Interplay of miRNA-TF-Gene Through a Novel Six-node Feed-forward Loop Identified Inflammatory Genes as Key Regulators in Type-2 Diabetes


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Abstract

Background:Intricacy in the pathological processes of type 2 diabetes (T2D) invites a need to understand gene regulation at the systems level. However, deciphering the complex gene modulation requires regulatory network construction,

Objective:The study aims to construct a six-node feed-forward loop (FFL) to analyze all the diverse inter- and intra- interactions between microRNAs (miRNA) and transcription factors (TF) involved in gene regulation.

Methods:The study included 644 genes, 64 TF, and 448 miRNA. A cumulative hypergeometric test was employed to identify the significant miRNA-miRNA and miRNA-TF interaction pairs. In addition, experimentally proven TF-TF pairs were incorporated for the first time in the regulatory network to discern gene regulation. The networks were analyzed to identify crucial genes involved in T2D. Following this, gene ontology was predicted to recognize the biological function that is crucial in T2D.

Results:In T2D, the lowest gene regulation for a composite FFL occurs through a four-node FFL variant1 (TF- miRNA-miRNA-Gene, n=14) and the highest regulation via a five-node FFL variant2 (TF-TF-miRNA-Gene, n=353). However, the maximum gene regulation occurs via six-node miRNA FFL (miRNA-miRNA-TF-TF-gene-gene, n=23987). Subnetworks derived from the six-node miRNATF- gene regulatory networks identified interactions among TP53 and NFkB, hsa-miR-125-5p and hsamiR- 155-5p.

Conclusion:The core regulation occurs through TP53, NFkB, hsa-miR-125-5p, and hsa-miR-155-5p FFL implicating the association of inflammation in the pathogenesis of T2D, which occurs majorly via six-node miRNA FFL. Thus regulatory network provides broader insights into the pathogenesis of T2D and can be extended to study the inflammatory mechanisms in various infections.

About the authors

Gayathri Bhat

Department of Biotechnology, Manipal Institute of Technology,, Manipal Academy of Higher Education

Email: info@benthamscience.net

Tarakad Keshav

Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education

Email: info@benthamscience.net

Raghu Hariharapura

Department of Pharmaceutical Biotechnology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education

