Biology subjects

Frioux, C.

Publications and source records attributed to Frioux, C..

15 recordsLinked to original sources

bioRxiv2026

Hydrogen metabolism shapes gut microbiome into health-associated configurations

The human gut microbiome exhibits reproducible configurations, yet the ecological forces connecting them to health remain unclear. Here, using enterosignature-based stratification of 5,170 individuals from the Le French Gut cohort, we identified hydrogen disposal as a key determinant of population-scale microbiome configurations, independently replicated in a meta-cohort (n = 5,107). Microbial configurations followed a continuum of hydrogen recycling capacity and redox-associated functions, aligned with dietary patterns and health indicators. Methanogenesis-dominant partitions were associated with more favorable health profiles, whereas acetogenesis-enriched partitions exhibited features of low-grade inflammation, and increased digestive symptoms, perceived stress and antidepressant use. Experimental characterization of mucin profiles highlighted differences across partitions and alterations in Bacteroides-enriched configurations. Together, our findings support an ecological host-microbiome framework linking hydrogen metabolism, redox ecology, and host health, offering microbiome-informed targets for precision intervention. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=128 SRC="FIGDIR/small/722951v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@e7b8d1org.highwire.dtl.DTLVardef@116ab09org.highwire.dtl.DTLVardef@136f1f8org.highwire.dtl.DTLVardef@480097_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2026

Integrating metagenome-scale metabolic modelling and metabolomics to identify biochemical interactions in Microcystis phycospheres

Favoured by global changes, freshwater cyanobacterial harmful blooms generate major ecological, economical and public health challenges. Microcystis, one of the most widespread cyanobacterial genera, grows within a phycosphere where specialised interactions with its microbiome occur, and are suspected to influence bloom appearance and its potential toxicity. Using a combination of metagenomic, metabolomic and metabolic modelling, we characterised the phycospheres of twelve Microcystis strains isolated from a French pond. The distribution of metabolic reactions within Microcystis was consistent with their genospecies, whereas the metabolic landscape at the community level diverged from cyanobacterial phylogeny indicating functional decoupling between cyanobacteria and their associated microbiomes. Phycosphere-associated bacteria substantially expand the metabolic repertoire of the system, while maintaining functional redundancy within and across communities. On the other hand, metabolomic profiles were largely driven by cyanobacterial metabolic outputs. Metabolic modelling, together with the identification of toxic specialised metabolites produced by specific biosynthetic gene clusters, further highlighted differences in metabolic potential among phycospheres. Together, these findings deepen the understanding of Microcystis phycosphere functioning, demonstrate the value of multi-omics systems biology approaches, and underscore the ecological relevance of interspecies and inter-phycosphere metabolic interactions as a structuring process in bloom-associated microbiomes.

systems biology↗

bioRxiv2026

MetaNetMap: automatic mapping of metabolomic data onto metabolic networks

MotivationMetabolic networks represent genome-derived information about the biochemical reactions that cells are capable of performing. Mapping omic data onto these networks is important to refine model simulations. However, metabolomic data mapping remains very challenging due to difficulties in identifier reconciliation between annotation profiles and metabolic networks. ResultsMetaNetMap is a Python package designed to automatise the process of mapping metabolomic data onto metabolic networks. It includes several layers of identifier matching, the use of customisable databases, and molecular ontology integration to suggest the most matches between experimentally-identified metabolites and molecules defined in the network. We demonstrate its usability and the quality of automated mapping using two datasets. Availability and ImplementationMetaNetMap is an open source python package and publicly available under the GPLv3 licence. Source code is freely available on GitHub: https://github.com/coraliemuller/metanetmap. Data and code used in the application cases of this paper can be found at: https://doi.org/10.57745/ESFLR8.

systems biology↗

bioRxiv2025

NeighborFinder: an R package inferring local microbial network around a species of interest

