In Silico Exploration Of Glyphosate's Binding Affinity And Inhibitory Effects On Key Metabolic Enzymes Implicated In Type 2 Diabetes Pathogenesis

Main Article Content

Dr. S. Mahalakshmi B.H.M.S., M.D.(Hom)
Deviniraikalai, MSC

Abstract

The widespread use of glyphosate, a prominent herbicide, has raised concerns regarding its potential impact on human health, particularly in relation to metabolic disorders like Type 2 Diabetes (T2DM). This study presents an in-depth in silico exploration of glyphosate's interaction with key metabolic enzymes implicated in T2DM pathogenesis, providing insights into the molecular mechanisms that might underlie glyphosate-induced metabolic dysregulation.


Utilizing advanced computational modeling techniques, including molecular docking and dynamic simulations, we systematically investigated the binding affinity of glyphosate to a series of enzymes integral to glucose metabolism and insulin signaling pathways. These enzymes include glucokinase, insulin receptor, protein kinase B (Akt), and phosphoenolpyruvate carboxykinase (PEPCK), among others. The study aimed to elucidate the potential inhibitory effects of glyphosate on these enzymes, thereby implicating its role in disrupting normal metabolic processes.


Our results demonstrate a significant binding affinity of glyphosate to these enzymes, with binding patterns suggesting possible competitive or allosteric inhibition. The molecular docking scores indicated a strong interaction, especially with the insulin receptor and Akt, which are crucial for insulin signaling and glucose uptake. Furthermore, dynamic simulation analyses revealed conformational changes in the enzyme structures upon glyphosate binding, potentially affecting their functional activity.


These findings suggest a novel mechanism by which glyphosate exposure could contribute to the development of insulin resistance, a key feature of T2DM. The study highlights the importance of considering environmental factors like herbicide exposure in the etiology of metabolic diseases. It also underscores the potential of in silico methods as powerful tools in toxicological research, enabling the prediction and analysis of biochemical interactions at a molecular level.


While our study provides compelling theoretical evidence, it also emphasizes the need for experimental validation. Further research, both in vitro and in vivo, is essential to confirm the biological relevance of these findings and to understand the broader implications of glyphosate exposure on human health, particularly in the context of increasing global rates of T2DM

Downloads

Download data is not yet available.

Article Details

How to Cite
Dr. S. Mahalakshmi B.H.M.S., M.D.(Hom), & Deviniraikalai, MSC. (2024). In Silico Exploration Of Glyphosate’s Binding Affinity And Inhibitory Effects On Key Metabolic Enzymes Implicated In Type 2 Diabetes Pathogenesis. Journal of Advanced Zoology, 45(1), 465–471. https://doi.org/10.53555/jaz.v45i1.3274
Section
Articles
Author Biographies

Dr. S. Mahalakshmi B.H.M.S., M.D.(Hom)

Professor and Head - Department of Physiology, Sri Sairam Homoeopathy Medical College and Research Centre, West Tambaram, Chennai - 600044

Deviniraikalai, MSC

Tutor, Department of Anatomy, SRM Medical college & Hospital, kattankulathur, Potheri, Tamil Nadu, India

References

Berman, H. M., et al. (2000). The Protein Data Bank. Nucleic Acids Research, 28(1), 235-242.

Biovia, D. S. (2017). Discovery Studio Visualizer.

Cronin, M. T. D., & Livingstone, D. J. (2004). Predicting chemical toxicity and fate. CRC Press.

DeFronzo, R. A., et al. (2015). Type 2 diabetes mellitus. Nature Reviews Disease Primers, 1, 15019.

Duke, S. O., & Powles, S. B. (2008). Glyphosate: a once-in-a-century herbicide. Pest Management Science, 64(4), 319-325.

Gasnier, C., et al. (2009). Glyphosate-based herbicides are toxic and endocrine disruptors in human cell lines. Toxicology, 262(3), 184-191.

Gore, A. C., et al. (2015). EDC-2: The Endocrine Society's second scientific statement on endocrine-disrupting chemicals. Endocrine Reviews, 36(6), E1-E150.

