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Clinical Phytoscience

International Journal of Phytomedicine and Phytotherapy

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Triterpene saponins from Barringtonia acutangula (L.) Gaertn as a potent inhibitor of 11β-HSD1 for type 2 diabetes mellitus, obesity, and metabolic syndrome

Clinical Phytoscience volume 6, Article number: 61 (2020) Cite this article

Abstract

Background

Barringtonia acutangula (L.) Gaertn, Garcinia indica (Thouars) Choisy, and Feronia limonia (L.) Swingle is widely utilized in traditional folk medicine against diabetes, obesity, and metabolic syndrome but lacks the evidence of compound-protein interaction for the treatment.

Methods

Phytocompounds were retrieved from herbs databases and public repositories. Probable protein targets were predicted using BindingDB (p ≥ 0.7). The pathways modulated by compounds were analyzed using the STRING and KEGG pathways. The compound-protein-pathway network was constructed using Cytoscape v3.6.1. Druglikeness was predicted by Molsoft. Docking was performed by AutoDock vina by PyRx 0.8v.

Results

Among three plants, eleven triterpene saponins from B. acutangula showed druggable characteristics and identified to inhibit the 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1/HSD11B1) as a key protein target and also inhibit/modulate other 27 protein molecules involved in the 3 major pathways i.e. Metabolic syndrome, cGMP-PKG signaling, and insulin resistance pathways and also these compounds showed interactions with the active site amino acid residues of 11β-HSD1. Among eleven compounds Barringtogenol B scored the highest binding affinity by forming a hydrogen bond with Ile218 active site residue of 11β-HSD1.

Conclusion

Triterpene saponins contained in B. acutangula bark and seed inhibits 11Β-HSD1 and this multi-compound contained enriched fraction could be the potent treatment regimen for T2DM, obesity, and MetS.

Introduction

Metabolic syndrome (MetS) is a group of metabolic abnormalities involves both genetic and acquired factors and gained considerable attention worldwide due to exponentially increased risk of cardiovascular disease, hypertension, obesity and type 2 diabetes mellitus (T2DM) that includes dyslipidemia, insulin resistance (IR), hypertension and visceral obesity [1, 2]. Currently, conventional drugs such as insulin sensitizers, PPAR-γ agonists, statins, etc. are utilized for the management of T2DM, obesity, and MetS. However, these agents target a single protein molecule that could regulate the mechanism of other protein, alters the homeostatic proteins/pathways, and causes numerous side effects such as ketoacidosis, pancreatitis, genital mycosis, neuropathy risk, nausea, and vomiting and have various limitations [3, 4]. Limiting the use of single drug molecules at high doses and utilizing the multiple drug candidates belongs to the same drug class targeting multiple proteins at a low dose could be the potential treatment strategy for complex diseases like T2DM, obesity, and MetS [5].

Herbal plants play a key role in the treatment of complex diseases in humans/animals due to their complex mixture of secondary metabolites on the modulation of corresponding molecular protein targets [6]. Currently, the network pharmacology concept of multi-drug, multi-target, and multi-pathway interactions opened up new systematic insights into the holistic understanding of the effects of herbal compounds at a molecular level and can open up opportunities for the identification potent drug candidates against complex diseases like T2DM, obesity, and MetS [4, 7, 8].

Barringtonia acutangula (L.) Gaertn, Garcinia indica (Thouars) Choisy, and Feronia limonia (L.) Swingle are traditionally utilized herbs for the management of diabetes, obesity, hypertension, and cardiovascular diseases and used in various polyherbal formulations. Researchers demonstrated their anti-obesity, anti-diabetic potency, the anti-hypertensive effect under various animal models [9,10,11,12,13]. We aimed the current study to identify the specific group of phytocompounds from these plants responsible for the regulation of protein targets and modulation of disease-associated pathways in the management of T2DM, obesity, and MetS. Interestingly, we identified the triterpene saponins group contained in aqueous extract of B. acutangula bark, seed, fruit, and leaves [14,15,16] to inhibit 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1/HSD11B1) as a key target. Several studies have been shown its pathogenic role and as a therapeutic target in the treatment of T2DM, obesity, and MetS associated hypertension, cardiovascular disease [17,18,19].

