Identifying Drivers of Parallel Evolution: A Regression Model Approach

Mutation bias and the predictability of evolution

Predicting evolutionary outcomes is an important research goal in a diversity of contexts. The focus of evolutionary forecasting is usually on adaptive processes, and efforts to improve prediction typically focus on selection. However, adaptive processes often rely on new mutations, which can be strongly influenced by predictable biases in mutation. Here, we provide an overview of existing theory and evidence for such mutation-biased adaptation and consider the implications of these results for the problem of prediction, in regard to topics such as the evolution of infectious diseases, resistance to biochemical agents, as well as cancer and other kinds of somatic evolution. We argue that empirical knowledge of mutational biases is likely to improve in the near future, and that this knowledge is readily applicable to the challenges of short-term prediction. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

Towards evolutionary predictions: Current promises and challenges

Evolution has traditionally been a historical and descriptive science, and predicting future evolutionary processes has long been considered impossible. However, evolutionary predictions are increasingly being developed and used in medicine, agriculture, biotechnology and conservation biology. Evolutionary predictions may be used for different purposes, such as to prepare for the future, to try and change the course of evolution or to determine how well we understand evolutionary processes. Similarly, the exact aspect of the evolved population that we want to predict may also differ. For example, we could try to predict which genotype will dominate, the fitness of the population or the extinction probability of a population. In addition, there are many uses of evolutionary predictions that may not always be recognized as such. The main goal of this review is to increase awareness of methods and data in different research fields by showing the breadth of situations in which evolutionary predictions are made. We describe how diverse evolutionary predictions share a common structure described by the predictive scope, time scale and precision. Then, by using examples ranging from SARS-CoV2 and influenza to CRISPR-based gene drives and sustainable product formation in biotechnology, we discuss the methods for predicting evolution, the factors that affect predictability and how predictions can be used to prevent evolution in undesirable directions or to promote beneficial evolution (i.e. evolutionary control). We hope that this review will stimulate collaboration between fields by establishing a common language for evolutionary predictions.

Discovery of positive and purifying selection in metagenomic time series of hypermutator microbial populations

A general method to infer both positive and purifying selection during the real-time evolution of hypermutator pathogens would be broadly useful. To this end, we introduce a Simple Test to Infer Mode of Selection (STIMS) from metagenomic time series of evolving microbial populations. We test STIMS on metagenomic data generated by simulations of bacterial evolution, and on metagenomic data spanning 62,750 generations of Lenski’s long-term evolution experiment with Escherichia coli (LTEE). This benchmarking shows that STIMS detects positive selection in both nonmutator and hypermutator populations, and purifying selection in hypermutator populations. Using STIMS, we find strong evidence of ongoing positive selection on key regulators of the E. coli gene regulatory network, even in some hypermutator populations. STIMS also detects positive selection on regulatory genes in hypermutator populations of Pseudomonas aeruginosa that adapted to subinhibitory concentrations of colistin–an antibiotic of last resort–for just twenty-six days of laboratory evolution. Our results show that the fine-tuning of gene regulatory networks is a general mechanism for rapid and ongoing adaptation. The simplicity of STIMS, together with its intuitive visual interpretation, make it a useful test for positive and purifying selection in metagenomic data sets that track microbial evolution in real-time.

Organisms often evolve elevated mutation rates as they adapt to new environments. That said, the genomic basis of adaptation in asexual hypermutator populations has been difficult to discern, because the few mutations driving adaptation are often associated with large numbers of non-adaptive “hitchhiker” mutations elsewhere in the genome. To address this research gap, we present a Simple Test to Infer Mode of Selection (STIMS) which aggregates the overall signature of selection seen over hundreds of genes. Using STIMS, we find strong positive selection on key regulators of the E. coli gene regulatory network across nonmutator and hypermutator populations of Lenski’s long-term evolution experiment with Escherichia coli. We also find positive selection on regulatory genes in populations of Pseudomonas aeruginosa that evolved hypermutability as they evolved to resist colistin, an antibiotic of last resort. Our work shows that positive and purifying selection can be resolved in hypermutator populations, given sufficient genomic data, and show that regulatory mutations are a major driver of adaptation in experimental populations of microbes.

Predicting Parallelism and Quantifying Divergence in Microbial Evolution Experiments

The degree to which independent populations subjected to identical environmental conditions evolve in similar ways is a fundamental question in evolution. To address this question, microbial populations are often experimentally passaged in a given environment and sequenced to examine the tendency for similar mutations to repeatedly arise. However, there remains the need to develop an appropriate statistical framework to identify genes that acquired more mutations in one environment than in another (i.e., divergent evolution), genes that serve as genetic candidates of adaptation. Here, we develop a mathematical model to evaluate evolutionary outcomes among replicate populations in the same environment (i.e., parallel evolution), which can then be used to identify genes that contribute to divergent evolution. Applying this approach to data sets from evolve-and-resequence experiments, we found that the distribution of mutation counts among genes can be predicted as an ensemble of independent Poisson random variables with zero free parameters. Building on this result, we propose that the degree of divergent evolution at a given gene between populations from two different environments can be modeled as the difference between two Poisson random variables, known as the Skellam distribution. We then propose and apply a statistical test to identify specific genes that contribute to divergent evolution. By focusing on predicting patterns among replicate populations in a given environment, we are able to identify an appropriate test for divergence between environments that is grounded in first principles.

