Probing Evolutionary Repeatability: Neutral and Double Changes and the Predictability of Evolutionary Adaptation

The question of how organisms adapt is among the most fundamental in evolutionary biology. Two recent studies investigated the evolution of Escherichia coli in response to challenge with the antibiotic cefotaxime. Studying five mutations in the beta-lactamase gene that together confer significant antibiotic resistance, the authors showed a complex fitness landscape that greatly constrained the identity and order of intermediates leading from the initial wildtype genotype to the final resistant genotype. Out of 18 billion possible orders of single mutations leading from non-resistant to fully-resistant form, they found that only 27 (1.5x10(-7)%) pathways were characterized by consistently increasing resistance, thus only a tiny fraction of possible paths are accessible by positive selection. I further explore these data in several ways.Allowing neutral changes (those that do not affect resistance) increases the number of accessible pathways considerably, from 27 to 629. Allowing multiple simultaneous mutations also greatly increases the number of accessible pathways. Allowing a single case of double mutation to occur along a pathway increases the number of pathways from 27 to 259, and allowing arbitrarily many pairs of simultaneous changes increases the number of possible pathways by more than 100 fold, to 4800. I introduce the metric 'repeatability,' the probability that two random trials will proceed via the exact same pathway. In general, I find that while the total number of accessible pathways is dramatically affected by allowing neutral or double mutations, the overall evolutionary repeatability is generally much less affected.These results probe the conceivable pathways available to evolution. Even when many of the assumptions of the analysis of Weinreich et al. (2006) are relaxed, I find that evolution to more highly cefotaxime resistant beta-lactamase proteins is still highly repeatable

Effect of host species on the topography of fitness landscape for a plant RNA virus

[EN] Adaptive fitness landscapes are a fundamental concept in evolutionary biology that relate the genotype of individuals with their fitness. At the end, the evolutionary fate of evolving populations depends on the topography of the landscape, that is, the number of accessible mutational pathways and of possible fitness peaks (i.e, adaptive solutions). For long time, fitness landscapes were only theoretical constructions due to a lack of precise information on the mapping between genotypes and phenotypes. In recent years, however, efforts have been devoted to characterize the properties of empirical fitness landscapes for individual proteins or for microbes adapting to artificial environments. In a previous study, we had characterized the properties of the empirical fitness landscape defined by the first five mutations fixed during adaptation of tobacco etch potyvirus (TEV) to a new experimental host, Arabidopsis thaliana. Here we evaluate the topography of this landscape in the ancestral host Nicotiana tabacum. Comparing the topographies of the landscape in the two hosts, we found that some features remain similar, such as the existence of fitness holes and the prevalence of epistasis, including cases of sign and of reciprocal sign that create rugged, uncorrelated and highly random topographies. However, we also observed significant differences in the fine grained details among both landscapes due to changes in the fitness and epistatic interactions of some genotypes. Our results support the idea that not only fitness tradeoffs between hosts but also topographical incongruences among fitness landscapes in alternative hosts may contribute to virus specialization.This project was funded by grants BFU2012-30805 and BFU2015-65037P from the Spanish Ministry of Economy and Competitiveness (MINECO), PROMETEOII/2014/021 from the Generalitat Valenciana, and EvoEvo (ICT610427) from the European Commission 7th Framework Program to S.F.E. H.C. was supported by contract BES2013-065595 from MINECO. J.L. was supported by a JAE-pre contract from CSIC.Cervera-Benet, H.; Lalic, J.; Elena Fito, SF. (2016). Effect of host species on the topography of fitness landscape for a plant RNA virus. Journal of Virology. 90(22):10160-10169. https://doi.org/10.1128/JVI.01243-16S1016010169902

Predictability of evolutionary trajectories in fitness landscapes

Experimental studies on enzyme evolution show that only a small fraction of all possible mutation trajectories are accessible to evolution. However, these experiments deal with individual enzymes and explore a tiny part of the fitness landscape. We report an exhaustive analysis of fitness landscapes constructed with an off-lattice model of protein folding where fitness is equated with robustness to misfolding. This model mimics the essential features of the interactions between amino acids, is consistent with the key paradigms of protein folding and reproduces the universal distribution of evolutionary rates among orthologous proteins. We introduce mean path divergence as a quantitative measure of the degree to which the starting and ending points determine the path of evolution in fitness landscapes. Global measures of landscape roughness are good predictors of path divergence in all studied landscapes: the mean path divergence is greater in smooth landscapes than in rough ones. The model-derived and experimental landscapes are significantly smoother than random landscapes and resemble additive landscapes perturbed with moderate amounts of noise; thus, these landscapes are substantially robust to mutation. The model landscapes show a deficit of suboptimal peaks even compared with noisy additive landscapes with similar overall roughness. We suggest that smoothness and the substantial deficit of peaks in the fitness landscapes of protein evolution are fundamental consequences of the physics of protein folding.Comment: 14 pages, 7 figure

