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mSystems. 2019 Feb 12;4(1). pii: e00337-18. doi: 10.1128/mSystems.00337-18. eCollection 2019 Jan-Feb.

Evaluating Metagenomic Prediction of the Metaproteome in a 4.5-Year Study of a Patient with Crohn's Disease.

Author information

1
Department of Pharmacology, University of California, San Diego, San Diego, California, USA.
2
Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, California, USA.
3
Department of Pediatrics, and Department of Computer Science and Engineering, University of California, San Diego, San Diego, California, USA.
4
Center for Microbiome Innovation, University of California, San Diego, San Diego, California, USA.
5
Department of Family Medicine and Public Health, University of California, San Diego, San Diego, California, USA.
6
Department of Computer Science and Engineering, University of California, San Diego, San Diego, California, USA.
7
California Institute for Telecommunications and Information Technology, University of California, San Diego, San Diego, California, USA.

Abstract

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 using samples from a single patient with colonic Crohn's disease. Utilizing multiplexed quantitative proteomics and shotgun metagenomic sequencing of eight time points in technical triplicate, we quantified over 29,000 protein groups and 110,000 genes and compared them to five protein biomarkers of disease activity. Broad-scale observations were consistent between data types, including overall clustering by principal-coordinate 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 conserved taxonomic differences relevant to Crohn's disease, including a negative association of Faecalibacterium and a positive association of Escherichia with calprotectin. Despite concordant associations of genera, the specific genes correlated with these metrics were drastically different between metagenomic and metaproteomic data sets. This resulted in the generation of unique functional interpretations dependent on the data type, with metaproteome evidence for previously investigated mechanisms of dysbiosis. An example of one such mechanism was a connection between urease enzymes, amino acid metabolism, and the local inflammation state within the patient. This proof-of-concept approach prompts further investigation of the metaproteome and its relationship with the metagenome in biologically complex systems such as the microbiome. IMPORTANCE A majority of current microbiome research relies heavily on DNA analysis. However, as the field moves toward understanding the microbial functions related to healthy and disease states, it is critical to evaluate how changes in DNA relate to changes in proteins, which are functional units of the genome. This study tracked the abundance of genes and proteins as they fluctuated during various inflammatory states in a 4.5-year study of a patient with colonic Crohn's disease. Our results indicate that despite a low level of correlation, taxonomic associations were consistent in the two data types. While there was overlap of the data types, several associations were uniquely discovered by analyzing the metaproteome component. This case study provides unique and important insights into the fundamental relationship between the genes and proteins of a single individual's fecal microbiome associated with clinical consequences.

KEYWORDS:

colonic Crohn's disease; gut inflammation; inflammatory bowel disease; metagenomics; metaproteomics; microbiome; multiomics; proteomics; tandem mass tags; time series

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