Skip to main content
SpringerLink
Log in

Gene set enrichment analysis of PPAR-γ regulators from Murraya odorata Blanco

  • Research article
  • Published:
Journal of Diabetes & Metabolic Disorders Aims and scope Submit manuscript

Abstract

Background

Peroxisome proliferator-activated receptor gamma (PPAR-γ) is reported to regulate insulin sensitivity and progression of Type 2 diabetes mellitus (T2DM). Hence the present study is aimed to identify PPAR-γ regulators from Murraya odorata Blanco and predict their role to manage T2DM.

Methods

Multiple in-silico tools and databases like SwissTargetPrediction, ADVERPred, PubChem, and MolSoft, were used to retrieve the information related to bioactives, targets, druglikeness character, and probable side effects as applicable. Similarly, the Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to identify the regulated pathways. Further, the bioactives-protein-pathways network interaction was constructed using Cytoscape. Finally, molecular docking was performed using Autodock4.

Results

Twenty-five bioactives were shortlisted in which six were predicted as PPAR-γ modulators. Among them, stigmasterol was predicted to possess the best binding affinity towards PPAR-γ and possessed no side effects. Similarly, n-hexadecanoic acid was predicted to modulate the highest number of proteins, and protein CD14 was targeted by the highest number of bioactives. Further, the PI3K-Akt pathway was predicted as the maximum modulated genes.

Conclusions

The anti-diabetic property of the Murraya odorata Blanco of fruit pulp may be due to the presence of n-hexadecanoic acid and stigmasterol; may also involve in the regulation of the PI3K-Akt pathway which needs further investigated by in-vitro and in-vivo protocols.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Baynes HW. Classification, pathophysiology, diagnosis and management of diabetes mellitus. J Diabetes Metab. 2015;6(5):1–9. https://doi.org/10.4172/2155-6156.1000541.

    Article  CAS  Google Scholar 

  2. Lehmann JM, Moore LB, Smith-Oliver TA, Wilkison WO, Willson TM, Kliewer SA. An antidiabetic thiazolidinedione is a high affinity ligand for peroxisome proliferator-activated receptor γ (PPARγ). J Biol Chem. 1995;270(22):12953–6. https://doi.org/10.1074/jbc.270.22.12953.

    Article  CAS  PubMed  Google Scholar 

  3. Spiegelman BM. PPAR-gamma: adipogenic regulator and thiazolidinedione receptor. J Diabetes. 1998;47(4):507–14. https://doi.org/10.2337/diabetes.47.4.507.

    Article  CAS  Google Scholar 

  4. Larsen TM, Toubro S, Astrup A. PPARgamma agonists in the treatment of type II diabetes: is increased fatness commensurate with long-term efficacy? Int J Obes (Lond). 2003;27(2):147–61. https://doi.org/10.1038/sj.ijo.802223.

    Article  CAS  Google Scholar 

  5. Krentz AJ, Bailey CJ. Oral antidiabetic agents. Drugs. 2005;65(3):385–411. https://doi.org/10.2165/00003495-200565030-00005.

    Article  CAS  PubMed  Google Scholar 

  6. Bailey CJ, Day C. Traditional plant medicines as treatments for diabetes. Diabetes Care. 1989;12(8):553–64. https://doi.org/10.2337/diacare.12.8.553.

    Article  CAS  PubMed  Google Scholar 

  7. Khanal P, Patil BM. Gene ontology enrichment analysis of α-amylase inhibitors from Duranta repens in diabetes mellitus. J Diabetes Metab Disord. 2020. https://doi.org/10.1007/s40200-020-00554-9.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Jayakumar R. Herbal medicines for type-2 diabetes. Int J Diabetes Dev Ctries. 2010;30(3):111. https://doi.org/10.4103/0973-3930.66501.

    Article  Google Scholar 

  9. Terstappen GC, Reggiani A. In silico research in drug discovery. Trends Pharmacol Sci. 2001;22(1):23–6. https://doi.org/10.1016/S0165-6147(00)01584-4.

    Article  CAS  PubMed  Google Scholar 

  10. Khanal P, Chikhale R, Dey YN, Pasha I, Chand S, Gurav N, Ayyanar M, Patil BM, Gurav S. Withanolides from Withania somnifera as an immunity booster and their therapeutic options against COVID-19. J Biomol Struct Dyn. 2021. https://doi.org/10.1080/07391102.2020.1869588.

  11. Muthulakshmi AR, Margret RJ, Mohan VR. GC-MS analysis of bioactive components of Feronia elephantum Correa (Rutaceae). J Appl Pharm Sci. 2012;2(02):69–74.

    Google Scholar 

  12. Maity P, Hansda D, Bandyopadhyay U, Mishra DK. Biological activities of crude extracts and chemical constituents of Bael, Aegle marmelos (L.) Corr. Indian J Exp Biol. 2009;47:849–61. 

  13. Sabu MC, Kuttan R. Antidiabetic activity of Aegle marmelos and its relationship with its antioxidant properties. Indian J Physiol Pharmacol. 2004;48(1):81–8.

    CAS  PubMed  Google Scholar 

  14. Mishra A, Garg GP. Antidiabetic activity of fruit pulp of Feronia elephantum Corr. Pharmacogn J. 2011;3(20):27–32. https://doi.org/10.5530/pj.2011.20.6.

    Article  Google Scholar 

  15. Intekhab J, Aslam M. Constituents from Feronia limonia. An Univ Bucuresti Chimie. 2009;18(2):95–101.

    CAS  Google Scholar 

  16. Pandey S, Satpathy G, Gupta RK. Evaluation of nutritional, phytochemical, antioxidant and antibacterial activity of exotic fruit “Limonia acidissima”. J Pharmacogn Phytochem. 2014;3(2):81–8.

