Centrality analysis of protein-protein interaction networks and molecular docking prioritize potential drug-targets in type 1 diabetes

Document Type : Research article

Authors

1 Department of Physical Chemistry, School of Chemistry, College of Sciences, University of Tehran, Tehran, Iran.

2 Bioinformatics Department, Institute of Biochemistry and Biophysics, Tehran University, Tehran, Iran.

3 Medical Informatics Department, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.

4 Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

5 Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

6 Immunology Department, Faculty of Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

7 Endocrine Research Center, Institute of Endocrinology and Metabolism, Iran University of Medical Sciences, Tehran, Iran.

Abstract

Type 1 diabetes (T1D) occurs as a consequence of an autoimmune attack against pancreatic-β cells. Due to a lack of a clear understanding of the T1D pathogenesis, the identification of effective therapies for T1D is the active area in the research. The study purpose was to prioritize potential drugs and targets in T1D via systems biology approach. Gene expression data of peripheral blood mononuclear cells (PBMCs) and pancreatic-β cells in T1D were analyzed and differential expressed genes were integrated with protein-protein interactions (PPI) data. Multiple topological centrality parameters of extracted query-query PPI (QQPPI) networks were calculated and the interaction of more central proteins with drugs was investigated. Molecular docking was performed to further predict the interactions between drugs and the binding sites of targets. Central proteins were identified by the analysis of PBMC (MYC, ERBB2, PSMA1, ABL1 and HSP90AA1) and pancreatic β-cells QQPPI networks (HSP90AB1, ESR1, RELA, RAC1, NFKB1, NFKB2, IKBKE, ARRB2, SRC). Thirteen drugs targeted eight central proteins were identified by further analysis of drug-target interactions. Some drugs which investigated for diabetes treatment in the experimental models of T1D were prioritized by literature verification, including melatonin, resveratrol, lapatinib, geldanamycin, eugenol and fostaminib. Finally, according on molecular docking analysis, lapatinib-ERBB2 and eugenol-ESR1 exhibited highest and lowest binding energy, respectively. This study presented promising results for the prioritization of potential drug-targets which might facilitate T1D targeted therapy and its drug discovery process more effectively.

Graphical Abstract

Centrality analysis of protein-protein interaction networks and molecular docking prioritize potential drug-targets in type 1 diabetes

