Fitness Effects Of Mutations Research Articles

Predictable properties of fitness landscapes induced by adaptational tradeoffs.

Fitness effects of mutations depend on environmental parameters. For example, mutations that increase fitness of bacteria at high antibiotic concentration often decrease fitness in the absence of antibiotic, exemplifying a tradeoff between adaptation to environmental extremes. We develop a mathematical model for fitness landscapes generated by such tradeoffs, based on experiments that determine the antibiotic dose-response curves of Escherichia coli strains, and previous observations on antibiotic resistance mutations. Our model generates a succession of landscapes with predictable properties as antibiotic concentration is varied. The landscape is nearly smooth at low and high concentrations, but the tradeoff induces a high ruggedness at intermediate antibiotic concentrations. Despite this high ruggedness, however, all the fitness maxima in the landscapes are evolutionarily accessible from the wild type. This implies that selection for antibiotic resistance in multiple mutational steps is relatively facile despite the complexity of the underlying landscape.

Collateral fitness effects of mutations

The distribution of fitness effects of mutation plays a central role in constraining protein evolution. The underlying mechanisms by which mutations lead to fitness effects are typically attributed to changes in protein specific activity or abundance. Here, we reveal the importance of a mutation's collateral fitness effects, which we define as effects that do not derive from changes in the protein's ability to perform its physiological function. We comprehensively measured the collateral fitness effects of missense mutations in the Escherichia coli TEM-1 β-lactamase antibiotic resistance gene using growth competition experiments in the absence of antibiotic. At least 42% of missense mutations in TEM-1 were deleterious, indicating that for some proteins collateral fitness effects occur as frequently as effects on protein activity and abundance. Deleterious mutations caused improper posttranslational processing, incorrect disulfide-bond formation, protein aggregation, changes in gene expression, and pleiotropic effects on cell phenotype. Deleterious collateral fitness effects occurred more frequently in TEM-1 than deleterious effects on antibiotic resistance in environments with low concentrations of the antibiotic. The surprising prevalence of deleterious collateral fitness effects suggests they may play a role in constraining protein evolution, particularly for highly expressed proteins, for proteins under intermittent selection for their physiological function, and for proteins whose contribution to fitness is buffered against deleterious effects on protein activity and protein abundance.

Why is cancer not more common? A changing microenvironment may help to explain why, and suggests strategies for anti-cancer therapy.

One of the great unsolved puzzles in cancer biology is not why cancers occur, but rather explaining why so few cancers occur compared with the theoretical number that could occur, given the number of progenitor cells in the body and the normal mutation rate. We hypothesized that a contributory explanation is that the tumour microenvironment (TME) is not fixed due to factors such as immune cell infiltration, and that this could impair the ability of neoplastic cells to retain a high enough fitness to become a cancer. The TME has implicitly been assumed to be static in most cancer evolution models, and we therefore developed a mathematical model of spatial cancer evolution assuming that the TME, and thus the optimum cancer phenotype, changes over time. Based on simulations, we show how cancer cell populations adapt to diverse changing TME conditions and fitness landscapes. Compared with static TMEs, which generate neutral dynamics, changing TMEs lead to complex adaptations with characteristic spatio-temporal heterogeneity involving variable fitness effects of driver mutations, subclonal mixing, subclonal competition and phylogeny patterns. In many cases, cancer cell populations fail to grow or undergo spontaneous regression, and even extinction. Our analyses predict that cancer evolution in a changing TME is challenging, and can help to explain why cancer is neither inevitable nor as common as expected. Should cancer driver mutations with effects dependent of the TME exist, they are likely to be selected. Anti-cancer prevention and treatment strategies based on changing the TME are feasible and potentially effective.

From Drift to Draft: How Much Do Beneficial Mutations Actually Contribute to Predictions of Ohta's Slightly Deleterious Model of Molecular Evolution?

