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

Abstract

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.

Evolution of genome fragility enables microbial division of labor

“…Mathematical models indicate that the organization of genetic information along the chromosome is influenced by the mutational operators that act on it [22, 36–39]. As a consequence of this organization, mutations may be more likely to generate mutant offspring with specific characteristics, such as reduced competition with the wildtype due to low fitness [40–43], lower propensity for social conflicts [44] or accelerated re-adaptation to variable environments [45]. In prokaryotes, mutational operators that can drive functional mutagenesis include horizontal gene transfer, which drives the rapid evolution of gene content [31, 46–48], and the CRISPR-Cas system, that generates immunity to viral infections through targeted incorporation of viral genomes [49].…”

Evolving Living Technologies—Insights from the EvoEvo Project

“…Hence, an evolvable genetic structure enables evolution to fine tune the distribution of offspring fitness, selecting for robustness and/or evolvability when needed. This fine tuning can even allow for simultaneous selection of robustness and evolvability, as exemplified by the results of Jaap Rutten [29]. His experiments show that organisms in which the point mutation rate is raised by a factor of 100 react by reorganizing their genome.…”

Capturing the facets of evolvability in a mechanistic framework

“…At the core of these issues lies a common element: the capability of organisms to adaptevolvability [1]. Evolvability research sheds new light on genomic architecture [2], the structure of regulatory networks [3,4], and many other features of biological systems (see Glossary). It has yielded surprising new insights, such as: adaptive evolution can proceed at a pace similar to ecological change, resulting in intricate and unexpected ecoevolutionary dynamics [5,6]; evolvability and robustness do not conflict but mutually reinforce each other [3,7,8]; and organisms with high evolvability can 'generalize' over environments [9,10].…”

“…First, determinants may affect evolvability by providing variation. The mutation rate is the most obvious example of such a determinant [2,[15][16][17]. Second, determinants may affect evolvability by influencing the effect of variation on fitness.…”