Identifying Targets of Selection in Laboratory Evolution Experiments.

Adaptive evolution navigates a balance between chance and determinism. The stochastic processes of mutation and drift generate phenotypic variation; however, once mutations reach an appreciable frequency in the population, their fate is governed by the deterministic action of selection, enriching for favorable genotypes and purging the less-favorable ones. The net result is that replicate populations will traverse similar-but not identical-pathways to higher fitness. This parallelism in evolutionary outcomes can be leveraged to identify the genes and pathways under selection. However, distinguishing between beneficial and neutral mutations is challenging because many beneficial mutations will be lost due to drift and clonal interference, and many neutral (and even deleterious) mutations will fix by hitchhiking. Here, we review the best practices that our laboratory uses to identify genetic targets of selection from next-generation sequencing data of evolved yeast populations. The general principles for identifying the mutations driving adaptation will apply more broadly.

Long-Term Adaptation to Galactose as a Sole Carbon Source Selects for Mutations Outside the Canonical GAL Pathway.

Galactose is a secondary fermentable sugar that requires specific regulatory and structural genes for its assimilation, which are under catabolite repression by glucose. When glucose is absent, the catabolic repression is attenuated, and the structural GAL genes are fully activated. In Saccharomyces cerevisiae, the GAL pathway is under selection in environments where galactose is present. However, it is unclear the adaptive strategies in response to long-term propagation in galactose as a sole carbon source in laboratory evolution experiments. Here, we performed a 4,000-generation evolution experiment using 48 diploid Saccharomyces cerevisiae populations to study adaptation in galactose. We show that fitness gains were greater in the galactose-evolved population than in identically evolved populations with glucose as a sole carbon source. Whole-genome sequencing of 96 evolved clones revealed recurrent de novo single nucleotide mutations in candidate targets of selection, copy number variations, and ploidy changes. We find that most mutations that improve fitness in galactose lie outside of the canonical GAL pathway. Reconstruction of specific evolved alleles in candidate target of selection, SEC23 and IRA1, showed a significant increase in fitness in galactose compared to glucose. In addition, most of our evolved populations (28/46; 61%) fixed aneuploidies on Chromosome VIII, suggesting a parallel adaptive amplification. Finally, we show greater loss of extrachromosomal elements in our glucose-evolved lineages compared with previous glucose evolution. Broadly, these data further our understanding of the evolutionary pressures that drive adaptation to less-preferred carbon sources.

Exploring a Local Genetic Interaction Network Using Evolutionary Replay Experiments.

Understanding how genes interact is a central challenge in biology. Experimental evolution provides a useful, but underutilized, tool for identifying genetic interactions, particularly those that involve non-loss-of-function mutations or mutations in essential genes. We previously identified a strong positive genetic interaction between specific mutations in KEL1 (P344T) and HSL7 (A695fs) that arose in an experimentally evolved Saccharomyces cerevisiae population. Because this genetic interaction is not phenocopied by gene deletion, it was previously unknown. Using "evolutionary replay" experiments, we identified additional mutations that have positive genetic interactions with the kel1-P344T mutation. We replayed the evolution of this population 672 times from six timepoints. We identified 30 populations where the kel1-P344T mutation reached high frequency. We performed whole-genome sequencing on these populations to identify genes in which mutations arose specifically in the kel1-P344T background. We reconstructed mutations in the ancestral and kel1-P344T backgrounds to validate positive genetic interactions. We identify several genetic interactors with KEL1, we validate these interactions by reconstruction experiments, and we show these interactions are not recapitulated by loss-of-function mutations. Our results demonstrate the power of experimental evolution to identify genetic interactions that are positive, allele specific, and not readily detected by other methods, shedding light on an underexplored region of the yeast genetic interaction network.

Adaptive genome duplication affects patterns of molecular evolution in Saccharomyces cerevisiae.

Genome duplications are important evolutionary events that impact the rate and spectrum of beneficial mutations and thus the rate of adaptation. Laboratory evolution experiments initiated with haploid Saccharomyces cerevisiae cultures repeatedly experience whole-genome duplication (WGD). We report recurrent genome duplication in 46 haploid yeast populations evolved for 4,000 generations. We find that WGD confers a fitness advantage, and this immediate fitness gain is accompanied by a shift in genomic and phenotypic evolution. The presence of ploidy-enriched targets of selection and structural variants reveals that autodiploids utilize adaptive paths inaccessible to haploids. We find that autodiploids accumulate recessive deleterious mutations, indicating an increased susceptibility for nonadaptive evolution. Finally, we report that WGD results in a reduced adaptation rate, indicating a trade-off between immediate fitness gains and long-term adaptability.

