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.

Automatic Chemical Profiling of Wine by Proton Nuclear Magnetic Resonance Spectroscopy

“…Optimization of MagMet for Wine Profiling. After identifying 70 reliably measurable wine compounds through manual profiling, we then determined if our earlier versions of MagMet 37,38 could correctly process the wine NMR spectra. We found that the original MagMet could indeed process wine spectra but needed slight modifications.…”

“…Each wine sample was prepared for NMR analysis following the standard procedures for MagMet. 37,38 However, for wine samples, some steps may be best performed with some minor methodological alterations (detailed in the Results and Discussion section). Initially, 0.5 mL of each sample was filtered through a thoroughly prewashed Amicon 3 kDa MWCO centrifugal filter.…”

“…The algorithm, design and testing of the MagMet software was described previously. 37,38 MagMet is a 1D…”

“…We have demonstrated the application of MagMet to the automated analysis of human serum, plasma, 38 and fecal samples. 37 MagMet's data processing is robust and very efficient. It performs automatic Fourier transformation, phase correction, baseline optimization, chemical shift referencing, water signal removal, and peak picking.…”

Explainable AI to Facilitate Understanding of Neural Network-Based Metabolite Profiling Using NMR Spectroscopy

“…Traditionally, the manual quantification of analytes in NMR spectra has been a tedious, slow, user-dependent process requiring a trained spectroscopist with domain knowledge of the analytes. Semi-automated and automated software have been developed for metabolite profiling from NMR data (e.g., Chenomx, Magmet, and LipSpin) [ 5 , 6 ]; however, these techniques are slower and less scalable compared to processing using neural networks (NNs). Despite past approaches described in the literature [ 7 , 8 ] and the current surge of interest in artificial intelligence (AI), NNs have yet to gain traction in the realm of targeted NMR profiling.…”