RNA sequencing on Solanum lycopersicum trichomes identifies transcription factors that activate terpene synthase promoters
© Spyropoulou et al.; licensee BioMed Central Ltd. 2014
Received: 19 February 2014
Accepted: 9 May 2014
Published: 27 May 2014
Glandular trichomes are production and storage organs of specialized metabolites such as terpenes, which play a role in the plant’s defense system. The present study aimed to shed light on the regulation of terpene biosynthesis in Solanum lycopersicum trichomes by identification of transcription factors (TFs) that control the expression of terpene synthases.
A trichome transcriptome database was created with a total of 27,195 contigs that contained 743 annotated TFs. Furthermore a quantitative expression database was obtained of jasmonic acid-treated trichomes. Sixteen candidate TFs were selected for further analysis. One TF of the MYC bHLH class and one of the WRKY class were able to transiently transactivate S. lycopersicum terpene synthase promoters in Nicotiana benthamiana leaves. Strikingly, SlMYC1 was shown to act synergistically with a previously identified zinc finger-like TF, Expression of Terpenoids 1 (SlEOT1) in transactivating the SlTPS5 promoter.
High-throughput sequencing of tomato stem trichomes led to the discovery of two transcription factors that activated several terpene synthase promoters. Our results identified new elements of the transcriptional regulation of tomato terpene biosynthesis in trichomes, a largely unexplored field.
KeywordsTomato trichomes Terpene biosynthesis Transcription factor High-throughput sequencing
Specialized glandular trichomes can produce and accumulate large quantities of terpenoids, phenylpropanoids, flavonoids and alkaloids, which they can also secrete . RNA sequencing in combination with metabolite profile analysis of glandular trichomes and proteomics have shed light on the biosynthesis of specialized metabolites in the trichomes of various plant species . Through the production of EST libraries, micro-arrays and high-throughput sequencing of (glandular) trichome RNA, genes have been identified that are involved in the terpenoid, phenylpropanoid, alkaloid and flavonoid biosynthesis in various plant species, including tomato [3–5], sweet basil [6, 7], tobacco [8, 9], mint , alfalfa , Artemisia annua and hop . Although EST sequencing has been instrumental in the discovery of enzymes of trichome-specialized metabolism , next generation sequencing (NGS) can give a more in-depth picture of transcriptomes. NGS technologies (i.e. RNA sequencing) has been used for characterization of several trichome transcriptomes, for example from plants of medical importance like Artemisia annua (Asteraceae; ) or Huperzia serrata and Phlegmariurus carinatus (Huperziaceae; ). NGS has also been used for gene discovery, for example in combination with shotgun proteomics and metabolite analysis of tomato (Solanum lycopersicum) trichomes, leading to the discovery of the leaf-trichome-specific β-caryophyllene/α-humulene synthase (CAHS; ). NGS of trichomes RNA from wild and cultivated tomato varieties led to the discovery and characterization of various sesquiterpene synthases, providing insight into the evolution of terpene synthases .
Terpene biosynthesis in tomato plants is of major interest because terpenes play an important role in the plant’s defense [16–20]. The sequencing of the cultivated tomato genome has enabled the characterization of its terpene synthase (TPS) gene family [21, 22], but not much is known about the regulation of the terpenoid pathway. Transcriptional control of biosynthetic genes is a major mechanism by which secondary metabolite production is regulated [23, 24].
There are not many transcription factors (TFs) known to be involved in regulation of terpenoid pathways. ORCA3, a jasmonate-responsive APETALA2 (AP2)-domain transcription factor from Catharanthus roseus, has been shown to regulate expression of Strictosidine Synthase (STR) involved in terpene indole alkaloid biosynthesis . Subsequently, a methyl-jasmonate (MeJA)-inducible transcription factor of the MYC family (CrMYC2) was shown to positively regulate ORCA3. CrWRKY1 was identified as being involved in the root-specific accumulation of serpentine in C. roseus plants and as being induced by phytohormones including JA . This TF appeared to negatively regulate ORCA3 and to a lesser extend CrMYC2. A MeJA-inducible WRKY transcription factor from Gossypium arboreum that regulates the sesquiterpene synthase (+)-δ-cadiene synthase A in cotton fibers was identified by Xu et al.. Ma et al.  demonstrated that a MeJA-inducible WRKY transcription factor from Artemisia annua is involved in the regulation of artemisinin biosynthesis. More recently two JA-responsive AP2 family transcription factors from A. annua (AaERF1 and 2) were found to regulate Amorpha-4,11-diene synthase (ADS), a sesquiterpene synthase involved in the biosynthesis of artemisinin  whereas Lu et al.  identified AaORA, a AP2/ERF TF, that regulates several genes in the artemisinin biosynthetic pathway including AaERF1. Most recently, the MeJA-inducible Arabidopsis thaliana MYC2 transcription factor  was shown to regulate sesquiterpene synthases AtTPS21 and AtTPS11.
