Transcriptomic analysis of rice in response to iron deficiency and excess
© Bashir et al.; licensee Springer. 2014
Received: 10 April 2014
Accepted: 23 July 2014
Published: 12 September 2014
Iron (Fe) is essential micronutrient for plants and its deficiency as well as toxicity is a serious agricultural problem. The mechanisms of Fe deficiency are reasonably understood, however our knowledge about plants response to excess Fe is limited. Moreover, the regulation of small open reading frames (sORFs) in response to abiotic stress has not been reported in rice. Understanding the regulation of rice transcriptome in response to Fe deficiency and excess could provide bases for developing strategies to breed plants tolerant to Fe deficiency as well as excess Fe.
We used a novel rice 110 K microarray harbouring ~48,620 sORFs to understand the transcriptomic changes that occur in response to Fe deficiency and excess. In roots, 36 genes were upregulated by excess Fe, of which three were sORFs. In contrast, 1509 genes were upregulated by Fe deficiency, of which 90 (6%) were sORFs. Co-expression analysis revealed that the expression of some sORFs was positively correlated with the genes upregulated by Fe deficiency. In shoots, 50 (19%) of the genes upregulated by Fe deficiency and 1076 out of 2480 (43%) genes upregulated by excess Fe were sORFs. These results suggest that excess Fe may significantly alter metabolism, particularly in shoots.
These data not only reveal the genes regulated by excess Fe, but also suggest that sORFs might play an important role in the response of plants to Fe deficiency and excess.
KeywordsExcess Fe Fe deficiency Iron Peptides Rice Small open reading frames
Iron (Fe) is an essential micronutrient for all higher organisms, and its deficiency causes a serious nutritional problem in both humans and plants. Although mineral soils are rich in Fe (>5%), various factors such as a high soil pH and the presence of sodium carbonate adversely affect the availability and uptake of Fe through plant roots (Marschner ; Mori ). In contrast, a low soil pH and anaerobic conditions, such as in a paddy field, lead to the reduction of Fe3+ to Fe2+, which can result in increased absorption and conditions of excess Fe (Neue et al. ; Quinet et al. ). Fe toxicity can occur in flooded soils with a pH below 5.8 under aerobic conditions, and at a pH below 6.5 under anaerobic conditions (Fageria et al. ). Fe toxicity is a serious agricultural problem, particularly when plants are grown in acidic soils (Guerinot and Ying ; Quinet et al. ). Developing plants that can grow in problematic soils requires an understanding of the molecular mechanisms of Fe uptake, transport, and storage in plants under conditions of varying Fe availability (Bashir et al. [2013a]). The molecular mechanisms of Fe uptake from soil have been extensively studied (Bashir et al. ; Bashir et al. [2011b]; Bashir et al. [2013a]; Guerinot ; Guerinot and Ying ; Ishimaru et al. [2011b]; Ishimaru et al. [2011a]; Kobayashi and Nishizawa ; Marschner ). Plants are divided into two broad categories (strategies I and II) based on how they uptake Fe from the soil (Marschner ; Marschner and Römheld ). Rice is a strategy II plant, and secretes 2'-deoxymugineic acid (DMA) to acquire soil Fe. The genes involved in DMA synthesis have been cloned and characterized (Bashir et al. ; Bashir and Nishizawa ; Inoue et al. ; Inoue et al. ; Nozoye et al. ; Suzuki et al. ; Suzuki et al. ; Suzuki et al. ; Takahashi et al. ). Specifically, L-methionine is converted to nicotianamine (NA) by NA synthase 1-3 (OsNAS1-3), and is then converted to 3'-keto acid by NA aminotransferase 1 (OsNAAT1) and finally DMA synthase (OsDMAS1) converts this 3'-keto acid to DMA (Bashir et al. ; Bashir and Nishizawa ; Bashir et al. ; Inoue et al. ; Inoue et al. ; Ma et al. ; Ma et al. ; Mori and Nishizawa ; Nozoye et al. [2014a]; Nozoye et al. [2014b]). DMA is then secreted to the rhizosphere via the mugineic acid transporter (OsTOM1) Nozoye et al. . In the rhizosphere, DMA binds to Fe(III), and the resulting DMA-Fe (III) complex is taken up by OsYSL15 (Inoue et al. ; Lee et al. ). Rice also uses OsIRT1 to uptake ferrous Fe under paddy field conditions, and secretes phenolics to solubilize apoplasmic Fe (Bashir et al. [2011b]; Ishimaru et al. [2011a]; Ishimaru et al. [2011b]). Once Fe is absorbed through roots, it is translocated to the aerial parts of the plant. The genes involved in root-to-shoot translocation and the transport of Fe to subcellular organelles have also been characterized (Aoyama et al. ; Bashir et al. [2011a]; Bashir et al. [2011c]; Bashir et al. [2013b]; Ishimaru et al. ; Ishimaru et al. ; Ishimaru et al. [2011a]; Ishimaru et al. [2011b]; Ishimaru et al. ; Kakei et al. ; Koike et al. ; Lee et al. ; Yokosho et al. ; Zhang et al. [2012b]).
