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. 2017 Oct 24;114(43):E9026-E9035.
doi: 10.1073/pnas.1705887114. Epub 2017 Oct 10.

Mutator genomes decay, despite sustained fitness gains, in a long-term experiment with bacteria

Alejandro Couce  1 , Larissa Viraphong Caudwell  2 , Christoph Feinauer  3 , Thomas Hindré  2 , Jean-Paul Feugeas  1 , Martin Weigt  4 , Richard E Lenski  3   5 , Dominique Schneider  2 , Olivier Tenaillon  6
Affiliations

Mutator genomes decay, despite sustained fitness gains, in a long-term experiment with bacteria

Alejandro Couce et al. Proc Natl Acad Sci U S A. .

Abstract

Understanding the extreme variation among bacterial genomes remains an unsolved challenge in evolutionary biology, despite long-standing debate about the relative importance of natural selection, mutation, and random drift. A potentially important confounding factor is the variation in mutation rates between lineages and over evolutionary history, which has been documented in several species. Mutation accumulation experiments have shown that hypermutability can erode genomes over short timescales. These results, however, were obtained under conditions of extremely weak selection, casting doubt on their general relevance. Here, we circumvent this limitation by analyzing genomes from mutator populations that arose during a long-term experiment with Escherichia coli, in which populations have been adaptively evolving for >50,000 generations. We develop an analytical framework to quantify the relative contributions of mutation and selection in shaping genomic characteristics, and we validate it using genomes evolved under regimes of high mutation rates with weak selection (mutation accumulation experiments) and low mutation rates with strong selection (natural isolates). Our results show that, despite sustained adaptive evolution in the long-term experiment, the signature of selection is much weaker than that of mutational biases in mutator genomes. This finding suggests that relatively brief periods of hypermutability can play an outsized role in shaping extant bacterial genomes. Overall, these results highlight the importance of genomic draft, in which strong linkage limits the ability of selection to purge deleterious mutations. These insights are also relevant to other biological systems evolving under strong linkage and high mutation rates, including viruses and cancer cells.

