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1. Evolutionary Dynamics of Asexual Hypermutators Adapting to a Novel Environment

   https://doi.org/10.1093/gbe/evab257  

   Ho, Wei-Chin; Behringer, Megan G; Miller, Samuel F; Gonzales, Jadon; Nguyen, Amber; Allahwerdy, Meriem; Boyer, Gwyneth F; Lynch, Michael (December 2021, Genome Biology and Evolution)

   Gaut, Brandon (Ed.)

   Abstract How microbes adapt to a novel environment is a central question in evolutionary biology. Although adaptive evolution must be fueled by beneficial mutations, whether higher mutation rates facilitate the rate of adaptive evolution remains unclear. To address this question, we cultured Escherichia coli hypermutating populations, in which a defective methyl-directed mismatch repair pathway causes a 140-fold increase in single-nucleotide mutation rates. In parallel with wild-type E. coli, populations were cultured in tubes containing Luria-Bertani broth, a complex medium known to promote the evolution of subpopulation structure. After 900 days of evolution, in three transfer schemes with different population-size bottlenecks, hypermutators always exhibited similar levels of improved fitness as controls. Fluctuation tests revealed that the mutation rates of hypermutator lines converged evolutionarily on those of wild-type populations, which may have contributed to the absence of fitness differences. Further genome-sequence analysis revealed that, although hypermutator populations have higher rates of genomic evolution, this largely reflects strong genetic linkage. Despite these linkage effects, the evolved population exhibits parallelism in fixed mutations, including those potentially related to biofilm formation, transcription regulation, and mutation-rate evolution. Together, these results are generally inconsistent with a hypothesized positive relationship between the mutation rate and the adaptive speed of evolution, and provide insight into how clonal adaptation occurs in novel environments. 

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2. Interplay Between Polymorphic Short Tandem Repeats and Gene Expression Variation in Caenorhabditis elegans

   https://doi.org/10.1093/molbev/msad067  

   Zhang, Gaotian; Andersen, Erik C (April 2023, Molecular Biology and Evolution)

   Larracuente, Amanda (Ed.)

   Abstract Short tandem repeats (STRs) have orders of magnitude higher mutation rates than single nucleotide variants (SNVs) and have been proposed to accelerate evolution in many organisms. However, only few studies have addressed the impact of STR variation on phenotypic variation at both the organismal and molecular levels. Potential driving forces underlying the high mutation rates of STRs also remain largely unknown. Here, we leverage the recently generated expression and STR variation data among wild Caenorhabditis elegans strains to conduct a genome-wide analysis of how STRs affect gene expression variation. We identify thousands of expression STRs (eSTRs) showing regulatory effects and demonstrate that they explain missing heritability beyond SNV-based expression quantitative trait loci. We illustrate specific regulatory mechanisms such as how eSTRs affect splicing sites and alternative splicing efficiency. We also show that differential expression of antioxidant genes and oxidative stresses might affect STR mutations systematically using both wild strains and mutation accumulation lines. Overall, we reveal the interplay between STRs and gene expression variation by providing novel insights into regulatory mechanisms of STRs and highlighting that oxidative stress could lead to higher STR mutation rates. 

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3. The effect of weak clonal interference on average fitness trajectories in the presence of macroscopic epistasis

   https://doi.org/10.1093/genetics/iyac028  

   Guo, Yipei; Amir, Ariel; Jain, ed., K. (February 2022, Genetics)

   Abstract Adaptation dynamics on fitness landscapes is often studied theoretically in the strong-selection, weak-mutation regime. However, in a large population, multiple beneficial mutants can emerge before any of them fixes in the population. Competition between mutants is known as clonal interference, and while it is known to slow down the rate of adaptation (when compared to the strong-selection, weak-mutation model with the same parameters), how it affects the shape of long-term fitness trajectories in the presence of epistasis is an open question. Here, by considering how changes in fixation probabilities arising from weak clonal interference affect the dynamics of adaptation on fitness-parameterized landscapes, we find that the change in the shape of fitness trajectory arises only through changes in the supply of beneficial mutations (or equivalently, the beneficial mutation rate). Furthermore, a depletion of beneficial mutations as a population climbs up the fitness landscape can speed up the rescaled fitness trajectory (where adaptation speed is measured relative to its value at the start of the experiment), while an enhancement of the beneficial mutation rate does the opposite of slowing it down. Our findings suggest that by carrying out evolution experiments in both regimes (with and without clonal interference), one could potentially distinguish the different sources of macroscopic epistasis (fitness effect of mutations vs change in fraction of beneficial mutations). 

