Bias and Evolution of the Mutationally Accessible Phenotypic Space in a Developmental System

Genetic and developmental architecture may bias the mutationally available phenotypic spectrum. Although such asymmetries in the introduction of variation may influence possible evolutionary trajectories, we lack quantitative characterization of biases in mutationally inducible phenotypic variation, their genotype-dependence, and their underlying molecular and developmental causes. Here we quantify the mutationally accessible phenotypic spectrum of the vulval developmental system using mutation accumulation (MA) lines derived from four wild isolates of the nematodes Caenorhabditis elegans and C. briggsae. The results confirm that on average, spontaneous mutations degrade developmental precision, with MA lines showing a low, yet consistently increased, proportion of developmental defects and variants. This result indicates strong purifying selection acting to maintain an invariant vulval phenotype. Both developmental system and genotype significantly bias the spectrum of mutationally inducible phenotypic variants. First, irrespective of genotype, there is a developmental bias, such that certain phenotypic variants are commonly induced by MA, while others are very rarely or never induced. Second, we found that both the degree and spectrum of mutationally accessible phenotypic variation are genotype-dependent. Overall, C. briggsae MA lines exhibited a two-fold higher decline in precision than the C. elegans MA lines. Moreover, the propensity to generate specific developmental variants depended on the genetic background. We show that such genotype-specific developmental biases are likely due to cryptic quantitative variation in activities of underlying molecular cascades. This analysis allowed us to identify the mutationally most sensitive elements of the vulval developmental system, which may indicate axes of potential evolutionary variation. Consistent with this scenario, we found that evolutionary trends in the vulval system concern the phenotypic characters that are most easily affected by mutation. This study provides an empirical assessment of developmental bias and the evolution of mutationally accessible phenotypes and supports the notion that such bias may influence the directions of evolutionary change.     

Quantitative evidence for early life fitness defects from 32 longevity-associated alleles in yeast

Reduced fecundity has been associated with some alleles that enhance longevity in invertebrate and mammalian models. This observation has been suggested to support the antagonistic pleiotropy theory of aging, which predicts that alleles of some genes promoting fitness early in life have detrimental effects later in life that limit survival. In only a few cases, however, has the relative fitness of long-lived mutants been quantified through direct competition with the wild type genotype. Here we report the first comprehensive analysis of longevity/fitness trade-offs by measuring the relative fitness of 49 long-lived yeast variants in a direct competition assay with wild type cells. We find that 32 (65%) of these variants show a significant defect in fitness in this competition assay. In 26 (81%) of these cases, this reduction in fitness can be partially accounted for by reduced maximal growth rate during early life, usually resulting from a G₀/G₁-specific cell cycle defect. A majority of the less fit longevity-enhancing variants are associated with reduced mRNA translation. These findings are therefore consistent with the idea that enhanced longevity often comes with a fitness cost and suggest that this cost is often associated with variation in a subset of longevity factors, such as those regulating mRNA translation, growth and reproduction.Key words: yeast, aging, antagonistic pleiotropy, fitness, translation, longevity, evolution     

The ascent of the abundant: how mutational networks constrain evolution

Evolution by natural selection is fundamentally shaped by the fitness landscapes in which it occurs. Yet fitness landscapes are vast and complex, and thus we know relatively little about the long-range constraints they impose on evolutionary dynamics. Here, we exhaustively survey the structural landscapes of RNA molecules of lengths 12 to 18 nucleotides, and develop a network model to describe the relationship between sequence and structure. We find that phenotype abundance—the number of genotypes producing a particular phenotype—varies in a predictable manner and critically influences evolutionary dynamics. A study of naturally occurring functional RNA molecules using a new structural statistic suggests that these molecules are biased toward abundant phenotypes. This supports an “ascent of the abundant” hypothesis, in which evolution yields abundant phenotypes even when they are not the most fit.     

