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The study aimed to investigate time-course transcriptomes in myocardial ischaemia reperfusion injury (IRI) via RNA-Seq. Transcriptomes of 10 samples derived from patients with acute ST-segment elevation myocardial infarction (ASTEMI) who were assigned to percutaneous coronary intervention (PCI), were sequenced at the time of 0 (before PCI), 2, 12, 24 and 72 hours after PCI, respectively. Using the genefilter package in r, wgcna and stem, different expression lncRNA (DEL) and mRNA (DEM) were analysed. Out of 756 mRNAs and 206 lncRNAs shared by enrolled patients, 135 RNAs were screened to be significantly associated with the IRI. Furthermore, combined with lncRNA-mRNA, lncRNA-miRNA and miRNA-mRNA network, 51 RNAs and 131 relationship pairs were ascertained in the competing endogenous RNAs (ceRNA) network. Among these nodes, SH2D3C and GTF2H4 were significantly enriched in cellular response to stress and their interaction module were isolated from functional ceRNA network. Subsequently, their critical role was confirmed via down-regulation of SH2D3C and GTF2H4 expression in vitro model. These results identified that lncRNA-mRNA ceRNA network, associated significantly with IRI, functioned as critical regulative pivotal roles after PCI-AMI, and SH2D3C and GTF2H4 may be the most responsive transcriptional regulator in the early-phase of IRI.
Pubmed ID: 30657634
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Bioinformatics resource system including web server and web service for functional annotation and enrichment analyses of gene lists. Consists of comprehensive knowledgebase and set of functional analysis tools. Includes gene centered database integrating heterogeneous gene annotation resources to facilitate high throughput gene functional analysis.
View all literature mentionsSoftware platform for complex network analysis and visualization. Used for visualization of molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles and other state data.
View all literature mentionsThe Short Time-series Expression Miner (STEM) is a Java program for clustering, comparing, and visualizing short time series gene expression data from microarray experiments (~8 time points or fewer). STEM allows researchers to identify significant temporal expression profiles and the genes associated with these profiles and to compare the behavior of these genes across multiple conditions. STEM is fully integrated with the Gene Ontology (GO) database supporting GO category gene enrichment analyses for sets of genes having the same temporal expression pattern. STEM also supports the ability to easily determine and visualize the behavior of genes belonging to a given GO category or user defined gene set, identifying which temporal expression profiles were enriched for these genes. (Note: While STEM is designed primarily to analyze data from short time course experiments it can be used to analyze data from any small set of experiments which can naturally be ordered sequentially including dose response experiments.) Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible
View all literature mentionsIntegrated database resource consisting of 16 main databases, broadly categorized into systems information, genomic information, and chemical information. In particular, gene catalogs in completely sequenced genomes are linked to higher-level systemic functions of cell, organism, and ecosystem. Analysis tools are also available. KEGG may be used as reference knowledge base for biological interpretation of large-scale datasets generated by sequencing and other high-throughput experimental technologies.
View all literature mentionsSoftware tool for transcriptome assembly and differential expression analysis for RNA-Seq. Includes script called cuffmerge that can be used to merge together several Cufflinks assemblies. It also handles running Cuffcompare as well as automatically filtering a number of transfrags that are likely to be artifacts. If the researcher has a reference GTF file, the researcher can provide it to the script to more effectively merge novel isoforms and maximize overall assembly quality.
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