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CC BY 4.0 · TH Open 2024; 08(01): e81-e92
DOI: 10.1055/a-2222-9126
Original Article

Unraveling Epigenetic Interplay between Inflammation, Thrombosis, and Immune-Related Disorders through a Network Meta-analysis

Shankar Chanchal
1   Department of Biotechnology, Faculty of Natural Sciences, Jamia Millia Islamia, Delhi, India
,
Swati Sharma
1   Department of Biotechnology, Faculty of Natural Sciences, Jamia Millia Islamia, Delhi, India
,
Syed Mohd
1   Department of Biotechnology, Faculty of Natural Sciences, Jamia Millia Islamia, Delhi, India
,
Armiya Sultan
1   Department of Biotechnology, Faculty of Natural Sciences, Jamia Millia Islamia, Delhi, India
,
Aastha Mishra
2   Cardio Respiratory Disease unit, CSIR- Institute of Genomics and Integrative Biology, Delhi, India
,
Mohammad Zahid Ashraf
1   Department of Biotechnology, Faculty of Natural Sciences, Jamia Millia Islamia, Delhi, India
› Author Affiliations Funding This work was supported by the research project sponsored under Scheme for Promotion of Academic and Research Collaboration (SPARC).
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Abstract

Inflammation and thrombosis are two distinct yet interdependent physiological processes. The inflammation results in the activation of the coagulation system that directs the immune system and its activation, resulting in the initiation of the pathophysiology of thrombosis, a process termed immune-thrombosis. Still, the shared underlying molecular mechanism related to the immune system and coagulation has not yet been explored extensively. Inspired to answer this, we carried out a comprehensive gene expression meta-analysis using publicly available datasets of four diseases, including venous thrombosis, systemic lupus erythematosus, rheumatoid arthritis, and inflammatory bowel disease. A total of 609 differentially expressed genes (DEGs) shared by all four datasets were identified based on the combined effect size approach. The pathway enrichment analysis of the DEGs showed enrichment of various epigenetic pathways such as histone-modifying enzymes, posttranslational protein modification, chromatin organization, chromatin-modifying enzymes, HATs acetylate proteins. Network-based protein–protein interaction analysis showed epigenetic enzyme coding genes dominating among the top hub genes. The miRNA-interacting partner of the top 10 hub genes was determined. The predomination of epitranscriptomics regulation opens a layout for the meta-analysis of miRNA datasets of the same four diseases. We identified 30 DEmiRs shared by these diseases. There were 9 common DEmiRs selected from the list of miRNA-interacting partners of top 10 hub genes and shared significant DEmiRs from microRNAs dataset acquisition. These common DEmiRs were found to regulate genes involved in epigenetic modulation and indicate a promising epigenetic aspect that needs to be explored for future molecular studies in the context of immunothrombosis and inflammatory disease.

Keywords

thrombosis - inflammation - hypoxia - epigenetic modulators - miRNA - meta-analysis

Author Contributions

S. Chanchal and M. Z. Ashraf wrote the manuscript and designed the study. S. Chanchal participated in data analysis. A. Mishra, S. Sharma, S. Mohd, and A. Sultan helped in data interpretation and all authors edited and approved the final version of the manuscript.


