Sandeep Venkataram

Quantitative evolutionary dynamics using high-resolution lineage tracking

Evolution of large asexual cell populations underlies ∼30% of deaths worldwide, including those caused by bacteria, fungi, parasites, and cancer. However, the dynamics underlying these evolutionary processes remain poorly understood because they involve many competing beneficial lineages, most of which never rise above extremely low frequencies in the population. To observe these normally hidden evolutionary dynamics, we constructed a sequencing-based ultra high-resolution lineage tracking system in Saccharomyces cerevisiae that allowed us to monitor the relative frequencies of ∼500,000 lineages simultaneously. In contrast to some expectations, we found that the spectrum of fitness effects of beneficial mutations is neither exponential nor monotonic. Early adaptation is a predictable consequence of this spectrum and is strikingly reproducible, but the initial small-effect mutations are soon outcompeted by rarer large-effect mutations that result in variability between replicates. These results suggest that early evolutionary dynamics may be deterministic for a period of time before stochastic effects become important.

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.

Is Transcription Factor Binding Site Turnover a Sufficient Explanation for Cis-Regulatory Sequence Divergence?

The molecular evolution of cis-regulatory sequences is not well understood. Comparisons of closely related species show that cis-regulatory sequences contain a large number of sites constrained by purifying selection. In contrast, there are a number of examples from distantly related species where cis-regulatory sequences retain little to no sequence similarity but drive similar patterns of gene expression. Binding site turnover, whereby the gain of a redundant binding site enables loss of a previously functional site, is one model by which cis-regulatory sequences can diverge without a concurrent change in function. To determine whether cis-regulatory sequence divergence is consistent with binding site turnover, we examined binding site evolution within orthologous intergenic sequences from 14 yeast species defined by their syntenic relationships with adjacent coding sequences. Both local and global alignments show that nearly all distantly related orthologous cis-regulatory sequences have no significant level of sequence similarity but are enriched for experimentally identified binding sites. Yet, a significant proportion of experimentally identified binding sites that are conserved in closely related species are absent in distantly related species and so cannot be explained by binding site turnover. Depletion of binding sites depends on the transcription factor but is detectable for a quarter of all transcription factors examined. Our results imply that binding site turnover is not a sufficient explanation for cis-regulatory sequence evolution.

High-resolution mapping of cis-regulatory variation in budding yeast

Significance Genetic variants affecting gene-expression levels are a major source of phenotypic variation. Using 85 diverse isolates of Saccharomyces cerevisiae, we mapped genetic variants that affect gene expression with 50-fold higher resolution than previously possible. By doing so, we were able to pinpoint likely causal variants and investigate their molecular mechanisms. We found that these genetic variants are generally under negative selection, but also that clinical yeast isolates have undergone positive selection for up-regulation of genes involved in biofilm suppression. Altogether, our results demonstrate the power of high-resolution mapping of genetic variants that affect gene expression, particularly in understanding the molecular mechanisms of regulatory variation and the natural selection acting on this variation. Genetic variants affecting gene-expression levels are a major source of phenotypic variation. The approximate locations of these variants can be mapped as expression quantitative trait loci (eQTLs); however, a major limitation of eQTLs is their low resolution, which precludes investigation of the causal variants and their molecular mechanisms. Here we report RNA-seq and full genome sequences for 85 diverse isolates of the yeast Saccharomyces cerevisiae—including wild, domesticated, and human clinical strains—which allowed us to perform eQTL mapping with 50-fold higher resolution than previously possible. In addition to variants in promoters, we uncovered an important role for variants in 3′UTRs, especially those affecting binding of the PUF family of RNA-binding proteins. The eQTLs are predominantly under negative selection, particularly those affecting essential genes and conserved genes. However, applying the sign test for lineage-specific selection revealed the polygenic up-regulation of dozens of biofilm suppressor genes in strains isolated from human patients, consistent with the key role of biofilms in fungal pathogenicity. In addition, a single variant in the promoter of a biofilm suppressor, NIT3, showed the strongest genome-wide association with clinical origin. Altogether, our results demonstrate the power of high-resolution eQTL mapping in understanding the molecular mechanisms of regulatory variation, as well as the natural selection acting on this variation that drives adaptation to environments, ranging from laboratories to vineyards to the human body.

