Open Access

Autotoxicity mechanism of Oryza sativa: transcriptome response in rice roots exposed to ferulic acid

  • Wen-Chang Chi1,
  • Yun-An Chen1, 2,
  • Yu-Chywan Hsiung1,
  • Shih-Feng Fu3,
  • Chang-Hung Chou4,
  • Ngoc Nam Trinh1,
  • Ying-Chih Chen1 and
  • Hao-Jen Huang1Email author
Contributed equally
BMC Genomics201314:351

DOI: 10.1186/1471-2164-14-351

Received: 25 May 2012

Accepted: 2 May 2013

Published: 25 May 2013

Abstract

Background

Autotoxicity plays an important role in regulating crop yield and quality. To help characterize the autotoxicity mechanism of rice, we performed a large-scale, transcriptomic analysis of the rice root response to ferulic acid, an autotoxin from rice straw.

Results

Root growth rate was decreased and reactive oxygen species, calcium content and lipoxygenase activity were increased with increasing ferulic acid concentration in roots. Transcriptome analysis revealed more transcripts responsive to short ferulic-acid exposure (1- and 3-h treatments, 1,204 genes) than long exposure (24 h, 176 genes). Induced genes were involved in cell wall formation, chemical detoxification, secondary metabolism, signal transduction, and abiotic stress response. Genes associated with signaling and biosynthesis for ethylene and jasmonic acid were upregulated with ferulic acid. Ferulic acid upregulated ATP-binding cassette and amino acid/auxin permease transporters as well as genes encoding signaling components such as leucine-rich repeat VIII and receptor-like cytoplasmic kinases VII protein kinases, APETALA2/ethylene response factor, WRKY, MYB and Zinc-finger protein expressed in inflorescence meristem transcription factors.

Conclusions

The results of a transcriptome analysis suggest the molecular mechanisms of plants in response to FA, including toxicity, detoxicification and signaling machinery. FA may have a significant effect on inhibiting rice root elongation through modulating ET and JA hormone homeostasis. FA-induced gene expression of AAAP transporters may contribute to detoxicification of the autotoxin. Moreover, the WRKY and Myb TFs and LRR-VIII and SD-2b kinases might regulate downstream genes under FA stress but not general allelochemical stress. This comprehensive description of gene expression information could greatly facilitate our understanding of the mechanisms of autotoxicity in plants.

Keywords

Allelochemical Ferulic acid Microarray Protein kinase Rice Autotoxicity

Background

Monoculture of crops leads to decreased growth and yield in the next season, with autotoxicity the major culprit [13]. Autotoxicity occurs when a plant releases toxic chemical substances into the environment that inhibit germination and growth of conspecific plants [4]. Recently, an increasing number of reports have provided evidence for the role of autotoxicity in replant failure and soil sickness [1]. Autotoxicity is a common problem in continuous monocropping of rice [2] because decomposing rice straw is left in fallow fields [5]. A range of secondary metabolites in rice straws, such as phenolic acids [6] and a few flavones and terpenoids [7], are potent autotoxins.

Phenolic compounds are common in soils. Whitehead [8] reported that the concentration of phenolic compounds in rhizosphere soil solution may reach 90 ppm. Various phenolic compounds such as ferulic acid (FA), o-hydroxy phenyl acetic acid, and p-coumaric acid have been isolated from decomposing rice residues in soil [5]. These compounds inhibit the growth of rice seedlings in the order of FA > p-coumaric acid > o-hydroxy phenyl acetic acid [9]. Exposure of plant roots to FA reduces water use [10], inhibits foliar expansion [11] and root elongation [12], and decreases nutrient uptake [1315]. Further, FA exposure rapidly depolarizes root cell membranes, causing a generalized increase in membrane permeability, inducing lipid peroxidation and affecting certain enzymatic activities [1618]. Ferulic acid may be esterified with cell wall polysaccharides, be incorporated into lignin structures, or form bridges that connect lignin with wall polysaccharides, thus resulting in cell wall rigidity and restriction of cell growth [19, 20]. Ferulic acid affects cell wall-bound peroxidase (POD) and phenylalanine ammonia-lyase (PAL) activities, lignin content, and root growth in seedlings [21].

Several reports demonstrated that autotoxins induce oxidative stress in plants [22, 23]. Reactive oxygen species (ROS) play a vital role in the plant defense against stresses and in cell growth and development [24, 25]. Low concentrations of ROS, as a signal, can lead to repair of cellular damage, but high levels can lead to programmed cell death [26]. Calcium is a crucial regulator of growth and development in plants [27]. ROS-activated calcium channel activity is required during the growth of cells in the elongation zone of the root [28].

Both allelopathy and autotoxicity play important roles in regulating plant biodiversity and productivity [3]. Autotoxins can impact many physiological and biochemical reactions in plants such as rice, alfalfa, cucumber, tomato, corn, wheat, sugarcane [1, 23]. The potential mechanisms underlying autotoxicity have been explored in alfalfa and cucumber [22, 29]. In alfafa, cinnamic acid is a phenolic acid and the major autotoxin in leaves and root exudates [30]. In cucumber, autotoxins can inhibit the membrane H+-ATPase activity that drives the uptake of essential ions, other solutes and water [22]. However, our knowledge of an autotoxicity mechanism is poorly understood. Transcriptional profiling experiments using microarrays are being conducted to examine the effects of natural phytotoxins on the plant transcriptome [31]. Microarray analyses were used to analyze gene expression profiles of plants exposed to the allelochemicals 2(3H)-benzoxazolinone [32], fagomine, gallic acid, rutin [33], 3-(3',4'-dihydroxyphenyl)-L-alanine [34], and juglone [35].

Rice (Oryza sativa L.) is a model for genomic research into the responses of monocot species to environmental stresses. In this study, we used FA as a rice-model autotoxin and used microarray assay to assess alterations in rice root gene expression induced by the autotoxin. We discuss the possible involvement of reactive oxygen species (ROS) and calcium in allelochemical signal transduction pathways. These data significantly expand on previous studies examining plant transcriptional responses to allelochemicals and provides a foundation for elucidating the autotoxicity mechanism of O. sativa, particularly the phytotoxic effect of decomposing rice residues in soil.

