期刊
METABOLITES
卷 10, 期 4, 页码 -出版社
MDPI
DOI: 10.3390/metabo10040160
关键词
metabolomics; metabolite annotation; enzyme promiscuity; extended metabolic models
资金
- National Institute of General Medical Sciences of the National Institutes of Health [R01GM132391]
- National Science Foundation [1909536]
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [1909536] Funding Source: National Science Foundation
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 extended metabolic model filtering (EMMF), that aims to engineer a candidate set, a listing of putative chemical identities to be used during annotation, through an extended 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. EMMF aims to strike a balance between discovering previously uncharacterized metabolites and the computational burden of annotation. EMMF 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 EMMF are not catalogued in PubChem. For the CHO cell, we experimentally confirmed the presence of 4-hydroxyphenyllactate, a metabolite predicted by EMMF that has not been previously documented as part of the CHO cell metabolic model.
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