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Adaptive tracking with antagonistic pleiotropy results in seemingly neutral molecular evolution

Nature Ecology & Evolution volume 9, pages 2358–2373 (2025)Cite this article

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Abstract

The neutral theory of molecular evolution, positing that most amino acid substitutions in protein evolution are neutral, is supported by vast comparative genomic data. However, here we report that the key premise of the theory—beneficial mutations are extremely scarce—is violated. Deep mutational scanning data from 12,267 amino acid-altering mutations in 24 prokaryotic and eukaryotic genes reveal that > 1% of these mutations are beneficial, predicting that > 99% of amino acid substitutions would be adaptive. This observation demands a new theory that is compatible with both the high beneficial mutation rate and the comparative genomic data considered consistent with the neutral theory. We propose such a theory named adaptive tracking with antagonistic pleiotropy. In this theory, virtually all beneficial mutations observed are environment specific. Frequent environmental changes and mutational antagonistic pleiotropy across environments render most of the beneficial mutations seen at one time deleterious soon after and hence rarely fixed. Consequently, despite the occurrence of adaptive tracking—continuous adaptation to a changing environment fuelled by beneficial mutations—neutral substitutions prevail. We show that this theory is supported by population genetics simulation, empirical observations and experimental evolution and has implications for the adaptedness of natural populations and the tempo and mode of evolution.

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Fig. 1: Empirical DFEs of non-synonymous mutations refute the neutral theory for functional genes.
Fig. 2: Derived allele frequency spectra, polymorphisms, substitutions and substitution rates under various evolutionary models simulated.
Fig. 3: The molecular clock is insensitive to variations in Ne and Fben under Neutral or AdapTrack, relative to that under Adaptive.
Fig. 4: Mutational and fitness trajectories under Neutral and AdapTrack models in a population genetics simulation with Ne = 104.
Fig. 5: Yeast experimental evolution in changing and corresponding constant environments.

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Data availability

The Illumina sequencing data have been deposited to NCBI SRA under the accession number PRJNA1181288. Data for generating figures are available via Zenodo at https://doi.org/10.5281/zenodo.17149945 (ref. 93).

Code availability

Custom code is available via Github at https://github.com/song88180/Adaptive_Tracking_with_Antagonistic_Pleiotropy/releases/tag/v1.

References

  1. Kimura, M. Evolutionary rate at the molecular level. Nature 217, 624–626 (1968).

    Article  CAS  PubMed  Google Scholar 

  2. King, J. L. & Jukes, T. H. Non-Darwinian evolution. Science 164, 788–798 (1969).

    Article  CAS  PubMed  Google Scholar 

  3. Kimura, M. The Neutral Theory of Molecular Evolution (Cambridge Univ. Press, 1983).

  4. Zhang, J. in Evolution Since Darwin: The First 150 Years (eds Bell, M. A. et al.) 87–118 (Sinauer, 2010).

  5. Zuckerkandl, E. & Pauling, L. in Horizons in Biochemistry (eds Kasha, M. & Pullman, B.) 189–225 (Academic Press, 1962).

  6. Ohta, T. The nearly neutral theory of molecular evolution. Annu Rev. Ecol. Syst. 23, 263–286 (1992).

    Article  Google Scholar 

  7. Zhang, J. in Oxford Bibliographies in Evolutionary Biology (ed. K. Pfennig) (Oxford Univ. Press, 2016).

  8. Kern, A. D. & Hahn, M. W. The neutral theory in light of natural selection. Mol. Biol. Evol. 35, 1366–1371 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Smith, N. G. & Eyre-Walker, A. Adaptive protein evolution in Drosophila. Nature 415, 1022–1024 (2002).

    Article  CAS  PubMed  Google Scholar 

  10. Jensen, J. D. et al. The importance of the neutral theory in 1968 and 50 years on: a response to Kern and Hahn 2018. Evolution 73, 111–114 (2019).

    Article  PubMed  Google Scholar 

  11. Nei, M., Suzuki, Y. & Nozawa, M. The neutral theory of molecular evolution in the genomic era. Annu Rev. Genomics Hum. Genet 11, 265–289 (2010).

