In the summer of 2012, 1 year after the nuclear accident in March 2011 at the Fukushima Daiichi nuclear power plant, we examined the effects of gamma radiation on rice at a highly contaminated field of Iitate village in Fukushima, Japan. We investigated the morphological and molecular changes on healthy rice seedlings exposed to continuous low-dose gamma radiation up to 4 µSv h−1, about 80 times higher than natural background level. After exposure to gamma rays, expression profiles of selected genes involved in DNA replication/repair, oxidative stress, photosynthesis, and defense/stress functions were examined by RT-PCR, which revealed their differential expression in leaves in a time-dependent manner over 3 days (6, 12, 24, 48, and 72h). For example, OsPCNA mRNA rapidly increased at 6, 12, and 24h, suggesting that rice cells responded to radiation stress by activating a gene involved in DNA repair mechanisms. At 72h, genes related to the phenylpropanoid pathway (OsPAL2) and cell death (OsPR1oa) were strongly induced, indicating activation of defense/stress responses. We next profiled the transcriptome using a customized rice whole-genome 4×44K DNA microarray at early (6h) and late (72h) time periods. Low-level gamma radiation differentially regulated rice leaf gene expression (induced 4481 and suppressed 3740 at 6h and induced 2291 and suppressed 1474 genes at 72h) by at least 2-fold. Using the highly upregulated and downregulated gene list, MapMan bioinformatics tool generated diagrams of early and late pathways operating in cells responding to gamma ray exposure. An inventory of a large number of gamma radiation–responsive genes provides new information on novel regulatory processes in rice.

Living organisms are affected by numerous environmental factors related with normal growth and development. Radiation, in particular radioactive contamination—both external and internal, is a stress factor that is highly damaging to life on this planet (Bertell 1985). Radiation has the capacity to severely affect growth and development of cells, tissues/organs, and organisms, although much of the current focus is on mammalian models for obvious reasons of anxiety related to the effects of radiation on humans (Smirnova 2010). What is the effect of radiation on plants was the question that this research by Rakwal and Agrawal sought to address in the year 2003. Our first study on the effects of ultralow-level dose of gamma radiation (Kimura et al. 2008) examined specifically the morphological and molecular genetic levels in the cereal crop/grass model rice, Oryza sativa L., using the japonica cultivar Nipponbare—a model genome (Goff et al. 2002; Yu et al. 2002; Kikuchi et al. 2003; Kikuchi 2008; International Rice Genome Sequencing Project 2005; Agrawal and Rakwal 2006, 2011). To remind the readers, rice is the crop that feeds the world, and rice is life (2004 was the International Year of Rice, http://www.fao.org/rice2004/index_en.htm; http://www.fao.org/rice2004/en/concept.htm). Considering the above characteristics of rice plant biology and a move toward understanding rice as a whole, the rice species has become a model on par with the human/mammalian models to study environmental stress, including the effects of radiation.

How does gamma radiation affect rice or how do rice plants respond to the environment with abnormal radiation? Our first 2 studies (Kimura et al. 2008; Rakwal et al. 2009) used ultralow-dose gamma radiation exposure on leaves of rice seedlings, for which the 2-week-old rice seedling model system was established to demonstrate the stress responses at the molecular level (Jwa et al. 2006). Initial studies examined the effects of external radiation exposure on rice plants, in particular on cut leaf segments, for a short period of 72h. In the first study, early genome-wide transcriptional profiling data in rice leaf segments exposed to gamma radiation (5.34 µGy/day; 10.90-fold relative to natural background control level) emitted from contaminated soil sample (Masany, 10 km from the Chernobyl nuclear reactor) revealed 516 differentially expressed genes that were categorized into the following 3 main functions: Information storage and processing, cellular processes and signaling, and metabolism (Kimura et al. 2008). The second study was built up on the incredulous claim of the first study (Kimura et al. 2008) that ultralow-level gamma radiation affects rice self-defense mechanisms and replicated the experiment using an in-lab fabricated gamma ray 137Cs source at 6 dose rates (13±1, 25±2, 45±2, 110±10, 190±10, and 380±20 µGy/3 days) on leaves of rice seedlings (Rakwal et al. 2009). The results arising from the use of both naturally emitting and in-lab fabricated gamma ray sources provided the first evidence for ultralow-level gamma radiation triggering changes at the molecular level in the multilayered defense/stress-related biological processes in rice leaves, laying the foundation for future studies. Meanwhile, our group has carried out additional research using whole plants exposed to high-dose ionizing radiation, such as carbon ion beams (Rakwal et al. 2008), gamma rays, and X-rays (Rakwal R, unpublished data). These data are yet to be published, but they indicate a wide-ranging response (related to defense/stress) at the level of the genome in rice leaves after exposure to high-dose radiation.

The events following the 11 March 2011 nuclear accident at the Fukushima Daiichi Nuclear Power Plant (FDNPP) after the Great Tohoku Earthquake unexpectedly provided an opportunity to initiate a new research project with fellow physicists/radiation experts at the highly contaminated fields in Iitate village of Fukushima Prefecture, Japan (Imanaka et al. 2012). The highly contaminated Iitate Farm (ITF), which is located 31 km from the damaged nuclear power plant and has a field radiation level more than 100 times (~5 µSv/h) higher than the natural background level, was the designated place for the reexamination of low-level gamma radiation experiments using rice as a model system (Figure 1). Because our group had a decade of experience, in addition to data on the effects of gamma radiation on leaf segments (Kimura et al. 2008; Rakwal et al. 2009), the experiment was designed in such a way as to expose whole rice plants to gamma radiation being emitted from the contaminated ground and examine the morphological and molecular genetic changes in the leaves after growth under varying radiation doses. The experiment was performed 3 times in July, August, and September 2012. Results presented here provided the first support to our previous research conducted in the laboratory using cut rice leaf segments (in vitro experiment), which revealed gamma radiation–induced self-defense response. Second, the current research provided new details on the genomewide response of rice plants to low-level gamma radiation in a radioactively contaminated field environment. This is the first article in a series of research reports that will examine, present, and discuss how rice plants behave in response to low-level gamma radiation directly in the field.

Figure 1.

Iitate village in Fukushima Prefecture, and the location of the Iitate farm (ITF). (A) Part of Fukushima Prefecture is shown. (B) Enlarged view of Iitate village, and contours (µSv/h) of measured radiation dose (each dot represents the point of the survey) on 23 March 2012; for details, see Imanaka et al. (2012). The location of ITF is marked by a colored circle.

Figure 1.

Iitate village in Fukushima Prefecture, and the location of the Iitate farm (ITF). (A) Part of Fukushima Prefecture is shown. (B) Enlarged view of Iitate village, and contours (µSv/h) of measured radiation dose (each dot represents the point of the survey) on 23 March 2012; for details, see Imanaka et al. (2012). The location of ITF is marked by a colored circle.