IMPORTANCE There is currently no universally accepted framework for identifying genes that contribute to molecular divergence between microbial populations in different environments. To address this absence, we developed a null model to describe the distribution of mutation counts among genes. We find that divergent evolution within a given gene can be modeled as the absolute difference in the total number of mutations observed between two environments. This quantity is effectively captured by a probability distribution known as the Skellam distribution, providing an appropriate statistical test for researchers seeking to identify the set of genes that contribute to divergent evolution in microbial evolution experiments.

A mutational hotspot that determines highly repeatable evolution can be built and broken by silent genetic changes

Mutational hotspots can determine evolutionary outcomes and make evolution repeatable. Hotspots are products of multiple evolutionary forces including mutation rate heterogeneity, but this variable is often hard to identify. In this work, we reveal that a near-deterministic genetic hotspot can be built and broken by a handful of silent mutations. We observe this when studying homologous immotile variants of the bacteria Pseudomonas fluorescens, AR2 and Pf0-2x. AR2 resurrects motility through highly repeatable de novo mutation of the same nucleotide in >95% lines in minimal media (ntrB A289C). Pf0-2x, however, evolves via a number of mutations meaning the two strains diverge significantly during adaptation. We determine that this evolutionary disparity is owed to just 6 synonymous variations within the ntrB locus, which we demonstrate by swapping the sites and observing that we are able to both break (>95% to 0%) and build (0% to 80%) a deterministic mutational hotspot. Our work reveals a key role for silent genetic variation in determining adaptive outcomes.

High-throughput analysis of adaptation using barcoded strains of Saccharomyces cerevisiae

BACKGROUND

Experimental evolution of microbes can be used to empirically address a wide range of questions about evolution and is increasingly employed to study complex phenomena ranging from genetic evolution to evolutionary rescue. Regardless of experimental aims, fitness assays are a central component of this type of research, and low-throughput often limits the scope and complexity of experimental evolution studies. We created an experimental evolution system in Saccharomyces cerevisiae that utilizes genetic barcoding to overcome this challenge.

RESULTS

We first confirm that barcode insertions do not alter fitness and that barcode sequencing can be used to efficiently detect fitness differences via pooled competition-based fitness assays. Next, we examine the effects of ploidy, chemical stress, and population bottleneck size on the evolutionary dynamics and fitness gains (adaptation) in a total of 76 experimentally evolving, asexual populations by conducting 1,216 fitness assays and analyzing 532 longitudinal-evolutionary samples collected from the evolving populations. In our analysis of these data we describe the strengths of this experimental evolution system and explore sources of error in our measurements of fitness and evolutionary dynamics.

CONCLUSIONS

Our experimental treatments generated distinct fitness effects and evolutionary dynamics, respectively quantified via multiplexed fitness assays and barcode lineage tracking. These findings demonstrate the utility of this new resource for designing and improving high-throughput studies of experimental evolution. The approach described here provides a framework for future studies employing experimental designs that require high-throughput multiplexed fitness measurements.

Genomic consequences of dietary diversification and parallel evolution due to nectarivory in leaf-nosed bats

BACKGROUND

The New World leaf-nosed bats (Phyllostomids) exhibit a diverse spectrum of feeding habits and innovations in their nutrient acquisition and foraging mechanisms. However, the genomic signatures associated with their distinct diets are unknown.

RESULTS

We conducted a genomic comparative analysis to study the evolutionary dynamics related to dietary diversification and specialization. We sequenced, assembled, and annotated the genomes of five Phyllostomid species: one insect feeder (Macrotus waterhousii), one fruit feeder (Artibeus jamaicensis), and three nectar feeders from the Glossophaginae subfamily (Leptonycteris yerbabuenae, Leptonycteris nivalis, and Musonycteris harrisoni), also including the previously sequenced vampire Desmodus rotundus. Our phylogenomic analysis based on 22,388 gene families displayed differences in expansion and contraction events across the Phyllostomid lineages. Independently of diet, genes relevant for feeding strategies and food intake experienced multiple expansions and signatures of positive selection. We also found adaptation signatures associated with specialized diets: the vampire exhibited traits associated with a blood diet (i.e., coagulation mechanisms), whereas the nectarivore clade shares a group of positively selected genes involved in sugar, lipid, and iron metabolism. Interestingly, in fruit-nectar-feeding Phyllostomid and Pteropodids bats, we detected positive selection in two genes: AACS and ALKBH7, which are crucial in sugar and fat metabolism. Moreover, in these two proteins we found parallel amino acid substitutions in conserved positions exclusive to the tribe Glossophagini and to Pteropodids.

