Tempo and mode of genome evolution in a 50,000-generation experiment

Journal name:
Nature
Volume:
536,
Pages:
165–170
Date published:
DOI:
doi:10.1038/nature18959
Received
Accepted
Published online

Abstract

Adaptation by natural selection depends on the rates, effects and interactions of many mutations, making it difficult to determine what proportion of mutations in an evolving lineage are beneficial. Here we analysed 264 complete genomes from 12 Escherichia coli populations to characterize their dynamics over 50,000 generations. The populations that retained the ancestral mutation rate support a model in which most fixed mutations are beneficial, the fraction of beneficial mutations declines as fitness rises, and neutral mutations accumulate at a constant rate. We also compared these populations to mutation-accumulation lines evolved under a bottlenecking regime that minimizes selection. Nonsynonymous mutations, intergenic mutations, insertions and deletions are overrepresented in the long-term populations, further supporting the inference that most mutations that reached high frequency were favoured by selection. These results illuminate the shifting balance of forces that govern genome evolution in populations adapting to a new environment.

At a glance

Figures

  1. Total number of mutations over time in the 12 LTEE populations.
    Figure 1: Total number of mutations over time in the 12 LTEE populations.

    a, Total mutations in each population. b, Total mutations rescaled to reveal the trajectories for the six populations that did not become hypermutable for point mutations, and for the other six before they evolved hypermutability. Each symbol shows a sequenced genome; some points are hidden behind others. Each line passes through the average of the genomes from the same population and generation.

  2. Phylogenetic trees for LTEE populations.
    Figure 2: Phylogenetic trees for LTEE populations.

    a, Phylogenies for 22 genomes from each population, based on point mutations. b, The same trees, except branches are rescaled as follows: branches for lineages with mismatch-repair defects are orange and shortened by a factor of 25; branches for mutT mutators are red and shortened by a factor of 50. Strain REL606 (on the left) is the ancestor. No early mutations are shared between any populations, confirming their independent evolution. Most populations have multiple basal lineages that reflect early diversification and extinction; some have deeply divergent lineages with sustained persistence, most notably Ara−2.

  3. Alternative models fit to the trajectory of genome evolution.
    Figure 3: Alternative models fit to the trajectory of genome evolution.

    Each symbol shows total mutations in a clone from five populations that never became mutators and seven before point mutation or IS150 hypermutability evolved. Colours are the same as in Fig. 1; open triangles indicate grand means. Dashed grey line shows the best fit to the linear model, m = at. Solid grey curve shows the fit to the square-root model, m = bt. Black curve is fit to the composite model, m = at + bt, where a = 0.000944 and b = 0.134856. See text for statistical analysis.

  4. Trajectories for synonymous, nonsynonymous and intergenic point mutations.
    Figure 4: Trajectories for synonymous, nonsynonymous and intergenic point mutations.

    a, Synonymous mutations, scaled so that the mean of five non-mutator populations (excluding point mutation and IS150 hypermutators) is unity at 50,000 generations. b, Nonsynonymous mutations, scaled using the same rate as synonymous mutations after adjusting for sites at risk for both classes. c, Intergenic point mutations, scaled using the same rate as synonymous mutations after adjusting for sites at risk. Each symbol shows the mean for sequenced genomes from a non-mutator or premutator lineage. Colours are as in Fig. 1. Note the discontinuous scale; populations with zero mutations are plotted below. Black lines connect grand means; shading shows standard errors calculated from replicate populations.

  5. Changes in genome size during the LTEE.
    Extended Data Fig. 1: Changes in genome size during the LTEE.

    Box-and-whiskers plot showing the distribution of average genome length (Mb) for each of the 12 LTEE populations based on the two clones sequenced at each time point shown from 500 to 50,000 generations. The red line shows the length of the ancestral genome. The boxes are the interquartile range (IQR), which spans the second and third quartiles of the data (25th to 75th percentiles); the thick black lines are medians; the whiskers extend to the outermost values that are within 1.5 times the IQR; and the points show all outlier values beyond the whiskers.