Results

Effect of FA on growth and root architecture of rice

To select an appropriate concentration of FA for stress treatments, we conducted a dose-response analysis of rice root growth 3 days after FA treatment (Figure 1A). Compared with the control, 25 ppm FA significantly reduced root growth. With 50 ppm FA, root growth was about half of the control growth, and with 200 ppm, growth was almost completely inhibited.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-14-351/MediaObjects/12864_2012_Article_7106_Fig1_HTML.jpg
Figure 1

Ferulic acid (FA) stress inhibits root elongation of rice seedlings. (A) Rice roots were measured after 3 d of treatment with different concentrations of FA (0, 25, 50, 100, or 200 ppm). Results represent the means ± SD (n = 30) of 3 independent experiments. Asterisks indicate significant differences (P < 0.05) from the control treatment. (B) To assess reactive oxygen species (ROS) production under FA stress, root samples were labeled with 10 μM CM-H2DCF-DA for 30 min and treated with 50 ppm FA for 1–3 h. Green fluorescence indicates the presence of ROS. (C) To evaluate calcium accumulation under FA stress, root samples were labeled with 10 μM Oregon Green 488 BAPTA-1, a calcium indicator, for 30 min and treated with 50 ppm FA for 1–3 h. Green fluorescence indicates the presence of calcium. Five control and 5 treated roots showed similar results. Magnification for representing images was × 100.

Compared with the control, 50 ppm FA inhibited crown root, lateral root and root hair formation. Both the number and length of lateral roots and root hairs were significantly reduced with 50 ppm FA as compared with the control (Table 1).
Table 1

Effect of ferulic acid treatment on number and length of crown root, lateral root and root hairs in rice

Character a

Water

50 ppm FA

Crown root number

6.93 ± 0.88

2.93 ± 0.70

Lateral root number/seedling

20.73 ± 2.63

6.67 ± 1.54

Lateral root length (mm)

6.15 ± 0.93

2.45 ± 0.60

Root hair number/mmb

73.5 ± 6.50

40.1 ± 3.07

Root hair length (μm) c

599.05 ± 30.34

205.55 ± 27.29

a Number and length of root hairs on seminal root measured after 24 h FA treatment; other characters were determined on seedlings after 3-day FA treatment. Data are mean ± SD.

b Number of root hairs from one side of 1-mm sections at the root hair zone (3–4 mm behind the root tip) on seminal roots. Data are mean of 10 seedlings.

c Length of the 20 longest root hairs from the root hair zone of each seminal root. Data represent the mean of 10 seedlings.

Ferulic acid rapidly induced ROS and calcium accumulation in rice roots

To determine whether FA treatment induced ROS production, we labeled roots with the ROS-sensitive dye CM-H2DCFDA (Figure 1B) or nitroblue tetrazolium (Additional file 1: Figure S1), then treated them with 50 ppm FA for 1 or 3 h. Ferulic-acid stress significantly increased the levels of dihydrodichlorofluorescein (DCF), and thus ROS, in roots (Figure 1B, Additional file 2: Figure S2). To determine whether FA treatment induced calcium accumulation, we used a calcium indicator, Oregon green 488 BAPTA-1, before FA treatment. Calcium level was significantly increased in root tip regions with 50 ppm FA treatment for 1 or 3 h (Figure 1C and Additional file 2: Figure S2).

Effect of FA on lipid peroxidation

Ferulic-acid–induced oxidative damage of roots was positively confirmed by Schiff’s staining in the meristem and elongation zone of roots (Additional file 3: Figure S3). Ferulic-acid–induced root oxidative damage was measured by LOX activity with non-denaturing PAGE. We detected 3 LOX isozymes in rice roots treated with 50 ppm FA for 3, 6, 12, and 24 h (Additional file 3: Figure S3).

Expression profiling by microarray assay

To identify genes and biological pathways associated with FA toxicity and tolerance in rice roots, we used large-scale expression profiling. RNA samples were collected from root tips early (1 and 3 h) after FA treatment to examine rapid changes in global patterns of gene expression. We pooled RNA isolated from the two short (1 and 3 h) FA exposures to maximize gene discovery. Mechanisms of adaptation after long-term (24 h) FA exposure are important, but the physiological and metabolic parameters measured after long treatment periods might be distorted by the severe toxic effects of FA. We aimed to understand the primary response to FA exposure as opposed to responses to nonspecific cellular damage.

We performed microarray assays with RNA extracted from roots treated with 50 ppm FA after short (pooled from 1- and 3-h treatments) and long (24 h) exposure. This FA level is comparable to that found in rice-field soils [8, 36]. In all, 1,204 genes were responsive to short FA exposure and 176 to long exposure. After short FA treatment, 972 genes were upregulated (FDR < 0.1, fold change ≥ 2) and 232 were downregulated (FDR < 0.1, fold change ≤ 0.5) (Additional file 4: Table S1).

We used GO analysis [37] to determine the functions of the 972 upregulated genes (Table 2, Additional file 5: Table S2). The most significantly enriched GO term was “response to stress” (GO:0006950, FDR 2.00E-47). Other enriched terms were “phenylpropanoid metabolic process” (GO:0009698, FDR 2.10E-07), “transmembrane transport” (GO:0055085, FDR 1.10E-12), “proteolysis” (GO:0006508, FDR 1.30E-14), “cell wall macromolecule metabolic process” (GO:0044036, FDR 6.10E-13) and “signal transduction” (GO:0007165, FDR 6.30E-05). For molecular function, the significant GO terms were “kinase activity” (GO:0016301, 1.10E-32), “calcium ion binding” (GO:0005509, FDR 7.80E-23), “transcription factor activity” (GO:0003700, FDR 9.00E-19), and “chitinase activity” (GO:0004568, FDR 1.00E-09).
Table 2

Gene ontology analysis of 972 up-regulated genes

GO ID

GO term

Query item

Background item

FDR p-value

 

biological process

 

Regulation of biological process

regulation of metabolic process

GO:0080090

regulation of primary metabolic process

53

324

8.30E-30

GO:0045449

regulation of transcription

51

321

3.00E-28

GO:0060255

regulation of macromolecule metabolic process

53

326

1.10E-29

GO:0010556

regulation of macromolecule biosynthetic process

52

322

4.40E-29

GO:0010468

regulation of gene expression

52

324

5.50E-29

GO:0009889

regulation of biosynthetic process

52

322

4.40E-29

GO:0051171

regulation of nitrogen compound metabolic process

51

323

3.90E-28

regulation of cellular process

    

GO:0007165

signal transduction

11

106

6.30E-05

GO:0007242

intracellular signaling cascade

10

68

6.90E-06

Biological regulation

    

GO:0065008

regulation of biological quality

12

14

4.40E-18

Multi-organism process

    

GO:0051707

response to other organism

9

39

4.80E-07

GO:0009617

response to bacterium

6

7

3.80E-09

Cellular process

    

cellular response to stimulus

    