CONCLUSIONS

Our findings illuminate the genomic and molecular shifts associated with the evolution of nectarivory and shed light on how nectar-feeding bats can avoid the adverse effects of diets with high glucose content.

Nitrogen starvation reveals the mitotic potential of mutants in the S/MAPK pathways

The genetics of quiescence is an emerging field compared to that of growth, yet both states generate spontaneous mutations and genetic diversity fueling evolution. Reconciling mutation rates in dividing conditions and mutation accumulation as a function of time in non-dividing situations remains a challenge. Nitrogen-starved fission yeast cells reversibly arrest proliferation, are metabolically active and highly resistant to a variety of stresses. Here, we show that mutations in stress- and mitogen-activated protein kinase (S/MAPK) signaling pathways are enriched in aging cultures. Targeted resequencing and competition experiments indicate that these mutants arise in the first month of quiescence and expand clonally during the second month at the expense of the parental population. Reconstitution experiments show that S/MAPK modules mediate the sacrifice of many cells for the benefit of some mutants. These findings suggest that non-dividing conditions promote genetic diversity to generate a social cellular environment prone to kin selection.

Nitrogen-starved fission yeast cells survive for weeks without dividing. Here, the authors show that some of these surviving cells accumulate mutations in the stress- and mitogen-activated protein kinase pathways and outcompete their parental cells, which provide nutrients for the mutant cells.

Replicated Landscape Genomics Identifies Evidence of Local Adaptation to Urbanization in Wood Frogs

Native species that persist in urban environments may benefit from local adaptation to novel selection factors. We used double-digest restriction-side associated DNA (RAD) sequencing to evaluate shifts in genome-wide genetic diversity and investigate the presence of parallel evolution associated with urban-specific selection factors in wood frogs (Lithobates sylvaticus). Our replicated paired study design involved 12 individuals from each of 4 rural and urban populations to improve our confidence that detected signals of selection are indeed associated with urbanization. Genetic diversity measures were less for urban populations; however, the effect size was small, suggesting little biological consequence. Using an FST outlier approach, we identified 37 of 8344 genotyped single nucleotide polymorphisms with consistent evidence of directional selection across replicates. A genome-wide association study analysis detected modest support for an association between environment type and 12 of the 37 FST outlier loci. Discriminant analysis of principal components using the 37 FST outlier loci produced correct reassignment for 87.5% of rural samples and 93.8% of urban samples. Eighteen of the 37 FST outlier loci mapped to the American bullfrog (Rana [Lithobates] catesbeiana) genome, although none were in coding regions. This evidence of parallel evolution to urban environments provides a powerful example of the ability of urban landscapes to direct evolutionary processes.

Quantifying how constraints limit the diversity of viable routes to adaptation

Convergent adaptation occurs at the genome scale when independently evolving lineages use the same genes to respond to similar selection pressures. These patterns of genetic repeatability provide insights into the factors that facilitate or constrain the diversity of genetic responses that contribute to adaptive evolution. A first step in studying such factors is to quantify the observed amount of repeatability relative to expectations under a null hypothesis. Here, we formulate a novel index to quantify the constraints driving the observed amount of repeated adaptation in pairwise contrasts based on the hypergeometric distribution, and then generalize this for simultaneous analysis of multiple lineages. This index is explicitly based on the probability of observing a given amount of repeatability by chance under a given null hypothesis and is readily compared among different species and types of trait. We also formulate an index to quantify the effective proportion of genes in the genome that have the potential to contribute to adaptation. As an example of how these indices can be used to draw inferences, we assess the amount of repeatability observed in existing datasets on adaptation to stress in yeast and climate in conifers. This approach provides a method to test a wide range of hypotheses about how different kinds of factors can facilitate or constrain the diversity of genetic responses observed during adaptive evolution.

How many ways can evolution solve the same adaptive problem? While convergent adaptation is evident in many organisms at the phenotypic level, we are only beginning to understand how commonly this convergence extends to the genome scale. Quantifying the repeatability of adaptation at the genome scale is therefore critical for assessing how constraints affect the diversity of viable genetic responses. Here, we develop probability-based indices to quantify the deviation between observed repeatability and expectations under a range of null hypotheses, and an estimator of the proportion of loci in the genome that can contribute to adaptation. We demonstrate the usage of these indices with individual-based simulations and example datasets from yeast and conifers and discuss how they differ from previously developed approaches to studying repeatability. Because these indices are unitless, they provide a general approach to quantifying and comparing how constraints drive convergence at the genome scale across a wide range of traits and taxa.