Keywords


  1. Craig ME, Jefferies C, Dabelea D, Balde N, Seth A and Donaghue KC. Definition, epidemiology, and classification of diabetes in children and adolescents. Pediatr. Diabetes (2014) 15: 4-17.
  2. DiMeglio LA, Evans-Molina C and Oram RA. Type 1 diabetes. Lancet (2018). 391: 2449-62.
  3. Atkinson MA, Eisenbarth GS and Michels AW. Type 1 diabetes. Lancet (2014) 383: 69-82.
  4. Harris K, Boland C, Meade L and Battise D. Adjunctive therapy for glucose control in patients with type 1 diabetes. Diabetes Metab. Syndr. Obes. (2018) 11: 159-73.
  5. Hartemann A, Bensimon G, Payan CA, Jacqueminet S, Bourron O, Nicolas N, Fonfrede M, Rosenzwajg M, Bernard C and Klatzmann D. Low-dose interleukin 2 in patients with type 1 diabetes: a phase 1/2 randomised, double-blind, placebo-controlled trial. Lancet Diabetes Endocrinol. (2013) 1: 295-305.
  6. Skyler JS. Hope vs hype: where are we in type 1 diabetes?. Diabetologia (2018) 61: 509-16.
  7. Sams-Dodd F. Is poor research the cause of the declining productivity of the pharmaceutical industry? An industry in need of a paradigm shift. Drug. Discov. Today (2013) 18: 211-7.
  8. Berger SI and Iyengar R. Role of systems pharmacology in understanding drug adverse events. Wiley Interdiscip. Rev. Syst. Biol. Med. (2011) 3: 129-35.
  9. Eizirik DL, Sammeth M, Bouckenooghe T, Bottu G, Sisino G, Igoillo-Esteve M, Ortis F, Santin I, Colli ML and Barthson J. The human pancreatic islet transcriptome: expression of candidate genes for type 1 diabetes and the impact of pro-inflammatory cytokines. PLoS Genet. (2012) 8: e1002552.
  10. Kaizer EC, Glaser CL, Chaussabel D, Banchereau J, Pascual V and White PC. Gene expression in peripheral blood mononuclear cells from children with diabetes. J. Clin. Endocrinol. Metab. (2007) 92: 3705-11.
  11. Chuang HY, Lee E, Liu YT, Lee D and Ideker T. Network‐based classification of breast cancer metastasis. Mol. Syst. Biol. (2007) 3: 140-50.
  12. Pujana MA, Han J-DJ, Starita LM, Stevens KN, Tewari M, Ahn JS, Rennert G, Moreno V, Kirchhoff T and Gold B. Network modeling links breast cancer susceptibility and centrosome dysfunction. Nat. Genet. (2007) 39: 1338-49.
  13. Taylor IW, Linding R, Warde-Farley D, Liu Y, Pesquita C, Faria D, Bull S, Pawson T, Morris Q and Wrana JL. Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat. Biotechnol. (2009) 27: 199-204.
  14. Melak T and Gakkhar S. Comparative genome and network centrality analysis to identify drug targets of mycobacterium tuberculosis h37rv. Biomed. Res. Int. (2015) 2015.
  15. Módos D, Bulusu KC, Fazekas D, Kubisch J, Brooks J, Marczell I, Szabó PM, Vellai T, Csermely P and Lenti K. Neighbours of cancer-related proteins have key influence on pathogenesis and could increase the drug target space for anticancer therapies. NPJ. Syst. Biol. Appl. (2017) 3: 2.
  16. Mahboubi M, Azodi MZ, Tavirani MR, Mansouri V, Ahmadi NA, Hamdieh M, Tavirani MR and Gargari BN. Protein-Protein Interaction Analysis of Common Top Genes in Obsessive-Compulsive Disorder (OCD) and Schizophrenia: Towards New Drug Approach Obsessive-Compulsive disorder (OCD) and Schizophrenia Comorbidity Gene Analysis. Iran. J. Pharm. Res. (2018) 17: 173-86.
  17. Piñero J, Berenstein A, Gonzalez-Perez A, Chernomoretz A and Furlong LI. Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing. Sci. Rep. (2016) 6: 24570.
  18. Safari-Alighiarloo N, Rezaei-Tavirani M, Taghizadeh M, Tabatabaei SM and Namaki S. Analysis of protein-protein interactions network based on differentially expressed genes in cerebrospinal fluid for multiple sclerosis. Koomesh (2018) 20: 81-8.
  19. Safari‐Alighiarloo N, Taghizadeh M, Tabatabaei SM, Shahsavari S, Namaki S, Khodakarim S and Rezaei‐Tavirani M. Identification of new key genes for type 1 diabetes through construction and analysis of protein–protein interaction networks based on blood and pancreatic islet transcriptomes. J. Diabetes (2017) 9: 764-77.
