Metabolomic Characterization of Ovarian Epithelial Carcinomas by HRMAS-NMR Spectroscopy

Objectives. The objectives of the present study are to determine if a metabolomic study by HRMAS-NMR can (i) discriminate between different histological types of epithelial ovarian carcinomas and healthy ovarian tissue, (ii) generate statistical models capable of classifying borderline tumors and (iii) establish a potential relationship with patient's survival or response to chemotherapy. Methods. 36 human epithelial ovarian tumor biopsies and 3 healthy ovarian tissues were studied using 1H HRMAS NMR spectroscopy and multivariate statistical analysis. Results. The results presented in this study demonstrate that the three histological types of epithelial ovarian carcinomas present an effective metabolic pattern difference. Furthermore, a metabolic signature specific of serous (N-acetyl-aspartate) and mucinous (N-acetyl-lysine) carcinomas was found. The statistical models generated in this study are able to predict borderline tumors characterized by an intermediate metabolic pattern similar to the normal ovarian tissue. Finally and importantly, the statistical model of serous carcinomas provided good predictions of both patient's survival rates and the patient's response to chemotherapy. Conclusions. Despite the small number of samples used in this study, the results indicate that metabolomic analysis of intact tissues by HRMAS-NMR is a promising technique which might be applicable to the therapeutic management of patients

High-resolution magic angle spinning 1H nuclear magnetic resonance spectroscopy metabolomics of hyperfunctioning parathyroid glands

Background Primary hyperparathyroidism (PHPT) may be related to a single gland disease or multiglandular disease, which requires specific treatments. At present, an operation is the only curative treatment for PHPT. Currently, there are no biomarkers available to identify these 2 entities (single vs. multiple gland disease). The aims of the present study were to compare (1) the tissue metabolomics profiles between PHPT and renal hyperparathyroidism (secondary and tertiary) and (2) single gland disease with multiglandular disease in PHPT using metabolomics analysis. Methods The method used was 1H high-resolution magic angle spinning nuclear magnetic resonance spectroscopy. Forty-three samples from 32 patients suffering from hyperparathyroidism were included in this study. Results Significant differences in the metabolomics profile were assessed according to PHPT and renal hyperparathyroidism. A bicomponent orthogonal partial least square-discriminant analysis showed a clear distinction between PHPT and renal hyperparathyroidism (R2Y = 0.85, Q2 = 0.63). Interestingly, the model also distinguished single gland disease from multiglandular disease (R2Y = 0.96, Q2 = 0.55). A network analysis was also performed using the Algorithm to Determine Expected Metabolite Level Alterations Using Mutual Information (ADEMA). Single gland disease was accurately predicted by ADEMA and was associated with higher levels of phosphorylcholine, choline, glycerophosphocholine, fumarate, succinate, lactate, glucose, glutamine, and ascorbate compared with multiglandular disease. Conclusion This study shows for the first time that 1H high-resolution magic angle spinning nuclear magnetic resonance spectroscopy is a reliable and fast technique to distinguish single gland disease from multiglandular disease in patients with PHPT. The potential use of this method as an intraoperative tool requires specific further studies. © 2016 Elsevier Inc

A new specific succinate-glutamate metabolomic hallmark in SDHx-related paragangliomas

Paragangliomas (PGLs) are frequently associated with germline mutations in genes involved in energy metabolism. The purpose of the present study was to assess whether the tumor metabolomic profile of patients with hereditary and apparently sporadic PGLs enables the distinction of different subtypes of tumors. Twenty-eight unrelated patients with a histological diagnosis of PGLs were included in the present study. Twelve had germline mutations in SDHx genes (5 SDHB, 7 SDHD), 6 VHL, and 10 were apparently sporadic. Intact tumor samples from these patients (one per patient) were evaluated with (1)H high-resolution magic angle spinning (HRMAS) NMR spectroscopy. SDHx-related tumors were characterized by an increase in succinate levels in comparison to other tumor subtypes (p = 0.0001 vs VHL and p = 0.000003 vs apparently sporadic). Furthermore, we found significantly lower values of glutamate in SDHx-related tumors compared to other subtypes (p = 0.0007 vs VHL and p = 0.003 vs apparently sporadic). Moreover, SDHx-tumors also exhibited lower values of ATP/ADP/AMP (p = 0.01) compared to VHL. VHL tumors were found to have the highest values of glutathione (GSH) compared to other tumors. Based on 4 metabolites (succinate, glutamate, GSH, and ATP/ADP/AMP), tumors were accurately distinguished from the other ones on both 3- and 2-class PLS-DA models. The present study shows that HRMAS NMR spectroscopy is a very promising method for investigating the metabolomic profile of various PGLs. The present data suggest the existence of a specific succinate-glutamate hallmark of SDHx PGLs. The relevance of such a metabolomic hallmark is expected to be very useful in designing novel treatment options as well as improving the diagnosis and follow-up of these tumors, including metastatic ones

Investigation of the surfactants in CTAB-templated mesoporous silica by 1H HRMAS NMR

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Serum analysis by¹H Nuclear Magnetic Resonance spectroscopy: a new tool for distinguishing neuromyelitis optica from multiple sclerosis

Background:Neuromyelitis optica (NMO) and multiple sclerosis (MS), two inflammatory demyelinating diseases, are characterized by different therapeutic strategies. Currently, the only biological diagnostic tool available to distinguish NMO from MS is the specific serum autoantibody that targets aquaporin 4, but its sensitivity is low.Objective:To assess the diagnostic accuracy of metabolomic biomarker profiles in these two neurological conditions, compared to control patients.Methods:We acquired serum spectra (47 MS, 44 NMO and 42 controls) using proton nuclear magnetic resonance (1H-NMR) spectroscopy. We used multivariate pattern recognition analysis to identify disease-specific metabolic profiles.Results:The1H-NMR spectroscopic analysis evidenced two metabolites, originating probably from astrocytes, scyllo-inositol and acetate, as promising serum biomarkers of MS and NMO, respectively. In 87.8% of MS patients, scyllo-inositol increased 0.15 to 3-fold, compared to controls and in 74.3% of NMO patients, acetate increased 0.4 to 7-fold, compared to controls. Using these two metabolites simultaneously, we can discriminate MS versus NMO patients (sensitivity, 94.3%; specificity, 90.2%).Conclusion:This study demonstrates the potential of1H-NMR spectroscopy of serum as a novel, promising analytical tool to discriminate populations of patients affected by NMO or MS.</jats:sec