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4. Natural variation in C. elegans short tandem repeats

   https://doi.org/10.1101/gr.277067.122  

   Zhang, Gaotian; Wang, Ye; Andersen, Erik C. (October 2022, Genome Research)

   Short tandem repeats (STRs) represent an important class of genetic variation that can contribute to phenotypic differences. Although millions of single nucleotide variants (SNVs) and short indels have been identified among wild Caenorhabditis elegans strains, the natural diversity in STRs remains unknown. Here, we characterized the distribution of 31,991 STRs with motif lengths of 1–6 bp in the reference genome of C. elegans . Of these STRs, 27,667 harbored polymorphisms across 540 wild strains and only 9691 polymorphic STRs (pSTRs) had complete genotype data for more than 90% of the strains. Compared with the reference genome, the pSTRs showed more contraction than expansion. We found that STRs with different motif lengths were enriched in different genomic features, among which coding regions showed the lowest STR diversity and constrained STR mutations. STR diversity also showed similar genetic divergence and selection signatures among wild strains as in previous studies using SNVs. We further identified STR variation in two mutation accumulation line panels that were derived from two wild strains and found background-dependent and fitness-dependent STR mutations. We also performed the first genome-wide association analyses between natural variation in STRs and organismal phenotypic variation among wild C. elegans strains. Overall, our results delineate the first large-scale characterization of STR variation in wild C. elegans strains and highlight the effects of selection on STR mutations. 

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5. Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics

   https://doi.org/10.1371/journal.pbio.3001633  

   Avecilla, Grace; Chuong, Julie N.; Li, Fangfei; Sherlock, Gavin; Gresham, David; Ram, Yoav (May 2022, PLOS Biology)

   de Visser, J. Arjan (Ed.)

   The rate of adaptive evolution depends on the rate at which beneficial mutations are introduced into a population and the fitness effects of those mutations. The rate of beneficial mutations and their expected fitness effects is often difficult to empirically quantify. As these 2 parameters determine the pace of evolutionary change in a population, the dynamics of adaptive evolution may enable inference of their values. Copy number variants (CNVs) are a pervasive source of heritable variation that can facilitate rapid adaptive evolution. Previously, we developed a locus-specific fluorescent CNV reporter to quantify CNV dynamics in evolving populations maintained in nutrient-limiting conditions using chemostats. Here, we use CNV adaptation dynamics to estimate the rate at which beneficial CNVs are introduced through de novo mutation and their fitness effects using simulation-based likelihood–free inference approaches. We tested the suitability of 2 evolutionary models: a standard Wright–Fisher model and a chemostat model. We evaluated 2 likelihood-free inference algorithms: the well-established Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC) algorithm, and the recently developed Neural Posterior Estimation (NPE) algorithm, which applies an artificial neural network to directly estimate the posterior distribution. By systematically evaluating the suitability of different inference methods and models, we show that NPE has several advantages over ABC-SMC and that a Wright–Fisher evolutionary model suffices in most cases. Using our validated inference framework, we estimate the CNV formation rate at the GAP1 locus in the yeast Saccharomyces cerevisiae to be 10 −4.7 to 10 −4 CNVs per cell division and a fitness coefficient of 0.04 to 0.1 per generation for GAP1 CNVs in glutamine-limited chemostats. We experimentally validated our inference-based estimates using 2 distinct experimental methods—barcode lineage tracking and pairwise fitness assays—which provide independent confirmation of the accuracy of our approach. Our results are consistent with a beneficial CNV supply rate that is 10-fold greater than the estimated rates of beneficial single-nucleotide mutations, explaining the outsized importance of CNVs in rapid adaptive evolution. More generally, our study demonstrates the utility of novel neural network–based likelihood–free inference methods for inferring the rates and effects of evolutionary processes from empirical data with possible applications ranging from tumor to viral evolution. 

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