  20. Safari-Alighiarloo N, Rezaei-Tavirani M, Taghizadeh M, Tabatabaei SM and Namaki S. Network-based analysis of differentially expressed genes in cerebrospinal fluid (CSF) and blood reveals new candidate genes for multiple sclerosis. Peer J. (2016) 4: e2775.
  21. Safari-Alighiarloo N, Taghizadeh M, Tabatabaei SM, Namaki S and Rezaei-Tavirani M. Identification of common key genes and pathways between type 1 diabetes and multiple sclerosis using transcriptome and interactome analysis. Endocrine (2020) 68: 81-92.
  22. Csermely P, Korcsmáros T, Kiss HJ, London G and Nussinov R. Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review. Pharmacol. Ther. (2013) 138: 333-408.
  23. Peng Q and Schork N. Utility of network integrity methods in therapeutic target identification. Front. Genet. (2014) 3: 5-12.
  24. Kotlyar M, Fortney K and Jurisica I. Network-based characterization of drug-regulated genes, drug targets, and toxicity. Methods (2012) 57: 499-507.
  25. Kerrien S, Aranda B, Breuza L, Bridge A, Broackes-Carter F, Chen C, Duesbury M, Dumousseau M, Feuermann M and Hinz U. The IntAct molecular interaction database in 2012. Nucleic Acids Res. (2011) 40: D841-D6.
  26. Licata L, Briganti L, Peluso D, Perfetto L, Iannuccelli M, Galeota E, Sacco F, Palma A, Nardozza AP and Santonico E. MINT, the molecular interaction database: 2012 update. Nucleic Acids Res. (2011) 40: D857-D61.
  27. Xenarios I, Salwinski L, Duan XJ, Higney P, Kim S-M and Eisenberg D. DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Res. (2002) 30: 303-5.
  28. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. (2003) 13: 2498-504.
  29. Junker BH, Koschützki D and Schreiber F. Exploration of biological network centralities with CentiBiN. BMC Bioinformatics (2006) 7: 219.
  30. Barabási A-L, Gulbahce N and Loscalzo J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. (2011) 12: 56-68.
  31. Zhang A. Protein interaction networks: computational Analysis. Cambridge University Press (2009).
  32. Lee JH, Gao J, Kosinski PA, Elliman SJ, Hughes TE, Gromada J and Kemp DM. Heat shock protein 90 (HSP90) inhibitors activate the heat shock factor 1 (HSF1) stress response pathway and improve glucose regulation in diabetic mice. Biochem. Biophys. Res. Commun. (2013) 430: 1109-13.
  33. Al-Trad B, Alkhateeb H, Alsmadi W and Al-Zoubi M. Eugenol ameliorates insulin resistance, oxidative stress and inflammation in high fat-diet/streptozotocin-induced diabetic rat. Life Sci. (2019) 216: 183-8.
  34. Benter IF, Sarkhou F, Al-Khaldi AT, Chandrasekhar B, Attur S, Dhaunsi GS, Yousif MH and Akhtar S. The dual targeting of EGFR and ErbB2 with the inhibitor Lapatinib corrects high glucose-induced apoptosis and vascular dysfunction by opposing multiple diabetes-induced signaling changes. J. Drug Target (2015) 23: 506-18.
  35. Turanli B, Altay O, Borén J, Turkez H, Nielsen J, Uhlen M, Arga KY and Mardinoglu A. Systems biology based drug repositioning for development of cancer therapy. in Seminars in Cancer Biology. (2019). Elsevier.
  36. Guo JH, Chen H, Ruan YC, Zhang XL, Zhang XH, Fok KL, Tsang LL, Yu MK, Huang WQ and Sun X. Glucose-induced electrical activities and insulin secretion in pancreatic islet β-cells are modulated by CFTR. Nat. Commun. (2014) 5: 4420.
  37. Grishman EK, White PC and Savani RC. Toll-like receptors, the NLRP3 inflammasome, and interleukin-1β in the development and progression of type 1 diabetes. Pediatr. Res. (2012) 71: 626-632.
  38. Morita S, Villalta SA, Feldman HC, Register AC, Rosenthal W, Hoffmann-Petersen IT, Mehdizadeh M, Ghosh R, Wang L and Colon-Negron K. Targeting ABL-IRE1α signaling spares ER-stressed pancreatic β cells to reverse autoimmune diabetes. Cell Metab. (2017) 25: 883-97.
