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Eccleston RC, Manko E, Campino S, Clark TG, Furnham N. A computational method for predicting the most likely evolutionary trajectories in the stepwise accumulation of resistance mutations. eLife 2023; 12:e84756. [PMID: 38132182 PMCID: PMC10807863 DOI: 10.7554/elife.84756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 12/21/2023] [Indexed: 12/23/2023] Open
Abstract
Pathogen evolution of drug resistance often occurs in a stepwise manner via the accumulation of multiple mutations that in combination have a non-additive impact on fitness, a phenomenon known as epistasis. The evolution of resistance via the accumulation of point mutations in the DHFR genes of Plasmodium falciparum (Pf) and Plasmodium vivax (Pv) has been studied extensively and multiple studies have shown epistatic interactions between these mutations determine the accessible evolutionary trajectories to highly resistant multiple mutations. Here, we simulated these evolutionary trajectories using a model of molecular evolution, parameterised using Rosetta Flex ddG predictions, where selection acts to reduce the target-drug binding affinity. We observe strong agreement with pathways determined using experimentally measured IC50 values of pyrimethamine binding, which suggests binding affinity is strongly predictive of resistance and epistasis in binding affinity strongly influences the order of fixation of resistance mutations. We also infer pathways directly from the frequency of mutations found in isolate data, and observe remarkable agreement with the most likely pathways predicted by our mechanistic model, as well as those determined experimentally. This suggests mutation frequency data can be used to intuitively infer evolutionary pathways, provided sufficient sampling of the population.
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Affiliation(s)
- Ruth Charlotte Eccleston
- Department of Infection Biology, London School of Hygiene and Tropical MedicineLondonUnited Kingdom
| | - Emilia Manko
- Department of Infection Biology, London School of Hygiene and Tropical MedicineLondonUnited Kingdom
| | - Susana Campino
- Department of Infection Biology, London School of Hygiene and Tropical MedicineLondonUnited Kingdom
| | - Taane G Clark
- Department of Infection Biology, London School of Hygiene and Tropical MedicineLondonUnited Kingdom
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical MedicineLondonUnited Kingdom
| | - Nicholas Furnham
- Department of Infection Biology, London School of Hygiene and Tropical MedicineLondonUnited Kingdom
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2
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Baquero F, Martínez JL, F. Lanza V, Rodríguez-Beltrán J, Galán JC, San Millán A, Cantón R, Coque TM. Evolutionary Pathways and Trajectories in Antibiotic Resistance. Clin Microbiol Rev 2021; 34:e0005019. [PMID: 34190572 PMCID: PMC8404696 DOI: 10.1128/cmr.00050-19] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Evolution is the hallmark of life. Descriptions of the evolution of microorganisms have provided a wealth of information, but knowledge regarding "what happened" has precluded a deeper understanding of "how" evolution has proceeded, as in the case of antimicrobial resistance. The difficulty in answering the "how" question lies in the multihierarchical dimensions of evolutionary processes, nested in complex networks, encompassing all units of selection, from genes to communities and ecosystems. At the simplest ontological level (as resistance genes), evolution proceeds by random (mutation and drift) and directional (natural selection) processes; however, sequential pathways of adaptive variation can occasionally be observed, and under fixed circumstances (particular fitness landscapes), evolution is predictable. At the highest level (such as that of plasmids, clones, species, microbiotas), the systems' degrees of freedom increase dramatically, related to the variable dispersal, fragmentation, relatedness, or coalescence of bacterial populations, depending on heterogeneous and changing niches and selective gradients in complex environments. Evolutionary trajectories of antibiotic resistance find their way in these changing landscapes subjected to random variations, becoming highly entropic and therefore unpredictable. However, experimental, phylogenetic, and ecogenetic analyses reveal preferential frequented paths (highways) where antibiotic resistance flows and propagates, allowing some understanding of evolutionary dynamics, modeling and designing interventions. Studies on antibiotic resistance have an applied aspect in improving individual health, One Health, and Global Health, as well as an academic value for understanding evolution. Most importantly, they have a heuristic significance as a model to reduce the negative influence of anthropogenic effects on the environment.
