Demographic feedbacks during evolutionary rescue can slow or speed adaptive evolution.

Populations declining toward extinction can persist via genetic adaptation in a process called evolutionary rescue. Predicting evolutionary rescue has applications ranging from conservation biology to medicine, but requires understanding and integrating the multiple effects of a stressful environmental change on population processes. Here we derive a simple expression for how generation time, a key determinant of the rate of evolution, varies with population size during evolutionary rescue. Change in generation time is quantitatively predicted by comparing how intraspecific competition and the source of maladaptation each affect the rates of births and deaths in the population. Depending on the difference between two parameters quantifying these effects, the model predicts that populations may experience substantial changes in their rate of adaptation in both positive and negative directions, or adapt consistently despite severe stress. These predictions were then tested by comparison to the results of individual-based simulations of evolutionary rescue, which validated that the tolerable rate of environmental change varied considerably as described by analytical results. We discuss how these results inform efforts to understand wildlife disease and adaptation to climate change, evolution in managed populations and treatment resistance in pathogens.

Evolvability Costs of Niche Expansion.

What prevents generalists from displacing specialists, despite obvious competitive advantages of utilizing a broad niche? The classic genetic explanation is antagonistic pleiotropy: genes underlying the generalism produce 'jacks-of-all-trades' that are masters of none. However, experiments challenge this assumption that mutations enabling niche expansion must reduce fitness in other environments. Theory suggests an alternative cost of generalism: decreased evolvability, or the reduced capacity to adapt. Generalists using multiple environments experience relaxed selection in any one environment, producing greater relative lag load. Additionally, mutations fixed by generalist lineages early during their evolution that avoid or compensate for antagonistic pleiotropy may limit access to certain future evolutionary trajectories. Hypothesized evolvability costs of generalism warrant further exploration, and we suggest outstanding questions meriting attention.

Exploring the expanse between theoretical questions and experimental approaches in the modern study of evolvability.

Despite several decades of computational and experimental work across many systems, evolvability remains on the periphery with regards to its status as a widely accepted and regularly applied theoretical concept. Here we propose that its marginal status is partly a result of large gaps between the diverse but disconnected theoretical treatments of evolvability and the relatively narrower range of studies that have tested it empirically. To make this case, we draw on a range of examples-from experimental evolution in microbes, to molecular evolution in proteins-where attempts have been made to mend this disconnect. We highlight some examples of progress that has been made and point to areas where synthesis and translation of existing theory can lead to further progress in the still-new field of empirical measurements of evolvability.

Bet-hedging via dispersal aids the evolution of plastic responses to unreliable cues.

Adaptive plasticity is expected to evolve when informative cues predict environmental variation. However, plastic responses can be maladaptive even when those cues are informative, if prediction mistakes are shared across members of a generation. These fitness costs can constrain the evolution of plasticity when initial plastic mutants use of cues of only moderate reliability. Here, we model the barriers to the evolution of plasticity produced by these constraints and show that dispersal across a metapopulation can overcome them. Constraints are also lessened, though not eliminated, when plastic responses are free to evolve gradually and in concert with increased reliability. Each of these factors be viewed as a form of bet-hedging: by lessening correlations in the fates of relatives, dispersal acts as diversifying bet-hedging, while producing submaximal responses to a cue can be understood as a conservative bet-hedging strategy. While poor information may constrain the evolution of plasticity, the opportunity for bet-hedging may predict when that constraint can be overcome.

Asymmetric Evolvability Leads to Specialization without Trade-Offs.

AbstractMany organisms are specialized, and these narrow niches are often explained with trade-offs-the inability for one organism to express maximal performance in two or more environments. However, evidence is lacking that trade-offs are sufficient to explain specialists. Several lines of theoretical inquiry suggest that populations can specialize without explicit trade-offs, as a result of relaxed selection in generalists for their performance in rare environments. Here, I synthesize and extend these approaches, showing that emergent asymmetries in evolvability can push a population toward specialization in the absence of trade-offs and in the presence of substantial ecological costs of specialism. Simulations are used to demonstrate how adaptation to a more common environment interferes with adaptation to a less common but otherwise equal alternative environment and that this interference is greatly exacerbated at low recombination rates. This adaptive process of specialization can effectively trap populations in a suboptimal niche. These modeling results predict that transient differences in evolvability across traits during a single episode of adaptation could have long-term consequences for a population's niche.

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.

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.

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.

Range expansion can promote the evolution of plastic generalism in coarse-grained landscapes.

Phenotypic plasticity is one way for organisms to deal with variable environments through generalism. However, plasticity is not found universally and its evolution may be constrained by costs and other limitations such as complexity: the need for multiple mutational steps before the adaptation is realized. Theory predicts that greater experienced heterogeneity, such as organisms may encounter when spatial heterogeneity is fine-grained relative to dispersal, should favor the evolution of a broader niche. Here we tested this prediction via simulation. We found that, contrary to classical predictions, coarse-grained landscapes can be the most favorable for the evolution of plasticity, but only when populations encounter those landscapes through range expansion. During these range expansions, coarse-grained landscapes select for each step in the complex mutational pathway to plastic generalism by blocking the dispersal of specialists. These circumstances provide ecological opportunities for innovative mutations that change the niche. Our results indicate a new mechanism by which range expansion and spatially structured landscapes interact to shape evolution and reveal that the environments in which a complex adaptation has the highest fitness may not be the most favorable for its evolution.

Modeling poliovirus replication dynamics from live time-lapse single-cell imaging data.

