Biology subjects

Pascal Andreu, V.

Publications and source records attributed to Pascal Andreu, V..

4 recordsLinked to original sources

bioRxiv2024

ClonalTracker: a tool to elucidate dissemination patterns between vancomycin-resistant Enterococcus faecium isolates

The global spread of vancomycin-resistant Enterococcus faecium (VRE), which commonly occurs in hospital environments, has become a major public health concern. To facilitate genomic surveillance and tracking the transmission of VRE, ClonalTracker was designed. This tool assesses the clonal relatedness between two VRE isolates given the respective assembled genomes by analyzing the van operon, the respective transposon type and the whole genome similarity. ClonalTracker has been validated using two previously analyzed publicly available datasets and I showcase its applicability on a yet unprocessed third dataset. While the method agrees with previously published results, it is able to provide more resolution at the clustering level even in the absence of plasmid information and using as reference the minimal version of the vancomycin resistance transposon. Within this third dataset composed of 323 vanB VRE isolates, ClonalTracker found that clonal expansion is the most common dissemination mode. All in all, this tool provides new bioinformatic means to uncover dissemination patterns and elucidate links between vancomycin-resistance isolates and can be broadly accessible via its webserver hosted at www.clonaltracker.nl (as of January 2024). The local version of this tool is also available at: https://github.com/victoriapascal/clonaltracker

bioinformatics↗

bioRxiv2021

A systematic analysis of metabolic pathways in the human gut microbiota

The gut microbiota produce hundreds of small molecules, many of which modulate host physiology. Although efforts have been made to identify biosynthetic genes for secondary metabolites, the chemical output of the gut microbiome consists predominantly of primary metabolites. Here, we systematically profile primary metabolic genes from the gut microbiome, identifying 19,885 gene clusters in 4,240 high-quality microbial genomes. We find marked differences in pathway distribution among phyla, reflecting distinct strategies for energy capture. These data explain taxonomic differences in short-chain fatty acid production and suggest a characteristic metabolic niche for each taxon. Analysis of 1,135 subjects from a Dutch population-based cohort shows that the level of 14 microbiome-derived metabolites in plasma is almost completely uncorrelated with the metagenomic abundance of the corresponding biosynthetic genes, revealing a crucial role for pathway-specific gene regulation and metabolite flux. This work is a starting point for understanding differences in how bacterial taxa contribute to the chemistry of the microbiome.

bioinformatics↗

bioRxiv2020

BiG-MAP: an automated pipeline to profile metabolic gene cluster abundance and expression in microbiomes

Microbial gene clusters encoding the biosynthesis of primary and secondary metabolites play key roles in shaping microbial ecosystems and driving microbiome-associated phenotypes. Although effective approaches exist to evaluate the metabolic potential of such bacteria through identification of metabolic gene clusters in their genomes, no automated pipelines exist to profile the abundance and expression levels of such gene clusters in microbiome samples to generate hypotheses about their functional roles and to find associations with phenotypes of interest. Here, we describe BiG-MAP, a bioinformatic tool to profile abundance and expression levels of gene clusters across metagenomic and metatranscriptomic data and evaluate their differential abundance and expression between different conditions. To illustrate its usefulness, we analyzed 47 metagenomic samples from healthy and caries-associated human oral microbiome samples and identified 58 gene clusters, including unreported ones, that were significantly more abundant in either phenotype. Among them, we found the muc operon, a gene cluster known to be associated to tooth decay. Additionally, we found a putative reuterin biosynthetic gene cluster from a Streptococcus strain to be enriched but not exclusively found in healthy samples; metabolomic data from the same samples showed masses with fragmentation patterns consistent with (poly)acrolein, which is known to spontaneously form from the products of the reuterin pathway and has been previously shown to inhibit pathogenic Streptococcus mutans strains. Thus, we show how BiG-MAP can be used to generate new hypotheses on potential drivers of microbiome-associated phenotypes and prioritize the experimental characterization of relevant gene clusters that may mediate them. ImportanceMicrobes play an increasingly recognized role in determining host-associated phenotypes by producing small molecules that interact with other microorganisms or host cells. The production of these molecules is often encoded in syntenic genomic regions, also known as gene clusters. With the increasing numbers of (multi-)omics datasets that can help understanding complex ecosystems at a much deeper level, there is a need to create tools that can automate the process of analyzing these gene clusters across omics datasets. The current study presents a new software tool called BiG-MAP, which allows assessing gene cluster abundance and expression in microbiome samples using metagenomic and metatranscriptomic data. In this manuscript, we describe the tool and its functionalities, and how it has been validated using a mock community. Finally, using an oral microbiome dataset, we show how it can be used to generate hypotheses regarding the functional roles of gene clusters in mediating host phenotypes.

bioinformatics↗

bioRxiv2020

Computational genomic discovery of diverse gene clusters harboring Fe-S flavoenzymes in anaerobic gut microbiota

The gut contains an enormous diversity of simple as well as complex molecules from highly diverse food sources as well as host-secreted molecules. This presents a large metabolic opportunity for the gut microbiota, but little is known on how gut microbes are able to catabolize this large chemical diversity. Recently, Fe-S flavoenzymes were found to be key in the transformation of bile acids, catalysing the key step in the 7-dehydroxylation pathway that allows gut bacteria to transform cholic acid (CA) into deoxycholic acid (DCA), an exclusively microbe-derived molecule with major implications for human health. While this enzyme family has also been implicated in a limited number of other catalytic transformations, little is known about the extent to which it is of more global importance in gut microbial metabolism. Here, we use large-scale computational genomic analysis to show that this enzyme superfamily has undergone a remarkable expansion in Clostridiales, and occurs throughout a diverse array of >1,000 different families of putative metabolic gene clusters. Analysis of the enzyme content of these gene clusters suggests that they encode pathways with a wide range of predicted substrate classes, including saccharides, amino acids/peptides and lipids. Altogether, these results indicate a potentially important role of this protein superfamily in the human gut, and our dataset provides significant opportunities for the discovery of novel pathways that may have significant effects on human health.

bioinformatics↗