Here, we used NGS of tomato stem trichomes as a tool for gene discovery. First, a transcript database was created from normalized cDNA, which was mined for transcription factors. Then, in order to narrow down the number of TFs potentially involved in terpene biosynthesis, an expression profiling database was created using Illumina sequencing of trichome RNAs from plants treated with or without jasmonic acid (JA), since JA is known to induce terpene emission in tomato and to regulate several terpene synthases [16, 21, 34, 35]. To identify TFs that regulate terpene biosynthesis we used a transient assay based on the transactivation of tomato terpene synthase promoters in planta.
Assembly of RNAseq data and Genome Analyzer II transcript profiling
We created a tomato trichome EST database by sequencing a mixture of glandular and non-glandular trichome RNAs, derived from stems of Solanum lycopersicum cv. Moneymaker plants. The resulting cDNA was normalized prior to being used as input for 454 GS FLX Titanium pyrosequencing. A full plate was sequenced consisting of two halves: one with cDNAs originating from control plants and the other half with cDNAs originating from plants treated with JA. In total we obtained 979,076 high-quality reads with an average length of 337 bp. The reads from control and JA-treated samples were assembled de novo resulting in 27,195 contigs with an average length of 931 bp, leaving 24,187 reads unmatched (singletons), with an average length of 241 bp. Nucleotide sequences of the contigs were blasted against the Solanaceae Genomics Network (SGN) tomato database for annotation, using a local E-Blast tool; 3,295 contigs were not annotated.
For creating the transcript profiling databases with Genome Analyzer II, the same RNA material as for the 454 sequencing was used, but this time the cDNA derived from control and JA-treated stem trichomes was not normalized before being processed. We specifically obtained 5,631,975 3’ sequences from the Control sample and 5,882,547 from the JA-treated sample. 4,840,738 and 5,169,891 reads from the Control and JA-samples, respectively, were mapped to one unique contig of the trichome database. In addition, 38,699 (C) and 45,375 (JA) reads were mapped to multiple contigs and 791,237 (C) and 712,656 (JA) remained unmapped.
Both the 454 GS FLX Titanium reads and the Genome Analyzer II reads can be found in the Sequence Read Archive of NCBI (http://www.ncbi.nlm.nih.gov/sra) under accession number SRP041373.
Annotation, gene ontology and protein families
In order to characterize the S. lycopersicum stem trichome transcriptome the unique contigs (27,195 ESTs) were submitted to homology searches (BLASTX) in the National Center for Biotechnology Information (NCBI) non-redundant protein database using Blast2GO . 4,733 contigs did not return a BLASTX hit. The majority of the top hits were to protein sequences of Vitis vinifera, followed by Populus trichocarpa, Ricinus communis and Solanum lycopersicum.
Next, gene ontology (GO) and enzyme classifications (EC) were performed in order to classify the ESTs. It must be noted that one sequence could be assigned to more than one GO term. For the cellular component class the assignments were mostly given to cell and organelle (54,82% and 29,35% respectively; Additional file 1: Figure S1a). The highest percentage of molecular function GO terms were in binding and catalytic activity (42,96% and 41,38% respectively; Additional file 1: Figure S1c). In the biological processes, the majority of the GO terms was grouped into two categories- those of metabolic and cellular process (36,55% and 32,79% respectively; Additional file 1: Figure S1b). Finally, within the predicted ECs, the prevailing categories of enzymes were transferases and oxidoreductases (31,38% and 29,65% respectively; Additional file 1: Figure S1d).
