Biology subjects

Weinreich, D. M.

Publications and source records attributed to Weinreich, D. M..

13 recordsLinked to original sources

bioRxiv2025

The Population Genetics of Biological Noise

Information transmission is intrinsic to life, and noise is intrinsic to information transmission. Biological noise during development is essential for the flexibility and plasticity of individual organisms, but also underlies some diseases. Biological noise during reproduction is the fuel for evolution, including the evolution of therapy resistance in pathogenic microbes and in cancer. Recent technological advances in our ability to characterize many sources of biological noise have demonstrated that its amount is often heritable. Here, we frame the population genetics of loci that influence the amount of any source of biological noise. While analogous theory for heritable changes in mean trait values has been established for nearly a century, to our knowledge this is the first general approach for studying the evolution of heritable changes in their statistical distributions. This represents a critical theoretical contribution to an important and rapidly growing domain of intellectual inquiry. It also sheds light on the hypothesis that natural selection can increase evolvability, and generalizes modifier theory used in the tradition of Feldman and colleagues. Author summaryBiological noise is a fact of life. Genetically identical organisms reared in identical environments invariably exhibit random phenotypic differences. And siblings born of the same parent(s) in the same environment are endowed with inheritances that invariably differ at random. While the specific consequences of biological noise are unpredictable, extraordinary experimental advances now make clear that its amount can be influenced by an organisms genetics. For example, high- and low-noise promoter, and high- and low-noise DNA polymerase alleles are well known. This raises the question of when and how natural selection favors high- or low-noise alleles. While biological noise is on average deleterious, it can also occasionally induce high fitness phenotypes. Here, we solve a simple analytic model for the fitness difference between noise alleles that captures both these features. Our model predicts the existence of an evolutionary equilibrium in the amount of noise, whose location reflects just four features of an organisms biology. In light the clinical importance of biological noise, as well as its central role more broadly in both development and evolution, this work provides an urgently needed evolutionary framework for understanding its long-term determinants.

evolutionary biology↗

bioRxiv2024

Balancing the risks of mating: biogeographic evidence of cleistogamy as a bet hedging strategy

Cleistogamy is a mating system in which plants produce some proportion of closed, autonomously self-pollinating flowers. Cleistogamous flowers differ from chasmogamous flowers, which are open flowers capable of outcrossing. Both dimorphic cleistogamy (cleistogamous and chasmogamous flowers produced on the same plant) and complete cleistogamy occur. Cleistogamy has been hypothesized to be a bet hedging strategy for reducing risk in the face of unpredictable pollinator availability. However, conflicting results across species and challenges connecting theory to data have prevented researchers from proving that cleistogamy is bet hedging. To test the bet hedging hypothesis, we investigated the distribution of over 400 cleistogamous species through biogeographical analyses. We find that cleistogamy is more prevalent in cooler, more variable environments. Additionally, we find that among cleistogamous species, complete cleistogamy is more likely to occur in warmer, more stable, tropical and subtropical environments. We hypothesize that the difference in distribution between complete and dimorphic cleistogamy may be driven by the opposing forces of selection to increase cleistogamy proportion and extinction risk, which we test using a heuristic Markov transition model. We conclude that the distribution of cleistogamy suggests that the strategy has evolved in variable environments, consistent with expectations for bet hedging.

evolutionary biology↗

bioRxiv2023

Beyond the (geometric) mean: stochastic models undermine deterministic predictions of bet hedger evolution

