Academic journal article Genetics

Mutational Effects and Population Dynamics during Viral Adaptation Challenge Current Models

Academic journal article Genetics

Mutational Effects and Population Dynamics during Viral Adaptation Challenge Current Models

Article excerpt

ABSTRACT

Adaptation in haploid organisms has been extensively modeled but little tested. Using a microvirid bacteriophage (ID11), we conducted serial passage adaptations at two bottleneck sizes (10^sup 4^ and 10^sup 6^), followed by fitness assays and whole-genome sequencing of 631 individual isolates. Extensive genetic variation was observed including 22 beneficial, several nearly neutral, and several deleterious mutations. In the three large bottleneck lines, up to eight different haplotypes were observed in samples of 23 genomes from the final time point. The small bottleneck lines were less diverse. The small bottleneck lines appeared to operate near the transition between isolated selective sweeps and conditions of complex dynamics (e.g., clonal interference). The large bottleneck lines exhibited extensive interference and less stochasticity, with multiple beneficial mutations establishing on a variety of backgrounds. Several leapfrog events occurred. The distribution of first-step adaptive mutations differed significantly from the distribution of second-steps, and a surprisingly large number of second-step beneficial mutations were observed on a highly fit first-step background. Furthermore, few first-step mutations appeared as second-steps and second-steps had substantially smaller selection coefficients. Collectively, the results indicate that the fitness landscape falls between the extremes of smooth and fully uncorrelated, violating the assumptions of many current mutational landscape models.

THE question ofhow populations adapt to changes in their environment has long been a central one in evolutionary biology. While Fisher (1930) andWright (1932) proposed thefoundationalmodels of adaptation, the discoveries linking DNA sequence to amino acid sequence to functional protein changed the way that biologists conceived of adaptation. In the mutational landscape framework proposed by Maynard Smith (1970) and further developed especially by Gillespie (1984, 1991) and Orr (2000, 2002, 2005), organisms occupy points in discrete sequence space, where sequences are either DNA or protein. Spatially adjacent sequences differ from each other by one mutational change. Each sequence has an associated fitness yielding a fitness surface. An altered environment changes the fitness surface and shifts the peak away from the wild type. In a nonrecombinant haploid system,as is our focus here, mutationsonthe parental background(s) allow the population to explore neighboring regions of sequence space and thereby climb a fitness peak.

A conceptual map showing how the assumptions of many of the mutational landscape models relate one to another is presented in Figure 1. Our objective here is to first describe several of these assumptions in the Introduction and then examine their validity using real data from experimental evolution. We caution from the outset, however, that neither our survey of models nor our list of important modeling questions is exhaustive. We do not, for example, address extensions of Fisher's geometrical model, the effect of deleterious mutations, recombination, or a continually changing environment to the adaptive process, and we generally omit models that delve into these realms.

A central issue in all models of adaptation involves how frequently beneficial mutations arise in the population. When rare, the population will be fixed for one background for an extended time, ''waiting'' for a beneficial mutation to arise. Upon arising, it sweeps to fixation rapidly (relative to the waiting time).We refer to this situation as selective sweep dynamics, and the conditions that produce it are known as strong selection, weak mutation (SSWM) (Gillespie 1984, 1991). Selective sweep dynamics depend on the probability of fixation of each of the possible beneficial mutations. Some models have assumed that the population fixes a random beneficial mutation (e.g., the NK models in Figure 1), but Gillespie (1984) showed that mutations should be chosen with probability equal to their selection coefficient divided by the sum of all beneficial selection coefficients (Figure 1, bottom left). …

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