bioRxivGenomicsPreprint
Transcriptomic analyses reveal tissue-specific selection on genes related to apoptotic processes in the subterranean rodent, Ctenomys sociabilis
Andrew S. LangiD, Lauren L. KordonowyiD, Eileen Anne LaceyiD, Matthew D. MacManesiD
Abstract
Specialization for a subterranean existence is expected to impact multiple aspects of an organisms biology, including behavior, physiology, and genomic structure. While the phenotypic correlates of life underground have been extensively characterized, the genetic bases for these traits are not well understood, due in part to the challenges of generating large, multi-locus data sets using traditional DNA sequencing strategies. To begin exploring the genomic architecture of adaptation to a subterranean existence, we generated high-quality de novo transcriptome assemblies for 8 different tissue types (hippocampus, hypothalamus, kidney, liver, spleen, ovary, testis, skin) obtained from the colonial tuco-tuco (Ctenomys sociabilis), a group-living species of subterranean rodent that is endemic to southwestern Argentina. From these transcriptomes, we identified genes that are evolving more rapidly in the C. sociabilis lineage compared to other subterranean species of rodents. These comparisons suggest that genes associated with immune response, cell-cycle regulation, and heavy metal detoxification have been subject to positive selection in C. sociabilis. Comparisons of transcripts from different tissues suggest that the spleen and liver - organs involved in immune function and detoxification - may be particularly important sites for these adaptations, thereby underscoring the importance of including multiple tissue types in analyses of transcriptomic variation. In addition to providing an important resource for future genomic studies of C. sociabilis, our analyses generate new insights into the genomic architecture of functionally significant phenotypic traits in free-living mammals.
- organism: mouse
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 1
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