Quantitative evolutionary dynamics using high-resolution lineage tracking

Levy, Sasha F.; Blundell, Jamie R.; Venkataram, Sandeep; Petrov, Dmitri A.; Fisher, Daniel S.; Sherlock, Gavin

doi:10.1038/nature14279

Cited by 431 publications

(797 citation statements)

References 53 publications

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“…Second, a phenomenon called clonal interference occurs in evolving asexual populations because beneficial mutations that arise in different lineages in the same population cannot be brought together by recombination. As a consequence, lineages with highly beneficial mutations outcompete other lineages with less beneficial mutations, such that only the most beneficial mutations available typically can achieve fixation on the timescale of this experiment (39)(40)(41). For example, even though mutations in nadR may be beneficial under all of the temperature regimes, they are able to reach high frequency in competition against other mutations only in the 32°C environment, whereas mutations in other genes will dominate the initial waves of adaptation at other temperatures.…”

Section: Discussionmentioning

confidence: 99%

Specificity of genome evolution in experimental populations of Escherichia coli evolved at different temperatures

Deatherage

Kepner

Bennett

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

121

101

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Isolated populations derived from a common ancestor are expected to diverge genetically and phenotypically as they adapt to different local environments. To examine this process, 30 populations ofEscherichia coliwere evolved for 2,000 generations, with six in each of five different thermal regimes: constant 20 °C, 32 °C, 37 °C, 42 °C, and daily alternations between 32 °C and 42 °C. Here, we sequenced the genomes of one endpoint clone from each population to test whether the history of adaptation in different thermal regimes was evident at the genomic level. The evolved strains had accumulated ∼5.3 mutations, on average, and exhibited distinct signatures of adaptation to the different environments. On average, two strains that evolved under the same regime exhibited ∼17% overlap in which genes were mutated, whereas pairs that evolved under different conditions shared only ∼4%. For example, all six strains evolved at 32 °C had mutations innadR, whereas none of the other 24 strains did. However, a population evolved at 37 °C for an additional 18,000 generations eventually accumulated mutations in the signature genes strongly associated with adaptation to the other temperature regimes. Two mutations that arose in one temperature treatment tended to be beneficial when tested in the others, although less so than in the regime in which they evolved. These findings demonstrate that genomic signatures of adaptation can be highly specific, even with respect to subtle environmental differences, but that this imprint may become obscured over longer timescales as populations continue to change and adapt to the shared features of their environments.

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Section: Discussionmentioning

confidence: 99%

Specificity of genome evolution in experimental populations of Escherichia coli evolved at different temperatures

Deatherage

Kepner

Bennett

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

121

101

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“…If one then regresses t 0 on the speed v of adaptation, one could infer the fitness variance D per generation that is generated by mutations. Genetic barcoding techniques allow tracking of many subpopulations (Levy et al 2015). This will make it possible to study, in a similar way, the higher-order cross-correlation functions in Equation 13, which are generalizations of the heterozygosity.…”

Section: Discussionmentioning

confidence: 99%

“…It may also effectively apply in island models with low migration rates, where the fitness effect of a mutation is reduced by potentially low migration rates. But, in well-mixed populations, the diffusion approach breaks down when the probability per generation to acquire a beneficial mutation is much smaller than their typical effect (Rouzine et al 2003), which has been confirmed for a number of microbial species when they adapt to new environments (Perfeito et al 2007;Gordo et al 2011;Levy et al 2015).The stochastic term ffiffiffiffiffiffiffiffi ebc t p h t in Equation 1 accounts for all random factors that influence the reproduction process. The function h t ðxÞ represents standard white noise, i.e., a set of d-correlated random numbers,…”