GO:0070887

cellular response to chemical stimulus

7

43

0.00013

GO:0055085

transmembrane transport

9

12

1.10E-12

Metabolic process

    

primary metabolic process

    

GO:0005975

carbohydrate metabolic process

37

138

3.60E-29

GO:0005976

polysaccharide metabolic process

12

57

1.10E-08

GO:0006022

aminoglycan metabolic process

6

21

1.70E-05

GO:0006030

chitin metabolic process

6

21

1.70E-05

GO:0016052

carbohydrate catabolic process

17

45

1.10E-16

GO:0006629

lipid metabolic process

21

81

1.20E-16

GO:0019538

protein metabolic process

80

487

4.30E-44

GO:0006508

proteolysis

23

126

1.30E-14

secondary metabolic process

    

GO:0006721

terpenoid metabolic process

5

54

0.025

GO:0016101

diterpenoid metabolic process

5

35

0.0036

GO:0009698

phenylpropanoid metabolic process

6

11

2.10E-07

macromolecule metabolic process

    

GO:0019538

protein metabolic process

80

487

4.30E-44

GO:0043412

macromolecule modification

50

265

3.60E-31

GO:0006464

protein modification process

50

264

3.10E-31

GO:0044036

cell wall macromolecule metabolic process

11

21

6.10E-13

GO:0016998

cell wall macromolecule catabolic process

7

21

8.60E-07

GO:0010467

gene expression

55

419

3.50E-26

GO:0009059

macromolecule biosynthetic process

56

569

1.30E-20

Establishment of localization

    

transport

    

GO:0006811

ion transport

13

66

5.40E-09

GO:0006812

cation transport

10

65

4.60E-06

GO:0030001

metal ion transport

10

36

1.30E-08

Response to stimulus

    

GO:0009719

response to endogenous stimulus

8

106

0.0075

GO:0009628

response to abiotic stimulus

7

41

9.70E-05

GO:0009607

response to biotic stimulus

13

39

4.10E-12

GO:0006950

response to stress

46

103

2.00E-47

GO:0006952

defense response

16

59

3.40E-13

GO:0006979

response to oxidative stress

11

17

2.40E-14

GO:0042221

response to chemical stimulus

27

133

3.00E-18

GO:0010033

response to organic substance

8

106

0.0075

 

molecular function

 

Molecular transducer activity

    

GO:0004871

signal transducer activity

13

32

2.00E-13

Transporter activity

    

substrate-specific transporter activity

    

GO:0022891

substrate-specific transmembrane transporter

14

79

4.90E-09

GO:0015075

ion transmembrane transporter activity

9

68

5.20E-05

GO:0008324

cation transmembrane transporter activity

5

62

0.04

transmembrane transporter activity

    

GO:0016820

hydrolase activity, acting on acid anhydrides, catalyzing transmembrane movement of substances

5

17

9.30E-05

GO:0042626

ATPase activity, coupled to transmembrane movement of substances

5

17

9.30E-05

active transmembrane transporter activity

    

GO:0015291

secondary active transmembrane transporter

7

24

2.20E-06

GO:0015399

primary active transmembrane transporter activity

5

25

0.00064

Antioxidant activity

    

GO:0004601

peroxidase activity

8

68

0.00036

Transcription regulator activity

    

GO:0003700

transcription factor activity

26

116

9.00E-19

Catalytic activity

    

oxidoreductase activity

    

GO:0004497

monooxygenase activity

21

47

2.40E-22

GO:0051213

dioxygenase activity

7

7

1.10E-11

GO:0015036

disulfide oxidoreductase activity

7

10

1.20E-09

transferase activity

    

GO:0016757

transferase activity, transferring glycosyl groups

24

31

1.00E-33

GO:0016758

transferase activity, transferring hexosyl groups

19

30

3.70E-24

GO:0016772

transferase activity, transferring phosphorus-containing groups

54

426

5.10E-25

GO:0016773

phosphotransferase activity, alcohol group as

47

244

6.30E-30

GO:0004672

protein kinase activity

42

235

1.40E-25

GO:0016301

kinase activity

51

261

1.10E-32

hydrolase activity

    

GO:0016798

hydrolase activity, acting on glycosyl bonds

25

87

6.40E-21

GO:0004553

hydrolase activity, hydrolyzing O-glycosy

24

85

6.50E-20

GO:0004568

chitinase activity

9

21

1.00E-09

Binding

    

carbohydrate binding

    

GO:0005529

sugar binding

6

10

9.10E-08

nucleic acid binding

    

ion binding

    

GO:0043169

cation binding

126

175

6.50E-166

GO:0046872

metal ion binding

111

173

1.60E-137

GO:0046914

transition metal ion binding

80

132

9.80E-97

GO:0008270

zinc ion binding

30

89

3.60E-27

GO:0005507

copper ion binding

6

19

9.00E-06

GO:0005509

calcium ion binding

20

39

7.80E-23

These observations were further supported by comparison of metabolism genes with use of MapMan. The genes encoding enzymes related to detoxification were cytochrome P450, UDP glycosyltransferases, and glutathione-S-transferases (Figure 2A). RT-PCR validated the microarray findings (Additional file 6: Figure S4).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-14-351/MediaObjects/12864_2012_Article_7106_Fig2_HTML.jpg
Figure 2

Genes up- or down-regulated by FA stress. MapMan was used to visualize the detoxification enzyme (A), transporter (B), and phytohormone (C) genes. Each BIN or subBIN is represented as a block, within which upregulated transcripts are displayed as red squares and downregulated transcripts as blue squares. Functional bins identified by the Wilcoxon rank sum statistic as being significantly changed by FA are outlined in blue.

Expression profiles of root architecture-related genes

To investigate the involvement of root architecture related genes in FA-induced stress, we analyzed the global expression profiles of genes related to 3 such gene families (Table 3). In total, 3 of the 18 root architecture related genes were slightly downregulated by FA (FDR < 0.1). FA repressed the expression of two lateral-root genes (ARF-16, Os06g0196700, downregulated 1.7-fold; OsCel9C, Os05g0212300, downregulated 1.6-fold) and one root-hair–related gene (OsCSLD1, Os10g0578200, downregulated 1.5-fold) in rice roots (Table 3).
Table 3

List of rice genes associated with crown root, lateral root, root hair formation after FA exposures

Gene name

RAB-DB Locus ID

TIGR Locus ID

Short exposures

Long exposures

Description

   

Fold change a

Fold change a

 

Crown root-related genes

OsCRL1

Os03g0149000

LOC_Os03g05500

−1.09

1.15

Protein of unknown function DUF260 domain containing protein.

