Alexandre Jousset

This study shows bacterial endosymbionts in fungal pathogens enhance virulence by altering the rhizosphere microbiome and reducing disease-suppressive bacteria.

KuafuPrimer uses machine learning to design 16S rRNA gene primers with minimal bias, improving taxonomic accuracy and detecting rare taxa missed by universal primers.

eBiota designs microbial communities from large strain pools to generate target products using optimization and deep learning, showing accurate predictions and experimental validation.

This study shows that common pharmaceuticals increase pathogen abundance and biodiversity in wastewater, posing a biohazard risk.

LorMe provides a unified R framework for microbiome analysis, offering an end-to-end pipeline with reproducibility and interoperability across tools.

This study uses genome-scale models and synthetic communities to show that substrate utilization and cross-feeding synergistically determine microbiome resistance to pathogen invasion.

The paper develops a co-evolution algorithm to predict bacterial iron interaction networks based on siderophore synthetase-receptor gene pairs across Pseudomonas genomes, revealing habitat- and pathogen-specific interactions.

The paper predicts plant health using spatiotemporal changes in soil properties with 84% accuracy across multiple climates and cropping systems.

The paper develops a bioinformatic pipeline to predict bacterial siderophore structures and receptors from genome sequences, revealing significant diversity in pyoverdines and their receptors.

The paper shows that pollutome complexity drives biodegradation of recalcitrant pharmaceuticals through microbial community shifts and high-order interactions.

This study investigates how host re-infection affects pathogen founding population size, showing that increased temperature reduces founding population size by limiting subsequent re-infection contributions.