkarS. Phylogenetic Analysis of Lamellidenscorrianus obtained from Konkan Region of Maharashtra by 28s rRNA and 18s rRNA sequences. Res. J. Biotech. 2009; 4, 37–44.
  35. Šlapeta J., Moreira D., López-García P. The extent of protist diversity: insights from molecular ecology of freshwater eukaryotes. Proc. R. Soc. B: Biol. Sci. 2005; 272: 2073–2081.
    CrossRef
  36. Krüger M., Krüger C., Walker C., Stockinger H., Schüßler A. Phylogenetic reference data for systematics and phylotaxonomy of arbuscular mycorrhizal fungi from phylum to species level. New Phytol 2012; 193: 970–984.
    CrossRef
  37. Buse H.Y., Lu J., Struewing I.T., Ashbolt N.J. Eukaryotic diversity in premise drinking water using 18S rDNA sequencing: implications for health risks. Environ. Sci. Pollut. Res. 2013; 20: 6351–6366.
    CrossRef
  38. Fonseca V.G., Carvalho G.R., Nichols B., Quince C., Johnson H.F., Neill S.P., Lambshead J.D., Thomas W.K., Power D.M., Creer S. Metagenetic analysis of patterns of distribution and diversity of marine meiobenthic eukaryotes. Glob. Ecol. Biogeogr. 2014; 23: 1293–1302.
    CrossRef
  39. Tang C.Q., Leasi F., Obertegger U., Kieneke A., Barraclough T.G., Fontaneto D. The widely used small subunit 18S rDNA molecule greatly underestimates true diversity in biodiversity surveys of the meiofauna. Proc. Natl. Acad. Sci. 2012; 109: 16208–16212.
    CrossRef
  40. Ramakrishna, Dey A.: Handbook on Indian Freshwater Molluscs. Zoological Survey of India, Kolkata. 2007.
  41. Subba Rao N.V.: Handbook on Indian Freshwater Molluscs. Zoological Survey of India, Kolkata. 1989
  42. Kumar S., Stecher G., Tamura K. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 2016; 33(7): 1870-1874.
    CrossRef
  43. Simpson C.T. Synopsis of the naiades, or pearly freshwater mussels. Proc. U. S. Natl. Mus. 1900; 22: 501–1044.
    CrossRef
  44. Modell H. Das natürliche system der najaden.  Arch. Molluskenkd. 1942; 74: 161–191.
  45. Starobogatov Y.I.: Molluscan Fauna and Zoogeographic Zonation of Continental Freshwater Bodies of the World. Leningrad: ZoologiceskijInstitut, AkademijaNauk SSSR, 1970.
  46. Hoeh W.R., Bogan A.E., Heard W.H.: A phylogenetic perspective on the evolution of morphological and reproductive characteristics in the Unionoida. In: Ecology and evolution of the freshwater mussels Unionoida. Springer, Berlin, Heidelberg. 2001; pp 257–280.
    CrossRef
  47. Budha P.: Lamellidensnarainpirensis. The IUCN Red List of Threatened Species 2010, e.T173030A6961298. https://dx.doi.org/ 10.2305/ IUCN.UK.2010–4.RLTS.T173030A6961298.en.
    CrossRef
  48. Budha P.B., Daniel B.A.: Lamellidenslamellatus. The IUCN Red List of Threatened Species 2010, e.T166687A6259747.  http:// dx.doi.org/ 10.2305/IUCN.UK.2010–4.RLTS.T166687A6259747.en
    CrossRef
  49. Madhyastha A., Daniel B.A., Bogan, A.E.: Lamellidensscutum. The IUCN Red List of Threatened Species 2014, e.T173032A1376335. https://dx.doi.org/10.2305/IUCN.UK.2014–3.RLTS.T173032A1376335.en.
    CrossRef
  50. Madhyastha A.: LamellidensunioidesThe IUCN Red List of Threatened Species 2010, e.T177460A7439640.  https://dx.doi.org/ 10.2305/IUCN.UK.2010–4.RLTS.T177460A7439640.en.
    CrossRef
  51. Preston H.B.: The Fauna of British India including Ceylon and Burma. Mollusca (Freshwater Gastropoda and Pelecypoda) Taylor and Francis, London, 1915.

Influence of sewage treatment plant effluent on the presence of culturable pathogenic bacteria in the water body

$
0
0

Introduction

A large portion of water bodies is impacted by different kinds of wastewater released from various sources: household, business properties, industry, and farming 1. Modern wastewater management systems treat the wastewater into low-nutrient and low-organic content for release into the surface water without risk to human wellbeing or harm to the environment.The proficiency of any Sewage Treatment Plant (STP) is specified by the general execution of plant and effluent quality fit from an environmental standpoint 2. In this manner, the system is scrutinized to specify the general pollution associated with it. The efficiency of the system is vulnerable to several factors. Sewage from different sources, such as residential and industrial, produced intricate blends of inorganic and organic constituents causing incompatibility with the system’s operation. The system can also be overwhelmed bythe influent of raw sewage beyond the system’s capacity. High operational cost makes maintenance difficultas well and causessystemsto poor performance 3. These threats can reduce the efficiency of the system, causing the release of faecal bacteria, parasites, and viruses with potential health risks such as gastrointestinal and respiratory illness 4.Furthermore, Despitetreatment, some contaminants remain in treated wastewater released into surface waters, such as microbes (mostly intestinal) as well as chemicals from personal care items 5. Al-gheethi and Ismail (2014) studied the bacterial assorted variety in treated sewage plus biosolid produced from five STPs in Yemen. 160 bacterial strains were isolated of which, E. coli was the most widely recognized. Osuolale and Okoh (2017) study from the five wastewater treatment plants (WWTPs) in South Africa, Eastern Cape showed that in the treated effluents, the existence of faecal coliforms and E. coli was higher than that of rotaviruses or enteroviruses. Shigellaspp, Salmonellaspp, Staphylococcusspp, Vibriospp, as well as Listeria spp. were isolated from STP in Aswan, Egypt8. Similarly, Pant and Mittal (2007) reported that all three faecal-oral pathogens, Shigella, Salmonella, and Vibrio were notable in all the effluent samples from the plant alongside indicator microorganisms. Their findings recommended that treated sewage routinely contained pathogens as well as faecal coliform (FC) and faecal streptococci (FS).

This demands the question: how do regulated effluent from wastewater treatment plants (STPs) impacts surface water? Metagenomics studies identified changes in the microbial abundance and diversity of surface water that received effluents from STP 10, 11. For instance, the significant increase in abundance of human gut bacteria and decrease of phototrophic microorganisms or even disappearing after mixing upstream and outflow in surface water receiving effluents from (STP) effluent 12.Metagenomeisa powerful tool, capable of identifying bacteria, viruses, fungi, and parasites in complex samples through sequencing of DNA fragments. However, the presence of DNA fragments does notguarantee the presence of viable microbes. In this regard, metagenome analyses could not perceive the actual risk of STP effluent on health or the environment.

Unregulated discharges of untreated wastewater are also serious threat causing faecal contamination of surface water. The coliforms are bacteria from the Enterobacteriaceae family resistant to bile to adapt to gut condition. They are common in faecal materials, and can be found in the aquatic environment contaminated with faeces. Because they are easy to grow, and are reliable faecal contamination indicator 13, 14, 15. However, the coliforms are universal faecal bacteria found in all mammals, not only humans. In this sense, they are not effective for differentiating the source of faecal contamination, whether from human such as the STP, runoff from agriculture farms, or even wildlife normally inhabiting near water bodies 16. Recognising the main bacterial residence of the gut or bile-resistant bacteria,the frequently isolated in faecal contaminant can be indicative of potential source indicator 15.

A study showed that even the appropriately treated sewerages are capable of negative ramifications on the self-purification capacities of water reservoirs1. The serious ramifications on human health due to deficient wastewater treatment is underlined by the United Nation: globally, 2 million tons of sewage plus industrial as well as horticultural wastes are released into the world’s waterways. At any time, 1.8 million children under five years of age passed away each year from water-related sicknesses. People who died as a consequence of polluted water exceeded those who perished by all kinds of violence, including wars. A report from the American Academy of Microbiology shows the built-up of global complacency on wastewater treatment could be hazardous; causing widespread sickness every year 16. Therefore, there is an intense need for checking the water quality, the assessment for the presence of infectious bacteria in the water that is harmful to humanand animal wellness 17.

This study aims to preliminary assess the effect of STP effluent on surface water by studying the population of culturable gut bile-resistant bacteria in the surface water. Bile-resistant bacteria wereselectively grown on MacConkey agar and partially identified by Triple Suga Iron agar. If bile-resistant bacteria can be isolated from surface water downstream of STP, it is possible the bacteria were faecal-origin and from STP. However, the surface water is open to contamination by animal faeces. Thus, the population of bile-resistant bacteria that closely resembles the population from STP effluent, but different fromundisturbed water upstream of STP means that bacteria would likely originate from STP effluent rather than the environment.

Material and methods

Sampling

Samples were collected from the stream receiving effluent of Kolej 9 sewage treatment plant in UniversitiTeknologi Malaysia (UTM) Skudai, Johor Bahru (Figure 1).  The stream was sampled at three different points: (1) 5 m upstream from effluent outflow, which does not receive the effluent of STP to determine the presence of microbes in those water, which are not affected by STP effluent (2) effluent outflow, and (3) 5 m downstream of effluent outflow, which can receive the effluent of STP outflow as well as upstream to identify the impact of STP on surface water. Samples were collected by the grab method using a plastic scoop. The scoop was rinsed with water from the sampling site before sample-grab. Water samples were placed in a sterile screw-cap container. Then, the culturing of bacteria was carried out as soon as after sampling.

Vol18No1_Inf_Gul_fig1 Figure 1: Kolej 9 stream sampling sites.

Click here to view figure 

Detection of Bile Resistance Bacteria in Water Samples

Enumeration of bile resistant bacteria

Isolation plus enumeration of bile resistant bacteria was carried out using standard membrane filtration methods on MacConkey agar (MAC) (Figure 2). MAC agar was used because the incorporation of bile salts and crystal violet make the medium selective towards Gram-negative coliforms and Coccus (bacteria from the guts). The phenol red allows differentiation of lactose fermenting coliform from non-lactose fermenting coliforms. The 50 gr of MacConkey agar powder (Catalogue No: 1.05465.0500 & Brand: Mark KGaA) was dissolved in 1 litre of distilled water using a magnetic stirrer and was autoclaved for 15 minutes at 121°C. The medium was poured into the Petri dishes and solidified at room temperature 18. MacConkey agar plates were labelled with the sample number/identification as well as the sample volume to be analysed. Then, a sterile filter membrane (0.45 μm, Whatman) was placed on the porous plate filter housing (Nalgene) using flamed forceps. The funnel was then attached to the filter unit base 19. Next, 100 ml of the water sample was measured and poured into the funnel. The vacuum pump was switched on for the water sample to pass through the filter membrane. The filter membrane was picked by its side with flamed forceps, gently lifted, and placed face-up on a labelled MacConkey agar plate. To prevent trapping air bubbles between the underlying agar and the filter membrane, the filter was slide onto the agar using a rolling technique. Finally, the agar plate was inverted and incubated at 35°C for 22 to 24 hours. The next day, colonies grown on the filter were counted to find out the bacterial population in surface water. The final values of the colony-forming unit in the water sample were calculated using the following formula: 20

Vol18No1_Inf_Gul_eq1

For the diluted samples, the dilution factor was also included as the following equation.

Vol18No1_Inf_Gul_eq2

Vol18No1_Inf_Gul_fig2 Figure ‎2: Method of membrane filtration used for enumeration of bacteria from water samples [21].

Click here to view figure

Purification of bile resistant bacteria

After the enumeration of bacteria colony-forming units (CFU/100 ml) membrane filter plate, the colonies were differentiated visually according to shape and colour for initial identification. For this, plates with the countable number of colonies used for enumeration, were selected for streaking. Every colony of bacteria on the filter membrane from this countable plate was streaked onto fresh MacConkey agar plates for purification. Each streak plate was labelled and incubated for 22 to 24 hours at 35 °C.

Partial identification of bile resistant bacteria

Bile resistant bacteria growing on MacConkey agar were partially identified using triple sugar iron (TSI) agar (due to the limitation of time). The agar is commonly used to distinguish groups of Enterobacteriaceae, especially for intestinal pathogens based on the ability to ferment carbohydrates and reducing sulphur 22. TSI agar contains glucose, sucrose, and lactose in a concentration of 0.1%, 1%, and 1%, respectively. Phenol red (pH indicator) was used to detect carbohydrate fermentation, which is yellow when below the pH of 6.8. Therefore, the uninoculated medium (pH 7.6) is red from phenol red. In addition, the medium contains two indictors for detecting H2S formation, which are sodium thiosulfate and ferrous sulphate. Thus, it is a two steps process. The H2S is formed from sodium thiosulfate, in the first step. As H2S is a colourless gas, ferrous sulphate, a second indicator is required for visually detecting its production23 . The 65gr of TSI agar (Catalogue No: 1.03915.0500 & Brand: Merck KGaA) was dissolved in 1 litre of distilled water using a magnetic stirrer and was autoclaved for 15 minutes at 121°C. Then, the TSI agar was poured into the sterile universal tubes and was solidified to give agar slant 24.

Next, a small number of bacteria from the 24-hour streak plate was inoculated using the stab and streak inoculation method into the tubes with inoculating wire lope. Then, the tubes were incubated for 22 to 24 hours at 35 °C to identify the opportunistic pathogenic bile resistant bacteria in surface water.

Results

Bile resistant bacteria in surface water before and after receiving STP effluent

The culture was collected twice at the one-month interval (2nd of February and 2nd of March 2020). For the first time the samples, which were collected after rainfall, three different colony colours on MAC were detected after 22 hours to 24 hours incubation at 35°C. The colonies observed were: pink to red, yellow to white, and black(Figure 3).Also, there was less obvious presence of lactose fermenters (pink colonies) in the upstream, and more obvious presence in the downstream. The obvious presence of lactose fermenter colonies in downstream compared to upstream can be an impact of STP outflow, rich in lactose fermenters. Besides, the number of bacterial colonies in outflow was higher than upstream, which caused the downstream to have a high number of bacteria as well (Table 2).

Vol18No1_Inf_Gul_fig3 Figure ‎3: Bile-resistant bacteria on MacConkey agar from first sampling, after raining (2nd February 2020). From A to F, are bacterial isolated colonies on membrane filter using MacConkey agar plates with different dilution numbers. From G to I are streaking plates for purification of a bacterial colony. pH readings of upstream, outflow, and downstream were 6.31, 6.81, and 6.19 respectively.

Click here to view figure 

Sampling for the second time was done one-month later, (2nd March 2020). The second time sampling did not have black colonies in any of the samples(Figure 4). As the first-time sapling was done after raining and rainfall can accumulate microbes from the environment, and second time sampling was done when there was no rainfall, thus, rainfall can be the reason for the verity and presence of black colour colonies in the first-time water samples or possibly, the   differences between first- and second-time sampling could be caused by changes that happened within one month (interval of two times sampling). Regarding this, studies that monitor the impact of STP on stream showed microbial differences over different times. These studies showed microbes in the stream changed by wetter antecedent moisture conditions, environmental perturbation, physiochemical properties and toxicity of sewage, or hydraulic mixture 25, 26. The change of pattern was high microbial diversity occurring after rainfall, lower microbial diversity after precipitation, and increasing or even disappearing of microorganisms after mixing between upstream and outflow.

In general, culturable bile resistant bacteria in surface water that received STP effluent tend to have majority lactose fermenter. It resembles culturable bile resistant bacteria of STP outflow more than undisturbed upstream.

Vol18No1_Inf_Gul_fig4 Figure ‎4: Bile-resistant bacteria on MacConkey agar from second sampling when there was no rain (2nd March 2020). From A to F are bacterial isolated colonies on membrane filter using MacConkey agar plates with different dilution numbers. pH reading of upstream, outflow, and downstream were 6.33, 7.13, and 7.17 respectively.

Click here to view figure

Partial identification using triple sugar iron agar test

All isolates were partially identified using triple sugar iron (TSI) agar slants incubated for 22 hours to 24 hours at 35°C. Growth on TSI yields nine combinations of characters, (Table 1), and each combination of characters can be attributed to several types of Enterobacteriaceae. The number of times colonies with TSI combination of characters, which were found in enumerated plates, were recorded (Table 2) This qualitative assessment provides a general idea of the occurrence of the types of Enterobacteriaceae in the samples.

Vol18No1_Inf_Gul_tab1 Table ‎1: Triple sugar iron agar slant tubes after incubation with the TSI reactions: Acid reaction (A) = yellow colour, Alkaline reaction (K) = red colour, Hydrogen sulphide production (H2S) = black precipitate, Gas productions   (G) = bubbles, cracks or media displacement, No change (NC).

Click here to view table

Table 2: Possible ID and number of colonies of both time sampling using MacConkey and TSI agar, from upstream, outflow and downstream sampling.

Presumptive Bacteria ID Upstream Outflow Downstream
Number of colonies (10-3) Number of colonies (10-4) Number of colonies (10-2)
Citrobacter freundii 2&2 0&1 1 & 1
Citrobacter diversus 0&1 0&2 0&0
E. coli, E. aerogenes,
E. cloacae
3&36 7&64 64 &63
Aeromonas hydrophilia 1&3 38& 4 22 &0
Alcaligenes faecalis 6&1 5&1 1 &0
Serratia, vibrio cholera 4&13 15&10 11 &0
Pseudomonas aeruginosa,

pseudomonas putida

1&1 1&0 0&0
Klebsiella pneumonia 4&5 2&3 4 &1
Shigella dysenteriae, Shigella boydii, Shigella flexneri 1&4 0&0 0&0
Salmonella cholerasus, Morganilla morganii 0&2 12&2 0&0
Unknown 3& 3 1& 16 11& 0
No growth on TSI 2& 0 2 & 5 2 & 0
CFU/100 ml (Total) 3.0 x 103 [27]&7.9×103 [71] 92×104 [83]&120×104 [108] 1.29×102 [116]&72×104 [65]

Growth on TSI showed including some unknown bacteria, Enterobacteriaceae can be found in every sample not only STP outflow but in samples before and after receiving outflow. However, the Enterobacteriaceae population from the sample after STP effluent was introduced into the stream (downstream) was very different from upstream.Outflow from STP changed downstream population to favour 3 to 6 Enterobacteriaceae groups, even though both upstream and outflow almost always carried all 10 members of Enterobacteriaceae. The favoured groups in downstream are Escherichia and Enterobacter, Citrobacter, Klebsiella, Aeromonas, Alcaligenes, and Serratia and Vibrio. Of those favoured, Escherichia and Enterobacter, Citrobacter, and Klebsiella seemed to be a constant feature and Escherichia and Enterobacter as the dominant changes. The abundance of bacterial content (CFU/100 ml) downstream was affected by the entry of outflow into the stream. This is because bacterial count in upstream was3.0x103 CFU/100 ml, very far from the outflowcount,which was92x104 CFU/100 ml. On the other hand, bacterial counts downstream were consistently higher,1.29×102 CFU/100 ml.

Discussion

Sewage treatment processes are capable of decreasing the concentration of faecal pathogens 14, 27, 28. However, studies also showed the public health risk of streams impacted by STP effluent as metagenome analyses showed pathogenic bacteria can escape STP treatment processes 29–32. These metagenomic studies found nucleic acid indicators of pathogens such as Bacteroides HF183, Helicobacterspp, E. coli, Enterococci, and Acinetobacter baumannii. In addition, many metagenome studies also showed STP effluent changes the microbial landscape of streams 26, 33,35. And these studies explained changes from the perspective of microbial metabolism. It showed that under long term nutrient stress conditions, such as in wastewater treatment plants, microbial communities developed special metabolic patterns such as specific amino acid metabolism and membrane transporters to maintain optimal cellular activity. However, all of the studies had relied on metagenomic strategy. The very limited study assessed the impact of STP effluent on changes to culturable or living bacteria in the STP and the stream, including pathogenic strains. Isolation of living pathogenic bacteria provide realistic health risk assessment compared to the metagenome survey alone36. Therefore, there is a need to determine the actual presence of culturable bacteria in water impacted by STP effluent to assess any impending public health risk from pathogenic or potentially pathogenic strains. Findings from this study complete the big picture of microbial changes in a stream impacted by STP effluent revealed by metagenome studies and opened up an avenue to potential source-specific bacterial indicators.

In this study, STP effluent (outflow from Kolej 9 STP) was shown to cause the water of the receiving stream to have higher selected groups of Enterobacteriaceae. In addition, the number of bile resistance bacteria in the outflow of STP was higher than upstream, which indicates the presence of bacteria (opportunistic pathogenic bacteria) in the treated wastewater of kolej9 STP and can contribute to the water pollution. The effluent makes the Enterobacteriaceae population in the sample downstream of STP different from that upstream from STP. Particularly, Escherichia (E. coli), Enterobacter (E. aerogenes and E. cloacae), Citrobacter (C. freundii), and Klebsiella (K. pneumoniae) are favoured features. All these are known as opportunistic pathogens for humans. Unlike obligate pathogens, opportunistic pathogens cause infection to those who are immunocompromised either from diseases or poor diet 30.  For instance, E. coli is a gut organism. It causes infection of the intestine and causes diarrhoea when contaminated food or water is consumed 37. C. freundii is another intestinal inhabitant of humans, which can be found in environments such as water, sewage, soil, and food. C. freundii may sometimes acquire the ability to produce an enterotoxin, mostly causing abnormal inflammatory changes in the intestinal tract affecting biliary, urinary, and respiratory tracts, and blood of patients with the weak immune system 38. K pneumoniae is present as commensal in the nasopharynx and the intestinal tract. Occasionally, Klebsiella spp. causes human diseases, including asymptomatic colonization of the intestinal, urinary, or respiratory tract, and even fatal septicaemia 38,39. Apart from these favoured feature groups, the presence of Aeromonas hydrophilia and Serratia marcescens or Vibrio cholera, which were also detected in samples, are concerning as these bacteria. Similarly, these bacteria are opportunistic human pathogens. A. hydrophilia causes gastroenteritis, septicaemia, meningitis, and wound infections 39 whereas Serratia marcescens causes respiratory tract infection, urinary tract infection, pneumonia and meningitis 40. Vibrio cholera is responsible for intestinal infections of humans causing cholera worldwide when the bacteria-contaminated drinking water is consumed38.

Does isolation of the mentioned bile resistant bacteria (opportunistic pathogens) imply the health risk of surface water impacted by STP effluent? One of the main bacterial indicators of faecal contamination is Faecal Coliform E. coli. Studies have shown that gastrointestinal and respiratory diseases are linked to polluted waters with high numbers of indicator bacteria16, 41.  WHO suggested that Faecal Coliform must be less than 1000 cells/100 ml for harmless recycling of sewage treated effluents 42. The results of the current study showed the presence of opportunistic pathogenic bacteria in the samples taken from the stream impacted by STP and also showed that the total number of E. coli and Enterobacter in downstream is 63×104 CFU/100 ml.

The presence of gut organisms and opportunistic pathogens, such as E. coli, is proof of faecal contamination. However, studies showed E. coli as an indicator of faecal contamination could not tell the source of faecal either from humans or animals. This is because, these indicators are universal faecal indicators found in all mammals, not only humans[16]. Besides, sources of faecal pollution in water varies. For instance, it can be from the human sewage treatment plant, runoff from agriculture farms, or even wildlife that are normal inhabitants around water bodies. [16], [43]. Thus, the inability to differentiate the source of faecal would prevent effective control of faecal pollution. In this study, gut bacteria other than E. coli were also detected in downstream samples, which particularly received the effluent from Kolej9 (a residential place) STP. Partial identification by TSI suggested Enterobacter were very high. Thus, Enterobacter can be a candidate for a source-specific faecal indicator. Common featured bacteria, which are Citrobacter and Klebsiella, or the occasionally detected in high number Serratia, Vibrio, and Aeromonas could be considered as candidates as well.

To date, other studies that researched alternative of E. coli as faecal bacterial indicator had identified bacteria such as Clostridia, Bacteroides, Bifidobacter, enterococci as the possible source-specific faecal indicator 44, 45. However, these bacteria have problems, or their use is limited. There is considerable debate regarding the use of Clostridium perfringens as an indicator of water quality due to its persistence in the environment 46. On the other hand, the need to maintain anoxic conditions for cultivation, isolation, and biochemical identification limits the use of anaerobic Bacteroide species as a faecal indicator 45. Bifidobacterium tolerates some oxygen but is a fastidious bacterium that grows very slowly in culture media, and are the least studied of all faecal bacteria due to the technical difficulties in their isolation and cultivation 39. Several studies have identified difficulties to find media that can efficiently enumerate a wide variety of Enterococcus spp. Not sacrificing the specificity of the Enterococcus genus and the detection of enterococci isolates from environmental matrices (e.g. sediments, soil, sand, plants, plus water) remains challenging 47]. However, it is still too early to suggest bacteria as alternatives to E. coli but, as such bacteria cannot be definitive proof of faecal contamination. Results from current work open up the possibility of other possible source-specific faecal indicator candidates that can be further researched.

Furthermore, the characteristics of the ideal indicator organism are: 1. suitable for all types of water. 2. Present in greater quantities than pathogens. 3. Present in sewage 4. They should be at least as resistant as the pathogen to environmental threats and disinfection processes of wastewater treatment plants. 5. The indicator organism should be non-pathogenic. 6. Occur in large numbers in the intestine and faeces. 8. Simple, accurate, and cheap to observe and enumerate. Not multiplying outside the enteric environment is the desired character as well 39, 45, 47, 48.  A perfect organism with all the criteria does not exist. Even existing faecal coliform E. coli is having concern with replication in the environment 49. But studies that focused on the viable count of faecal or gallbladder bacteria from pig, human, and poultry sources, found the E. coli as the majority and most abundant across the different sources. Accompanying the E. coli, other bacteria such as Pseudomonas and Aeromonas were easily found in poultry 50, Enterobacter for humans[51], and Salmonella for pigs52. In this study, E. coli and Enterobacter were also found in favour of samples related to STP effluent, instead of the upstream sample without effluent. Perhaps a consortium of faecal bacteria, instead of a single type of coliform, is the way to go for source tracking.

Furthermore, the black colour colony of bacteria on MacConkey agar, found in first time sampling of the current study are the colony, which is not reported about in the previous studies thus, it can provide an avenue for other researchers to do further researches to find about the risk or usefulness of this black colour colony of bacteria on MacConkey agar.

Conclusion

The characteristics of treated sewage for discharge according to Malaysia Standard are low nutrient substances and organic materials. The coliform count is not included. Thus, it is not clear how Malaysia Standard comply STP effluent would affect bacterial diversity and health safety of surface water. This study showed that bile resistance bacteria were high in surface water that received STP effluent than upstream, which does not receive effluent from STP. In addition, STP effluent increased faecal-related Enterobacteriaceae in the surface water. The Enterobacteriaceae are also known to be opportunistic pathogenic bacteria. The presence of culturable opportunistic pathogenic bacteria could be a concern of public health risk. Besides, the detection of opportunistic pathogens in the wastewater of this research would facilitate decision-making for effective technology and management solutions to decrease microbial risks in receiving water bodies. Thus, further research and additional treatment are required to improve the treatment process and reduce the concentration of pathogens in treated sewage effluents. Additionally, this study found that STP effluents contain bile resistance bacteria associated with the human that can be suitable as a source-specific faecal indicator for human sewage.

Acknowledgement

This study was made possible through a post-graduate scholarship funded by the Ministry of Higher Education Afghanistan and Higher Education Development Program (HEDP), and Faculty of Science, UniversitiTeknologi Malaysia (UTM) for researchfacilities and amenities.