Materials and methods

Mining of phytocompounds and target identification

Phytocompounds were retrieved from Dr. Duke’s DB (https://phytochem.nal.usda.gov), Phytochemical Interaction DB (https://www.genome.jp/db/pcidb/), ChEBI (https://www.ebi.ac.uk/chebi/) and public repositories (Supplementary Table 1). Probable targets of each compound were predicted by BindingDB at a probable score of ≥0.7 with corresponding to the standard small molecules targeting the specific protein of interest [20]. Gene ID of each protein was retrieved from UniProt. Further, the phytocompounds targeting therapeutic targets involved in the pathogenesis of DM and obesity were retrieved from the Therapeutic Target Database (http://db.idrblab.net/ttd/). The probable targets modulated by the phytocompounds were shown in Supplementary Table 2.

Gene ontology, enrichment analysis, and network construction

Search Tool for the Retrieval of Interacting Genes/Proteins v11.0 (STRING; https://string-db.org) and its annotated tool KEGG pathway (https://www.genome.jp/kegg/pathway.html) was utilized to identify the phytocompounds modulating enriched pathways associated with the pathogenesis of DM and obesity. Further, the compound-protein-pathway network was constructed using Cytoscape v3.6.1 software. During the network construction, the edge count parameter was applied to identify the potentially modulated target by the phytocompounds. A degree sorted circular layout was applied to design the network.

Retrieval and preparation of ligand and protein

The phytocompounds were retrieved from the PubChem in 2D and 3D .sdf format. To eliminate the clashes within ligand atoms and to produce the reasonable staring pose, the mmff94 force field was applied for each compound for energy minimization using MarvinSketch. Further, pose having the lowest energy conformation was chosen and saved in protein data bank format (.pdb). The .pdb molecule then converted into AutoDock molecule (.pdbqt). The three dimensional (3D) x-ray crystallographic structure of 11β -HSD1 (PDB ID: 1XU7 and 1.8 A0) was retrieved from the RCSB PDB [21]. The backbone dihedral angles ϕ and ψ were analyzed by Ramachandran plot obtained through PROCHECK (https://servicesn.mbi.ucla.edu/PROCHECK/) and ERRAT server was used to check the overall protein quality (https://servicesn.mbi.ucla.edu/ERRAT/).

Ligand- protein docking

The binding affinity of the compound with protein target was checked by AutoDock vina by PyRx 0.8v. The grid box for ligand and protein was set to maximum and other parameters were kept default. Discovery Studio Visualizer 2019v was used to visualize the non-covalent interactions of the ligand with active amino acid residues of the protein target.

Results and discussion

Twenty-nine isolated compounds from three plant viz., B. acutangula (12), G. indica (9), and F. limonia (8) were retrieved from the herbs databases and public repositories. Twelve compounds from B. acutangula bark, seed, and fruit and leaves were identified as triterpene saponins and predicted to modulate 28 protein targets involved in the T2DM, obesity, and MetS i.e. HSD11B1, HSD11B2, HMGCR, AKR1B10, AR, ALOX15, CYP19A1, CYP17A1, LSS, NR1H3, PTPN1, PTGS1, PTAFR, PRKCA, PRKCE, F2RL1, ATP5B, HSP90AA1, ATP2A2, SQLE, FDFT1, XDH, ATP1A1, ATP1A2, PPP2R5D, PPP2CA, PPP1CC, STAT3. Whereas, among 9 compounds from G. indica, 3 compounds were predicted to modulate 16 protein targets i.e. AKR1B1, ADRA2A, CA2, PDE5A, CHRM5, CYP3A4, IL2RA, ALPI, AMY2A, PTPN1, RPS6KA3, TNF, ALOX12, PTGS1, KDR, and MAOA. Among 8 compounds from F. limonia, 6 compounds predicted to modulate 14 protein targets i.e. CA2, CBR1, CYP19A1, CYP2A6, CYP2C9, GPR35, HSD17B3, HSP90AA1, MAOB, PDE5A, PGR, PTGS1, PTPN1, and TYR associated with T2DM, obesity, and MetS (Supplementary Table 3). Further, the probable protein targets were queried into STRING to understand the protein-protein interaction network and to obtain disease pathways modulated by the phytocompounds. The result revealed that 79 pathways were highly enriched within the network. The peer interpretation of the pathways revealed that among 79 pathways, 10 pathways were associated with T2DM, obesity, and MetS. Among 10 pathways, metabolic pathway followed by insulin resistance and cGMP-PKG signaling pathway were identified as highly enriched pathways (Supplementary Table 4). Further, we constructed the network interaction between compounds with their probable targets and enriched pathways. Among all the compounds, a group of triterpene saponins from B. acutangula were highly enriched to interact with 11β -HSD1 (Fig. 1). The enzyme 11β-HSD1 is an ER-localized membrane protein catalyzes the inter-conversion of cortisone and cortisol. Excessive production of cortisol in adipose tissue by 11β -HSD1 progresses the pathogenesis of T2DM and obesity [21]. Based on the mode of action, 83 inhibitors and 2 modulators of 11β-HSD1 were developed and none of them progressed beyond Phase III [22]. Numerous researchers suggested a group of natural compounds could be suitable inhibitors against 11β-HSD1 for satisfactory pharmacological treatment and also utilization of triterpenes as a potent inhibitor of 11β-HSD1 [18, 23,24,25].

