Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products.


Journal

Natural product reports
ISSN: 1460-4752
Titre abrégé: Nat Prod Rep
Pays: England
ID NLM: 8502408

Informations de publication

Date de publication:
14 Feb 2024
Historique:
medline: 14 2 2024
pubmed: 14 2 2024
entrez: 14 2 2024
Statut: aheadofprint

Résumé

Covering: 1995 to 2023Advances in bioanalytical methods, particularly mass spectrometry, have provided valuable molecular insights into the mechanisms of life. Non-targeted metabolomics aims to detect and (relatively) quantify all observable small molecules present in a biological system. By comparing small molecule abundances between different conditions or timepoints in a biological system, researchers can generate new hypotheses and begin to understand causes of observed phenotypes. Functional metabolomics aims to investigate the functional roles of metabolites at the scale of the metabolome. However, most functional metabolomics studies rely on indirect measurements and correlation analyses, which leads to ambiguity in the precise definition of functional metabolomics. In contrast, the field of natural products has a history of identifying the structures and bioactivities of primary and specialized metabolites. Here, we propose to expand and reframe functional metabolomics by integrating concepts from the fields of natural products and chemical biology. We highlight emerging functional metabolomics approaches that shift the focus from correlation to physical interactions, and we discuss how this allows researchers to uncover causal relationships between molecules and phenotypes.

Identifiants

pubmed: 38351834
doi: 10.1039/d3np00050h
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Auteurs

Giovanni Andrea Vitale (GA)

CMFI Cluster of Excellence, Interfaculty Institute of Microbiology and Medicine, University of Tuebingen, Tuebingen, Germany.

Christian Geibel (C)

CMFI Cluster of Excellence, Interfaculty Institute of Microbiology and Medicine, University of Tuebingen, Tuebingen, Germany.

Vidit Minda (V)

Division of Pharmacology and Pharmaceutical Sciences, University of Missouri - Kansas City, Kansas City, USA.
Department of Chemistry and Biochemistry, University of Denver, Denver, USA. allegra.aron@du.edu.

Mingxun Wang (M)

Department of Computer Science, University of California Riverside, Riverside, USA. mingxun.wang@ucr.edu.

Allegra T Aron (AT)

Department of Chemistry and Biochemistry, University of Denver, Denver, USA. allegra.aron@du.edu.

Daniel Petras (D)

CMFI Cluster of Excellence, Interfaculty Institute of Microbiology and Medicine, University of Tuebingen, Tuebingen, Germany.
Department of Biochemistry, University of California Riverside, Riverside, USA. dpetras@ucr.edu.

Classifications MeSH