Literature DB >> 26981369

Transcriptional profile of immediate response to ionizing radiation exposure.

Eric C Rouchka1, Robert M Flight2, Brigitte H Fasciotto3, Rosendo Estrada4, John W Eaton5, Phani K Patibandla6, Sabine J Waigel7, Dazhuo Li8, John K Kirtley8, Palaniappan Sethu6, Robert S Keynton9.   

Abstract

Astronauts participating in long duration space missions are likely to be exposed to ionizing radiation associated with highly energetic and charged heavy particles. Previously proposed gene biomarkers for radiation exposure include phosphorylated H2A Histone Family, Member XH2AX), Tumor Protein 53 (TP53), and Cyclin-Dependent Kinase Inhibitor 1A (CDKN1A). However, transcripts of these genes may not be the most suitable biomarkers for radiation exposure due to a lack of sensitivity or specificity. As part of a larger effort to develop lab-on-a-chip methods for detecting radiation exposure events using blood samples, we designed a dose-course microarray study in order to determine coding and non-coding RNA transcripts undergoing differential expression immediately following radiation exposure. The main goal was to elicit a small set of sensitive and specific radiation exposure biomarkers at low, medium, and high levels of ionizing radiation exposure. Four separate levels of radiation were considered: 0 Gray (Gy) control; 0.3 Gy; 1.5 Gy; and 3.0 Gy with four replicates at each radiation level. This report includes raw gene expression data files from the resulting microarray experiments from all three radiation levels ranging from a lower, typical exposure than an astronaut might see (0.3 Gy) to high, potentially lethal, levels of radiation (3.0 Gy). The data described here is available in NCBI's Gene Expression Omnibus (GEO), accession GSE64375.

Entities:  

Keywords:  Astronaut; Ionizing radiation; Long duration space travel; Radiation exposure

Year:  2015        PMID: 26981369      PMCID: PMC4778620          DOI: 10.1016/j.gdata.2015.11.027

Source DB:  PubMed          Journal:  Genom Data        ISSN: 2213-5960


Direct link to deposited data

Data is available in the Gene Expression Omnibus (GEO) [1], [2] accession GSE64375 through the direct link http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE64375

Value of the data

Available data on transcriptional profiling of ionizing radiation exposure is sparse at best and this dataset provides novel data on immediate transcriptional responses for both coding and non-coding RNAs at three distinct levels of radiation: 0.3 Gy (low), 1.5 Gy (medium), and 3.0 Gy (high). Immediate transcriptional response biomarkers to radiation exposure can be elucidated by combining the radiation exposure data and determining common transcriptional responses. Dose-specific transcriptional responses immediately following radiation exposure can be determined using the available dataset which can be used to extract sensitive and specific biomarkers. Identification of appropriate biomarkers for general radiation exposure as well as dose-dependent markers found within blood plasma samples makes it possible to design appropriate diagnostic tests for measuring radiation exposure. Such a test could be employed on long-term space flights to diagnose whether or not an astronaut has been exposed to radiation and at what level so appropriate treatment options can be explored.

Experimental design, materials and methods

Experimental design

All procedures were performed in accordance with published NASA and NIH Guidelines, the University of Louisville Institutional Review Board (IRB), and the University of Louisville Institutional Biosafety Committee (IBC). In this study, we sought to understand transcriptional changes in human blood samples resulting from exposure to three different levels of radiation. The experimental design consisted of blood draws from four volunteers which was separated into four samples. Blood from each volunteer was then exposed to 0.0 Gy, 0.3 Gy, 1.5 Gy, and 3.0 Gy of radiation independently as described in Section 3.2.

