To capture the functional diversity of microbiota, one must identify metabolic functions and species of interest within hundreds or thousands of microorganisms. We present Metage2Metabo (M2M) a resource that meets the need for de novo functional screening of genome-scale metabolic networks (GSMNs) at the scale of a metagenome, and the identification of critical species with respect to metabolic cooperation. M2M comprises a flexible pipeline for the characterisation of individual metabolisms and collective metabolic complementarity. In addition, M2M identifies key species, that are meaningful members of the community for functions of interest. We demonstrate that M2M is applicable to collections of genomes as well as metagenome-assembled genomes, permits an efficient GSMN reconstruction with Pathway Tools, and assesses the cooperation potential between species. M2M identifies key organisms by reducing the complexity of a large-scale microbiota into minimal communities with equivalent properties, suitable for further analyses.

The contribution of surrounding plant microbiota to disease development has led to the 'pathobiome' concept, which represents the interaction between the pathogen, the host plant and the associated biotic microbial community, resulting or not in plant disease. The aim herein is to understand how the soil microbial environment may influence the functions of a pathogen and its pathogenesis, and the molecular response of the plant to the infection, with a dual-RNAseq transcriptomics approach. We address this question using Brassica napus and Plasmodiophora brassicae, the pathogen responsible for clubroot. A time-course experiment was conducted to study interactions between P. brassicae, two B. napus genotypes and three soils harbouring high, medium or low microbiota diversities and levels of richness. The soil microbial diversity levels had an impact on disease development (symptom levels and pathogen quantity). The P. brassicae and B. napus transcriptional patterns were modulated by these microbial diversities, these modulations being dependent on the host genotype plant and the kinetic time. The functional analysis of gene expressions allowed the identification of pathogen and plant host functions potentially involved in the change of plant disease level, such as pathogenicity-related genes (NUDIX effector) in P. brassicae and plant defence-related genes (glucosinolate metabolism) in B. napus.