    Article  CAS  PubMed  Google Scholar 

  12. Kimura, M. On the probability of fixation of mutant genes in a population. Genetics 47, 713–719 (1962).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Eyre-Walker, A. & Keightley, P. D. The distribution of fitness effects of new mutations. Nat. Rev. Genet. 8, 610–618 (2007).

    Article  CAS  PubMed  Google Scholar 

  14. Wei, H. & Li, X. Deep mutational scanning: a versatile tool in systematically mapping genotypes to phenotypes. Front. Genet. 14, 1087267 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Shen, X., Song, S., Li, C. & Zhang, J. Synonymous mutations in representative yeast genes are mostly strongly non-neutral. Nature 606, 725–731 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Shen, X., Song, S., Li, C. & Zhang, J. Further evidence for strong non-neutrality of yeast synonymous mutations. Mol. Biol. Evol. 41, msae224 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Chen, P. & Zhang, J. Asexual experimental evolution of yeast does not curtail transposable elements. Mol. Biol. Evol. 38, 2831–2842 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Flynn, J. M. et al. Comprehensive fitness maps of Hsp90 show widespread environmental dependence. eLife 9, e53810 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Roscoe, B. P., Thayer, K. M., Zeldovich, K. B., Fushman, D. & Bolon, D. N. Analyses of the effects of all ubiquitin point mutants on yeast growth rate. J. Mol. Biol. 425, 1363–1377 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Thompson, S., Zhang, Y., Ingle, C., Reynolds, K. A. & Kortemme, T. Altered expression of a quality control protease in E. coli reshapes the in vivo mutational landscape of a model enzyme. eLife 9, e53476 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Levy, S. F. et al. Quantitative evolutionary dynamics using high-resolution lineage tracking. Nature 519, 181–186 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Couce, A. et al. Changing fitness effects of mutations through long-term bacterial evolution. Science 383, eadd1417 (2024).

    Article  CAS  PubMed  Google Scholar 

  23. Joseph, S. B. & Hall, D. W. Spontaneous mutations in diploid Saccharomyces cerevisiae: more beneficial than expected. Genetics 168, 1817–1825 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Bondel, K. B. et al. Inferring the distribution of fitness effects of spontaneous mutations in Chlamydomonas reinhardtii. PLoS Biol. 17, e3000192 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Bondel, K. B. et al. The distribution of fitness effects of spontaneous mutations in Chlamydomonas reinhardtii inferred using frequency changes under experimental evolution. PLoS Genet. 18, e1009840 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Rutter, M. T., Roles, A. J. & Fenster, C. B. Quantifying natural seasonal variation in mutation parameters with mutation accumulation lines. Ecol. Evol. 8, 5575–5585 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Wiser, M. J., Ribeck, N. & Lenski, R. E. Long-term dynamics of adaptation in asexual populations. Science 342, 1364–1367 (2013).

    Article  CAS  PubMed  Google Scholar 

  28. Tenaillon, O. et al. Tempo and mode of genome evolution in a 50,000-generation experiment. Nature 536, 165–170 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Bailey, S. F., Alonso Morales, L. A. & Kassen, R. Effects of synonymous mutations beyond codon bias: the evidence for adaptive synonymous substitutions from microbial evolution experiments. Genome Biol. Evol. 13, evab141 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Zhang, J. & Qian, W. Functional synonymous mutations and their evolutionary consequences. Nat. Rev. Genet. https://doi.org/10.1038/s41576-025-00850-1 (2025).

  31. Johnson, M. S. et al. Phenotypic and molecular evolution across 10,000 generations in laboratory budding yeast populations. eLife 10, e63910 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Li, W. H., Gojobori, T. & Nei, M. Pseudogenes as a paradigm of neutral evolution. Nature 292, 237–239 (1981).

