bioRxiv Science⌕ Search

Biology subjects

Godfroid, M.

Publications and source records attributed to Godfroid, M..

6 recordsLinked to original sources

Chance favors the prepared genomes: horizontal transfer shapes the emergence of antibiotic resistance mutations in core genes.

Bacterial lineages vary in the frequency with which they acquire novel traits, like antibiotic resistance or virulence. While previous studies have highlighted the impact of the genetic background on the successful acquisition of novel traits through horizontal gene transfer, the impact of the latter on the subsequent evolution of bacterial genomes by point mutations remains poorly understood. Here, we studied the evolution of resistance to quinolones in thousands of Escherichia coli genomes. Resistance-conferring point mutations in the core genes are frequent and accumulate very quickly. We searched for gene gains and losses significantly associated with the subsequent acquisition of these resistance mutations. This revealed 60 groups of genes in genetic linkage whose gain or loss induced a change in the probability of subsequently becoming resistant to quinolones by point mutations in gyrA and parC. Although some of these chronologies may reflect epidemiological trends, most of these groups encoded functions that were previously associated with antibiotic resistance, tolerance, or persistence, often specifically under quinolone treatment. A lot of the largest groups were found in prophages or plasmids, and they usually increased the likelihood of subsequent resistance mutations. Conversely groups of lost genes were typically small and chromosomal. Quinolone resistance was among the first resistances acquired in the extant lineages of E. coli and its acquisition was associated with an increased likelihood of acquiring other types of resistances, including to aminoglycosides and beta-lactams. Our findings suggest that gene flow shapes the subsequent fixation rate of adaptive mutations in core genes. Given the substantial gene flow within bacterial genomes, interactions between horizontal transfer and point mutations in core genes may be key to the success of adaptation processes.

evolutionary biology↗

Closely related Bacteroides of the murine intestinal microbiota affect each other's growth positively or negatively

The mammalian intestine is a unique ecosystem for thousands of bacterial species and strains. How naturally coexisting bacteria of the microbiota interact with each other is not yet fully understood. Here, we isolated formerly coexisting, closely related strains of the genus Bacteroides from the intestines of healthy, wild-derived mice. The effect of one strain on another strains growth was tested in 169 pairs in vitro. We find a vast diversity of growth promoting and growth inhibiting activities. A strong positive effect was observed between two strains with differing metabolisms. Growth inhibition among a subset of strains was associated with the known bacterial toxin bacteroidetocin B. Across all strains, we observed growth promotion more often than growth inhibition. The effects were independent of two strains belonging to the same or different species. In some cases, one species differed in its effect on another according to host origin. These findings on obligate host-associated bacteria demonstrate that closely related and naturally coexisting strains have the potential to affect each others growth positively or negatively. These results have implications for our basic understanding of host-associated microbes and the design of synthetic microbial communities.

microbiology↗

Comparative genomics of novel Bacteroides acidifaciens isolates reveals candidates for adaptation to host subspecies in house mice

The breadth of phenotypes influenced by the gut microbiome in multicellular hosts has attracted the keen and renewed interest of evolutionary biologists. Comparative studies suggest that coevolutionary processes may occur as hosts and their associated microbes (i.e., holobionts) diverge. The majority of studies to date however lack information beyond that of 16S rRNA gene profiling, and thus fail to capture potential underlying genomic changes among microbes. In this study, we conducted a comparative genomic analysis of 19 newly sampled Bacteroides acidifaciens isolates derived from the eastern and western house mouse subspecies, Mus musculus musculus and M. m. domesticus. Through a panel of genome-wide association (GWAS) analyses applied to pangenomic content, structural gene rearrangements, and SNPs, we reveal several candidates for adaptation to the host subspecies environment. The proportion of significant loci in each respective category is small, indicating low levels of differentiation according host subspecies. However, consistent signal is observed for genes involved in processes such as carbohydrate acquisition/utilization (SusD/RagB, amyA and amyS) and de novo purine nucleotide biosynthesis (purD), which serve as promising candidates for future experimental investigation in the house mouse as a model of holobiont evolution.

evolutionary biology↗

Evo-Scope: Fully automated assessment of correlated evolution on phylogenetic trees

