Network analysis of atherosclerotic genes elucidates druggable targets

Abstract

Background Atherosclerosis is one of the major causes of cardiovascular disease. It is characterized by the accumulation of atherosclerotic plaque in arteries under the influence of inflammatory responses, proliferation of smooth muscle cell, accumulation of modified low density lipoprotein. The pathophysiology of atherosclerosis involves the interplay of a number of genes and metabolic pathways. In traditional translation method, only a limited number of genes and pathways can be studied at once. However, the new paradigm of network medicine can be explored to study the interaction of a large array of genes and their functional partners and their connections with the concerned disease pathogenesis. Thus, in our study we employed a branch of network medicine, gene network analysis as a tool to identify the most crucial genes and the miRNAs that regulate these genes at the post transcriptional level responsible for pathogenesis of atherosclerosis. Result From NCBI database 988 atherosclerotic genes were retrieved. The protein–protein interaction using STRING database resulted in 22,693 PPI interactions among 872 nodes (genes) at different confidence score. The cluster analysis of the 872 genes using MCODE, a plug-in of Cytoscape software revealed a total of 18 clusters, the topological parameter and gene ontology analysis facilitated in the selection of four influential genes viz., AGT, LPL, ITGB2, IRS1 from cluster 3. Further, the miRNAs (miR-26, miR-27, and miR-29 families) targeting these genes were obtained by employing MIENTURNET webtool. Conclusion Gene network analysis assisted in filtering out the 4 probable influential genes and 3 miRNA families in the pathogenesis of atherosclerosis. These genes, miRNAs can be targeted to restrict the occurrence of atherosclerosis. Given the importance of atherosclerosis, any approach in the understanding the genes involved in its pathogenesis can substantially enhance the health care system.

In silico and in vitro analysis of microRNAs with therapeutic potential in atherosclerosis

“…The highest confidence score lies between 0.9 and 1, high confidence score between 0.7 and 0.9, medium confidence score between 0.4 and 0.7 and low confidence score is less than 0.4. In the network, each gene is designated as a node and the interactions between the nodes are designated as edges 46 . The results of the analysis with highest, high, medium and low confidence scores are 7, 8, < 1.11e−16; 7, 14, < 1.0e−16; 7, 20, < 1.0e−16 (nodes, edges, and p value, respectively).…”

Risk prediction model construction for heart failure after myocardial infarction by immune B cells in blood

“…ITGB2 is a transmembrane adhesion and signaling receptor expressed only on leukocytes and extracellular vesicles. It promotes an in ammatory reaction and assists leukocytes adhere to tissue and move around [39,40]. Liu et al demonstrated that suppressing the expression of ITGB2 in macrophages in a mouse HF model decreased the in ltration of myocardial immune in ammatory cells and cardiac hypertrophy [41].…”

Risk prediction model construction for post myocardial infarction heart failure by blood immune B cells

“…ITGB2 is a transmembrane adhesion and signaling receptor expressed only in leukocytes and extracellular vesicles. It promotes an inflammatory reaction and assists leukocytes in adhering to tissues and to move around ( 42 , 43 ). Liu et al.…”