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Talk by Dr. Niklas Mejhert

Title: "Standardized metabolomics to map mice, microbes and mankind"
Occasion: SFB - Special Seminar
Start: 09.05.2025 2:15 pm
Location: CellNanOs, 38/201

About the speaker: Prof. Dr. Oliver Fiehn conducts research at West Coast Metabolomics Center, University of California, Davis, CA, USA.

Most utilization of human diets occurs in the small intestine, which remains largely unstudied. Stool is an inadequate surrogate for intestinal microbiome or metabolome studies for these diseases. Peroral or endoscopic gut aspirates and mucosal biopsies are highly invasive, and are conducted in the fasted state. Instead, we used a novel non-invasive, ingestible sampling device to probe the spatiotemporal variation of upper intestinal luminal contents during routine daily digestion in 15 healthy subjects. To demonstrate the biological and clinical utility of the capsule sampling device, we profiled the microbiome and metabolites present in these samples. We analyzed 274 intestinal samples and 60 corresponding stool homogenates by metabolomics and 16S rRNA sequencing. 275 samples were taken from 15 healthy humans using four capsules after lunch and again after dinner over two subsequent days.

Using these samples, we showcase the computational infrastructure to make nontargeted metabolomic analyses standardized, validated, and useful for comparisons comparable across multiple organs and studies. The LC-BinBase environment continually monitors data acquisitions, including QC chart violations of upper and lower intervention limits and missed internal standards. LC-BinBase standardizes retention times into retention index values by normalizing to 42 internal standards for the BEH-amide (HILIC) assay, and 76 internal standards for the BEH-C18 lipidomics assay. LC-BinBase then generates ‘accurate mass_MS/MS_retention time’ triplets (Bins) that are mapped to unique Spectral Hash Keys. The public MassWiki sites includes open libraries such as MassBank.us and GNPS, while licensed libraries such as NIST23 are kept in-house. Normalized retention times are matched against machine learning predictions in Retip 2.0 software.

We identified 1,909 metabolites, including novel bile acids, in addition to numerous unknown compounds. Stool metabolomes were dramatically different than intestinal tract samples. Trends in chemical abundance were observed based on intestinal location, subject-specific metabolite expression, and diet-linked factors. As expected, essential nutrients and dietary lipids decreased along the intestinal tract, while other diet related metabolites increased along the intestine. Interestingly, bile acids showed both expected and novel variations. Two subjects with antibiotic treatments up to 6 months prior to collection showed markedly different metabolome patterns. Exposome metabolism could be readily followed in different modifications along the intestinal tract. From inter-individual variation, we annotated Blautia species as a candidate to be involved in FAHFA metabolism.

For the first time, the metabolome of the human intestine was analyzed to reveal subject specific chemical phenotypes.