Biology subjects

Pauvert, C.

Publications and source records attributed to Pauvert, C..

6 recordsLinked to original sources

bioRxiv2026

Swimming motility in the gut microbiota is diverse and increased in inflammation.

Swimming motility has long been studied as a virulence mechanism of enteric pathogens, while the resident microbiota's motility has only been inferred from proxies or explored through a few model species. This study presents a direct, functional analysis of gut bacterial motility in health and inflammation. Using phase-contrast microscopy and high-throughput 3D tracking, we quantified motile bacteria and characterized their swimming behaviours directly in diluted fresh gut content. In healthy mice, fewer than 3% of bacteria were motile along the digestive tract, and their swimming patterns were dominated not by the run-tumble behavior of model gut species but by diverse behaviours rich in reverses. In five mouse models with intestinal inflammation (spanning chemical, genetic, dietary and infectious etiologies) the motile fraction rose by at least 4-fold, correlating with elevated Lipocalin-2 where measured. Reverse-rich patterns remained prevalent in these inflamed conditions, with the notable exception of Salmonella infection. Paired metagenomics and metatranscriptomics showed enrichment of flagellar genes, while communities transferred into cecal water from inflamed mice raised their motile fraction within an hour, indicating that the rise reflects both enrichment of motile taxa and rapid modulation within existing populations. In vitro assays with human-derived isolates confirmed motility across several phyla, with variability down to strain level, and identified oxygen and viscosity as key modulators. These findings support increased motility as a hallmark of the inflamed gut and challenge established assumptions about gut bacterial motility.

microbiology↗

bioRxiv2025

Robust genome-based delineation of bacterial genera

BackgroundGenomic analysis has become essential in bacterial taxonomy, enabling fast classification of bacteria. However, quantifiable measures to make informed decision on the taxonomic placement of bacterial taxa above the level of species are rare, which hampers the stability of knowledge in databases and articles. In this work, we focused on bacterial classification at the genus level and revisited the concept of Percentage Of Conserved Proteins (POCP). Whilst POCP is broadly used as an overall genome relatedness index, the underlying tool and method of calculation differ, resulting in disparate implementations and misnomers unfit for the increasing wealth of public genomes available. ResultsWe evaluated 10 protein alignments methods and found evidence for a scalable yet robust alternative to BLASTP for POCP based on 2,358,466 pairwise comparisons of 4,767 genomes across 35 families. However, we showed that certain combinations of tools and parameters are not suited for POCP calculations owing to drastic over-or underestimation. Therefore, we bring forward a clearer definition of POCP using only unique matches, termed POCPu, that showed better genus delineation than with POCP. We suggested tentative family-specific thresholds when the standard of 50% was not resolutive enough. ConclusionsWe propose that faster bacterial genus delineation should be calculated with DIAMOND using very-sensitive settings and only unique matches (POCPu). We advise microbiologists to carefully assess that the tools used for genus assignment match their biological assumptions to avoid misguided inferences.

microbiology↗

bioRxiv2024

Broad diversity of human gut bacteria accessible via a traceable strain deposition system

Numerous bacteria in the human gut microbiome remain unknown and/or have yet to be cultured. While collections of human gut bacteria have been published, few strains have been made publicly available. A major hurdle in making strains publicly available is their deposition to public culture collections. We propose a framework for the bulk-deposition of strains to culture collections, which removes many of the barriers previously identified (www.dsmz.de/bulk-deposit). Using this bulk-deposition system we have created a publicly available collection of human gut isolates. The Human intestinal Bacteria Collection (HiBC) (www.hibc.rwth-aachen.de) contains 340 strains representing 198 species within 29 families and 7 phyla, of which 29 previously unknown species are taxonomically described and named. These included two butyrate-producing species of Faecalibacterium and new dominant species associated with health and inflammatory bowel disease, Ruminococcoides intestinale and Blautia intestinihominis, respectively. Plasmids were prolific within the HiBC isolates, with almost half (46%) of strains containing plasmids, with a maximum of six within a strain. This included a broadly occurring plasmid (pBAC) that exists in three diverse forms across Bacteroidales species. Megaplasmids were identified within two strains, the pMMCAT megaplasmid is globally present within multiple Bacteroidales species. This collection of easily searchable and publicly available gut bacterial isolates will facilitate functional studies of the gut microbiome.

