Genomics & Informatics

Korea Genome Organization (한국유전체학회)
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Comparative co-expression analysis of RNA-Seq transcriptome revealing key genes, miRNA and transcription factor in distinct metabolic pathways in diabetic nerve, eye, and kidney disease

  • Received : 2022.04.27
  • Accepted : 2022.09.02
  • Published : 2022.09.30
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Abstract

Diabetes and its related complications are associated with long term damage and failure of various organ systems. The microvascular complications of diabetes considered in this study are diabetic retinopathy, diabetic neuropathy, and diabetic nephropathy. The aim is to identify the weighted co-expressed and differentially expressed genes (DEGs), major pathways, and their miRNA, transcription factors (TFs) and drugs interacting in all the three conditions. The primary goal is to identify vital DEGs in all the three conditions. The overlapped five genes (AKT1, NFKB1, MAPK3, PDPK1, and TNF) from the DEGs and the co-expressed genes were defined as key genes, which differentially expressed in all the three cases. Then the protein-protein interaction network and gene set linkage analysis (GSLA) of key genes was performed. GSLA, gene ontology, and pathway enrichment analysis of the key genes elucidates nine major pathways in diabetes. Subsequently, we constructed the miRNA-gene and transcription factor-gene regulatory network of the five gene of interest in the nine major pathways were studied. hsa-mir-34a-5p, a major miRNA that interacted with all the five genes. RELA, FOXO3, PDX1, and SREBF1 were the TFs interacting with the major five gene of interest. Finally, drug-gene interaction network elucidates five potential drugs to treat the genes of interest. This research reveals biomarker genes, miRNA, TFs, and therapeutic drugs in the key signaling pathways, which may help us, understand the processes of all three secondary microvascular problems and aid in disease detection and management.

