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Complex traits such as fitness are difficult to study because they are determined by multiple genetic factors or loci. The difficulty arises from the unpredictable nature of epistasis, the interactions between these loci, which limit our ability to predict the ultimate form of complex traits from their underlying parts. Therefore, to understand the evolution of a complex trait, it is first necessary to understand how genetic interactions constrain changes in the trait. Our general approach is to conduct laboratory evolution experiments in model systems with sufficiently small numbers of genes that we can uncover both the mechanistic causes and evolutionary consequences of genetic interactions. We employ a variety of bacteriophages in these experiments, the dsRNA phage φ6, ssDNA phage φX174, and dsDNA phage T7, and have begun to couple our work on bacteriophages with computational models of gene interaction networks.

Genetic Interaction and the Evolution of Sex

Although a variety of theories have been proposed to explain the ubiquity of sexual reproduction, the evolutionary reasons for its success remain unclear.  We propose that previous theoretical investigations have missed an important advantage of sex by treating the genetic architecture of organisms (see box below) as static.  We are developing a new modeling and experimental framework using the bacteriophage T7 to test our central hypothesis that sexual reproduction selects for a genetic architecture that favors its own maintenance.  This hypothesis follows from our discovery, using an artificial gene network model, that sex selects for a lower deleterious mutation rate, lower recombination load and negative epistasis — three factors predicted to favor the maintenance of sex.

Genetic architecture refers to the patterns of gene action and interaction that specify a given phenotype and its variational properties. The distribution of allelic and mutational effects, and the patterns of pleiotropy, dominance, and epistasis are all determined by the underlying genetic architecture (Hansen 2006).

We are now following up on the results from the artificial gene network model using evolution experiments in both the bacteriophage T7 and in a computational model of the T7 gene interaction network.  These experiments offer the real possibility of characterizing both the causes and consequences of gene interactions for important evolutionary phenomena such as the evolution of sex.

Publications on this topic:

R. Lohaus, C. L. Burch, R. B. R. Azevedo. Genetic Architecture and the Evolution of Sex. Journal of Heredity 101, S142–S157 (2010). Link

Ricardo B. R. Azevedo, Rolf Lohaus, Suraj Srinivasan, Kristen K. Dang, Christina L. Burch. Sexual reproduction selects for robustness and negative epistasis in artificial gene networks. Nature 440, 87–90 (2006). Link

Genetic Interaction and the Distribution of Mutational Effects

Evolution experiments consistently demonstrate that the rates of adaptation in large populations and of fitness losses in small populations decline over time. This change in rate implies that the distribution of mutational effects is dynamic, changing with fitness. Over the last century, the conceptual framework that has been most often used to explain or predict the nature of that change is Fisher’s Geometric Model (Fisher 1958), which describes adaptation within a multi-dimensional phenotypic landscape. Fisher’s Geometric Model has been used both as a basis for theoretical predictions of mutational effect distribution dynamics and as an explanation for empirical observations. Despite the wide popularity of Fisher’s Geometric Model among evolutionary biologists, the model’s most basic prediction – that the frequency of beneficial mutations should tend toward 50% as fitness declines toward zero – remains untested. Our current goal is to provide a direct test of this prediction using Mutation Accumulation experiments to estimate the distribution of spontaneous mutational effects in five genotypes of the RNA virus φ6 that span a wide range of fitnesses. Unlike previous MA experiments, we measured fitness every day over the course of mutation accumulation to ensure the direct measurement of individual mutation effects. Our data match Fisher’s prediction. Beneficial mutations were rare in high fitness genomes, but increased to a frequency near 50% as fitness declined.

