Open Access

Substantial reprogramming of the Eutrema salsugineum (Thellungiella salsuginea) transcriptome in response to UV and silver nitrate challenge

  • Stefanie Mucha1,
  • Dirk Walther2,
  • Teresa M Müller1,
  • Dirk K Hincha2 and
  • Erich Glawischnig1Email author
BMC Plant Biology201515:137

https://doi.org/10.1186/s12870-015-0506-5

Received: 27 February 2015

Accepted: 24 April 2015

Published: 12 June 2015

Abstract

Background

Cruciferous plants synthesize a large variety of tryptophan-derived phytoalexins in response to pathogen infection, UV irradiation, or high dosages of heavy metals. The major phytoalexins of Eutrema salsugineum (Thellungiella salsuginea), which has recently been established as an extremophile model plant, are probably derivatives of indole glucosinolates, in contrast to Arabidopsis, which synthesizes characteristic camalexin from the glucosinolate precursor indole-3-acetaldoxime.

Results

The transcriptional response of E. salsugineum to UV irradiation and AgNO3 was monitored by RNAseq and microarray analysis. Most transcripts (respectively 70% and 78%) were significantly differentially regulated and a large overlap between the two treatments was observed (54% of total). While core genes of the biosynthesis of aliphatic glucosinolates were repressed, tryptophan and indole glucosinolate biosynthetic genes, as well as defence-related WRKY transcription factors, were consistently upregulated. The putative Eutrema WRKY33 ortholog was functionally tested and shown to complement camalexin deficiency in Atwrky33 mutant.

Conclusions

In E. salsugineum, UV irradiation or heavy metal application resulted in substantial transcriptional reprogramming. Consistently induced genes of indole glucosinolate biosynthesis and modification will serve as candidate genes for the biosynthesis of Eutrema-specific phytoalexins.

Keywords

Eutrema salsugineum Thellungiella salsuginea Transcriptomics Glucosinolate biosynthesis Phytoalexin

Background

The synthesis of bioactive compounds for adaptation to abiotic stress conditions and for defence against herbivores and pathogen infections is a fundamental survival strategy of plants. The biosynthesis of phytoalexins, which contain an indole moiety substituted with additional ring systems or side chains, often containing sulphur and nitrogen, is characteristic for cruciferous plants [1]. The individual structures are very diverse even among different Brassica cultivars. In Arabidopsis thaliana, a variety of compounds are synthesized from the intermediate indole-3-acetonitrile (IAN) in response to pathogen infection or heavy metal stress [2,3] with camalexin as the most prominent metabolite. The camalexin biosynthetic pathway from tryptophan and glutathione and its role in defence against a number of fungal pathogens has been investigated in detail [4]. Phytoalexin biosynthesis is induced upon pathogen infection, but also under harsh abiotic conditions, such as high dosages of heavy metal ions or UV light, which lead to the generation of reactive oxygen species and ultimately to programmed cell death. For studies on plant metabolism, abiotic stress treatments provide the advantage that no interference of pathogen metabolism, which is often strain specific [5], has to be taken into account.

Eutrema salsugineum has been established recently as an alternative model system for crucifers in addition to Arabidopsis, because of its high tolerance of various abiotic stresses [6]. The E. salsugineum genome sequence [7,8], as well as a reference transcriptome, [9] are available and additional transcriptomics data were published recently [8,10]. E. salsugineum is also referred to as Thellungiella salsuginea. The ecotype Shandong analysed in this study was initially assigned as T. halophila and this species name was used in a number of publications [11-13]. Consequently, gene and transcript sequences isolated from Shandong ecotype have been deposited under the species names T. halophila, T. salsuginea and E. salsugineum. According to work by Koch and German [14], the species name T. salsuginea is acceptable, but E. salsugineum, which we refer to in this manuscript, is preferred.

Within the Brassicaceae, Eutrema and Arabidopsis are rather distantly related and their last common ancestor is estimated to have lived 43 million years ago [8]. Still, large stretches of syntenic regions were identified in the genomes, allowing clear assignment of putative orthologs [7,8]. At the protein level, for the number of best hit pairs between Eutrema and Arabidopsis a peak at 85% amino acid sequence identity was determined [8].

Eutrema and Arabidopsis have developed a diversified spectrum of defence compounds, such as glucosinolates [11,15,16] and indolic phytoalexins. In Arabidopsis, these phytoalexins are predominantly synthesized from the intermediate indole-3-acetaldoxime [2,17], while the characteristic Eutrema phytoalexins are most likely derivatives of 1-methoxy-indole glucosinolate [18]. The identification of biosynthetic genes for presumably glucosinolate-derived (Eutrema) and glucosinolate-independent (Arabidopsis) phytoalexins will build the basis for metabolic engineering studies of indolic phytoalexins and for establishment of a model for phytoalexin evolution in the Brassicaceae.

In this work, we analysed the transcriptional reprogramming of E. salsugineum in response to abiotic stress conditions, which lead to the accumulation of phytoalexins. We show that genes of tryptophan and indole glucosinolate biosynthesis and modification are highly upregulated providing candidates for phytoalexin biosynthesis. Also the Eutrema ortholog of WRKY33, a key regulator of Arabidopsis phytoalexin induction, was highly upregulated, even though known WRKY33 target genes, such as CYP71B15 [19] are apparently missing in E. salsugineum.

Results and Discussion

Induction of phytoalexin biosynthesis in response to UV light and silver nitrate spraying

The biosynthesis of phytoalexins by Brassicaceae species is induced by pathogen infection, but also specific abiotic stress treatments, such as high dosages of heavy metals and UV light. Applying abiotic stressors provides the advantage of a high degree of experimental reproducibility and excludes the modulation of plant defence reactions and metabolism by the pathogen. Induction of phytoalexin biosynthesis by the heavy metal salt CuCl and UV treatment was previously established by Pedras and coworkers [12,13]. Here, wasalexin induction was confirmed for 10-week old E. salsugineum (Shandong) leaves in response to UVC light, silver nitrate application, and Botrytis cinerea infection (Additional file 1: Figure S1).

In Arabidopsis, expression of camalexin biosynthetic genes is coregulated with expression of ASA1, encoding the committing enzyme of tryptophan biosynthesis. We therefore assumed that also in E. salsugineum tryptophan biosynthesis is upregulated under phytoalexin inducing conditions, which we later confirmed (see below). Quantitative RT-PCR was used to determine the induction kinetics of EsASA1 (Figure 1). For both treatments, transcript levels were highly elevated 7.5 h and 10 h after the onset of induction. Therefore, for transcriptomics analysis 8 h induction was selected.
Figure 1

RT-qPCR analysis. Time course of expression after treatment with UV light (A) and AgNO3 (B). EsASA1 (Thhalv10013041m), EsIGMT5 (Thhalv10018739m), EsPEN2 (Thhalv10001354m), EsBGLU18-1 (Thhalv10011384m), EsBGLU18-2 (Thhalv10011385m), and EsWRKY33 (Thhalv10016542m), were analysed. The expression levels, relative to the mean for 0 h, were determined by RT-qPCR, normalized to the geometric mean of three reference genes (EsActin1, EsYLS8 and EsPP2AA2). Values are means of three independent experiments ± SE.

