Biology subjects

Desai, M. M.

Publications and source records attributed to Desai, M. M..

At least 19 recordsLinked to original sources

bioRxiv2026

Joint QTL mapping reveals patterns of pleiotropy across structured environments in budding yeast

Quantitative trait locus (QTL) mapping studies in model organisms have long been used to infer the structure of genotype-phenotype maps. However, prior work has largely focused on analyzing the genetic architecture of complex traits individually. Here, we sought to instead infer genotype-phenotype maps for a large set of related phenotypes simultaneously. To do so, we used bulk assays to measure the competitive fitness of each of 100,000 individuals from a large budding yeast QTL mapping panel across a set of 101 phenotypes related by a ``hub-and-perturbation'' pattern, and then used a forward search approach to jointly infer the genetic basis of all phenotypes together. Our results reveal complex patterns of pleiotropy in the genetic architecture of these traits. We find that these patterns of pleiotropy reflect the structure of the environmental relationships as well as functional information about individual variants and the interactions between them. This suggests that high-throughput QTL mapping across structured environments can help uncover biologically meaningful genetic interactions, along with their variation across related environmental contexts.

evolutionary biology↗

bioRxiv2026

Mutation choice biases the structure of empirical fitness landscapes

Evolution can be thought of as taking place in a fitness landscape which maps each genotype to a fitness value. This abstraction has motivated significant efforts to quantify the statistical properties of random fitness landscapes with different amounts and types of interactions among loci---broadly referred to as epistasis. New high-throughput experimental techniques measuring the phenotypes of thousands of genotypes promise to bridge this theory with data. Yet, while impressive, thousands is still small relative to the size of genotype space. Moreover, experiments often assay a highly non-random subset of the fitness landscape. For example, a common experimental approach is to generate all possible 2M sequence intermediates between ancestral and evolved sequences differing by M much less than L mutations. Here, we ask to what extent the statistics of an empirical landscape constructed under distinct ascertainment protocols recapitulate the statistics of a random subset of the global landscape in a few well-studied random landscape models. We show that, in most landscapes, selecting mutations which occurred along an adaptive trajectory inflates the variance explained by lower order epistasis, reduces the number of local maxima, and increases the number of accessible paths relative to a random fitness landscape. Selecting the most beneficial or deleterious mutations, on the other hand, results in distortions to these statistics which depend more critically on the amount of epistasis in the global landscape. We then take advantage of several large-scale mutagenesis datasets to simulate the effects of ascertainment in more realistic settings.

genetics↗

bioRxiv2026

Interactions between epistasis and pleiotropy in generic models of complex traits

Evolution in fluctuating environments depends critically on epistatic interactions between mutations and the pleiotropic effects these mutations have on fitness across multiple conditions. However, while these effects of epistasis and pleiotropy have been extensively studied individually, the interactions between them are less well understood. In this study, we analyze how genetic background shapes the effects of mutations across multiple environmental conditions in generic models of complex traits. We show that when epistasis is widespread and random, systematic patterns of cross-environment pleiotropy can emerge naturally. Specifically we outline two regimes, widespread random epistasis (WRE) and structured pleiotropy (SP), in which patterns of global epistasis in one environment can be predicted from those in other conditions. We outline these predictions in several classes of generic null models, and test these predictions in laboratory budding yeast by measuring the fitness effects of a large set of mutations across many genetic backgrounds in several growth environments. We find that only a subset of mutations fall within the WRE and SP regimes, but that in these cases two of our models provide accurate fits to the data. Finally, we use this framework to show how modular, pleiotropic architectures shape long-term evolutionary steady states in fluctuating environments, revealing how tradeoffs between environments can develop from the geometry of shared latent interaction structure.

evolutionary biology↗

bioRxiv2026

Laboratory yeast crosses reveal limited epistasis in the genetic basis of complex traits

Mapping the genetic basis of complex traits is complicated by the presence of epistatic interactions between loci. While work in molecular genetics identifies numerous specific genetic interactions, statistical analyses of quantitative traits frequently conclude that additive (nonepistatic) models explain most heritable variation. However, these conclusions are typically limited by the narrow range of genetic relatedness(e.g. in F1 offspring of a biparental or circular cross). Here, we use a barcoded panel of Saccharomyces cerevisiae genotypes with a broad range of relatedness to quantify the effects of epistasis on the genetic architecture of seven complex traits. We find limited contributions of epistasis to the genetic basis of these traits. These results indicate that epistasis beyond that detected in standard yeast crosses may exist, yet it contributes little to phenotypic variance in these systems.

