Jeremy A. Draghi

Mutational robustness can facilitate adaptation

Robustness seems to be the opposite of evolvability. If phenotypes are robust against mutation, we might expect that a population will have difficulty adapting to an environmental change, as several studies have suggested. However, other studies contend that robust organisms are more adaptable. A quantitative understanding of the relationship between robustness and evolvability will help resolve these conflicting reports and will clarify outstanding problems in molecular and experimental evolution, evolutionary developmental biology and protein engineering. Here we demonstrate, using a general population genetics model, that mutational robustness can either impede or facilitate adaptation, depending on the population size, the mutation rate and the structure of the fitness landscape. In particular, neutral diversity in a robust population can accelerate adaptation as long as the number of phenotypes accessible to an individual by mutation is smaller than the total number of phenotypes in the fitness landscape. These results provide a quantitative resolution to a significant ambiguity in evolutionary theory.

Phenotypic plasticity facilitates mutational variance, genetic variance, and evolvability along the major axis of environmental variation.

Phenotypically plastic genotypes express different phenotypes in different environments, often in adaptive ways. The evolution of phenotypic plasticity creates developmental systems that are more flexible along the trait dimensions that are more plastic, and as a result, we hypothesize that such traits will express greater mutational variance, genetic variance, and evolvability. We develop an explicit gene network model with three components: some genes can receive environmental cues about the adult selective environment, some genes that interact repeatedly to determine each others’ final state, and other factors that translate these final expression states into the phenotype. We show that the evolution of phenotypic plasticity is an important determinant of mutational patterns, genetic variance, and evolutionary potential of a population. Phenotypic plasticity tends to lead to populations with greater mutational variance, greater standing genetic variance, and, when the optimal phenotypes of two traits vary in concert, greater mutational and genetic correlations. However, plastic populations do not tend to respond much more rapidly to selection than do populations evolved in a static environment. We find that the quantitative genetic descriptions of traits created by explicit developmental network models are evolutionarily labile, with genetic correlations that change rapidly with shifts in the selection regime.

Evolution of Evolvability in a Developmental Model

Abstract Evolvability, the ability of populations to adapt, can evolve through changes in the mechanisms determining genetic variation and in the processes of development. Here we construct and evolve a simple developmental model in which the pleiotropic effects of genes can evolve. We demonstrate that selection in a changing environment favors a specific pattern of variability, and that this favored pattern maximizes evolvability. Our analysis shows that mutant genotypes with higher evolvability are more likely to increase to fixation. We also show that populations of highly evolvable genotypes are much less likely to be invaded by mutants with lower evolvability, and that this dynamic primarily shapes evolvability. We examine several theoretical objections to the evolution of evolvability in light of this result. We also show that this result is robust to the presence or absence of recombination, and explore how nonrandom environmental change can select for a modular pattern of variability.

SELECTION BIASES THE PREVALENCE AND TYPE OF EPISTASIS ALONG ADAPTIVE TRAJECTORIES

The contribution to an organisms phenotype from one genetic locus may depend upon the status of other loci. Such epistatic interactions among loci are now recognized as fundamental to shaping the process of adaptation in evolving populations. Although little is known about the structure of epistasis in most organisms, recent experiments with bacterial populations have concluded that antagonistic interactions abound and tend to deaccelerate the pace of adaptation over time. Here, we use the NK model of fitness landscapes to examine how natural selection biases the mutations that substitute during evolution based on their epistatic interactions. We find that, even when beneficial mutations are rare, these biases are strong and change substantially throughout the course of adaptation. In particular, epistasis is less prevalent than the neutral expectation early in adaptation and much more prevalent later, with a concomitant shift from predominantly antagonistic interactions early in adaptation to synergistic and sign epistasis later in adaptation. We observe the same patterns when reanalyzing data from a recent microbial evolution experiment. These results show that when the order of substitutions is not known, standard methods of analysis may suggest that epistasis retards adaptation when in fact it accelerates it.

Mapping the Fitness Landscape of Gene Expression Uncovers the Cause of Antagonism and Sign Epistasis between Adaptive Mutations

How do adapting populations navigate the tensions between the costs of gene expression and the benefits of gene products to optimize the levels of many genes at once? Here we combined independently-arising beneficial mutations that altered enzyme levels in the central metabolism of Methylobacterium extorquens to uncover the fitness landscape defined by gene expression levels. We found strong antagonism and sign epistasis between these beneficial mutations. Mutations with the largest individual benefit interacted the most antagonistically with other mutations, a trend we also uncovered through analyses of datasets from other model systems. However, these beneficial mutations interacted multiplicatively (i.e., no epistasis) at the level of enzyme expression. By generating a model that predicts fitness from enzyme levels we could explain the observed sign epistasis as a result of overshooting the optimum defined by a balance between enzyme catalysis benefits and fitness costs. Knowledge of the phenotypic landscape also illuminated that, although the fitness peak was phenotypically far from the ancestral state, it was not genetically distant. Single beneficial mutations jumped straight toward the global optimum rather than being constrained to change the expression phenotypes in the correlated fashion expected by the genetic architecture. Given that adaptation in nature often results from optimizing gene expression, these conclusions can be widely applicable to other organisms and selective conditions. Poor interactions between individually beneficial alleles affecting gene expression may thus compromise the benefit of sex during adaptation and promote genetic differentiation.