    Article  CAS  PubMed  Google Scholar 

  33. Otto, S. P. & Whitlock, M. C. in Encyclopedia of Life Sciences (John Wiley & Sons, 2013).

  34. Roles, A. J., Rutter, M. T., Dworkin, I., Fenster, C. B. & Conner, J. K. Field measurements of genotype by environment interaction for fitness caused by spontaneous mutations in Arabidopsis thaliana. Evolution 70, 1039–1050 (2016).

    Article  CAS  PubMed  Google Scholar 

  35. Wang, A. D., Sharp, N. P., Spencer, C. C., Tedman-Aucoin, K. & Agrawal, A. F. Selection, epistasis, and parent-of-origin effects on deleterious mutations across environments in Drosophila melanogaster. Am. Nat. 174, 863–874 (2009).

    Article  PubMed  Google Scholar 

  36. Li, C. & Zhang, J. Multi-environment fitness landscapes of a tRNA gene. Nat. Ecol. Evol. 2, 1025–1032 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Qian, W., Ma, D., Xiao, C., Wang, Z. & Zhang, J. The genomic landscape and evolutionary resolution of antagonistic pleiotropy in yeast. Cell Rep. 2, 1399–1410 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Wei, X. & Zhang, J. The genomic architecture of interactions between natural genetic polymorphisms and environments in yeast growth. Genetics 205, 925–937 (2017).

    Article  CAS  PubMed  Google Scholar 

  39. Wei, X. & Zhang, J. Environment-dependent pleiotropic effects of mutations on the maximum growth rate r and carrying capacity K of population growth. PLoS Biol. 17, e3000121 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Chen, P. & Zhang, J. Antagonistic pleiotropy conceals molecular adaptations in changing environments. Nat. Ecol. Evol. 4, 461–469 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Chen, P. & Zhang, J. The loci of environmental adaptation in a model eukaryote. Nat. Commun. 15, 5672 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Zhang, J. Patterns and evolutionary consequences of pleiotropy. Annu Rev. Ecol. Evol. Syst. 54, 1–19 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Simons, A. M. Modes of response to environmental change and the elusive empirical evidence for bet hedging. Proc. Biol. Sci. 278, 1601–1609 (2011).

    PubMed  PubMed Central  Google Scholar 

  44. Haller, B. C. & Messer, P. W. SLiM 2: flexible, interactive forward genetic simulations. Mol. Biol. Evol. 34, 230–240 (2017).

    Article  CAS  PubMed  Google Scholar 

  45. Bromham, L. & Penny, D. The modern molecular clock. Nat. Rev. Genet. 4, 216–224 (2003).

    Article  CAS  PubMed  Google Scholar 

  46. Machado, H. E. et al. Broad geographic sampling reveals the shared basis and environmental correlates of seasonal adaptation in Drosophila. eLife 10, e67577 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Rudman, S. M. et al. Direct observation of adaptive tracking on ecological time scales in Drosophila. Science 375, eabj7484 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Tenaillon, O. et al. The molecular diversity of adaptive convergence. Science 335, 457–461 (2012).

    Article  CAS  PubMed  Google Scholar 

  49. Huang, C. J., Lu, M. Y., Chang, Y. W. & Li, W. H. Experimental evolution of yeast for high-temperature tolerance. Mol. Biol. Evol. 35, 1823–1839 (2018).

    CAS  PubMed  Google Scholar 

  50. Toprak, E. et al. Evolutionary paths to antibiotic resistance under dynamically sustained drug selection. Nat. Genet. 44, 101–105 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Barrick, J. E. & Lenski, R. E. Genome dynamics during experimental evolution. Nat. Rev. Genet. 14, 827–839 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. McDonald, M. J. Microbial experimental evolution—a proving ground for evolutionary theory and a tool for discovery. EMBO Rep. 20, e46992 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Miura, S., Zhang, Z. & Nei, M. Random fluctuation of selection coefficients and the extent of nucleotide variation in human populations. Proc. Natl Acad. Sci. USA 110, 10676–10681 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Abdul-Rahman, F., Tranchina, D. & Gresham, D. Fluctuating environments maintain genetic diversity through neutral fitness effects and balancing selection. Mol. Biol. Evol. 38, 4362–4375 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Lourenco, J. M., Glemin, S. & Galtier, N. The rate of molecular adaptation in a changing environment. Mol. Biol. Evol. 30, 1292–1301 (2013).

