Cellulose is the most abundant biopolymer on Earth. Optimising energy recovery from this renewable but recalcitrant material is a key issue. The metaproteome expressed by thermophilic communities during cellulose anaerobic digestion was investigated in microcosms. By multiplying the analytical replicates (65 protein fractions analysed by MS/MS) and relying solely on public protein databases, more than 500 non-redundant protein functions were identified. The taxonomic community structure as inferred from the metaproteomic data set was in good overall agreement with 16S rRNA gene tag pyrosequencing and fluorescent in situ hybridisation analyses. Numerous functions related to cellulose and hemicellulose hydrolysis and fermentation catalysed by bacteria related to Caldicellulosiruptor spp. and Clostridium thermocellum were retrieved, indicating their key role in the cellulose-degradation process and also suggesting their complementary action. Despite the abundance of acetate as a major fermentation product, key methanogenesis enzymes from the acetoclastic pathway were not detected. In contrast, enzymes from the hydrogenotrophic pathway affiliated to Methanothermobacter were almost exclusively identified for methanogenesis, suggesting a syntrophic acetate oxidation process coupled to hydrogenotrophic methanogenesis. Isotopic analyses confirmed the high dominance of the hydrogenotrophic methanogenesis. Very surprising was the identification of an abundant proteolytic activity from Coprothermobacter proteolyticus strains, probably acting as scavenger and/or predator performing proteolysis and fermentation. Metaproteomics thus appeared as an efficient tool to unravel and characterise metabolic networks as well as ecological interactions during methanisation bioprocesses. More generally, metaproteomics provides direct functional insights at a limited cost, and its attractiveness should increase in the future as sequence databases are growing exponentially.

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Simultaneous profiling of biospecimens using different technological platforms enables the study of many data types, encompassing microbial communities, omics, and meta-omics as well as clinical or chemistry variables. Reduction in costs now enables longitudinal or time course studies on the same biological material or system. The overall aim of such studies is to investigate relationships between these longitudinal measures in a holistic manner to further decipher the link between molecular mechanisms and microbial community structures, or host-microbiota interactions. However, analytical frameworks enabling an integrated analysis between microbial communities and other types of biological, clinical, or phenotypic data are still in their infancy. The challenges include few time points that may be unevenly spaced and unmatched between different data types, a small number of unique individual biospecimens, and high individual variability. Those challenges are further exacerbated by the inherent characteristics of microbial communities-derived data (e.g., sparse, compositional). We propose a generic data-driven framework to integrate different types of longitudinal data measured on the same biological specimens with microbial community data and select key temporal features with strong associations within the same sample group. The framework ranges from filtering and modeling to integration using smoothing splines and multivariate dimension reduction methods to address some of the analytical challenges of microbiome-derived data. We illustrate our framework on different types of multi-omics case studies in bioreactor experiments as well as human studies.

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