Molecular specificity, convergence and constraint shape adaptive evolution in nutrient-poor environments

Whi2 is a conserved negative regulator of TORC1 in response to low amino acids

Yeast WHI2 was originally identified in a genetic screen for regulators of cell cycle arrest and later suggested to function in general stress responses. However, the function of Whi2 is unknown. Whi2 has predicted structure and sequence similarity to human KCTD family proteins, which have been implicated in several cancers and are causally associated with neurological disorders but are largely uncharacterized. The identification of conserved functions between these yeast and human proteins may provide insight into disease mechanisms. We report that yeast WHI2 is a new negative regulator of TORC1 required to suppress TORC1 activity and cell growth specifically in response to low amino acids. In contrast to current opinion, WHI2 is dispensable for TORC1 inhibition in low glucose. The only widely conserved mechanism that actively suppresses both yeast and mammalian TORC1 specifically in response to low amino acids is the conserved SEACIT/GATOR1 complex that inactivates the TORC1-activating RAG-like GTPases. Unexpectedly, Whi2 acts independently and simultaneously with these established GATOR1-like Npr2-Npr3-Iml1 and RAG-like Gtr1-Gtr2 complexes, and also acts independently of the PKA pathway. Instead, Whi2 inhibits TORC1 activity through its binding partners, protein phosphatases Psr1 and Psr2, which were previously thought to only regulate amino acid levels downstream of TORC1. Furthermore, the ability to suppress TORC1 is conserved in the SKP1/BTB/POZ domain-containing, Whi2-like human protein KCTD11 but not other KCTD family members tested.

Border collies of the genome: domestication of an autonomous retrovirus-like transposon

Retrotransposons often spread rapidly through eukaryotic genomes until they are neutralized by host-mediated silencing mechanisms, reduced by recombination and mutation, and lost or transformed into benevolent entities. But the Ty1 retrotransposon appears to have been domesticated to guard the genome of Saccharomyces cerevisiae.

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.

Whole genome duplications—the simultaneous doubling of each chromosome—can have a profound influence on evolution. Evidence of ancient whole genome duplications can be seen in most modern genomes. Experimental evolution, the long-term propagation of organisms under well-controlled laboratory conditions, yields valuable insight into the processes of adaptation and genome evolution. One interesting, and common, outcome of laboratory evolution experiments that start with haploid yeast populations is the emergence of diploid lineages via whole genome duplication. We show that, under our laboratory conditions, whole genome duplication provides a direct fitness benefit, and we identify several consequences of whole genome duplication on adaptation. Following whole-genome duplication, the rate of adaptation slows, the biological targets of selection change, and aneuploidies, copy-number variants and recessive lethal mutations accumulate. By studying the effect of whole genome duplication on adaptation, we can better understand how selection acts on ploidy, a fundamental biological parameter that varies considerably across life.

Genome plasticity in response to stress in Tetrahymena thermophila: selective and reversible chromosome amplification and paralogous expansion of metallothionein genes

Extreme stress situations can induce genetic variations including genome reorganization. In ciliates like Tetrahymena thermophila, the approximately 45-fold ploidy of the somatic macronucleus may enable adaptive responses that depend on genome plasticity. To identify potential genome-level adaptations related to metal toxicity, we isolated three Tetrahymena thermophila strains after an extended adaptation period to extreme metal concentrations (Cd²⁺ , Cu²⁺ or Pb²⁺ ). In the Cd-adapted strain, we found a approximately five-fold copy number increase of three genes located in the same macronuclear chromosome, including two metallothionein genes, MTT1 and MTT3. The apparent amplification of this macronuclear chromosome was reversible and reproducible, depending on the presence of environmental metal. We also analysed three knockout (KO) and/or knockdown (KD) strains for MTT1 and/or MTT5. In the MTT5KD strain, we found at least two new genes arising from paralogous expansion of MTT1, which encode truncated variants of MTT1. The expansion can be explained by a model based on somatic recombination between MTT1 genes on pairs of macronuclear chromosomes. At least two of the new paralogs are transcribed and upregulated in response to Cd²⁺ . Altogether, we have thus identified two distinct mechanisms, both involving genomic plasticity in the polyploid macronucleus that may represent adaptive responses to metal-related stress.