The Eutrema transcriptome in response to UV light and heavy metal stress

RNA was isolated from non-treated leaves and from leaves treated with either AgNO3 or UV light. cDNA libraries were prepared and approximately 33 Mio to 45 Mio 50 bp reads per library were obtained by Illumina sequencing. Reads were mapped to the JGI genome [8]. For each cDNA library, approx. 75% of total transcript models were covered (Table 1) and a large overlap between treatments was observed (Additional file 2: Figure S2). Transcript models were analysed for read-counts in the different samples and annotated for best hit in the Arabidopsis thaliana genome (Additional file 3: Table S1).
Table 1

RNAseq metrics and alignments

  

n.i.

UV

AgNO 3

B.c.

reads

total fragments

33,445,682

45,326,703

33,278,110

35,924,995

 

uncounted

8,100,893

22,525,573

7,470,863

12,091,407

 

counted

25,344,789

22,801,130

25,807,247

23,833,588

 

- uniquely

17,567,426

14,322,990

19,065,875

16,287,764

 

- non-specific

7,777,363

8,478,140

6,741,372

7,545,824

transcripts

hit (reads > 0)

23,237

23,730

23,985

23,655

 

uniquely hit

21,589

21,875

22,216

22,048

 

(% of total)

(73,7%)

(74,7%)

(75,9%)

(75,3%)

Reads were mapped to the JGI genome (Yang et al., [8]), 29284 reference transcripts (2 mismatches allowed); uncounted/counted: number of unmapped/mapped reads; uniquely: number of uniquely mapped reads; non-specific: number of reads with multiple locations in the reference.

Similarly, we have analysed the transcriptome 48 h after infection of plants with B. cinerea (Additional file 3: Table S1). 3139 transcripts were identified as more than 2-fold upregulated with respect to untreated leaves. Of this set, 56% and 61% were also upregulated more than 2-fold after UV and AgNO3 treatment, respectively, indicating overlapping responses to the abiotic and biotic stressors. However, as transcriptional changes in response to UV light and AgNO3 were much more pronounced, we focussed on these treatments for further analysis.

Microarray analysis of four biological replicates was conducted with Agilent arrays based on the design by Lee et al. [9]. Statistically robust differential regulation was observed for the majority of transcripts (Additional file 4: Table S2). Of a total of 42562 oligonucleotide probes, signal intensities of 11930 (28%) and 15384 (36%) probes were significantly (t-test FDR corrected p < 0.01) elevated, while signal intensities of 11562 (27%) and 11879 (28%) probes were significantly reduced in response to UV light and AgNO3, respectively.

These array data were compared with the RNAseq data, which in addition provide information about absolute expression levels. A correlation analysis with the log2 fold-change values obtained by the two methods in response to UV and AgNO3 is shown in Additional file 5: Figure S3.

We matched RNAseq and array data based on the comparison of array probe and transcript model sequences and omitted those probes from further analysis for which no match was found. Duplicated genes with highly homologous sequences were sometimes indistinguishable on array level (e.g. TsCYP79B2, see below). Here, the more highly abundant transcript from the RNAseq analysis was chosen for the matched dataset. Log2 fold-change values based on RNAseq and array analyses were correlated (r = 0.66 for UV light, r = 0.65 for AgNO3). For further analysis, we worked with a set of 14,706 genes, for which both array and RNAseq data are available (Additional file 6: Table S3). Correlations of log2 fold-change values in response to UV and AgNO3 treatment obtained by microarray hybridization are shown in Figure 2. For a large proportion of these transcripts (88%), significant changes in abundance were detected in response to UV or AgNO3 treatment (Figure 2). 4502 (31%) transcripts were upregulated, 3433 (23%) downregulated in response to both treatments, indicating substantial overlap in metabolic and regulatory responses.
Figure 2

Global analysis of transcriptomics data. The set of 14,706 genes, for which RNAseq and array data could be matched, was analysed for significant (FDR P <0.05) transcriptional changes (array data) in response to UV light and AgNO3. A large overlap in response to the two stressors was observed.

Figure 3 shows a Mapman [20] representation of log2-fold transcriptional changes, in response to UV light (Figure 3A) and AgNO3 (Figure 3B), based on array data. Strongly repressed processes include photosynthesis and starch synthesis. The tricarboxylic acid cycle, providing precursors of aromatic amino acid and the biosynthesis of cell wall precursors are induced on the level of transcript abundance, consistent with plant defence reactions.
Figure 3

Mapman visualisation of transcript abundance changes for metabolic genes. Metabolism overview for microarray data. A: UV versus not induced (n.i.). B: AgNO3 versus n.i.. Red indicates downregulated, blue upregulated genes. The colour code indicates log2-fold changes in expression.

Transcriptional changes induced upon both UV and heavy metal stress

Transcripts that were strongly and consistently upregulated in response to both UV light and AgNO3 include a number of genes that encode enzymes involved in biosynthesis or modification of hormones and signalling compounds. This indicated that reprogramming the hormone balance is one of the key elements in the adaptation of Eutrema to high dosages of UV light or heavy metals. Genes upregulated most strongly in response to both stressors include EsSOT12 and, based on NGS data, EsST2a/EsSOT1 (Additional file 3: Table S1 and Additional file 6: Table S3). The corresponding Arabidopsis orthologs encode a sulfotransferase, which sulphonates salicylic acid, thereby positively regulating salicylic acid accumulation [21], and a sulfotransferase, which sulphonates hydroxyjasmonic acid [22]. SOT12 is also strongly induced in A. thaliana seedlings in response to UVB light [23]. Furthermore, we observed that genes encoding Eutrema orthologs of 1-amino-cyclopropane-1-carboxylate synthase 2 (ethylene biosynthesis) and cis-zeatin O-β-D-glucosyltransferase (UGT85A1, cytokinin metabolism) [24] were highly upregulated in response to both UV light and AgNO3. Other induced processes are senescence and regulation of cell death. Here, examples of highly upregulated genes include the Eutrema orthologs of AtDLAH [25] and AtBAP2, an inhibitor of programmed cell death [26].