Summary of the most common InterPro entries found in the S. lycopersicum stem trichome transcriptome
Protein kinase-like domain
Protein kinase, catalytic domain
Serine/threonine-/dual-specificity protein kinase, catalytic domain
Serine/threonine-protein kinase, active site
Tyrosine-protein kinase, catalytic domain
Zinc finger, RING/FYVE/PHD-type
Protein kinase, ATP binding site
WD40/YVTN repeat-like-containing domain
Zinc finger, RING-type
Nucleotide-binding, alpha-beta plait
Serine-threonine/tyrosine-protein kinase catalytic domain
RNA recognition motif domain
WD40 repeat-like-containing domain
Glycoside hydrolase, superfamily
Major facilitator superfamily domain, general substrate transporter
Glycoside hydrolase, subgroup, catalytic domain
Cytochrome P450, E-class, group I
KEGG pathways related to biosynthesis of secondary metabolites found in the S. lycopersicum stem trichome transcriptome
Nr of sequences
4-hydroxy-3-methylbut-2-enyl diphosphate reductase
2-C-methyl-D-erythritol 4-phosphate cytidylyltransferase
4-(cytidine 5′-diphospho)-2-C-methyl-D-erythritol kinase
hydroxymethylglutaryl-CoA reductase (NADPH)
List of selected enzymes involved in the jasmonic acid biosynthesis and signaling
Transcript length (bases)
Expression values JA*
Expression values C*
Fold JA induction
Allene oxide cyclase
Jasmonate ZIM-domain 1
Jasmonate ZIM-domain 3
Enzymes involved in the biosynthesis of precursors of mono-, sesqui- and diterpenes
Expression values JA*
Expression values C*
Fold JA induction
acetoacetyl-coenzyme A thiolase
mevalonate diphosphate decarboxylase
1-deoxy-D-xylulose 5-phosphate synthase 1
1-deoxy-D-xylulose 5-phosphate synthase 2
1-deoxy-D-xylulose 5-phosphate reductoisomerase
2C-methyl-D-erythritol 2,4-cyclodiphosphate synthase
1-hydroxy-2-methyl-2-(E)-butenyl 4-diphosphate synthase
1-hydroxy-2-methyl-2-(E)-butenyl 4-diphosphate reductase
isopentenyl diphosphate isomerase
geranyl diphosphate synthase
neryl diphosphate synthase
farnesyl diphosphate synthase
geranyl geranyl diphosphate synthase
cis-prenyl transferase 3
cis-prenyl transferase 4
cis-prenyl transferase 5
cis-prenyl transferase 7
Terpene synthases (TPS) found in S. lycopersicum stem trichomes
Transcript length (bases)
Expression values JA*
Expression values C*
Fold JA induction
Selection of transcription factors potentially involved in regulating terpene synthases
List of selected S. lycopersicum transcription factors (TF) potentially involved in terpene biosynthesis
Transcript length (bases)
Expression values JA*
Expression values C*
Fold JA induction
ERF (ethylene response factor) subfamily B-3 of ERF/AP2 TF family
DREB subfamily A-1 of ERF/AP2 TF family
AP2 domain-containing TF
DREB subfamily A-1 of ERF/AP2 TF family
DREB subfamily A-4 of ERF/AP2 TF family
ERF (ethylene response factor) subfamily B-3 of ERF/AP2 TF family
AP2 domain-containing TF
ERF (ethylene response factor) subfamily B-4 of ERF/AP2 TF family
ERF (ethylene response factor) subfamily B-2 of ERF/AP2 TF
ERF (ethylene response factor) subfamily B-3 of ERF/AP2 TF family
Ethylene-responsive element-binding factor 4 homolog
WRKY family TF
WRKY family TF
WRKY family TF
WRKY family TF
Tissue specificity and JA responsiveness of selected transcription factors
SlMYC1 and SlWRKY73 can transactivate terpene synthase promoters in Nicotiana benthamianaleaves
As shown in Figure 3a, SlWRKY73 could transactivate the SlTPS5 promoter, albeit to a lower extent than SlEOT1. SlWRKY73 transactivated the SlTPS3 and SlTPS7 promoters only weakly, and the SlTPS8 and SlTPS9 promoters not at all (35S:RFP negative controls shown in Additional file 1: Figure S2). SlWRKY78 or SlWRKY28 did not transactivate any of the terpene synthase promoters (Additional file 1: Figure S3).