Email: info@benthamscience.net

Shaik Mahammad Fayaz

Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education

Author for correspondence.
Email: info@benthamscience.net

References

  1. Chen K, Rajewsky N. The evolution of gene regulation by transcription factors and microRNAs. Nat Rev Genet 2007; 8(2): 93-103. doi: 10.1038/nrg1990 PMID: 17230196
  2. Kaneto H, Matsuoka TA. Down-regulation of pancreatic transcription factors and incretin receptors in type 2 diabetes. World J Diabetes 2013; 4(6): 263-9. doi: 10.4239/wjd.v4.i6.263 PMID: 24379916
  3. Guo S, Dai C, Guo M, et al. Inactivation of specific β cell transcription factors in type 2 diabetes. J Clin Invest 2013; 123(8): 3305-16. doi: 10.1172/JCI65390 PMID: 23863625
  4. Calderari S, Diawara MR, Garaud A, et al. Biological roles of microRNAs in the control of insulin secretion and action. Physiol Genomics 2017; 49(1): 1-10. doi: 10.1152/physiolgenomics.00079.2016 PMID: 27815534
  5. Eliasson L, Esguerra JLS. MicroRNA networks in pancreatic islet cells: Normal function and type 2 diabetes. Diabetes 2020; 69(5): 804-12. doi: 10.2337/dbi19-0016 PMID: 32312896
  6. O’Brien J, Hayder H, Zayed Y, Peng C. Overview of microRNA biogenesis, mechanisms of actions, and circulation. Front Endocrinol 2018; 9: 402. doi: 10.3389/fendo.2018.00402 PMID: 30123182
  7. Pu M, Chen J, Tao Z, et al. Regulatory network of miRNA on its target: Coordination between transcriptional and post-transcriptional regulation of gene expression. Cell Mol Life Sci 2019; 76(3): 441-51. doi: 10.1007/s00018-018-2940-7 PMID: 30374521
  8. Ramchandran R, Chaluvally-Raghavan P. MiRNA-mediated RNA activation in mammalian cells. Adv Exp Med Biol 2017; 983: 81-9. doi: 10.1007/978-981-10-4310-9_6 PMID: 28639193
  9. Vaschetto LM. miRNA activation is an endogenous gene expression pathway. RNA Biol 2018; 15(6): 1-3. doi: 10.1080/15476286.2018.1451722 PMID: 29537927
  10. Ørom UA, Nielsen FC, Lund AH. MicroRNA-10a binds the 5'UTR of ribosomal protein mRNAs and enhances their translation. Mol Cell 2008; 30(4): 460-71. doi: 10.1016/j.molcel.2008.05.001 PMID: 18498749
  11. Kim J, Choi M, Kim JR, Jin H, Kim VN, Cho KH. The co-regulation mechanism of transcription factors in the human gene regulatory network. Nucleic Acids Res 2012; 40(18): 8849-61. doi: 10.1093/nar/gks664 PMID: 22798495
  12. Sanda T, Lawton LN, Barrasa MI, et al. Core transcriptional regulatory circuit controlled by the TAL1 complex in human T cell acute lymphoblastic leukemia. Cancer Cell 2012; 22(2): 209-21. doi: 10.1016/j.ccr.2012.06.007 PMID: 22897851
  13. Mangan S, Alon U. Structure and function of the feed-forward loop network motif. Proc Natl Acad Sci 2003; 100(21): 11980-5. doi: 10.1073/pnas.2133841100 PMID: 14530388
  14. Sun J, Gong X, Purow B, Zhao Z. Uncovering microRNA and transcription factor mediated regulatory networks in glioblastoma. PLOS Comput Biol 2012; 8(7): e1002488. doi: 10.1371/journal.pcbi.1002488 PMID: 22829753
  15. Nampoothiri SS, Fayaz SM, Rajanikant GK. A novel five-node feed-forward loop unravels miRNA-gene-TF regulatory relationships in ischemic stroke. Mol Neurobiol 2018; 55(11): 8251-62. doi: 10.1007/s12035-018-0963-6 PMID: 29524052
  16. Kim W, Li M, Wang J, Pan Y. Biological network motif detection and evaluation. BMC Syst Biol 2011; 5(S3): S5. doi: 10.1186/1752-0509-5-S3-S5 PMID: 22784624
  17. Shalgi R, Lieber D, Oren M, Pilpel Y. Global and local architecture of the mammalian microRNA-transcription factor regulatory network. PLOS Comput Biol 2007; 3(7): e131. doi: 10.1371/journal.pcbi.0030131 PMID: 17630826
  18. Hill M, Tran N. miRNA interplay: Mechanisms and consequences in cancer. Dis Model Mech 2021; 14(4): dmm047662. doi: 10.1242/dmm.047662 PMID: 33973623
  19. Cui Y, Chen W, Chi J, Wang L. Comparison of transcriptome between type 2 diabetes mellitus and impaired fasting glucose. Med Sci Monit 2016; 22: 4699-706. doi: 10.12659/MSM.896772 PMID: 27906902
  20. Irhimeh MR, Hamed M, Barthelmes D, et al. Identification of novel diabetes impaired miRNA-transcription factor co-regulatory networks in bone marrow-derived Lin-/VEGF-R2+ endothelial progenitor cells. PLoS One 2018; 13(7): e0200194. doi: 10.1371/journal.pone.0200194 PMID: 29995913
  21. Stoffel M, Duncan SA. The maturity-onset diabetes of the young (MODY1) transcription factor HNF4α regulates expression of genes required for glucose transport and metabolism. Proc Natl Acad Sci 1997; 94(24): 13209-14. doi: 10.1073/pnas.94.24.13209 PMID: 9371825