We observed significant transcriptional reprogramming of phenylpropanoid metabolism. Genes of the core phenylpropanoid biosynthetic pathway, i.e. E. salsugineum orthologs putatively encoding phenylalanine ammonia-lyase 1 and 2, cinnamate-4-hydroxylase, cinnamoyl CoA reductase, and cinnamyl alcohol dehydrogenase were upregulated in response to UV and AgNO3. The E. salsugineum ortholog of TT4, encoding naringenine chalcone synthase, was strongly downregulated. Interestingly, in Arabidopsis strong TT4 upregulation was observed in response to UV light [27]. Whether this is due to experimental differences, such as plant age or UV wavelength or reflects a species-specific difference in adaptation with respect to the phenylpropanoids that are synthesized remains to be investigated. Further, fundamental changes in the transcript abundance of genes encoding enzymes involved in the biosynthesis of defence-related secondary metabolites were observed, which are discussed in detail below.

A number of genes have been functionally associated with the halophytic lifestyle of E. salsugineum. These include the sodium transporter EsHKT1 [28] and EsERF1 [29], which are also strongly and significantly upregulated under both AgNO3 and UV treatment (Additional file 6: Table S3). Arabidopsis ERF1 is an integrator of different abiotic and biotic stress responses [30]. For other genes associated with salt tolerance, such as SOS1 and iron superoxide dismutase this was not observed [31]. We have surveyed transcriptional changes in response to AgNO3 and UV in E. salsugineum for similarity to changes in response to drought or cold [32]. There was a clear overlap among downregulated genes, which are mainly related to photosynthesis. A common pattern among upregulated genes was not observed (Additional file 7: Figure S4A). Apparently, the responses of E. salsugineum to drought/cold and to UV/heavy metal stresses differ substantially.

The effect of silver treatment on the Arabidopsis transcriptome was investigated previously by Kaveh and coworkers [33]. The number of significantly upregulated genes was much lower than in our work on Eutrema, probably due to differences in the experimental setup. Only for a few genes, the corresponding orthologs were identified in both studies, including the orthologs of the β-glucosidase genes 18 and 46.

Recently, genes were identified in A. thaliana which are upregulated in response to both B. cinerea infection and oxidative stress [34]. For 115 out of these 175 transcripts, corresponding E. salsuginea orthologs were identified here. Strikingly, for a large fraction of these genes (76; 66%), including e.g. EsCYP79B3 and EsCYP83B1 (see below), we observed upregulation by both UV and AgNO3 treatments (Additional file 7: Figure S4B). Possibly, all these processes lead to the generation of reactive oxygen species, inducing transcriptional reactions that are largely conserved between Arabidopsis and Eutrema.

Tryptophan biosynthetic genes

In Brassicaceae, tryptophan is a precursor of indole glucosinolates and indolic phytoalexins [4], which constitute the major tryptophan sinks. As cellular tryptophan concentrations are low in Arabidopsis leaves, tryptophan biosynthesis is strongly coregulated with the biosynthesis of camalexin [35,36].

Here, we observed significant and strong increases in transcript levels associated with the tryptophan biosynthetic pathway in response to UV light and AgNO3 (Table 2). This includes genes encoding tryptophan synthase β (TSB) type 1 isoforms, while the ortholog of TSBtype2, of which the biological function is unknown [37], is significantly downregulated in response to UV light.
Table 2

Analysis of transcript abundance changes of genes associated with the biosynthesis of defence-related metabolites

Transcript ID

Best Ath hit

Gene symbol

Annotation

UV

Ag +

Fold change log2 (UV/n.i.)

FDR-p-value test

Fold change log2 (Ag/n.i.)

FDR-p-value test

RNAseq Unique reads

n.i.

UV

AgNO3

Tryptophan biosynthesis

           

Thhalv10013041m

AT5G05730.1

ASA1,TRP5,WEI2

anthranilate synthase alpha subunit 1

up

up

4,83

0,000

5,31

0,000

431

20815

28637

Thhalv10010558m

AT3G54640.1

TRP3,TSA1

tryptophan synthase alpha chain

up

up

4,34

0,000

3,76

0,000

264

9972

9692

Thhalv10013439m

AT4G27070.1

TSB2

tryptophan synthase beta-subunit 2

up

up

4,07

0,000

3,13

0,000

710

3153

2355

Thhalv10025097m

AT4G27070.1

TSB2

tryptophan synthase beta-subunit 2

up

up

3,37

0,000

2,47

0,000

2187

7481

5464

Thhalv10013857m

AT5G17990.1

PAT1,TRP1

tryptophan biosynthesis 1

up

up

2,26

0,000

3,26

0,000

22

207

268

Thhalv10014630m

AT4G27070.1

TSB2

tryptophan synthase beta-subunit 2

up

up

1,67

0,002

1,11

0,002

2

35

17

Thhalv10002557m

AT2G04400.1

IGPS

indole-3-glycerol phosphate synthase

up

up

1,16

0,000

1,40

0,000

684

6529

8538

Thhalv10016377m

AT2G29690.1

ASA2

anthranilate synthase 2

 

down

−0,20

0,356

−0,43

0,004

482

436

449

Thhalv10027732m

AT5G38530.1

TSBtype2

tryptophan synthase beta type 2

down

 

−1,47

0,000

−1,40

0,000

876

407

1012

Biosynthesis of aliphatic glucosinolates

          

Thhalv10023453m

AT1G62570.1

FMO GS-OX4

glucosinolate S-oxygenase 4

up

up

4,12

0,001

3,92

0,001

243

8147

7570

Thhalv10007582m

AT1G12140.1

FMO GS-OX5

glucosinolate S-oxygenase 5

up

up

2,12

0,000

1,48

0,000

604

1102

1077

Thhalv10018813m

AT1G74090.1

ATST5B,SOT18

desulfo-glucosinolate sulfotransf. 18

  

2,12

0,000

1,75

0,000

1133

461

175

Thhalv10007073m

AT1G18500.1

IPMS1,MAML-4

methylthioalkylmalate synthase-like 4

 

0,31

0,084

0,32

0,165

1695

1815

2672

Thhalv10004037m

AT5G23010.1

IMS3,MAM1

methylthioalkylmalate synthase 1

 

down

−0,60

0,055

−2,92

0,000

51

1

4

Thhalv10017125m

AT2G43100.1

ATLEUD1,IPMI2

isopropylmalate isomerase 2

down

 

−0,97

0,002

−1,22

0,003

655

462

942

Thhalv10013695m

AT5G14200.1

IMD1

isopropylmalate dehydrogenase 1

down

down

−1,97

0,000

−1,91

0,000

1667

266

779

Thhalv10028851m

AT4G12030.2

BASS5,BAT5

bile acid transporter 5

down

down

−2,05

0,007

−4,56

0,000

11

3

2

Thhalv10024982m

AT4G13770.1

CYP83A1,REF2

cytochrome P450 83A1

down

down

−2,79

0,000

−3,97

0,000

20919

1569

2903

Thhalv10007301m

AT1G16410.1

CYP79F1

cytochrome P450 79 F1

down

down

−2,92

0,005

−5,72

0,000

15712

1101

2740

Thhalv10013952m

AT5G07690.1

MYB29

myb domain protein 29

down

down

−3,22

0,002

−4,35

0,000

3421

380

61

Thhalv10004406m

AT5G61420.2

MYB28,HAG1

myb domain protein 28

down

down

−5,60

0,000

−6,10

0,001

427

10

10

Indole and general glucosinolate biosynthesis

          