The Fitness Consequences of Aneuploidy Are Driven by Condition-Dependent Gene Effects

Aneuploidy is a hallmark of tumor cells, and yet the precise relationship between aneuploidy and a cell’s proliferative ability, or cellular fitness, has remained elusive. In this study, we have combined a detailed analysis of aneuploid clones isolated from laboratory-evolved populations of Saccharomyces cerevisiae with a systematic, genome-wide screen for the fitness effects of telomeric amplifications to address the relationship between aneuploidy and cellular fitness. We found that aneuploid clones rise to high population frequencies in nutrient-limited evolution experiments and show increased fitness relative to wild type. Direct competition experiments confirmed that three out of four aneuploid events isolated from evolved populations were themselves sufficient to improve fitness. To expand the scope beyond this small number of exemplars, we created a genome-wide collection of >1,800 diploid yeast strains, each containing a different telomeric amplicon (Tamp), ranging in size from 0.4 to 1,000 kb. Using pooled competition experiments in nutrient-limited chemostats followed by high-throughput sequencing of strain-identifying barcodes, we determined the fitness effects of these >1,800 Tamps under three different conditions. Our data revealed that the fitness landscape explored by telomeric amplifications is much broader than that explored by single-gene amplifications. As also observed in the evolved clones, we found the fitness effects of most Tamps to be condition specific, with a minority showing common effects in all three conditions. By integrating our data with previous work that examined the fitness effects of single-gene amplifications genome-wide, we found that a small number of genes within each Tamp are centrally responsible for each Tamp’s fitness effects. Our genome-wide Tamp screen confirmed that telomeric amplifications identified in laboratory-evolved populations generally increased fitness. Our results show that Tamps are mutations that produce large, typically condition-dependent changes in fitness that are important drivers of increased fitness in asexually evolving populations.     

Evolution in a Changing Environment

We propose a simple model for genetic adaptation to a changing environment, describing a fitness landscape characterized by two maxima. One is associated with “specialist” individuals that are adapted to the environment; this maximum moves over time as the environment changes. The other maximum is static, and represents “generalist” individuals not affected by environmental changes. The rest of the landscape is occupied by “maladapted” individuals. Our analysis considers the evolution of these three subpopulations. Our main result is that, in presence of a sufficiently stable environmental feature, as in the case of an unchanging aspect of a physical habitat, specialists can dominate the population. By contrast, rapidly changing environmental features, such as language or cultural habits, are a moving target for the genes; here, generalists dominate, because the best evolutionary strategy is to adopt neutral alleles not specialized for any specific environment. The model we propose is based on simple assumptions about evolutionary dynamics and describes all possible scenarios in a non-trivial phase diagram. The approach provides a general framework to address such fundamental issues as the Baldwin effect, the biological basis for language, or the ecological consequences of a rapid climate change.     

Quantitative evidence for early life fitness defects from 32 longevity-associated alleles in yeast

Reduced fecundity has been associated with some alleles that enhance longevity in invertebrate and mammalian models. This observation has been suggested to support the antagonistic pleiotropy theory of aging, which predicts that alleles of some genes promoting fitness early in life have detrimental effects later in life that limit survival. In only a few cases, however, has the relative fitness of long-lived mutants been quantified through direct competition with the wild type genotype. Here we report the first comprehensive analysis of longevity/fitness trade-offs by measuring the relative fitness of 49 long-lived yeast variants in a direct competition assay with wild type cells. We find that 32 (65%) of these variants show a significant defect in fitness in this competition assay. In 26 (81%) of these cases, this reduction in fitness can be partially accounted for by reduced maximal growth rate during early life, usually resulting from a G0/G1-specific cell cycle defect. A majority of the less fit longevity-enhancing variants are associated with reduced mRNA translation. These findings are therefore consistent with the idea that enhanced longevity often comes with a fitness cost and suggest that this cost is often associated with variation in a subset of longevity factors, such as those regulating mRNA translation, growth, and reproduction.     

The Impact of Macroscopic Epistasis on Long-Term Evolutionary Dynamics

Genetic interactions can strongly influence the fitness effects of individual mutations, yet the impact of these epistatic interactions on evolutionary dynamics remains poorly understood. Here we investigate the evolutionary role of epistasis over 50,000 generations in a well-studied laboratory evolution experiment in Escherichia coli. The extensive duration of this experiment provides a unique window into the effects of epistasis during long-term adaptation to a constant environment. Guided by analytical results in the weak-mutation limit, we develop a computational framework to assess the compatibility of a given epistatic model with the observed patterns of fitness gain and mutation accumulation through time. We find that a decelerating fitness trajectory alone provides little power to distinguish between competing models, including those that lack any direct epistatic interactions between mutations. However, when combined with the mutation trajectory, these observables place strong constraints on the set of possible models of epistasis, ruling out many existing explanations of the data. Instead, we find that the data are consistent with a “two-epoch” model of adaptation, in which an initial burst of diminishing-returns epistasis is followed by a steady accumulation of mutations under a constant distribution of fitness effects. Our results highlight the need for additional DNA sequencing of these populations, as well as for more sophisticated models of epistasis that are compatible with all of the experimental data.     