OsCRL4

Os03g0666100

LOC_Os03g46330

−1.21

−1.04

SEC7-like domain containing protein.

OsCRL5

Os07g0124700

LOC_Os07g03250

1.64

1.06

ANT (Ovule development protein aintegumenta).

OsARL1

Os03g0149100

LOC_Os03g05510

   

WOX11

Os07g0684900

LOC_Os07g48560

1.86

−1.39

Homeobox domain containing protein.

Lateral root-related genes

ARF-16

Os06g0196700

LOC_Os06g09660

−1.73*

−1.43*

Auxin response factor 1.

AUX/IAA-1

Os01g0178500

LOC_Os01g08320

1.88*

−1.20

AUX/IAA protein family protein.

AUX/IAA-11

Os03g0633500

LOC_Os03g43400

1.07

−1.40

Auxin-responsive protein IAA17 (Indoleacetic acid-induced protein 17) (Auxin response 3).

AUX/IAA-13

Os03g0742900

LOC_Os03g53150

1.22

−1.04

AUX/IAA protein family protein.

AUX/IAA-23

Os06g0597000

LOC_Os06g39590

1.77*

−1.07

Auxin-responsive protein IAA14 (Indoleacetic acid-induced protein 14) (SOLITARY-ROOT protein).

AUX/IAA-29

Os12g0601400

LOC_Os12g40900

1.16*

−1.28

Auxin-responsive protein (Aux/IAA) (Fragment).

OsCel9C

Os05g0212300

LOC_Os05g12150

−1.59*

1.43*

Endo-beta-1,4-glucanase precursor (EC 3.2.1.4).

Root hair-related genes

OsEXPA30

Os10g0535900

LOC_Os10g39110

   

OsRHL1

Os06g0184000

LOC_Os06g08500

   

OsCSLD1

Os10g0578200

LOC_Os10g42750

1.47

−1.94*

Cellulose synthase-9.

OsEXPA17

Os06g0108600

LOC_Os06g01920

   

OsEXPB5

Os04g0552200

LOC_Os04g46650

1.22

1.11

Beta-expansin 5.

OsAPY1

Os07g0682800

LOC_Os07g48430

1.35

−1.05

Apyrase.

a The fold change in expression of each gene after FA treatment was calculated by the mean from 3 biological replicates and false discovery rate <0.1 is shown with a asterisk.

Polysaccharide and cell wall metabolism

To investigate the involvement of cell-wall–related genes in FA-induced stress, we analyzed the global expression profiles of genes related to 34 such gene families (Additional file 7: Table S3). In total, 30 of the 639 cell-wall–related genes showed significant responses to FA: 16 were upregulated and 14 downregulated. Upregulated genes predominantly belonged to the expansins (EXP), yieldins (GH18), xyloglucan endotransglycosylases/hydrolases (XTH), beta-galactosidases (BGAL), glycoside hydrolases 17 (GH17), pectin acetylesterases (PAE). and glycosyl transferases 21A (GT31a).

Expression profiles of ROS-related genes

We analyzed the global expression profiles of genes related to 15 ROS-related gene families (Table 4, Additional file 8: Table S4). Among the 343 ROS response-network genes spotted on our arrays, transcripts of 270 showed changed expression after FA treatment (Additional file 8: Table S4): 55 were significantly regulated, 51 upregulated and four downregulated. The genes included alternative oxidases (AOX), glutathione peroxidase (GPx), glutathione reductase (GR), glutaredoxins (Grx), glutathione-S-transferases (GST), monodehydroascorbate reductase (MDAR), class III peroxidase (Prx), peroxiredoxin (PrxR), respiratory burst oxidase homolog (Rboh; NADPH oxidase), and thioredoxin (Trx). Almost all AOX genes were induced by FA, and 25 of the 79 GST genes were significantly upregulated by FA.
Table 4

Ferulic acid-responsive transcripts related to ROS

 

Short exposures

Long exposures

Functional categories

In genome

On arrary

Detected

Increased a

Decreased

Increased

Decreased

Reactive oxygen species (ROS) network

343

323

270

51

4

5

2

AOX (Alternative oxidases) genes

4

4

4

4*

0

0

0

APx (Ascorbate peroxidase) genes

11

11

8

0

0

0

0

Cat (Catalase) genes

3

3

3

0

0

0

0

DiOx (Alpha-dioxygenase)

1

1

1

0

0

0

0

Ferritin genes

2

2

2

0

0

0

0

GPx(Glutathione peroxidase) genes

5

5

5

1

0

0

0

GR (Glutathione reductase) genes

3

3

3

2

0

0

0

Grx (Glutaredoxins) genes

27

22

17

5

0

0

0

GST (Glutathione-S-transferases) genes

79

74

67

25*

0

3

2

MDAR (monodehydroascorbate reductase) genes

15

14

8

2

0

0

0

Prx (Class III Peroxidase) genes

138

130

103

7*

4

2

0

PrxR(Peroxiredoxin) genes

8

8

7

2

0

0

0

Rboh (Respiratory burst oxidase homolog; NADPH oxidase) genes

9

9

7

1

0

0

0

SOD (superoxide dismutase) genes

8

8

8

0

0

0

0

Trx (thioredoxin) genes

30

29

27

2

0

0

0

a Functional categories of genes, total number of genes found within the rice genome, numbers of genes present on and detected on arrays, and numbers of genes showing significant differences (FDR <0.1) in transcript abundance are shown in rows and columns labeled accordingly.

ROS families that are overrepresented in the response group are shown with asterisks (P < 0.05).

Expression profiles of transporter genes

In the rice genome, transporter families are grouped by mode of transport and energy-coupling mechanism into four types: ATP-dependent transporters, secondary transporters, ion channels, and unclassified transporters with unknown mechanisms of action. Among 1,286 transporter-related genes, 1,113 were present on our arrays, and 64 were significantly upregulated with FA treatment (Table 5, Additional file 9: Table S5). Nearly all of the transporters responding to FA were ATP-dependent and secondary transporters. Transporters with changed expression were 17 of the 130 ATP-binding cassette (ABC) transporters and three of the P-type ATPase (P-ATPase) transporters. The major facilitator superfamily (MFS) is the largest family of secondary transporters in the rice genome. Ferulic-acid treatment upregulated nine MFS genes and downregulated two. Transcripts for five proton-dependent oligopeptide transporter (POT) genes and five amino acid/auxin permease (AAAP) genes were upregulated. In addition, four of 123 drug/metabolite transporter (DMT) genes belonging to secondary transporters were upregulated by FA treatment.
Table 5

Ferulic acid-responsive transcripts related to transporter

 

