Elucidation of Dose-Dependent Transcriptional Events Immediately Following Ionizing Radiation Exposure

bioRxiv preprint doi: https://doi.org/10.1101/207951; this version posted October 23, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

1 2 3 4 Elucidation of dose-dependent transcriptional events immediately following 5 ionizing radiation exposure 6

7 Eric C. Rouchka1,2,*, Robert M. Flight3, Brigitte H. Fasciotto4, Rosendo Estrada5, John W.

8 Eaton6,7,8, Phani K. Patibandla5, Sabine J. Waigel8, Dazhuo Li1, John K. Kirtley1, Palaniappan

9 Sethu9,10, and Robert S. Keynton5

10 11 1Department of Computer Engineering and Computer Science, University of Louisville, 12 Louisville, Kentucky, United States of America 13 14 2Kentucky Biomedical Research Infrastructure Network Bioinformatics Core, University of 15 Louisville, Louisville, Kentucky, United States of America 16 17 3Department of Molecular and Cellular Biochemistry, University of Kentucky, Lexington, 18 Kentucky, United States of America 19 20 4The ElectroOptics Research Institute and Nanotechnology Center, University of Louisville, 21 Louisville, Kentucky, United States of America 22 23 5Department of Bioengineering, University of Louisville, Louisville, Kentucky, United States of 24 America 25 26 6Department of Medicine, University of Louisville, Louisville, Kentucky, United States of 27 America 28 29 7Department of Pharmacology and Toxicology, University of Louisville, Louisville, Kentucky, 30 United States of America 31 32 8James Graham Brown Cancer Center, University of Louisville, Louisville, Kentucky, United 33 States of America 34 35 9Division of Cardiovascular Disease, Department of Medicine, University of Alabama at 36 Birmingham, Birmingham, Alabama, United States of America 37 38 10Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, 39 Alabama, United States of America

41 *Corresponding author

42 E-mail: [email protected] (ECR)

44 Author contributions

45 ECR, RMF, DL, and JKK designed and performed all computational analyses. BHF, RE, JWE,

46 PKP and PS designed the wet lab experiments and oversaw blood sample collection. SJW

47 prepared and ran the microarray experiments. ECR drafted manuscript. RSK provided oversight

48 and input into the overall experimental design.

50 Short Title: Transcriptional events following early radiation exposure

51 Abstract 52

53 Long duration space missions expose astronauts to ionizing radiation events associated with

54 highly energetic and charged heavy particles. Such exposure can result in chromosomal

55 aberrations increasing the likelihood of the development of cancer. Early detection and

56 mitigation of these events is critical in providing positive outcomes. In order to aid in the

57 development of portable devices used to measure radiation exposure, we constructed a genome-

58 wide screen to detect transcriptional changes in peripheral blood lymphocytes shortly after

59 (approximately 1 hour) radiation exposure at low (0.3 Gy), medium (1.5 Gy) and high (3.0 Gy)

60 doses compared to control (0.0 Gy) using Affymetrix® Human Gene 1.0 ST v1 microarrays.

61 Our results indicate a number of sensitive and specific transcriptional profiles induced by

62 radiation exposure that can potentially be implemented as biomarkers for radiation exposure as

63 well as dose effect. For overall immediate radiation exposure, KDELC1, MRPS30, RARS, and

64 HEXIM1 were determined to be effective biomarkers while PRDM9, CHST4, and SLC26A10

65 were determined to be biomarkers specific to 0.3 Gy exposure; RPH, CCDC96, WDYHV1, and

66 IFNA16 were identified for 1.5 Gy exposure; and CWC15, CHCHD7, and DNAAF2 were

67 determined to be sensitive and specific to 3.0 Gy exposure. The resulting raw and analyzed data

68 are publicly available through NCBI’s Gene Expression Ominibus via accession GSE64375.

70 Introduction

71 The National Aeronautics and Space Administration Authorization Act of 2010 (1) and the

72 National Space Policy of the United States of America (2010) (2) set in motion the goals for

73 cislunar and deep space exploration. Among these goals are manned missions for the Asteroid

74 Redirect Mission (3) and Journey to Mars (4). The long duration of these missions (up to 1,100

75 days), necessitates the development of lightweight and portable devices for monitoring health.

76 The top health risk for astronauts on such missions beyond low earth orbit (LEO) is exposure to

77 ionizing radiation associated with highly energetic and charged heavy particles originating from

78 constant galactic cosmic rays and sporadic solar particle events (5-7). This exposure can lead to

79 increased risk of cancer (8-14); deficits in the central nervous system (15-23); degenerative

80 tissue effects (24, 25) including changes attributed to the increase in oxidative stress that are

81 normally associated with aging, such as cataract formation (26-28) and vascular degeneration

82 (29, 30); and acute radiation syndrome (31-34) marked by decreased circulating blood cells (35,

83 36), lung damage (37), decreased cardiac function (38, 39), and immune system suppression (5).

84 Current methodologies for measuring radiation exposure still have a high degree of

85 uncertainty when it comes to determining the level of radiation to which an astronaut crew

86 member has been exposed (5, 40-42). Most of the methods to date have focused on detecting

87 chromosomal aberrations, including single- (SSB) and double-stranded (DSB) breaks,

88 translocations, and exchanges. DSBs have been used extensively, due to their direct relation

89 with radio-induced biological effects, including unequal crossover, chromosomal rings (43),

90 inversions, and dicentric chromosomes (44-46). Detection of these aberrations using either

91 Giemsa staining techniques or fluorescent in-situ hybridization (FISH) (7, 47) have become the

92 de facto gold standard for biological dosimetry, showing a dosage exposure accuracy rate of ±

93 10% (48). While these are currently the most reliable methods in detecting chromosomal

94 aberrations, it is likely the development of high-throughput genome-wide screens of radiation

95 exposure will allow for the detection of sensitive and specific transcriptional biomarkers which

96 can be used in biodosimeters for detecting changes in response to radiation (47).

97 On an individual gene level, a number of genes have been used as biomarkers for

98 radiation exposure at either the transcript, protein, or modified protein level, including

99 phosphorylated H2A Histone Family, Member X (γH2AFX), Tumor Protein 53 (TP53), and

100 Cyclin-Dependent Kinase Inhibitor 1A (CDKN1A). Detection of the phosphorylation of H2AFX

101 has been used in assays to determine radiation exposure due to its role in DNA double-stranded

102 break repair (49-53) and is perhaps the best example of a single protein modification used for

103 radiation detection. TP53 is known to function as a transcription factor, which is radiation-

104 modulated (54-59), and CDKN1A is a downstream target of TP53, which regulates progression

105 through the cell cycle (60-62).

106 While each of these biomarkers have been shown to be sensitive to radiation exposure,

107 they fall short in their specificity at a transcriptional level. Therefore, the purpose of this work is

108 to identify additional transcriptional biomarkers which are both sensitive and specific to low,

109 medium, and high levels of radiation at 1 hr post-exposure. The focus of our study is specifically

110 on ionizing radiation from γ-rays and does not consider the specific effects of other sources of

111 ionizing radiation (such as α-particles, β-particles, and positrons) which may also have an effect

112 on transcriptional activity.

