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.

Synonymous mutations in representative yeast genes are mostly strongly non-neutral.

Synonymous mutations in protein-coding genes do not alter protein sequences and are thus generally presumed to be neutral or nearly neutral1-5. Here, to experimentally verify this presumption, we constructed 8,341 yeast mutants each carrying a synonymous, nonsynonymous or nonsense mutation in one of 21 endogenous genes with diverse functions and expression levels and measured their fitness relative to the wild type in a rich medium. Three-quarters of synonymous mutations resulted in a significant reduction in fitness, and the distribution of fitness effects was overall similar-albeit nonidentical-between synonymous and nonsynonymous mutations. Both synonymous and nonsynonymous mutations frequently disturbed the level of mRNA expression of the mutated gene, and the extent of the disturbance partially predicted the fitness effect. Investigations in additional environments revealed greater across-environment fitness variations for nonsynonymous mutants than for synonymous mutants despite their similar fitness distributions in each environment, suggesting that a smaller proportion of nonsynonymous mutants than synonymous mutants are always non-deleterious in a changing environment to permit fixation, potentially explaining the common observation of substantially lower nonsynonymous than synonymous substitution rates. The strong non-neutrality of most synonymous mutations, if it holds true for other genes and in other organisms, would require re-examination of numerous biological conclusions about mutation, selection, effective population size, divergence time and disease mechanisms that rely on the assumption that synoymous mutations are neutral.

Characterization of SARS-CoV-2 Omicron BA.4 and BA.5 isolates in rodents.

The BA.2 sublineage of the SARS-CoV-2 Omicron variant has become dominant in most countries around the world; however, the prevalence of BA.4 and BA.5 is increasing rapidly in several regions. BA.2 is less pathogenic in animal models than previously circulating variants of concern1-4. Compared with BA.2, however, BA.4 and BA.5 possess additional substitutions in the spike protein, which play a key role in viral entry, raising concerns that the replication capacity and pathogenicity of BA.4 and BA.5 are higher than those of BA.2. Here we have evaluated the replicative ability and pathogenicity of BA.4 and BA.5 isolates in wild-type Syrian hamsters, human ACE2 (hACE2) transgenic hamsters and hACE2 transgenic mice. We have observed no obvious differences among BA.2, BA.4 and BA.5 isolates in growth ability or pathogenicity in rodent models, and less pathogenicity compared to a previously circulating Delta (B.1.617.2 lineage) isolate. In addition, in vivo competition experiments revealed that BA.5 outcompeted BA.2 in hamsters, whereas BA.4 and BA.2 exhibited similar fitness. These findings suggest that BA.4 and BA.5 clinical isolates have similar pathogenicity to BA.2 in rodents and that BA.5 possesses viral fitness superior to that of BA.2.

Disease variant prediction with deep generative models of evolutionary data.

Quantifying the pathogenicity of protein variants in human disease-related genes would have a marked effect on clinical decisions, yet the overwhelming majority (over 98%) of these variants still have unknown consequences1-3. In principle, computational methods could support the large-scale interpretation of genetic variants. However, state-of-the-art methods4-10 have relied on training machine learning models on known disease labels. As these labels are sparse, biased and of variable quality, the resulting models have been considered insufficiently reliable11. Here we propose an approach that leverages deep generative models to predict variant pathogenicity without relying on labels. By modelling the distribution of sequence variation across organisms, we implicitly capture constraints on the protein sequences that maintain fitness. Our model EVE (evolutionary model of variant effect) not only outperforms computational approaches that rely on labelled data but also performs on par with, if not better than, predictions from high-throughput experiments, which are increasingly used as evidence for variant classification12-16. We predict the pathogenicity of more than 36 million variants across 3,219 disease genes and provide evidence for the classification of more than 256,000 variants of unknown significance. Our work suggests that models of evolutionary information can provide valuable independent evidence for variant interpretation that will be widely useful in research and clinical settings.

Mutation Rate Heterogeneity Increases Odds of Survival in Unpredictable Environments.

Mutation rates affect both a population's present fitness and its capacity to adapt to future environmental changes. When the available genetic variability limits adaptation to environmental change, natural selection favors high mutations rates. However, constitutively high mutation rates compromise the fitness of a population in stable environments. This problem may be resolved if an increase in mutation rates is limited to times of stress, restricted to some genomic regions, and occurs only in a subpopulation of cells. Such within-population heterogeneity of mutation rates can result from genetic, environmental, and stochastic effects. The presence of subpopulations of transient mutator cells does not jeopardize the overall fitness of a population under stable environmental conditions. However, they can increase the odds of survival in changing environments because they represent reservoirs of increased genetic variability. This article presents evidence that such heterogeneity of mutation rates is more the norm than the exception.