Thhalv10007957m

AT1G21100.1

IGMT1

O-methyltransferase family protein

up

up

5,97

0,000

4,54

0,001

165

10234

6500

Thhalv10000114m

AT2G22330.1

CYP79B3

cytochrome P450 79B3

up

up

5,92

0,000

5,05

0,000

16

7685

4205

Thhalv10008152m

AT1G18570.1

AtMYB51,HIG1

myb domain protein 51

up

up

5,68

0,000

4,31

0,000

164

13093

7845

Thhalv10024861m

AT4G39950.1

CYP79B2

cytochrome P450 79B2

up

up

5,06

0,000

5,38

0,000

254

24763

30609

Thhalv10007964m

AT1G21120.1

IGMT2

O-methyltransferase family protein

up

up

4,93

0,000

2,70

0,001

434

9009

9298

Thhalv10018795m

AT1G74100.1

ATST5A,SOT16

sulfotransferase 16

up

up

4,54

0,000

4,45

0,000

665

26889

29842

Thhalv10024979m

AT4G37410.1

CYP81F4

cytochrome P450 81 F4

up

up

4,52

0,000

5,27

0,000

300

21717

48198

Thhalv10008073m

AT1G18590.1

ATST5C,SOT17

sulfotransferase 17

up

up

4,07

0,000

2,59

0,002

877

8651

4339

Thhalv10026067m

AT4G30530.1

GGP1

gammaglutamyl peptidase 1

up

up

3,31

0,000

3,55

0,000

5315

66528

98213

Thhalv10001994m

AT2G20610.1

SUR1,ALF1,RTY1

superroot1

up

up

3,22

0,000

2,48

0,000

14

28

23

Thhalv10018739m

AT1G76790.1

IGMT5

O-methyltransferase family protein

up

up

2,63

0,001

3,03

0,000

1747

42057

94252

Thhalv10007574m

AT1G24100.1

UGT74B1

UDP-glucosyl transferase 74B1

up

up

2,44

0,000

2,15

0,000

1062

6408

5926

Thhalv10024981m

AT4G37430.1

CYP81F1

cytochrome P450 81 F1

 

up

1,99

0,002

4,94

0,000

131

84

393

Thhalv10004064m

AT4G31500.1

CYP83B1,SUR2

cytochrome P450 83B1

up

up

1,70

0,001

2,06

0,000

2841

51916

96397

Thhalv10027443m

AT4G37400.1

CYP81F3

cytochrome P450 81 F3

 

up

−3,97

0,000

1,14

0,043

5

9

544

Phenylpropanoid biosynthesis

           

Thhalv10025563m

AT4G34230.1

CAD5

cinnamyl alcohol dehydrogenase 5

up

up

4,90

0,000

4,92

0,000

590

14915

11374

Thhalv10016314m

AT2G37040.1

PAL1

PHE ammonia lyase 1

up

up

4,54

0,000

4,32

0,000

5639

43919

53026

Thhalv10010153m

AT3G53260.1

ATPAL2,PAL2

PHE ammonia lyase 2

up

up

4,47

0,000

4,08

0,000

5516

24942

29448

Thhalv10016545m

AT2G30490.1

C4H,CYP73A5

cinnamate-4-hydroxylase

up

up

4,47

0,000

3,92

0,000

3465

40914

24286

Thhalv10018849m

AT1G80820.1

CCR2

cinnamoyl coa reductase

up

up

4,38

0,000

3,95

0,000

9

2796

6118

Thhalv10016544m

AT2G30490.1

CYP73A5,REF3

cinnamate-4-hydroxylase

up

up

4,34

0,000

4,28

0,000

200

5351

6505

Thhalv10020406m

AT3G21230.1

4CL5

4-coumarate:CoA ligase 5

up

up

2,86

0,000

1,30

0,000

160

961

882

Thhalv10001440m

AT2G43820.1

SGT1,UGT74F2

UDP-glucosyltransferase 74 F2

up

up

2,58

0,001

4,45

0,000

98

1100

1193

Thhalv10004662m

AT5G08640.1

FLS1

flavonol synthase 1

up

up

2,41

0,003

0,80

0,026

52

416

498

Thhalv10016538m

AT2G40890.1

CYP98A3

cytochrome P450, 98A3

  

2,32

0,001

2,10

0,000

1482

847

699

Thhalv10011357m

AT1G51680.3

4CL1

4-coumarate:CoA ligase 1

up

up

1,85

0,002

1,35

0,000

875

3071

2269

Thhalv10010658m

AT3G55120.1

A11,CFI,TT5

Chalcone-flavanone isomerase

up

up

1,76

0,001

2,04

0,000

112

352

573

Thhalv10024928m

AT4G36220.1

CYP84A1,FAH1

ferulic acid 5-hydroxylase 1

up

up

1,51

0,002

1,83

0,000

5064

5573

8972

Thhalv10026028m

AT4G34050.1

CCoAOMT1

SAM-dependent methyltransferase

up

 

0,99

0,010

0,04

0,779

851

3093

3791

Thhalv10000324m

AT3G21230.1

4CL5

4-coumarate:CoA ligase 5

 

up

0,85

0,021

0,75

0,001

28

16

240

Thhalv10008111m

AT1G15950.1

CCR1

cinnamoyl coa reductase 1

up

 

0,76

0,008

0,15

0,447

423

586

375

Thhalv10000513m

AT3G21230.1

4CL5

4-coumarate:CoA ligase 5

  

0,27

0,533

−0,03

0,957

21

17

339

Thhalv10018769m

AT1G72680.1

CAD1

cinnamyl-alcohol dehydrogenase

 

down

−0,15

0,461

−0,23

0,050

859

635

666

Thhalv10022462m

AT1G65060.1

4CL3

4-coumarate:CoA ligase 3

 

down

−0,25

0,141

−0,55

0,004

113

23

15

Thhalv10020439m

AT3G21230.1

4CL5

4-coumarate:CoA ligase 5

down

down

−0,83

0,015

−0,93

0,004

910

214

484

Thhalv10027317m

AT4G36220.1

CYP84A1,FAH1

ferulic acid 5-hydroxylase 1

  

−0,98

0,338

−1,38

0,170

1

1

3

Thhalv10013289m

AT5G07990.1

CYP75B1,TT7

cytochrome P450, 75B1

  

−1,26

0,080

−1,16

0,084

4

2

33

Thhalv10004668m

AT5G08640.1

ATFLS1,FLS,FLS1

flavonol synthase 1

down

down

−1,43

0,005

−2,38

0,002

137

17

41

Thhalv10005442m

AT1G43620.1

TT15,UGT80B1

UDP-Glycosyltransferase 80B1

down

up

−1,72

0,000

0,34

0,008

1061

827

4174

Thhalv10014054m

AT5G08640.1

FLS1

flavonol synthase 1

down

 

