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Gene ontology enrichment analysis of α-amylase inhibitors from Duranta repens in diabetes mellitus

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Abstract

Background

Although α-amylase is the choice of target to manage postprandial hyperglycemia, inhibitors of this enzyme may get absorbed into the systemic circulation and modulate proteins involved in the pathogenesis of diabetes mellitus. Hence, the present study aimed to identify α-amylase inhibitors from Duranta repens via in silico and in vitro and predict their role in the modulation of multiple pathways involved in diabetes mellitus.

Methods

α-amylase inhibitory activity of hydroalcoholic extract/fractions (s) and pure compounds from D. repens was performed using in vitro enzyme inhibitory assay. Multiple open-source databases and published literature were used to retrieve reported phytoconstituents present in D. repens and their targets. The network was constructed between α-amylase inhibitors, modulated proteins, and expressed pathways. Further, hit molecules were also confirmed for their potency to inhibit α-amylase using in silico molecular docking and in vitro enzyme inhibitory assay. The glucose uptake assay was performed to assess the effect of hydrolcoholic extract/fraction(s) using rat hemidiaphragm.

Results

Fraction rich in flavonoids showed the highest α-amylase inhibitory activity with a IC50 of 644.29 ± 4.36 µg/ml compared to other fractions. PI3K-Akt signaling pathway and p53 signaling pathway were predicted to be primarily modulated in the compound-protein-pathway network. Similarly, scutellarein was predicted as lead hit based on α-amylase inhibitory action, binding affinity, and regulated pathways. Further, α-amylase inhibitors were also predicted to modulate the pathways involved in diabetes complications like AGE-RAGE and FoxO signaling pathway. Fraction rich in flavonoids showed the highest glucose uptake in rat hemidiaphragm with an effective concentration of 534.73 ± 0.79 µg/ml.

Conclusions

The α-amylase inhibitors from D. repens may not be limited within the gastrointestinal tract to inhibit α-amylase but may get absorbed into the systemic circulation and modulate multiple pathways involved in the pathogenesis of diabetes mellitus to produce synergistic/additive effect.

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Data availability

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Abbreviations

ChEBI:

Chemical Entities of Biological Interest

DM:

Diabetes Mellitus

DPP4:

Dipeptidyl peptidase-4

GLUT:

Glucose transporter

IC50 :

Inhibitory Concentration 50

ICMR-NITM:

Indian Council of Medical Research -National Institute of Traditional Medicine

KEGG:

Kyoto Encyclopedia of Genes and Genomes

Pa:

Probable activity

PASS:

Prediction of Activity Spectra for Substances

PCIDB:

PhytoChemical Interactions DB

PDB:

Protein Data Bank

Pi:

Probable inactivity

PTPN1B:

Protein tyrosine phosphatase 1B

RCSB:

Research Collaboratory for Structural Bioinformatics

STRING:

Search Tool for the Retrieval of Interacting Genes/Proteins

T2DM:

Type 2 Diabetes Mellitus

References

  1. de Souza PM, de Oliveira Magalhães P. Application of microbial α-amylase in industry - A review. Braz J Microbiol. 2010;41(4):850–61. https://doi.org/10.1590/S1517-83822010000400004.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Telagari M, Hullatti K. In-vitro α-amylase and α-glucosidase inhibitory activity of Adiantum caudatum Linn. and Celosia argentea Linn. extracts and fractions. Indian J Pharmacol. 2015;47(4):425–9. https://doi.org/10.4103/0253-7613.161270.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Min SW, Han JS. Polyopes lancifolia extract, a potent α-Glucosidase Inhibitor, Alleviates Postprandial Hyperglycemia in diabetic mice. Prev Nutr Food Sci. 2014;19(1):5–9. https://doi.org/10.3746/pnf.2014.19.1.005.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Reuser AJ, Wisselaar HA. An evaluation of the potential side-effects of alpha-glucosidase inhibitors used for the management of diabetes mellitus. Eur J Clin Invest. 1994;24(S3):19–24. https://doi.org/10.1111/j.1365-2362.1994.tb02251.x.

