Selection for chaperone-like mediated genetic robustness at low mutation rate: impact of drift, epistasis and complexity

Human deleterious mutation rate slows adaptation and implies high fitness variance

Each new human has an expectedU d = 2 - 10 new deleterious mutations. Using a novel approach to capture complex linkage disequilibria from highU d using genome-wide simulations, we confirm that fitness decline due to the fixation of many slightly deleterious mutations can be compensated by rarer beneficial mutations of larger effect. The evolution of increased genome size and complexity have previously been attributed to a similarly asymmetric pattern of fixations, but we propose that the cause might be highU d rather than the small population size posited as causal by drift barrier theory. High within-population variance in relative fitness is an inevitable consequence of highU d ∼ 2 - 10 combined with inferred human deleterious effect sizes; two individuals will typically differ in fitness by 15-40%. The need to compensate for the deluge of deleterious mutations slows net adaptation (i.e. to the external environment) by ~13%-55%. The rate of beneficial fixations is more sensitive to changes in the mutation rate than the rate of deleterious fixations is. As a surprising consequence of this, an increase (e.g. 10%) in overall mutation rate leads to faster adaptation; this puts to rest dysgenic fears about increasing mutation rates due to rising paternal age.

Evolution of drift robustness in small populations

Most mutations are deleterious and cause a reduction in population fitness known as the mutational load. In small populations, weakened selection against slightly-deleterious mutations results in an additional fitness reduction. Many studies have established that populations can evolve a reduced mutational load by evolving mutational robustness, but it is uncertain whether small populations can evolve a reduced susceptibility to drift-related fitness declines. Here, using mathematical modeling and digital experimental evolution, we show that small populations do evolve a reduced vulnerability to drift, or ‘drift robustness’. We find that, compared to genotypes from large populations, genotypes from small populations have a decreased likelihood of small-effect deleterious mutations, thus causing small-population genotypes to be drift-robust. We further show that drift robustness is not adaptive, but instead arises because small populations can only maintain fitness on drift-robust fitness peaks. These results have implications for genome evolution in organisms with small effective population sizes.

Genetic drift can reduce fitness in small populations by counteracting selection against deleterious mutations. Here, LaBar and Adami demonstrate through a mathematical model and simulations that small populations tend to evolve to drift-robust fitness peaks, which have a low likelihood of slightly-deleterious mutations.

Determining the factors driving selective effects of new nonsynonymous mutations

The distribution of fitness effects (DFE) of new mutations plays a fundamental role in evolutionary genetics. However, the extent to which the DFE differs across species has yet to be systematically investigated. Furthermore, the biological mechanisms determining the DFE in natural populations remain unclear. Here, we show that theoretical models emphasizing different biological factors at determining the DFE, such as protein stability, back-mutations, species complexity, and mutational robustness make distinct predictions about how the DFE will differ between species. Analyzing amino acid-changing variants from natural populations in a comparative population genomic framework, we find that humans have a higher proportion of strongly deleterious mutations than Drosophila melanogaster. Furthermore, when comparing the DFE across yeast, Drosophila, mice, and humans, the average selection coefficient becomes more deleterious with increasing species complexity. Last, pleiotropic genes have a DFE that is less variable than that of nonpleiotropic genes. Comparing four categories of theoretical models, only Fisher's geometrical model (FGM) is consistent with our findings. FGM assumes that multiple phenotypes are under stabilizing selection, with the number of phenotypes defining the complexity of the organism. Our results suggest that long-term population size and cost of complexity drive the evolution of the DFE, with many implications for evolutionary and medical genomics.