Initial Mutations Direct Alternative Pathways of Protein Evolution

Whether evolution is erratic due to random historical details, or is repeatedly directed along similar paths by certain constraints, remains unclear. Epistasis (i.e. non-additive interaction between mutations that affect fitness) is a mechanism that can contribute to both scenarios. Epistasis can constrain the type and order of selected mutations, but it can also make adaptive trajectories contingent upon the first random substitution. This effect is particularly strong under sign epistasis, when the sign of the fitness effects of a mutation depends on its genetic background. In the current study, we examine how epistatic interactions between mutations determine alternative evolutionary pathways, using in vitro evolution of the antibiotic resistance enzyme TEM-1 β-lactamase. First, we describe the diversity of adaptive pathways among replicate lines during evolution for resistance to a novel antibiotic (cefotaxime). Consistent with the prediction of epistatic constraints, most lines increased resistance by acquiring three mutations in a fixed order. However, a few lines deviated from this pattern. Next, to test whether negative interactions between alternative initial substitutions drive this divergence, alleles containing initial substitutions from the deviating lines were evolved under identical conditions. Indeed, these alternative initial substitutions consistently led to lower adaptive peaks, involving more and other substitutions than those observed in the common pathway. We found that a combination of decreased enzymatic activity and lower folding cooperativity underlies negative sign epistasis in the clash between key mutations in the common and deviating lines (Gly238Ser and Arg164Ser, respectively). Our results demonstrate that epistasis contributes to contingency in protein evolution by amplifying the selective consequences of random mutations

Role of chance and history during evolution in Chlamydomonas reinhardtii

The extent to which evolution is repeatable has important implications. If evolution is highly repeatable, the trajectories and outcomes of evolution in different lineages will always be the same. On the other hand, if evolution is not repeatable, then trajectories and outcomes will be diverse. Thus, the repeatability of evolution affects our understanding of the nature of biodiversity and can inform the extent to which evolutionary theory can be used to make predictions. The repeatability of evolution depends on the relative contribution of selection, chance, and history. To determine what factors affect the importance of chance and history during evolution, I propagated replicated populations of the unicellular green alga Chlamydomonas reinhardtii in controlled environments. I measured the change in fitness after a few hundred generations and determined how much variation had arisen among replicate populations and among populations with different histories. I applied a similar approach to study the importance of history in extinctions, and measured rates of extinction in populations with different histories. I found that evolution is much less repeatable in small than in large populations because history is more constraining and selection less efficient in small than in large populations. There is also a significant effect of sex and recombination on the repeatability of evolution at the fitness level, but this effect is highly dependent on the environment of selection. Sex can increase the importance of chance or history in some environments, but lower their importance in others, thereby leading to convergence or divergence depending on the environment. Thirdly, I found that the importance of history during evolution does not appear to come from the accumulation of past evolutionary selection pressures, but rather comes from only the most recent selection pressure as it determines genetic correlations for growth between different environments and the amount of genetic variance. Finally, I found that extinction risks are extremely high during continuous environmental deterioration, although a history of sexual reproduction and phenotypic plasticity play an important role in adaptation. By focusing not solely on the effect of treatments on mean trait values, but also on the variance that arises in our evolution experiments, we can gain a better understanding of the contribution that chance and history make to evolution. The repeatability of evolution can therefore inform us about the adaptive vs. stochastic nature of the diversity we see today, and about the specificity or generality of evolutionary outcomes

Molecular mechanisms of drug resistance in natural Leishmania populations vary with genetic background