The strength and pattern of natural selection on gene expression in rice.

Levels of gene expression underpin organismal phenotypes1,2, but the nature of selection that acts on gene expression and its role in adaptive evolution remain unknown1,2. Here we assayed gene expression in rice (Oryza sativa)3, and used phenotypic selection analysis to estimate the type and strength of selection on the levels of more than 15,000 transcripts4,5. Variation in most transcripts appears (nearly) neutral or under very weak stabilizing selection in wet paddy conditions (with median standardized selection differentials near zero), but selection is stronger under drought conditions. Overall, more transcripts are conditionally neutral (2.83%) than are antagonistically pleiotropic6 (0.04%), and transcripts that display lower levels of expression and stochastic noise7-9 and higher levels of plasticity9 are under stronger selection. Selection strength was further weakly negatively associated with levels of cis-regulation and network connectivity9. Our multivariate analysis suggests that selection acts on the expression of photosynthesis genes4,5, but that the efficacy of selection is genetically constrained under drought conditions10. Drought selected for earlier flowering11,12 and a higher expression of OsMADS18 (Os07g0605200), which encodes a MADS-box transcription factor and is a known regulator of early flowering13-marking this gene as a drought-escape gene11,12. The ability to estimate selection strengths provides insights into how selection can shape molecular traits at the core of gene action.

A yeast living ancestor reveals the origin of genomic introgressions.

Genome introgressions drive evolution across the animal1, plant2 and fungal3 kingdoms. Introgressions initiate from archaic admixtures followed by repeated backcrossing to one parental species. However, how introgressions arise in reproductively isolated species, such as yeast4, has remained unclear. Here we identify a clonal descendant of the ancestral yeast hybrid that founded the extant Saccharomyces cerevisiae Alpechin lineage5, which carries abundant Saccharomyces paradoxus introgressions. We show that this clonal descendant, hereafter defined as a 'living ancestor', retained the ancestral genome structure of the first-generation hybrid with contiguous S. cerevisiae and S. paradoxus subgenomes. The ancestral first-generation hybrid underwent catastrophic genomic instability through more than a hundred mitotic recombination events, mainly manifesting as homozygous genome blocks generated by loss of heterozygosity. These homozygous sequence blocks rescue hybrid fertility by restoring meiotic recombination and are the direct origins of the introgressions present in the Alpechin lineage. We suggest a plausible route for introgression evolution through the reconstruction of extinct stages and propose that genome instability allows hybrids to overcome reproductive isolation and enables introgressions to emerge.

Unraveling the dynamics of hepatitis C virus adaptive mutations and their impact on antiviral responses in primary human hepatocytes.

Hepatitis C virus (HCV) infection progresses to chronicity in the majority of infected individuals. Its high intra-host genetic variability enables HCV to evade the continuous selection pressure exerted by the host, contributing to persistent infection. Utilizing a cell culture-adapted HCV population (p100pop) which exhibits increased replicative capacity in various liver cell lines, this study investigated virus and host determinants that underlie enhanced viral fitness. Characterization of a panel of molecular p100 clones revealed that cell culture adaptive mutations optimize a range of virus-host interactions, resulting in expanded cell tropism, altered dependence on the cellular co-factor micro-RNA 122 and increased rates of virus spread. On the host side, comparative transcriptional profiling of hepatoma cells infected either with p100pop or its progenitor virus revealed that enhanced replicative fitness correlated with activation of endoplasmic reticulum stress signaling and the unfolded protein response. In contrast, infection of primary human hepatocytes with p100pop led to a mild attenuation of virion production which correlated with a greater induction of cell-intrinsic antiviral defense responses. In summary, long-term passage experiments in cells where selective pressure from innate immunity is lacking improves multiple virus-host interactions, enhancing HCV replicative fitness. However, this study further indicates that HCV has evolved to replicate at low levels in primary human hepatocytes to minimize innate immune activation, highlighting that an optimal balance between replicative fitness and innate immune induction is key to establish persistence. IMPORTANCE: Hepatitis C virus (HCV) infection remains a global health burden with 58 million people currently chronically infected. However, the detailed molecular mechanisms that underly persistence are incompletely defined. We utilized a long-term cell culture-adapted HCV, exhibiting enhanced replicative fitness in different human liver cell lines, in order to identify molecular principles by which HCV optimizes its replication fitness. Our experimental data revealed that cell culture adaptive mutations confer changes in the host response and usage of various host factors. The latter allows functional flexibility at different stages of the viral replication cycle. However, increased replicative fitness resulted in an increased activation of the innate immune system, which likely poses boundary for functional variation in authentic hepatocytes, explaining the observed attenuation of the adapted virus population in primary hepatocytes.

