Radiation Biodosimetry Using Gene Expression Signatures

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Our studies to date have laid the groundwork for applied gene expression biodosimetry, focusing on signature development for whole-body high dose rate external photon exposure. Other types of radiation exposures, including partial-body exposure, internal emitters, low dose rate, and neutron exposure, will also impact triage needs, and may produce distinct responses, or variations in the dosimetric signatures already identified. As estimates of dose provide only a general idea ofthe radiation injury expected across a population, it will also be important to develop signatures that may provide a more accurate prediction of radiation injury response and outcome on an individual basis. Project 2 will use a functional genomics approach to develop refined gene expression signatures of radiation exposure and dose addressing the two main renewal themes: first, the impact of different radiation modalities (partial-body exposure, internal emitters, low dose rate, and neutron exposure), and second, prediction of individual radiation sensitivity. Microarray analysis will be applied to human and murine samples to build upon the predictive signatures we have developed in the first funding period of this grant and to better adapt them to realistic radiation exposure scenarios. Mouse models will also be used to nvestigate the mechanistic underpinnings ofthe gene expression signatures that predict radiation dose and sensitivity. Project 2 will be tightly integrated with Projects 1 and 3 through the Irradiation Core (Core C), the Informatics Core (Core E), and through a sample sharing approach using both human blood irradiated ex vivo and in vivo irradiated mice. This sample sharing approach will also help to enable development by the Informatics Core of integrative analysis approaches spanning all three Projects and using data from the microRNA, mRNA, metabolomic, and cellular levels. Such an integrative approach will help provide mechanistic insight into the underpinnings of both transcriptomic and metabolomic signatures, as well as suggesting the best combinations of high-throughput biodosimetry assays to apply in specific practical scenarios.