High rate of adaptation of mammalian proteins that interact with Plasmodium and related parasites

Plasmodium parasites, along with their Piroplasm relatives, have caused malaria-like illnesses in terrestrial mammals for millions of years. Several Plasmodium-protective alleles have recently evolved in human populations, but little is known about host adaptation to blood parasites over deeper evolutionary timescales. In this work, we analyze mammalian adaptation in ~500 Plasmodium- or Piroplasm- interacting proteins (PPIPs) manually curated from the scientific literature. We show that (i) PPIPs are enriched for both immune functions and pleiotropy with other pathogens, and (ii) the rate of adaptation across mammals is significantly elevated in PPIPs, compared to carefully matched control proteins. PPIPs with high pathogen pleiotropy show the strongest signatures of adaptation, but this pattern is fully explained by their immune enrichment. Several pieces of evidence suggest that blood parasites specifically have imposed selection on PPIPs. First, even non-immune PPIPs that lack interactions with other pathogens have adapted at twice the rate of matched controls. Second, PPIP adaptation is linked to high expression in the liver, a critical organ in the parasite life cycle. Finally, our detailed investigation of alpha-spectrin, a major red blood cell membrane protein, shows that domains with particularly high rates of adaptation are those known to interact specifically with P. falciparum. Overall, we show that host proteins that interact with Plasmodium and Piroplasm parasites have experienced elevated rates of adaptation across mammals, and provide evidence that some of this adaptation has likely been driven by blood parasites.

Ploidy and the Predictability of Evolution in Fishers Geometric Model

Predicting the future evolutionary state of a population is a primary goal of evolutionary biology. One can differentiate between forward and backward predictability, where forward predictability is the probability of the same adaptive outcome occurring in independent evolutionary trials, and backward predictability is the likelihood of a particular adaptive path given the knowledge of the starting and final states. Most studies of evolutionary predictability assume that alleles along an adaptive walk fix in succession with individual adaptive mutations occurring in monomorphic populations. However, in nature, adaptation generally occurs within polymorphic populations, and there are a number of mechanisms by which polymorphisms can be stably maintained by natural selection. Here we investigate the predictability of evolution in monomorphic and polymorphic situations by studying adaptive walks in diploid populations using Fishers geometric model, which has been previously found to generate balanced polymorphisms through overdominant mutations. We show that overdominant mutations cause a decrease in forward predictability and an increase in backward predictability relative to diploid walks lacking balanced states. We also show that in the presence of balanced polymorphisms, backward predictability analysis can lead to counterintuitive outcomes such as reaching different final adapted population states depending on the order in which mutations are introduced and cases where the true adaptive trajectory appears inviable. As stable polymorphisms can be generated in both haploid and diploid natural populations through a number of mechanisms, we argue that natural populations may contain complex evolutionary histories that may not be easily inferred without historical sampling.Predicting the course of evolution is critical for solving current biomedical challenges such as cancer and the evolution of drug resistant pathogens. One approach to studying evolutionary predictability is to observe repeated, independent evolutionary trajectories of similar organisms under similar selection pressures in order to empirically characterize this adaptive fitness landscape. As this approach is infeasible for many natural systems, a number of recent studies have attempted to gain insight into the adaptive fitness landscape by testing the plausibility of different orders of appearance for a specific set of adaptive mutations in a single adaptive trajectory. While this approach is technically feasible for systems with very few available adaptive mutations, the usefulness of this approach for predicting evolution in situations with highly polygenic adaptation is unknown. It is also unclear whether the presence of stable adaptive polymorphisms can influence the predictability of evolution as measured by these methods. In this work, we simulate adaptive evolution under Fisher’s geometric model to study evolutionary predictability. Remarkably, we find that the predictability estimated by these methods are anti-correlated, and that the presence of stable adaptive polymorphisms can both qualitatively and quantitatively change the predictability of evolution.Several recent experimental studies assessed the likelihood of all possible evolutionary paths between ancestral and evolved sequences. All of these studies measured the fitness of the intermediate genotypes and assumed that the advantageous genotypes fix in the population before acquiring the next adaptive mutation along the path. Unfortunately, the successive fixation assumption used by these studies is typically invalid, given that natural selection often maintain alleles at intermediate frequency by a variety of mechanisms such as frequency-dependent selection, local adaptation, clonal interference, and fitness overdominance. Here we simulate adaptive walks using Fishers geometric model in diploid populations where previous work has shown that adaptation commonly generates balanced polymorphisms through overdominant mutations. We use these simulations to show that the use of the successive fixation assumption in this simple model is largely justified if the goal is to separate viable and inviable paths from each other. However, the estimates of the relative likelihoods of the viable paths become unreliable. We also show that the presence of balanced states along the true path significantly affects the number and likelihood distribution of viable paths when compared to walks without balanced states. These simple simulations highlight the importance of considering the effect of polymorphisms during adaptation especially given the prevalence of functional polymorphisms in natural populations.

