Abstract
Each amino acid except two is encoded by multiple synonymous codons, but at unequal frequencies. Such codon usage bias (CUB) is observable in almost all species, and commonly assumed as the result of natural selection towards an optimal CUB that matches the cellular tRNA supply. Here we hypothesize instead that the optimal CUB of a gene should slightly mismatch the tRNA supply to avoid excessive translational costs, while ensuring adequate functional payoff. By modifying the CUB of a resistance gene expressed in bacteria under antibiotic selection, we demonstrate that a small mismatch with the tRNA supply confers faster bacterial growth than those with minimized or large CUB-tRNA mismatches. Intriguingly, the optimal degree of CUB-tRNA mismatch increases as the resistance gene becomes less important in media with lower antibiotic concentrations, which is explainable by our model as a shift in the balance between the gene’s functional payoff and translational cost. Furthermore, genomic analyses in model organisms suggest that the optimal degree of CUB-tRNA mismatch is larger for endogenous genes with lower functional importance and higher mRNA abundance, respectively supporting the impact of functional payoff and translational cost. Finally, we find that mutations increasing or decreasing the CUB-tRNA mismatch of native genes are both predominantly deleterious, such that the CUB-tRNA mismatch is likely selectively maintained rather than minimized to that achievable in the presence of genetic drift and mutational bias. These results challenge the commonly assumed unidirectional selection on CUB and highlight the CUB-modulated balance between functional payoff and translational cost.
Similar content being viewed by others
Data availability
The data supporting the findings of this study are available from the corresponding authors upon request. Raw sequencing reads from Ribo-Seq are available in NCBI BioProjects under the accession number PRJNA1335396. Source data for the figures and Supplementary Figs. are provided as a Source Data file. Source data are provided with this paper.
Code availability
Custom R scripts were used in data analysis and are available on GitHub (https://github.com/chenfengokha/GmRevol) or Zenodo (https://zenodo.org/records/18425169).
References
Shen, X., Song, S., Li, C. & Zhang, J. Synonymous mutations in representative yeast genes are mostly strongly non-neutral. Nature 606, 725–731 (2022).
Kruglyak, L. et al. Insufficient evidence for non-neutrality of synonymous mutations. Nature 616, E8–E9 (2023).
Nyerges, A. et al. Synthetic genomes unveil the effects of synonymous recoding. bioRxiv (2024).
Plotkin, J. B. & Kudla, G. Synonymous but not the same: the causes and consequences of codon bias. Nat. Rev. Genet. 12, 32 (2010).
Pechmann, S., Chartron, J. W. & Frydman, J. Local slowdown of translation by nonoptimal codons promotes nascent-chain recognition by SRP in vivo. Nat. Struct. Mol. Biol. 21, 1100–1105 (2014).
Tuller, T. et al. An evolutionarily conserved mechanism for controlling the efficiency of protein translation. Cell 141, 344–354 (2010).
Xu, Y. et al. Non-optimal codon usage is a mechanism to achieve circadian clock conditionality. Nature 495, 116–120 (2013).
Zhou, M. et al. Non-optimal codon usage affects expression, structure and function of clock protein FRQ. Nature 495, 111–115 (2013).
Hahn, F. E. & Sarre, S. G. Mechanism of action of gentamicin. J. Infect. Dis. 119, 364–369 (1969).
Hershberg, R. & Petrov, D. A. Selection on codon bias. Annu Rev. Genet 42, 287–299 (2008).
Lynch, M. et al. Genetic drift, selection and the evolution of the mutation rate. Nat. Rev. Genet 17, 704–714 (2016).
Gingold, H. & Pilpel, Y. Determinants of translation efficiency and accuracy. Mol. Syst. Biol. 7, 481 (2011).
Qian, W., Yang, J. R., Pearson, N. M., Maclean, C. & Zhang, J. Balanced codon usage optimizes eukaryotic translational efficiency. PLoS Genet 8, e1002603 (2012).
Xia, X. Maximizing transcription efficiency causes codon usage bias. Genetics 144, 1309–1320 (1996).
Xia, X. A major controversy in codon-anticodon adaptation resolved by a new codon usage index. Genetics 199, 573–579 (2015).
Chen, F. et al. Dissimilation of synonymous codon usage bias in virus-host coevolution due to translational selection. Nat. Ecol. Evol. 4, 589–600 (2020).
Chen, F. & Yang, J.-R. Distinct codon usage bias evolutionary patterns between weakly and strongly virulent respiratory viruses. iScience 25, 103682 (2022).
Love, A. M. & Nair, N. U. Specific codons control cellular resources and fitness. Sci. Adv. 10, eadk3485 (2024).
Guo, X. et al. Non-adaptive evolution in codon usage of human-origin monkeypox virus. Comp. Immunol. Microbiol Infect. Dis. 100, 102024 (2023).
Chu, D., Barnes, D. J. & von der Haar, T. The role of tRNA and ribosome competition in coupling the expression of different mRNAs in Saccharomyces cerevisiae. Nucleic Acids Res 39, 6705–6714 (2011).
