Open Access

Transcriptomic analysis of rice in response to iron deficiency and excess

  • Khurram Bashir1, 2,
  • Kousuke Hanada3, 4,
  • Minami Shimizu4,
  • Motoaki Seki2, 5,
  • Hiromi Nakanishi1 and
  • Naoko K Nishizawa1, 6Email author
Rice20147:18

DOI: 10.1186/s12284-014-0018-1

Received: 10 April 2014

Accepted: 23 July 2014

Published: 12 September 2014

Background

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.

Results

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.

Conclusion

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.

Keywords

Excess Fe Fe deficiency Iron Peptides Rice Small open reading frames

Background

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 [1995]; Mori [1999]). 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. [1998]; Quinet et al. [2012]). 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. [2008]). Fe toxicity is a serious agricultural problem, particularly when plants are grown in acidic soils (Guerinot and Ying [1994]; Quinet et al. [2012]). 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. [2010]; Bashir et al. [2011b]; Bashir et al. [2013a]; Guerinot [2010]; Guerinot and Ying [1994]; Ishimaru et al. [2011b]; Ishimaru et al. [2011a]; Kobayashi and Nishizawa [2012]; Marschner [1995]). Plants are divided into two broad categories (strategies I and II) based on how they uptake Fe from the soil (Marschner [1995]; Marschner and Römheld [1994]). 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. [2006]; Bashir and Nishizawa [2006]; Inoue et al. [2003]; Inoue et al. [2008]; Nozoye et al. [2004]; Suzuki et al. [2006]; Suzuki et al. [2008]; Suzuki et al. [2012]; Takahashi et al. [1999]). 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. [2006]; Bashir and Nishizawa [2006]; Bashir et al. [2010]; Inoue et al. [2003]; Inoue et al. [2008]; Ma et al. [1995]; Ma et al. [1999]; Mori and Nishizawa [1987]; Nozoye et al. [2014a]; Nozoye et al. [2014b]). DMA is then secreted to the rhizosphere via the mugineic acid transporter (OsTOM1) Nozoye et al. [2011]. In the rhizosphere, DMA binds to Fe(III), and the resulting DMA-Fe (III) complex is taken up by OsYSL15 (Inoue et al. [2009]; Lee et al. [2009]). 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. [2009]; Bashir et al. [2011a]; Bashir et al. [2011c]; Bashir et al. [2013b]; Ishimaru et al. [2009]; Ishimaru et al. [2010]; Ishimaru et al. [2011a]; Ishimaru et al. [2011b]; Ishimaru et al. [2012]; Kakei et al. [2012]; Koike et al. [2004]; Lee et al. [2012]; Yokosho et al. [2009]; 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. [2012]). 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. [2013]), 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. [2013]; Hanada et al. [2010]; Hanada et al. [2007]). These sORFs play a critical role in morphogenesis in Arabidopsis thaliana (Hanada et al. [2013]; Hanada et al. [2010]; Hanada et al. [2007]). Although the potential role of sORF in rice is recently discussed (Okamoto et al. [2014]) 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.

Results

Morphological responses to Fe deficiency and excess Fe

When plants were grown under Fe-deficient conditions, the root and shoot length as well as the chlorophyll content decreased significantly compared with plants grown in the presence of 100 μM Fe-EDTA (Figure 1a-c). In contrast, when plants were grown under conditions of excess Fe, the root length was reduced, but no significant differences were observed in plant height or chlorophyll content compared with wild-type plants (Figure 1a-c). In the shoots of Fe-deficient plants, the concentrations of Fe were 50% lower than in plants grown with 100 μM Fe, whereas plants grown under conditions of excess Fe accumulated two-fold more Fe in their leaves (Figure 1d).
Figure 1

Morphological characteristics and metal profiling of plants grown under conditions of Fe deficiency and excess. a) Root length (cm). b) Shoot length (cm). c) Chlorophyll content. d) Shoot Fe. e) Shoot Zn. f) Shoot Mn. g) Shoot Cu. h) Root Fe. i) Root Zn. j) Root Mn. k) Root Cu (μg/g dry weight). Vertical bars followed by different letters are significantly different from each other, according to the Tukey-Kramer test (p < 0.05; n = 4).