Conflict of Interest

No conflict of interest

Funding Source

No sources

References

  1. Babko, T. Kuzmina, Z. Suchorab, M. K. Widomski, and M. Franus, “Influence of Treated Sewage Discharge on the Benthos Ciliate Assembly in the Lowland River,” Ecol. Chem. Eng. S, vol. 23, no. 3, pp. 461–471, Sep. 2016, doi: 10.1515/eces-2016-0033.
    CrossRef
  2. R. Gedekar, M. T. Scholar, E.- Scientist, M. P. Bhorkar, P. K. Baitule, and M. T. Scholar, “Performance Evaluation of Sewage Treatment Plant ( STP ) – A Review,” Int. J. Sci. Technol. Eng. |, vol. 2, no. 07, pp. 2011–2013, 2016.
  3. N. Edokpay, J. O. Odiyo, and O. S. Durowoju, “Impact of Wastewater on Surface Water Quality in Developing Countries: A Case Study of South Africa,” in School of Enviromental Sciences, 2012, pp. 401–416.
  4. Y. Lim, S. L. Ong, and J. Hu, “Recent advances in the use of chemical markers for tracing wastewater contamination in aquatic environment: A review,” Water (Switzerland), vol. 9, no. 2, p. 26, 2017, doi: 10.3390/w9020143.
    CrossRef
  5. N. Wakode and S. U. Sayyad, “Performance Evaluation of 25MLD Sewage Treatment Plant ( STP ) at Kalyan,” Am. J. Eng. Res., vol. 03, no. 03, pp. 310–316, 2014.
    CrossRef
  6. A. S. Al-gheethi and N. Ismail, “Biodegradation of Pharmaceutical Wastes in Treated Sewage Effluents by Bacillus subtilis 1556WTNC,” Env. Process, vol. 1, pp. 459–481, 2014, doi: 10.1007/s40710-014-0034-6.
  7. Osuolale and A. Okoh, “Human enteric bacteria and viruses in five wastewater treatment plants in the Eastern Cape, South Africa,” J. Infect. Public Health, no. 676, p. 7, 2017, doi: 10.1016/j.jiph.2016.11.012.
    CrossRef
  8. Younis, H. A. Soleiman, and K. A. Elmagd, “Microbiological and Chemical Evaluation of Bentonite as a New Technique for Sewage Water Treatment, Aswan City , Egypt,” Seventh Int. Water Technol. Conf. Egypt 1-3, pp. 323–334, 2003.
  9. Pant and A. K. Mittal, “Monitoring of Pathogenicity of Effluents from the UASB Based Sewage Treatment Plant,” Env. Monit Assess, vol. 133, pp. 43–51, 2007, doi: 10.1007/s10661-006-9558-1.
    CrossRef
  10. Chaudhary, I. Kauser, and A. Ray, “Taxon-Driven Functional Shifts Associated with Storm Flow in,” Appl. Environ. Sci., vol. 3, no. 4, pp. e00194-18, 2018.
    CrossRef
  11. Garcia, N. Brion, and P. Servais, “Seasonal Variations and Resilience of Bacterial Communities in a Sewage Polluted Urban River,” PLoS One, vol. 9, no. 3, p. e92579, 2014, doi: 10.1371/journal.pone.0092579.
    CrossRef
  12. Clinton et al., “Sediment Microbial Diversity in Urban Piedmont North Carolina Watersheds Receiving Wastewater Input,” water, vol. 12, no. 6, p. 1557, 2020.
    CrossRef
  13. Dhakal and N. Roshan, “Microbiological quality of slaughterhouses and antibiotic susceptibility pattern of some isolates,” Science (80-. )., pp. 1–52, 2014, doi: 10.13140/RG.2.1.3819.3689.
  14. Hendricks and E. J. Pool, “The effectiveness of sewage treatment processes to remove faecal pathogens and antibiotic residues,” J. Environ. Sci. Heal. – Part A Toxic/Hazardous Subst. Environ. Eng., vol. 47, no. 2, pp. 289–297, 2012, doi: 10.1080/10934529.2012.637432.
    CrossRef
  15. Ivaylo, Y. Todorova, L. Kenderov, and Y. Topalova, “Assessment of Contamination With Opportunistic Pathogenic Bacteria From Family Enterobacteriaceae in Sediments of Iskar River,” Ecol. Eng. Environ. Prot., no. IX, pp. 47–55, 2017.
  16. M. Monsalvo, “Wastewater and Public Health: Bacterial and Pharmaceutical Exposures,” in Wastewater and Public Health, 2015, pp. 3–279.
    CrossRef
  17. Páll, M. Niculae, C. D. Şandru, and M. Spînu, “Human impact on the microbiological water quality of the rivers,” J. Med. Microbiol., vol. 62, no. PART 11, pp. 1635–1640, 2013, doi: 10.1099/jmm.0.055749-0.
    CrossRef
  18. Ji and Y. tin Wang, “Selenium reduction by a defined co-culture of Shigella fergusonii strain TB42616 and Pantoea vagans strain EWB32213-2,” Bioprocess Biosyst. Eng., vol. 42, no. 8, pp. 1343–1351, 2019, doi: 10.1007/s00449-019-02134-5.
    CrossRef
  19. Oshiro, “Method 1604: Total Coliforms and Escherichia coli in Water by Membrane Filtration Using a Simultaneous Detection Technique (MI Medium).,” United States Environ. Prot. Agency, p. Washington, DC 20460, 2002, [Online]. Available: http://www.epa.gov/nerlcwww/1604sp02.pdf.
  20. S. and T. U.S. EPA Office of Water, “Method 1604 : Total Coliforms and Escherichia coli in Water by Membrane Filtration Using a Simultaneous Detection Technique ( MI Medium ),” 2002, pp. 1–14.
  21. Kenneth, “Seasonal prevalence of faecal indicators and enteric pathogens in Suva lagoon,” 2011.
  22. Y. Aditi, S. S. Rahman, and M. Hossain, “A Study on the Microbiological Status of Mineral Drinking Water,” Open Microbiol. Journal, vol. 11, pp. 31–44, 2017, doi: 10.2174/1874285801711010031.
    CrossRef
  23. K. O. M. Paníková, Special Bacteriology Basic Laboratory Test. 2016.
  24. T. Aung and P. P. Oo, “Isolation and Characterization of Rhizobium From Root Nodules of Arachis Hypogaea L . ( Groundnut ),” J. Myanmar Acad. Arts Sci., vol. XVIII, no. 4, pp. 197–210, 2020.
  25. Tryland et al., “Impact of rainfall on microbial contamination of surface water,” Int. J. Clim. Chang. Strateg. Manag., vol. 3, no. 4, pp. 361–373, 2011.
    CrossRef
  26. Li, X. Jiang, J. Wang, K. Wang, and B. Zheng, “Effect of Sewage and Industrial Effluents on Bacterial and Archaeal Communities of Creek Sediments in the Taihu Basin,” water, vol. 9, no. 373, pp. 1–19, 2017, doi: 10.3390/w9060373.
    CrossRef
  27. Drury, E. Rosi-Marshall, and J. J. Kelly, “Wastewater Treatment Effluent Reduces the Abundance and Diversity of Benthic Bacterial Communities in Urban and Suburban Rivers,” Appl. Environ. Microbiol., vol. 79, no. 6, pp. 1897–1905, 2013, doi: 10.1128/aem.03527-12.
    CrossRef
  28. Henrique and O. Dias, “Bacteriophages as surrogates of viral pathogens in wastewater treatment processes,” 2016.
  29. Ahmed, S. Payyappat, M. Cassidy, and C. Besley, “Enhanced insights from human and animal host-associated molecular marker genes in a freshwater lake receiving wet weather overflows,” Sci. Rep., no. 9, pp. 1–13, 2019, doi: 10.1038/s41598-019-48682-4.
    CrossRef
  30. Cui and S. Liang, “Monitoring Opportunistic Pathogens in Domestic Wastewater from a Pilot-Scale Anaerobic Biofilm Reactor to Reuse in Agricultural Irrigation,” water, vol. 11, no. 1283, pp. 1–14, 2019.
    CrossRef
  31. Hembach, J. Alexander, C. Hiller, A. Wieland, and T. Schwartz, “Dissemination prevention of antibiotic resistant and facultative pathogenic bacteria by ultrafiltration and ozone treatment at an urban wastewater treatment plant,” Sci. Rep., no. 9, pp. 1–12, 2019, doi: 10.1038/s41598-019-49263-1.
    CrossRef
  32. Numberger et al., “Characterization of bacterial communities in wastewater with enhanced taxonomic resolution by full-length 16S rRNA sequencing,” Sci. Rep., vol. 9, pp. 1–14, 2019, doi: 10.1038/s41598-019-46015-z.
    CrossRef
  33. R. Price, S. H. Ledford, M. O. Ryan, L. Toran, and C. M. Sales, “Wastewater treatment plant effluent introduces recoverable shifts in microbial community composition in receiving streams,” Sci. Total Environ., vol. 613–614, pp. 1104–1116, 2018, doi: 10.1016/j.scitotenv.2017.09.162.
    CrossRef
  34. Yang, L. Wang, F. Xiang, L. Zhao, and Z. Qiao, “Activated Sludge Microbial Community and Treatment Performance of Wastewater Treatment Plants in Industrial and Municipal Zones,” Environ. Res. Public Heal., vol. 17, no. 436, pp. 1–15, 2020.
    CrossRef
  35. Michael et al., “Trace levels of sewage effluent are sufficient to increase class 1 integron prevalence in freshwater biofilms without changing the core community,” Water Res., vol. 106, pp. 163–170, 2016, doi: 10.1016/j.watres.2016.09.035.
    CrossRef
  36. Ben Maamar et al., “Mobilizable antibiotic resistance genes are present in dust microbial communities,” PLOS Pathog., pp. 1–21, 2020.
    CrossRef
  37. Makvana and L. R. Krilov, “Escherichia coli Infections,” Am. Acad. Pediatr., vol. 36, no. 4, pp. 167–171, 2015, doi: 10.1542/pir.36-4-167.
    CrossRef
  38. M. Amin, “Perspectives on Gastro-Intestinal Pathogenic Bacteria Infections in Humans,” EC Microbiol., vol. 8, no. 11, pp. 1173–1185, 2019.
  39. Cabral, “Water microbiology. Bacterial pathogens and water,” Int. J. Environ. Res. Public Health, vol. 7, no. 10, pp. 3657–3703, 2010, doi: 10.3390/ijerph7103657.
    CrossRef
  40. T. Rudhy, “Isolation , Identification and Molecular Characterization of Pathogenic Organisms Obtained from Meat samples ( Cooked , Semi- cooked and Raw ) of Different Areas of Dhaka City,” 2017.
  41. F. Arnold et al., “Original Contribution Acute Illness Among Surfers After Exposure to Seawater in Dry- and Wet-Weather Conditions,” Am J Epidemiol, vol. 186, no. 7, pp. 866–875, 2017, doi: 10.1093/aje/kwx019.
    CrossRef
  42. Jeong, H. Kim, and T. Jang, “Irrigation Water Quality Standards for Indirect Wastewater Reuse in Agriculture : A Contribution toward Sustainable Wastewater Reuse in South Korea,” WATER, vol. 8, no. 169, pp. 1–18, 2016, doi: 10.3390/w8040169.
    CrossRef
  43. W. S. Domingo and T. A. Edge, “Identification of primary sources of faecal pollution,” in Safe Management of Shellfish and HarvestWaters, 2010, pp. 52–80.
  44. Saeidi et al., “Occurrence of Traditional and Alternative Fecal Indicators in Tropical Urban Environments under Different Land Use,” Appl. Environ. Microbiol., vol. 84, no. 14, pp. 1–16, 2018.
    CrossRef
  45. Saxena, R. N. Bhargava, and A. Raj, “Microbial indicators , pathogens and methods for their monitoring in water environment,” J. Water Health, vol. 13, no. 2, pp. 319–339, 2015, doi: 10.2166/wh.2014.275.
    CrossRef
  46. M. Scott, J. B. Rose, T. M. Jenkins, and S. R. Farrah, “Microbial Source Tracking : Current Methodology and Future Directions †,” Appl. Environ. Microbiol., vol. 68, no. 12, pp. 5796–5803, 2002, doi: 10.1128/AEM.68.12.5796.
    CrossRef
  47. N. Byappanahalli et al., “Enterococci in the Environment,” Microbiol. Mol. Biol. Rev., vol. 76, no. 4, pp. 685–706, 2012, doi: 10.1128/MMBR.00023-12.
    CrossRef
  48. D. N. Myers, D. M. Stoeckel, R. N. Bushon, D. S. Francy, and A. M. G. Brady, “Fecal Indicator Bacteria,” in Biological Indicators, vol. 1, 2015, pp. 5–73.
  49. J. Horan, “Faecal indicator organisms,” in Handbook of Water and Wastewater Microbiology, Elsevier, 2003, pp. 105–112.
    CrossRef
  50. Ali, T. Molla, S. Mahmud, K. A. Talukder, and A. K. M. Mohiuddin, “PTC & B Therapeutic Potential of Plant Extracts Against Multidrug Resistance Poultry Bacteria,” Plant Tissue Cult. Biotechnol., vol. 30, no. 1, pp. 119–130, 2020.
    CrossRef
  51. D. Perry et al., “Prevalence of faecal carriage of Enterobacteriaceae with NDM-1 carbapenemase at military hospitals in Pakistan, and evaluation of two chromogenic media,” J. Antimicrob. Chemother., vol. 10, no. 66, pp. 2288–2294, 2011, doi: 10.1093/jac/dkr299.
    CrossRef
  52. Evangelopoulou, G. Filioussis, and S. Kritas, “Isolation and Antimicrobial Testing of Aeromonas spp ., Citrobacter spp ., Cronobacter spp ., Enterobacter spp ., Escherichia spp ., Klebsiella spp ., and Trabulsiella spp . from the Gall … Isolation and Antimicrobial Testing of Aeromonas spp ., Citroba,” Polish J. Microbiol., vol. 2, no. 64, pp. 185–188, 2017.
    CrossRef

Adaptation of Congo Red Agar Method and Microtiter Plate Assay to Study Biofilm Formation in Streptomyces

$
0
0

Introduction

A Biofilm isdescribed as an irreversible association of microbes  with  biotic or abiotic surfaces1. The elaboration of the extracellular polymeric matrix (EPS), also called slime, is a determining factor in the detection of biofilms. The molecular composition of this EPS is specific to the microbial species2. According to Flemming, living inside biofilm community is part of the life cycle of most if not all bacteria. This lifestyle provides microbial cells-for a while-  better survive in harsh environmental conditions3,4.In clinical settings, biofilms pose serious problems. They are commonly found on medical devices5,6also increases the resistance of  microbial species responsible of nosocomiale infections7. In industrial fields, biofilms are responsible of clogging, blockages, energy transfer inefficiency. They are also responsible of surfaces corrosion5,6.

In wasterwater treatements, biofilm reactors are more efficient than others involving the use of sessile microorganisms, also in biotechnology/bioconversion  field  for the production of chemical substance with economic values 8. Curently, the manipulation of the biofilm in biorectors is difficult and poorly studied. However, they are used  to respect economics and space-time yield 9,10.

Streptomyces, the representative genus in the phylum Actinobacteria, having the ability to produce valuable molecules and enzymes. Exploration of  biofilm formation in this genus could provide crucial informations for controlling production of secondary metabolites and efficiency in environmental process.

The most commonly used methods for detecting and quantifying biofilms in bacteria are crystal violet staining methods (including microtiter plate and tube methods), congo red agar method, EPS assay, hydrophobic interaction chromatography assay, contact-angle measurements, bacterial adhesion to hydrocarbon assay, autoaggregation method and scanning electron microscopy, atomic force microscopy, confocal scanning microscopy…etc 11–16.

There is always an urgent need for easy and reliable methods as alternative to expensive technics. This research aims to test an easy and low-cost approach for screening of Streptomyces with ability to form biofilm for further purposes.

In the present study, two methods are tested with slight modification for screening biofilm formation ability. EightStreptomyces strains are tested. A modified congo red agar method for qualitative investigation, inspired from freeman’s method which is used for the first time for Streptomyces species and crystal violet assay for quantification of biofilm formed.

Materialand  Methods

Growth conditions of Streptomyces strains

Streptomyces strains arebelonging to bioprocess and bio-interfaces laboratory are used in this study17. Strains arecultivated on Bennett liquid medium for further tests. Spore stock is obtained by cultivating Streptomyces strains on agar Bennett for 10 days.

Testing Biofilm formation ability

Microtiteration plates Assay

Biofilms were cultivated in microtiter plates following the modified method proposed by Stepanović18. In Brief, tests are carried out in micotiter plate 96-well flat bottom (polystyrene, orange scientific, France). Streptomyces strains are cultivated over-night in Bennett liquid media in erlemeyer flask at 28°C. Then optical density of the bacterial was measured at 600 nm wavelenght and the concentration adjusted to 0.02-0.03. Each well was filled in aseptic conditions with a volume of 200µl of cell suspension. Plates were incubated in 28°C. The wells has been filled after 24h and rinsed by 200 µl of sterile water three times in order to eliminate non adhered bacteria. After washing, the remaining adhered biomass was heat-fixed through exposition to hot air at 80°C for 30 min. Biofilm formation was evaluated at 1day, 2days, 3days, 4 days, 5days, 6 days and 7days. Wells filled only with liquid Bennett medium without bacterial cells are the negative control.

The attached biomass has been stained for 5 min by 200 µl per well of cristal violet (5%). Stain surplus was evacuated under tap water18. Then plates were dried, the cristal violet bound to the biomass was resolubilized using  200 µl per well of (ethanol/ acetone) solution (80/20) (v/v). The OD of the obtained solutions were measured at 595 nm using a microtiter plates reader. The OD595 values reflects the amount of attached biomass to the microtiter plate wells. Experiments were replicated three times.

The OD ranges were evaluated for all strains and controls. The cut off values (ODc) was calculated. It is characterized as the sum OD control and three standard deviations (SD).

ODcut off = range OD of negative control + (3SD of negative control).

For each microtiter plate, ODcut off value was calculated inedependentely.

Results are interpretated and strains were divided to four categories:

OD ≤ ODcut off   indicates Non biofilm forming (0).

ODcut off <OD ≤ 2xODcut off    indicates Weak biofilm producer (* or 1).

2xODcutoff<OD ≤ 4xODcut off   indicates Moderate biofilm producer (** or 2).

4xODcut off <OD indicates   Strong biofilm producer (*** or 3).

Adaptedcongo red method

Two phenotypic methods were tested to study biofilm formation by Streptomycesstrains. The qualitative study based on Congo red agar method,while the a quantitative oneused in this investigation is microtiter plate assay.

This test was performed following Freeman’s method with moderate adjustment16. The growth  medium contain 10g/l of D-Glucose, 2g/l yeast extract, 1g/l meat extract, 2g/l peptone, 50 g/l sucrose, 10 g/l bacteriological agar and 1ml of autaclaved congo red (0.8g/l). Plates were inoculated and incubated at 37 °C.Then, the color of colonies was observed at 1 day, 2 days, 3 days, 4days, 5 days, 6 days, 7 days, 8 days, 9 days, and 10 days.19. Interactions of congo red with metabolites form complex product wich give darkness to colonies.  Black colonies are categorized as biofilm-producers, whereas red colonies are considered as non-biofilm-formingbacteria. Tests are repeated 3 times.

Results and Discussion

Biofilm formation by streptomyces in microtiter-plates devices:

Figure 1 shows the quantification of biofilm formation for 7 days. Biofilm strength is reflected by the average of crystal violet optical density. As described in the methods part biomass attached to microtiter plate wells is stained using crystal violet. After dissolution using ethanol/acetone mixture crystal violet contained in the cells is found then in the alcoholic mixture. The optical density of this mixture measured 595nm reflect the amount of biomass adhered to well walls. Based on Stepanović classification 18 which refers to a comparison between Doc and DOtest it is possible to describe biofilm formation as strong, moderate, weak, or none.

Vol18No1_Ada_Rab_fig1 Figure 1: Quantification and following of biofilm formation Streptomyces strainsusing microtiter plate method.

Click here to view figure

Streptomycesstrains (A3, A4, A10, A14, A15 and A23) areclassified as weak biofilm-forming strain, even after 7 days of incubation. Biofilms of those Streptomyces remain weak throughout the follow-up. On the other hand, Streptomyces bellus A43 and Streptomyces bellus A61 can form strong biofilm from 3 days of incubation. The evolution of the intensity of formed biofilms changes from weak to strong at 7 days. Streptomyces bellus A43 and Streptomyces bellus A61 are able to establish a strong attachment to wells. Colonization and kinetics of biofilm formation mainly depend on surface properties of both of Streptomyces and microtiter plate walls.

The measured density of the crystal violet reflects, in reality, the attached biomass to the wall which is composed of cells and extracellular matrix. Crystal violet is found in the microtiter plate wells after dissolution by ethanol/acetone. The high optical density of the mixture indicates a strong amount of adhered cells to the wells. According to the literature, few studies are conducted on the Streptomyces  biofilm. In 2014, Winn characterized biofilm formation in the Streptomyces griseus ATCC 13273 cultivated in a tubular reactor; their results does not reveal any fixation of this strain in wells of polystyrene microtiter plates, they showed that the growth of this strain occurs at the liquid-air interface 20Streptomyces biofilm formed in tubular reactor undergoes detachment at 24 h. According to Winn and collaborators, biofilm formation is a cyclical phenomenon; each attachment is followed by a detachment. These results 20 does not comply with ours which shows an increase of biofilm intensity by time incubation 20. However, Streptomyces was able to form stable biofilms during co-culture with Bacillus biofilm-forming 20 and Lactobacillus 21. This mixed culture lead to the formation of stable biofilms in bioreactors. 

Ability of Streptomyces griseorubens and Streptomyces bellus strains to produce extracellular matrix (Slime).

Our tests are based on Freeman’s method CRA established on the differenceof colony color to distinguish between slime negative andslime positive bacteria  (freeeman et al., 1989). The obtained results of the congo red test are presented in Table (1); as indicated the strains Streptomyces griseorubens A3, Streptomyces bellus A7, Streptomyces bellus A10, Streptomyces griseorubens A14, Streptomyces griseorubens A15 and Streptomyces griseorubens A23 showed a red colonies at 2 days of incubation (48h) which indicates the absence of slime production. While Streptomyces bellus A43 and Streptomyces bellus A61 reveal black colonies denoted consequently the presence of slime production.

Following colony color evolution for 10 days leads to observe changes in slime production over time. Red colonies which appear in slime negative strains undergo a progressive modification, it varies from red to grey (Streptomyces griseorubens A3, Streptomyces bellus A7 and Streptomyces bellus A10 see Figure 2,3 and 4  ) or from red to white Streptomyces griseorubens A14and Streptomyces griseorubens A23(Figure 5 and 7).}

Vol18No1_Ada_Rab_fig2 Figure 2 : Observation of colony color of Streptomyces griseorubens A3 onto modified CRA plate. U : Streptomyces griseorubens A3at 2 days;  V: Streptomyces griseorubens A3at 4 days;  W: Streptomycesgriseorubens A3at 6days; X: Streptomyces griseorubens A3at 8 days.

Click here to view figure

 

Vol18No1_Ada_Rab_fig3 Figure 3: Observation of colony color of Streptomyces bellus A7 onto modified CRA plate. Q : Streptomyces bellus A7at 2 days;  R: Streptomyces bellus A61 at 4 days;  R: Streptomyces bellus A7at 6days; T: Streptomyces bellus A7at 8 days.

Click here to view figure

 

Vol18No1_Ada_Rab_fig4 Figure 4: Observation of colony color of Streptomyces bellus A10 onto modified  CRA plate. M : Streptomyces bellus A10at 2 days;  N: Streptomyces bellus A10 at 4 days;  O: Streptomyces bellus A10at 6days; P: Streptomyces bellus A10at 8 days.

Click here to view figure

As known the genus Streptomyces has a complex developmental cycle including physiological and structural modification, spores germinate and give rise to vegetative hypha. The later is characterized by theability to anchor in the growth medium.A typical variation in cell surface properties occurs during the differenciation from vegetative growth to aerial growth. Aerial hypha risen during the aerial phase had typic color, it depends on the strain22–24 The observed color innon slime forming strainsand their derivates are due to the interactions of congo red with aerial hypha. Furthermore, these phenotypes observed in our case does not exist in Freeman’s  or Arciola’s statements.

Vol18No1_Ada_Rab_fig5 Figure 5: Observation of colony color of Streptomyces griseorubens A14 onto modified CRA plate. E’ : Streptomyces griseorubens A14at 2 days;  F’: Streptomyces griseorubens A14 at4 days;  G’: Streptomyces griseorubens A14at 6days; H’: Streptomyces griseorubens A14at 8 days.

Click here to view figure

 

Vol18No1_Ada_Rab_fig6 Figure 6: Observation of colony color of Streptomyces griseorubens A15 onto modified CRA plate. A’: Streptomyces griseorubens A15at 2 days; B’: Streptomyces griseorubens A15 at 4 days; C’: Streptomyces griseorubens A15at 6days; D’: Streptomyces griseorubens A15at 8 days.

Click here to view figure

 

Vol18No1_Ada_Rab_fig7 Figure 7: Observation of colony color of Streptomyces griseorubens A23 onto modified CRA plate. A : Streptomyces griseorubens at 2 days;  B: Streptomyces griseorubens at 4 days;  C: Streptomyces griseorubens at 6days; D: Streptomyces griseorubens at 8 days.

Click here to view figure

We note that Streptomyces bellus A43 and Streptomyces bellus A61 (Table 1, figure 8 and figure 9) at 2 days are considered as slime positive. Following the incubation, at 10 days it is showed that the black color changes to brown at 5days and became red at 9 days. It is suggested that slime formed at 2 days is used by those strains as a source of nutrients. The polysaccharides which interact with the congo red and give the dark color are probably metabolized.

Table 1: Phenotypic characterization and follow of slime production in  Streptomyces griseorubens and  Streptomyces bellus strains by modified   congo red agar plate during 10days.

1day 2 day 3 day 4 day 5 day 6 day 7 day 8 day 9 day 10 day
A3 No growth Red red red pink white white grey grey grey
A7 No growth Red red pink pink white white grey grey grey
A10 No growth Red red red white white white white white grey
A14 No growth Red red red white white white white white white
A15 No growth  Red red red pink pink pink pink grey grey
A23 No growth  Red red red pink pink pink pink grey grey
A43 No growth Black black black brown brown brown brown red red
A61 No growth Black black brown brown brown brown red  red  red

 

Vol18No1_Ada_Rab_fig8 Figure 8: Observation of colony color of Streptomyces bellus A43 onto modified  CRA plate. A : Streptomyces bellus A43 at 2 days;  B: Streptomyces bellus A43 at 4 days;  C: Streptomyces bellus A43 at 6days; D: Streptomyces bellus A43 at 8 days.

Click here to view figure

 

Vol18No1_Ada_Rab_fig9 Figure 9: Observation of colony color of Streptomyces bellus A61 onto modified CRA plate. A : Streptomyces bellus A61 at 2 days;  B: Streptomyces bellus A61 at 4 days;  C: Streptomyces bellus A61 at 6days; D: Streptomyces bellus A61 at 8 days.

Click here to view figure

To our knowledge, there is no qualitative study elaborated based on congo red dye to describe biofilm formation in Streptomyces. However, several studies used congo red agar method to detect slime production  for  Staphylococcusstrains 2,25,26. The TSB broth supplemented with sucrose was most  accurate medium for slime detection27. Mateo and collaborators investigated slime production in Staphylococcus epidermidis using congo red agar method 28. Although, the multitude of studies in slime detection, the mechanism by which interact congo red with EPS matrix and give black coloration still unknown16,29

The present study shows the effect of time incubation on slime production.No studies have been carried out on this parameter in Streptomyces or other bacteria. Following the incubation, at 10 days extracellular polysaccharides are probably metabolized. Our results demonstrate that slime production can be altered during incubationperiod; time is an important factor in the description of slime production. It is also proved that color of aerial hypha influence the interpretation since color indicated by freeman don’t includes those obtained in our case.

The complex developmental cycle of Streptomyces start with spore germination then development of aerial hypha and spores are formed and start a new lifecycle. Cell changes on cell Streptomyces surfaces are characterized by Atomic force microscopy, it was demonstrated that vegetative cell produce extracellular matrix, while aerial hypha are characterized by the absence of extracellular matrix 30. Treatments of Streptomyces pellets by DNase and protease demonstrate that extracellular matrix is mainly proteins20. The later suggestion is in agreement with Petrus investigation which approved the presence of rodlet layer composed mainly of proteins assembly31. The extracellular layer exist during aerial growth, its major role is to mediate the attachment to hydrophobic surfaces.

Many modified methods are proposed to detect biofilm formation through slime production. This modification occurs in medium composition 2,32. It is shown that accuracy is dependent on the growth medium. In Staphylococcus, it was found that accuracy is generally confirmed by analysis of intercellular aggregation locus (icaAB genes) 33.