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Affiliation(s)
- F. Baquero
- Department of Microbiology, Ramón y Cajal University Hospital, Ramón y Cajal Institute for Health Research (IRYCIS), Network Center for Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - J. L. Martínez
- National Center for Biotechnology (CNB-CSIC), Madrid, Spain
| | - V. F. Lanza
- Department of Microbiology, Ramón y Cajal University Hospital, Ramón y Cajal Institute for Health Research (IRYCIS), Network Center for Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Central Bioinformatics Unit, Ramón y Cajal Institute for Health Research (IRYCIS), Madrid, Spain
| | - J. Rodríguez-Beltrán
- Department of Microbiology, Ramón y Cajal University Hospital, Ramón y Cajal Institute for Health Research (IRYCIS), Network Center for Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - J. C. Galán
- Department of Microbiology, Ramón y Cajal University Hospital, Ramón y Cajal Institute for Health Research (IRYCIS), Network Center for Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - A. San Millán
- National Center for Biotechnology (CNB-CSIC), Madrid, Spain
| | - R. Cantón
- Department of Microbiology, Ramón y Cajal University Hospital, Ramón y Cajal Institute for Health Research (IRYCIS), Network Center for Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - T. M. Coque
- Department of Microbiology, Ramón y Cajal University Hospital, Ramón y Cajal Institute for Health Research (IRYCIS), Network Center for Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
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3
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Behdenna A, Godfroid M, Petot P, Pothier J, Lambert A, Achaz G. A minimal yet flexible likelihood framework to assess correlated evolution. Syst Biol 2021; 71:823-838. [PMID: 34792608 DOI: 10.1093/sysbio/syab092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 11/04/2021] [Accepted: 11/09/2021] [Indexed: 11/14/2022] Open
Abstract
An evolutionary process is reflected in the sequence of changes of any trait (e.g. morphological or molecular) through time. Yet, a better understanding of evolution would be procured by characterizing correlated evolution, or when two or more evolutionary processes interact. Previously developed parametric methods often require significant computing time as they rely on the estimation of many parameters. Here we propose a minimal likelihood framework modelling the joint evolution of two traits on a known phylogenetic tree. The type and strength of correlated evolution is characterized by a few parameters tuning mutation rates of each trait and interdependencies between these rates. The framework can be applied to study any discrete trait or character ranging from nucleotide substitution to gain or loss of a biological function. More specifically, it can be used to 1) test for independence between two evolutionary processes, 2) identify the type of interaction between them and 3) estimate parameter values of the most likely model of interaction. In the current implementation, the method takes as input a phylogenetic tree with discrete evolutionary events mapped on its branches. The method then maximizes the likelihood for one or several chosen scenarios. The strengths and limits of the method, as well as its relative power compared to a few other methods, are assessed using both simulations and data from 16S rRNA sequences in a sample of 54 γ-enterobacteria. We show that, even with datasets of fewer than 100 species, the method performs well in parameter estimation and in evolutionary model selection.
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Affiliation(s)
- Abdelkader Behdenna
- Institut de Systématique, Évolution, Biodiversité (ISYEB), Muséum National d'Histoire Naturelle, CNRS UMR 7205, Sorbonne Université, École Pratique des Hautes Études, Université des Antilles, 45 rue Buffon, 75005 Paris, France
- SMILE Group, Center for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, Université PSL, 11, place Marcellin Berthelot, 75005 Paris, France
- Epigene Labs, 7 Square Gabriel Fauré, 75017 Paris, France
| | - Maxime Godfroid
- SMILE Group, Center for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, Université PSL, 11, place Marcellin Berthelot, 75005 Paris, France
| | - Patrice Petot
- Institut de Systématique, Évolution, Biodiversité (ISYEB), Muséum National d'Histoire Naturelle, CNRS UMR 7205, Sorbonne Université, École Pratique des Hautes Études, Université des Antilles, 45 rue Buffon, 75005 Paris, France
- SMILE Group, Center for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, Université PSL, 11, place Marcellin Berthelot, 75005 Paris, France
| | - Joël Pothier
- Institut de Systématique, Évolution, Biodiversité (ISYEB), Muséum National d'Histoire Naturelle, CNRS UMR 7205, Sorbonne Université, École Pratique des Hautes Études, Université des Antilles, 45 rue Buffon, 75005 Paris, France
| | - Amaury Lambert
- SMILE Group, Center for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, Université PSL, 11, place Marcellin Berthelot, 75005 Paris, France
- Laboratoire de Probabilités, Statistique et Modélisation (LPSM), Sorbonne Université, CNRS UMR 8001, Université de Paris, 4, place Jussieu, 75005 Paris, France
| | - Guillaume Achaz
- Institut de Systématique, Évolution, Biodiversité (ISYEB), Muséum National d'Histoire Naturelle, CNRS UMR 7205, Sorbonne Université, École Pratique des Hautes Études, Université des Antilles, 45 rue Buffon, 75005 Paris, France
- SMILE Group, Center for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, Université PSL, 11, place Marcellin Berthelot, 75005 Paris, France
- Éco-anthropologie, Muséum National d'Histoire Naturelle, CNRS UMR 7206, Université de Paris, place du Trocadéro, 75016 Paris, France
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4
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Lambros M, Pechuan-Jorge X, Biro D, Ye K, Bergman A. Emerging Adaptive Strategies Under Temperature Fluctuations in a Laboratory Evolution Experiment of Escherichia Coli. Front Microbiol 2021; 12:724982. [PMID: 34745030 PMCID: PMC8569431 DOI: 10.3389/fmicb.2021.724982] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 09/27/2021] [Indexed: 11/22/2022] Open
Abstract
Generalists and specialists are types of strategies individuals can employ that can evolve in fluctuating environments depending on the extremity and periodicity of the fluctuation. To evaluate whether the evolution of specialists or generalists occurs under environmental fluctuation regimes with different levels of periodicity, 24 populations of Escherichia coli underwent laboratory evolution with temperatures alternating between 15 and 43°C in three fluctuation regimes: two periodic regimes dependent on culture's cell density and one random (non-periodic) regime with no such dependency, serving as a control. To investigate contingencies on the genetic background, we seeded our experiment with two different strains. After the experiment, growth rate measurements at the two temperatures showed that the evolution of specialists was favored in the random regime, while generalists were favored in the periodic regimes. Whole genome sequencing demonstrated that several gene mutations were selected in parallel in the evolving populations with some dependency on the starting genetic background. Given the genes mutated, we hypothesized that the driving force behind the observed adaptations is the restoration of the internal physiology of the starting strains' unstressed states at 37°C, which may be a means of improving fitness in the new environments. Phenotypic array measurements supported our hypothesis by demonstrating a tendency of the phenotypic response of the evolved strains to move closer to the starting strains' response at the optimum of 37°C, especially for strains classified as generalists.