  39. Louvet C, Szot GL, Lang J, Lee MR, Martinier N, Bollag G, Zhu S, Weiss A and Bluestone JA. Tyrosine kinase inhibitors reverse type 1 diabetes in nonobese diabetic mice. Proc. Natl. Acad. Sci. USA (2008) 105: 18895-900.
  40. Catanuto P, Xia X, Pereira-Simon S and Elliot S. Estrogen receptor subtype ratio change protects against podocyte damage. Curr. Trends Endocinol. (2017) 9: 19-29.
  41. Szkudelski T and Szkudelska K. Resveratrol and diabetes: from animal to human studies. Biochim. Biophys. Acta (2015) 1852: 1145-54.
  42. Li Y, Huang J, Yan Y, Liang J, Liang Q, Lu Y, Zhao L and Li H. Preventative effects of resveratrol and estradiol on streptozotocin-induced diabetes in ovariectomized mice and the related mechanisms. PloS One (2018) 13: e0204499.
  43. del Río B, Pedrero JMG, Martínez-Campa C, Zuazua P, Lazo PS and Ramos S. Melatonin, an endogenous-specific inhibitor of estrogen receptor α via calmodulin. J. Biol. Chem. (2004) 279: 38294-302.
  44. Peschke E, Wolgast S, Bazwinsky I, Pönicke K and Muhlbauer E. Increased melatonin synthesis in pineal glands of rats in streptozotocin induced type 1 diabetes. J. Pineal. Res. (2008) 45: 439-48.
  45. Lin GJ, Huang SH, Chen SJ, Wang CH, Chang DM and Sytwu HK. Modulation by melatonin of the pathogenesis of inflammatory autoimmune diseases. Int. J. Mol. Sci. (2013) 14: 11742-66.
  46. Lazaro I, Oguiza A, Recio C, Mallavia B, Madrigal-Matute J, Blanco J, Egido J, Martin-Ventura JL and Gomez-Guerrero C. Targeting HSP90 ameliorates nephropathy and atherosclerosis through suppression of NF-κB and STAT signaling pathways in diabetic mice. Diabetes (2015) 64: 3600-13.
  47. Vega VL, Charles W and Alexander LEC. Rescuing of deficient killing and phagocytic activities of macrophages derived from non-obese diabetic mice by treatment with geldanamycin or heat shock: potential clinical implications. Cell Stress Chaperones (2011) 16: 573.
  48. Yan X, Zhang G, Bie F, Lv Y, Ma Y, Ma M, Wang Y, Hao X, Yuan N and Jiang X. Eugenol inhibits oxidative phosphorylation and fatty acid oxidation via downregulation of c-Myc/PGC-1β/ERRα signaling pathway in MCF10A-ras cells. Sci. Rep. (2017) 7: 12920.
  49. Agostino NM, Chinchilli VM, Lynch CJ, Koszyk-Szewczyk A, Gingrich R, Sivik J and Drabick JJ. Effect of the tyrosine kinase inhibitors (sunitinib, sorafenib, dasatinib, and imatinib) on blood glucose levels in diabetic and nondiabetic patients in general clinical practice. J. Oncol. Pharm. Pract. (2011) 17: 197-202.
  50. Karbownik A, Szałek E, Sobańska K, Klupczynska A, Plewa S, Grabowski T, Wolc A, Moch M, Kokot ZJ and Grześkowiak E. A pharmacokinetic study on lapatinib in type 2 diabetic rats. Pharmacol. Rep. (2018) 70: 191-5.
  51. Roskoski Jr R. The ErbB/HER family of protein-tyrosine kinases and cancer. Pharmacol. Rep. (2014) 79: 34-74.
  52. Cook JJ, Hudson I, Harrison LC, Dean B, Colman PG, Werther G and Warne GL. Double-blind controlled trial of azathioprine in children with newly diagnosed type I diabetes. Diabetes (1989) 38: 779-83..
  53. Tiede I, Fritz G, Strand S, Poppe D, Dvorsky R, Strand D, Lehr HA, Wirtz S, Becker C and Atreya R. CD28-dependent Rac1 activation is the molecular target of azathioprine in primary human CD4 + T lymphocytes. J. Clin. Invest. (2003) 111: 1133-45.
  54. Veluthakal R, Sidarala V and Kowluru A. NSC23766, a known inhibitor of Tiam1-Rac1 signaling module, prevents the onset of type 1 diabetes in the NOD mouse model. Cell Physiol. Biochem. (2016) 39: 760-7.
  55. Harrison LC, Colman PG, Dean B, Baxter R and Martin F. Increase in remission rate in newly diagnosed type I diabetic subjects treated with azathioprine. Diabetes (1985) 34: 1306-8.
  56. Geliebter R, Chia D and Derrick K. SUN-275 Azathioprine for Tertiary Prevention of Diabetes in Patients with Newly Diagnosed Type 1 Diabetes. J. Endocr. Soc. (2019) 3: SUN-275.