Epistasis Increases the Rate of Conditionally Neutral Substitution in an Adapting Population

Kimura observed that the rate of neutral substitution should equal the neutral mutation rate. This classic result is central to our understanding of molecular evolution, and it continues to influence phylogenetics, genomics, and the interpretation of evolution experiments. By demonstrating that neutral mutations substitute at a rate independent of population size and selection at linked sites, Kimura provided an influential justification for the idea of a molecular clock and emphasized the importance of genetic drift in shaping molecular evolution. But when epistasis among sites is common, as numerous empirical studies suggest, do neutral mutations substitute according to Kimuras expectation? Here we study simulated, asexual populations of RNA molecules, and we observe that conditionally neutral mutations—i.e., mutations that do not alter the fitness of the individual in which they arise, but that may alter the fitness effects of subsequent mutations—substitute much more often than expected while a population is adapting. We quantify these effects using a simple population-genetic model that elucidates how the substitution rate at conditionally neutral sites depends on the population size, mutation rate, strength of selection, and prevalence of epistasis. We discuss the implications of these results for our understanding of the molecular clock, and for the interpretation of molecular variation in laboratory and natural populations.

Local Adaptation Interacts with Expansion Load during Range Expansion: Maladaptation Reduces Expansion Load

The biotic and abiotic factors that facilitate or hinder species range expansions are many and complex. We examine the impact of two genetic processes and their interaction on fitness at expanding range edges: local maladaptation resulting from the presence of an environmental gradient and expansion load resulting from increased genetic drift at the range edge. Results from spatially explicit simulations indicate that the presence of an environmental gradient during range expansion reduces expansion load; conversely, increasing expansion load allows only locally adapted populations to persist at the range edge. Increased maladaptation reduces the speed of range expansion, resulting in less genetic drift at the expanding front and more immigration from the range center, therefore reducing expansion load at the range edge. These results may have ramifications for species being forced to shift their ranges because of climate change or other anthropogenic changes. If rapidly changing climate leads to faster expansion as populations track their shifting climatic optima, populations may suffer increased expansion load beyond previous expectations.

Experimentally guided models reveal replication principles that shape the mutation distribution of RNA viruses

Life history theory posits that the sequence and timing of events in an organisms lifespan are fine-tuned by evolution to maximize the production of viable offspring. In a virus, a life history strategy is largely manifested in its replication mode. Here, we develop a stochastic mathematical model to infer the replication mode shaping the structure and mutation distribution of a poliovirus population in an intact single infected cell. We measure production of RNA and poliovirus particles through the infection cycle, and use these data to infer the parameters of our model. We find that on average the viral progeny produced from each cell are approximately five generations removed from the infecting virus. Multiple generations within a single cell infection provide opportunities for significant accumulation of mutations per viral genome and for intracellular selection. DOI: http://dx.doi.org/10.7554/eLife.03753.001

In Evolution, the Sum Is Less than Its Parts

Laboratory experiments with bacteria shed light on how epistatic interactions influence the pace of evolution. Propagating bacteria in a lab for thousands of generations may seem tedious, or even irrelevant, to most evolutionary biologists. Nonetheless, such experiments provide an opportunity to deduce quantitative principles of evolution and directly test them in controlled environments. Combined with modern sequencing technologies, as well as theory, recent microbial experiments have suggested a critical role for genetic interactions among mutations, called epistasis, in determining the pace of evolution. Two papers in this issue, by Khan et al. on page 1193 (1) and Chou et al. (2) on page 1190, present precise experimental measurements of these epistatic interactions.

Robustness to noise in gene expression evolves despite epistatic constraints in a model of gene networks

Stochastic noise in gene expression causes variation in the development of phenotypes, making such noise a potential target of stabilizing selection. Here, we develop a new simulation model of gene networks to study the adaptive landscape underlying the evolution of robustness to noise. We find that epistatic interactions between the determinants of the expression of a gene and its downstream effect impose significant constraints on evolution, but these interactions do allow the gradual evolution of increased robustness. Despite strong sign epistasis, adaptation rarely proceeds via deleterious intermediate steps, but instead occurs primarily through small beneficial mutations. A simple mathematical model captures the relevant features of the single‐gene fitness landscape and explains counterintuitive patterns, such as a correlation between the mean and standard deviation of phenotypes. In more complex networks, mutations in regulatory regions provide evolutionary pathways to increased robustness. These results chart the constraints and possibilities of adaptation to reduce expression noise and demonstrate the potential of a novel modeling framework for gene networks.