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Affiliation(s)
- Maryl Lambros
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Ximo Pechuan-Jorge
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Daniel Biro
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Kenny Ye
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, United States.,Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Aviv Bergman
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, United States.,Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, United States.,Department of Pathology, Albert Einstein College of Medicine, Bronx, NY, United States.,Santa Fe Institute, Santa Fe, NM, United States
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5
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Genomic evolution of antibiotic resistance is contingent on genetic background following a long-term experiment with Escherichia coli. Proc Natl Acad Sci U S A 2021; 118:2016886118. [PMID: 33441451 DOI: 10.1073/pnas.2016886118] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Antibiotic resistance is a growing health concern. Efforts to control resistance would benefit from an improved ability to forecast when and how it will evolve. Epistatic interactions between mutations can promote divergent evolutionary trajectories, which complicates our ability to predict evolution. We recently showed that differences between genetic backgrounds can lead to idiosyncratic responses in the evolvability of phenotypic resistance, even among closely related Escherichia coli strains. In this study, we examined whether a strain's genetic background also influences the genotypic evolution of resistance. Do lineages founded by different genotypes take parallel or divergent mutational paths to achieve their evolved resistance states? We addressed this question by sequencing the complete genomes of antibiotic-resistant clones that evolved from several different genetic starting points during our earlier experiments. We first validated our statistical approach by quantifying the specificity of genomic evolution with respect to antibiotic treatment. As expected, mutations in particular genes were strongly associated with each drug. Then, we determined that replicate lines evolved from the same founding genotypes had more parallel mutations at the gene level than lines evolved from different founding genotypes, although these effects were more subtle than those showing antibiotic specificity. Taken together with our previous work, we conclude that historical contingency can alter both genotypic and phenotypic pathways to antibiotic resistance.
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6
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Meroz N, Tovi N, Sorokin Y, Friedman J. Community composition of microbial microcosms follows simple assembly rules at evolutionary timescales. Nat Commun 2021; 12:2891. [PMID: 33976223 PMCID: PMC8113234 DOI: 10.1038/s41467-021-23247-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 04/20/2021] [Indexed: 12/14/2022] Open
Abstract
Managing and engineering microbial communities relies on the ability to predict their composition. While progress has been made on predicting compositions on short, ecological timescales, there is still little work aimed at predicting compositions on evolutionary timescales. Therefore, it is still unknown for how long communities typically remain stable after reaching ecological equilibrium, and how repeatable and predictable are changes when they occur. Here, we address this knowledge gap by tracking the composition of 87 two- and three-species bacterial communities, with 3-18 replicates each, for ~400 generations. We find that community composition typically changed during evolution, but that the composition of replicate communities remained similar. Furthermore, these changes were predictable in a bottom-up approach-changes in the composition of trios were consistent with those that occurred in pairs during coevolution. Our results demonstrate that simple assembly rules can hold even on evolutionary timescales, suggesting it may be possible to forecast the evolution of microbial communities.
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Affiliation(s)
- Nittay Meroz
- Department of Plant Pathology and Microbiology, The Hebrew University of Jerusalem, Rehovot, Israel.
| | - Nesli Tovi
- Department of Plant Pathology and Microbiology, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Yael Sorokin
- Department of Plant Pathology and Microbiology, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Jonathan Friedman
- Department of Plant Pathology and Microbiology, The Hebrew University of Jerusalem, Rehovot, Israel.
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7
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Desjardins E, Kurtz J, Kranke N, Lindeza A, Richter SH. Beyond Standardization: Improving External Validity and Reproducibility in Experimental Evolution. Bioscience 2021. [DOI: 10.1093/biosci/biab008] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Discussions of reproducibility are casting doubts on the credibility of experimental outcomes in the life sciences. Although experimental evolution is not typically included in these discussions, this field is also subject to low reproducibility, partly because of the inherent contingencies affecting the evolutionary process. A received view in experimental studies more generally is that standardization (i.e., rigorous homogenization of experimental conditions) is a solution to some issues of significance and internal validity. However, this solution hides several difficulties, including a reduction of external validity and reproducibility. After explaining the meaning of these two notions in the context of experimental evolution, we import from the fields of animal research and ecology and suggests that systematic heterogenization of experimental factors could prove a promising alternative. We also incorporate into our analysis some philosophical reflections on the nature and diversity of research objectives in experimental evolution.