Inference of annealed protein fitness landscapes with AnnealDCA.

The design of proteins with specific tasks is a major challenge in molecular biology with important diagnostic and therapeutic applications. High-throughput screening methods have been developed to systematically evaluate protein activity, but only a small fraction of possible protein variants can be tested using these techniques. Computational models that explore the sequence space in-silico to identify the fittest molecules for a given function are needed to overcome this limitation. In this article, we propose AnnealDCA, a machine-learning framework to learn the protein fitness landscape from sequencing data derived from a broad range of experiments that use selection and sequencing to quantify protein activity. We demonstrate the effectiveness of our method by applying it to antibody Rep-Seq data of immunized mice and screening experiments, assessing the quality of the fitness landscape reconstructions. Our method can be applied to several experimental cases where a population of protein variants undergoes various rounds of selection and sequencing, without relying on the computation of variants enrichment ratios, and thus can be used even in cases of disjoint sequence samples.

High-resolution lineage tracking reveals travelling wave of adaptation in laboratory yeast.

In rapidly adapting asexual populations, including many microbial pathogens and viruses, numerous mutant lineages often compete for dominance within the population1-5. These complex evolutionary dynamics determine the outcomes of adaptation, but have been difficult to observe directly. Previous studies have used whole-genome sequencing to follow molecular adaptation6-10; however, these methods have limited resolution in microbial populations. Here we introduce a renewable barcoding system to observe evolutionary dynamics at high resolution in laboratory budding yeast. We find nested patterns of interference and hitchhiking even at low frequencies. These events are driven by the continuous appearance of new mutations that modify the fates of existing lineages before they reach substantial frequencies. We observe how the distribution of fitness within the population changes over time, and find a travelling wave of adaptation that has been predicted by theory11-17. We show that clonal competition creates a dynamical 'rich-get-richer' effect: fitness advantages that are acquired early in evolution drive clonal expansions, which increase the chances of acquiring future mutations. However, less-fit lineages also routinely leapfrog over strains of higher fitness. Our results demonstrate that this combination of factors, which is not accounted for in existing models of evolutionary dynamics, is critical in determining the rate, predictability and molecular basis of adaptation.

Gain without pain: adaptation and increased virulence of Zika virus in vertebrate host without fitness cost in mosquito vector.

IMPORTANCE: Previously, we modeled direct transmission chains of Zika virus (ZIKV) by serially passaging ZIKV in mice and mosquitoes and found that direct mouse transmission chains selected for viruses with increased virulence in mice and the acquisition of non-synonymous amino acid substitutions. Here, we show that these same mouse-passaged viruses also maintain fitness and transmission capacity in mosquitoes. We used infectious clone-derived viruses to demonstrate that the substitution in nonstructural protein 4A contributes to increased virulence in mice.

Infer global, predict local: Quantity-relevance trade-off in protein fitness predictions from sequence data.

Predicting the effects of mutations on protein function is an important issue in evolutionary biology and biomedical applications. Computational approaches, ranging from graphical models to deep-learning architectures, can capture the statistical properties of sequence data and predict the outcome of high-throughput mutagenesis experiments probing the fitness landscape around some wild-type protein. However, how the complexity of the models and the characteristics of the data combine to determine the predictive performance remains unclear. Here, based on a theoretical analysis of the prediction error, we propose descriptors of the sequence data, characterizing their quantity and relevance relative to the model. Our theoretical framework identifies a trade-off between these two quantities, and determines the optimal subset of data for the prediction task, showing that simple models can outperform complex ones when inferred from adequately-selected sequences. We also show how repeated subsampling of the sequence data is informative about how much epistasis in the fitness landscape is not captured by the computational model. Our approach is illustrated on several protein families, as well as on in silico solvable protein models.

Inferring protein fitness landscapes from laboratory evolution experiments.