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

Several studies reported the upregulation of genes in response to Fe deficiency in rice (Bashir et al. [2013c]; Bashir and Nishizawa [2013]; Ishimaru et al. [2009]; Nozoye et al. [2011]), however little attention is paid to identify genes regulated by excess Fe. Before carrying out our microarray analysis, we used RT-PCR to assess the expression of OsDMAS1 and OsVIT2 to confirm the effects of excess Fe and deficiency treatments. OsDMAS1 is upregulated by Fe deficiency, while expression of vacuolar Fe transporter OsVIT2 is reported to be upregulated by excess Fe (Bashir et al. [2011c]; Zhang et al. [2012b]; Bashir et al. [2013b]). In our study, OsDMAS1 was upregulated by Fe deficiency in both roots and shoots, and was downregulated by excess Fe. As expected, the expression of OsVIT2 was upregulated by excess Fe in both shoots and roots (Additional file 1: Figure S1). In general, the transcriptomic changes in roots were clearer in response to Fe-deficiency as compared to excess Fe. On the other hand in shoot tissue, the expression of secondary metabolism related genes was more significantly altered by excess Fe compared to Fe deficiency. Our microarray results revealed the upregulation of 1509 genes in response to Fe deficiency in roots (Figure 2a, e and Additional file 2: Table S1), of which 90 (6%) were sORFs. In addition, 116 genes were downregulated by excess Fe (Figure 2a, Additional file 2: Table S2). Of the 1509 genes upregulated by Fe deficiency, 43 were downregulated by excess Fe (Figure 2a, Table 1). The genes presented in Table 1 are therefore highly responsive to Fe availability in roots. Consistent with previous microarray reports, the genes upregulated by Fe deficiency included those involved in the synthesis of DMA such as OsNAAT1, and OsDMAS1, those involved in Fe-NA or DMA complex transport (OsYSL2 and OsYSL15), and the DMA efflux transporter (OsTOM1) (Table 1 (Ishimaru et al. [2009])). In addition, OsIRO2 and two other basic helix loop helix (bHLH)-type transcription factors were upregulated by Fe deficiency (Tables 1 and S1). Two ABC transporters that are upregulated by excess Cu (Lin et al. [2013]), were upregulated by Fe deficiency as were two amino acid transporters, of which Os02g0788800 is also upregulated by excess Cu (Lin et al. [2013]). MapMan analysis revealed that many metabolic genes were upregulated or downregulated in response to Fe deficiency, and many of these were upregulated in response to excess Fe in roots (Additional file 1: Figure S2). Changes in the expression of OsDMAS1 and sORF chr9_-_4113943-4114041 were also confirmed through real time PCR and the data was in line with microarray analysis (Figure 3).
Figure 2

Venn diagram representing the transcriptional changes in response to Fe deficiency and excess. a) Number of genes upregulated by Fe deficiency and downregulated by excess Fe in roots. b) Number of genes downregulated by Fe deficiency and upregulated by excess Fe in roots. c) Number of genes upregulated by Fe deficiency and downregulated by excess Fe in shoots. d) Number of genes downregulated by Fe deficiency and upregulated by excess Fe in shoots. e) Genes upregulated by Fe deficiency both in roots and shoots. f) Genes upregulated by excess Fe both in roots and shoots. g) Genes downregulated by Fe deficiency both in roots and shoots. h) Genes downregulated by excess Fe both in roots and shoots.

Table 1

Genes upregulated by Fe-deficiency and downregulated by excess Fe in roots

Locus

Gene

-Fe/+Fe

-Fe/+Fe

++Fe/+Fe

++Fe/+Fe

Os02g0306401

OsNAAT1

4.072

4.665

0.211

0.160

Os03g0237100

OsDMAS1

6.852

3.768

0.109

0.096

Os02g0649900

OsYSL2

53.191

54.448

0.439

0.498

Os02g0650300

OsYSL15

5.035

6.444

0.125

0.094

Os11g0134900

OsTOM1

10.903

5.724

0.078

0.064

Os03g0667300

OsIRT2 (0.67)

8.568

10.120

0.183

0.107

Os01g0952800

OsIRO2 (0.89) (0.91)

2.394

3.313

0.038

0.029

Os12g0282000

MIR (0.848), (0.94)

13.538

10.484

0.100

0.084

Os12g0570700

OsIDS1

5.992

9.164

0.370

0.318

Os03g0751100

OPT (0.91) (0.82)