113 114 Materials and methods 115 116 Experimental design

117 Details of the study design have been previous described in Rouchka et al. (63). In summary, the

118 design was constructed to determine transcriptional changes within white blood cells (WBC) at

119 an early time point (1 hr) following ionizing radiation exposure. Whole blood was drawn from

120 four volunteers prior to irradiation. Samples were maintained at room temperature throughout

121 the radiation and WBC isolation process. The blood samples were then irradiated with γ-

122 radiation using a Gammacell 1000 Elite (Noridon) for 0s (0.0 Gy – control), 3s (0.3 Gy), 16s (1.5

123 Gy), or 32s (3.0 Gy) with four biological replicates at each level (n=4). After completion of the

124 radiation cycle, WBC were separated according to the methods in (63). Total RNA was then

125 extracted from the WBC for use in the microarray experiments. All procedures were done in

126 accordance with published NASA and NIH Guidelines, the University of Louisville Institutional

127 Review Board (IRB), and the University of Louisville Institutional Biosafety Committee (IBC).

128 129 Microarray preparation 130 Microarrays were prepared using Affymetrix® Human Gene 1.0 ST v1 microarrays (Gene

131 Expression Omnibus (GEO) (64) platform GPL6244 transcript version; GPL10739 probeset

132 version). After the samples were prepared and the biotinylated cDNA was hybridized to the

133 arrays, microarrays were scanned using an Affymetrix® GeneChip® Scanner 3000 and the

134 GeneChip® Command Console® software version 3.1 (Affymetrix®) resulting in 16 raw CEL

135 files (Table 1).

136 Table 1. Microarray samples.

Sample CEL File Name Volunteer Radiation Time Number Number Dose 1 PS_Vol1_0.0GY.CEL 1 0.0 Gy 1 hr 2 PS_Vol2_0.0GY.CEL 2 0.0 Gy 1 hr 3 PS_Vol3_0.0GY.CEL 3 0.0 Gy 1 hr 4 PS_Vol4_0.0GY.CEL 4 0.0 Gy 1 hr 5 PS_Vol1_0.3GY.CEL 1 0.3 Gy 1 hr 6 PS_Vol2_0.3GY.CEL 2 0.3 Gy 1 hr 7 PS_Vol3_0.3GY.CEL 3 0.3 Gy 1 hr 8 PS_Vol4_0.3GY.CEL 4 0.3 Gy 1 hr 9 PS_Vol1_1.5GY.CEL 1 1.5 Gy 1 hr 10 PS_Vol2_1.5GY.CEL 2 1.5 Gy 1 hr 11 PS_Vol3_1.5GY.CEL 3 1.5 Gy 1 hr 12 PS_Vol4_1.5GY.CEL 4 1.5 Gy 1 hr 13 PS_Vol1_3.0GY.CEL 1 3.0 Gy 1 hr 14 PS_Vol2_3.0GY.CEL 2 3.0 Gy 1 hr 15 PS_Vol3_3.0GY.CEL 3 3.0 Gy 1 hr 16 PS_Vol4_3.0GY.CEL 4 3.0 Gy 1 hr 137

138 6

139 Microarray analysis

140 Raw microarray files were processed in RStudio (65) (version 0.98.501) using R (66) (version

141 3.0.1 2013-05-16 “Good Sport”) and packages obtained from CRAN (67) and Bioconductor

142 (68). CEL files were preprocessed and normalized using the oligo package (69) and robust

143 multichip averaging (RMA) (70) based on the default Affymetrix® transcript chip description

144 format (CDF) (GEO platform GPL6244) which organizes the probes into 33,297 transcripts.

145 CEL files were organized into four phenotypic categories according to the descriptions in Table

146 1, including four biological replicates at each of the four radiation doses of 0.0 Gy (control), 0.3

147 Gy, 1.5 Gy, and 3.0 Gy. A single contrast matrix was constructed in order to complete three

148 dose-dependent comparisons: 0.3 Gy (n=4) vs. control (n=4); 1.5 Gy (n=4) vs. control; and 3.0

149 Gy (n=4) vs. control. Note that in each comparison, the control remains the same untreated

150 samples for the four volunteers (n=4). Differentially expressed genes (DEGs) (defined as

151 Affymetrix® transcript sets) were determined using the Limma (71) linear modeling

152 Bioconductor package.

153 Categorical enrichment 154 155 Entrez identifiers for differentially expressed probe sets were used as inputs into category

156 Compare (72) for further analysis of enriched Gene Ontology Biological Processes (GO::BP)

157 (73) and KEGG Pathways (74). For the GO::BP categories, a minimum gene count of 5 was

158 used along with a p-value cutoff of 0.01, which was calculated using a hypergeometic test.

159 160 Sensitive and Specific Transcriptional Biomarkers 161 162 In order to account for specificity, we analyzed publicly available GEO DataSets, which were

163 highly curated and standardized experiments contained within GEO. As of 12/13/2015, a total of

164 3,848 GEO DataSets existed, out of a total of 63,462 GEO Series. Using the GEO DataSets as a

165 standard, the corresponding gene symbols for the differentially expressed probe sets from our

166 microarray study were searched against GEO DataSets to filter common genes that were not

167 frequently up- or down-regulated. Filtering by commonly measured genes removed nearly all of

168 the noncoding RNAs (snRNA, snoRNA, miRNA, lncRNA), since they were not commonly

169 represented on all microarray platforms. For our purposes, a gene was determined to be common

170 if it was found in at least 2,000 of the GEO DataSets, and infrequently differentially expressed if

171 it was up- or down-regulated in fewer than 100 of the datasets. We further reduced the analysis

172 to only up-regulated genes from our dataset, since the primary goal of our study was to identify

173 biomarkers for developing technologies that would work best on determining detectable levels of

174 gene products.

175 176 Results 177 178 Differentially expressed probesets 179 180 Using a p-value cutoff of 0.05, a total of 439 probesets were determined to be differentially

181 expressed at 0.3 Gy vs. control; while 403 were found to be differentially expressed at 1.5 Gy

182 and 494 at 3.0 Gy out of a total of 28,869 non-control probesets (Table 2). Among the top 10

183 transcripts differentially expressed at 0.3 Gy were DPP10, KDELC1, MAGEA10, MS4A5,

184 OR8I2, WNT5A which were up-regulated and CXCL11, OR1F2P, and STRA8 which were down-

185 regulated. At 1.5 Gy, the top 10 differentially expressed transcripts included the up-regulated

186 transcripts KDELC1, METTL2A, and PCNA and down-regulated transcripts HIST2H2BE,

187 HSPB8, OR4M2, PRR32, ROPN1, and STRA8. The top transcripts differentially expressed at 3.0

188 Gy exposure included HEXIM1, KDELC1, OR8I2, PCNA, and ZNF22 which were up-regulated,

189 and FAM47A, HIST2H2BE, HSD11B2, KLHDC9, and OR4M2 which were down-regulated.

190 Expression patterns for the top 10 differentially expressed transcripts at each exposure level are

191 given in Fig 1.

