Research Article | Volume 11, Issue 1, January, 2023

Meta-analysis of Type 2 diabetes and insulin resistance gene expression datasets to decipher their associated pathways

Aditya Saxena Nitish Mathur Uma Chaudhary Utkarsh Raj Sneha Rai Sandeep Kumar Mathur   

Open Access   

Published:  Nov 22, 2022

DOI: 10.7324/JABB.2023.110116

Type 2 diabetes (T2D) is a chronic complex disease which is difficult to cure using existing drugs so new, it is essential to identify novel disease-associated genes/proteins which can be targeted for effective treatment. In this study, we identified that key genes (proteins) whose involvement in T2D have been mined from multiple level of evidence, that is, their presence in large-scale meta-analysis of T2D, and insulin resistance (IR) gene-expression datasets, their reported association with T2D, their enrichment in T2D-implicated cellular pathways, as well as their topological positions in the disease-network. We have carried out probably the most comprehensive meta-analysis of T2D, and IR gene expression datasets to identify meta-signature of genes. These genes were further subjected to pathway- and network-based analysis to extract a small number of genes. We foresee that modulation of their protein-products by small molecules could be a promising strategy for therapy. We expect that our identified genes can be validated by qPCR and/or western blot experiments and, further, investigated as their potential role in T2D and IR. Our approach is generic and can be used for other disorders.

Keyword:     Type 2 diabetes Insulin resistance Meta-analysis Network biology Microarray data Hyperglycemia


Saxena A, Mathur N, Chaudhary U, Raj U, Rai S, Mathur SK. Meta-analysis of Type 2 Diabetes and Insulin Resistance gene expression Datasets to decipher their associated pathways. J App Biol Biotech. 2023;11(1)::109-115.

Copyright: Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike license.

HTML Full Text

1. Saeedi P, Salpea P, Karuranga S, Petersohn I, Malanda B, Gregg EW, et al. Mortality attributable to diabetes in 20-79 years old adults, 2019 estimates: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract 2020;162:108086.

2. Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract 2019;157:107843.

3. Skov V, Glintborg D, Knudsen S, Jensen T, Kruse TA, Tan Q, et al. Reduced expression of nuclear-encoded genes involved in mitochondrial oxidative metabolism in skeletal muscle of insulin-resistant women with polycystic ovary syndrome. Diabetes 2007;56:2349-55.

4. Hardy OT, Perugini RA, Nicoloro SM, Gallagher-Dorval K, Puri V, Straubhaar J, et al. Body mass index-independent inflammation in omental adipose tissue associated with insulin resistance in morbid obesity. Surg Obes Relat Dis 2011;7:60-7.

5. Wu X, Wang J, Cui X, Maianu L, Rhees B, Rosinski J, et al. The effect of insulin on expression of genes and biochemical pathways in human skeletal muscle. Endocrine 2007;31:5-17.

6. Soronen J, Laurila PP, Naukkarinen J, Surakka I, Ripatti S, Jauhiainen M, et al. Adipose tissue gene expression analysis reveals changes in inflammatory, mitochondrial respiratory and lipid metabolic pathways in obese insulin-resistant subjects. BMC Med Genomics 2012;5:9.

7. Kaur S, Archer KJ, Devi MG, Kriplani A, Strauss JF 3rd, Singh R. Differential gene expression in granulosa cells from polycystic ovary syndrome patients with and without insulin resistance: Identification of susceptibility gene sets through network analysis. J Clin Endocrinol Metab 2012;97:E2016-21.

8. Kristensen JM, Skov V, Petersson SJ, Ørtenblad N, Wojtaszewski JF, Beck-Nielsen H, et al. A PGC-1α- and muscle fibre type-related decrease in markers of mitochondrial oxidative metabolism in skeletal muscle of humans with inherited insulin resistance. Diabetologia 2014;57:1006-15.

9. Winnier DA, Fourcaudot M, Norton L, Abdul-Ghani MA, Hu SL, Farook VS, et al. Transcriptomic identification of ADH1B as a novel candidate gene for obesity and insulin resistance in human adipose tissue in Mexican Americans from the Veterans Administration Genetic Epidemiology Study (VAGES). PLoS One 2015;10:e0119941.

10. Frederiksen CM, Højlund K, Hansen L, Oakeley EJ, Hemmings B, Abdallah BM, et al. Transcriptional profiling of myotubes from patients with type 2 diabetes: No evidence for a primary defect in oxidative phosphorylation genes. Diabetologia 2008;51:2068-77.