Different adaptive strategies in E. coli populations evolving under macronutrient limitation and metal ion limitation

BACKGROUND

Adaptive responses to nutrient limitation involve mutations that increase the efficiency of usage or uptake of the limiting nutrient. However, starvation of different nutrients has contrasting effects on physiology, resulting in different evolutionary responses. Most studies performed to understand these evolutionary responses have focused only on macronutrient limitation. Hence our understanding of adaptation under limitation of other forms of nutrients is limited. In this study, we compared the evolutionary response in populations evolving under growth-limiting conditions for a macronutrient and a major cation.

RESULTS

We evolved eight populations of E. coli in nutrient-limited chemostats for 400 generations to identify the genetic basis of the mechanisms involved in efficient usage of two nutrients: nitrogen and magnesium. Our population genomic sequencing work, based on this study and previous work, allowed us to identify targets of selection under these nutrient limiting conditions. Global transcriptional regulators glnGL were targets of selection under nitrogen starvation, while proteins involved in outer-membrane biogenesis (genes from the lpt operon) were targets of selection under magnesium starvation. The protein involved in cell-cycle arrest (yhaV) was a target of selection in both environments. We re-constructed specific mutants to analyze the effect of individual mutations on fitness in nutrient limiting conditions in chemostats and in batch cultures. We further demonstrated that adaptation to nitrogen starvation proceeds via a nutrient specific mechanism, while that to magnesium starvation involves a more general mechanism.

CONCLUSIONS

Our results show two different forms of adaptive strategies under limitation of nutrients that effect cellular physiology in different ways. Adaptation to nitrogen starvation proceeds by upregulation of transcriptional regulator glnG and subsequently of transporter protein amtB, both of which results in increased nitrogen scavenging ability of the cell. On the other hand, adaptation to magnesium starvation proceeds via the restructuring of the cell outer-membrane, allowing magnesium to be redistributed to other biological processes. Also, adaptation to the chemostat environment involves selection for loss of function mutations in genes that under nutrient-limiting conditions interfere with continuous growth.

An incoherent feedforward loop facilitates adaptive tuning of gene expression

We studied adaptive evolution of gene expression using long-term experimental evolution of Saccharomyces cerevisiae in ammonium-limited chemostats. We found repeated selection for non-synonymous variation in the DNA binding domain of the transcriptional activator, GAT1, which functions with the repressor, DAL80 in an incoherent type-1 feedforward loop (I1-FFL) to control expression of the high affinity ammonium transporter gene, MEP2. Missense mutations in the DNA binding domain of GAT1 reduce its binding to the GATAA consensus sequence. However, we show experimentally, and using mathematical modeling, that decreases in GAT1 binding result in increased expression of MEP2 as a consequence of properties of I1-FFLs. Our results show that I1-FFLs, one of the most commonly occurring network motifs in transcriptional networks, can facilitate adaptive tuning of gene expression through modulation of transcription factor binding affinities. Our findings highlight the importance of gene regulatory architectures in the evolution of gene expression.

Network-Based Identification of Adaptive Pathways in Evolved Ethanol-Tolerant Bacterial Populations

Efficient production of ethanol for use as a renewable fuel requires organisms with a high level of ethanol tolerance. However, this trait is complex and increased tolerance therefore requires mutations in multiple genes and pathways. Here, we use experimental evolution for a system-level analysis of adaptation of Escherichia coli to high ethanol stress. As adaptation to extreme stress often results in complex mutational data sets consisting of both causal and noncausal passenger mutations, identifying the true adaptive mutations in these settings is not trivial. Therefore, we developed a novel method named IAMBEE (Identification of Adaptive Mutations in Bacterial Evolution Experiments). IAMBEE exploits the temporal profile of the acquisition of mutations during evolution in combination with the functional implications of each mutation at the protein level. These data are mapped to a genome-wide interaction network to search for adaptive mutations at the level of pathways. The 16 evolved populations in our data set together harbored 2,286 mutated genes with 4,470 unique mutations. Analysis by IAMBEE significantly reduced this number and resulted in identification of 90 mutated genes and 345 unique mutations that are most likely to be adaptive. Moreover, IAMBEE not only enabled the identification of previously known pathways involved in ethanol tolerance, but also identified novel systems such as the AcrAB-TolC efflux pump and fatty acids biosynthesis and even allowed to gain insight into the temporal profile of adaptation to ethanol stress. Furthermore, this method offers a solid framework for identifying the molecular underpinnings of other complex traits as well.