Sample preparation

Whole blood was drawn from four (4) volunteers using a Safety Winged IV blood draw set (Exel International, St. Petersburg, FL) in 7-ml lavender topped Ethylenediamineteraacetic acid (EDTA) anticoagulant-containing vacutainers. Blood samples were aliquoted and kept at room temperature throughout the radiation and white blood cell (WBC) isolation process. Whole blood samples were radiated at the Kentucky Lion Eye Center using a Gammacell 1000 Elite (Cs-137) (Best Theratronics Ltd., Ottawa, Canada) for 0 s (control — 0.0 Gy exposure), 3 s (0.30 Gy exposure), 16 s (1.5 Gy exposure), or 32 s (3.0 Gy exposure). Approximately 30 min after completion of the radiation cycle, red blood cells (RBC) were lysed by adding 15 ml of NH4Cl RBC lysis buffer for each ml of whole blood (1:15 v/v dilution) in order to isolate leukocytes. The tubes were agitated for 5 min on a rocker platform and centrifuged for 5 min at 1500 RPM at room temperature. Cells were suspended in 10 ml of phosphate-buffered saline (PBS) and centrifuged again twice for 5 min at 1500 RPM. WBCs were suspended in 2 ml PBS, equivalent to the initial volume of the whole blood. WBCs were centrifuged 5 min at 1500 rpm. Supernatant was discarded and cell pellets were suspended in 600 ul RLT lysis buffer (Qiagen, Venlo, The Netherlands) and tubes were vortexed vigorously and stored at − 70 °C until RNA purification. Purification of total RNA was performed using the RNAeasy Mini Kit (Qiagen). Optional on-column DNase digestion was performed to eliminate genomic DNA contamination. Total RNA was eluted in 60 ul of RNase-free water. The quantity analysis of the total RNA was performed with a Nanodrop spectrophotometer (Thermo Fisher Scientific, Wilmington, DE). The quality of the total RNA was checked using an Agilent 2100 Bioanalyzer (Agilent Technologies, Inc., Santa Clara, CA) (Table 1).
Table 1

Concentration and purity data for microarray samples.

Sample NumberVolunteer NumberRadiation LevelConcentration ng/ul260/280260/230RIN
110.0 Gy812.082.208.20
220.0 Gy992.092.025.90
330.0 Gy932.082.136.60
440.0 Gy622.082.057.90
510.3 Gy672.062.148.30
620.3 Gy1302.082.235.40
730.3 Gy972.032.046.70
840.3 Gy682.082.147.30
911.5 Gy682.062.197.90
1021.5 Gy1672.082.136.50
1131.5 Gy992.092.136.20
1241.5 Gy772.072.187.50
1313.0 Gy621.992.067.30
1423.0 Gy882.112.066.90
1533.0 Gy942.092.026.70
1643.0 Gy682.102.046.80
Biotinylated cDNA were prepared according to the standard protocol for Affymetrix® GeneChip® WT Expression protocol (Affymetrix® Inc., Santa Clara, CA) from 100 ng total RNA, which includes an Ambion WT Expression kit followed by a GeneChip® WT Terminal Labeling and Hybridization kit. Following fragmentation, microarrays were hybridized at the University of Louisville Genomics Core Facility in a single batch. cDNA were hybridized for 16 h at 45 °C to Affymetrix® GeneChip® Human Gene 1.0 ST v1 Arrays (GEO platform GPL6244) according to the GeneChip® WT Terminal Labeling and Hybridization User Manual from Affymetrix®.

Data acquisition

GeneChips® were scanned using an Affymetrix® GeneChip® Scanner 3000 7G (Affymetrix®) and the GeneChip® Command Console® software version 3.1 (Affymetrix®), resulting in 16 raw CEL files which were subsequently submitted to GEO (Table 2). These CEL files were processed in RStudio version 0.98.501) [3] using R (version 3.0.1 2013-05-16 “Good Sport”) [4] and Bioconductor packages [5]. CEL files were pre-processed and normalized in R using the oligo package [6] and robust multichip averaging (RMA) [7]. CEL files were organized into a single category for comparison, based on dose-dependent responses at an early time point averaging roughly 1 h post-exposure. Low radiation is defined as 0.3 Gy; mid radiation as 1.5 Gy; and high radiation as 3.0 Gy.
Table 2

Sample information.