3.410

2.244

0.147

0.091

Os01g0871600

TGF-beta receptor, type I/II (0.74) (0.79)

12.263

8.808

0.045

0.036

Os10g0567400

Rieske_[2Fe-2S]_region_domain_containing_protein

7.620

5.157

0.370

0.325

Os08g0527700

TGF-beta_receptor,_type_I/II_extracellular_region_family_protein (0.80) (0.83)

5.608

6.219

0.391

0.241

Os01g0871500

TGF-beta_receptor,_type_I/II_extracellular_region_family_protein (0.86) (0.89)

3.336

2.311

0.246

0.198

Os09g0129600

Site-specific_recombinase_family_protein

7.529

6.035

0.275

0.197

Os04g0306400

Ribose_5-phosphate_isomerase_family_protein

2.639

2.213

0.276

0.202

Os03g0439700

Protein_of_unknown_function_DUF1230_family_protein (0.81), (0.89)

7.024

8.104

0.071

0.055

Os01g0655500

Protein_kinase-like_domain_containing_protein (0.80) (0.88)

5.218

3.746

0.157

0.106

Os01g0494300

Non-protein_coding_transcript,_putative_npRNA (0.72) (0.74)

4.468

5.140

0.470

0.400

Os12g0236200

Non-protein_coding_transcript,_unclassifiable_transcript (0.82)

30.420

11.722

0.058

0.132

Os02g0707633

NONE Category (0.81) (0.85)

11.858

8.096

0.051

0.042

Os09g0118650

NONE Category (0.98) (0.88)

7.410

7.636

0.048

0.043

Os02g0779400

NONE Category

6.789

3.371

0.156

0.131

Os12g0508500

NONE Category

6.502

7.113

0.142

0.135

Os03g0615600

NONE Category (0.91) (0.96)

4.090

3.454

0.061

0.049

Os12g0435466

NONE Category (0.81) (0.85)

11.679

5.892

0.063

0.052

Os01g0608300

Conserved_hypothetical_protein (0.98) (0.94)

11.851

9.647

0.158

0.134

Os11g0262600

Conserved_hypothetical_protein (0.95) (0.98)

7.386

6.953

0.044

0.045

Os03g0431600

Conserved_hypothetical_protein (0.94) (0.99)

7.084

5.026

0.056

0.042

Os03g0725200

Conserved_hypothetical_protein

7.066

3.573

0.054

0.048

Os10g0195250

Conserved_hypothetical_protein (0.95) (0.96)

6.610

5.194

0.040

0.039

Os02g0594600

Conserved_hypothetical_protein

5.792

4.971

0.071

0.065

Os06g0294950

Conserved_hypothetical_protein (0.96) (0.96)

5.407

5.059

0.040

0.039

LOC_Os06g19095

Conserved_hypothetical_protein (0.98) (0.96)

5.407

5.059

0.040

0.039

Os01g0332200

Conserved_hypothetical_protein

5.279

5.794

0.459

0.203

Os10g0159066

Conserved_hypothetical_protein (0.80) (0.71)

4.907

5.267

0.110

0.082

Os05g0554000

Conserved_hypothetical_protein (0.91)

4.784

4.416

0.355

0.395

Os01g0689300

Conserved_hypothetical_protein (0.75) (0.76)

4.646

4.288

0.511

0.406

Os12g0236100

Conserved_hypothetical_protein (0.91) (0.95)

4.560

3.175

0.073

0.061

Os01g0953000

Conserved_hypothetical_protein

3.215

2.956

0.470

0.373

chr9_-_4113943-4114041

sORF (1.00) (0.94)

6.364

5.478

0.042

0.042

chr4_-_5708578-5708748

sORF (0.94) (1.00)

5.376

3.090

0.054

0.036

chr1_ + _43772594-43772752

sORF (0.83) (0.83)

3.181

4.281

0.249

0.263

The expression of genes listed in Table 1 is up or down regulated at least two fold in both biological replications. Coexpression analysis 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.

The values written in bold indicate the co-expression coefficient for chr9_-_4113943-4114041, while the values written in bold Italic indicate the co-expression coefficient for sORF chr4_-_5708578-5708748.