Since its inception in 1973, the slightly deleterious model of molecular evolution, also known as the nearly neutral theory of molecular evolution, remains a central model to explain the main patterns of DNA polymorphism in natural populations. This is not to say that the quantitative fit to data are perfect. A recent study used polymorphism data from Drosophila melanogaster to test whether, as predicted by the nearly neutral theory, the proportion of effectively neutral mutations depends on the effective population size (Ne). It showed that a nearly neutral model simply scaling with Ne variation across the genome could not alone explain the data, but that consideration of linked positive selection improves the fit between observations and predictions. In the present article, we extended the work in two main directions. First, we confirmed the observed pattern on a set of 59 species, including high-quality genomic data from 11 animal and plant species with different mating systems and effective population sizes, hence a priori different levels of linked selection. Second, for the 11 species with high-quality genomic data we also estimated the full distribution of fitness effects (DFE) of mutations, and not solely the DFE of deleterious mutations. Both Ne and beneficial mutations contributed to the relationship between the proportion of effectively neutral mutations and local Ne across the genome. In conclusion, the predictions of the slightly deleterious model of molecular evolution hold well for species with small Ne, but for species with large Ne, the fit is improved by incorporating linked positive selection to the model.

Low mutational load and high mutation rate variation in gut commensal bacteria.

Bacteria generally live in species-rich communities, such as the gut microbiota. Yet little is known about bacterial evolution in natural ecosystems. Here, we followed the long-term evolution of commensal Escherichia coli in the mouse gut. We observe the emergence of mutation rate polymorphism, ranging from wild-type levels to 1,000-fold higher. By combining experiments, whole-genome sequencing, and in silico simulations, we identify the molecular causes and explore the evolutionary conditions allowing these hypermutators to emerge and coexist within the microbiota. The hypermutator phenotype is caused by mutations in DNA polymerase III proofreading and catalytic subunits, which increase mutation rate by approximately 1,000-fold and stabilise hypermutator fitness, respectively. Strong mutation rate variation persists for >1,000 generations, with coexistence between lineages carrying 4 to >600 mutations. The in vivo molecular evolution pattern is consistent with fitness effects of deleterious mutations sd ≤ 10−4/generation, assuming a constant effect or exponentially distributed effects with a constant mean. Such effects are lower than typical in vitro estimates, leading to a low mutational load, an inference that is observed in in vivo and in vitro competitions. Despite large numbers of deleterious mutations, we identify multiple beneficial mutations that do not reach fixation over long periods of time. This indicates that the dynamics of beneficial mutations are not shaped by constant positive Darwinian selection but could be explained by other evolutionary mechanisms that maintain genetic diversity. Thus, microbial evolution in the gut is likely characterised by partial sweeps of beneficial mutations combined with hitchhiking of slightly deleterious mutations, which take a long time to be purged because they impose a low mutational load. The combination of these two processes could allow for the long-term maintenance of intraspecies genetic diversity, including mutation rate polymorphism. These results are consistent with the pattern of genetic polymorphism that is emerging from metagenomics studies of the human gut microbiota, suggesting that we have identified key evolutionary processes shaping the genetic composition of this community.

Sex alters molecular evolution in diploid experimental populations of S. cerevisiae.

Sex is common among eukaryotes, but entails considerable costs. The selective conditions that drive the evolutionary maintenance of sexual reproduction remain an open question. One long-standing explanation is that sex and recombination facilitate adaptation to fluctuating environmental conditions, although the genetic mechanisms that underlie such a benefit have not been empirically observed. In this study, we compare the dynamics and fitness effects of mutations in sexual and asexual diploid populations of the yeast Saccharomyces cerevisiae during adaptation to a fluctuating environment. While we find no detectable difference in the rate of adaptation between sexual and asexual populations, only the former evolve high fitness mutations in parallel, a genetic signature of adaptation. Using genetic reconstructions and fitness assays, we demonstrate that evolved, overdominant mutations can be beneficial in asexual populations, but maintained at lower frequencies in sexual populations due to segregation load. Overall these data show that sex alters the molecular basis of adaptation in diploids, and confers both costs and benefits.