  6. Accumulation of synonymous mutations in populations that evolved point-mutation hypermutability.
    Extended Data Fig. 2: Accumulation of synonymous mutations in populations that evolved point-mutation hypermutability.

    Each symbol shows a sequenced genome from a hypermutable lineage. Colours are the same as those in Fig. 1. The accumulation of synonymous substitutions serves as a proxy for the underlying point-mutation rate. All four of the populations that became hypermutable before 10,000 generations accumulated synonymous mutations at higher rates between 10,000 and 20,000 generations than between 40,000 and 50,000 generations, indicating the evolution of reduced mutability.

  7. Alternative models fit to trajectory of genome evolution for each LTEE population.
    Extended Data Fig. 3: Alternative models fit to trajectory of genome evolution for each LTEE population.

    a, Ara−1. b, Ara+1. c, Ara−2. d, Ara+2. e, Ara−3. f, Ara+3. g, Ara−4. h, Ara+4. i, Ara−5. j, Ara+5. k, Ara−6. l, Ara+6. Each symbol shows the total mutations in a sequenced genome; in many cases, the symbols for the two genomes from the same population and generation are not distinguishable because they have the same, or almost the same, number of mutations. For the populations that evolved hypermutability, data are shown only for time points before mutators arose. In each panel, the dashed grey line shows the best fit to the linear model; the solid grey curve shows the best fit to the square-root model; and the solid black curve shows the best fit to the composite model with both linear and square-root terms.

  8. Uncertainty in parameter estimation for the model describing the rates of accumulation for neutral and beneficial mutations.
    Extended Data Fig. 4: Uncertainty in parameter estimation for the model describing the rates of accumulation for neutral and beneficial mutations.

    Contours show relative likelihoods for simultaneously estimating the linear and square-root coefficients from the observed numbers of mutations that accumulated over time in non-mutator and premutator lineages (Fig. 3). The black central point shows the maximum likelihood estimates, and the three black contours show solutions 2, 6 and 10 log units away. The points on the horizontal and vertical axes show values for the best one-parameter models.

  9. Accumulation of synonymous substitutions in non-mutator lineages.
    Extended Data Fig. 5: Accumulation of synonymous substitutions in non-mutator lineages.

    Each filled symbol shows the mean number of synonymous mutations in the (usually two) non-mutator genomes from an LTEE population that were sequenced at that time point; non-integer values can occur if the two genomes have different numbers. Small horizontal offsets were added so that overlapping points are visible. Colours are the same as in Fig. 1. Open triangles show the grand means of the replicate populations. The grey line extends from the intercept to the final grand mean. The slope of that line was used to scale the relative rates of synonymous, nonsynonymous and intergenic point mutations in Fig. 4.

  10. Temporal trend in accumulation of nonsynonymous mutations relative to the neutral expectation in non-mutator lineages.
    Extended Data Fig. 6: Temporal trend in accumulation of nonsynonymous mutations relative to the neutral expectation in non-mutator lineages.

    Interval-specific accumulation of nonsynonymous mutations calculated from changes in the total number of nonsynonymous mutations between successive samples. As with the cumulative data in Fig. 4b, values are scaled by the average rate of accumulation for synonymous mutations over 50,000 generations, after adjusting for the numbers of genomic sites at risk for nonsynonymous and synonymous mutations. Each point shows the average rate calculated for a non-mutator or premutator population; small horizontal offsets were added so that overlapping points are visible. Note the discontinuous scale; populations with no additional mutations over an interval are plotted below. Colours are the same as in Fig. 1. Black lines connect grand means; the grey shading shows standard errors calculated from the replicate populations.

  11. Mutational spectrum for non-mutator lineages in the LTEE.
    Extended Data Fig. 7: Mutational spectrum for non-mutator lineages in the LTEE.