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Affiliation(s)
- Eric Desjardins
- Rotman Institute of Philosophy, Department of Philosophy, University of Western Ontario, London, Ontario, Canada
| | | | | | | | - S Helene Richter
- RG Behavioural Biology and Animal Welfare, Institute of Neuro and Behavioural Biology all at the WWU, Münster, Germany
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8
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Bacterial fitness landscapes stratify based on proteome allocation associated with discrete aero-types. PLoS Comput Biol 2021; 17:e1008596. [PMID: 33465077 PMCID: PMC7846111 DOI: 10.1371/journal.pcbi.1008596] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 01/29/2021] [Accepted: 12/01/2020] [Indexed: 11/19/2022] Open
Abstract
The fitness landscape is a concept commonly used to describe evolution towards optimal phenotypes. It can be reduced to mechanistic detail using genome-scale models (GEMs) from systems biology. We use recently developed GEMs of Metabolism and protein Expression (ME-models) to study the distribution of Escherichia coli phenotypes on the rate-yield plane. We found that the measured phenotypes distribute non-uniformly to form a highly stratified fitness landscape. Systems analysis of the ME-model simulations suggest that this stratification results from discrete ATP generation strategies. Accordingly, we define "aero-types", a phenotypic trait that characterizes how a balanced proteome can achieve a given growth rate by modulating 1) the relative utilization of oxidative phosphorylation, glycolysis, and fermentation pathways; and 2) the differential employment of electron-transport-chain enzymes. This global, quantitative, and mechanistic systems biology interpretation of fitness landscape formed upon proteome allocation offers a fundamental understanding of bacterial physiology and evolution dynamics.
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9
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Abstract
MOTIVATION How predictable is the evolution of cancer? This fundamental question is of immense relevance for the diagnosis, prognosis and treatment of cancer. Evolutionary biologists have approached the question of predictability based on the underlying fitness landscape. However, empirical fitness landscapes of tumor cells are impossible to determine in vivo. Thus, in order to quantify the predictability of cancer evolution, alternative approaches are required that circumvent the need for fitness landscapes. RESULTS We developed a computational method based on conjunctive Bayesian networks (CBNs) to quantify the predictability of cancer evolution directly from mutational data, without the need for measuring or estimating fitness. Using simulated data derived from >200 different fitness landscapes, we show that our CBN-based notion of evolutionary predictability strongly correlates with the classical notion of predictability based on fitness landscapes under the strong selection weak mutation assumption. The statistical framework enables robust and scalable quantification of evolutionary predictability. We applied our approach to driver mutation data from the TCGA and the MSK-IMPACT clinical cohorts to systematically compare the predictability of 15 different cancer types. We found that cancer evolution is remarkably predictable as only a small fraction of evolutionary trajectories are feasible during cancer progression. AVAILABILITY AND IMPLEMENTATION https://github.com/cbg-ethz/predictability\_of\_cancer\_evolution. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sayed-Rzgar Hosseini
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Ramon Diaz-Uriarte
- Department of Biochemistry, Universidad Autónoma de Madrid, Instituto de Investigaciones Biomédicas “Alberto Sols (UAM-CSIC)”, Madrid, Spain
| | - Florian Markowetz
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
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10
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Zhong Z, Liu CC. Probing pathways of adaptation with continuous evolution. CURRENT OPINION IN SYSTEMS BIOLOGY 2019; 14:18-24. [PMID: 31608311 PMCID: PMC6788780 DOI: 10.1016/j.coisb.2019.02.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Ziwei Zhong
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA 92697, USA
| | - Chang C. Liu
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA 92697, USA
- Department of Chemistry, University of California, Irvine, Irvine, CA 92697, USA
- Department of Molecular Biology and Biochemistry, University of California, Irvine, Irvine, CA 92697, USA
- Lead Contact
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11
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Lumby CK, Nene NR, Illingworth CJR. A novel framework for inferring parameters of transmission from viral sequence data. PLoS Genet 2018; 14:e1007718. [PMID: 30325921 PMCID: PMC6203404 DOI: 10.1371/journal.pgen.1007718] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Revised: 10/26/2018] [Accepted: 09/26/2018] [Indexed: 11/18/2022] Open
Abstract
Transmission between hosts is a critical part of the viral lifecycle. Recent studies of viral transmission have used genome sequence data to evaluate the number of particles transmitted between hosts, and the role of selection as it operates during the transmission process. However, the interpretation of sequence data describing transmission events is a challenging task. We here present a novel and comprehensive framework for using short-read sequence data to understand viral transmission events, designed for influenza virus, but adaptable to other viral species. Our approach solves multiple shortcomings of previous methods for this purpose; for example, we consider transmission as an event involving whole viruses, rather than sets of independent alleles. We demonstrate how selection during transmission and noisy sequence data may each affect naive inferences of the population bottleneck, accounting for these in our framework so as to achieve a correct inference. We identify circumstances in which selection for increased viral transmission may or may not be identified from data. Applying our method to experimental data in which transmission occurs in the presence of strong selection, we show that our framework grants a more quantitative insight into transmission events than previous approaches, inferring the bottleneck in a manner that accounts for selection, both for within-host virulence, and for inherent viral transmissibility. Our work provides new opportunities for studying transmission processes in influenza, and by extension, in other infectious diseases.