Plants can accumulate varying levels of Fe and the response of rice to Fe toxicity was recently summarized after comprehensive transcriptomic and physiological analyses (Quinet et al. ). In the current study, our main objective was to understand the transcriptomic response of rice to different conditions of Fe availability. We therefore performed a microarray analysis of plants accumulating high, yet not physiologically toxic, levels of Fe. Although the rice genome has been sequenced (Kawahara et al. ), the identification of small open reading frames (sORFs) typically consisting of fewer than 100 codons was not addressed in plants until recently (Hanada et al. ; Hanada et al. ; Hanada et al. ). These sORFs play a critical role in morphogenesis in Arabidopsis thaliana (Hanada et al. ; Hanada et al. ; Hanada et al. ). Although the potential role of sORF in rice is recently discussed (Okamoto et al. ) their regulation in response to different abiotic stresses has not been assessed in rice. In this study, we used a novel 110 K rice microarray that, along with previously identified genes, includes ~48,620 sORFs to identify transcriptional changes in response to Fe deficiency and excess in rice roots and shoots. This will allow a better understanding of the response of plants to these stresses, and suggests the involvement of sORFs in Fe metabolism under different conditions of Fe availability.
Morphological responses to Fe deficiency and excess Fe
In plants grown under Fe-deficient conditions, the concentrations of zinc and copper (Cu) increased in the shoots, whereas the manganese (Mn) concentrations were comparable to plants grown in the presence of Fe (Figure 1e-g). In contrast, plants grown in the presence of excess Fe accumulated more Mn in their shoots compared to plants supplied with 100 μM Fe (Figure 1f). In the roots of plants grown under Fe-deficient conditions, the concentrations of Fe and Mn decreased significantly, whereas the concentration of Cu increased compared to plants grown with 100 μM Fe (Figure 1h-k).
