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

Combining behavioral models with metabolomics can help identify which metabolites are most relevant to substance use disorders. In a preclinical experiment, untargeted metabolomics was performed on 336 microdialysis samples from the medial striatum of 21 male Sprague-Dawley rats during an MDMA-induced behavioral sensitization study. Orthogonal partial least squares analysis, using behavioral data as the Y variable and relative concentrations of 737 detected features as X variables, revealed that MDMA and its derivatives, serotonin, and several dopamine/norepinephrine metabolites were the strongest predictors of acute MDMA-produced behavior. Repeated MDMA exposure significantly altered MDMA metabolism, which may contribute to increased abuse liability with repeated use. Including behavioral data guides metabolomics analysis and enhances relevance to the phenotype of interest.

Study at a glance

Abstract

Metabolomics produces vast quantities of data but determining which metabolites are the most relevant to the disease or disorder of interest can be challenging. This study sought to demonstrate how behavioral models of psychiatric disorders can be combined with metabolomics research to overcome this limitation. 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. 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. 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.