microbiology↗

bioRxiv2020

Microbial association networks give relevant insights into plant pathobiomes

Interactions between plant pathogens and other plant-associated microorganisms regulate disease. Deciphering the networks formed by these interactions, termed pathobiomes, is crucial to disease management. Our aim was to investigate whether microbial association networks inferred from metabarcoding data give relevant insights into pathobiomes, by testing whether inferred associations contain signals of ecological interactions. We used Poisson Lognormal Models to construct microbial association networks from metabarcoding data and then investigated whether some of these associations corresponded to interactions measurable in co-cultures or known in the literature, by using grapevine (Vitis vinifera) and the fungal pathogen causing powdery mildew (Erysiphe necator) as a model system. Our model suggested that the pathogen species was associated with 23 other fungal species, forming its putative pathobiome. These associations were not known as interactions in the literature, but one of them was confirmed by our co-culture experiments. The yeast Buckleyzyma aurantiaca impeded pathogen growth and reproduction, in line with the negative association found in the microbial network. Co-cultures also supported another association involving two yeast species. Together, these findings indicate that microbial networks can provide plausible hypotheses of ecological interactions that could be used to develop microbiome-based strategies for crop protection.

ecology↗

bioRxiv2019

Maternal effects and environmental filtering shape seed fungal communities in oak trees

O_LITrees, as foundation species, play a pivotal role in the species interaction networks that constitute forest ecosystems. From the seed stage, they interact with microbial communities that affect their growth, health and fitness. Despite their eco-evolutionary importance, the processes shaping seed microbial communities in natural forests have received little attention.\nC_LIO_LITo unravel these processes, we analyzed the microbial communities of seeds collected in populations of sessile oak (Quercus petraea) growing along elevation gradients. We focused on the fungal communities as this group includes seed pathogens. Ecological processes shaping the communities were quantified using joint species distribution models.\nC_LIO_LIFungi were present in all seed tissues, including the embryo. Fungal communities differed significantly among oak populations along the elevation gradients, and among mother trees within the same population. These maternal effects remained significant after seed fall, despite colonization by fungal species on the ground. Associations between tree pathogens and their antagonists were detected in the seeds.\nC_LIO_LIOur results demonstrate that both maternal effects and environmental filtering shape seed microbial communities of sessile oak. They provide a starting point for future research aimed at identifying the seed extended phenotypic traits that influence seed dispersal and germination, and seedling survival and growth across environments.\nC_LI

ecology↗

bioRxiv2019

Microbial networks inferred from metabarcoding data lack replicability: consequences for next-generation biomonitoring

Plant-associated microbial interaction networks protect plants against disease. There is, therefore, a need to monitor in real time their responses to environmental changes to predict disease risk and adjust crop protection strategies. Next-Generation Biomonitoring (NGB) proposes to reconstruct automatically these networks from metabarcoding data, to complement ecological community properties commonly used for ecosystem health assessment. This study aimed to evaluate the benefits and shortcomings of community-level and network-level properties for biomonitoring. We specifically investigated whether microbial networks inferred from metabarcoding data show robust responses to agricultural practices, using the grapevine microbiota as a study system. Our results demonstrate a strong footprint of the agricultural practice on the metabarcoding data, when analyzed at the community level. The richness, diversity and evenness of fungal communities were significantly higher in organic than conventional plots. The cropping system also affected the composition of grapevine foliar fungal communities significantly. Contrary to our expectations, microbial networks were less sensitive to changes in agricultural practices than microbial communities, confirming that NGB should not only consider network-level properties but also community-level properties. Moreover, we found that microbial networks lacked replicability within a cropping system but that consensus networks, built from several network replicates, could generate relevant hypotheses of microbial interactions. As things stand, community-level properties appear to be a more reliable and statistically powerful monitoring option than network-level properties. Future developments, especially in network inference methods, are likely to challenge our findings and help to improve the monitoring of the ecosystem services provided by the plant microbiota.

ecology↗