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Morel M, Zhukova A, Lemoine F, Gascuel O. Accurate Detection of Convergent Mutations in Large Protein Alignments With ConDor. Genome Biol Evol 2024; 16:evae040. [PMID: 38451738 PMCID: PMC10986858 DOI: 10.1093/gbe/evae040] [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: 05/09/2023] [Revised: 01/30/2024] [Accepted: 02/22/2024] [Indexed: 03/09/2024] Open
Abstract
Evolutionary convergences are observed at all levels, from phenotype to DNA and protein sequences, and changes at these different levels tend to be correlated. Notably, convergent mutations can lead to convergent changes in phenotype, such as changes in metabolism, drug resistance, and other adaptations to changing environments. We propose a two-component approach to detect mutations subject to convergent evolution in protein alignments. The "Emergence" component selects mutations that emerge more often than expected, while the "Correlation" component selects mutations that correlate with the convergent phenotype under study. With regard to Emergence, a phylogeny deduced from the alignment is provided by the user and is used to simulate the evolution of each alignment position. These simulations allow us to estimate the expected number of mutations in a neutral model, which is compared to the observed number of mutations in the data studied. In Correlation, a comparative phylogenetic approach, is used to measure whether the presence of each of the observed mutations is correlated with the convergent phenotype. Each component can be used on its own, for example Emergence when no phenotype is available. Our method is implemented in a standalone workflow and a webserver, called ConDor. We evaluate the properties of ConDor using simulated data, and we apply it to three real datasets: sedge PEPC proteins, HIV reverse transcriptase, and fish rhodopsin. The results show that the two components of ConDor complement each other, with an overall accuracy that compares favorably to other available tools, especially on large datasets.
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Affiliation(s)
- Marie Morel
- Institut Pasteur, Université Paris Cité, Unité Bioinformatique Evolutive, Paris, France
- Université Claude Bernard Lyon 1, LBBE, UMR 5558, CNRS, VAS, Villeurbanne, 69100, France
| | - Anna Zhukova
- Institut Pasteur, Université Paris Cité, Unité Bioinformatique Evolutive, Paris, France
- Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, Paris, France
| | - Frédéric Lemoine
- Institut Pasteur, Université Paris Cité, Unité Bioinformatique Evolutive, Paris, France
- Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, Paris, France
- Institut Pasteur, Université Paris Cité, CNR Virus Des Infections Respiratoires, Paris, France
| | - Olivier Gascuel
- Institut Pasteur, Université Paris Cité, Unité Bioinformatique Evolutive, Paris, France
- Institut de Systématique, Evolution, Biodiversité (UMR 7205—CNRS, Muséum National d’Histoire Naturelle, SU, EPHE, UA), Paris, France
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Tamuri AU, Dos Reis M. A mutation-selection model of protein evolution under persistent positive selection. Mol Biol Evol 2021; 39:6409866. [PMID: 34694387 PMCID: PMC8760937 DOI: 10.1093/molbev/msab309] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
We use first principles of population genetics to model the evolution of proteins under persistent positive selection (PPS). PPS may occur when organisms are subjected to persistent environmental change, during adaptive radiations, or in host–pathogen interactions. Our mutation–selection model indicates protein evolution under PPS is an irreversible Markov process, and thus proteins under PPS show a strongly asymmetrical distribution of selection coefficients among amino acid substitutions. Our model shows the criteria ω>1 (where ω is the ratio of nonsynonymous over synonymous codon substitution rates) to detect positive selection is conservative and indeed arbitrary, because in real proteins many mutations are highly deleterious and are removed by selection even at positively selected sites. We use a penalized-likelihood implementation of the PPS model to successfully detect PPS in plant RuBisCO and influenza HA proteins. By directly estimating selection coefficients at protein sites, our inference procedure bypasses the need for using ω as a surrogate measure of selection and improves our ability to detect molecular adaptation in proteins.