−2,67

0,000

−2,92

0,001

17

11

19

Thhalv10013745m

AT5G13930.1

CHS,TT4

Chalcone and stilbene synth. Fam.

down

down

−4,04

0,000

−3,32

0,005

214

11

200

Glucosinolate biosynthesis and modification

Members of the order Brassicales synthesize glucosinolates from non-polar amino acids as major defence compounds against herbivores and pathogens. In Arabidopsis thaliana, almost exclusively methionine-derived aliphatic and tryptophan-derived indole glucosinolates are found. Their biosynthetic pathways are known in great detail [38]. In Eutrema salsugineum Shandong, the short chain aliphatic allyl-2-phenylethyl-, 3-methylsulfinylpropyl-, and 3-methylthiopropylglucosinolate, the very-long-chain aliphatic 10-methylsulfinyldecylglucosinolate, as well as 3-indolylmethyl- and 1-methoxy-3-indolylmethylglucosinolate were identified as major compounds [11] (E. salsugineum denoted in this publication as T. halophila). According to labelling experiments, 1-methoxy-3-indolylmethylglucosinolate is likely to be a biosynthetic intermediate of the phytoalexins 1-methoxybrassinin and wasalexin A and B [18].

For all defined steps of the core aliphatic and indole glucosinolate biosynthetic pathways, putative orthologs of the genes encoding the corresponding enzymes were found in Eutrema salsugineum, based on homology and synteny to A. thaliana. Some additional duplication events or losses of tandem copies were detected. In contrast to the tandem duplicates CYP79F1 and CYP79F2 in A. thaliana, only one copy, designated as EsCYP79F1 was found in E. salsugineum, suggesting that this single gene is essential for the biosynthesis of aliphatic glucosinolates. A putative CYP79A2 [39] ortholog was found, which is expressed at very low levels (0, 0, and 1 reads in n.i., UV, and AgNO3 samples, respectively) consistent with the apparent absence of phenylalanine-derived glucosinolates [11]. E. salsugineum contains three CYP79B genes due to a recent duplication of CYP79B2 leading to two transcripts hybridizing to the same array probe and generating proteins with 98.6% identity of their amino acid sequences. These two duplicates strongly differ in expression level based on RNAseq data (254, 24763 and 30609, versus 0, 3, and 5 reads in n.i., UV, and AgNO3 samples, respectively).

In response to UV light and AgNO3, the core genes of indole glucosinolate biosynthesis are strongly upregulated, consistent with the proposed role of 1-methoxy-3-indolylmethylglucosinolate as precursor of the characteristic Eutrema phytoalexins (Table 2). Also, the ortholog of MYB51/HIG1, encoding a master regulator of indole glucosinolate biosynthesis in Arabidopsis [40], is consistently induced. Strikingly, in response to these stressors, transcripts encoding indole glucosinolate biosynthetic genes, such as EsCYP83B1 and EsGGP1 are among the most highly abundant, according to our RNAseq data, indicating an important metabolic response.

In Arabidopsis, a time course experiment has been performed for UV response [41]. We surveyed these data for the responses of orthologs of E. salsugineum genes we analysed by RT-qPCR (Figure 1). Moderate upregulation with respect to 0 h, peaking at 3 h for AtASA1 (5.0-fold) and AtPEN2 (2.0-fold), and at 6 h for AtIGMT5 (3.6-fold) and AtBGLU18 (3.3-fold) was observed. More generally, we surveyed these data for core indole glucosinolate biosynthetic genes and again observed only modest transcript induction 6 h after UV treatment (less than 5-fold upregulation of CYP83B1, SUR1, GGP1, SOT16 and UGT74B1). In contrast, the camalexin biosynthetic genes CYP71B15 and CYP71A13 were induced approximately 121-fold and 66-fold, respectively [41]. These differential responses are consistent with the proposed phytoalexin biosynthetic pathways in the two species.

In Arabidopsis, unmodified indole glucosinolate is methoxylated in response to pathogen infection, involving members of the CYP81F family and indole glucosinolate methyl transferases (IGMTs) [42]. E. salsugineum contains five CYP81F members, due to an additional gene copy in the CYP81F1/3/4 cluster. For three of these genes, microarray and RNAseq data were obtained and matched. Based on its expression pattern, EsCYP81F4 (Thhalv10024979m) is a candidate gene for catalysing N-hydroxylation of 3-indolylmethylglucosinolate in the biosynthesis of Eutrema phytoalexins. EsCYP81F3 (Thhalv10027443m) was induced by AgNO3 but not by UV light. Also, EsIGMT5, highly expressed in response to stress treatment (Table 2, Figure 1), is a candidate for involvement in the biosynthesis of N-methoxylated indolic compounds.

In response to pathogen infection, in Arabidopsis indole glucosinolates are degraded to bioactive compounds by the β-glucosidase PEN2 (BGLU26) [43,44]. We hypothesize that β-glucosidases are also involved in the biosynthesis of Eutrema phytoalexins. A number of β-glucosidase-encoding genes were significantly upregulated in response to AgNO3 and UV challenge (Table 3), including EsPEN2 (Thhalv10001354m), EsBGLU18-1 (Thhalv10011384m), and EsBGLU18-2 (Thhalv10011385m). The time course of induction of these genes was monitored by quantitative RT-PCR and strong induction responses to AgNO3 and UV treatment were confirmed (Figure 1). In conclusion, the Eutrema orthologs of PEN2 (BGLU26) and BGLU18 are candidates for an involvement in phytoalexin biosynthesis.
Table 3

Analysis of transcript abundance changes of genes encoding β-glucosidases

Transcript ID

Best Ath hit

Gene symbol

UV

Ag +

Fold change log2 (UV/n.i.)

FDR-p-value test

Fold change log2 (Ag/n.i.)

FDR-p-value test

RNAseq Unique reads

  

n.i.