Bet hedging is a ubiquitous strategy for risk reduction in the face of unpredictable environmental change where a lineage lowers its variance in fitness across environments at the expense of also lowering its arithmetic mean fitness. Classically, the benefit of bet hedging has been quantified using geometric mean fitness (GMF); bet hedging is expected to evolve if and only if it has a higher GMF than the wild-type. We build upon previous research on the effect of incorporating stochasticity in phenotypic distribution, environment, and reproduction to investigate the extent to which these sources of stochasticity will impact the evolution of real-world bet hedging traits. We utilize both individual-based simulations and Markov chain numerics to demonstrate that modeling stochasticity can alter the sign of selection for the bet hedger compared to deterministic predictions. We find that bet hedging can be deleterious at small population sizes and beneficial at larger population sizes. This non-monotonic dependence of the sign of selection on population size, known as sign inversion, exists across parameter space for both conservative and diversified bet hedgers. We apply our model to published data of bet hedging strategies to show that sign inversion exists for biologically relevant parameters in two study systems: Papaver dubium, an annual poppy with variable germination phenology, and Salmonella typhimurium, a pathogenic bacteria that exhibits antibiotic persistence. Taken together, our results suggest that GMF is not enough to predict when bet hedging is adaptive.

evolutionary biology↗

bioRxiv2022

Leveraging the Genetic Correlation between Traits Improves the Detection of Epistasis in Genome-wide Association Studies

Epistasis, commonly defined as the interaction between genetic loci, is known to play an important role in the phenotypic variation of complex traits. As a result, many statistical methods have been developed to identify genetic variants that are involved in epistasis, and nearly all of these approaches carry out this task by focusing on analyzing one trait at a time. Previous studies have shown that jointly modeling multiple phenotypes can often dramatically increase statistical power for association mapping. In this study, we present the "multivariate MArginal ePIstasis Test" (mvMAPIT) -- a multi-outcome generalization of a recently proposed epistatic detection method which seeks to detect marginal epistasis or the combined pairwise interaction effects between a given variant and all other variants. By searching for marginal epistatic effects, one can identify genetic variants that are involved in epistasis without the need to identify the exact partners with which the variants interact -- thus, potentially alleviating much of the statistical and computational burden associated with conventional explicit search-based methods. Our proposed mvMAPIT builds upon this strategy by taking advantage of correlation structure between traits to improve the identification of variants involved in epistasis. We formulate mvMAPIT as a multivariate linear mixed model and develop a multi-trait variance component estimation algorithm for efficient parameter inference and P-value computation. Together with reasonable model approximations, our proposed approach is scalable to moderately sized GWA studies. With simulations, we illustrate the benefits of mvMAPIT over univariate (or single-trait) epistatic mapping strategies. We also apply mvMAPIT framework to protein sequence data from two broadly neutralizing anti-influenza antibodies and approximately 2,000 heterogenous stock of mice from the Wellcome Trust Centre for Human Genetics. The mvMAPIT R package can be downloaded at https://github.com/lcrawlab/mvMAPIT.

genetics↗

bioRxiv2021

Sign inversion in selection on ploidy

Ploidy - the number of homologous chromosome sets in a cell - is remarkably variable across the natural world, yet the evolutionary processes that have resulted in such diversity remain poorly understood. Here we use stochastic agent-based simulations to model ploidy evolution under the influence of indirect selection, i.e., selection mediated solely by statistical associations with fitness-affecting mutations. We find that in non-equilibrium asexual populations, the sign of selection on ploidy can change with population size - a phenomenon we have previously termed sign inversion. In large populations, ploidy dynamics are dominated by indirect effects of selection on beneficial mutations, which favors haploids over diploids. However, as population size declines, selection for beneficial mutations is neutralized by random genetic drift before drift can overwhelm selection against the cost of the deleterious mutational load. As a result, in small populations indirect selection is dominated by the cost of the deleterious load, which favors diploids over haploids. Our work adds to the growing body of evidence challenging established evolutionary theory that population size can affect only the efficiency, but not the sign, of natural selection.

evolutionary biology↗

bioRxiv2021

When good mutations go bad: how population size can change the direction of natural selection

