Evaluating Metagenomic Prediction of the Metaproteome in a 4.5 Year Study of a Crohn's Patient

Although genetic approaches are the standard in microbiome analysis, proteome-level information is largely absent. This discrepancy warrants a better understanding of the relationship between gene copy number and protein abundance, as this is crucial information for inferring protein level changes from metagenomic data. As it remains unknown how metaproteomic systems evolve during dynamic disease states, we leveraged a 4.5-year fecal time series of a single patient with colonic Crohn's disease. Utilizing multiplexed quantitative proteomics and shotgun metagenomic sequencing, we quantified over 29,000 protein groups and 110,000 genes and compare them to five protein biomarkers of disease activity. Results indicate that many broad-scale observations were consistent between data types, including overall clustering by Principal Coordinates Analysis and fluctuations in Gene Ontology terms related to Crohn's disease. Through linear regression, we determined genes and proteins fluctuating in conjunction with inflammatory metrics. We discovered many conserved taxonomic differences relevant to Crohn's disease including a negative association of Faecalibacterium and a positive association of Escherichia to Calprotectin. Despite concordant genera associations, the specific genes correlated with these metrics were drastically different between metagenomic and metaproteomic datasets. This resulted in the generation of unique functional interpretations dependent on the data type, and metaproteome data provided new evidence for previously investigated mechanisms of dysbiosis. One such example was a connection between urease enzymes, amino acid metabolism and local inflammation. This proof-of-concept approach prompts further investigation of the metaproteome and its relations to the metagenome in biologically complex systems such as the microbiome.