192 193 Table 2. Number of differentially expressed transcript probesets for radiation dependent 194 results (p-value ≤ 0.05; 1 hr post-exposure). 195 Comparison DEG Direction 0.3 Gy vs. 0 Gy 1.5 Gy vs. 0 Gy 3.0 Gy vs. 0 Gy UP 223 165 202 DOWN 216 238 292 COMBINED 439 403 494 196 197 198 Fig 1. Gene expression patterns for top 10 differentially expressed transcripts at 0.3 Gy 199 (left), 1.5 Gy (center) and 3.0 Gy (right). 200

201 Among the top differentially expressed transcripts that were up-regulated, KDELC1

202 (KDEL motif-containing 1) was shared at all radiation levels. KDELC1 is an endosplasmic

203 reticulum protein. The function of the KDEL motif is to prevent all endoplasmic reticulum

204 resident proteins from being secreted. PCNA (proliferating cell nuclear antigen) was among the

205 top 10 differentially up-regulated genes at 1.5 and 3.0 Gy. Due to the higher level of radiation

206 exposure, the up-regulation of PCNA was likely functioning in DNA repair through the RAD6

207 pathway.

208 Shared differentially expressed down-regulated transcripts included STRA8 (stimulated

209 by retinoic acid) and HIST2H2BE (histone cluster 2, H2be). STRA8 is thought to play a role in

210 spermatogenesis. The down regulation of STRA8 along with SPATA2 and ROPN1 could help to

211 potentially reduce germ cell mutagenesis induced by radiation exposure (75). As a histone core

212 protein, the down-regulation of HIST2H2BE may be related to the activity of one of its binding

213 partners, H2AFX and likely plays a role in DNA accessibility and repair.

214 Two olfactory receptors, OR8I2 and OR4M2 were shared as differentially expressed

215 transcripts. In the first case, OR8I2 was up-regulated, while in the second case, OR4M2 was

216 down-regulated. The differential expression of olfactory receptors is not surprising, given the

217 association of changes in sensory perception with radiation exposure (76-79).

218 Fig 2 shows a Venn diagram of shared gene symbols (based on Entrez (80) identifier)

219 across the three radiation levels, with those shared between all three sets given in Table 3. As

220 Table 3 illustrates, a number of the common transcript probes belong to small non-coding

221 snRNA and snoRNA, which are involved in rRNA maturation. In order to illustrate that the

222 direction of change was rather consistent no matter the radiation level, a heatmap was

223 constructed to show the log2fold-change for all probes where the absolute value of the fold

224 change was greater than or equal to 1.2 in at least one of the radiation exposure datasets (Fig 3).

225 226 Fig 2. Venn diagram of shared gene symbols across radiation levels. Shown are the number

227 of symbols that are A) up-regulated; B) down-regulated; C) up- and/or down-regulated.

228 Additional probes without corresponding Entrez gene identifiers or gene symbols are not shown.

229 230

231 Table 3. Common differentially expressed transcript probesets across all radiation levels. 232 Probe ID Entrez Gene Gene Description Regulation Gene Symbol Direction 7925446 NA MTND5P18 MT-ND5 pseudogene 18 DN 7926819 NA snoU13 snoU13 snoRNA DN 7928363 NA RNU6-833P RNU6-833P snRNA DN 7928815 NA RNU6-325P RNU6-325P snRNA UP 7940835 1351 COX8A Cytochrome c oxidase subunit 8A mitochondrial DN 7962916 NA snoU13 snoU13 snoRNA DN 7972682 79070 KDELC1 KDEL (Lys-Asp-Glu-Leu) containing 1 UP 7981781 390538, OR4M2 olfactory receptor, family 4, subfamily M, member 2 DN 101927079, 102724796 8045814 NA snoU13 snoU13 snoRNA DN 8066953 9825 SPATA2 spermatogenesis associated 2 DN 8080853 NA RNU6-139P RNU6-139P snRNA DN 8086503 51304 ZDHHC3 zinc finger, DHHC-type containing 3 DN 8090152 54763, ROPN1, rhophilin associated tail protein 1 DN 152015 ROPN1B rhophilin associated tail protein 1B 8107996 NA RNA5SP192 RNA, 5S ribosomal pseudogene 192 UP 8126432 NA U3 Small nucleolar RNA U3 DN 8134550 NA MYH16 Processed transcript DN 8144473 55894, DEFB103B, defensin, beta 103B, UP 414325 DEFB103A defensin, beta 103A 8149172 55894, DEFB103B, defensin, beta 103B, UP 414325 DEFB103A defensin, beta 103A 8162490 84641 MFSD14B Major facilitator superfamily domain containing 14B DN 8162568 NA NA Y RNA DN 8169808 100130613 PRR32 proline rich 32 DN 233 234

235 Fig 3. Heatmap of genes with at least one probe having FC of ±1.2 in at least one radiation

236 level for the given condition in treated vs. control. Up-regulated genes are in red; down-

237 regulated in green. Shown are radiation dependent results (three radiation levels vs. no radiation

238 at time 1 hr).

239 240 Differentially expressed microRNAs

241 In addition to protein coding genes, a small number of non-coding microRNAs

242 (miRNAs) were found to be differentially expressed (Table 4; Fig 4). Among the differentially

243 expressed miRNAs at 0.3 Gy radiation level, MIR200A has been shown to play a role in

244 controlling epithelial-to-mesenchymal transition (EMT) (81, 82) as well as controlling oxidative

245 stress response (83) and MIN181B1, which was down-regulated, modulates expression of PTEN

246 (phosphatase and tension homolog) (84), a tumor suppressing gene mutated in a multitude of

247 cancers. MIR191 helps to trigger keratinocyte senescence by down-regulating the cell cycle

248 regulator CDK6 (cyclin-dependent kinase 6) (85). The miRNA MIR520C has been implicated in

249 reduction of protein translation and induces cell senescence (86). Little is known about the

250 function of the additional significantly differentially expressed miRNAs MIR194-1, MIR323A,

251 and MIR519A1.

252

253 Table 4. Differentially expressed miRNAs with known function.

miRNA Radiation Regulation Function Level Direction MIR181B1 0.3 Gy DN Modulation of PTEN MIR191 0.3 Gy UP Downregulates cell cycle regulator CDK6 MIR200A 0.3 Gy UP Controls epithelial-to-mesenchymal transition and oxidative stress response MIR520C 0.3 Gy DN Reduces protein translation and induces cell senescence MIR17 1.5 Gy, 3.0 UP Controls cell-cycle progression; targets CDKN1A and BRCA1 Gy MIR29B1 1.5 Gy UP Tumor suppression; immune mediation MIR208A 3.0 Gy DN Cardiac-specific miRNA 254

255 Fig 4. MicroRNA (miRNA) expression patterns for differentially expressed miRNA

256 transcripts at 0.3 Gy (left), 1.5 Gy (center) and 3.0 Gy (right).

257

258 At 1.5 Gy, MIR29B1 has been shown to have a tumor suppressor role through its control

259 of methylation patterns by modulating the DNA methyltransferases DNMT3A and DNMT3B

260 (87), its control of CD276 (88), and regulation of a number of genes involved in acute myeloid

261 leukemia (89). In addition, MIR29B1 helped control the anti-apoptotic protein MCL1 (myeloid

262 cell leukemia 1) (90) and was down-regulated by the transcription factor MYC (91) as well as

263 served a role in immune mediation (92). Little was known about the function of MIR492.