Fig. 1
figure 1

Network representation of compound-target-pathway interaction

Full size image

In obese and diabetic rodents and humans, there is an increased expression of the 11β-HSD1 in adipose tissue. Increased expression of 11β-HSD1 results in MetS with visceral obesity, dyslipidemia, T2DM, and hypertension [26]. The in vitro and in vivo studies reflects the potency of B. acutangula leaves, bark, seed, and fruits to play an important role in the treatment of DM, obesity, and MetS [27,28,29,30]. Babre et al. reported, due to the presence of saponins in the hydroalcoholic extract of B. acutangula root delayed the intestinal absorption of dietary fat via pancreatic lipase activity inhibition [27]. Further, 500 mg/kg dose significantly reduced blood glucose levels from day 7 in STZ-induced diabetic rats [28]. Aqueous extract of B. acutangula fruit showed hypoglycaemic activity in the STZ-induced hyperglycaemic rat [29]. Grogery et al. study results reflected B. acutangula leaf aqueous and ethanolic extracts (500 mg/kg) and glibenclamide (10 mg/kg) to exhibit equal anti-diabetic efficacy in STZ-induced diabetic rats [30]. This suggests triterpene saponins from B. acutangula interact with 28 protein molecules and 11β-HSD1 as a potential protein target and modulate the pathways associated with T2DM, obesity, and MetS.

The affinity and hydrogen bond interactions of eleven triterpenes from B. acutangula bark and seed with 11Β-HSD1 active amino acid residue were analyzed by docking study. The active site residues were identified using the PDB ID 1XU7 and the protein quality was found to be 92.7%. The characteristics of 11β-HSD1 are shown in Supplementary Table 5. All eleven compounds from B. acutangula showed druggable characteristics (Supplementary Table 6), among them, Barringtogenol B scored the highest binding affinity (− 11.3 kcal/mol) by forming one hydrogen bond i.e. Ile218 and eight Alkyl, Pi-alkyl interaction i.e. Lys44, Ile46, Ile121, Val180, Val227, Leu126, Tyr183, and Ala223 with the active site residue of 11β-HSD1 (Fig. 2). Interestingly, all the compounds showed hydrogen bond, Alkyl, Pi-alkyl, Pi-sigma interaction with active site residue which suggests having the best fit with the target (Supplementary Table 7). It is important to note that, the triterpenes from B. acutangula not only predicted to interact with the 11β-HSD1 but also with other 27 protein targets involved in the T2DM, obesity, and Mets.

Fig. 2
figure 2

Interaction of Barringtogenol B with 11Β-HSD1 a 3D representation b 2D representation and c Barringtogenol B fit in active site 1 of 11Β-HSD1

Full size image

Conclusion

In conclusion, eleven triterpene saponins contained in aqueous extract of B. acutangula bark, seed, fruit, and leaves were identified to have druggable characteristics and these compounds targeted 28 protein molecules and 11β-HSD1 as a potential therapeutic target and identified to interact with the active site amino acid residues. The compound-gene set enrichment, network pharmacology, and docking analysis identified triterpene saponins from B. acutangula modulated 10 enriched disease pathways of T2DM, obesity, and MetS which suggest a potent therapy. However, the current study findings are based on the prediction using experimental based available database and computer simulations, so that subsequent wet-lab studies can be designed accordingly to test/verify 11β-HSD1 inhibitory activity using enriched fraction or isolated compound.

Availability of data and materials

All data generated or analyzed during this study are included in this published article [and its supplementary information files].

Abbreviations

DM:

Diabetes Mellitus

T2DM:

Type 2 Diabetes Mellitus

MetS:

Metabolic Syndrome

11β-HSD1 or HSD11B1:

11β-hydroxysteroid dehydrogenase type 1

BindingDB:

Binding database

STRING:

Search Tool for the Retrieval of Interacting Genes/Proteins

KEGG1:

Kyoto Encyclopedia of Genes and Genomes

PDB:

Protein Data Bank

BE:

Binding Energy

References

  1. Rochlani Y, Pothineni NV, Kovelamudi S, Mehta JL. Metabolic syndrome: pathophysiology, management, and modulation by natural compounds. Ther Adv Cardiovasc Dis. 2017;11(8):215–25.