genetics↗

bioRxiv2026

DYNAMICS OF CONTAMINANT MICROBES IN BIOETHANOL PRODUCTION FROM SUGARCANE

The dynamics and impact of microbial contaminants in industrial sugarcane bioethanol production in Brazil were investigated through a two-year metagenomic study across two biorefineries. Shotgun metagenomic sequencing revealed that temporal shifts in the contaminant microbiome dynamics within production seasons were more pronounced than inter-annual or inter-mill variations. While Saccharomyces spp. dominated, bacterial communities, primarily within the Firmicutes phylum and dominated by the genera Lactobacillus, Limosilactobacillus, and Bacillus, exhibited dynamic changes. Correlation analyses with industrial process parameters revealed a complex interplay: lower Lactobacillus levels in one mill were associated with increased ethanol yield, whereas higher levels in another mill correlated with reduced yeast viability and increased flocculation. The presence of Limosilactobacillus was linked to decreased yeast viability, whereas Bacillus showed potential for inhibiting both Lactobacillus and Limosilactobacillus. These findings highlight the nuanced and species-specific impacts of bacterial contaminants on bioethanol production, underscoring the need for strain-level functional studies and targeted interventions to optimize fermentation efficiency and stability in industrial settings.

microbiology↗

bioRxiv2025

Impact of fluctuating environments on the fitness and robustness of evolving laboratory and industrial Saccharomyces cerevisiae strains

Adaptation occurs through the selection of beneficial mutations enhancing fitness in a specific environment. However, since environments vary across time and space, mutations that are positively selected in one environment may be less beneficial or detrimental in others. Here, we investigate the evolution of microbial robustness (i.e. a consistent fitness across many diverse environments) through the adaptive evolution of two genetically distinct Saccharomyces cerevisiae populations in fluctuating conditions, followed by fitness assays and whole-genome sequencing. Our results indicate that the haploid laboratory strain S288C achieved higher average fitness than its parental strain, particularly when evolved in fluctuating environments compared to constant environments, but did not show increased robustness. In contrast, populations of the industrial diploid strain Ethanol Red failed to achieve significant fitness improvement under both fluctuating and constant evolution regimes but became more robust. Populations that adapted to fluctuating conditions acquired mutations in genes involved with cell morphology and protein degradation. Overall, our results emphasise the importance of parental traits in shaping fitness and robustness during adaptive laboratory evolution.

evolutionary biology↗

bioRxiv2025

Epistasis and background dependence in the evolution of Omicron variants of the SARS-CoV-2 Spike protein

The rapid and repeated emergence of SARS-CoV-2 variants, particularly within the Omicron lineage, highlights the viruss remarkable ability to adapt under shifting immune pressures. A central molecular battleground in this evolutionary arms race is the spike receptor-binding domain (RBD), which must simultaneously maintain high affinity for the human ACE2 receptor while evading recognition by neutralizing antibodies. In this study, we construct and analyze multiple combinatorial libraries of SARS-CoV-2 RBD variants spanning major branches of Omicron evolution, including BA.1, BA.2, BA.5, XBB, and JN.1. Using high-throughput yeast display and binding assays, we map the effects of thousands of mutations and their combinations on ACE2 binding and antibody evasion. Our results reveal that while many RBD mutations exhibit additive effects, several mutations interact epistatically in a background-dependent manner. In particular, we identify synergistic interactions between BA.1 and BA.5 mutations that enhance antibody evasion, likely facilitating the rise of recombinant variants and convergent evolution. Conversely, some mutations show lineage-restricted compatibility, suggesting potential constraints on future evolutionary trajectories. Our comprehensive genotype-to-phenotype maps uncover both rugged and smooth regions of the viral fitness landscape and underscore the importance of epistasis in shaping SARS-CoV-2 evolution. These findings improve our ability to anticipate future viral variants and provide a framework for understanding how host-pathogen co-evolution unfolds at the molecular level.

evolutionary biology↗

bioRxiv2025

Parallel but distinct adaptive routes in the budding and fission yeasts after 10,000 generations of experimental evolution