Conclusion

To study biofilms in Streptomyces, slight modifications are performed to the congo red method and microtiter plate assay.We concluded that, the modified congo red agar method could be used to follow extracellular matrix production in Streptomyces just for 3 days, because the lifecycle of Streptomyces is complex and the color of aerial hypha interfer with the phenotypic characters. The aerial mycelia reveal a color charactristic to the Streptomyces strain. Theses colors that appears during aerial growth does not exist in the average proposed by Arciola or Freeman. Streptomyces bellus A43 and Streptomyces bellus A61are able to form strong biofilms from 3 days and the biofilm intensity increases over time. Streptomyces griseorubens A3, Streptomyces bellus A7, Streptomyces bellus A10, Streptomyces griseorubens A14, Streptomyces griseorubens A15 and Streptomyces griseorubens A23cannot form stable biofilm even after 7 days of incubation. The monitoring of biofilm formation by microtiter plate assayindicate that biofilm forming strains slime positive strains have the ability to establish strong biofilm at 3 days. The results obtained by congo red agar method are confirmed by microtiter plate assay. The combination of these easy methods could be an alternative to the complicated technics.

Acknowledgement

The authors recognize the help of the national center of scientific and technical research Rabat Morocco which provided us a scholarship.

Conflict of interest

The authors have no conflict of interest.

Funding Source

No financial support has been provided for this work.

References

  1. O’Toole, G., Kaplan, H. B. & Kolter, R. Biofilm Formation as Microbial Development. Annu. Rev. Microbiol.54, 49–79 (2000).
    CrossRef
  2. Arciola, C. R. et al. Detection of slime production by means of an optimised Congo red agar plate test based on a colourimetric scale in Staphylococcus epidermidis clinical isolates genotyped for ica locus. Biomaterials23, 4233–4239 (2002).
    CrossRef
  3. Flemming, H. & Wingender, J. The biofilm matrix. Publ. Gr.8, 623–633 (2010).
    CrossRef
  4. Kokare, C. R., Chakraborty, S., Khopade, A. N. & Mahadik, K. R. Biofilm : Importance and applications. 8, 159–168 (2009).
  5. Hall-Stoodley, L., Costerton, J. W. & Stoodley, P. Bacterial biofilms: From the natural environment to infectious diseases. Nature Reviews Microbiology2, 95–108 (2004).
    CrossRef
  6. Flemming, H. C. Biofouling in water systems – Cases, causes and countermeasures. Applied Microbiology and Biotechnology59, 629–640 (2002).
    CrossRef
  7. Muffler, K., Lakatos, M., Schlegel, C., Strieth, D. & Kuhne, S. Application of Biofilm Bioreactors in White Biotechnology. (2013). doi:10.1007/10
    CrossRef
  8. Qureshi, N., Annous, B. A., Ezeji, T. C., Karcher, P. & Maddox, I. S. Biofilm reactors for industrial bioconversion processes : employing potential of enhanced reaction rates. 21, 1–21 (2005).
  9. Conroy, J. & Couturier, M. Dissolution of minerals during hydrolysis of fish waste solids. Aquaculture298, 220–225 (2010).
    CrossRef
  10. Asri, M., Elabed, S., Ibnsouda Koraichi, S. & El Ghachtouli, N. Biofilm-Based Systems for Industrial Wastewater Treatment. in Handbook of Environmental Materials Management 1–21 (Springer International Publishing, 2018). doi:10.1007/978-3-319-58538-3_137-1
    CrossRef
  11. Taj, Y., Essa, F., Aziz, F. & Kazmi, S. U. Original Article Study on biofilm-forming properties of clinical isolates of Staphylococcus aureus. J Infect Dev Ctries5, :403-409 (2012).
    CrossRef
  12. Wright, C. J., Shah, M. K., Powell, L. C. & Armstrong, I. Application of AFM from microbial cell to biofilm. Scanning32, 134–149 (2010).
    CrossRef
  13. Faust, J., Ba, M., Follo, M. & Wolkewitz, M. Biofilm formation and composition on different implant materials in vivo. 101–109 (2010). doi:10.1002/jbm.b.31688
    CrossRef
  14. Priester, J. H. et al. Enhanced visualization of microbial biofilms by staining and environmental scanning electron microscopy. Microbiol. Methods68, 577–587 (2007).
    CrossRef
  15. Christensen, G. D., Simpson, W. A., Bisno, A. L. & Beachey, E. H. Adherence of slime-producing strains of Staphylococcus epidermidis to smooth surfaces. Immun.37, 318–326 (1982).
    CrossRef
  16. Freeman, D. J., Falkiner, F. R. & Patrick, S. New method for detecting slime production by coagulase negative staphylococci. 872–874 (1989).
    CrossRef
  17. Zanane, C., Latrache, H., Elfazazi, K., Zahir, H. & Ellouali, M. Isolation of actinomycetes from different soils of Beni Amir Morocco. Mater. Environ. Sci9, 2994–3000 (2018).
  18. Stepanović, S., Vuković, D., Dakić, I., Savić, B. & Švabić-Vlahović, M. A modified microtiter-plate test for quantification of staphylococcal biofilm formation. Microbiol. Methods40, 175–179 (2000).
    CrossRef
  19. Arciola, C. R. & Baldassarri, L. Presence of icaA and icaD Genes and Slime Production in a Collection of Staphylococcal Strains from Catheter-Associated Infections. 39, 2151–2156 (2001).
    CrossRef
  20. Winn, M., Casey, E., Habimana, O. & Murphy, C. D. Characteristics of Streptomyces griseus biofilms in continuous flow tubular reactors. FEMS Microbiol Lett.352, 157–164 (2014).
    CrossRef
  21. Demirci, A., Pometto, A. L. & Johnson, K. E. Evaluation of biofilm reactor solid support for mixed-culture lactic acid production. Microbiol. Biotechnol. (1993). doi:10.1007/BF00167135
    CrossRef
  22. Chater, K. F. Genetics of Differentiation in Streptomyces. Rev. Microbiol.47, 685–711 (1993).
    CrossRef
  23. Mendez, C., Brana, A. F., Manzanal, M. B. & Hardisson, C. Role of substrate mycelium in colony development in Streptomyces. J. Microbiol.31, 446–450 (1985).
    CrossRef
  24. Hopwood, D. A. Streptomyces in Nature and Medicine. Oxford university press1, (2015).
    CrossRef
  25. Kaiser, T. D. L. et al. Modification of the Congo red agar method to detect biofilm production by Staphylococcus epidermidis. Microbiol. Infect. Dis.75, 235–239 (2013).
    CrossRef
  26. Croes, S. et al. Staphylococcus aureus biofilm formation at the physiologic glucose concentration depends on the S. aureus lineage. BMC Microbiol.9, 1–9 (2009).
    CrossRef
  27. Lee, J., Bae, Y. & Lee, S. Development of Congo red broth method for the detection of biofilm-forming or slime-producing Staphylococcus sp. LWT – Food Sci. Technol. (2016). doi:10.1016/j.lwt.2016.03.023
    CrossRef
  28. Mateo, M., Maestre, J. R., Aguilar, L. & Giménez, M. J. Strong slime production is a marker of clinical significance in Staphylococcus epidermidis isolated from intravascular catheters. 311–314 (2008). doi:10.1007/s10096-007-0433-y
    CrossRef
  29. Dadawala, A. I. et al. Assessment of Escherichia coli isolates for in vitro biofilm production. World3, 364–366 (2010).
  30. Del Sol, R., Armstrong, I., Wright, C. & Dyson, P. Characterization of changes to the cell surface during the life cycle of Streptomyces coelicolor. Atomic force microscopy of living cells. Bacteriol.189, 2219–2225 (2007).
    CrossRef
  31. Petrus, M. L. C. & Claessen, D. Pivotal roles for Streptomyces cell surface polymers in morphological differentiation, attachment and mycelial architecture. Antonie van Leeuwenhoek, Int. J. Gen. Mol. Microbiol.106, 127–139 (2014).
    CrossRef
  32. Mariana, N. S., Salman, S. A., Neela, V. & Zamberi, S. Evaluation of modified Congo red agar for detection of biofilm produced by clinical isolates of methicillin – resistance Staphylococcus aureus. 3, 330–338 (2009).
  33. Dias, T. et al. Modi fi cation of the Congo red agar method to detect bio fi lm production by Staphylococcus epidermidis ☆. 75, 235–239 (2013).
    CrossRef

Structural Analysis of the Polymerase Protein for Multiepitopes Vaccine Prediction against Hepatitis B Virus

$
0
0

Introduction

Hepatitis B virus (HBV) is spherical enveloped double stranded DNA virus (dsDNA), that belongs to the genus Orthohepadnavirusa part of the Hepadnaviridae family [Francki et al., 2012]. Hepatitis caused by HBV can be acute or chronic resulting inhepatocellular carcinoma and liver cirrhosis [Beasley et al., 1981]. It has also been suggested that this virus might cause pancreatitis[Shepard et al., 2006]. HBV is the main cause of viral hepatitis worldwide with chronic carriers exceeding 240 million[Ott et al., 2012]. It is estimated that 1 million of United States citizen at risk of the viral infection specially individuals with sexual behaviors, drug users, healthcare workers, during organ transplantation, individuals with frequent blood transfusions, newborns during parturition and patients with kidney dialysis[Ott et al., 2012]. In addition to that,780,000 people were succumbed to death due to hepatitis B infection worldwide. The most regions that are endemic with HBV are East Asia and sub-Sahara of Africa. In these regions more that 10% of adults were considered as chronic carriers [Yousif et al., 2013]. In developed countries this figure significantly reduced, includingonly less than 1% of the population [Yousif et al., 2013].

Sudan is one of the countries with high sero-prevalence of HBV with 47%–78%people exposed to Hepatitis B surface Antigen (HBsAg). The prevalence varied from about 6.8% to 26% in Central and South Sudan, respectively [Schattner, 2005]. Studies demonstrated that the infection was mainly concentrated in Southern Sudan in early childhood, while the rate of infection was highin Northern Sudanbased on increasing patient’sage. As HBV was recorded asthe main cause of hepatocellular carcinoma and chronic liver disease, in Sudan the disease was recorded as the second main etiology of acute liver failure [Rehermann et al., 1995; Mudawi, 2008].

Few studies demonstrated the prevalence and the risk factors associated with hepatitis B infection in rural Sudan [Rehermann et al., 1995; Mudawi, 2008]. The studies demonstrated that the prevalence of the HBsAg was highest in patients less than five years of age (32.3%). Further more Hepatitis B e Antigen (HBeAg) was recorded in 70% of the pregnant women with HBsAg-positive. The prevalence of the infection in residence with parenteral Malaria therapy was found to be independent. However the studies showed that age, crowding and tattooing were predictive of sero-positivity for hepatitis marker [Mudawi, 2008].

HBV contains many antigens like M glycoprotein, L glycoprotein, S glycoprotein and DNA polymerase protein. Multiple studies showed that DNA polymerase protein is the best protein with immune response against  both B and T cells of the immune system [Mudawi, 2008; Bekele et al., 2015; Parkin and Cohen, 2001; Percus et al., 1993].  Polymerase protein is considered as the only immunogene of Hepatitis B virus that induced immune response. Polymerase‐specific cells were present in peripheral blood mono nuclear cells (PBMCs) from the different HBV patients [Mudawi, 2008; Bekele et al., 2015]. Moreover the polymerase includes three domains that differ from each other such asreverse transcriptase (RT), terminal protein (TP) and RNase H. The humoral immune responses related to these proteins is not well clarified, despitethe antibody topolymerase was shown to bepresent in the serum of patients with chronic hepatitis Base [Enshell-Seijffers et al., 2003].

Vaccination against HBV is the effective way to combat the HBV infection. However HBV vaccination such as subunit vaccine-HBsAg demonstrated multiple drawbacks. For instance, three doses are required to achieve full effective course of vaccination. This resulted in difficulties to achieve because of the difficult logistic conditions in some areas and the poor compliance of the patients. In addition to that, there is a comparably high rate, about 5% in adults, considered asnon-responders to the vaccine [Enshell-Seijffers et al., 2003]. Finally, the possibility of  some strains of HBV that demonstrate mutations in HBsAg could escape the immunity induced by the present vaccines [Enshell-Seijffers et al., 2003]. Most importantly the association between multiple sclerosis and the recombinant hepatitis B vaccine was become prominent. There was a clear association between hepatitis B recombinant vaccine and the development of the multiple sclerosis in adults that requires multiple precautions [Schattner, 2005]. The vaccination against hepatitis B post liver transplantation in case of hepatitis B–related liver disease was analyzed as an alternative strategy. However this strategy ofreinfection against prophylaxis of hepatitis B immunoglobulin (HBIG) provided conflicting results. In the majority of the studies, HBIG remedy was not continued before vaccination [Potocnakova et al., 2016]. A good significant response via vaccination was achieved under the continuous HBIG injection using hepatitis B surface antigen (HBsAg)‐based vaccine with special adjuvants. The special adjuvants and the continuous HBIG injections were extensively discussed as important factors to enhance good response. However the conventional HBsAg,despite the continued HBIG treatment, the vaccine was potentially incapable in inducing a significant humoral immune response in most treated patients [Potocnakova et al., 2016]. The prepared DNA vaccine to treat hepatitis B virus showed no response or non-sustainable responses compared to the hepatitis B conventional vaccine. Despite this vaccine was shown to be safe with remarkable tolerance and elicited antibodies responses in the vaccinated subjects, failed to induce long lasting immunity[Frikha-Gargouri et al., 2008].

Thus in this regard the need for an efficient, safe vaccine free from the future complications is required. Thus this study attempted to exploit the immunoinformatics approaches to predict epitopes from DNA polymerase protein that provoke the human immune system and to work as safe and effective vaccine against HBV.

Material and Methods

The polymerase protein sequences retrieval

A total of 148Hepatitis B polymerase sequences wereobtained and downloadedfrom NCBI database at (http://www.ncbi.nlm.nih.gov/ protein/polymerase). These quences of the strains were from different countries. The retrieved strains accession numbers, country and the year of collection were presented in table (1).

Table 1: Hepatitis B polymerase retrieved strains with their accession numbers, country and area of collection.

Accession No Year Country Accession No Year Country Accession No Year Country
YP_009173866* NA USA ACF95205.1 NA Siberia AFB76718.1 1998 Canada
ABD36968 NA Eastern India AGB97500.1 1994 New Zealand AFB76714.1 1998 Canada
AJR19220 2003 Brazil AGB97371.1 1994 New Zealand AFB76726.1 1998 Canada
AJR19215 2003 Brazil AGB97364.1 1994 New Zealand AFB76722.1 1998 Canada
ALS87644 2007 South Africans AGB97322.1 1994 New Zealand AFB76734.1 1998 Canada
AJR19235 2003 Brazil AGB97315.1 1994 New Zealand AFB76730.1 1998 Canada
AIZ95362 NA South Africans AGB97540.1 1994 New Zealand AEK67261.1 2006 Iran
AIJ49989 2005 South Africans AGB97506.1 1994 New Zealand AEK67288. 2006 Iran
AJR19230 2003 Brazil AGB97496.1 1994 New Zealand AFQ36956.1 2005 Latvia”
ACF95157 NA Siberia AGB97405.1 1994 New Zealand AIG21752.1 2007 Belgium
ACF95157 NA Siberia AGB97308.1 1994 New Zealand AIG21738.1 2007 Belgium
ACF95157 NA Siberia AGB97506.1 1994 New Zealand AEK67268.1 2006 Iran
AJR19230 2003 Brazil AGB97357.1 1994 New Zealand AIG21684.1 2007 Belgium
AIW81563 2009 Argentina: AGB97350.1 1994 New Zealand AGB97429.1 1994 New Zealand
ACF95275 NA Siberia AGB97442.1 1994 New Zealand AGB97329.1 1994 New Zealand
ACF95293 NA Siberia AGB97462.1 1994 New Zealand AIG21651.1 2007 Belgium
ACF95257 NA Siberia AGB97455.1 1994 New Zealand AIG21664.1 2007 Belgium
ACF95224 NA Siberia AGB97564.1 1994 New Zealand AIG21675.1 2007 Belgium
ACF95305 NA Siberia AGB97526.1 1994 New Zealand ADB03542.1 2005 Indonesia
ACF95118. NA Siberia AGB97435.1 1994 New Zealand AEK66943.1 2006 Iran
ACF95251 NA Siberia AGB97378.1 1994 New Zealand AEK67300.1 2006 Iran
ACF95238 NA Siberia AGB97301.1 1994 New Zealand AEK67241.1 2006 Iran
ACF95268 NA Siberia AGB97448.1 1994 New Zealand AEK67248.1 2006 Iran
AGB97469.1 1994 New Zealand AGB97343.1 1994 New Zealand AFB76750.1 1998 Canada
AFQ36977.1 2005 Latvia AGB97336.1 1994 New Zealand AFB76746.1 1998 Canada
ACF95261.1 NA Siberia AGB97513.1 1994 New Zealand AGB97476.1 1994 New Zealand
ACF95245.1 NA Siberia AGB97557.1 1994 New Zealand ABD36978.1 NA India
AFQ36977.1 2005 Latvia” AGB97412.1 1994 New Zealand AGR65533.1 2008 Sudan
ACF95331.1 NA Siberia AGB97533.1 1994 New Zealand AGB97877.1 1994 New Zealand
ACF95325.1 NA Siberia AGB97489.1 1994 New Zealand AGB97613.1 1994 New Zealand
ACF95123 NA Russia AGB97520.1 1994 New Zealand AGB97640.1 1994 New Zealand
ACF95311.1 NA Siberia AGB97385.1 1994 New Zealand AGB97634.1 1994 New Zealand
ACF95279.1 NA Siberia AIG21771.1 2007 Belgium AGB97851.1 1994 New Zealand
ACF95133.1 NA Siberia AEK67275.1 2006 Iran AGB97890.1 1994 New Zealand
ACF95128.1 NA Siberia AEK67294.1 2006 Iran AGB97845.1 1994 New Zealand
ACF95338.1 NA Siberia AIG21690.1 2007 Belgium AGB97870.1 1994 New Zealand
ACF95318.1 NA Siberia AIG21698.1 2007 Belgium AGB97884.1 1994 New Zealand
AFQ36970.1 2005 Latvia AIG21719.1 2007 Belgium AGB97587.1 1994 New Zealand
AFQ36949.1 2007 Latvia AIG21658.1 2007 Belgium AGB97654.1 1994 New Zealand
ACF95299.1 NA Siberia AIG21681.1 2007 Belgium AGB97399.1 1994 New Zealand
ACF95286.1 NA Siberia AIG21759.1 2007 Belgium AGB97581.1 1994 New Zealand
AGB97728.1 2001 New Zealand AIG21745.1 2007 Belgium AGB97593.1 1994 New Zealand
AGB97714.1 2001 New Zealand AIG21765.1 2007 Belgium AIW68019.1 NA Cuba
AGB97721.1 2001 New Zealand AIG21731.1 2007 Belgium AEK66844.1 2006 Iran
AGB97701.1 2001 New Zealand AGB97707.1 1994 New Zealand AFB76738.1 1998 Canada
ACF95212.1 NA Siberia AEK67255.1 2006 Iran ACH58048.1 2005 China
ACF95231.1 NA Siberia AIG21705.1 2007 Belgium AFB76742.1 1998 Canada
ACF95345.1 NA Siberia AGB97482.1 1994 New Zealand ACF95218.1 NA Siberia
AGB97857.1 1994 New Zealand AEK67282.1 2006 Iran ACF95152.1 NA Russia
AGB97885.1 1994 New Zealand

*Ref sequence

N/A: not available

Multiple sequences alignment and epitopes conservancy

Multiple sequence alignment was used to align the strains sequences usingthe offline BioEdit program, version 7.0.9.0[Hall 1999]. Epitopes that demonstrated 100% conservancy from the aligned sequences of the variant HBV strains were obtained by finding their positions in the sequences with no mutation. The conserved epitopes were further subjected and analyzed by the free Immune Epitope Database prediction resources (IEDB)at (http://www.iedb.org/)

B cell epitopes prediction

Epitope is a discrete part of the antigen recognized by the immune system, particularly, B and T cells and to which an antibodies bind. Antibodies bound to epitopes viaparatopes that formed by continuous sequences of particular amino acids from the antigen[Percus et al., 1993]. B cells are subtypes of white blood cells known as lymphocyte sub type. They function as humoral immunity component of the adaptive immune system by secreting antibodies[Parkin and Cohen, 2001]. Additionally, B cells present antigen (they are also classified as professional antigen-presenting cells (APCs) and secrete cytokines [Parkin and Cohen, 2001].The IEDB resource tool at (http://toolsiedb.ofg/bcell/) was used to predict B cell epitopes. Eg; BepiPred was used to predict the linearB-cell epitopes [Larsen et al., 2006; Ponomarenko and Bourne, 2007; Haste Andersen et al., 2006]. Antigen surface epitopes were predicted by Emini surfaceaccessibility tools[Emini et al., 1985]. Kolaskar and Tongaonker antigenicity method was used to determine the antigenicity of the predicted epitopes [Kolaskar and Tongaonkar, 1990].The threshold of each prediction tool was obtained by IEDB that calculated the threshold as the average prediction score for each corresponding tool.

T cells epitopes prediction

T lymphocyte plays a central role in cell-mediated immunity. They functioned by recognition of major histocompatibility complex molecules (MHC) in a set of surface proteins in antigen presenting cells. The MHC is important for the immune system to recognize foreign molecules in vertebrates and determines their histocompatibility [Janeway, 1989].

Cytotoxic T lymphocytes epitopes prediction

IEDB MHC I prediction tool at (http://tools.iedb.org/mhci/) was used to analyze the peptides bound to MHC I molecules. The prediction undergoesmultiple steps; the prediction of the cleaved epitopes that boundto MHC groove, followed by choosing ArtificialNeural Network (ANN) as a prediction method. Before prediction initiated, the epitopes lengths were adjusted as9mers. The half-maximal inhibitory concentration (IC50) wasset to be ≤300 [Kim et al., 2012; Nielsen et al., 2003; Lundegaard et al., 2008; Sidney et al., 2008].

Helper T lymphocytes epitopes prediction

IEDB MHC II prediction tool at (http://tools.immuneepitope.org/mhcii/) was used to analyzethe peptide bound to MHC class II molecules. A set of reference alleles was used to predict the binding of epitopes toMHC II groove with different lengths. Such binding variability features makes prediction process difficult with low accuracy. The prediction methodused was NN- align that uses the artificial neural networks that permits simultaneous identification of epitopes bound to MHC II with high binding affinity. The half-maximal inhibitory concentration (IC50) was set to be ≤3000[Wang et al., 2008].

Antigenicity, allergenicity and toxicity of the predicted epitopes

VaxiJen v2.0 server at (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html) was used to investigate the antigenicity of the predicted epitopes using the default threshold of the server (0.4). AllerTOP server at (http://www.ddg-pharmfac.net/AllerTop/)[Dimitrov et al., 2013]and Toxin Pred server at (http://crdd.osdd.net/raghava/toxinpred/) [Gupta et al., 2013] werethe methods used to predict the allergenicity and toxicity of the epitopes interacting with B and T lymphocytes, respectively.

Calculation of the population coverage

IEDB tool at (http://tools.iedb.org/tools/population/iedb_input) for calculation of the population coverage was used to calculate the population coverage for each T lymphocytes predicted epitopes. The prediction of theMHC I and MHC II potential binders from polymerase protein was determined against the whole world.

Assemblage of the multi-epitope vaccine

Antigenic, nonallergic and nontoxic epitopes that interacted with T helper, T cytotoxic and B lymphocytes were used to generate vaccine construct. The T cytotoxic epitopes were fused the GPGPG linker [Shey et al., 2019] while the B and T helper cells epitopes were fused with KK linker [Hasan et al., 2019]. Insertion of linkers between two epitopes provides efficient separation that is required for the efficient functioning of each epitope [Nezafat et al., 2014; Ali et al., 2017]. The 50S ribosomal protein L7/L12 of Mycobacterium tuberculosis (strain ATCC 25618/ H37Rv, with uniprot accession no P9WHE3)was used as an adjuvant on the amino terminal of the vaccine sequence to enhance the immunogenicity of the chimeric vaccine. The adjuvant was joined via EAAAK linker to the epitopes. Six His-tags were added at the carboxyl terminal for purification and identification of the chimeric vaccine.

Physical and chemical characterisitics of the chimeric vaccine 

ProtParam server at (https://web.expasy.org/protparam/) was used to analyze the physical and chemical properties of the chimeric vaccine. The calculated features comprises: molecular weight (MW), theoretical isoelectric point (pI), atomic composition, amino acid composition, extinction coefficient, estimated half-life, aliphatic index, instability index and grand average of hydropathicity (GRAVY)

Secondary structure prediction

Self-optimized prediction method (SOPMA) at (https://npsa-prabi.ibcp.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_sopma.html) was used to predict the secondary structure of the multi-epitope vaccine protein [Combet et al., 2000].

Tertiary structure prediction

The tertiary structure of final multi-epitope vaccine was predicted by submitting the sequence of the chimeric vaccine sequence to PHYRE-2 protein folding recognition server (http://www.sbg.bio.ic.ac.uk/~phyre2/html/page.cgi?id=index ) [Kelley et al., 2015]. The server used to analyze and predict the3D structure of the protein, functionsand mutations. The output PDB file obtained was used for refinement and adaptation of the chimeric vaccine structure.

Tertiary structure refinement and validation

The PDB file obtained by PHYRE-2 server was submitted to Galaxy WEB web server for protein structure prediction and refinement [Shin et al., 2014; Ko et al., 2012]. Template based refinement model of the 3D tertiary structure was performed to ameliorate both the local and global structural quality. Thestructure validation was performed through Ramachandran plot at RAMPAGE [Al-Hakim et al., 2015; Lovell et al., 2002]. Moreover the PDB file obtained by PHYRE-2 server was analyzed by ProSA server for structure potential errors [Wiederstein and Sippl, 2007].

Prediction of discontinuous B-cell epitopes

The prediction of discontinuous B-cell epitopes was performed using ElliPro in theIEDB at (http://tools.iedb.org/ellipro/ ).ElliProtool predicts discontinuous and linear antibody epitopes depending on the protein (antigen) 3D structure. ElliProcombines each of the predicted epitope with a score, known as Protrusion Index (PI) value, averaged over the epitope residues. The minimum score and the maximum distance (Angstrom) of the prediction were set to default (0.5) and (6), respectively [Ponomarenko et al., 2008].

Solubility of the chimeric vaccine

The solubility of the chimeric protein was measured compared to the proteins of the E. coli using protein-sol server at (https://protein-sol.manchester.ac.uk/). Protein sol is a web based suite of theoretical calculations and predictive algorithms for understanding protein solubility of a given protein (QuerySol, scaled solubility value) compared to the E. coli experimental dataset (PopAvrSol) with a population average of 0.45[Hebditch et al., 2017]. Protein scored greater than 0.45 is expected to be soluble compared to the average solubility of E. coli proteins[Hebditch et al., 2017; Niwa et al., 2009].

Stability of the chimeric vaccine

The geometric conformation made by disulfide bonds engineering strengthens the chimeric vaccine with significant stability. The Disulfide by Design 2.0 (DbD2) is a web-based tool for disulfide engineering in proteins was used to engineer disulfide bonds in the chimeric vaccine [Craig and Dombkowski, 2013]. The position of the predicted disulfide bonds for residue pairs in the protein located in the high mobile regions. It is calculated based on the chi3 residue screening, B-factor value and energy value (equal to or less that 3.5), assuming the residue pairs mutated to cysteine.

Molecular dynamics simulation

iMODS is an online server (http://imods.chaconlab.org/) explores the collective motions of proteins and nucleic acids using normal modes analysis (NMA) in internal coordinates[Lopez-Blanco et al., 2014]. The server was used to analyze the stability of protein-protein complex and further effectively assess the structural dynamics of protein complex [Prabhakar et al., 2016; Awan et al., 2017]. The iMODS provided the direction and magnitude of the motions in protein complex in the form of deformability, eigenvalues, B-factors, covariance, variance map in the residue index and elastic network in the atoms index [Lopez-Blanco et al., 2011].

Molecular docking of the chimeric vaccine with TLR4

The automated docking server,ClusPro 2.0, that uses the discrimination method for prediction of protein complexeswas used for molecular interaction between the vaccine protein and Toll like Receptor 4 (TLR4) [Kozakov et al., 2017; Vajda et al., 2017]. ClusPro 2.0server rapidly filters the docked conformations and ranks them according to their clustering properties. The chimeric vaccine construct PDB file was submitted to the server with TLR4 (PDB4G8A) as a receptor. The docking process was performed in TLR4 chain A and chain B separately. The advance method was used as a docking method.The interaction between the vaccine and TLR4 chains was visualize by thePyMOL visualization tool.