Directed laboratory evolution applies iterative rounds of mutation and selection to explore the protein fitness landscape and provides rich information regarding the underlying relationships between protein sequence, structure, and function. Laboratory evolution data consist of protein sequences sampled from evolving populations over multiple generations and this data type does not fit into established supervised and unsupervised machine learning approaches. We develop a statistical learning framework that models the evolutionary process and can infer the protein fitness landscape from multiple snapshots along an evolutionary trajectory. We apply our modeling approach to dihydrofolate reductase (DHFR) laboratory evolution data and the resulting landscape parameters capture important aspects of DHFR structure and function. We use the resulting model to understand the structure of the fitness landscape and find numerous examples of epistasis but an overall global peak that is evolutionarily accessible from most starting sequences. Finally, we use the model to perform an in silico extrapolation of the DHFR laboratory evolution trajectory and computationally design proteins from future evolutionary rounds.

Dysregulation of expression correlates with rare-allele burden and fitness loss in maize.

Here we report a multi-tissue gene expression resource that represents the genotypic and phenotypic diversity of modern inbred maize, and includes transcriptomes in an average of 255 lines in seven tissues. We mapped expression quantitative trait loci and characterized the contribution of rare genetic variants to extremes in gene expression. Some of the new mutations that arise in the maize genome can be deleterious; although selection acts to keep deleterious variants rare, their complete removal is impeded by genetic linkage to favourable loci and by finite population size. Modern maize breeders have systematically reduced the effects of this constant mutational pressure through artificial selection and self-fertilization, which have exposed rare recessive variants in elite inbred lines. However, the ongoing effect of these rare alleles on modern inbred maize is unknown. By analysing this gene expression resource and exploiting the extreme diversity and rapid linkage disequilibrium decay of maize, we characterize the effect of rare alleles and evolutionary history on the regulation of expression. Rare alleles are associated with the dysregulation of expression, and we correlate this dysregulation to seed-weight fitness. We find enrichment of ancestral rare variants among expression quantitative trait loci mapped in modern inbred lines, which suggests that historic bottlenecks have shaped regulation. Our results suggest that one path for further genetic improvement in agricultural species lies in purging the rare deleterious variants that have been associated with crop fitness.

Creating a functional single-chromosome yeast.

Eukaryotic genomes are generally organized in multiple chromosomes. Here we have created a functional single-chromosome yeast from a Saccharomyces cerevisiae haploid cell containing sixteen linear chromosomes, by successive end-to-end chromosome fusions and centromere deletions. The fusion of sixteen native linear chromosomes into a single chromosome results in marked changes to the global three-dimensional structure of the chromosome due to the loss of all centromere-associated inter-chromosomal interactions, most telomere-associated inter-chromosomal interactions and 67.4% of intra-chromosomal interactions. However, the single-chromosome and wild-type yeast cells have nearly identical transcriptome and similar phenome profiles. The giant single chromosome can support cell life, although this strain shows reduced growth across environments, competitiveness, gamete production and viability. This synthetic biology study demonstrates an approach to exploration of eukaryote evolution with respect to chromosome structure and function.

Probing the aggregated effects of purifying selection per individual on 1,380 medical phenotypes in the UK Biobank.

Understanding the relationship between natural selection and phenotypic variation has been a long-standing challenge in human population genetics. With the emergence of biobank-scale datasets, along with new statistical metrics to approximate strength of purifying selection at the variant level, it is now possible to correlate a proxy of individual relative fitness with a range of medical phenotypes. We calculated a per-individual deleterious load score by summing the total number of derived alleles per individual after incorporating a weight that approximates strength of purifying selection. We assessed four methods for the weight, including GERP, phyloP, CADD, and fitcons. By quantitatively tracking each of these scores with the site frequency spectrum, we identified phyloP as the most appropriate weight. The phyloP-weighted load score was then calculated across 15,129,142 variants in 335,161 individuals from the UK Biobank and tested for association on 1,380 medical phenotypes. After accounting for multiple test correction, we observed a strong association of the load score amongst coding sites only on 27 traits including body mass, adiposity and metabolic rate. We further observed that the association signals were driven by common variants (derived allele frequency > 5%) with high phyloP score (phyloP > 2). Finally, through permutation analyses, we showed that the load score amongst coding sites had an excess of nominally significant associations on many medical phenotypes. These results suggest a broad impact of deleterious load on medical phenotypes and highlight the deleterious load score as a tool to disentangle the complex relationship between natural selection and medical phenotypes.

Predicting evolutionary change at the DNA level in a natural Mimulus population.