Hidden Complexity of Yeast Adaptation under Simple Evolutionary Conditions

Few studies have "quantitatively" probed how adaptive mutations result in increased fitness. Even in simple microbial evolution experiments, with full knowledge of the underlying mutations and specific growth conditions, it is challenging to determine where within a growth-saturation cycle those fitness gains occur. A common implicit assumption is that most benefits derive from an increased exponential growth rate. Here, we instead show that, in batch serial transfer experiments, adaptive mutants' fitness gains can be dominated by benefits that are accrued in one growth cycle, but not realized until the next growth cycle. For thousands of evolved clones (most with only a single mutation), we systematically varied the lengths of fermentation, respiration, and stationary phases to assess how their fitness, as measured by barcode sequencing, depends on these phases of the growth-saturation-dilution cycles. These data revealed that, whereas all adaptive lineages gained similar and modest benefits from fermentation, most of the benefits for the highest fitness mutants came instead from the time spent in respiration. From monoculture and high-resolution pairwise fitness competition experiments for a dozen of these clones, we determined that the benefits "accrued" during respiration are only largely "realized" later as a shorter duration of lag phase in the following growth cycle. These results reveal hidden complexities of the adaptive process even under ostensibly simple evolutionary conditions, in which fitness gains can accrue during time spent in a growth phase with little cell division, and reveal that the memory of those gains can be realized in the subsequent growth cycle.

Genomics of Adaptation Depends on the Rate of Environmental Change in Experimental Yeast Populations

The rate of directional environmental change may have profound consequences for evolutionary dynamics and outcomes. Yet, most evolution experiments impose a sudden large change in the environment, after which the environment is kept constant. We previously cultured replicate Saccharomyces cerevisiae populations for 500 generations in the presence of either gradually increasing or constant high concentrations of the heavy metals cadmium, nickel, and zinc. Here, we investigate how each of these treatments affected genomic evolution. Whole-genome sequencing of evolved clones revealed that adaptation occurred via a combination of SNPs, small indels, and whole-genome duplications and other large-scale structural changes. In contrast to some theoretical predictions, gradual and abrupt environmental change caused similar numbers of genomic changes. For cadmium, which is toxic already at comparatively low concentrations, mutations in the same genes were used for adaptation to both gradual and abrupt increase in concentration. Conversely, for nickel and zinc, which are toxic at high concentrations only, mutations in different genes were used for adaptation depending on the rate of change. Moreover, evolution was more repeatable following a sudden change in the environment, particularly for nickel and zinc. Our results show that the rate of environmental change and the nature of the selection pressure are important drivers of evolutionary dynamics and outcomes, which has implications for a better understanding of societal problems such as climate change and pollution.

The Influence of Polyploidy on the Evolution of Yeast Grown in a Sub-Optimal Carbon Source

Polyploidization events have occurred during the evolution of many fungi, plant, and animal species and are thought to contribute to speciation and tumorigenesis, however little is known about how ploidy level contributes to adaptation at the molecular level. Here we integrate whole genome sequencing, RNA expression analysis, and relative fitness of ∼100 evolved clones at three ploidy levels. Independent haploid, diploid, and tetraploid populations were grown in a low carbon environment for 250 generations. We demonstrate that the key adaptive mutation in the evolved clones is predicted by a gene expression signature of just five genes. All of the adaptive mutations identified encompass a narrow set of genes, however the tetraploid clones gain a broader spectrum of adaptive mutations than haploid or diploid clones. While many of the adaptive mutations occur in genes that encode proteins with known roles in glucose sensing and transport, we discover mutations in genes with no canonical role in carbon utilization (IPT1 and MOT3), as well as identify novel dominant mutations in glucose signal transducers thought to only accumulate recessive mutations in carbon limited environments (MTH1 and RGT1). We conclude that polyploid cells explore more genotypic and phenotypic space than lower ploidy cells. Our study provides strong evidence for the beneficial role of polyploidization events that occur during the evolution of many species and during tumorigenesis.