Short exposures

Long exposures

Family name

In genome

On array

Detected

Increase a

Decrease

Increase

Decrease

ATP-dependent

       

ATP-binding Cassette (ABC) Superfamily

130

115

80

17*

1

3

0

P-type ATPase (P-ATPase) Superfamily

45

42

37

3

0

0

0

Ion channels

       

Ammonia Transporter Channel (Amt) Family

12

8

7

0

2

0

0

Annexin (Annexin) Family

9

6

6

1

0

1

0

Glutamate-gated Ion Channel (GIC) Family of Neurotransmitter

21

12

10

1

0

1

0

Major Intrinsic Protein (MIP) Family

37

33

24

0

0

0

1

Secondary transporter

       

Amino Acid/Auxin Permease (AAAP) Family

63

52

41

5

0

0

0

Auxin Efflux Carrier (AEC) Family

19

16

11

2

1

0

0

Amino Acid-Polyamine-Organocation (APC) Family

27

22

20

1

0

0

0

Aromatic Acid Exporter (ArAE) Family

14

13

6

1

0

1

0

Arsenite-Antimonite (ArsB) Efflux Family

3

3

3

1

0

0

0

Ca2+:Cation Antiporter (CaCA) Family

16

15

13

1

0

0

0

Chloride Carrier/Channel (ClC) Family

9

9

8

0

1

0

0

Divalent Anion:Na + Symporter (DASS) Family

7

7

5

1

0

0

0

Drug/Metabolite Transporter (DMT) Superfamily

123

106

86

4

1

0

1

K + Transporter (Trk) Family

7

7

2

1

0

0

0

Mitochondrial Carrier (MC) Family

61

59

55

2

0

0

0

Major Facilitator Superfamily (MFS)

151

133

103

9

2

1

1

Multidrug/Oligosaccharidyl-lipid/Polysaccharide (MOP) Flippase Superfamily

57

44

34

5

1

0

0

Monovalent Cation:Proton Antiporter-2 (CPA2) Family

20

18

4

1

0

1

0

Proton-dependent Oligopeptide Transporter (POT) Family

86

74

50

5

0

0

0

Telurite-resistance/Dicarboxylate Transporter (TDT) Family

9

8

4

0

1

1

0

Sulfate Permease (SulP) Family

14

14

13

1

0

0

0

Zinc (Zn2+)-Iron (Fe2+) Permease (ZIP) Family

18

16

12

2

0

0

0

a Functional categories of genes, total number of genes found within the rice genome, numbers of genes present on and detected on arrays, and numbers of genes showing significant differences (FDR <0.1) in transcript abundance are shown in rows and columns labeled accordingly. Transporter families that are overrepresented in the response group are shown with asterisks (P < 0.05).

These observations were further supported by comparison of metabolism genes by use of MapMan. Genes encoding ATP-binding cassette-type and AAAP transporters were differentially regulated in the early (1 and 3 h) response to FA (Figure 2B). MapMan analysis revealed that AAAP transporters were significantly upregulated by FA treatment.

Expression profiles of phytohormone-related genes

Among 324 phytohormone-related genes, 297 were present on our arrays, and 25 were significantly upregulated with FA treatment (Table 6, Additional file 10: Table S6). One jasmonic acid (JA) biosynthesis gene, OsAOS2 (Os03g0767000) and six JA signaling genes (Os03g0180900, Os10g0392400, Os03g0402800, Os03g0181100, Os03g0180800, and Os09g0439200) were upregulated by FA exposure; none were downregulated during the same time of exposure. MapMan analysis revealed that ethylene (ET) synthesis and signaling genes were significantly upregulated by FA treatment (Figure 2C).
Table 6

Ferulic acid-responsive transcripts related to phytohormones

 

Short exposures

Long exposures

Functional categories

In genome

On arrary

Detected

Increased a

Decreased

Increased

Decreased

Ethylene

Total

29

27

22

3

0

0

0

 

Biosynthesis

13

11

10

3

0

0

0

 

Signaling

16

16

12

0

0

0

0

JA

Total

38

34

34

7*

0

0

0

 

Biosynthesis

27

24

16

1

0

0

0

 

Signaling

11

10

10

6

0

0

0

a Functional categories of genes, total number of genes found within the rice genome, numbers of genes present on and detected on arrays, and numbers of genes showing significant differences (FDR <0.1) in transcript abundance are shown in rows and columns labeled accordingly.

Phytohormone families that are overrepresented in the response group are shown with asterisks.

Expression profiles of signaling genes and TFs

Perception and transmission of stress signals are important aspects of the plant response to environment stress. Protein kinases are crucial in these signaling pathways. The activation of signal transduction pathways connects the actions of protein kinases, TFs and the downstream stress-responsive genes. In total, 51 protein kinase genes were upregulated by FA, and 16 were downregulated (Figure 3A, Additional file 11: Table S7). Nearly all of the FA-responsive kinases were associated with the receptor-like kinase (RLK) family. In total, 40 RLK family genes were significantly upregulated and 15 were downregulated after short and long FA exposure. The leucine-rich repeat VIII (LRR-VIII) and receptor-like cytoplasmic kinases VII (RLCK-VII) subfamilies of the RLK family were significantly upregulated with FA treatment.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-14-351/MediaObjects/12864_2012_Article_7106_Fig3_HTML.jpg
Figure 3

Family classifications of FA stress response genes. Twenty-one receptor like kinase (RLK) protein kinase families (A) and 24 transcription factor families (B) were regulated by FA stress. Red bars represent the percentage of upregulated genes (fold change ≥2; FDR < 0.1) in each protein kinase or transcription factor family. Blue bars refer to the percentage of genes per kinase or transcription factor categories with respect to the entire number of kinases or transcription factors in the genome. Fisher’s exact test was used to assess the significance of overrepresented protein kinase and transcription factor families. Asterisks indicate families that were significantly overrepresented in the response group (P < 0.05).

We found 107 TFs significantly regulated by FA: 85 were significantly upregulated and 22 downregulated after short and long exposure. Transcription factors regulated by FA stress predominantly belong to the APETALA2/ET response factor (AP2/ERF), MYB, WRKY and Zinc-finger protein expressed in inflorescence meristem (ZIM) families (Figure 3B, Additional file 12: Table S8). From rice genome sequence data, 164, 129, 100 and 18 genes have been identified for the AP2/ERF, MYB, WRKY and ZIM families, respectively. In our rice roots, FA induced 14 AP2/ERF, 11 MYB, 17 WRKY and 6 ZIM families.

