Network‐based computational approach to identify genetic links between cardiomyopathy and its risk factors. Issue 2 (1st April 2020)

• Record Type: Journal Article
• Authors: Haidar, Md. Nasim; Islam, M. Babul; Chowdhury, Utpala Nanda; Rahman, Md. Rezanur; Huq, Fazlul; Quinn, Julian M.W.; Moni, Mohammad Ali
• Is Part Of: IET systems biology. Volume 14:Issue 2(2020)
• Page Start: 75
• Page End: 84
• Publication Date: 2020-04-01
• Subjects: cellular biophysics -- diseases -- molecular biophysics -- proteins -- RNA -- neurophysiology -- bioinformatics -- medical disorders -- biochemistry -- data analysis -- biology computing -- genomics -- genetics; network‐based computational approach -- cardiomyopathy -- lifestyle factors -- inflammatory CMP development -- systems biology approach -- microarray gene expression datasets -- risk factors including smoking -- ageing factors -- clinical depression status -- high dietary red meat intake -- high‐calorie diet -- high‐fat diet -- differentially expressed genes -- risk factor datasets -- protein–protein interaction network analysis identified protein subnetworks -- CDT1 -- HIST1H4C -- HIST1H4D -- transcription factors -- FOXC1 -- FOXL1 -- YY1 -- CREB1 -- authors -- important risk factors -- managing CMP; Systems biology -- Periodicals; Cell physiology -- Periodicals; Biological systems -- Mathematical models -- Periodicals; Genetics -- Mathematical models -- Periodicals; Computational biology -- Periodicals; 573
• DOI: 10.1049/iet-syb.2019.0074
• Languages: English
• ISSNs: 1751-8849

Abstract

Abstract : Cardiomyopathy (CMP) is a group of myocardial diseases that progressively impair cardiac function. The mechanisms underlying CMP development are poorly understood, but lifestyle factors are clearly implicated as risk factors. This study aimed to identify molecular biomarkers involved in inflammatory CMP development and progression using a systems biology approach. The authors analysed microarray gene expression datasets from CMP and tissues affected by risk factors including smoking, ageing factors, high body fat, clinical depression status, insulin resistance, high dietary red meat intake, chronic alcohol consumption, obesity, high‐calorie diet and high‐fat diet. The authors identified differentially expressed genes (DEGs) from each dataset and compared those from CMP and risk factor datasets to identify common DEGs. Gene set enrichment analyses identified metabolic and signalling pathways, including MAPK, RAS signalling and cardiomyopathy pathways. Protein–protein interaction (PPI) network analysis identified protein subnetworks and ten hub proteins (CDK2, ATM, CDT1, NCOR2, HIST1H4A, HIST1H4B, HIST1H4C, HIST1H4D, HIST1H4E and HIST1H4L). Five transcription factors (FOXC1, GATA2, FOXL1, YY1, CREB1) and five miRNAs were also identified in CMP. Thus the authors' approach reveals candidate biomarkers that may enhance understanding of mechanisms underlying CMP and their link to risk factors. Such biomarkers may also be useful to develop new therapeutics for CMP.