264 The miRNA MIR17 was significantly up-regulated at both 1.5 Gy and 3.0 Gy. It has

265 been shown to be activated by MYC and negatively regulated the E2F1 transcription factor (93),

266 thus providing a control mechanism for cell cycle progression. By controlling E2F1, MIR17

267 helped to limit double-stranded breaks (94) and targets CDKN1A (cyclin-dependent kinase

268 inhibitor 1A; P21) (95) and BRCA1 (96) and, thus, was likely trying to recover from the effects

269 of the damage induced by the radiation exposure. MIR208A, which was significantly down-

270 regulated at 3.0 Gy, has been shown to serve as a cardiac-specific miRNA by controlling

271 expression of the cardiac muscle myosin heavy chain 7b, MYH7B (97).

272

273 Differentially expressed ncRNAs

274 In addition to miRNAs, a number of non-coding RNAs (ncRNAs) were determined to be

275 significantly differentially expressed, particularly at higher radiation levels (Table 5; Fig 5).

276 Among these were long, non-coding RNAs (lncRNAs), small nuclear RNAs (snRNAs), and

277 small nucleolar RNAs (snoRNAs). The snRNAs and snoRNAs are largely functional in

278 ribosomal RNA maturation.

279 280

281 Table 5. Differentially expressed ncRNAs and their associated function.

ncRNA Radiation Level Direction Role SNORA36A 0.3 Gy UP Mediates pseudouridation of 18S rRNA ILF3-AS1 0.3 Gy DN UNK LINC00910 0.3 Gy UNK RNU2-38P 0.3 Gy U2-38 pseudogene LINC00474 1.5 Gy, 3.0 Gy DN UNK SNHG1 1.5 Gy, 3.0 Gy UP 18S rRNA maturation [7985025] host gene SNORA6 1.5 Gy UP pseudouridation SNORA8 1.5 Gy UP pseudouridation SNORD25 1.5 Gy, 3.0 Gy UP rRNA maturation SNORD26 1.5 Gy, 3.0 Gy UP rRNA maturation SNORD30 1.5 Gy, 3.0 Gy UP rRNA maturation SNRPG 1.5 Gy UP Pre-mRNA splicing SSTR5-AS1 1.5 Gy DN UNK TMEM51-AS1 1.5 Gy DN UNK LINC01356 3.0 Gy UP UNK SCARNA1 3.0 Gy UP UNK SNHG23 3.0 Gy DN rRNA maturation SNORA63 3.0 Gy UP Ribosome formation SNORD20 3.0 Gy UP rRNA maturation 282

283 Fig 5. Non-coding RNA (ncRNA) expression patterns for differentially expressed ncRNA

284 transcripts at 0.3 Gy (left), 1.5 Gy (center) and 3.0 Gy (right).

285 286 287 Differentially expressed pseudogenes

288 Interestingly, a number of the significant differentially expressed probes belonged to

289 pseudogenes for both protein coding and non-coding RNAs (Fig 6). Among the up-regulated

290 pseudogenes were a number of ribosomal associated pseudogenes; two that were associated with

291 olfaction (VN2R17P, OR2U1P); and one, GCOM2 (GRINC1B complex locus 2, pseudogene)

292 that was implicated in both myeloid leukemia (98) and telomeric repeat binding (99).

293

294 Fig 6. mRNA and ncRNA pseudogene expression patterns for differentially expressed

295 mRNA (top) and ncRNA (bottom) pseudogene transcripts at 0.3 Gy (left), 1.5 Gy (center)

296 and 3.0 Gy (right).

297

298 The majority of the down-regulated pseudogenes were pseudogenes for the

299 mitochondrially encoded NADH dehydrogenase 1 (MTND1) or were related to ATP activity,

300 suggesting the down-regulation of mitochondrial specific pseudogenes which may act as sponges

301 for regulatory elements for their parent genes. Other down-regulated pseudogenes were related

302 to ribosomal processes. In fact, all of the top ncRNA pseudogenes (both up- and down-

303 regulated) fell into this category.

304 Categorical enrichments 305 306 Significantly enriched GO::BP categories with a minimum gene list of five (Tables 6-8)

307 showed a molecular response to detection of chemical stimulus that was independent of the

308 dosage. This agreed with prior studies that showed radiation exposure induced olfactory

309 sensations and reduced smell acuity (76-79). At 1.5 Gy, metabolic processes were enriched

310 (Table 7). Previous work showed that global changes in the metabolome occurred with radiation

311 exposure (100), specifically with DNA and RNA metabolism (101-105). The highest dosage,

312 3.0 Gy, was enriched for a number of cell cycle categories (Table 8), indicating the cell was

313 likely trying to compensate for a large number of DNA damage events and shut down the

314 cellular mechanisms for mitotic reproduction.

315

316 Table 6. Enriched GO::BP with at least 5 genes at 0.3 Gy radiation. 317 GO ID GO Description P-Value Gene Count GO:0050911 detection of chemical stimulus involved in sensory perception of smell 4.50E-05 10 GO:0050906 detection of stimulus involved in sensory perception 6.61E-05 11 GO:0007608 sensory perception of smell 9.14E-05 10 GO:0050907 detection of chemical stimulus involved in sensory perception 0.0001 10 GO:0009593 detection of chemical stimulus 0.0003 10 GO:0007606 sensory perception of chemical stimulus 0.0003 10 GO:0051606 detection of stimulus 0.0005 12 GO:0007186 G-protein coupled receptor signaling pathway 0.0010 17

318 319 Table 7. Enriched GO::BP with at least 5 genes at 1.5 Gy radiation. 320 GO ID GO Description P-Value Gene Count External Stimulus GO:0009581 detection of external stimulus 0.0017 6 GO:0009582 detection of abiotic stimulus 0.0020 6 GO:0071466 cellular response to xenobiotic stimulus 0.0064 5 GO:0009410 response to xenobiotic stimulus 0.0072 5 GO:0051606 detection of stimulus 0.0098 10

Metabolic Process GO:0044267 cellular protein metabolic process 0.0022 44 GO:0010562 positive regulation of phosphorus metabolic process 0.0026 15 GO:0045937 positive regulation of phosphate metabolic process 0.0026 15 GO:0044238 primary metabolic process 0.0037 91 GO:0019538 protein metabolic process 0.0040 50 GO:0006520 cellular amino acid metabolic process 0.0043 10 GO:0034660 ncRNA metabolic process 0.0045 8 GO:0019220 regulation of phosphate metabolic process 0.0051 24 GO:0051174 regulation of phosphorus metabolic process 0.0056 24 GO:0044237 cellular metabolic process 0.0057 89 GO:0006805 xenobiotic metabolic process 0.0062 5 GO:1901564 organonitrogen compound metabolic process 0.0072 30

Other GO:0007601 visual perception 0.0039 6 GO:0006959 humoral immune response 0.0042 5 GO:0050953 sensory perception of light stimulus 0.0044 6 GO:0006457 protein folding 0.0057 6 GO:0042325 regulation of phosphorylation 0.0078 17 321 322

