EvoEvo Deliverable 6.1 : Project Website

Project website: Public website of the project. Private website for collaborative work

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

International audienc

A Model for Genome Size Evolution

International audienceWe present a model for genome size evolution that takes into account both local mutations such as small insertions and small deletions, and large chromosomal rearrangements such as duplications and large deletions. We introduce the possibility of undergoing several mutations within one generation. The model, albeit minimalist, reveals a non-trivial spontaneous dynamics of genome size: in the absence of selection, an arbitrary large part of genomes remains beneath a finite size, even for a duplication rate 2.6-fold higher than the rate of large deletions, and even if there is also a systematic bias toward small insertions compared to small deletions. Specifically, we show that the condition of existence of an asymptotic stationary distribution for genome size non-trivially depends on the rates and mean sizes of the different mutation types. We also give upper bounds for the median and other quantiles of the genome size distribution, and argue that these bounds cannot be overcome by selection. Taken together, our results show that the spontaneous dynamics of genome size naturally prevents it from growing infinitely, even in cases where intuition would suggest an infinite growth. Using quantitative numerical examples, we show that, in practice, a shrinkage bias appears very quickly in genomes undergoing mutation accumulation, even though DNA gains and losses appear to be perfectly symmetrical at first sight. We discuss this spontaneous dynamics in the light of the other evolutionary forces 123 2250 S. Fischer et al. proposed in the literature and argue that it provides them a stability-related size limit below which they can act

Simulating short-and long-term evolutionary dynamics on rugged landscapes

International audienceWe propose a minimal model to simulate long waiting times followed by evolutionary bursts on rugged landscapes. It combines point and inversions-like mutations as sources of genetic variation. The inversions are intended to simulate one of the main chromosomal rearrangements. Using the wellknown family of NK fitness landscapes, we simulate random adaptive walks, i.e. successive mutational events constrained to incremental fitness selection. We report the emergence of different time scales: a short-term dynamics mainly driven by point mutations, followed by a long-term (stasislike) waiting period until a new mutation arises. This new mutation is an inversion which can trigger a burst of successive point mutations, and then drives the system to new short-term increasing-fitness period. We analyse the effect of genes epistatic interactions on the evolutionary time scales. We suggest that the present model mimics the process of evolutionary innovation and punctuated equilibrium

Subspace Clustering for all Seasons

International audienceSubspace clustering is recognized as a more general and difficult task than standard clustering since it requires to identify not only objects sharing similar feature values but also the various subspaces where these similarities appear. Many approaches have been investigated for subspace clustering in the literature using various clustering paradigms. In this paper, we present Chameleoclust, an evolutionary subspace clustering algorithm that incorporates a genome having an evolvable structure. The genome is a coarse grained genome defined as a list of tuples (the "genes"),each tuple containing numbers. These tuples are mapped at the phenotype level to denote core point locations in different dimensions, which are then used to collectively build the subspace clusters, by grouping the data around the core points. The algorithm has been assessed using a reference framework for subspace clustering evaluation, and compared to state-of-the-art algorithms on both real and synthetic datasets. The results obtained with the Chameleoclust algorithm show that evolution of evolution, through an evolvable genome structure, is usefull to solve a difficult problem such as subspace clustering

Evolutionary escape from local fitness peaks through inversion mutations

CCS 2021 - Conference on Complex SystemsInternational audienc

Jump then Climb: can rearrangements predict the occurrence of mutational bursts?

International audienc

.: .

International audienceMolecular evolution is often conceptualised as adaptive walks on rugged fitness landscapes, driven by mutations and constrained by incremental fitness selection. It is well known that epistasis shapes the ruggedness of the landscape’s surface, outlining their topography (with high-fitness peaks separated by valleys of lower fitness genotypes). However, within the strong selection weak mutation (SSWM) limit, once an adaptive walk reaches a local peak, natural selection restricts passage through downstream paths and hampers any possibility of reaching higher fitness values. Here, in addition to the widely used point mutations, we introduce a minimal model of sequence inversions to simulate adaptive walks. We use the well known NK model to instantiate rugged landscapes. We show that adaptive walks can reach higher fitness values through inversion mutations, which, compared to point mutations, allows the evolutionary process to escape local fitness peaks. To elucidate the effects of this chromosomal rearrangement, we use a graph-theoretical representation of accessible mutants and show how new evolutionary paths are uncovered. The present model suggests a simple mechanistic rationale to analyse escapes from local fitness peaks in molecular evolution driven by (intragenic) structural inversions and reveals some consequences of the limits of point mutations for simulations of molecular evolution

Evolution of mutator populations in constant environments

International audienceWe simulate the evolution of mutator strains using the aevol simulator. These strains show a striking resistance to mutational burden. Analysis of their genomes show that this resistance is obtained through two different mechanisms: contraction of the coding part of the genome (directly reducing the burden) and expansion of the non-coding part of the genome (undirectly favoring the reproduction of the best individuals)

A Genome-Wide Evolutionary Simulation of the Transcription-Supercoiling Coupling

International audienceDNA supercoiling, the level of under-or overwinding of the DNA polymer around itself, is widely recognized as an ancestral regulation mechanism of gene expression in bacteria. Higher levels of negative supercoiling facilitate the opening of the DNA double helix at gene promoters, and thereby increase gene transcription rates. Different levels of supercoiling have been measured in bacteria exposed to different environments, leading to the hypothesis that variations in supercoiling could be a response to changes in the environment. Moreover, DNA transcription has been shown to generate local variations in the supercoiling level, and therefore to impact the transcription rate of neighboring genes. In this work, we study the coupled dynamics of DNA supercoiling and transcription at the genome scale. We implement a genome-wide model of gene expression based on the transcriptionsupercoiling coupling. We show that, in this model, a simple change in global DNA supercoiling is sufficient to trigger differentiated responses in gene expression levels via the transcription-1 supercoiling coupling. Then, studying our model in the light of evolution, we demonstrate that this non-linear response to different environments, mediated by the transcription-supercoiling coupling, can serve as the basis for the evolution of specialized phenotypes