Supplementary Material



Publication History

Received: 10 July 2023

Accepted: 25 September 2023

Accepted Manuscript online:
05 December 2023

Article published online:
02 February 2024

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Ferrero-Miliani L, Nielsen OH, Andersen PS, Girardin SE. Chronic inflammation: importance of NOD2 and NALP3 in interleukin-1beta generation. Clin Exp Immunol 2007; 147 (02) 227-235
    Google Scholar
  • 2 Halonen JI, Zanobetti A, Sparrow D, Vokonas PS, Schwartz J. Associations between outdoor temperature and markers of inflammation: a cohort study. Environ Health 2010; 9: 42
    Google Scholar
  • 3 Gupta N, Sahu A, Prabhakar A. et al. Activation of NLRP3 inflammasome complex potentiates venous thrombosis in response to hypoxia. Proc Natl Acad Sci U S A 2017; 114 (18) 4763-4768
    Google Scholar
  • 4 Shanmugam MK, Sethi G. Role of epigenetics in inflammation-associated diseases. Subcell Biochem 2013; 61: 627-657
    Google Scholar
  • 5 Foley JH, Conway EM. Cross talk pathways between coagulation and inflammation. Circ Res 2016; 118 (09) 1392-1408
    Google Scholar
  • 6 Margetic S. Inflammation and haemostasis. Biochem Med (Zagreb) 2012; 22 (01) 49-62
    Google Scholar
  • 7 Chanchal S, Mishra A, Singh MK, Ashraf MZ. Understanding inflammatory responses in the manifestation of prothrombotic phenotypes. Front Cell Dev Biol 2020; 8: 73
    Google Scholar
  • 8 Afeltra A, Vadacca M, Conti L. et al. Thrombosis in systemic lupus erythematosus: congenital and acquired risk factors. Arthritis Rheum 2005; 53 (03) 452-459
    Google Scholar
  • 9 Burgos PI, Alarcón GS. Thrombosis in systemic lupus erythematosus: risk and protection. Expert Rev Cardiovasc Ther 2009; 7 (12) 1541-1549
    Google Scholar
  • 10 Mameli A, Barcellona D, Marongiu F. Rheumatoid arthritis and thrombosis. Clin Exp Rheumatol 2009; 27 (05) 846-855
    Google Scholar
  • 11 Bargen JA, Barker NW. Extensive arterial and venous thrombosis complicating chronic ulcerative colitis. Arch Intern Med (Chic) 1936; 58 (01) 17-31
    Google Scholar
  • 12 Murthy SK, Nguyen GC. Venous thromboembolism in inflammatory bowel disease: an epidemiological review. Am J Gastroenterol 2011; 106 (04) 713-718
    Google Scholar
  • 13 Kume K, Yamasaki M, Tashiro M, Yoshikawa I, Otsuki M. Activations of coagulation and fibrinolysis secondary to bowel inflammation in patients with ulcerative colitis. Intern Med 2007; 46 (17) 1323-1329
    Google Scholar
  • 14 Xia J, Fjell CD, Mayer ML, Pena OM, Wishart DS, Hancock RE. INMEX–a web-based tool for integrative meta-analysis of expression data. Nucleic Acids Res 2013; 41 (Web Server issue): W63-70
    Google Scholar
  • 15 Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 2007; 8 (01) 118-127
    Google Scholar
  • 16 Smyth GK. Limma: linear models for microarray data. In: Gentleman R, Carey V, Dudoit S, Irizarry R, Huber W. eds. Bioinformatics and Computational Biology Solutions Using R and Bioconductor. New York, NY: Springer; 2005: 397-420
    Google Scholar
  • 17 Marot G, Foulley JL, Mayer CD, Jaffrézic F. Moderated effect size and P-value combinations for microarray meta-analyses. Bioinformatics 2009; 25 (20) 2692-2699
    Google Scholar
  • 18 Cochran WG. The combination of estimates from different experiments. Biometrics 1954; 10 (01) 101-129
    Google Scholar
  • 19 Shannon P, Markiel A, Ozier O. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003; 13 (11) 2498-2504