Materials and methods

Rice Seedling Growth and ITF

Japonica type rice (Oryza sativa L.) cv. Nipponbare was used as the test material. The seeds were received from the National Institute for Environmental Studies (NIES), Tsukuba, Japan. Rice seedlings were grown in the greenhouse facility at NIES (Supplementary Figure 1). Briefly, the healthy seeds of cv. Nipponbare were allowed to imbibe water for 1–2 days under darkness at 30 °C and allowed to germinate. Similarly germinated seeds were placed in neat rows in seedling pots (4 rows per pot having 10–12 seeds each) having commercial soil (nursery soil for rice seedling growth and transplantation, purchased from JA Zen-Noh, Japan; https://www.zennoh.or.jp/) with recommended NPK (nitrogen, phosphorus, and potassium) doses in a controlled (25 °C, 70% relative humidity, and natural light conditions) greenhouse at NIES, Tsukuba, Japan during July, August, and September 2012. At the age of 14 days (from start of germination protocol), healthy rice seedlings were transported to designated experimental sites at ITF (Iitate village, Fukushima, Japan) for initiating the experiment. To know the radiation levels during growth and transport of the rice to ITF, accumulated radiation dose was calculated using a MYDOSE mini electronic pocket dosimeter (model PDM-222–52, ALOKA, Japan) (Supplementary Figure 1). To observe the gene expression level in leaves of seedlings after reaching ITF, leaves were sampled at 05.00 AM (called the 0-h NIES sample), the time just before departure to Iitate village. The rice leaves were also sampled on reaching ITF (09:40 AM); this sample was called the 0-h ITF sample and marked the start of gamma radiation exposure). In this study, the results of the experiment performed in July 2012 are presented and discussed.

Plot Design, Gamma Radiation Exposure, and Sampling

The plot design is schematically presented in Figure 2. At the ITF, a leveled ground was overlaid with a blue tarpaulin sheet in the designated area that had an average contamination level (ground 137Cs) of −700 kBq/m2 (Supplementary Figure 2) and that emitted a constant radiation dose of ~5 µSv/h. This area was defined as a low-level gamma field. As shown in Figure 2A,B, the 3 cylindrical boxes were placed at a distance of 2 m apart and were shielded with a recently fabricated shielding material (Nihon Matai Co., Ltd., Moriyama, Shiga, Japan; http://www.matai.co.jp/r02_factory/s_sheet.html) to control the amount of radiation reaching the target in the target area, namely, rice seedlings at the center of the box. The effect of the shielding material around and below the boxes 1 (double shield, ~1.6 µSv/h: low dose) and 2 (single shield, ~2.6 µSv/h: middle dose) can be seen by the amount of gamma ray dose reaching inside (Figure 2C). Box number 3 was not shielded and served as the high-dose (~4.2 µSv/h) condition. The rice plants in the 3 cases of exposures were placed in the center of each box, and the gamma ray dose was recorded by 2 MYDOSE mini electronic pocket dosimeters placed near the 3rd fully formed leaf. Gamma ray exposure times were set at 6, 12, 24, 48, and 72h after arrival at ITF, and the rice leaves at the 3rd position (from the base) from 6 to 10 seedlings were sampled, by cutting the 3rd fully formed leaf at the base of attachment to the sheath, for each dose (low, middle, and high). Postcutting, the leaves were placed in an aluminum foil under dry ice and immediately stored in dry ice packs in the deep freezer (−30 °C). Photographs of the leaves were taken by a digital camera (Coolpix S9100, Nikon, Tokyo, Japan). As a control, rice leaves were sampled in Tsukuba (NIES) and immediately after arrival at ITF; a sample set was also taken at 72h from healthy rice seedlings in the greenhouse in NIES. Samples were taken back to the laboratory and analyzed.

Figure 2.

Experimental plot and placement of the shielded boxes containing rice plants. (A) The dimensions of the plot of land, measured radiation levels, and distances between each shielded box [1, double shield (++); 2, single shield (+); 3, no shield (−)] that contained the rice seedlings. (B) Enlarged view of a circular box (and its dimensions) showing the placement of the seedling box within, and the points where each radiation dose was measured. (C) The actual photograph of the experimental plot showing the 3 circular boxes used in the experiment. (D) The measured radiation dose data in each box (1, 2, and 3) at the bottom (B), center (C), and top (T) as indicated by the crossed lines, and at each direction (South, S; North, N; East, E; and West, W) including in the center of the box, indicated by black filled circles. Details are mentioned in the text.

Figure 2.

Experimental plot and placement of the shielded boxes containing rice plants. (A) The dimensions of the plot of land, measured radiation levels, and distances between each shielded box [1, double shield (++); 2, single shield (+); 3, no shield (−)] that contained the rice seedlings. (B) Enlarged view of a circular box (and its dimensions) showing the placement of the seedling box within, and the points where each radiation dose was measured. (C) The actual photograph of the experimental plot showing the 3 circular boxes used in the experiment. (D) The measured radiation dose data in each box (1, 2, and 3) at the bottom (B), center (C), and top (T) as indicated by the crossed lines, and at each direction (South, S; North, N; East, E; and West, W) including in the center of the box, indicated by black filled circles. Details are mentioned in the text.

Grinding of Leaf Samples in Liquid Nitrogen

Prior to the downstream molecular analyses for gene expression changes, rice leaf powders were prepared as described in the study by Agrawal et al. (2013). Individual leaves taken from each seedling under each dose condition were pooled to give a sample for each treatment condition dose—low, middle, and high, prior to grinding; to repeat, data presented below are for pooled samples from the experiment carried out in July 2012. Rice leaves were ground to a very fine powder with a prechilled mortar and pestle in liquid nitrogen and stored at −80 °C until further analysis (Supplementary Figure 3). The advantage of preparing fine powders is their use in extracting total RNA (gene expression analysis), protein, and metabolites from the same sample and in extremely low amounts (Agrawal et al. 2013).

Total RNA Extraction and Quantity and Quality Control Analyses

Fine powders were used for extracting total RNA following a previously published protocol (Cho et al. 2012). Briefly, the RNeasy Plant Mini Kit (QIAGEN, MD) was used as per manufacturer’s instructions. A detailed step-by-step protocol is schematically presented in Supplementary Figure 4. The quality of RNA, the yield, and its purity were determined spectrophotometrically (NanoDrop, Wilmington, DE) and were visually confirmed using formaldehyde–agarose gel electrophoresis (Supplementary Figure 5).

Complementary DNA Synthesis and Reverse Transcription–Polymerase Chain Reaction

Prior to the gene expression analyses using reverse transcription–polymerase chain reaction (RT-PCR) and the DNA microarray chip analysis, complementary DNA (cDNA) was synthesized, and to check the quality of synthesized cDNA, RT-PCR was performed on the beta-actin (AK100267) gene using the following primer pairs: RJSR43 forward, 5′–CTCCTAGCAGCATGAAGATCAA–3′; and RJSR44 reverse 5′–ATGATAACAGATAGGCCGGTTG–3′ (Cho et al. 2012; Cho et al. 2013). Total RNA samples were first treated with RNase-free DNase (Stratagene, Agilent Technologies, La Jolla, CA). First-strand cDNA was then synthesized in a 20-μL reaction mixture with an AffinityScript QPCR cDNA Synthesis Kit (Stratagene) according to the protocol provided by the manufacturer using 1 μg of total RNA. The reaction conditions were 25 °C for 5min, 42 °C for 5min, 55 °C for 40min, and 95 °C for 5min. The synthesized cDNA was made up to a volume of 50 μL with sterile water supplied in the kit. The reaction mixture contained 0.6 μL of the first-strand cDNA, 7 pmols of each primer set, and 6.0 μL of the Emerald Amp PCR Master Mix (2× premix) (TaKaRa Shuzo, Shiga, Japan) in a total volume of 12 μL. Thermal cycling (Applied Biosystems, Tokyo, Japan) parameters were as follows: After an initial denaturation at 97 °C for 5min, samples were subjected to a cycling regime of 20–40 cycles at 95 °C for 45 s, 55 °C for 45 s, and 72 °C for 1min. At the end of the final cycle, an additional extension step was carried out for 10min at 72 °C. After completion of the PCR, the total reaction mixture was spun down and mixed (3 μL), before being loaded into the wells of a 1.2/1.8% agarose (Agarose [fine powder] Cat no. 02468-95, Nacalai Tesque, Kyoto, Japan) gel. Electrophoresis was then performed for ~22min at 100V in 1× TAE buffer using a Mupid-ex electrophoresis system (ADVANCE, Tokyo, Japan). The gels were stained (8 μL of 10mg/mL ethidium bromide in 200mL 1× TAE buffer) for ~7min, and the stained bands were visualized with the ChemiDoc XRS+ imaging system (Bio-Rad) (Supplementary Figure 6). RT-PCR analysis was also carried out on selected genes based on previous experiments (Kimura et al. 2008; Rakwal et al. 2008, 2009) and unpublished data (Rakwal R) and are listed in Table 1. Each gene candidate was analyzed by RT-PCR more than once to confirm and reconfirm the data on expression change, and finally, a representative data set from each analysis is shown as the relative abundance of mRNA. Moreover, based on the RT-PCR data, the middle dose sample was selected for global gene expression analysis.

