Cell ReportsGenomicsPeer-reviewed journal article
Decoding the transcriptional response to ischemic stroke in young and aged mouse brain
Peter AndroviciD, Denisa KirdajováiD, Jana TurečkováiD, Daniel ŽuchaiD, Eva RohlováiD, Pavel AbaffyiD, Ján KriškaiD, Martin ValnýiD, Miroslava AnděrováiD, Mikael KubistaiD, Lukáš ValihrachiD
Abstract
Ischemic stroke is one of the leading causes of mortality and major healthcare and economic burden. It is a well-recognized disease of aging, yet it is unclear how the age-dependent vulnerability occurs and what are the underlying mechanisms. To address these issues, we performed a comprehensive RNA-Seq analysis of aging, ischemic stroke and their interaction using a model of permanent middle cerebral artery occlusion (MCAO) in 3 and 18 month old female mice. We assessed differential gene expression across injury status and age, estimated cell type proportion changes, assayed the results against a range of transcriptional signatures from the literature and performed unsupervised co-expression analysis, identifying modules of genes with varying response to injury. We uncovered selective vulnerability of neuronal populations and increased activation of type-I interferon (IFN-I) signaling and several other inflammatory pathways in aged mice. We extended these findings via targeted expression analysis in tissue as well as acutely purified cellular populations to show differential temporal dynamics of IFN-I signaling between age groups and contribution of individual cell types. Together, these results paint a picture of ischemic stroke as a complex age-related disease and provide insights into interaction of aging and stroke on cellular and molecular level.\n\nGraphical summary\n\nO_FIG O_LINKSMALLFIG WIDTH=156 HEIGHT=200 SRC=\"FIGDIR/small/769331v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (46K):\norg.highwire.dtl.DTLVardef@1590c10org.highwire.dtl.DTLVardef@126fc76org.highwire.dtl.DTLVardef@88e20aorg.highwire.dtl.DTLVardef@b000d9_HPS_FORMAT_FIGEXP M_FIG C_FIG
- organism: mouse
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 118
- FWCI
- 5.51
- Field percentile
- top 3%
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