bioRxivBioinformaticsPreprint
Evolutionary divergence of induced versus constitutive antiviral gene expression between primates and rodents
Abstract
BackgroundHundreds of genes are upregulated in response to viral infection. These genes sequences often diverge across mammals, to counteract rapid virus evolution. However, the transcriptional divergence of these genes, their relative levels before and after infection in different host species, remains poorly understood. ResultsWe studied this divergence by comparing gene expression before and after viral stimulation in cells from primates and rodents. We developed a method to identify orthologs upregulated in one species that are unchanged in response to stimulus in another species. Using human and mouse data, we detected 578 transcriptionally divergent orthologous genes. While most divergent genes do not belong to the same cellular process, several pathways and protein complexes are enriched in this set, suggesting that divergence in immune responses between closely related mammals is limited to specific modules rather than involving entire pathways. Transcriptional divergence between human and mouse orthologs was also observed when ortholog expression from different primates and rodents were compared, when responses were studied in several cell types, and was recapitulated at the chromatin level, using histone mark patterns that denote active promoter regions. ConclusionsIn summary, we found genes whose orthologs diverge between primates and rodents in response to immune stimulation. Some of these genes are constitutively expressed in one species even before infection, potentially facilitating rapid antiviral activity, and suggesting clade-specific adaptation to confer greater resistance against viruses. Further comparative studies on diverse infections can point to additional species-specific responses and how they enable different species to overcome infection.
- organism: human
- organism: mouse
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
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