In silico cloning

The insilico cloning was performed to guarantee the expression of the chimeric vaccine in the selected host. The protein of the chimeric vaccine was reversed translated to DNA sequence using Java Codon Adaptation Tool (JCAT) server (http://www.prodoric.de/JCat ). The best codon adaptation index (CAI) score is 1.0 but >0.8 is considered a good score [Morla et al., 2016]. The favourable GC content ranged between 30–70%. The Ndel and Xho1restriction enzymes cutting sites sequences were added to the ends of theDNA sequence. The sequence was inserted into pET28a (+) vector between the Ndel and Xho1 restriction enzymes using SnapGene restriction cloning module[Shey et al., 2019; Pandey et al., 2018].

Results

Epitopes conservancy

Sequence alignment of all retrieved strains of polymerase proteins were performed using ClustalW that presented by Bioedit software. Sequence alignment was performed to obtain 100% conserved epitopes from the retrieved strains. Epitopes conservancy assessed between the reference sequence and all the retrieved sequences via alignment. As shown in figure (1) the retrieved sequences of the polymerases showed conservancy upon aligned. The identity of amino acidswithin the sequences clearly identified the conserved regions.

Vol18No1_Str_Rol_fig1 Figure 1: Multiple sequence alignment showed the conservancy between sequences of the retrieved strains of the polymerase protein. Dots showed the conserved regions while letters within rectangles showed the mutated or the unconserved region between strains

Click here to view figure

B-cell Epitopes Prediction

IEDB server was used to predict B–cell epitopes. As shown in figure (2) scores equal to or greater than the thresholds of 0.06 (for linear epitope), 1.000 (for surface accessible epitopes) and 1.049(for antigenic epitopes)were considered as potential epitopes determinants of B cell.The three tools predicted 76 linear conserved epitopes, 46 epitopes on the surface and 30 antigenic epitopes.However only 14 epitopes overlapped the three tools and were further investigated for antigenicity using Vaxijen software with default threshold (0.4), allergenicity and toxicity. Upon investigation only one epitope was shown to be antigenic, nonallergic and nontoxic.This epitope was provided in table (2).

Vol18No1_Str_Rol_fig2 Figure 2: The threshold values of B-cell epitopes prediction tools indicated by the red lines in the figure.(a) The threshold was 0.06 forBepipred linear epitope prediction. (b) The threshold was 1.000 for Emini surface accessibility and (c)The threshold was 1.049 forKolaskar&Tongaonkar antigenicity methods. Areas above the threshold line (yellow colors) are considered as B cell epitopes, while areas below the threshold line (green colors) are not considered as B cells epitopes.

Click here to view figure

Table 2: Only one epitope predicted against B cell. The length and the threshold of each IEDB prediction tools were shown.

Epitope Start End Emini

Surface accessibility (1.000)

Kolaskar and Tongaonkar antigenicity (1.049) Vaxijen

antigenicity (0.4)

Allergenicity Toxicity
YLPLDKGIKPYY 122 133 1.923 1.072 0.6637 non-allergen Non-Toxin

Cytotoxic T lymphocytes epitopes prediction

Based on the ANN prediction methodwith IC50≤300,only 22 epitopes were found interacting with different MHC1alleles.Among the 22 epitopes only 7 epitopes were found antigenic, nonallergic and nontoxic. The 7 epitopes and their population coverage scores provided in table (3) and wereelected as MHC-1 epitopes.

Table 3: The 7 predicted epitopes interacted against cytotoxic T cells. The Population coverage against whole world was shown for each epitope.

Peptide Start End Vaxijin

antigenicity (0.4)

Allergenicity Toxicity Population coverage
YFQTRHYLH 141 149 0.6793 non-allergen Non-Toxin 9.14%
SLDVSAAFY 416 424 0.8683 non-allergen Non-Toxin 23.03%
QRIVGLLGF 623 631 0.5111 non-allergen Non-Toxin 4.78%
AELLAACFA 717 725 0.514 non-allergen Non-Toxin 3.45%
LAACFARSR 720 728 1.0703 non-allergen Non-Toxin 5.83%
DNSVVLSRK 737 745 1.3699 non-allergen Non-Toxin 5.83%
RVHFASPLH 818 826 0.4556 non-allergen Non-Toxin 3.89%

Helper T lymphocytes epitopes prediction

Based on NN-align method with IC50 ≤ 3000, a total of 77 predictedepitopes interacted against MHC-II alleles.Among them, only 19 epitopes were antigenic, nonallergic and nontoxic. Table (4) provided the 19 epitopes with their population coverage scores.

Table 4: The 19 predicted epitopes interacted against helper T cells. The Population coverage against whole world was shown for each epitope.

Core epitope Peptide Start End Vaxijen antigenicity (0.4) Allergenicity Toxicity Population coverage
CSVVRRAFP CSVVRRAFPHCLAFS 523 537 0.4739 Non-allergen Non-Toxin 76.04%
DATPTGWGL DATPTGWGLVMGHQR 689 703 2.0429 Non-allergen Non-Toxin 11.53%
ESRLVVDFS ESRLVVDFSQFSRGN 374 388 0.511 Non-allergen Non-Toxin 56.72%
KTKRWGYSL KTKRWGYSLNFMGYV 574 588 0.9557 Non-allergen Non-Toxin 20.51%
KYLPLDKGI KYLPLDKGIKPYYPE 121 135 0.806 Non-allergen Non-Toxin 48.66%
LAACFARSR LAACFARSRSGANII 720 734 1.0703 Non-allergen Non-Toxin 52.28%
LDKGIKPYY LDKGIKPYYPEHLVN 125 139 0.6475 Non-allergen Non-Toxin 77.23%
LDVSAAFYH LDVSAAFYHLPLHPA 417 431 0.906 Non-allergen Non-Toxin 89.97%
LGFAAPFTQ LGFAAPFTQCGYPAL 629 643 0.6846 Non-allergen Non-Toxin 91.70%
LPLDKGIKP LPLDKGIKPYYPEHL 123 137 0.8282 Non-allergen Non-Toxin 32.01%
NSVVLSRKY NSVVLSRKYTSFPWL 738 752 1.2913 Non-allergen Non-Toxin 27.73%
NWILRGTSF NWILRGTSFVYVPSA 758 772 1.3089 Non-allergen Non-Toxin 78.80%
SVVLSRKYT SVVLSRKYTSFPWLL 739 753 1.4231 Non-allergen Non-Toxin 27.90%
TAELLAACF TAELLAACFARSRSG 716 730 0.5238 Non-allergen Non-Toxin 88.45%
VHFASPLHV DRVHFASPLHVAWRP 817 831 0.4149 Non-allergen Non-Toxin 99.83%
VNHYFQTRH VNHYFQTRHYLHTLW 138 152 0.6801 Non-allergen Non-Toxin 77.52%
VVLSRKYTS VVLSRKYTSFPWLLG 740 754 1.194 Non-allergen Non-Toxin 87.65%
WKVCQRIVG WKVCQRIVGLLGFAA 619 633 0.7487 Non-allergen Non-Toxin 63.93%
YLPLDKGIK YLPLDKGIKPYYPEH 122 136 0.5373 Non-allergen Non-Toxin 55.74%

Construction of muli-epitopes vaccine (chimeric vaccine)

The chimeric vaccine includes the B cell and T cell predicted epitopes. One epitope was proposed as B cell epitope, seven epitopes as cytotoxic T cell and nineteen epitopes as helper T cell. The chimeric vaccine composed of 457 amino acids after addition of the adjuvants, linkers and 6-His- tag (figure 3). The chimeric vaccine demonstrated antigenicity in Vaxigen server with score of 0.5110 and was nonallergen in the Allertop server.

Vol18No1_Str_Rol_fig3 Figure 3: The chimeric vaccine structure. T helper epitopes (purple color) and B cell epitopes (red color) were linked by the short peptide linker KK, while T cytotoxic epitopes (blue color) were linked by GPGPG linker. The adjuvant (green color) was at amino terminal and the 6-his-tag at the carboxyl terminal.

Click here to view figure 

Physical and chemical characteristics of the vaccine construct

The MW of the chimeric vaccine was 50.03 KDa with pI value of 10.04. The negatively and positively charged residues in the vaccine structure were 39 and 92 respectively. The Extinction coefficient was 46215 indicating all pairs of Cys residues form cystines. The estimated half-life was 30 hours (mammalian reticulocytes, in vitro), >20 hours (yeast, in vivo) and >10 hours (Escherichia coli, in vivo). The instability index (II) was computed to be 25.78 indicating the stability of the chimeric vaccine. Aliphatic index was 81.82 and the GRAVY was -0.354 indicating the hydrophilicity of the chimeric vaccine.

Secondary structure prediction

Figure (4) demonstrated that Self-optimized prediction method (SOPMA) provided that among the 457 amino acids of the chimeric vaccine 164 amino acid (35.89%) involved in formation of alpha helices, 83 amino acids (18.16%) were extended strands, 43 amino acids (9.41%) were beta turns while 167 amino acids (36.54%) were random coils with no unambiguous or any other states.

Vol18No1_Str_Rol_fig4 Figure 4: Secondary structure prediction plot of the vaccine construct.  Alpha Helices were shown in blue color, while extended strands and beta turns were shown by red and green colors, respectively. The visualization of the prediction (a) and the score curves for each predicted state (b) were shown.

Click here to view figure

Tertiary structure prediction

Figure (5) provided the 3D structure of the chimeric vaccine predicted by PHYRE2 server.The 3D structure was refined with Galaxyrefine server. The model was further assessed by Ramachandran plot after refinement and demonstrated that the number of residues in favoured, allowed and outlier region were 90.1%, 7.3% and 2.6%,respectively. Moreover proSA server Z-score of the chimeric vaccine was -3.69 which represents the good quality of the model.

Vol18No1_Str_Rol_fig5 Figure 5: (a) the 3D tertiary structure of the chimeric vaccine predicted by PHYRE2 server. (b) the chimeric vaccine after refinement by Galaxyrefiner. (c) ProSA-web, showed a Z-score of −3.69. (d) Showed the validation of chimeric vaccine tertiary structure by ramachandran plot (90.1% of the residues in favored region, 7.3% of the residues in the allowed region and 2.6% residues lies in outlier region).

Click here to view figure 

Discontinuous B-cell epitopes prediction

Table (5) and figure (6) demonstrated 12 the B-cell discontinuous epitopes. The scores of these epitopes were ranged from 0.558 to 0.769. The total of residues predicted locating in these discontinuous epitopes was 251 residues. The size of the conformational epitopes ranged from 4 to 119 residues.

Table 5: The showed the number of the predicted discontinuous B cell epitopes with the number of the residues and their scores.

No. Residues Number of residues Score
1 _:E390, _:A394, _:C395, _:K397, _:K398, _:V399, _:H400 7 0.769
2 _:A251, _:F252, _:P253, _:K254, _:K255, _:D256, _:A257, _:T258, _:P259, _:T260, _:G261, _:W262, _:V271, _:V272, _:D273, _:F274, _:S275, _:K276, _:K277, _:K278, _:T279, _:K280 22 0.754
3 _:F63, _:L67, _:E68, _:A69, _:A70, _:G71, _:D72, _:K73, _:K74, _:I75, _:G76, _:V77, _:I78, _:K79, _:V80, _:E83, _:K94, _:D95, _:V97, _:D98, _:G99, _:A100, _:P101, _:K102, _:A109, _:K110, _:E111, _:D114, _:E115, _:K117, _:A118, _:K119, _:L120, _:E121, _:A122, _:A123, _:G124, _:A125, _:T126, _:V127, _:T128, _:L143 42 0.703
4 _:G159, _:P160, _:G161, _:P162, _:G163, _:Q164, _:R165, _:I166, _:V167, _:G168, _:L169, _:L170, _:G171, _:F172, _:G173, _:P174, _:G175, _:P176, _:G177, _:A178, _:E179, _:L180, _:L181, _:A182, _:A183, _:C184, _:F185, _:A186, _:G187, _:P188, _:G189, _:P190, _:G191, _:L192, _:A193, _:A194, _:C195, _:F196, _:A197, _:S199, _:D206, _:N207, _:S208, _:V209, _:V210, _:L211, _:S212, _:R213, _:K214, _:G215, _:P216, _:G217, _:P218, _:G219, _:R220, _:V221, _:H222, _:F223, _:A224, _:S225, _:P226, _:L227, _:H228, _:K229, _:K230, _:L232, _:D312, _:K313, _:G314, _:I315, _:K316, _:P317, _:Y318, _:Y319, _:K320, _:K321, _:L322, _:D323, _:V324, _:S325, _:A326, _:A327, _:F328, _:Y329, _:H330, _:K331, _:K332, _:L333, _:G334, _:F335, _:A336, _:A337, _:P338, _:F339, _:K342, _:K343, _:L344, _:P345, _:L346, _:K348, _:G349, _:I350, _:K351, _:P352, _:K353, _:K354, _:N355, _:S356, _:R370, _:G371, _:T372, _:S373, _:F374, _:K375, _:K376, _:S424, _:K426, _:Y427, _:H457 119 0.701
5 _:E131, _:A132, _:A133, _:A134, _:K135, _:Y136, _:F137, _:Q138, _:T139, _:Y142, _:F401, _:A402, _:S403, _:P404, _:L405, _:H406 16 0.663
6 _:A302, _:C303, _:A305, _:R306, _:S307, _:K309, _:K310 7 0.659
7 _:G263, _:L264, _:K265, _:K266, _:E267, _:L270 6 0.624
8 _:E59, _:Y290, _:L291, _:P292, _:L293, _:D294, _:K298 7 0.605
9 _:M1, _:A2, _:K3, _:L4, _:S5, _:E8, _:F32, _:E33, _:V34, _:S246, _:R249, _:R250 12 0.602
10 _:V378, _:V379, _:K383 3 0.587
11 _:R82, _:G87, _:L88, _:G89, _:L90, _:K91 6 0.56
12 _:G283, _:L286, _:K287, _:K289 4 0.558

 

Vol18No1_Str_Rol_fig6 Figure 6: (a) showed the 3D structures of the 12 discontinuous B-cell epitopes predicted by the ElliPro (1-12). Epitopes were shown in yellow color, while grey color showed the chimeric protein. (b): the red line showed the threshold of the residues score. The yellow color demonstrated the discontinuous epitopes while the green color was the continuous epitopes.

Click here to view figure 

Solubility of the chimeric vaccine

Figure (7) showed the solubility of the chimeric vaccine, QuerySol scaled solubility value, was 0.567 compared to the experimental data set (PopAvrSol) of 0.45 for E. coli proteins. This result showed that the chimeric vaccine is potentially soluble.

Vol18No1_Str_Rol_fig7 Figure 7: The solubility of the chimeric vaccine predicted by protein sol server. The solubility of the vaccine construct was shown to be 0.567 compared to 0.45 of the population average solubility of E. coli.

Click here to view figure

Stability of the chimeric vaccine

Figure (8), showed the disulfide engineered residues in the chimeric protein sequence when mutated to cysteine. A total 36 pairs of amino acids residues probably forming disulfide bond. But only five regions were evaluated to form disulfide bond based on the chi3 residue screening (between −87 and +97), B-factor value (ranged 6.950 – 17.410) and energy value less than 3.5. The five residue pairs were 63PHE-113ALA; 147GLY-443TYR; 219GLY-333LEU; 277LYS-280LYS and 293LEU-296GLY.

Vol18No1_Str_Rol_fig8 Figure 8: Stability of the chimeric vaccine by disulfide bond engineering in (a) the original form and (b) the mutant form. Five disulfide bond regions were shown in golden sticky forms in the mutant form

Click here to view figure 

Molecular dynamics simulation

The stability and the large scale mobility of chimeric protein were shown in figure (9) that performed by NMA (Normal mode analysis) in the iMODS server. The direction of the mobility of each residue in the chimeric vaccine protein was indicated by arrows(figure 9-a). Moreover the deformability of the molecule was associated with the distortion of the individual residues. This was indicated by hinges in the chain (figure 9-b). The experimental B-factor was obtained from the corresponding PDB field and calculated from NMA(figure 9-c). The eigenvalue which represents the motion stiffness was calculated to be 2.142287e−04 (figure 9-d), where the least the eigenvalue, the easier the deformation. Figure (9-e)provided the covariance matrix that represents the coupling between pairs of residues, i.e. whether they experience correlated motion (red color), uncorrelated motion (white color) or anti-correlated motion (blue color). The elastic network model defines which pairs of atoms are joined by springs in which each dot represents one spring between the corresponding pair of atoms. Dots are colored based on their stiffness. The darker grays represents stiffer springs and vice versa (figure 9-f).

Vol18No1_Str_Rol_fig9 Figure 9. Molecular dynamics simulation of the chimeric vaccine protein complex performed by iMODS server. The protein complex was investigated via the direction of the mobility (a), protein complex deformability (b), B-factor (c), eigenvalue (d), covariance (e) and elastic stiffness network (f).

Click here to view figure 

Molecular docking of the chimeric vaccine with TLR4

Molecular docking was performed between the TLR4 and the chimeric vaccine protein and provided biologically significant results indicated by the terms of free binding energy. The docking process for TLR4 chain A provided that the representative lower energy score was-1458.7 (figure 10-a). For chain B the representative lower energy score was -1410.3 (figure 10-b).These negatively scored values demonstrated the strong binding between the chimeric vaccine protein and TLR4 chains.

Vol18No1_Str_Rol_fig10 Figure 10: (a) Represents the interaction between TLR4 chain A (green color) and the chimeric vaccine (cyan color). (b) Represents the interaction between TLR4 chain B (green color) and the chimeric vaccine (cyan color). The interaction between the chimeric vaccine and the TLR chains was zoomed to visualize the bonds interactions (red dots).

Click here to view figure

In silico cloning of the chimeric vaccine

The DNA sequence of the chimeric protein provided CAI-Value of 0.9199, representing the higher proportion of most abundant codons. While the GC-content was 51.58%, indicating favourable GC content. Figure (11), showed that DNA sequence was cloned into pET28a (+) vector betweenNdel and Xho1restriction enzymes cutting sites.

Vol18No1_Str_Rol_fig11 Figure 11: In silico cloning of the chimeric vaccine DNA sequence into the pET30a (+) vector. Red color in the vector represents the DNA sequenceand the black color represents the vector backbone. The enzymes used in the cloning process and the length of the insert were also shown.

Click here to view figure 

Discussion

HBV infection includes large spectrum of hepatic diseases ranged from acute to chronic hepatitis, hepatocellular carcinoma (HCC) and liver cirrhosis [Hollinger and Liang, 2001]. Multiple novel remedies against HBV were reported. For instance the Direct Acting Anti-virals (DAA) directly targeted the virus via multiple inhibition events. This includes entry inhibitors, site-specific cleavage of DNA agents, polymerase inhibitors, inhibitors of relax-circular DNA to covalently closed circular DNA (cccDNA) conversion. Inhibition events also includes the inhibitors of nucleocapsid assembly, agents that knockdown HBV RNA, viral proteins and HBV DNA knockdown, capsid inhibitors and agents that block HBsAg secretion [Ward et al., 2016; Zheng et al., 2017]. Moreover, beside the inhibition events, the host targeting agents (HTA) acted by improving the ability of the host immunity. This includes inhibitors of the immune checkpoints, stimulators of exogenous interferon, therapeutic vaccines and agents that enhances APOBEC3A and APOBEC3B [Ward et al., 2016; Zheng et al., 2017; Lin and Kao, 2016; Jia et al., 2015].The advances made in the field of the recombinant DNA technology guided to the development of second-generation recombinant vaccines production against HBV in yeast [Sitrin et al., 1993; McMahon et al., 1992; McAleer et al., 1984].Althoughthe second-generation HBV vaccines shown to be as an efficient vaccine, the immunization failure might occur and can be explained by variable factors such as the non-responsiveness to immunization that might be genetically determined resistance [Craven et al., 1986; Alper et al., 1989]. Moreover the ability to produce antibodies in response to immunization is controlled by autosomal dominantly expressed HLA class II molecules [Craven et al., 1986; Alper et al., 1989; Milich, 1988; Hohler et al., 1998]. Therefore the need of effective vaccine with fewer drawbacks is highly appreciated. This study aimed to use the polymerase protein of the HBV for multiple epitopes prediction that would act as vaccine candidates against the disease. The polymerase protein is considered as the only immunogene of Heptitis B virus that induced immune response[Mudawi, 2008; Bekele et al., 2015; Parkin and Cohen, 2001; Percus et al., 1993]. Multiple immunoinformatics approaches were exploited to investigate the efficacy of the chimeric vaccine construct.

One report by Depla et al (2008) described the design and synthesis of multi-epitopes vaccine against HBV in plasmid DNA construct and a recombinant MVA viral vector each contain a single gene encoding epitopes from cytotoxic and helper T lymphocytes. Their successful designed vaccine enhanced the immune responses of the host. Their results indicating the capability of multi-epitopes therapeutic vaccine in stimulating cellular immune responses and are essential for controlling and clearing HBV infection in in vitro and in HLA transgenic mice. Thus the coupled efforts of vaccination, interruption of transmission and the effective remedy are important factors to combat HBV [Chen 2009]

To generate the chimeric vaccine, short protein sequences act as linkers (spacers) were introduced between the B and T cells predicted epitopes to generate multi-epitopes peptides [Meza et al., 2017]. The linkers were reported to cause minimal junctional immunogenicity [Shey et al., 2019; Hasan et al., 2019; Ali et al., 2107;Pandey et al., 2018; Khatoon et al., 2017] and to ameliorate the bioactivity of the chimeric vaccine and to reach a high level of expression[Shey et al., 2019;Meza et al., 2017].  Moreover the chimeric vaccine was supported with an adjuvant in the N terminal of the vaccine. The adjuvants were previously reported as immunomodulator to ameliorate the activity of multiple vaccines [Mohan et al., 2013; Solanki and Tiwari, 2018]. It is noteworthy that bioinformatics and immunologic analysis tools provided that the chimeric vaccine should comprise MHCI and MHCII possessed epitopes in addition to linear and discontinuous B-cell epitopes [Ali et al., 2017]. Our chimeric vaccine was shown comprising linear and discontinuous B cell epitopes as well as MHC1 and MHCII epitopes. Also the vaccine construct was investigated for antigenicity and allergenicity. It demonstrated antigenicity in Vaxijen server and was shown to be nonallergic in Allertop server. This result indicated that the antigenic property of the vaccine without allergenicity further affirm its potentiality as a vaccine candidate. Furthermore the physical and chemical properties of the chimeric vaccine were analyzed.The analysis showed the protein was stable, contains aliphatic side chains, hydrophilic and demonstrated thermal stability. These features provided the suitability of the chimeric protein construct as good vaccine against the HBV.

Structural stability of protein secondary and tertiary structures are of crucial importance for efficient presentation of antigenic peptides on MHC for triggering strong immune reactions[Scheiblhofer et al., 2017]. Moreover fold stability directly impacted the existence of B-cell epitopes. At the same time protein destabilization leads to improper or unfoldingof the protein tertiary structure. This resulted in loss of conformational epitopes[Scheiblhofer et al., 2017]. Thus in this study the secondary and tertiary structure of the chimeric vaccine was investigated using multiple bioinformatics tools.The results showed that the secondary and tertiary structures were significantly important for generating a vaccine candidate. For instance the secondary structure of  the chimeric protein contained alpha helices, extended strands, beta turns and random coils with no unambiguous or any other states. The 3D structure of the chimeric vaccine was ameliorated by the refined software and demonstrated desirable characteristics on Ramachandran plot predictions. The result indicated that most of the residues were in the favored areas with very minor residues in the outlier region, representing satisfactory model with good designed quality. One of the most cornerstones in designing vaccine is to analyze the solubility and the stability of the generated vaccine construct. It was previously reported that low solubility of the vaccine protein represents a drawback for production of large amounts of Hepatitis A virus proteins in BEVS vector[Silva et al., 2016]. In this study the solubility of the chimeric vaccine was measured compared to the solubility of E. coli proteins using protein sol server[Hebditch et al., 2017]. The solubility of the chimeric vaccine exceeds that of the E. coli proteins indicating the solubility of the chimeric protein. Moreover protein stability depends on disulfide bonds that considered as cornerstone forstructural folding and stability of a protein. Disulfide bonds changing the conformation of the protein based on the redox state of the environment and are critical for their folding. Disulfide bonds significantly reduced the number of conformations for a particular protein, resulting increased thermostability and decreased entropy[Berkmen, 2012; Zhang et al., 1994]. The chimeric protein in this study demonstrated five positions for disulfide bond formation depending on the chi3 residue screening, B-factor value and energy value. These five positions if mutated to cysteine would result in five disulfide bonds formation assisted in the stability of the vaccine construct. In addition to that, molecular dynamics analysis was performed to assess the complex stability of the chimeric vaccine. Previously a subset of atoms and covariance analysis has been analyzed for structural dynamics of proteins stability[Hasan et al., 2019; Aaltenetal., 1997]. Moreover previous studies correlated the stability of macromolecules with the fluctuation of atoms [Hasan et al., 2019; Caspar 1995; Clarage et al., 1995]. The molecular dynamics analysis of the chimeric vaccine in this study showed insignificant deformability of the vaccine residues strengthening the result of the chimeric protein stability.

Molecular docking between the chimeric vaccine protein and the TLR4 was performed to determine the favourable protein-protein interaction. The binding energies calculated from the interaction between our chimeric vaccine construct with TLR4 receptor further strengthen that the developed vaccine could potentially provoke protective immune response. It is of great significance is to express and validate the designed vaccine protein in suitable vector for immunoreactivity [Gori et al., 2013]. For the production of chimeric protein, E. coli expression systems are the most preferable choice [Rosano and Ceccarelli 2014; Chen, 2012]. Expression and translation of the vaccine construct in E. coli (strain K12) codon optimization was performed. The CAI was 0.9199, representing high proportion of most abundant codons. The GC- content was 51.58% representing high-level of expression of the vaccine protein in bacterial host.

Conclusion

Novel and effective vaccine against HBV is essentially required. The approach of reverse vaccinology was exploited in this study for designing potential multi epitopes vaccine from the polymerase protein of the HBV eliciting both B and T cells lymphocytes. The proposed epitopes were clonally expressed in the E. coli thus, providing the ability to work as putative vaccine candidates to combat the action of HBV.

Acknowledgments

Authors would like to thank the staff members of Department of Molecular Biology and Bioinformatics, College of Veterinary Medicine, University of Bahri/ Sudan for their cooperation and support.

Funding Sourse 

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Competing Interest

The authors declare that they have no competing interests.