Sample numberSample nameCEL fileVolunteer numberDoseGEO sample ID
1SAMPLE_0.0Gy_1h-1PS_Vol1_0.0GY.CEL10.0 GyGSM1569806
2SAMPLE_0.0Gy_1h-2PS_Vol2_0.0GY.CEL20.0 GyGSM1569807
3SAMPLE_0.0Gy_1h-3PS_Vol3_0.0GY.CEL30.0 GyGSM1569808
4SAMPLE_0.0Gy_1h-4PS_Vol4_0.0GY.CEL40.0 GyGSM1569809
5SAMPLE_0.3Gy_1h-1PS_Vol1_0.3GY.CEL10.3 GyGSM1569810
6SAMPLE_0.3Gy_1h-2PS_Vol2_0.3GY.CEL20.3 GyGSM1569811
7SAMPLE_0.3Gy_1h-3PS_Vol3_0.3GY.CEL30.3 GyGSM1569812
8SAMPLE_0.3Gy_1h-4PS_Vol4_0.3GY.CEL40.3 GyGSM1569813
9SAMPLE_1.5Gy_1h-1PS_Vol1_1.5GY.CEL11.5 GyGSM1569814
10SAMPLE_1.5Gy_1h-2PS_Vol2_1.5GY.CEL21.5 GyGSM1569815
11SAMPLE_1.5Gy_1h-3PS_Vol3_1.5GY.CEL31.5 GyGSM1569816
12SAMPLE_1.5Gy_2h-4PS_Vol4_1.5GY.CEL41.5 GyGSM1569817
13SAMPLE_3.0Gy_2h-1PS_Vol1_3.0GY.CEL13.0 GyGSM1569818
14SAMPLE_3.0Gy_2h-2PS_Vol2_3.0GY.CEL23.0 GyGSM1569819
15SAMPLE_3.0Gy_2h-3PS_Vol3_3.0GY.CEL33.0 GyGSM1569820
16SAMPLE_3.0Gy_2h-4PS_Vol4_3.0GY.CEL43.0 GyGSM1569821
Differentially expressed genes (DEGs), defined as Affymetrix® transcript sets, were determined using Limma [8] and a p-value cutoff of 0.05. Using these levels, approximately 400–500 differentially DEGs were determined at each radiation level, relative to control (Table 3). Many of these DEGs appear to be specific to a particular level of radiation exposure, while a small number are shared as general radiation response biomarkers. Further analysis of these genes based on categorical enrichments was performed using categoryCompare [9] (results not shown).
Table 3

Number of differentially expressed genes (DEGs) detected by Limma at p ≤ 0.05 for low (0.3 Gy vs. 0.0 Gy), mid (1.5 Gy vs. 0.0 Gy) and high (3.0 vs. 0.0 Gy) radiation levels.

DEG TypeLow radiationMid radiationHigh radiation
Up223165202
Down216238292
Combined439403494

Expression of radiation-modulated biomarkers

Several genes have previously been considered as potential biomarkers for radiation exposure, including most prominently γH2AFX, TP53, and CDKN1A. Phosphorylation of H2AFX has been used in assays to determine radiation exposure due to its role in DNA double-stranded break repair [10], [11], [12], [13], [14] while TP53 is known to function as a transcription factor which is radiation-modulated [15], [16], [17], [18], [19], [20] and CDKN1A is a downstream target of TP53 which regulates progression through the cell cycle [21], [22], [23]. A plot of the dose-dependent changes in expression of these three biomarkers (Fig. 1) illustrates that two of the three may not be the best to use at a transcriptional level, due to the lack of measurement of protein modifications of H2AX as well as low detectable changes of TP53 which may still affect downstream targets [16]. Taken together, this illustrates the potential value of this dataset in detection of either independent or sets of biomarkers for ionizing radiation exposure at low, mid, and high radiation levels.
Fig. 1

Dose-dependent gene expression patterns for CDKN1A, H2AFX, and TP53. At a transcriptional level, only CDKN1A shows a response that could be used as a biomarker. Phosphorylation of H2AFX cannot be measured transcriptionally, and TP53 shows only a slight (statistically insignificant) change which results in statistically significant downstream effects.

Conflict of interest

The authors declare they have no conflict of interest.
Specifications
Organism/cell line/tissueHomo sapiens
SexMixed
Sequencer or array typeAffymetrix® Human Gene 1.0 ST v1 Arrays
Data formatRaw; CEL files
Experimental factors0.3 Gy exposure vs. 0.0 Gy (low radiation response);1.5 Gy exposure vs. 0.0 Gy (mid-radiation response);3.0 Gy exposure vs. 0.0 Gy (high radiation response)
Experimental featuresGene expression profiling of radiation exposure using:0.0 Gy (control; n = 4);0.3 Gy (low radiation; n = 4);1.5 Gy (mid radiation; n = 4);3.0 Gy (high radiation; n = 4)
ConsentNot applicable
Sample source locationNot applicable
  20 in total

1.  TP53 is not required for the constitutive or induced repair of DNA damage produced by ionizing radiation at the G1/S-phase border.

