This proposal combines our metabolomics and radiation-signaling expertise with the expertise of team members in instrumentation for rapid and cost-effective assessment of select metabolites. The overall goals are to develop a reliable database of radiation metabolomic biomarkers in humans from easily-accessible biofluids, and then to refine subset(s) that will allow assessment of select biomarkers with instrumentation that could provide the basis for application in clinical and potentially in-field scenarios. The propose study builds on an extensive track record of accomplishments in radiation metabolomics, as well as the use of approaches, particularly differential mobility spectrometry (DMS), that allows for high-throughput assessment of metabolite biomarkers of interest. In the case of radiation metabolomics, our laboratory and its collaborators have made major contributions in establishing this field using a modern liquid chromatography (LC) mass spectrometry (MS) approach in a variety of animal models as well as in human cells. We have shown in publications over the last several years that they are dose-dependent and timecourse-dependent responses in urine metabolomics profiles after doses of ionizing radiation (IR) that are a NIAID priority. Recently, we have shown that there is evidence for specificity in the IR response in vivo compared to another relevant stressor, which mimics the inflammatory response to sepsis. Our team has also published extensively on the development and refinement of DMS, which should allow for selective """"""""tuning"""""""" for metabolite biomarkers without cumbersome LC. We have demonstrated that DMS allows isolation of select metabolites so that they can then be detected with a simplified and miniature MS system. In the case of human biomarker development, we have a collection of biofluids from a large number of patients undergoing total body irradiation (TBI) and have already demonstrated significant metabolomic responses in urine after TBI.
Aim 1 will be to develop a robust metabolomic biomarker database for human exposure using our high-end laboratory LCMS approach. Since dose and timecourse sampling is limited in TBI patients, we will use our extensive on-going mouse model datasets, which are funded by a different mechanism, as well as non-human primate samples provided by collaborators to model a much wider range of exposures. In the case of radiation toxicity, we have exciting preliminary data demonstrating that toxicity and lethality, which occurs approximately 2 wk after irradiation, can be distinguished as early as 1 day after irradiation in mice.
Aim 2 will develop biomarker panels to distinguish IR biomarker signatures from other injury and disease processes. A modern bioinformatics pipeline, which includes novel in-house algorithms, has been developed to facilitate biomarker discovery. Having developed a robust IR dataset, we will then focus in aim 3 on development of convenient and cost-effective instrumentation that can provide the basis for use in clinical and ultimately in-field scenarios, and refine subsets of IR biomarkers that can be effectively measured with our approaches such as DMS-MS.
Modern metabolomics offers the opportunity to assess radiation exposure and toxicity using a small molecule approach in a manner analogous to instrumentation currently in use to detect traces of explosives. Having already established a dataset of metabolomics biomarkers in animal models and human cells, we now plan to develop a robust dataset of biomarkers for human exposures using high-end laboratory instrumentation, and then refine a subset that will allow assessment of select biomarkers with instrumentation that could be effectively used in clinical and potentially in-field scenarios. This will be carriedout in combination with instrument refinement with the long-term goal to provide the basis for a new class of instrumentation to measure easily accessible biofluids, such as urine and blood.
|Chen, Zhidan; Coy, Stephen L; Pannkuk, Evan L et al. (2016) Rapid and High-Throughput Detection and Quantitation of Radiation Biomarkers in Human and Nonhuman Primates by Differential Mobility Spectrometry-Mass Spectrometry. J Am Soc Mass Spectrom 27:1626-36|
|Mak, Tytus D; Tyburski, John B; Krausz, Kristopher W et al. (2015) Exposure to ionizing radiation reveals global dose- and time-dependent changes in the urinary metabolome of rat. Metabolomics 11:1082-1094|
|Laiakis, Evagelia C; Trani, Daniela; Moon, Bo-Hyun et al. (2015) Metabolomic profiling of urine samples from mice exposed to protons reveals radiation quality and dose specific differences. Radiat Res 183:382-90|
|Pannkuk, Evan L; Laiakis, Evagelia C; Authier, Simon et al. (2015) Global Metabolomic Identification of Long-Term Dose-Dependent Urinary Biomarkers in Nonhuman Primates Exposed to Ionizing Radiation. Radiat Res 184:121-33|
|Mak, Tytus D; Laiakis, Evagelia C; Goudarzi, Maryam et al. (2015) Selective paired ion contrast analysis: a novel algorithm for analyzing postprocessed LC-MS metabolomics data possessing high experimental noise. Anal Chem 87:3177-86|
|Goudarzi, Maryam; Mak, Tytus D; Chen, Congju et al. (2014) The effect of low dose rate on metabolomic response to radiation in mice. Radiat Environ Biophys 53:645-57|
|Gathungu, Rose M; Bird, Susan S; Sheldon, Diane P et al. (2014) Identification of metabolites from liquid chromatography-coulometric array detection profiling: gas chromatography-mass spectrometry and refractionation provide essential information orthogonal to LC-MS/microNMR. Anal Biochem 454:23-32|
|Laiakis, Evagelia C; Mak, Tytus D; Anizan, Sebastien et al. (2014) Development of a metabolomic radiation signature in urine from patients undergoing total body irradiation. Radiat Res 181:350-61|
|Kafle, Amol; Coy, Stephen L; Wong, Bryan M et al. (2014) Understanding gas phase modifier interactions in rapid analysis by differential mobility-tandem mass spectrometry. J Am Soc Mass Spectrom 25:1098-113|
|Laiakis, Evagelia C; Strassburg, Katrin; Bogumil, Ralf et al. (2014) Metabolic phenotyping reveals a lipid mediator response to ionizing radiation. J Proteome Res 13:4143-54|
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