Antagonistic pleiotropy conceals molecular adaptations in changing environments.

The importance of positive selection in molecular evolution is debated. Evolution experiments under invariant laboratory conditions typically show a higher rate of nonsynonymous nucleotide change than that of synonymous change, demonstrating prevalent molecular adaptations. Natural evolution inferred from genomic comparisons, however, almost always exhibits the opposite pattern even among closely related conspecifics, indicative of a paucity of positive selection. We hypothesize that this apparent contradiction is at least in part attributable to ubiquitous and frequent environmental changes in nature, causing nonsynonymous mutations beneficial at one time deleterious soon after because of antagonistic pleiotropy and hindering their fixations relative to synonymous mutations despite continued population adaptations. To test this hypothesis, we performed yeast evolution experiments in changing and corresponding constant environments, followed by genome sequencing of the evolving populations. We observed a lower nonsynonymous to synonymous rate ratio in antagonistic changing environments than in the corresponding constant environments, and the population dynamics of mutations supports our hypothesis. These findings and the accompanying population genetic simulations suggest that molecular adaptation is consistently underestimated in nature due to the antagonistic fitness effects of mutations in changing environments.

Molecular signatures of aneuploidy-driven adaptive evolution

Alteration of normal ploidy (aneuploidy) can have a number of opposing effects, such as unbalancing protein abundances and inhibiting cell growth but also accelerating genetic diversification and rapid adaptation. The interplay of these detrimental and beneficial effects remains puzzling. Here, to understand how cells develop tolerance to aneuploidy, we subject disomic (i.e. with an extra chromosome copy) strains of yeast to long-term experimental evolution under strong selection, by forcing disomy maintenance and daily population dilution. We characterize mutations, karyotype alterations and gene expression changes, and dissect the associated molecular strategies. Cells with different extra chromosomes accumulated mutations at distinct rates and displayed diverse adaptive events. They tended to evolve towards normal ploidy through chromosomal DNA loss and gene expression changes. We identify genes with recurrent mutations and altered expression in multiple lines, revealing a variant that improves growth under genotoxic stresses. These findings support rapid evolvability of disomic strains that can be used to characterize fitness effects of mutations under different stress conditions.

PolyDFE: Inferring the Distribution of Fitness Effects and Properties of Beneficial Mutations from Polymorphism Data.

The possible evolutionary trajectories a population can follow is determined by the fitness effects of new mutations. Their relative frequencies are best specified through a distribution of fitness effects (DFE) that spans deleterious, neutral, and beneficial mutations. As such, the DFE is key to several aspects of the evolution of a population, and particularly the rate of adaptive molecular evolution (α). Inference of DFE from patterns of polymorphism and divergence has been a longstanding goal of evolutionary genetics.polyDFE provides a flexible statistical framework to estimate the DFE and α from site frequency spectrum (SFS) data. Several probability distributions can be fitted to the data to model the DFE. The method also jointly estimates a series of nuisance parameters that model the effect of unknown demography as well data imperfections, in particular possible errors in polarizing SNPs. This chapter is organized as a tutorial for polyDFE. We start by briefly reviewing the concept of DFE, α, and the principles underlying the method, and then provide an example using central chimpanzees data (Tataru et al., Genetics 207(3):1103-1119, 2017; Bataillon et al., Genome Biol Evol 7(4):1122-1132, 2015) to guide the user through the different steps of an analysis: formatting the data as input to polyDFE, fitting different models, obtaining estimates of parameters uncertainty and performing statistical tests, as well as model averaging procedures to obtain robust estimates of model parameters.

Remodeling enzyme active sites by stepwise loop insertion.