UV

AgNO3

Thhalv10006515m

AT4G27830.1

BGLU10

up

 

1,80

0,001

−1,56

0,000

3

30

6

Thhalv10006510m

AT4G27830.1

BGLU10

up

 

1,42

0,000

−1,94

0,000

40

389

105

Thhalv10005908m

AT4G27830.1

BGLU10

down

 

−2,08

0,000

−1,85

0,000

217

127

587

Thhalv10001447m

AT2G44450.1

BGLU15

up

up

1,14

0,003

2,08

0,000

2

20

29

Thhalv10011384m

AT1G52400.1

BGLU18

down

up

−5,32

0,000

0,47

0,000

4823

2272

195299

Thhalv10011385m

AT1G52400.1

BGLU18

  

−7,87

0,000

0,41

0,279

2

6

1246

Thhalv10020508m

AT3G09260.1

BGLU23,PYK10

 

up

0,18

0,650

1,20

0,045

19

106

861

Thhalv10020496m

AT3G03640.1

BGLU25,GLUC

 

up

−0,13

0,582

1,46

0,001

415

811

1634

Thhalv10001354m

AT2G44490.1

BGLU26,PEN2

up

up

1,94

0,000

1,70

0,000

2401

11117

21129

Thhalv10002501m

AT4G22100.1

BGLU3

  

−1,19

0,036

−0,22

0,547

2

20

542

Thhalv10004297m

AT4G22100.1

BGLU3

down

down

−1,30

0,000

−2,00

0,000

11

3

8

Thhalv10028552m

AT4G22100.1

BGLU3

  

−1,85

0,000

−1,19

0,000

85

89

170

Thhalv10002493m

AT4G22100.1

BGLU3

down

 

−2,57

0,000

−1,31

0,001

100

92

571

Thhalv10005858m

AT3G60140.1

BGLU30,SRG2

 

up

0,50

0,566

6,19

0,000

5

18

344

Thhalv10018387m

AT5G24550.1

BGLU32

up

up

5,96

0,000

6,62

0,000

729

31451

52650

Thhalv10002474m

AT5G26000.1

BGLU38,TGG1

  

−0,37

0,529

−0,38

0,598

7

0

2

Thhalv10004165m

AT5G26000.1

BGLU38,TGG1

  

−0,68

0,303

−0,73

0,375

11

1

2

Thhalv10003954m

AT5G26000.1

BGLU38,TGG1

  

−1,29

0,085

−0,88

0,219

6

0

10

Thhalv10007404m

AT1G26560.1

BGLU40

up

up

2,11

0,000

2,54

0,000

167

652

1015

Thhalv10027734m

AT5G36890.1

BGLU42

down

 

−1,09

0,000

0,24

0,001

773

351

731

Thhalv10020536m

AT3G18080.1

BGLU44

  

1,81

0,002

1,01

0,006

8

5

3

Thhalv10023411m

AT1G61820.1

BGLU46

up

up

2,81

0,005

7,02

0,000

1

157

964

In response to UV light and AgNO3, most genes involved in aliphatic glucosinolate biosynthesis were strongly downregulated, with the exception of the putative orthologs of flavin-containing monooxygenase (FMO) genes encoding glucosinolate S-oxygenases (Table 2), probably resulting in a metabolic shift towards indolic and oxidized aliphatic glucosinolates. Based on homology and chromosomal position Thhalv10008073m is orthologous to AtSOT17/AtST5c (At1g18590), encoding a sulfotransferase with a preference for aliphatic desulfoglucosinolates as substrates [45,46]. Here, we observed strong transcriptional upregulation of EsSOT17 (Thhalv10008073m) in response to UV irradiation and AgNO3 treatment, similar to genes involved in indole glucosinolate biosynthesis. We speculate that in the two species the two orthologs have acquired different substrate specificities and that the Eutrema gene functions in indole glucosinolate biosynthesis. The two other confirmed desulfoglucosinolate sulfotransferases AtSOT18/AtST5b and AtSOT16/AtST5a have probably retained their function in aliphatic and indole glucosinolate biosynthesis, respectively.

WRKY transcription factors

In Arabidopsis, WRKY transcription factors play an essential role in the regulation of phytoalexin responses. Our data show that also in Eutrema several WRKY genes are upregulated, including the orthologs of WRKY40, WRKY75, WRKY33, WRKY6, WRKY51 and WRKY18 (Table 4). WRKY18 and WRKY40 are central regulators of indole glucosinolate modification in response to pathogens [47]. WRKY6 is associated with both senescence- and defence-related processes [48] and WRKY75, besides its role in phosphate acquisition [49], is also linked to senescence and pathogen defence [50,51]. WRKY51 plays a role in modulation of salicylate- and jasmonate signalling in defence [52]. In summary, these transcriptional changes indicate that also in Eutrema WRKYs are crucial for induced metabolic defence.
Table 4

Analysis of transcript abundance changes of genes encoding WRKY transcription factors

Transcript ID

Best Ath hit

WRKY

UV

Ag +

Fold change log2 (UV/n.i.)

FDR-p-value test

Fold change log2 (Ag/n.i.)

FDR-p-value test

RNAseq Unique reads

n.i.

UV

AgNO3

Thhalv10002516m

AT2G04880.2

1

down

down

−0,16

0,026

−0,39

0,000

170

79

89

Thhalv10012829m

AT5G56270.1

2

 

up

0,35

0,144

0,37

0,014

742

649

786

Thhalv10004015m

AT2G03340.1

3

  

−0,27

0,315

−0,02

0,923

746

866

648

Thhalv10007428m

AT1G13960.1

4

up

up

2,27

0,000

2,56

0,000

1482

1556

2016

Thhalv10023390m

AT1G62300.1

6

up

up

4,84

0,000

5,03

0,000

83

7722

9852

Thhalv10025630m

AT4G24240.1

7

  

−0,25

0,122

−0,21

0,241

384

161

260

Thhalv10025646m

AT4G31550.1

11

up

up

1,93

0,001

3,53

0,000

120

700

3144

Thhalv10000270m

AT2G23320.1

15

up

up

4,43

0,000

3,38

0,000

757

4909

2951

Thhalv10001165m

AT5G45050.2

16

  

−0,22

0,641

0,64

0,116

5963

1782

5082

Thhalv10000242m

AT2G24570.1

17

up

up

1,64

0,001

2,40

0,000

300

1089

2481

Thhalv10025785m

AT4G31800.1

18

up

up

2,15

0,002

3,61

0,000

173

1582

2793

Thhalv10024810m

AT4G26640.2

20

 

down

−0,17

0,133

−0,38

0,029

646

333

300

Thhalv10013115m

AT4G26640.2

20

down

 

−1,46

0,035

0,40

0,637

367

124

210

Thhalv10016852m

AT2G30590.1

21

up

 

0,69

0,003

0,40

0,001

8

12

7

Thhalv10028843m

AT4G01250.1

22

up

up

2,88

0,000

4,37

0,000

7

50

211

Thhalv10016764m

AT2G30250.1

25

up

up

1,79

0,001

2,06

0,000

239

1182

1844

Thhalv10017017m

AT2G30250.1

25

up

up

1,55

0,010

2,22

0,000

22

120

136

Thhalv10013872m

AT5G52830.1

27

 

down

0,72

0,036

−1,29

0,005

48

27

10

Thhalv10025799m

AT4G23550.1

29

  