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Affiliation(s)
- Asif U Tamuri
- Centre for Advanced Research Computing, University College London, Gower St, London, WC1E 6BT, UK.,EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Mario Dos Reis
- School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End Road, London, E1 4NS, UK
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Stark TL, Liberles DA. Characterizing Amino Acid Substitution with Complete Linkage of Sites on a Lineage. Genome Biol Evol 2021; 13:6377338. [PMID: 34581792 PMCID: PMC8557849 DOI: 10.1093/gbe/evab225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/17/2021] [Indexed: 11/16/2022] Open
Abstract
Amino acid substitution models are commonly used for phylogenetic inference, for ancestral sequence reconstruction, and for the inference of positive selection. All commonly used models explicitly assume that each site evolves independently, an assumption that is violated by both linkage and protein structural and functional constraints. We introduce two new models for amino acid substitution which incorporate linkage between sites, each based on the (population-genetic) Moran model. The first model is a generalized population process tracking arbitrarily many sites which undergo mutation, with individuals replaced according to their fitnesses. This model provides a reasonably complete framework for simulations but is numerically and analytically intractable. We also introduce a second model which includes several simplifying assumptions but for which some theoretical results can be derived. We analyze the simplified model to determine conditions where linkage is likely to have meaningful effects on sitewise substitution probabilities, as well as conditions under which the effects are likely to be negligible. These findings are an important step in the generation of tractable phylogenetic models that parameterize selective coefficients for amino acid substitution while accounting for linkage of sites leading to both hitchhiking and background selection.
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Affiliation(s)
- Tristan L Stark
- Department of Biology and Center for Computational Genetics and Genomics, Temple University, Philadelphia, PA, USA
| | - David A Liberles
- Department of Biology and Center for Computational Genetics and Genomics, Temple University, Philadelphia, PA, USA
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Kosakovsky Pond SL, Wisotsky SR, Escalante A, Magalis BR, Weaver S. Contrast-FEL-A Test for Differences in Selective Pressures at Individual Sites among Clades and Sets of Branches. Mol Biol Evol 2021; 38:1184-1198. [PMID: 33064823 PMCID: PMC7947784 DOI: 10.1093/molbev/msaa263] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
A number of evolutionary hypotheses can be tested by comparing selective pressures among sets of branches in a phylogenetic tree. When the question of interest is to identify specific sites within genes that may be evolving differently, a common approach is to perform separate analyses on subsets of sequences and compare parameter estimates in a post hoc fashion. This approach is statistically suboptimal and not always applicable. Here, we develop a simple extension of a popular fixed effects likelihood method in the context of codon-based evolutionary phylogenetic maximum likelihood testing, Contrast-FEL. It is suitable for identifying individual alignment sites where any among the K≥2 sets of branches in a phylogenetic tree have detectably different ω ratios, indicative of different selective regimes. Using extensive simulations, we show that Contrast-FEL delivers good power, exceeding 90% for sufficiently large differences, while maintaining tight control over false positive rates, when the model is correctly specified. We conclude by applying Contrast-FEL to data from five previously published studies spanning a diverse range of organisms and focusing on different evolutionary questions.
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Affiliation(s)
| | - Sadie R Wisotsky
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA
| | - Ananias Escalante
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA
| | - Brittany Rife Magalis
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA.,Emerging Pathogens Institute, University of Florida, Gainesville, FL
| | - Steven Weaver
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA
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Ritchie AM, Stark TL, Liberles DA. Inferring the number and position of changes in selective regime in a non-equilibrium mutation-selection framework. BMC Ecol Evol 2021; 21:39. [PMID: 33691618 PMCID: PMC7944921 DOI: 10.1186/s12862-021-01770-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 02/25/2021] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Recovering the historical patterns of selection acting on a protein coding sequence is a major goal of evolutionary biology. Mutation-selection models address this problem by explicitly modelling fixation rates as a function of site-specific amino acid fitness values.However, they are restricted in their utility for investigating directional evolution because they require prior knowledge of the locations of fitness changes in the lineages of a phylogeny. RESULTS We apply a modified mutation-selection methodology that relaxes assumptions of equlibrium and time-reversibility. Our implementation allows us to identify branches where adaptive or compensatory shifts in the fitness landscape have taken place, signalled by a change in amino acid fitness profiles. Through simulation and analysis of an empirical data set of [Formula: see text]-lactamase genes, we test our ability to recover the position of adaptive events within the tree and successfully reconstruct initial codon frequencies and fitness profile parameters generated under the non-stationary model. CONCLUSION We demonstrate successful detection of selective shifts and identification of the affected branch on partitions of 300 codons or more. We successfully reconstruct fitness parameters and initial codon frequencies in simulated data and demonstrate that failing to account for non-equilibrium evolution can increase the error in fitness profile estimation. We also demonstrate reconstruction of plausible shifts in amino acid fitnesses in the bacterial [Formula: see text]-lactamase family and discuss some caveats for interpretation.
