Peripheral tryptophan, serotonin, kynurenine, and their metabolites in major depression: A case-control study.

AIM: Tryptophan is the sole precursor of both peripherally and centrally produced serotonin and kynurenine. In depressed patients, tryptophan, serotonin, kynurenine, and their metabolite levels remain unclear. Therefore, peripheral tryptophan and metabolites of serotonin and kynurenine were investigated extensively in 173 patients suffering from a current major depressive episode (MDE) and compared to 214 healthy controls (HC). METHODS: Fasting plasma levels of 11 peripheral metabolites were quantified: tryptophan, serotonin pathway (serotonin, its precursor 5-hydroxytryptophan and its metabolite 5-hydroxyindoleacetic acid), and kynurenine pathway (kynurenine and six of its metabolites: anthranilic acid, kynurenic acid, nicotinamide, picolinic acid, xanthurenic acid, and 3-hydroxyanthranilic acid). RESULTS: Sixty (34.7%) patients were antidepressant-drug free. Tryptophan levels did not differ between MDE patients and HC. Serotonin and its precursor (5-hydroxytryptophan) levels were lower in MDE patients than in HC, whereas, its metabolite (5-hydroxyindoleacetic acid) levels were within the standard range. Kynurenine and four of its metabolites (kynurenic acid, nicotinamide, picolinic acid, and xanthurenic acid) were lower in MDE patients. CONCLUSION: Whilst the results of this study demonstrate an association between the metabolites studied and depression, conclusions about causality cannot be made. This study uses the largest ever sample of MDE patients, with an extensive assessment of peripheral tryptophan metabolism in plasma. These findings provide new insights into the peripheral signature of MDE. The reasons for these changes should be further investigated. These results might suggest new antidepressant therapeutic strategies.

Mass spectrometry-based metabolomics: accelerating the characterization of discriminating signals by combining statistical correlations and ultrahigh resolution.

A strategy combining autocorrelation matrices and ultrahigh resolution mass spectrometry (MS) was developed to optimize the characterization of discriminating ions highlighted by metabolomics. As an example, urine samples from rats treated with phenobarbital (PB) were analyzed by ultrahigh-pressure chromatography with two different eluting conditions coupled to time-of-flight mass spectrometric detection in both the positive and negative electrospray ionization modes. Multivariate data analyses were performed to highlight discriminating variables from several thousand detected signals: a few hundred signals were found to be affected by PB, whereas a few tenths of them were linked to its metabolism. Autocorrelation matrices were then applied to eliminate adduct and fragment ions. Finally, the characterization of the ions of interest was performed with ultrahigh-resolution mass spectrometry and sequential MS(n) experiments, by using a LC-LTQ-Orbitrap system. The use of different eluting conditions was shown to drastically impact on the chromatographic retention and ionization of compounds, thus providing a way to obtain more exhaustive metabolic fingerprints, whereas autocorrelation matrices allowed one to focus the identification work on the most relevant ions. By using such an approach, 14 PB metabolites were characterized in rat urines, some of which have not been reported in the literature.

Mass spectrometry for the identification of the discriminating signals from metabolomics: current status and future trends.

The metabolome is characterized by a large number of molecules exhibiting a high diversity of chemical structures and abundances, requiring complementary analytical platforms to reach its extensive coverage. Among them, atmospheric pressure ionization mass spectrometry (API-MS)-based technologies, and especially those using electrospray ionization are now very popular. In this context, this review deals with strengths, limitations and future trends in the identification of signals highlighted by API-MS-based metabolomics. It covers the identification process from the determination of the molecular mass and/or its elemental composition to the confirmation of structural hypotheses. Furthermore, some tools that were developed in order to address the MS signal redundancy and some approaches that could facilitate identification by improving the visualization and organization of complex data sets are also reported and discussed.

The human plasma-metabolome: Reference values in 800 French healthy volunteers; impact of cholesterol, gender and age.

Metabolomic approaches are increasingly used to identify new disease biomarkers, yet normal values of many plasma metabolites remain poorly defined. The aim of this study was to define the "normal" metabolome in healthy volunteers. We included 800 French volunteers aged between 18 and 86, equally distributed according to sex, free of any medication and considered healthy on the basis of their medical history, clinical examination and standard laboratory tests. We quantified 185 plasma metabolites, including amino acids, biogenic amines, acylcarnitines, phosphatidylcholines, sphingomyelins and hexose, using tandem mass spectrometry with the Biocrates AbsoluteIDQ p180 kit. Principal components analysis was applied to identify the main factors responsible for metabolome variability and orthogonal projection to latent structures analysis was employed to confirm the observed patterns and identify pattern-related metabolites. We established a plasma metabolite reference dataset for 144/185 metabolites. Total blood cholesterol, gender and age were identified as the principal factors explaining metabolome variability. High total blood cholesterol levels were associated with higher plasma sphingomyelins and phosphatidylcholines concentrations. Compared to women, men had higher concentrations of creatinine, branched-chain amino acids and lysophosphatidylcholines, and lower concentrations of sphingomyelins and phosphatidylcholines. Elderly healthy subjects had higher sphingomyelins and phosphatidylcholines plasma levels than young subjects. We established reference human metabolome values in a large and well-defined population of French healthy volunteers. This study provides an essential baseline for defining the "normal" metabolome and its main sources of variation.

Ultra performance liquid chromatography - mass spectrometry studies of formalin-induced alterations of human brain lipidome.

The development of 'omics' sciences offers new opportunities for the study of neurodegenerative diseases but increases at the same time the sample demand on brain banks that collect and store valuable human post-mortem tissue. Our study aims to evaluate in lipidomics the potential of formalin-fixed tissue compared with the cryopreservation method, considered as the gold standard for biochemical research. Two complementary liquid chromatography/mass spectrometry analytical platforms were used on the basis of hybrid quadrupole time-of-flight and triple quadrupole mass spectrometers. Untargeted fingerprinting, semitargeted profiling of specific lipid classes and targeted monitoring of lipid species were performed in formalin-fixed and cryopreserved samples to provide detailed information at the molecular level on the formalin-induced alterations of the brain tissue. In vitro incubations of lipid standards were also performed to further describe the degradation processes induced by formaldehyde. Phospholipid compounds were found to be extensively hydrolysed, whilst the sphingolipid ones were preserved. N-methylation and N-formylation of amine-containing phospholipids have also been evidenced. These findings show that the potential detrimental effect of formalin on the analytes of interest must be taken into account when analysing formalin-fixed samples.