1,71

0,009

−0,42

0,428

18

16

23

Thhalv10025126m

AT4G30935.1

32

 

down

−0,17

0,244

−0,33

0,049

1203

391

285

Thhalv10016542m

AT2G38470.1

33

up

up

5,31

0,000

4,37

0,000

325

15433

11838

Thhalv10021115m

AT3G04670.1

39

 

up

1,14

0,001

1,98

0,000

169

143

195

Thhalv10018925m

AT1G80840.1

40

up

up

6,44

0,000

4,50

0,000

23

7398

7919

Thhalv10028794m

AT4G11070.1

41

up

up

4,24

0,000

3,22

0,000

6

46

85

Thhalv10001568m

AT2G46400.1

46

up

up

3,15

0,000

4,01

0,000

72

925

2639

Thhalv10005029m

AT5G26170.1

50

 

up

0,17

0,680

1,30

0,005

41

67

93

Thhalv10004930m

AT5G64810.1

51

up

up

4,29

0,000

4,30

0,000

55

1520

1853

Thhalv10016713m

AT2G40750.1

54

up

up

1,27

0,005

2,38

0,000

407

1840

3229

Thhalv10017926m

AT2G40740.1

55

up

up

6,27

0,000

4,45

0,000

31

241

411

Thhalv10018976m

AT1G69310.1

57

  

0,12

0,559

0,28

0,173

410

196

249

Thhalv10000288m

AT2G25000.1

60

down

down

−2,14

0,000

−0,70

0,002

99

37

90

Thhalv10006157m

AT3G58710.1

69

up

 

−0,52

0,162

1,54

0,001

34

48

194

Thhalv10006146m

AT3G56400.1

70

up

up

1,66

0,002

4,40

0,000

216

2525

12004

Thhalv10013146m

AT5G15130.1

72

 

up

0,30

0,450

3,54

0,000

1

1

25

Thhalv10014943m

AT5G13080.1

75

up

up

6,26

0,000

7,02

0,000

9

1076

1311

EsWRKY33 complements camalexin deficiency in an Arabidopsis WRKY mutant

In Arabidopsis, WRKY33 is an essential regulator of camalexin biosynthesis and directly binds to the promoter of CYP71B15 (PAD3) [19]. Accordingly, its expression is induced by Pathogen-associated molecular patterns (PAMPs) and it is important for resistance against necrotrophic fungal pathogens [53-56]. Camalexin has not been detected in Eutrema and it does not contain a clear ortholog of CYP71B15. The closest CYP71B15 homolog in E. salsugineum shares only 66.7% identical amino acids. Nevertheless, EsWRKY33 is strongly upregulated upon phytoalexin inducing conditions (Figure 1; Table 4).

We investigated whether EsWRKY33 can functionally replace AtWRKY33 as a positive regulator of camalexin biosynthesis and expressed EsWRKY33 in the Arabidopsis wrky33-1 mutant [54]. While in wrky33 leaves camalexin levels were significantly reduced in relation to wild type, wild type levels were restored in the complementing line (Figure 4). This indicates that even though Eutrema does not synthesize camalexin, EsWRKY33 can act as a positive regulator of camalexin biosynthesis in Arabidopsis.
Figure 4

Complementation of camalexin deficiency in Arabidopsis wrky33 knockout mutant by EsWRKY33 expression. All plants were induced by UV light and analysed 20 h after the onset of induction. Mean and standard deviation is depicted. Different letters above the bars indicate significantly different amounts of camalexin in the respective samples, as determined by one-way ANOVA (Bonferrfoni; p < 0.05); n = 11.

Conclusions

In E. salsugineum, UV irradiation or heavy metal application resulted in substantial transcriptional reprogramming consistent with the induction of defence responses. Photosynthesis and starch synthesis were transcriptionally downregulated, while processes providing precursors for aromatic defence metabolites and cell wall compounds were transcriptionally induced. Strikingly, a shift in expression is observed from orthologs of genes for the biosynthesis of aliphatic glucosinolates, probably functioning primarily in insect defence, to orthologs of genes for the biosynthesis of indole glucosinolates, serving as precursors of phytoalexins.

WRKY33 is an essential regulator of the camalexin biosynthetic gene CYP71B15 (PAD3) [19], for which there is probably no functional homolog in E. salsugineum, consistent with the absence of camalexin in this species [12]. Nevertheless, there is a putative Eutrema WRKY33 ortholog, which is strongly upregulated under phytoalexin inducing conditions. EsWRKY33 was functionally tested and shown to complement camalexin deficiency in an Atwrky33 mutant. We hypothesize that regulatory mechanisms for phytoalexin induction are conserved among members of the Brassicaceae, while the individual chemical structures have strongly diversified.

Methods

Plant growth conditions and stress treatments

After 10 days (E. salsugineum) or two days (A. thaliana) of stratification at 6°C, plants were grown in a growth chamber at a 12/12 h photoperiod at a light intensity of 80 to 100 μmol m−2 s−1 at 21°C and 40% relative humidity. For stress treatment leaves were sprayed with 5 mM AgNO3 or placed under a UV lamp (Desaga UVVIS, λ = 254 nm, 8 W) at a distance of 20 cm and radiated for 2 h. For Botrytis cinerea infection a spore suspension (strain B05.10, 2 × 105 spores per ml) was sprayed on the leaf surface.

RNA isolation, cDNA preparation and RT-qPCR

RNA extraction, cDNA synthesis and RT-qPCR, performed with the SYBRGreen/Light Cycler system (Roche), has been described previously [57]. The following primers were used:
  • AtActin1: 5′TGGAACTGGAATGGTTAAGGCTGG3′ and 5′TCTCCAGAGTCGAGCACAATACCG3′

  • AtGAPC: 5′GCACCTTTCCGACAGCCTTG3′ and 5′ATTAGGATCGGAATCAACGG3′

  • EsActin1: 5′TGGAACTGGAATGGTTAAGGCTGG3′ and 5′TCTCCAGAGTCGAGCACAATACCG3′

  • EsYLS8: 5′GCGATTCTGGCTGAGGAAGA3′ and 5′CTTCCTTGCACCACGGTAGA3′

  • EsPP2AA2: 5′TGCTGAAGATAGGCACTGGA3′ and 5′CATTGAATTTGATGTTGGGAAC3′

  • EsASA1: 5′ATGTCTAGCGTTGGTCGTTATAGCG3′ and 5′CTTGACCACAGCCTCCTTGTACTCT3′

  • EsIGMT5: 5′AGTGCCAAGTCGTTGATGGT3′ and 5′TTGATACCCTTGATGTTTGGA3′

  • EsBGLU18-1: 5′AGAGGACCTTGGAGACCTTC3′ and 5′AGTTCTTCCCTCACTAACTTGGA3′

  • EsBGLU18-2: 5′CCTACTCGTGCTCTACTGGA3′ and 5′TCCCGGCTTAAGGAAATCAGA3′

  • EsPEN2: 5′CCAACAGGACTCAGAAACGT3′ and 5′GCAGTGACAACGAACAAGCT3′

  • EsWRKY33: 5′TATCCATTCACAGGAACAACAGAG3′ and 5′GGATGGTTATGGCTTCCCTT3′.

