Biology subjects

Rousset, F.

Publications and source records attributed to Rousset, F..

24 records · Page 2Linked to original sources

bioRxiv2020

The impact of genetic diversity on gene essentiality within the E. coli species

Bacteria from the same species can differ widely in their gene content. In E. coli, the set of genes shared by all strains, known as the core genome, represents about half the number of genes present in any strain. While recent advances in bacterial genomics have enabled to unravel genes required for fitness in various experimental conditions at the genome scale, most studies have focused on model strains. As a result, the impact of this genetic diversity on core processes of the bacterial cell largely remains to be investigated. Here, we developed a new CRISPR interference platform for high-throughput gene repression that is compatible with most E. coli isolates and closely-related species. We applied it to assess the importance of [~]3,400 nearly ubiquitous genes in 3 growth media in 18 representative E. coli strains spanning most common phylogroups and lifestyles of the species. Our screens highlighted extensive variations in gene essentiality between strains and conditions. Unlike variations in gene expression level, variations in gene essentiality do not recapitulate the strains phylogeny. Investigation of the genetic determinants for these variations highlighted the importance of epistatic interactions with mobile genetic elements. In particular, we showed how mobile genetic elements can trigger the essentiality of core genes that are usually nonessential. This study provides new insights into the evolvability of gene essentiality and argues for the importance of studying various isolates from the same species in bacterial genomics.

genomics↗

bioRxiv2019

Does extrinsic mortality accelerate the pace of life? A bare-bones approach

1It is commonly asserted that when extrinsic mortality is high, individuals should invest early in reproduction. This intuition thrives in the literature on life-history theory and human behavior, yet it has been criticized repeatedly on the basis of mathematical models. The intuition is indeed wrong; but a recent theoretical criticism has confused the reason why it is wrong, thereby obscuring earlier and sounder criticisms. In the present article, based on the simplest possible model, we sought to clarify these issues. We confirm earlier findings that extrinsic mortality can affect the evolution of pace of life, not because it leaves little time to reproduce, but through its effects on density-dependent competition. This result highlights the importance of accounting for density-dependence in theoretical models and data analyses. Further, we find little support for the recent claim that the direction of selection on a reaction norm in a variable environment cannot be easily inferred from models made in homogeneous environments. In conclusion, although life-history theory is still imperfect, it has provided simple results that deserve to be understood.

evolutionary biology↗

bioRxiv2019

When do individuals maximize their inclusive fitness?

Adaptation is often described in behavioral ecology as individuals maximizing their inclusive fitness. Under what conditions does this hold and how does this relate to the gene-centered perspective of adaptation? We unify and extend the literature on these questions to class-structured populations. We demonstrate that the maximization (in the best-response sense) of class-specific inclusive fitness obtains in uninvadable population states (meaning that all deviating mutant go extinct). This defines a genuine actor-centered perspective on adaptation. But this inclusive fitness is assigned to all bearers of a mutant allele in a given class and depends on distributions of demographic and genetic contexts. These distributions, in turn, usually depend on events in previous generations and are thus not under individual control. This prevents, in general, from envisioning individuals themselves as autonomous fitness-maximizers, each with its own inclusive fitness. For weak selection, however, the dependence on earlier events can be neglected. We then show that each individual in each class appears to maximize its own inclusive fitness when all other individuals exhibit fitness-maximizing behavior. This defines a genuine individual-centered perspective of adaptation and justifies formally, as a first-order approximation, the long-heralded view of individuals appearing to maximize their own inclusive fitness.

evolutionary biology↗

bioRxiv2018

Genome-wide CRISPR-dCas9 screens in E. coli identify essential genes and phage host factors

High-throughput genetic screens are powerful methods to identify genes linked to a given phenotype. The catalytic null mutant of the Cas9 RNA-guided nuclease (dCas9) can be conveniently used to silence genes of interest in a method also known as CRISPRi. Here, we report a genome-wide CRISPR-dCas9 screen using a pool of ~ 92,000 sgRNAs which target random positions in the chromosome of E. coli. We first investigate the utility of this method for the prediction of essential genes and various unusual features in the genome of E. coli. We then apply the screen to discover E. coli genes required by phages {lambda}, T4 and 186 to kill their host. In particular, we show that colanic acid capsule is a barrier to all three phages. Finally, cloning the library on a plasmid that can be packaged by {lambda} enables to identify genes required for the formation of functional {lambda} capsids. This study demonstrates the usefulness and convenience of pooled genome-wide CRISPR-dCas9 screens in bacteria in order to identify genes linked to a given phenotype.