Hitchhiking and epistasis give rise to cohort dynamics in adapting populations.

Beneficial mutations are the driving force of adaptive evolution. In asexual populations, the identification of beneficial alleles is confounded by the presence of genetically linked hitchhiker mutations. Parallel evolution experiments enable the recognition of common targets of selection; yet these targets are inherently enriched for genes of large target size and mutations of large effect. A comprehensive study of individual mutations is necessary to create a realistic picture of the evolutionarily significant spectrum of beneficial mutations. Here we use a bulk-segregant approach to identify the beneficial mutations across 11 lineages of experimentally evolved yeast populations. We report that nearly 80% of detected mutations have no discernible effects on fitness and less than 1% are deleterious. We determine the distribution of driver and hitchhiker mutations in 31 mutational cohorts, groups of mutations that arise synchronously from low frequency and track tightly with one another. Surprisingly, we find that one-third of cohorts lack identifiable driver mutations. In addition, we identify intracohort synergistic epistasis between alleles of hsl7 and kel1, which arose together in a low-frequency lineage.

Pervasive genetic hitchhiking and clonal interference in forty evolving yeast populations.

The dynamics of adaptation determine which mutations fix in a population, and hence how reproducible evolution will be. This is central to understanding the spectra of mutations recovered in the evolution of antibiotic resistance, the response of pathogens to immune selection, and the dynamics of cancer progression. In laboratory evolution experiments, demonstrably beneficial mutations are found repeatedly, but are often accompanied by other mutations with no obvious benefit. Here we use whole-genome whole-population sequencing to examine the dynamics of genome sequence evolution at high temporal resolution in 40 replicate Saccharomyces cerevisiae populations growing in rich medium for 1,000 generations. We find pervasive genetic hitchhiking: multiple mutations arise and move synchronously through the population as mutational 'cohorts'. Multiple clonal cohorts are often present simultaneously, competing with each other in the same population. Our results show that patterns of sequence evolution are driven by a balance between these chance effects of hitchhiking and interference, which increase stochastic variation in evolutionary outcomes, and the deterministic action of selection on individual mutations, which favours parallel evolutionary solutions in replicate populations.

Crowded growth leads to the spontaneous evolution of semistable coexistence in laboratory yeast populations.

Identifying the mechanisms that create and maintain biodiversity is a central challenge in biology. Stable diversification of microbial populations often requires the evolution of differences in resource utilization. Alternatively, coexistence can be maintained by specialization to exploit spatial heterogeneity in the environment. Here, we report spontaneous diversification maintained by a related but distinct mechanism: crowding avoidance. During experimental evolution of laboratory Saccharomyces cerevisiae populations, we observed the repeated appearance of "adherent" (A) lineages able to grow as a dispersed film, in contrast to their crowded "bottom-dweller" (B) ancestors. These two types stably coexist because dispersal reduces interference competition for nutrients among kin, at the cost of a slower maximum growth rate. This tradeoff causes the frequencies of the two types to oscillate around equilibrium over the course of repeated cycles of growth, crowding, and dispersal. However, further coevolution of the A and B types can perturb and eventually destroy their coexistence over longer time scales. We introduce a simple mathematical model of this "semistable" coexistence, which explains the interplay between ecological and evolutionary dynamics. Because crowded growth generally limits nutrient access in biofilms, the mechanism we report here may be broadly important in maintaining diversity in these natural environments.

Experimental evolution in fungi: An untapped resource.

Historically, evolutionary biology has been considered an observational science. Examining populations and inferring evolutionary histories mold evolutionary theories. In contrast, laboratory evolution experiments make use of the amenability of traditional model organisms to study fundamental processes underlying evolution in real time in simple, but well-controlled, environments. With advances in high-throughput biology and next generation sequencing, it is now possible to propagate hundreds of parallel populations over thousands of generations and to quantify precisely the frequencies of various mutations over time. Experimental evolution combines the ability to simultaneously monitor replicate populations with the power to vary individual parameters to test specific evolutionary hypotheses, something that is impractical or infeasible in natural populations. Many labs are now conducting laboratory evolution experiments in nearly all model systems including viruses, bacteria, yeast, nematodes, and fruit flies. Among these systems, fungi occupy a unique niche: with a short generation time, small compact genomes, and sexual cycles, fungi are a particularly valuable and largely untapped resource for propelling future growth in the field of experimental evolution. Here, we describe the current state of fungal experimental evolution and why fungi are uniquely positioned to answer many of the outstanding questions in the field. We also review which fungal species are most well suited for experimental evolution.