    Article  CAS  PubMed  Google Scholar 

  5. Tahrani AA, Barnett AH, Bailey CJ. Pharmacology and therapeutic implications of current drugs for type 2 diabetes mellitus. Nat Rev Endocrinol. 2016;12(10):566–92. https://doi.org/10.1038/nrendo.2016.86.

    Article  CAS  PubMed  Google Scholar 

  6. Sacks DB, McDonald JM. The pathogenesis of type II diabetes mellitus. A polygenic disease. Am J Clin Pathol. 1996;105(2):149–56. https://doi.org/10.1093/ajcp/105.2.149.

    Article  CAS  PubMed  Google Scholar 

  7. Wang X, Xu X, Tao W, Li Y, Wang Y, Yang L. A systems biology approach to uncovering pharmacological synergy in herbal medicines with applications to cardiovascular disease. Evid Based Complement Alternat Med. 2012;2012:519031. https://doi.org/10.1155/2012/519031.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Subsongsang R, Jiraungkoorskul W. An Updated Review on Phytochemical Properties of “Golden Dewdrop” Duranta erecta. Pharmacogn Rev. 2016;10(20):115–7. https://doi.org/10.4103/0973-7847.194042.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Rauter AP, Martins A, Borges C, et al. Antihyperglycaemic and protective effects of flavonoids on streptozotocin-induced diabetic rats. Phytother Res. 2010;24(S2):133–8. https://doi.org/10.1002/ptr.3017.

    Article  Google Scholar 

  10. Agrawal R, Sethiya NK, Mishra SH. Antidiabetic activity of alkaloids of Aerva lanata roots on streptozotocin-nicotinamide induced type-II diabetes in rats. Pharm Biol. 2013;51(5):635–42. https://doi.org/10.3109/13880209.2012.761244.

    Article  CAS  PubMed  Google Scholar 

  11. Ong KW, Hsu A, Song L, Huang D, Tan BK. Polyphenols-rich Vernonia amygdalina shows anti-diabetic effects in streptozotocin-induced diabetic rats. J Ethnopharmacol. 2011;133(2):598–607. https://doi.org/10.1016/j.jep.2010.10.046.

    Article  PubMed  Google Scholar 

  12. Zheng T, Shu G, Yang Z, Mo S, Zhao Y, Mei Z. Antidiabetic effect of total saponins from Entada phaseoloides (L.) Merr. in type 2 diabetic rats. J Ethnopharmacol. 2012;139(3):814–21. https://doi.org/10.1016/j.jep.2011.12.025.

    Article  CAS  PubMed  Google Scholar 

  13. Ponnusamy S, Ravindran R, Zinjarde S, Bhargava S, Ravi Kumar A. Evaluation of traditional Indian antidiabetic medicinal plants for human pancreatic amylase inhibitory effect in vitro. Evid Based Complement Alternat Med. 2011;2011:515647. https://doi.org/10.1155/2011/515647.

    Article  PubMed  Google Scholar 

  14. Iqbal K, Malik A, Mukhtar N, Anis I, Khan SN, Choudhary MI. Alpha-glucosidase inhibitory constituents from Duranta repens. Chem Pharm Bull (Tokyo). 2004;52:785–9. https://doi.org/10.1248/cpb.52.785.

    Article  CAS  Google Scholar 

  15. Khanal P, Patil BM. α–Glucosidase inhibitors from Duranta repens modulate p53 signaling pathway in diabetes mellitus. Adv Tradit Med (ADTM). 2020. https://doi.org/10.1007/s13596-020-00426-w.

    Article  Google Scholar 

  16. Cos P, Vlietinck AJ, Berghe DV, Maes L. Anti-infective potential of natural products: how to develop a stronger in vitro ‘proof-of-concept.’ J Ethnopharmacol. 2006;106(3):290–302. https://doi.org/10.1016/j.jep.2006.04.003.