What are housekeeping genes?

The concept of “housekeeping gene” has been used for four decades but remains loosely defined. Housekeeping genes are commonly described as “essential for cellular existence regardless of their specific function in the tissue or organism”, and “stably expressed irrespective of tissue type, developmental stage, cell cycle state, or external signal”. However, experimental support for the tenet that gene essentiality is linked to stable expression across cell types, conditions, and organisms has been limited. Here we use genome-scale functional genomic screens together with bulk and single-cell sequencing technologies to test this link and optimize a quantitative and experimentally validated definition of housekeeping gene. Using the optimized definition, we identify, characterize, and provide as resources, housekeeping gene lists extracted from several human datasets, and 10 other animal species that include primates, chicken, and C. elegans. We find that stably expressed genes are not necessarily essential, and that the individual genes that are essential and stably expressed can considerably differ across organisms; yet the pathways enriched among these genes are conserved. Further, the level of conservation of housekeeping genes across the analyzed organisms captures their taxonomic groups, showing evolutionary relevance for our definition. Therefore, we present a quantitative and experimentally supported definition of housekeeping genes that can contribute to better understanding of their unique biological and evolutionary characteristics.     

Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics

The rate of adaptive evolution depends on the rate at which beneficial mutations are introduced into a population and the fitness effects of those mutations. The rate of beneficial mutations and their expected fitness effects is often difficult to empirically quantify. As these 2 parameters determine the pace of evolutionary change in a population, the dynamics of adaptive evolution may enable inference of their values. Copy number variants (CNVs) are a pervasive source of heritable variation that can facilitate rapid adaptive evolution. Previously, we developed a locus-specific fluorescent CNV reporter to quantify CNV dynamics in evolving populations maintained in nutrient-limiting conditions using chemostats. Here, we use CNV adaptation dynamics to estimate the rate at which beneficial CNVs are introduced through de novo mutation and their fitness effects using simulation-based likelihood–free inference approaches. We tested the suitability of 2 evolutionary models: a standard Wright–Fisher model and a chemostat model. We evaluated 2 likelihood-free inference algorithms: the well-established Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC) algorithm, and the recently developed Neural Posterior Estimation (NPE) algorithm, which applies an artificial neural network to directly estimate the posterior distribution. By systematically evaluating the suitability of different inference methods and models, we show that NPE has several advantages over ABC-SMC and that a Wright–Fisher evolutionary model suffices in most cases. Using our validated inference framework, we estimate the CNV formation rate at the GAP1 locus in the yeast Saccharomyces cerevisiae to be 10^−4.7 to 10⁻⁴ CNVs per cell division and a fitness coefficient of 0.04 to 0.1 per generation for GAP1 CNVs in glutamine-limited chemostats. We experimentally validated our inference-based estimates using 2 distinct experimental methods—barcode lineage tracking and pairwise fitness assays—which provide independent confirmation of the accuracy of our approach. Our results are consistent with a beneficial CNV supply rate that is 10-fold greater than the estimated rates of beneficial single-nucleotide mutations, explaining the outsized importance of CNVs in rapid adaptive evolution. More generally, our study demonstrates the utility of novel neural network–based likelihood–free inference methods for inferring the rates and effects of evolutionary processes from empirical data with possible applications ranging from tumor to viral evolution.

This study shows that simulation-based inference of evolutionary dynamics using neural networks can yield parameter values for fitness and mutation rate that are difficult to determine experimentally, including those of copy number variants (CNVs) during experimental adaptive evolution of yeast.     

