Network models of TEM β-lactamase mutations coevolving under antibiotic selection show modular structure and anticipate evolutionary trajectories.

Understanding how novel functions evolve (genetic adaptation) is a critical goal of evolutionary biology. Among asexual organisms, genetic adaptation involves multiple mutations that frequently interact in a non-linear fashion (epistasis). Non-linear interactions pose a formidable challenge for the...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Violeta Beleva Guthrie, Jennifer Allen, Manel Camps, Rachel Karchin
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/a9f432f0a0fa41bdba756aae9aa33595
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:a9f432f0a0fa41bdba756aae9aa33595
record_format	dspace
spelling	oai:doaj.org-article:a9f432f0a0fa41bdba756aae9aa335952021-11-04T05:42:45ZNetwork models of TEM β-lactamase mutations coevolving under antibiotic selection show modular structure and anticipate evolutionary trajectories.1553-734X1553-735810.1371/journal.pcbi.1002184https://doaj.org/article/a9f432f0a0fa41bdba756aae9aa335952011-09-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21966264/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Understanding how novel functions evolve (genetic adaptation) is a critical goal of evolutionary biology. Among asexual organisms, genetic adaptation involves multiple mutations that frequently interact in a non-linear fashion (epistasis). Non-linear interactions pose a formidable challenge for the computational prediction of mutation effects. Here we use the recent evolution of β-lactamase under antibiotic selection as a model for genetic adaptation. We build a network of coevolving residues (possible functional interactions), in which nodes are mutant residue positions and links represent two positions found mutated together in the same sequence. Most often these pairs occur in the setting of more complex mutants. Focusing on extended-spectrum resistant sequences, we use network-theoretical tools to identify triple mutant trajectories of likely special significance for adaptation. We extrapolate evolutionary paths (n = 3) that increase resistance and that are longer than the units used to build the network (n = 2). These paths consist of a limited number of residue positions and are enriched for known triple mutant combinations that increase cefotaxime resistance. We find that the pairs of residues used to build the network frequently decrease resistance compared to their corresponding singlets. This is a surprising result, given that their coevolution suggests a selective advantage. Thus, β-lactamase adaptation is highly epistatic. Our method can identify triplets that increase resistance despite the underlying rugged fitness landscape and has the unique ability to make predictions by placing each mutant residue position in its functional context. Our approach requires only sequence information, sufficient genetic diversity, and discrete selective pressures. Thus, it can be used to analyze recent evolutionary events, where coevolution analysis methods that use phylogeny or statistical coupling are not possible. Improving our ability to assess evolutionary trajectories will help predict the evolution of clinically relevant genes and aid in protein design.Violeta Beleva GuthrieJennifer AllenManel CampsRachel KarchinPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 7, Iss 9, p e1002184 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Violeta Beleva Guthrie Jennifer Allen Manel Camps Rachel Karchin Network models of TEM β-lactamase mutations coevolving under antibiotic selection show modular structure and anticipate evolutionary trajectories.
description	Understanding how novel functions evolve (genetic adaptation) is a critical goal of evolutionary biology. Among asexual organisms, genetic adaptation involves multiple mutations that frequently interact in a non-linear fashion (epistasis). Non-linear interactions pose a formidable challenge for the computational prediction of mutation effects. Here we use the recent evolution of β-lactamase under antibiotic selection as a model for genetic adaptation. We build a network of coevolving residues (possible functional interactions), in which nodes are mutant residue positions and links represent two positions found mutated together in the same sequence. Most often these pairs occur in the setting of more complex mutants. Focusing on extended-spectrum resistant sequences, we use network-theoretical tools to identify triple mutant trajectories of likely special significance for adaptation. We extrapolate evolutionary paths (n = 3) that increase resistance and that are longer than the units used to build the network (n = 2). These paths consist of a limited number of residue positions and are enriched for known triple mutant combinations that increase cefotaxime resistance. We find that the pairs of residues used to build the network frequently decrease resistance compared to their corresponding singlets. This is a surprising result, given that their coevolution suggests a selective advantage. Thus, β-lactamase adaptation is highly epistatic. Our method can identify triplets that increase resistance despite the underlying rugged fitness landscape and has the unique ability to make predictions by placing each mutant residue position in its functional context. Our approach requires only sequence information, sufficient genetic diversity, and discrete selective pressures. Thus, it can be used to analyze recent evolutionary events, where coevolution analysis methods that use phylogeny or statistical coupling are not possible. Improving our ability to assess evolutionary trajectories will help predict the evolution of clinically relevant genes and aid in protein design.
format	article
author	Violeta Beleva Guthrie Jennifer Allen Manel Camps Rachel Karchin
author_facet	Violeta Beleva Guthrie Jennifer Allen Manel Camps Rachel Karchin
author_sort	Violeta Beleva Guthrie
title	Network models of TEM β-lactamase mutations coevolving under antibiotic selection show modular structure and anticipate evolutionary trajectories.
title_short	Network models of TEM β-lactamase mutations coevolving under antibiotic selection show modular structure and anticipate evolutionary trajectories.
title_full	Network models of TEM β-lactamase mutations coevolving under antibiotic selection show modular structure and anticipate evolutionary trajectories.
title_fullStr	Network models of TEM β-lactamase mutations coevolving under antibiotic selection show modular structure and anticipate evolutionary trajectories.
title_full_unstemmed	Network models of TEM β-lactamase mutations coevolving under antibiotic selection show modular structure and anticipate evolutionary trajectories.
title_sort	network models of tem β-lactamase mutations coevolving under antibiotic selection show modular structure and anticipate evolutionary trajectories.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/a9f432f0a0fa41bdba756aae9aa33595
work_keys_str_mv	AT violetabelevaguthrie networkmodelsoftemblactamasemutationscoevolvingunderantibioticselectionshowmodularstructureandanticipateevolutionarytrajectories AT jenniferallen networkmodelsoftemblactamasemutationscoevolvingunderantibioticselectionshowmodularstructureandanticipateevolutionarytrajectories AT manelcamps networkmodelsoftemblactamasemutationscoevolvingunderantibioticselectionshowmodularstructureandanticipateevolutionarytrajectories AT rachelkarchin networkmodelsoftemblactamasemutationscoevolvingunderantibioticselectionshowmodularstructureandanticipateevolutionarytrajectories
_version_	1718445232141369344

Network models of TEM β-lactamase mutations coevolving under antibiotic selection show modular structure and anticipate evolutionary trajectories.

Ejemplares similares