Authors:  N Whisnant-Hurst; S A Leadon
Journal:  Radiat Res       Date:  1999-03       Impact factor: 2.841

2.  A comparison of normalization methods for high density oligonucleotide array data based on variance and bias.

Authors:  B M Bolstad; R A Irizarry; M Astrand; T P Speed
Journal:  Bioinformatics       Date:  2003-01-22       Impact factor: 6.937

3.  DNA double-stranded breaks induce histone H2AX phosphorylation on serine 139.

Authors:  E P Rogakou; D R Pilch; A H Orr; V S Ivanova; W M Bonner
Journal:  J Biol Chem       Date:  1998-03-06       Impact factor: 5.157

4.  Apoptosis is signalled early by low doses of ionising radiation in a radiation-induced bystander effect.

Authors:  Hayley Furlong; Carmel Mothersill; Fiona M Lyng; Orla Howe
Journal:  Mutat Res       Date:  2013-02-20       Impact factor: 2.433

5.  Changes in phosphorylation of histone H2A.X and p53 in response of peripheral blood lymphocytes to gamma irradiation.

Authors:  Zdenka Vilasová; Martina Rezácová; Jirina Vávrová; Ales Tichý; Doris Vokurková; Friedo Zoelzer; Zuzana Reháková; Jan Osterreicher; Emilie Lukásová
Journal:  Acta Biochim Pol       Date:  2008       Impact factor: 2.149

6.  Radiation sensitivity, H2AX phosphorylation, and kinetics of repair of DNA strand breaks in irradiated cervical cancer cell lines.

Authors:  Judit P Banáth; Susan H Macphail; Peggy L Olive
Journal:  Cancer Res       Date:  2004-10-01       Impact factor: 12.701

7.  Bioconductor: open software development for computational biology and bioinformatics.

Authors:  Robert C Gentleman; Vincent J Carey; Douglas M Bates; Ben Bolstad; Marcel Dettling; Sandrine Dudoit; Byron Ellis; Laurent Gautier; Yongchao Ge; Jeff Gentry; Kurt Hornik; Torsten Hothorn; Wolfgang Huber; Stefano Iacus; Rafael Irizarry; Friedrich Leisch; Cheng Li; Martin Maechler; Anthony J Rossini; Gunther Sawitzki; Colin Smith; Gordon Smyth; Luke Tierney; Jean Y H Yang; Jianhua Zhang
Journal:  Genome Biol       Date:  2004-09-15       Impact factor: 13.583

8.  Radiation risk prediction and genetics: the influence of the TP53 gene in vivo.

Authors:  R E J Mitchel
Journal:  Dose Response       Date:  2006-05-22       Impact factor: 2.658

9.  Identification of gene expression biomarkers for predicting radiation exposure.

Authors:  Tzu-Pin Lu; Yi-Yao Hsu; Liang-Chuan Lai; Mong-Hsun Tsai; Eric Y Chuang
Journal:  Sci Rep       Date:  2014-09-05       Impact factor: 4.379

10.  categoryCompare, an analytical tool based on feature annotations.

Authors:  Robert M Flight; Benjamin J Harrison; Fahim Mohammad; Mary B Bunge; Lawrence D F Moon; Jeffrey C Petruska; Eric C Rouchka
Journal:  Front Genet       Date:  2014-04-29       Impact factor: 4.599

View more
  3 in total

1.  Synergistic effect of aluminum and ionizing radiation upon ultrastructure, oxidative stress and apoptotic alterations in Paneth cells of rat intestine.

Authors:  N A Eltahawy; S M Elsonbaty; S Abunour; W E Zahran
Journal:  Environ Sci Pollut Res Int       Date:  2017-01-12       Impact factor: 4.223

2.  Candidate gene biodosimetry markers of exposure to external ionizing radiation in human blood: A systematic review.

Authors:  Jerome Lacombe; Chao Sima; Sally A Amundson; Frederic Zenhausern
Journal:  PLoS One       Date:  2018-06-07       Impact factor: 3.240

3.  Dataset for dose and time-dependent transcriptional response to ionizing radiation exposure.

Authors:  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
Journal:  Data Brief       Date:  2019-10-05
  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.