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⇱ The dynamics of molecular evolution over 60,000 generations | Nature

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Abstract

The outcomes of evolution are determined by a stochastic dynamical process that governs how mutations arise and spread through a population. However, it is difficult to observe these dynamics directly over long periods and across entire genomes. Here we analyse the dynamics of molecular evolution in twelve experimental populations of Escherichia coli, using whole-genome metagenomic sequencing at five hundred-generation intervals through sixty thousand generations. Although the rate of fitness gain declines over time, molecular evolution is characterized by signatures of rapid adaptation throughout the duration of the experiment, with multiple beneficial variants simultaneously competing for dominance in each population. Interactions between ecological and evolutionary processes play an important role, as long-term quasi-stable coexistence arises spontaneously in most populations, and evolution continues within each clade. We also present evidence that the targets of natural selection change over time, as epistasis and historical contingency alter the strength of selection on different genes. Together, these results show that long-term adaptation to a constant environment can be a more complex and dynamic process than is often assumed.

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Figure 1: The dynamics of molecular evolution.
Figure 2: Rates of molecular evolution.
Figure 3: Long-term coexistence of competing clades.
Figure 4: Evolutionary dynamics within clades.
Figure 5: Parallelism.
Figure 6: Epistasis and contingency.

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Acknowledgements

We thank N. Hajela, A. N. Nguyen Ba, and E. Jerison for assistance. B.H.G. acknowledges support from the US National Science Foundation (DEB-1501580) and the Miller Institute for Basic Research in Science at the University of California Berkeley. R.E.L. acknowledges support from the US National Science Foundation (DEB-1451740) and BEACON Center for the Study of Evolution in Action (DBI-0939454). M.M.D. acknowledges support from the Simons Foundation (grant 376196), the US National Science Foundation (PHY-1313638), and the US National Institutes of Health (GM104239). Computational work was performed on the Odyssey cluster supported by the Research Computing Group at Harvard University.

Author information

Author notes

  1. Benjamin H. Good and Michael J. McDonald: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, 02138, Massachusetts, USA

    Benjamin H. Good, Michael J. McDonald & Michael M. Desai

  2. FAS Center for Systems Biology, Harvard University, Cambridge, 02138, Massachusetts, USA

    Benjamin H. Good, Michael J. McDonald & Michael M. Desai

  3. Department of Physics, Harvard University, Cambridge, 02138, Massachusetts, USA

    Benjamin H. Good & Michael M. Desai

  4. Department of Physics, University of California Berkeley, Berkeley, 94720, California, USA

    Benjamin H. Good

  5. Department of Bioengineering, University of California Berkeley, Berkeley, 94720, California, USA

    Benjamin H. Good

  6. Centre for Geometric Biology, School of Biological Sciences, Monash University, Clayton, 3800, Victoria, Australia

    Michael J. McDonald

  7. Department of Molecular Biosciences, The University of Texas, Austin, 78712, Texas, USA

    Jeffrey E. Barrick

  8. BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, Michigan, 48824, USA

    Jeffrey E. Barrick & Richard E. Lenski

  9. Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan, 48824, USA

    Richard E. Lenski

Authors
  1. Benjamin H. Good
  2. Michael J. McDonald
  3. Jeffrey E. Barrick
  4. Richard E. Lenski
  5. Michael M. Desai

Contributions

B.H.G., M.J.M., R.E.L. and M.M.D. designed the project; B.H.G. and M.J.M. conducted the experiments and generated the sequence data; B.H.G. and J.E.B. designed and conducted the bioinformatics analyses; B.H.G. developed theory and statistical methods; B.H.G., M.J.M., J.E.B., R.E.L. and M.M.D. analysed the data and wrote the paper.