Publications on this topic:

Burch, C. L. and L. Chao. 1999. Evolution by small steps and rugged landscapes in the RNA virus phi-6. Genetics. 151: 921-927. Link

Christina L. Burch, Lin Chao. Evolvability of an RNA virus is determined by its mutational neighbourhood. Nature 406, 625–628 (2000). Link

C. L. Burch. Epistasis and Its Relationship to Canalization in the RNA Virus ϕ6. Genetics 167, 559–567 (2004). Link

C. L. Burch, S. Guyader, D. Samarov, H. Shen. Experimental Estimate of the Abundance and Effects of Nearly Neutral Mutations in the RNA Virus ϕ6. Genetics 176, 467–476 (2007). Link

Darin R. Rokyta, Craig J. Beisel, Paul Joyce, Martin T. Ferris, Christina L. Burch, Holly A. Wichman. Beneficial Fitness Effects Are Not Exponential for Two Viruses. Journal of Molecular Evolution 67, 368–376 (2008). Link

Mihee Lee, Haipeng Shen, Christina Burch, J. S. Marron. Direct deconvolution density estimation of a mixture distribution motivated by mutation effects distribution. Journal of Nonparametric Statistics 22, 1–22 (2010). Link

Host Range Evolution and Speciation

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.

Our initial characterizations of host range evolution in φ6 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 φ6. We have discovered that host specificity is mediated primarily by the host attachment gene, and that a subset of mutations that confer improved performance on a novel host (ecological adaptation) also confer increased host specificity (assortative mating). Although the advantages of the φ6 genetic model differ from the advantages of ecological model systems, this pleiotropy between host performance and host specificity make φ6 similarly useful for studying ecological speciation. Current research capitalizes on the genetic tractability of the φ6 system to measure the effects of competition and gene flow on the evolution of host specificity.

Publications on this topic:

M. T. Ferris, P. Joyce, C. L. Burch. High Frequency of Mutations That Expand the Host Range of an RNA Virus. Genetics 176, 1013–1022 (2006). Link

Siobain Duffy, Christina L. Burch, Paul E. Turner. Evolution of host specificity drives reproductive isolation among RNA viruses. Evolution 61, 2614–2622 (2007). Link

L. M. Bono, C. L. Gensel, D. W. Pfennig, C. L. Burch. Competition and the origins of novelty: experimental evolution of niche-width expansion in a virus. Biology Letters 9, 20120616–20120616 (2012). Link

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. To determine whether biochemical constraints govern evolutionary responses to temperature, we quantify 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.

Publications on this topic:

Jennifer L. Knies, Rima Izem, Katie L. Supler, Joel G. Kingsolver, Christina L. Burch. The Genetic Basis of Thermal Reaction Norm Evolution in Lab and Natural Phage Populations. Plos Biol 4, e201 (2006). Link

Jennifer L. Knies, Joel G. Kingsolver, Christina L. Burch. Hotter Is Better and Broader: Thermal Sensitivity of Fitness in a Population of Bacteriophages. Am Nat 173, 419–430 (2009). Link

RNA Secondary Structure Evolution

Description coming soon!

Publications on this topic:

J. L. Knies, K. K. Dang, T. J. Vision, N. G. Hoffman, R. Swanstrom, C. L. Burch. Compensatory Evolution in RNA Secondary Structures Increases Substitution Rate Variation among Sites. Molecular Biology and Evolution 25, 1778–1787 (2008). Link

Joseph M. Watts, Kristen K. Dang, Robert J. Gorelick, Christopher W. Leonard, Julian W. Bess Jr, Ronald Swanstrom, Christina L. Burch, Kevin M. Weeks. Architecture and secondary structure of an entire HIV-1 RNA genome. Nature 460, 711–716 (2009). Link

Elizabeth Pollom, Kristen K. Dang, E. Lake Potter, Robert J. Gorelick, Christina L. Burch, Kevin M. Weeks, Ronald Swanstrom. Comparison of SIV and HIV-1 Genomic RNA Structures Reveals Impact of Sequence Evolution on Conserved and Non-Conserved Structural Motifs. PLoS Pathogens 9, e1003294 (2013). Link