    Google Scholar
  • 20 Han JD, Bertin N, Hao T. et al. Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature 2004; 430 (6995): 88-93 [published correction appears in Nature. 2004 Jul 15;430(6997):380]
    Google Scholar
  • 21 Bindea G, Mlecnik B, Hackl H. et al. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 2009; 25 (08) 1091-1093
    Google Scholar
  • 22 Maere S, Heymans K, Kuiper M. BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 2005; 21 (16) 3448-3449
    Google Scholar
  • 23 Hasstedt SJ, Bezemer ID, Callas PW. et al. Cell adhesion molecule 1: a novel risk factor for venous thrombosis. Blood 2009; 114 (14) 3084-3091
    Google Scholar
  • 24 Rodriguez-Pla A, Patel P, Maecker HT. et al. IFN priming is necessary but not sufficient to turn on a migratory dendritic cell program in lupus monocytes. J Immunol 2014; 192 (12) 5586-5598
    Google Scholar
  • 25 Barnes MG, Aronow BJ, Luyrink LK. et al. Gene expression in juvenile arthritis and spondyloarthropathy: pro-angiogenic ELR+ chemokine genes relate to course of arthritis. Rheumatology (Oxford) 2004; 43 (08) 973-979 [published correction appears in Rheumatology (Oxford). 2004 Oct;43(10):1320]
    Google Scholar
  • 26 Burczynski ME, Peterson RL, Twine NC. et al. Molecular classification of Crohn's disease and ulcerative colitis patients using transcriptional profiles in peripheral blood mononuclear cells. J Mol Diagn 2006; 8 (01) 51-61
    Google Scholar
  • 27 Junjie xiao, MicroRNAs as potential biomarkers for APE, 2010, Accessed December 18, 2023 at: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE24149
  • 28 Wang Y, Liang J, Qin H. et al. Elevated expression of miR-142-3p is related to the pro-inflammatory function of monocyte-derived dendritic cells in SLE. Arthritis Res Ther 2016; 18 (01) 263
    Google Scholar
  • 29 He P, Mo XB, Lei SF, Deng FY. Epigenetically regulated co-expression network of genes significant for rheumatoid arthritis. Epigenomics 2019; 11 (14) 1601-1612
    Google Scholar
  • 30 Duttagupta R, DiRienzo S, Jiang R. et al. Genome-wide maps of circulating miRNA biomarkers for ulcerative colitis. PLoS One 2012; 7 (02) e31241
    Google Scholar
  • 31 Al-Homood IA. Thrombosis in systemic lupus erythematosus: a review article. ISRN Rheumatol 2012; 2012: 428269
    Google Scholar
  • 32 Kim SC, Schneeweiss S, Liu J, Solomon DH. Risk of venous thromboembolism in patients with rheumatoid arthritis. Arthritis Care Res (Hoboken) 2013; 65 (10) 1600-1607
    Google Scholar
  • 33 Cohen JB, Comer DM, Yabes JG, Ragni MV. Inflammatory bowel disease and thrombosis: a national inpatient sample study. TH Open 2020; 4 (01) e51-e58
    Google Scholar
  • 34 Perez-Sanchez C, Barbarroja N, Messineo S. et al. Gene profiling reveals specific molecular pathways in the pathogenesis of atherosclerosis and cardiovascular disease in antiphospholipid syndrome, systemic lupus erythematosus and antiphospholipid syndrome with lupus. Ann Rheum Dis 2015; 74 (07) 1441-1449
    Google Scholar
  • 35 Ouyang Q, Wu J, Jiang Z. et al. Microarray expression profile of circular RNAs in peripheral blood mononuclear cells from rheumatoid arthritis patients. Cell Physiol Biochem 2017; 42 (02) 651-659
    Google Scholar
  • 36 Kader HA, Tchernev VT, Satyaraj E. et al. Protein microarray analysis of disease activity in pediatric inflammatory bowel disease demonstrates elevated serum PLGF, IL-7, TGF-beta1, and IL-12p40 levels in Crohn's disease and ulcerative colitis patients in remission versus active disease. Am J Gastroenterol 2005; 100 (02) 414-423