Local Fitness Landscapes Predict Yeast Evolutionary Dynamics in Directionally Changing Environments

The fitness landscape is a concept that is widely used for understanding and predicting evolutionary adaptation. The topography of the fitness landscape depends critically on the environment, with potentially far-reaching consequences for evolution under changing conditions. However, few studies have assessed directly how empirical fitness landscapes change across conditions, or validated the predicted consequences of such change. We previously evolved replicate yeast populations in the presence of either gradually increasing, or constant high, concentrations of the heavy metals cadmium (Cd), nickel (Ni), and zinc (Zn), and analyzed their phenotypic and genomic changes. Here, we reconstructed the local fitness landscapes underlying adaptation to each metal by deleting all repeatedly mutated genes both by themselves and in combination. Fitness assays revealed that the height, and/or shape, of each local fitness landscape changed considerably across metal concentrations, with distinct qualitative differences between unconditionally (Cd) and conditionally toxic metals (Ni and Zn). This change in topography had particularly crucial consequences in the case of Ni, where a substantial part of the individual mutational fitness effects changed in sign across concentrations. Based on the Ni landscape analyses, we made several predictions about which mutations had been selected when during the evolution experiment. Deep sequencing of population samples from different time points generally confirmed these predictions, demonstrating the power of landscape reconstruction analyses for understanding and ultimately predicting evolutionary dynamics, even under complex scenarios of environmental change.

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.

Experimental Evolution Reveals Favored Adaptive Routes to Cell Aggregation in Yeast

Yeast flocculation is a community-building cell aggregation trait that is an important mechanism of stress resistance and a useful phenotype for brewers; however, it is also a nuisance in many industrial processes, in clinical settings, and in the laboratory. Chemostat-based evolution experiments are impaired by inadvertent selection for aggregation, which we observe in 35% of populations. These populations provide a testing ground for understanding the breadth of genetic mechanisms Saccharomyces cerevisiae uses to flocculate, and which of those mechanisms provide the biggest adaptive advantages. In this study, we employed experimental evolution as a tool to ask whether one or many routes to flocculation are favored, and to engineer a strain with reduced flocculation potential. Using a combination of whole genome sequencing and bulk segregant analysis, we identified causal mutations in 23 independent clones that had evolved cell aggregation during hundreds of generations of chemostat growth. In 12 of those clones, we identified a transposable element insertion in the promoter region of known flocculation gene FLO1, and, in an additional five clones, we recovered loss-of-function mutations in transcriptional repressor TUP1, which regulates FLO1 and other related genes. Other causal mutations were found in genes that have not been previously connected to flocculation. Evolving a flo1 deletion strain revealed that this single deletion reduces flocculation occurrences to 3%, and demonstrated the efficacy of using experimental evolution as a tool to identify and eliminate the primary adaptive routes for undesirable traits.

Seeking Goldilocks During Evolution of Drug Resistance

Speciation can occur when a population is split and the resulting subpopulations evolve independently, accumulating mutations over time that make them incompatible with one another. It is thought that such incompatible mutations, known as Bateson–Dobzhansky–Muller (BDM) incompatibilities, may arise when the two populations face different environments, which impose different selective pressures. However, a new study in PLOS Biology by Ono et al. finds that the first-step mutations selected in yeast populations evolving in parallel in the presence of the antifungal drug nystatin are frequently incompatible with one another. This incompatibility is environment dependent, such that the combination of two incompatible alleles can become advantageous under increasing drug concentrations. This suggests that the activity for the affected pathway must have an optimum level, the value of which varies according to the drug concentration. It is likely that many biological processes similarly have an optimum under a given environment and many single-step adaptive ways to reach it; thus, not only should BDM incompatibilities commonly arise during parallel evolution, they might be virtually inevitable, as the combination of two such steps is likely to overshoot the optimum.

Stability of Cross-Feeding Polymorphisms in Microbial Communities

Cross-feeding, a relationship wherein one organism consumes metabolites excreted by another, is a ubiquitous feature of natural and clinically-relevant microbial communities and could be a key factor promoting diversity in extreme and/or nutrient-poor environments. However, it remains unclear how readily cross-feeding interactions form, and therefore our ability to predict their emergence is limited. In this paper we developed a mathematical model parameterized using data from the biochemistry and ecology of an E. coli cross-feeding laboratory system. The model accurately captures short-term dynamics of the two competitors that have been observed empirically and we use it to systematically explore the stability of cross-feeding interactions for a range of environmental conditions. We find that our simple system can display complex dynamics including multi-stable behavior separated by a critical point. Therefore whether cross-feeding interactions form depends on the complex interplay between density and frequency of the competitors as well as on the concentration of resources in the environment. Moreover, we find that subtly different environmental conditions can lead to dramatically different results regarding the establishment of cross-feeding, which could explain the apparently unpredictable between-population differences in experimental outcomes. We argue that mathematical models are essential tools for disentangling the complexities of cross-feeding interactions.

