4.6 Article

Temporal Effects on Radiation Responses in Nonhuman Primates: Identification of Biofluid Small Molecule Signatures by Gas Chromatography-Mass Spectrometry Metabolomics

Journal

METABOLITES
Volume 9, Issue 5, Pages -

Publisher

MDPI
DOI: 10.3390/metabo9050098

Keywords

biodosimetry; ionizing radiation; nonhuman primates; GC-MS; metabolomics; acute radiation syndrome; mass spectrometry

Funding

  1. pilot grant from the Opportunity Funds Management Core of the Centers for Medical Countermeasures against Radiation
  2. National Institute of Allergy and Infectious Diseases (NIAID), under HHS Contract [U19AI067773, HHSN272201500013I]
  3. NIAID grant [1RO1AI101798]

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Whole body exposure to ionizing radiation damages tissues leading to physical symptoms which contribute to acute radiation syndrome. Radiation biodosimetry aims to determine characteristic early biomarkers indicative of radiation exposure and is necessary for effective triage after an unanticipated radiological incident. Radiation metabolomics can address this aim by assessing metabolic perturbations following exposure. Gas chromatography-mass spectrometry (GC-MS) is a standardized platform ideal for compound identification. We performed GC time-of-flight MS for the global profiling of nonhuman primate urine and serum samples up to 60 d after a single 4 Gy -ray total body exposure. Multivariate statistical analysis showed higher group separation in urine vs. serum. We identified biofluid markers involved in amino acid, lipid, purine, and serotonin metabolism, some of which may indicate host microbiome dysbiosis. Sex differences were observed for amino acid fold changes in serum samples. Additionally, we explored mitochondrial dysfunction by tricarboxylic acid intermediate analysis in the first week with a GC tandem quadrupole MS platform. By adding this temporal component to our previous work exploring dose effects at 7 d, we observed the highest fold changes occurring at 3 d, returning closer to basal levels by 7 d. These results emphasize the utility of both MS-based metabolomics for biodosimetry and complementary analytical platforms for increased metabolome coverage.

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