Antigenic diversification is correlated with increased thermostability in a mammalian virus

The theory of plastogenetic congruence posits that ultimately, the pressure to maintain function in the face of biomolecular destabilization produces robustness. As temperature goes up so does destabilization. Thus, genetic robustness, defined as phenotypic constancy despite mutation, should correlate with survival during thermal challenge. We tested this hypothesis using vesicular stomatitis virus (VSV). We produced two sets of evolved strains after selection for higher thermostability by either preincubation at 37°C or by incubation at 40°C during infection. These VSV populations became more thermostable and also more fit in the absence of thermal selection, demonstrating an absence of tradeoffs. Eleven out of 12 evolved populations had a fixed, nonsynonymous substitution in the nucleocapsid (N) open reading frame. There was a partial correlation between thermostability and mutational robustness that was observed when the former was measured at 42°C, but not at 37°C. These results are consistent with our earlier work and suggest that the relationship between robustness and thermostability is complex. Surprisingly, many of the thermostable strains also showed increased resistance to monoclonal antibody and polyclonal sera, including sera from natural hosts. These data suggest that evolved thermostability may lead to antigenic diversification and an increased ability to escape immune surveillance in febrile hosts, and potentially to an improved robustness. These relationships have important implications not only in terms of viral pathogenesis, but also for the development of vaccine vectors and oncolytic agents.

Evolution of small prokaryotic genomes

As revealed by genome sequencing, the biology of prokaryotes with reduced genomes is strikingly diverse. These include free-living prokaryotes with ∼800 genes as well as endosymbiotic bacteria with as few as ∼140 genes. Comparative genomics is revealing the evolutionary mechanisms that led to these small genomes. In the case of free-living prokaryotes, natural selection directly favored genome reduction, while in the case of endosymbiotic prokaryotes neutral processes played a more prominent role. However, new experimental data suggest that selective processes may be at operation as well for endosymbiotic prokaryotes at least during the first stages of genome reduction. Endosymbiotic prokaryotes have evolved diverse strategies for living with reduced gene sets inside a host-defined medium. These include utilization of host-encoded functions (some of them coded by genes acquired by gene transfer from the endosymbiont and/or other bacteria); metabolic complementation between co-symbionts; and forming consortiums with other bacteria within the host. Recent genome sequencing projects of intracellular mutualistic bacteria showed that previously believed universal evolutionary trends like reduced G+C content and conservation of genome synteny are not always present in highly reduced genomes. Finally, the simplified molecular machinery of some of these organisms with small genomes may be used to aid in the design of artificial minimal cells. Here we review recent genomic discoveries of the biology of prokaryotes endowed with small gene sets and discuss the evolutionary mechanisms that have been proposed to explain their peculiar nature.

Gravitational Effects on Human Physiology

Physical working capacity decreases with age and also in microgravity. Regardless of age, increased physical activity can always improve the physical adaptability of the body, although the mechanisms of this adaptability are unknown. Physical exercise produces various mechanical stimuli in the body, and these stimuli may be essential for cell survival in organisms. The cytoskeleton plays an important role in maintaining cell shape and tension development, and in various molecular and/or cellular organelles involved in cellular trafficking. Both intra and extracellular stimuli send signals through the cytoskeleton to the nucleus and modulate gene expression via an intrinsic property, namely the "dynamic instability" of cytoskeletal proteins. αB-crystallin is an important chaperone for cytoskeletal proteins in muscle cells. Decreases in the levels of αB-crystallin are specifically associated with a marked decrease in muscle mass (atrophy) in a rat hindlimb suspension model that mimics muscle and bone atrophy that occurs in space and increases with passive stretch. Moreover, immunofluorescence data show complete co-localization of αB-crystallin and the tubulin/microtubule system in myoblast cells. This association was further confirmed in biochemical experiments carried out in vitro showing that αB-crystallin acts as a chaperone for heat-denatured tubulin and prevents microtubule disassembly induced by calcium. Physical activity induces the constitutive expression of αB-crystallin, which helps to maintain the homeostasis of cytoskeleton dynamics in response to gravitational forces. This relationship between chaperone expression levels and regulation of cytoskeletal dynamics observed in slow anti-gravitational muscles as well as in mammalian striated muscles, such as those in the heart, diaphragm and tongue, may have been especially essential for human evolution in particular. Elucidation of the intrinsic properties of the tubulin/microtubule and chaperone αB-crystallin protein complex systems is expected to provide valuable information for high-pressure bioscience and gravity health science.