323 Table 8. Enriched GO::BP with at least 5 genes at 3.0 Gy radiation. GO ID GO Description P-Value Gene Count Sensory Perception GO:0050907 detection of chemical stimulus involved in sensory perception 0.0006 11 GO:0050911 detection of chemical stimulus involved in sensory perception of smell 0.0010 10 GO:0009593 detection of chemical stimulus 0.0014 11 GO:0007606 sensory perception of chemical stimulus 0.0016 11 GO:0050906 detection of stimulus involved in sensory perception 0.0017 11 GO:0007608 sensory perception of smell 0.0018 10

Cell Cycle GO:0051726 regulation of cell cycle 0.0009 19 GO:0051781 positive regulation of cell division 0.0011 5 GO:0007049 cell cycle 0.0016 29 GO:0045787 positive regulation of cell cycle 0.0081 5 GO:0006275 regulation of DNA replication 0.0088 5

System Process GO:0003008 system process 0.0021 30 GO:0050877 neurological system process 0.0028 22

Other GO:0007267 cell-cell signaling 0.0045 22 GO:0007186 G-protein coupled receptor signaling pathway 0.0055 20 GO:0071363 cellular response to growth factor stimulus 0.0093 14 324

325 Fig 7-9 highlight the significantly enriched GO::BP categories detected regardless of the

326 number of genes in each list. Additional categories showed an up-regulation of protein transport

327 and secretion at 0.3 Gy; increased immune response at 3.0 Gy; decreased cAMP biosynthesis and

328 metabolism at 1.5 Gy; and decreased T-cell migration at 1.5 Gy.

329

330 Fig 7. Enriched GO:BP results from categoryCompare for up-regulated DEGs at 1 hr post-

331 exposure.

332

333 Fig 8. Enriched GO:BP results from categoryCompare for down-regulated DEGs at 1 hr

334 post-exposure.

335

336 Fig 9. Enriched GO:BP results from categoryCompare for up- and/or down-regulated

337 DEGs at 1 hr post-exposure.

338

339 Enriched KEGG metabolic pathways are shown in Fig 10. Among the enriched pathways

340 were oxidative phosphorylation which, along with mitochondrial electron transport, is increased

341 with ionizing radiation exposure (106-109). In addition, the ribosome pathway was enriched at

342 3.0 Gy. This was consistent with an increase in small RNA molecules responsible for rRNA

343 maturation (Table 5) and previous results (110, 111).

344 345 Fig 10. Enriched KEGG results from categoryCompare for up- and/or down-regulated

346 DEGs at 1 hr post-exposure.

347 348 Dose-dependent responses 349 350 Examination of the Entrez gene identifiers mapped by the differentially expressed probesets

351 yielded 182 transcripts uniquely differentially expressed at 0.3 Gy, 161 uniquely differentially

352 expressed at 1.5 Gy, and 223 uniquely differentially expressed at 3.0 Gy, indicating the majority

353 of differentially expressed transcripts were dose-dependent (Fig 2). The top 10 significant dose-

354 dependent, differentially expressed transcripts are shown in Table 9 for each of the three

355 radiation doses.

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356 Table 9. Top 10 differentially expressed dose-specific transcripts. 357 doi:

0.3 Gy 1.5 Gy 3.0 Gy https://doi.org/10.1101/207951 Symbol Description Symbol Description Symbol Description OR1F2P olfactory receptor, family 1, KRTAP13-2 keratin associated protein 13-2 MIR208A microRNA 208a subfamily F, member 2 WNT5A wingless-type MMTV integration HSP8 heat shock 22kDa protein 8 HSD11B2 hydroxysteroid (11-beta) dehydrogenase 2 site family, member 5A STRA8 stimulated by retinoic acid 8 SNOR8 small nucleolar RNA, H/ACA KLHDC9 kelch domain containing 9 box 8 MS4A5 membrane-spanning 4-domains, TTLL13 tubulin tyrosine ligase-like NKX2-3 NK2 homeobox 3 a ; CC-BY-NC-ND 4.0Internationallicense subfamily A, member 5 family, member 13 this versionpostedOctober23,2017. PRDM9 PR domain containing 9 GKN2 gastrokine 2 IFNW1 interferon, omega 1 DNAL1 dynein, axonemal, light chain 1 UNK45B unc-45 homolog B (C. elegans) TNNI3K TNNI3 interacting kinase PCDHB7 protocadherin beta 7 IFNB1 interferon, beta 1, fibroblast CEP57L1 centrosomal protein 57kDa-like 1 ODF3B outer dense fiber of sperm tails 3B TEX36 testis expressed 36 EGR3 early growth response 3 NSA2 NSA2 ribosome biogenesis TMEM51- TMEM51 antisense RNA 1 GABRA2 gamma-aminobutyric acid (GABA) A homolog (S. cerevisiae) AS1 receptor, alpha 2 PAM peptidylglycine alpha-amidating CERCAM cerebral endothelial cell GNAI1 guanine nucleotide binding protein (G monooxygenase adhesion molecule protein), alpha inhibiting activity polypeptide 1 358 The copyrightholderforthispreprint(whichwasnot . 359

360 DAVID (112) GO::BP enrichment analysis of genes differentially expressed at a specific

361 radiation level was performed with a minimum gene count of 6 and a significance cutoff of p ≥

362 0.05 (Table 10). Since this was a reduced gene list, the number of enriched categories was

363 smaller than that reported for all differentially expressed transcripts at each dosage. The results

364 suggest that cell adhesion and signaling were affected at low-dose radiation (0.3 Gy), consistent

365 with prior studies (113, 114). At the same time, an intermediate dose of radiation (1.5 Gy)

366 elicited a response from transcripts involved in sensory perception. A higher level of radiation

367 (3.0 Gy) more directly affected transcription of genes involved in cell cycle regulation and

368 cellular homeostasis, which are more traditionally observed responses in radiation exposure

369 events.

370 371 Table 10. Enriched GO::BP for dose-dependent transcriptional responses.

GO ID GO Description P-value Gene Count 0.3 Gy GO:0016337 cell-cell adhesion 2.60E-02 6 GO:0007186 G-protein coupled receptor protein signaling pathway 3.80E-02 13 GO:0043933 macromolecular complex subunit organization 6.50E-02 9

1.5 Gy GO:0007601 visual perception 6.20E-03 6 GO:0050953 sensory perception of light stimulus 6.20E-03 6 GO:0050877 neurological system process 2.80E-02 13 GO:0008283 cell proliferation 3.00E-02 7

3.0 Gy GO:0051726 regulation of cell cycle 2.20E-03 10 GO:0022502 cell cycle process 9.20E-03 12 GO:0008284 positive regulation of cell proliferation 9.20E-03 10 GO:0000278 mitotic cell cycle 1.40E-02 9 GO:0051240 positive regulation of multicellular organismal process 1.90E-02 7 GO:0042592 homeostatic process 2.70E-02 13 GO:0048878 chemical homeostasis 3.20E-02 10 GO:0007049 cell cycle 3.40E-02 13 GO:0042127 regulation of cell proliferation 3.70E-02 13 GO:0042493 response to drug 3.80E-02 6 GO:0006873 cellular ion homeostasis 4.20E-02 8 GO:0055082 cellular chemical homeostasis 4.50E-02 8 GO:0019725 cellular homeostasis 4.70E-02 9 372

373 374 375 Sensitive and specific transcriptional biomarkers 376 377 In general, differentially expressed transcripts detected within microarray experiments yield

378 biomarkers sensitive to a specific condition when all other factors are reasonably controlled.