The spectrum of adaptive mutations in experimental evolution.

A primary goal of recent work in experimental evolution is to probe the molecular basis of adaptation. This requires an understanding of the individual mutations in evolving populations: their identity, their physiological and fitness effects, and the interactions between them. The combination of high-throughput methods for laboratory evolution and next-generation sequencing methods now makes it possible to identify and quantify mutations in hundreds of replicate populations over thousands of generations, and to directly measure fitness effects and epistatic interactions. Many laboratories are now leveraging these tools to study the molecular basis of adaptation and the reproducibility of evolutionary outcomes across a variety of model systems. Genetic analyses on evolved populations are shedding light on the statistics of epistasis between evolved mutations. Here we review the current understanding of the spectrum of mutations observed across these systems, with a focus on epistatic interactions between beneficial mutations and constraints on evolutionary outcomes. We emphasize evolution in asexual microbes, where next generation sequencing methods have been widely applied.

Cytoduction preserves genetic diversity following plasmid transfer into pooled yeast libraries.

Much of our understanding of functional genomics derives from insights gained from large strain libraries including the yeast deletion collection, the GFP and TAP-tagged libraries, QTL mapping populations, among others [1-5]. A limitation of these libraries is that it is not easy to introduce reporters or make genetic perturbations to all strains in these collections. Tools such as Synthetic Genetic Arrays allow for the genetic manipulation of these libraries but are labor intensive and require specialized equipment for high throughput pinning [6]. Manipulating a diverse library en mass without losing diversity remains challenging. Ultimately, this limitation stems from the inefficiency of transformation, which is the standard method for genetic manipulation in yeast. Here, we develop a method that uses cytoduction (mating without nuclear fusion) to transfer plasmids directionally from a "Donor" to a diverse pool of "Recipient" strains. Because cytoduction uses mating, it is a natural process and is orders-of-magnitude more efficient than transformation, enabling the introduction of plasmids into high-diversity libraries with minimal impact on the diversity of the population.

The cost of gene expression underlies a fitness trade-off in yeast.

Natural selection optimizes an organism's genotype within the context of its environment. Adaptations to one environment can decrease fitness in another, revealing evolutionary trade-offs. Here, we show that the cost of gene expression underlies a trade-off between growth rate and mating efficiency in the yeast Saccharomyces cerevisiae. During asexual growth, mutations that eliminate the ability to mate provide an approximately 2% per-generation growth-rate advantage. Some strains, including most laboratory strains, carry an allele of GPA1 (an upstream component of the mating pathway) that increases mating efficiency by approximately 30% per round of mating at the cost of an approximately 1% per-generation growth-rate disadvantage. In addition to demonstrating a trade-off between growth rate and mating efficiency, our results illustrate differences in the selective pressures defining fitness in the laboratory versus the natural environment and show that selection, acting on the cost of gene expression, can optimize expression levels and promote gene loss.

Overdominant and partially dominant mutations drive clonal adaptation in diploid Saccharomyces cerevisiae.

Identification of adaptive targets in experimental evolution typically relies on extensive replication and genetic reconstruction. An alternative approach is to directly assay all mutations in an evolved clone by generating pools of segregants that contain random combinations of evolved mutations. Here, we apply this method to 6 Saccharomyces cerevisiae clones isolated from 4 diploid populations that were clonally evolved for 2,000 generations in rich glucose medium. Each clone contains 17-26 mutations relative to the ancestor. We derived intermediate genotypes between the founder and the evolved clones by bulk mating sporulated cultures of the evolved clones to a barcoded haploid version of the ancestor. We competed the resulting barcoded diploids en masse and quantified fitness in the experimental and alternative environments by barcode sequencing. We estimated average fitness effects of evolved mutations using barcode-based fitness assays and whole-genome sequencing for a subset of segregants. In contrast to our previous work with haploid evolved clones, we find that diploids carry fewer beneficial mutations, with modest fitness effects (up to 5.4%) in the environment in which they arose. In agreement with theoretical expectations, reconstruction experiments show that all mutations with a detectable fitness effect manifest some degree of dominance over the ancestral allele, and most are overdominant. Genotypes with lower fitness effects in alternative environments allowed us to identify conditions that drive adaptation in our system.

Evolutionary rescue of phosphomannomutase deficiency in yeast models of human disease.