Multiple Transcript Properties Related to Translation Affect mRNA Degradation Rates in Saccharomyces cerevisiae

Degradation of mRNA contributes to variation in transcript abundance. Studies of individual mRNAs have shown that both cis and trans factors affect mRNA degradation rates. However, the factors underlying transcriptome-wide variation in mRNA degradation rates are poorly understood. We investigated the contribution of different transcript properties to transcriptome-wide degradation rate variation in the budding yeast, Saccharomyces cerevisiae, using multiple regression analysis. We find that multiple transcript properties are significantly associated with variation in mRNA degradation rates, and that a model incorporating these properties explains ∼50% of the genome-wide variance. Predictors of mRNA degradation rates include transcript length, ribosome density, biased codon usage, and GC content of the third position in codons. To experimentally validate these factors, we studied individual transcripts expressed from identical promoters. We find that decreasing ribosome density by mutating the first translational start site of a transcript increases its degradation rate. Using coding sequence variants of green fluorescent protein (GFP) that differ only at synonymous sites, we show that increased GC content of the third position of codons results in decreased rates of mRNA degradation. Thus, in steady-state conditions, a large fraction of genome-wide variation in mRNA degradation rates is determined by inherent properties of transcripts, many of which are related to translation, rather than specific regulatory mechanisms.

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

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 Saccharomyces cerevisiae using sets of single-gene deletions and amplifications of almost all the genes in the S. cerevisiae 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.

Experimental evolution allows us to observe evolution in real time. New advances in genome sequencing make it trivial to discover the mutations that have arisen in evolved cultures; however, linking those mutations to particular adaptive traits remains difficult. We evaluated the fitness impacts of thousands of single-gene losses and amplifications in yeast. We discovered that only a fraction of the hundreds of possible beneficial mutations were actually detected in evolution experiments performed previously. Our results provide evidence that 35% of the mutations identified in experimentally evolved populations are advantageous and that the distribution of beneficial fitness effects depends on the genetic background and the selective conditions. Furthermore, we show that it is possible to select for alternative mutations that improve fitness by blocking particularly high-fitness routes to adaptation.

Development of a Comprehensive Genotype-to-Fitness Map of Adaptation-Driving Mutations in Yeast

Adaptive evolution plays a large role in generating the phenotypic diversity observed in nature, yet current methods are impractical for characterizing the molecular basis and fitness effects of large numbers of individual adaptive mutations. Here, we used a DNA barcoding approach to generate the genotype-to-fitness map for adaptation-driving mutations from a Saccharomyces cerevisiae population experimentally evolved by serial transfer under limiting glucose. We isolated and measured the fitness of thousands of independent adaptive clones and sequenced the genomes of hundreds of clones. We found only two major classes of adaptive mutations: self-diploidization and mutations in the nutrient-responsive Ras/PKA and TOR/Sch9 pathways. Our large sample size and precision of measurement allowed us to determine that there are significant differences in fitness between mutations in different genes, between different paralogs, and even between different classes of mutations within the same gene.

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.

Hard-Wired Control of Bacterial Processes by Chromosomal Gene Location

Bacterial processes, such as stress responses and cell differentiation, are controlled at many different levels. While some factors, such as transcriptional regulation, are well appreciated, the importance of chromosomal gene location is often underestimated or even completely neglected. A combination of environmental parameters and the chromosomal location of a gene determine how many copies of its DNA are present at a given time during the cell cycle. Here, we review bacterial processes that rely, completely or partially, on the chromosomal location of involved genes and their fluctuating copy numbers. Special attention will be given to the several different ways in which these copy-number fluctuations can be used for bacterial cell fate determination or coordination of interdependent processes in a bacterial cell.