1MotivationUnderstanding interactions from microbiome data is a central aspect in microbial ecology, as it provides insights into ecosystem stability, disease mechanisms, and can be used to design synthetic communities. Current network inference tools reconstruct global networks from co-abundance data, which means they capture the overall correlation structure for the entire set of taxa considered. These approaches are computationally intensive and suboptimal when the focus is on the local neighborhood of specific taxa of interest. ResultsWe introduce NeighborFinder, a local network inference method that enables the targeted discovery of direct neighbors around a species of interest. Using cross-validated multiple linear regression with[l] 1 penalty and microbiome-specific filters, our approach infers interpretable species-centered interactions, with F1 score [≥] 0.95 on simulated cohorts ranging from 250 to 1000 samples. This method is well-suited for large metagenomic datasets and is particularly valuable for exploratory studies where the targeted hypotheses outweigh the need for global community structure. The approach complements existing methods by being a biologically intuitive and computationally efficient. Availability and ImplementationThe R package is freely available on GitHub: https://github.com/metagenopolis/NeighborFinder. The data and source code used to calculate performances and produce the use case example in this paper can be found respectively at: https://doi.org/10.57745/UPITJ0 and https://doi.org/10.57745/HJLWW4. Supplementary informationSupplementary data are available

systems biology↗

bioRxiv2025

Accurate MAG reconstruction from complex soil microbiome through combined short- and HiFi long-reads metagenomics

BackgroundAdvances in high-fidelity long-read (HiFi-LR) sequencing technologies have opened new opportunities to explore the microbial genomic diversity of complex environments, such as soils. While short-read (SR) sequencing has enabled broad insights at the gene level, the limited read length constrains the reconstruction of complete genomes. HiFi-LRs, in contrast, improve assembly continuity and completeness, supporting higher-resolution taxonomic and functional annotation. However, the cost and relatively low throughput of HiFi-LR sequencing can limit genome recovery--particularly at the binning stage, where coverage depth is critical. In this study, we assess the benefit of combining HiFi-LR and SR sequencing for genome-resolved characterization of a soil microbiome. ResultsWe generated metagenomic data for a tunnel-cultivated soil sample using high coverage Illumina SRs as well as a combination of two HiFi-LR sequencing platforms (PacBio Sequel II and PacBio Revio). We found that assemblies generated from pooled HiFi-LR data alone exhibited higher completeness compared to those from ultra-deep SR data. Incorporating SR-derived coverage information for the binning of HiFi-LR contigs further increased both the number and quality of recovered metagenome-assembled genomes (MAGs), with a 24% increase in MAG recovery (313 vs. 252) and lower contamination levels (116 vs. 132 contaminated bins; mean 7.09 vs. 8.07), compared to using HiFi-LR data alone. This approach enabled the recovery of 61 additional MAGs, including 67% of low-abundance and taxonomically diverse lineages such as Archaea, representing 36 novel lineages. ConclusionOur results demonstrate that integrating HiFi-LR and SR sequencing markedly enhances genome recovery and binning accuracy in a highly diverse environment such as soil. The hybrid approach employed leverages the strengths of both technologies, leading to more contiguous assemblies and enabling the recovery of a broader range of genomes, including low-abundance and taxonomically diverse taxa. While factors such as sequencing depth, cost, and DNA quality remain important considerations, our study provides practical guidance for designing future soil metagenomics projects and underscores the value of adopting long-read technologies for more comprehensive characterization of complex microbial communities.

genomics↗

bioRxiv2025

Mapler: Assessing assembly quality in taxonomically rich metagenomes sequenced with HiFi reads

SummaryMetagenome assembly seeks to reconstruct the most high-quality genomes from sequencing data of microbial ecosystems. Despite technological advancements that facilitate assembly, such as Hi-Fi long reads, the process remains challenging in complex environmental samples consisting of hundreds to thousands of populations. Mapler is a metagenome assembly and evaluation pipeline with a focus on evaluating the quality of Hi-Fi long read metagenome assemblies. It incorporates several state-of-the-art metrics, as well as novel metrics assessing the diversity that remains uncaptured by the assembly process. Mapler facilitates the comparison of assembly strategies and helps identify methodological bottlenecks that hinder genome reconstruction. Availability and ImplementationMapler is open source and publicly available under the AGPL-3.0 licence at https://github.com/Nimauric/Mapler. Source code is implemented in Python and Bash as a Snakemake pipeline. Contactsnicolas.maurice@inria.fr, clemence.frioux@inria.fr. Supplementary informationAvailable online.