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Affiliation(s)
- Casper K. Lumby
- Department of Genetics, University of Cambridge, Cambridge, United Kingdom
| | - Nuno R. Nene
- Department of Genetics, University of Cambridge, Cambridge, United Kingdom
| | - Christopher J. R. Illingworth
- Department of Genetics, University of Cambridge, Cambridge, United Kingdom
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
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12
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Dick C, Hinh J, Hayashi CY, Reznick DN. Convergent evolution of coloration in experimental introductions of the guppy ( Poecilia reticulata). Ecol Evol 2018; 8:8999-9006. [PMID: 30271561 PMCID: PMC6157698 DOI: 10.1002/ece3.4418] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Revised: 06/30/2018] [Accepted: 07/04/2018] [Indexed: 01/01/2023] Open
Abstract
Despite the multitude of examples of evolution in action, relatively fewer studies have taken a replicated approach to understand the repeatability of evolution. Here, we examine the convergent evolution of adaptive coloration in experimental introductions of guppies from a high-predation (HP) environment into four low-predation (LP) environments. LP introductions were replicated across 2 years and in two different forest canopy cover types. We take a complementary approach by examining both phenotypes and genetics. For phenotypes, we categorize the whole color pattern on the tail fin of male guppies and analyze evolution using a correspondence analysis. We find that coloration in the introduction sites diverged from the founding Guanapo HP site. Sites group together based on canopy cover, indicating convergence in response to light environment. However, the axis that explains the most variation indicates a lack of convergence. Therefore, evolution may proceed along similar phenotypic trajectories, but still maintain unique variation within sites. For the genetics underlying the divergent phenotypes, we examine expression levels of color genes. We find no evidence for differential expression, indicating that the genetic basis for the color changes remains undetermined.
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Affiliation(s)
- Cynthia Dick
- Department of Evolution, Ecology and Organismal BiologyUniversity of California‐RiversideRiversideCalifornia
| | - Jasmine Hinh
- Department of Evolution, Ecology and Organismal BiologyUniversity of California‐RiversideRiversideCalifornia
| | - Cheryl Y. Hayashi
- Division of Invertebrate Zoology and Sackler Institute for Comparative GenomicsAmerican Museum of Natural HistoryNew YorkNew York
| | - David N. Reznick
- Department of Evolution, Ecology and Organismal BiologyUniversity of California‐RiversideRiversideCalifornia
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13
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Evolutionary constraints in fitness landscapes. Heredity (Edinb) 2018; 121:466-481. [PMID: 29993041 DOI: 10.1038/s41437-018-0110-1] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Revised: 06/01/2018] [Accepted: 06/03/2018] [Indexed: 12/29/2022] Open
Abstract
In the last years, several genotypic fitness landscapes-combinations of a small number of mutations-have been experimentally resolved. To learn about the general properties of "real" fitness landscapes, it is key to characterize these experimental landscapes via simple measures of their structure, related to evolutionary features. Some of the most relevant measures are based on the selectively acessible paths and their properties. In this paper, we present some measures of evolutionary constraints based on (i) the similarity between accessible paths and (ii) the abundance and characteristics of "chains" of obligatory mutations, that are paths going through genotypes with a single fitter neighbor. These measures have a clear evolutionary interpretation. Furthermore, we show that chains are only weakly correlated to classical measures of epistasis. In fact, some of these measures of constraint are non-monotonic in the amount of epistatic interactions, but have instead a maximum for intermediate values. Finally, we show how these measures shed light on evolutionary constraints and predictability in experimentally resolved landscapes.
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14
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15
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Westphal LL, Lau J, Negro Z, Moreno IJ, Ismail Mohammed W, Lee H, Tang H, Finkel SE, Kram KE. Adaptation of Escherichia coli to long-term batch culture in various rich media. Res Microbiol 2018; 169:145-156. [PMID: 29454026 DOI: 10.1016/j.resmic.2018.01.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2017] [Revised: 12/22/2017] [Accepted: 01/23/2018] [Indexed: 12/29/2022]
Abstract
Experimental evolution studies have characterized the genetic strategies microbes utilize to adapt to their environments, mainly focusing on how microbes adapt to constant and/or defined environments. Using a system that incubates Escherichia coli in different complex media in long-term batch culture, we have focused on how heterogeneity and environment affects adaptive landscapes. In this system, there is no passaging of cells, and therefore genetic diversity is lost only through negative selection, without the experimentally-imposed bottlenecking common in other platforms. In contrast with other experimental evolution systems, because of cycling of nutrients and waste products, this is a heterogeneous environment, where selective pressures change over time, similar to natural environments. We determined that incubation in each environment leads to different adaptations by observing the growth advantage in stationary phase (GASP) phenotype. Re-sequencing whole genomes of populations identified both mutant alleles in a conserved set of genes and differences in evolutionary trajectories between environments. Reconstructing identified mutations in the parental strain background confirmed the adaptive advantage of some alleles, but also identified a surprising number of neutral or even deleterious mutations. This result indicates that complex epistatic interactions may be under positive selection within these heterogeneous environments.