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References

  1. Faselis C, Katsimardou A, Imprialos K, Deligkaris P, Kallistratos M, Dimitriadis K. Microvascular complications of type 2 diabetes mellitus. Curr Vasc Pharmacol 2020;18:117-124. https://doi.org/10.2174/1570161117666190502103733
  2. Chawla A, Chawla R, Jaggi S. Microvasular and macrovascular complications in diabetes mellitus: distinct or continuum? Indian J Endocrinol Metab 2016;20:546-551. https://doi.org/10.4103/2230-8210.183480
  3. IDF Diabetes Atlas. Brussels: International Diabetes Federation, 2021. Accessed 2021 Dec 23. Available from: https://diabetesatlas.org/.
  4. Cade WT. Diabetes-related microvascular and macrovascular diseases in the physical therapy setting. Phys Ther 2008;88:1322- 1335. https://doi.org/10.2522/ptj.20080008
  5. Brosius FC, Khoury CC, Buller CL, Chen S. Abnormalities in signaling pathways in diabetic nephropathy. Expert Rev Endocrinol Metab 2010;5:51-64. https://doi.org/10.1586/eem.09.70
  6. Lim A. Diabetic nephropathy: complications and treatment. Int J Nephrol Renovasc Dis 2014;7:361-381. https://doi.org/10.2147/IJNRD.S40172
  7. Pop-Busui R, Boulton AJ, Feldman EL, Bril V, Freeman R, Malik RA, et al. Diabetic neuropathy: a position statement by the American Diabetes Association. Diabetes Care 2017;40:136-154. https://doi.org/10.2337/dc16-2042
  8. Oguntibeju OO. Type 2 diabetes mellitus, oxidative stress and inflammation: examining the links. Int J Physiol Pathophysiol Pharmacol 2019;11:45-63.
  9. Satoh J, Yagihashi S, Toyota T. The possible role of tumor necrosis factor-alpha in diabetic polyneuropathy. Exp Diabesity Res 2003;4:65-71. https://doi.org/10.1155/EDR.2003.65
  10. Wang Y, Schmeichel AM, Iida H, Schmelzer JD, Low PA. Enhanced inflammatory response via activation of NF-kappaB in acute experimental diabetic neuropathy subjected to ischemia-reperfusion injury. J Neurol Sci 2006;247:47-52. https://doi.org/10.1016/j.jns.2006.03.011
  11. Kumar A, Sharma SS. NF-kappaB inhibitory action of resveratrol: a probable mechanism of neuroprotection in experimental diabetic neuropathy. Biochem Biophys Res Commun 2010;394: 360-365. https://doi.org/10.1016/j.bbrc.2010.03.014
  12. Yamagishi S, Ogasawara S, Mizukami H, Yajima N, Wada R, Sugawara A, et al. Correction of protein kinase C activity and macrophage migration in peripheral nerve by pioglitazone, peroxisome proliferator activated-gamma-ligand, in insulin-deficient diabetic rats. J Neurochem 2008;104:491-499.
  13. Tomlinson DR. Mitogen-activated protein kinases as glucose transducers for diabetic complications. Diabetologia 1999;42: 1271-1281. https://doi.org/10.1007/s001250051439
  14. Saini DC, Kochar A, Poonia R. Clinical correlation of diabetic retinopathy with nephropathy and neuropathy. Indian J Ophthalmol 2021;69:3364-3368. https://doi.org/10.4103/ijo.IJO_1237_21
  15. Hotta N, Kawamori R, Fukuda M, Shigeta Y; Aldose Reductase Inhibitor-Diabetes Complications Trial Study Group. Long-term clinical effects of epalrestat, an aldose reductase inhibitor, on progression of diabetic neuropathy and other microvascular complications: multivariate epidemiological analysis based on patient background factors and severity of diabetic neuropathy. Diabet Med 2012;29:1529-1533. https://doi.org/10.1111/j.1464-5491.2012.03684.x
  16. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014;15:550. https://doi.org/10.1186/s13059-014-0550-8
  17. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008;9:559. https://doi.org/10.1186/1471-2105-9-559
  18. Chen J, Bardes EE, Aronow BJ, Jegga AG. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res 2009;37:W305-W311. https://doi.org/10.1093/nar/gkp427
  19. Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, et al. The reactome pathway knowledgebase. Nucleic Acids Res 2018;46:D649-D655. https://doi.org/10.1093/nar/gkx1132
  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:D362-D368. https://doi.org/10.1093/nar/gkw937
  21. Demchak B, Hull T, Reich M, Liefeld T, Smoot M, Ideker T, et al. Cytoscape: the network visualization tool for GenomeSpace workflows. F1000Res 2014;3:151. https://doi.org/10.12688/f1000research.4492.2
  22. Guo WP, Ding XB, Jin J, Zhang HB, Yang QL, Chen PC, et al. HIR V2: a human interactome resource for the biological interpretation of differentially expressed genes via gene set linkage analysis. Database (Oxford) 2021;2021:baab009. https://doi.org/10.1093/database/baab009
  23. 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-W241. https://doi.org/10.1093/nar/gkz240
  24. Fan Y, Xia J. miRNet-functional analysis and visual exploration of miRNA-target interactions in a network context. Methods Mol Biol 2018;1819:215-233. https://doi.org/10.1007/978-1-4939-8618-7_10
  25. Griffith M, Griffith OL, Coffman AC, Weible JV, McMichael JF, Spies NC, et al. DGIdb: mining the druggable genome. Nat Methods 2013;10:1209-1210. https://doi.org/10.1038/nmeth.2689
  26. Ozaki KI, Awazu M, Tamiya M, Iwasaki Y, Harada A, Kugisaki S, et al. Targeting the ERK signaling pathway as a potential treatment for insulin resistance and type 2 diabetes. Am J Physiol Endocrinol Metab 2016;310:E643-E651. https://doi.org/10.1152/ajpendo.00445.2015
  27. Saxena A, Mathur N, Tiwari P, Mathur SK. Whole transcriptome RNA-seq reveals key regulatory factors involved in type 2 diabetes pathology in peripheral fat of Asian Indians. Sci Rep 2021;11: 10632. https://doi.org/10.1038/s41598-021-90148-z
  28. Gupta S, Gambhir JK, Kalra O, Gautam A, Shukla K, Mehndiratta M, et al. Association of biomarkers of inflammation and oxidative stress with the risk of chronic kidney disease in type 2 diabetes mellitus in North Indian population. J Diabetes Complications 2013;27:548-552. https://doi.org/10.1016/j.jdiacomp.2013.07.005
  29. Gupta S, Mehndiratta M, Kalra S, Kalra OP, Shukla R, Gambhir JK. Association of tumor necrosis factor (TNF) promoter polymorphisms with plasma TNF-alpha levels and susceptibility to diabetic nephropathy in North Indian population. J Diabetes Complications 2015;29:338-342. https://doi.org/10.1016/j.jdiacomp.2015.01.002
  30. Oeckinghaus A, Ghosh S. The NF-kappaB family of transcription factors and its regulation. Cold Spring Harb Perspect Biol 2009;1:a000034.
  31. O'Neill BT, Bhardwaj G, Penniman CM, Krumpoch MT, Suarez Beltran PA, Klaus K, et al. FoxO transcription factors are critical regulators of diabetes-related muscle atrophy. Diabetes 2019; 68:556-570. https://doi.org/10.2337/db18-0416
  32. Su T, Hou J, Liu T, Dai P, Qin L, Ding L, et al. MiR-34a-5p and miR-452-5p: the novel regulators of pancreatic endocrine dysfunction in diabetic Zucker rats? Int J Med Sci 2021;18:3171- 3181. https://doi.org/10.7150/ijms.62843
  33. Gholaminejad A, Roointan A, Gheisari Y. Transmembrane signaling molecules play a key role in the pathogenesis of IgA nephropathy: a weighted gene co-expression network analysis study. BMC Immunol 2021;22:73. https://doi.org/10.1186/s12865-021-00468-y
  34. Ito T, Yagi S, Yamakuchi M. MicroRNA-34a regulation of endothelial senescence. Biochem Biophys Res Commun 2010;398: 735-740. https://doi.org/10.1016/j.bbrc.2010.07.012
  35. Banerjee J, Khanna S, Bhattacharya A. MicroRNA regulation of oxidative stress. Oxid Med Cell Longev 2017;2017:2872156.
  36. Hermeking H. The miR-34 family in cancer and apoptosis. Cell Death Differ 2010;17:193-199. https://doi.org/10.1038/cdd.2009.56
  37. Slabakova E, Culig Z, Remsik J, Soucek K. Alternative mechanisms of miR-34a regulation in cancer. Cell Death Dis 2017;8: e3100. https://doi.org/10.1038/cddis.2017.495
  38. Li N, Wang K, Li PF. MicroRNA-34 family and its role in cardiovascular disease. Crit Rev Eukaryot Gene Expr 2015;25:293-297. https://doi.org/10.1615/CritRevEukaryotGeneExpr.2015015396