Transporters, TFs, and protein kinases specifically altered by FA and juglone

We compared transporters, TFs, and protein kinases regulated by exposure to FA and to the ROS-generating allelochemical juglone (Figure 4). Genes encoding AAAP transporters responded relatively specifically to FA (Additional file 13: Table S9). Comparison of the TFs induced by juglone after short FA exposure revealed that only half of the genes (48 of 84) reported in our previous study [35] showed changed expression in this study (Additional file 14: Table S10). The WRKY and Myb TFs responded significantly to FA stress. Comparison of the protein kinase genes induced by juglone revealed that the LRR-VIII and SD-2b families responded significantly to FA stress (Additional file 15: Table S11).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-14-351/MediaObjects/12864_2012_Article_7106_Fig4_HTML.jpg
Figure 4

Comparison of gene regulation by FA and juglone. FA and juglone specific regulation of transporters (A), transcription factors (B), and protein kinases (C). Genes repressed or activated by FA or juglone are in blue (fold change ≤0.5) and red (fold change ≥2), respectively.

Discussion

Autotoxicity is intraspecific allelopathy and plays an important role in plant growth inhibition and structuring plant communities [1]. Here, we observed that treatment with 50 ppm of the autotoxic chemical FA inhibited rice root length by 50% (Figure 1). Plant growth as well as response to stress is controlled by phytohormones [38]. Our microarray assay revealed changed expression of ET and JA biosynthesis and signaling genes in rice roots with FA treatment (Table 6). Xu et al. [39] reported that the combination of ET and JA synergistically induced the expression of defense genes in plants. Hua and Meyerowitz [40] and Staswick et al. [41] reported that JA and ET treatment inhibited plant root elongation. Our results suggest that these two hormones may be involved in FA-induced inhibition of root growth in rice. In addition, we found that FA inhibited crown root, lateral root and root hair formation. FA repressed the expression of two lateral-root genes (ARF-16, Os06g0196700; OsCel9C, Os05g0212300) and one root-hair–related gene in rice roots (OsCSLD1, Os10g0578200) (Table 3). Examination of the RiceXPro database revealed that JA repressed the expression of these genes (ARF-16, OsCel9C and OsCSLD1) [42]. Therefore, our results suggest that JA may involve in FA-induced morphogenic response in roots by regulating these root architecture- related genes.

In general, cell walls become lignified when cell expansion decreases or when the cell is under stress [43]. Previous study indicated that lignification may be an important step in root growth reduction in FA-stressed soybean [21]. Our FA treatment upregulated genes involved in the cell-wall macromolecule metabolic process (GO:0044036, FDR 6.10E-13), particularly cell-wall reassembly. The expression of cell-wall-related genes, most notably the expansins, was enriched by FA treatment. Cosgrove found that expansins are a group of wall proteins that induce wall stress relaxation and extension [44]. Increased expansin gene activity may be involved in stress relaxation in FA-treated roots.

Increased ROS levels are an important component of the stress induced by allelochemicals [45]. Ferulic acid modifies various oxidative reactions in vitro by acting as a substrate, activator or inhibitor depending on the concentration [46, 47]. Reactive oxygen species are toxic to plant tissues and can trigger cell growth inhibition and cell death. In addition, they may act as signal molecules involved in triggering tolerance against various environmental stresses. In this study, FA rapidly induced ROS production in rice roots. Ferulic acid-induced lipid peroxidation of roots was positively confirmed by Schiff’s reagent staining (Additional file 3: Figure S3). We found a steady increase in LOX activity in response to FA (Additional file 3: Figure S3). Thus, FA may increase ROS accumulation, lipid peroxidation, and LOX activity to affect cell integrity in rice roots and contribute to FA-induced root growth inhibition.

Many studies have provided evidence that ROS signaling is integrated with calcium signaling networks in plants. Saijo et al. [48] and Martín and Busconi [49] demonstrated rapid increase in cytoplasmic calcium concentrations in plant cells in response to multiple stress stimuli. The change in cytoplasmic calcium concentrations is critical for activating various defense responses [50]. We found that FA increased calcium levels in rice roots. Thus, ROS and calcium may act as early second messengers in the transcriptional activation of an array of defense-related genes in rice roots under FA stress.

Conjugated forms of xenobiotics can be recognized by specific membrane-associated transporters in the final detoxification phase [32]. Our GO analysis notably revealed the term “primary active transmembrane transporter activity”. We found 64 membrane-transporter–like sequences induced by FA, including 17 putative ABC, nine MFS, and five AAAP transporters. In plants, ABC and MFS transporters represent different multidrug efflux protein superfamilies associated with resistance to xenobiotics [32]. The ABC transporters facilitate the movement of glutathionylated toxins and other substrates across biological membranes [51]. We found 17 and three ABC transporter genes upregulated by short and long FA exposure, respectively. Thus, expression of ABC transporters, which work in conjunction with other detoxifying systems, was found primarily with early stages of FA stress. The AAAPs are efficient transporters of proline and betaine [52] that accumulate in higher plants under stress conditions such as drought, salinity, extreme temperatures, UV radiation, and heavy metals [53, 54]. Previous reports have demonstrated a positive relationship between proline and betaine accumulation and plant stress tolerance [55, 56]. Our observed induction of AAAPs by FA indicates their possible involvement in plant tolerance to autotoxin stress.

Protein kinases are important signaling molecules in the plant response to environment stress. Multiple plant RLK members are involved in the stress response [5759]. Among 40 RLK genes we found upregulated with FA treatment, LRR-VIII and RLCK-VII subfamilies were identified as significantly participating in transcriptional regulation (Additional file 11: Table S7). The involvement of LRR-VIII and RLCK-VII in stress responses was previously reported [58]. Thus, differential expression of a number of transmembrane receptor kinases with FA exposure suggests that multiple receptors belonging to different families may have unique regulatory mechanisms.

Responses to abiotic stresses require the production of important regulatory proteins such as TFs to mediate the expression of downstream stress-responsive genes. We found that the major TFs, AP2/ERF, MYB, WRKY, and ZIM, were overrepresented in the response to FA. The AP2/ERF, MYB, and WRKY TFs have been isolated from different plants and are important candidates for the stress tolerance response; in rice, the overexpression of AP2/ERF, MYB, and WRKY conferred significant tolerance to abiotic stresses [6063]. Transcription factors of ZIM have been intensively investigated because of the role of these proteins as key regulators of the jasmonate hormonal response in Arabidopsis and rice [64]. Here, we found that FA upregulated six ZIM genes. Overexpression of ZIM-3 (Os03g0180800), a stress-inducible gene, was found to significantly increase tolerance to salt and dehydration stresses [64]. The observed induction of AP2/ERF, MYB, WRKY, and ZIM TFs during FA treatment indicates their possible involvement in plant resistance to autotoxin stress.