The remolding active site loops via residue insertion/deletion as well as substitution is thought to play a key role in enzyme divergent evolution. However, enzyme engineering by residue insertion in active site loops often severely perturbs the protein structural integrity and causes protein misfolding and activity loss. We have designed a stepwise loop insertion strategy (StLois), in which a pair of randomized residues is introduced in a stepwise manner, efficiently collating mutational fitness effects. The strategy of StLois constitutes three key steps. First, the target regions should be identified through structural and functional analysis on the counterpart enzymes. Second, pair residues can be introduced in loop regions through insertion with NNK codon degeneracy. Third, the best hit used as a template for the next round mutagenesis. The residue insertion process can repeat as many times as necessary. By using the StLois method, we have evolved the substrate preference of a lactonase to phosphotriesterase. In this chapter, we describe the detailed StLois technique, which efficiently expands the residue in the loop region and remolds the architecture of enzyme active site for novel catalytic properties.

Toward Inferring Potts Models for Phylogenetically Correlated Sequence Data

Global coevolutionary models of protein families have become increasingly popular due to their capacity to predict residue–residue contacts from sequence information, but also to predict fitness effects of amino acid substitutions or to infer protein–protein interactions. The central idea in these models is to construct a probability distribution, a Potts model, that reproduces single and pairwise frequencies of amino acids found in natural sequences of the protein family. This approach treats sequences from the family as independent samples, completely ignoring phylogenetic relations between them. This simplification is known to lead to potentially biased estimates of the parameters of the model, decreasing their biological relevance. Current workarounds for this problem, such as reweighting sequences, are poorly understood and not principled. Here, we propose an inference scheme that takes the phylogeny of a protein family into account in order to correct biases in estimating the frequencies of amino acids. Using artificial data, we show that a Potts model inferred using these corrected frequencies performs better in predicting contacts and fitness effect of mutations. First, only partially successful tests on real protein data are presented, too.

Comparison of the Full Distribution of Fitness Effects of New Amino Acid Mutations Across Great Apes.

The distribution of fitness effects (DFE) is central to many questions in evolutionary biology. However, little is known about the differences in DFE between closely related species. We use >9000 coding genes orthologous one-to-one across great apes, gibbons, and macaques to assess the stability of the DFE across great apes. We use the unfolded site frequency spectrum of polymorphic mutations (n = 8 haploid chromosomes per population) to estimate the DFE. We find that the shape of the deleterious DFE is strikingly similar across great apes. We confirm that effective population size (Ne ) is a strong predictor of the strength of negative selection, consistent with the nearly neutral theory. However, we also find that the strength of negative selection varies more than expected given the differences in Ne between species. Across species, mean fitness effects of new deleterious mutations covaries with Ne , consistent with positive epistasis among deleterious mutations. We find that the strength of negative selection for the smallest populations, bonobos and western chimpanzees, is higher than expected given their Ne This may result from a more efficient purging of strongly deleterious recessive variants in these populations. Forward simulations confirm that these findings are not artifacts of the way we are inferring Ne and DFE parameters. All findings are replicated using only GC-conservative mutations, thereby confirming that GC-biased gene conversion is not affecting our conclusions.

Asymmetric Distribution of Gene Trees Can Arise under Purifying Selection If Differences in Population Size Exist.

Incomplete lineage sorting (ILS) is an important factor that causes gene tree discordance. For gene trees of three species, under neutrality, random mating, and the absence of interspecific gene flow, ILS creates a symmetric distribution of gene trees: the gene tree that accords with the species tree has the highest frequency, and the two discordant trees are equally frequent. If the neutral condition is violated, the impact of ILS may change, altering the gene tree distribution. Here, we show that under purifying selection, even assuming that the fitness effect of mutations is constant throughout the species tree, if differences in population size exist among species, asymmetric distributions of gene trees will arise, which is different from the expectation under neutrality. In extremes, one of the discordant trees rather than the concordant tree becomes the most frequent gene tree. In addition, we found that in a real case, the position of Scandentia relative to Primate and Glires, the symmetry in the gene tree distribution can be influenced by the strength of purifying selection. In current phylogenetic inference, the impact of purifying selection on the gene tree distribution is rarely considered by researchers. This study highlights the necessity of considering this impact.

