Metabolomics applied to urine samples in childhood asthma; differentiation between asthma phenotypes and identification of relevant metabolites

Asthma is a heterogeneous disorder and one of the most common chronic childhood diseases. An improved characterization of asthma phenotypes would be invaluable for the understanding of the pathogenic mechanisms and the correct treatment of this disease. The aim of this pilot study was to explore the potential of metabolomics applied to urine samples in characterizing asthma, and to identify the most representative metabolites. Urine samples of 41 atopic asthmatic children (further subdivided in sub-groups according to the symptoms) and 12 age-matched controls were analyzed. Untargeted metabolic profiles were collected by LC-MS, and studied by multivariate analysis. The group of the asthmatics was differentiated by a model that proved to be uncorrelated with the chronic assumption of controller drugs by part of the patients. The distinct sub-groups were also appropriately modeled. Further investigations revealed a reduced excretion of urocanic acid, methyl-imidazoleacetic acid and of a metabolite resembling the structure of an Ile-Pro fragment in the asthmatics. The meaning of these findings was discussed and mainly correlated with the modulation of immunity in asthma. Metabolic profiles from urines have revealed the potential to characterize asthma and enabled the identification of metabolites which may have a role in the underlying inflammation.JRC.I.6-Systems toxicolog

The protective role of transferrin in Müller glial cells after iron-induced toxicity

PURPOSE: Transferrin (Tf) expression is enhanced by aging and inflammation in humans. We investigated the role of transferrin in glial protection. METHODS: We generated transgenic mice (Tg) carrying the complete human transferrin gene on a C57Bl/6J genetic background. We studied human (hTf) and mouse (mTf) transferrin localization in Tg and wild-type (WT) C57Bl/6J mice using immunochemistry with specific antibodies. Müller glial (MG) cells were cultured from explants and characterized using cellular retinaldehyde binding protein (CRALBP) and vimentin antibodies. They were further subcultured for study. We incubated cells with FeCl(3)-nitrilotriacetate to test for the iron-induced stress response; viability was determined by direct counting and measurement of lactate dehydrogenase (LDH) activity. Tf expression was determined by reverse transcriptase-quantitative PCR with human- or mouse-specific probes. hTf and mTf in the medium were assayed by ELISA or radioimmunoassay (RIA), respectively. RESULTS: mTf was mainly localized in retinal pigment epithelium and ganglion cell layers in retina sections of both mouse lines. hTf was abundant in MG cells. The distribution of mTf and hTf mRNA was consistent with these findings. mTf and hTf were secreted into the medium of MG cell primary cultures. Cells from Tg mice secreted hTf at a particularly high level. However, both WT and Tg cell cultures lose their ability to secrete Tf after a few passages. Tg MG cells secreting hTf were more resistant to iron-induced stress toxicity than those no longer secreted hTf. Similarly, exogenous human apo-Tf, but not human holo-Tf, conferred resistance to iron-induced stress on MG cells from WT mice. CONCLUSIONS: hTf localization in MG cells from Tg mice was reminiscent of that reported for aged human retina and age-related macular degeneration, both conditions associated with iron deposition. The role of hTf in protection against toxicity in Tg MG cells probably involves an adaptive mechanism developed in neural retina to control iron-induced stress

Critical Exponents of the pure and random-field Ising models

We show that current estimates of the critical exponents of the three-dimensional random-field Ising model are in agreement with the exponents of the pure Ising system in dimension 3 - theta where theta is the exponent that governs the hyperscaling violation in the random case.Comment: 9 pages, 4 encapsulated Postscript figures, REVTeX 3.

Calcul de stabilité des berges d'un canal : Application au réseau de canaux de la Sèvre Niortaise

Les berges de la Sèvre Niortaise sont régulièrement endommagées par des glissements circulaires. Des chantiers de restauration utilisant des soutènements par pieux, des géotextiles et des plantations ont déjà été réalisés. L'étendue des dégradations étant importante, l'optimisation des travaux est devenue nécessaire. Ainsi, nous travaillons à la réalisation d'un outil de prédiction des évolutions topographiques du canal et des berges. Celui-ci sert à la proposition de solutions de restauration. Le paramétrage du modèle s'appuie sur des essais mécaniques réalisés sur des échantillons de sol prélevés in situ. Une berge située à Damvix (85) a été modélisée. Cette étude a permis de déterminer la géométrie des surfaces de rupture potentielles et de tester l'influence des différents facteurs déstabilisants

Application of Multivariate Analysis, Support Vector Machines and Artificial Neural Network to the Processing of Nuclear Magnetic Resonance data of olive oil and fish oil samples for classification of geographic origin and discrimination between wild and farm fish.

