Biology subjects

Jousset, A.

Publications and source records attributed to Jousset, A..

14 recordsLinked to original sources

bioRxiv2026

eBiota: Designing microbial communities from large seed pools with desired function using rapid optimization and deep learning

Designing microbial communities to generate target products is crucial for biotechnology, agriculture, and disease treatment. However, rationally designing such communities from large seed pools has become a major challenge, as the rapidly expanding number of complete microbial genomes greatly expands the search space and sharply increases the required screening time and computational cost. Here, we introduce eBiota, a platform for ab initio design of microbial communities from a pool of 21,514 strains to generate target products. eBiota not only identifies optimal strain combinations but also simulates community behaviors, including microbial interactions and relative abundances. eBiota integrates three modules: CoreBFS, a graph-based search algorithm that rapidly screens for bacteria with complete metabolic pathways related to the target product; ProdFBA, an extended flux balance analysis that identifies microbial consortia with maximal production efficiency; and DeepCooc, a deep learning model trained on 23,323 microbiome samples across various environments to infer co-occurrence patterns. We validated eBiotas capabilities in microbial community design and production efficiency calculation using public microbiome datasets, ranging from single strains to six-member consortia. Further in vitro experiments involving 94 strains confirmed eBiotas ability to identify species that inhibit pathogen growth and to accurately model the relative abundances within complex microbial communities. As an initial digital twin, eBiota provides a powerful platform for the rational design of functional microbial communities, offering new opportunities for metabolic engineering and synthetic biology.

bioengineering↗

bioRxiv2026

KuafuPrimer: Machine learning empowers the design of 16S amplicon sequencing primers toward minimal bias for bacterial communities

Amplicon sequencing protocol targeting the 16S rRNA gene is a widely used and cost-effective method for exploring bacterial communities. However, its performance is often limited by primer bias arising from the arbitrary use of universal primers across diverse microbial communities and habitats. We propose KuafuPrimer to design the optimal 16S rRNA gene primers toward minimal bias for targeted bacterial communities, using few-shot machine learning to guide the primer design procedure based on a small number of samples. Simulations on 809 samples across 26 representative environments and habitats showed that KuafuPrimer-designed primers outperformed the universal primers in taxonomic accuracy, achieving an averaged 16.31% relative reduction in primer bias, with reductions up to 46.08% in plant samples. Notably, KuafuPrimer detected 29 rare and key taxa undetectable by the universal primers. Validation with 317 longitudinal gut microbiota samples demonstrated that KuafuPrimer-designed primers consistently outperformed the universal primers across temporal, individual, and cohort levels, with relative bias reductions of 5.03%, 3.53%, and 3.10%, respectively. Finally, in real PCR experiments on human gut samples from Clostridioides difficile-infected and healthy groups showed that polymerase chain reaction products using KuafuPrimer-designed primers correlated better with metagenomic data compared to the universal primers. More importantly, KuafuPrimer successfully detected Clostridioides difficile, the key pathogen missed by the universal primers, highlighting its potential for improving clinical diagnostics. In summary, KuafuPrimer provides a machine learning-based primer design strategy for targeted bacterial communities, with demonstrated utility in large-scale microbiome initiatives, longitudinal surveys and clinical diagnostics.

bioinformatics↗

bioRxiv2026

Bacterial endosymbionts enhance fungal virulence by disrupting the disease-suppressive rhizobiome

The mechanisms by which bacterial endosymbionts (bacteria living within fungal cells) enhance the fitness and virulence of fungal pathogens remain poorly understood. Here, we report that the tomato Fusarium wilt pathogen Fusarium oxysporum f. sp. lycopersici (FOL) hosts Achromobacter sp. endosymbionts that enhance fungal virulence. This virulence potentiation is partially dependent on interactions with the native rhizosphere microbiota. We show that bacterial endosymbiont-harboring FOL reshapes the rhizosphere bacterial community during pathogen infection and decreased the abundance of disease{square}suppressive bacteria, including Streptomyces sp. taxa. This inhibitory effect is mediated by bacterial endosymbiont{square}stimulated production of beauvericin (an antibacterial cyclic hexadepsipeptide) by FOL. Together, our findings reveal a tripartite interaction in which a fungal pathogen leverages its bacterial endosymbiont to weaken rhizosphere microbiome{square}based disease suppression by inhibiting plant-protective bacterial taxa. This work highlights how cross{square}kingdom symbioses can modulate pathogen ecology and virulence in soil environments.

microbiology↗

bioRxiv2025

LorMe: a streamlined and interoperable R framework for end-to-end microbiome analysis