Corresponding author

Correspondence to Michael M. Desai.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

Reviewer Information Nature thanks R. Kishony, J. Plotkin, G. Sherlock and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Figure 1 Between-line variability in the rate of mutation accumulation.

a, Coarse-grained mutation gains Ξ”Mp,k (Supplementary Information 5.1) for the six nonmutator populations, plotted using the same colour scheme as in Fig. 2. For comparison, the original mutation trajectories Mp(t) are shown in light grey. b, Between-line variability in πŸ‘ Image
, with and without the Ara+1 population. Observed values are indicated as symbols; solid lines show the corresponding null distribution obtained by randomly permuting Ξ”Mp,k across the six populations.

Extended Data Figure 2 Nonsynonymous versus synonymous mutations.

The ratio of nonsynonymous to synonymous mutations (dN/dS) in the entire pool of detected mutations, as well as the subset that fixed within their respective clades. Symbols denote individual populations; bars denote pooled estimates across either the nonmutator or mutator populations. In a, this ratio is normalized by the relative number of synonymous and nonsynonymous sites. Panel b corrects for the observed spectrum of single-nucleotide mutations in each population.

Extended Data Figure 3 Parallelism at the nucleotide level.

a, b, The distribution of nucleotide multiplicity (Supplementary Information 6.2) for the nonmutator (a) and mutator (b) populations. Observed data are shown in coloured lines, and the null expectations are shown in grey for comparison.

Extended Data Figure 4 Mutations in hslU.

Mutations that arose in the hslU gene in the six nonmutator populations. The inferred appearance times are indicated by the star symbols.

Extended Data Figure 5 Mutations in atoS.

Mutations that arose in the atoS gene in the six nonmutator populations. The inferred appearance times are indicated by stars.

Extended Data Figure 6 Temporal similarity among two-hit genes.

The distribution of the difference between the earliest and latest appearance times in genes with exactly two detected mutations in the nonmutator lines. The null distribution is obtained by randomly permuting appearance times among the two-hit genes for 10,000 bootstrap iterations.

Extended Data Figure 7 Realized mutation spectrum in different time windows.

a, Fraction of mutations contributed by each gene in Fig. 6a, including time windows before and after the median appearance time of all mutations in those genes. b, Differences between the early and late distributions in a as a function of the partition time t*. Dashed line denotes the median appearance time used to divide in a. Solid line shows the value of the likelihood ratio test (LRT) between these two distributions for different choices of t* (Supplementary Information 6.3.2). Shaded region represents a one-sided 95% confidence interval obtained by randomly permuting appearance times across the subset of genes in a for 10,000 bootstrap iterations.

Extended Data Figure 8 Mutations in argR.

Mutations that arose in the argR gene in the six nonmutator populations. The inferred appearance times are indicated by stars.

Extended Data Figure 9 Missed opportunities.

Net missed opportunities in the nonmutator populations as a function of the partition time t*. Lines denote the net missed opportunities for genes with median appearance times before and after t*, as defined by the formula in Supplementary Information 6.3.3. Shaded regions denote one-sided 95% confidence intervals obtained by bootstrap resampling from the corresponding null model 10,000 times (see Supplementary Information 6.3.3).

Supplementary information

Supplementary Information (download PDF )

This file contains Supplementary Information sections 1–6, Supplementary Figures 1–19, full legends for Supplementary Tables 1–4 and a Data Availability Statement – see contents page for details. (PDF 5269 kb)

Supplementary Table 1 (download CSV )

This file contains a comma-separated list of metagenomic samples used in this study. (CSV 87 kb)

Supplementary Table 2 (download CSV )

This file contains a comma-separated list of clonal isolates used in this study. (CSV 7 kb)

Supplementary Table 3 (download CSV )

This file contains a comma-separated list of genes showing significant parallelism in the nonmutator populations. (CSV 2 kb)

Supplementary Table 4 (download CSV )

This file contains a comma-separated list of operons showing significant parallelism in the nonmutator populations. (CSV 3 kb)

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Good, B., McDonald, M., Barrick, J. et al. The dynamics of molecular evolution over 60,000 generations. Nature 551, 45–50 (2017). https://doi.org/10.1038/nature24287

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