Properties of selected mutations and genotypic landscapes under Fisher's geometric model

The fitness landscape-the mapping between genotypes and fitness-determines properties of the process of adaptation. Several small genotypic fitness landscapes have recently been built by selecting a handful of beneficial mutations and measuring fitness of all combinations of these mutations. Here, we generate several testable predictions for the properties of these small genotypic landscapes under Fisher's geometric model of adaptation. When the ancestral strain is far from the fitness optimum, we analytically compute the fitness effect of selected mutations and their epistatic interactions. Epistasis may be negative or positive on average depending on the distance of the ancestral genotype to the optimum and whether mutations were independently selected, or coselected in an adaptive walk. Simulations show that genotypic landscapes built from Fisher's model are very close to an additive landscape when the ancestral strain is far from the optimum. However, when it is close to the optimum, a large diversity of landscape with substantial roughness and sign epistasis emerged. Strikingly, small genotypic landscapes built from several replicate adaptive walks on the same underlying landscape were highly variable, suggesting that several realizations of small genotypic landscapes are needed to gain information about the underlying architecture of the fitness landscape.

The Utility of Fisher's Geometric Model in Evolutionary Genetics

The accumulation of data on the genomic bases of adaptation has triggered renewed interest in theoretical models of adaptation. Among these models, Fisher Geometric Model (FGM) has received a lot of attention over the last two decades. FGM is based on a continuous multidimensional phenotypic landscape, but it is for the emerging properties of individual mutation effects that it is mostly used. Despite an apparent simplicity and a limited number of parameters, FGM integrates a full model of mutation and epistatic interactions that allows the study of both beneficial and deleterious mutations, and subsequently the fate of evolving populations. In this review, I present the different properties of FGM and the qualitative and quantitative support they have received from experimental evolution data. I later discuss how to estimate the different parameters of the model and outline some future directions to connect FGM and the molecular determinants of adaptation.

Modeling evolution using the probability of fixation: history and implications

Many models of evolution calculate the rate of evolution by multiplying the rate at which new mutations originate within a population by a probability of fixation. Here we review the historical origins, contemporary applications, and evolutionary implications of these "origin-fixation" models, which are widely used in evolutionary genetics, molecular evolution, and phylogenetics. Origin-fixation models were first introduced in 1969, in association with an emerging view of "molecular" evolution. Early origin-fixation models were used to calculate an instantaneous rate of evolution across a large number of independently evolving loci; in the 1980s and 1990s, a second wave of origin-fixation models emerged to address a sequence of fixation events at a single locus. Although origin fixation models have been applied to a broad array of problems in contemporary evolutionary research, their rise in popularity has not been accompanied by an increased appreciation of their restrictive assumptions or their distinctive implications. We argue that origin-fixation models constitute a coherent theory of mutation-limited evolution that contrasts sharply with theories of evolution that rely on the presence of standing genetic variation. A major unsolved question in evolutionary biology is the degree to which these models provide an accurate approximation of evolution in natural populations.

Fisher's geometrical model emerges as a property of complex integrated phenotypic networks

Models relating phenotype space to fitness (phenotype-fitness landscapes) have seen important developments recently. They can roughly be divided into mechanistic models (e.g., metabolic networks) and more heuristic models like Fisher's geometrical model. Each has its own drawbacks, but both yield testable predictions on how the context (genomic background or environment) affects the distribution of mutation effects on fitness and thus adaptation. Both have received some empirical validation. This article aims at bridging the gap between these approaches. A derivation of the Fisher model "from first principles" is proposed, where the basic assumptions emerge from a more general model, inspired by mechanistic networks. I start from a general phenotypic network relating unspecified phenotypic traits and fitness. A limited set of qualitative assumptions is then imposed, mostly corresponding to known features of phenotypic networks: a large set of traits is pleiotropically affected by mutations and determines a much smaller set of traits under optimizing selection. Otherwise, the model remains fairly general regarding the phenotypic processes involved or the distribution of mutation effects affecting the network. A statistical treatment and a local approximation close to a fitness optimum yield a landscape that is effectively the isotropic Fisher model or its extension with a single dominant phenotypic direction. The fit of the resulting alternative distributions is illustrated in an empirical data set. These results bear implications on the validity of Fisher's model's assumptions and on which features of mutation fitness effects may vary (or not) across genomic or environmental contexts.

