Abstract
Synonymous mutations in protein-coding genes do not alter protein sequences and are thus generally presumed to be neutral or nearly neutral1,2,3,4,5. Here, to experimentally verify this presumption, we constructed 8,341 yeast mutants each carrying a synonymous, nonsynonymous or nonsense mutation in one of 21 endogenous genes with diverse functions and expression levels and measured their fitness relative to the wild type in a rich medium. Three-quarters of synonymous mutations resulted in a significant reduction in fitness, and the distribution of fitness effects was overall similar—albeit nonidentical—between synonymous and nonsynonymous mutations. Both synonymous and nonsynonymous mutations frequently disturbed the level of mRNA expression of the mutated gene, and the extent of the disturbance partially predicted the fitness effect. Investigations in additional environments revealed greater across-environment fitness variations for nonsynonymous mutants than for synonymous mutants despite their similar fitness distributions in each environment, suggesting that a smaller proportion of nonsynonymous mutants than synonymous mutants are always non-deleterious in a changing environment to permit fixation, potentially explaining the common observation of substantially lower nonsynonymous than synonymous substitution rates. The strong non-neutrality of most synonymous mutations, if it holds true for other genes and in other organisms, would require re-examination of numerous biological conclusions about mutation, selection, effective population size, divergence time and disease mechanisms that rely on the assumption that synoymous mutations are neutral.
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Data availability
Sequencing data generated in this study have been deposited into NCBI with the Bioproject ID PRJNA750109. Public data used include gene function annotations in the Saccharomyces Genome Database (https://www.yeastgenome.org/) and genomic coding sequences of S. paradoxus, C. glabrata, and S. castellii and genomic sequences of S. mikatae and S. uvarum from the NCBI genome assembly database (https://www.ncbi.nlm.nih.gov/assembly/). Source data are provided with this paper.
Code availability
Custom code is available at https://github.com/song88180/Mutational-Fitness-Effects and https://doi.org/10.5281/zenodo.5908478.
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Acknowledgements
We thank P. Chen, H. Liu, and H. Xu for technical assistance and W. Qian, X. Wei, J.-R. Yang, and members of the Zhang laboratory for valuable comments. This work was supported by the U.S. National Institutes of Health research grant R35GM139484 to J.Z.
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J.Z. conceived the study. X.S., C.L., and J.Z. designed the study. X.S. performed the experiments. X.S. and S.S. analysed the data. X.S. and J.Z. wrote the paper with input from all authors.
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Extended data figures and tables
Extended Data Fig. 1 Properties of wild-type and mutant strains analyzed.
a, Experimental procedure for testing cellular respiratory functions. Cells from each of the 21 mutant libraries were spread on YPD and YPG plates, followed by colony counting after growth. Respiration is needed for cell growth on YPG but not on YPD. b, Mean ratio of YPD colony number to YPG colony number for each mutant library, based on three replicates per library. Error bars show the standard error of the mean. The negative control is deficient in respiration due to gene deletions (see Methods). c, Maximum growth rates of three reconstituted wild-type strains and BY4742. WT1 was used as the wild-type control in en masse competitions with mutants. The red error bar indicates the standard error of the mean based on 16 replicates each shown by a dot (15 for BY4742). P-values are from two-tailed t-tests. The growth rate is not significantly different among the four strains (P = 0.58, one-factor ANOVA test). d, Ploidy of one T48 population per mutant library assessed by flow cytometry. SYTOX Green fluorescence was analyzed using the BL2 detector that measured the output from the 488-nm laser (blue). In control flow cytometry profiles, the two peaks respectively represent cells in the G1 and G2/M cell-cycle stages (1C and 2C DNA content for haploids while 2C and 4C for diploids)
Extended Data Fig. 2 Mutant fitness quantification.
a, Fractions of synonymous (yellow) and nonsynonymous (blue) mutants among designed but unobserved mutants and those among observed mutants. Nonsense mutants are not considered. Numbers in the bars are numbers of mutants. The distributions of synonymous and nonsynonymous mutants among the unobserved and observed mutant groups are not significantly different (P > 0.05, Fisher’s exact test). b–f, Correlation between every two of the four replicates in estimated mutant fitness under YPD at 30 °C. The correlation between replicate 1 and replicate 2 is presented in Fig. 1c. Each dot is a mutant and the dotted line indicates the diagonal. Pearson’s correlation r and its associated P-value are presented. Among-genotype sum of squares explains 93.8% of the total sum of squares (one-factor ANOVA)
Extended Data Fig. 3 Mutant fitness distribution under YPD at 30 °C.
