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TrpNet: Understanding Tryptophan Metabolism across Gut Microbiome.
Lu, Yao; Chong, Jasmine; Shen, Shiqian; Chammas, Joey-Bahige; Chalifour, Lorraine; Xia, Jianguo.
  • Lu Y; Department of Microbiology and Immunology, McGill University, Montreal, QC H3A 2T5, Canada.
  • Chong J; Institute of Parasitology, McGill University, Montreal, QC H3A 2T5, Canada.
  • Shen S; MGH Center for Translational Pain Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02215, USA.
  • Chammas JB; Department of Medicine, McGill University, Montreal, QC H3A 2T5, Canada.
  • Chalifour L; Lady Davis Institute for Medical Research, Montreal, QC H3T 1E2, Canada.
  • Xia J; Department of Medicine, McGill University, Montreal, QC H3A 2T5, Canada.
Metabolites ; 12(1)2021 Dec 23.
Article en En | MEDLINE | ID: mdl-35050132
ABSTRACT
Crosstalk between the gut microbiome and the host plays an important role in animal development and health. Small compounds are key mediators in this host-gut microbiome dialogue. For instance, tryptophan metabolites, generated by biotransformation of tryptophan through complex host-microbiome co-metabolism can trigger immune, metabolic, and neuronal effects at local and distant sites. However, the origin of tryptophan metabolites and the underlying tryptophan metabolic pathway(s) are not well characterized in the current literature. A large number of the microbial contributors of tryptophan metabolism remain unknown, and there is a growing interest in predicting tryptophan metabolites for a given microbiome. Here, we introduce TrpNet, a comprehensive database and analytics platform dedicated to tryptophan metabolism within the context of host (human and mouse) and gut microbiome interactions. TrpNet contains data on tryptophan metabolism involving 130 reactions, 108 metabolites and 91 enzymes across 1246 human gut bacterial species and 88 mouse gut bacterial species. Users can browse, search, and highlight the tryptophan metabolic pathway, as well as predict tryptophan metabolites on the basis of a given taxonomy profile using a Bayesian logistic regression model. We validated our approach using two gut microbiome metabolomics studies and demonstrated that TrpNet was able to better predict alterations in in indole derivatives compared to other established methods.
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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Año: 2021 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Año: 2021 Tipo del documento: Article