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Affiliation(s)
- Andrew M Ritchie
- Department of Biology, Temple University, 1900 North 12th Street, Philadelphia, PA, USA
| | - Tristan L Stark
- Department of Biology, Temple University, 1900 North 12th Street, Philadelphia, PA, USA
| | - David A Liberles
- Department of Biology, Temple University, 1900 North 12th Street, Philadelphia, PA, USA.
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Kazmi SO, Rodrigue N. Detecting amino acid preference shifts with codon-level mutation-selection mixture models. BMC Evol Biol 2019; 19:62. [PMID: 30808289 PMCID: PMC6390532 DOI: 10.1186/s12862-019-1358-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Accepted: 01/11/2019] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND In recent years, increasing attention has been placed on the development of phylogeny-based statistical methodologies for uncovering site-specific changes in amino acid fitness profiles over time. The few available random-effects approaches, modelling across-site variation in amino acid profiles as random variables drawn from a statistical law, either lack a mechanistic codon-level formulation, or pose significant computational challenges. RESULTS Here, we bring together a few existing ideas to explore a simple and fast method based on a predefined finite mixture of amino acid profiles within a codon-level substitution model following the mutation-selection formulation. Our study is focused on the detection of site-specific shifts in amino acid profiles over a known sub-clade of a tree, using simulations with and without shifts over the sub-clade to study the properties of the method. Through modifications of the values of the amino acid profiles, our simulations show different levels of reliability under different forms of finite mixture models. Sites identified by our method in a real data set show obvious overlap with those identified using previous methods, with some notable differences. CONCLUSION Overall, our results show that when a site-specific shift in amino acid profile is strongly pronounced, involving two clearly different sets of profiles, the method performs very well; but shifts between profiles that share many features are difficult to correctly identify, highlighting the challenging nature of the problem.
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Affiliation(s)
- S Omar Kazmi
- Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, K1S 5B6, Canada
| | - Nicolas Rodrigue
- Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, K1S 5B6, Canada. .,Institute of Biochemistry and School of Mathematics and Statistics, Carleton University, 1125 Colonel By Drive, Ottawa, K1S 5B6, Canada.
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Abstract
In the history of life, some phenotypes have been acquired several times independently, through convergent evolution. Recently, lots of genome-scale studies have been devoted to identify nucleotides or amino acids that changed in a convergent manner when the convergent phenotypes evolved. These efforts have had mixed results, probably because of differences in the detection methods, and because of conceptual differences about the definition of a convergent substitution. Some methods contend that substitutions are convergent only if they occur on all branches where the phenotype changed toward the exact same state at a given nucleotide or amino acid position. Others are much looser in their requirements and define a convergent substitution as one that leads the site at which they occur to prefer a phylogeny in which species with the convergent phenotype group together. Here, we suggest to look for convergent shifts in amino acid preferences instead of convergent substitutions to the exact same amino acid. We define as convergent shifts substitutions that occur on all branches where the phenotype changed and such that they correspond to a change in the type of amino acid preferred at this position. We implement the corresponding model into a method named PCOC. We show on simulations that PCOC better recovers convergent shifts than existing methods in terms of sensitivity and specificity. We test it on a plant protein alignment where convergent evolution has been studied in detail and find that our method recovers several previously identified convergent substitutions and proposes credible new candidates.