Table 1

The primer pairs used for analyzing gene expression changes in rice by RT–PCR using specific primer pairs

 Forward primer Reverse primer Product size (bp) Description 
Accession (gene) Primer name Nucleotide sequence (5′–3′) Primer name Nucleotide sequence (5′–3′) 
AK100267 RJSR43 CTCCTAGCAGCATGAAGATCAA RJSR44 ATGATAACAGATAGGCCGGTTG 294 Actin 
AB037144 RJSR665 AAGCAGAAACAAGATGGAGGAG RJSR666 ATTACTGGACCATCCAACCAAC 323 OsUV-DDB1 
AB111944 RJSR667 GATCAGCTTCCAATCACACATC RJSR668 ACTGGTAGTCAGGTTTCAGCAC 279 OsCSB 
X54046 RJSR669 GTCACTAACCTTTGCCCTGAGGTACA RJSR670 GGTAAAAGCATTCCGTCGTAAG 305 OsPCNA 
AK111418 RJSR663 AACTTCTGCTATTACCAACCTC RJSR664 CTGGTCCACTAGTCCATTCTAG 251 CDP photolyase 
AB021666 RJSR673 CCGATGAGGAAGGTCTTGTAGAGT RJSR674 CAGGAGGTCTTGTTGATGAATG 276 OsFEN-1a 
AB042415 RJSR679 ACCCTCGGTTTGCAGACAC RJSR680 ACGAGCGAGCAGCTGATAGAGTAG 224 OsRPA70a 
AK060582 RJSR681 GTGATGACAGTTACCTTCTCAA RJSR682 CATGGACTCTTCAAGCTTCACC 226 OsRPA70b 
AB037145 RJSR671 GCACATTGATGAAATCGTGAAG RJSR672 TGTAATTTCACTGGATGGAGCA 285 OsRPA32 
AB037135 RJSR675 GCAAGCTTGGTGAAGGTAAGAT RJSR676 CCTTCGAGTCGATATCTTTTGG 300 OsORC1 
D45423 RJSR343 GACAAGAAACCCTCTGCAGTTT RJSR344 GTAGTCTGCTGGTTCACACTGG 305 OsAPX1 
AB053297 RJSR345 GACAAGAAACCCTCTGCAGTTT RJSR346 GTAGTCTGCTGGTTCACACTGG 302 OsAPX2 
AK099923 RJSR103 GACGATACACAAGCAGAACGAC RJSR104 TGACATTGTCTGGCCTTATTTG 299 OsCATc 
AK106109 RJSR11 CACTCCGACCAGGAGCTCTAC RJSR12 CGTTGCGCACTTATACATATCG 310 OsPOX8.1 
AK073202 RJSR123 ACAACGCCTACTACAGCAACCT RJSR124 TATATGTGGTGTGGCCCGTTTA 306 OsPOX22.3 
AK062772 RJSR849 TGCACCCCTGTACAAGTATCTG RJSR850 ATAAGGATTCAGGATGCAAGGA 312 OsGPX1 
NC_001320 RJSR919 GGCCTACTTCTTCACATTCACC RJSR920 ATCTCCAAAGATTTCGGTCAGA 327 OsRBS LSU 
AY445627 RJSR921 GCTAACTAACTACGTGGCTATGG RJSR922 ACTTGGATCGAAGCAGGTACTC 272 OsRBS SSU 
X87946 RJSR351 CGATTCCCAGCAGAATCACC RJSR352 GCCTCCACACTCCACTGTTATT 254 OsPAL2 
X89859 RJSR37 CTGGACAAGGAGAGGATGAGG RJSR38 ATAAAAGATGACGTGTGGCGTA 290 OsCHS1 
AK060005 RJSR29 GGAGAAGGGCTCCTACGACTAC RJSR30 GCGCATATATATCTACYGAGAGCA 314 OsPR1b 
AK071613 RJSR493 AGTCGGATGTGCTCGAGGCAGAA RJSR494 ATAGAGGCAGTATTCCTCTTCA 260 OsPR10a (PBZ1) 
 Forward primer Reverse primer Product size (bp) Description 
Accession (gene) Primer name Nucleotide sequence (5′–3′) Primer name Nucleotide sequence (5′–3′) 
AK100267 RJSR43 CTCCTAGCAGCATGAAGATCAA RJSR44 ATGATAACAGATAGGCCGGTTG 294 Actin 
AB037144 RJSR665 AAGCAGAAACAAGATGGAGGAG RJSR666 ATTACTGGACCATCCAACCAAC 323 OsUV-DDB1 
AB111944 RJSR667 GATCAGCTTCCAATCACACATC RJSR668 ACTGGTAGTCAGGTTTCAGCAC 279 OsCSB 
X54046 RJSR669 GTCACTAACCTTTGCCCTGAGGTACA RJSR670 GGTAAAAGCATTCCGTCGTAAG 305 OsPCNA 
AK111418 RJSR663 AACTTCTGCTATTACCAACCTC RJSR664 CTGGTCCACTAGTCCATTCTAG 251 CDP photolyase 
AB021666 RJSR673 CCGATGAGGAAGGTCTTGTAGAGT RJSR674 CAGGAGGTCTTGTTGATGAATG 276 OsFEN-1a 
AB042415 RJSR679 ACCCTCGGTTTGCAGACAC RJSR680 ACGAGCGAGCAGCTGATAGAGTAG 224 OsRPA70a 
AK060582 RJSR681 GTGATGACAGTTACCTTCTCAA RJSR682 CATGGACTCTTCAAGCTTCACC 226 OsRPA70b 
AB037145 RJSR671 GCACATTGATGAAATCGTGAAG RJSR672 TGTAATTTCACTGGATGGAGCA 285 OsRPA32 
AB037135 RJSR675 GCAAGCTTGGTGAAGGTAAGAT RJSR676 CCTTCGAGTCGATATCTTTTGG 300 OsORC1 
D45423 RJSR343 GACAAGAAACCCTCTGCAGTTT RJSR344 GTAGTCTGCTGGTTCACACTGG 305 OsAPX1 
AB053297 RJSR345 GACAAGAAACCCTCTGCAGTTT RJSR346 GTAGTCTGCTGGTTCACACTGG 302 OsAPX2 
AK099923 RJSR103 GACGATACACAAGCAGAACGAC RJSR104 TGACATTGTCTGGCCTTATTTG 299 OsCATc 
AK106109 RJSR11 CACTCCGACCAGGAGCTCTAC RJSR12 CGTTGCGCACTTATACATATCG 310 OsPOX8.1 
AK073202 RJSR123 ACAACGCCTACTACAGCAACCT RJSR124 TATATGTGGTGTGGCCCGTTTA 306 OsPOX22.3 
AK062772 RJSR849 TGCACCCCTGTACAAGTATCTG RJSR850 ATAAGGATTCAGGATGCAAGGA 312 OsGPX1 
NC_001320 RJSR919 GGCCTACTTCTTCACATTCACC RJSR920 ATCTCCAAAGATTTCGGTCAGA 327 OsRBS LSU 
AY445627 RJSR921 GCTAACTAACTACGTGGCTATGG RJSR922 ACTTGGATCGAAGCAGGTACTC 272 OsRBS SSU 
X87946 RJSR351 CGATTCCCAGCAGAATCACC RJSR352 GCCTCCACACTCCACTGTTATT 254 OsPAL2 
X89859 RJSR37 CTGGACAAGGAGAGGATGAGG RJSR38 ATAAAAGATGACGTGTGGCGTA 290 OsCHS1 
AK060005 RJSR29 GGAGAAGGGCTCCTACGACTAC RJSR30 GCGCATATATATCTACYGAGAGCA 314 OsPR1b 
AK071613 RJSR493 AGTCGGATGTGCTCGAGGCAGAA RJSR494 ATAGAGGCAGTATTCCTCTTCA 260 OsPR10a (PBZ1) 