The thermostability and specificity of ancient proteins

Were ancient proteins systematically different than modern proteins? The answer to this question is profoundly important, shaping how we understand the origins of protein biochemical, biophysical, and functional properties. Ancestral sequence reconstruction (ASR), a phylogenetic approach to infer the sequences of ancestral proteins, may reveal such trends. We discuss two proposed trends: a transition from higher to lower thermostability and a tendency for proteins to acquire higher specificity over time. We review the evidence for elevated ancestral thermostability and discuss its possible origins in a changing environmental temperature and/or reconstruction bias. We also conclude that there is, as yet, insufficient data to support a trend from promiscuity to specificity. Finally, we propose future work to understand these proposed evolutionary trends.

Network-Based Analysis of eQTL Data to Prioritize Driver Mutations

In clonal systems, interpreting driver genes in terms of molecular networks helps understanding how these drivers elicit an adaptive phenotype. Obtaining such a network-based understanding depends on the correct identification of driver genes. In clonal systems, independent evolved lines can acquire a similar adaptive phenotype by affecting the same molecular pathways, a phenomenon referred to as parallelism at the molecular pathway level. This implies that successful driver identification depends on interpreting mutated genes in terms of molecular networks. Driver identification and obtaining a network-based understanding of the adaptive phenotype are thus confounded problems that ideally should be solved simultaneously. In this study, a network-based eQTL method is presented that solves both the driver identification and the network-based interpretation problem. As input the method uses coupled genotype-expression phenotype data (eQTL data) of independently evolved lines with similar adaptive phenotypes and an organism-specific genome-wide interaction network. The search for mutational consistency at pathway level is defined as a subnetwork inference problem, which consists of inferring a subnetwork from the genome-wide interaction network that best connects the genes containing mutations to differentially expressed genes. Based on their connectivity with the differentially expressed genes, mutated genes are prioritized as driver genes. Based on semisynthetic data and two publicly available data sets, we illustrate the potential of the network-based eQTL method to prioritize driver genes and to gain insights in the molecular mechanisms underlying an adaptive phenotype. The method is available at http://bioinformatics.intec.ugent.be/phenetic_eqtl/index.html

Experimental Evolution and Resequencing Analysis of Yeast

Experimental evolution of microbes is a powerful tool to study adaptation to strong selection, the mechanism of evolution and the development of new traits. The development of high-throughput sequencing methods has given researchers a new ability to cheaply and easily identify mutations genome wide that are selected during the course of experimental evolution. Here we provide a protocol for conducting experimental evolution of yeast using chemostats, including fitness measurement and whole genome sequencing of evolved clones or populations collected during the experiment. Depending on the number of generations appropriate for the experiment, the number of samples tested and the sequencing platform, this protocol takes from 1 month to several months to be completed, with the possibility of processing several strains or mutants at once.

Adaptation to High Ethanol Reveals Complex Evolutionary Pathways

Tolerance to high levels of ethanol is an ecologically and industrially relevant phenotype of microbes, but the molecular mechanisms underlying this complex trait remain largely unknown. Here, we use long-term experimental evolution of isogenic yeast populations of different initial ploidy to study adaptation to increasing levels of ethanol. Whole-genome sequencing of more than 30 evolved populations and over 100 adapted clones isolated throughout this two-year evolution experiment revealed how a complex interplay of de novo single nucleotide mutations, copy number variation, ploidy changes, mutator phenotypes, and clonal interference led to a significant increase in ethanol tolerance. Although the specific mutations differ between different evolved lineages, application of a novel computational pipeline, PheNetic, revealed that many mutations target functional modules involved in stress response, cell cycle regulation, DNA repair and respiration. Measuring the fitness effects of selected mutations introduced in non-evolved ethanol-sensitive cells revealed several adaptive mutations that had previously not been implicated in ethanol tolerance, including mutations in PRT1, VPS70 and MEX67. Interestingly, variation in VPS70 was recently identified as a QTL for ethanol tolerance in an industrial bio-ethanol strain. Taken together, our results show how, in contrast to adaptation to some other stresses, adaptation to a continuous complex and severe stress involves interplay of different evolutionary mechanisms. In addition, our study reveals functional modules involved in ethanol resistance and identifies several mutations that could help to improve the ethanol tolerance of industrial yeasts.