11. Kaizer EC, Glaser CL, Chaussabel D, Banchereau J, Pascual V, White PC. Gene expression in peripheral blood mononuclear cells from children with diabetes. J Clin Endocrinol Metab 2007;92:3705-11.

12. Skov V, Knudsen S, Olesen M, Hansen ML, Rasmussen LM. Global gene expression profiling displays a network of dysregulated genes in non-atherosclerotic arterial tissue from patients with type 2 diabetes. Cardiovasc Diabetol 2012;11:15.

13. Pihlajamäki J, Boes T, Kim EY, Dearie F, Kim BW, Schroeder J, et al. Thyroid hormone-related regulation of gene expression in human fatty liver. J Clin Endocrinol Metab 2009;94:3521-9.

14. van Tienen FH, Praet SF, de Feyter HM, van den Broek NM, Lindsey PJ, Schoonderwoerd KG, et al. Physical activity is the key determinant of skeletal muscle mitochondrial function in type 2 diabetes. J Clin Endocrinol Metab 2012;97:3261-9.

15. Misu H, Takamura T, Takayama H, Hayashi H, Matsuzawa-Nagata N, Kurita S, et al. A liver-derived secretory protein, selenoprotein P, causes insulin resistance. Cell Metab 2010;12:483-95.

16. Dominguez V, Raimondi C, Somanath S, Bugliani M, Loder MK, Edling CE, et al. Class II phosphoinositide 3-kinase regulates exocytosis of insulin granules in pancreatic beta cells. J Biol Chem 2011;286:4216-25.

17. Jain P, Vig S, Datta M, Jindel D, Mathur AK, Mathur SK, et al. Systems biology approach reveals genome to phenome correlation in type 2 diabetes. PLoS One 2013;8:e53522.

18. Davis S, Meltzer PS. GEOquery: A bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics 2007;23:1846-7.

19. Zhou G, Soufan O, Ewald J, Hancock RE, Basu N, Xia J. NetworkAnalyst 3.0: A visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res 2019;47:W234-41.

20. Piñero J, Bravo À, Queralt-Rosinach N, Gutiérrez-Sacristán A, Deu- Pons J, Centeno E, et al. DisGeNET: A comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res 2017;45:D833-9.

21. Mlecnik B, Galon J, Bindea G. Comprehensive functional analysis of large lists of genes and proteins. J Proteomics 2018;171:2-10.

22. Kelder T, van Iersel MP, Hanspers K, Kutmon M, Conklin BR, Evelo CT, et al. WikiPathways: Building research communities on biological pathways. Nucleic Acids Res 2012;40:D1301-7.

23. 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:D607-13.

24. Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY. cytoHubba: Identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 2014;8 Suppl 4:S11.

25. Rehfeld JF. CCK, gastrin and diabetes mellitus. Biomark Med 2016;10:1125-7.

26. Pendharkar SA, Drury M, Walia M, Korc M, Petrov MS. Gastrin-releasing peptide and glucose metabolism following pancreatitis. Gastroenterol Res 2017;10:224-34.

27. Matsuzaka T, Shimano H, Yahagi N, Amemiya-Kudo M, Okazaki H, Tamura Y, et al. Insulin-independent induction of sterol regulatory element-binding protein-1c expression in the livers of streptozotocin-treated mice. Diabetes 2004;53:560-9.

28. Jiang WJ, Peng YC, Yang KM. Cellular signaling pathways regulating β-cell proliferation as a promising therapeutic target in the treatment of diabetes (Review). Exp Ther Med 2018;16:3275-85.

29. Saxena A, Sachin K, Bhatia AK. System level meta-analysis of microarray datasets for elucidation of diabetes mellitus pathobiology. Curr Genomics 2017;18:298-304.

30. Saxena A, Sachin K. A network biology approach for assessing the role of pathologic adipose tissues in insulin resistance using meta-analysis of microarray datasets. Curr Genomics 2018;19:630-66. 31. Saxena A, Tiwari P, Wahi N, Soni A, Bansiwal RC, Kumar A, et al. Transcriptome profiling reveals association of peripheral adipose tissue pathology with type-2 diabetes in Asian Indians. Adipocyte 2019;8:125-36.

32. Kumar A, Tiwari P, Saxena A, Purwar N, Wahi N, Sharma B, et al. The transcriptomic evidence on the role of abdominal visceral vs. Subcutaneous adipose tissue in the pathophysiology of diabetes in Asian Indians indicates the involvement of both. Biomolecules 2020;10:E1230.

Article Metrics

29 Absract views 75 PDF Downloads 104 Total views

Related Search

By author names

Citiaion Alert By Google Scholar

Similar Articles