Cargando…

Adapting the engine to the fuel: mutator populations can reduce the mutational load by reorganizing their genome structure

BACKGROUND: Mutators are common in bacterial populations, both in natural isolates and in the lab. The fate of these lineages, which mutation rate is increased up to 100 ×, has long been studied using population genetics models, showing that they can spread in a population following an environmental...

Descripción completa

Detalles Bibliográficos
Autores principales: Rutten, Jacob Pieter, Hogeweg, Paulien, Beslon, Guillaume
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6800497/
https://www.ncbi.nlm.nih.gov/pubmed/31627727
http://dx.doi.org/10.1186/s12862-019-1507-z
_version_ 1783460456476704768
author Rutten, Jacob Pieter
Hogeweg, Paulien
Beslon, Guillaume
author_facet Rutten, Jacob Pieter
Hogeweg, Paulien
Beslon, Guillaume
author_sort Rutten, Jacob Pieter
collection PubMed
description BACKGROUND: Mutators are common in bacterial populations, both in natural isolates and in the lab. The fate of these lineages, which mutation rate is increased up to 100 ×, has long been studied using population genetics models, showing that they can spread in a population following an environmental change. However in stable conditions, they suffer from the increased mutational load, hence being overcome by non-mutators. However, these results don’t take into account the fact that an elevated mutation rate can impact the genetic structure, hence changing the sensitivity of the population to mutations. Here we used Aevol, an in silico experimental evolution platform in which genomic structures are free to evolve, in order to study the fate of mutator populations evolving for a long time in constant conditions. RESULTS: Starting from wild-types that were pre-evolved for 300,000 generations, we let 100 mutator populations (point mutation rate ×100) evolve for 100,000 further generations in constant conditions. As expected all populations initially undergo a fitness loss. However, after that the mutator populations started to recover. Most populations ultimately recovered their ancestors fitness, and a significant fraction became even fitter than the non-mutator control clones that evolved in parallel. By analyzing the genomes of the mutators, we show that the fitness recovery is due to two mechanisms: i. an increase in robustness through compaction of the coding part of the mutator genomes, ii. an increase of the selection coefficient that decreases the mean-fitness of the population. Strikingly the latter is due to the accumulation of non-coding sequences in the mutators genomes. CONCLUSION: Our results show that the mutational burden that is classically thought to be associated with mutator phenotype is escapable. On the long run mutators adapted their genomes and reshaped the distribution of mutation effects. Therewith the lineage is able to recover fitness even though the population still suffers the elevated mutation rate. Overall these results change our view of mutator dynamics: by being able to reduce the deleterious effect of the elevated mutation rate, mutator populations may be able to last for a very long time; A situation commonly observed in nature.
format Online
Article
Text
id pubmed-6800497
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-68004972019-10-22 Adapting the engine to the fuel: mutator populations can reduce the mutational load by reorganizing their genome structure Rutten, Jacob Pieter Hogeweg, Paulien Beslon, Guillaume BMC Evol Biol Research Article BACKGROUND: Mutators are common in bacterial populations, both in natural isolates and in the lab. The fate of these lineages, which mutation rate is increased up to 100 ×, has long been studied using population genetics models, showing that they can spread in a population following an environmental change. However in stable conditions, they suffer from the increased mutational load, hence being overcome by non-mutators. However, these results don’t take into account the fact that an elevated mutation rate can impact the genetic structure, hence changing the sensitivity of the population to mutations. Here we used Aevol, an in silico experimental evolution platform in which genomic structures are free to evolve, in order to study the fate of mutator populations evolving for a long time in constant conditions. RESULTS: Starting from wild-types that were pre-evolved for 300,000 generations, we let 100 mutator populations (point mutation rate ×100) evolve for 100,000 further generations in constant conditions. As expected all populations initially undergo a fitness loss. However, after that the mutator populations started to recover. Most populations ultimately recovered their ancestors fitness, and a significant fraction became even fitter than the non-mutator control clones that evolved in parallel. By analyzing the genomes of the mutators, we show that the fitness recovery is due to two mechanisms: i. an increase in robustness through compaction of the coding part of the mutator genomes, ii. an increase of the selection coefficient that decreases the mean-fitness of the population. Strikingly the latter is due to the accumulation of non-coding sequences in the mutators genomes. CONCLUSION: Our results show that the mutational burden that is classically thought to be associated with mutator phenotype is escapable. On the long run mutators adapted their genomes and reshaped the distribution of mutation effects. Therewith the lineage is able to recover fitness even though the population still suffers the elevated mutation rate. Overall these results change our view of mutator dynamics: by being able to reduce the deleterious effect of the elevated mutation rate, mutator populations may be able to last for a very long time; A situation commonly observed in nature. BioMed Central 2019-10-18 /pmc/articles/PMC6800497/ /pubmed/31627727 http://dx.doi.org/10.1186/s12862-019-1507-z Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Rutten, Jacob Pieter
Hogeweg, Paulien
Beslon, Guillaume
Adapting the engine to the fuel: mutator populations can reduce the mutational load by reorganizing their genome structure
title Adapting the engine to the fuel: mutator populations can reduce the mutational load by reorganizing their genome structure
title_full Adapting the engine to the fuel: mutator populations can reduce the mutational load by reorganizing their genome structure
title_fullStr Adapting the engine to the fuel: mutator populations can reduce the mutational load by reorganizing their genome structure
title_full_unstemmed Adapting the engine to the fuel: mutator populations can reduce the mutational load by reorganizing their genome structure
title_short Adapting the engine to the fuel: mutator populations can reduce the mutational load by reorganizing their genome structure
title_sort adapting the engine to the fuel: mutator populations can reduce the mutational load by reorganizing their genome structure
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6800497/
https://www.ncbi.nlm.nih.gov/pubmed/31627727
http://dx.doi.org/10.1186/s12862-019-1507-z
work_keys_str_mv AT ruttenjacobpieter adaptingtheenginetothefuelmutatorpopulationscanreducethemutationalloadbyreorganizingtheirgenomestructure
AT hogewegpaulien adaptingtheenginetothefuelmutatorpopulationscanreducethemutationalloadbyreorganizingtheirgenomestructure
AT beslonguillaume adaptingtheenginetothefuelmutatorpopulationscanreducethemutationalloadbyreorganizingtheirgenomestructure