    Shaded bars show the distribution of different types of genetic change for all independent mutations found in the set of non-mutator clones that were sequenced at each generation. The total number of mutations in this set at each time point (N) is shown above each column. Base substitutions are divided into synonymous, nonsynonymous, intergenic, and other categories; the nonsynonymous category includes nonsense mutations, and the ‘other’ category includes rare point mutations in noncoding RNA genes and pseudogenes.

  12. Changes in fitness of MAE lines after 550 single-cell bottlenecks and ~13,750 generations.
    Extended Data Fig. 8: Changes in fitness of MAE lines after 550 single-cell bottlenecks and ~13,750 generations.

    Each point shows the mean fitness based on nine competition assays between the MAE ancestor (REL1207) or one of the 15 MAE lineages (JEB807–JEB821) and the Ara variant of the MAE ancestor (REL1206). One-day competition assays were performed using the standard procedures and same conditions as for the LTEE16, 17. Error bars show 95% confidence intervals. *P < 0.05, **P < 0.01, based on two-tailed t-tests of the null hypothesis that relative fitness equals 1. Ten of the fifteen MAE lines experienced significant fitness declines, while none had significant gains.

  13. Trajectories for mutations by class in the LTEE in comparison with neutral expectations based on the MAE.
    Extended Data Fig. 9: Trajectories for mutations by class in the LTEE in comparison with neutral expectations based on the MAE.

    af, Accumulation of nonsynonymous mutations (a), intergenic point mutations (b), IS150 insertions (c), all other IS-element insertions (d), small indels (e) and large indels (f). Colours are the same as in Fig. 1. All values are expressed relative to the rate at which synonymous mutations accumulated in non-mutator LTEE lineages over 50,000 generations (Fig. 4a), and then scaled by the ratio of the number of the indicated class of mutation relative to the number of synonymous mutations in the MAE lines. In all panels, each symbol shows a non-mutator or premutator population. Note the discontinuous scale, in which populations with no mutations of the indicated type are plotted below. Black lines connect grand means over the replicate LTEE populations; the grey shading shows the corresponding standard errors.

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Author information

  1. Present address: Department of Internal Medicine, Washington University School of Medicine, St Louis, Missouri 63110, USA.

    • Aurko Dasgupta
  2. These authors contributed equally to this work.

    • Olivier Tenaillon,
    • Jeffrey E. Barrick &
    • Richard E. Lenski

Affiliations

  1. IAME, UMR 1137, INSERM, Université Paris Diderot, Sorbonne Paris Cité, F-75018 Paris, France

    • Olivier Tenaillon
  2. Department of Molecular Biosciences, Institute for Cellular and Molecular Biology, Center for Systems and Synthetic Biology, Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, Texas 78712, USA

    • Jeffrey E. Barrick,
    • Daniel E. Deatherage,
    • Aurko Dasgupta &
    • Gabriel C. Wu
  3. BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, Michigan 48824, USA

    • Jeffrey E. Barrick,
    • Noah Ribeck &
    • Richard E. Lenski
  4. Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan 48824, USA

    • Noah Ribeck &
    • Richard E. Lenski
  5. Department of Biology, University of Massachusetts, Amherst, Massachusetts 01003, USA

    • Jeffrey L. Blanchard
  6. Institute of Integrative Biology, ETH Zürich, Universitätstrasse 16, Zürich 8092, Switzerland

    • Sébastien Wielgoss
  7. Université Grenoble Alpes, Laboratoire Technologies de l’Ingénierie Médicale et de la Complexité — Informatique, Mathématiques et Applications (TIMC-IMAG), F-38000 Grenoble, France

    • Sébastien Wielgoss &
    • Dominique Schneider
  8. UMR 8030, CNRS, Université Évry-Val-d’Essonne, CEA, Institut de Génomique, Laboratoire d’Analyses Bioinformatiques pour la Génomique et le Métabolisme, F-91000 Évry, France