Os, Oryza sativa; UV, ultraviolet; DDB, damaged DNA binding ; CSB, Cockayne syndrome group B; PCNA, proliferating cell nuclear antigen; CDP, cyclobutane pyrimidine dimer; FEN, flap endonuclease; RPA, replication protein A; ORC, origin recognition complex; APX, ascorbate peroxidase; CAT, catalase; POX, peroxidase; GPX, glutathione peroxidase; RBS, ribulose-1,5-bisphosphate carboxylase/oxygenase; LSU, large subunit; SSU, small subunit; PAL, phenylalanine ammonia-lyase; CHS, chalcone synthase; PR, pathogenesis-related; PBZ, probenazole.

Whole-Genome DNA Microarray Analysis and GEO Accession

A rice 4×44K custom (eARRAY, AMAdid-017845) oligo-DNA microarray chip (G2514F, Agilent Technologies, Palo Alto, CA) was used for genomewide gene profiling of expressions of early (6h) and late (72h) genes, as described previously (Satoh et al. 2010; Cho et al. 2012, 2013). Total RNA (900ng) was labeled with either Cy3 or Cy5 using a Low RNA Input Fluorescent Linear Amplification Kit (Agilent). Fluorescently labeled targets of control (0h at ITF and at NIES greenhouse, prior to transport to ITF) and treated (rice exposed to gamma rays for 6 and 72h, middle dose) samples were hybridized to the same microarray slide containing 60-mer probes. Supplementary Figure 7 shows the chip design used here. A flip-labeling (dye swap or reverse labeling with Cy3 and Cy5 dyes) procedure was followed in order to nullify the dye bias associated with unequal incorporation of the 2 Cy dyes into cDNA. To select differentially expressed genes by the dye-swap approach, we considered genes that were upregulated in chip 1 (Cy3 and Cy5 label for control and treatment, respectively) but downregulated in chip 2 (Cy3 and Cy5 label for treatment and control, respectively). The use of a dye-swap approach has 2 benefits. First and most importantly, it provides a highly stringent selection condition for changed gene expression profiling over use of a single/2-color approach (Rosenzweig et al. 2004; Altman 2005). Second, it provides 2 technical chip replicates on the same slide for 1 sample set (Supplementary Figure 7). Additionally, it avoids the prohibitively high cost of a DNA microarray chip in such an experiment, where statistically significant 7–8 replications using 7–8 individual chips are impractical.

Hybridization and wash processes were performed according to the manufacturer’s instructions (Agilent), and hybridized microarray slides were scanned using an Agilent microarray scanner G2505C. For detection of significantly differentially expressed genes between control and treatment, each slide image was processed by Agilent Feature Extraction Software (version 11.0.1.1). The program measures Cy3 and Cy5 signal intensities of whole probes. Dye bias tends to be dependent on signal intensity; therefore, the software selects probes using a set by rank consistency filter for dye normalization. The said normalization was performed by LOWESS (locally weighted linear regression) that calculates the log ratio of dye-normalized Cy3 and Cy5 signals, as well as the final error of log ratio. The significance (P) value is based on the propagated error and universal error models. In this analysis, the threshold of significant differentially expressed genes was < 0.01 (for the confidence that the feature was not differentially expressed). In addition, erroneous data generated due to artifacts were eliminated prior to data analysis using the software. The gamma radiation–responsive up- and downregulated gene lists (≥2.0-fold, ≥ 0.5-fold) are detailed in Supplementary Tables 1 (6h up), 2 (6h down), 3 (72h down), 4 (72h down), 5 (0h ITF up), 6 (0h ITF down), 7 (72h NIES up), and 8 (72h NIES down).

The data discussed in this publication have been deposited in NCBI’s Gene Expression Omnibus (GEO) and are accessible through GEO Series accession number GSE53055 (http://www.ncbi.nlm.nih.gov/geo/info/linking.html).

Functional Classification of Differentially Expressed Genes

Due to the large number of differentially expressed genes, we further selected the highly up- and downregulated genes based on simple criteria highlighting those genes that were only differentially expressed after exposure to gamma radiation (middle dose) at ITF for 6 and 72h. This implies that those genes that were expressed between the time period of 5 AM (NIES 0-h greenhouse sample) to 10 AM (ITF 0-h sample) and after 3 days (NIES 72-h greenhouse sample), that is, time- and growth-dependent gene expressions, were subtracted from the total number of genes up- and downregulated using data from chips 1 and 2 (Supplementary Figure 8). These genes are listed in Supplementary Tables 9 (highly up at 6h), 10 (up at 6h), 11 (highly down at 6h), 12 (down at 6h), 13 (highly up at 72h), 14 (up at 72h), 15 (highly down at 72h), and 16 (down at 72h). The nonredundant gamma radiation–highly responsive up- and down-regulated genes listed in Supplementary Tables 9, 11, 13, and 15 were further considered candidate genes for specific bioinformatics analysis using the MapMan program, version 3.1.1, at the Max Plant Institute of Molecular Plant Physiology, Germany (Thimm et al. 2004; Usadel et al. 2009). Gene expression fold values were transformed to Log2 (fold), and then their means were calculated. These nonredundant genes were classified into MapMan BINs, and their annotated functions were visualized using the MapMan program, based on a newly constructed rice mapping file for all the genes on Agilent 4×44K rice DNA chip. The mapping file was established by automated searches using the systematic names (as locus identifiers) of all the genes on the DNA chip released from the GeneSpring program (version GX 10, Agilent) and a MapCave tool (http://mapman.gabipd.org/web/guest/mapcave), which is linked with 6 different databases, such as Arabidopsis thaliana TAIR8, Arabidopsis thaliana TAIR9, Hordeum vulgare, Oryza sativa TIGR5, SwissProt/PPAP, and Vitis vinifera Gene Index R5.