    Article  CAS  PubMed  Google Scholar 

  56. Huerta-Sanchez, E., Durrett, R. & Bustamante, C. D. Population genetics of polymorphism and divergence under fluctuating selection. Genetics 178, 325–337 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Gordo, I. & Campos, P. R. Evolution of clonal populations approaching a fitness peak. Biol. Lett. 9, 20120239 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Johnson, O. L., Tobler, R., Schmidt, J. M. & Huber, C. D. Fluctuating selection and the determinants of genetic variation. Trends Genet. 39, 491–504 (2023).

    Article  CAS  PubMed  Google Scholar 

  59. Wittmann, M. J., Bergland, A. O., Feldman, M. W., Schmidt, P. S. & Petrov, D. A. Seasonally fluctuating selection can maintain polymorphism at many loci via segregation lift. Proc. Natl Acad. Sci. USA 114, E9932–E9941 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Haldane, J. B. S. & Jayakar, S. D. Polymorphism due to selection of varying direction. J. Genet. 58, 237–242 (1963).

    Article  Google Scholar 

  61. Cvijovic, I., Good, B. H., Jerison, E. R. & Desai, M. M. Fate of a mutation in a fluctuating environment. Proc. Natl Acad. Sci. USA 112, E5021–E5028 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Gillespie, J. H. The Causes of Molecular Evolution (Oxford Univ. Press, 1991).

  63. Levins, R. Evolution in Changing Environments; Some Theoretical Explorations (Princeton Univ. Press, 1968).

  64. Bell, G. Fluctuating selection: the perpetual renewal of adaptation in variable environments. Philos. Trans. R. Soc. Lond. B Biol. Sci. 365, 87–97 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Kimura, M. Process leading to quasi-fixation of genes in natural populations due to random fluctuation of selection intensities. Genetics 39, 280–295 (1954).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Lynch, M., Wei, W., Ye, Z. & Pfrender, M. The genome-wide signature of short-term temporal selection. Proc. Natl Acad. Sci. USA 121, e2307107121 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Torrillo, P. A. & Lieberman, T. D. Reversions mask the contribution of adaptive evolution in microbiomes. eLife 13, e93146 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Neel, J. V. Diabetes mellitus: a ‘thrifty’ genotype rendered detrimental by ‘progress’?. Am. J. Hum. Genet 14, 353–362 (1962).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Kassen, R. The experimental evolution of specialists, generalists, and the maintenance of diversity. J. Evol. Biol. 15, 173–190 (2002).

    Article  Google Scholar 

  70. Ghalambor, C. K. et al. Non-adaptive plasticity potentiates rapid adaptive evolution of gene expression in nature. Nature 525, 372–375 (2015).

    Article  CAS  PubMed  Google Scholar 

  71. Ho, W. C. & Zhang, J. Evolutionary adaptations to new environments generally reverse plastic phenotypic changes. Nat. Commun. 9, 350 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Ho, W. C. & Zhang, J. Genetic gene expression changes during environmental adaptations tend to reverse plastic changes even after the correction for statistical nonindependence. Mol. Biol. Evol. 36, 1847–1848 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Kruglyak, L. et al. Insufficient evidence for non-neutrality of synonymous mutations. Nature 616, E8–E9 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Baba, T. et al. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol. Syst. Biol. 2, 2006.0008 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Winzeler, E. A. et al. Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science 285, 901–906 (1999).

    Article  CAS  PubMed  Google Scholar 

  76. Lynch, M. & Marinov, G. K. The bioenergetic costs of a gene. Proc. Natl Acad. Sci. USA 112, 15690–15695 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Gerrish, P. J. & Lenski, R. E. The fate of competing beneficial mutations in an asexual population. Genetica 102/103, 127–144 (1998).

