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We analyzed for the first time the metabolic profile of Lebanese children affected by autistic disorders to compare this profile to other metabolomics studies and to identify the associated metabolic disturbances. Urine samples of 40 patients with Autism spectrum disorder (ASD) and 40 healthy matched controls were analyzed using nuclear magnetic resonance (NMR) and liquid chromatography coupled to high-resolution mass spectrometry (LC-MS). Multivariate analysis on analytical data fusion was conducted on the training set of 50 urine samples, and then validated with a test set of 30 samples, this repeated 10 times. The model was also evaluated using a receiver operating characteristic curve showing a specificity and a sensitivity of 86% and 80%, respectively. Among the most significant metabolites that contributed to the discrimination between ASD and controls, we confirmed the perturbations of tyrosine, 2-hydroxybutyrate, creatine and glutamate. We found new metabolites such as trigonelline, cysteic acid and guanine. We found metabolic perturbations including amino acids, carbohydrates and oxidative stress pathways which added value for the contribution of known metabolic disturbances in ASD observed in populations of other ethnic and geographic origins., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Objectives: Metabolomics is an emerging science based on diverse high throughput methods that are rapidly evolving to improve metabolic coverage of biological fluids and tissues. Technical progress has led researchers to combine several analytical methods without reporting the impact on metabolic coverage of such a strategy. The objective of our study was to develop and validate several analytical techniques (mass spectrometry coupled to gas or liquid chromatography and nuclear magnetic resonance) for the metabolomic analysis of small muscle samples and evaluate the impact of combining methods for more exhaustive metabolite covering., Design and Methods: We evaluated the muscle metabolome from the same pool of mouse muscle samples after 2 metabolite extraction protocols. Four analytical methods were used: targeted flow injection analysis coupled with mass spectrometry (FIA-MS/MS), gas chromatography coupled with mass spectrometry (GC-MS), liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS), and nuclear magnetic resonance (NMR) analysis. We evaluated the global variability of each compound i.e., analytical (from quality controls) and extraction variability (from muscle extracts). We determined the best extraction method and we reported the common and distinct metabolites identified based on the number and identity of the compounds detected with low analytical variability (variation coefficient<30%) for each method. Finally, we assessed the coverage of muscle metabolic pathways obtained., Results: Methanol/chloroform/water and water/methanol were the best extraction solvent for muscle metabolome analysis by NMR and MS, respectively. We identified 38 metabolites by nuclear magnetic resonance, 37 by FIA-MS/MS, 18 by GC-MS, and 80 by LC-HRMS. The combination led us to identify a total of 132 metabolites with low variability partitioned into 58 metabolic pathways, such as amino acid, nitrogen, purine, and pyrimidine metabolism, and the citric acid cycle. This combination also showed that the contribution of GC-MS was low when used in combination with other mass spectrometry methods and nuclear magnetic resonance to explore muscle samples., Conclusion: This study reports the validation of several analytical methods, based on nuclear magnetic resonance and several mass spectrometry methods, to explore the muscle metabolome from a small amount of tissue, comparable to that obtained during a clinical trial. The combination of several techniques may be relevant for the exploration of muscle metabolism, with acceptable analytical variability and overlap between methods However, the difficult and time-consuming data pre-processing, processing, and statistical analysis steps do not justify systematically combining analytical methods., (Copyright © 2017 Elsevier B.V. All rights reserved.)

We developed a multi-platform approach for the metabolome exploration of rat brain tissue, using liquid chromatography coupled with mass spectrometry (LC-MS), nuclear magnetic resonance spectroscopy (NMR) and gas-chromatography coupled with mass spectrometry (GC-MS). The critical steps for metabolite exploration of cerebral tissues are tissue lysis and metabolites extraction. We first evaluated the impact of freeze-drying compared to wet tissue metabolites extraction using NMR and LC-MS with a reversed phase liquid chromatography. Then, we compared four metabolite extraction methods Based on the number of metabolites extracted, their intensity and their coefficient of variation (%CV), the most reproducible protocol (one-step extraction with acetonitrile on lyophilized material) was chosen to further evaluate the impact of sample mass on method performance (3, 6, and 9mg were essayed). GC-MS analysis was also investigated by analyzing four different methoximation/silylation derivatization combinations. The optimal analytical protocols were proposed to establish the reliability required to realize untargeted brain tissue metabolomics exploration. The most reliable workflow was then exemplified by analyzing three rat brain regions (cerebellum, frontal and parietal cortices, n=12) by 1 H NMR, LC-MS and GC-MS, allowing their clustering based on their metabolic profiles. We present here an example of development of methodology that should be done before running analysis campaigns., (Copyright © 2017 Elsevier B.V. All rights reserved.)