Decoupling of evolutionary changes in mRNA and protein levels.
| Autor: | Jiang D; Department of Quantitative and Computational Biology, University of Southern California, USA., Cope AL; Department of Genetics, Rutgers University, USA., Zhang J; Department of Ecology and Evolutionary Biology, University of Michigan, USA., Pennell M; Department of Quantitative and Computational Biology, University of Southern California, USA.; Department of Biological Sciences, University of Southern California, USA. |
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| Jazyk: | angličtina |
| Zdroj: | BioRxiv : the preprint server for biology [bioRxiv] 2023 Apr 08. Date of Electronic Publication: 2023 Apr 08. |
| DOI: | 10.1101/2023.04.08.536110 |
| Abstrakt: | Variation in gene expression across lineages is thought to explain much of the observed phenotypic variation and adaptation. The protein is closer to the target of natural selection but gene expression is typically measured as the amount of mRNA. The broad assumption that mRNA levels are good proxies for protein levels has been undermined by a number of studies reporting moderate or weak correlations between the two measures across species. One biological explanation for this discrepancy is that there has been compensatory evolution between the mRNA level and regulation of translation. However, we do not understand the evolutionary conditions necessary for this to occur nor the expected strength of the correlation between mRNA and protein levels. Here we develop a theoretical model for the coevolution of mRNA and protein levels and investigate the dynamics of the model over time. We find that compensatory evolution is widespread when there is stabilizing selection on the protein level, which is true across a variety of regulatory pathways. When the protein level is under directional selection, the mRNA level of a gene and its translation rate of the same gene were negatively correlated across lineages but positively correlated across genes. These findings help explain results from comparative studies of gene expression and potentially enable researchers to disentangle biological and statistical hypotheses for the mismatch between transcriptomic and proteomic studies. |
| Databáze: | MEDLINE |
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