Large scale enzyme based xenobiotic identification for exposomics.
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ABSTRACT:
SUBMITTER: Liu KH
PROVIDER: S-EPMC8440538 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Exposomics methods are limited by low abundance of xenobiotic metabolites and lack of authentic standards, which precludes identification using solely mass spectrometry-based criteria. Here, we validate a method for enzymatic generation of xenobiotic metabolites for use with high-resolution mass spectrometry for chemical identification. Generated xenobiotic metabolites were used to confirm identities of respective metabolites in mice and human samples based upon accurate mass, retention time, and co-occurrence with related xenobiotic metabolites. The data shared here are high-resolution Orbitrap MS data for S9 incubations of 140 xenobiotic compounds with 0 and 24 hour time points for all reactions.
2021-02-21 | ST001715 | MetabolomicsWorkbench
Project description:The hepatic xenobiotic metabolizing enzyme flavin-containing monooxygenase 3 (FMO3) has been implicated in the development of cardiometabolic disease primarily due to its enzymatic product trimethylamine-N oxide (TMAO), which has recently been shown to be associated with multiple chronic diseases, including kidney and coronary artery diseases. Although TMAO may have causative roles as a pro-inflammatory mediator, the possibility for roles in metabolic disease for FMO3, irrespective of TMAO formation, does exist. We hypothesized that FMO3 may interact with other proteins known to be involved in cardiometabolic diseases and that modulating the expression of FMO3 may impact on these interaction partners. Here, we combine a co-immunoprecipitation strategy coupled to unbiased proteomic workflow to report a novel protein:protein interaction network for FMO3. We identified 51 FMO3 protein interaction partners, and through gene ontology analysis, have identified urea cycle as an enriched pathway. Using mice deficient in FMO3 on two separate backgrounds, we validated and further investigated expressional and functional associations between FMO3 and the identified urea cycle genes. FMO3-deficient mice showed hepatic overexpression of carbamoylphosphate synthetase (CPS1), the rate-limiting gene of urea cycle, and increased hepatic urea levels, especially in mice of FVB (Friend leukemia virus B strain) background. Finally, overexpression of FMO3 in murine AML12 hepatocytes led to downregulation of CPS1. Although there is past literature linking TMAO to urea cycle, this is the first published work showing that FMO3 and CPS1 may directly interact, implicating a role for FMO3 in chronic kidney disease irrespective of TMAO formation.
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Project description:Compound (or chemical) databases are an invaluable resource for many scientific disciplines. Exposomics researchers need to find and identify relevant chemicals that cover the entirety of potential (chemical and other) exposures over entire lifetimes. This daunting task, with over 100 million chemicals in the largest chemical databases, coupled with broadly acknowledged knowledge gaps in these resources, leaves researchers faced with too much-yet not enough-information at the same time to perform comprehensive exposomics research. Furthermore, the improvements in analytical technologies and computational mass spectrometry workflows coupled with the rapid growth in databases and increasing demand for high throughput "big data" services from the research community present significant challenges for both data hosts and workflow developers. This article explores how to reduce candidate search spaces in non-target small molecule identification workflows, while increasing content usability in the context of environmental and exposomics analyses, so as to profit from the increasing size and information content of large compound databases, while increasing efficiency at the same time. In this article, these methods are explored using PubChem, the NORMAN Network Suspect List Exchange and the in silico fragmentation approach MetFrag. A subset of the PubChem database relevant for exposomics, PubChemLite, is presented as a database resource that can be (and has been) integrated into current workflows for high resolution mass spectrometry. Benchmarking datasets from earlier publications are used to show how experimental knowledge and existing datasets can be used to detect and fill gaps in compound databases to progressively improve large resources such as PubChem, and topic-specific subsets such as PubChemLite. PubChemLite is a living collection, updating as annotation content in PubChem is updated, and exported to allow direct integration into existing workflows such as MetFrag. The source code and files necessary to recreate or adjust this are jointly hosted between the research parties (see data availability statement). This effort shows that enhancing the FAIRness (Findability, Accessibility, Interoperability and Reusability) of open resources can mutually enhance several resources for whole community benefit. The authors explicitly welcome additional community input on ideas for future developments.
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