    Article  CAS  Google Scholar 

  2. Halpern A, Mancini MC, Magalhães ME, Fisberg M, Radominski R, Bertolami MC, et al. Metabolic syndrome, dyslipidemia, hypertension and type 2 diabetes in youth: from diagnosis to treatment. Diabetol Metab Syndr. 2010;2(1):55.

    Article  Google Scholar 

  3. 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 Phytoscience. 2019;5(1):35.

    Article  Google Scholar 

  4. Chaudhury A, Duvoor C, Dendi R, Sena V, Kraleti S, Chada A, et al. Clinical review of antidiabetic drugs: implications for type 2 diabetes mellitus management. Front Endocrinol. 2017;24(8):6.

    Google Scholar 

  5. McCulloch DK. Management of persistent hyperglycemia in type 2 diabetes mellitus. Waltham MA UpToDate. http://www.uptodate.com/contents/management-of-persistent-hyperglycemia-in-type-2-diabetes-mellitus. Accessed 26 Apr 2020.

  6. Wink M. Modes of action of herbal medicines and plant secondary metabolites. Medicines. 2015;2(3):251–86.

    Article  CAS  Google Scholar 

  7. Duyu T, Khatib NA, Khanal P, Patil BM, Hullatti KK. Network pharmacology-based prediction and experimental validation of Mimosa pudica for Alzheimer's disease. J Phyto. 2020;9(1):46–53.

    Google Scholar 

  8. Khanal P, Patil BM. α-Glucosidase inhibitors from Duranta repens modulate p53 signaling pathway in diabetes mellitus. Adv Tradit Med. 2020:1–12. https://doi.org/10.1007/s13596-020-00426-w.

  9. Subramoniam A. Plants with anti-diabetes Mellitus Properties. Barringtonia acutangula. 1st ed. Boca Raton: CRC Press; 2016. p. 20.

  10. Sood SK, Bhardwaj R, Lakhanpal TN. Ethnic Indian plants in cure of diabetes. Barringtonia acutangula. Jodhpur: Scientific publishers; 2005. p. 150.

  11. Pullaiah T, Naidu KC. Antidiabetic plants in India and herbal based antidiabetic research. Garcinia indica (Dupetit-Thours) Choisy. New Delhi: Regency publications; 2003. p. 186.

  12. Soumyanath A. Traditional medicines for modern times: antidiabetic plants. Feronia limonia. 1st ed. Boca Raton: CRC Press; 2005. p. 261.

  13. Subramoniam A. Plants with Anti-Diabetes Mellitus Properties. Limonia acidissima (Feronia limonia). 1st ed. Boca Raton: CRC Press; 2016. p. 46.

  14. Mills C, Carroll AR, Quinn RJ. Acutangulosides a− F, Monodesmosidic Saponins from the bark of Barringtonia acutangula. J Nat. 2005;68(3):311–8.

    CAS  Google Scholar 

  15. Pal BC, Chaudhuri T, Yoshikawa K, Arihara S. Saponins from Barringtonia acutangula. Phytochemistry. 1994;35(5):1315–8.

    Article  CAS  Google Scholar 

  16. Barua AK, Dutta SP, Das BC. Triterpenoids—XXIX: the structure of barringtogenol B—A new triterpenoid sapogenin from Barringtonia acutangula Gaertn. Tetrahedron. 1968;24(3):1113–7.

    Article  CAS  Google Scholar 

  17. Pereira CD, Azevedo I, Monteiro R, Martins MJ. 11β-Hydroxysteroid dehydrogenase type 1: relevance of its modulation in the pathophysiology of obesity, the metabolic syndrome and type 2 diabetes mellitus. Diabetes Obes Metab. 2012;14(10):869–81.

    Article  CAS  Google Scholar 

  18. Hu GX, Lin H, Lian QQ, Zhou SH, Guo J, Zhou HY, et al. Curcumin as a potent and selective inhibitor of 11β-hydroxysteroid dehydrogenase 1: improving lipid profiles in high-fat-diet-treated rats. PLoS One. 2013;8(3):1–7.

    Google Scholar 

  19. Bujalska IJ, Gathercole LL, Tomlinson JW, Darimont C, Ermolieff J, Fanjul AN, Rejto PA, Stewart PM. A novel selective 11beta-hydroxysteroid dehydrogenase type 1 inhibitor prevents human adipogenesis. J Endocrinol. 2008;197(2):297–307.