Figure 3

Expressionanalysis of selected genes in response to varying Fe availability. Expression of a, f) OsDMAS1. b, g) chr9_-_4113943-4114041. c) chr6_ + _23392831-23392944. d) chr6_ + _29900249-29900395. e) Os01g0127000. h) OsFRO2. i) Os07g0142100. j) chr7_-_23991237-23991350. a-e) Root. f-g) Shoot. The graph shows mean ± s.d. relative to the expression of α-tubulin. Vertical bars followed by different letters are significantly different from each other, according to the Tukey-Kramer test (p < 0.05; n = 3).

Genes upregulated by excess Fe and downregulated by Fe deficiency in roots

In roots, 36 genes were upregulated by excess Fe, of which three were sORFs (Table 2), while 2655 genes were downregulated by Fe deficiency, of which 1225 (46%) were sORFs (Additional file 2: Table S3). However, only nine genes were upregulated by excess Fe and downregulated by Fe deficiency. The genes upregulated by excess Fe included four peroxidases, multi-Cu oxidase (Os01g0127000), and alcohol dehydrogenase, suggesting that excess Fe causes oxidative stress. Three cytochrome P450 family proteins, which may play a role in electron transport, were also upregulated, as was the expression of one subtilase family gene. Five uncharacterized proteins and three sORFs genes were also upregulated by excess Fe (Table 2). Changes in the expression of multicopper oxidase Os01g0127000 and two sORFs chr6_ + _29900249-29900395 and chr6_ + _23392831-23392944 were also confirmed through real time PCR (Figure 3).
Table 2

Genes upregulated by excess Fe in roots

Locus

Gene

-Fe/+Fe

-Fe/+Fe

++Fe/+Fe

++Fe/+Fe

Os06g0597600

Aromatic-ring_hydroxylase_family_protein

1.695

1.047

2.401

2.054

Os09g0388400

Cof_protein_family_protein

1.528

0.532

4.731

2.708

Os01g0895300

Cytochrome b561, eukaryote domain containing protein

0.369

0.401

2.098

2.163

Os01g0803800

Cytochrome_P450_family_protein

0.397

0.391

5.581

5.668

Os01g0803900

Cytochrome_P450_family_protein

0.760

0.925

6.990

5.304

Os11g0138300

Cytochrome_P450_family_protei

1.590

2.129

6.240

4.700

Os01g0893700

DOMON_related_domain_containing_protein

0.818

0.756

25.037

23.014

Os01g0895200

DOMON_related_domain_containing_protein

0.312

0.205

3.241

2.876

Os06g0695300

Haem_peroxidase,_plant/fungal/bacterial_family_protein

0.148

0.147

9.393

7.456

Os01g0736500

Harpin-induced_1_domain_containing_protein

1.650

1.563

2.312

1.979

Os04g0542000

HAT_dimerisation_domain_containing_protein

1.942

1.176

2.360

2.143

Os04g0469000

Heavy_metal_transport/detoxification_protein

3.068

3.461

1.986

3.308

Os01g0129600

LBD40,

0.539

0.553

2.823

3.143

Os01g0127000

Multicopper_oxidase,copper_ion_binding

0.040

0.038

10.058

10.692

Os07g0681200

Plant_acid_phosphatase_family_protein

0.522

0.414

2.480

2.531

Os05g0253200

Protein_kinase-like_domain_containing_protein

2.880

1.837

2.600

3.229

Os02g0586000

Quinonprotein_alcohol_dehydrogenase-like_domain

1.112

0.772

3.192

3.130

Os01g0941400

Beta-1,3-glucanase

0.790

0.989

1.982

8.542

Os01g0940700

Glucan_endo-1,3-beta-glucosidase

1.219

1.726

2.520

14.386

Os03g0273200

Similar_to_Laccase_(EC_1.10.3.2) copper_ion_binding

5.600

5.213

2.697

3.305

Os03g0234100

Similar to Non-symbiotic hemoglobin 4 (rHb4)

1.286

1.206

2.168

2.152

Os03g0368300

Similar to Peroxidase 1

0.482

0.529

2.593

2.244

Os03g0369000

Similar to Peroxidase 1

0.763

0.738

2.608

2.861

Os07g0531400

Similar to Peroxidase 27 precursor (EC_1.11.1.7)

