Biological Filtering and Substrate Promiscuity Prediction for Annotating Untargeted Metabolomics

Abstract

Mass spectrometry coupled with chromatography separation techniques provides a powerful platform for untargeted metabolomics. Determining the chemical identities of detected compounds however remains a major challenge. Here, we present a novel computational workflow, termed Expanded Metabolic Model Annotation (EMMA), that aims to strike a balance between discovering previously uncharacterized metabolites and the computational burden of annotation. EMMA engineers a candidate set, a listing of putative chemical identities to be used during annotation, through an expanded metabolic model (EMM). An EMM includes not only canonical substrates and products of enzymes already cataloged in a database through a reference metabolic model, but also metabolites that can form due to substrate promiscuity. EMMA was applied to untargeted LC-MS data collected from cultures of Chinese hamster ovary (CHO) cells and murine cecal microbiota. EMM metabolites matched, on average, to 23.92% of measured masses, providing a > 7-fold increase in the candidate set size when compared to a reference metabolic model. Many metabolites suggested by EMMA are not catalogued in PubChem. For the CHO cell, we experimentally confirmed the presence of 4-hydroxy-phenyllactate, a metabolite predicted by EMMA that has not been previously identified as part of CHO cell metabolism.