The reproducibility of adaptation in the light of experimental evolution with whole genome sequencing

A key question in evolutionary biology is the reproducibility of adaptation. This question can now be quantitatively analyzed using experimental evolution coupled to whole genome sequencing (WGS). With complete sequence data, one can assess convergence among replicate populations. In turn, convergence reflects the action of natural selection and also the breadth of the field of possible adaptive solutions. That is, it provides insight into how many genetic solutions or adaptive paths may lead to adaptation in a given environment. Convergence is both a property of an adaptive landscape and, reciprocally, a tool to study that landscape. In this chapter we present the links between convergence and the properties of adaptive landscapes with respect to two types of microbial experimental evolution. The first tries to reconstruct a full adaptive landscape using a handful of carefully identified mutations (the reductionist approach), while the second uses WGS of replicate experiments to infer properties of the adaptive landscape. Reductionist approaches have highlighted the importance of epistasis in shaping the adaptive landscape, but have also uncovered a wide diversity of landscape architectures. The WGS approach has uncovered a very high diversity of beneficial mutations that affect a limited set of genes or functions and also suggests some shortcomings of the reductionist approach. We conclude that convergence may be better defined at an integrated level, such as the genic level or even at a phenotypic level, and that integrated mechanistic models derived from systems biology may offer an interesting perspective for the analysis of convergence at all levels.

The cost of antibiotic resistance depends on evolutionary history in Escherichia coli

BACKGROUND

The persistence of antibiotic resistance depends on the fitness effects of resistance elements in the absence of antibiotics. Recent work shows that the fitness effect of a given resistance mutation is influenced by other resistance mutations on the same genome. However, resistant bacteria acquire additional beneficial mutations during evolution in the absence of antibiotics that do not alter resistance directly but may modify the fitness effects of new resistance mutations.

RESULTS

We experimentally evolved rifampicin-resistant and sensitive Escherichia coli in a drug-free environment, before measuring the effects of new resistance elements on fitness in antibiotic-free conditions. Streptomycin-resistance mutations had small fitness effects in rifampicin-resistant genotypes that had adapted to antibiotic-free growth medium, compared to the same genotypes without adaptation. We observed a similar effect when resistance was encoded by a different mechanism and carried on a plasmid. Antibiotic-sensitive bacteria that adapted to the same conditions showed the same pattern for some resistance elements but not others.

CONCLUSIONS

Epistatic variation of costs of resistance can result from evolution in the absence of antibiotics, as well as the presence of other resistance mutations.

Capturing the mutational landscape of the beta-lactamase TEM-1

Adaptation proceeds through the selection of mutations. The distribution of mutant fitness effect and the forces shaping this distribution are therefore keys to predict the evolutionary fate of organisms and their constituents such as enzymes. Here, by producing and sequencing a comprehensive collection of 10,000 mutants, we explore the mutational landscape of one enzyme involved in the spread of antibiotic resistance, the beta-lactamase TEM-1. We measured mutation impact on the enzyme activity through the estimation of amoxicillin minimum inhibitory concentration on a subset of 990 mutants carrying a unique missense mutation, representing 64% of possible amino acid changes in that protein reachable by point mutation. We established that mutation type, solvent accessibility of residues, and the predicted effect of mutations on protein stability primarily determined alone or in combination changes in minimum inhibitory concentration of mutants. Moreover, we were able to capture the drastic modification of the mutational landscape induced by a single stabilizing point mutation (M182T) by a simple model of protein stability. This work thereby provides an integrated framework to study mutation effects and a tool to understand/define better the epistatic interactions.

