Biology subjects

Arnoux, J.

Publications and source records attributed to Arnoux, J..

3 recordsLinked to original sources

bioRxiv2026

PanGBank: a large-scale resource of precomputed microbial pangenomes built with PPanGGOLiN

PanGBank (https://pangbank.genoscope.cns.fr) is a comprehensive open-access database providing precomputed prokaryotic pangenomes at a broad taxonomic scale. Built upon PPanGGOLiN partitioned pangenome graphs, PanGBank addresses the growing need for large-scale comparative genomics through a standardized, regularly updated, and fully accessible resource. The initial release comprises two complementary collections covering more than 4,600 prokaryotic species from the Genome Taxonomy Database (GTDB), encompassing over 393,000 genomes: GTDB all, maximizing taxonomic and environmental diversity through the inclusion of MAGs and SAGs, and GTDB refseq, focusing on high-quality, annotation-rich genomes. Each species-level pangenome integrates graph-based statistical partitions into persistent, shell, and cloud gene families, together with regions of genomic plasticity (panRGP) and co-localized functional modules (panModule). PanGBank offers multiple access modes, including a REST API, a command-line interface (PanGBank-cli), and an interactive web interface. By combining large-scale pangenome resources with advanced graph-based analyses, PanGBank provides a scalable framework for exploring microbial diversity, genome evolution, functional variation, and the dissemination of adaptive traits across prokaryotic populations, as illustrated by a use case on Acinetobacter baumannii pangenome investigating the distribution and evolution of antimicrobial resistance determinants. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=63 SRC="FIGDIR/small/742796v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@12c441org.highwire.dtl.DTLVardef@12b079org.highwire.dtl.DTLVardef@ffd7f8org.highwire.dtl.DTLVardef@bc013f_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

Bacterial Stress Responses Lower mRNA-Protein Level Correlations

Diverse bacterial pathogens have evolved complex regulatory mechanisms to adapt to various environmental stresses during infection. The uncertainty in mRNA-protein levels in response to environmental stressors complicates our understanding of bacterial physiology and their adaptation to stressful environments. To examine this issue, we have integrated transcriptomics and proteomics data on three human bacterial pathogens Salmonella enterica Typhimurium, Yersinia pseudotuberculosis, and Staphylococcus aureus under ten infection-relevant stress conditions. We observed positive correlations between mRNA and protein levels, which were decreased under different stress conditions. Essential genes exhibited higher expression levels with lower variation across the conditions and stronger mRNA-protein correlations compared to non-essential genes, highlighting their critical role in bacterial adaptability and survival. Moreover, we identified a substantial number of genes with stress-induced non-correlating mRNA-protein levels, especially under conditions triggering strong stress responses. Particularly this level was dramatically lowered for osmotic stress specific genes affected by impaired translational activity under osmotic stress. Our findings highlight the prevalence of non-correlating mRNA-protein levels and the potential role of post-translational modifications in modulating protein levels in response to environmental stressors during infection. This study provides a comprehensive framework for integrating transcriptomics and proteomics data and identifies potential gene products that might significantly impact the ability of diverse bacterial pathogens to adapt to hostile infection environments. Significance StatementUnderstanding how bacteria adapt to host environments is crucial for combating infections. We employed an integrative transcriptomics and proteomics approach to investigate mRNA-protein correlations in three clinically relevant pathogens under ten infection-relevant stress conditions. We identified genes whose mRNA-protein relationships are significantly disrupted by specific stressors. This study provides a deeper understanding of mRNA-protein level uncertainty, coupling it to stress responses known to trigger critical post-transcriptional and post-translational regulations. These findings help reveal novel mechanisms of bacterial adaptation and pathogenesis. By providing a comprehensive, multi-species dataset, this study serves as a foundational resource for infection biology, driving future advancements in understanding complex regulatory networks and identifying potential antimicrobial targets.

microbiology↗

bioRxiv2025

Panorama: a robust pangenome-based method for predicting and comparing biological systems across species

Over the last decade, the expansion in the number of available genomes has profoundly transformed the study of genetic diversity, evolution, and ecological adaptation in prokaryotes. However, traditional bioinformatic approaches based on the analysis of individual genomes are showing their limitations when faced with the sheer scale of the data. To overcome these constraints, the concept of pangenome has emerged, offering a comprehensive framework to capture the full genetic repertoire of a species. In this study, we present PANORAMA, an innovative pangenomic tool designed to exploit pangenome graphs and enable them to be annotated and compared in order to explore the genomic diversity of several species. Based on the PPanGGOLiN pangenome graphs, PANORAMA integrates advanced methods for rule-based prediction of macromolecular systems and comparative analysis of conserved features between different pangenomes, such as spots of insertion. We illustrate the use of PANORAMA on a dataset of 941 Pseudomonas aeruginosa genomes, evaluating its performance against reference defense system prediction tools such as PADLOC and DefenseFinder. The analysis was then extended to a larger set, including four species of Enterobacteriaceae (>6,000 genomes), demonstrating PANORAMAs ability to annotate, compare, and explore the diversity and distribution of biological systems across multiple species. This work provides new methods for the large-scale comparative study of microbial genomes and underlines the relevance of pangenome approaches in deciphering their evolutionary dynamics. PANORAMA is freely available and accessible through: https://github.com/labgem/PANORAMA Author summaryMicroorganisms are present in nearly all environments on Earth. Uncovering their diversity through the study of their genomes is essential for understanding their biology and evolution. This includes characterizing the species complete genetic repertoire, known as the pangenome. Such research also enables new applications in health, ecology, biotechnology, etc. Here, we present PANORAMA, a novel computational tool designed to predict macromolecular systems, such as defense mechanisms against phages, and to compare pangenome graphs across different species. By using rule-based models that combine gene function and genomic context, we can search for systems directly within pangenome graphs. This graph-based approach provides a global view of the functional content of entire species, moving beyond the analysis of individual genomes. It greatly facilitates the analysis of thousands of genomes by reducing the required computation time and directly integrating the results to identify shared and specific systems. Furthermore, PANORAMAs comparative functionality enables the identification of conserved structures across species, such as shared spots of insertion, revealing common evolutionary mechanisms and functional modules. This work establishes a foundation for comparative pangenomics, offering an unprecedented framework to explore the adaptive potential and evolutionary dynamics of prokaryotes at scale.

bioinformatics↗