379 However, in order for a biomarker to be useful in terms of a diagnostic test, it must also be

380 specific to the condition being tested. For example, within our dataset, PCNA (proliferating cell

381 nuclear antigen) was highly differentially expressed at both 1.5 Gy and 3.0 Gy, but has been

382 determined to be differentially regulated under a large number of conditions.

383 Using the methodology for determining sensitive and specific transcriptional biomarkers

384 outlined in Materials and methods, a total of 23 genes at 0.3 Gy were considered for possible up-

385 regulated biomarkers (Table 11), while 20 genes at 1.5 Gy (Table 12) and 23 genes at 3.0 Gy

386 were considered (Table 13). Only one of these genes, KDELC1 (KDEL (Lys-Asp-Glu-Leu)

387 containing 1) was found to be differentially expressed at all three radiation levels, while MRPS30

388 (mitochondrial ribosomal protein S30), RARS (arginyl-tRNA synthetase), and HEXIM1

389 (hexamethylene bis-acetamide inducible 1) were found to be differentially expressed at both 1.5

390 Gy and 3.0 Gy. For each of the datasets of possible transcriptional biomarkers, we then looked

391 at their pairwise occurrence within GEO DataSets. From this analysis, we focused on the three

392 potential transcriptional biomarkers with the fewest pairwise occurrences within the list (Tables

393 14-16). For 0.3 Gy, this yielded PRDM9 (PR domain containing 9), CHST4 (carbohydrate (N-

394 acetylglucosamine 6-O) sulfotransferase 4), and SLC26A10 (solute carrier family 26, member

395 10). The reduced transcriptional biomarker set at 1.5 Gy included RPH (retinal pigment

396 epithelium-derived rhodopsin homolog), CCDC96 (coiled-coil domain containing 96), WDYHV1

397 (WDYHV motif containing 1), and IFNA16 (interferon, alpha 16) while those detected at 3.0 Gy

398 included CWC15 (CWC15 spliceosome-associated protein), CHCHD7 (coiled-coil-helix-coiled-

399 helix domain containing 7), and DNAAF2 (dynein, axonemal, assembly factor 2).

400 401 Table 11. Filtered up-regulated biomarkers at 0.3 Gy radiation.

ID Gene Symbol Gene Name P-value Number of Number of Geo Datasets Up or Down Regulated 7972682 KDELC1 KDEL (Lys-Asp-Glu-Leu) containing 1 0.0034 2490 31 8104634 PRDM9 PR domain containing 9 0.0081 2155 9 7898725 CELA3A chymotrypsin-like elastase family, member 3A 0.0115 2166 57 8121066 SPACA1 sperm acrosome associated 1 0.0121 2005 16 7997152 CHST4 carbohydrate (N-acetylglucosamine 6-O) sulfotransferase 4 0.0147 2239 22 8051241 ALK anaplastic lymphoma receptor tyrosine kinase 0.0154 2815 37 8132151 ADCYAP1R1 adenylate cyclase activating polypeptide 1 (pituitary) receptor type I 0.0194 3195 85 8021245 DCC DCC netrin 1 receptor 0.0217 3055 75 8073548 SEPT3 septin 3 0.0239 2150 58 7943530 GRIA4 glutamate receptor, ionotropic, AMPA 4 0.0253 2942 80 7998700 PKD1 polycystic kidney disease 1 (autosomal dominant) 0.0257 2942 44 7956573 SLC26A10 solute carrier family 26, member 10 0.0261 2223 22 7947076 PTPN5 protein tyrosine phosphatase, non-receptor type 5 (striatum-enriched) 0.0287 2460 71 8060660 HSPA12B heat shock 70kD protein 12B 0.0312 2064 41 7976402 COX8C cytochrome c oxidase subunit VIIIC 0.0315 2056 24 8049044 ARMC9 armadillo repeat containing 9 0.0342 2317 71 8064415 TMEM74B transmembrane protein 74B 0.0362 2077 24 8097236 ADAD1 adenosine deaminase domain containing 1 (testis-specific) 0.0389 2076 32 7918936 VTCN1 V-set domain containing T cell activation inhibitor 1 0.0399 2281 69 7906838 NOS1AP nitric oxide synthase 1 (neuronal) adaptor protein 0.0447 2494 39 8144315 ARHGEF10 Rho guanine nucleotide exchange factor (GEF) 10 0.0451 2602 60 7978676 NKX2-1 NK2 homeobox 1 0.0465 2794 52 402 403

404 Table 12. Filtered up-regulated biomarkers at 1.5 Gy radiation.

ID Gene Gene Name P-value Number of Number of Symbol Geo Datasets Up or Down Regulated 7972682 KDELC1 KDEL (Lys-Asp-Glu-Leu) containing 1 0.0028 2490 31 8007745 HEXIM1 hexamethylene bis-acetamide inducible 1 0.0055 2753 91 8105181 MRPS30 mitochondrial ribosomal protein S30 0.0100 2605 23 8109802 RARS arginyl-tRNA synthetase 0.0143 2997 61 8173825 RPS6KA6 ribosomal protein S6 kinase, 90kDa, polypeptide 6 0.0158 2370 79 7974190 DNAJC19 DnaJ (Hsp40) homolog, subfamily C, member 19 0.0185 2334 43 8096830 RRH retinal pigment epithelium-derived rhodopsin homolog 0.0193 2666 23 8016870 MRPS23 mitochondrial ribosomal protein S23 0.0237 2218 36 8160392 IFNA16 interferon, alpha 16 0.0301 2460 27 7979888 SLC8A3 solute carrier family 8 (sodium/calcium 0.0302 2093 40 exchanger), member 3 8099242 CCDC96 coiled-coil domain containing 96 0.0303 2243 26 7961507 ART4 ADP-ribosyltransferase 4 (Dombrock blood group) 0.0323 2590 44 8148198 WDYHV1 WDYHV motif containing 1 0.0352 2320 28 7981439 BAG5 BCL2-associated athanogene 5 0.0369 2754 38 8009366 NOL11 nucleolar protein 11 0.0389 2392 36 8145097 PIWIL2 piwi-like RNA-mediated gene silencing 2 0.0390 2380 32 8058837 PECR peroxisomal trans-2-enoyl-CoA reductase 0.0409 2574 71 7928401 CHCHD1 coiled-coil-helix-coiled-coil-helix domain containing 1 0.0420 2193 41 8027416 URI1 URI1, prefoldin-like chaperone 0.0471 2897 90 7936809 CUZD1 family with sequence similarity 24, member B CUB 0.0491 2838 79 and zona pellucida-like domains 1 405 406 Table 13. Filtered up-regulated biomarkers at 3.0 Gy radiation.