Organisms can evolve resistance to specific stress factors, which allows them to thrive in environments where non-adapted organisms fail to grow. However, the molecular mechanisms that underlie adaptation to complex stress factors that interfere with basic cellular processes are poorly understood. In this study, we reveal how yeast populations adapt to high ethanol concentrations, an ecologically and industrially relevant stress that is still poorly understood. We exposed six independent populations of genetically identical yeast cells to gradually increasing ethanol levels, and we monitored the changes in their DNA sequence over a two-year period. Together with novel computational analyses, we could identify the mutational dynamics and molecular mechanisms underlying increased ethanol resistance. Our results show how adaptation to high ethanol is complex and can be reached through different mutational pathways. Together, our study offers a detailed picture of how populations adapt to a complex continuous stress and identifies several mutations that increase ethanol resistance, which opens new routes to obtain superior biofuel yeast strains.

Genomic investigations of evolutionary dynamics and epistasis in microbial evolution experiments

Microbial evolution experiments enable us to watch adaptation in real time, and to quantify the repeatability and predictability of evolution by comparing identical replicate populations. Further, we can resurrect ancestral types to examine changes over evolutionary time. Until recently, experimental evolution has been limited to measuring phenotypic changes, or to tracking a few genetic markers over time. However, recent advances in sequencing technology now make it possible to extensively sequence clones or whole-population samples from microbial evolution experiments. Here, we review recent work exploiting these techniques to understand the genomic basis of evolutionary change in experimental systems. We first focus on studies that analyze the dynamics of genome evolution in microbial systems. We then survey work that uses observations of sequence evolution to infer aspects of the underlying fitness landscape, concentrating on the epistatic interactions between mutations and the constraints these interactions impose on adaptation.

Experimental evolution of the model eukaryote Saccharomyces cerevisiae yields insight into the molecular mechanisms underlying adaptation

Understanding how changes in DNA drive the emergence of new phenotypes and fuel evolution remains a major challenge. One major hurdle is the lack of a fossil record of DNA that allows linking mutations to phenotypic changes. However, the emergence of high-throughput sequencing technologies now allows sequencing genomes of natural and experimentally evolved microbial populations to study how mutations arise and spread through a population, how new phenotypes arise and how this ultimately leads to adaptation. Here, we highlight key studies that have increased our mechanistic understanding of evolution. We specifically focus on the model eukaryote Saccharomyces cerevisiae because its relatively short replication time, much-studied biology and available molecular toolbox have made it a prime model for molecular evolution studies.

Mechanisms of x chromosome dosage compensation

In many animals, males have one X and females have two X chromosomes. The difference in X chromosome dosage between the two sexes is compensated by mechanisms that regulate X chromosome transcription. Recent advances in genomic techniques have provided new insights into the molecular mechanisms of X chromosome dosage compensation. In this review, I summarize our current understanding of dosage imbalance in general, and then review the molecular mechanisms of X chromosome dosage compensation with an emphasis on the parallels and differences between the three well-studied model systems, M. musculus, D. melanogaster and C. elegans.

The functional basis of adaptive evolution in chemostats

Two of the central problems in biology are determining the molecular basis of adaptive evolution and understanding how cells regulate their growth. The chemostat is a device for culturing cells that provides great utility in tackling both of these problems: it enables precise control of the selective pressure under which organisms evolve and it facilitates experimental control of cell growth rate. The aim of this review is to synthesize results from studies of the functional basis of adaptive evolution in long-term chemostat selections using Escherichia coli and Saccharomyces cerevisiae. We describe the principle of the chemostat, provide a summary of studies of experimental evolution in chemostats, and use these studies to assess our current understanding of selection in the chemostat. Functional studies of adaptive evolution in chemostats provide a unique means of interrogating the genetic networks that control cell growth, which complements functional genomic approaches and quantitative trait loci (QTL) mapping in natural populations. An integrated approach to the study of adaptive evolution that accounts for both molecular function and evolutionary processes is critical to advancing our understanding of evolution. By renewing efforts to integrate these two research programs, experimental evolution in chemostats is ideally suited to extending the functional synthesis to the study of genetic networks.