bioinformatics↗

bioRxiv2024

Modeling the emergent metabolic potential of soil microbiomes in Atacama landscapes

Soil microbiomes harbor complex communities and exhibit important ecological roles resulting from biochemical transformations and microbial interactions. Difficulties in characterizing the mechanisms and consequences of such interactions together with the multidimensionality of niches hinder our understanding of these ecosystems. The Atacama Desert is an extreme environment that includes unique combinations of stressful abiotic factors affecting microbial life. In particular, the Talabre Lejia transect has been proposed as a unique natural laboratory for understanding adaptation mechanisms. We propose a systems biology-based computational framework for the reconstruction and simulation of community-wide and genome-resolved metabolic models, in order to provide an overview of the metabolic potential as a proxy of how microbial communities are prepared to respond to the environment. Through a multifaceted approach that includes taxonomic and functional profiling of microbiomes, simulation of the metabolic potential, and multivariate analyses, we were able to identify key species and functions from six contrasting soil samples across the Talabre Lejia transect. We highlight the functional redundancy of whole metagenomes, which act as a gene reservoir from which site-specific functions emerge at the species level. We also link the physicochemistry from the puna and the lagoon samples to specific metabolic machineries that could be associated with their adaptation to the unique environmental conditions found there. We further provide an abstraction of community composition and structure for each site that allows to describe them as sensitive or resilient to environmental shifts through putative cooperation events. Our results show that the study of community-wide and genome-resolved metabolic potential, together with targeted modeling, may help to elucidate the role of producible metabolites in the adaptation of microbial communities. Our framework was designed to handle non-model microorganisms, making it suitable for any (meta)genomic dataset that includes nucleotide sequence data and high-quality environmental metadata for different samples.

systems biology↗

bioRxiv2024

Seed2LP: seed inference in metabolic networks for reverse ecology applications

A challenging problem in microbiology is to determine nutritional requirements of microorganisms and culture them, especially for the microbial dark matter detected solely with culture-independent methods. The latter foster an increasing amount of genomic sequences that can be explored with reverse ecology approaches to raise hypotheses on the corresponding populations. Building upon genome scale metabolic networks (GSMNs) obtained from genome annotations, metabolic models predict contextualised phenotypes using nutrient information. We developed the tool Seed2LP, addressing the inverse problem of predicting source nutrients, or seeds, from a GSMN and a metabolic objective. The originality of Seed2LP is its hybrid model, combining a scalable and discrete Boolean approximation of metabolic activity, with the numerically accurate flux balance analysis (FBA). Seed inference is highly customisable, with multiple search and solving modes, exploring the search space of external and internal metabolites combinations. Application to a benchmark of 107 curated GSMNs highlights the usefulness of a logic modelling method over a graph-based approach to predict seeds, and the relevance of hybrid solving to satisfy FBA constraints. Focusing on the dependency between metabolism and environment, Seed2LP is a computational support contributing to address the multifactorial challenge of culturing possibly uncultured microorganisms. Seed2LP is available on https://github.com/bioasp/seed2lp.

bioinformatics↗

bioRxiv2023

A digital twin of bacterial metabolism during cheese production

Cheese organoleptic properties result from complex metabolic processes occurring in microbial communities. A deeper understanding of such mechanisms makes it possible to improve both industrial production processes and end-product quality through the design of microbial consortia. In this work, we caracterise the metabolism of a three-species community consisting of Lactococcus lactis, Lactobacillus plantarum and Propionibacterium freudenreichii during a seven-week cheese production process. Using genome-scale metabolic models and omics data integration, we modeled and calibrated individual dynamics using monoculture experiments, and coupled these models to capture the metabolism of the community. This digital twin accurately predicted the dynamics of the community, enlightening the contribution of each microbial species to organoleptic compound production. Further metabolic exploration raised additional possible interactions between the bacterial species. This work provides a methodological framework for the prediction of community-wide metabolism and highlights the added-value of dynamic metabolic modeling for the comprehension of fermented food processes.

bioinformatics↗

bioRxiv2022

AuCoMe: inferring and comparing metabolisms across heterogeneous sets of annotated genomes

