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Overview

Ultimately, the ability of a population to adapt to a new environmental challenge depends on whether there is genetic raw material available to produce the adaptation. Thus, the absence of resistance mutations in American Chestnut populations explains why the American Chestnut was essentially eliminated from North America by chestnut blight during the last century. The major focus of research in my lab is to characterize the raw material available for adaptation in the context of particular environmental challenges. We measure the distribution of effects of new spontaneous mutations, determine how selection acts to shape that distribution in the current environment, and use the resulting information to predict the ability and consequences of adaptation to the new environmental challenge.

Our general approach is to conduct controlled evolution experiments in which we monitor adaptation of a virus population to a novel environment. In general, we use as our model system the bacterial virus (bacteriophage) Φ6, an RNA virus that infects the bacterium Pseudomonas syringae. We work with phi-6 because it has a rich history in experimental evolution, it makes a suitable model for the study of emerging diseases, which are dominated by RNA viruses, and its molecular biology has been well characterized. In addition, we have recently added alternative model systems to our repertoire because they are better suited to particular research questions. These models include the DNA bacteriophage G4 (a ΦX174 relative), and computational models of artificial gene networks.

A major advantage of the bacteriophage model systems is that they allow us to watch evolution in action. The following pictures were taken at different timepoints during the evolution of a low fitness phage genotype toward an adaptive optimum. What you're seeing is a pale gray background that is the bacterial lawn and dark circles where phage have landed, replicated, and killed bacteria to make cleared circles that we call plaques. It is easy to monitor adaptation in phage because plaque size is a strong correlate of fitness (i.e. relative growth rate). As beneficial mutations appear and become common in adapting populations, fitness improves and plaque size increases.

t = 0t = 50 generationst = 100 generations


The Genetics of Adaptation

The primary goal of our research is to characterize the effects of and the interactions between spontaneous mutations, and in particular, to determine whether the distribution of mutation effects differs predictably among genotypes. We use the bacteriophage Φ6 as our model experimental system and examine, simply, the effects of mutations on fitness in the laboratory environment. Several recent findings illustrate the nature of this work. We made the most detailed measure of the distribution of spontaneous mutation effects on fitness to date, providing data that challenge the emerging paradigm that very few mutations have large effects on fitness [14]*. We showed that the mutation effect distribution changes shape when fitness is reduced by the fixation of deleterious mutations, so that the distribution contains more beneficial mutations [3] and fewer deleterious mutations of large effect [8]. We further showed that these differences in the mutation effect distribution can have a dramatic effect on the ability of a population to adapt to a new environment [5]. These results are relevant to applied concerns in medicine and conservation biology, including the ability of pathogens to recover fitness following acquisition of drug or antibiotic resistance mutations, and the ability of small populations to persist in the face of recurrent deleterious mutations.

Current and future work in this area focuses on the idea that natural selection may act to shape the mutation effect distribution. This idea is not new, but the mechanisms by which such selection could act and the potential ramifications have not been fully explored. We began with explorations of a computational model of genetic networks because the model lets us manipulate population genetic parameters, such as mutation and recombination rate, that are difficult to manipulate empirically. So far, the most surprising result has been that genetic recombination imposes selection for robustness (insensitivity) to mutations. This selection caused the effects of mutations to decrease, and also caused the nature of the interactions between mutations to evolve. Evolved genomes exhibited a type of genetic interaction (negative epistasis) that makes recombination more advantageous. Thus, recombination selected for conditions that favor its own maintenance [10].


Adaptation to a novel host

We study virus adaptation to a novel host as a model for investigating the genetics of adaptation because it represents a rare scenario in which adaptive mutations are easily obtained and identified. In addition, the increasing threat of disease emergence, especially among RNA viruses, provides considerable incentive for predicting whether and when virus populations will acquire the ability to colonize and adapt to a novel host. In order to make such predictions we need better characterizations of the rates and effects of spontaneous mutations that allow successful infection of novel hosts. Thus, the applied interest in predicting the genetics of adaptation to novel hosts coincides nicely with the basic science interest in predicting the genetics of adaptation more generally.