    • Stéphane Cruveiller &
    • Claudine Médigue
  9. Centre National de la Recherche Scientifique, TIMC-IMAG, F-38000 Grenoble, France

    • Dominique Schneider

Contributions

O.T., J.E.B., D.S. and R.E.L. conceived the project; R.E.L. and J.L.B. provided strains; O.T., J.E.B., D.E.D., A.D., G.C.W., S.W., S.C. and C.M. analysed genomes and generated other data; N.R. developed theory; R.E.L., O.T. and J.E.B. wrote the paper. All authors approved the submitted version.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to:

All sequencing data sets are available in the NCBI BioProject database under accession number PRJNA294072. The breseq analysis pipeline is available at GitHub (http://github.com/barricklab/breseq). Other analysis scripts are available at the Dryad Digital Repository (http://dx.doi.org/10.5061/dryad.6226d). R.E.L. will make strains available to qualified recipients, subject to a material transfer agreement.

Reviewer Information

Nature thanks M. Desai, G. Sherlock and C. Zeyl for their contribution to the peer review of this work.

Author details

Extended data figures and tables

Extended Data Figures

  1. Extended Data Figure 1: Changes in genome size during the LTEE. (43 KB)

    Box-and-whiskers plot showing the distribution of average genome length (Mb) for each of the 12 LTEE populations based on the two clones sequenced at each time point shown from 500 to 50,000 generations. The red line shows the length of the ancestral genome. The boxes are the interquartile range (IQR), which spans the second and third quartiles of the data (25th to 75th percentiles); the thick black lines are medians; the whiskers extend to the outermost values that are within 1.5 times the IQR; and the points show all outlier values beyond the whiskers.

  2. Extended Data Figure 2: Accumulation of synonymous mutations in populations that evolved point-mutation hypermutability. (91 KB)

    Each symbol shows a sequenced genome from a hypermutable lineage. Colours are the same as those in Fig. 1. The accumulation of synonymous substitutions serves as a proxy for the underlying point-mutation rate. All four of the populations that became hypermutable before 10,000 generations accumulated synonymous mutations at higher rates between 10,000 and 20,000 generations than between 40,000 and 50,000 generations, indicating the evolution of reduced mutability.

  3. Extended Data Figure 3: Alternative models fit to trajectory of genome evolution for each LTEE population. (128 KB)

    a, Ara−1. b, Ara+1. c, Ara−2. d, Ara+2. e, Ara−3. f, Ara+3. g, Ara−4. h, Ara+4. i, Ara−5. j, Ara+5. k, Ara−6. l, Ara+6. Each symbol shows the total mutations in a sequenced genome; in many cases, the symbols for the two genomes from the same population and generation are not distinguishable because they have the same, or almost the same, number of mutations. For the populations that evolved hypermutability, data are shown only for time points before mutators arose. In each panel, the dashed grey line shows the best fit to the linear model; the solid grey curve shows the best fit to the square-root model; and the solid black curve shows the best fit to the composite model with both linear and square-root terms.

  4. Extended Data Figure 4: Uncertainty in parameter estimation for the model describing the rates of accumulation for neutral and beneficial mutations. (171 KB)

    Contours show relative likelihoods for simultaneously estimating the linear and square-root coefficients from the observed numbers of mutations that accumulated over time in non-mutator and premutator lineages (Fig. 3). The black central point shows the maximum likelihood estimates, and the three black contours show solutions 2, 6 and 10 log units away. The points on the horizontal and vertical axes show values for the best one-parameter models.

  5. Extended Data Figure 5: Accumulation of synonymous substitutions in non-mutator lineages. (58 KB)

    Each filled symbol shows the mean number of synonymous mutations in the (usually two) non-mutator genomes from an LTEE population that were sequenced at that time point; non-integer values can occur if the two genomes have different numbers. Small horizontal offsets were added so that overlapping points are visible. Colours are the same as in Fig. 1. Open triangles show the grand means of the replicate populations. The grey line extends from the intercept to the final grand mean. The slope of that line was used to scale the relative rates of synonymous, nonsynonymous and intergenic point mutations in Fig. 4.