Stress response, behavior, and development are shaped by transposable element-induced mutations in Drosophila

Mapping genotype to phenotype is challenging because of the difficulties in identifying both the traits under selection and the specific genetic variants underlying these traits. Most of the current knowledge of the genetic basis of adaptive evolution is based on the analysis of single nucleotide polymorphisms (SNPs). Despite increasing evidence for their causal role, the contribution of structural variants to adaptive evolution remains largely unexplored. In this work, we analyzed the population frequencies of 1,615 Transposable Element (TE) insertions in 91 samples from 60 worldwide natural populations of Drosophila melanogaster. We identified a set of 300 TEs that are present at high population frequencies, and located in genomic regions with high recombination rate, where the efficiency of natural selection is high. The age and the length of these 300 TEs are consistent with relatively young and long insertions reaching high frequencies due to the action of positive selection. Indeed, we, and others, found evidence of selective sweeps and/or population differentiation for 65 of them. The analysis of the genes located nearby these 65 candidate adaptive insertions suggested that the functional response to selection is related with the GO categories of response to stimulus, behavior, and development. We further showed that a subset of the candidate adaptive TEs affect expression of nearby genes, and five of them have already been linked to an ecologically relevant phenotypic effect. Our results provide a more complete understanding of the genetic variation and the fitness-related traits relevant for adaptive evolution. Similar studies should help uncover the importance of TE-induced adaptive mutations in other species as well.

On the study of evolutionary predictability using historical reconstruction

Predicting the future evolutionary state of a population is a primary goal of evolutionary biology. One can differentiate between forward and backward predictability, where forward predictability is the probability of the same adaptive outcome occurring in independent evolutionary trials, and backward predictability is the likelihood of a particular adaptive path given the knowledge of the starting and final states. Most studies of evolutionary predictability assume that alleles along an adaptive walk fix in succession with individual adaptive mutations occurring in monomorphic populations. However, in nature, adaptation generally occurs within polymorphic populations, and there are a number of mechanisms by which polymorphisms can be stably maintained by natural selection. Here we investigate the predictability of evolution in monomorphic and polymorphic situations by studying adaptive walks in diploid populations using Fishers geometric model, which has been previously found to generate balanced polymorphisms through overdominant mutations. We show that overdominant mutations cause a decrease in forward predictability and an increase in backward predictability relative to diploid walks lacking balanced states. We also show that in the presence of balanced polymorphisms, backward predictability analysis can lead to counterintuitive outcomes such as reaching different final adapted population states depending on the order in which mutations are introduced and cases where the true adaptive trajectory appears inviable. As stable polymorphisms can be generated in both haploid and diploid natural populations through a number of mechanisms, we argue that natural populations may contain complex evolutionary histories that may not be easily inferred without historical sampling.Predicting the course of evolution is critical for solving current biomedical challenges such as cancer and the evolution of drug resistant pathogens. One approach to studying evolutionary predictability is to observe repeated, independent evolutionary trajectories of similar organisms under similar selection pressures in order to empirically characterize this adaptive fitness landscape. As this approach is infeasible for many natural systems, a number of recent studies have attempted to gain insight into the adaptive fitness landscape by testing the plausibility of different orders of appearance for a specific set of adaptive mutations in a single adaptive trajectory. While this approach is technically feasible for systems with very few available adaptive mutations, the usefulness of this approach for predicting evolution in situations with highly polygenic adaptation is unknown. It is also unclear whether the presence of stable adaptive polymorphisms can influence the predictability of evolution as measured by these methods. In this work, we simulate adaptive evolution under Fisher’s geometric model to study evolutionary predictability. Remarkably, we find that the predictability estimated by these methods are anti-correlated, and that the presence of stable adaptive polymorphisms can both qualitatively and quantitatively change the predictability of evolution.Several recent experimental studies assessed the likelihood of all possible evolutionary paths between ancestral and evolved sequences. All of these studies measured the fitness of the intermediate genotypes and assumed that the advantageous genotypes fix in the population before acquiring the next adaptive mutation along the path. Unfortunately, the successive fixation assumption used by these studies is typically invalid, given that natural selection often maintain alleles at intermediate frequency by a variety of mechanisms such as frequency-dependent selection, local adaptation, clonal interference, and fitness overdominance. Here we simulate adaptive walks using Fishers geometric model in diploid populations where previous work has shown that adaptation commonly generates balanced polymorphisms through overdominant mutations. We use these simulations to show that the use of the successive fixation assumption in this simple model is largely justified if the goal is to separate viable and inviable paths from each other. However, the estimates of the relative likelihoods of the viable paths become unreliable. We also show that the presence of balanced states along the true path significantly affects the number and likelihood distribution of viable paths when compared to walks without balanced states. These simple simulations highlight the importance of considering the effect of polymorphisms during adaptation especially given the prevalence of functional polymorphisms in natural populations.