References

  1. FranckiI.B., FauquetC., Knudson D. Classification and Nomenclature of Viruses: Fifth Report of the International Committee on Taxonomy of Viruses. Virology Division of the International Union of Microbiological Societies.2012; Vol. 2: Springer Science & Business Media.
  2. Beasley R.P., Hwang L.Y., Lin C.C., Chien C.S. Hepatocellular carcinoma and hepatitis B virus. A prospective study of 22 707 men in Taiwan.Lancet. 1981; 21;2(8256):1129-33. doi: 10.1016/s0140-6736(81)90585-7. PMID: 6118576.
    CrossRef
  3. Shepard C.W., Simard E.P., Finelli L., Fiore A.E., Bell B.P. Hepatitis B virus infection: epidemiology and vaccination. Epidemiol Rev. 2006;28:112-25. doi: 10.1093/epirev/mxj009.
    CrossRef
  4. Ott J.J., Stevens G.A., Groeger J., Wiersma S.T. Global epidemiology of hepatitis B virus infection: new estimates of age-specific HBsAgseroprevalence and endemicity. Vaccine. 2012; 9;30(12):2212-9. doi: 10.1016/j
    CrossRef
  5. Yousif M., Mudawi H., Bakhiet S. et al. Molecular characterization of hepatitis B virus in liver disease patients and asymptomatic carriers of the virus in Sudan. BMC Infect Dis. 2013;13,328. https://doi.org/10.1186/1471-2334-13-328
    CrossRef
  6. Schattner A. Consequence or coincidence? The occurrence, pathogenesis and significance of autoimmune manifestations after viral vaccines.Vaccine. 2005; 10;23(30):3876-86. doi: 10.1016/j.vaccine.2005.03.005.
    CrossRef
  7. Rehermann B., Fowler P., Sidney J., Person J., Redeker A., Brown M., Moss B., Sette A., Chisari F.V. The cytotoxic T lymphocyte response to multiple hepatitis B virus polymerase epitopes during and after acute viral hepatitis. J Exp Med. 1995 Mar 1;181(3):1047-58. doi: 10.1084/jem.181.3.1047.
    CrossRef
  8. Mudawi H.M. Epidemiology of viral hepatitis in Sudan. ClinExpGastroenterol. 2008;1:9-13. doi: 10.2147/ceg.s3887.
    CrossRef
  9. Bekele Y., Amu S., Bobosha K., Lantto R., Nilsson A., Endale B., Gebre M., Aseffa A., RéthiB., Howe R., Chiodi F. Impaired phenotype and function of T follicular Helper cells in HIV-1-infected children receiving ART. Medicine.2015; 94.
    CrossRef
  10. Parkin J., Cohen B. An overview of the immune system.Lancet. 2001; 2;357(9270):1777-89. doi: 10.1016/S0140-6736(00)04904-7.
    CrossRef
  11. Percus J. K., PercusO. E., Perelson A. S. Predicting the size of the T-cell receptor and antibody combining region from consideration of efficient self-nonself discrimination.Proceedings of the National Academy of Sciences of the United States of America. 1993; 90(5), 1691–1695.
    CrossRef
  12. Enshell-Seijffers D., Denisov D., Groisman B., Smelyanski L., Meyuhas R., Gross G., Denisova G., Gershoni J.M. The mapping and reconstitution of a conformational discontinuous B-cell epitope of HIV-1. J Mol Biol. 2003; 14;334(1):87-101. doi: 10.1016/j.jmb.2003.09.002.
    CrossRef
  13. Potocnakova L., Bhide M., Pulzova L.B.An introduction to B-cell epitope mapping and in silico epitope prediction.Journal of immunology research.2016, Article ID 6760830, 11 pages http://dx.doi.org/10.1155/2016/6760830.
    CrossRef
  14. Frikha-Gargouri O., Gdoura R., Znazen A. et al. Evaluation of an in silico predicted specific and immunogenic antigen from the OmcB protein for the serodiagnosis of Chlamydia trachomatis BMC Microbiol. 2008; 8,217https://doi.org/10.1186/1471-2180-8-217
    CrossRef
  15. Hall T.A. BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. in Nucleic acids symposium series. [London]: Information Retrieval Ltd. 1999; c1979-c2000.
  16. Larsen J.E., Lund O., Nielsen M. Improved method for predicting linear B-cell epitopes. Immunome Res.2006; 2:2.
    CrossRef
  17. Ponomarenko J.V., Bourne P.E. Antibody-protein interactions: benchmark datasets and prediction tools evaluation. BMC Struct Biol. 2007; 7:64.
    CrossRef
  18. Haste Andersen P., Nielsen M., Lund O. Prediction of residues in discontinuous B-cell epitopes using protein 3D structures. Protein Sci.2006; 15:2558-2567.
    CrossRef
  19. Emini E.A., Hughes J.V., Perlow D.S., Boger J. Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide. J Virol.1985; 55:836-839.
    CrossRef
  20. Kolaskar A.S., Tongaonkar P.C. A semi-empirical method for prediction of antigenic determinants on protein antigens.FEBS Lett.1990; 276:172-174.
    CrossRef
  21. JanewayC.A. Approaching the asymptote? Evolution and revolution in immunology.in Cold Spring Harbor symposia on quantitative biology. Cold Spring Harbor Laboratory Press. 1989
    CrossRef
  22. Kim Y., Ponomarenko J., Zhu Z., Tamang D., Wang P., et al. Immune epitope database analysis resource. Nucleic Acids Res. 2012; 438.
    CrossRef
  23. Nielsen M., Lundegaard C., Worning P., Lauemøller S.L., Lamberth K., et al. Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci.2003; 12: 1007-1017.
    CrossRef
  24. Lundegaard C., Lamberth K., Harndahl M., Buus S., Lund O., et al. NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8–11. Nucleic Acids Res. 2008; 36: W509-W12.
    CrossRef
  25. Sidney J., AssarssonE.,Moore C., Ngo S., Pinilla C., et al. Quantitative peptide binding motifs for 19 human and mouse MHC class I molecules derived using positional scanning combinatorial peptide libraries. Immunome Res. 2008; 4: 2
    CrossRef
  26. Wang P., Sidney J., Dow C., Mothe B., Sette A.A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach.PLoSComput Biol.2008; 4: e1000048.
    CrossRef
  27. Dimitrov I., Bangov I., Flower D.R., Doytchinova I.A.AllerTOP v.2- a server for in silico prediction of allergens. J Mol. Model.2013; 20, 2278.
    CrossRef
  28. Gupta S., Kapoor P.,Chaudhary K., Gautam A., Kumar R. Open source drug discovery consortium, Raghava GP in silico approach for predicting toxicity of peptides and proteins. PLoS One.2013; 8 (9), e73957.
    CrossRef
  29. Shey R.A., Ghogomu S.M., Esoh K.K., Nebangwa N.D., Shintouo C.M., Nongley N.F., Asa B.F., Ngale F.N., Vanhamme L., Souopgui J. In-silico design of a multi-epitope vaccine candidate against onchocerciasis and related filarial diseases.Sci Rep. 2019; 13; 9 (1):4409. doi: 10.1038/s41598-019-40833-x.
    CrossRef
  30. Hasan M., Ghosh P.P., Azim K.F., Mukta S., Abir R.A., Nahar J., Hasan Khan M.M. Reverse vaccinology approach to design a novel multi-epitope subunit vaccine against avian influenza A (H7N9) virus. Microb Pathog.;130:19-37. doi: 10.1016/j.micpath.2019; 02.023. Epub 2019 Feb 26.
    CrossRef
  31. Nezafat N., Ghasemi Y., Javadi G., Khoshnoud M.J., Omidinia E. A novel multi-epitope peptide vaccine against cancer: an in silico approach. J. Theor. Biol. 2014; 349, 121–134.
    CrossRef
  32. Ali, PandeyR., KhatoonN., NarulaA.,  MishraA., PrajapatiV. Exploring dengue genome to construct a multi-epitope based subunit vaccine by utilizing immunoinformatics approach to battle against dengue infection. Sci Rep. 2017; 7, 9232.
    CrossRef
  33. Combet C., Blanchet C., GeourjonC. and Deléage G. NPS@ Network Protein Sequence Analysis TIBS. 2000; Vol. 25, No 3 [291]:147-150.
    CrossRef
  34. Kelley L.,Mezulis S., Yates C., et al.The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc. 2015; 10, 845–858. https://doi.org/10.1038/nprot.2015.053
    CrossRef
  35. Shin W.H., LeeG. R., Heo L., Lee H., and Seok C. Prediction of protein structure and interaction by GALAXY protein modeling programs, Bio Design. 2014; 2 (1), 1-11.
  36. KoJ., Park H., Heo L., and Seok C.Galaxy WEB server for protein structure prediction and refinement, Nucleic Acids Res. 2012; 40 (W1), W294-W297.
    CrossRef
  37. Lovell S.C., Davis I.W., Arendall W.B., Bakker P.I.W., Word J.M., Prisant M.G.,Richardson J.S. and Richardson D.C. Structure validation by Calpha geometry: phi, psiand C beta deviation, Protein Struct. Funct.Genet. 2002; 50, 437–450.
    CrossRef
  38. Al-Hakim M., Hasan R., Ali M.F., Rabbee J., Marufatuzzahan Z.F. In-silicocharacterization and homology modeling of catechol 1,2 dioxygenase involved inprocessing of catechol- an intermediate of aromatic compound degradationpathway, Glob. J. Sci. Front. Res. G Bio-Tech Genet. 2015; 15, 1–13.
  39. Wiederstein and SipplM.J. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res Jul; 35(Web Server issue).2007; W407–W410.
    CrossRef
  40. Ponomarenko J.V., Bui H., Li W., Fusseder N., Bourne P.E., Sette A., Peters B.ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC Bioinformatics.2008; 9:514.
    CrossRef
  41. Hebditch M., Carballo-Amador M.A., Charonis S., Curtis R., Warwicker J.Protein-Sol: a web tool for predicting protein solubility from sequence.2017; 1;33(19):3098-3100. doi: 10.1093/bioinformatics/btx345.
    CrossRef
  42. Niwa T., Ying B.W., Saito K., Jin W., Takada S., Ueda T., Taguchi H.Bimodal protein solubility distribution revealed by an aggregation analysis of the entire ensemble of Escherichia coli proteins, Proc. Natl. Acad. Sci. Unit. States Am. 2009; 106, 4201–4206.
    CrossRef
  43. Craig D.B.,Dombkowski A.A. Disulfide by Design 2.0: a web-based tool for disulfide engineering in proteins. BMC Bioinformatics .2013; 1; 14: 346. DOI: 1186/1471-2105-14-346 PMID: 24289175
    CrossRef
  44. Lopez-Blanco J.R., Aliaga J.I., Quintana-Orti E.S., Chacon P.iMODS: internal coordinates normal mode analysis server, Nucleic Acids Res. 2014; 42W271–W276.
    CrossRef
  45. AwanF.M., Obaid A., Ikram A., Janjua H.A. Mutation-structure function relationship based integrated strategy reveals the potential impact of deleterious missense mutations in autophagy related proteins on hepatocellular carcinoma (HCC): a comprehensive informatics approach, Int. J. Mol. Sci. 2017;18 (1) 139.
    CrossRef
  46. Prabhakar P.K., Srivastava A., Rao K.K., Balaji P.V.Monomerization alters the dynamics of the lid region in campylobacter jejuniCstII: an MD simulation study, J. Biomol. Struct.Dyn. 2016; 34 (4) 778–779.
    CrossRef
  47. Lopez-Blanco J.R., Garzon J.I., Chacon P.iMod, multipurpose normal mode analysis in internal coordinates, Bioinformatics. 2011; 27 (20) 2843–2850.
    CrossRef
  48. Vajda S., Yueh C., Beglov D., Bohnuud T., Mottarella S.E., Xia B., Hall D.R., Kozakov D. New additions to the ClusPro server motivated by CAPRI.Proteins: Structure, Function, and Bioinformatics. 2017; 85(3):435-444.
    CrossRef
  49. Kozakov D., Hall D.R., Xia B., Porter K.A., Padhorny D., Yueh C., Beglov D., Vajda S. The ClusPro web server for protein-protein docking.Nature Protocols.2017; Feb;12(2):255-278.
    CrossRef
  50. Morla S., Makhija A., Kumar S. Synonymous codon usage pattern in glycoprotein gene of rabies virus. Gene. 2016; 584, 1–6
    CrossRef
  51. Pandey R.K., Ojha R., Aathmanathan V.S., Krishnan M., Prajapati V.K.Immunoinformatics approaches to design a novel multi-epitope subunit vaccine against HIV infection, Vaccine. 2018; 36, 2262–2272
    CrossRef
  52. Hollinger F.B., Liang T.J. Hepatitis B virus. In: Knipe DM, Howley PM, Griffin DE, Lamb RA, Martin MA, Roizman B, et al., editors. Fields Virology. Philadelphia, PA: Lippincott-Raven Publishers. 2001; pp. 2971–3036.
  53. Ward,Tang L.,Poonia B.,Kottilil S. Treatment of hepatitis B virus: an update. Future Microbiol. 2016; 11(12): 1581–1597.
    CrossRef
  54. Zheng J., Lin X., Wang X., Zheng L.,Lan S., Jin S., Ou Z., Wu J. In Silico Analysis of Epitope-Based Vaccine Candidates against Hepatitis B Virus Polymerase Protein.Viruses. 2017;  9, 112; doi:10.3390/v9050112
    CrossRef
  55. Lin C.L., Kao J.H. Review article: Novel therapies for hepatitis B virus cure—Advances and perspectives. Aliment.Pharmacol.Ther. 2016; 44, 213–222.
    CrossRef
  56. Jia H., Rai D., Zhan P., Chen X., Jiang X, Liu X. Recent advance of the hepatitis B virus inhibitors: A medicinal chemistry overview. Future Med. Chem. 2015; 7, 587–607.
    CrossRef
  57. Sitrin R.D., Wampler D.E., Ellis R.W. Survey of hepatitis B vaccines and their production processes. In: Ellis RW, editor. Hepatitis B vaccine in clinical practice. New York: Marcel Dekker. 1993; 83–101.
  58. McMahon B.J., Helminiak C., Wainwright R.B., Bulkow L., Trimble B.A,Wainright K., et al. Frequency of adverse reactions to hepatitis Bvaccine in 43618 persons. Am J Med. 1992; 92:254 – 6.
    CrossRef
  59. McAleer W.J., Buynak E.B., Maigetter R.Z., Wampler D.E., Miller W.J.,Hilleman M.R., et al. Human hepatitis B vaccine from recombinantyeast.Nature.1984; 307:178 – 81.
    CrossRef
  60. Craven D.E., Awdeh Z.L., Kunches L.M., Yunis E.J., Deinstag J.L., WernerB.G., et al. Nonresponsiveness to hepatitis B vaccine in health careworkers: results of revaccination and genetic typing. Ann Intern Med. 1986; 105:356 – 60.
    CrossRef
  61. Alper C.A., Kruskall M.S., Marcus-Bagley D., Craven D.E., Katz A.J.,Brink S.J., et al. Genetic prediction of non-responsiveness to hepatitis Bvaccine. N Engl J Med. 1989; 321:708 – 12.
    CrossRef
  62. Milich D.R.T and B cell recognition of hepatitis B viral antigens.Immunol Today. 1988; 9:380 – 91.
    CrossRef
  63. Hohler T., Meyer C.U., Notghi A., Stradmann-Bellinghausen B., Schneider P.M., Starke R., et al.  Influence of major histocompatibility complex class II and T cell V beta repertoire on response toimmunization with HBsAg. Hum Immunol. 1998; 59:212 – 8
    CrossRef
  64. Depla E., Van der Aa A., Livingston B.D., Crimi C., Allosery K., De Brabandere V., Krakover J., Murthy S., Huang M., Power S., Babé L., Dahlberg C., McKinney D., Sette A., Southwood S., Philip R., Newman M.J., Meheus L. Rational design of a multiepitope vaccine encoding T-lymphocyte epitopes for treatment of chronic hepatitis B virus infections. J Virol. 2008;82(1):435-50. doi: 10.1128/JVI.01505-07.
    CrossRef
  65. Chen D.S. Hepatitis B vaccination: The key towards elimination and eradication of hepatitis B. J Hepatol. 2009;50(4):805-16. doi: 10.1016/j.jhep.2009.01.002.
    CrossRef
  66. Meza B., Ascencio F., Sierra-Beltrán A.P., Torres J.&Angulo C. A novel design of a multi-antigenic, multistage and multi-epitope vaccine against Helicobacter pylori: An in silico approach. Infection, Genetics and Evolution. 2017; 49, 309–317.
    CrossRef
  67. Khatoon N., Pandey R.K., Prajapati V.K. Exploring Leishmania secretory proteins to design B and T cell multi-epitope subunit vaccine using immunoinformatics approach. Sci Rep. 2017; 7, 82-85.
    CrossRef
  68. Mohan T., Verma P., Rao D.N. Novel adjuvants & delivery vehicles for vaccines development: a road ahead, Indian J. Med. Res.2013;138 (5) 779.
  69. Solanki V., Tiwari V. Subtractive proteomics to identify novel drug targets and reverse vaccinology for the development of chimeric vaccine against Acinetobacterbaumannii, Scientific report. 2018; 8 (1) 9044.
    CrossRef
  70. Scheiblhofer S., Laimer J., Machado Y., Weiss R., Thalhamer J. Influence of protein fold stability on immunogenicity and its implications for vaccine design. Expert Rev Vaccines. 2107; 16(5):479–89. doi:10.1080/14760584-1306441
    CrossRef
  71. Silva H.C. J., PestanaC.P., Galler R., Medeiros M.A. Solubility as a limiting factor for expression of hepatitis A virus proteins in insect cell-baculovirus system. MemInstOswaldo Cruz. 2016; 111(8):535‐ doi:10.1590/0074-02760160153
    CrossRef
  72. Berkmen M. Production of disulfide-bonded proteins in Escherichia coli. Protein Expression and Purification. 2012; 82(1), 240–251. doi:10.1016/j.pep.2011.10.009
    CrossRef
  73. Zhang T., Bertelsen E., Alber T. Entropic effects of disulphide bonds on protein stability, Nat. Struct. Biol.1994; (1) 434–438.
    CrossRef
  74. Aalten D.M.F., Groot B.L., Findlay J.B.C., Berendsen H.J.C., Amadei A.A comparison of techniques for calculating protein essential dynamics. J. Comput. Chem. 1997; 18 (2) 169–181.
    CrossRef
  75. Clarage J.B., Romo T., Andrews B.K., Pettitt B.M., Phillips G.N. A sampling problem in molecular dynamics simulations of macromolecules, Proc. Natl. Acad. Sci. U.S.A.1995; 92, 3288–3292.
    CrossRef
  76. Caspar D.L.D. Problems in simulating macromolecular movements, Structure.1995; 3, 327–329.
    CrossRef
  77. Gori A., LonghiR., Peri C. & Colombo G. Peptides for immunological purposes: design, strategies and applications. Amino Acids. 2013; 45, 257–268.
    CrossRef
  78. Chen R.Bacterial expression systems for recombinant protein production: E. coli and beyond. Biotechnol Adv.2012; 30, 1102–7.
    CrossRef
  79. Rosano G.L.,Ceccarelli E.A. Recombinant protein expression in Escherichia coli: advances and challenges. Frontiers in Microbiology.2014; 5, 172.
    CrossRef

Isolation of Fluoride Resistant Microorganisms from Fluoride Contaminated Ground Water Samples of Nalgonda district and their role in Bioremediation

$
0
0

Introduction

Fluorine is one amongst the copious elements on earth as well as, acts as a major environmental pollutant arising from usual as well as industrial sources (Whitford GM.,1983). The fluoride ion (F) concentration in the surface water is day by day increasing withquick industrialization and pollution with some insecticides (Aguirre-Sierra A et al., 2013). Ground water samples of greater than twenty countries including India have reported increased concentrations of Fluoride in them (Meenakshi  and R.C.Maheshwari, 2006). TheWHO has recommended a maximum allowed concentration (MAC) of Fin source of drinking water is 1.5 mg/L, above which health problems are common. (WHO, 1984)

The fluoride ion acts as a protoplasmic toxin with which many biochemical reactions can get effected inside living cells, when present even at low concentrations (Erenet al., 2005). Fluoride stimulates oxidative damage by producing reactive oxygen species inside the cells which changes intracellular redox homeostasis (Podderet al., 2010 and Chattopadhyay et al., 2011) Fluoride binds with DNA resulting ininjury to DNA which possibly causes chemical carcinogenesis (Zhan  et al., 2006; Podderet al., 2008;Choubisa, 2012).

One of the important concern with respect to environment nowadays is the escalating levelof fluoride in resources of groundwater (Susheela, 2013). These fluoride deposits are causing fluorosis in 17 states of India (UNICEF, 1999). Most of the researchers worked on fluoride concentration in ground water of Nalgonda district had reported high concentrations (Ayoob and Gupta, 2006; Ibrahim, 2011).

Increased levels of fluoride can affect the growth of microbes, their activity and organic matter degradation.  (Zang et al., 2019) Hence, fluoride can be used as an additive in toothpastes to inhibit the formation of caries (Liao Y et al., 2017).

During evolution, microorganisms attained various abilities to resist certain concentrations of fluoride (Maltz  and Emilson., 1982) as the result of genomic modifications (Liao Y et al., 2015; Mitsuhataet al., 2014).With the help of different mechanisms like metal sorption, oxidoreduction reactions and pumping out toxic elements present in them, microorganisms can overcome the unsuitable environments and use the various hazardous elements (Banerjee et al., 2016). To pump out fluoride ions some microorganisms consists of Fluoride antiporters with which they export these ions out of the cell and import protons, which causes the reduction in intracellular level of fluoride. Hence, mutations in genes related to fluoride antiporters decreased the resistance of S. mutans to fluoride(Liao Y et al., 2018).

Fluoride resistant microorganisms play a significant function in bioremediation of fluoride and convert it as less hazardous type (Chouhan et al., 2012) However, studies regarding the bioremediation of Fluoride are rare. In this paper,a study about fluoride-resistant bacterial strainsisolated from Fluoride contaminated ground water of Nalgonda district and analysis of their bioremediation capability was reported.

Materials and Methods

Sample Collection

Ground water samples were brought from water pumps and electric bore wells of fourteen different locations in Nalgonda district. For sample collection, bottles of 20mL capacity were washed rigorously with soap water and 8M HNO­3 followed by distilled water. Before collecting samples from the bore wells or hand pumps,water was pumped out from them for about 5 to 10 min to get rid of standing water from the pipe lines of the same. Sample bottles were cleaned with the samples of ground water before final sample collection.

Fluoride estimation and pH analysis of water samples

Thesamples of ground water were collected from various places in Nalgonda disctrict were analysed for fluoride levelsusing a Fluriode ion selective electrode after calibration. In this test,1 mL of TISAB III (Total ionic strength adjustment buffer III solution)reagent was carefully mixed with 10 mL of ground water sample and measured using the fluoride ion selective electrode. The ground water samples which were brought from various sites in Nalgonda district were also tested with pH meter to know their pH profile.

Isolation and adjustment of bacterial isolates on media with Sodium Fluoride

All the glassware was rinsed thoroughly with 10 % HCL before use. High fluoride containing samples of ground water were used for serial dilution and then plated on to Luria–Bertani (LB) agar medium consisting of (g/L-1) Casein Enzyme Hydrolysate (Tryptone) (10), Yeast Extract (5),NaCl (5g),Agar (15). Plates were then incubated at pH 7.0±0.2 and temperature 30°C for 24 h. Bacterial isolates were arbitrarily choosen and purified. After purification of bacterial strains on LB agar, each and every strain was plated on to media with various sodium fluoride (NaF) amounts(25mg/L,50mg/L,100mg/L,200mg/L, 300mg/L, 400mg/L, 500mg/L and 600mg/L)in stepwise manner for their adaptation and resistance study.

Initially, inoculation of LB broth consisting of 25mg/L sodium fluoride (as in the plate medium)was done with Fluoride resistant micro organisms and incubated at 30° C, 120 rpm for 24 h. Sudden exposure of isolates to the high concentrations of fluoride may inhibit the growth of the same hence low concentrations of fluoride was initially selected to let the micro-organisms to get adapted to the fluoride containing media. The isolates were then exposed to a high fluoride concentration on Luria Bertani agar plates with 50 mg/L sodium fluoride concentration. After incubation, the same bacteria were then grown in LB broth consisting of 50 mg/L of sodium fluoride. The same approach was redone for the media with till 600 mg/L fluoride concentration. After three subcultures, Fluoride-resistant isolates were selected from the LB-NaF agar plates.

Characterization of selected bacterial isolates

Morphological, physiological and biochemical characteristics of the high fluoride resistant three isolates (MB1, F and G) were observed in this study. In morphological characteristics, colony morphology and gram’s test were performed.

Fluoride resistant organisms were tested for optimum pH and temperature for their optimum growth using LB broth with respective NaF concentration. Growth of the purified fluoride resistant organisms were checked in LB broth with respective NaF concentration at different temperatures i.e. 5°C to 45°C  with every 5°C increment in an incubator for 48h. Growth ofthe purified fluoride resistant organisms was checked in LB broth consisting of respective NaF concentration at different pH, ranging from 2 to 12 in an incubator for 48hat 30 °C.

Biochemical tests performed on fluoride resistant organisms were to test their protease and amylase producing capabilities. To understand the capacity of protease enzymatic activities of the purified strains, they were first grown on skim milk agar (skim milk powder 28g/L; Yeast extract 2-5g/L; Dextrose 1g/L; Agar 25g/L; distilled water 1L) for 48 hours and observed for proteolysis activity. Fluoride resistant organisms with protease activity hydrolyzed casein and formed clear zone encircling the colonies.

To test the Amylase activity of the purified fluoride resistant isolates, first they were grown on starch agar media (peptone 5g /L; meat extract 3g /L;starch 2g/L;  agar 25g/L; distilled water 1L) for 48h and plates were flooded with iodine solution (Iodine crystals 0.34g; KI 0.66g; distilled water 100mL). Amylase positive isolates formed a transparent halo area encircling the colony. The transparent halo area diameter is directly proportional to the starch hydrolyzing activity of the particular strain under study.

Determination of Bioremediation of fluoride by the selected three high fluoride resistant strains

To determine the fluoride removal activity of the three high fluoride resistant strains ( MB1, F and G), they were inoculated and incubated at temperature 30°C and pH 7 in 100 mL LB broth with 20 mg/L fluoride concentration (sodium fluoride (NaF)) in 250 ml conical flasks at 120 rpm with dextrose (10 g) as carbon source. Fluoride content was determined on every second day till incubation of 8 days. After which there was no considerable reduction in fluoride concentration, which must be due to bacterial cell count reduction. Maximum fluoride concentration observed in fluoride contaminated water was 19.2 mg/L, hence broth with 20 mg/L fluoride concentration was selected for the test. Sample (15ml) was taken out from the broth on every other day (i.e. days 0, 2, 4, 6, 8 and 10),during incubation of 10 days and centrifuged at 4500 rpm for 15 min, and then supernatant was tested for analyzing fluoride concentration with fluoride ion selective electrode(Mukherjee, S., et al., 2017).The above test was performed in triplicate.

Results and Discussion

Fluoride Estimation and pH analysis of water samples

Ten samples brought from different sites of Nalgonda district were analyzed for their fluoride concentration using fluriode ion selective electrode (Figure 1) and their pH (Table 1). Narketpally ground water sample was showing the highest Fluoride concentration i.e. 9.18 ppm, which was selected for isolation of Fluoride resistant bacteria.Whereas, Ramdas thanda (Nampally) ground water sample has less Fluoride concentration of 1.55 ppm.

Vol18No1_-Iso_Thi_fig1 Figure 1: Thermo Scientific ORION STAR A214 ISE meter used for Fluoride estimation of ground water samples.

Click here to view figure

Table 1: Fluoride concentration and pH values of the ten collected samples from ground water in Nalgonda district.

Sample No Location Sample pH Concentration of F
(ppm)
1 Narketpally 8 9.18
2 Chandur 7 1.96
3 Bangarigadda (Chandur) 7 4.88
4 Nermata (Chandur) 8 5.61
5 Ramdas thanda (Nampally) 7 2.23
6 Ramdas thanda (Nampally) 8 1.55
7 Peddapuram (Nampally) 7 1.60
8 Nerellapally (Nampally) 7 7.55
9 PadamatithallaGudem (Chandur) 8 6.24
10 MadaYadavally (Narketpally) 7 6.55

Adjustment of bacterial isolates on media with Sodium Fluoride

In adaptation and resistance study, a total  of eight fluoride resistant strains (MA, MB, MB1, MB1, D2, D1, E1, F and G) were isolated with varying fluoride resistance on LB agar with fluoride concentraion range between 25 mg/L to 600 mg/Lat pH 7 (Table 2). Among eight strains isolated, three strains (MB1, F and G) were showing high fluoride resistance at 500 mg/L (Figure 2), which were further explored for their role in Bioremediation of Fluoride.

Vol18No1_-Iso_Thi_fig2 Figure2: Growth of MB1,F,G strains on LB agar with 50mg/100ml NaF

 Click here to view figure

Table 2: Fluoride resistant bacterial isolates grown on Luria Bertani agar with different fluoride concentrations.

CONCENTRAION OF FLUORIDE
(mg/1000ml)
25 50 100 200 300 400 500
 Fluoride resistant strains MA, MB, MB1, MB1D2, D1, E1,  F, G MA, MB, MB1, MB1D2, D1, E1,  F, G MA, MB, MB1, MB1D2, D1, E1,  F, G MA, MB1, MB1D2, D1, E1, F, G MB1, MB1D2, D1, F, G MB1,MB1D2, D1, F, G MB1, F, G

Characterization of Bacterial isolates

Three strains (MB1, F and G) showing high fluoride resistance at 500mg were characterized morphologically, physiologically and biochemically. All the three strains (MB1, F and G) were in cream colour, MB1, F were in circular shape, whereas, colonies of G strain were slightly irregular in shape. MB1 and G strains were Gram positive whereas, F strain was Gram negative. Optimum temperature for the growth of all the above three strains was 30°C. Optimum pH for the growth of MB1 and G strains was 7.5, whereas, F strain has grown optimally at pH 8. All the mentioned three organisms were protease negative. When observed Amylase activity, G strainwas Amylase positive, MB1 and F strains were Amylase negative (Table 3).

Table 3: Morphological, physiological and biochemical characteristics of the purified fluoride resistant isolates.