    Google Scholar
  • 37 Chanchal S, Sharma S, Mohd S. et al. Dominance of epigenetic modulators shared among the inflammatory disorders with prominent thrombotic phenotypic feature. 01 March 2023, PREPRINT (Version 1) available at Research Square
    Google Scholar
  • 38 Patsouras MD, Vlachoyiannopoulos PG. Evidence of epigenetic alterations in thrombosis and coagulation: a systematic review. J Autoimmun 2019; 104: 102347
    Google Scholar
  • 39 Barranco-Medina S, Pozzi N, Vogt AD, Di Cera E. Histone H4 promotes prothrombin autoactivation. J Biol Chem 2013; 288 (50) 35749-35757
    Google Scholar
  • 40 Semeraro F, Ammollo CT, Esmon NL, Esmon CT. Histones induce phosphatidylserine exposure and a procoagulant phenotype in human red blood cells. J Thromb Haemost 2014; 12 (10) 1697-1702
    Google Scholar
  • 41 Semeraro F, Ammollo CT, Morrissey JH. et al. Extracellular histones promote thrombin generation through platelet-dependent mechanisms: involvement of platelet TLR2 and TLR4. Blood 2011; 118 (07) 1952-1961
    Google Scholar
  • 42 Ammollo CT, Semeraro F, Xu J, Esmon NL, Esmon CT. Extracellular histones increase plasma thrombin generation by impairing thrombomodulin-dependent protein C activation. J Thromb Haemost 2011; 9 (09) 1795-1803
    Google Scholar
  • 43 Longstaff C, Varjú I, Sótonyi P. et al. Mechanical stability and fibrinolytic resistance of clots containing fibrin, DNA, and histones. J Biol Chem 2013; 288 (10) 6946-6956
    Google Scholar
  • 44 Zapilko V, Fish RJ, Garcia A. et al. MicroRNA-126 is a regulator of platelet-supported thrombin generation. Platelets 2020; 31 (06) 746-755
    Google Scholar
  • 45 Wan RJ, Li YH. MicroRNA-146a/NAPDH oxidase4 decreases reactive oxygen species generation and inflammation in a diabetic nephropathy model. Mol Med Rep 2018; 17 (03) 4759-4766
    Google Scholar
  • 46 Miao CG, Yang YY, He X. et al. The emerging role of microRNAs in the pathogenesis of systemic lupus erythematosus. Cell Signal 2013; 25 (09) 1828-1836
    Google Scholar
  • 47 Singh RP, Hahn BH, Bischoff DS. Identification and contribution of inflammation-induced novel MicroRNA in the pathogenesis of systemic lupus erythematosus. Front Immunol 2022; 13: 848149
    Google Scholar
  • 48 Shi C, Liang Y, Yang J. et al. MicroRNA-21 knockout improve the survival rate in DSS induced fatal colitis through protecting against inflammation and tissue injury. PLoS One 2013; 8 (06) e66814
    Google Scholar
  • 49 Zhao Y, Ma T, Chen W. et al. MicroRNA-124 promotes intestinal inflammation by targeting aryl hydrocarbon receptor in crohn's disease. J Crohn's Colitis 2016; 10 (06) 703-712
    Google Scholar
  • 50 Sahu A, Jha PK, Prabhakar A. et al. MicroRNA-145 impedes thrombus formation via targeting tissue factor in venous thrombosis. EBioMedicine 2017; 26: 175-186
    Google Scholar
  • 51 Chen SP, Liu BX, Xu J. et al. MiR-449a suppresses the epithelial-mesenchymal transition and metastasis of hepatocellular carcinoma by multiple targets. BMC Cancer 2015; 15: 706
    Google Scholar
  • 52 Rössig L, Li H, Fisslthaler B. et al. Inhibitors of histone deacetylation downregulate the expression of endothelial nitric oxide synthase and compromise endothelial cell function in vasorelaxation and angiogenesis. Circ Res 2002; 91 (09) 837-844
    Google Scholar