Quantitative genetics approaches designed to understand the evolution of traits have helped improve our understanding of the genetic basis of adaptation. However, they often overlook crucial aspects of adaptation, including the long-term temporal evolutionary dynamics, the predictability of evolutionary outcomes, the influence of past evolution on future evolutionary trajectories (contingency), and the diversity of molecular mechanisms underlying adaptation. Experimental evolution has been a useful tool for answering these questions, but extracting fundamental principles and predictive features of evolutionary outcomes from these datasets remains challenging due to the large number of covariates and confounding effects, such as differences in experimental setup, species lifestyle, gene content, and evolution rate. Here, we sought to circumvent these challenges by comparing distant yeast species that share several evolutionary features but differ mainly in evolutionary history and genome architecture, i.e. Saccharomyces cerevisiae and Schizosaccharomyces pombe. Thus, we evolved 10 populations of the fission yeast for 10,000 generations in the same conditions as a pre-existing budding yeast dataset (i.e. high-sugar media and hypoxic conditions), allowing us to observe repeatable evolutionary outcomes within species but diverse molecular mechanisms and targets of adaptation across species. The most frequent adapting route in these conditions involved upregulating fermentation genes and downregulating the glycolysis gene pyk1, which has not previously been observed in S. cerevisiae evolved populations or in wild Kluyveromyces lactis, but similar evolutionary paths have been observed in Schizosaccharomyces japonicus and in clinically relevant populations, such as some cancer cells. This suggests that parallelism is pervasive in the tree of life and that mechanisms of adaptation can be shared among closely related or distant species. Despite similar gene content and identical environments, recurrent adaptation across S. pombe populations involved different genes than in S. cerevisiae and was mostly detectable at the transcriptomic level. This suggests that trans-regulatory effects may play an important role in adaptation on short evolutionary timescales and that differences in evolutionary outcomes between these species may be attributed to contingency.

evolutionary biology↗

bioRxiv2025

Amino acid exchangeability and surface accessibility underpin the effects of single substitutions

Deep mutational scans have measured the effects of many mutations on many different proteins. Here we use a collection of such scans to perform a statistical meta-analysis of the effects of single amino acid substitutions. Specifically, we model the relative deleteriousness of each substitution in each deep mutational scan with respect to the identities of the wildtype and mutant residues, and the wildtype residues surface accessibility. This model explains much of the variance in mutational effects and quantifies physicochemical trends underlying them, including by yielding an empirical amino acid exchangeability matrix.

bioinformatics↗

bioRxiv2025

Inferring genotype-phenotype maps using attention models

Predicting phenotype from genotype is a central challenge in genetics. Traditional approaches in quantitative genetics typically analyze this problem using methods based on linear regression. These methods generally assume that the genetic architecture of complex traits can be parameterized in terms of an additive model, where the effects of loci are independent, plus (in some cases) pair-wise epistatic interactions between loci. However, these models struggle to analyze more complex patterns of epistasis or subtle gene-environment interactions. Recent advances in machine learning, particularly attention-based models, offer a promising alternative. Initially developed for natural language processing, attention-based models excel at capturing context-dependent interactions and have shown exceptional performance in predicting protein structure and function. Here, we apply attention-based models to quantitative genetics. We analyze the performance of this attention-based approach in predicting phenotype from genotype using simulated data across a range of models with increasing epistatic complexity, and using experimental data from a recent quantitative trait locus mapping study in budding yeast. We find that our model demonstrates superior out-of-sample predictions in epistatic regimes compared to standard methods. We also explore a more general multi-environment attention-based model to jointly analyze genotype-phenotype maps across multiple environments and show that such architectures can be used for "transfer learning" - predicting phenotypes in novel environments with limited training data.

genetics↗

bioRxiv2025

Sex decreases the pleiotropic costs of local adaptation

Understanding the evolutionary mechanisms that maintain sex throughout nature despite its substantial direct costs is a longstanding challenge in biology. Previous work has shown that sexual recombination provides a key advantage in speeding adaptation, in part by separating beneficial mutations from deleterious hitchhikers. However, these earlier studies have focused on the effects of sex in a constant environment. Here, we show that recombination also provides a key advantage in fluctuating conditions, promoting the evolution of generalist phenotypes by reducing the pleiotropic costs of local adaptation. Using laboratory evolution in S. cerevisiae as a model system, we show that hitchhiking genetic load leads to pleiotropic costs and hence specialization in response to local adaptation in asexual but not in sexual lineages. This provides the first direct evidence that sex can be maintained over longer evolutionary timescales because it enables lineages to persist in the face of environmental change.