0.161

0.097

9.404

8.606

Os01g0795100

Similar to Subtilase.";category_

2.699

2.176

7.865

4.144

Os06g0578100

Von Willebrand factor, type A domain containing protein

0.737

1.932

5.243

5.210

Os11g0687100

Von Willebrand_factor, type A domain containing protein

1.657

3.269

4.657

4.324

Os01g0838600

Zinc finger, C2H2-type domain containing proteinc

3.505

3.445

2.704

2.022

Os02g0582900

Conserved hypothetical protein

0.443

0.263

3.946

3.242

Os04g0438600

Conserved hypothetical protein

1.375

1.398

2.466

2.099

Os04g0538300

Conserved_hypothetical_protein

0.147

0.236

18.160

29.066

Os01g0803600

NONE";category_"NONE

0.661

0.783

3.882

3.204

Os10g0451601

NONE";category_"NONE

2.712

2.123

23.277

14.910

chr2_-_1866365-1866487

sORF

5.403

2.693

4.059

2.612

chr6_ + _23392831-23392944

sORF

0.768

1.221

9.650

7.388

chr6_ + _29900249-29900395

sORF

0.106

0.111

7.392

6.886

The expression of genes listed in Table 2 is up regulated at least two fold in both biological replications.

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

In shoots, 258 genes were upregulated by Fe deficiency, of which 35 genes were also downregulated by excess Fe (Figure 2c). Consistent with previous reports, genes involved in DMA synthesis and transport (such as OsNAS1-2 and OsDMAS1, OsTOM1), Fe-NA or DMA complex transport (OsYSL2) were upregulated by Fe deficiency (Additional file 2: Table S4). Other genes regulated by Fe deficiency included OsIRT2, OsIDS1, OsIRO2 and OsFRO2. OsIDS1 is a metallothionein (MT) gene highly responsive to Fe deficiency (Itai et al. [2013]). Of the genes upregulated by Fe deficiency in shoots, 50 (19%) were sORFs, but only two of these were also downregulated by excess Fe, whereas 1655 genes were downregulated by excess Fe (Additional file 2: Table S5). The genes downregulated by excess Fe include NADPH-dependent oxidoreductases and peroxidases. Two bHLH transcription factors, a cyclin-like F-box domain-containing protein, a protein kinase, and two sORFs (chr6_ + _7967232-7967441 and chr9_-_4113943-4114041) were upregulated by Fe deficiency and downregulated by excess Fe (Table 3). A total of 74 genes were upregulated by Fe deficiency in both roots and shoots (Figure 2e), but only 17 genes were downregulated by excess Fe in both roots and shoots (Figure 2h).
Table 3