Reactive oxygen species are secondary messengers for the activation of specific TFs. We found that FA induced ROS production. Therefore, we compared the set of our FA-regulated TFs to those regulated by exposure to juglone, an ROS-generating allelochemical [35]. Our results suggest that WRKY and Myb TFs and LRR-VIII and SD-2b kinases might regulate downstream genes under FA stress but not general allelochemical stress (Figure 4). Moreover, 64 transporters were upregulated by FA, but only 31 transporters were upregulated by juglone. The number of upregulated genes encoding transporters was more under FA than juglone stress. Especially, the AAAP transporter family was regulated significantly by FA stress but not by juglone (Figure 4). The AAAPs are efficient transporters of osmoprotectants such as proline, glycinebetaine and gamma-aminobutyric acid [52] that accumulate in higher plants under stress conditions. This observation could be related to detoxification of the autotoxin in rice roots. The AAAP transporters may play an important role in the FA-triggered autotoxicity mechanism.

Conclusions

FA may have a significant effect on inhibiting rice root elongation through ET and JA gene regulation. Detoxification enzymes such as cytochrome, GST, and ROS scavengers are involved in protecting against FA toxicity. Moreover, proteins involved in regulatory functions and signal transduction, including TFs, calcium-regulated proteins, and various protein kinases, play important roles in the response to FA stress (Figure 5). Future studies with rice mutants or overexpressors with altered expression of the genes identified in this work will be helpful to elucidate their biological significance and clarify new pathways involved in toxicity and tolerance to FA.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-14-351/MediaObjects/12864_2012_Article_7106_Fig5_HTML.jpg
Figure 5

Molecular mode of action of the allelochemical FA in cellular processes and response/regulatory pathways.

Methods

Plant materials

Rice plants (O. sativa L. cv. TN-67) were grown as previously described [65]. Rice seedlings were exposed to FA (25 to 200 ppm) for 1 to 24 h. Control plants were treated with water in parallel for the indicated times.

Analysis of growth

Rice seeds were surface-disinfected with 2.5% (v/v) sodium hypochlorite (Katayama, Osaka, Japan) for 15 min, then thoroughly washed in distilled water. Seeds were placed in 9-cm Petri dishes containing 20 ml distilled water and left at 37°C in the dark. After 2 d of incubation, uniformly germinated seeds were transferred to Petri dishes with filter paper discs (Advantec, Tokyo) moistened with 10 ml distilled water. Each Petri dish contained 15 germinated seeds grown at 27°C in the dark for 3 d. Once the roots reached 0.2 cm in length, they were used for experiments of exposure to FA (Sigma, St. Louis, MO, USA) under sterile conditions in the same Petri dish. Ferulic acid was added at final concentrations of 0 to 200 ppm for varying treatment durations. Root length was measured after 3 d of incubation at 26°C in darkness. Mean root length was obtained from 15 individual seedlings from at least 3 separate experiments. To determine the number of crown root and lateral roots, the number of all emerged lateral roots on seminal roots was counted by the naked eye. Root samples of 6-day-old rice seedlings were treated with FA for 3 days. The values of crown root and lateral root number represent the mean of 15 seedlings. The value of lateral root length represents the mean of 200 lateral roots. For root hair measurement, after 24-h FA treatment, the number and length of root hairs on the root hair zone immediately behind the root tip (3–4 mm behind the root tip) of seminal roots were determined by microscopy (Leica MZ125) (Leica Microsystems, Heerbrugg, Switzerland). To determine number of root hairs, the number of root hairs from one side of the root hair zone of seminal roots was counted. To determine the length of root hairs, the length of the 20 longest root hairs from the root hair zone of seminal roots was measured.

Detection of ROS and calcium levels in rice roots

Root samples of 6-day-old rice seedlings were labeled with 10 μM CM-H2DCF-DA (Molecular Probes, Eugene, OR, USA) or Oregon Green 488 BAPTA-1 (Molecular Probes) for 30 min to determine ROS or calcium levels, respectively, then treated with 50 ppm FA for 1–3 h. Fluorescence images were visualized under a confocal microscope (EZ-C1; Nikon, Tokyo, Japan) with the 488-nm laser line of an Ar laser (2 mW optical fiber output; 500–530 nm). Exposure times were equal for all samples.

Purification of total RNA

Total RNA was extracted from rice plants grown as described above [35] and treated with 50 ppm FA for 1–24 h. Roots were separated from shoots, and total RNA was isolated from root tissues with use of the RNeasy Plant Mini kit (QIAGEN, Hilden, Germany). The RNA was further treated with DNase (QIAGEN) to eliminated DNA contamination. The concentrations of total RNA samples were measured with use of NanoDrop ND2000 (NanoDrop Technologies, Wilmington, DE, USA). The purity of RNA samples was determined by OD260/280 and OD260/230. RNA samples of more than 2 μg/μl concentration and high purity (OD260/280 > 2, OD260/230 > 2) were used for microarray assay and RT-PCR.

Microarray preparation and analysis

Six-day-old rice seedlings were exposed to 50 ppm FA for short (1 and 3 h) or long (24 h) durations, then RNA was isolated from root tips to examine rapid changes in global patterns of gene expression. We pooled RNA from the two short exposures to maximize gene discovery. RNA from water-treated (control) and FA-treated roots was used with the Agilent Rice Oligo microarray (4 × 44 K, custom-made; Agilent Technologies, Palo Alto, CA, USA) for RNA labeling and microarray hybridizations involved 3 biological replicate samples.

For the microarray assay, 0.5 μg total RNA was amplified by use of the Fluorescent Linear Amplification Kit (Agilent) and labeled with Cy3-CTP (control samples) or Cy5-CTP (FA-treated) (CyDye, PerkinElmer, Norwalk, CT, USA) during the in vitro transcription process. In total, 0.825 μg Cy-labeled cRNA was fragmented to a mean size of about 50–100 nt by incubation with fragmentation buffer (Agilent) at 60°C for 30 min. The fragmented-labeled cRNA was then pooled and hybridized to the Rice Oligo DNA Microarray 44 K RAP-DB (G2519F#15241; Agilent) at 60°C for 17 h. After a washing and blow-drying with a nitrogen gun, microarrays were scanned with use of an Agilent microarray scanner at 535 nm for Cy3 and 625 nm for Cy5. Scanned images were analyzed with use of Feature Extraction v9.5.3 (Agilent), with LOWESS normalization.