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confidence: 99%

Collective Fluctuations in the Dynamics of Adaptation and Other Traveling Waves

Hallatschek

Geyrhofer

2016

Genetics

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The dynamics of adaptation are difficult to predict because it is highly stochastic even in large populations. The uncertainty emerges from random genetic drift arising in a vanguard of particularly fit individuals of the population. Several approaches have been developed to analyze the crucial role of genetic drift on the expected dynamics of adaptation, including the mean fitness of the entire population, or the fate of newly arising beneficial deleterious mutations. However, little is known about how genetic drift causes fluctuations to emerge on the population level, where it becomes palpable as variations in the adaptation speed and the fitness distribution. Yet these phenomena control the decay of genetic diversity and variability in evolution experiments and are key to a truly predictive understanding of evolutionary processes. Here, we show that correlations induced by these emergent fluctuations can be computed at any arbitrary order by a suitable choice of a dynamical constraint. The resulting linear equations exhibit fluctuationinduced terms that amplify short-distance correlations and suppress long-distance ones. These terms, which are in general not small, control the decay of genetic diversity and, for wave-tip dominated ("pulled") waves, lead to anticorrelations between the tip of the wave and the lagging bulk of the population. While it is natural to consider the process of adaptation as a branching random walk in fitness space subject to a constraint (due to finite resources), we show that other traveling wave phenomena in ecology and evolution likewise fall into this class of constrained branching random walks. Our methods, therefore, provide a systematic approach toward analyzing fluctuations in a wide range of population biological processes, such as adaptation, genetic meltdown, species invasions, or epidemics.KEYWORDS traveling-wave models; asexual adaptation; Fisher waves; tuned models; stochastic modeling; higher-order correlation functions; exact approaches M ANY important evolutionary and ecological processes rely on the behavior of a small number of individuals that have a large dynamical influence on the population as a whole. This is, perhaps, most obvious in the case of biological adaptation in asexual species, such as microbes (over timescales where horizontal gene transfer is irrelevant). Future generations descend from a small number of currently welladapted individuals. The genetic footprint of the large majority of the population is wiped out over time by the fixation of more fit genotypes. For a large influx of beneficial mutations, these dynamics can be visualized as a traveling wave in fitness space; see Figure 1A. At any time, the currently most fit "pioneer" individuals reside in the small tip of the wave. As time elapses, the wave moves toward higher fitness and the formerly rare most fit individuals dominate the population. By that time, however, a new wave tip of even more fit mutants has formed by mutations and the cycle of transient dominance continues. Evolu...

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“…Thus, one possible explanation for the high level of HSF1-independent chaperone expression in mammalian cells is to create a chaperone buffer that dampens variability between cells in a tissue caused by random protein misfolding. To test this notion, one experimental approach is to engineer a chaperone buffer in yeast by expressing synHDGs to varying degrees above their endogenous levels and measure the fitness distribution across a clonal population using a recently developed high-throughput technique based on deepsequencing of genetically encoded cell barcodes (Levy et al, 2015). If random fluctuations in the protein-folding environment between cells are a major driver of fitness variation, then variability should decrease as the chaperone buffer increases.…”

Section: Chapter 4: Future Directionsmentioning

confidence: 99%

Defining the Essential Function of Yeast Hsf1 Reveals a Compact Transcriptional Program for Maintaining Eukaryotic Proteostasis

Solís

Pandey

et al. 2018

Molecular Cell

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All rights reserved. I am especially thankful to my mom, dad and brother, all of whom were critical for getting me to grad school in the first place. I'm also forever thankful that they were always there to support and listen to me through my many struggles, even when the problems I was describing were unrelatable or unintelligibly described. know it would have been significantly less silly, ludicrous and off-putting to everyone else ix on the periphery if Patrick weren't down the hall from me, but I'm so glad I didn't have to live in that counterfactual universe. We have spent countless hours talking about science, politics, our personal lives, and many other things that I hope never get repeated, and I have enjoyed it all so very much.I have also been extremely fortunate for my academic advisors Vlad Denic and Edo Airoldi, along with my mentor and friend David Pincus. The three of them put an incredible amount of time into helping and guiding me as I grew as a scientist. When I reflect on the progress I've made since I started graduate school, I know that it would not have been possible without them constantly pushing me to be better. While it wasn't always fun or easy, I am so thankful for the time, energy and effort they put into me personally. They all went so far beyond what could reasonably be expected from an advisor, and even though all three were at challenging early stages of their own careers, they made an exceptional effort to help me as I started my own. I will always be thankful and grateful to Edo, Vlad and Dave for molding me into the scientist that I am today. In order to adapt when stress causes the folding environment to deteriorate, cells need a mechanism to adjust the availability of chaperones according to need. One simple mechanism employed by some bacteria to regulate expression of chaperones during heat shock involves RNA secondary structure that occludes the ribosome binding site at low 2 temperatures, but melts at high temperature de-repressing translation during thermal stress (Cimdins et al., 2014). However, rather than regulating each chaperone gene individually, eukaryotic cells have evolved a highly conserved feedback mechanism that coordinates up-regulation of many proteostasis factors in response to stress. The first indication of this mechanism were made by Ferruccio Ritossa in 1962 when he noticed a novel pattern of "puffs" on the polytene salivary gland chromosomes of Drosophila larvae that were subjected to a serendipitous heat shock when a co-worker increased their incubator's temperature (Ritossa, 1996). Subsequent controlled experiments revealed that shifting larvae from 25°C to 30°C caused puffs that were stable at the lower temperature to rapidly dissipate and induced the appearance of novel, reproducible puffs (Ritossa, 1962). It was soon revealed that the temperature stress caused by the actions of an unwitting co-worker has spurred the first observation of the dramatic changes in gene expression that comprise the heat shock response.It was subsequently revealed tha...

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Quantitative evolutionary dynamics using high-resolution lineage tracking

Cited by 431 publications

References 53 publications

Specificity of genome evolution in experimental populations of Escherichia coli evolved at different temperatures

Specificity of genome evolution in experimental populations of Escherichia coli evolved at different temperatures

Collective Fluctuations in the Dynamics of Adaptation and Other Traveling Waves

Defining the Essential Function of Yeast Hsf1 Reveals a Compact Transcriptional Program for Maintaining Eukaryotic Proteostasis

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