Chen, S. et al. Codon-Resolution Analysis Reveals a Direct and Context-Dependent Impact of Individual Synonymous Mutations on mRNA Level. Mol. Biol. Evol. 34, 2944–2958 (2017).
Zhou, Z. et al. Codon usage is an important determinant of gene expression levels largely through its effects on transcription. Proc. Natl. Acad. Sci. USA 113, E6117–E6125 (2016).
Weinberg, D. E. et al. Improved Ribosome-Footprint and mRNA Measurements Provide Insights into Dynamics and Regulation of Yeast Translation. Cell Rep. 14, 1787–1799 (2016).
Yu, C. H. et al. Codon Usage Influences the Local Rate of Translation Elongation to Regulate Co-translational Protein Folding. Mol. Cell 59, 744–754 (2015).
Hersch, S. J., Elgamal, S., Katz, A., Ibba, M. & Navarre, W. W. Translation initiation rate determines the impact of ribosome stalling on bacterial protein synthesis. J. Biol. Chem. 289, 28160–28171 (2014).
Frumkin, I. et al. Codon usage of highly expressed genes affects proteome-wide translation efficiency. Proc. Natl. Acad. Sci. USA 115, E4940–E4949 (2018).
Mordret, E. et al. Systematic Detection of Amino Acid Substitutions in Proteomes Reveals Mechanistic Basis of Ribosome Errors and Selection for Translation Fidelity. Mol. Cell 75, 427–441.e5 (2019).
Cope, A. L. & Gilchrist, M. A. Quantifying shifts in natural selection on codon usage between protein regions: a population genetics approach. BMC Genomics 23, 408 (2022).
Tong, M. et al. Gene Dispensability in Escherichia coli Grown in Thirty Different Carbon Environments. mBio 11 (2020).
Yoshikawa, K. et al. Comprehensive phenotypic analysis for identification of genes affecting growth under ethanol stress in Saccharomyces cerevisiae. FEMS Yeast Res 9, 32–44 (2009).
Turco, G. et al. Global analysis of the yeast knockout phenome. Sci. Adv. 9, eadg5702 (2023).
Zhang, J. & Yang, J. R. Determinants of the rate of protein sequence evolution. Nat. Rev. Genet 16, 409–420 (2015).
Benitiere, F., Lefebure, T. & Duret, L. Variation in the fitness impact of translationally optimal codons among animals. Genome Res 35, 446–458 (2025).
Liu, H. & Zhang, J. The rate and molecular spectrum of mutation are selectively maintained in yeast. Nat. Commun. 12, 4044 (2021).
Wei, W. et al. Rapid evolution of mutation rate and spectrum in response to environmental and population-genetic challenges. Nat. Commun. 13, 4752 (2022).
Liu, H. & Zhang, J. Yeast Spontaneous Mutation Rate and Spectrum Vary with Environment. Curr. Biol. 29, 1584–1591.e3 (2019).
Zhu, Y. O., Siegal, M. L., Hall, D. W. & Petrov, D. A. Precise estimates of mutation rate and spectrum in yeast. Proc. Natl. Acad. Sci. USA 111, E2310–E2318 (2014).
Wu, Z. et al. Expression level is a major modifier of the fitness landscape of a protein coding gene. Nat. Ecol. Evol. 6, 103–115 (2022).
Blair, J. M. A., Webber, M. A., Baylay, A. J., Ogbolu, D. O. & Piddock, L. J. V. Molecular mechanisms of antibiotic resistance. Nat. Rev. Microbiol. 13, 42–51 (2015).
Wein, T. & Dagan, T. Plasmid evolution. Curr. Biol. 30, R1158–R1163 (2020).
Sharp, P. M. & Li, W. H. The codon Adaptation Index-a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res 15, 1281–1295 (1987).
Kudla, G., Murray, A. W., Tollervey, D. & Plotkin, J. B. Coding-sequence determinants of gene expression in Escherichia coli. Science 324, 255–258 (2009).
Nieuwkoop, T. et al. Revealing determinants of translation efficiency via whole-gene codon randomization and machine learning. Nucleic Acids Res 51, 2363–2376 (2023).
Cherry, J. L. Expression level, evolutionary rate, and the cost of expression. Genome Biol. Evol. 2, 757–769 (2010).
Frumkin, I. et al. Gene Architectures that Minimize Cost of Gene Expression. Mol. Cell 65, 142–153 (2017).
Mittal, P., Brindle, J., Stephen, J., Plotkin, J. B. & Kudla, G. Codon usage influences fitness through RNA toxicity. Proc. Natl. Acad. Sci. USA 115, 8639–8644 (2018).
Ma, L., Cui, P., Zhu, J., Zhang, Z. & Zhang, Z. Translational selection in human: more pronounced in housekeeping genes. Biol. Direct 9, 17 (2014).
Pardi, N., Hogan, M. J., Porter, F. W. & Weissman, D. mRNA vaccines - a new era in vaccinology. Nat. Rev. Drug Discov. 17, 261–279 (2018).