  • 53 Hooper WC. The relationship between inflammation and the anticoagulant pathway: the emerging role of endothelial nitric oxide synthase (eNOS). Curr Pharm Des 2004; 10 (08) 923-927
    Google Scholar
  • 54 Wu S, Huang S, Ding J. et al. Multiple microRNAs modulate p21Cip1/Waf1 expression by directly targeting its 3′ untranslated region. Oncogene 2010; 29 (15) 2302-2308
    Google Scholar
  • 55 Ghaemi Z, Soltani BM, Mowla SJ. MicroRNA-326 functions as a tumor suppressor in breast cancer by targeting ErbB/PI3K signaling pathway. Front Oncol 2019; 9: 653
    Google Scholar
  • 56 Li L, Jia L, Ding Y. Upregulation of miR-375 inhibits human liver cancer cell growth by modulating cell proliferation and apoptosis via targeting ErbB2. Oncol Lett 2018; 16 (03) 3319-3326
    Google Scholar
  • 57 Zeng S, Yang Y, Tan Y. et al. ERBB2-induced inflammation in lung carcinogenesis. Mol Biol Rep 2012; 39 (08) 7911-7917
    Google Scholar
  • 58 Si Y, Zhang Y, Chen Z. et al. Posttranslational modification control of inflammatory signaling. Adv Exp Med Biol 2017; 1024: 37-61
    Google Scholar
  • 59 Gongol B, Marin T, Zhang J. et al. Shear stress regulation of miR-93 and miR-484 maturation through nucleolin. Proc Natl Acad Sci U S A 2019; 116 (26) 12974-12979
    Google Scholar
  • 60 Jia YZ, Liu J, Wang GQ, Song ZF. miR-484: a potential biomarker in health and disease. Front Oncol 2022; 12: 830420
    Google Scholar
  • 61 Kadam S, Ghosh B, Apte K. et al. Metabolic changes in peripheral blood mononuclear cells (PBMCs) of subjects with chronic obstructive pulmonary disease (COPD). Eur Respir J 2017; 50: PA3917
    Google Scholar
  • 62 Mosallaei M, Ehtesham N, Rahimirad S, Saghi M, Vatandoost N, Khosravi S. PBMCs: a new source of diagnostic and prognostic biomarkers. Arch Physiol Biochem 2022; 128 (04) 1081-1087
    Google Scholar
  • 63 Chen C, Grennan K, Badner J. et al. Removing batch effects in analysis of expression microarray data: an evaluation of six batch adjustment methods. PLoS One 2011; 6 (02) e17238
    Google Scholar
  • 64 Müller C, Schillert A, Röthemeier C. et al. Removing batch effects from longitudinal gene expression - quantile normalization plus ComBat as best approach for microarray transcriptome data. PLoS One 2016; 11 (06) e0156594
    Google Scholar
  • 65 Delcuve GP, Khan DH, Davie JR. Roles of histone deacetylases in epigenetic regulation: emerging paradigms from studies with inhibitors. Clin Epigenetics 2012; 4 (01) 5
    Google Scholar
  • 66 Neumann PA, Dong Y. Molecular and cellular mechanisms of addiction. In: Miller PM. ed. Biological Research on Addiction: Comprehensive Addictive Behaviors and Disorders. Vol. 2; 1st ed.. Cambridge, MA: Academic Press Inc.; 2013: 251
    Google Scholar
  • 67 Bendjennat M, Boulaire J, Jascur T. et al. UV irradiation triggers ubiquitin-dependent degradation of p21(WAF1) to promote DNA repair. Cell 2003; 114 (05) 599-610
    Google Scholar
  • 68 Koprinarova M, Schnekenburger M, Diederich M. Role of histone acetylation in cell cycle regulation. Curr Top Med Chem 2016; 16 (07) 732-744
    Google Scholar
  • 69 Mungamuri SK, Murk W, Grumolato L, Bernstein E, Aaronson SA. Chromatin modifications sequentially enhance ErbB2 expression in ErbB2-positive breast cancers. Cell Rep 2013; 5 (02) 302-313
    Google Scholar
  • 70 Gordon S, Akopyan G, Garban H, Bonavida B. Transcription factor YY1: structure, function, and therapeutic implications in cancer biology. Oncogene 2006; 25 (08) 1125-1142
    Google Scholar