Genes upregulated by Fe deficiency and downregulated by excess Fe in roots
Genes upregulated by Fe-deficiency and downregulated by excess Fe in roots
OsIRO2 (0.89) (0.91)
MIR (0.848), (0.94)
OPT (0.91) (0.82)
TGF-beta receptor, type I/II (0.74) (0.79)
TGF-beta_receptor,_type_I/II_extracellular_region_family_protein (0.80) (0.83)
TGF-beta_receptor,_type_I/II_extracellular_region_family_protein (0.86) (0.89)
Protein_of_unknown_function_DUF1230_family_protein (0.81), (0.89)
Protein_kinase-like_domain_containing_protein (0.80) (0.88)
Non-protein_coding_transcript,_putative_npRNA (0.72) (0.74)
NONE Category (0.81) (0.85)
NONE Category (0.98) (0.88)
NONE Category (0.91) (0.96)
NONE Category (0.81) (0.85)
Conserved_hypothetical_protein (0.98) (0.94)
Conserved_hypothetical_protein (0.95) (0.98)
Conserved_hypothetical_protein (0.94) (0.99)
Conserved_hypothetical_protein (0.95) (0.96)
Conserved_hypothetical_protein (0.96) (0.96)
Conserved_hypothetical_protein (0.98) (0.96)
Conserved_hypothetical_protein (0.80) (0.71)
Conserved_hypothetical_protein (0.75) (0.76)
Conserved_hypothetical_protein (0.91) (0.95)
sORF (1.00) (0.94)
sORF (0.94) (1.00)
chr1_ + _43772594-43772752
sORF (0.83) (0.83)
Genes upregulated by excess Fe and downregulated by Fe deficiency in roots
Genes upregulated by excess Fe in roots
Cytochrome b561, eukaryote domain containing protein
Similar to Non-symbiotic hemoglobin 4 (rHb4)
Similar to Peroxidase 1
Similar to Peroxidase 1
Similar to Peroxidase 27 precursor (EC_220.127.116.11)
Similar to Subtilase.";category_
Von Willebrand factor, type A domain containing protein
Von Willebrand_factor, type A domain containing protein
Zinc finger, C2H2-type domain containing proteinc
Conserved hypothetical protein
Conserved hypothetical protein
chr6_ + _23392831-23392944
chr6_ + _29900249-29900395
Most of the genes downregulated by Fe deficiency (1225; 46%) were categorized as sORFs. Other downregulated genes include 15 Zn finger proteins, two WRKY transcription factors, 11 peptidase, eight heme peroxidases, and genes involved in the ethylene response and other metabolic pathways such as methionine metabolism (Additional file 2: Table S3).
Genes upregulated by Fe deficiency and downregulated by excess Fe in shoots
Genes upregulated by Fe deficiency and downregulated by excess Fe in shoots
chr6_ + _7967232-7967441
Genes upregulated by excess Fe and downregulated by Fe deficiency in shoots
Gene ontology analysis of genes upregulated by excess Fe in shoots
Cellular biosynthetic process
Cellular protein metabolic process
Cellular macromolecule biosynthetic process
Nucleobase, nucleoside and nucleotide metabolic
Generation of precursor metabolites and energy
Cellular metabolic process
Structural constituent of ribosome
Structural molecule activity
Small ribosomal subunit
Genes upregulated by excess Fe and downregulated by Fe deficiency in shoots
cDNA transposon protein, putative, unclassified
chr3_ + _35148650-35148835
chr5_ + _8517789-8518034
chr8_ + _9042728-9042955
chr9_ + _5568388-5568600
Both Fe deficiency and toxicity cause significant losses in crop yield and quality. In plants, Fe is essential for various cellular processes, as it serves as a cofactor for a range of plant enzymes, including cytochromes, catalase, peroxidase isozymes, ferredoxin, and isozymes of superoxide dismutase (Marschner, ). It was therefore expected that the expression of these genes would be downregulated by Fe deficiency. Genes upregulated during Fe deficiency-associated stress in graminaceous crops have been described extensively (Bashir et al. ; Ishimaru et al. ; Ishimaru et al. [2011b]; Kobayashi et al. ; Nagasaka et al. ; Negishi et al. ; Nozoye et al. ), and our microarray data are consistent with those of previous reports. We have therefore not discussed these genes in detail. Similarly the morphological changes in response to Fe availability as well as the effects of availability of Fe on accumulation of other metals have been widely reported in rice (Ishimaru et al. ; Bashir et al. [2011c]).