Learning the local landscape of protein structures with convolutional neural networks

One fundamental problem of protein biochemistry is to predict protein structure from amino acid sequence. The inverse problem, predicting either entire sequences or individual mutations that are consistent with a given protein structure, has received much less attention even though it has important applications in both protein engineering and evolutionary biology. Here, we ask whether 3D convolutional neural networks (3D CNNs) can learn the local fitness landscape of protein structure to reliably predict either the wild-type amino acid or the consensus in a multiple sequence alignment from the local structural context surrounding site of interest. We find that the network can predict wild type with good accuracy, and that network confidence is a reliable measure of whether a given prediction is likely going to be correct or not. Predictions of consensus are less accurate and are primarily driven by whether or not the consensus matches the wild type. Our work suggests that high-confidence mis-predictions of the wild type may identify sites that are primed for mutation and likely targets for protein engineering.

     

Balance between promiscuity and specificity in phage λ host range

As hosts acquire resistance to viruses, viruses must overcome that resistance to re-establish infectivity, or go extinct. Despite the significant hurdles associated with adapting to a resistant host, viruses are evolutionarily successful and maintain stable coevolutionary relationships with their hosts. To investigate the factors underlying how pathogens adapt to their hosts, we performed a deep mutational scan of the region of the λ tail fiber tip protein that mediates contact with the receptor on λ’s host, Escherichia coli. Phages harboring amino acid substitutions were subjected to selection for infectivity on wild type E. coli, revealing a highly restrictive fitness landscape, in which most substitutions completely abrogate function. A subset of positions that are tolerant of mutation in this assay, but diverse over evolutionary time, are associated with host range expansion. Imposing selection for phage infectivity on three λ-resistant hosts, each harboring a different missense mutation in the λ receptor, reveals hundreds of adaptive variants in λ. We distinguish λ variants that confer promiscuity, a general ability to overcome host resistance, from those that drive host-specific infectivity. Both processes may be important in driving adaptation to a novel host.Subject terms: Bacteriophages, Molecular evolution, Viral genetics     

A Study on the Fundamental Mechanism and the Evolutionary Driving Forces behind Aerobic Fermentation in Yeast

Baker’s yeast Saccharomyces cerevisiae rapidly converts sugars to ethanol and carbon dioxide at both anaerobic and aerobic conditions. The later phenomenon is called Crabtree effect and has been described in two forms, long-term and short-term effect. We have previously studied under fully controlled aerobic conditions forty yeast species for their central carbon metabolism and the presence of long-term Crabtree effect. We have also studied ten steady-state yeast cultures, pulsed them with glucose, and followed the central carbon metabolism and the appearance of ethanol at dynamic conditions. In this paper we analyzed those wet laboratory data to elucidate possible mechanisms that determine the fate of glucose in different yeast species that cover approximately 250 million years of evolutionary history. We determine overflow metabolism to be the fundamental mechanism behind both long- and short-term Crabtree effect, which originated approximately 125–150 million years ago in the Saccharomyces lineage. The “invention” of overflow metabolism was the first step in the evolution of aerobic fermentation in yeast. It provides a general strategy to increase energy production rates, which we show is positively correlated to growth. The “invention” of overflow has also simultaneously enabled rapid glucose consumption in yeast, which is a trait that could have been selected for, to “starve” competitors in nature. We also show that glucose repression of respiration is confined mainly among S. cerevisiae and closely related species that diverged after the whole genome duplication event, less than 100 million years ago. Thus, glucose repression of respiration was apparently “invented” as a second step to further increase overflow and ethanol production, to inhibit growth of other microbes. The driving force behind the initial evolutionary steps was most likely competition with other microbes to faster consume and convert sugar into biomass, in niches that were semi-anaerobic.     