ID Gene Symbol Gene Name P-value Number of Number of Geo Datasets Up or Down Regulated 8007745 HEXIM1 hexamethylene bis-acetamide inducible 1 0.0017 2753 91 7972682 KDELC1 KDEL (Lys-Asp-Glu-Leu) containing 1 0.0021 2490 31 7902317 TNNI3K TNNI3 interacting kinase 0.0047 2256 35 8121392 CEP57L1 centrosomal protein 57kDa-like 1 0.0049 2095 37 8100109 GABRA2 gamma-aminobutyric acid (GABA) A 0.0078 2958 86 receptor, alpha 2 8105181 MRPS30 mitochondrial ribosomal protein S30 0.0138 2605 23 7978838 DNAAF2 dynein, axonemal, assembly factor 2 0.0194 2187 22 8147101 E2F5 E2F transcription factor 5, p130-binding 0.0212 3051 67 8109802 RARS arginyl-tRNA synthetase 0.0238 2997 61 7950764 DLG2 discs, large homolog 2 (Drosophila) 0.0263 2813 69 7989915 TIPIN TIMELESS interacting protein 0.0305 2742 63 8147000 ZFHX4 zinc finger homeobox 4 0.0319 2775 66 8171313 ARHGAP6 Rho GTPase activating protein 6 0.0331 2371 91 8083690 IL12A interleukin 12A 0.0346 2853 47 8160401 IFNA5 interferon, alpha 5 0.0359 2549 43 8146517 CHCHD7 coiled-coil-helix-coiled-coil-helix domain 0.0385 2818 41 containing 7 8070279 KCNJ6 potassium inwardly-rectifying channel, 0.0398 2958 48 subfamily J, member 6 8129045 HDAC2 histone deacetylase 2 0.0423 3071 41 7951068 CWC15 CWC15 spliceosome-associated protein 0.0450 2542 19 7979416 TIMM9 translocase of inner mitochondrial membrane 0.0456 2791 27 9 homolog (yeast) 7916643 TM2D1 TM2 domain containing 1 0.0460 2902 45 7918936 VTCN1 V-set domain containing T cell activation inhibitor 1 0.0472 2281 69 8162570 HSD17B3 hydroxysteroid (17-beta) dehydrogenase 3 0.0473 2935 49 407

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408 Table 14. Number of shared GEO DataSets where both genes are differentially expressed for LOW radiation biomarkers. 409 doi: * https://doi.org/10.1101/207951 COX8C GRIA4 DCC ARMC9 HSPA12B PTPN5 CHST4 SPACA1 CELA3A PKD1 ARHGEF10 NKX2-1 SLC26A10 KDELC1 VTCN1 TMEM74B ADCYAP1R1 PRDM9 ADAD1 SEPT3 NOS1AP ALK TOTAL COX8C 3 2 2 0 0 0 1 1 1 0 2 0 0 0 0 2 0 4 0 0 0 18 GRIA4 13 2 2 6 0 0 5 2 0 2 0 2 4 0 8 1 1 4 1 1 57 DCC 4 3 7 0 0 2 1 2 1 1 1 1 1 7 0 1 3 0 0 50 ARMC9 2 4 0 1 2 0 3 1 0 0 3 2 2 2 1 3 3 1 38 HSPA12B 1 0 2 2 1 0 1 0 1 0 1 1 0 0 0 1 0 18 a

PTPN5 0 0 0 0 5 0 0 0 2 0 4 1 0 2 2 3 37 ; CC-BY-NC-ND 4.0Internationallicense this versionpostedOctober23,2017. CHST4 0 1 0 0 1 0 2 0 0 2 0 0 2 0 0 8 SPACA1 2 1 0 0 1 0 2 1 0 0 1 0 0 1 13 CELA3A 1 1 1 1 0 1 1 4 1 1 1 1 2 31 PKD1 1 1 0 1 2 0 1 0 1 0 2 4 20 ARHGEF10 1 1 1 3 1 0 0 0 3 2 1 25 NKX2-1 2 0 1 0 2 0 1 0 2 0 19 SLC26A10 0 0 1 0 0 0 2 1 0 10 KDELC1* 1 1 1 0 0 0 0 0 11 VTCN1 3 6 0 0 2 3 3 37 TMEM74B 2 0 0 2 0 0 16 ADCYAP1R1 1 2 4 3 3 55 The copyrightholderforthispreprint(whichwasnot PRDM9 0 0 0 0 6 . ADAD1 0 1 0 14 SEPT3 1 0 29 NOS1AP 2 25 ALK 21 410 411 Legend: Total is the sum from the corresponding row and column for each gene. *KDELC1 is found as a biomarker for LOW, MID, and HIGH 412 radiation levels. 413

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414 Table 15. Number of shared GEO DataSets where both genes are differentially expressed for MID radiation biomarkers. 415 doi:

* † †

https://doi.org/10.1101/207951 † NOL11 NOL11 DNAJC19 HEXIM1 RPS6KA6 SLC8A3 WDYHV1 RPH URI1 BAG5 MRPS23 ART4 PECR PIWIL2 MRPS30 KDELC1 IFNA16 CHCHD1 CCDC96 CUZD1 RARS TOTAL RPS6KA6 2 0 0 2 2 1 1 3 1 0 2 0 1 0 2 3 0 0 1 21 SLC8A3 2 0 0 1 0 3 0 0 0 0 0 1 0 0 4 1 2 2 18 WDYHV1 0 2 2 0 1 2 1 0 1 0 0 0 1 2 0 0 1 15 RPH 1 0 1 1 1 0 0 0 1 0 1 0 2 0 2 0 10 URI1 2 2 3 5 2 1 3 2 0 2 3 4 1 1 3 39 BAG5 0 0 2 0 3 0 2 0 1 0 0 0 1 5 21 a ; CC-BY-NC-ND 4.0Internationallicense

MRPS23 1 0 1 1 0 1 3 0 3 2 4 1 1 22 this versionpostedOctober23,2017. ART4 2 3 0 1 1 1 0 1 0 0 1 3 23 PECR 0 0 2 1 2 0 2 1 0 0 9 32 PIWIL2 0 0 1 0 3 3 0 1 0 1 17 MRPS30† 0 0 1 0 0 2 1 0 3 12 KDELC1* 0 0 0 0 1 0 0 1 11 IFNA16 0 0 2 2 0 2 0 15 CHCHD1 1 0 2 5 4 0 21 CCDC96 0 1 0 3 1 13 CUZD1 1 0 0 1 19 RARS† 3 2 2 34 18

NOL11 0 2 The copyrightholderforthispreprint(whichwasnot . DNAJC19 0 19 HEXIM1† 36 416 417 Legend: Total is the sum from the corresponding row and column for each gene. *KDELC1 is found as a biomarker for LOW, MID, and HIGH 418 radiation levels. †MRPS30, RARS, and HEXM1 are found as biomarkers at both MID and HIGH radiation levels. 419 420

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421 Table 16. Number of shared GEO DataSets where both genes are differentially expressed for HIGH radiation biomarkers. 422 doi:

* † †

https://doi.org/10.1101/207951 † CWC15 TOTAL CHCHD7 CEP57L1 IFNA5 GABRA2 KCNJ6 TIPIN ZFHX4 E2F5 DLG2 DNAAF2 ARHGAP6 TNNI3K HDAC2 MRPS30 TIMM9 TIMM9 KDELC1 VTCN1 IL12A HSD17B3 TM2D1 RARS HEXIM1