Keywords: GC content; experimental evolution; genetic draft; hypermutability; selection.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Three evolutionary regimes from which the E. coli genomes analyzed in this study were derived. (A) Hypermutable populations that arose during the LTEE experienced high mutation rates and strong selection. (B) MAEs conducted with hypermutable strains experienced high mutation rates but weak selection. (C) Natural divergence: Strains diverged for millions of years in natural environments (before being isolated) experienced low mutation rates and strong selection. The diagram shows the relationships between strains belonging to two of several major E. coli phylogroups. REL606 is a derivative of E. coli B and the ancestor of the LTEE. MG1655, which belongs to the same group, is a derivative of E. coli K-12 and the ancestor of the MAEs analyzed in this study. The genome of ED1a is more distant from REL606 and was chosen for analyzing mutational differences that arose over long periods in nature. The red branches connecting them represent an estimated divergence time of >20 million years.
Fig. 2.
Fig. 2.
Base substitution frequencies observed in genomes from the three evolutionary regimes studied here. Only synonymous mutations are included for all populations, except for those from the MAE regime, in which both nonsynonymous and synonymous mutations were included. The color code at right shows the six possible substitutions. Both mutator types show marked biases toward specific substitutions—AT to CG transversions for GO-defective (i.e., mutT) mutators, and AT to GC and GC to AT transitions for MMR-defective mutators—under both the MAE and LTEE regimes. The six LTEE populations that independently evolved one or the other mode of hypermutability are indicated by their population name (e.g., Ara+6). The various base substitutions are more evenly represented in the genomes derived under natural conditions. The total substitutions for each sample are indicated to the right of each distribution.
Fig. S1.
Fig. S1.
Examples of complications that arise when determining whether a gene is essential or not, including several well-known essential and nonessential genes. The blue dashed lines mark the C-terminal 25% portion of each ORF. (A) The rpoB gene is essential and shows few insertions, which might be in duplicated copies of the gene. (B) The purD gene is nonessential, but, as the result of a hotspot, shows an excess of insertions near the C terminus. (C) The topA gene is essential, but it tolerates insertions in the C terminus. (D) The uppS gene is essential; the low diversity of insertion sites suggests a mutational hotspot or outgrowth of a mutation in a duplicated copy. (E) The yqfE gene is nonessential; the low diversity of insertion sites reflects its short length, mutational hotspots, or both.
Fig. S2.
Fig. S2.
(A) Distribution of insertions in the C-terminal 25% portion of each ORF, expressed as the fold increase compared with the remainder of the ORF. The dashed line corresponds to 80% of the insertions within the C-terminal portion. (B) The two histograms show (Top) the diversity (based on Shannon’s H index) and (Bottom) abundance of insertions in the subset of ORFs with >80% of insertions concentrated in the C-terminal portion. The dashed lines mark the empirical cutoffs discussed in SI Materials and Methods.
Fig. S3.
Fig. S3.
Properties of ORFs that do not display excess insertions in their C termini. The histograms for the abundance and diversity of insertions in each ORF were used to establish the empirical cutoffs for each dimension, shown by the straight dashed lines. The product of the two cutoffs is shown by the dashed curve, and the 309 genes that lie below that curve were deemed to be essential or nearly so in the LTEE environment. The area of each point is proportional to the gene length.
Fig. 3.
Fig. 3.
Predictions of the fitness effects of nonsynonymous mutation using the nonepistatic IND (blue) and epistatic DCA (red) models. (A) Ranks of the native amino acid score, when comparing all 21 possible variants (including amino acid deletion) in the same position. (B) Distribution over all proteins of the fraction of single-residue mutants with better scores (i.e., higher predicted fitnesses) than the native sequence. For each protein, the scores for all amino acid substitutions were estimated, and the fraction of mutations that improved on the native sequence was calculated. (C) Distribution of scores for both methods shows that a large fraction of possible mutations produce an unlikely sequence (positive score) that probably corresponds to a low-fitness protein. Only a small fraction (negative scores) is predicted to be beneficial.
Fig. 4.
Fig. 4.
Odds ratios in the three evolutionary regimes for AT to CG transversions, the typical signature of GO-defective (i.e., mutT) mutators. Odd ratios (shown on a log2 scale) show the importance of various factors in determining whether a particular base has experienced this type of mutation. For quantitative factors, interquartile odds ratios are shown. The green bar represents the synonymous versus nonsynonymous state. Dark blue bars show the impacts of amino acid changes based on their DCA score, IND score, and Grantham distance. Light blue bars represent factors at the gene level including gene expression level, Codon Adaptation Index (CAI), and essentiality. Pink and purple bars show the impact of the presence of a given base immediately before or after, respectively, the mutated base. Red bars represent the impact of the triplet in which the mutated base occurs. All background effects are measured on the leading strand. The intensity of each color is proportional to the log10-transformed P value of the test, as indicated on the scale bars. For the MAE and LTEE, only the GO-defective populations are included.
Fig. 5.
Fig. 5.
Odds ratios in the three evolutionary regimes for AT to GC transitions, the most pronounced base substitution signature of MMR-defective mutators. Colors and legends are as described in Fig. 4. For the MAE and LTEE, only the MMR-defective populations are included.
Fig. S4.
Fig. S4.
Odds ratios in the three evolutionary regimes for GC to AT transitions, the second-most pronounced base substitution signature of MMR-defective mutators. Colors and legends are as described in Fig. 4. For the MAE and LTEE, only the MMR-defective populations are included.
Fig. 6.
Fig. 6.
Comparison of contributions of a selection-related factor (DCA score measured on nonsynonymous mutations) and local mutational effects. (A) Distribution of DCA scores in mutated (blue) and unmutated (red) sites. A score of 0 suggests no impact of the mutation on fitness, a positive score suggests a deleterious effect, and a negative score suggests a beneficial effect. Distributions are shown for AT to CG transversions in the case of the GO-defective (i.e., mutT) MAE and LTEE populations, and for all mutations in the case of the strains that diverged in nature over millions of years. For the LTEE populations, results are also shown separately for the subset of mutations that affect genes that are essential under the LTEE conditions. (B) Contribution of selection- and mutation-related effects to the probability of nonsynonymous sites being mutated for the three mutation types enriched in mutator populations under each of the three evolutionary regimes. For the LTEE populations, results are again shown separately for those mutations affecting essential genes. The selection-related DCA score strongly influences genomic evolution in nature, whereas mutational biases have greater influences in the MAE and LTEE populations, even when the analysis is restricted to essential genes.
Fig. 7.
Fig. 7.
Correlation between odds ratios for factors associated with mutational patterns observed for different evolutionary regimes. (A) Correlation between GO-defective (i.e., mutT) mutator populations under the LTEE and MAE regimes. (B) Correlation between MMR-defective mutator populations under the LTEE and MAE regimes. (C) Correlation between GO-defective (i.e., mutT) mutator populations in the LTEE and lineages that diverged for millions of years in nature. (D) Correlation between MMR-defective mutator populations in the LTEE and lineages that diverged for millions of years in nature. In each panel, the blue line shows the overall correlation using all factors; the red line shows the correlation restricted to the local mutational biases; and the dotted line shows the hypothetical perfect correlation. Symbol colors are the same as used in Figs. 4–6.
Fig. S5.
Fig. S5.
Odds ratios for AT to CG transversions in the GO-defective mutators from the LTEE. Colors and legends are as described in Fig. 4. The label “LTEE” denotes all mutations found in the sequenced mutator genomes with mutT defects. The label “LTEE conserved >10K” refers to the subset of mutations that were found in at least two clones sampled at least 10,000 generations apart; mutations that appear twice independently in a population, based on the phylogeny, are counted as two different mutations. The label “LTEE singletons” refers to those mutations that were found in only one clone; they may include mutations that have not yet been strongly filtered by natural selection. All three sets show similar patterns.
Fig. S6.
Fig. S6.
Odds ratios for AT to GC transitions in the MMR-defective mutators from the LTEE. Colors, legends, and labels are as in Fig. S4.
Fig. S7.
Fig. S7.
Odds ratios for CG to TA transitions in the MMR-defective mutators from the LTEE. Colors, legends, and labels are as in Fig. S4.
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