A New Catalog of Structural Variants in 1,301 A. thaliana Lines from Africa,Eurasia, and North America Reveals a Signature of Balancing Selection at Defense Response Genes

Genomic variation in the model plant Arabidopsis thaliana has been extensively used to understand evolutionary processes in natural populations, mainly focusing on single-nucleotide polymorphisms. Conversely, structural variation has been largely ignored in spite of its potential to dramatically affect phenotype. Here, we identify 155,440 indels and structural variants ranging in size from 1 bp to 10 kb, including presence/absence variants (PAVs), inversions, and tandem duplications in 1,301 A. thaliana natural accessions from Morocco, Madeira, Europe, Asia, and North America. We show evidence for strong purifying selection on PAVs in genes, in particular for housekeeping genes and homeobox genes, and we find that PAVs are concentrated in defense-related genes (R-genes, secondary metabolites) and F-box genes. This implies the presence of a “core” genome underlying basic cellular processes and a “flexible” genome that includes genes that may be important in spatially or temporally varying selection. Further, we find an excess of intermediate frequency PAVs in defense response genes in nearly all populations studied, consistent with a history of balancing selection on this class of genes. Finally, we find that PAVs in genes involved in the cold requirement for flowering (vernalization) and drought response are strongly associated with temperature at the sites of origin.     

A Mixture of “Cheats” and “Co-Operators” Can Enable Maximal Group Benefit

Is a group best off if everyone co-operates? Theory often considers this to be so (e.g. the “conspiracy of doves”), this understanding underpinning social and economic policy. We observe, however, that after competition between “cheat” and “co-operator” strains of yeast, population fitness is maximized under co-existence. To address whether this might just be a peculiarity of our experimental system or a result with broader applicability, we assemble, benchmark, dissect, and test a systems model. This reveals the conditions necessary to recover the unexpected result. These are 3-fold: (a) that resources are used inefficiently when they are abundant, (b) that the amount of co-operation needed cannot be accurately assessed, and (c) the population is structured, such that co-operators receive more of the resource than the cheats. Relaxing any of the assumptions can lead to population fitness being maximized when cheats are absent, which we experimentally demonstrate. These three conditions will often be relevant, and hence in order to understand the trajectory of social interactions, understanding the dynamics of the efficiency of resource utilization and accuracy of information will be necessary.     

Conditional prediction of consecutive tumor evolution using cancer progression models: What genotype comes next?

Accurate prediction of tumor progression is key for adaptive therapy and precision medicine. Cancer progression models (CPMs) can be used to infer dependencies in mutation accumulation from cross-sectional data and provide predictions of tumor progression paths. However, their performance when predicting complete evolutionary trajectories is limited by violations of assumptions and the size of available data sets. Instead of predicting full tumor progression paths, here we focus on short-term predictions, more relevant for diagnostic and therapeutic purposes. We examine whether five distinct CPMs can be used to answer the question “Given that a genotype with n mutations has been observed, what genotype with n + 1 mutations is next in the path of tumor progression?” or, shortly, “What genotype comes next?”. Using simulated data we find that under specific combinations of genotype and fitness landscape characteristics CPMs can provide predictions of short-term evolution that closely match the true probabilities, and that some genotype characteristics can be much more relevant than global features. Application of these methods to 25 cancer data sets shows that their use is hampered by a lack of information needed to make principled decisions about method choice. Fruitful use of these methods for short-term predictions requires adapting method’s use to local genotype characteristics and obtaining reliable indicators of performance; it will also be necessary to clarify the interpretation of the method’s results when key assumptions do not hold.     

Modeling the Evolutionary Architectures of Transcribed Human Enhancer Sequences Reveals Distinct Origins,Functions, and Associations with Human Trait Variation

Despite the importance of gene regulatory enhancers in human biology and evolution, we lack a comprehensive model of enhancer evolution and function. This substantially limits our understanding of the genetic basis of species divergence and our ability to interpret the effects of noncoding variants on human traits.To explore enhancer sequence evolution and its relationship to regulatory function, we traced the evolutionary origins of transcribed human enhancer sequences with activity across diverse tissues and cellular contexts from the FANTOM5 consortium. The transcribed enhancers are enriched for sequences of a single evolutionary age (“simple” evolutionary architectures) compared with enhancers that are composites of sequences of multiple evolutionary ages (“complex” evolutionary architectures), likely indicating constraint against genomic rearrangements. Complex enhancers are older, more pleiotropic, and more active across species than simple enhancers. Genetic variants within complex enhancers are also less likely to associate with human traits and biochemical activity. Transposable-element-derived sequences (TEDS) have made diverse contributions to enhancers of both architectures; the majority of TEDS are found in enhancers with simple architectures, while a minority have remodeled older sequences to create complex architectures. Finally, we compare the evolutionary architectures of transcribed enhancers with histone-mark-defined enhancers.Our results reveal that most human transcribed enhancers are ancient sequences of a single age, and thus the evolution of most human enhancers was not driven by increases in evolutionary complexity over time. Our analyses further suggest that considering enhancer evolutionary histories provides context that can aid interpretation of the effects of variants on enhancer function. Based on these results, we propose a framework for analyzing enhancer evolutionary architecture.     

