Integrating metagenomics data into accurate mass stool metabolite identifications

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Project Summary Prof. Oliver Fiehn will work with his key persons, statistician Dr. Christopher Brydges, bioinformatics specialist Dr. Yuanyue Li and programmer Gert Wohlgemuth (all UC Davis) to generate new pipelines that integrate stool microbial metagenomics data and stool mass spectrometry data to better associate metabolites with disease progression in inflammatory bowel disease. We will work in consultation with Dr. Clary Clish (Broad Institute) who generated and deposited the data to the NIH Common Funds MetabolomicsWorkbench and the iHMP integrated human microbiome data. We will prioritize the enormous set of more than 80,000 yet unidentified stool metabolic signals using longitudinal disease progression over 1 year in subjects with inflammatory bowel disease, in comparison to healthy subjects. For this limited set of not more than 1,000 metabolites that will show significant association with health outcomes, we will use all available accurate mass MS/MS data and all stool microbiome data to obtain metabolite class information and likely metabolite structures or substructures. Dr. Clish will review our results and share new annotations that his group will release. To this end, we will develop the tools for metabolome predictions that have been built by the KBase collaborative research consortium over the past 10 years. KBase uses microbial genomic sequences (or even transcriptomics data) to automatically build metabolic pathways through enzyme predictions and gap filling. KBase also empowers utilization of microbial communities, modeling import and export of metabolites that other microbes can use as carbon sources. In consultation with Dr. Chris Henry (Argonne National Lab) from the KBase consortium, we will then build pipelines within the KBase environment to include mass spectrometry tools that the Fiehn laboratory has built through its past NIH funding, specifically formula predictions and substructure predictions (in MS-FINDER), retention time predictions (in Retip.app), hybrid-shift MS/MS similarity matching (in NIST search), and entropy similarity MS/MS matching (in MassBank.us). This specific project will have large impact on other, similar microbiome/metabolome projects that will be uploaded to the NIH Common Funds databases in the future. The project addresses the huge complexity in stool metagenomics and stool metabolomics data, and delivers key pipelines (called ‘narratives’ in KBase) that can be used by the research community at large.