bioRxivBioinformaticsPreprint
Identification of molecular specificity between reprogrammed cardiomyocytes and normal cardiomyocytes by bioinformatics
Abstract
The adult human heart lacks an effective endogenous repair mechanism and cannot fully restore heart function after injury. We obtained two microarray data sets GSE99814 and GSE22292 from the NCBI GEO database, which include 5 groups of directly reprogrammed cardiomyocyte-like cells (iCMs) and 5 groups of normal mouse cardiomyocytes (CM). And through the GEO2R tool and Venn diagram software to screen the differentially expressed genes (DEG) between iCM and CM. Next, we performed functional enrichment analysis on these DEGs. The protein-protein interaction network (PPI) was constructed by STRING and Cytoscape for module analysis. We have selected a total of 243 DEGs consistently expressed genes in the two data sets, of which 127 up-regulated genes function and pathway enrichment mainly concentrated on biological processes such as innate immune response, inflammatory response, immune system process, positive regulation of apoptosis process and complement and coagulation cascade, while the 116 down-regulated genes are mainly enriched in cell cycle, cell division, cardiac development, myocardial contraction and myocardial cell development and signaling pathways such as cell cycle and adrenergic signaling in cardiomyocytes. Then in the PPI network, we found 27 central genes when analyzing these 243 DEGs by the Molecular Complex Detection (MCODE) plug-in. Finally, we reanalyzed these 27 central genes through DAVID and found that 6 genes (CCNB1, CCNB2, BUB1, TTK, CDC25C, CCNA2) significantly enriched the cell cycle signaling pathway (p <1.20E-07). Therefore, through integrated bioinformatics methods, we found that compary with iCMs and CMs, iCMs were induced by direct reprogramming of neonatal mouse cardiomyocytes, the DEGs were mainly enriched in immune-related processes and myocardial contraction and cell cycle signaling pathways (p < 0.05).
- organism: mouse
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
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