a, Distribution of the fitness of 169 nonsense mutants. The peak around 0.94 is caused by 26 nonsense mutants of GET1 that all have fitness of about 0.94. b, Cumulative frequency distributions of log10(mutant fitness) of nonsynonymous (blue) and synonymous (yellow) mutants. c, The full figure of Fig. 2c, including low-fitness mutants that are not shown in Fig. 2c. d, The full figure of Fig. 2e, including low-fitness and high-fitness mutants that are not shown in Fig. 2e
Extended Data Fig. 4 Coding mutations influence the mRNA level of the mutated gene.
a, Non-significant negative correlation between the mean fitness of synonymous mutants of a gene and the expression level of the gene. Each dot represents a gene. Spearman’s correlation ρ and associated P-value are presented. b–g, Correlation in mutant REL between replicates, which are indicated on the axes of each panel. Each dot is a mutant, and the dotted line indicates the diagonal. Pearson’s correlation r and its associated P-value are presented. Among-genotype sum of squares explains 89.7% of total sum of squares (one-factor ANOVA). h, Cumulative frequency distributions of REL of nonsynonymous and synonymous mutants. i, Relative expression level (REL) distributions of nonsynonymous (blue) and synonymous (yellow) mutants of 20 individual genes shown by box plots. The lower and upper edges of a box represent the first (qu1) and third (qu3) quartiles, respectively, the horizontal line inside the box indicates the median (md), the whiskers extend to the most extreme values inside inner fences, md ± 1.5(qu3-qu1), and the dots show outliers. Nonsynonymous and synonymous distributions of each gene are compared by a two-tailed Wilcoxon rank-sum test, with FDR-adjusted P-values indicated as follows: *, P < 0.05; ⁑, P < 0.01, ⁂, P < 0.001. j, Distribution of REL of nonsense mutants
Extended Data Fig. 5 Mechanisms underlying coding mutations’ fitness effects.
a–b, Box plots showing similar absolute fractional changes in the mRNA level induced by nonsynonymous (a) or synonymous (b) mutations within and outside TF-binding sites. The lower and upper edges of a box represent the first (qu1) and third (qu3) quartiles, respectively, the horizontal line inside the box indicates the median (md), the whiskers extend to the most extreme values inside inner fences, md ± 1.5(qu3-qu1), and the dots show outliers. P-values are from two-tailed Wilcoxon rank-sum test (n = 1191, 4736, 367, and 1411, respectively, for the four bars from left to right). c–d. Positive correlation between rCAI and rescaled fitness among nonsynonymous (c) and synonymous (d) mutants, respectively. e, Fraction of synonymous mutations lowering CAI increases with the expression level of the gene. f, Fraction of synonymous mutations lowering the expression level increases with the expression level of the gene. g, Fraction of nonsynonymous mutations lowering CAI increases with the expression level of the gene. h, Fraction of nonsynonymous mutations lowering the expression level increases with the expression level of the gene. i, Mean rescaled fitness of synonymous mutants declines with the expression level of the gene. j, Mean rescaled fitness of nonsynonymous mutants declines with the expression level of the gene. Because deleting a more highly expressed gene tends to cause a greater fitness reduction60, the finding in panel j means that the mean fitness reduction caused by a nonsynonymous mutation should rise with the expression level of the gene. In e-j, each dot represents a gene. k–l, positive correlation between the relative mRNA folding strength (rMFS) of a nonsynonymous (k) or synonymous (l) mutant and its rescaled fitness when rMFS is below 1. The rMFS of a mutant is its mRNA folding strength (i.e., the absolute value of its minimal folding energy) divided by that of the wild-type. In each panel, the correlation is separately computed for mutants with rMFS < 1 and those with rMFS > 1. In c-l, rank correlations (ρ) and associated P-values are shown
Extended Data Fig. 6 A higher coefficient of variation (CV) of fitness across environments for nonsynonymous than synonymous mutants can create a nonsynonymous to synonymous substitution rate ratio (dN/dS) that is substantially below 1 despite similar fitness effects of synonymous and nonsynonymous mutations in each environment.
a, Mean expected dN/dS from 1000 simulations of a population that experiences multiple different environments. A mutant is purged if its fitness is lower than a preset cutoff such as 0.98 or 0.99 in any environment. Shaded areas represent 95% confidence intervals. a. Results with CV = 0.004 for synonymous mutants. b, Results with CV = 0.005 for synonymous mutants. Note that, under the fitness cutoff of 0.99, dN/dS starts to increase with the number (m) of environments when m is large. Raising m reduces the fraction of synonymous mutations that are always neutral (FANS) as well as the fraction of nonsynonymous mutations that are always neural (FANN). Because the fitness CV is larger for nonsynonymous than synonymous mutants in the simulation, FANN decreases with m more quickly than does FANS when m is small. When m is large, FANN is small, making it possible for FANS to decrease with m more quickly than FANN. As a result, dN/dS might increase with m when m is large
Extended Data Fig. 7 Pairwise correlation between replicates in estimated mutant fitness in each of the three additional environments used.
