Behavioral metabolomics: how behavioral data can guide metabolomics research on neuropsychiatric disorders

• Published in: Metabolomics 2023-08, Vol.19 (8), p.69-69, Article 69
• Main Authors: van de Wetering, Ross, Vorster, Jan A., Geyrhofer, Sophie, Harvey, Joanne E., Keyzers, Robert A., Schenk, Susan
• Format: Article
• Language: English
• Subjects: Animals; Behavior; Biochemistry; Biomedical and Life Sciences; Biomedicine; Cell Biology; Developmental Biology; Dopamine - metabolism; Drug abuse; Ecstasy; Life Sciences; Liquid chromatography; Male; Mass spectroscopy; MDMA; Mental disorders; Metabolites; Metabolomics; Metabolomics - methods; Microdialysis; Molecular Medicine; N-Methyl-3,4-methylenedioxyamphetamine - metabolism; N-Methyl-3,4-methylenedioxyamphetamine - pharmacology; Neostriatum; Norepinephrine; Original; Original Article; Phenotypes; Rats; Rats, Sprague-Dawley; Serotonin; Substance use
• ISSN: 1573-3890; 1573-3882; 1573-3890
• DOI: 10.1007/s11306-023-02034-6

Introduction Metabolomics produces vast quantities of data but determining which metabolites are the most relevant to the disease or disorder of interest can be challenging. Objectives This study sought to demonstrate how behavioral models of psychiatric disorders can be combined with metabolomics research to overcome this limitation. Methods We designed a preclinical, untargeted metabolomics procedure, that focuses on the determination of central metabolites relevant to substance use disorders that are (a) associated with changes in behavior produced by acute drug exposure and (b) impacted by repeated drug exposure. Untargeted metabolomics analysis was carried out on liquid chromatography-mass spectrometry data obtained from 336 microdialysis samples. Samples were collected from the medial striatum of male Sprague-Dawley ( N  = 21) rats whilst behavioral data were simultaneously collected as part of a (±)-3,4-methylenedioxymethamphetamine (MDMA)-induced behavioral sensitization experiment. Analysis was conducted by orthogonal partial least squares, where the Y variable was the behavioral data, and the X variables were the relative concentrations of the 737 detected features. Results MDMA and its derivatives, serotonin, and several dopamine/norepinephrine metabolites were the greatest predictors of acute MDMA-produced behavior. Subsequent univariate analyses showed that repeated MDMA exposure produced significant changes in MDMA metabolism, which may contribute to the increased abuse liability of the drug as a function of repeated exposure. Conclusion These findings highlight how the inclusion of behavioral data can guide metabolomics data analysis and increase the relevance of the results to the phenotype of interest.