  22. Smith SB, Watada H, Scheel DW, Mrejen C, German MS. Autoregulation and maturity onset diabetes of the young transcription factors control the human PAX4 promoter. J Biol Chem 2000; 275(47): 36910-9. doi: 10.1074/jbc.M005202200 PMID: 10967107
  23. Serocki M, Bartoszewska S, Janaszak-Jasiecka A, Ochocka RJ, Collawn JF, Bartoszewski R. miRNAs regulate the HIF switch during hypoxia: A novel therapeutic target. Angiogenesis 2018; 21(2): 183-202. doi: 10.1007/s10456-018-9600-2 PMID: 29383635
  24. Williams AL, Walton CB, MacCannell KA, Avelar A, Shohet RV. HIF-1 regulation of miR-29c impairs SERCA2 expression and cardiac contractility. Am J Physiol Heart Circ Physiol 2019; 316(3): H554-65. doi: 10.1152/ajpheart.00617.2018 PMID: 30575439
  25. Perscheid C, Grasnick B, Uflacker M. Integrative gene selection on gene expression data: Providing biological context to traditional approaches. J Integr Bioinform 2019; 16(1): 20180064. doi: 10.1515/jib-2018-0064 PMID: 30785707
  26. Overbey EG, da Silveira WA, Stanbouly S, et al. Spaceflight influences gene expression, photoreceptor integrity, and oxidative stress-related damage in the murine retina. Sci Rep 2019; 9(1): 13304. doi: 10.1038/s41598-019-49453-x PMID: 31527661
  27. Lambert SA, Jolma A, Campitelli LF, et al. The human transcription factors. Cell 2018; 172(4): 650-65. doi: 10.1016/j.cell.2018.01.029 PMID: 29425488
  28. Aftabuddin M, Mal C, Deb A, Kundu S. C2Analyzer: Co-target–co-function analyzer. Genomics Proteomics Bioinformatics 2014; 12(3): 133-6. doi: 10.1016/j.gpb.2014.03.003 PMID: 24862384
  29. Martinez NJ, Ow MC, Barrasa MI, et al. A C. elegans genome-scale microRNA network contains composite feedback motifs with high flux capacity. Genes Dev 2008; 22(18): 2535-49. doi: 10.1101/gad.1678608 PMID: 18794350
  30. Kashtan N, Itzkovitz S, Milo R, Alon U. Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs. Bioinformatics 2004; 20(11): 1746-58. doi: 10.1093/bioinformatics/bth163 PMID: 15001476
  31. 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(S4): S11. doi: 10.1186/1752-0509-8-S4-S11 PMID: 25521941
  32. Bae GD, Park EY, Kim K, Jang SE, Jun HS, Oh YS. Upregulation of caveolin-1 and its colocalization with cytokine receptors contributes to beta cell apoptosis. Sci Rep 2019; 9(1): 16785. doi: 10.1038/s41598-019-53278-z PMID: 31728004
  33. Lee SW, Song KE, Shin DS, et al. Alterations in peripheral blood levels of TIMP-1, MMP-2, and MMP-9 in patients with type-2 diabetes. Diabetes Res Clin Pract 2005; 69(2): 175-9. doi: 10.1016/j.diabres.2004.12.010 PMID: 16005367
  34. Zhang F, Xu X, Zhang Y, Zhou B, He Z, Zhai Q. Gene expression profile analysis of type 2 diabetic mouse liver. PLoS One 2013; 8(3): e57766. doi: 10.1371/journal.pone.0057766 PMID: 23469233
  35. Andersen E, Ingerslev LR, Fabre O, et al. Preadipocytes from obese humans with type 2 diabetes are epigenetically reprogrammed at genes controlling adipose tissue function. Int J Obes 2019; 43(2): 306-18. doi: 10.1038/s41366-018-0031-3 PMID: 29511320
  36. Karczewska-Kupczewska M, Nikołajuk A, Majewski R, et al. Changes in adipose tissue lipolysis gene expression and insulin sensitivity after weight loss. Endocr Connect 2020; 9(2): 90-100. doi: 10.1530/EC-19-0507 PMID: 31905163
  37. Fjære E, Andersen C, Myrmel LS, et al. Tissue inhibitor of matrix metalloproteinase-1 is required for high-fat diet-induced glucose intolerance and hepatic steatosis in Mice. PLoS One 2015; 10(7): e0132910. doi: 10.1371/journal.pone.0132910 PMID: 26168159
  38. Haddad D, Al Madhoun A, Nizam R, Al-Mulla F. Role of caveolin-1 in diabetes and its complications. Oxid Med Cell Longev 2020; 2020: 9761539. doi: 10.1155/2020/9761539 PMID: 32082483
  39. Xu G, Ji C, Song G, et al. MiR-26b modulates insulin sensitivity in adipocytes by interrupting the PTEN/PI3K/AKT pathway. Int J Obes 2015; 39(10): 1523-30. doi: 10.1038/ijo.2015.95 PMID: 25999046
  40. Chen C, Deng Y, Hu X, et al. miR-128-3p regulates 3T3-L1 adipogenesis and lipolysis by targeting Pparg and Sertad2. J Physiol Biochem 2018; 74(3): 381-93. doi: 10.1007/s13105-018-0625-1 PMID: 29654510
  41. Su T, Hou J, Liu T, 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(14): 3171-81. doi: 10.7150/ijms.62843 PMID: 34400887
  42. Salunkhe VA, Ofori JK, Gandasi NR, et al. MiR-335 overexpression impairs insulin secretion through defective priming of insulin vesicles. Physiol Rep 2017; 5(21): e13493. doi: 10.14814/phy2.13493 PMID: 29122960
