Dataset for dose and time-dependent transcriptional response to ionizing radiation exposure
Eric C. Rouchka,
Robert M. Flight,
Bridgitte H. Fasciotto,
Rosendo Estrada,
John W. Eaton,
Phani K. Patibandla,
Sabine J. Waigel,
Dazhuo Li,
John K. Kirtley,
Palaniappan Sethu,
Robert S. Keynton
Affiliations
Eric C. Rouchka
Department of Computer Engineering and Computer Science, University of Louisville, Louisville, KY, 40292, United States; Kentucky Biomedical Research Infrastructure Network Bioinformatics Core, University of Louisville, Louisville, KY, 40292, United States; Corresponding author. Department of Computer Engineering and Computer Science, University of Louisville, Louisville, KY, 40292, United States.
Robert M. Flight
Department of Molecular and Cellular Biochemistry, University of Kentucky, Lexington, KY, 40356, United States
Bridgitte H. Fasciotto
Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, 40292, United States; The ElectroOptics Research Institute and Nanotechnology Center, University of Louisville, Louisville, KY, 40292, United States
Rosendo Estrada
Department of Bioengineering, University of Louisville, Louisville, KY, 40292, United States
John W. Eaton
Department of Medicine, University of Louisville, Louisville, KY, 40292, United States; Department of Pharmacology and Toxicology, University of Louisville, Louisville, KY, 40292, United States; James Graham Brown Cancer Center, University of Louisville, Louisville, KY, 40202, United States
Phani K. Patibandla
Division of Cardiovascular Disease, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, 35294, United States; Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, 35294, United States
Sabine J. Waigel
James Graham Brown Cancer Center, University of Louisville, Louisville, KY, 40202, United States
Dazhuo Li
Department of Computer Engineering and Computer Science, University of Louisville, Louisville, KY, 40292, United States
John K. Kirtley
Department of Computer Engineering and Computer Science, University of Louisville, Louisville, KY, 40292, United States
Palaniappan Sethu
Division of Cardiovascular Disease, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, 35294, United States; Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, 35294, United States
Robert S. Keynton
Department of Bioengineering, University of Louisville, Louisville, KY, 40292, United States
Exposure to ionizing radiation associated with highly energetic and charged heavy particles is an inherent risk astronauts face in long duration space missions. We have previously considered the transcriptional effects that three levels of radiation (0.3 Gy, 1.5 Gy, and 3.0 Gy) have at an immediate time point (1 hr) post-exposure [1]. Our analysis of these results suggest effects on transcript levels that could be modulated at lower radiation doses [2]. In addition, a time dependent effect is likely to be present. Therefore, in order to develop a lab-on-a-chip approach for detection of radiation exposure in terms of both radiation level and time since exposure, we developed a time- and dose-course study to determine appropriate sensitive and specific transcript biomarkers that are detectable in blood samples. The data described herein was developed from a study measuring exposure to 0.15 Gy, 0.30 Gy, and 1.5 Gy of radiation at 1 hr, 2 hr, and 6 hr post-exposure using Affymetrix® GeneChip® PrimeView™ microarrays. This report includes raw gene expression data files from the resulting microarray experiments representing typical radiation exposure levels an astronaut may experience as part of a long duration space mission. The data described here is available in NCBI's Gene Expression Omnibus (GEO), accession GSE63952. Keywords: Radiation exposure, Microarrays, Space flight, Gene expression, Lab-on-a-chip
