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MIA: Gautam Reddy, Predictable patterns in phenotypic evolution; Primer by Michael Desai

Models, Inference and Algorithms January 27, 2021 Broad Institute Meeting:    • Видео   Gautam Reddy NSF-Simons Center for Mathematical and Statistical Analysis of Biology; Harvard University Predictable patterns in phenotypic evolution Epistasis between mutations can make adaptation contingent on evolutionary history. Yet despite idiosyncratic epistasis between the mutations involved, microbial evolution experiments show consistent patterns of fitness increase between replicate lines. Recent work shows that this consistency is driven in part by patterns of diminishing-returns and increasing-costs epistasis, which make mutations systematically less beneficial (or more deleterious) on fitter genetic backgrounds. In this talk, we will argue that these predictable patterns emerge generically due to widespread, idiosyncratic epistasis. Extending this idea, we develop a new Fourier analysis-based framework to quantify how macroscopic features of the genotype-phenotype map impact the dynamics of phenotypic evolution. Using this framework, we show that the distribution of fitness effects takes on a universal form when epistasis is widespread and introduce a novel fitness landscape model to rationalize why phenotypic evolution can be repeatable despite sequence-level stochasticity. Michael Desai Depts. Organismic and Evolutionary Biology, Physics, Harvard University Primer: Genomic investigations of evolutionary dynamics and epistasis in microbial evolution experiments Microbial evolution experiments enable us to watch adaptation in real time, and to quantify the repeatability and predictability of evolution by comparing identical replicate populations. Further, we can resurrect ancestral types to examine changes over evolutionary time. Until recently, experimental evolution has been limited to measuring phenotypic changes, or to tracking a few genetic markers over time. However, recent advances in sequencing technology now make it possible to extensively sequence clones or whole-population samples from microbial evolution experiments. Here, we review recent work exploiting these techniques to understand the genomic basis of evolutionary change in experimental systems. We first focus on studies that analyze the dynamics of genome evolution in microbial systems. We then survey work that uses observations of sequence evolution to infer aspects of the underlying fitness landscape, concentrating on the epistatic interactions between mutations and the constraints these interactions impose on adaptation. For more information on the Broad Institute and Models, Inference and Algorithms visit: https://www.broadinstitute.org/mia Chapters: 00:00 Primer - Michael Desai 48:26 Meeting - Gautam Reddy Copyright Broad Institute, 2021. All rights reserved.