  17. Dassault Systèmes BIOVIA, Discovery S. 2019, San Diego: DassaultSystèmes, 2019.

  18. Poroikov VV, Filimonov DA, Ihlenfeldt WD, Gloriozova TA, Lagunin AA, Borodina YV, et al. PASS biological activity spectrum predictions in the enhanced open NCI database browser. J Chem Inf Comput Sci. 2003;43(1):228–36. https://doi.org/10.1021/ci020048r.

    Article  CAS  PubMed  Google Scholar 

  19. Lagunin A, Ivanov S, Rudik A, Filimonov D, Poroikov V. DIGEP-Pred: Web service for in silico prediction of drug-induced gene expression profiles based on structural formula. Bioinformatics. 2013;29(16):2062–3. https://doi.org/10.1093/bioinformatics/btt322.

    Article  CAS  PubMed  Google Scholar 

  20. Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, et al. The STRING database in 2017: Quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 2017;45(D1):D362-8. https://doi.org/10.1093/nar/gkw937.

    Article  CAS  PubMed  Google Scholar 

  21. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504. https://doi.org/10.1101/gr.1239303.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Halgren TA. Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. J Comput Chem. 1996;17:490–519. https://doi.org/10.1002/(SICI)1096-987X(199604)17:5/6490::AID-JCC1>3.0.CO;2-P.

    Article  CAS  Google Scholar 

  23. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, et al. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem. 2009;30(16):2785–91. https://doi.org/10.1002/jcc.21256.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Kumar M, Prasad SK, Hemalatha S. In Vitro study on glucose utilization capacity of bioactive fractions of Houttuynia cordata in isolated rat hemidiaphragm and its major phytoconstituent. Adv Pharmacol Sci. 2016;2016:2573604. https://doi.org/10.1155/2016/2573604.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Chandran U, Mehendale N, Tillu G, Patwardhan B. Network pharmacology of ayurveda formulation Triphala with special reference to anti-cancer property. Comb Chem High Throughput Screen. 2015;18(9):846–54. https://doi.org/10.2174/1386207318666151019093606.

    Article  CAS  PubMed  Google Scholar 

  26. Khanal P, Patil BM. Gene set enrichment analysis of alpha-glucosidase inhibitors from Ficus benghalensis. Asian Pac J Trop Biomed. 2019;9(6):263–70. https://doi.org/10.4103/2221-1691.260399.

    Article  CAS  Google Scholar 

  27. Khanal P, Patil BM, Mandar BK, Dey YN, Duyu T. Network pharmacology-based assessment to elucidate the molecular mechanism of anti-diabetic action of Tinospora cordifolia. Clin Phytosci. 2019;5:35. https://doi.org/10.1186/s40816-019-0131-1.

    Article  CAS  Google Scholar 

  28. Gene Ontology Unifying Biology. GO enrichment analysis. [Internet] Available at: http://geneontology.org/docs/go-enrichment-analysis/. Accessed 4 May 2020.

  29. Huang X, Liu G, Guo J, Su Z. The PI3K/AKT pathway in obesity and type 2 diabetes. Int J Biol Sci. 2018;14(11):1483–96. https://doi.org/10.7150/ijbs.27173.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Leahy JL. Pathogenesis of type 2 diabetes mellitus. Arch Med Res. 2005;36(3):197–209. https://doi.org/10.1016/j.arcmed.2005.01.003.

    Article  CAS  PubMed  Google Scholar 

  31. Khorami SA, Movahedi A, Sokhini AM. PI3K/AKT pathway in modulating glucose homeostasis and its alteration in Diabetes. Int J Biol Sci. 2018;14(11):1483–96. https://doi.org/10.7150/ijbs.27173.