Expression values of candidate genes were normalized to the geometric mean of three reference genes [58] (EsActin1, EsYELLOW-LEAF-SPECIFIC GENE 8 (EsYLS8), and EsPROTEIN PHOSPHATASE 2A SUBUNIT A2 (EsPP2AA2)). Expression level of EsWRKY33 in the A. thaliana background was normalized to AtActin1 and AtGAPC.

RNAseq setup and analysis

Total RNA was isolated from three biological replicates of either control leaves or from leaves treated either with UV light for 2 h followed by 6 h recovery, leaves sprayed with 5 mM AgNO3 and incubated 8 h, or 48 h after infection of plants with B. cinerea, using the NucleoSpin® RNA II Kit (Machery-Nagel). Single-end cDNA libraries were prepared and sequenced using Illumina HiSeq 2000 technology at LGC Genomics [59] to obtain 50 bp reads. Demultiplexing was done using Illumina’s CASAVA software [60]. Reads were adapter-clipped and reads shorter than 20 bases were discarded. Read quality was assessed using FASTQC [61]. Table 1 lists the resulting number of reads used for analyses.

CLC Genomics workbench Version 6.5.1 [62] was used for RNAseq analysis including mapping to the Eutrema reference transcriptome [9] using default settings, allowing for at most two mismatches and a maximum of 10 transcript hits per read and generation of RPKM value statistic. Differential gene expression was detected using Fisher exact tests based on mapped read counts and with FDR-based correction for multiple testing errors [63]. Fold changes were computed using RPKM-values.

Microarray setup and statistical analysis

For each treatment, four biological replicates were investigated, generated from pooled tissues of 4 plants. Total RNA was extracted with NucleoSpin® RNA II Kit (Machery-Nagel). After DNase treatment, concentration and quality of extracted RNA was measured photometrically and with a Bioanalyzer (Agilent Technologies, Santa Clara, CA). Samples were hybridized to Agilent 8 × 60 k microarrays by OakLabs GmbH [64]. The arrays contain 42,562 oligonucleotide probes and are based on the recently developed Agilent 4 × 44 k Eutrema array [9].

Raw hybridization signals were quantile-normalized and log-base-2 transformed. Differential gene expression was assessed using ANOVA across all conditions and repeats and t-test statistic for pairwise comparisons with FDR-multiple testing correction [63]. Differential gene expression was mapped to metabolic pathways using the MAPMAN software [20].

RNAseq and microarray data match, functional annotation/candidate orthologs in Arabidopsis thaliana

Mapping of array probe identifiers to reference transcriptome identifiers was based on sequence matches using BLASTN with an E-value cutoff of 1E-05. Candidate ortholog genes in Arabidopsis thaliana were identified as best sequence identity hits using BLASTN with the same cutoff. The set of representative Arabidopsis transcripts available from TAIR10 [65] was used.

Generation of WRKY33 complementation lines

EsWRKY33 (Thhalv10016542m) coding sequence was amplified from cDNA (E. salsugineum leaves, 5 h after UV treatment) using the primer pair 5′GGCTTAAUATGGCTGCTTCTTCTCTTC3′ and 3′GGTTTAAUTCACGACAAAAACGAATCAAA5′ and cloned into pCAMBIA330035Su via USER technology [66]. After confirmation of the correct cDNA sequence, Agrobacterium-mediated transformation of Arabidopsis wrky33-1 knockout mutant (SALK_006603; [54]) was performed via floral-dipping, and successful transformants were confirmed by BASTA resistance of the seedlings and by PCR analysis. Primary transformants were analysed for EsWRKY33 expression by RT-qPCR. One low (#1, 0.48 ± 0.26 fg/fg AtActin1, 0.11 ± 0.09 fg/fg GAPC) and one high (#2, 16.2 ± 7.8 fg/fg AtActin1, 2.8 ± 1.7 fg/fg GAPC) expression line was selected for phenotype analysis.

Metabolite analysis

Camalexin formation was induced in six-week old A. thaliana plants by treatment with UV (see above). Camalexin was isolated 20 h after the onset of induction and quantified applying HPLC with fluorescence detection as described [67]. For monitoring wasalexin A formation, E. salsugineum leaves were treated with either UV light for 2 h followed by 22 h incubation, sprayed with 5 mM AgNO3 and incubated 24 h, or sprayed with B. cinerea spore suspension and incubated 48 h. Plant material was frozen in liquid nitrogen. Leaves were ground and 900 μl methanol were added. The samples were incubated at room temperature for 30 min under agitation, centrifuged for 15 min at 14,000 rpm and the supernatant was transferred to a new tube. To increase the yield of metabolites the extraction was repeated once and supernatants were combined. The solvent was evaporated completely (SA-Speed Concentrator, H.Saur Laborbedarf) and metabolites were dissolved in 400 μl 100% methanol. Quantification of Wasalexin A was done via reverse-phase HPLC (Göhler Multohigh100 RP-18, 5 μm, 250mmx4mm; flow rate: 1 ml/min; solvents: acetonitrile and 0.3% formic acid in H2O; 20% acetonitrile for 2 min, followed by a 17 min linear gradient to 70% acetonitrile and then 3 min to 100% acetonitrile). The peaks at 20.2 min and 21.5 min (ODmax: 362 nm) were identified as Wasalexin B and Wasalexin A, respectively by comparison with authentic standard with respect to retention time and UV spectrum.

Availability of supporting data

All curated supporting data are included as additional files. The raw RNAseq data have been deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database under the accession number SRP048695. Microarray data was deposited at Gene Expression Omnibus (GEO) database under the accession numbers GSM1530883 to GSM1530894 (platform accession GPL19319).

Abbreviations

ANOVA: 

Analysis of variance

FDR: 

False discovery rate

JGI: 

Joint Genome Institute

RPKM: 

Reads per kilobase of transcript per million reads mapped

RT-qPCR: 

Reverse transcription quantitative polymerase chain reaction

Declarations

Acknowledgements

We kindly thank Prof. Soledade Pedras for providing Wasalexin A standard, Prof. Barbara Halkier for providing pCAMBIA330035Su, and Prof. Paul Tudzynski for providing Botrytis cinerea B05.10. We thank Alexandra Chapman for assisting in establishment of Eutrema work and Heidi Miller-Mommerskamp for plant propagation. This work has been supported by the Deutsche Forschungsgemeinschaft (DFG), grants 346/7 and 346/5 (Heisenberg fellowship to E.G.), and the Hans-Fischer-Gesellschaft für Bioorganische Chemie.

Authors’ Affiliations

(1)
Lehrstuhl für Genetik, Technische Universität München
(2)
Max-Planck-Institut für Molekulare Pflanzenphysiologie

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© Mucha et al. 2015

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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