Pervasive adaptation in Plasmodium-interacting proteins in mammals

The protozoan genus Plasmodium causes malaria in dozens of mammal species, including humans, non-human primates, rodents, and bats. In humans, Plasmodium infections have caused hundreds of millions of documented deaths, imposing strong selection on certain populations and driving the emergence of several resistance alleles. Over the deep timescale of mammalian evolution, however, little is known about host adaptation to Plasmodium. In this work, we expand the collection of known Plasmodium-interacting-proteins (PIPs) in mammalian hosts from ~10 to 410, by manually curating thousands of scientific abstracts. We use comparative tests of adaptation to show that PIPs have experienced >3 times more positive selection than similar mammalian proteins, consistent with Plasmodium as a major and long-standing selective pressure. PIP adaptation is strongly linked to gene expression in the blood, liver, and lung, all of which are clinically relevant tissues in Plasmodium infection. Interestingly, we find that PIPs with immune functions are especially enriched for additional interactions with viruses or bacteria, which together drive a 3.7-fold excess of adaptation. These pleiotropic interactions with unrelated pathogens, along with pressure from other Plasmodium-like Apicomplexan parasites, may help explain the PIP adaptation we observe in all clades of the mammalian tree. As a case study, we also show that alpha-spectrin, the major membrane component of mammalian red blood cells, has experienced accelerated adaptation in domains known to interact specifically with Plasmodium proteins. Similar interactions with Plasmodium-like parasites appear to have driven substantial adaptation in hundreds of host proteins throughout mammalian evolution.

Predictability of adaptive evolution under the successive fixation assumption

Predicting the future evolutionary state of a population is a primary goal of evolutionary biology. One can differentiate between forward and backward predictability, where forward predictability is the probability of the same adaptive outcome occurring in independent evolutionary trials, and backward predictability is the likelihood of a particular adaptive path given the knowledge of the starting and final states. Most studies of evolutionary predictability assume that alleles along an adaptive walk fix in succession with individual adaptive mutations occurring in monomorphic populations. However, in nature, adaptation generally occurs within polymorphic populations, and there are a number of mechanisms by which polymorphisms can be stably maintained by natural selection. Here we investigate the predictability of evolution in monomorphic and polymorphic situations by studying adaptive walks in diploid populations using Fishers geometric model, which has been previously found to generate balanced polymorphisms through overdominant mutations. We show that overdominant mutations cause a decrease in forward predictability and an increase in backward predictability relative to diploid walks lacking balanced states. We also show that in the presence of balanced polymorphisms, backward predictability analysis can lead to counterintuitive outcomes such as reaching different final adapted population states depending on the order in which mutations are introduced and cases where the true adaptive trajectory appears inviable. As stable polymorphisms can be generated in both haploid and diploid natural populations through a number of mechanisms, we argue that natural populations may contain complex evolutionary histories that may not be easily inferred without historical sampling.Predicting the course of evolution is critical for solving current biomedical challenges such as cancer and the evolution of drug resistant pathogens. One approach to studying evolutionary predictability is to observe repeated, independent evolutionary trajectories of similar organisms under similar selection pressures in order to empirically characterize this adaptive fitness landscape. As this approach is infeasible for many natural systems, a number of recent studies have attempted to gain insight into the adaptive fitness landscape by testing the plausibility of different orders of appearance for a specific set of adaptive mutations in a single adaptive trajectory. While this approach is technically feasible for systems with very few available adaptive mutations, the usefulness of this approach for predicting evolution in situations with highly polygenic adaptation is unknown. It is also unclear whether the presence of stable adaptive polymorphisms can influence the predictability of evolution as measured by these methods. In this work, we simulate adaptive evolution under Fisher’s geometric model to study evolutionary predictability. Remarkably, we find that the predictability estimated by these methods are anti-correlated, and that the presence of stable adaptive polymorphisms can both qualitatively and quantitatively change the predictability of evolution.Several recent experimental studies assessed the likelihood of all possible evolutionary paths between ancestral and evolved sequences. All of these studies measured the fitness of the intermediate genotypes and assumed that the advantageous genotypes fix in the population before acquiring the next adaptive mutation along the path. Unfortunately, the successive fixation assumption used by these studies is typically invalid, given that natural selection often maintain alleles at intermediate frequency by a variety of mechanisms such as frequency-dependent selection, local adaptation, clonal interference, and fitness overdominance. Here we simulate adaptive walks using Fishers geometric model in diploid populations where previous work has shown that adaptation commonly generates balanced polymorphisms through overdominant mutations. We use these simulations to show that the use of the successive fixation assumption in this simple model is largely justified if the goal is to separate viable and inviable paths from each other. However, the estimates of the relative likelihoods of the viable paths become unreliable. We also show that the presence of balanced states along the true path significantly affects the number and likelihood distribution of viable paths when compared to walks without balanced states. These simple simulations highlight the importance of considering the effect of polymorphisms during adaptation especially given the prevalence of functional polymorphisms in natural populations.