    Article  PubMed  Google Scholar 

  78. Liu, H. & Zhang, J. Yeast spontaneous mutation rate and spectrum vary with environment. Curr. Biol. 29, 1584–1591 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Agrawal, A. F. & Whitlock, M. C. Inferences about the distribution of dominance drawn from yeast gene knockout data. Genetics 187, 553–566 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Grieshop, K. & Arnqvist, G. Sex-specific dominance reversal of genetic variation for fitness. PLoS Biol. 16, e2006810 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Chao, L. & Carr, D. E. The molecular clock and the relationship between population size and generation time. Evolution 47, 688–690 (1993).

    Article  PubMed  Google Scholar 

  82. Tajima, F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123, 585–595 (1989).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Fu, Y. X. & Li, W. H. Statistical tests of neutrality of mutations. Genetics 133, 693–709 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Fay, J. C. & Wu, C. I. Hitchhiking under positive Darwinian selection. Genetics 155, 1405–1413 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Zeng, K., Fu, Y. X., Shi, S. & Wu, C. I. Statistical tests for detecting positive selection by utilizing high-frequency variants. Genetics 174, 1431–1439 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Garud, N. R., Messer, P. W., Buzbas, E. O. & Petrov, D. A. Recent selective sweeps in North American Drosophila melanogaster show signatures of soft sweeps. PLoS Genet. 11, e1005004 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  87. Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).

    Article  CAS  PubMed  Google Scholar 

  88. Ranwez, V., Douzery, E. J. P., Cambon, C., Chantret, N. & Delsuc, F. MACSE v2: toolkit for the alignment of coding sequences accounting for frameshifts and stop codons. Mol. Biol. Evol. 35, 2582–2584 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Park, C., Chen, X., Yang, J. R. & Zhang, J. Differential requirements for mRNA folding partially explain why highly expressed proteins evolve slowly. Proc. Natl Acad. Sci. USA 110, E678–E686 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Zhang, J., Rosenberg, H. F. & Nei, M. Positive Darwinian selection after gene duplication in primate ribonuclease genes. Proc. Natl Acad. Sci. USA 95, 3708–3713 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Song, S., Chen, P. Shen, X. & Zhang, J. Adaptive tracking with antagonistic pleiotropy results in seemingly neutral molecular evolution. Zenodo https://doi.org/10.5281/zenodo.17149945 (2025).

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Acknowledgements

We thank D. Jiang, W. Qian, X. Wei, H. Xu and J. Yang for valuable comments. This work was supported by the US National Institutes of Health research grant R35GM139484 to J.Z.

Author information

Author notes

  1. Piaopiao Chen

    Present address: College of Life Sciences, Zhejiang University, Hangzhou, China

  2. Xukang Shen

    Present address: Department of Molecular Biology, Princeton University, Princeton, NJ, USA

  3. These authors contributed equally: Siliang Song, Piaopiao Chen.

Authors and Affiliations

  1. Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA

    Siliang Song, Piaopiao Chen, Xukang Shen & Jianzhi Zhang

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  1. Siliang Song
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Contributions

J.Z. conceived of the study. S.S., P.C. and J.Z. designed the study. S.S. performed the simulation. P.C. performed the experiments. S.S., P.C. and X.S. analysed the data. S.S., P.C. and J.Z. wrote the paper with input from all authors.

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Correspondence to Jianzhi Zhang.

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Extended data

Extended Data Fig. 1 Distribution of individual mutational fitness effects and inferred Ω and 1-α under various conditions.

a, Fitness effects of individual beneficial non-synonymous mutations in the 21 yeast genes. Each dot represents the point estimate of the fitness effect of a mutation, with its standard error shown by the error bar. Red indicates a significant fitness effect (nominal P < 0.05), whereas grey indicates a non-significant fitness effect. b–c, Inferred Ω (b) and 1-α (c) when various statistical stringencies are applied in calling significant fitness effects of mutations. Under a statistical cutoff, Ω and 1-α are estimated by setting all non-significant fitness effects at 0. d–e, Inferred Ω (d) and 1-α (e) when asexual populations are considered. f–g, Inferred Ω (f) and 1-α (g) when the effective population size (Ne) is 104. See Fig. 1b,c for symbol definitions.

