Quantifying shifts in natural selection on codon usage between protein regions: a population genetics approach
Abstract
Codon usage bias (CUB), the non-uniform usage of synonymous codons, occurs across all domains of life. Adaptive CUB is hypothesized to result from various selective pressures, including selection for efficient ribosome elongation, accurate translation, mRNA secondary structure, and/or protein folding. Given the critical link between protein folding and protein function, numerous studies have analyzed the relationship between codon usage and protein structure. The results from these studies have often been contradictory, likely reflecting the differing methods used for measuring codon usage and the failure to appropriately control for confounding factors, such as differences in amino acid usage between protein structures and changes in the frequency of different structures with gene expression. Here we take an explicit population genetics approach to quantify codon-specific shifts in natural selection related to protein structure in S. cerevisiae and E. coli. Unlike other metrics of codon usage, our approach explicitly separates the effects of natural selection, scaled by gene expression, and mutation bias while naturally accounting for a region’s amino acid usage. Bayesian model comparisons suggest selection on codon usage varies only slightly between helix, sheet, and coil secondary structures and, similarly, between structured and intrinsically-disordered regions. Similarly, in contrast to previous findings, we findmore »
- Authors:
-
- Univ. of Tennessee, Knoxville, TN (United States); Rutgers Univ., Piscataway, NJ (United States)
- Univ. of Tennessee, Knoxville, TN (United States); National Institute for Mathematical and Biological Synthesis, Knoxville, TN (United States)
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Center for Bioenergy Innovation (CBI)
- Sponsoring Org.:
- USDOE; National Science Foundation (NSF)
- OSTI Identifier:
- 1983037
- Grant/Contract Number:
- AC05-00OR22725; MCB-1546402; DBI-1936046; DBI-1300426
- Resource Type:
- Accepted Manuscript
- Journal Name:
- BMC Genomics
- Additional Journal Information:
- Journal Volume: 23; Journal Issue: 1; Journal ID: ISSN 1471-2164
- Publisher:
- Springer
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; codon usage bias; protein folding; protein secondary structure; population genetics
Citation Formats
Cope, Alexander L., and Gilchrist, Michael A. Quantifying shifts in natural selection on codon usage between protein regions: a population genetics approach. United States: N. p., 2022.
Web. doi:10.1186/s12864-022-08635-0.
Cope, Alexander L., & Gilchrist, Michael A. Quantifying shifts in natural selection on codon usage between protein regions: a population genetics approach. United States. https://doi.org/10.1186/s12864-022-08635-0
Cope, Alexander L., and Gilchrist, Michael A. Mon .
"Quantifying shifts in natural selection on codon usage between protein regions: a population genetics approach". United States. https://doi.org/10.1186/s12864-022-08635-0. https://www.osti.gov/servlets/purl/1983037.
@article{osti_1983037,
title = {Quantifying shifts in natural selection on codon usage between protein regions: a population genetics approach},
author = {Cope, Alexander L. and Gilchrist, Michael A.},
abstractNote = {Codon usage bias (CUB), the non-uniform usage of synonymous codons, occurs across all domains of life. Adaptive CUB is hypothesized to result from various selective pressures, including selection for efficient ribosome elongation, accurate translation, mRNA secondary structure, and/or protein folding. Given the critical link between protein folding and protein function, numerous studies have analyzed the relationship between codon usage and protein structure. The results from these studies have often been contradictory, likely reflecting the differing methods used for measuring codon usage and the failure to appropriately control for confounding factors, such as differences in amino acid usage between protein structures and changes in the frequency of different structures with gene expression. Here we take an explicit population genetics approach to quantify codon-specific shifts in natural selection related to protein structure in S. cerevisiae and E. coli. Unlike other metrics of codon usage, our approach explicitly separates the effects of natural selection, scaled by gene expression, and mutation bias while naturally accounting for a region’s amino acid usage. Bayesian model comparisons suggest selection on codon usage varies only slightly between helix, sheet, and coil secondary structures and, similarly, between structured and intrinsically-disordered regions. Similarly, in contrast to previous findings, we find selection on codon usage only varies slightly at the termini of helices in E. coli. Using simulated data, we show this previous work indicating “non-optimal” codons are enriched at the beginning of helices in S. cerevisiae was due to failure to control for various confounding factors (e.g. amino acid biases, gene expression, etc.), and rather than selection to modulate cotranslational folding. Our results reveal a weak relationship between codon usage and protein structure, indicating that differences in selection on codon usage between structures are slight. In addition to the magnitude of differences in selection between protein structures being slight, the observed shifts appear to be idiosyncratic and largely codon-specific rather than systematic reversals in the nature of selection. Overall, our work demonstrates the statistical power and benefits of studying selective shifts on codon usage or other genomic features from an explicitly evolutionary approach. Limitations of this approach and future potential research avenues are discussed.},
doi = {10.1186/s12864-022-08635-0},
journal = {BMC Genomics},
number = 1,
volume = 23,
place = {United States},
year = {Mon May 30 00:00:00 EDT 2022},
month = {Mon May 30 00:00:00 EDT 2022}
}
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