microbiology↗

bioRxiv2017

Modelling isoscapes using mixed models

AbstractIsoscapes are maps depicting the continuous spatial (and sometimes temporal) variation in isotope composition. They have various applications ranging from the study of isotope circulation in the main earth systems to the determination of the provenance of migratory animals. Isoscapes can be produced from the fit of statistical models to observations originating from a set of discrete locations. Mixed models are powerful tools for drawing inferences from correlated data. While they are widely used to study non-spatial variation, they are often overlooked in spatial analyses. In particular, they have not been used to study the spatial variation of isotope composition. Here, we introduce this statistical framework and illustrate the methodology by building isoscapes of the isotope composition of hydrogen (measured in{delta} 2H) for precipitation water in Europe. For this example, the approach based on mixed models presents a higher predictive power than a widespread alternative approach. We discuss other advantages offered by mixed models including: the ability to model the residual variance in isotope composition, the quantification of prediction uncertainty, and the simplicity of model comparison and selection using an adequate information criterion: the conditional AIC (cAIC). We provide all source code required for the replication of the results of this paper as a small R package to foster a transparent comparison between alternative frameworks used to model isoscapes.\n\nAbbreviations used in this paper O_LIAIC: Akaike Information Criterion\nC_LIO_LIBLUP: Best Linear Unbiased Predictor\nC_LIO_LIBWR: a method for building isoscape introduced by Bowen and Wilkinson (2002) and Bowen and Revenaugh (2003)\nC_LIO_LIcAIC: conditional Akaike Information Criterion\nC_LIO_LIDHGLM: Double Hierarchical Generalised Linear Model\nC_LIO_LIGLM: Generalised Linear Model\nC_LIO_LIGLMM: Generalised Linear Mixed-effects Model\nC_LIO_LIGNIP: Global Network for Isotopes in Precipitation\nC_LIO_LILM: Linear Model\nC_LIO_LILMM: Linear Mixed-effects Model\nC_LIO_LIMAE: Mean Absolute Error\nC_LIO_LIML: Maximum Likelihood\nC_LIO_LIREML: Restricted Maximum Likelihood\nC_LIO_LIRMSE: Root Mean Squared Error\nC_LI

ecology↗

bioRxiv2017

Likelihood analysis of population genetic data under coalescent models: computational and inferential aspects

Likelihood methods are being developed for inference of migration rates and past demographic changes from population genetic data. We survey an approach for such inference using sequential importance sampling techniques derived from coalescent and diffusion theory. The consistent application and assessment of this approach has required the re-implementation of methods often considered in the context of computer experiments methods, in particular of Kriging which is used as a smoothing technique to infer a likelihood surface from likelihoods estimated in various parameter points, as well as reconsideration of methods for sampling the parameter space appropriately for such inference. We illustrate the performance and application of the whole tool chain on simulated and actual data, and highlight desirable developments in terms of data types and biological scenarios.\n\nResumeDiverses approches ont ete developpees pour linference des taux de migration et des changements demo-graphiques passes a partir de la variation genetique des populations. Nous decrivons une de ces approches utilisant des techniques dechantillonnage pondere sequentiel, fondees sur la modelisation par approches de coalescence et de diffusion de levolution de ces polymorphismes. Lapplication et levaluation systematique de cette approche ont requis la re-implementation de methodes souvent considerees pour lanalyse de fonctions simulees, en particulier le krigeage, ici utilise pour inferer une surface de vraisemblance a partir de vraisemblances estimees en differents points de lespace des parametres, ainsi que des techniques dechantillonage de ces points. Nous illustrons la performance et lapplication de cette serie de methodes sur donnees simulees et reelles, et indiquons les ameliorations souhaitables en termes de types de donnees et de scenarios biologiques.\n\nMots-cleshistoire demographique, processus de coalescence, importance sampling, genetic polymorphism\n\nAMS 2000 subject classifications92D10, 62M05, 65C05

evolutionary biology↗