Transcriptomic time-series data analysis of an ischemia-reperfusion mouse model

Abstract

Myocardial infarction (MI) is a major threat worldwide and despite progress in clinical care, its morbi-mortality remains high. To study the pathology, biological modeling is being used since biopsy is impossible on MI patients. Mouse models of ischemia-reperfusion have enabled a better understanding of the pathology and have helped testing therapeutic strategies. Temporal characterization is further needed to identify better therapeutic administration windows. Since the time is an important key to describe this pathology and most of the studies are conducted on the same specific time points, we need a strong temporal characterization of the ischemia-reperfusion response between those specific known times. Ischemia in the mouse model is obtained by surgical occlusion of the coronary artery for 45 minutes. When the wire is released, we reproduce the MI treatment: the reperfusion. A crucial phase which causes cell death and increases inflammation over time. In order to reliably characterize this model, RNAseq cohort has been designed by the collection of mouse heart samples at 10 different time points. From the first 5 minutes of ischemia to the 24 hours after reperfusion. Two types of samples were collected: one from the ischemic area and the second from a remote area. Analysis of the differentially expressed genes enables the identification of 7500 transcripts involved in the ischemia-reperfusion response in the area at risk and 900 in the remote area. Several clusters of genes having the same temporal expression evolution were identified with heatmap plotting, which reports a temporal cascade of transcript responses. The results were coupled with a GO ontology enrichment that identified the main biological processes represented throughout time. These given biological functions can then be described with simplified kinetic profiles. Further investigations will be conducted to infer the temporal interactions between transcripts within these subnetworks. This work will result in a transcriptional mapping of the temporal regulation of the genes involved in MI and may open new insights in the behind-the-scenes mechanisms.