  6. Extended Data Figure 6: Temporal trend in accumulation of nonsynonymous mutations relative to the neutral expectation in non-mutator lineages. (78 KB)

    Interval-specific accumulation of nonsynonymous mutations calculated from changes in the total number of nonsynonymous mutations between successive samples. As with the cumulative data in Fig. 4b, values are scaled by the average rate of accumulation for synonymous mutations over 50,000 generations, after adjusting for the numbers of genomic sites at risk for nonsynonymous and synonymous mutations. Each point shows the average rate calculated for a non-mutator or premutator population; small horizontal offsets were added so that overlapping points are visible. Note the discontinuous scale; populations with no additional mutations over an interval are plotted below. Colours are the same as in Fig. 1. Black lines connect grand means; the grey shading shows standard errors calculated from the replicate populations.

  7. Extended Data Figure 7: Mutational spectrum for non-mutator lineages in the LTEE. (107 KB)

    Shaded bars show the distribution of different types of genetic change for all independent mutations found in the set of non-mutator clones that were sequenced at each generation. The total number of mutations in this set at each time point (N) is shown above each column. Base substitutions are divided into synonymous, nonsynonymous, intergenic, and other categories; the nonsynonymous category includes nonsense mutations, and the ‘other’ category includes rare point mutations in noncoding RNA genes and pseudogenes.

  8. Extended Data Figure 8: Changes in fitness of MAE lines after 550 single-cell bottlenecks and ~13,750 generations. (68 KB)

    Each point shows the mean fitness based on nine competition assays between the MAE ancestor (REL1207) or one of the 15 MAE lineages (JEB807–JEB821) and the Ara variant of the MAE ancestor (REL1206). One-day competition assays were performed using the standard procedures and same conditions as for the LTEE16, 17. Error bars show 95% confidence intervals. *P < 0.05, **P < 0.01, based on two-tailed t-tests of the null hypothesis that relative fitness equals 1. Ten of the fifteen MAE lines experienced significant fitness declines, while none had significant gains.

  9. Extended Data Figure 9: Trajectories for mutations by class in the LTEE in comparison with neutral expectations based on the MAE. (178 KB)

    af, Accumulation of nonsynonymous mutations (a), intergenic point mutations (b), IS150 insertions (c), all other IS-element insertions (d), small indels (e) and large indels (f). Colours are the same as in Fig. 1. All values are expressed relative to the rate at which synonymous mutations accumulated in non-mutator LTEE lineages over 50,000 generations (Fig. 4a), and then scaled by the ratio of the number of the indicated class of mutation relative to the number of synonymous mutations in the MAE lines. In all panels, each symbol shows a non-mutator or premutator population. Note the discontinuous scale, in which populations with no mutations of the indicated type are plotted below. Black lines connect grand means over the replicate LTEE populations; the grey shading shows the corresponding standard errors.

Supplementary information

Excel files

  1. Supplementary Data 1 (65 KB)

    This file contains descriptions of column titles (first sheet) and information on the 264 LTEE clones (second sheet) and 15 MAE clones (third sheet) sequenced and analyzed in this study.

  2. Supplementary Data 2 (1.2 MB)

    This file contains the analysis of parallel evolution for nonmutator populations and premutator lineages sorted by gene order (first sheet), G score (second sheet), and excluding nonsynonymous and synonymous mutations (third sheet).

  3. Supplementary Data 3 (1.3 MB)

    This file contains the analysis of parallel evolution for populations that evolved hypermutability sorted by gene order (first sheet), G score (second sheet), and excluding nonsynonymous and synonymous mutations (third sheet).

  4. Supplementary Data 4 (72 KB)

    This file contains the numbers of each type of mutation inferred from sequencing the 264 LTEE genomes (first sheet) and 15 MAE (second sheet) genomes.

Additional data