Results and Discussion

Rationale and Experimental Strategy

On the basis of previously conducted experiments, the effect of ultralow, low, and high doses of ionizing radiation in rice plants was apparent at the morphological and molecular genetic levels (Kimura et al. 2008; Rakwal et al. 2008, 2009; Rakwal R, unpublished data). In the case of gamma radiation—our main focus—the effects of ultralow- and low-level gamma rays were examined in cut leaf segments obtained from 2-week-old rice seedlings, whereby the experiment could be considered in vitro, that is, “Petri dish” experiments. Considering the fact that it was not feasible to conduct such a low radiation dose experiment in the laboratory and this being what we wished to examine at the whole plant level or in vivo, the ill-fated FDNPP accident in March 2011 provided such an unexpected opportunity. Being able to visit, see, and meet up with physicist colleagues at Iitate village (Fukushima) was a starting point for the ongoing project under the Iitate-mura (=village) Society for Radioecology (http://iitate-sora.net/). The experimental site was chosen at ITF based on the continuous emission of gamma rays (~5 µSv/h; 100 times greater than natural background level) from the highly contaminated soil there (Imanaka et al. 2012). The radiation dose was similar to the previously conducted in-house experiment with fabricated gamma ray–emitting sources (Rakwal et al. 2009) and formed the basis for a 3-dose (~1.5/2.5/4.5 µSv/h) experiment to confirm previous findings and provide new information on gamma radiation–exposed whole rice plants. As diagrammatically depicted in Figure 2A, there was no direct contact between the seedlings and the contaminated soil, thus ensuring that we primarily observed the effects of gamma radiation alone. The 3rd leaf was used as the experimental sample. Each dose—low, middle, and high—was determined as described in the Materials and Methods, and the data are graphically presented in Figure 3 for the months of July, August, and September 2012. The experimental strategy from the design of the experiment to the sampling, methodology, and analyses steps that led to the list of identified gamma radiation–responsive molecular factors is presented in Figure 4.

Figure 3.

Accumulated radiation dose for each day of the experimental periods in July, August, and September of 2012. In each month, the values indicated at the right-hand side of each point line indicate the maximum accumulated dose that was measured at the last time point sampled. Details are mentioned in the text.

Figure 3.

Accumulated radiation dose for each day of the experimental periods in July, August, and September of 2012. In each month, the values indicated at the right-hand side of each point line indicate the maximum accumulated dose that was measured at the last time point sampled. Details are mentioned in the text.

Figure 4.

Experimental design and strategy for measuring the effect of low-level dose of gamma radiation on rice plants. A 2-week-old seedling model system was used. Briefly, the upper panel shows the rice plants at the start of the experiment before transporting the rice seedlings from Tsukuba to ITF in Iitate village. The middle panel shows a representative sampling photo of rice leaf cutting and storage in dry ice and a deep freezer. The lower set of photographs shows ground rice leaf powder in a mortar and pestle in liquid nitrogen; filled area in the 3 microtubes represents the amount of powdered sample just above the triangular base. Further details are in the text.

Figure 4.

Experimental design and strategy for measuring the effect of low-level dose of gamma radiation on rice plants. A 2-week-old seedling model system was used. Briefly, the upper panel shows the rice plants at the start of the experiment before transporting the rice seedlings from Tsukuba to ITF in Iitate village. The middle panel shows a representative sampling photo of rice leaf cutting and storage in dry ice and a deep freezer. The lower set of photographs shows ground rice leaf powder in a mortar and pestle in liquid nitrogen; filled area in the 3 microtubes represents the amount of powdered sample just above the triangular base. Further details are in the text.

Selection of July 2012 Experiment for Downstream Analysis Based on Climate Parameters and Leaf Morphology

Three independent experiments were carried out in the months of July, August, and September 2012. On the basis of the ground (field) conditions of temperature, humidity, light, and rain, along with observations of the leaf morphology after 3-day exposure to gamma radiation, the July experiment was selected for further molecular analyses. The ground and interior (boxes containing seedlings) temperatures (°C), humidity (%), and light intensity (lux) are graphically shown in Supplementary Figure 9 for the time periods of the experiment. In the month of July, the temperature in Iitate village hovered around 26 °C for the month of July, except for day 1, when the temperature was measured as being around 33.5 °C in the experimental field at ITF. Similar readings were obtained for the temperature inside the sample boxes. Additionally, the July sky was clear and sunny, and there was no rain. On the other hand, the temperature increased to around 40.8 °C at the maximum on day 1 and decreased to 31.8 °C on day 2 in August, and due to rain, the boxes were placed under a greenhouse with only the top cover with open sides. In September, the temperature dropped down to around 19 °C, and there was heavy rain, resulting in use of an almost fully closed-type greenhouse during the final 2 days. The humidity also varied with each month, and compared with the levels in July and August, the humidity peaked in September due to the use of the greenhouse. For light intensity, similar lux readings were obtained in July and August compared with the relatively low intensity measured in September. In addition, the optimum temperature, humidity, and light conditions in the control greenhouse (NIES, Tsukuba), where a part of the seedlings were left to grow, were almost similar to that of the July experimental period.

After exposure to gamma radiation, the 3rd leaves were examined for changes in morphology. As seen in Figure 5, the tips of the 3rd leaves (fully formed) showed drying/withering at the dose (~241 µSv/3 days) in the unshielded box (Figure 5A). Following removal of the seedlings from ITF and placement back in the greenhouse in Tsukuba, the tips further withered, as seen in Figure 5B. In comparison, healthy seedlings (Figure 5C) showed no such damage on the leaves, suggesting that the drying at the tips could be due to radiation exposure. The observed leaf tip damage was also seen in the case of high-dose gamma ray and ionizing radiation in previous experiments (Rakwal et al. 2008; Rakwal R, unpublished data). Unfortunately, we could not observe such symptoms on leaves during August and September. One reason might be the changes in temperature, humidity, and light/rain, due to which we had to cover the seedlings by enclosing within a greenhouse.

Figure 5.

Gamma radiation affects the tips of rice seedling leaves. (A) Leaf tips at 3 days after exposure to gamma radiation; 3rd leaves are marked by arrows. (B) 3-day-exposed seedlings showing the progression of the drying of the leaf (3rd) tips (marked by arrows) at 30 days postgermination, in the control greenhouse (NIES, Tsukuba). (C) Healthy seedlings show no such damage to the 3rd leaf or any other leaf.

Figure 5.

Gamma radiation affects the tips of rice seedling leaves. (A) Leaf tips at 3 days after exposure to gamma radiation; 3rd leaves are marked by arrows. (B) 3-day-exposed seedlings showing the progression of the drying of the leaf (3rd) tips (marked by arrows) at 30 days postgermination, in the control greenhouse (NIES, Tsukuba). (C) Healthy seedlings show no such damage to the 3rd leaf or any other leaf.

Prior to downstream molecular analysis using RT-PCR and DNA microarray, the leaves were ground in liquid nitrogen to yield fine powders (Figure 4). In the following sections, the results of these gene expression analyses using 2 different approaches are presented and discussed.

RT-PCR Analysis of Selected Candidate Genes

On the basis of previously conducted experiments, we had a general idea of the genes that might be differentially affected by ionizing radiation (Kimura et al. 2008; Rakwal et al. 2008, 2009; Rakwal R, unpublished data). Therefore, we first examined whether these genes indeed are affected by gamma radiation exposure using RT-PCR. The gene names and primers are described in Table 1. The RT-PCR experiment was conducted using blind samples, and once the results were obtained, the data were reformatted to the time-course series from 0 to 72h. The gene expression results are graphically presented in Figure 6. Five groups of gene functions were examined: Genes related to DNA replication/repair, oxidative stress, photosynthesis, secondary metabolism, and defense/stress (see Table 1). Although for most of the genes, a correlation with the dose (low, middle, and high) was found, we are not able to discuss that feature (dose dependency) in detail in this article. Therefore, we will mainly discuss the increase or decrease in gene expression following gamma radiation exposure relative to the 0-h start at ITF using some examples from each above-mentioned functional category.

