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 ¹H NMR of human fecal extracts. To optimize the program, we identified 82 potential fecal metabolites using 1D ¹H 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 ¹H 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 ¹H NMR spectral profiling of fecal samples. MagMet-F is available at https://www.magmet.ca.