Microarray analyses were performed after one week of Fe deficiency and excess treatment and at this point, plants correspond to a new transcriptomic/metabolic steady state. Many genes upregulated by Fe deficiency are also upregulated by other stresses such as cadmium (Egan et al. ) toxicity (Nakanishi et al. ; Takahashi et al. ). Consistent with this, we observed the upregulation of several genes (Additional file 2: Table S1) that are also regulated by other abiotic stresses, including Cd toxicity (Takahashi et al. ) (OsNRAMP1), Cu toxicity (Lin et al. ) (Os04g0588700, Os02g0208300, and Os04g0512300), and heat stress (amino acid transporter and heat shock proteins). Many genes reported to be regulated by disease pathogenesis are also upregulated by Fe deficiency (Additional file 2: Table S1). The expression of symbiotic hemoglobin 2 (rHb2; Os03g0226200), which plays an important role in plant adaptation to unfavorable environment (Zhang et al. [2012a]), was also upregulated by Fe deficiency. These results suggest that Fe-deficient plants undergo oxidative stress, since oxidative stress is common during times of biotic or abiotic stress.
The expression of 1-aminocyclopropane-1-carboxylate oxidase 1 (Os09g0451400) was significantly upregulated in Fe-deficient shoots. This gene encodes an intermediate during the formation of ethylene, which plays a role in abiotic stress signaling (Lingam et al. ). Auxins interact with ethylene metabolism, and the expression of four auxin-responsive genes (two auxin-responsive SAUR protein family proteins, one auxin-induced gene, and indoleacetic acid-induced protein 18) was also upregulated by Fe deficiency (Additional file 2: Table S1). These results suggest that ethylene signaling and the reprograming of plant metabolism may be an important strategy of rice in response to Fe deficiency.
Transcriptomic changes in response to Excess Fe
The expression of several genes was upregulated by excess Fe in roots and shoots. In roots, members of the cytochrome family, oxidases, alcohol dehydrogenase, a protein kinase, a Zn finger domain-containing protein, and a heavy metal transporter were all significantly upregulated. Many of these genes are also regulated by other stresses. For example, the cytochrome_P450_family gene Os01g0803800 is upregulated by diclofop methyl (Qian et al. ), Os11g0138300 is regulated by ionizing radiation (Kim et al. ), a heavy metal transporter is regulated by excess silicon and rice blast (Brunings et al. ). One laccase gene that plays a role in lignin formation and two peroxidases (Os03g0369000 and Os07g0531400) are also upregulated by Fe toxicity (Quinet et al. ). These results suggest that under conditions of excess Fe, the generation of reactive oxygen species (ROS) increases, as ROS production is common during times of abiotic or biotic stress. However, it is unknown if the generation of ROS is a direct effect of increased Fe concentrations or is the result of an increased metabolic rate, as suggested by our MapMan analysis.
In shoots, the expression of OsLhcb1.3 (Os01g0720500) was significantly upregulated after treatment with excess Fe. The photosynthetic apparatus of barley adapts to Fe deficiency by remodeling its PSII antenna system, in which the expression of two Hvlhcb1 genes (HvLhcb1.11 and HvLhcb1.12) is upregulated, and four genes (HvLhcb1.6-9) are downregulated by Fe deficiency (Saito et al. ). Although it was not assessed experimentally, it is possible that these downregulated genes would be upregulated in response to excess Fe. Additional genes related to PSII were also upregulated, suggesting that the rate of photosynthesis is increased due to the increased availability of Fe.
The role of ethylene signaling in abiotic stress, including Fe deficiency, has been discussed extensively (Lingam et al. ). Ethylene may also play a significant role in signaling under conditions of excess Fe, since two rice ethylene response factor-3 (OsERF3) genes which regulate ethylene synthesis (Zhang et al. ) were upregulated in shoots in the presence of excess Fe. The expression of OsRab8A5, which may be involved in signal transduction, was also upregulated. Upregulation of LONELY GUY, a cytokinin-activating enzyme that regulates activation pathways in rice shoot meristems (Kurakawa et al. ), transcription factors such as OsMADS18 and OsMADS56 involved in regulating long-day-dependent flowering (Ryu et al. ) suggest that plant growth and cell division are significantly increased in shoots under conditions of excess Fe. In addition, our MapMan analysis suggested that genes that regulate metabolism are also upregulated in shoots in the response to excess amounts of Fe.