    CAS  Google Scholar 

  17. Kim S, Thiessen PA, Bolton EE, Chen J, Fu G, Gindulyte A, Han L, He J, He S, Shoemaker BA, Wang J. PubChem substance and compound databases. Nucleic Acids Res. 2016;44(D1):D1202-13. https://doi.org/10.1093/nar/gkv951.

    Article  CAS  PubMed  Google Scholar 

  18. Drug-Likeness and molecular property prediction. molsoft molecules in silico. Available at: https://molsoft.com/mprop/. Accessed 18 Nov 2020.

  19. Ivanov SM, Lagunin AA, Rudik AV, Filimonov DA, Poroikov VV. ADVERPred–Web service for prediction of adverse effects of drugs. J Chem Inf Model. 2018;58(1):8–11. https://doi.org/10.1021/acs.jcim.7b00568.

    Article  CAS  PubMed  Google Scholar 

  20. 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 

  21. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47(D1):D607–13.

    Article  CAS  Google Scholar 

  22. Khanal P, Dey YN, Patil R, Chikhale R, Wanjari MM, Gurav SS, Patil BM, Srivastava B, Gaidhani SN. Combination of system biology to probe the anti-viral activity of andrographolide and its derivative against COVID-19. RSC Adv. 2021;11(9):5065–79. https://doi.org/10.1039/D0RA10529E.

    Article  CAS  Google Scholar 

  23. Bindea G, Galon J, Mlecnik B. CluePedia Cytoscape plugin: pathway insights using integrated experimental and in silico data. Bioinformatics. 2013;29(5):661–3. https://doi.org/10.1093/bioinformatics/btt019.

    Article  CAS  Google Scholar 

  24. Khanal P, Duyu T, Patil BM, Dey YN, Pasha I, Wanjari M, Gurav SS, Maity A. Network pharmacology of AYUSH recommended immune-boosting medicinal plants against COVID-19. J Ayurveda Integr Med. 2020. https://doi.org/10.1016/j.jaim.2020.11.004. 

  25. Halgren TA. Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. J Comput Chem. 1996;17(5-6):490–519. https://doi.org/10.1002/(SICI)1096-987X(199604)17:5/6<490::AID-JCC1>3.0.CO;2-P.

    Article  CAS  Google Scholar 

  26. Biovia DS. Discovery studio visualizer. San Diego, CA, USA. 2017. pp. 936. Available link:https://discover.3ds.com/discovery-studio-visualizer-download.

  27. Oo WM, Khine MM. Pharmacological properties of Feronia limonia fruit pulp–a review. Indo Am J Pharm Sci. 2017;7(04):8213–9.

  28. Moreno E, Cordobilla R, Calvet T, Lahoz FJ, Balana AI. The C form of n-hexadecanoic acid. Acta Crystallogr Sect C Cryst Struct Commun. 2006;62(3):o129-31. https://doi.org/10.1107/S0108270106003106.

    Article  CAS  Google Scholar 

  29. Bibo AM, Peterson IR. Phase diagrams of monolayers of the long chain fatty acids. Adv Mater. 1990;2(6-7):309–11. https://doi.org/10.1002/adma.19900020608.

    Article  CAS  Google Scholar 

  30. Khanal P, Patil BM. Integration of network and experimental pharmacology to decipher the antidiabetic action of Duranta repens L. J Integr Med. 2020. https://doi.org/10.1016/j.joim.2020.10.003.

    Article  PubMed  Google Scholar 

  31. Walters WP, Murcko MA. Prediction of ‘drug-likeness’. Adv Drug Deliv Rev. 2002;54(3):255–71. https://doi.org/10.1016/S0169-409X(02)00003-0.

    Article  CAS  PubMed  Google Scholar 

  32. 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  Google Scholar 

  33. Xiao H, Gu Z, Wang G, Zhao T. The possible mechanisms underlying the impairment of HIF-1α pathway signaling in hyperglycemia and the beneficial effects of certain therapies. Int J Med Sci. 2013;10(10):1412–21. https://doi.org/10.7150/ijms.5630.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Kay AM, Simpson CL, Stewart JA Jr. The Role of AGE/RAGE Signaling in Diabetes-Mediated Vascular Calcification. J Diabetes Res. 2016;2016:6809703. doi:https://doi.org/10.1155/2016/6809703.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Author notes

  1. Ekta Kotharkar and Shailaja Nare contributed equally to this work.

Authors and Affiliations

  1. Department of Pharmacology and Toxicology, KLE College of Pharmacy Belagavi, KLE Academy of Higher Education and Research (KAHER), Belagavi, 590010, India

    Prarambh SR Dwivedi, V. P. Rasal, Ekta Kotharkar, Shailaja Nare & Pukar Khanal

Authors
  1. Prarambh SR Dwivedi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. P. Rasal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ekta Kotharkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shailaja Nare
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pukar Khanal
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Prarambh SR Dwivedi performed the work and drafted the manuscript. VP Rasal and Pukar Khanal had an equal contribution in designing the work, supervising, and final review of the manuscript. Ekta Kotharkar and Shailaja Nare assisted Prarambh SR Dwivedi in data mining and data analysis. All the authors of this manuscript approved the final manuscript.

Corresponding authors

Correspondence to V. P. Rasal or Pukar Khanal.

Ethics declarations

Conflict of interest

Nil.

Ethical statement

Not applicable.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dwivedi, P.S., Rasal, V.P., Kotharkar, E. et al. Gene set enrichment analysis of PPAR-γ regulators from Murraya odorata Blanco. J Diabetes Metab Disord 20, 369–375 (2021). https://doi.org/10.1007/s40200-021-00754-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40200-021-00754-x

Keywords

Search

Navigation