To characterize the rates and effects of mutations that allow infection of novel hosts, we screened spontaneous 6 mutants for the ability to infect a novel Pseudomonas syringae host, and characterized the genetic and phenotypic bases of the host range expansion. Several findings resulted from this work. 1) We developed a new statistical method to estimate from the resulting data that a total of 55 different mutations in the host attachment gene enable infection of the novel host. This number comprises 1.3% of the possible non-synonymous mutations in that gene, and indicates that the proportion of mutations that are beneficial in a novel environment can be surprisingly high [13]. 2) We demonstrated an environmental (non-genetic) cost of broader host ranges associated with transitioning between host types [12]. The environmental cost is intriguing from an evolutionary perspective because it imposes a cost on host generalists that further evolution may be unable to eliminate. 3) We provided one of the first, most detailed, and largest sample size measures of the distribution of beneficial mutation effects, which allowed us to reject the common assumption of most theoretical models of adaptation – that the distribution of beneficial mutation effects is approximately exponential [17].

Our initial characterizations of host range evolution in 6 have us well poised for future investigations of the evolutionary ecology underlying host use in viruses. The ability to sequence whole genomes of evolved bacteriophages and to generate defined mutations allows perfect knowledge of the genetics of host adaptation in 6. We have discovered that host specificity is mediated primarily by the host attachment gene [12, 13](Duffy et al. 2006; Ferris et al 2006), and that a subset of mutations that confer improved performance on a novel host (ecological adaptation) also confer increased host specificity (assortative mating) [15]. Although the advantages of the 6 genetic model differ from the advantages of ecological model systems, this pleiotropy between host performance and host specificity should make 6 similarly useful for studying ecological speciation. In future, we plan to capitalize on the genetic tractability of the 6 system to compare, for example, the effects of competition and reinforcement (selection against the production of hybrids) on the evolution of host specificity.


Adaptation to novel temperatures

We study evolutionary responses to temperature as a model for understanding the process of adaptation to novel environments because the evolutionary and mechanistic causes of thermal adaptation are relatively transparent. Temperature is a component of the environment that varies predictably, and for which we have knowledge of the proximate mechanisms (e.g. biochemical rate processes) that determine the effects of temperature on growth or performance. Our main approach is to use what is known about temperature effects on biochemical rate processes to predict the nature of adaptation to alternative temperature environments.

For this work, we use as our model system the bacteriophage G4 because it has a small, easily sequenced DNA genome, and because protein function and structure has been more thoroughly characterized in G4 than in 6. In addition, we have amassed a large collection of completely sequenced, related G4-like bacteriophages isolated from natural populations [11]. This collection represents a rich resource for current and future investigations of natural genetic and phenotypic variation, and allows a comparison of the outcomes of laboratory evolution with observations from nature.

To determine whether biochemical constraints govern evolutionary responses to temperature, we quantified differences in thermal reaction norms—the curves that describe the effect of temperature on the growth rate of the bacteriophages—both among genotypes that resulted from laboratory adaptation to high and low temperature, and among genotypes isolated from natural populations. In initial characterizations of laboratory adaptation to high temperature, we discovered that the adaptation was accomplished primarily by increases in the bacteriophages’ thermal optimum, rather than a general increase in growth rate across temperatures or a broadening of the thermal niche [9]. Variation among bacteriophages isolated from nature showed a similar pattern, resulting largely from differences in thermal optima. In addition, we provided strong evidence for a thermodynamic constraint that limits adaptation to low temperature. We found that genotypes adapted to low temperatures have lower maximum growth rates than those adapted to high temperatures , indicating that adaptation can not overcome the rate-depressing effects of low temperature [manuscript in preparation].

The future direction of this work will be to combine these characterizations of reaction norms with more detailed investigations of the genetic bases and proximate mechanisms of temperature adaptation. We have already had some success on both of these fronts. For instance, we identified a single nucleotide mutation that was responsible for similar reaction norm changes during laboratory adaptation and among the natural bacteriophage isolates [9]. In addition, we demonstrated that adaptation to high temperature in the laboratory was accomplished by increasing protein thermostability. We are particularly interested in clarifying the role of biochemically predicted constraints on adaptation, such as a tradeoff between protein stability and enzyme activity.