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1. Divergent Evolution of Mutation Rates and Biases in the Long-Term Evolution Experiment with Escherichia coli

   https://doi.org/10.1093/gbe/evaa178  

   Maddamsetti, Rohan; Grant, Nkrumah A (September 2020, Genome Biology and Evolution)

   Zhang, George (Ed.)

   Abstract All organisms encode enzymes that replicate, maintain, pack, recombine, and repair their genetic material. For this reason, mutation rates and biases also evolve by mutation, variation, and natural selection. By examining metagenomic time series of the Lenski long-term evolution experiment (LTEE) with Escherichia coli (Good BH, McDonald MJ, Barrick JE, Lenski RE, Desai MM. 2017. The dynamics of molecular evolution over 60,000 generations. Nature 551(7678):45–50.), we find that local mutation rate variation has evolved during the LTEE. Each LTEE population has evolved idiosyncratic differences in their rates of point mutations, indels, and mobile element insertions, due to the fixation of various hypermutator and antimutator alleles. One LTEE population, called Ara+3, shows a strong, symmetric wave pattern in its density of point mutations, radiating from the origin of replication. This pattern is largely missing from the other LTEE populations, most of which evolved missense, indel, or structural mutations in topA, fis, and dusB—loci that all affect DNA topology. The distribution of mutations in those genes over time suggests epistasis and historical contingency in the evolution of DNA topology, which may have in turn affected local mutation rates. Overall, the replicate populations of the LTEE have largely diverged in their mutation rates and biases, even though they have adapted to identical abiotic conditions. 

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2. Parallel and population-specific gene regulatory evolution in cold-adapted fly populations

   https://doi.org/10.1093/genetics/iyab077  

   Huang, Yuheng; Lack, Justin B; Hoppel, Grant T; Pool, John E (May 2021, Genetics)

   Begun, D (Ed.)

   Abstract Changes in gene regulation at multiple levels may comprise an important share of the molecular changes underlying adaptive evolution in nature. However, few studies have assayed within- and between-population variation in gene regulatory traits at a transcriptomic scale, and therefore inferences about the characteristics of adaptive regulatory changes have been elusive. Here, we assess quantitative trait differentiation in gene expression levels and alternative splicing (intron usage) between three closely related pairs of natural populations of Drosophila melanogaster from contrasting thermal environments that reflect three separate instances of cold tolerance evolution. The cold-adapted populations were known to show population genetic evidence for parallel evolution at the SNP level, and here we find evidence for parallel expression evolution between them, with stronger parallelism at larval and adult stages than for pupae. We also implement a flexible method to estimate cis- vs trans-encoded contributions to expression or splicing differences at the adult stage. The apparent contributions of cis- vs trans-regulation to adaptive evolution vary substantially among population pairs. While two of three population pairs show a greater enrichment of cis-regulatory differences among adaptation candidates, trans-regulatory differences are more likely to be implicated in parallel expression changes between population pairs. Genes with significant cis-effects are enriched for signals of elevated genetic differentiation between cold- and warm-adapted populations, suggesting that they are potential targets of local adaptation. These findings expand our knowledge of adaptive gene regulatory evolution and our ability to make inferences about this important and widespread process. 

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3. How Do Cells Adapt? Stories Told in Landscapes

   https://doi.org/10.1146/annurev-chembioeng-011720-103410  

   Agozzino, Luca; Balázsi, Gábor; Wang, Jin; Dill, Ken A. (June 2020, Annual Review of Chemical and Biomolecular Engineering)

   null (Ed.)

   Cells adapt to changing environments. Perturb a cell and it returns to a point of homeostasis. Perturb a population and it evolves toward a fitness peak. We review quantitative models of the forces of adaptation and their visualizations on landscapes. While some adaptations result from single mutations or few-gene effects, others are more cooperative, more delocalized in the genome, and more universal and physical. For example, homeostasis and evolution depend on protein folding and aggregation, energy and protein production, protein diffusion, molecular motor speeds and efficiencies, and protein expression levels. Models provide a way to learn about the fitness of cells and cell populations by making and testing hypotheses. 

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4. Experimental test of the contributions of initial variation and new mutations to adaptive evolution in a novel environment

   https://doi.org/10.3389/fevo.2022.958406  

   Izutsu, Minako; Lenski, Richard E. (October 2022, Frontiers in Ecology and Evolution)

   Experimental evolution is an approach that allows researchers to study organisms as they evolve in controlled environments. Despite the growing popularity of this approach, there are conceptual gaps among projects that use different experimental designs. One such gap concerns the contributions to adaptation of genetic variation present at the start of an experiment and that of new mutations that arise during an experiment. The primary source of genetic variation has historically depended largely on the study organisms. In the long-term evolution experiment (LTEE) using Escherichia coli , for example, each population started from a single haploid cell, and therefore, adaptation depended entirely on new mutations. Most other microbial evolution experiments have followed the same strategy. By contrast, evolution experiments using multicellular, sexually reproducing organisms typically start with preexisting variation that fuels the response to selection. New mutations may also come into play in later generations of these experiments, but it is generally difficult to quantify their contribution in these studies. Here, we performed an experiment using E. coli to compare the contributions of initial genetic variation and new mutations to adaptation in a new environment. Our experiment had four treatments that varied in their starting diversity, with 18 populations in each treatment. One treatment depended entirely on new mutations, while the other three began with mixtures of clones, whole-population samples, or mixtures of whole-population samples from the LTEE. We tracked a genetic marker associated with different founders in two treatments. These data revealed significant variation in fitness among the founders, and that variation impacted evolution in the early generations of our experiment. However, there were no differences in fitness among the treatments after 500 or 2,000 generations in the new environment, despite the variation in fitness among the founders. These results indicate that new mutations quickly dominated, and eventually they contributed more to adaptation than did the initial variation. Our study thus shows that preexisting genetic variation can have a strong impact on early evolution in a new environment, but new beneficial mutations may contribute more to later evolution and can even drive some initially beneficial variants to extinction. 

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5. Universal Constraints on Protein Evolution in the Long-Term Evolution Experiment with Escherichia coli

   https://doi.org/10.1093/gbe/evab070  

   Maddamsetti, Rohan (June 2021, Genome Biology and Evolution)

   Golding, Brian (Ed.)

   Abstract Although it is well known that abundant proteins evolve slowly across the tree of life, there is little consensus for why this is true. Here, I report that abundant proteins evolve slowly in the hypermutator populations of Lenski’s long-term evolution experiment with Escherichia coli (LTEE). Specifically, the density of all observed mutations per gene, as measured in metagenomic time series covering 60,000 generations of the LTEE, significantly anticorrelates with mRNA abundance, protein abundance, and degree of protein–protein interaction. The same pattern holds for nonsynonymous mutation density. However, synonymous mutation density, measured across the LTEE hypermutator populations, positively correlates with protein abundance. These results show that universal constraints on protein evolution are visible in data spanning three decades of experimental evolution. Therefore, it should be possible to design experiments to answer why abundant proteins evolve slowly. 

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