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Updated: Sep 3, 2025
Cardiac Pressure-Volume Loop Analysis Using Conductance Catheters in Mice
Published on: September 17, 2015
Identification of Differential Expression Genes between Volume and Pressure Overloaded Hearts Based on Bioinformatics
Yuanfeng Fu1, Di Zhao1, Yufei Zhou1
1Shanghai Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Insights
This study identifies key genes involved in volume overload (VO) cardiac disease using bioinformatics. It highlights PPARα, ACADM, and PNPLA2 as potential biomarkers for VO diagnosis and treatment, regulating myocardial changes via fatty acid metabolism.
Area of Science:
- Cardiovascular Research
- Genomics
- Biomarker Discovery
Background:
- Volume overload (VO) and pressure overload (PO) are critical in cardiac disease.
- Clinically meaningful molecular markers for VO are lacking.
- Bioinformatics analysis is crucial for identifying novel disease markers.
Purpose of the Study:
- To identify differentially expressed genes (DEGs) in VO and PO using bioinformatics.
- To discover potential molecular biomarkers for VO.
- To elucidate the molecular pathways involved in VO.
Main Methods:
- Utilized gene expression datasets GSE97363 (VO) and GSE52796 (PO).
- Employed the LIMMA algorithm to identify DEGs.
- Performed Gene Ontology (GO) analysis, KEGG pathway analysis, and constructed a protein-protein interaction (PPI) network using Cytoscape.
Main Results:
- Identified 2761 DEGs in VO and 1093 DEGs in PO.
- Found 305 overlapping DEGs, with 255 showing opposing regulation.
- Six hub genes, including PPARα, ACADM, and PNPLA2, were identified, particularly associated with fatty acid metabolism pathways.
Conclusions:
- The study identified key DEGs and hub genes in VO and PO.
- PPARα, ACADM, and PNPLA2 are predicted to regulate VO myocardial changes via fatty acid metabolism.
- These identified genes show promise as potential biomarkers for VO diagnosis and treatment.
Abstract:
Volume overload (VO) and pressure overload (PO) are two common pathophysiological conditions associated with cardiac disease. VO, in particular, often occurs in a number of diseases, and no clinically meaningful molecular marker has yet been established. We intend to find the main differential gene expression using bioinformatics analysis. GSE97363 and GSE52796 are the two gene expression array datasets related with VO and PO, respectively. The LIMMA algorithm was used to identify differentially expressed genes (DEGs) of VO and PO. The DEGs were divided into three groups and subjected to functional enrichment analysis, which comprised GO analysis, KEGG analysis, and the protein-protein interaction (PPI) network. To validate the sequencing data, cardiomyocytes from AR and TAC mouse models were used to extract RNA for qRT-PCR. The three genes with random absolute values of LogFC and indicators of heart failure (natriuretic peptide B, NPPB) were detected: carboxylesterase 1D (CES1D), whirlin (WHRN), and WNK lysine deficient protein kinase 2 (WNK2). The DEGs in VO and PO were determined to be 2761 and 1093, respectively, in this study. Following the intersection, 305 genes were obtained, 255 of which expressed the opposing regulation and 50 of which expressed the same regulation. According to the GO and pathway enrichment studies, DEGs with opposing regulation are mostly common in fatty acid degradation, propanoate metabolism, and other signaling pathways. Finally, we used Cytoscape's three techniques to identify six hub genes by intersecting 255 with the opposite expression and constructing a PPI network. Peroxisome proliferator-activated receptor (PPARα), acyl-CoA dehydrogenase medium chain (ACADM), patatin-like phospholipase domain containing 2 (PNPLA2), isocitrate dehydrogenase 3 (IDH3), heat shock protein family D member 1 (HSPD1), and dihydrolipoamide S-acetyltransferase (DLAT) were identified as six potential genes. Furthermore, we predict that the hub genes PPARα, ACADM, and PNPLA2 regulate VO myocardial changes via fatty acid metabolism and acyl-Coa dehydrogenase activity, and that these genes could be employed as basic biomarkers for VO diagnosis and treatment.