The most common cause of human congenital disorders of glycosylation (CDG) are mutations in the phosphomannomutase gene PMM2, which affect protein N-linked glycosylation. The yeast gene SEC53 encodes a homolog of human PMM2. We evolved 384 populations of yeast harboring one of two human-disease-associated alleles, sec53-V238M and sec53-F126L, or wild-type SEC53. We find that after 1000 generations, most populations compensate for the slow-growth phenotype associated with the sec53 human-disease-associated alleles. Through whole-genome sequencing we identify compensatory mutations, including known SEC53 genetic interactors. We observe an enrichment of compensatory mutations in other genes whose human homologs are associated with Type 1 CDG, including PGM1, which encodes the minor isoform of phosphoglucomutase in yeast. By genetic reconstruction, we show that evolved pgm1 mutations are dominant and allele-specific genetic interactors that restore both protein glycosylation and growth of yeast harboring the sec53-V238M allele. Finally, we characterize the enzymatic activity of purified Pgm1 mutant proteins. We find that reduction, but not elimination, of Pgm1 activity best compensates for the deleterious phenotypes associated with the sec53-V238M allele. Broadly, our results demonstrate the power of experimental evolution as a tool for identifying genes and pathways that compensate for human-disease-associated alleles.

Overdominant Mutations Restrict Adaptive Loss of Heterozygosity at Linked Loci.

Loss of heterozygosity is a common mode of adaptation in asexual diploid populations. Because mitotic recombination frequently extends the full length of a chromosome arm, the selective benefit of loss of heterozygosity may be constrained by linked heterozygous mutations. In a previous laboratory evolution experiment with diploid yeast, we frequently observed homozygous mutations in the WHI2 gene on the right arm of Chromosome XV. However, when heterozygous mutations arose in the STE4 gene, another common target on Chromosome XV, loss of heterozygosity at WHI2 was not observed. Here, we show that mutations at WHI2 are partially dominant and that mutations at STE4 are overdominant. We test whether beneficial heterozygous mutations at these two loci interfere with one another by measuring loss of heterozygosity at WHI2 over 1,000 generations for ∼300 populations that differed initially only at STE4 and WHI2. We show that the presence of an overdominant mutation in STE4 reduces, but does not eliminate, loss of heterozygosity at WHI2. By sequencing 40 evolved clones, we show that populations with linked overdominant and partially dominant mutations show less parallelism at the gene level, more varied evolutionary outcomes, and increased rates of aneuploidy. Our results show that the degree of dominance and the phasing of heterozygous beneficial mutations can constrain loss of heterozygosity along a chromosome arm, and that conflicts between partially dominant and overdominant mutations can affect evolutionary outcomes.

Adaptive evolution of nontransitive fitness in yeast.

A common misconception is that evolution is a linear 'march of progress', where each organism along a line of descent is more fit than all those that came before it. Rejecting this misconception implies that evolution is nontransitive: a series of adaptive events will, on occasion, produce organisms that are less fit compared to a distant ancestor. Here we identify a nontransitive evolutionary sequence in a 1000-generation yeast evolution experiment. We show that nontransitivity arises due to adaptation in the yeast nuclear genome combined with the stepwise deterioration of an intracellular virus, which provides an advantage over viral competitors within host cells. Extending our analysis, we find that nearly half of our ~140 populations experience multilevel selection, fixing adaptive mutations in both the nuclear and viral genomes. Our results provide a mechanistic case-study for the adaptive evolution of nontransitivity due to multilevel selection in a 1000-generation host/virus evolution experiment.

It is widely accepted in biology that all life on Earth gradually evolved over billions of years from a single ancestor. Yet, there is still much about this process that is not fully understood. Evolution is often thought of as progressing in a linear fashion, with each new generation being better adapted to its environment than the last. But it has been proposed that evolution is also nontransitive: this means even if each generation is 'fitter' than its immediate predecessor, these series of adaptive changes will occasionally result in organisms that are less fit than their distant ancestors. Laboratory experiments of evolution are a good way to test evolutionary theories because they allow researchers to create scenarios that are impossible to observe in natural populations, such as an organism competing against its extinct ancestors. Buskirk et al. set up such an experiment using yeast to determine whether nontransitive effects can be observed in the direct descendants of an organism. At the start of the experiment, the yeast cells were host to a non-infectious 'killer' virus that is common among yeast. Cells containing the virus produce a toxin that destroys other yeast that lack the virus. The populations of yeast were given a nutrient-rich broth in which to grow and subjected to a simple evolutionary pressure: to grow fast, which limits the amount of resources available. As the yeast evolved, they gained beneficial genetic mutations that allowed them to outcompete their neighbors, and they passed these traits down to their descendants. Some of these mutations occurred not in the yeast genome, but in the genome of the killer virus, and this stopped the yeast infected with the virus from producing the killer toxin. Over time, other mutations resulted in the infected yeast no longer being immune to the toxin. Thus, when Buskirk et al. pitted these yeast against their distant ancestors, the new generation were destroyed by the toxins the older generation produced. These findings provide the first experimental evidence for nontransitivity along a line of descent. The results have broad implications for our understanding of how evolution works, casting doubts over the idea that evolution always involves a direct progression towards new, improved traits.

