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.
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Accession codes
Data deposits
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.
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Acknowledgements
We thank N. Hajela for assistance, R. Maddamsetti and Z. Blount for discussions, and M. Lynch for starting the MAE lines. This research was supported by the US National Science Foundation (DEB-1451740 to R.E.L.), BEACON Center for the Study of Evolution in Action (DBI-0939454), European Research Council (FP7 grant 310944 to O.T.), European Union (FP7 grant 610427 to D.S.), French National Funding Agency (ANR-08-GENM-023-001 to D.S., O.T. and C.M.), French CNRS International Associated Laboratory (to D.S. and R.E.L.), and US National Institutes of Health (R00-GM087550 to J.E.B.). D.E.D. was supported by a traineeship from the Cancer Prevention and Research Institute of Texas. We acknowledge the use of high-performance computing resources at the Texas Advanced Computing Center.
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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.
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Nature thanks M. Desai, G. Sherlock and C. Zeyl for their contribution to the peer review of this work.
Extended data figures and tables
Extended Data Figure 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.
Extended Data Figure 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.
Extended Data Figure 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.
Extended Data Figure 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.
Extended Data Figure 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.
Extended Data Figure 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.
Extended Data Figure 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.
Extended Data Figure 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.
Extended Data Figure 9 Trajectories for mutations by class in the LTEE in comparison with neutral expectations based on the MAE.
a–f, 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
Supplementary Data 1
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. (XLSX 65 kb)
Supplementary Data 2
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). (XLSX 1240 kb)
Supplementary Data 3
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). (XLSX 1398 kb)
Supplementary Data 4
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. (XLSX 72 kb)
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Tenaillon, O., Barrick, J., Ribeck, N. et al. Tempo and mode of genome evolution in a 50,000-generation experiment. Nature 536, 165–170 (2016). https://doi.org/10.1038/nature18959
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DOI: https://doi.org/10.1038/nature18959
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