Genes upregulated by Fe deficiency and downregulated by excess Fe in shoots

Locus

Gene

-Fe/+Fe

-Fe/+Fe

++Fe/+Fe

++Fe/+Fe

Os03g0379300

bHLH_domain_containing_protein

4.969

7.570

0.108

0.145

Os04g0578600

OsFRO2

5.457

8.123

0.021

0.027

Os01g0655500

Protein_kinase-like_domain_containing_protein

8.077

4.031

0.161

0.077

Os03g0736900

Conserved_hypothetical_protein

2.415

3.145

0.322

0.384

Os07g0438300

Conserved_hypothetical_protein

3.543

3.060

0.428

0.365

Os01g0689300

Conserved_hypothetical_protein

6.633

6.891

0.362

0.325

Os03g0725200

Conserved_hypothetical_protein

7.943

10.426

0.007

0.023

Os10g0159066

Conserved_hypothetical_protein

8.220

13.331

0.081

0.124

Os10g0195250

Conserved_hypothetical_protein

10.161

14.816

0.003

0.024

Os02g0594600

Conserved_hypothetical_protein

11.980

17.788

0.037

0.073

Os06g0294950

Conserved_hypothetical_protein

14.289

17.805

0.002

0.013

LOC_Os06g19095

Conserved_hypothetical_protein

14.289

17.805

0.002

0.013

Os01g0608300

Conserved_hypothetical_protein

16.000

20.626

0.129

0.159

Os11g0262600

Conserved_hypothetical_protein

18.554

29.444

0.026

0.037

Os07g0142100

Conserved_hypothetical_protein

98.700

168.002

0.006

0.044

Os01g0659900

Cyclin-like_F-box_domain_containing_protein

2.137

2.187

0.401

0.476

Os07g0475300

NONE";category_"NONE

2.349

2.029

0.531

0.503

Os02g0746500

NONE";category_"NONE

2.390

2.909

0.425

0.477

Os04g0380900

NONE";category_"NONE

5.670

7.148

0.397

0.464

Os10g0193700

NONE";category_"NONE

7.163

6.096

0.530

0.497

Os09g0118650

NONE";category_"NONE

17.954

27.631

0.004

0.020

Os10g0524300

Peptidoglycan-binding_LysM_domain_containing

3.627

3.952

0.518

0.413

Os05g0592300

Protein_of_unknown_function_DUF1637_family_protein

6.181

6.361

0.159

0.285

Os07g0150100

Protein_of_unknown_function_DUF221_domain

2.185

2.185

0.511

0.435

Os08g0425700

Similar_to_Annexin-like_protein

2.654

1.975

0.431

0.395

Os03g0718800

Similar_to_Physical_impedance_induced_protein

2.118

2.299

0.437

0.288

Os04g0672100

Similar_to_Phytosulfokine_receptor_precursor_(EC_2.7.1.37)

3.066

2.988

0.402

0.452

Os09g0442600

Similar_to_RSH2

6.774

6.480

0.426

0.417

Os05g0566200

Similar_to_Small_CTD_phosphatase_1_splice_variant

2.441

2.265

0.480

0.483

Os01g0871500

TGF-beta_receptor,_type_I/II_extracellular_region_family_protein

2.591

3.208

0.309

0.322

Os01g0871600

TGF-beta_receptor,_type_I/II_extracellular_region_family_protein

36.333

35.382

0.110

0.140

Os09g0442400

t-snare_domain_containing_protein

2.908

2.694

0.311

0.388

Os05g0551000

Zinc_finger,_CHY-type_domain_containing_protein

7.155

4.812

0.461

0.379

chr6_ + _7967232-7967441

sORF

4.663

6.381

0.406

0.477

chr9_-_4113943-4114041

sORF

16.207

25.106

0.020

0.033

The expression of genes listed in Table 3 is up or down regulated at least two fold in both biological replications.

Genes upregulated by excess Fe and downregulated by Fe deficiency in shoots

In shoots, 2480 genes were upregulated by excess Fe, of which 1076 (43%) were sORFs (Additional file 2: Table S6). The genes upregulated by excess Fe included a 2-oxoglutarate (OG)-Fe(II) oxygenase domain-containing protein, and an ATPase, and 17 transporter genes belonging to different families. These transporters include two putative plasma membrane ABC transporter domain-containing proteins [a putative subfamily B ABC-type transporter and an MRP-like ABC transporter], two putative amino acid transporters, and two transporters belonging to the multidrug and toxic compound extrusion (MATE) transporter family, which transports small organic compounds (Omote et al. [2006]). The MATE transporter Os03g0571700 is highly homologous to rice phenolics efflux zero 1, which transports phenolics to solubilize apoplasmic Fe (Ishimaru et al. [2011b]; Ishimaru et al. [2011a]). Additional transporters that putatively transport Cu, magnesium, phosphate or other anions, and oligopeptides were also upregulated (Additional file 2: Table S7). Other genes upregulated by excess Fe include those that participate in cellular metabolic processes, gene expression and translation, and the generation of precursor metabolites and energy (Table 4).
Table 4

Gene ontology analysis of genes upregulated by excess Fe in shoots

GO ID

GO term

Query

Total

*FDR

GO:0006412

Translation

19

683

2.40E-14

GO:0010467

Gene expression

25

2581

6.40E-09

GO:0044249

Cellular biosynthetic process

29

5899

0.00013

GO:0044267

Cellular protein metabolic process

19

2983

0.00013

GO:0034645

Cellular macromolecule biosynthetic process

24

5248

0.00082

GO:0055086

Nucleobase, nucleoside and nucleotide metabolic

5

275

0.0013

GO:0006091

Generation of precursor metabolites and energy

5

308

0.002

GO:0044237

Cellular metabolic process

32

10813

0.041

GO:0003735

Structural constituent of ribosome

19

455

2.20E-18

GO:0005198

Structural molecule activity

19

531

1.80E-17

GO:0015935

Small ribosomal subunit

17

59

7.60E-31

GO:0030529

Ribonucleoprotein complex

20

503

1.20E-18

GO:0005840

Ribosome

19

456

2.70E-18

GO:0032991

Macromolecular complex

25

1365

3.10E-15

GO:0005737

Cytoplasm

20

1271

1.40E-11

GO:0043228

Non-membrane-bounded organelle

19

1590

2.90E-09

GO:0005622

intracellular

28

4460

5.10E-07

GO:0043226

organelle

22

3164

1.10E-06

GO:0005623

cell

28

6353

0.00015

GO:0043234

protein complex

5

799

0.04

*FDR; False discovery rate.

