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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 loss of protein structure or function. 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 variants with 95% positive predictive value. Our method, called SNPMuSiC, can be used with both experimental and modeled structures and compares favorably with other prediction tools on several independent test sets. It constitutes a step towards interpreting variant effects at the molecular scale. SNPMuSiC is freely available at https://soft.dezyme.com/ .

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-conferring variants 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 frequency and 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 23 times lower than 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.

The influx of whole genome sequencing (WGS) data in the public health and clinical diagnostic sectors has created a need for data analysis methods and bioinformatics expertise, which can be a bottleneck for many laboratories. At Sciensano, the Belgian national public health institute, an intuitive and user-friendly bioinformatics tool portal was implemented using Galaxy, an open-source platform for data analysis and workflow creation. The Galaxy @Sciensano instance is available to both internal and external scientists and offers a wide range of tools provided by the community, complemented by over 50 custom tools and pipelines developed in-house. The tool selection is currently focused primarily on the analysis of WGS data generated using Illumina sequencing for microbial pathogen typing, characterization and outbreak detection, but it also addresses specific use cases for other data types. Our Galaxy instance includes several custom-developed 'push-button' pipelines, which are user-friendly and intuitive stand-alone tools that perform complete characterization of bacterial isolates based on WGS data and generate interactive HTML output reports with key findings. These pipelines include quality control, de novo assembly, sequence typing, antimicrobial resistance prediction and several relevant species-specific assays. They are tailored for pathogens with active genomic surveillance programs, and clinical relevance, such as Escherichia coli , Listeria monocytogenes , Salmonella spp. and Mycobacterium tuberculosis . These tools and pipelines utilize internationally recognized databases such as PubMLST, EnteroBase, and the NCBI National Database of Antibiotic Resistant Organisms, which are automatically synchronized on a regular basis to ensure up-to-date results. Many of these pipelines are part of the routine activities of Belgian national reference centers and laboratories, some of which use them under ISO accreditation. This resource is publicly available for noncommercial use at https://galaxy.sciensano.be/ and can help other laboratories establish reliable, traceable and reproducible bioinformatics analyses for pathogens encountered in public health settings.

Correlated evolution describes how multiple biological traits evolve together. Recently developed methods provide increasingly detailed results of correlated evolution, sometimes at elevated computational costs. Here, 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. Furthermore, 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. As an application, we scan a publicly available 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).

Abstract Bacterial lineages acquire novel traits at diverse rates in part because the genetic background impacts the successful acquisition of novel genes by horizontal transfer. Yet, how horizontal transfer affects the subsequent evolution of core genes remains poorly understood. Here, we studied the evolution of resistance to quinolones in Escherichia coli accounting for population structure. We found 60 groups of genes whose gain or loss induced an increase in the probability of subsequently becoming resistant to quinolones by point mutations in the gyrase and topoisomerase genes. These groups include functions known to be associated with direct mitigation of the effect of quinolones, with metal uptake, cell growth inhibition, biofilm formation, and sugar metabolism. Many of them are encoded in phages or plasmids. Although some of the chronologies may reflect epidemiological trends, many of these groups encoded functions providing latent phenotypes of antibiotic low-level resistance, tolerance, or persistence under quinolone treatment. The mutations providing resistance were frequent and accumulated very quickly. Their emergence was found to increase the rate of acquisition of other antibiotic resistances setting the path for multidrug resistance. Hence, our findings show that horizontal gene transfer shapes the subsequent emergence of adaptive mutations in core genes. In turn, these mutations further affect the subsequent evolution of resistance by horizontal gene transfer. Given the substantial gene flow within bacterial genomes, interactions between horizontal transfer and point mutations in core genes may be a key to the success of adaptation processes.

Correlated evolution describes how multiple biological traits evolve together. Recently developed methods provide increasingly detailed results of correlated evolution, sometimes at elevated computational costs. Here, 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. Furthermore, 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. As 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).Competing Interest StatementThe authors have declared no competing interest.Footnotes* Correct accession to the published galaxy workflow.* https://github.com/Maxime5G/EvoScope* https://galaxy.pasteur.fr/u/maximeg/w/evoscope

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.

An evolutionary process is reflected in the sequence of changes through time of any trait (e.g. morphological, molecular). Yet, a better understanding of evolution would be procured by characterizing correlated evolution, or when two or more evolutionary processes interact. A wide range of parametric methods have previously been proposed to detect correlated evolution but they often require significant computing time as they rely on the estimation of many parameters. Here we propose a minimal likelihood framework modelling the joint evolution of two traits on a known phylogenetic tree. The type and strength of correlated evolution is characterized by few parameters tuning mutation rates of each trait and interdependencies between these rates. The framework can be applied to study any discrete trait or character ranging from nucleotide substitution to gain or loss of a biological function. More specifically, it can be used to 1) test for independence between two evolutionary processes, 2) identify the type of interaction between them and 3) estimate parameter values of the most likely model of interaction. In its current implementation, the method takes as input a phylogenetic tree together with mapped discrete evolutionary events on it and then maximizes the likelihood for one or several chosen scenarios. The strengths and limits of the method, as well as its relative power when compared to a few other methods, are assessed using both simulations and data from 16S rRNA sequences in a sample of 54 γ-enterobacteria. We show that even with datasets of less than 100 species, the method performs well in parameter estimation and in the selection of evolutionary scenario. Competing Interest Statement The authors have declared no competing interest. Footnotes * https://doi.org/10.5061/dryad.dbrv15dzq

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 strain's 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 other's growth positively or negatively. These results have implications for our basic understanding of host-associated microbes and the design of synthetic microbial communities.Competing Interest StatementThe authors have declared no competing interest.

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.Competing Interest StatementThe authors have declared no competing interest.