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

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: epigenetic modulators; hypoxia; inflammation; meta-analysis; miRNA; thrombosis.

Fig. 1

Workflow and processing of mRNA and microRNA microarray datasets. ( A ) Diagram depicting the workflow of the retrieval and selection of the microarray datasets along with inclusion and exclusion criteria of individual datasets included in the meta-analysis. ( B ) Workflow of the process of microarray and microRNA datasets through meta-analysis. Depiction of the flow chart of the process involved in integrated meta-analysis of selected microarray datasets of mRNA and miRNA expression. BOEC, blood outgrowth endothelial cells; GEO, gene expression omnibus; IBD, inflammatory bowel disease; PBMCs, peripheral blood mononuclear cells; PE, pulmonary embolism; RA, rheumatoid arthritis; SLE, systemic lupus erythematosus; VT, venous thrombosis.

Fig. 2

Analysis of datasets for the identification of gene and its share with other disease. ( A ) Density plot to compare clustering and distribution patterns before batch removal and ( B ) after applying batch removal using the Combat procedure. ( C ) Visualization of volcano plot showing DEGs of the microarray datasets. ( D ) The Venn diagram showing the distribution of DEGs between individual diseases and their relationship between them. DEGs, differentially expressed genes.

Fig. 3

Hub gene expression and their regulated pathways. ( A ) Expression pattern of selected hub gene which shows the expression in different disease conditions. I. HDAC1 ( p -value = 0.0125), II. HDAC2 ( p -value = 0.002), III. PRKDC ( p -value = 0.002), IV. CDKN1A ( p -value = 0.000769), V. RBBP4 ( p -value = 0.0074). ( B ) Pathway enrichment interaction showing the significant pathway. Network overrepresentations of enriched pathway and gene ontology integrating the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome pathways for the top 20 hub genes using Cytoscape plug-in, ClueGO. Right-sided hypergeometric distribution tests, with an adjusted p -value of 0.05, followed by the Bonferroni adjustment based on the highest significance. ( C ) Enriched pathway of shared DEGs using online tool Kobas3.0 in a tabular format. HATs, Histone acetyltransferases; HIF, hypoxia inducible factor; NF-κB, Nuclear factor-κB; Kyoto encyclopedia of genes; NOD, nucleotide-binding, oligomerization domain; TP53, tumor protein p53.

Fig. 4

Analysis of miRNA dataset for the identification of DEmiRs and its interaction with the associated DEGs. ( A ) The Venn diagram showing the distribution of DEmiRs between individual diseases, their relationship between them, and the common DEmiRs among all the diseases. ( B ) Interaction of top 10 hub genes with the common DEmiRs from the microRNA list of the top 10 hub genes and DEmiRs selected from the microRNAs datasets.