MapMan analysis revealed that many metabolic related genes were upregulated or downregulated in response to Fe deficiency, and many of these were upregulated in response to excess Fe in shoots (Additional file 1: Figure S3). A total of 43 genes were upregulated by excess Fe and downregulated by Fe deficiency (Figure 2d), of which 9 (21%) were sORFs (Table 5). In shoots, 318 genes were downregulated by Fe deficiency (Additional file 2: Table S7). Interestingly, only one gene (belonging to the cytochrome family) was upregulated in both roots and shoots in response to excess Fe (Figure 2f), whereas 53 genes were downregulated in response to Fe deficiency in both roots and shoots (Figure 2g). Genes that were downregulated by Fe deficiency included Fe sulfur [4Fe-4S] cluster assembly factor, mitochondrial substrate carrier family protein, heavy metal transporters, ferredoxin domain-containing proteins, a bHLH domain-containing protein, heme peroxidases, isocitrate dehydrogenase, OsNAS3, a ferritin gene, OsZIP7 and OsZIP10, six peroxidases, and 43 sORFs (Additional file 2: Table S7). A summary of the transcriptomic changes in chloroplasts in response to Fe deficiency and excess is shown in Additional file 1: Figure S4. The expression of photosystem II genes was either unchanged or downregulated during Fe deficiency, whereas photosystem I genes were both upregulated and downregulated. In contrast, almost all of the genes involved in ATP synthesis, PS1, and PSII were upregulated in response to excess Fe.
Table 5

Genes upregulated by excess Fe and downregulated by Fe deficiency in shoots

Locus

Gene

-Fe/+Fe

-Fe/+Fe

++Fe/+Fe

+Fe/+Fe

Os11g0140600

Annexin,_type_VII_family_protein

0.134

0.381

8.732

11.011

LOC_Os03g26100

cDNA transposon protein, putative, unclassified

0.165

0.406

13.344

8.324

LOC_Os05g22840

Conserved_hypothetical_protein

0.154

0.264

4.270

3.790

LOC_Os08g38140

Conserved_hypothetical_protein

0.520

0.542

2.090

2.512

Os01g0559200

Conserved_hypothetical_protein

0.175

0.308

2.070

2.532

Os02g0184100

Conserved_hypothetical_protein

0.447

0.534

2.255

2.249

Os08g0359900

Conserved_hypothetical_protein

0.442

0.448

2.791

3.276

Os05g0556400

DOMON_related_domain_containing_protein

0.415

0.486

2.534

2.959

Os02g0802200

Glycoside_hydrolase_family_79

0.538

0.536

2.711

2.190

Os05g0134400

Heme_peroxidase

0.544

0.530

2.600

2.714

Os02g0135100

NONE";category_"NONE

0.534

0.502

2.722

2.843

Os05g0124900

NONE";category_"NONE

0.041

0.184

4.779

4.932

Os06g0104800

NONE";category_"NONE

0.434

0.449

5.629

8.412

Os07g0407300

NONE";category_"NONE

0.247

0.491

6.269

8.481

Os08g0149701

NONE";category_"NONE

0.208

0.413

2.114

2.395

Os09g0286700

NONE";category_"NONE

0.104

0.388

13.768

14.613

Os09g0332540

NONE";category_"NONE

0.037

0.181

15.617

15.984

LOC_Os09g16320

NONE";category_"NONE

0.037

0.181

15.617

15.984

Os09g0377400

NONE";category_"NONE

0.351

0.390

3.487

4.615

Os10g0330950

NONE";category_"NONE

0.305

0.382

3.141

3.059

Os11g0586700

NONE";category_"NONE

0.121

0.312

2.744

2.357

Os01g0619900

Non-protein_coding_transcript

0.246

0.492

5.356

3.120

Os03g0846250

Non-protein_coding_transcript

0.326

0.541

2.406

2.861

Os01g0720500

OsLhcb1.3

0.298

0.423

2.166

2.575

Os02g0443000

Prefoldin_domain_containing_protein

0.173

0.333

3.485

2.818

Os04g0649900

Protein_of_unknown_function_DUF579,family_protein

0.371

0.546

2.357

2.273

Os01g0909400

Protein_of_unknown_function_DUF868,family_protein

0.349

0.521

2.585

3.464

Os03g0305000

Similar_to_AMP-binding_protein

0.283

0.508

2.729

2.443

Os09g0426800

Similar_to_Glossy1_protein.";category_"II_:

