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The Metabolomic Approach Identifies a Biological Signature of Low-dose Chronic Exposure to Cesium 137

Authors :
Jean-Charles Martin
Stéphane Grison
Catherine Defoort
Line Grandcolas
Isabelle Dublineau
Patrick Gourmelon
Gaëlle Favé
Romain Bott
Maâmar Souidi
Nathalie Banzet
Elie Tourlonias
Eric Blanchardon
Institut de Radioprotection et de Sûreté Nucléaire (IRSN)
Nutrition, obésité et risque thrombotique (NORT)
Aix Marseille Université (AMU)-Institut National de la Recherche Agronomique (INRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)
Institut de Radioprotection et de Surete Nucleaire (IRSN)
Electricite de France (EDF)
Institut National de la Recherche Agronomique (INRA)-Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM)
ProdInra, Migration
Source :
Journal of Radiation Research, Journal of Radiation Research, Oxford University Press (OUP), 2012, 53 (1), pp.33-43. ⟨10.1269/jrr.11071⟩, Journal of Radiation Research, 2012, 53 (1), pp.33-43. ⟨10.1269/jrr.11071⟩, Journal of Radiation Research 1 (53), 33-43. (2012)
Publication Year :
2012
Publisher :
HAL CCSD, 2012.

Abstract

International audience; Reports have described apparent biological effects of Cs-137 (the most persistent dispersed radionuclide) irradiation in people living in Chernobyl-contaminated territory. The sensitive analytical technology described here should now help assess the relation of this contamination to the observed effects. A rat model chronically exposed to Cs-137 through drinking water was developed to identify biomarkers of radiation-induced metabolic disorders, and the biological impact was evaluated by a metabolomic approach that allowed us to detect several hundred metabolites in biofluids and assess their association with disease states. After collection of plasma and urine from contaminated and non-contaminated rats at the end of the 9-months contamination period, analysis with a LC-MS system detected 742 features in urine and 1309 in plasma. Biostatistical discriminant analysis extracted a subset of 26 metabolite signals (2 urinary, 4 plasma non-polar, and 19 plasma polar metabolites) that in combination were able to predict from 68 up to 94% of the contaminated rats, depending on the prediction method used, with a misclassification rate as low as 5.3%. The difference in this metabolic score between the contaminated and non-contaminated rats was highly significant (P = 0.019 after ANOVA cross-validation). In conclusion, our proof-of-principle study demonstrated for the first time the usefulness of a metabolomic approach for addressing biological effects of chronic low-dose contamination. We can conclude that a metabolomic signature discriminated Cs-137-contaminated from control animals in our model. Further validation is nevertheless required together with full annotation of the metabolic indicators.

Details

Language :
English
ISSN :
04493060 and 13499157
Database :
OpenAIRE
Journal :
Journal of Radiation Research, Journal of Radiation Research, Oxford University Press (OUP), 2012, 53 (1), pp.33-43. ⟨10.1269/jrr.11071⟩, Journal of Radiation Research, 2012, 53 (1), pp.33-43. ⟨10.1269/jrr.11071⟩, Journal of Radiation Research 1 (53), 33-43. (2012)
Accession number :
edsair.doi.dedup.....bd9abd86cbddc2d8b13e988ba7c07021
Full Text :
https://doi.org/10.1269/jrr.11071⟩