Michael E. Palmer

Dynamics of hybrid incompatibility in gene networks in a constant environment.

After an ancestral population splits into two allopatric populations, different mutations may fix in each. When pairs of mutations are brought together in a hybrid offspring, epistasis may cause reduced fitness. Such pairs are known as Bateson-Dobzhansky-Muller (BDM) incompatibilities. A well-known model of BDM incompatibility due to Orr suggests that the fitness load on hybrids should initially accelerate, and continue to increase as the number of potentially incompatible substitutions increases (the “snowball effect”). In the gene networks model, which violates a key assumption of Orrs model (independence of fixation probabilities), the snowball effect often does not occur. Instead, we describe three possible dynamics in a constant environment: (1) Stabilizing selection can constrain two allopatric populations to remain near-perfectly compatible. (2) Despite constancy of environment, punctuated evolution may obtain; populations may experience rare adaptations asynchronously, permitting incompatibility. (3) Despite stabilizing selection, developmental system drift may permit genetic change, allowing two populations to drift in and out of compatibility. We reinterpret Orrs model in terms of genetic distance. We extend Orrs model to the finite loci case, which can limit incompatibility. Finally, we suggest that neutral evolution of gene regulation in nature, to the point of speciation, is a distinct possibility.

Evolution of simple sequence repeat–mediated phase variation in bacterial genomes

Mutability as mechanism for rapid adaptation to environmental challenge is an alluringly simple concept whose apotheosis is realized in simple sequence repeats (SSR). Bacterial genomes of several species contain SSRs with a proven role in adaptation to environmental fluctuations. SSRs are hypermutable and generate reversible mutations in localized regions of bacterial genomes, leading to phase variable ON/OFF switches in gene expression. The application of genetic, bioinformatic, and mathematical/computational modeling approaches are revolutionizing our current understanding of how genomic molecular forces and environmental factors influence SSR‐mediated adaptation and led to evolution of this mechanism of localized hypermutation in bacterial genomes.

Broad Conditions Favor the Evolution of Phase-Variable Loci

ABSTRACT Simple sequence repeat (SSR) tracts produce stochastic on-off switching, or phase variation, in the expression of a panoply of surface molecules in many bacterial commensals and pathogens. A change to the number of repeats in a tract may alter the phase of the translational reading frame, which toggles the on-off state of the switch. Here, we construct an in silico SSR locus with mutational dynamics calibrated to those of the Haemophilus influenzae mod locus. We simulate its evolution in a regimen of two alternating environments, simultaneously varying the selection coefficient, s, and the epoch length, T. Some recent work in a simpler (two-locus) model suggested that stochastic switching in a regimen of two alternating environments may be evolutionarily favored only if the selection coefficients in the two environments are nearly equal (“symmetric”) or selection is very strong. This finding was puzzling, as it greatly restricted the conditions under which stochastic switching might evolve. Instead, we find agreement with other recent theoretical work, observing selective utility for stochastic switching if the product sT is large enough for the favored state to nearly fix in both environments. Symmetry is required neither in s nor in sT. Because we simulate finite populations and use a detailed model of the SSR locus, we are also able to examine the impact of population size and of several SSR locus parameters. Our results indicate that conditions favoring evolution and maintenance of SSR loci in bacteria are quite broad. IMPORTANCE Bacteria experience frequent changes of environment during the infection cycle. One means to rapidly adapt is stochastic switching: a bacterial lineage will stochastically produce a variety of genotypes, so that some descendants will survive if the environment changes. Stochastic switching mediated by simple sequence repeat (SSR) loci is widespread among bacterial commensals and pathogens and influences critical interactions with host surfaces or immune effectors, thereby affecting host persistence, transmission, and virulence. Here, we use the most detailed in silico model of an SSR locus to date, with its phase variation calibrated to match the mod locus of Haemophilus influenzae. The type III restriction-modification system encoded by mod participates in the regulation of multiple other genes; thus, SSR-mediated phase variation of mod has far-reaching cis-regulatory effects. This coupling of phase-variable switching to complex phenotypic effects has been described as the “phasevarion” and is central to understanding the infection cycle of bacterial commensals and pathogens. Bacteria experience frequent changes of environment during the infection cycle. One means to rapidly adapt is stochastic switching: a bacterial lineage will stochastically produce a variety of genotypes, so that some descendants will survive if the environment changes. Stochastic switching mediated by simple sequence repeat (SSR) loci is widespread among bacterial commensals and pathogens and influences critical interactions with host surfaces or immune effectors, thereby affecting host persistence, transmission, and virulence. Here, we use the most detailed in silico model of an SSR locus to date, with its phase variation calibrated to match the mod locus of Haemophilus influenzae. The type III restriction-modification system encoded by mod participates in the regulation of multiple other genes; thus, SSR-mediated phase variation of mod has far-reaching cis-regulatory effects. This coupling of phase-variable switching to complex phenotypic effects has been described as the “phasevarion” and is central to understanding the infection cycle of bacterial commensals and pathogens.