Microbiome research is rapidly evolving accompanied by increasingly diverse analytical strategies and visualization formats. However, existing R-based tools remain fragmented, differ in data structures, and often require extensive package-specific pre-processing. This increases command-line complexity and limits both interoperability and reproducibility across packages. Here, we present LorMe (Lightweight One-line Resolving Microbial Ecology), an R package that provides a unified, interoperable, and user-friendly framework for microbiome data analysis. LorMe incorporates a standardized S4 object system with fully bidirectional compatibility with phyloseq and microeco, which enables seamless data exchange across major microbial analysis packages. A global configuration system ensures consistent parameter control and visualization standards, and a modular architecture supports both complete end-to-end workflows and flexible execution of individual analytical components. Based on these, LorMe further offers an one-command pipeline that conducts the full spectrum of microbial community analyses, including alpha and beta diversity, differential abundance testing, co-occurrence network inference, and meta-network construction, while archiving all intermediate objects and source data to ensure complete reproducibility. Demonstrations using sample datasets show that LorMe performs comprehensive analyses within minute, produces publication-ready outputs, and maintains methodological transparency. Application to a real rhizosphere dataset revealed biologically coherent patterns across diversity metrics, differential taxa, and network modules, highlighting the capacity of LorMe to support robust ecological interpretation. Collectively, LorMe provides a lightweight, extensible, and flexible solution that reduces technical barriers and enhances reproducibility in microbiome research. LorMe package is freely accessible from both CRAN (https://cran.r-project.org/web/packages/LorMe) and github (https://github.com/wangnq111/LorMe)

bioinformatics↗

bioRxiv2025

Pharmaceuticals residues lead to enrichment of Human, Animal, and Plant Pathogens in Wastewater

Wastewater forms a reservoir for diverse microbial pathogens, posing significant risks to public health and aquatic ecosystems. They further contain several pharmaceutical residues that can induce profound shifts in the wastewater microbiome. In this study, we assessed whether common non-antimicrobial pharmaceuticals such as pain killers and blood pressure regulators impact wastewater pathogen profiles. We spiked wastewater with caffeine (stimulant), atenolol (beta-blocker), paracetamol (analgesic), ibuprofen (anti-inflammatory) and enalapril (ACE-inhibitor), and monitored microbiome 16S rRNA gene profiles. We then matched species composition to a comprehensive database of human, animal, and plant pathogens. All pharmaceuticals significatively increased abundance and biodiversity for zoonotic, plant, and animal pathogens. These findings highlight that pharmaceutical contamination poses a biohazard risk by fostering pathogen growth. We call for a paired, continuous monitoring of chemical and biological pollutants and a more stringent removal of pharmaceutical residues from wastewater.

microbiology↗

bioRxiv2025

Spatiotemporal changes in soil properties predict plant health at continental scale

Abiotic and biotic soil properties are strong predictors of plant yield globally1-5, but they become unreliable over large areas when plant health is threatened by pathogen6-8. Here we present a novel approach to predict plant health based on spatiotemporal changes in soil chemical and biological properties. We first demonstrate that plant health and soil properties consistently respond to environmental change (organic fertilization) regardless of the soil type or geographical origin. Second, we experimentally show that trackable shifts in soil properties reliably explain soil suppressiveness to the Ralstonia solanacearum bacterial pathogen and that a scalable spatiotemporal model predicts plant health with 84% accuracy across multiple climatic zones and cropping systems. Our results suggest that this tight coupling between soil properties and plant health could facilitate the development of agricultural practices aimed at sustainably improving crop yields while safeguarding crop health.

ecology↗

bioRxiv2024

Substrate utilization and cross-feeding synergistically determine microbiome resistance to pathogen invasion

Understanding how microbiomes resist pathogen invasion remains a key challenge in natural ecosystems. Here, we combined genome-scale metabolic models with synthetic community experiments to unravel the mechanisms driving pathogen suppression. We developed curated genome-scale models for each strain, incorporating 48 common resource utilization profiles to fully capture their metabolic capacities. Trophic interactions inferred from models accurately predicted pathogen invasion outcomes, achieving an F1 score of 96% across 620 invasion tests involving diverse microbial communities and nutrient environments. Importantly, considering both substrate and metabolite features provided a more holistic understanding of pathogen suppression. In particular, cross-feeding metabolites within the native community emerged as crucial yet often overlooked predictors of community resistance, disproportionally favoring native species over invaders. This study lays the foundation for designing disease-resistant microbiomes, with broad implications for mitigating pathogen exposure in diverse environments.