Comparative analysis of Genome-Scale Metabolic Networks (GSMNs) may yield important information on the biology, evolution, and adaptation of species. However, it is impeded by the high heterogeneity of the quality and completeness of structural and functional genome annotations, which may bias the results of such comparisons. To address this issue, we developed AuCoMe - a pipeline to automatically reconstruct homogeneous GSMNs from a heterogeneous set of annotated genomes without discarding available manual annotations. We tested AuCoMe with three datasets, one bacterial, one fungal, and one algal, and demonstrated that it successfully reduces technical biases while capturing the metabolic specificities of each organism. Our results also point out shared metabolic traits and divergence points among evolutionarily distant species, such as algae, underlining the potential of AuCoMe to accelerate the broad exploration of metabolic evolution across the tree of life.

systems biology↗

bioRxiv2022

EsMeCaTa: Estimating metabolic capabilities from taxonomic affiliations

PurposeMetabarcoding, and metagenomic sequencing have enabled the characterization of highly diverse environmental communities. The challenge of estimating the metabolic functions carried out by these communities has led to the development of several state-of-the-art methods, most of which are tailored to a specific gene marker. However, the increasing diversity of approaches resulting from advances in sequencing technologies drives the need for methods capable of handling heterogeneous microbial community data. Moreover, predictions often depend on their internal analysis pipelines and are influenced by the underlying databases, which link marker genes to specific functional annotations. This limits users ability to evaluate the quality of predictions by tracing internal data and processes. Finally, users are constrained by the specific annotations provided by these methods (e.g. EC numbers), limiting their ability to conduct further specialized analyses based on intermediate results. MethodsEsMeCaTa predicts consensus proteomes and their associated functions from taxonomic affiliations. A key feature of EsMeCaTa is its explainability and flexibility. To support the flexible integration of heterogeneous sequencing data, EsMeCaTa utilizes taxonomic affiliations obtained through analyses of diverse sequencing datasets. To provide insight into the knowledge available for each taxonomic affliation and to interpret the relevance of predicted functions, EsMeCaTa identifies a taxonomic rank within a given affliation that is suffciently represented by documented proteomes in the UniProt database. The proteins of the UniProt proteomes are clustered and filtered according to a threshold to create consensus proteomes. These consensus proteomes are automatically annotated with functional information (e.g., EC numbers, GO terms) but they are also designed to be used in further customized annotation workflows. Functional annotations are reported in a functional table, which can be enriched with taxon abundances to generate comprehensive functional profiles. ResultsEsMeCaTa predictions have been validated using multiple datasets and compared to a state-of-the-art method. Additionally, it was applied to a novel metabarcoding dataset from a methanogenic reactor, characterizing the microbial community and biogas production across different time points and intake condition. Our results demonstrate the link between biogas production, intake condition and the dynamics of the metabolic functions predicted by EsMeCaTa in the microbial communities.

bioinformatics↗

bioRxiv2021

Changing microbial activities during low salinity acclimation in the brown alga Ectocarpus subulatus

Ectocarpus subulatus is one of the few brown algae found in river habitats. Its ability to tolerate freshwater is due, in part, to its uncultivated microbiome. We investigated this phenomenon by modifying the microbiome of laboratory-grown E. subulatus using mild antibiotic treatments, which affected its ability to grow in low salinity. The acclimation to low salinity of fresh water-tolerant and intolerant holobionts was then compared. Salinity had a significant impact on bacterial gene expression as well as the expression of algae- and bacteria-associated viruses in all holobionts, albeit in different ways for each holobiont. On the other hand, gene expression of the algal host and metabolite profiles were affected almost exclusively in the fresh water intolerant holobiont. We found no evidence of bacterial protein production that would directly improve algal stress tolerance. However, we identified vitamin K synthesis as one possible bacterial service missing specifically in the fresh water-intolerant holobiont in low salinity. We also noticed an increase in bacterial transcriptomic activity and the induction of microbial genes involved in the biosynthesis of the autoinducer AI-1, a compound that regulates quorum sensing. This could have caused a shift in bacterial behavior in the intolerant holobiont, resulting in virulence or dysbiosis. Originality-Significance StatementThe importance of symbiotic microbes for the health and stress resistance of multicellular eukaryotes is widely acknowledged, but understanding the mechanisms underlying these interactions is challenging. They are especially difficult to separate in systems with one or more uncultivable components. We bridge the gap between fully controlled, cultivable model systems and purely environmental studies through the use of a multi-omics approach and metabolic models on experimentally modified "holobiont" systems. This allows us to generate two promising working hypotheses on the mechanisms by which uncultivated bacteria influence their brown algal hosts fresh water tolerance.