Chaperones: needed for both the good times and the bad times

In this issue, we explore the assembly roles of protein chaperones, mainly through the portal of their associated human diseases (e.g. cardiomyopathy, cataract, neurodegeneration, cancer and neuropathy). There is a diversity to chaperone function that goes beyond the current emphasis in the scientific literature on their undoubted roles in protein folding and refolding. The focus on chaperone-mediated protein folding needs to be broadened by the original Laskey discovery that a chaperone assists the assembly of an oligomeric structure, the nucleosome, and the subsequent suggestion by Ellis that other chaperones may function in assembly processes, as well as in folding. There have been a number of recent discoveries that extend this relatively neglected aspect of chaperone biology to include proteostasis, maintenance of the cellular redox potential, genome stability, transcriptional regulation and cytoskeletal dynamics. So central are these processes that we propose that chaperones stand at the crossroads of life and death because they mediate essential functions, not only during the bad times, but also in the good times. We suggest that chaperones facilitate the success of a species, and hence the evolution of individuals within populations, because of their contributions to so many key cellular processes, of which protein folding is only one.

Evolutionary layering and the limits to cellular perfection

Although observations from biochemistry and cell biology seemingly illustrate hundreds of examples of exquisite molecular adaptations, the fact that experimental manipulation can often result in improvements in cellular infrastructure raises the question as to what ultimately limits the level of molecular perfection achievable by natural selection. Here, it is argued that random genetic drift can impose a strong barrier to the advancement of molecular refinements by adaptive processes. Moreover, although substantial improvements in fitness may sometimes be accomplished via the emergence of novel cellular features that improve on previously established mechanisms, such advances are expected to often be transient, with overall fitness eventually returning to the level before incorporation of the genetic novelty. As a consequence of such changes, increased molecular/cellular complexity can arise by Darwinian processes, while yielding no long-term increase in adaptation and imposing increased energetic and mutational costs.

The emergence of complexity and restricted pleiotropy in adapting networks

Background

The emergence of organismal complexity has been a difficult subject for researchers because it is not readily amenable to investigation by experimental approaches. Complexity has a myriad of untested definitions and our understanding of its evolution comes primarily from static snapshots gleaned from organisms ranked on an intuitive scale. Fisher's geometric model of adaptation, which defines complexity as the number of phenotypes an organism exposes to natural selection, provides a theoretical framework to study complexity. Yet investigations of this model reveal phenotypic complexity as costly and therefore unlikely to emerge.

Results

We have developed a computational approach to study the emergence of complexity by subjecting neural networks to adaptive evolution in environments exacting different levels of demands. We monitored complexity by a variety of metrics. Top down metrics derived from Fisher's geometric model correlated better with the environmental demands than bottom up ones such as network size. Phenotypic complexity was found to increase towards an environment-dependent level through the emergence of restricted pleiotropy. Such pleiotropy, which confined the action of mutations to only a subset of traits, better tuned phenotypes in challenging environments. However, restricted pleiotropy also came at a cost in the form of a higher genetic load, as it required the maintenance by natural selection of more independent traits. Consequently, networks of different sizes converged in complexity when facing similar environment.

Conclusions

Phenotypic complexity evolved as a function of the demands of the selective pressures, rather than the physical properties of the network architecture, such as functional size. Our results show that complexity may be more predictable, and understandable, if analyzed from the perspective of the integrated task the organism performs, rather than the physical architecture used to accomplish such tasks. Thus, top down metrics emphasizing selection may be better for describing biological complexity than bottom up ones representing size and other physical attributes.