The activity and expression of glutathione reductase (GR) is already reported to change in response to Fe deficiency (Bashir et al. ), while in present experiment upregulation of OsGR1 was observed in response to excess Fe. Similarly, the expression of NADPH HC toxin reductase, which is reported to be regulated by Cu toxicity (Lin et al. ), also increased by excess Fe. Genes involved in brassinosteroids synthesis were also upregulated. In rice, brassinosteroids regulate multiple developmental processes and modulate several important traits such as height, leaf angle, fertility, and seed filling (Wang et al. ). These results further support the hypothesis that plant metabolism and growth are stimulated under conditions of excess Fe.
The expression of OsWSL2, which is associated with the elongation of very long-chain fatty acids, and Os9BGlu32 was significantly upregulated by excess Fe. Although the function of Os9BGlu32 is unknown, it is a close homolog of Os9BGlu31, which equilibrates the levels of phenolic acids and carboxylated phytohormones and their gluco-conjugates (Luang et al. ). The role of phenolic transport in Fe deficiency has been reported (Bashir et al. [2011b]; Ishimaru et al. [2011b]; Ishimaru et al. [2011a]; Jin et al. ), and it is possible that these phenolics act as antioxidants in the presence of excess Fe. Although the microarray analysis indicates that metabolic rate may increase in response to excess Fe, plants still retain many responses common to different biotic and abiotic stresses. Despite the increased metabolic rate, excess Fe cannot therefore be considered optimal for rice plants, at least under the current growth conditions.
The expression of one 2OG-Fe(II) oxygenase (Os10g0559500) was upregulated by Fe deficiency, whereas one gene (Os08g0392100) was upregulated by excess Fe. In plants, 2OG-Fe(II) oxygenase are involved in the synthesis of phytosiderophores (Nakanishi et al. ) and numerous other biosynthesis pathways. It was recently suggested that plant 2OG-Fe(II) oxygenases play a role in Fe sensing and metabolism reprograming under Fe-deficient conditions (Vigani et al. ). The upregulation of different 2'-OG dioxygenases by opposing conditions of Fe deficiency and excess suggests that these genes are involved in Fe sensing during altered Fe availability.
Changes in expression of sORFs in response to Fe deficiency and Excess
In roots, three sORF genes were upregulated by Fe deficiency and downregulated by excess Fe. Our co-expression analysis revealed that these three sORFs are not only positively co-regulated with each other, but also with several other genes presented in Table 1. Specifically, OsIRO2, MIR, OPT, eight conserved hypothetical protein genes, and two sORF genes showed a strong positive correlation (r < 0.8) when co-expression analysis was carried out for the third sORF (chr9_-_4113943-4114041). Seven sORF genes (chr1_ + _43772594-43772752, chr12_-_7456469-7456567, chr4_-_24346205-24346330, chr4_-_5708578-5708748, chr5_ + _27469071-27469241, chr6_ + _7967232-7967441, and chr9_-_4113943-4114041) were upregulated by Fe deficiency in both roots and shoots (Additional file 2: Table S8). The upregulation of several sORFs was also confirmed through real time PCR analysis (Figure 3). Among these, very high expression of chr9_-_4113943-4114041 was observed particularly in shoot tissue (Figure 3b, g). Among these sORFs, the expression of chr1_ + _43772594-43772752 is not regulated by any other known stresses, according to HanaDB-OS (http://evolver.psc.riken.jp/seiken/OS/index.html), whereas the expression of chr9_-_4113943-4114041 is significantly downregulated in roots in response to other abiotic stresses such as drought, heat, and salt. It is therefore possible that these sORFs play a significant role (e.g., signalling) during Fe deficiency. Further characterization of these sORFs will help clarify their role in abiotic stress responses.