Classical evolutionary theory holds that the efficiency, but not the direction, of natural selection depends on population size. In small populations, drift overwhelms selection, rendering all fitness-affecting mutations selectively neutral. Yet, beneficial mutations never become deleterious and deleterious mutations never become beneficial. Remarkably, several mutations, including in modifiers of recombination and mutation rate, have now been shown to be favored at some population sizes but disfavored at others, challenging established theory. Previously, we have designated this phenomenon sign inversion. Here we show that, unlike selected mutations in the classical framework, mutations susceptible to sign inversion confer both fitness costs and fitness benefits, that vary among their carriers. Furthermore, all such mutations can be classified based on whether their effects differ between or within mutant lineages. Using computer simulations, we demonstrate that both between-lineage and within-lineage variability can cause sign inversion and elucidate the common underlying mechanism. Our results confirm that variability in the sign of selective effects is necessary for sign inversion, which occurs because drift overwhelms selection on carriers bearing the cost and carriers enjoying the benefit at different population sizes.

evolutionary biology↗

bioRxiv2021

In vitro and in vivo preclinical studies predict REGEN-COV protection against emergence of viral escape in humans

Monoclonal antibodies against SARS-CoV-2 are a clinically validated therapeutic option against COVID-19. As rapidly emerging virus mutants are becoming the next major concern in the fight against the global pandemic, it is imperative that these therapeutic treatments provide coverage against circulating variants and do not contribute to development of treatment emergent resistance. To this end, we investigated the sequence diversity of the spike protein and monitored emergence of minor virus variants in SARS-COV-2 isolates found in COVID-19 patients or identified from preclinical in vitro and in vivo studies. This study demonstrates that a combination of non-competing antibodies, REGEN-COV, not only provides full coverage against current variants of concern/interest but also protects against emergence of new such variants and their potential seeding into the population in a clinical setting.

microbiology↗

bioRxiv2020

Predicting the Viability of Beta-Lactamase: How Folding and Binding Free Energies Correlate with Beta-Lactamase Fitness

One of the long-standing holy grails of molecular evolution has been the ability to predict an organisms fitness directly from its genotype. With such predictive abilities in hand, researchers would be able to more accurately forecast how organisms will evolve and how proteins with novel functions could be engineered, leading to revolutionary advances in medicine and biotechnology. In this work, we assemble the largest reported set of experimental TEM-1 {beta}-lactamase folding free energies and use this data in conjunction with previously acquired fitness data and computational free energy predictions to determine how much of the fitness of {beta}-lactamase can be directly predicted by thermodynamic folding and binding free energies. We focus upon {beta}-lactamase because of its long history as a model enzyme and its central role in antibiotic resistance. Based upon a set of 21 {beta}-lactamase single and double mutants expressly designed to influence protein folding, we first demonstrate that modeling software such as FoldX and PyRosetta designed to compute folding free energies can meaningfully, although not perfectly, predict the experimental folding free energies of single mutants. Interestingly, while these techniques also yield sensible double mutant free energies, we show that they do so for the wrong physical reasons. We then go on to assess how well both experimental and computational folding free energies explain single mutant fitness. We find that folding free energies account for, at most, 24% of the variance in {beta}-lactamase fitness values according to linear models and, somewhat surprisingly, complementing folding free energies with computationally-predicted binding free energies of residues near the active site only increases the folding-only figure by a few percent. This strongly suggests that the majority of {beta}-lactamases fitness is controlled by factors other than free energies. Overall, our results shed a bright light on to what extent the community is justified in using thermodynamic measures to infer protein fitness as well as how applicable modern computational techniques for predicting free energies will be to the large data sets of multiply-mutated proteins forthcoming.

biophysics↗

bioRxiv2019

Selection on mutators is not frequency-dependent

The evolutionary fate of mutator mutations - i.e., genetic variants that raise the genome-wide mutation rate - in asexual populations is often described as being frequency (or number) dependent. This common intuition suggests that mutators can invade a population by hitchhiking with a sweeping beneficial mutation, but only when sufficiently frequent to produce such a mutation before non-mutators do. Here, we use stochastic, agent-based simulations to show that neither the strength nor the sign of selection on mutators depend on their initial frequency, and while the overall probability of hitchhiking increases predictably with frequency, the per-capita probability of fixation remains unchanged.