ecology↗

bioRxiv2023

Pollutome complexity determines the removal of recalcitrant pharmaceuticals

Organic pollutants are an increasing threat for wildlife and humans. Managing their removal is however complicated by the difficulties in predicting degradation rates. In this work we demonstrate that the complexity of the pollutome, the set of co-existing contaminants, is a major driver of biodegradation. We built representative assemblages out of one to five common pharmaceuticals (caffeine, atenolol, paracetamol, ibuprofen, and enalapril) selected along a gradient of biodegradability. We followed their individual removal by wastewater microbial communities. The presence of multichemical background pollution was essential for the removal of recalcitrant molecules such as ibuprofen. Crucially, high order interactions between pollutants were a determinant, with the addition of new molecules particularly impacting assemblages of multiple compounds. We explain these interactions by shifts in the microbiome, with degradable molecules such as paracetamol enriching species and pathways involved in the removal of several organic molecules. We conclude that pollutants should be treated as part of a complex system, with emerging pollutants potentially showing cascading effects and offering leverage to promote bioremediation.

microbiology↗

bioRxiv2023

From sequence to ecology: siderophore-receptor coevolution algorithm predicts bacterial interactions in complex communities

Predicting bacterial social interactions from genome sequences is notoriously difficult. Here, we developed bioinformatic tools to predict whether secreted iron-scavenging siderophores stimulate or inhibit the growth of community members. Siderophores are chemically diverse and can be stimulatory or inhibitory depending on whether bacteria possess or lack corresponding uptake receptors. We focused on 1928 representative Pseudomonas genomes and developed a co-evolution algorithm to match all encoded siderophore synthetases to corresponding receptor gene groups with >90% accuracy based on experimental validation. We derived community-level iron interaction networks to show that selection for siderophore-mediated interactions differs across habitats and lifestyles. Specifically, dense networks of siderophore sharing and competition were observed among environmental (soil/water/plant) strains and non-pathogenic species, while only fragmented networks occurred among human-derived strains and pathogenic species. Altogether, our sequence-to-ecology approach empowers the analyses of social interactions among thousands of bacterial strains and uncovers ways for targeted intervention to microbial communities. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=87 SRC="FIGDIR/small/565711v3_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@60d907org.highwire.dtl.DTLVardef@48756eorg.highwire.dtl.DTLVardef@115aac1org.highwire.dtl.DTLVardef@17d76e5_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2023

From sequence to molecules: Feature sequence-based genome mining uncovers the hidden diversity of bacterial siderophore pathways

Microbial secondary metabolites are a rich source for pharmaceutical discoveries and play crucial ecological functions. While tools exist to identify secondary metabolite clusters in genomes, precise sequence-to-function mapping remains challenging because neither function nor substrate specificity of biosynthesis enzymes can accurately be predicted. Here we developed a knowledge-guided bioinformatic pipeline to solve these issues. We analyzed 1928 genomes of Pseudomonas bacteria and focused on iron-scavenging pyoverdines as model metabolites. Our pipeline predicted 188 chemically different pyoverdines with nearly 100% structural accuracy and the presence of 94 distinct receptor groups required for the uptake of iron-loaded pyoverdines. Our pipeline unveils an enormous yet overlooked diversity of siderophores (151 new structures) and receptors (91 new groups). Our approach, combining feature sequence with phylogenetic approaches, is extendable to other metabolites and microbial genera, and thus emerges as powerful tool to reconstruct bacterial secondary metabolism pathways based on sequence data. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=118 SRC="FIGDIR/small/564663v5_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@1dedad4org.highwire.dtl.DTLVardef@1f0a39eorg.highwire.dtl.DTLVardef@185e345org.highwire.dtl.DTLVardef@10beec6_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2022

Engineering ecologically complementary rhizosphere probiotics using consortia of specialized bacterial mutants

While bacterial diversity is beneficial for the functioning of rhizosphere microbiomes, multi-species bioinoculants often fail to promote plant growth. One potential reason for this is that competition between inoculated consortia members create conflicts for their survival and functioning. To circumvent this, we used transposon mutagenesis to increase the functional diversity within Bacillus amyloliquefaciens bacterial species and tested if we could improve plant growth-promotion by assembling consortia of closely related but functionally specialized mutants. While most insertion mutations were harmful, some improved strains plant growth-promotion traits without increasing antagonism between them. Crucially, plant growth-promotion could be improved by applying these specialist mutants as consortia, leading to clear positive relationships between consortia richness, plant root colonization and protection from bacterial wilt disease. Together, our results suggest that increasing intra-species diversity could be an effective way to increase probiotic consortia multifunctionality, leading to more stable plant growth-promotion throughout growth cycle via insurance effects.

ecology↗

bioRxiv2021

Introduction of probiotic bacterial consortia promotes plant growth via impacts on the resident rhizosphere microbiome