  43. Zhang C, Qian D, Zhao H, Lv N, Yu P, Sun Z. MiR17 improves insulin sensitivity through inhibiting expression of ASK1 and anti-inflammation of macrophages. Biomed Pharmacother 2018; 100: 448-54. doi: 10.1016/j.biopha.2018.02.012 PMID: 29477089
  44. Yu CY, Yang CY, Rui ZL. MicroRNA-125b-5p improves pancreatic β-cell function through inhibiting JNK signaling pathway by targeting DACT1 in mice with type 2 diabetes mellitus. Life Sci 2019; 224: 67-75. doi: 10.1016/j.lfs.2019.01.031 PMID: 30684546
  45. Lin X, Qin Y, Jia J, et al. MiR-155 enhances insulin sensitivity by coordinated regulation of multiple genes in mice. PLoS Genet 2016; 12(10): e1006308. doi: 10.1371/journal.pgen.1006308 PMID: 27711113
  46. Lee HM, Kim TS, Jo EK. MiR-146 and miR-125 in the regulation of innate immunity and inflammation. BMB Rep 2016; 49(6): 311-8. doi: 10.5483/BMBRep.2016.49.6.056 PMID: 26996343
  47. Alivernini S, Gremese E, McSharry C, et al. MicroRNA-155—at the critical interface of innate and adaptive immunity in arthritis. Front Immunol 2018; 8: 1932. doi: 10.3389/fimmu.2017.01932 PMID: 29354135
  48. Corral-Fernández N, Salgado-Bustamante M, Martínez-Leija M, et al. Dysregulated miR-155 expression in peripheral blood mononuclear cells from patients with type 2 diabetes. Exp Clin Endocrinol Diabetes 2013; 121(6): 347-53. doi: 10.1055/s-0033-1341516 PMID: 23616185
  49. Brovkina O, Nikitin A, Khodyrev D, et al. Role of microRNAs in the regulation of subcutaneous white adipose tissue in individuals with obesity and without type 2 diabetes. Front Endocrinol 2019; 10: 840. doi: 10.3389/fendo.2019.00840 PMID: 31866945
  50. Wadgaonkar R, Phelps KM, Haque Z, Williams AJ, Silverman ES, Collins T. CREB-binding protein is a nuclear integrator of nuclear factor-kappaB and p53 signaling. J Biol Chem 1999; 274(4): 1879-82. doi: 10.1074/jbc.274.4.1879 PMID: 9890939
  51. Salminen A, Kaarniranta K. Control of p53 and NF-κB signaling by WIP1 and MIF: Role in cellular senescence and organismal aging. Cell Signal 2011; 23(5): 747-52. doi: 10.1016/j.cellsig.2010.10.012 PMID: 20940041
  52. Taneera J, Fadista J, Ahlqvist E, et al. Expression profiling of cell cycle genes in human pancreatic islets with and without type 2 diabetes. Mol Cell Endocrinol 2013; 375(1-2): 35-42. doi: 10.1016/j.mce.2013.05.003 PMID: 23707792
  53. Ling C. Epigenetic regulation of insulin action and secretion – role in the pathogenesis of type 2 diabetes. J Intern Med 2020; 288(2): 158-67. doi: 10.1111/joim.13049 PMID: 32363639
  54. Szołtysek K, Janus P, Zając G, et al. RRAD, IL4I1, CDKN1A, and SERPINE1 genes are potentially co-regulated by NF-κB and p53 transcription factors in cells exposed to high doses of ionizing radiation. BMC Genomics 2018; 19(1): 813. doi: 10.1186/s12864-018-5211-y PMID: 30419821
  55. Toniolo A, Cassani G, Puggioni A, et al. The diabetes pandemic and associated infections: Suggestions for clinical microbiology. Rev Med Microbiol 2019; 30(1): 1-17. doi: 10.1097/MRM.0000000000000155 PMID: 30662163
  56. National Institute of Health. Knowledge Portal Network Available from: https://kp4cd.org/ Accessed on : Dec 29, 2021
  57. Rani J, Mittal I, Pramanik A, et al. T2DiACoD: A gene atlas of type 2 diabetes mellitus associated complex disorders. Sci Rep 2017; 7(1): 6892. doi: 10.1038/s41598-017-07238-0 PMID: 28761062
  58. Liu G, Wan Q, Li J, Hu X, Gu X, Xu S. Silencing miR-125b-5p attenuates inflammatory response and apoptosis inhibition in mycobacterium tuberculosis-infected human macrophages by targeting DNA damage-regulated autophagy modulator 2 (DRAM2). Cell Cycle 2020; 19(22): 3182-94. doi: 10.1080/15384101.2020.1838792 PMID: 33121314
  59. Etna MP, Sinigaglia A, Grassi A, et al. Mycobacterium tuberculosis-induced miR-155 subverts autophagy by targeting ATG3 in human dendritic cells. PLoS Pathog 2018; 14(1): e1006790. doi: 10.1371/journal.ppat.1006790 PMID: 29300789
  60. Lim YJ, Lee J, Choi JA, et al. M1 macrophage dependent-p53 regulates the intracellular survival of mycobacteria. Apoptosis 2020; 25(1-2): 42-55. doi: 10.1007/s10495-019-01578-0 PMID: 31691131
  61. Bai X, Feldman NE, Chmura K, et al. Inhibition of nuclear factor-kappa B activation decreases survival of Mycobacterium tuberculosis in human macrophages. PLoS One 2013; 8(4): e61925. doi: 10.1371/journal.pone.0061925 PMID: 23634218

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