ecology↗

bioRxiv2019

Metabolic complementarity between a brown alga and associated cultivable bacteria provide indications of beneficial interactions

Brown algae are key components of marine ecosystems and live in association with bacteria that are essential for their growth and development. Ectocarpus siliculosus is a genetic and genomic model for brown algae. Here we use this model to start disentangling the complex interactions that may occur between the algal host and its associated bacteria. We report the genome-sequencing of 10 alga-associated bacteria and the genome-based reconstruction of their metabolic networks. The predicted metabolic capacities were then used to identify metabolic complementarities between the algal host and the bacteria, highlighting a range of potentially beneficial metabolite exchanges between them. These putative exchanges allowed us to predict consortia consisting of a subset of these ten bacteria that would best complement the algal metabolism. Finally, co-culture experiments were set up with a subset of these consortia to monitor algal growth as well as the presence of key algal metabolites. Although we did not fully control but only modify bacterial communities in our experiments, our data demonstrated a significant increase in algal growth in cultures inoculated with the selected consortia. In several cases, we also detected, in algal extracts, the presence of key metabolites predicted to become producible via an exchange of metabolites between the alga and the microbiome. Thus, although further methodological developments will be necessary to better control and understand microbial interactions in Ectocarpus, our data suggest that metabolic complementarity is a good indicator of beneficial metabolite exchanges in holobiont.

systems biology↗

bioRxiv2019

Metage2Metabo: metabolic complementarity applied to genomes of large-scale microbiotas for the identification of keystone species

Capturing the functional diversity of microbiotas entails identifying metabolic functions and species of interest within hundreds or thousands. Starting from genomes, a way to functionally analyse genetic information is to build metabolic networks. Yet, no method enables a functional screening of such a large number of metabolic networks nor the identification of critical species with respect to metabolic cooperation.\n\nMetage2Metabo (M2M) addresses scalability issues raised by metagenomics datasets to identify keystone, essential and alternative symbionts in large microbiotas communities with respect to individual metabolism and collective metabolic complementarity. Genome-scale metabolic networks for the community can be either provided by the user or very efficiently reconstructed from a large family of genomes thanks to a multi-processing solution to run the Pathway Tools software. The pipeline was applied to 1,520 genomes from the gut microbiota and 913 metagenome-assembled genomes of the rumen microbiota. Reconstruction of metabolic networks and subsequent metabolic analyses were performed in a reasonable time.\n\nM2M identifies keystone, essential and alternative organisms by reducing the complexity of a large-scale microbiota into minimal communities with equivalent properties, suitable for further analyses.

bioinformatics↗

bioRxiv2018

The genome of Ectocarpus subulatus highlights unique mechanisms for stress tolerance in brown algae

Brown algae are multicellular photosynthetic stramenopiles that colonize marine rocky shores worldwide. Ectocarpus sp. Ec32 has been established as a genomic model for brown algae. Here we present the genome and metabolic network of the closely related species, Ectocarpus subulatus Kutzing, which is characterized by high abiotic stress tolerance. Since their separation, both strains show new traces of viral sequences and the activity of large retrotransposons, which may also be related to the expansion of a family of chlorophyll-binding proteins. Further features suspected to contribute to stress tolerance include an expanded family of heat shock proteins, the reduction of genes involved in the production of halogenated defence compounds, and the presence of fewer cell wall polysaccharide-modifying enzymes. Overall, E. subulatus has mainly lost members of gene families down-regulated in low salinities, and conserved those that were up-regulated in the same condition. However, 96% of genes that differed between the two examined Ectocarpus species, as well as all genes under positive selection, were found to encode proteins of unknown function. This underlines the uniqueness of brown algal stress tolerance mechanisms as well as the significance of establishing E. subulatus as a comparative model for future functional studies.

genomics↗