Extended Data Fig. 2 Conceptual illustration of (a) neutral, (b) adaptive, and (c) adaptive tracking models of molecular evolution in sexual populations.

In each panel, the left diagram shows mutant frequencies over time, whereas the right diagram shows fractions of deleterious, neutral, and beneficial mutations (upper bar) and substitutions (lower bar), respectively. All three models assume that most mutations are deleterious. The neutral model assumes negligible beneficial mutations, so most substitutions are due to random fixations of neutral mutations. The adaptive model allows a non-negligible fraction of beneficial mutations, resulting in substitutions being largely beneficial and driven to fixation by positive selection. The adaptive tracking (with antagonistic pleiotropy) model allows a non-negligible fraction of beneficial mutations but assumes that these beneficial mutations soon become deleterious when the environment changes and thereby cannot reach fixation; consequently, most substitutions are neutral.

Extended Data Fig. 3 Derived allele frequency spectra, polymorphisms, substitutions, and substitution rates under various evolutionary models simulated, with conditions not considered in Fig. 2.

a–d, Allele frequency spectra (a), polymorphisms (b), substitutions (c), and Ω (d) under AdapTrack with a constant or fluctuating population size (indicated by “fluc”). e–h, Allele frequency spectra (e), polymorphisms (f), substitutions (g), and Ω (h) under AdapTrack with different fractions of neutral mutations that reflect different levels of gene importance. i–l, Allele frequency spectra (i), polymorphisms (j), substitutions (k), and Ω (l) under AdapTrack with different ranges of the probability that a sometimes-beneficial mutation can be beneficial in an environment. m–p, Allele frequency spectra (m), polymorphisms (n), substitutions (o), and Ω (p) under AdapTrack in which the magnitude of an environmental change that occurs every generation follows an exponential distribution (see Methods). The larger the mean of the exponential distribution, the greater the mean and variance of the magnitude of the environment changes. q–t, Allele frequency spectra (q), polymorphisms (r), substitutions (s), and Ω (t) under Neutral, Adaptive, and AdapTrack with or without dominance. With dominance (indicated by “dom”), the coefficient of dominance of a mutation in an environment is h = 0.75 if the mutation is beneficial in the environment, 0.50 if the mutation is neutral, and 0.25 if the mutation is deleterious. Without dominance, h = 0.50 regardless of the mutational fitness effect.

Extended Data Fig. 4 non-synonymous substitution rates (dN) of 90 fly genes likely under parallel seasonal (antagonistic) selections and those of 100 negative control genes.

The violin plot shows the frequency distribution, with the red dot representing the mean and the top and bottom horizontal bars respectively indicating the maximal and minimal values. Genes under antagonistic selections have significantly lower dN than the negative control genes (P = 0.0025, t-test).

Extended Data Fig. 5 Population dynamics of non-synonymous SNVs in a changing environment and corresponding constant environments.

a–b, Data from ref. 40 are reanalyzed to generate non-synonymous SNV frequency trajectories in five representative populations evolving in an antagonistic changing environment (a) and in five populations evolving in corresponding constant environments (b). In (a), the environment changed every 224 generations from one to the next of the five environments shown in (b). In (a), each line shows the allele frequency trajectory of a non-synonymous mutation at the beginning of the experimental evolution, four time points marking the four environmental changes, and the end of the experimental evolution. In (b), each line shows the allele frequency trajectory of a non-synonymous mutation at the same time points as in (a). Trajectories of all non-synonymous SNVs in each population are displayed, and different SNVs are shown using different colors.

Extended Data Fig. 6 Results from a SLiM simulation mimicking the asexual, diploid yeast experimental evolution.