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Affiliation(s)
- Lacey L Westphal
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, RRI 303, 1050 Child's Way, Los Angeles, CA, 90089-2910, USA.
| | - Jasmine Lau
- Department of Biology, California State University, Dominguez Hills, NSM A-137, 1000 E. Victoria Street, Carson, CA, 90747, USA.
| | - Zuly Negro
- Department of Biology, California State University, Dominguez Hills, NSM A-137, 1000 E. Victoria Street, Carson, CA, 90747, USA.
| | - Ivan J Moreno
- Department of Biology, California State University, Dominguez Hills, NSM A-137, 1000 E. Victoria Street, Carson, CA, 90747, USA.
| | - Wazim Ismail Mohammed
- School of Informatics and Computing, Indiana University, 150 S. Woodlawn Avenue, Bloomington, IN, 47405, USA.
| | - Heewook Lee
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, GHC 7719, 5000 Forbes Ave., Pittsburgh, PA, 15213, USA.
| | - Haixu Tang
- School of Informatics and Computing, Indiana University, 150 S. Woodlawn Avenue, Bloomington, IN, 47405, USA.
| | - Steven E Finkel
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, RRI 303, 1050 Child's Way, Los Angeles, CA, 90089-2910, USA.
| | - Karin E Kram
- Department of Biology, California State University, Dominguez Hills, NSM A-137, 1000 E. Victoria Street, Carson, CA, 90747, USA.
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16
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Jahn LJ, Munck C, Ellabaan MMH, Sommer MOA. Adaptive Laboratory Evolution of Antibiotic Resistance Using Different Selection Regimes Lead to Similar Phenotypes and Genotypes. Front Microbiol 2017; 8:816. [PMID: 28553265 PMCID: PMC5425606 DOI: 10.3389/fmicb.2017.00816] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2016] [Accepted: 04/21/2017] [Indexed: 12/01/2022] Open
Abstract
Antibiotic resistance is a global threat to human health, wherefore it is crucial to study the mechanisms of antibiotic resistance as well as its emergence and dissemination. One way to analyze the acquisition of de novo mutations conferring antibiotic resistance is adaptive laboratory evolution. However, various evolution methods exist that utilize different population sizes, selection strengths, and bottlenecks. While evolution in increasing drug gradients guarantees high-level antibiotic resistance promising to identify the most potent resistance conferring mutations, other selection regimes are simpler to implement and therefore allow higher throughput. The specific regimen of adaptive evolution may have a profound impact on the adapted cell state. Indeed, substantial effects of the selection regime on the resulting geno- and phenotypes have been reported in the literature. In this study we compare the geno- and phenotypes of Escherichia coli after evolution to Amikacin, Piperacillin, and Tetracycline under four different selection regimes. Interestingly, key mutations that confer antibiotic resistance as well as phenotypic changes like collateral sensitivity and cross-resistance emerge independently of the selection regime. Yet, lineages that underwent evolution under mild selection displayed a growth advantage independently of the acquired level of antibiotic resistance compared to lineages adapted under maximal selection in a drug gradient. Our data suggests that even though different selection regimens result in subtle genotypic and phenotypic differences key adaptations appear independently of the selection regime.
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Affiliation(s)
- Leonie J Jahn
- Novo Nordisk Foundation Center for Biosustainability, Technical University of DenmarkHørsholm, Denmark
| | - Christian Munck
- Novo Nordisk Foundation Center for Biosustainability, Technical University of DenmarkHørsholm, Denmark
| | - Mostafa M H Ellabaan
- Novo Nordisk Foundation Center for Biosustainability, Technical University of DenmarkHørsholm, Denmark
| | - Morten O A Sommer
- Novo Nordisk Foundation Center for Biosustainability, Technical University of DenmarkHørsholm, Denmark
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17
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Speed MP, Arbuckle K. Quantification provides a conceptual basis for convergent evolution. Biol Rev Camb Philos Soc 2017; 92:815-829. [PMID: 26932796 PMCID: PMC6849873 DOI: 10.1111/brv.12257] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2015] [Revised: 01/28/2016] [Accepted: 02/02/2016] [Indexed: 12/26/2022]
Abstract
While much of evolutionary biology attempts to explain the processes of diversification, there is an important place for the study of phenotypic similarity across life forms. When similar phenotypes evolve independently in different lineages this is referred to as convergent evolution. Although long recognised, evolutionary convergence is receiving a resurgence of interest. This is in part because new genomic data sets allow detailed and tractable analysis of the genetic underpinnings of convergent phenotypes, and in part because of renewed recognition that convergence may reflect limitations in the diversification of life. In this review we propose that although convergent evolution itself does not require a new evolutionary framework, none the less there is room to generate a more systematic approach which will enable evaluation of the importance of convergent phenotypes in limiting the diversity of life's forms. We therefore propose that quantification of the frequency and strength of convergence, rather than simply identifying cases of convergence, should be considered central to its systematic comprehension. We provide a non-technical review of existing methods that could be used to measure evolutionary convergence, bringing together a wide range of methods. We then argue that quantification also requires clear specification of the level at which the phenotype is being considered, and argue that the most constrained examples of convergence show similarity both in function and in several layers of underlying form. Finally, we argue that the most important and impressive examples of convergence are those that pertain, in form and function, across a wide diversity of selective contexts as these persist in the likely presence of different selection pressures within the environment.
