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

Abstract

Whole body exposure to ionizing radiation (IR) (> 0.7 Gy) damages tissues leading to a range of physical symptoms contributing to acute radiation syndrome (ARS). Radiation biodosimetry aims to determine characteristic early biomarkers indicative of radiation exposure (generally at doses > 2 Gy) and is a necessity for effective triage in the event of an unanticipated radiological incident and emergency preparedness. Radiation metabolomics can address this aim by assessing metabolic perturbations following various emergency scenarios (e.g., elapsed time to medical care, absorbed dose, combined injury). Gas chromatography - mass spectrometry (GC-MS) is a standardized platform ideal for chromatographic separation, identification, and quantification of metabolites to discriminate molecular signatures that can be utilized in assessing radiation injury. We performed GC time-of-flight (TOF) MS for global profiling of nonhuman primate (NHP) urine and serum samples up to 60 d after a single 4 Gy {gamma}-ray total body exposure. Multivariate statistical analysis showed a higher separation of groups from urine signatures vs. serum signatures. 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 amino acid fold changes in serum samples. Additionally, we explored mitochondrial dysfunction by analysis of tricarboxylic acid (TCA) intermediates with a GC tandem quadrupole (TQ) MS platform in samples collected in a time course during the first week (1, 3, 5, and 7 d) after exposure. By adding this temporal component to our previous work exploring dose effects at a single time point of 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.