Your browser doesn't support javascript.
loading
Stem cell transcriptional profiles from mouse subspecies reveal cis-regulatory evolution at translation genes.
Simon, Noah M; Kim, Yujin; Bautista, Diana M; Dutton, James R; Brem, Rachel B.
Afiliación
  • Simon NM; Biology of Aging Doctoral Program, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089.
  • Kim Y; Buck Institute for Research on Aging, Novato, CA 94945, USA.
  • Bautista DM; Department of Plant and Microbial Biology, University of California, Berkeley, Berkeley CA 94720, USA.
  • Dutton JR; Stem Cell Institute, University of Minnesota, Minneapolis, MN 55455, USA.
  • Brem RB; Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN 55455, USA.
bioRxiv ; 2024 Apr 10.
Article en En | MEDLINE | ID: mdl-37503246
A key goal of evolutionary genomics is to harness molecular data to draw inferences about selective forces that have acted on genomes. The field progresses in large part through the development of advanced molecular-evolution analysis methods. Here we explored the intersection between classical sequence-based tests for selection and an empirical expression-based approach, using stem cells from Mus musculus subspecies as a model. Using a test of directional, cis-regulatory evolution across genes in pathways, we discovered a unique program of induction of translation genes in stem cells of the Southeast Asian mouse M. m. castaneus relative to its sister taxa. As a complement, we used sequence analyses to find population-genomic signatures of selection in M. m. castaneus, at the upstream regions of the translation genes, including at transcription factor binding sites. We interpret our data under a model of changes in lineage-specific pressures across Mus musculus in stem cells with high translational capacity. Together, our findings underscore the rigor of integrating expression and sequence-based methods to generate hypotheses about evolutionary events from long ago.

Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: BioRxiv Año: 2024 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: BioRxiv Año: 2024 Tipo del documento: Article