Title: Identifying radiation exposure biomarkers from mouse blood transcriptome

Authors: Daniel R. Hyduke; Evagelia C. Laiakis; Heng-Hong Li; Albert J. Fornace Jr.

Addresses: Department of Biochemistry and Molecular and Cellular Biology, and Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20057, USA; John B. Little Center for the Radiation Sciences and Environmental Health, Harvard School of Public Health, Boston, MA 02115, USA; Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA ' Department of Biochemistry and Molecular and Cellular Biology, and Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20057, USA ' Department of Biochemistry and Molecular and Cellular Biology, and Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20057, USA; John B. Little Center for the Radiation Sciences and Environmental Health, Harvard School of Public Health, Boston, MA 02115, USA ' Department of Biochemistry and Molecular and Cellular Biology, and Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20057, USA; John B. Little Center for the Radiation Sciences and Environmental Health, Harvard School of Public Health, Boston, MA 02115, USA; Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia

Abstract: Ionising radiation is a pleiotropic stress agent that may induce a variety of adverse effects. Molecular biomarker approaches possess promise to assess radiation exposure, however, the pleiotropic nature of ionising radiation induced transcriptional responses and the historically poor inter-laboratory performance of omics-derived biomarkers serve as barriers to identification of unequivocal biomarker sets. Here, we present a whole-genome survey of the murine transcriptomic response to physiologically relevant radiation doses, 2 Gy and 8 Gy. We used this dataset with the Random Forest algorithm to correctly classify independently generated data and to identify putative metabolite biomarkers for radiation exposure.

Keywords: ionising radiation; radiation exposure; radiation response biomarkers; murine blood transcriptome; reporter metabolites; inter-laboratory omics variation; random forests; radiation doses.

DOI: 10.1504/IJBRA.2013.054701

International Journal of Bioinformatics Research and Applications, 2013 Vol.9 No.4, pp.365 - 385

Received: 24 Jan 2011
Accepted: 21 Oct 2011
Published online: 18 Sep 2014 *

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