Viruses experience selective pressure on the timing and order of events during infection to maximize the number of viable offspring they produce. Additionally, they may experience variability in cellular environments encountered, as individual eukaryotic cells can display variation in gene expression among cells. This leads to a dynamic phenotypic landscape that viruses must face to replicate. To examine replication dynamics displayed by viruses faced with this variable landscape, we have developed a method for fitting a stochastic mechanistic model of viral infection to time-lapse imaging data from high-throughput single-cell poliovirus infection experiments. The model's mechanistic parameters provide estimates of several aspects associated with the virus's intracellular dynamics. We examine distributions of parameter estimates and assess their variability to gain insight into the root causes of variability in viral growth dynamics. We also fit our model to experiments performed under various drug treatments and examine which parameters differ under these conditions. We find that parameters associated with translation and early stage viral replication processes are essential for the model to capture experimentally observed dynamics. In aggregate, our results suggest that differences in viral growth data generated under different treatments can largely be captured by steps that occur early in the replication process.

Genetic Context Significantly Influences the Maintenance and Evolution of Degenerate Pathways.

Understanding the evolution of novel physiological traits is highly relevant for expanding the characterization and manipulation of biological systems. Acquisition of new traits can be achieved through horizontal gene transfer (HGT). Here, we investigate drivers that promote or deter the maintenance of HGT-driven degeneracy, occurring when processes accomplish identical functions through nonidentical components. Subsequent evolution can optimize newly acquired functions; for example, beneficial alleles identified in an engineered Methylorubrum extorquens strain allowed it to utilize a "Foreign" formaldehyde oxidation pathway substituted for its Native pathway for methylotrophic growth. We examined the fitness consequences of interactions between these alleles when they were combined with the Native pathway or both (Dual) pathways. Unlike the Foreign pathway context where they evolved, these alleles were often neutral or deleterious when moved into these alternative genetic backgrounds. However, there were instances where combinations of multiple alleles resulted in higher fitness outcomes than individual allelic substitutions could provide. Importantly, the genetic context accompanying these allelic substitutions significantly altered the fitness landscape, shifting local fitness peaks and restricting the set of accessible evolutionary trajectories. These findings highlight how genetic context can negatively impact the probability of maintaining native and HGT-introduced functions together, making it difficult for degeneracy to evolve. However, in cases where the cost of maintaining degeneracy was mitigated by adding evolved alleles impacting the function of these pathways, we observed rare opportunities for pathway coevolution to occur. Together, our results highlight the importance of genetic context and resulting epistasis in retaining or losing HGT-acquired degenerate functions.

Plasticity via feedback reduces the cost of developmental instability.

Costs of plasticity are thought to have important physiological and evolutionary consequences. A commonly predicted cost to plasticity is that plastic genotypes are likely to suffer from developmental instability. Adaptive plasticity requires that the developing organism can in some way sense what environment it is in or how well it is performing in that environment. These two information pathways-an "environmental signal" or a "performance signal" that indicates how well a developing phenotype matches the optimum in the current environment-can differ in their consequences for the organism and its evolution. Here, we consider how developmental instability might emerge as a side-effect of these two distinct mechanisms. Because a performance cue allows a regulatory feedback loop connecting a trait to a feedback signal, we hypothesized that plastic genotypes using a performance signal would be more developmentally robust compared to those using a purely environmental signal. Using a numerical model of a network of gene interactions, we show that plasticity comes at a cost of developmental instability when the plastic response is mediated via an environmental signal, but not when it is mediated via a performance signal. We also show that a performance signal mechanism can evolve even in a constant environment, leading to genotypes preadapted for plasticity to novel environments even in populations without a history of environmental heterogeneity.

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 organism's 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.

Selection biases the prevalence and type of epistasis along adaptive trajectories.

The contribution to an organism's 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.

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.

High-throughput analysis of growth differences among phage strains.

Although methods such as spectrophotometry are useful for identifying growth differences among bacterial strains, it is currently difficult to similarly determine whether bacteriophage strains differ in growth using high throughput methods. Here we use automated spectrophotometry to develop an in vitro method for indirectly distinguishing fitness (growth) differences among virus strains, based on direct measures of their infected bacterial hosts. We used computer simulations of a mathematical model for phage growth to predict which features of bacterial growth curves were best associated with differences in growth among phage strains. We then tested these predictions using the in vitro method to confirm which of the inferred viral growth traits best reflected known fitness differences among genotypes of the RNA phage phi-6, when infecting a Pseudomonas syringae host. Results showed that the inferred phage trait of time-to-extinction (time required to drive bacterial density below detectable optical density) reliably correlated with genotype rankings based on absolute fitness (phage titer per ml). These data suggested that the high-throughput analysis was valuable for identifying growth differences among virus strains, and that the method may be especially useful for high throughput analyses of fitness differences among phage strains cultured and/or evolved in liquid (unstructured) environments.

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 Kimura's 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.

DNA secretion and gene-level selection in bacteria.

Natural genetic transformation can facilitate gene transfer in many genera of bacteria and requires the presence of extracellular DNA. Although cell lysis can contribute to this extracellular DNA pool, several studies have suggested that the secretion of DNA from living bacteria may also provide genetic material for transformation. This paper reviews the evidence for specific secretion of DNA from intact bacteria into the extracellular environment and examines this behaviour from a population-genetics perspective. A mathematical model demonstrates that the joint action of DNA secretion and transformation creates a novel type of gene-level natural selection. This model demonstrates that gene-level selection could explain the existence of DNA secretion mechanisms that provide no benefit to individual cells or populations of bacteria. Additionally, the model predicts that any trait affecting DNA secretion will experience selection at the gene level in a transforming population. This analysis confirms that the secretion of DNA from intact bacterial cells is fully explicable with evolutionary theory, and reveals a novel mechanism for bacterial evolution.