O_LICorrelated evolution describes how multiple biological traits evolve together. Recently developed methods provide increasingly detailed results of correlated evolution, sometimes at elevated computational costs. C_LIO_LIHere, we present evo-scope, a fast and fully-automated pipeline with minimal input requirements to compute correlation between discrete traits evolving on a phylogenetic tree. Notably, we improve two of our previously developed tools that efficiently compute statistics of correlated evolution to characterize the nature, such as synergy or antagonism, and the strength of the interdependence between the traits. C_LIO_LIFurthermore, we improved the running time and implemented several additional features, such as genetic mapping, Bayesian Markov Chain Monte Carlo estimation, consideration of missing data and phylogenetic uncertainty. C_LIO_LIAs an application, we scan a published penicillin resistance data set of Streptococcus pneumoniae and characterize genetic mutations that correlate with antibiotic resistance. The pipeline is accessible both as a self-contained github repository (https://github.com/Maxime5G/EvoScope) and through a graphical galaxy interface (https://galaxy.pasteur.fr/u/maximeg/w/evoscope). C_LI

evolutionary biology↗

Insertion and deletion evolution reflects antibiotics selection pressure in a Mycobacterium tuberculosis outbreak.

In genome evolution, genetic variants are the source of diversity, which natural selection acts upon. Treatment of human tuberculosis (TB) induces a strong selection pressure for the emergence of antibiotic resistance in the infecting Mycobacterium tuberculosis (MTB) strains. MTB evolution in response to treatment has been intensively studied and mainly attributed to point substitutions. However, the contribution of insertions and deletions (indels) to MTB genome evolution remains poorly understood. Here, we analyzed a multi-drug resistant MTB outbreak for the presence of high-quality indels and substitutions. We find that indels are significantly enriched in genes conferring antibiotic resistance. Furthermore, we show that indels are inherited during the outbreak and follow a molecular clock with an evolutionary rate of 5.37e-9 indels/site/year, which is 23x lower compared to the substitution rate. Inherited indels may co-occur with substitutions in genes along related biological pathways; examples are iron storage and resistance to second-line antibiotics. This suggests that epistatic interactions between indels and substitutions affect antibiotic resistance and compensatory evolution in MTB. Author summaryMycobacterium tuberculosis (MTB) is a human pathogen causing millions of deaths every year. Its genome evolution has been intensively characterized through point substitutions, i.e., nucleotide exchanges that are inherited. Additional mutations are short or long insertions and deletions of nucleotides, termed indels. Short indels in genes might change the reading frame and disrupt the gene product. Here we show that antibiotic treatment has a strong impact on indel evolution in an MTB outbreak. Namely, indels occur frequently in genes causing antibiotic resistance upon disruption. Furthermore, we show that the molecular clock, i.e., the temporal emergence of variants over time, holds for short indels in MTB genomes. Finally, we observe that indels may co-occur with substitutions in genes along related biological pathways. These results support the notion that indels are important contributors to MTB evolution. We anticipate that including indels in the analyses of MTB outbreaks will improve our understanding of antibiotic resistance evolution.

microbiology↗

Prediction and interpretation of deleterious coding variants in terms of protein structural stability

The classification of human genetic variants into deleterious and neutral is a challenging issue, whose complexity is rooted in the large variety of biophysical mechanisms that can be responsible for disease conditions. For non-synonymous mutations in structured proteins, one of these is the protein stability change, which can lead to functionality loss. We developed a stability-driven knowledge-based classifier that uses protein structure, artificial neural networks and solvent accessibility-dependent combinations of statistical potentials to predict whether destabilizing or stabilizing mutations are disease-causing. Our predictor yields a balanced accuracy of 71% in cross validation. As expected, it has a very high positive predictive value of 89%: it predicts with high accuracy the subset of mutations that are deleterious because of stability issues, but is by construction unable of classifying variants that are deleterious for other reasons. Its combination with an evolutionary-based predictor increases the balanced accuracy up to 75%, and allowed predicting more than 1/4 of the deleterious variants with 95% positive predictive value. Our method, called SNPMuSiC, can be used with both experimental and structural models and compares favorably with other prediction tools on several independent test sets. It constitutes a step towards interpreting variant effects at the molecular scale.

bioinformatics↗