适合度的相对性与路径依赖的自然选择

自然选择理论认为生物个体或者种群在进化的过程中, 其基因或者性状、行为策略的选择一定是能够提高其适合度或者达到某个可期的“目标”。然而, 随着某个突变基因或者性状特征、行为策略在种群中扩散, 其期望收益将随着其在种群中分布的密度变化或环境改变而发生改变, 这就是适合度景观的悖论, 即静态的、固定可期望的收益可能因此而不存在。基于动态而非静态适合度景观的概念, 我们提出路径依赖的自然选择概念。路径依赖的自然选择过程中, 一个突变的基因或表型在某种环境下随机产生, 但是该基因或表型在某些特定环境下会产生正反馈。尤其是在正反馈与随机漂变的共同作用下, 多条路径的演化就可能发生, 并且其路径的形成将同时受到其种群进化历史过程和空间特征分布等因素的强烈影响。而在不同路径下, 由于观测维度、角度和尺度的不同, 适合度意义将因此而存在不同。在此意义下, 自然选择更可能选择路径频率而不是适合度大小。基于上述概念, 我们借鉴现代物理学中复函数的方法, 来描述多重动力对物种形成或者生物特征、种群进化等路径依赖的演化过程, 以期为同域物种、隐存种形成以及生物多样性演化提供解释机制。     

Rational Design of Antibiotic Treatment Plans: A Treatment Strategy for Managing Evolution and Reversing Resistance

The development of reliable methods for restoring susceptibility after antibiotic resistance arises has proven elusive. A greater understanding of the relationship between antibiotic administration and the evolution of resistance is key to overcoming this challenge. Here we present a data-driven mathematical approach for developing antibiotic treatment plans that can reverse the evolution of antibiotic resistance determinants. We have generated adaptive landscapes for 16 genotypes of the TEM β-lactamase that vary from the wild type genotype “TEM-1” through all combinations of four amino acid substitutions. We determined the growth rate of each genotype when treated with each of 15 β-lactam antibiotics. By using growth rates as a measure of fitness, we computed the probability of each amino acid substitution in each β-lactam treatment using two different models named the Correlated Probability Model (CPM) and the Equal Probability Model (EPM). We then performed an exhaustive search through the 15 treatments for substitution paths leading from each of the 16 genotypes back to the wild type TEM-1. We identified optimized treatment paths that returned the highest probabilities of selecting for reversions of amino acid substitutions and returning TEM to the wild type state. For the CPM model, the optimized probabilities ranged between 0.6 and 1.0. For the EPM model, the optimized probabilities ranged between 0.38 and 1.0. For cyclical CPM treatment plans in which the starting and ending genotype was the wild type, the probabilities were between 0.62 and 0.7. Overall this study shows that there is promise for reversing the evolution of resistance through antibiotic treatment plans.     

Evolutionary rescue in vertebrates: evidence,applications and uncertainty

The current rapid rate of human-driven environmental change presents wild populations with novel conditions and stresses. Theory and experimental evidence for evolutionary rescue present a promising case for species facing environmental change persisting via adaptation. Here, we assess the potential for evolutionary rescue in wild vertebrates. Available information on evolutionary rescue was rare and restricted to abundant and highly fecund species that faced severe intentional anthropogenic selective pressures. However, examples from adaptive tracking in common species and genetic rescues in species of conservation concern provide convincing evidence in favour of the mechanisms of evolutionary rescue. We conclude that low population size, long generation times and limited genetic variability will result in evolutionary rescue occurring rarely for endangered species without intervention. Owing to the risks presented by current environmental change and the possibility of evolutionary rescue in nature, we suggest means to study evolutionary rescue by mapping genotype → phenotype → demography → fitness relationships, and priorities for applying evolutionary rescue to wild populations.