Real-time visualization of mutations and their fitness effects in single bacteria.

Mutations are the driving force of evolution and the source of important pathologies. The characterization of the dynamics and effects of mutations on fitness is therefore central to our understanding of evolution and to human health. This protocol describes how to implement two methods that we recently developed: mutation visualization (MV) and microfluidic mutation accumulation (µMA), which allow the occurrence of mutations created by DNA replication errors (MV) and the evolution of cell fitness during MA (µMA) to be followed directly in individual cells of Escherichia coli. MV provides a quantitative characterization of the dynamics of mutation occurrences, and µMA allows precise estimation of the distribution of fitness effects (DFEs) of mutations. Both methods use microfluidics and time-lapse microscopy, and a fluorescent mismatch repair (MMR) MutL protein is used as a marker for nascent mutations. Here, we present a single protocol describing how to implement the MV and µMA methods, including detailed procedures for microfluidic setup installation, data acquisition and data analysis and interpretation. Using this procedure, the microfluidic setup installation can be completed within 1 d, and automated data acquisition takes 2-4 d.

Developmental structuring of phenotypic variation: A case study with a cellular automata model of ontogeny.

Developmental mechanisms not only produce an organismal phenotype, but they also structure the way genetic variation maps to phenotypic variation. Here, we revisit a computational model for the evolution of ontogeny based on cellular automata, in which evolution regularly discovered two alternative mechanisms for achieving a selected phenotype, one showing high modularity, the other showing morphological integration. We measure a primary variational property of the systems, their distribution of fitness effects of mutation. We find that the modular ontogeny shows the evolution of mutational robustness and ontogenic simplification, while the integrated ontogeny does not. We discuss the wider use of this methodology on other computational models of development as well as real organisms.

Coupling adaptive molecular evolution to phylodynamics using fitness-dependent birth-death models.

Beneficial and deleterious mutations cause the fitness of lineages to vary across a phylogeny and thereby shape its branching structure. While standard phylogenetic models do not allow mutations to feedback and shape trees, birth-death models can account for this feedback by letting the fitness of lineages depend on their type. To date, these multi-type birth-death models have only been applied to cases where a lineage's fitness is determined by a single character state. We extend these models to track sequence evolution at multiple sites. This approach remains computationally tractable by tracking the genotype and fitness of lineages probabilistically in an approximate manner. Although approximate, we show that we can accurately estimate the fitness of lineages and site-specific mutational fitness effects from phylogenies. We apply this approach to estimate the population-level fitness effects of mutations in Ebola and influenza virus, and compare our estimates with in vitro fitness measurements for these mutations.

The distribution of fitness effects among synonymous mutations in a gene under directional selection.

The fitness effects of synonymous mutations, nucleotide changes that do not alter the encoded amino acid, have often been assumed to be neutral, but a growing body of evidence suggests otherwise. We used site-directed mutagenesis coupled with direct measures of competitive fitness to estimate the distribution of fitness effects among synonymous mutations for a gene under directional selection and capable of adapting via synonymous nucleotide changes. Synonymous mutations had highly variable fitness effects, both deleterious and beneficial, resembling those of nonsynonymous mutations in the same gene. This variation in fitness was underlain by changes in transcription linked to the creation of internal promoter sites. A positive correlation between fitness and the presence of synonymous substitutions across a phylogeny of related Pseudomonads suggests these mutations may be common in nature. Taken together, our results provide the most compelling evidence to date that synonymous mutations with non-neutral fitness effects may in fact be commonplace.