Fluoride resistant strains Colony shape Gram’s

character

Optimum temperature of growth Optimum pH of growth Amylase activity
MB1 ciruclar +ve 30°C 7.5 -ve
F circular -ve 30°C 8 -ve
G Slightly irregular +ve 30°C 7.5 +ve

Bioremediation of fluoride by the high fluoride resistant three strains

Bioremediation activity of high fluoride resistant three isolates (MB1, F and G) were performed on every alternate day, i.e on days 0th, 2nd, 4th, 6th, 8th and 10th. Reduced fluoride concentration was observed till 8th day, after that there was no considerable defluoridation due to the reduction in cell viability.When fluoride concentration was present at 20 mgL-1, MB1 strain showed maximum fluoride removal of 68%, whereas, F and G showed 57% and 44%fluoride removal respectively, at pH 7 and 30 °C temperature with carbon source (dextrose) 10gper 100 mL after 8 days of incubation. Results indicate that,MB1 possibly a potential fluoride resistant bacterium with high fluoride bioremediation capacity. Xu et al., 2011 reported a bacterial species with maximum of 22.1% fluoride removal.

Conclusion

In the present study, three fluoride resistant bacteria (MB1, F, G) showing high fluoride resistance (up to 500 mg/L NaF) have been purified from ground water sample of highly fluoride affected location of Nalgonda district i.e. Narketpally. The above three strains showed varying bioremediation activity, in which MB1 strain showed maximum fluoride elimination of 68%, whereas, F and G strains showed 57% and 44%fluoride degradation respectively, when fluoride concentration was present at 20 mgL-1 at 30°C temperature and pH 7 with dextrose (10 g) as carbon source per 100 mL media after incubation of 8 days. Though the MB1 strain was showing promising results in Fluoride bioremediation, further study needs to be conducted in future to achieve longevity and maintenance of bacteria through immobilization studies.

Acknowledgment

Authors feel thankful to the village people in Nalgonda district for their support during the field study.Authors feel thankful to Mr.Veera reedy, lab incharge, Water quality analysis lab, Mission Bhageeratha Section, Panagal for allowing them to analyse fluoride in samples collected from various sites of Nalgonda district ground water,in their lab.

Conflict of Interest

The authors disclosed no potential conflicts of interest, financial or otherwise.

Funding source

At present there is no funding source to support the study.

References

  1. Aguirre-Sierra A., Alonso A., Camargo J.A. Fluoride bioaccumulation and toxic effects on the survival and behavior of the endangered white-clawed cray-fish Austropotamobiuspallipes(Lereboullet). Environ.Contam.Toxicol.2013;65:244-50.
    CrossRef
  2. Ayoob S., Gupta A.K. Fluoride in drinking water: A review on the status and stress effects. Monit. Assess Crit. Rev. Environ. Sci. Technol.2006;36:433–487.
    CrossRef
  3. Banerjee G., Sengupta A., Roy T., Banerjee P., Chattopadhyaya A., Ray A. K. Isolation and Characterization of Fluoride resistant bacterial strains from Fluoride endemic areas of west Bengal, India: Assesment of their Fluoride absorption efficiency. Fl. 2016; 49(4 pt 1):429-440
  4. Chattopadhyay A., Podder S., Agarwal S., Bhattacharya S.Fluoride-induced Histopathology and Synthesis of stress protein in Liver and Kidney of mice.Toxicol.2011;85:327-35.
    CrossRef
  5. Choubisa S.L. Status of Fluorosis in Animals. Natl. Acad. Sci. India Sect. B Biol. Sci. 2012;82(3):331-339.
    CrossRef
  6. Chouhan S., Tuteja U., Flora S.J.S. Isolation, Identification and Characterization of Fluoride Resistant Bacteria: Possible role in bioremediation. Biochem.Microbiol.2012;48:43-50.
    CrossRef
  7. Eren E., Ozturk M., Mumcu E.F., Canatan D. Fluorosis and its Hematological effects. Ind. Health2005;21:255-8.
    CrossRef
  8. Ibrahim M., Rasheed A., Sumalatha M., Prabhakar P. Effects of fluoride contents in ground water: a review. J. Pharm. Appl.2011;2(2):128–134.
  9. Liao Y., Chen J., Brandt B.W., Zhu Y., Li J., Loveren C., Deng D. M. Identification and functional analysis of genome mutations in a fluoride-resistant Streptococcus mutans Plos one. 2015; 10.
    CrossRef
  10. Liao Y., Yang J., Brandt B.W., Li J., Crielaard W., Loveren C.V. Deng D.M. Genetic loci associated with Fluoride resistance in Streptococcus mutans. Microbiol. 2018.https://doi.org/10.3389/fmicb.2018.03093.
    CrossRef
  11. Maltz M., Emilson C.G. Susceptibility of oral bacteria to various fluoride salts. Dent. Res. 1982; 61: 786-790.
    CrossRef
  12. Meenakshi, MaheshwariC.Fluoride in Drinking water and its Removal. J. Hazard. Mat.2006; 137 (1): 456-463.
    CrossRef
  13. Mitsuhata C., Puteri M.M., Ohara Y., Tatsukawa N., Kozai K. Possible involvement of enolase in fluoride resistance in Streptococcus mutans. Dentl. J.2014;24:12-16.
    CrossRef
  14. Mukherjee S., Yadav V., Mondal M.,Banerjee S., Halder Charaterization of a Fluoride-resistant bacterium Acinetobacter sp. RH5 towards assessment of its water defluoridation capability. Appl. Wat. Sci.2017;7:1923-1930.
    CrossRef
  15. Podder S., Chattopadhyay A., Bhattacharya S. In vivo suppression by Fluoride of chromosome aberrations induced by mitomycin-c in mouse bone marrow cells. 2008;41(1):40-3.
  16. Podder S., Chattopadhyay A., Bhattacharya S., Ray M.R. Histopathology and cell cycle alteration in the spleen of mice from low and high doses of sodium fluoride. 2010;43(4):237-45.
  17. Susheela A.K. Dental fluorosis and its extended effect. J.Pediatr.2013;80:715-7.
    CrossRef
  18. States of the art report on the extent of Fluoride in drinking water and the resulting endemicity in India. Report by Fluorosis and Rural Development Foundation for UNICEF, New Delhi. Environ. Monit. Assess.1999;145: 1–65.
  19. Whitford G.M. Fluoride: Metabolism, Mechanism of action and Safety. Hyg. 1983; 57:16-8.
  20. WHO, “Guidelines for Drinking Water Quality (Vol. II): Health Criteria and Supporting Information, Geneva, Switzerland, 1984.
  21. Xu J., Song X.A., Zhang Q., Pan H., Liang Y., Fan X.W., Zhi Y. Characterization of metal removal of immobilized Bacillus strain CR-7 biomass from acqueous solutions. Hazard. Mater.2011;187: 450-458.
    CrossRef
  22. Liao Y., Brandt B.W., Li J., Crielaard W., Loveren C. V., Deng D. M., Fluoride resistance in Streptococcus mutans: a mini review. Oral Microbiol.2017;9(1):DOI: 10.1080/20002297.2017.1344509
    CrossRef
  23. Zhan X.A., Wang M., Xu Z.R., Li J.X. Toxic effects of Fluoride on Kidney function and Histological structure in young pigs. 2006;39(1):22-6.
  24. Zhang X., Gao X., Li C., Luo X., Wang Y. Fluoride contributes to the shaping of microbial community in high fluoride groundwater in Qiji county, Yuncheng city, china. Rep. 9, 2019; https://doi.org/10.1038/s41598-019-50914-6.
    CrossRef

The Use of Biomaterials as Adsorbents for Removing Colorants from Aqueous Solution Case of Straw with Respect to Methylene Blue

$
0
0

Introduction

The presence of dyes in the effluents is of concerning because of its negative impact on many forms of life 1. It is estimated that there are more than 100,000 dyes available on the market with an annual production of 7 x 105 to 1 x 106 tonnes 2. Dyes are among other things that are used in many industries such as textiles, leather, paper and plastics. Furthermore, contamination by the dyes is a critical issue in the world, thus the issue needs to be taken care of and solved by looking for alternatives. The reduction or even elimination of these dyes is therefore necessary given the proven toxicity of some of them. Methods of operational treatments at the laboratory and at an industrial scale already exist, they include physicochemical methods (adsorption, membrane filtration, methods of solid-liquid separations: precipitation, coagulation, flocculation and settling), chemical methods (exchange resin ions, oxidation by oxygen, ozone …) and biological methods (anaerobic and aerobic treatment) 3, 4. These revelations could help industries to design a better waste­water treatment system which would help them to mitigate environmental pollution 5.

We will give an overview of the advantages and disadvantages of each process.

Flocculation and coagulation

Flocculation and coagulation is the technique in which a chemical is used to form colloid of the pollutants in wastewater and make them either settle down or float on top 6. This helps in easy removal of the contaminant. Flocculation is one of the most commonly used techniques in industrial wastewater treatment 7. this helps to easily remove the contaminant. In addition, large quantities of sludge are formed with this process: their regeneration or reuse remains the only way out but requires additional investments 8.

Membrane filtration

Membrane filtration at controlled hydraulic pressure is available in micro filtration, ultra filtration, nano filtration and reverse osmosis. Membrane technologies are environmentally friendly, but they are not applicable to all types of dyes and have limitations when used in the treatment of large volumes of water due to equipment costs and high energy consumption 9.

Oxidation

Oxidation is a method in which wastewater is treated through the use of oxidizing agents. Liakou and al used ozone for the degradation of ado dyes 10, while Malik and Saha used hydrogen peroxide for the oxidation of direct dyes 11. Oxidation methods are among the most widely used techniques for the decoloration of water, as they require small amounts of reagents and reaction times are relatively short But they have the disadvantage 12. of generating by-products of the oxidation reaction which can even be more toxic than the dye itself.

Advanced oxidation processes

They are techniques that involve more than one oxidation process. Advanced oxidation processes can be considered to be an alternative to the above-mentioned treatment procedure. In such processes hydroxyl radi­cals are generated which has the ability to degrade most of the organic pollutants present in the wastewater 13. Advanced oxidation has a very high degradation capacity and the process is very fast, it has been studied by other researchers; kumar and al (2016) in their work coupled carbon adsorption with advanced oxidation5. The Biotechnological methods are green technologies that ensure better aesthetics and a healthier environment while assisting the industries to minimize toxic compounds efficiency of these processes depends on many parameters such as the oxidant concentration, the intensity of UV light, pH, temperature .Thus cost and complication incurred during pH change would also be a big hindrance to commercial acceptance of such process 5.

Biological treatment

These processes are based on the presence of microorganisms in an oxygenated environment (aerobic), in the absence of oxygen (anaerobic) or on a combination of both 12 . Biotechnological methods are green technologies that ensure better aesthetics and a healthier environment while assisting the industries to minimize toxic compounds 14. The solution to this is the biological treatment that breaks down the organic loads of the wastewater into fundamental constituents forms a sludge which does not contain any chemicals like those used for chemical oxidation 15.

This makes it possible for the generated sludge to be used as fertilizer 16 Biological treatment methods seem to be an effective alter­native treatment methods, but the prolong treatment time and high sensitivity of the biological degrading agents – bacteria and fungi poses a major threat to its commercial large scale implementation 5.

Adsorption

Adsorption is a surface phenomenon that involves the accumulation or concentration of substances on a surface or an interface 17. Adsorption is one of the most useful treatment processes of being cheaper, fast, efficient and eco-friendly 18. Moreover adsorption process edges over other processes as it is a sludge free process which provide efficient removal of toxic contaminants from their aqueous solutions 19. In addition, it is important to mention that, unlike the oxidation and electrochemical processes when treating water contaminated with dyes by adsorption, toxic by-products are not formed and in some cases the eliminated species can be recovered 20. In this study we chose the adsorption process as it is considered one of the most widely used techniques in treating water from metal ions and organic compounds. Adsorption process carried over the surface of activated carbon is considered as the most useful strategy among all the techniques owing to its several attributes such as being economical, faster, efficient and eco-benign 18. Several drawbacks limit its frequent use, of course the costs of treatment with this support are interesting and leading to the operational incumbencies during activated carbon regeneration, pore blocking and hygroscopic nature 21. Several studies have been conducted regarding removing of dyes with natural materials, they include the seaweeds 22 industrial waste: red mud 23, agricultural by-products 24 seafood waste: chitin 25. rice husk ash 26, Eichhorniacrassipes 19.

The objective of this study is to value the straw in the elimination of Methylene Blue (MB).

Material and methods

Adsorbat 

The choice of MB dye meets the following criteria: their high solubility in water, simple and rapid analysis by UV spectrophotometry. Table1 represents the main physico-chemical properties of MB 27.

Table 1: Main physico-chemical characteristic of the MB.

Name Methylene Blue (MB)

 

Family Basic dyes
Brute formula C16H18N3SCl
Chemical name 3.7-bis (dimethylamino) phenazathionium
Molar mass (g/mol) 320g/mol
Dimensions (Å) 15 (diameter)
λmax 663nm
 

Structure

 Vol18No1_The_Kas_eq1

Adsorbent

The straw used in this study was washed until all impurities were removed, then dried in the open air for 72 hours, then crushed and sifted to have particles of the same size (<125nm).

Material characterization

The morphology of the straw powder was observed using a scanning electron microscope coupled with EDX (SEM / EDX).Further, Fourier transform infrared spectrograph (FTIR) analysis of surface of raw straw and straw after adsorption of MB for the study of surface chemistry and identification of functional groups that exist. The spectra were collected from 400 cm−1to 4000 cm−1.

Adsorption procedure

After each adsorption experiment, and agitation speed of 150 rpm, the adsorbent was eliminated by centrifugation. Then the obtained liquid was analyzed by UV-visible spectrophotometry watching the changes in absorbance at λmax (MB) = 663 nm. The quantity adsorbed is calculated using the following formula:

Vol18No1_The_Kas_eq2

Qads: Quantity adsorbed at the moment t in mg/g.

V: volume of the solution in ml.

and : are the initial concentration and the concentration at the moment t of the dye respectively in mg/l.

m: mass of the adsorbent in g.

Results and Discussion

Characterization of the adsorbent

Scanning Electron Microscopy (SEM)

Figure 1 show the SEM of the biomaterial before contact between the dye and the biomaterial and after contact. The morphology illustrates the cellulose microfibrils present in a lignin and hemicelluloses matrix.

Vol18No1_The_Kas_fig1 Figure 1: SEM biomaterial before (a)and after (b) the contact time

Click here to view figure

In figure 2 (a), we have the composition of the biomaterial, which shows the presence of carbon elements, oxygen, aluminum, sodium…. Figure 2 (b) shows the SEM of the biomaterial after the contact time. As is clearly shown, there is not much difference in composition; except for the presence of the element nitrogen and sulfur present in the dye. This is mainly because the components of the biomaterial and the dye are of the same nature, but there is a slight decrease in the percentages of carbon and oxygen, this difference being due to the possibility that they participate in the production of carbon dioxide during the absorption process.

Vol18No1_The_Kas_fig2 Figure 2: EDX biomaterial before (a) and after (b) the contact time

Click here to view figure 

Fourier Transforms Infrared Spectroscopy

Figures3(a) and 3(b) are showing respectively the infrared spectra (400-4000 cm-1) of the front straw powder and after adsorption by MB. In the figure 3(a), the spectrum has the peak (very broad) at 3443.16 cm-1 due to the valence vibration of the hydroxyl group (OH) (alcohol bound). The absorption peak at 2922.66 cm-1 could be attributed to the C-H valence vibration (aromatic). The adsorption peak at 1632.03 cm-1 confirms the presence of the carboxyle group C-O (aldehyde or ketone) and two peaks at 1399.51 cm-1-1349 cm-1 are due to the deformation vibrations of the aliphatic C-H grouping while the one at 1112.72 cm-1 corresponds to the valence vibration of the C-O, the band near 1034.18 cm-1 is due to stretching vibrations of the aliphatic C-H, finally the bands 672.32 cm-1 662.90 cm-1; 617.76 cm-1 correspond to the vibrations of mineral materials 29 .In figure 3(b) we observe the same spectra with the offset wave number corresponding to the variation of the functional energy groups. This indicates the existence of a binding process MB made on the surface of the straw powder, with the appearance of certain peak which are: two peak between 2397 cm-1 and 2249 cm-1 which indicates the presence of conjugated carbon or unsaturated di- or possibly the production of CO2 per decarboxylation of the biomass 29 between the bands 1554 cm-1– 1538 cm-1 which indicates the presence of amines primaries N-H, and finally the presence of peak at approximately 1035 cm-1 is due to CN vibration.

Vol18No1_The_Kas_fig3 Figure 3: Infrared biomaterial before the contact time (a). Infrared biomaterial after the contact time (b)

Click here to view figure

Point Of Zero Charge (PZC)

Point of zero charge PZC, is defined as the pH value which is the total net charge (external and internal) the particles on the surface of the adsorbent material is neutral, that is, the number of positive and negative sites is equal 29. 50 ml of distilled water was taken in 100 ml of Erlenmeyers, adjusting the pH of each solution between 2.0 and 11, by adding the appropriate quantities of HCl 1N and of NaOH 1N.0.05g of the straw powder was added to these solutions and after 48 hours under agitation and at room temperature, the final pH value was measured. The PZC is the point where the final pH curve as a function of the initial pH cuts the diagonal. The straw PZC is 6.33, meaning that the adsorbent surface is positively charged at pH less than 6.33, and negatively at a pH greater than 6.33. Therefore, the determination of this parameter is very useful to establish favorable conditions in terms of pH value effectively removing a particular dye. In the case of straw, anionic dyes are expected to be retained at a pH of less than 6.33 and that the removal of cationic dyes is promoted at a pH greater than this PZC value 30.Figure 4.

Vol18No1_The_Kas_fig4 Figure 4: Point of zero charge of the straw

Click here to view figure

Effect of different parameters on adsorption

Effect of Adsorbent Mass

The influence of the adsorbent mass was studied by shaking 50 ml of MB solution at 50mg/l, with different adsorbent masses (straw) ranging from 0.03 to 0.4g under constant agitation for 16 hours at room temperature and initial pH of the solution (pH free MB=4). The study of the influence of the mass of the MB on the adsorbed quantities of this pollutant is represented by the curve in Figure 5.

Vol18No1_The_Kas_fig5 Figure 5: Adsorbed amount of MB as a function of straw mass (condition: concentration= 50 mg/l, room temperature, pH= 4, contact time = 16 h).

Click here to view figure

We note that from the mass 0.1g, the adsorbed quantities of MB no longer evolve. This behavior can be due to the number of adsorption sites which increases with the amount of adsorbent up to a mass of 0.1g, from which the number of sites becomes stable 31, 32. On the other hand, some authors have shown that: the amount of adsorbent added to the dye solution is low, dye cations can easily access the adsorption sites; Adding adsorbent increases the numbers of adsorption sites but dye cations have more difficulty approaching these sites because of the clutter 33. In addition, a large amount of adsorbent creates the particles agglomerations, thus reducing the total adsorption area, therefore, a decrease in the amount of adsorbate per unit mass of adsorbent 33.

Effect of Initial Dye Concentration

To see the effect of MB concentration on adsorption capacity, the process was carried out with an initial dye concentration between 40 and 300 mg/l, the other parameters are constant (m=0.05g, t=16h, room temperature, pH=4). In figure 6, the elimination yield showed a downward trend when the initial MB concentration has been increased 34. At lower concentrations, all the MB present in the adsorption medium can interact with the bonding sites on the adsorbent surface; therefore higher adsorption yields were obtained. At higher concentrations, lower adsorption yields were observed due to saturation of adsorption sites 34.

Vol18No1_The_Kas_fig6 Figure 6: Effect of Initial MB Concentration Variation on Adsorption (condition: m=0.05g, t=16h, room temperature, pH=4)

Click here to view figure

Effect of contact time

Adsorption of the MB on the straw is carried out at different contact times (0 to 180min) with an initial concentration of 70mg/l. The results are shown in Figure 7. They that the dye reduction by adsorption on the straw increases with the increase in contact time. This adsorption is fast and takes place in the first 15 to 20 minutes, beyond this time; the dye adsorption is constant at its maximum value. It can be deduced that the contact time or the appropriate equilibrium time is beyond 20min. For all other adsorption tests with an initial concentration of 70mg/l and to ensure the right choice of contact time, we opted for 60 minutes of agitation.

Vol18No1_The_Kas_fig7 Figure 7: Effect of Contact Time Variation on MB Adsorption (condition: m=0.05g, C0=70mg/l, room temperature, pH=4)

Click here to view figure

pH effect

The pH influence on MB removal rate on straw (Figure 8), was studied using a. VENGO 6230 brand pH meter. The experiments were carried out by mixing 0.05g of straw with 70mg/l concentration MB solution, and at room temperature. The pH of the solution has been adjusted to specific values of: 2; 4; 6; 8; 10 and 12 by adding a few drops of HCl (0.1M) and NaOH (0.1 M). The mixtures are subjected to constant agitation for 1h. After centrifugation, the liquid is recovered and analysed by UV-Visible. The initial pH for the adsorption is an important process parameter [35]. This parameter influences the distribution of adsorbent species as well as the ionization of functional groups on the surface of the adsorbent [36].Figure 8 shows the percentage of elimination of BM by the straw as a function of the pH of the medium (2, 4, 6, 8, 10 and 12). It can be seen that at pH = 2 the lowest percentage of elimination was obtained. This can be attributed to increased competition between hydronium ions and BM cations for adsorption sites on the surface of the biosorbent. In the pH range of 6 to 12, a large increase in the percentage of elimination.

Vol18No1_The_Kas_fig8 Figure 8: Effect of pH of medium on MB removal (condition: m=0.05g,  C0=70mg/l, room temperature, t=16h).

Click here to view figure

The change in pH of the sample affected the surface charge of the adsorbents and the adsorptive process. This might be due to the dissociation of functional groups present on the active sites of the adsorbent [37].The straw exhibited a pHpzc (6.33), therefore the greater increase in percent removal corresponds to a greater increase in the number of negatively charged sites. At pH values of the solution above the pHpzc, the number of negative charges predominates over the positive ones and, therefore, the feasibility of the BM adsorption process by ion exchange increases.

Effect of temperature

It can also affect the adsorption process, temperature factor. The adsorption of the MB of an aqueous solution at different temperatures was studied in a temperature range from 20 to 60°C.

Vol18No1_The_Kas_fig9 Figure 9: Effect of temperature change on MB adsorption (condition: m=0.05g, C0=70mg/l, room temperature, t=16h)

Click here to view figure 

As is clear in Figure 9, an increase in temperature from 20°C to 60°C is accompanied by an increase in the removal percentage of the MB dye from 53% to 93%. This phenomenon, suggests that the reaction is endothermic whose temperature increase favors the adsorption mechanism. This endothermic phenomenon of adsorption was also observed with other materials for the same dye studied 38, 39.

Kinetic Adsorption Models

The determination of the batch adsorption kinetic parameters is essential for the design of adsorption columns in pilot plants for further scale up 40. In this study, pseudo first order model, pseudo second order model were the key kinetic models that were investigated in the adsorption process by straw, their parameters and the MB correlation coefficients were calculated from figure (11) and listed in Table 2.

Pseudo-first order kinetic model (Lagergren model) 41 and Pseudo second order kinetics model: (Ho and Mckay)42

Vol18No1_The_Kas_eq3

With: K1 (L.min-1): rate constant of adsorption of the kinetics of the pseudo first order.

Vol18No1_The_Kas_eq4

With: K2 (g.mg-1.min): rate of adsorption constant of the pseudo second order kinetics.

Vol18No1_The_Kas_fig10 Figure 10: Pseudofirst order (a) and pseudo second order (b) of the MB adsorbed by straw.

Click here to view figure 

Figure10 (a) shows that for the first order kinetic model the coefficient of determination R2 is equal to 0.787 and the adsorption capacity is much lower than that obtained experimentally. On the other hand, the coefficient of determination for the second-order kinetic model shown in Figure10 (b)  is 0.994 were closer to one, and the values obtained are comparable with the experimental values (Qexp=51.67mg/g). These observations lead us to say that the adsorption with methylene blue does not express a controlled diffusion process because it does not follow the pseudo-first equation; given by Lagergren the adsorption process therefore follows a second-order kinetic model, which considers the external mass transfer, the intra-particulate diffusion.

Table 2: Methylene blue adsorption kinetic parameters

dye K Qe R2
Pseudo first order MB 0.011515 22.08 0.787
Pseudo second order MB 0.001402 55.56 0.994

Adsorption isotherm

The study of adsorption isotherms, allows us to better understand how adsorbed MB molecules interact with the adsorbent (straw), when the adsorption process approaches to a state of equilibrium. Adsorption isotherms provide many fundamental physico-chemical data to estimate the applicability of the adsorption process, to express surface properties and the adsorbent affinity which can also be used to find the maximum adsorption capacity of a mass [43]. Many isothermal models are available in the literature, Freundlich and Langmuir models are the most frequently used to describe the experimental adsorption isotherm data, due to their simplicity

Langmuir model

Langmuir isotherm model predicts the formation of a single layer of adsorbed molecules (molecular monolayer) on specific sites and without interaction between them with an adsorption heat independent of the surface. The Langmuir model is defined by the following equation 44:

Vol18No1_The_Kas_eq5

With Ce (mg.l-1) is the concentration at equilibrium, Qe and Qm (mg.g-1)are the amount adsorbed to equilibrium and the maximum quantity adsorbed to the saturation of the monolayer, KL (l.mg-1) is the adsorption equilibrium constant depending on the temperature.

The linear transform of this model has as equation:

Vol18No1_The_Kas_eq6

If Langmuir’s equation is checked, by carrying

Vol18No1_The_Kas_eq7based on

Vol18No1_The_Kas_eq8 , the slope line is

Vol18No1_The_Kas_eq9and ordered originally

Vol18No1_The_Kas_eq10, which allows us to determine Qe and KL.

The graphical presentation of the Langmuir isotherm is shown in Figure11 (a).

The adimensional parameter of Hall RL can verify the favorability of the Langmuir isotherm in the following form 45:

Vol18No1_The_Kas_eq11

With C0 the initial concentration in mg L-1. If RL<1 Isothermal favourable, RL>1 Isothermal unfavourable and if RL=1 Isothermal linear.

Freundlich model

The Freundlich isotherm model assumes heterogeneity of the adsorption surface with sites of different adsorption energies, as well as the possibility of multi-layer formation of adsorbed molecules with interactions between them [46]. The Freundlich model is described by the following equation:

Vol18No1_The_Kas_eq12

Where Qe (mg.g-1) is the amount adsorbed to equilibrium, KF and n are Freundlich constants characteristic of the efficiency of a given adsorbent towards of a given solute, this (mg .L-1) is the solute concentration at equilibrium.

The logarithmic scaling of this equation makes it possible to verify its linear transformation:

Vol18No1_The_Kas_eq13

By tracing lnQe based on lnCe, slope line 1/n is obtained and ordered originally lnKF.

The graphical presentation of the Freundlich isotherm is shown in Figure 11 (b)

Vol18No1_The_Kas_fig11 Figure 11: Langmuir (a) and Freundlich (b) isotherm for MB adsorption on straw

Click here to view figure

Table (3) shows the values of the Langmuir and Freundlich constants, extrapolated from the lines of these two models.

Table 3: Langmuir and Freundlich constant

Langmuir model Freundlich model
Qm KL RL R2 KF n R2
58.8235 0.163461 0.08037 0.930 23.9507 5.49 0.961

Based on coefficients of determination, presented in table (3), it can be said that the Freundlich model (Figure11 (b)). Better describes the adsorption isotherm of the MB on the straw, with a correlation coefficient R2=0.961, and like n>1, then it is a physical adsorption.

Conclusion

The results of the adsorption of methylene blue on the straw showed that retention is fast. The temperature has a favorable effect on the percentage dye removal which suggests that the adsorption of MB on the straw was an endothermic process. The MB maximum adsorption occurred at pH> PZC meaning that the electrostatic forces influence on the dye binding power. The modeling of adsorption results showed that the model of pseudo-second-order offers better correlation of kinetic data and the Freundlich’s model better describes the adsorption phenomenon of methylene blue on the straw. The powder of the straw is a biomaterial having interesting adsorption capacities and can be an alternative to other commercial supports.

Acknowledgement

The authors would like to thank the stuff in charge of the laboratory “Materials and Interfacial Systems”; Abdelmalek Essadi University, Science Faculty, Tetouan, Morocco, for their assistance in making this study a success.

Conflict of Interest

All the authors acknowledge that they have no conflicts of interest.