Transcriptomic and physiological changes that occur in response to short- and long-term Fe toxicity have been reported (Quinet et al. ). However, our aim was to study the response to excess Fe, and to understand the specific responses of rice to varying Fe concentrations in roots and shoots. Our microarray analysis revealed that cellular metabolism was significantly reprogrammed in response to Fe deficiency and upregulated by excess Fe in shoots even though no morphological changes were observed in shoots under conditions of excess Fe. In addition to the upregulation of genes involved in various metabolic processes, our data suggest increased production of flavonoids and phenols, which may act as antioxidants. The expression of various transporters was also significantly upregulated, which suggests that these transporters coordinate the metabolic changes. Although the responses to Fe deficiency and excess share components with other stress responses, it does not significantly overlap with one particular stress. Moreover, our data reveal that the expression of several sORFs changes with varying Fe availability and that sORFs are co-regulated with other genes involved in Fe deficiency response, suggesting that they are involved in the response to Fe deficiency and/or excess in rice plants. However, the precise function of these sORFs is unclear. Because the products of these sORFs do not contain any characterized domains, it will be challenging to assess their function in response to different abiotic stresses.
It should be noted that the changes in the transcriptome are not specific to Fe, because the concentrations of Cu, Zn, and Mn changed in shoots with perturbations in the Fe level: Cu and Zn were increased during Fe deficiency, while Mn and Cu were increased with excess Fe (Figure 1). As a result, the observed changes in the transcriptome also represent changes in the availability of other metals. Indeed many of the genes reported to be regulated by metal deficiencies such as Zn deficiency changes in response to varying Fe availability (Ishimaru et al. ; Suzuki et al. ; Bashir et al. ; Takahashi et al. ). These analyses also reveal significant information about the regulation of sORFs in response to Fe deficiency and excess. Despite the rapid progress in genomics, uncharacterized and hypothetical genes still represent a large proportion of the rice genome. Understanding the role of these uncharacterized genes, including sORFs, is an important step in comprehensive understanding of the plants' response to different abiotic stresses (Hanada et al. ; Hanada et al. ).
Plant materials and growth conditions
Rice seeds (Oryza sativa L. cv. Nipponbare) were germinated for one week at room temperature on paper towels soaked with distilled water. After germination, the seedlings were transferred to a saran net floating on nutrient solution in a glasshouse for one week. Two-week-old plants were transferred to a 20 L plastic box containing nutrient solution with the following composition: 0.7 mM K2SO4, 0.1 mM KCl, 0.1 mM KH2PO4, 2.0 mM Ca(NO3)2, 0.5 mM MgSO4, 10 μM H3BO3, 0.5 μM MnSO4, 0.2 μM CuSO4, 0.5 μM ZnSO4, 0.05 μΜ Na2MoO4, and 100 μΜ Fe-EDTA as described previously (Suzuki et al. ) and grown for one more week. Plants were grown at 25°C for 14 h of light at 320 μmol photons m-2-s-1; and at 20°C for 10 h in dark. The nutrient solution was adjusted daily to pH 5.5 with 1 M HCl and was renewed weekly. 30 plants were grown per box (2 plants per hole) and two boxes were prepared for each treatment. For the Fe deficiency and excess treatments, four-week-old plants were transferred to nutrient solution containing 0 (Fe deficiency), 100 (Control), or 500 (excess Fe) μM Fe-EDTA and cultivated for one week. The pH of the nutrient solution was adjusted daily to 5.5, and was renewed weekly. The plants were harvested at noon.