Ne = 4×105, genome size = 1.6×105, mutation rate = 1×10−7 per site per generation, and other conditions followed the basal AdapTrack and Adaptive models. The simulation was run for 800 generations, and the environment changed every 80 generations under AdapTrack but remained constant under Adaptive. a, Fractions of “substitutions” belonging to various categories, where “substitutions” refer to mutational differences between the progenitor and a single sampled individual at the end of the simulation. b, Ω computed from the “substitutions” above defined. Shown are the results from 100 simulation replications. In (b), the lower and upper edges of a box represent the first (Q1) and third (Q3) quartiles, respectively, the horizontal line inside the box indicates the median, the whiskers extend to the most extreme values inside inner fences from Q1 – 1.5 × (Q3 – Q1) to Q3 + 1.5 × (Q3 – Q1), and the dots show outliers.

Extended Data Fig. 7 Additional comparisons of beneficial substitutions and ω between yeast experimental evolution in constant and changing environments.

a–b, The fraction of beneficial substitutions is significantly lower in changing environments than in corresponding constant environments. Same as Fig. 5c, except that only non-synonymous SNVs, nonsense SNVs, and frame shifting indels (a), or only non-synonymous SNVs (b) are considered in identifying beneficial substitutions. c, The fraction of beneficial substitutions in a changing environment increases with the similarity among the 10 media making up the changing environment. Each dot represents one of the 10 changing environments. Spearman’s correlation and associated one-tailed P-value are presented. d–f, Same as Fig. 5c except that the 12 potentially contaminated populations are excluded. Results are obtained when all substitution types (d), only non-synonymous SNVs, nonsense SNVs, and frame shifting indels (e), or only non-synonymous SNVs (f) are considered in identifying beneficial substitutions. g, ω is significantly lower in changing environments than in constant environments, as in Fig. 5d, except that the 12 potentially contaminated populations are excluded.

Extended Data Fig. 8 Results of SLiM simulations under AdapTrack and other models of fluctuating selection.

a-d, Derived allele frequency spectra (a), polymorphisms (b), substitutions (c), and Ω (d). Under the quasi-neutral model (QuasiNeu), beneficial mutations are subject to fluctuating selection with zero expected selection coefficients across environments. Under the fluctuating positive selection model (FluPosSel), beneficial mutations are subject to fluctuating selection with positive expected selection coefficients across environments.

Extended Data Fig. 9 Signals of selective sweeps in SLiM simulated data.

a-e, Distributions of Tajima’s D (a), Fu and Li’s D (b), Fay and Wu’s H (c), Zeng et al.’s E (d), and Garud et al.’s H12 (e) for populations simulated under five different models. Each distribution, presented as a violin plot, is based on the aggregated data from 20 timepoints of each of 30 simulation replications. A t-test is conducted between Neutral and AdapTrack (inf env) or AdapTrack (20 env) as well as between Adaptive and AdapTrack (inf env) or AdapTrack (20 env). Here, “inf env” stands for infinite number of environments whereas “20 env” stands for 20 rotating environments. *, P < 0.05, **, P < 0.005; ***, P < 0.0005. In the violin plot, the white dot represents the median, the dark rectangular spans from the first (Q1) to third (Q3) quartile, and the dark vertical line represents the range of the distribution after removing outliers that lie outside the domain from Q1 – 1.5 × (Q3 – Q1) to Q3 + 1.5 × (Q3 – Q1).

Extended Data Fig. 10 Results of SLiM simulations under AdapTrack with temporal vs. spatial heterogeneity in fitness effects of mutations.

a-d, Derived allele frequency spectra (a), polymorphisms (b), substitutions (c), and Ω (d). We consider the average fitness across all environments when classifying beneficial and deleterious polymorphisms/substitutions under the spatial heterogeneity model. The results of the simulation of temporal heterogeneity are from Fig. 2a-d.

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Supplementary Data 1

Inferred non-synonymous substitution rate relative to the neutral expectation (Ω) and inferred fraction of non-synonymous substitutions that are beneficial (α), under various empirical DFEs of non-synonymous mutations in sexual and asexual populations.

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Supplementary Tables 1–5.

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Song, S., Chen, P., Shen, X. et al. Adaptive tracking with antagonistic pleiotropy results in seemingly neutral molecular evolution. Nat Ecol Evol 9, 2358–2373 (2025). https://doi.org/10.1038/s41559-025-02887-1

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