Plant growth depends on a range of functions provided by their associated rhizosphere microbiome, including nutrient mineralization, hormone co-regulation and pathogen suppression. Improving the ability of plant associated microbiome to deliver these functions is thus important for developing robust and sustainable crop production. However, it is yet unclear how beneficial effects of probiotic microbial inoculants can be optimised and how they are mediated. Here, we sought to enhance the tomato plant growth by targeted introduction of probiotic bacterial consortia consisting of up to eight plant-associated Pseudomonas strains. We found that the effect of probiotic consortium inoculation was richness-dependent: consortia that contained more Pseudomonas strains, reached higher densities in the tomato rhizosphere and had clearer beneficial effects on multiple plant growth characteristics. Crucially, these effects were best explained by changes in the resident community diversity, composition and increase in the relative abundance of initially rare taxa, instead of introduction of plant-beneficial traits into the existing community along with probiotic consortia. Together, our results suggest that beneficial effects of microbial introductions can be driven indirectly through effects on the diversity and composition of resident plant rhizosphere microbiome.

ecology↗

bioRxiv2020

Rapid evolution of bacterial mutualism in the plant rhizosphere

Even though beneficial plant-microbe interactions are commonly observed in nature, direct evidence for the evolution of bacterial mutualism in the rhizosphere remains elusive. Here we use experimental evolution to causally show that initially plant-antagonistic Pseudomonas protegens bacterium evolves into mutualists in the rhizosphere of Arabidopsis thaliana within six plant growth cycles (6 months). This evolutionary transition was accompanied with increased mutualist fitness via two mechanisms: i) improved competitiveness for root exudates and ii) enhanced capacity for activating the root-specific transcription factor gene MYB72, which triggers the production of plant-secreted scopoletin antimicrobial for which the mutualists evolved relatively higher tolerance to. Genetically, mutualism was predominantly associated with different mutations in the GacS/GacA two-component regulator system, which conferred high fitness benefits only in the presence of plants. Together, our results show that bacteria can rapidly evolve along the parasitism-mutualism continuum in the plant rhizosphere at an agriculturally relevant evolutionary timescale.

microbiology↗

bioRxiv2020

Experimental evolution-driven identification of Arabidopsis rhizosphere competence genes in Pseudomonas protegens

Beneficial plant root-associated microorganisms carry out a range of functions that are essential for plant performance. Establishment of a bacterium on plant roots, however, requires overcoming several challenges, including competition with neighboring microorganisms and host immunity. Forward and reverse genetics has led to the identification of mechanisms that are used by beneficial microorganisms to overcome these challenges such as the production of iron-chelating compounds, the formation of strong biofilms, or the concealment of characteristic microbial molecular patterns that trigger the host immune system. However, how such mechanisms arose from an evolutionary perspective is much less understood. To study bacterial adaptation in the rhizosphere, we employed experimental evolution to track the physiological and genetic dynamics of root-dwelling Pseudomonas protegens in the Arabidopsis thaliana rhizosphere under axenic conditions. This simplified binary one plant-one bacterium system allows for the amplification of key adaptive mechanisms for bacterial rhizosphere colonization. We identified 35 mutations, including single-nucleotide polymorphisms, insertions, and deletions, distributed over 28 genes. We found that mutations in genes encoding global regulators, and in genes for siderophore production, cell surface decoration, attachment, and motility accumulated in parallel, underlining that bacterial adaptation to the rhizosphere follows multiple strategies. Notably, we observed that motility increased in parallel across multiple independent evolutionary lines. Altogether these results underscore the strength of experimental evolution to identify key genes, pathways, and processes for bacterial rhizosphere colonization, and a methodology for the development of elite beneficial microorganisms with enhanced root-colonizing capacities that can support sustainable agriculture in the future. ImportanceBeneficial root-associated microorganisms carry out many functions that are essential for plant performance. Establishment of a bacterium on plant roots, however, requires overcoming many challenges. Previously, diverse mechanisms that are used by beneficial microorganisms to overcome these challenges were identified. However, how such mechanisms have developed from an evolutionary perspective is much less understood. Here, we employed experimental evolution to track the evolutionary dynamics of a root-dwelling pseudomonad on the root of Arabidopsis. We find that mutations in global regulators, as well as in genes for siderophore production, cell surface decoration, attachment, and motility accumulate in parallel, underlining these strategies for bacterial adaptation to the rhizosphere. We identified 35 mutations distributed over 28 genes. Altogether our results demonstrate the power of experimental evolution to identify key pathways for rhizosphere colonization and a methodology for the development of elite beneficial microorganisms that can support sustainable agriculture.

microbiology↗