    Article  CAS  Google Scholar 

  32. Strycharz J, Drzewoski J, Szemraj J, Sliwinska A. Is p53 Involved in Tissue-Specific Insulin Resistance Formation? Oxid Med Cell Longev. 2017;2017:9270549. https://doi.org/10.1155/2017/9270549.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Talevi A. Multi-target pharmacology: possibilities and limitations of the “skeleton key approach” from a medicinal chemist perspective. Front Pharmacol. 2015;6:205. https://doi.org/10.3389/fphar.2015.00205.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Ramsay RR, Popovic-Nikolic MR, Nikolic K, Uliassi E, Bolognesi ML. A perspective on multi-target drug discovery and design for complex diseases. Clin Transl Med. 2018;7(1):3. https://doi.org/10.1186/s40169-017-0181-2.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Nickerson HD, Dutta S. Diabetic complications: current challenges and opportunities. J Cardiovasc Transl Res. 2012;5(4):375–9. https://doi.org/10.1007/s12265-012-9388-1.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Zhang L, Chen Q, Li L, Kwong JS, Jia P, Zhao P, Wang W, Zhou X, Zhang M, Sun X. Alpha-glucosidase inhibitors and hepatotoxicity in type 2 diabetes: a systematic review and meta-analysis. Sci Rep. 2016;6:32649. https://doi.org/10.1038/srep32649.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Tran ND, Hunter SK, Yankowitz J. Oral hypoglycemic agents in pregnancy. Obstet Gynecol Surv. 2004;59(6):456–85. https://doi.org/10.1097/00006254-200406000-00024.

    Article  PubMed  Google Scholar 

  38. KEGG Pathway [hsa04933.]. AGE-RAGE signaling pathway in diabetic complications - Homo sapiens (human). Available at: https://www.genome.jp/dbget-bin/www_bget?hsa04933. Accessed 4 Dec 2019.

  39. KEGG Pathway [hsa04068]. FoxO signaling pathway - Homo sapiens (human). Available at: https://www.genome.jp/dbget-bin/www_bget?pathway+hsa04068. Accessed 4 Dec 2019.

  40. KEGG Pathway [map04211]. Longevity regulating pathway. Available at: https://www.genome.jp/dbget-bin/www_bget?map04211. Accessed 4 Dec2019.

  41. Disney A. KeyLines FAQs: Social network analysis. Cambridge intelligence. 2014. Available at: https://cambridge-intelligence.com/keylines-faqs-social-network-analysis/. Accessed 8 Dec 2019.

  42. Max Planck Institute for Informatics. Network analyzer settings. 2018. Available at: https://med.bioinf.mpi-inf.mpg.de/netanalyzer/help/2.7/#neighborConn. Accessed 8 Dec 2019.

  43. Schwartzenberg-Bar-Yoseph F, Armoni M, Karnieli E. The tumor suppressor p53 down-regulates glucose transporters GLUT1 and GLUT4 gene expression. Cancer Res. 2004;64(7):2627–33. https://doi.org/10.1158/0008-5472.can-03-0846.

    Article  CAS  PubMed  Google Scholar 

  44. Richter EA, Hargreaves M, Exercise. GLUT4, and skeletal muscle glucose uptake. Physiol Rev. 2013;93(3):993–1017. https://doi.org/10.1152/physrev.00038.2012.

    Article  CAS  PubMed  Google Scholar 

  45. Mogyorósi A, Ziyadeh FN. GLUT1 and TGF-beta: the link between hyperglycaemia and diabetic nephropathy. Nephrol Dial Transplant. 1999;14(12):2827–9. https://doi.org/10.1093/ndt/14.12.2827.

    Article  PubMed  Google Scholar 

  46. Kung CP, Murphy ME. The role of the p53 tumor suppressor in metabolism and diabetes. J Endocrinol. 2016;231(2):R61-75. https://doi.org/10.1530/JOE-16-0324.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors are thankful to Principal KLE College of Pharmacy, Belagavi for providing necessary facilities and Head of Department of Pharmacology and Toxicology, KLE College of Pharmacy, Belagavi, for supporting to complete the work. Pukar Khanal is also thankful to Ms. Taaza Duyu for her assistance during the enzyme inhibitory activity.

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This work has not received any funds from national and international agencies.

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Correspondence to Pukar Khanal.

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Khanal, P., Patil, B.M. Gene ontology enrichment analysis of α-amylase inhibitors from Duranta repens in diabetes mellitus. J Diabetes Metab Disord 19, 735–747 (2020). https://doi.org/10.1007/s40200-020-00554-9

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