Transcriptome data analysis of primary cardiomyopathies reveals perturbations in arachidonic acid metabolism

Front Cardiovasc Med. 2023 May 23:10:1110119. doi: 10.3389/fcvm.2023.1110119. eCollection 2023.


Introduction: Cardiomyopathies are complex heart diseases with significant prevalence around the world. Among these, primary forms are the major contributors to heart failure and sudden cardiac death. As a high-energy demanding engine, the heart utilizes fatty acids, glucose, amino acid, lactate and ketone bodies for energy to meet its requirement. However, continuous myocardial stress and cardiomyopathies drive towards metabolic impairment that advances heart failure (HF) pathogenesis. So far, metabolic profile correlation across different cardiomyopathies remains poorly understood.

Methods: In this study, we systematically explore metabolic differences amongst primary cardiomyopathies. By assessing the metabolic gene expression of all primary cardiomyopathies, we highlight the significantly shared and distinct metabolic pathways that may represent specialized adaptations to unique cellular demands. We utilized publicly available RNA-seq datasets to profile global changes in the above diseases (|log2FC| ≥ 0.28 and BH adjusted p-val 0.1) and performed gene set analysis (GSA) using the PAGE statistics on KEGG pathways.

Results: Our analysis demonstrates that genes in arachidonic acid metabolism (AA) are significantly perturbed across cardiomyopathies. In particular, the arachidonic acid metabolism gene PLA2G2A interacts with fibroblast marker genes and can potentially influence fibrosis during cardiomyopathy.

Conclusion: The profound significance of AA metabolism within the cardiovascular system renders it a key player in modulating the phenotypes of cardiomyopathies.

Keywords: GSA; arachidonic acid; cardiomyopathies; heart failure; metabolism; transcriptome.

Grants and funding

RS acknowledges funding and support provided by JC Bose Fellowship (JBR/2021/000006) from Science and Engineering Research Board, India and Bioinformatics Centre Grant funded by Department of Biotechnology, India (BT/PR40187/BTIS/137/9/2021). RS would also like to thank Institute of Bioinformatics and Applied Biotechnology for the funding through her Mazumdar-Shaw Chair in Computational Biology (IBAB/MSCB/182/2022).