    Article  CAS  Google Scholar 

  20. Gilson MK, Liu T, Baitaluk M, Nicola G, Hwang L, Chong J. BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res. 2016;44(D1):D1045–53.

    Article  CAS  Google Scholar 

  21. Hosfield DJ, Wu Y, Skene RJ, Hilgers M, Jennings A, Snell GP, Aertgeerts K. Conformational flexibility in crystal structures of human 11β-hydroxysteroid dehydrogenase type I provide insights into glucocorticoid interconversion and enzyme regulation. J Biol Chem. 2005;280(6):4639–48.

    Article  CAS  Google Scholar 

  22. Therapeutic Target Database. Corticosteroid 11-beta-dehydrogenase 1 (HSD11B1) http://db.idrblab.net/ttd/search/ttd/target?search_api_fulltext=HSD11B1. Accessed 27 Apr 2020.

  23. Mosquera C, Panay AJ, Montoya G. Pentacyclic triterpenes from Cecropia telenitida can function as inhibitors of 11β-hydroxysteroid dehydrogenase type 1. Molecules. 2018;23(6):1444.

    Article  Google Scholar 

  24. Shao Y, Qiao L, Wu L, Sun X, Zhu D, Yang G, et al. Structure identification and anti-cancer pharmacological prediction of triterpenes from Ganoderma lucidum. Molecules. 2016;21(5):678.

    Article  Google Scholar 

  25. Classen-Houben D, Schuster D, Da Cunha T, Odermatt A, Wolber G, Jordis U, Kueenburg B. Selective inhibition of 11β-hydroxysteroid dehydrogenase 1 by 18α-glycyrrhetinic acid but not 18β-glycyrrhetinic acid. J Steroid Biochem. 2009;113(3–5):248–52.

    Article  CAS  Google Scholar 

  26. Paterson JM, Morton NM, Fievet C, Kenyon CJ, Holmes MC, Staels B, et al. Metabolic syndrome without obesity: hepatic overexpression of 11β-hydroxysteroid dehydrogenase type 1 in transgenic mice. PNAS. 2004;101(18):7088–93.

    Article  CAS  Google Scholar 

  27. Babre NP, Debnath S, Manjunath YS, Deshmaukh G, Hariprasath K, Sharon K. Hypolipidemic effect of hydro-alcoholic extract of Barringtonia acutangula Linn. Root extract on streptozotocin-induced diabetic rats. J Pharm Sci Tech. 2010;2:368–71.

    Google Scholar 

  28. Babre NP, Debnath S, Manjunath SY, Panda M, Manoj G. Antidiabetic effect of hydroalcoholic extract of Barringtonia acutangula Linn. Root on streptozotocin-induced diabetic rats. Int J Pharm Sci Nanotech. 2010;3(3):1158–64.

    Google Scholar 

  29. Khatib NA, Patil PA. Evaluation of hypoglycemic activity of Barringtonia acutangula fruit extracts in streptozotocin induced hyperglycemic wistar rats. Cell Tissue Res. 2011;11(1):2573.

    Google Scholar 

  30. Gregory M, Khandelwal VK, Mary RA, Kalaichelvan VK, Palanivel V. Barringtonia acutangula improves the biochemical parameters in diabetic rats. Chin J Nat Med. 2014;12(2):126–30.

    CAS  PubMed  Google Scholar 

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Declarations

VSP and NAK have approved this manuscript. The content of this manuscript or any portion thereof has not been published or submitted for publication elsewhere.

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

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  1. Vishal Shivalingappa Patil and Nayeem A. Khatib contributed equally to this work.

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  1. Department of pharmacology and toxicology, KLE College of Pharmacy, KLE Academy of Higher Education and Research, 590010, Belagavi, Karnataka, India

    Vishal Shivalingappa Patil & Nayeem A. Khatib

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  1. Vishal Shivalingappa Patil
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VSP and NAK equally participated in the study design, literature search, data analysis, preparing, and revision of the manuscript. The authors read and approved the final draft of the manuscript.

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Patil, V.S., Khatib, N.A. Triterpene saponins from Barringtonia acutangula (L.) Gaertn as a potent inhibitor of 11β-HSD1 for type 2 diabetes mellitus, obesity, and metabolic syndrome. Clin Phytosci 6, 61 (2020). https://doi.org/10.1186/s40816-020-00210-y

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