Funding Source

There is no funding source

References

  1. Hammed B.H. Evaluation of papaya seeds as a novel non-conventional low-cost adsorbent for removal of methylene blue. Journal ofHazardous Materials. 2009; 162:939-944.
    CrossRef
  2. Ravi K., Deebika B., Balu K. Decolourization of aqueous dye solutions by a novel adsorbent: application of statistical designs and surface plots for the optimization and regression analysis. Journal of Hazardous Materials. 2005; 122:75-83.
    CrossRef
  3. Pokhel D., Viraraghavan T. Treatment of pulp and paper mill wastewater: a review. Science of the Total Environement.2004; 333:37-58.
    CrossRef
  4. Robinson T., McMullan G., Marchant R., Nigam P. Remediation of dyes in textiles effluent: a critical review on current treatment technologies with a proposed alternative. 2001; 77:247-255.
    CrossRef
  5. Kumar A., Sengupta B., Kannaujiya M.C, Priyadarshinee R., Singha S.,Dasguptamandal D., Mandal T. Treatment of coke oven using ozone with hydrogen peroxide and activated carbon. Desalin Water Treat. 2017; 69:352–365.
    CrossRef
  6. Hargeaves A.J., Vale P., Whelan J., Alibardi L., Constantino C., Dotro G., Cartmell E., Campo P. Coagulation-floculation process with metal salts, synthetic polymers and biopolymers for the removal of trace metals (Cu, Pb, Ni, Zn) from municipal .Wasterwater Clean Environ . 2018; 20(2) : 393-402.
    CrossRef
  7. Nair A.T., Ahammed M.M. The reuse of water treatment sludge as acoagulant for post-treatment of UASB reactor treating urban wasterwater. J Clean Prod .2015; 96:272-281.
    CrossRef
  8. Errais E .Réactivité de surface d’argiles naturelles étude de l’adsorption de colorants anioniques. Thèse Université de Strasbourg, France.2011 p75-86.
  9. Kumar K.V., Porkodi K. Mass transfer, kinetics and equilibrium studies for the biosorption of methylene blue using Paspalum notatum. Journal of Hazardous Materials. 2007; 146: 214-226.
    CrossRef
  10. Liakou S., Pavlou S., Lyberatos G. Ozonation of azo dyes. Water Science and Technology.1997; 35: 279-286.
    CrossRef
  11. Malik P.K., Saha S.K. Oxidation of direct dyes with hydrogen peroxide using ferrous ion as catalyst. Separation and Purification Technology. 2003; 31: 241-250.
    CrossRef
  12. Gupta V.K., Carrott P.J., Ribeiro M.M., Suhas. Low-cost adsorbents: growing approach to wastewater treatment. Critical Reviews in Environmental Science and Technology. 2009; 39: 783-842.
    CrossRef
  13. Akpotu S.O., Oseghe E.O., Ayanda O.S., Skelton A.A., Msagati T.A., Ofomaja A.E. Photocatalysis and biodegradation of pharmaceuticals in wasterwater. Effect of abiotic and biotic factors. Clean Technol environ. 2019
    CrossRef
  14. Kumar A., Priyadarshinee R., Singha S., Sengupt B., roy A., Dasgupta D., Mandal T.Biodegradation of alkali lignin by Bacillus flexus RMWWII: analyzing performance for abatement of rice mill wastewater. Waterscience and Tecnology 2019; 80(9): 1623-1632.
    CrossRef
  15. Bokare A.D., Choi W. Review of iron-free Fenton-like systems for activating H2O2 in advanced oxidation processes. J Hazard Mater. 2014; 275:121–135.
    CrossRef
  16. Cartes J., Neumann P., Hospido A., Vidal G. Life cycle assessment of management alternatives for sludge from sewage treatment plants in Chile: does advanced anaerobic digestion improve environmental performance compared to current practices?J Mater Cycles Waste.2018; 20:1530–1540.
    CrossRef
  17. Cooney D. Adsorption design for wastewater treatment. Lewis Publishers. 1999 p. 30.
  18. Pizarro J., Castillo X., Jara S., Ortiz C., Navarro P., Cid H., Rioseco H., Barros D., Barros N. Adsorption of Cu2+on coal fly ash modified withfunctionalized mesoporous silica. Fuel.2015; 156:  96–102.
    CrossRef
  19. Kumar A., Jash A., priyadarshinee R., Sengupta B., Dasgupta D., Halder G., Mandal T. Removal of catechol from aqueous solutions by adsorption using low cost activated carbon prepared from Eichhornia crassipes.Desalin Water Treat.2017; 73: 389-398
    CrossRef
  20. Rafatullah M., Sulaiman O., Hashim R., Ahmad A. Adsorption of methylene blue on low-cost adsorbents. Journal of Hazardous Materials. 2010; 177:  70-80.
    CrossRef
  21. Mohan D., Pittman C. U., Steele P. H. Journal Colloid interface Sci. 2006; 297:489-504.
    CrossRef
  22. Ranam A., Rao J.R., Nair B.U. Adsorption of phenol onto activated carbon from seaweed, determination of the optimal experimental parameters using factorial design. Taiwan Inst. Chem. Eng. 2011; 142:952-956.
    CrossRef
  23. Bhatnagar A., Vilar V. J. P., Botelho C. M. S., Boaventura R. A. R. A review of the use of red mud as adsorbent for the removal of toxic pollutants from water and wastewater. Technol. 2011; 32:231-249.
    CrossRef
  24. Djilani C., Zaghdoudi R., Modarressi A., Rogalski M., DjaziF.,Lallam Elimination of organic micropollutants by adsorption on activated carbon prepared from agricultural waste.  Chem. Eng 2012; 189-190, 203-212.
    CrossRef
  25. Liu Y., Zheng Y., Wang A. Enhanced adsorption of methylene blue from aqueous solution by chitosan-g-poly, acrylic acid./vermiculite hydrogel composites. J. Environ. Sci. 2010; 22:486-493.
    CrossRef
  26. Kumar A., Singha S., Sengupta B., Dasgupta D., Datta S., Mandal T. Intensive insight into the enhanced utilization of rice hisk ash: abatement of rice mill wastewater and recovery of silica as avalue added product. Eco Eng. 2016 ;  91: 270-281
    CrossRef
  27. Bouchemal N., Merzougui Z., Addoun F. Adsorption en milieux aqueux de deux colorants sur charbons actifs à base de noyaux de dattes.Journal de la société Algérienne de chimie. 2011; 21:1-14
  28. Albis A., López A., Romero M. Removal of methylene blue from aqueous solutions using cassava peel (Manihot esculenta) modified with phosphoric acid. 2017; 15:60-73.
    CrossRef
  29. Franks G. V., Meagher L. The isoelectric points of sapphire crystals and alpha-alumina powder. Colloids and Surfaces A:Physicochemical and Engineering Aspects. 2003; 214(1-3): 99-110.
    CrossRef
  30. Villa F., Anaguano A. Detemination of the point of zero change andisoelectric point of two agricultural wastes and their application in the removal of colorants. Revista de investigacionAgraria y ambiental. 2013; 4:27-36.
  31. Gupta V.K., Mittal A., Gajbe V. Adsorption and desorption studies of a water soluble dye, Quinoline Yellow, using waste materials .Colloid and Interface Science. 2005; 284(1) : 89-98
    CrossRef
  32. Tsai W.T., Hsu H.C., SuT.Y., Lin K.Y., Lin C.M., Dai T.H. The adsorption of cationic dye from aqueous solution onto acid-activated andesite. Hazard Mater. 2007; 147(3):1056‑1062.
    CrossRef
  33. Bennani K. A., Mounir B., Hachkar M., Bakasse M., Yaacoubi A. Removal of basic dye Methylene Blue in aqueous solution by Safi clay .Sci. Eau.2010; 23(4) :375-388
    CrossRef
  34. Ozer A., DursunG.Removal of methylene blue from aqueous solution by dehydrated wheat bran carbon. J. Hazard. 2007; 146 : 262-269
    CrossRef
  35. Kushwaha J.P., Srivastava V.C., Mall I.D. Treatment of dairy wastewater bycommercial activated carbon and bagasse fly ash parametric, kinetic andequilibrium modelling, disposal studies. Bioresour. Technol.2010; 101: 3474–3483).
    CrossRef
  36. Rosas C.J.M Aplicacion de residues agricolas para el tratamiento de agua contaminada con colorants. Theses Universidad Autónoma de Nuevo León, México.2012p4
  37. Totlani K., Mehta R., Mandavgane S.A. 2012. Comparative study of adsorption of Ni (II) on RHA and carbon embedded silica obtained from RHA. Chem. Eng. 2012; 181: 376–386.).
    CrossRef
  38. Somasekhara R. M. C., Sivaramakrishna L., Varada R. A. The use of an agricultural waste material, Jujuba seeds for the removal of anionicdye (Congo red) from aqueous medium. Journal of Hazardous Materials 2012; 203-204:118– 127.
    CrossRef
  39. Venkat S ., Vijay B. P. V.Kinetic and equilibrium studies on the removal of Congo red from aqueous solution using Eucalyptus wood (Eucalyptus globulus) saw dust. Journal of the Taiwan Institute of Chemical Engineers 2013; 44:81-88.
    CrossRef
  40. Chatterjee S., Kumar A., Basu S., Dutta S. Application of response surfacemethodology for methylene blue dye removal from aqueous solution usinglow cost adsorbent. Chem. Eng.2012; 181: 289–299.).
    CrossRef
  41. Lagergren S.Kungliga Svenka Vetenspsakademiens. Handlingar. 1898; 24:1-39
  42. Ho Y. S., McKay G. The Kinetics of Sorption of Divalent Metal Ions onto Sphagnum Moss Peat. Water Research2000; 34: 735-742.
    CrossRef
  43. Senturk H. B., ozdes D., Duran C. Biosorption of Rhodamine 6G from aqueous solutions onto almond shell (Prunus dulcis) as a low cost biosorbent. Desalination. 2010; 252: 81-87.
    CrossRef
  44. Langmuir I. The Constituction and fundamental properties of solids and liquids. Part I. Solids. Journal of the American Chemical Soc.1916; 38: 2221-2295.
    CrossRef
  45. Hall K. R., Eagleton L. C., Acrivos A., Vermeulen T., Industrial and Engineering Chemistry Fundamentals. 1966; 5(2): 212-223.
    CrossRef
  46. Freundlich H.M.F. Uber Die Adsorption in Losungen. Industrial and Engineering Chemistry Fundamentals. 1906; 57: 385- 470.
    CrossRef

Carbon and Nitrogen Sources Effect on Pectinase Synthesis by Aspergillusniger Under Submerged Fermentation

$
0
0

Introduction

Pectinase enzymes which acts on the glycosidic bonds breakdown in the galacturonic acid chains of the pectin materials17. The enzymes production has developed rapidly and currently, pectinase are the most important products obtained for human needs through microbial sources. Because of their wide applications in food industry29, pectinases accounts for 25% of the global food enzyme market7. Because of wide industrial applications and important cost, there is a need to minimize the production costs. Agricultural wastes, like wheat bran, containing large amount of pectin can be considered as other source of substrate for pectinase production by avoiding the use of expensive chemical components in the media formulation. Agroindustrial by products can be successfully employed for pectinolytic enzyme production and as these residues are low cost raw materials available locally, they can be used for cost effective enzyme production.

Pectinolytic enzymes have been observed in a large variety of bacteria and fungi, mostly commercial preparations of pectic enzymes are acquired from fungal sources. Due to pH optima of enzymes produced by fungal strains are in a range naturally found in materials to be processed. But Aspergillus species are employed in production of pectinases which are used in various applications like extraction, clarification of fruit juices, in maceration of vegetables to produce purees and also in wine making16.

Pectinases are commercially produced using either submerged or solid state fermentation cultures. Semi-solid or submerged fermentation medium is more favorable to fungal growth since in the medium, the moisture content, agro-waste (substrate) and pH are the main factors determining enzyme yield. In solid state fermentation culture, microbial growth and product formation occur at or near the surface of solid substrate particles with low moisture content whereas, in submerged fermentation culture, production of pectinases usually depends on medium compositions such as nitrogen source, pH of the medium, temperature, pectin concentration, and fermentation time.23Inspite of a lot of researchers recommended solid state fermentation for production of microbial enzymes (ex: pectinase), submerged fermentation technique is the suitable system on large-scale5. Therefore, utilization of agricultural residues as carbon sources in enzyme production media has increased enzyme activity with reduction of the production cost30.The aim of the present work is to assess the enhancement of enzyme production by A. nigerin different carbon and nitrogen sources in submerged fermentation using wheat bran as substrate.

Materials and Methods

Sample collection

Soil samples collected from the site where the vegetable wastes were dumped which were obtained from different areas of Warangal, in sterile polythene bags and were immediately transferred to the laboratory for microbial assessment and analysis.

Isolation of fungi

Potato dextrose agar (PDA) medium consists of (gm/L): Dextrose, 20.0, agar, 15.0 and potato infusion, 200; was chosen as growth medium for preliminary isolation of fungi. All the above mentioned samples were suspended in sterile distilled water. These suspensions were stirred for 20 minutes before making serial dilutions. The dilution-plate method was employed for the isolation purpose12.  After serial dilutions, suspensions were spread on potato dextrose agar medium containing 0.08% streptomycin to avoid bacterial contamination. These plates were incubated in an inverted position at 28˚C for 7 days14. Fungi growing on the agar plates were sub-cultured and were preserved on potato dextrose agar slants under refrigeration condition at 4˚C prior to use and maintained for further identification and enzyme studies.

Morphological identification of the fungi

Based on their morphology, mycelia structure and spore formation, the fungal isolates were identified. The identified fungal strains were stained by lacto phenol cotton blue staining.

Primary Screening of Pectinolytic Fungi

Selection of 30 potential pectinolytic fungi were assessed by using 0.1 mL of inoculum from the enriched medium, they were plated on pectin agar media contains 1% pectin, 0.1%, K2HPO4; 0.2%, NaNO3; 0.05%, MgSO4. 7H2O; 0.05%, KCl; 10mg, FeSO4.7H2O; 3%, Sucrose; 0.001%, CuSO4 and 0.001%, ZnSO4 and incubated at 28±1ºC for 4-5 days. After 5 days of incubation, plates were flooded with iodine- potassium iodide solution and observed for zone of hydrolysis around the wells29. Among 30 isolates, two fungal strains showed good activity and identified them as A. niger and A. flavus. Among these two isolates A. niger showed better activity over A. flavus, hence only A. niger presented in this communication.

Pectinases Production Under Submerged Fermentation (Smf)

250 ml Erlenmeyer flask containing 100 ml of culture broth (pH 7.0), contains 1% wheat bran as pectin substrate, 3%, sucrose; 0.2%, NaNO3; 0.1%, MgSO4.7H2O; 0.05%, KCl; 0.01%, K2HPO4; 0.05%, FeSO4.7H2O; 0.001% CuSO4  and 0.001%, ZnSO4; were used for assay of pectinases. After sterilization of the Erlenmeyer flasks containing fermentation medium, young fungal mycelia of 3 day old cultures at the growing edges were inoculated aseptically. Inoculated flasks were incubated in the orbital shaker operating at 120-180 rpm at 28±1ºC for 16 days. 10 ml of incubated broth was withdrawn from the culture flasks at different time intervals. The supernatants obtained from the centrifugations were used as partially purified enzyme sources for assay.

Effect of various carbon sources on the pectinases production was assessed in submerged fermentation (SmF) by culturing Aspergillusniger.  The above mentioned production media were supplemented with various carbon sources at 1% (w/v). The carbon sources used in this investigation were fructose, dextrose, lactose, maltose, sucrose, mannitol, arabinose, cellulose. Fermentation medium lacking carbon source was considered as control. Concentration of best carbon source was optimized for pectinase production by using its different concentration (w/v) viz., 0.05%, 1.00%, 1.50%, 2.00%, 2.50%, 3.00% and 3.50%. The inoculated flasks were incubated at temperature 28±1˚C for 16 days enzyme assay was carried from the enzyme source.

To determine the influence of several inorganic and organic nitrogen compounds on pectinase production, ingredients considered as supplying nitrogen in the basal medium were replaced with various nitrogen sources at 0.2% (w/v). The employed nitrogen sources were (casein, peptone, yeast extract, ammonium chloride, urea, ammonium molybdate, sodium dihydrogen phosphate, potassium nitrate, sodium nitrate). Fermentation medium lacking nitrogen source was considered as control. Different concentrations 0.05%, 0.10%, 0.20%, 0.30%, 0.40%, 0.50% w/v of best nitrogen source were added to the production media to study the effect of concentration of nitrogen on enzyme production. After incubation the inoculated flask at temperature 28±1˚for 16 days pectinolytic activity were detected as previously described in SmF

Enzyme recovery

After incubation, to remove mycelium, the culture medium was filtered using filter paper Whatmann No.5, then filtrate was centrifuged at 5000 rpm for 10 min and the clear supernatant was used as the extracellular enzyme source.

Quantitative assay for exo polygalacturonase (Exo-PG)

Exo polygalacturonases, activity was assayed by estimating reducing sugars using DNS method20. The exo-PGase activity was measured using 1% polygalacturonic acid (PGA) as substrate, prepared in sodium acetate buffer (0.1M; pH 4.5). The reaction mixture (2mL) contained equal amounts of enzyme (1.0mL) and substrate (1.0mL) and incubating at 50ºC for 30min in a water bath.   By addition of 3ml of 3,5- dinitrosalicilic acid (DNS) reagent, the reaction was stopped and the contents were boiled for 15 minutes. The color developed was read at 540 nm. D-galactouronic acid (1mg/mL) standard curve was prepared to find out the amount of reducing sugars liberated. Enzymatic activity expressed as unit per ml (U/ml), which is defined as the amount of enzyme, which liberates 1μmole of galacturonic acid (reducing sugar) per mL per minute under assay conditions.

Quantitative assay for Endo Polygalacturonase (Endo-PG)

Wood’s viscometric method36 was employed to estimate the endo-PG. Polygalacturonic acid (0.5%) was prepared by dissolving 0.5g of polygalacturonic acid in 100ml citrate buffer (pH 5.5). The reaction mixture for the estimation of endo-PG contained polygalacturonic acid (0.5%) substrate, citrate buffer (pH 5.5) and enzyme source in 4:1:2 ratios. The reaction mixture consisting of 12ml of substrate, 4ml of enzyme and 1ml of citrate buffer. The viscosity loss was measured for every 10 minutes over a period of 30 minutes. The reaction mixture with inactivated enzyme (heat killed) and distilled water as control. The viscosity loss percentage was calculated by the formula as given below.

Vol18No1_Car_Gou_eq1

Where,

V = loss of viscosity percentage

ti = flow time of reaction mixture + inactive enzyme.

ta = flow time of reaction mixture + active enzyme

t0= flow time of distilled water + active enzyme at ‘‘O’’ time

The Relative Enzyme Activity (REA) of endo-PG was calculated by dividing 1000 with time required for 50% loss of viscosity (t50) and in relative viscometric units (RVU).

Vol18No1_Car_Gou_eq2

Where tv50 = time required in minutes taken for 50% loss of initial viscosity

Quantitative assay for pectin methyl esterase activity (PME)

Pectin methyl esterase activity was measured by the standard method15. PME activity can be measured either by measuring the amount of methanol released or increase in free carboxyl group by monitoring pH changes.

Pectin methyl esterase activity was estimated by titration method against NaOH with phenolphthalein as a pH indicator. PME activity was assayed by 20ml of 1% pectin (dissolved in 0.15M NaCl, pH-7.0) and 4ml of enzyme extract were taken in a beaker and incubated for 1hr. After incubation, the solution was titrated against the 0.02N NaOH to reach pH 7.0 using the phenolphthalein as indicator (colour change from colourless to pink). The heat killed enzyme extract was used as control.

Pectin esterase activity = Vs – V b (Normality of NaOH) × 100/Vt

Where, Vs-volume of NaOH used to titer the sample (ml), V b-volume of NaOH used to titer the blank (ml), V-volume of incubation mixture (ml), t-Reaction time (min). PME activity was expressed as milliequivalents of NaOH consumed min-1 ml-1 of enzyme extract under the assay conditions.

Quantitative assay for endo pectinlyase (Endo-PL)

Endo-PL activity was assayed viscometric method 36. 1% pectin was used as substrate in this assay. Four ml of culture supernatant and 0.8 ml of tris-HCl buffer pH (8.0) were added to 12ml of pectin solution. Viscosity changes of reaction mixture were determined by using Ostwald viscometer. Initial reading time and the reading after 30 minutes of incubation were determined. The loss of viscosity was estimated for every 10 minutes over a period of 30 minutes. The reaction mixture with inactivated enzyme (heat killed) and distilled water served as control. Enzyme activity was expressed in RVU units (relative viscometric units).

Statistical analysis

The enzyme activities are presented as Mean±SE of all values. Results found in this study were subjected to analysis of variance using oneway ANOVA and difference between means were separated by Duncan Multiple Range Test using SPSS software 17.0 version33. The results are presented in tables (1-2) and figures (1-8)

Results and discussion

Influence of carbon sources on pectinases

The influence of carbon source on exo-PG was recorded minimum on 8th day and maximum on 12th day of incubation period. The highest exo-PG (0.690U/ml) was obtained in the medium containing sucrose on 12th day of incubation followed by dextrose (0.610U/ml) and fructose (0.520 U/ml). Cellulose (0.310 U/ml) and lactose (0.300 U/ml) showed intermediate production (fig. 1). Rest of the carbon sources showed meager activity. Very less activity was obtained in arabinose (0.024U/ml). Similar results were expressed by Banuet al.6 in sucrose (29.1U/ml) and by Mojsov21 in galactose (140.0UL-1) by A. niger.

Vol18No1_Car_Gou_fig1 Figure 1: Effect of carbon source on exo-PG production by A. niger under SmF using wheat bran

Click here to view figure

The influence of carbon source on endo-PG was recorded maximum on 8th day comparatively 12th day of incubation period. The maximum endo-PG activity was found in the medium containing sucrose (105.0RVU) on 8th day of incubation (fig 2). Very less activity was obtained in dextrose (27.76RVU). In another study Patil and Dayanand25 reported that 4-6% glucose increased the production of pectinase in submerged fermentation.

Vol18No1_Car_Gou_fig2 Figure 2: Effect of carbon source on endo-PG production by A. niger under SmF using wheat bran.

Click here to view figure

Results from the figure 3 reveal highest endo-PL activity recorded in the medium containing sucrose (58.0RVU) followed by maltose (57.0RVU) on 8th day of incubation. Very less activity recorded in arabinose (28.20 RVU) and no activity was in fructose on 8th day. Maximum production was recorded on 8th day of incubation comparatively 12th day. Control failed to show the enzyme activity. Several workers reported that fructose influenced the maximal production of pectinase out of all carbon sources used2. The reason could be that the fructose is a simple sugar and pectinolytic microorganism utilizes simple sugars more efficiently as compared to complex polysaccharide such as starch and produced maximum, galacturonic acid from their substrates.

Vol18No1_Car_Gou_fig3 Figure 3: Effect of carbon source on endo-PL production by A. niger under SmF using wheat bran.

Click here to view figure

The highest PME activity was obtained in the medium containing sucrose (0.051 meq. of NaOH consumed/min/ml) followed by maltose (0.049 meq. of NaOH consumed/min/ml) on 8th day of incubation (fig 4). Rest of the carbon sources showed minimum production. Very less activity obtained in arabinose (0.004 meq. of NaOH consumed/ min/ml) on 12th day of incubation. Solis-Pereira et al.32 was proved that there was a catabolite repression of pectic enzymes in presence of glucose and other sugars.

Vol18No1_Car_Gou_fig4 Figure 4: Effect of carbon source on PME production by  A. niger under SmF using wheat bran.

Click here to view figure

Enzyme production started at 0.50% concentration, reached optimum at 1.0% and gradually decreased in subsequent concentrations (Table. 1.).  Sucrose at a concentration of 1% was observed to be the best carbon source for all types of pectinases by A. niger. Among different concentration of sucrose a significant exo-PG activity (1.529U/ml) was observed on 12th day of incubation. While increased endo-PG (83.0RVU) and endo-PL (86.7RVU) activity observed on 8th day of incubation. Similarly increased PME activity also observed (0.060 meq. of NaOH consumed/min/ml) on 8th day of incubation. Twelfth day of incubation favoured more exo-PG activity and rest of the pectinases shows maximum production on 8th day.

The present results are on par with the findings of Palaniyappan and his associates who reported an enhanced activity of pectinases in glucose as carbon source by A. fumigatus24. Similar findings were observed earlier in which low enzyme production with other carbon sources is maybe due to catabolite repression3. Previous studies reported the same results that glucose followed by lactose observed with the highest production of pectinase24. Jayaniet al.11 revealed different results about glucose. Phutelaet al.26 mention the positive effect of sucrose on the production of pectinolytic enzymes, produced by Aspergillus fumigatus, which leads to the enhanced production of the enzymes.

Table 1: Effect of sucrose concentration on pectinases production by A. niger under SmF using wheat bran.

Sucrose (%) Exo -PG 

(U/ml)

Endo-PG

(RVU)

Endo-PL

(RVU)

PME (meq. of NaOH

consumed/min/ml)

8th day 12th day 8th day 12th day 8th day 12th day 8th day 12th day
0.50% 0.807bc±0.001 1.480b±0.005 65.0b±0.001 28.50cd±0.001 55.0cd±0.003 17.5b±0.001 0.0020d±0.001 0.001f±0.001
1.00% 1.099a±0.001 1.529a±0.005 83.0a±0.001 46.0a±0.001 86.7a±0.003 20.0a±0.001 0.060a±0.001 0.030a±0.001
1.50% 0.893b±0.001 1.391cd±0.005 57.0cd±0.001 30.0bc±0.001 57.8b±0.003 10.1c±0.001 0.0046b±0.001 0.020b±0.001
2.00% 0.893b±0.001 1.305cd±0.005 57.0cd±0.001 30.0bc±0.001 46.7e±0.003 8.6d±0.001 0.0031c±0.001 0.020b±0.001
2.50% 0.893b±0.001 0.960ef±0.005 40.5e±0.001 28.5cd±0.001 40.0f±0.003 6.5e±0.001 0.0031e±0.001
3.00% 0.893b±0.001 0.807g±0.005 24.0f±0.001 24.0d±0.001 35.5g±0.003 4.2f±0.001 0.0020d±0.001
3.50% 0.893b±0.001 0.910ef±0.005 12.5g±0.001 10.5e±0.001 28.0h±0.003 2.1g±0.001 0.0020d±0.001

Values are significant at P< 0.005

— No activity

Effect of nitrogen source on pectinases

The effect of nitrogen sources on exo-PG production by A. nigerunder SmF shown in figure 5. It was observed that sodium nitrate (0.613U/ml) followed by urea (0.550U/ml), peptone (0.506U/ml), ammonium chloride (0.480U/ ml), potassium nitrate (0.430U/ml) supported maximum for the production of exo-PG on 12th day of incubation. Casein (0.255U/ml), sodium dihydrogen phosphate (0.235U/ml), yeast extract (0.210U/ml), control (0.200U/ml) caused poor induction of exo-PG on both the incubation periods. Fawole and Odunfa9 who found that ammonium sulphate and ammonium nitrate were best nitrogen sources for A. niger while glycine and tryptophane were not supported for enzyme production. El Garhyet al,8 observed that among five nitrogen sources tested for screening their effect on pectinase production, yeast extract was reported to be the good nitrogen source producing the optimum level of pectinase activity by P. chrysogenum.

Vol18No1_Car_Gou_fig5 Figure 5: Effect of nitrogen source on exo-PG production by   A. niger under SmF using wheat bran.

Click here to view figure

Results from the figure 6. reveal the maximum endo-PG activity in sodium nitrate (163RVU) followed by urea (70.66RVU) whereas, sodium dihydrogen phosphate (57.6RVU), ammonium molybdate (54.33RVU), potassium nitrate (53.66RVU), ammonium chloride (48.66RVU), peptone (48.66RVU), casein (46.0RVU) and yeast extract (42.5RVU) showed moderate levels while, control (23.0RVU) caused poor production of enzyme on 8th day of incubation. Mrudula and Anitharaj22 they were found highest pectinase activity of 223.3Ug-1 in yeast extract + (NH4)2SO4 by A. niger. The current result is on par with the result of Rajmane and Korekar28. They showed maximum pectinase activity of 89.0U/ml by Botryodiplodia theobromae.

Vol18No1_Car_Gou_fig6 Figure 6: Effect of nitrogen source on endo-PG production by A. niger under SmF using wheat bran.