Figure 6.

Gene expression analysis of 22 selected genes. Beta-actin gene was used to check the quality of cDNA and as a positive control. Relative abundance of gene expression calculated from the bands on agarose gels (see Materials and methods and Supplementary Figure 6 for further details) were plotted against treatment (gamma radiation) time and dose. Details are mentioned in the text.

Figure 6.

Gene expression analysis of 22 selected genes. Beta-actin gene was used to check the quality of cDNA and as a positive control. Relative abundance of gene expression calculated from the bands on agarose gels (see Materials and methods and Supplementary Figure 6 for further details) were plotted against treatment (gamma radiation) time and dose. Details are mentioned in the text.

In the DNA replication/repair category, the clearest change/increase in abundance of gene expression was seen at the early time points for OsCSB, OsPCNA, CDP photolyase, OsFEN-1a, OsRPA70a, OsRPA70b, OsRPA32, and OsORC1 (Kimura et al. 2004). This is also in line with previous experiments, wherein high-dose gamma radiation and ionizing radiation increased their expressions (Rakwal et al. 2008; Rakwal R, unpublished data). In particular, we identified that the OsPCNA gene expression was very high only during the early time period (6, 12, and 24h) of gamma radiation exposure (Figure 6). Interestingly, OsPCNA is the only well-studied and reported gene in rice among other DNA replication/repair genes (Kimura et al. 2001, 2004; Yamamoto et al. 2005; Strzalka and Ziemienowicz 2011). In rice plants, PCNA has been shown to interact with DnaJ that is induced under DNA damage (Yamamoto et al. 2005) and recently also with X-ray repair cross-complementing 1 (XRCC1), a well-known base excision repair protein (Uchiyama et al. 2008). Although we could not find the previously reported DnaJ gene (Yamamoto et al. 2005) from among the 163 probes corresponding to numerous DnaJ-related genes in the rice genome, we found that the XRCC1 gene was induced in the 6-h sample but suppressed in the 72-h sample used for microarray analysis (data are available under the GEO series accession number GSE53055) described below. Similarly, the OsPCNA gene was found to be induced and suppressed at 6 and 72h, respectively, based on the obtained DNA microarray data (GSE53055). This shows a preconfirmation of the gene expression–profiling data obtained using DNA microarray chip discussed below. On the basis of our present finding, it can be suggested that OsPCNA is involved in DNA repair processes in gamma ray–exposed cells in the rice leaves. On the other hand, the OsUV-DDB1 gene did not show any strong change in expression. To date, the OsUV-DDB1 gene, along with OsUV-DDB2, has been shown to be responsive to treatment with ultraviolet radiation in rice seedlings (Ishibashi et al. 2003). The expression of OsUV-DDB genes was correlated with cell proliferation, and its expression might be necessary for predominantly undergoing DNA repair during DNA replication. These results suggest that gamma radiation specifically alters the expression of certain known genes involved in DNA replication/repair, which might be accelerated due to the gamma rays penetrating the cells. Moreover, this response is early, within 6–24h, and not late, again suggesting the specificity of the observed effect (radiation).

In the category of oxidative stress–related genes, the genes encoding ascorbate peroxidases (APX), catalase (CAT), peroxidases (POX), and glutathione peroxidase (GPX) were found to be differentially expressed, indicating their individual time-dependent responses to the gamma radiation (Figure 6). In particular, OsAPX1/2 genes showed a slight increase in expression from 0 to 72h, peaking around 24 and 48h postexposure. The OsAPX1/2 genes are the most well characterized among the genes examined herein and have been shown to be responsive to oxidative and abiotic stresses in rice (Morita et al. 1997, 2011; Lu et al. 2005). The OsCATc gene showed a downregulation at 24 and 48h, followed by a recovery at 72h postexposure. Interesting, the OsPOX8.1/22.3 genes showed a strong decrease in expression, except for a peak at 12h, compared with the 0-h control for OsPOX8.1. The OsGPX1 gene was induced relative to the 0-h control prominently at 6 and 24h postexposure. The OsGPX gene family has been recently shown to be induced in response to exogenous hydrogen peroxide (H2O2) and cold stress (Passaia et al. 2013). These results suggest that the exposed leaves have oxidative stress response mechanisms, resulting in the differential expression of the genes encoding the antioxidant enzymes. From these data, it is clear that both induction (OsAPX1/2 and OsGPX) and suppression (OsCATc and OsPOZ8.1/22.3) of gene expression occur in cells and that the effect may depend on the variety and amount of free radicals being generated. In future studies, the production of free radicals, such as H2O2, would have to be examined along with the activities of the antioxidant enzymes in the gamma-irradiated leaves.

For the photosynthesis-related genes, OsRBS (ribulose bisphosphate carboxylase/oxygenase) encoding the large subunit (LSU) and small subunit (SSU), no clear differences were observed until 24h, but at 48 and 72h, an increase in gene expression was seen (Figure 6). In general, climatic factors cause variation in RuBisCO content and activity (Galmes et al. 2013). It is difficult to explain the results obtained here, but under field conditions, multiple environmental factors are working together. Thus, the increased transcription of RuBisCO observed at late time periods may be due to the plant’s response to the low-level stress being perceived, but with no major damage to the chloroplastic apparatus, which is a major cause of reduced RuBisCO transcription, translation, and activity. Compared with other major abiotic stresses, wherein the general trend is reduction of RuBisCO, a major effect is on depression of photosynthesis (Galmes et al. 2013), which may not be the case in the current stress condition of gamma ray exposure because the leaves are healthy except for the symptom of drying at the extreme tip (Figure 5). As a next step, we are conducting proteomics analysis to see how the proteins, especially the RuBisCO subunits, behave under gamma irradiation.

Both the secondary metabolism–related genes OsPAL2 and OsCHS1 examined here showed a strong increase in expression after exposure to gamma radiation (Figure 6), which is expected under both abiotic and biotic stresses. The OsPAL2 gene has been reported to be both developmentally regulated and stress inducible (Zhu et al. 1995; Hyun et al. 2011). The OsCHS1 gene expression was below the detectable limit of the RT-PCR experiment at 0h, but it showed a strong increase at 6h and thereafter, making it an interesting candidate for further investigation as a specific gamma ray–responsive gene. DNA microarray analysis (see below) also revealed the high fold induction of 15 and 9 and 8 and 11 OsPAL and OsCHS genes at 6 and 72 h, respectively, again providing preconfirmation of PAL and CHS gene expression at the whole-genome level. Chalcone synthase (CHS) is a key enzyme of the flavonoid/isoflavonoid biosynthesis pathway, and in addition to being developmentally regulated similar to the PAL genes, it is known to be induced in response to stress conditions, including ultraviolet light and pathogen attack (Dao et al. 2011). OsCHS1 (Scheffler et al. 1995) encodes a naringenin CHS, which is mostly likely behind the production of antimicrobial phytoalexins including sakuranetin; we also previously identified this gene in rice leaves exposed to ultralow-level dose of gamma radiation emitted from contaminated soil obtained from the exclusion zone around the Chernobyl reactor site (Rakwal et al. 2009). It would also be interesting to identify the proteins catalyzing these reactions toward phytoalexin production in rice leaves in our ongoing proteomics analysis. Nonetheless, differential induction of secondary metabolism–related genes by gamma radiation indicates activation of the self-defense mechanism in rice leaves.

