Empirical determinants of adaptive mutations in yeast experimental evolution

High-throughput sequencing technologies have enabled expansion of the scope of genetic screens to identify mutations that underlie quantitative phenotypes, such as fitness improvements that occur during the course of experimental evolution. This new capability has allowed us to describe the relationship between fitness and genotype at a level never possible before, and ask deeper questions, such as how genome structure, available mutation spectrum, and other factors drive evolution. Here we combined functional genomics and experimental evolution to first map on a genome scale the distribution of potential beneficial mutations available as a first step to an evolving population and then compare these to the mutations actually observed in order to define the constraints acting upon evolution. We first constructed a single-step fitness landscape for the yeast genome by using barcoded gene deletion and overexpression collections, competitive growth in continuous culture, and barcode sequencing. By quantifying the relative fitness effects of thousands of single-gene amplifications or deletions simultaneously we revealed the presence of hundreds of accessible evolutionary paths. To determine the actual mutation spectrum used in evolution, we built a catalog of &gt;1000 mutations selected during experimental evolution. By combining both datasets, we were able to ask how and why evolution is constrained. We identified adaptive mutations in laboratory evolved populations, derived mutational signatures in a variety of conditions and ploidy states, and determined that half of the mutations accumulated positively affect cellular fitness. We also uncovered hundreds of potential beneficial mutations never observed in the mutational spectrum derived from the experimental evolution catalog and found that those adaptive mutations become accessible in the absence of the dominant adaptive solution. This comprehensive functional screen explored the set of potential adaptive mutations on one genetic background, and allows us for the first time at this scale to compare the mutational path with the actual, spontaneously derived spectrum of mutations.</jats:p

High-Throughput Identification of Adaptive Mutations in Experimentally Evolved Yeast Populations

<div><p>High-throughput sequencing has enabled genetic screens that can rapidly identify mutations that occur during experimental evolution. The presence of a mutation in an evolved lineage does not, however, constitute proof that the mutation is adaptive, given the well-known and widespread phenomenon of genetic hitchhiking, in which a non-adaptive or even detrimental mutation can co-occur in a genome with a beneficial mutation and the combined genotype is carried to high frequency by selection. We approximated the spectrum of possible beneficial mutations in <i>Saccharomyces cerevisiae</i> using sets of single-gene deletions and amplifications of almost all the genes in the <i>S</i>. <i>cerevisiae</i> genome. We determined the fitness effects of each mutation in three different nutrient-limited conditions using pooled competitions followed by barcode sequencing. Although most of the mutations were neutral or deleterious, ~500 of them increased fitness. We then compared those results to the mutations that actually occurred during experimental evolution in the same three nutrient-limited conditions. On average, ~35% of the mutations that occurred during experimental evolution were predicted by the systematic screen to be beneficial. We found that the distribution of fitness effects depended on the selective conditions. In the phosphate-limited and glucose-limited conditions, a large number of beneficial mutations of nearly equivalent, small effects drove the fitness increases. In the sulfate-limited condition, one type of mutation, the amplification of the high-affinity sulfate transporter, dominated. In the absence of that mutation, evolution in the sulfate-limited condition involved mutations in other genes that were not observed previously—but were predicted by the systematic screen. Thus, gross functional screens have the potential to predict and identify adaptive mutations that occur during experimental evolution.</p></div

Distribution of the fitness effects of single-gene amplifications and deletions.

<p>Fitness distributions of the five yeast collections in glucose-limited, sulfate-limited, and phosphate-limited continuous-growth conditions. The fitness of each strain is shown as a small line. The fitness distribution of the control collection is shown in grey. The thick black line represents the mean. Dashed grey lines indicate the cutoff of ±10% measured using the control collection.</p

Comparison of the mutational signature between haploid and diploid strains (E set).

Comparison of the mutational signature between haploid and diploid strains (E set).</p

Number of strains for which fitness was measured in each collection.

<p>Number of strains for which fitness was measured in each collection.</p

Experimental design for the pooled competition experiments.

<p>The proportion of each strain was measured every three to four generations during pooled competition assays, in which all the strains from a single collection were mixed together in equal proportions and grown in continuous culture for 20 generations (<b>A</b>). The frequency of each barcode at each time point was measured using the barseq method (<b>B</b>). The fitness of each strain was computed based on the measured frequencies (<b>C</b>).</p

Recurrently mutated genes reveal how evolution is constrained.

<p>(<b>A</b>) Repeatability of adaptation and parallelism at the gene level. Genes were classified by the number of mutations detected during Evolve and Resequence studies: 154 genes were mutated in more than one sample; 48 genes with recurrent mutations were mutated in more than one condition (small panel). (<b>B</b>) Enrichment of recurrently mutated genes with high-impact mutations compared with genes mutated in only one sample. Enrichment is not observed for moderate or low impact mutations, or modifiers. Error bars are 95% CIs.</p

Alternative beneficial mutations are selected in the absence of the main driver.

<p>(<b>A</b>) The copy number of <i>SUL1</i> was assessed using qPCR of samples taken from two independent experiments in which <i>SUL1</i> was not amplified (green and pink) and compared with previously published data from wild-type strains (in grey) [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006339#pgen.1006339.ref040" target="_blank">40</a>]. (<b>B</b>) The fitness coefficient as compared to the ancestral strain of population samples at generations 5, 50, and 200 and the fitness of two clones isolated at generation 200. (<b>C</b>) A small deletion (~5kb) encompassing four genes on chromosome IV was detected in a population from one experiment (between brackets); polyT sequences are present at the breakpoints. The colors of the boxes represent the orientation of the genes (yellow: gene on the Watson strand, grey: genes on the Crick strand). (<b>D</b>) Fitness coefficients of the deletion strains <i>ipt1</i>Δ and <i>snf11</i>Δ and those of both deletion strains complemented with <i>IPT1</i> or <i>SNF11</i> on a low-copy plasmid grown in sulfate limitation.</p

Mutational catalog (E set) subdivided by conditions, ploidy, and sample type.

<p>Mutational catalog (E set) subdivided by conditions, ploidy, and sample type.</p

Alternative accessible evolutionary paths.

<p><b>(A)</b> The fitness of beneficial mutations found (F) in Evolve and Resequence studies is significantly higher than the fitness of beneficial mutations not found (NF) in sulfate-limitation but not in glucose-limitation. The significance of the difference between the two boxplots for each condition was estimated using a Wilcoxon-ranked test. (<b>B)</b> Each point represents the fitness of a strain and the proportion of Evolve and Resequence samples with the corresponding gene mutated. <i>SUL1</i> dominates the fitness and mutational spectrum. Several mutations have a high fitness but have never been detected in Evolve and Resequence studies and might correspond to potential drivers of adaptation.</p