evolutionary biology↗

bioRxiv2024

Effects of selection stringency on the outcomes of directed evolution

Directed evolution makes mutant lineages compete in climbing complicated sequence-function landscapes. Given this underlying complexity it is unclear how selection stringency, a ubiquitous parameter of directed evolution, impacts the outcome. Here we approach this question in terms of the fitnesses of the candidate variants at each round and the heterogeneity of their distributions of fitness effects. We show that even if the fittest mutant is most likely to yield the fittest mutants in the next round of selection, diversification can improve outcomes by sampling a larger variety of fitness effects. We find that heterogeneity in fitness effects between variants, larger population sizes, and evolution over a greater number of rounds all encourage diversification.

bioengineering↗

bioRxiv2024

Protein sequence landscapes are not so simple: on reference-free versus reference-based inference

In a recent preprint, Park, Metzger, and Thornton reanalyze 20 empirical protein sequence-function landscapes using a "reference-free analysis" (RFA) method they recently developed. They argue that these empirical landscapes are simpler and less epistatic than earlier work suggested, and attribute the difference to limitations of the methods used in the original analyses of these landscapes, which they claim are more sensitive to measurement noise, missing data, and other artifacts. Here, we show that these claims are incorrect. Instead, we find that the RFA method introduced by Park et al. is exactly equivalent to the reference-based least-squares methods used in the original analysis of many of these empirical landscapes (and also equivalent to a Hadamard-based approach they implement). Because the reanalyzed and original landscapes are in fact identical, the different conclusions drawn by Park et al. instead reflect different interpretations of the parameters describing the inferred landscapes; we argue that these do not support the conclusion that epistasis plays only a small role in protein sequence-function landscapes.

evolutionary biology↗

bioRxiv2023

A dynamical limit to evolutionary adaptation

Natural selection makes evolutionary adaptation possible even if the overwhelming majority of new mutations are deleterious. However, in rapidly evolving populations where numerous linked mutations occur and segregate simultaneously, clonal interference and genetic hitchhiking can limit the efficiency of selection, allowing deleterious mutations to accumulate over time. This can in principle overwhelm the fitness increases provided by beneficial mutations, leading to an overall fitness decline. Here, we analyze the conditions under which evolution will tend to drive populations to higher versus lower fitness. Our analysis focuses on quantifying the boundary between these two regimes, as a function of parameters such as population size, mutation rates, and selection pressures. This boundary represents a state in which adaptation is precisely balanced by Mullers ratchet, and we show that it can be characterized by rapid molecular evolution without any net fitness change. Finally, we consider the implications of global fitness-mediated epistasis, and find that under some circumstances this can drive populations towards the boundary state, which can thus represent a long-term evolutionary attractor.

evolutionary biology↗

bioRxiv2023

Far From Home: Evolution of haploid and diploid populations reveals common, strong, and variable pleiotropic effects in non-home environments

Adaptation is driven by the selection for beneficial mutations that provide a fitness advantage in the specific environment in which a population is evolving. However, environments are rarely constant or predictable. When an organism well adapted to one environment finds itself in another, pleiotropic effects of mutations that made it well adapted to its former environment will affect its success. To better understand such pleiotropic effects, we evolved both haploid and diploid barcoded budding yeast populations in multiple environments, isolated adaptive clones, and then determined the fitness effects of adaptive mutations in "non-home" environments in which they were not selected. We find that pleiotropy is common, with most adaptive evolved lineages showing fitness effects in non-home environments. Consistent with other studies, we find that these pleiotropic effects are unpredictable: they are beneficial in some environments and deleterious in others. However, we do find that lineages with adaptive mutations in the same genes tend to show similar pleiotropic effects. We also find that ploidy influences the observed adaptive mutational spectra in a condition-specific fashion. In some conditions, haploids and diploids are selected with adaptive mutations in identical genes, while in others they accumulate mutations in almost completely disjoint sets of genes.