0.260

0.204

2.998

2.006

Os12g0169000

Similar_to_N-acylethanolamine_amidohydrolase

0.434

0.470

2.253

4.054

Os04g0271000

Similar_to_NAD-dependent_deacetylase

0.252

0.505

2.511

2.397

Os04g0538400

Similar_to_Nodulin_21_(N-21)

0.003

0.003

3.385

3.991

Os03g0719900

Similar_to_Peptide_transporter_1

0.455

0.460

4.535

2.624

Os05g0242166

Similar_to_Photosystem_I_reaction_centre_subunit_N

0.210

0.470

2.200

3.465

chr1_-_1443442-1443819

sORF

0.447

0.516

3.455

3.908

chr1_-_10477792-10477944

sORF

0.193

0.308

19.218

21.518

chr3_ + _35148650-35148835

sORF

0.071

0.413

52.628

88.277

chr4_-_7821106-7821402

sORF

0.252

0.464

10.953

12.886

chr4_-_16469013-16469153

sORF

0.142

0.389

2.807

2.190

chr5_ + _8517789-8518034

sORF

0.169

0.077

36.793

13.034

chr7_-_23991237-23991350

sORF

0.171

0.452

35.318

25.773

chr8_ + _9042728-9042955

sORF

0.110

0.366

12.118

9.071

chr9_ + _5568388-5568600

sORF

0.228

0.502

4.912

4.826

The expression of genes listed in Table 5 is up or down regulated at least two fold in both biological replications.

Discussion

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, [1995]). 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. [2010]; Ishimaru et al. [2009]; Ishimaru et al. [2011b]; Kobayashi et al. [2005]; Nagasaka et al. [2009]; Negishi et al. [2002]; Nozoye et al. [2007]), 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. [2009]; 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. [2007]) toxicity (Nakanishi et al. [2006]; Takahashi et al. [2011]). 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. [2011]) (OsNRAMP1), Cu toxicity (Lin et al. [2013]) (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. [2011]). 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. [2012]), Os11g0138300 is regulated by ionizing radiation (Kim et al. [2012]), a heavy metal transporter is regulated by excess silicon and rice blast (Brunings et al. [2009]). 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. [2012]). 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. [2010]). 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. [2011]). 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. [2013]) 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. [2007]), transcription factors such as OsMADS18 and OsMADS56 involved in regulating long-day-dependent flowering (Ryu et al. [2009]) 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. [2007]), 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. [2013]), 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. [2010]). 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. [2013]). 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. [2007]), 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. [2000]) 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. [2013]). 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.

Conclusion

Transcriptomic and physiological changes that occur in response to short- and long-term Fe toxicity have been reported (Quinet et al. [2012]). 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. [2012]; Suzuki et al. [2012]; Bashir et al. [2012]; Takahashi et al. [2012]). 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. [2007]; Hanada et al. [2010]).

Methods

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. [2006]) 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. [2013]) 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. [2013]). 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. [2004]). 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. [2007]).

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. [2012]).

Authors' contributions

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.

Additional files

Abbreviations

Fe: 

Iron

sORF: 

Small open reading frames

Declarations

Acknowledgements

This work was supported by a grant from the Ministry of Agriculture, Forestry, and Fisheries of Japan (Green Technology Project IP-5003).

Authors’ Affiliations

(1)
Laboratory of Plant Biotechnology, Department of Global Agricultural Sciences, Graduate School of Agricultural and Life Sciences, The University of Tokyo
(2)
Plant Genomics Network Research Team, Center for Sustainable Resource Science, RIKEN Yokohama Campus
(3)
Gene Discovery Research Group, Center for Sustainable Resource Science, RIKEN Yokohama Campus
(4)
Frontier Research Academy for Young Researchers, Department of Bioscience and Bioinformatics, Kyusyu Institute of Technology
(5)
Kihara Institute for Biological Research, Yokohama City University
(6)
Research Institute for Bioresources and Biotechnology, Ishikawa Prefectural University

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