Finally, 2 genes related to the biotic and abiotic stress responses were examined. The OsPR1b gene is a pathogenesis-related gene induced by pathogens and numerous other elicitors (Jwa et al. 2006). However, we could only observe an induction in its mRNA level predominantly at 12h, and at other time points, there was a general decrease in expression (Figure 6). On the other hand, OsPR10a (also known as the probenazole-inducible protein, PBZ1) was strongly induced starting at 6h, followed by a decline at 12h, but thereafter showing a strong increase until 72h. The PBZ1 gene has previously been shown to be strongly induced in response to ultralow-level dose of gamma radiation (Rakwal et al. 2009) and by other stresses (Jwa et al. 2006). Recently, the PBZ1 protein having RNase activity was suggested to play a key role in cell death in plants (Kim et al. 2011).

Taken together, the above results indicate that gamma radiation affects rice by causing the transcriptional activation of genes involved in rice self-defense mechanisms, including genes involved in DNA repair, antioxidant defense, photosynthesis, secondary metabolism, and cell death, in the leaves. It is emphasized that the genes selected above, although based on previous ionizing radiation exposure experiments, are also modulated by other biotic and abiotic stress factors. Therefore, gamma radiation as an environmental stimulus adds to the growing list of stresses being examined in rice and therein provides the ability to discern the expression and regulation of each gene under various differential stress conditions. Moreover, RT-PCR analysis of gene expression provided us with initial confirmatory data showing that these rice plants are uniquely gamma ray stressed.

DNA Microarray Analyses Reveal Numerous Differentially Expressed Genes Involved in the Early and Late Stress Responses

The data on the expression levels of the above-mentioned selected genes clearly revealed that gamma radiation triggers the differential expression of genes with diverse functions in a time-dependent manner, and these genes can be broadly categorized as early- and late-responsive genes (Figure 6). These data provided us further confidence to examine in detail the genomewide expression profiles in the same samples with an aim to unravel the pathways operating downstream in gamma radiation–stressed rice. DNA microarray analysis was performed as described in Materials and Methods (Supplementary Figure 7). Two chips were used to generate the lists of differentially expressed genes at 6 and 72h time points postexposure and to also know the changed gene expression levels at 0h, the start of the experiment at ITF, relative to the 0-h control at the greenhouse (NIES) in Tsukuba, and after 72h in the NIES greenhouse (Supplementary Figure 8). The up- and downregulated genes at 6 and 72h and at 0h at ITF and at 72h at the greenhouse are listed in Supplementary Tables 1–8. These gene inventories revealed that gamma radiation exposure causes the modulation of diverse gene functions. The gene resources for this experiment are available to the scientific community for study and scrutiny at the GEO database with accession number GSE53055.

On the basis of the criteria specified for identifying genes that were assumed to be more specific to the gamma radiation exposure, 4481 (upregulated) and 3740 (downregulated) genes were selected for the early—6 h—response period, compared with the 2291 (upregulated) and 1474 (downregulated) genes selected for the late—72 h—response period (Supplementary Tables 9–16). Among these, the nonredundant highly gamma radiation–responsive up- and downregulated genes are listed in Supplementary Table 9 (184 genes), 11 (225 genes), 13 (235 genes), and 15 (203 genes). Let us look at a few examples of the identified highly changed genes.

At 6h, the LOC_Os01g12440, a gene encoding the AP2 domain–containing protein was identified at the highest induction: Average fold value of 87.69 (Supplementary Table 9). The AP2 (APETALA2) and EREBPs (ethylene-responsive element–binding proteins) are plant-specific transcription factors that contain the AP2 DNA-binding domain and are key regulators of several developmental processes and, importantly, part of mechanisms used by plants to respond to environmental stress factors (Riechmann and Meyerowitz 1998; Gutterson and Reuber 2004). This becomes the first report of an AP2-EREBP family member to be induced by gamma radiation. Among the highly downregulated genes, the top hit was a 1,3;1,4-beta glucanase (Gns1; LOC_Os05g31140), which showed the lowest suppression: Average fold value of 0.00 (Supplementary Table 11). The Gns1 gene is known to be highly inducible by ethylene, wounding, salicylic acid, and fungal elicitors (Simmons et al. 1992); in transgenic plants that overexpress this gene and are associated with lesions on the leaves and that are under pathogen infection (Nishizawa et al. 2003); and by brown plant hopper attack (Wei et al. 2009). Our results indicate that for some reason unknown at present, gamma radiation strongly suppresses Gns1, which is involved in carbohydrate metabolism. At 72h, the most highly upregulated (average fold value of 404.11) gene was LOC_Os04g55159, a protease inhibitor/seed storage/LTP family protein precursor (Supplementary Table 13). These are small cysteine peptides resembling antimicrobial peptides, which have been underpredicted in plants (Silverstein et al. 2007). These are known to be induced under diverse environmental stresses, but this may be the first report of its strong induction by gamma ray. The highly downregulated (average fold value of 0.00) gene at 72h was LOC_Os10g26940 (Supplementary Table 15), which encodes a polygalacturonase, a hydrolase responsible for cell wall pectin degradation, organ consenescence, and biotic stress in plants (Liu et al. 2013, and references therein). Interestingly, the gene OsBURP16 (LOC_Os10g26940) encoding a PG1β subunit precursor was investigated at the transgenic level, and the results showed that its overexpression caused pectin degradation that affected the cell wall integrity as well as transpiration rate, which decreased tolerance to abiotic stress (Liu et al. 2013). We cannot explain the reason for OsBURP16 gene downregulation, but considering the results obtained above, protecting against possible cell damage may be a possibility.

General View of Gamma Radiation Response Pathways in Rice Cells

The above-mentioned highly changed genes (Supplementary Tables 9, 11, 13, and 15) were analyzed using the MapMan program and were functionally categorized into 35 groups, wherein the frequency of genes in each class was calculated as a percentage (Table 2). Looking at the categories that changed at 6 and 72h, protein functions were abundantly represented at 6h than at 72h, followed by RNA and DNA functions that were almost similarly represented at both time points but with a lower percentage for DNA. The stress category was also found to be highly represented at both 6 and 72h. In the case of signaling function, the genes were more mobile at the 72-h period, indicating the occurrence of secondary stress responses. On the other hand, miscellaneous and unassigned functions were highly represented, suggesting that many rice genes need to be annotated by further experiments. Understanding these gene functions will provide greater insight into the mechanisms operating behind gamma ray–induced rice self-defense mechanisms. Finally, to understand different gamma radiation responses in leaves, the expression levels of genes categorized into each subBINs were compared and visualized, as shown in Figure 7. A glance of the mapped genes and their expressions on various regulatory events presented major differences in the presence/absence of fundamental regulatory processes of hormonal and other signaling pathways, transcription factors, biotic and abiotic stress, redox reactions, and development at early (6h) and late (72h) periods. Without discussing the details of each gene here, we would like to show that first, abiotic stress–related gene processes are more induced at 72h than at 6h, compared with a strongly induced redox process at 6h relative to that at 72h, which correlates well with the strong expression of glutathione S-transferase early in the exposure period. Secondly, hormonal processes are more active at the 6-h period compared with the 72-h period. However, other signaling processes are more widely expressed at 72h, indicating secondary stress responses at later stages of gamma radiation exposure. Thirdly, transcription factors are differentially expressed at 6h (ERF/MYB strongly up), compared with the expression of bZIP and WRKY, strongly expressed at 72h, which might be directly related to the perception of gamma radiation itself. Fourthly, developmental processes are more highly expressed at 72h, which may be linked to the later-observed drying of the 3rd leaves (Figure 5). In this context, although the OsBURP16 gene shows strongly reduced expression at 72h, other cell wall–related genes are highly induced at 72h, which might lead us to speculate on their involvement in the observed leaf tip–drying phenomenon. Finally, heat shock proteins and secondary metabolites are strongly regulated at 72h, which can be correlated with the induction of secondary stress responses and the production of phytoalexins in leaves.