RT-PCR and microarray analyses
For each treatment, RNA was extracted from six plants in duplicate (two biological replicates, each including six plants). RT-PCR was performed as described previously (Bashir et al. [2011c]), using the primers OsDMAS1 RT (forward) 5`-GCCGGCATCCCGCAGCGGAAGATCA-3' and OsDMAS1 RT (reverse) 5`-CTCTCTCTCTCGCACGTGCTAGCGT-3'. The primers used to assess osvit2 by RT-PCR (qRT-PCR) were (forward) 5`-AAGGCCTGGCTCGAATTCATG-3' and (reverse) 5`-GTGTATTAGATGTTCTGGAGGTG-3'. The α-tubulin primers used were (forward) 5`-TCTTCCACCCTGAGCAGCTC-3' and (reverse) 5`-AACCTTGGAGACCAGTGCAG-3'. Primers used for real time PCR were as follows OsDMAS1, (forward) 5`-GAGGAGGAGAGGCAGAGGAT-3' and (reverse) 5`-TCAACACGATCGTCAAGAGC-3', OsFRO2 (forward) 5`-GCCAGATGTTCGAGCTCTTC-3' and (reverse) 5`-GGGCTTTTGCAGAAGTTGAG-3', Os01g0127000 (forward) 5`-GAGAACATGACGAGCAACGA-3' and (reverse) 5`-AGCATGCAGCTCTTGAAGGT-3', Os07g0142100 (forward) 5`-CGTCTTCCTCGATAGCCAAA-3' and (reverse) 5`-AGCTGGAGCCACATCGAC-3', chr6_ + _23392831-23392944 (forward) 5`-TCGTGTGTAATAATATGGGCTGTT-3' and (reverse) 5`-GGATACAATGGGAAATGAGCA-3', chr6_ + _29900249-29900395 (forward) 5`-CACACGTGCGAGATCTACCT-3' and (reverse) 5`-AAAGGAAAGATTGCCATCCA-3', chr7_-_23991237-23991350 (forward) 5`-ATGTTCTACCCCATGCCACT-3' and (reverse) 5`-ATGTCGCTGGACACCCTAAC-3', chr9_-_4113943-4114041 were (forward) 5`-GGCCTGTGCTAGTTTTGGTG-3' and (reverse) 5`-ATGGGCGCAAATTACATCAT-3' respectively. All experiments were performed in a minimum of triplicates.
The microarray slides were custom-designed and contained 101,720, 60 mer probes. Of these, 48,620 were for sORFs, 50,962 probes represented RAP-DB, and the rest belonged to TIGR. For our microarray analysis, RNA was labelled using an Agilent Low RNA Input Linear Amplification Kit (Agilent Technologies Inc., Santa Clara, CA), following the manufacturer's instructions. The microarray analyses were performed as described previously (Hanada et al. ) with the exception that two biological replicates were used. Data analysis was performed using Feature Extraction and Image Analysis software (Agilent Technologies Inc.) and Microarray Suite (Affymetrix, Santa Clara, CA), and normalized and processed as described (Hanada et al. ). Those genes with a low signal intensity (<300) were filtered to focus on genes that were highly expressed under conditions of Fe deficiency and excess. For our MapMan analysis, the average log2 value of both biological replicates was calculated for individual annotations in response to Fe deficiency and excess in roots and shoots. This log2 value was then used to compare the transcriptomic changes in metabolism-related genes using MapMan 3.5.1R2 (Thimm et al. ). Our gene ontology analyses were carried out at http://www.geneontology.org/. Coexpression analyses were done at http://evolver.psc.riken.jp/seiken/OS/co-express.html. This database contains microarray data of 40 different experimental conditions obtained through microarray analysis using the same custom microarray chip as described in this manuscript.
Determination of metal concentrations
Roots were washed with de-ionized water before harvesting. Leaf and root samples were dried for three days at 70°C, and then digested with 3 ml of 13 M HNO3 at 220 C for 40 min using a MARS XPRESS microwave reaction system (CEM, Matthews, NC). All samples were processed with four biological replicates. After digestion, the samples were collected, diluted to 5 ml, and analyzed by ICP-AES (SPS1200VR; Seiko, Tokyo, Japan), as described previously (Ishimaru et al. [2011b]; Ishimaru et al. ).
Recording of the morphological characteristics of the plants
Root and shoot lengths were measured using a scale. The degree of chlorosis in the youngest fully expanded leaf was determined using a SPAD-502 chlorophyll meter (Minolta Co., Tokyo, Japan), as described previously (Ishimaru et al. ).
KB, KH and NN designed the study, KB, MS and KH performed the research, and KB, KH, MS, HN and NN discussed the data and wrote the manuscript. All authors read and approved the final manuscript.
Small open reading frames
This work was supported by a grant from the Ministry of Agriculture, Forestry, and Fisheries of Japan (Green Technology Project IP-5003).
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