Validated High Resolution Mass Spectrometry-Based Approach for Metabolomic Fingerprinting of the Human Gut Phenotype

Fecal samples are an obvious choice for metabolomic approaches, since they can be obtained non-invasively and allow one to study the interactions between the gut microbiota and the host. The use of ultrahigh performance liquid chromatography hyphenated to Orbitrap high-resolution mass spectrometry (UHPLC-Orbitrap HRMS) in this field is unique. Hence, this study relied on Orbitrap HRMS to develop and validate a metabolic fingerprinting workflow for human feces and in vitro digestive fluids. After chemometric sample extraction optimization, an aqueous dilution appeared necessary to comply to the dynamic range of the MS. The method was proven fit-for-purpose through a validation procedure that monitored endogenous metabolites in quality control samples, which displayed in both matrices an excellent linearity (R-2 > 0.990), recoveries ranging from 93% to 105%, and precision with coefficients of variation (CVs) < 15%. Finally, feces from 10 healthy individuals and 13 patients diagnosed with inflammatory bowel disease were subjected to metabolomic fingerprinting. 9553 ions were detected, as well as differentiating profiles between Crohn's disease and ulcerative colitis by means of (orthogonal) partial least-square analysis ((O)PLS)-DA (discriminate analysis) models. Additionally, samples from the dynamic gastrointestinal tract simulator (SHIME (Simulator of the Human Intestinal Microbial Ecosystem) platform) were analyzed resulting in 6446 and 5010 ions for the proximal and distal colonic samples, respectively. Supplementing SHIME feed with antibiotics resulted in a significant shift (P < 0.05) of 27.7% of the metabolites from the proximal data set and 34.3% for the distal one. As a result, the presented fingerprinting approach provided predictive modeling of the gastrointestinal metabolome in vivo and in vitro, offering a window to reveal disease related biomarkers and potential insight into the mechanisms behind pathologies.