Urinary metabolic signatures and early triage of acute radiation exposure in rat model

Abstract

After a large-scale radiological accident, early-response biomarkers to assess radiation exposure over a broad dose range are not only the basis of rapid radiation triage, but are also the key to the rational use of limited medical resources and to the improvement of treatment efficiency. Because of its high throughput, rapid assays and minimally invasive sample collection, metabolomics has been applied to research into radiation exposure biomarkers in recent years. Due to the complexity of radiobiological effects, most of the potential biomarkers are both dose-dependent and time-dependent. In reality, it is very difficult to find a single biomarker that is both sensitive and specific in a given radiation exposure scenario. Therefore, a multi-parameters approach for radiation exposure assessment is more realistic in real nuclear accidents. In this study, untargeted metabolomic profiling based on gas chromatography-mass spectrometry (GC-MS) and targeted amino acid profiling based on LC-MS/MS were combined to investigate early urinary metabolite responses within 48 h post-exposure in a rat model. A few of the key early-response metabolites for radiation exposure were identified, which revealed the most relevant metabolic pathways. Furthermore, a panel of potential urinary biomarkers was selected through a multi-criteria approach and applied to early triage following irradiation. Our study suggests that it is feasible to use a multi-parameters approach to triage radiation damage, and the urinary excretion levels of the relevant metabolites provide insights into radiation damage and repair.

Early metabolic characterization of brain tissues after whole body radiation based on gas chromatography–mass spectrometry in a rat model

“…Since FA and MA are the key intermediates of the tricarboxylic acid (TCA) cycle and Pi is needed for ATP synthesis, both the level changes and the variations in metabolic correlation patterns in these energy‐metabolism relatives implied disturbed energy metabolism induced by ionizing radiation. Unlike the change patterns in the brain tissue, TCA cycle‐related metabolites were markedly decreased in plasma and urine samples from the rodents (Chen, Brenner, & Brown, ; Laiakis, Trani, Moon, Strawn, & Fornace, ; Lanz et al, ; Zhao et al, ). A partial cause of the different changing trends may be that urine or plasma metabolites reflect the whole body metabolic circulation state while the tissue metabolites only represent the local metabolic status.…”

“…Then 800 μL supernatant was dried by vacuum. After that, the dried sample was derivatized according to our previous report (Zhao et al, ). In order to prevent the tissue metabolites from degradation owing to activation of enzymatic catalytic activities or spontaneous reaction processes, ice‐cold chloroform–methanol–water was chosen as the extraction solvent.…”

“…The identification of metabolites in the rat brain tissue in GC–MS was performed in our previous work (Zhao et al, ). Briefly, the metabolites were identified by comparison of mass spectra with commercial libraries (NIST, Mainlib) and the available authentic standards.…”

“…Peak detection and alignment were adapted from our previous report (Zhao et al, ). The metabolic features and the corresponding response areas were imported into the on‐line MetaboAnalyst (http://www.metaboanalyst.ca) as CSV format.…”

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

“…In addition to its superior separation of isomers and small volatile compounds, the breadth of compounds available in reference databases far surpasses other metabolomic platforms [15]. As previous studies on radiation biofluid metabolomics using GC-MS platforms have been limited, including mice (plasma after low dose radiation exposure) [16], rats (urine time effects [17] and time/dose effects [18]; serum dose effects [19] to globally profile biofluids spanning a time course from pre-exposure to 60 d in an initial discovery phase approach. Identified metabolites implicate perturbation to amino acid, purine, and lipid metabolism.…”