A biophysical protein folding model accounts for most mutational fitness effects in viruses

Fitness effects of mutations fall on a continuum ranging from lethal to deleterious to beneficial. The distribution of fitness effects (DFE) among random mutations is an essential component of every evolutionary model and a mathematical portrait of robustness. Recent experiments on five viral species all revealed a characteristic bimodal-shaped DFE featuring peaks at neutrality and lethality. However, the phenotypic causes underlying observed fitness effects are still unknown and presumably, are thought to vary unpredictably from one mutation to another. By combining population genetics simulations with a simple biophysical protein folding model, we show that protein thermodynamic stability accounts for a large fraction of observed mutational effects. We assume that moderately destabilizing mutations inflict a fitness penalty proportional to the reduction in folded protein, which depends continuously on folding free energy (ΔG). Most mutations in our model affect fitness by altering ΔG, whereas based on simple estimates, ~10% abolish activity and are unconditionally lethal. Mutations pushing ΔG > 0 are also considered lethal. Contrary to neutral network theory, we find that, in mutation/selection/drift steady state, high mutation rates (m) lead to less stable proteins and a more dispersed DFE (i.e., less mutational robustness). Small population size (N) also decreases stability and robustness. In our model, a continuum of nonlethal mutations reduces fitness by ~2% on average, whereas ~10-35% of mutations are lethal depending on N and m. Compensatory mutations are common in small populations with high mutation rates. More broadly, we conclude that interplay between biophysical and population genetic forces shapes the DFE.

Function-specific accelerations in rates of sequence evolution suggest predictable epistatic responses to reduced effective population size

Changes in effective population size impinge on patterns of molecular evolution. Notably, slightly deleterious mutations are more likely to drift to fixation in smaller populations, which should typically also lead to an overall acceleration in the rates of evolution. This prediction has been validated empirically for several endosymbiont and island taxa. Here, we first show that rate accelerations are also evident in bacterial pathogens whose recent shifts in virulence make them prime candidates for reduced effective population size: Bacillus anthracis, Bordetella parapertussis, Mycobacterium leprae, Salmonella enterica typhi, Shigella spp., and Yersinia pestis. Using closely related genomes to analyze substitution rate dynamics across six phylogenetically independent bacterial clades, we demonstrate that relative rates of coding sequence evolution are biased according to gene functional category. Notably, genes that buffer against slightly deleterious mutations, such as chaperones, experience stronger rate accelerations than other functional classes at both nonsynonymous and synonymous sites. Although theory predicts altered evolutionary dynamics for buffer loci in the face of accumulating deleterious mutations, to observe even stronger rate accelerations is surprising. We suggest that buffer loci experience elevated substitution rates because the accumulation of deleterious mutations in the remainder of the genome favors compensatory substitutions in trans. Critically, the hyper-acceleration is evident across phylogenetically independent clades, supporting the hypothesis that reductions in effective population size predictably induce epistatic responses in genes that buffer against slightly deleterious mutations.

Evolution of molecular error rates and the consequences for evolvability

Making genes into gene products is subject to predictable errors, each with a phenotypic effect that depends on a normally cryptic sequence. Many cryptic sequences have strongly deleterious effects, for example when they cause protein misfolding. Strongly deleterious effects can be avoided globally by avoiding making errors (e.g., via proofreading machinery) or locally by ensuring that each error has a relatively benign effect. The local solution requires powerful selection acting on every cryptic site and so evolves only in large populations. Small populations with less effective selection evolve global solutions. Here we show that for a large range of realistic intermediate population sizes, the evolutionary dynamics are bistable and either solution may result. The local solution facilitates the genetic assimilation of cryptic genetic variation and therefore substantially increases evolvability.

The evolution of epistasis and its links with genetic robustness, complexity and drift in a phenotypic model of adaptation

The epistatic interactions among mutations have a large effect on the evolution of populations. In this article we provide a formalism under which epistatic interactions among pairs of mutations have a distribution whose mean can be modulated. We find that the mean epistasis is correlated to the effect of mutations or genetic robustness, which suggests that such formalism is in good agreement with most in silico models of evolution where the same pattern is observed. We further show that the evolution of epistasis is highly dependant on the intensity of drift and of how complex the organisms are, and that either positive or negative epistasis could be selected for, depending on the balance between the efficiency of selection and the intensity of drift.