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1 August 2009 Radiation Metabolomics. 3. Biomarker Discovery in the Urine of Gamma-Irradiated Rats Using a Simplified Metabolomics Protocol of Gas Chromatography-Mass Spectrometry Combined with Random Forests Machine Learning Algorithm
Christian Lanz, Andrew D. Patterson, Josef Slavík, Kristopher W. Krausz, Monika Ledermann, Frank J. Gonzalez, Jeffrey R. Idle
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Abstract

Radiation metabolomics employing mass spectral technologies represents a plausible means of high-throughput minimally invasive radiation biodosimetry. A simplified metabolomics protocol is described that employs ubiquitous gas chromatography-mass spectrometry and open source software including random forests machine learning algorithm to uncover latent biomarkers of 3 Gy γ radiation in rats. Urine was collected from six male Wistar rats and six sham-irradiated controls for 7 days, 4 prior to irradiation and 3 after irradiation. Water and food consumption, urine volume, body weight, and sodium, potassium, calcium, chloride, phosphate and urea excretion showed major effects from exposure to γ radiation. The metabolomics protocol uncovered several urinary metabolites that were significantly up-regulated (glyoxylate, threonate, thymine, uracil, p-cresol) and down-regulated (citrate, 2-oxoglutarate, adipate, pimelate, suberate, azelaate) as a result of radiation exposure. Thymine and uracil were shown to derive largely from thymidine and 2′-deoxyuridine, which are known radiation biomarkers in the mouse. The radiation metabolomic phenotype in rats appeared to derive from oxidative stress and effects on kidney function. Gas chromatography-mass spectrometry is a promising platform on which to develop the field of radiation metabolomics further and to assist in the design of instrumentation for use in detecting biological consequences of environmental radiation release.

Christian Lanz, Andrew D. Patterson, Josef Slavík, Kristopher W. Krausz, Monika Ledermann, Frank J. Gonzalez, and Jeffrey R. Idle "Radiation Metabolomics. 3. Biomarker Discovery in the Urine of Gamma-Irradiated Rats Using a Simplified Metabolomics Protocol of Gas Chromatography-Mass Spectrometry Combined with Random Forests Machine Learning Algorithm," Radiation Research 172(2), 198-212, (1 August 2009). https://doi.org/10.1667/RR1796.1
Received: 18 March 2009; Accepted: 1 April 2009; Published: 1 August 2009
JOURNAL ARTICLE
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