Signal intensities were extracted with use of Feature Extraction v9.5.3. For statistical analysis, we excluded genes with signal intensities < 100 in all experiments. Significant differences from 0 were identified by use of t test with GeneSpringGX11 (Agilent). The Benjamini-Hochberg false discovery rate (FDR) method was used to obtain P-values that were corrected for multiple testing. The fold change in expression of each gene after FA treatment was calculated by the mean from 3 biological replicates. Genes upregulated by FA treatment by more than two-fold (cutoff by FDR < 0.1) were extracted. Each probe was considered an individual gene and annotated according to the Rice Annotation Project Data Base (RAP-DB; http://rapdb.dna.affrc.go.jp/; Rice Annotation Project 2007, 2008). The dye swap was not included. Three biological replicates were performed with 3 independent microarray slides for both short- and long-term FA treatments. Total RNA control samples were labeled with Cy3, and total RNA experimental samples (FA treatment) were labeled with Cy5.

FA-responsive genes were annotated according to the RAP-DB and TIGR Rice Genome Annotation Resource (http://rice.plantbiology.msu.edu/) [66] and were classified into functional categories by AgriGO gene ontology (GO) functional enrichment analysis [67]. For signaling, transcription factor (TF), and peroxidase functions, the lists of rice genes encoding protein kinases (1,467 genes), TFs (1,930 genes), the main ROS (343 genes), cell-wall–related genes (639 genes), and transporters (1,286 genes) were obtained from the Rice Kinase Database (http://rkd.ucdavis.edu) [68], the Database of Rice Transcription Factors (DRTF; http://drtf.cbi.pku.edu.cn/) [69], the peroxidase database (http://peroxibase.toulouse.inra.fr/) [70], Cell Wall Navigator (CWN; http://bioinfo.ucr.edu/projects/Cellwall/index.pl) [71], and TransportDB (http://www.membranetransport.org) [72], respectively. Fisher’s exact test (P < 0.05) [73] was used to assess the significance of overrepresented ROS, cell-wall, transporters, protein kinases and TFs in the list of regulated genes in the genome. The microarray data described in this study have been deposited in the Gene Expression Omnibus and are accessible with the series accession number [GEO: GSE34899] (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE34899) [74].

MapMan display

The averaged signals for a given treatment were expressed relative to those for control samples, converted to a log2 scale and displayed by use of MapMan v3.5.1 [75]. O. sativa mapping files were imported into MapMan. Rice genes represented on the Rice Oligo DNA Microarray were organized by BINS and sub-BINS for display on the schematic map of the transport overview. Gene expression was analysed by the Wilcoxon Rank Sum test with uncorrected p value.

Semi-quantitative RT-PCR

Total RNA was isolated from root tissues treated with 50 ppm FA for 3, 12, or 24 h by use of the RNeasy Plant Mini kit (QIAGEN) and purified with the RNase-Free DNase Set (QIAGEN). Primer sequences are in Supporting Information (Additional file 16: Table S12). The number of PCR cycles in the experiments was adjusted to the optimal conditions. The data was shown on the basis of at least three biological replicates. Amplicons were analyzed by 1% agarose gel electrophoresis, and PCR products were sequenced.

Histochemical analyses and in-gel enzyme analyses

Histochemical detection of lipid peroxidation involved use of Schiff’s reagent [76]. In brief, freshly harvested rice roots were stained with Schiff’s reagent for 60 min, which detects aldehydes originating from lipid peroxides. Then roots were rinsed with potassium sulphite solution (0.5% [w/v] K2S2O5 prepared in 0.05 M HCl) and maintained in the solution. The isozymes of lipoxygenase (LOX) were separated on discontinuous polyacrylamide gels (stacking gel 4.5%, separating gel 10%) under non-denaturing and non-reducing conditions. Proteins were electrophoretically separated at 4°C and 80 V in the stacking gel, then 120 V in the separating gel. Isozymes of LOX were visualized as described [77].

Notes

Abbreviations

AAAP: 

Amino acid/auxin permease

ABC: 

ATP-binding cassette

AOX: 

Alternative oxidases

AP2/ERF: 

APETALA2/ET response factor

BGAL: 

Beta-galactosidases

CM-H2DCF-DA: 

5-(and-6)-chlormethyl-2′,7′-dichlordihydrofluorescein diacetate, acetyl ester

DCF: 

Dihydrodichlorofluorescein

DMT: 

Drug/metabolite transporter

DRTF: 

The Database of Rice Transcription Factors

ET: 

Ethylene

EXP: 

Expansins

FA: 

Ferulic acid

FDR: 

False discovery rate

GH17: 

Glycoside hydrolases 17

GH18: 

Yieldins

GO: 

Gene ontology

GPx: 

Glutathione peroxidase

GR: 

Glutathione reductase

Grx: 

Glutaredoxins

GST: 

Glutathione S-transferases

JA: 

Jasmonic acid

LOX: 

Lipoxygenase

LRR-VIII: 

Leucine-rich repeat VIII

MDAR: 

Monodehydroascorbate reductase

MFS: 

Major facilitator superfamily

PAE: 

Pectin acetylesterases

PAL: 

Phenylalanine ammonia-lyase

POD: 

Peroxidase

POT: 

Proton-dependent oligopeptide transporter

Prx: 

Class III peroxidase

PrxR: 

Peroxiredoxin

P-ATPase: 

P-type ATPase

Rboh: 

Respiratory burst oxidase homolog

RLCK: 

Receptor-like cytoplasmic kinase

RLK: 

Receptor-like kinase

ROS: 

Reactive oxygen species

TFs: 

Transcription factors

Trx: 

Thioredoxin

XTH: 

Xyloglucan endotransglycosylases/hydrolases

ZIM: 

Zinc-finger protein expressed in inflorescence meristem

Declarations

Acknowledgments

This work was supported by research grants from the National Science Council (NSC 98-2621-B-006-003-MY3 and NSC 101-2621-B-006-001-MY3) and the Ministry of Education, Taiwan (Aim for the Top University Project B024). Agilent DNA microarray assays were performed by the DNA Microarray Core Laboratory in the Institute of Plant and Microbial Biology, Academia Sinica. Expression profiling and data mining used the system provided by the Bioinformatics Core for Genomic Medicine and Biotechnology Development at National Cheng-Kung University, supported by a National Science Council grant (NSC 97-3112-B-006 -011).

Authors’ Affiliations

(1)
Department of Life Sciences, National Cheng Kung University
(2)
Department of Biological Sciences, National Sun Yat-Sen University
(3)
Department of Biology, National Changhua University of Education
(4)
Graduate Institute of Ecology and Evolutionary Biology, College of Life Sciences, China Medical University

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