Grun, F., & Blumberg, B. (2009). Endocrine disrupters as obesogens. Molecular and Cellular Endocrinology, 304(1-2), 19-29.

Heindel, J. J., et al. (2015). Metabolism disrupting chemicals and metabolic disorders. Reproductive Toxicology, 52, 93-99.

Hers, I., Vincent, E. E., & Tavare, J. M. (2011). Akt signalling in health and disease. Cellular Signalling, 23(10), 1515-1527.

Huang, J., & MacKerell, A. D. Jr. (2013). CHARMM36 all-atom additive protein force field: validation based on comparison to NMR data. Journal of Computational Chemistry, 34(25), 2135-2145.

Kim, S., et al. (2016). PubChem Substance and Compound databases. Nucleic Acids Research, 44(D1), D1202-D1213.

Kitchen, D. B., Decornez, H., Furr, J. R., & Bajorath, J. (2004). Docking and scoring in virtual screening for drug discovery: methods and applications. Nature Reviews Drug Discovery, 3(11), 935-949.

Lang, T., & Rayner, G. (2007). Overcoming policy cacophony on obesity: an ecological public health framework for policymakers. Obesity Reviews, 8(s1), 165-181.

Lee, D. H., Jacobs Jr, D. R., & Porta, M. (2016). Hypothesis: A unifying mechanism for nutrition and chemicals as lifelong modulators of DNA hypomethylation. Environmental Health Perspectives, 124(8), 1176-1182.

Manning, B. D., & Toker, A. (2017). AKT/PKB signaling: navigating the network. Cell, 169(3), 381-405.

Matschinsky, F. M. (2009). Assessing the potential of glucokinase activators in diabetes therapy. Nature Reviews Drug Discovery, 8(5), 399-416.

Mesnage, R., et al. (2015). Potential toxic effects of glyphosate and its commercial formulations below regulatory limits. Food and Chemical Toxicology, 84, 133-153.

Morris, G. M., et al. (2009). AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry, 30(16), 2785-2791.

Morris, G. M., & Geistlinger, T. R. (2008). Virtual screening in drug discovery: a computational perspective. Current Opinion in Chemical Biology, 12(4), 359-366.

Mostafalou, S., & Abdollahi, M. (2013). Pesticides and human chronic diseases: Evidences, mechanisms, and perspectives. Toxicology and Applied Pharmacology, 268(2), 157-177.

Postic, C., & Girard, J. (2008). The role of the liver in the control of carbohydrate and lipid homeostasis. Diabetes & Metabolism, 34(6), 649-658.

Rappaport, S. M., & Smith, M. T. (2010). Environment and disease risks. Science, 330(6003), 460-461.

Saltiel, A. R., & Kahn, C. R. (2001). Insulin signalling and the regulation of glucose and lipid metabolism. Nature, 414(6865), 799-806.

Samsel, A., & Seneff, S. (2013). Glyphosate, pathways to modern diseases II: Celiac sprue and gluten intolerance. Interdisciplinary Toxicology, 6(4), 159-184.

Swinburn, B. A., et al. (2011). The global obesity pandemic: shaped by global drivers and local environments. The Lancet, 378(9793), 804-814.

Tang-Peronard, J. L., et al. (2011). Endocrine-disrupting chemicals and obesity development in humans: a review. Obesity Reviews, 12(8), 622-636.

Thayer, K. A., et al. (2012). Role of environmental chemicals in diabetes and obesity: a National Toxicology Program workshop review. Environmental Health Perspectives, 120(6), 779-789.

Trott, O., & Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2), 455-461.

Van Der Spoel, D., et al. (2005). GROMACS: Fast, flexible, and free. Journal of Computational Chemistry, 26(16), 1701-1718.

Wild, C. P. (2005). Complementing the genome with an “exposome”: the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiology Biomarkers & Prevention, 14(8), 1847-1850.

World Health Organization. (2016). Global report on diabetes. World Health Organization.

Zoeller, R. T., et al. (2012). Endocrine-disrupting chemicals and public health protection: a statement of principles from The Endocrine Society. Endocrinology, 153(9), 4097-4110