Bitounis, D., Jacquinet, E., Rogers, M. A. & Amiji, M. M. Strategies to reduce the risks of mRNA drug and vaccine toxicity. Nat. Rev. Drug Discov. 23, 281–300 (2024).
Angov, E., Legler, P. M. & Mease, R. M. Adjustment of codon usage frequencies by codon harmonization improves protein expression and folding. Methods Mol. Biol. 705, 1–13 (2011).
Guo, Z. et al. Assessment of the reversibility of resistance in the absence of antibiotics and its relationship with the resistance gene’s fitness cost: a genetic study with mcr-1. Lancet Microbe, (2024).
Behrens, A., Rodschinka, G. & Nedialkova, D. D. High-resolution quantitative profiling of tRNA abundance and modification status in eukaryotes by mim-tRNAseq. Mol. Cell 81, 1802–1815.e7 (2021).
Tuller, T., Waldman, Y. Y., Kupiec, M. & Ruppin, E. Translation efficiency is determined by both codon bias and folding energy. Proc. Natl. Acad. Sci. 107, 3645–3650 (2010).
Vo, P. L. H. et al. CRISPR RNA-guided integrases for high-efficiency, multiplexed bacterial genome engineering. Nat. Biotechnol. 39, 480–489 (2021).
Elowitz, M. B. & Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature 403, 335–338 (2000).
Olins, P. O. & Rangwala, S. H. A novel sequence element derived from bacteriophage T7 mRNA acts as an enhancer of translation of the lacZ gene in Escherichia coli. J. Biol. Chem. 264, 16973–16976 (1989).
Postle, K. & Good, R. F. A bidirectional rho-independent transcription terminator between the E. coli tonB gene and an opposing gene. Cell 41, 577–585 (1985).
Green, M. R. & Sambrook, J. Molecular cloning: a laboratory manual. Anal. Biochem. 186, 182–183 (2001).
Clinical and Laboratory Standards Institute. Performance Standards for Antimicrobial Susceptibility Testing, (2023).
Murakami, C. & Kaeberlein, M. Quantifying yeast chronological life span by outgrowth of aged cells. J Vis Exp (2009).
Anindyajati, Artarini, A. A., Riani, C. & Retnoningrum, D. S. Plasmid Copy Number Determination by Quantitative Polymerase Chain Reaction. Sci. pharmaceutica 84, 89–101 (2016).
Skulj, M. et al. Improved determination of plasmid copy number using quantitative real-time PCR for monitoring fermentation processes. Micro. Cell Fact. 7, 6 (2008).
Lee, C., Kim, J., Shin, S. G. & Hwang, S. Absolute and relative QPCR quantification of plasmid copy number in Escherichia coli. J. Biotechnol. 123, 273–280 (2006).
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Ren, S., Li, Y. & Zhou, Z. RiboParser/RiboShiny: an integrated platform for comprehensive analysis and visualization of Ribo-seq data. J Genet Genomics (2025).
Harrison, P. W. et al. Ensembl 2024. Nucleic Acids Res 52, D891–D899 (2024).
Sayers, E. W. et al. Database resources of the national center for biotechnology information. Nucleic Acids Res 50, D20–D26 (2022).
Greenbaum, D., Colangelo, C., Williams, K. & Gerstein, M. Comparing protein abundance and mRNA expression levels on a genomic scale. Genome Biol. 4, 117 (2003).
Papatheodorou, I. et al. Expression Atlas: gene and protein expression across multiple studies and organisms. Nucleic Acids Res 46, D246–D251 (2018).
Sastry, A. V. et al. The Escherichia coli transcriptome mostly consists of independently regulated modules. Nat. Commun. 10, 5536 (2019).
Viswanatha, R., Li, Z., Hu, Y. & Perrimon, N. Pooled genome-wide CRISPR screening for basal and context-specific fitness gene essentiality in Drosophila cells. Elife 7 (2018).
Acknowledgements
This work was supported by the National Natural Science Foundation of China (2022YFA1106700 to F.C., 32122022, 32361133555 to J.-R. Y., 32000401 and 32270681 to F.C.), the National Key R&D Program of China (grant number 2021YFA1302500 to J.-R. Y.), Guangdong Basic and Applied Basic Research Foundation (2023A1515011926 to F.C.), the Science and Technology Planning Project of Guangdong Province, China (2024B1212070013 to J.-R. Y.), and the Science and Technology Projects in Guangzhou (2025A04J5439 to F.C.).
Author information
Authors and Affiliations
Contributions
J.-R.Y. conceived the idea and supervised the study; F.C., J.L., X. F., J. C. and J.-R.Y. designed the study and conducted formal data analyses; Y.L., Z.Z., J.L., X. F., Y.H. and Y. C. conducted experiments and acquired data; F.C. and J.-R.Y. wrote the paper with inputs from all authors.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interest.
Peer review
Peer review information
Nature Communications thanks Yitzhak Pilpel, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Source data
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Chen, F., Liu, Y., Zhou, Z. et al. A slight mismatch between a gene’s codon usage and the cellular tRNA supply is beneficial. Nat Commun (2026). https://doi.org/10.1038/s41467-026-69643-2
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41467-026-69643-2