Dissecting the Genetic Basis of a Complex cis -Regulatory Adaptation

Although single genes underlying several evolutionary adaptations have been identified, the genetic basis of complex, polygenic adaptations has been far more challenging to pinpoint. Here we report that the budding yeast Saccharomyces paradoxus has recently evolved resistance to citrinin, a naturally occurring mycotoxin. Applying a genome-wide test for selection on cis-regulation, we identified five genes involved in the citrinin response that are constitutively up-regulated in S. paradoxus. Four of these genes are necessary for resistance, and are also sufficient to increase the resistance of a sensitive strain when over-expressed. Moreover, cis-regulatory divergence in the promoters of these genes contributes to resistance, while exacting a cost in the absence of citrinin. Our results demonstrate how the subtle effects of individual regulatory elements can be combined, via natural selection, into a complex adaptation. Our approach can be applied to dissect the genetic basis of polygenic adaptations in a wide range of species.

SPATIAL ENVIRONMENTAL VARIATION CAN SELECT FOR EVOLVABILITY

Previous studies have shown that temporally fluctuating environments can create indirect selection for modifiers of evolvability. Here, we use a simple computational model to investigate whether spatially varying environments (multiple demes with limited migration among them, and a different, static selective optimum in each) can also create indirect selection for increased evolvability. The answer is surprisingly complicated. Spatial variation in the environment can sharply reduce the survival rate of migrants, because migrants may be maladapted to their new deme, relative to incumbents. The incumbent advantage can be removed by occasional extinctions in single demes. After all incumbents in a particular deme die, incoming migrants from other demes will, on average, be similarly maladapted to the new environment. This sets off a race to adapt rapidly. Over many extinction events, and the subsequent invasions by maladapted immigrants into a new environment, indirect selection for the ability to adapt rapidly, also known as high evolvability, may result.

Survivability Is More Fundamental Than Evolvability

For a lineage to survive over long time periods, it must sometimes change. This has given rise to the term evolvability, meaning the tendency to produce adaptive variation. One lineage may be superior to another in terms of its current standing variation, or it may tend to produce more adaptive variation. However, evolutionary outcomes depend on more than standing variation and produced adaptive variation: deleterious variation also matters. Evolvability, as most commonly interpreted, is not predictive of evolutionary outcomes. Here, we define a predictive measure of the evolutionary success of a lineage that we call the k-survivability, defined as the probability that the lineage avoids extinction for k generations. We estimate the k-survivability using multiple experimental replicates. Because we measure evolutionary outcomes, the initial standing variation, the full spectrum of generated variation, and the heritability of that variation are all incorporated. Survivability also accounts for the decreased joint likelihood of extinction of sub-lineages when they 1) disperse in space, or 2) diversify in lifestyle. We illustrate measurement of survivability with in silico models, and suggest that it may also be measured in vivo using multiple longitudinal replicates. The k-survivability is a metric that enables the quantitative study of, for example, the evolution of 1) mutation rates, 2) dispersal mechanisms, 3) the genotype-phenotype map, and 4) sexual reproduction, in temporally and spatially fluctuating environments. Although these disparate phenomena evolve by well-understood microevolutionary rules, they are also subject to the macroevolutionary constraint of long-term survivability.

Long-term evolution is surprisingly predictable in lattice proteins

It has long been debated whether natural selection acts primarily upon individual organisms, or whether it also commonly acts upon higher-level entities such as lineages. Two arguments against the effectiveness of long-term selection on lineages have been (i) that long-term evolutionary outcomes will not be sufficiently predictable to support a meaningful long-term fitness and (ii) that short-term selection on organisms will almost always overpower long-term selection. Here, we use a computational model of protein folding and binding called ‘lattice proteins’. We quantify the long-term evolutionary success of lineages with two metrics called the k-fitness and k-survivability. We show that long-term outcomes are surprisingly predictable in this model: only a small fraction of the possible outcomes are ever realized in multiple replicates. Furthermore, the long-term fitness of a lineage depends only partly on its short-term fitness; other factors are also important, including the ‘evolvability’ of a lineage—its capacity to produce adaptive variation. In a system with a distinct short-term and long-term fitness, evolution need not be ‘short-sighted’: lineages may be selected for their long-term properties, sometimes in opposition to short-term selection. Similar evolutionary basins of attraction have been observed in vivo, suggesting that natural biological lineages will also have a predictive long-term fitness.