Visualizing the gene expression network patterns and metabolic disturbances after acute myocardial infraction: a systems biological modeling based on transcriptomics data sets

Abstract

Objective Establishing a visualization tool to examine global gene expression regulatory networks changes after acute myocardial infractions (AMI).Methods We used a gene microarray dataset named GDS 2331.It contained gene expression profiling data for rat AMI modeling (the platform number is GPL 83).This data set was downloaded in a National Center for Biotechnology Information (NCBI) GEO database.We isolated datasets that contained gene expression profiles at 15 min,60 min,4 hours,12 hour,14 hours and 48 hours after AMI.We transformed the original data into computer readable structures for Matlab using Mathworks Bioinformatics Toolbox.The pattern recognition algorithm diminished background noise,and we got a significant gene expression disturbances spectrum.K-mean clustering was used to distinguish patterns of gene expressions.We used gene ontology analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) platform to establish the connection between transcriptomics and metabolic systems.Results In total 1,400 genes were selected for modeling.We found that during the first 48 hours after AMI,the changes in these genes could be categorized into 16 expression patterns.According to Gene Ontology analysis,we found:(1) in biological processing,there is a set of developmental related genes in which the regulatory status changed significantly that includes protein kinase B path,central nerve development path and cytochalasin B regulatory path; (2) in molecule functions,the changes were related to phosphorylation,actin binding and nucleic acid pairing/unzipping; (3) in cellular components,the changes were related with intra-cellular and inter-cellular membrane structures,exocytosis and cellular skeletons (especially related with dynactin complex).KEGG mapping successfully exhibited regulatory paths and identified the sites of regulation and discovered similar regulatory patterns in up-stream and/or down-stream genes.For example,when we found that actin was a core site for cellular skeleton signaling regulation,similar regulatory pattern existed at GF RTK Vav/tiam Rac MLCK Myosin paths.Conclusions We conclude that systematically biological method provides visualization tools to describe gene expression pattern changes after AMI.These patterns reflect profiling dynamics that integrates expression volume and sites.When we use gene ontology analysis and KEGG mapping,we get a powerful integrated tool that synchronously exhibits functional and locational information on gene expression regulatory networks. Key words: Acute myocardial infraction; Visualization; Transcriptomics; Regulatory network analysis ; Gene ontology ; Kyoto encyclopedia of genes and genomes (KEGG) ; Biostatistics ; Systematic biology