Table 2

The functional category of highly expressed gamma-responsive rice genes at 6 and 72h determined by MAPMAN analysis

BIN Functional category 6 h_up 6 h_down 72 h_up 72 h_down 
Count Count Count Count 
PS (photosynthesis) 1.1 0.4 0.4 0.0 
Major CHO (carbohydrate) metabolism 0.0 1.3 1.3 0.0 
Minor CHO (carbohydrate) metabolism 0.5 2.2 0.4 0.5 
Glycolysis 0.5 0.0 0.4 0.0 
Fermentation 0.5 0.0 0.0 0.5 
OPP (oxidative pentose phosphate pathway) 0.0 0.4 0.0 0.0 
TCA (tricarboxylic acid cycle) / org. transformation 0.5 0.4 0.0 1.5 
10 Cell wall 0.5 2.2 2.6 0.5 
11 Lipid metabolism 1.1 2.2 2.6 0.5 
12 N-metabolism 0.5 0.0 0.0 0.0 
13 Amino acid metabolism 0.5 0.9 1.7 0.0 
15 Metal handling 0.0 0.4 0.4 1.0 
16 Secondary metabolism 1.1 1.3 11 4.7 2.0 
17 Hormone metabolism 2.2 0.9 10 4.3 12 5.9 
18 Co-factor and vitamine metabolism 0.0 0.4 0.4 0.5 
19 Tetrapyrrole synthesis 0.0 0.0 0.9 0.0 
20 Stress 3.8 11 4.9 2.1 16 7.9 
21 Redox regulation 1.1 0.0 1.3 0.5 
22 Polyamine metabolism 0.5 0.0 0.0 0.0 
23 Nucleotide metabolism 0.0 0.0 0.9 0.5 
26 Miscellaneous 11 6.0 14 6.2 23 9.8 22 10.8 
27 RNA 17 9.2 16 7.1 15 6.4 20 9.9 
28 DNA 1.6 0.9 0.9 0.0 
29 Protein 45 24.5 19 8.4 25 10.6 11 5.4 
30 Signaling 1.6 22 9.8 15 6.4 19 9.4 
31 Cell 0.5 2.7 2.1 1.0 
33 Development 1.6 0.9 0.4 2.0 
34 Transport 3.3 3.1 3.8 3.0 
35 Not assigned 69 37.5 101 44.9 89 37.9 79 38.9 
 The number of nonredundant genes 184 100 225 100 235 100 203 100 
BIN Functional category 6 h_up 6 h_down 72 h_up 72 h_down 
Count Count Count Count 
PS (photosynthesis) 1.1 0.4 0.4 0.0 
Major CHO (carbohydrate) metabolism 0.0 1.3 1.3 0.0 
Minor CHO (carbohydrate) metabolism 0.5 2.2 0.4 0.5 
Glycolysis 0.5 0.0 0.4 0.0 
Fermentation 0.5 0.0 0.0 0.5 
OPP (oxidative pentose phosphate pathway) 0.0 0.4 0.0 0.0 
TCA (tricarboxylic acid cycle) / org. transformation 0.5 0.4 0.0 1.5 
10 Cell wall 0.5 2.2 2.6 0.5 
11 Lipid metabolism 1.1 2.2 2.6 0.5 
12 N-metabolism 0.5 0.0 0.0 0.0 
13 Amino acid metabolism 0.5 0.9 1.7 0.0 
15 Metal handling 0.0 0.4 0.4 1.0 
16 Secondary metabolism 1.1 1.3 11 4.7 2.0 
17 Hormone metabolism 2.2 0.9 10 4.3 12 5.9 
18 Co-factor and vitamine metabolism 0.0 0.4 0.4 0.5 
19 Tetrapyrrole synthesis 0.0 0.0 0.9 0.0 
20 Stress 3.8 11 4.9 2.1 16 7.9 
21 Redox regulation 1.1 0.0 1.3 0.5 
22 Polyamine metabolism 0.5 0.0 0.0 0.0 
23 Nucleotide metabolism 0.0 0.0 0.9 0.5 
26 Miscellaneous 11 6.0 14 6.2 23 9.8 22 10.8 
27 RNA 17 9.2 16 7.1 15 6.4 20 9.9 
28 DNA 1.6 0.9 0.9 0.0 
29 Protein 45 24.5 19 8.4 25 10.6 11 5.4 
30 Signaling 1.6 22 9.8 15 6.4 19 9.4 
31 Cell 0.5 2.7 2.1 1.0 
33 Development 1.6 0.9 0.4 2.0 
34 Transport 3.3 3.1 3.8 3.0 
35 Not assigned 69 37.5 101 44.9 89 37.9 79 38.9 
 The number of nonredundant genes 184 100 225 100 235 100 203 100 
Figure 7.

Molecular events and potential components for cellular response against gamma radiation stress in rice leaves. Gene expression changes are depicted in MapMan format, version 3.1.1, where (A) 6h posttreatment and (B) 72h posttreatment indicate the early- and late-responsive gene expressions; each square represents a gene. Red and blue colors indicate up- and downregulation in gene expression, respectively.

Figure 7.

Molecular events and potential components for cellular response against gamma radiation stress in rice leaves. Gene expression changes are depicted in MapMan format, version 3.1.1, where (A) 6h posttreatment and (B) 72h posttreatment indicate the early- and late-responsive gene expressions; each square represents a gene. Red and blue colors indicate up- and downregulation in gene expression, respectively.

Concluding Remarks

The herein-presented results provide an overview of the low-level gamma radiation–responsive rice transcriptome, showing both specific and common (to other abiotic stress) modulations of gene expression in the rice plant. Two important points can be highlighted from this study: 1) The experimental design and strategy provide a new way to study the effects of gamma radiation in cereal model systems, although the effects of dose dependency remain to be clarified, and 2) the large inventory of differentially expressed genes provides a great resource for genes that might be uniquely modulated by ionizing radiation. Considering the large number of changed genes, it will be possible to clarify the gamma ray response completely only by further experimentation and detailed bioinformatics analysis. Future studies will involve analyzing the leaf proteome to complement the genomics data reported here and to observe the effects of gamma radiation from the whole plant to the level of the seed.

Supplementary Material

Supplementary material can be found at http://www.jhered.oxfordjournals.org/.

Funding

There were no external funding sources for this work.

Acknowledgments

Authors appreciate the help of Mr K. Matsumoto (NIES, Tsukuba) for managing the growth of the rice seedlings used in these experiments. Authors thank the people of Iitate village (Fukushima) and all other people involved in this study at various parts of the experiment for their support and encouragement, without which this work could not have seen light. We also appreciate the support of Iitate-mura Society for Radioecology (IISORA) (http://iitate-sora.net/).

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Author notes

Corresponding editor: Tomoko Steen