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Contributions of cis- and trans-Regulatory Evolution to Transcriptomic Divergence across Populations in the Drosophila mojavensis Larval Brain

Natural selection on gene expression was originally predicted to result primarily in cis- rather than trans-regulatory evolution, due to the expectation of reduced pleiotropy. Despite this, numerous studies have ascribed recent evolutionary divergence in gene expression predominantly to trans-regula...

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Detalles Bibliográficos
Autores principales: Benowitz, Kyle M, Coleman, Joshua M, Allan, Carson W, Matzkin, Luciano M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7495911/
https://www.ncbi.nlm.nih.gov/pubmed/32653899
http://dx.doi.org/10.1093/gbe/evaa145
Descripción
Sumario:Natural selection on gene expression was originally predicted to result primarily in cis- rather than trans-regulatory evolution, due to the expectation of reduced pleiotropy. Despite this, numerous studies have ascribed recent evolutionary divergence in gene expression predominantly to trans-regulation. Performing RNA-seq on single isofemale lines from genetically distinct populations of the cactophilic fly Drosophila mojavensis and their F(1) hybrids, we recapitulated this pattern in both larval brains and whole bodies. However, we demonstrate that improving the measurement of brain expression divergence between populations by using seven additional genotypes considerably reduces the estimate of trans-regulatory contributions to expression evolution. We argue that the finding of trans-regulatory predominance can result from biases due to environmental variation in expression or other sources of noise, and that cis-regulation is likely a greater contributor to transcriptional evolution across D. mojavensis populations. Lastly, we merge these lines of data to identify several previously hypothesized and intriguing novel candidate genes, and suggest that the integration of regulatory and population-level transcriptomic data can provide useful filters for the identification of potentially adaptive genes.