CHCHD7 0 1 0 0 0 0 1 2 0 1 0 1 0 0 0 2 1 0 0 1 0 2 12 CEP57L1 2 1 1 4 1 0 1 0 0 0 0 1 0 2 4 1 0 0 2 1 1 22 IFNA5 4 3 4 2 2 3 0 3 1 0 0 2 0 1 1 0 1 3 2 1 36 GABRA2 3 4 2 3 5 0 4 6 2 0 0 0 1 0 1 5 2 2 1 46 KCNJ6 0 3 1 0 0 4 2 1 0 2 1 0 1 2 2 0 2 1 29 TIPIN 0 0 0 3 1 0 1 0 1 0 2 0 0 2 1 3 0 26 ZFHX4 3 0 0 5 1 0 0 2 0 3 1 3 2 1 2 0 31 a ; CC-BY-NC-ND 4.0Internationallicense E2F5 2 1 2 1 0 1 0 2 0 2 1 0 2 3 0 27 this versionpostedOctober23,2017. DLG2 0 1 2 1 1 0 1 1 1 1 0 0 2 0 24 DNAAF2 1 0 0 0 1 2 0 0 0 0 1 3 0 12 ARHGAP6 2 3 1 2 1 2 3 3 3 1 3 1 47 TNNI3K 1 0 0 0 1 0 1 0 1 1 0 20 HDAC2 2 0 0 0 0 2 1 3 0 0 18 MRPS30† 0 0 2 0 1 0 2 3 0 14 TIMM9 1 2 0 0 0 0 1 0 14 KDELC1* 1 1 0 1 1 1 0 15 VTCN1 2 2 2 1 4 0 33 IL12A 1 0 1 0 0 16 HSD17B3 1 1 4 1 25 The copyrightholderforthispreprint(whichwasnot TM2D1 1 4 0 25 . RARS† 2 1 28 HEXIM1† 0 43 CWC15 9 423 424 Legend: Total is the sum from the corresponding row and column for each gene. *KDELC1 is found as a biomarker for LOW, MID, and HIGH 425 radiation levels. †MRPS30, RARS, and HEXM1 are found as biomarkers at both MID and HIGH radiation levels. 426

427 Discussion

428 Our analysis of the transcriptional changes occurring immediately following ionizing radiation at

429 0.3 Gy, 1.5 Gy, and 3.0 Gy indicated a number of transcripts were significantly differentially

430 expressed at the ncRNA, pseudogene, miRNA, and mRNA levels. Nearly all of the ncRNAs and

431 pseudogenes with a known function or homolog that were regulated were involved in rRNA

432 maturation or mitochondrial specific genes, indicating that radiation exposure affected the

433 overall translation process as well as production of cellular energy. Not surprisingly, at the

434 miRNA level, most of the regulated miRNAs were involved in regulating genes associated with

435 cell cycle or tumor processes. The resulting responses at the mRNA level indicated both a

436 general radiation response that was highly enriched for genes involved in sensory perception as

437 well as dose-dependent responses where 1.5 Gy induced responses to genes involved in

438 metabolic processes and 3.0 Gy was enriched for genes involved in the cell cycle and cellular

439 signaling.

440 As a result of this study, we were also able to elucidate additional transcriptional

441 biomarkers for general radiation exposure, including KDELC1, MRPS30, RARS, and HEXIM1,

442 as well as dose-specific markers including PRDM9, CHST4, and SLC26A10 (0.3 Gy); RPH,

443 CCDC96, WDYHV1, and IFNA16 (1.5 Gy): and CWC15, CHCHD7, and DNAAF2 (3.0 Gy)

444 which were both sensitive and specific to their corresponding radiation exposure immediately (1

445 hr) following exposure. Many of the differentially expressed ncRNAs and genes (including the

446 elucidated biomarkers MRPS30 and CHCD7) were either localized within the mitochondria or

447 have mitochondrial functions, consistent with previous findings that ionizing radiation increased

448 mitochondrial oxidative stress (106, 107, 115, 116) and altered mitochondrial function (107, 117-

449 120). Many others, including the transcriptional biomarkers KDELC1, RARS, HEXIM1,

450 PRDM9, and CWC15 played a role in ribosomal maturation and activity, which has been shown

451 as key in survival after radiation damage (110, 121, 122).

452 Detection of globally enriched biological processes and pathways provides partial insight

453 into how the differentially expressed transcripts function. However, in some cases, the known

454 function of a particular gene in radiation exposure is not clearly evident. As one example,

455 KDELC1 is differentially up-regulated at all three doses. However, its known function as a

456 luminal protein allows resident proteins to escape the endoplasmic reticulum, which does not

457 yield clear insight into its function in radiation exposure. In this particular case, KDELC1 has

458 been shown to form a sense-antisense gene pair (SAGP) on chromosome 13 with basic

459 immunoglobulin-like variable motif (BIVM). SAGPs are transcribed together in pairs in

460 opposite direction on complementary DNA strands. This particular SAGP is part of a group of

461 12 whose differential expression has been determined to be highly predictive of breast cancer

462 survivability (123). Further analysis of BIVM shows that it often forms a read-through transcript

463 with excision repair cross-complementary rodent repair deficiency, complementation group 5

464 (ERCC5) (124). The BIVM-ERCC5 functions in single-stranded DNA binding, nucleotide

465 excision repair, nucleic acid phosphodiester bond hydrolysis, and endonuclease activity, all of

466 which may be in direct response to radiation exposure. Therefore, KDELC1 differential

467 expression could directly signal a high expression of BIVM-ERCC5.

468 Our transcriptional profile has led to a number of elucidated transcriptional biomarkers

469 that have not been previously implicated in radiation exposure. Many of these have roles in

470 related processes and show a specificity and sensitivity that make them prime candidates for

471 inclusion on diagnostic platforms. For others, functional annotation may not be complete,

472 making their use as transcriptional biomarkers subject to additional scrutiny. As the KDELC1

473 example illustrates, this functionality in radiation exposure may be an indirect association,

474 allowing for a novel transcriptional biomarker resulting from the complex interaction among

475 multiple transcripts. The results of our study using microarray technology have yielded a

476 number of transcriptional biomarkers that can be flagged for further transcriptomic analysis of

477 expression, including alternative 5’ and 3’ UTR usage and alternative splicing using current

478 RNA-Seq technologies.

479 480 Acknowledgements

481 The authors wish to thank members of the University of Louisville Research Group for

482 Diagnosing and Mitigating Human Exposure to Radiation Using Micro-Nanotechnology and

483 members of the Kentucky Biomedical Research Infrastructure Network Bioinformatics Core for

484 critical feedback. We wish to thank the anonymous reviewers for their time and feedback as

485 well.

486 487 References

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36 bioRxiv preprint doi: https://doi.org/10.1101/207951; this version posted October 23, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/207951; this version posted October 23, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/207951; this version posted October 23, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/207951; this version posted October 23, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/207951; this version posted October 23, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/207951; this version posted October 23, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/207951; this version posted October 23, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/207951; this version posted October 23, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/207951; this version posted October 23, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/207951; this version posted October 23, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.