evolutionary biology↗

bioRxiv2022

Yeast population dynamics in Brazilian bioethanol production

The large scale and non-aseptic fermentation of sugarcane feedstocks into fuel ethanol in biorefineries represents a unique ecological niche, in which the yeast Saccharomyces cerevisiae is the predominant organism. Several factors, such as sugarcane variety, process design, and operating and weather conditions, make each of the [~]400 industrial units currently operating in Brazil a unique ecosystem. Here, we track yeast population dynamics in two different biorefineries through two production seasons (April to November of 2018 and 2019), using a novel statistical framework on a combination of metagenomic and clonal sequencing data. We find that variation from season to season in one biorefinery is small compared to the differences between the two units. In one biorefinery, all lineages present during the entire production period derive from one of the starter strains, while in the other, invading lineages took over the population and displaced the starter strain. However, despite the presence of invading lineages and the non-aseptic nature of the process, all yeast clones we isolated are phylogenetically related to other previously sequenced bioethanol yeast strains, indicating a common origin from this industrial niche. Despite the substantial changes observed in yeast populations through time in each biorefinery, key process indicators remained quite stable through both production seasons, suggesting that the process is robust to the details of these population dynamics.

evolutionary biology↗

bioRxiv2022

Inferring sparse structure in genotype-phenotype maps

Correlation among multiple phenotypes across related individuals may reflect some pattern of shared genetic architecture: individual genetic loci affect multiple phenotypes (an effect known as pleiotropy), creating observable relationships between phenotypes. A natural hypothesis is that pleiotropic effects reflect a relatively small set of common "core" cellular processes: each genetic locus affects one or a few core processes, and these core processes in turn determine the observed phenotypes. Here, we propose a method to infer such structure in genotype-phenotype data. Our approach, sparse structure discovery (SSD) is based on a penalized matrix decomposition designed to identify latent structure that is low-dimensional (many fewer core processes than phenotypes and genetic loci), locus-sparse (each locus affects few core processes) and/or phenotype-sparse (each phenotype is influenced by few core processes). Our use of sparsity as a guide in the matrix decomposition is motivated by the results of a novel empirical test indicating evidence of sparse structure in several recent genotype-phenotype data sets. First, we use synthetic data to show that our SSD approach can accurately recover core processes if each genetic locus affects few core processes or if each phenotype is affected by few core processes. Next, we apply the method to three datasets spanning adaptive mutations in yeast, genotoxin robustness assay in human cell lines, and genetic loci identified from a yeast cross, and evaluate the biological plausibility of the core process identified. More generally, we propose sparsity as a guiding prior for resolving latent structure in empirical genotype-phenotype maps.

evolutionary biology↗

bioRxiv2022

Hierarchical sequence-affinity landscapes shape the evolution of breadth in an anti-influenza receptor binding site antibody

Broadly neutralizing antibodies (bnAbs) that neutralize diverse variants of a particular virus are of considerable therapeutic interest1. Recent advances have enabled us to isolate and engineer these antibodies as therapeutics, but eliciting them through vaccination remains challenging, in part due to our limited understanding of how antibodies evolve breadth2. Here, we analyze the landscape by which an anti-influenza receptor binding site (RBS) bnAb, CH65, evolved broad affinity to diverse H1 influenza strains3, 4. We do this by generating an antibody library of all possible evolutionary intermediates between the unmutated common ancestor (UCA) and the affinity-matured CH65 antibody and measure the affinity of each intermediate to three distinct H1 antigens. We find that affinity to each antigen requires a specific set of mutations - distributed across the variable light and heavy chains - that interact non-additively (i.e., epistatically). These sets of mutations form a hierarchical pattern across the antigens, with increasingly divergent antigens requiring additional epistatic mutations beyond those required to bind less divergent antigens. We investigate the underlying biochemical and structural basis for these hierarchical sets of epistatic mutations and find that epistasis between heavy chain mutations and a mutation in the light chain at the VH-VL interface is essential for binding a divergent H1. Collectively, this work is the first to comprehensively characterize epistasis between heavy and light chain mutations and shows that such interactions are both strong and widespread. Together with our previous study analyzing a different class of anti-influenza antibodies5, our results implicate epistasis as a general feature of antibody sequence-affinity landscapes that can potentiate and constrain the evolution of breadth.

evolutionary biology↗