Motivations Traceability and control of origin of food products are very important for the Consumers and for the European enforcement laboratories. For instance, The high added value of olive oil makes its control an important goal for EU producers and consumers. There is thus a need in developing analytical methods to ensure compliance with labeling, i.e.the control of geographical origin giving also support to the denominated protected origin (DPO) policy, and the determination of the genuineness of the product by the detection of eventual adulterations. Futhermore , EU regulations requires that origin, wild or farmed as well as geographic origin, of fish sold on the retail market be available to the consumers. Modern analytical techniques such as Nuclear Magnetic Resonance (NMR) provide very informative data on the composition in fatty acids and in other constituents of vegetable oils and fish oils. The combination of 1H NMR fingerprinting with multivariate analysis provides an original approach to study the profile of these oils in relation with geographical origin of olive oil or for discrimination between wild or farm origin for fish like salmons. Methods Concerning the experiment on fish oil, we used Support vector machines (SVMs) as a novel learning machine in the authentication of the origin of salmon. SVMs have the advantage of relying on a well-developed theory and have already proved to be successful in a number of practical applications. The method requires a very simple sample preparation of the fish oils extracted from the white muscle of salmon samples. Multivariate (chemometric) techniques are able to filter out the most relevant information from a spectrum, e.g. for a classification. In the experiment on olive oil samples, the principal component analysis (PCA) was carried out on the ~12,000 variables (chemical shifts) and four data sets were defined prior to PCA. Linear discriminant analysis (LDA) of the first 50 PC\u2019s was applied for classification of olive oil samples according to the geographic origin and year of production. The data analysis has been carried out with and without outliers, as well. Variable selection for LDA was achieved using: (i) the best five variables and (ii) an interactive forward stepwise manner. Results The use of SVMs for the discrimination between wild and farm salmon provides a new and effective method that eliminates the possibility of fraud through misrepresentation of the country of origin of salmon. The SVM has been able to distinguish correctly between the wild and farmed salmon; however ca. 5% of the country of origins were misclassified. Using LDA on the external validation sets the correct classification of olive oil varied between 47 and 75% (random selection), and between 35 and 92% (Kennard\u2013Stone selection (KS)) depending on geographic origin (country) and production years. A similar success rate could be achieved using partial least squares discriminant analysis (PLS DA). The success rate can be considerably improved by using probabilistic neural networks (PNN). Correct classification by PNN varied between 58 and 100% on the external validation sets. Other chemometric techniques, such as multiple linear regression, or generalized pair-wise correlation, did not give better results. Acknowledgements The authors are grateful to the Europeanproject COFAWS (European Commission DG RTD FP5 project GRD2\u20132000\u201331813) and to all the collaborators from the partners of this project (Eurofins Scientific (Nantes- France), North Atlantic Fisheries College (Scalloway, Shetland Islands - United Kingdom), SINTEF Fisheries and Aquaculture (Trondheim-Norway), Joint Research Centre (Ispra-Italy)) who contributed to the collection and preparation of fish samples, and for the authorization to exploit their NMR data in this work

Quality assurance and quality control processes:summary of a metabolomics community questionnaire

Introduction The Metabolomics Society Data Quality Task Group (DQTG) developed a questionnaire regarding quality assurance (QA) and quality control (QC) to provide baseline information about current QA and QC practices applied in the international metabolomics community. Objectives The DQTG has a long-term goal of promoting robust QA and QC in the metabolomics community through increased awareness via communication, outreach and education, and through the promotion of best working practices. An assessment of current QA and QC practices will serve as a foundation for future activities and development of appropriate guidelines. Method QA was defined as the set of procedures that are performed in advance of analysis of samples and that are used to improve data quality. QC was defined as the set of activities that a laboratory does during or immediately after analysis that are applied to demonstrate the quality of project data. A questionnaire was developed that included 70 questions covering demographic information, QA approaches and QC approaches and allowed all respondents to answer a subset or all of the questions. Result The DQTG questionnaire received 97 individual responses from 84 institutions in all fields of metabolomics covering NMR, LC-MS, GC-MS, and other analytical technologies. Conclusion There was a vast range of responses concerning the use of QA and QC approaches that indicated the limited availability of suitable training, lack of Standard Operating Procedures (SOPs) to review and make decisions on quality, and limited use of standard reference materials (SRMs) as QC materials. The DQTG QA/QC questionnaire has for the first time demonstrated that QA and QC usage is not uniform across metabolomics laboratories. Here we present recommendations on how to address the issues concerning QA and QC measurements and reporting in metabolomics