evolutionary biology↗

bioRxiv2017

The influence of higher-order epistasis on biological fitness landscape topography

The effect of a mutation on the organism often depends on what other mutations are already present in its genome. Geneticists refer to such mutational interactions as epistasis. Pairwise epistatic effects have been recognized for over a century, and their evolutionary implications have received theoretical attention for nearly as long. However, pairwise epistatic interactions themselves can vary with genomic background. This is called higher-order epistasis, and its consequences for evolution are much less well understood. Here, we assess the influence that higher-order epistasis has on the topography of 16 published, biological fitness landscapes. We find that on average, their effects on fitness landscape declines with order, and suggest that notable exceptions to this trend may deserve experimental scrutiny. We explore whether natural selection may have contributed to this finding, and conclude by highlight opportunities for further work dissecting the influence that epistasis of all orders has on the efficiency of natural selection.

evolutionary biology↗

bioRxiv2017

Sign of selection on mutation rate modifiers depends on population size

The influence of population size (N) on natural selection acting on alleles that affect fitness has been understood for over half a century1. As N declines, genetic drift overwhelms selection and alleles with direct fitness effects are rendered neutral. Often, though, alleles experience so called indirect selection, meaning they affect not the fitness of an individual but the fitness distribution of its offspring. Some of the best studied examples of indirect selection include alleles that modify aspects of the genetic system such as recombination2 and mutation3 rates. Here we use analytics, simulations and experimental populations of S. cerevisiae to show that modifiers that increase the genomic mutation rate (mutators) are favored by indirect selection in large populations but become disfavored as N declines. This surprising phenomenon of sign inversion in selective effect demonstrates that indirect selection on a mutator exhibits a qualitatively novel dependence on N. Sign inversion may help understand the relatively sporadic distribution of mutators in nature despite their frequent emergence in laboratory populations. More generally, sign inversion may be broadly applicable to other instances of indirect selection, suggesting a previously unappreciated but critical role of population size in evolution.

evolutionary biology↗

bioRxiv2017

Natural selection can favor the evolution of ratchet robustness over evolution of mutational robustness

The vast majority of fitness-affecting mutations are deleterious. How natural populations evolve to cope is a question of fundamental interest. Previous studies have reported the evolution of mutational robustness, that is, natural selection favoring populations with less deleterious mutations. By definition, mutational robustness provides a short-term fitness advantage. However, this overlooks the fact that mutational robustness decreases finite asexual populations ability to purge recurrent deleterious mutations. Thus, mutational robustness also results in higher risk of long-term extinction by Mullers ratchet. Here, we explore the tension between short- and long- term response to deleterious mutations. We first show that populations can resist the ratchet if either the selection coefficient or the ratio of beneficial to deleterious mutations increases as fitness declines. We designate these properties as ratchet robustness, which fundamentally reflects a negative feedback between mutation rate and the tendency to accumulate more mutations. We also find in simulations that populations can evolve ratchet robustness when challenged by deleterious mutations. We conclude that mutational robustness cannot be selected for in the long term, but it can be favored in the short-term, purely because of temporary fitness advantage. We also discuss other potential causes of mutational robustness in nature.

evolutionary biology↗

bioRxiv2017

Variability in fitness effects and the limitations of lineage selection

Evolutionary biologists commonly assess the evolutionary advantage of an allele based on its effects on the lifetime survival and reproduction of individuals. However, alleles affecting traits like sex, evolvability, and cooperation can cause fitness effects that depend heavily on differences in the environmental, genetic, and social context of individuals carrying the allele. This variability makes it difficult to summarize the evolutionary fate of an allele based solely on its effects on any one individual. In this review we show how attempts to average over variability in the fitness effects of an allele can sometimes cause misleading results. We then describe a number of intriguing new evolutionary phenomena that have emerged in studies that explicitly model the fate of alleles that influence long-term lineage dynamics. We conclude with prospects for generalizations of population genetics theory and discuss how this theory might be applied to the evolution of infectious diseases.

evolutionary biology↗