Evolution by natural selection occurs when the frequencies of genetic variants change because individuals differ in Darwinian fitness components such as survival or reproductive success. Differential fitness has been demonstrated in field studies of many organisms, but it remains unclear how well we can quantitatively predict allele frequency changes from fitness measurements. Here, we characterize natural selection on millions of Single Nucleotide Polymorphisms (SNPs) across the genome of the annual plant Mimulus guttatus. We use fitness estimates to calibrate population genetic models that effectively predict allele frequency changes into the next generation. Hundreds of SNPs experienced "male selection" in 2013 with one allele at each SNP elevated in frequency among successful male gametes relative to the entire population of adults. In the following generation, allele frequencies at these SNPs consistently shifted in the predicted direction. A second year of study revealed that SNPs had effects on both viability and reproductive success with pervasive trade-offs between fitness components. SNPs favored by male selection were, on average, detrimental to survival. These trade-offs (antagonistic pleiotropy and temporal fluctuations in fitness) may be essential to the long-term maintenance of alleles. Despite the challenges of measuring selection in the wild, the strong correlation between predicted and observed allele frequency changes suggests that population genetic models have a much greater role to play in forward-time prediction of evolutionary change.

Fitness maps to a large-effect locus in introduced stickleback populations.

Mutations of small effect underlie most adaptation to new environments, but beneficial variants with large fitness effects are expected to contribute under certain conditions. Genes and genomic regions having large effects on phenotypic differences between populations are known from numerous taxa, but fitness effect sizes have rarely been estimated. We mapped fitness over a generation in an F2 intercross between a marine and a lake stickleback population introduced to a freshwater pond. A quantitative trait locus map of the number of surviving offspring per F2 female detected a single, large-effect locus near Ectodysplasin (Eda), a gene having an ancient freshwater allele causing reduced bony armor and other changes. F2 females homozygous for the freshwater allele had twice the number of surviving offspring as homozygotes for the marine allele, producing a large selection coefficient, s = 0.50 ± 0.09 SE. Correspondingly, the frequency of the freshwater allele increased from 0.50 in F2 mothers to 0.58 in surviving offspring. We compare these results to allele frequency changes at the Eda gene in an Alaskan lake population colonized by marine stickleback in the 1980s. The frequency of the freshwater Eda allele rose steadily over multiple generations and reached 95% within 20 y, yielding a similar estimate of selection, s = 0.49 ± 0.05, but a different degree of dominance. These findings are consistent with other studies suggesting strong selection on this gene (and/or linked genes) in fresh water. Selection on ancient genetic variants carried by colonizing ancestors is likely to increase the prevalence of large-effect fitness variants in adaptive evolution.

The crucial role of genome-wide genetic variation in conservation.

The unprecedented rate of extinction calls for efficient use of genetics to help conserve biodiversity. Several recent genomic and simulation-based studies have argued that the field of conservation biology has placed too much focus on conserving genome-wide genetic variation, and that the field should instead focus on managing the subset of functional genetic variation that is thought to affect fitness. Here, we critically evaluate the feasibility and likely benefits of this approach in conservation. We find that population genetics theory and empirical results show that conserving genome-wide genetic variation is generally the best approach to prevent inbreeding depression and loss of adaptive potential from driving populations toward extinction. Focusing conservation efforts on presumably functional genetic variation will only be feasible occasionally, often misleading, and counterproductive when prioritized over genome-wide genetic variation. Given the increasing rate of habitat loss and other environmental changes, failure to recognize the detrimental effects of lost genome-wide genetic variation on long-term population viability will only worsen the biodiversity crisis.

The impact of identity by descent on fitness and disease in dogs.

Domestic dogs have experienced population bottlenecks, recent inbreeding, and strong artificial selection. These processes have simplified the genetic architecture of complex traits, allowed deleterious variation to persist, and increased both identity-by-descent (IBD) segments and runs of homozygosity (ROH). As such, dogs provide an excellent model for examining how these evolutionary processes influence disease. We assembled a dataset containing 4,414 breed dogs, 327 village dogs, and 380 wolves genotyped at 117,288 markers and data for clinical and morphological phenotypes. Breed dogs have an enrichment of IBD and ROH, relative to both village dogs and wolves, and we use these patterns to show that breed dogs have experienced differing severities of bottlenecks in their recent past. We then found that ROH burden is associated with phenotypes in breed dogs, such as lymphoma. We next test the prediction that breeds with greater ROH have more disease alleles reported in the Online Mendelian Inheritance in Animals (OMIA). Surprisingly, the number of causal variants identified correlates with the popularity of that breed rather than the ROH or IBD burden, suggesting an ascertainment bias in OMIA. Lastly, we use the distribution of ROH across the genome to identify genes with depletions of ROH as potential hotspots for inbreeding depression and find multiple exons where ROH are never observed. Our results suggest that inbreeding has played a large role in shaping genetic and phenotypic variation in dogs and that future work on understudied breeds may reveal new disease-causing variation.