Click here to view figure 

The highest endo-PL activity recorded in sodium nitrate (43.66RVU) followed by potassium nitrate (42.0RVU), whereas peptone (32.40RVU), urea (26.2RVU) showed moderate level while, ammonium chloride (18.0RVU), casein (16.63RVU), ammonium molybdate (14.66RVU), sodium dihydrogen phosphate (13.0RVU) yeast extract (12.50RVU) and control (10.0RVU) showed poor production on 8th day of incubation. (fig 7). Patil and Dayanand25 who found greater values of endo- and exo-pectinases when the medium was supplemented with ammonium sulphate, (0.3%) as nitrogen source in SmF and SSF by A. nigerDMF 27 and DMF 45. It has been demonstrated that yeast extract in combination with (NH4)2SO4 was observed to support maximum production of pectinase (831U/g) by A.niger   IM09 10.

Vol18No1_Car_Gou_fig7 Figure 7: Effect of nitrogen source on endo-PL production by   A. niger under SmF using wheat bran.

Click here to view figure

Figure 8. reveal highestPME activity obtained in sodium nitrate (0.043 meq. of NaOH consumed/min/ml) followed by urea (0.034 meq. of NaOH consumed/min/ml), and potassium nitrate (0.030 meq. of NaOH consumed/min/ml) on 8th day of incubation whereas, yeast extract 0.022 meq. of NaOH consumed/min/ml), casein (0.020 meq. of NaOH consumed/min/ml) and control (0.020 meq. of NaOH consumed/min/ml) showed moderate level of production, while ammonium chloride (0.017 meq. of NaOH consumed/min/ml), sodium dihydrogen phosphate (0.012 meq. of NaOH consumed/min/ml), ammonium molybdate (0.011 meq. of NaOH consumed/min/ml) and peptone (0.008 meq. of NaOH consumed/min/ml) showed very less production of PME on 8th day of incubation. Abbasiet al.1 also found finding of highest exo and endopectinase activity in ammonium sulphate (1.6Uml-1 and 0.0013Uml-1) than sodium nitrate (1.2Uml-1 and 0.0011Uml-1) as nitrogen source by A. niger.

Vol18No1_Car_Gou_fig8 Figure 8: Effect of nitrogen source on PME production by A. niger under SmF using wheat bran.

Click here to view figure 

Influence of different concentrations of sodium nitrate on pectinases production discussed in table 1.1. Sodium nitrate at 0.2% concentration was proved to be the best nitrogen source for pectinases production. An increasing trend in enzyme activity was observed in different concentrations (0.05%, 0.10%, 0.20%, 0.30%, 0.40% and 0.50%) up to 12 days and decreased in subsequent incubations. Exo-PG activity showed a significant level of 0.320U/ml in 0.2% sodium nitrate on 12th day of incubation and decreased subsequently. Similarly an increased endo-PG activity of 51.8U/ml was also observed in 0.2% sodium nitrate but on 8th day of incubation. Similar results of endo-PL activity (71.5U/ml) and PME activity (0.068U/ml) were also observed in 0.2% sodium nitrate on 8th day of incubation. Overall, highest pectinases production was recorded on 8th day incubation for all pectinases except exo-PG; it showed its maximum activity on 12th day. There was no PME enzyme activity on 12th day of incubation becausethe longer an enzyme is incubated with its substrate, the higher the amount of product that will be formed. As a result, the rate of formation of product slows down as along as the incubation proceeds, and if the incubation time is too long, then there is no activity of the enzyme.

It was also studied that ammonium sulphate had great effect on the production of pectinase (15.74IU/ml) by A. niger19. Previous studies reported that increased pectinase activity (85.0U/mg) in ammonium persulphate by Pencillium chrysogenum6. Similar view was expressed by other workers34 and reported increased pectinase activity of 65.0U/ml in ammonium sulphate by Pencillium chrysogenum. The current view is on par with the results of Lahaet al.18 and reported increased activity in ammonium sulphate (0.936mg/ml) by Pencillium chrysogenum.

Surprisingly, previous studies reported ammonium dihydrogen phosphate as the good nitrogen source for the growth and production of pectinase by A. niger13. Sethiet al.31 found regarding three-fold increase in pectinase activity with ammonium persulfate. Previous studies revealed as ammonium sulphate (1.69%) as a good nitrogen source for pectinase production in Penicillium chrysogenum4.  

Table 2: Effect of sodium nitrate concentration on pectinases production by A. niger under SmF using wheat bran.

NaNO3

(%)

Exo -PG 

(U/ml)

Endo-PG

(RVU)

Endo-PL

(RVU)

PME (meq. of NaOH

consumed/min/ml)

8th day 12th day 8th day 12th day 8th day 12th day 8th day 12th day
0.05% 0.061c±0.001 0.140cd±0.001 42.8c±0.005 20.5c±0.001 49.0de±0.005 25.5d±0.002 0.031d±0.001
0.1% 0.070b±0.001 0.175b±0.001 47.6b±0.005 22.7b±0.001 56.5c±0.005 32.0b±0.002 0.052b±0.001
0.2% 0.110a±0.001 0.320a±0.001 51.8a±0.005 30.5a±0.001 71.5a±0.005 41.5a±0.002 0.068a±0.001
0.3% 0.066c±0.001 0.160cd±0.001 41.3d±0.005 22.0b±0.001 66.6b±0.005 28.0c±0.002 0.036c±0.001
0.4% 0.047e±0.001 0.055e±0.001 33.3e±0.005 15.5d±0.001 53.6de±0.005 23.6e±0.002 0.028e±0.001
0.5% 0.0241f±0.001 0.020f±0.001 22.8f±0.005 11.0e±0.001 21.6f±0.005 20.6f±0.002 0.018f±0.001

Values are significant at P < 0.005

— No activity

Conclusion

In conclusion, A. niger was identified as the best strain for pectinase enzyme production by submerged fermentation. The effect of added carbon sources such as arabinose, cellulose, dextrose, fructose, lactose, maltose, mannitol and sucrose was also investigated. Out of all the different carbon added sources sucrose found to be best for optimum production of pectinase. Sucrose acts as an inducer and stimulate the production of enzyme. Various concentration of sucrose (0.50% to 3.50%) for maximal productivity of enzyme was screened. Highest enzyme activity was observed at 1.0 %. Above or below the optimal concentration of carbon source leads to decline in the productivity of enzyme.

The effect of various inorganic and organic nitrogen sources (ammonium chloride, ammonium molybdate, casein, peptone, urea, potassium nitrate, sodium dihydrogen phosphate, sodium nitrate, and yeast extract were studied. The nitrogen sources enhanced the fungal growth and provoked the secretion of enzyme. Several additional nitrogen sources added, out of all nitrogen sources, sodium nitrate proved to be maximum pectinase production. Various concentrations of sodium nitrate (0.05% to 0.50%) for maximum productivity of enzyme was screened. Maximal enzyme activity was recorded at 0.2%. Because inorganic nitrogen source like sodium nitrate maintains the fungal growth better than that of organic nitrogen sources. Fungal strain hydrolyzes the sodium nitrate easily so, various nutrient components and growth factors which were released assimilated into fungal metabolism that eventually increases their growth. But, further studies must be carried out to identify the strain in genetic levels and to satisfy its commercial application in large-scale food formulation and processing.

Acknowledgement

We are thankful to the Head, Department of Microbiology, Kakatiya University, Warangal for facilities and encouragement.

Conflict of Interest

All authors declare that there is no conflict of interest in this work.

Funding Source

There is no funding source

References

  1. Abbasi H., Mortazavipour R. and Setudeh  M., Polygalacturonase (PG) production by fungal strains using agro-industrial bioproduct in solid state fermentation. Chemical Engineering Research Bulletin, 15(1), 1-5 (2011).
    CrossRef
  2. Abdullah R., Jafer A., Nisar K., Kaleem A., Iqtedar M., Iftikhar T. and Naz S., Process optimization for pectinase production by locally isolated fungal strain using submerged fermentation. Bioscience Journal, 34(4),(2018).
    CrossRef
  3. Ahlawat S., Dhiman S. S., Battan B., Mandhan R. P. and Sharma J., Pectinase production by Bacillus subtilis and its potential application in biopreparation of cotton and micropoly fabric. Process Biochemistry, 44(5), 521-526 (2009).
    CrossRef
  4. Akhter N., Morshed M. A., Uddin A., Begum F., Sultan T. and Azad A. K., Production of pectinase by Aspergillusniger cultured in solid state media. Int J Biosci, 1(1), 33-42 (2011)
  5. Azzaz H. H., Aziz H. A., Alzahar H. and Murad H. A., Yeast and Trichodermaviride don’t synergistically work to improve olive trees by products digestibility and lactating Barki ewe’s productivity.  Biol. Sci, 18, 270-279 (2018)
    CrossRef
  6. Banu A. R., Devi,M. K., Gnanaprabhal G. R., Pradeep B. V. and Palaniswamy M., Production and characterization of pectinase enzyme from Penicilliumchrysogenum. Indian Journal of Science and Technology, 3(4), 377-381 (2010)
    CrossRef
  7. Chauhan S, Vohra A, Lakhanpal A. and Gupta R. Immobilization of commercial pectinase (Polygalacturonase) on celite and its application in juice clarification. Journal of Food Processing and Preservation. 9(6), 2135-41 (2015)
    CrossRef
  8. El Garhy G. M., Azzaz H. H., Abd El Mola A. M. and Mousa, G.A., Fungal Pectinase Production Optimization and its Application in Buffaloe’s Diets Degradation. (2020)
  9. Fawole O. B. and Odunfa S. A., Some factors affecting production of pectic enzymes by Aspergillusniger.  International Biodeterioration& Biodegradation, 52(4), 223-227 (2003)
    CrossRef
  10. Islam S., Feroza B., Alam A. K. M. R. and Begum S., Pectinase production by Aspergillusniger isolated from decomposed apple skin. Bangladesh Journal of Scientific and Industrial Research, 48(1), 25-32 (2013).
    CrossRef
  11. Jayani R. S., Saxena S. and Gupta R., Microbial pectinolytic enzymes: a review. Process Biochemistry, 40(9), 2931-2944 (2005).
    CrossRef
  12. Johnson, L. E. and Curl, E. A., Methods for Research on the Ecology of Soil-borne Plant Pathogens. Burgess Publ. Co. Minneapolis (1972).
  13. Joshi V. K., Parmar M., and Rana N. S., Pectin Esterase Production from Apple Pomace in Solid-State and Submerged Fermentations. Food Technology & Biotechnology, 44(2), (2006).
  14. Kaur, S., Kaur, H. P., Prasad, B. and Bharti, T., Production and optimization of pectinase by Bacillus sp. isolated from vegetable waste soil. Indo American Journal of Pharmaceutical Research, 6(1), 4185-41906 (2016).
  15. Kertesz Z. I. and Mccollocch R. J., Enzymes acting on pectic substances. In Advances in carbohydrate chemistryAcademic Press 5, 79-102 (1950).
    CrossRef
  16. Khan F. and Latif Z., Molecular characterization of polygalacturonase producing bacterial strains collected from different sources.  Anim. Plant Sci, 26(3), 612-618 (2016).
  17. Khattab M. S., Azzaz H. H., El Tawab A. M. A. and Murad H. A., Production optimization of fungal cellulase and its impact on ruminal degradability and fermentation of diet.  J. Dairy Sci, 14, 61-68 (2019).
    CrossRef
  18. Laha S., Sarkar D. and Chaki S., Optimization of production and molecular characterization of pectinase enzyme produced from penicilliumchrysogenum. Scholars Academic Journal of Biosciences, 2(5), 326-335 (2014).
  19. Meenakshisundaram V., Optimization of pectinase enzyme production by using sour orange peel as substrate in solid state fermentation. Asian J Biochem Pharm Res, 2(1), 16-26 (2012).
  20. Miller G. L., Use of dinitrosalicylic acid reagent for determination of reducing sugar. Analytical chemistry, 31(3), 426-428 (1959).
    CrossRef
  21. Mojsov K., Experimental investigations of submerged fermentation and synthesis of pectinolytic enzymes by Aspergillusniger: effect of inoculums size and age of spores. Applied Technologies & Innovations, 2(2), 40-46 (2010).
    CrossRef
  22. Mrudula S. and Anitharaj R., by Aspergillusniger Using Orange Peel as Substrate. Global journal of Biotechnology and Biochemistry, 6(2), 64-71(2011).
  23. Oluwayemisi O. A., Effect of Blanching, Ripening and Other Treatments on the Production Characteristics of Pectinolytic Enzymes from Banana Peels by Aspergillusniger. Glob J Sci Front Res Chem, 12(2), 37-46 (2012).
  24. Palaniyappan M., Vijayagopal V., Viswanathan R. and Viruthagiri T., Screening of natural substrates and optimization of operating variables on the production of pectinase by submerged fermentation using Aspergillusniger MTCC 281. African journal of Biotechnology, 8(4),(2009).
  25. Patil S. R. and Dayanand A., Production of pectinase from deseeded sunflower head by Aspergillusniger in submerged and solid-state conditions. Bioresource technology, 97(16), 2054-2058 (2006).
    CrossRef
  26. Phutela, U., Dhuna, V., Sandhu., S., and Chadha, B., “Pectinase and polygalacturonase production by a thermophilic Aspergillus fumigatus isolated from decomposting orange peels, , J. Microbiol.36 (1), 63-69 (2005).
    CrossRef
  27. Prakash D., Nawani N., Prakash M., Bodas M., Mandal A., Khetmalas M. and Kapadnis B., Actinomycetes: a repertory of green catalysts with a potential revenue resource. BioMed research international,  (2013).
    CrossRef
  28. Rajmane S. D. and Korekar S. L., Impact of carbon and nitrogen sources on pectinase production of post-harvest fungi. Current Botany. (2012).
  29. Reddy, P. L. and Sreeramulu, A., Isolation, identification and screening of pectinolytic fungi from different soil samples of Chittoor district. International Journal of Life Sciences Biotechnology and Pharma Research, 1(3), 1-10 (2012).
  30. Sabah M.H. Al-Shatty, Alaa Kareem Niamah, Bayan Y Abdullah., The ability of Trichodermaharzianum on cleavage of cellulose of date palm leaves. Journal of Tikrit University for Agricultural Sciences, 11(4), 2011.
  31. Sethi B. K., Nand, P. K. and Sahoo S., Enhanced production of pectinase by Aspergillusterreus NCFT 4269.10 using banana peels as substrate. 3 Biotech, 6(1), 36 (2016)
    CrossRef
  32. Solís-Pereira S., Favela-Torres E., Viniegra-González G. and Gutiérrez-Rojas M., Effects of different carbon sources on the synthesis of pectinase by Aspergillusniger in submerged and solid state fermentations. Applied Microbiology and Biotechnology, 39(1), 36-41 (1993).
    CrossRef
  33. SPSS Inc. Released 2008. SPSS Statistics for Windows, Version 17.0. Chicago: SPSS Inc; 2008.
  34. Tariq A. L. and Reyaz A. L., The influence of carbon and nitrogen sources on pectinase productivity of Penicillium chrysogenum in solid state fermentation.  Res. J. Microbiol., 3, 202-207 (2012).
  35. Teixeira, Maria F. S., Lima Filho, José L. and Durán, Nelson., Carbon sources effect on pectinase production from Aspergillusjaponicus 586. Brazilian Journal of Microbiology,31(4), 286-290 (2000).
    CrossRef
  36. Wood R. K. S., Studies in the Physiology of Parasitism: XVIII. Pectic Enzymes secreted by Bacterium aroideae. Annals of Botany, 19(1), 1-27 (1955).
    CrossRef

4D Printing: A Snapshot on an Evolving Field

$
0
0

Three-dimensional (3D) printing, or more formally Additive Manufacturing (AM), was introduced in the mid-80s and since then it has had a great impact on virtually all industry, market, and research areas, from automotive to healthcare, enabling the fabrication of complex structures with precise control on both internal and external geometries 1–3. After about 30 years, in 2013, Tibbit set al.proposed the term “four-dimensional (4D) printing” to denote the fabrication via AM of structures with the capability to shape transform over time, the “fourth dimension”, under a predefined stimulus.

Shape-changing, self-repairing, self-assembly, are some of the characteristics today associated with 4D printed objects, highlighting that these are no longer static objects but programmable active structures that accomplish their function thanks to their architecture and composition 5–8.

Indeed, “smart” or “responsive” materials constitute a main ingredient of 4D printing, undergoing a useful, predictive, reproducible, and macroscopic physical or chemical change as a consequence of an environmental variation. Several couples of “smart material-stimulus” could be enumerated spanning from metals to ceramics and polymers activated by both coherent and incoherent forms of energy, including electric field and heating, liquid flow, pH, magnetic field, light. 9–10

In this context, AM acts as an enabling technology by allowing a precise deposition of an exact amount of one or more stimulus-responsive materials in predefined positions, without any constraints on the geometric complexity. In this way, tiny variations can be transformed in macroscopic movements.

Although in all classes of materials there are examples of smart materials, nevertheless smart polymers (e.g., shape memory polymers SMP, liquid crystal elastomers) have been preferred for 4D printing, given their easier processability, and the large range of applicable stimuli.

The changes that occur in a 4D printed object can be one-way or two-way 11, 12. In a one-way change, the object transformation is irreversible and represents the target and final state of the object. Differently, in a two-way change, the transformation is reversible, and the object has two stablestates. Therefore, repetitive transformation can be achieved through the application and removal of the stimulus.

Indeed, 4D printing is influenced by several variables, thus mathematical models are a very useful tool to determine their right combination for achieving the desired transformation.

In 2018 the Gartner hype cycle, which forecasts the evolution of emerging technologies, pinpointed 4D printing as an innovation in its triggering stage, with a decade before reaching its mainstream 13. As a matter of facts, 4D printed structures present some key advantages over static 3D printed objects: i)an easier fabrication and storage, since 4D constructs are usually fabricated as a flatted object, achieving their complex 3D shape after printing; ii) a reduction of assembling costs, being often based on compliant mechanisms;iii)exploitation of reliable alternatives to electrical actuation, with the possibility to use 4D printed structures even in a harsh environment, such as the human body; iv)capability of multi-functionality, self-assembly and self-repair.

For these reasons, since its introduction, the 4D printing approach has been in rapid expansion in several fields, including smart textiles, autonomous and soft robotics, electronics, biomedical devices and tissue engineering (TE) 14,15. Giving a closer look at biomedicine, the use of the AM technologies to fabricate 3D constructs that are designed to interact with physiological systems at the cellular levelhas been referred to as Bioprinting 3. Bioprinting is mostly used to fabricate scaffold, namely 3D and porous structures providing physical support to growing cells. Some bioprinting technologies (e.g., extrusion based bioprinting and inkjet printing) allow the direct processing of both biomaterials and living cells.

Following the logical train of thought, but also the trend, the term 4D bioprinting has appeared in literature, indicating the application of the 4D printing approach to fabricate structures that are designed to be influenced by and to have an influence on cell behavior and functions thought their property variations. In this context, traction forces generated by cells attached on the 4D printed structure can be exploited to induce the desired shape-changing property. Conversely, environment-induced shape-changing could stimulate, for example, cell differentiation or alignment.

As clearly stated, 4D printing is influenced by several variables (e.g., stimulus, materials, geometries), thus mathematical models and template design strategies are a very useful tool to determine the combination of variables that leads to the maximum and desired movement of the 4D printed structures 12,16,17. The basic mechanisms of property changing in 4D printing and 4D bioprinting can be due to:i) the direct use of a single material; ii) the combination of different materials, that are characterized by different responses to the same stimulus; iii) the exploitation of cellular activities 7,18,19. These mechanisms can be synergistically combined to reach a more significant or a more complex change in the 4D structure 20,21 or, for example, to obtain activations at different timepoints thanks to different characteristic times of each phenomenon 22–24.Attempts to define a taxonomy of shape-changing movements, achievable through 4D printing, have been tried 25.

When a single smart material is used,the 3D printed geometry plays an essential role to induce the object transformation. Indeed, by precisely and spatially controlling the material deposition, local anisotropy and gradients of material can be introduced in the structure by the AM fabrication process itself, which lead the structure transformation. Although some studies have presented single-material 4D-printed structures, many researchers consider 4D printing in a multi-material fashion using smart (also referred ad active) materials that are selectively arranged with conventional (also referred as passive) materials to obtain the desired property changing behavior 26. The developments and progress in multi-material printing have boosted the progression of 4D printing 3. Indeed, some AM technologies (e.g., FDM, EBB, PolyJet) can be used to simultaneously deposit different materials, thus creating multi-material structures with spatially controlled chemical and mechanical properties.In 4D bioprinting, cells can be exploited to generate the property change in a 3D printing structure. In this case, the material involved must be biocompatible and cell-friendly, but they do not strictly require smart properties.Living cells, that are seeded on or into the scaffolds, can act as the active part of the constructs, performing topological changes, for example through cell traction forces, that originate from actin polymerization and actomyosin interactions 27. In this context, the use of the cell traction forces as a driving mechanism to fold 2D structures, on which they adhere, to create complex 3D structures, is named “cell origami”28.

Using the aforementioned materials and the related stimuli for their activation, it is possible to physically program many morphological transformations enabled by the proper organization guaranteed by 3D printing.By means of this technology smart devices can be manufactured in a single fabrication step and capable of carrying out tasks as a consequence of a change, over time, of their chemical-physical properties under a predefined stimulus.These smart devices can be used by a surgeon as support during surgery or can be designed on specific patient needs 16,29.Constructs for a controlled drug delivery or structures that self-bend in order to replace a damaged blood vessel can also be fabricated 30. Furthermore,4D printinghas influence onother biomedical applications, such asbioactuation, biorobotics, and biosensing 31,33.

Although some progress has been made, 4D printing development is still at an early stage, and several challenges need to bead dressed.The target application,the knowledge of the materials’ behavior, the correct stimulus, and the printing parameters are fundamental elements to be considered,globally increasing the complexity of this fabrication process. In addition, when manual intervention is required, for example during the programming phase when using SMPs, 4D printing is not a fully automated procedure. Being many phenomena temperature-dependent, the actuation speed of the 4D printed devices is limited. The actuation process occurs slowly, requiring along-time range for the accomplishing of the desired task. Furthermore, 4D printed devices based on polymeric matrices suffer in those applications where strength is needed.

In conclusion, 4D printing is emerging and still under-development fabrication technology that thanks to the constant progress in materials science, 3D printing and biology, is opening a new door in biomedical engineering and will serve as an enablingtool to solve problems in tissue engineering, drug delivery, and medical devices manufacturing.

 References

  1. T. Shafranek,et al., “Stimuli-responsive materials in additive manufacturing,” Progress in Polymer Science, vol. 93, pp. 36–67, 2019.
    CrossRef
  2. A. M. Tofail, et al., “Additive manufacturing: scientific and technological challenges, market uptake and opportunities,” Materials Today, vol. 21(1), pp. 22–37, 2018.
    CrossRef
  3. Moroni, et al., “Biofabrication: A Guide to Technology and Terminology,” Trends in Biotechnology, vol. 36(4), pp. 384–402, 2018.
    CrossRef
  4. Tibbits,et al., “4D printing: multi‐material shape change.,” Architectural Design, vol. 84(1), pp. 116–121, 2014.
    CrossRef
  5. X. Khoo, et al., “3D printing of smart materials: A review on recent progresses in 4D printing,” Virtual and Physical Prototyping, vol. 10(3), pp. 103–122, 2015.
    CrossRef
  6. Kuang,et al., “Advances in 4D Printing: Materials and Applications,” Advanced Functional Materials, vol. 29(2), pp. 1–23, 2019.
    CrossRef
  7. Bodaghi, et al., “4D printing self-morphing structures,” Materials, vol. 12(8), 2019.
    CrossRef
  8. Miao, et al., “4D printing of polymeric materials for tissue and organ regeneration,” Materials Today, vol. 20(10), pp. 577–591, 2017.
    CrossRef
  9. Chiesa, et al., “Modeling the Three-Dimensional Bioprinting Process of β-Sheet Self-Assembling Peptide Hydrogel Scaffolds,” Frontiers in Medical Technology, vol. 2(October), pp. 1–16, 2020.
    CrossRef
  10. S. Lui,et al., “4D printing and stimuli-responsive materials in biomedical aspects,” Acta Biomaterialia, vol. 92, pp. 19–36, 2019.
    CrossRef
  11. Y. Lee,et al., “Two-Way 4D Printing: A Review on the Reversibility of 3D-Printed Shape Memory Materials,” Engineering, vol. 3(5), pp. 663–674, 2017.
    CrossRef
  12. Momeni, et al., “A review of 4D printing,” Materials and Design, vol. 122, pp. 42–79, 2017.
    CrossRef
  13. “5 Trends Emerge in the Gartner Hype Cycle for Emerging Technologies, 2018 – Smarter With Gartner.” .
  14. Rayate, et al., “A Review on 4D Printing Material Composites and Their Applications,” Materials Today: Proceedings, vol. 5(9), pp. 20474–20484, 2018.
    CrossRef
  15. E. Bakarich, et al., “4D printing with mechanically robust, thermally actuating hydrogels,” Macromolecular Rapid Communications, vol. 36(12), pp. 1211–1217, 2015.
    CrossRef
  16. Bittolo Bon,et al., “Printable smart 3D architectures of regenerated silk on poly(3-hydroxybutyrate-co-3-hydroxyvalerate),” Materials and Design, vol. 201, p. 109492, 2021.
    CrossRef
  17. Micalizzi,et al., “Shape-memory actuators manufactured by dual extrusion multimaterial 3d printing of conductive and non-conductive filaments,” Smart Materials and Structures, vol. 28(10), Sep. 2019.
    CrossRef
  18. Zhu,et al., “4D printing smart biosystems for nanomedicine,” Nanomedicine, vol. 14(13), pp. 1643–1645, 2019.
    CrossRef
  19. Chan, et al., “Development of miniaturized walking biological machines,” Scientific Reports, vol. 2, 2012.
    CrossRef
  20. Kobayashi, et al., “Multitemperature Responsive Self-Folding Soft Biomimetic Structures,” Macromolecular Rapid Communications, vol. 39(4), pp. 1–7, 2018.
    CrossRef
  21. Liu, et al., “Dual-Gel 4D Printing of Bioinspired Tubes,” ACS Applied Materials and Interfaces, 2019.
    CrossRef
  22. Liu, et al., “Sequential self-folding of polymer sheets,” Science Advances, vol. 3(3), pp. 1–8, 2017.
    CrossRef
  23. Xie, et al., “Tunable polymer multi-shape memory effect,” Nature, vol. 464(7286), pp. 267–270, 2010.
    CrossRef
  24. Thérien-Aubin, et al., “Multiple shape transformations of composite hydrogel sheets,” Journal of the American Chemical Society, vol. 135(12), pp. 4834–4839, 2013.
    CrossRef
  25. Nam, et al., “A taxonomy of shape-changing behavior for 4D printed parts using shape-memory polymers,” Progress in Additive Manufacturing, vol. 4(2), pp. 167–184, 2019.
    CrossRef
  26. Momeni, et al., “Laws of 4D Printing,” Engineering, vol. 6(9), pp. 1035–1055, 2020.
    CrossRef
  27. M. Shewan, et al., “Myosin 2 Is a Key Rho Kinase Target Necessary for the Local Concentration of E-Cadherin at Cell-Cell Contacts □ D,” Molecular Biology of the Cell, vol. 16, pp. 4531–4542, 2005.
    CrossRef
  28. Kuribayashi-Shigetomi, et al., “Cell Origami: Self-Folding of Three-Dimensional Cell-Laden Microstructures Driven by Cell Traction Force,” PLoS ONE, vol. 7(12), Dec. 2012.
    CrossRef
  29. Lin, et al., “4D Printing of Bioinspired Absorbable Left Atrial Appendage Occluders: A Proof-of-Concept Study,” ACS Applied Materials and Interfaces, 2021.
  30. Melocchi, et al., “Retentive device for intravesical drug delivery based on water-induced shape memory response of poly(vinyl alcohol): design concept and 4D printing feasibility,” International Journal of Pharmaceutics, vol. 559, pp. 299–311, Mar. 2019.
    CrossRef
  31. L. Manzanares Palenzuela, et al., “(Bio)Analytical chemistry enabled by 3D printing: Sensors and biosensors,” TrAC – Trends in Analytical Chemistry, vol. 103, pp. 110–118, 2018.
    CrossRef
  32. Hines, et al., “Soft Actuators for Small-Scale Robotics,” Advanced Materials, vol. 29(13), 2017.
    CrossRef
  33. Miriyev, “Soft material for soft actuators,” Nature Communications, vol. 8(1), pp. 1–8, 2017.
    CrossRef
Viewing all 1376 articles
Browse latest View live