Estimating the per-base-pair mutation rate in the yeast Saccharomyces cerevisiae.

Although mutation rates are a key determinant of the rate of evolution they are difficult to measure precisely and global mutations rates (mutations per genome per generation) are often extrapolated from the per-base-pair mutation rate assuming that mutation rate is uniform across the genome. Using budding yeast, we describe an improved method for the accurate calculation of mutation rates based on the fluctuation assay. Our analysis suggests that the per-base-pair mutation rates at two genes differ significantly (3.80x10(-10) at URA3 and 6.44x10(-10) at CAN1) and we propose a definition for the effective target size of genes (the probability that a mutation inactivates the gene) that acknowledges that the mutation rate is nonuniform across the genome.

Detecting genetic interactions using parallel evolution in experimental populations.

Eukaryotic genomes contain thousands of genes organized into complex and interconnected genetic interaction networks. Most of our understanding of how genetic variation affects these networks comes from quantitative-trait loci mapping and from the systematic analysis of double-deletion (or knockdown) mutants, primarily in the yeast Saccharomyces cerevisiae. Evolve and re-sequence experiments are an alternative approach for identifying novel functional variants and genetic interactions, particularly between non-loss-of-function mutations. These experiments leverage natural selection to obtain genotypes with functionally important variants and positive genetic interactions. However, no systematic methods for detecting genetic interactions in these data are yet available. Here, we introduce a computational method based on the idea that variants in genes that interact will co-occur in evolved genotypes more often than expected by chance. We apply this method to a previously published yeast experimental evolution dataset. We find that genetic targets of selection are distributed non-uniformly among evolved genotypes, indicating that genetic interactions had a significant effect on evolutionary trajectories. We identify individual gene pairs with a statistically significant genetic interaction score. The strongest interaction is between genes TRK1 and PHO84, genes that have not been reported to interact in previous systematic studies. Our work demonstrates that leveraging parallelism in experimental evolution is useful for identifying genetic interactions that have escaped detection by other methods. This article is part of the theme issue 'Convergent evolution in the genomics era: new insights and directions'.

Integrative Meta-Assembly Pipeline (IMAP): Chromosome-level genome assembler combining multiple de novo assemblies.

BACKGROUND: Genomic data have become major resources to understand complex mechanisms at fine-scale temporal and spatial resolution in functional and evolutionary genetic studies, including human diseases, such as cancers. Recently, a large number of whole genomes of evolving populations of yeast (Saccharomyces cerevisiae W303 strain) were sequenced in a time-dependent manner to identify temporal evolutionary patterns. For this type of study, a chromosome-level sequence assembly of the strain or population at time zero is required to compare with the genomes derived later. However, there is no fully automated computational approach in experimental evolution studies to establish the chromosome-level genome assembly using unique features of sequencing data. METHODS AND RESULTS: In this study, we developed a new software pipeline, the integrative meta-assembly pipeline (IMAP), to build chromosome-level genome sequence assemblies by generating and combining multiple initial assemblies using three de novo assemblers from short-read sequencing data. We significantly improved the continuity and accuracy of the genome assembly using a large collection of sequencing data and hybrid assembly approaches. We validated our pipeline by generating chromosome-level assemblies of yeast strains W303 and SK1, and compared our results with assemblies built using long-read sequencing and various assembly evaluation metrics. We also constructed chromosome-level sequence assemblies of S. cerevisiae strain Sigma1278b, and three commonly used fungal strains: Aspergillus nidulans A713, Neurospora crassa 73, and Thielavia terrestris CBS 492.74, for which long-read sequencing data are not yet available. Finally, we examined the effect of IMAP parameters, such as reference and resolution, on the quality of the final assembly of the yeast strains W303 and SK1. CONCLUSIONS: We developed a cost-effective pipeline to generate chromosome-level sequence assemblies using only short-read sequencing data. Our pipeline combines the strengths of reference-guided and meta-assembly approaches. Our pipeline is available online at http://github.com/jkimlab/IMAP including a Docker image, as well as a Perl script, to help users install the IMAP package, including several prerequisite programs. Users can use IMAP to easily build the chromosome-level assembly for the genome of their interest.