a–c, Correlation between every two of the three replicates in estimated mutant fitness under SC at 37 °C. Each dot is a mutant and the dotted line indicates the diagonal. Pearson’s correlation r and its associated P-value are presented. Among-genotype sum of squares explains 96.1% of the total sum of squares (one-factor ANOVA). d–f, Correlation between every two of the three replicates in estimated mutant fitness under YPD + 0.375 mM H2O2. Among-genotype sum of squares explains 94.4% of the total sum of squares. g–i, Correlation between every two of the three replicates in estimated mutant fitness under YPE. j, Correlation between replicates 1 and 3 in estimated mutant fitness under YPE after the exclusion of SNF6 mutants. k, Correlation between replicates 2 and 3 in estimated mutant fitness under YPE after exclusion of SNF6 mutants. Panels g-k suggest that the fitness estimates of SNF6 mutants in replicate 3 under YPE are unreliable, so are unused in fitness estimation in YPE. When SNF6 is excluded, among-genotype sum of squares explains 91.0% of the total sum of squares in YPE
Extended Data Fig. 8 Mutant fitness in the three additional environments used.
a–c, Fractions of synonymous (yellow) and nonsynonymous (blue) mutants among designed but unobserved mutants and those among observed mutants in each environment. Nonsense mutants are not considered. Numbers in the bars are numbers of mutants. The distributions of synonymous and nonsynonymous mutants among the unobserved and observed mutant groups are not significantly different in each environment (P > 0.05, Fisher’s exact test). d–f, Cumulative frequency distributions of fitness of nonsynonymous and synonymous mutants in each environment. g–i, Fitness distributions of nonsynonymous and synonymous mutants of 19 individual genes shown by box plots in each environment. The lower and upper edges of a box represent the first (qu1) and third (qu3) quartiles, respectively, the horizontal line inside the box indicates the median (md), the whiskers extend to the most extreme values inside inner fences, md ± 1.5(qu3-qu1), and the dots show outliers. Nonsynonymous and synonymous distributions for each gene are compared by a two-tailed Wilcoxon sum-rank test, with the FDR-adjusted P-value indicated as follows: *, P < 0.05; ⁑, P < 0.01, ⁂, P < 0.001. j–l, Fractions of mutants with fitness significantly below 1 (P < 0.05), significantly above 1, and neither, respectively, in each environment. The error bar shows one standard error. The distributional difference between synonymous and nonsynonymous mutants among the three bins is tested by two-tailed Fisher’s exact test, with the P-value indicated. At FDR = 0.05, 40.7% and 0.7% of nonsynonymous mutations and 34.8% and 0.5% of synonymous mutations are significantly deleterious and beneficial, respectively, in SC+37 °C. These values become 35.5%, 1.7%, 31.9% and 1.6% in YPD+H2O2, and 47.6%, 1.4%, 45.6%, and 1.0% in YPE
Extended Data Fig. 9 Fractions of nonsynonymous (blue) and synonymous (yellow) neutral mutations in one environment (indicated on the X-axis) that become deleterious in any of the other three environments.
The fractions are higher for nonsynonymous than synonymous mutations (P < 0.05, paired t-test). A mutation is considered deleterious if its fitness is significantly lower than 1 (P < 0.05) and neutral if its fitness is not significantly different from 1
Extended Data Fig. 10 A new model explaining the widespread negative correlation between the mRNA level of a gene and its evolutionary rate measured by the nonsynonymous or amino acid substitution rate.
Compared with nonsynonymous mutations in lowly expressed genes, those in highly expressed genes tend to reduce the gene expression level and hence tend to be deleterious. As a result, the evolutionary rate of a gene measured by the nonsynonymous or amino acid substitution rate is negatively correlated with the gene expression level. The height of a symbol represents the quantity considered.
Supplementary information
Supplementary Data 1
Properties of the 21 genes studied.
Supplementary Data 2
Genotype frequencies in each replicate competition.
Supplementary Data 3
Fitness of all mutants in all environments.
Supplementary Data 4
Relative mRNA levels of all mutants in YPD.
Supplementary Data 5
Primers used in the study.
Source data
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Shen, X., Song, S., Li, C. et al. Synonymous mutations in representative yeast genes are mostly strongly non-neutral. Nature 606, 725–731 (2022). https://doi.org/10.1038/s41586-022-04823-w
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DOI: https://doi.org/10.1038/s41586-022-04823-w
