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Abstract The gut microbiome plays a fundamental role in human health and disease. Individual variations in the microbiome and the corresponding functional implications are key considerations to enhance precision health and medicine. Metaproteomics has recently revealed protein expression that might be associated with human health and disease. Existing studies focused on either human proteins or bacterial proteins that can be identified from (meta)proteomics data sets, but not both. In this study, we examined the feasibility of identifying both human and bacterial proteins that are differentially expressed between healthy and diseased individuals from metaproteomics data sets. We further evaluated different strategies of using identified peptides and proteins for building predictive models. By leveraging existing metaproteomics data sets and a tool that we have developed for metaproteomics data analysis (MetaProD), we were able to derive both human and bacterial differentially expressed proteins that could serve as potential biomarkers for all diseases we studied. We also built predictive models using identified peptides and proteins as features for prediction of human diseases. Our results showed peptide-based identifications over protein-based ones often produce the most accurate models and that feature selection can offer improvements. Prediction accuracy could be further improved, in some cases, by including bacterial identifications, but missing data in bacterial identifications remains problematic. 

Host-microbiome interactions and the microbial community have broad impact in human health and diseases. Most microbiome based studies are performed at the genome level based on next-generation sequencing techniques, but metaproteomics is emerging as a powerful technique to study microbiome functional activity by characterizing the complex and dynamic composition of microbial proteins. We conducted a large-scale survey of human gut microbiome metaproteomic data to identify generalist species that are ubiquitously expressed across all samples and specialists that are highly expressed in a small subset of samples associated with a certain phenotype. We were able to utilize the metaproteomic mass spectrometry data to reveal the protein landscapes of these species, which enables the characterization of the expression levels of proteins of different functions and underlying regulatory mechanisms, such as operons. Finally, we were able to recover a large number of open reading frames (ORFs) with spectral support, which were missed by de novo protein-coding gene predictors. We showed that a majority of the rescued ORFs overlapped with de novo predicted protein-coding genes, but on opposite strands or in different frames. Together, these demonstrate applications of metaproteomics for the characterization of important gut bacterial species.