The evolution of drug-resistance in pathogens is a major global health threat. Elucidating the molecular basis of pathogen drug-resistance has been the focus of many studies but rarely is it known whether a drug-resistance mechanism identified is universal for the studied pathogen; it has seldom been clarified whether drug-resistance mechanisms vary with the pathogen's genotype. Nevertheless this is of critical importance in gaining an understanding of the complexity of this global threat and in underpinning epidemiological surveillance of pathogen drug resistance in the field. This study aimed to assess the molecular and phenotypic heterogeneity that emerges in natural parasite populations under drug treatment pressure. We studied lines of the protozoan parasite Leishmania (L.) donovani with differential susceptibility to antimonial drugs; the lines being derived from clinical isolates belonging to two distinct genetic populations that circulate in the leishmaniasis endemic region of Nepal. Parasite pathways known to be affected by antimonial drugs were characterised on five experimental levels in the lines of the two populations. Characterisation of DNA sequence, gene expression, protein expression and thiol levels revealed a number of molecular features that mark antimonial-resistant parasites in only one of the two populations studied. A final series of in vitro stress phenotyping experiments confirmed this heterogeneity amongst drug-resistant parasites from the two populations. These data provide evidence that the molecular changes associated with antimonial-resistance in natural Leishmania populations depend on the genetic background of the Leishmania population, which has resulted in a divergent set of resistance markers in the Leishmania populations. This heterogeneity of parasite adaptations provides severe challenges for the control of drug resistance in the field and the design of molecular surveillance tools for widespread applicability

Reciprocal Sign Epistasis between Frequently Experimentally Evolved Adaptive Mutations Causes a Rugged Fitness Landscape

The fitness landscape captures the relationship between genotype and evolutionary fitness and is a pervasive metaphor used to describe the possible evolutionary trajectories of adaptation. However, little is known about the actual shape of fitness landscapes, including whether valleys of low fitness create local fitness optima, acting as barriers to adaptive change. Here we provide evidence of a rugged molecular fitness landscape arising during an evolution experiment in an asexual population of Saccharomyces cerevisiae. We identify the mutations that arose during the evolution using whole-genome sequencing and use competitive fitness assays to describe the mutations individually responsible for adaptation. In addition, we find that a fitness valley between two adaptive mutations in the genes MTH1 and HXT6/HXT7 is caused by reciprocal sign epistasis, where the fitness cost of the double mutant prohibits the two mutations from being selected in the same genetic background. The constraint enforced by reciprocal sign epistasis causes the mutations to remain mutually exclusive during the experiment, even though adaptive mutations in these two genes occur several times in independent lineages during the experiment. Our results show that epistasis plays a key role during adaptation and that inter-genic interactions can act as barriers between adaptive solutions. These results also provide a new interpretation on the classic Dobzhansky-Muller model of reproductive isolation and display some surprising parallels with mutations in genes often associated with tumors

Nonlinear Fitness Landscape of a Molecular Pathway

Genes are regulated because their expression involves a fitness cost to the organism. The production of proteins by transcription and translation is a well-known cost factor, but the enzymatic activity of the proteins produced can also reduce fitness, depending on the internal state and the environment of the cell. Here, we map the fitness costs of a key metabolic network, the lactose utilization pathway in Escherichia coli. We measure the growth of several regulatory lac operon mutants in different environments inducing expression of the lac genes. We find a strikingly nonlinear fitness landscape, which depends on the production rate and on the activity rate of the lac proteins. A simple fitness model of the lac pathway, based on elementary biophysical processes, predicts the growth rate of all observed strains. The nonlinearity of fitness is explained by a feedback loop: production and activity of the lac proteins reduce growth, but growth also affects the density of these molecules. This nonlinearity has important consequences for molecular function and evolution. It generates a cliff in the fitness landscape, beyond which populations cannot maintain growth. In viable populations, there is an expression barrier of the lac genes, which cannot be exceeded in any stationary growth process. Furthermore, the nonlinearity determines how the fitness of operon mutants depends on the inducer environment. We argue that fitness nonlinearities, expression barriers, and gene–environment interactions are generic features of fitness landscapes for metabolic pathways, and we discuss their implications for the evolution of regulation

Deep diversification of an AAV capsid protein by machine learning.

Modern experimental technologies can assay large numbers of biological sequences, but engineered protein libraries rarely exceed the sequence diversity of natural protein families. Machine learning (ML) models trained directly on experimental data without biophysical modeling provide one route to accessing the full potential diversity of engineered proteins. Here we apply deep learning to design highly diverse adeno-associated virus 2 (AAV2) capsid protein variants that remain viable for packaging of a DNA payload. Focusing on a 28-amino acid segment, we generated 201,426 variants of the AAV2 wild-type (WT) sequence yielding 110,689 viable engineered capsids, 57,348 of which surpass the average diversity of natural AAV serotype sequences, with 12-29 mutations across this region. Even when trained on limited data, deep neural network models accurately predict capsid viability across diverse variants. This approach unlocks vast areas of functional but previously unreachable sequence space, with many potential applications for the generation of improved viral vectors and protein therapeutics