Automated identification and quantification of metabolites in human fecal extracts by nuclear magnetic resonance spectroscopy

Abstract We report the development of a software program, called MagMet‐F, that automates the processing and quantification of 1D 1 H NMR of human fecal extracts. To optimize the program, we identified 82 potential fecal metabolites using 1D 1 H NMR of six human fecal extracts using manual profiling and a literature review of known fecal metabolites. We acquired pure versions of those metabolites and then acquired their 1D 1 H NMR spectra at 700 MHz to generate a fecal metabolite spectral library for MagMet‐F. The fitting of these metabolites by MagMet‐F was iteratively optimized to replicate manual profiling. We validated MagMet‐F's automated profiling using a test set of six fecal extracts. It correctly identified 80% of the compounds and quantified those within <20% of the values determined by manual profiling using Chenomx. We also compared MagMet‐F's profiling performance to two other open‐access NMR profiling tools, Bayesil and Batman. MagMet‐F outperformed both. Bayesil repeatedly overestimated metabolite concentrations by 10% to 40% while Batman was unable to properly quantify any compounds and took 10–20× longer. We have implemented MagMet‐F as a freely accessible web server to enable automated, fast and convenient 1D 1 H NMR spectral profiling of fecal samples. MagMet‐F is available at https://www.magmet.ca .