Concerted evolution of life stage performances signals recent selection on yeast nitrogen use

Exposing natural selection driving phenotypic and genotypic adaptive differentiation is an extraordinary challenge. Given that an organism's life stages are exposed to the same environmental variations, we reasoned that fitness components, such as the lag, rate, and efficiency of growth, directly reflecting performance in these life stages, should often be selected in concert. We therefore conjectured that correlations between fitness components over natural isolates, in a particular environmental context, would constitute a robust signal of recent selection. Critically, this test for selection requires fitness components to be determined by different genetic loci. To explore our conjecture, we exhaustively evaluated the lag, rate, and efficiency of asexual population growth of natural isolates of the model yeast Saccharomyces cerevisiae in a large variety of nitrogen-limited environments. Overall, fitness components were well correlated under nitrogen restriction. Yeast isolates were further crossed in all pairwise combinations and coinheritance of each fitness component and genetic markers were traced. Trait variations tended to map to quantitative trait loci (QTL) that were private to a single fitness component. We further traced QTLs down to single-nucleotide resolution and uncovered loss-of-function mutations in RIM15, PUT4, DAL1, and DAL4 as the genetic basis for nitrogen source use variations. Effects of SNPs were unique for a single fitness component, strongly arguing against pleiotropy between lag, rate, and efficiency of reproduction under nitrogen restriction. The strong correlations between life stage performances that cannot be explained by pleiotropy compellingly support adaptive differentiation of yeast nitrogen source use and suggest a generic approach for detecting selection.

The enduring utility of continuous culturing in experimental evolution

Studying evolution in the laboratory provides a means of understanding the processes, dynamics and outcomes of adaptive evolution in precisely controlled and readily replicated conditions. The advantages of experimental evolution are maximized when the selection is well defined, which enables linking genotype, phenotype and fitness. One means of maintaining a defined selection is continuous culturing: chemostats enable the study of adaptive evolution in constant nutrient-limited environments, whereas cells in turbidostats evolve in constant nutrient abundance. Although the experimental effort required for continuous culturing is considerable relative to the experimental simplicity of serial batch culture, the opposite is true of the environments they produce: continuous culturing results in simplified and invariant conditions whereas serially diluted batch cultures are complex and dynamic. The comparative simplicity of the selective environment that is unique to continuous culturing provides an ideal experimental system for addressing key questions in adaptive 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.

Chromosomal variation segregates within incipient species and correlates with reproductive isolation

Reproductive isolation is a critical step in the process of speciation. Among the most important factors driving reproductive isolation are genetic incompatibilities. Whether these incompatibilities are already present before extrinsic factors prevent gene flow between incipient species remains largely unresolved in natural systems. This question is particularly challenging because it requires that we catch speciating populations in the act before they reach the full-fledged species status. We measured the extent of intrinsic postzygotic isolation within and between phenotypically and genetically divergent lineages of the wild yeast Saccharomyces paradoxus that have partially overlapping geographical distributions. We find that hybrid viability between lineages progressively decreases with genetic divergence. A large proportion of postzygotic inviability within lineages is associated with chromosomal rearrangements, suggesting that chromosomal differences substantially contribute to the early steps of reproductive isolation within lineages before reaching fixation. Our observations show that polymorphic intrinsic factors may segregate within incipient species before they contribute to their full reproductive isolation and highlight the role of chromosomal rearrangements in speciation. We propose different hypotheses based on adaptation, biogeographical events and life history evolution that could explain these observations.

Lager yeast comes of age

Alcoholic fermentations have accompanied human civilizations throughout our history. Lager yeasts have a several-century-long tradition of providing fresh beer with clean taste. The yeast strains used for lager beer fermentation have long been recognized as hybrids between two Saccharomyces species. We summarize the initial findings on this hybrid nature, the genomics/transcriptomics of lager yeasts, and established targets of strain improvements. Next-generation sequencing has provided fast access to yeast genomes. Its use in population genomics has uncovered many more hybridization events within Saccharomyces species, so that lager yeast hybrids are no longer the exception from the rule. These findings have led us to propose network evolution within Saccharomyces species. This "web of life" recognizes the ability of closely related species to exchange DNA and thus drain from a combined gene pool rather than be limited to a gene pool restricted by speciation. Within the domesticated lager yeasts, two groups, the Saaz and Frohberg groups, can be distinguished based on fermentation characteristics. Recent evidence suggests that these groups share an evolutionary history. We thus propose to refer to the Saaz group as Saccharomyces carlsbergensis and to the Frohberg group as Saccharomyces pastorianus based on their distinct genomes. New insight into the hybrid nature of lager yeast will provide novel directions for future strain improvement.