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Affiliation(s)
- Carine Rey
- UnivLyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratoire de Biologie et Modélisation de la Cellule, Lyon, France
- Laboratoire de Biométrie et Biologie Évolutive (LBBE), Université de Lyon, Université Lyon 1, CNRS, Villeurbanne, France
| | - Laurent Guéguen
- Laboratoire de Biométrie et Biologie Évolutive (LBBE), Université de Lyon, Université Lyon 1, CNRS, Villeurbanne, France
| | - Marie Sémon
- UnivLyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratoire de Biologie et Modélisation de la Cellule, Lyon, France
| | - Bastien Boussau
- Laboratoire de Biométrie et Biologie Évolutive (LBBE), Université de Lyon, Université Lyon 1, CNRS, Villeurbanne, France
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Parto S, Lartillot N. Molecular adaptation in Rubisco: Discriminating between convergent evolution and positive selection using mechanistic and classical codon models. PLoS One 2018; 13:e0192697. [PMID: 29432438 PMCID: PMC5809049 DOI: 10.1371/journal.pone.0192697] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Accepted: 01/29/2018] [Indexed: 11/19/2022] Open
Abstract
Rubisco (Ribulose-1, 5-biphosphate carboxylase/oxygenase) is the most important enzyme on earth, catalyzing the first step of photosynthetic CO2 fixation. So, without it, there would be no storing of the sun's energy in plants. Molecular adaptation of Rubisco to C4 photosynthetic pathway has attracted a lot of attention. C4 plants, which comprise less than 5% of land plants, have evolved more efficient photosynthesis compared to C3 plants. Interestingly, a large number of independent transitions from C3 to C4 phenotype have occurred. Each time, the Rubisco enzyme has been subject to similar changes in selective pressure, thus providing an excellent model for convergent evolution at the molecular level. Molecular adaptation is often identified with positive selection and is typically characterized by an elevated ratio of non-synonymous to synonymous substitution rate (dN/dS). However, convergent adaptation is expected to leave a different molecular signature, taking the form of repeated transitions toward identical or similar amino acids. Here, we used a previously introduced codon-based differential-selection model to detect and quantify consistent patterns of convergent adaptation in Rubisco in eudicots. We further contrasted our results with those obtained by classical codon models based on the estimation of dN/dS. We found that the two classes of models tend to select distinct, although overlapping, sets of positions. This discrepancy in the results illustrates the conceptual difference between these models while emphasizing the need to better discriminate between qualitatively different selective regimes, by using a broader class of codon models than those currently considered in molecular evolutionary studies.
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Affiliation(s)
- Sahar Parto
- Department of Biochemistry and Molecular Medicine, Université de Montreal, Montreal, Quebec, Canada
- * E-mail:
| | - Nicolas Lartillot
- Laboratoire de Biométrie et Biologie Évolutive, Université Lyon 1, CNRS, UMR, Lyon, France
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Sánchez-Gracia A, Guirao-Rico S, Hinojosa-Alvarez S, Rozas J. Computational prediction of the phenotypic effects of genetic variants: basic concepts and some application examples in Drosophila nervous system genes. J Neurogenet 2017; 31:307-319. [DOI: 10.1080/01677063.2017.1398241] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Alejandro Sánchez-Gracia
- Departament de Genètica, Microbiologia i Estadística and Institut de Recerca de la Biodiversitat (IRBio), Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
| | - Sara Guirao-Rico
- Center for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB-UB, Bellaterra, Spain
| | - Silvia Hinojosa-Alvarez
- Departament de Genètica, Microbiologia i Estadística and Institut de Recerca de la Biodiversitat (IRBio), Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
| | - Julio Rozas
- Departament de Genètica, Microbiologia i Estadística and Institut de Recerca de la Biodiversitat (IRBio), Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
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