Biology subjects

Morgan, E. T.

Publications and source records attributed to Morgan, E. T..

2 recordsLinked to original sources

bioRxiv2024

Introducing identification probability for automated and transferable assessment of metabolite identification confidence in metabolomics and related studies

Methods for assessing compound identification confidence in metabolomics and related studies have been debated and actively researched for the past two decades. The earliest effort in 2007 focused primarily on mass spectrometry and nuclear magnetic resonance spectroscopy and resulted in four recommended levels of metabolite identification confidence - the Metabolite Standards Initiative (MSI) Levels. In 2014, the original MSI Levels were expanded to five levels (including two sublevels) to facilitate communication of compound identification confidence in high resolution mass spectrometry studies. Further refinement in identification levels have occurred, for example to accommodate use of ion mobility spectrometry in metabolomics workflows, and alternate approaches to communicate compound identification confidence also have been developed based on identification points schema. However, neither qualitative levels of identification confidence nor quantitative scoring systems address the degree of ambiguity in compound identifications in context of the chemical space being considered, are easily automated, or are transferable between analytical platforms. In this perspective, we propose that the metabolomics and related communities consider identification probability as an approach for automated and transferable assessment of compound identification and ambiguity in metabolomics and related studies. Identification probability is defined simply as 1/N, where N is the number of compounds in a reference library or chemical space that match to an experimentally measured molecule within user-defined measurement precision(s), for example mass measurement or retention time accuracy, etc. We demonstrate the utility of identification probability in an in silico analysis of multi-property reference libraries constructed from the Human Metabolome Database and computational property predictions, provide guidance to the community in transparent implementation of the concept, and invite the community to further evaluate this concept in parallel with their current preferred methods for assessing metabolite identification confidence.

biochemistry↗

bioRxiv2022

High-throughput Production of Diverse Xenobiotic Metabolites with P450-transduced Huh7 Hepatoma Cell Lines

Precision medicine requires methods to assess drug metabolism and distribution, including the identification of known and undocumented drug and chemical exposures as well as their metabolites. Recent work demonstrated high-throughput generation of xenobiotic metabolites with human liver S-9 fractions and detection in human plasma and urine. Here, we developed a panel of lentivirally transduced human hepatoma cell lines (Huh7) that stably express individual cytochrome P450 (P450) enzymes and generate P450-specific xenobiotic metabolites. We verified protein expression by immunoblotting and demonstrated that the cell lines generate P450-specific metabolites from probe substrates. To increase analytical throughput, we used a pooling strategy where 36 chemicals were grouped into 12 unique mixtures, each mixture containing 6 randomly selected compounds, and each compound being present in two separate mixtures. Each mixture of compounds was incubated with 8 different P450 cell lines with cell extracts analyzed at 0 and 2 h. Extracts were analyzed using liquid chromatography-high resolution mass spectrometry. Cell lines selectively metabolized test substrates, with pazopanib metabolized by CYP3A4 and CYP2C8 cells, bupropion by CYP2B6, and {beta}-naphthoflavone by CYP1A2 for example, showing substrate-enzyme specificity. Predicted metabolites from the remaining 33 compounds as well as many unidentified m/z features were detected. We also show that a specific metabolite generated by CYP2B6 cells, but not detected in the S9 system, was identified in human samples. Our data show that incubating these cell lines with chemical mixtures accelerated characterization of xenobiotic chemical space, while simultaneously allowing for the contributions of specific P450 enzymes to be identified. Significance statementHigh resolution mass spectrometry enables the identification of exposures to drugs and other xenobiotics in human samples. This paper demonstrates a workflow for high throughput production of xenobiotic metabolites using a panel of engineered cytochrome P450-expressing hepatoma cells. Active substrate-enzyme pairs can be identified using this workflow and generated metabolites can be used as surrogate standards to validate xenobiotic detection in humans.

pharmacology and toxicology↗