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Affiliation(s)
- Michael P. Speed
- Department of Evolution, Ecology and Behaviour, Institute of Integrative Biology, Faculty of Health & Life SciencesUniversity of LiverpoolLiverpoolL69 7ZBU.K.
| | - Kevin Arbuckle
- Department of Evolution, Ecology and Behaviour, Institute of Integrative Biology, Faculty of Health & Life SciencesUniversity of LiverpoolLiverpoolL69 7ZBU.K.
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18
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19
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Matuszewski S, Ormond L, Bank C, Jensen JD. Two sides of the same coin: A population genetics perspective on lethal mutagenesis and mutational meltdown. Virus Evol 2017; 3:vex004. [PMID: 29977604 PMCID: PMC6007402 DOI: 10.1093/ve/vex004] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
The extinction of RNA virus populations upon application of a mutagenic drug is frequently referred to as evidence for the existence of an error threshold, above which the population cannot sustain the mutational load. To explain the extinction process after reaching this threshold, models of lethal mutagenesis have been proposed, in which extinction is described as a deterministic (and thus population size-independent) process. As a separate body of literature, the population genetics community has developed models of mutational meltdown, which focus on the stochastic (and thus population-size dependent) processes governing extinction. However, recent extensions of both models have blurred these boundaries. Here, we first clarify definitions in terms of assumptions, expectations, and relevant parameter spaces, and then assess similarities and differences. As concepts from both fields converge, we argue for a unified theoretical framework that is focused on the evolutionary processes at play, rather than dispute over terminology.
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Affiliation(s)
- Sebastian Matuszewski
- School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne
1015, Switzerland
| | - Louise Ormond
- School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne
1015, Switzerland
| | - Claudia Bank
- Instituto Gulbenkian de Ciência, Oeiras 2780-156, Portugal
| | - Jeffrey D. Jensen
- School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne
1015, Switzerland
- Center for Evolution and Medicine, School of Life Sciences, Arizona State
University, Tempe, AZ 85287, USA
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20
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Nouhaud P, Tobler R, Nolte V, Schlötterer C. Ancestral population reconstitution from isofemale lines as a tool for experimental evolution. Ecol Evol 2016; 6:7169-7175. [PMID: 27895897 PMCID: PMC5114691 DOI: 10.1002/ece3.2402] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Revised: 08/04/2016] [Accepted: 08/05/2016] [Indexed: 02/03/2023] Open
Abstract
Experimental evolution is a powerful tool to study adaptation under controlled conditions. Laboratory natural selection experiments mimic adaptation in the wild with better‐adapted genotypes having more offspring. Because the selected traits are frequently not known, adaptation is typically measured as fitness increase by comparing evolved populations against an unselected reference population maintained in a laboratory environment. With adaptation to the laboratory conditions and genetic drift, however, it is not clear to what extent such comparisons provide unbiased estimates of adaptation. Alternatively, ancestral variation could be preserved in isofemale lines that can be combined to reconstitute the ancestral population. Here, we assess the impact of selection on alleles segregating in newly established Drosophila isofemale lines. We reconstituted two populations from isofemale lines and compared them to two original ancestral populations (AP) founded from the same lines shortly after collection. No significant allele frequency changes could be detected between both AP and simulations showed that drift had a low impact compared to Pool‐Seq‐associated sampling effects. We conclude that laboratory selection on segregating variation in isofemale lines is too weak to have detectable effects, which validates ancestral population reconstitution from isofemale lines as an unbiased approach for measuring adaptation in evolved populations.
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Affiliation(s)
- Pierre Nouhaud
- Institut für Populationsgenetik Vetmeduni Vienna Vienna Austria
| | - Ray Tobler
- Institut für Populationsgenetik Vetmeduni Vienna Vienna Austria; Present address: Ray Tobler, Australian Centre for Ancient DNA School of Biological Sciences University of Adelaide Adelaide SA Australia
| | - Viola Nolte
- Institut für Populationsgenetik Vetmeduni Vienna Vienna Austria
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21
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Abstract
Should the tape of life be replayed, would it produce similar living beings? A classical answer has long been ‘no’, but accumulating data are now challenging this view. Repeatability in experimental evolution, in phenotypic evolution of diverse species and in the genes underlying phenotypic evolution indicates that despite unpredictability at the level of basic evolutionary processes (such as apparition of mutations), a certain kind of predictability can emerge at higher levels over long time periods. For instance, a survey of the alleles described in the literature that cause non-deleterious phenotypic differences among animals, plants and yeasts indicates that similar phenotypes have often evolved in distinct taxa through independent mutations in the same genes. Does this mean that the range of possibilities for evolution is limited? Does this mean that we can predict the outcomes of a replayed tape of life? Imagining other possible paths for evolution runs into four important issues: (i) resolving the influence of contingency, (ii) imagining living organisms that are different from the ones we know, (iii) finding the relevant concepts for predicting evolution, and (iv) estimating the probability of occurrence for complex evolutionary events that occurred only once during the evolution of life on earth.
