Metabonomics reveals peripheral and central short-chain fatty acid and amino acid dysfunction in a naturally occurring depressive model of macaques.

PURPOSE: Depression is a complex psychiatric disorder. Various depressive rodent models are usually constructed based on different pathogenesis hypotheses. MATERIALS AND METHODS: Herein, using our previously established naturally occurring depressive (NOD) model in a non-human primate (cynomolgus monkey, Macaca fascularis), we performed metabolomics analysis of cerebrospinal fluid (CSF) from NOD female macaques (N=10) and age-and gender-matched healthy controls (HCs) (N=12). Multivariate statistical analysis was used to identify the differentially expressed metabolites between the two groups. Ingenuity Pathways Analysis and MetaboAnalyst were applied for predicted pathways and biological functions analysis. RESULTS: Totally, 37 metabolites responsible for discriminating the two groups were identified. The NOD macaques were mainly characterized by perturbations of fatty acid biosynthesis, ABC transport system, and amino acid metabolism (eg, aspartate, glycine, serine, and threonine metabolism). Interestingly, we found that eight altered CSF metabolites belonging to short-chain fatty acids and amino acids were also observed in the serum of NOD macaques (N=13 per group). CONCLUSION: Our findings suggest that peripheral and central short-chain fatty acids and amino acids are implicated in the onset of depression.

Diagnosis of major depressive disorder based on changes in multiple plasma neurotransmitters: a targeted metabolomics study.

Major depressive disorder (MDD) is a debilitating psychiatric illness. However, there is currently no objective laboratory-based diagnostic tests for this disorder. Although, perturbations in multiple neurotransmitter systems have been implicated in MDD, the biochemical changes underlying the disorder remain unclear, and a comprehensive global evaluation of neurotransmitters in MDD has not yet been performed. Here, using a GC-MS coupled with LC-MS/MS-based targeted metabolomics approach, we simultaneously quantified the levels of 19 plasma metabolites involved in GABAergic, catecholaminergic, and serotonergic neurotransmitter systems in 50 first-episode, antidepressant drug-naïve MDD subjects and 50 healthy controls to identify potential metabolite biomarkers for MDD (training set). Moreover, an independent sample cohort comprising 49 MDD patients, 30 bipolar disorder (BD) patients and 40 healthy controls (testing set) was further used to validate diagnostic generalizability and specificity of these candidate biomarkers. Among the 19 plasma neurotransmitter metabolites examined, nine were significantly changed in MDD subjects. These metabolites were mainly involved in GABAergic, catecholaminergic and serotonergic systems. The GABAergic and catecholaminergic had better diagnostic value than serotonergic pathway. A panel of four candidate plasma metabolite biomarkers (GABA, dopamine, tyramine, kynurenine) could distinguish MDD subjects from health controls with an AUC of 0.968 and 0.953 in the training and testing set, respectively. Furthermore, this panel distinguished MDD subjects from BD subjects with high accuracy. This study is the first to globally evaluate multiple neurotransmitters in MDD plasma. The altered plasma neurotransmitter metabolite profile has potential differential diagnostic value for MDD.

Biochemical effects of venlafaxine on astrocytes as revealed by 1H NMR-based metabolic profiling.

As a serotonin-norepinephrine reuptake inhibitor [SNRI], venlafaxine is one of the most commonly prescribed clinical antidepressants, with a broad range of antidepressant effects. Accumulating evidence shows that venlafaxine may target astrocytes to exert its antidepressant activity, although the underlying pharmacological mechanisms remained largely unknown. Here, we used a 1H nuclear magnetic resonance (NMR)-based metabonomics method coupled with multivariate statistical analysis to characterize the metabolic profiling of astrocytes treated with venlafaxine to explore the potential mechanism of its antidepressant effect. In total, 31 differential metabolites involved in energy, amino acid and lipid metabolism were identified. Ingenuity pathway analysis was used to identify the predicted pathways and biological functions with venlafaxine and fluoxetine. The most significantly altered network was "amino acid metabolism, cellular growth and proliferation", with a score above 20. Certain metabolites (lysine, tyrosine, glutamate, methionine, ethanolamine, fructose-6-phosphate, and phosphorylethanolamine) are involved in and play a central role in this network. Collectively, the biological effects of venlafaxine on astrocytes provide us with the further understanding of the mechanisms by which venlafaxine treats major depressive disorder.