Metabolic characterization in urine and hair from a rat model of methamphetamine self-administration using LC-QTOF-MS-based metabolomics

Abstract

Metabolomics is a powerful tool for understanding the comprehensive changes in metabolic responses associated with specific phenotypes. However, it has limitations with regards to data mining and metabolite assignment. Methamphetamine addiction is a critical issue owing to high potential for recurrence and lack of effective pharmacotherapy. However, the biological basis for methamphetamine addiction is not fully understood and no specific biomarkers have been identified. In the present study, the metabolic alterations in rat urine and hair by methamphetamine addiction were evaluated using a methamphetamine self-administration model with LC-QTOF-MS-based metabolomics. Firstly, an in-house database for 474 (MS mode) and 404 (MS/MS mode) metabolites, based on data on the multi-adduct formation and their fragmentation, was established using both positive and negative electrospray ionization (ESI) modes. Secondly, the metabolic characteristics of rat urine and hair were investigated before and after methamphetamine addiction. By multivariate statistical analysis, a clear clustering of samples collected before and after methamphetamine addiction was achieved. Fourteen (positive ESI) and thirty (negative ESI) ion features were altered in rat urine during methamphetamine addiction and extinction and those features were classified as potential markers for methamphetamine addiction and exposure. In rat hair, a total of 103 ion features for positive and 18 for negative ESI, including functional metabolites such as fatty acid amides and carnitines, were significantly changed. Hair was proposed as a more reliable diagnostic specimen for methamphetamine addiction. These findings provide a description of the metabolic alterations caused by methamphetamine addiction and will enable further studies to discovery of related diagnostic or prognostic markers.