Distribution of fitness effects of mutations obtained from a simple genetic regulatory network model

Beneficial and deleterious mutations change an organism’s fitness but the distribution of these mutational effects on fitness are unknown. Several experimental, theoretical, and computational studies have explored this question but are limited because of experimental restrictions, or disconnect with physiology. Here we attempt to characterize the distribution of fitness effects (DFE) due to mutations in a cellular regulatory motif. We use a simple mathematical model to describe the dynamics of gene expression in the lactose utilization network, and use a cost-benefit framework to link the model output to fitness. We simulate mutations by changing model parameters and computing altered fitness to obtain the DFE. We find beneficial mutations distributed exponentially, but distribution of deleterious mutations seems far more complex. In addition, we find neither the starting fitness, nor the exact location on the fitness landscape, affecting these distributions qualitatively. Lastly, we quantify epistasis in our model and find that the distribution of epistatic effects remains qualitatively conserved across different locations on the fitness landscape. Overall, we present a first attempt at exploring the specific statistical features of the fitness landscape associated with a system, by using the specific mathematical model associated with it.

Inferring the distribution of fitness effects of spontaneous mutations in Chlamydomonas reinhardtii.

Spontaneous mutations are the source of new genetic variation and are thus central to the evolutionary process. In molecular evolution and quantitative genetics, the nature of genetic variation depends critically on the distribution of effects of mutations on fitness and other quantitative traits. Spontaneous mutation accumulation (MA) experiments have been the principal approach for investigating the overall rate of occurrence and cumulative effect of mutations but have not allowed the phenotypic effects of individual mutations to be studied directly. Here, we crossed MA lines of the green alga Chlamydomonas reinhardtii with its unmutated ancestral strain to create haploid recombinant lines, each carrying an average of 50% of the accumulated mutations in a large number of combinations. With the aid of the genome sequences of the MA lines, we inferred the genotypes of the mutations, assayed their growth rate as a measure of fitness, and inferred the distribution of fitness effects (DFE) using a Bayesian mixture model. We infer that the DFE is highly leptokurtic (L-shaped). Of mutations with absolute fitness effects exceeding 1%, about one-sixth increase fitness in the laboratory environment. The inferred distribution of effects for deleterious mutations is consistent with a strong role for nearly neutral evolution. Specifically, such a distribution predicts that nucleotide variation and genetic variation for quantitative traits will be insensitive to change in the effective population size.

Mutational Landscape of Spontaneous Base Substitutions and Small Indels in Experimental Caenorhabditis elegans Populations of Differing Size.

Experimental investigations into the rates and fitness effects of spontaneous mutations are fundamental to our understanding of the evolutionary process. To gain insights into the molecular and fitness consequences of spontaneous mutations, we conducted a mutation accumulation (MA) experiment at varying population sizes in the nematode Caenorhabditis elegans, evolving 35 lines in parallel for 409 generations at three population sizes (N = 1, 10, and 100 individuals). Here, we focus on nuclear SNPs and small insertion/deletions (indels) under minimal influence of selection, as well as their accrual rates in larger populations under greater selection efficacy. The spontaneous rates of base substitutions and small indels are 1.84 (95% C.I. ± 0.14) × 10-9 substitutions and 6.84 (95% C.I. ± 0.97) × 10-10 changes/site/generation, respectively. Small indels exhibit a deletion bias with deletions exceeding insertions by threefold. Notably, there was no correlation between the frequency of base substitutions, nonsynonymous substitutions, or small indels with population size. These results contrast with our previous analysis of mitochondrial DNA mutations and nuclear copy-number changes in these MA lines, and suggest that nuclear base substitutions and small indels are under less stringent purifying selection compared to the former mutational classes. A transition bias was observed in exons as was a near universal base substitution bias toward A/T. Strongly context-dependent base substitutions, where 5'-Ts and 3'-As increase the frequency of A/T → T/A transversions, especially at the boundaries of A or T homopolymeric runs, manifest as higher mutation rates in (i) introns and intergenic regions relative to exons, (ii) chromosomal cores vs. arms and tips, and (iii) germline-expressed genes.