Effects of intra-gene fitness interactions on the benefit of sexual recombination

Parallel Evolution of High-Level Aminoglycoside Resistance in Escherichia coli Under Low and High Mutation Supply Rates

Antibiotic resistance is a major concern in public health worldwide, thus there is much interest in characterizing the mutational pathways through which susceptible bacteria evolve resistance. Here we use experimental evolution to explore the mutational pathways toward aminoglycoside resistance, using gentamicin as a model, under low and high mutation supply rates. Our results show that both normo and hypermutable strains of Escherichia coli are able to develop resistance to drug dosages > 1,000-fold higher than the minimal inhibitory concentration for their ancestors. Interestingly, such level of resistance was often associated with changes in susceptibility to other antibiotics, most prominently with increased resistance to fosfomycin. Whole-genome sequencing revealed that all resistant derivatives presented diverse mutations in five common genetic elements: fhuA, fusA and the atpIBEFHAGDC, cyoABCDE, and potABCD operons. Despite the large number of mutations acquired, hypermutable strains did not pay, apparently, fitness cost. In contrast to recent studies, we found that the mutation supply rate mainly affected the speed (tempo) but not the pattern (mode) of evolution: both backgrounds acquired the mutations in the same order, although the hypermutator strain did it faster. This observation is compatible with the adaptive landscape for high-level gentamicin resistance being relatively smooth, with few local maxima; which might be a common feature among antibiotics for which resistance involves multiple loci.

Breaking evolutionary constraint with a tradeoff ratchet

Epistatic interactions can frustrate and shape evolutionary change. Indeed, phenotypes may fail to evolve when essential mutations are only accessible through positive selection if they are fixed simultaneously. How environmental variability affects such constraints is poorly understood. Here, we studied genetic constraints in fixed and fluctuating environments using the Escherichia coli lac operon as a model system for genotype-environment interactions. We found that, in different fixed environments, all trajectories that were reconstructed by applying point mutations within the transcription factor-operator interface became trapped at suboptima, where no additional improvements were possible. Paradoxically, repeated switching between these same environments allows unconstrained adaptation by continuous improvements. This evolutionary mode is explained by pervasive cross-environmental tradeoffs that reposition the peaks in such a way that trapped genotypes can repeatedly climb ascending slopes and hence, escape adaptive stasis. Using a Markov approach, we developed a mathematical framework to quantify the landscape-crossing rates and show that this ratchet-like adaptive mechanism is robust in a wide spectrum of fluctuating environments. Overall, this study shows that genetic constraints can be overcome by environmental change and that cross-environmental tradeoffs do not necessarily impede but also, can facilitate adaptive evolution. Because tradeoffs and environmental variability are ubiquitous in nature, we speculate this evolutionary mode to be of general relevance.

Random field model reveals structure of the protein recombinational landscape

We are interested in how intragenic recombination contributes to the evolution of proteins and how this mechanism complements and enhances the diversity generated by random mutation. Experiments have revealed that proteins are highly tolerant to recombination with homologous sequences (mutation by recombination is conservative); more surprisingly, they have also shown that homologous sequence fragments make largely additive contributions to biophysical properties such as stability. Here, we develop a random field model to describe the statistical features of the subset of protein space accessible by recombination, which we refer to as the recombinational landscape. This model shows quantitative agreement with experimental results compiled from eight libraries of proteins that were generated by recombining gene fragments from homologous proteins. The model reveals a recombinational landscape that is highly enriched in functional sequences, with properties dominated by a large-scale additive structure. It also quantifies the relative contributions of parent sequence identity, crossover locations, and protein fold to the tolerance of proteins to recombination. Intragenic recombination explores a unique subset of sequence space that promotes rapid molecular diversification and functional adaptation.

Mutation and recombination are the primary sources of genetic variation in evolving populations. The relative benefit of these two diversification mechanisms and how they complement each other has been a long-standing question in evolutionary biology. While it is clear what types of genetic diversity these two mechanisms can create, a significant challenge is relating these sequence changes to changes in fitness. The fitness landscape, which describes this mapping from genotype to phenotype, is extraordinarily complex and defined over an incomprehensibly large space of sequences. Here, we develop a model of the landscape that relies not on the details of this mapping, but rather on the statistical relationships between sequences. By studying the expected values of landscape properties, we can gain insights into the structure of the landscape that are independent of the details of how genotype dictates phenotype. We use this random field model to understand how recombination explores a functionally enriched and diverse subset of protein sequence space.

Genome structure and the benefit of sex

We examine the behavior of sexual and asexual populations in modular multipeaked fitness landscapes and show that sexuals can systematically reach different, higher fitness adaptive peaks than asexuals. Whereas asexuals must move against selection to escape local optima, sexuals reach higher fitness peaks reliably because they create specific genetic variants that "skip over" fitness valleys, moving from peak to peak in the fitness landscape. This occurs because recombination can supply combinations of mutations in functional composites or "modules," that may include individually deleterious mutations. Thus when a beneficial module is substituted for another less-fit module by sexual recombination it provides a genetic variant that would require either several specific simultaneous mutations in an asexual population or a sequence of individual mutations some of which would be selected against. This effect requires modular genomes, such that subsets of strongly epistatic mutations are tightly physically linked. We argue that such a structure is provided simply by virtue of the fact that genomes contain many genes each containing many strongly epistatic nucleotides. We briefly discuss the connections with "building blocks" in the evolutionary computation literature. We conclude that there are conditions in which sexuals can systematically evolve high-fitness genotypes that are essentially unevolvable for asexuals.

Mutational effects and population dynamics during viral adaptation challenge current models

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(4) and 10(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.