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Affiliation(s)
- Virginie Orgogozo
- CNRS, UMR7592, Institut Jacques Monod , Univ Paris Diderot, Sorbonne Paris Cité , 15 rue Hélène Brion, 75013 Paris , France
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22
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Elucidating the molecular architecture of adaptation via evolve and resequence experiments. Nat Rev Genet 2015; 16:567-82. [PMID: 26347030 DOI: 10.1038/nrg3937] [Citation(s) in RCA: 150] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Evolve and resequence (E&R) experiments use experimental evolution to adapt populations to a novel environment, then next-generation sequencing to analyse genetic changes. They enable molecular evolution to be monitored in real time on a genome-wide scale. Here, we review the field of E&R experiments across diverse systems, ranging from simple non-living RNA to bacteria, yeast and the complex multicellular organism Drosophila melanogaster. We explore how different evolutionary outcomes in these systems are largely consistent with common population genetics principles. Differences in outcomes across systems are largely explained by different starting population sizes, levels of pre-existing genetic variation, recombination rates and adaptive landscapes. We highlight emerging themes and inconsistencies that future experiments must address.
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23
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Blanquart F, Achaz G, Bataillon T, Tenaillon O. Properties of selected mutations and genotypic landscapes under Fisher's geometric model. Evolution 2014; 68:3537-54. [PMID: 25311558 DOI: 10.1111/evo.12545] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2014] [Accepted: 10/05/2014] [Indexed: 02/06/2023]
Abstract
The fitness landscape-the mapping between genotypes and fitness-determines properties of the process of adaptation. Several small genotypic fitness landscapes have recently been built by selecting a handful of beneficial mutations and measuring fitness of all combinations of these mutations. Here, we generate several testable predictions for the properties of these small genotypic landscapes under Fisher's geometric model of adaptation. When the ancestral strain is far from the fitness optimum, we analytically compute the fitness effect of selected mutations and their epistatic interactions. Epistasis may be negative or positive on average depending on the distance of the ancestral genotype to the optimum and whether mutations were independently selected, or coselected in an adaptive walk. Simulations show that genotypic landscapes built from Fisher's model are very close to an additive landscape when the ancestral strain is far from the optimum. However, when it is close to the optimum, a large diversity of landscape with substantial roughness and sign epistasis emerged. Strikingly, small genotypic landscapes built from several replicate adaptive walks on the same underlying landscape were highly variable, suggesting that several realizations of small genotypic landscapes are needed to gain information about the underlying architecture of the fitness landscape.
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Affiliation(s)
- François Blanquart
- Bioinformatics Research Centre, University of Aarhus, 8000C, Aarhus, Denmark.
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24
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Tenaillon O. The Utility of Fisher's Geometric Model in Evolutionary Genetics. ANNUAL REVIEW OF ECOLOGY, EVOLUTION, AND SYSTEMATICS 2014; 45:179-201. [PMID: 26740803 PMCID: PMC4699269 DOI: 10.1146/annurev-ecolsys-120213-091846] [Citation(s) in RCA: 102] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The accumulation of data on the genomic bases of adaptation has triggered renewed interest in theoretical models of adaptation. Among these models, Fisher Geometric Model (FGM) has received a lot of attention over the last two decades. FGM is based on a continuous multidimensional phenotypic landscape, but it is for the emerging properties of individual mutation effects that it is mostly used. Despite an apparent simplicity and a limited number of parameters, FGM integrates a full model of mutation and epistatic interactions that allows the study of both beneficial and deleterious mutations, and subsequently the fate of evolving populations. In this review, I present the different properties of FGM and the qualitative and quantitative support they have received from experimental evolution data. I later discuss how to estimate the different parameters of the model and outline some future directions to connect FGM and the molecular determinants of adaptation.
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Affiliation(s)
- O Tenaillon
- IAME, UMR 1137, INSERM, F-75018 Paris, France ; IAME, UMR 1137, Univ. Paris Diderot, Sorbonne Paris Cité, F-75018 Paris, France
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25
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de Visser JAGM, Krug J. Empirical fitness landscapes and the predictability of evolution. Nat Rev Genet 2014; 15:480-90. [PMID: 24913663 DOI: 10.1038/nrg3744] [Citation(s) in RCA: 402] [Impact Index Per Article: 40.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
The genotype-fitness map (that is, the fitness landscape) is a key determinant of evolution, yet it has mostly been used as a superficial metaphor because we know little about its structure. This is now changing, as real fitness landscapes are being analysed by constructing genotypes with all possible combinations of small sets of mutations observed in phylogenies or in evolution experiments. In turn, these first glimpses of empirical fitness landscapes inspire theoretical analyses of the predictability of evolution. Here, we review these recent empirical and theoretical developments, identify methodological issues and organizing principles, and discuss possibilities to develop more realistic fitness landscape models.
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Affiliation(s)
- J Arjan G M de Visser
- Laboratory of Genetics, Wageningen University, Droevendaalsesteeg 1, 6708PB Wageningen, The Netherlands
| | - Joachim Krug
- Institute for Theoretical Physics, University of Cologne, Zülpicher Str. 77, 50937 Köln, Germany
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