SlMYC1 could transactivate all terpene synthase promoters tested except SlTPS8. Transactivation of the trichome-specific SlTPS5 and SlTPS3 promoters was strongest (Figure 3b; 35S:RFP negative control shown in Additional file 1: Figure S2). However, it should be noted that GUS activity of a promoter driving the GUSsYFP1 fusion was lower than when the same promoter driving GUS alone was transactivated by an effector construct (data not shown), possibly because the fusion protein was less stable or produced. Therefore, transactivation by SlMYC1 of the trichome-specific SlTPS9 promoter was potentially stronger than that detected here.
SlMYC1 and SlEOT1 act synergistically on the SlTPS5 promoter in N. benthamianaleaves
Discussion and conclusions
RNA sequencing of S. lycopersicum stem trichomes led to the identification of one MYC bHLH and one WRKY transcription factor that can transactivate several terpene synthase promoters. The observation that SlMYC1 acts synergistically with SlEOT1 in the transactivation of the SlTPS5 promoter suggests a complex regulatory network for terpene biosynthesis.
High-throughput sequencing of Solanum lycopersicumstem trichomes
We used massive parallel pyrosequencing on the 454 GS FLX Titanium platform to sequence S. lycopersicum stem trichome RNAs with the goal to identify transcription factors involved in terpene biosynthesis. We used normalized cDNA to maximize representation of low abundant transcripts and reduce representation of highly abundant transcripts. Attempts to map the obtained reads to the publicly available mixed tissue SGN database led to a high percentage of unmapped reads and assignment of the same reads to multiple unigenes and therefore the reads were assembled de novo. 2.5% of the reads could not be matched and were not used in further analysis. 87.9% of the resulting contigs were subsequently annotated after blasting against the SGN tomato database using a local E-Blast tool. In this database we identified annotated enzymes involved in several metabolic pathways (Additional file 1: Table S1). In short, compared to the study published by McDowell and colleagues  on S. lycopersicum cv. M82 trichomes, we identified in Moneymaker trichomes cDNAs encoding enzymes involved in for example the TCA cycle, starch and sucrose metabolism (Additional file 1: Table S1), as well as secondary metabolite biosynthesis (Table 2). Photosynthesis related genes were also identified but were not as prevalent (Additional file 1: Table S1) as in M82 trichomes. Such differences could originate from the fact that in our study we used a mix of Moneymaker trichome types, including stalks, whereas McDowell and colleagues focused on comparing different types of trichomes between Solanum species and so clipped off and analyzed only the secretory cells of glandular trichomes .
Furthermore we created an expression profiling database using Illumina sequencing in order to obtain genes regulated by JA. The success of the JA treatment is evident by the high induction of known JA markers, some of which are presented in Table 3 (LOXA, AOC ; JAZ1 ; JAZ3 ).
Jasmonic acid regulation of the terpene biosynthesis pathway in tomato trichomes
In order to investigate whether in stem trichomes of tomato Moneymaker plants, regulation of terpene biosynthesis by JA is also on the precursor level besides on the level of individual TPSs , the quantitative database was mined for enzymes of the precursor pathways. The copy number of these genes varies between different plant species  and, as shown in Table 4, different family members can vary in their expression levels and/or JA-inducibility. For example 1-deoxy-d-xylulose 5-phosphate synthase (DXS), in contrast with Arabidopsis, which contains a single functional gene, has diversified into two isogenes in other plant species including tomato . Whereas SlDXS1 is ubiquitously expressed, SlDXS2 is expressed only in a few tissues and in leaf trichomes its transcript abundance is much higher than that of SlDXS1, although this is not the case in stem trichomes (Table 4). Furthermore, SlDXS2 is moderately induced by wounding in the cultivar Moneymaker , which correlates with the observed moderate induction of SlDXS2 by JA (~1.6-fold, Table 4). SlDXS2 expression is also approximately threefold upregulated in the tomato cultivar Castlemart upon feeding by Manduca sexta larvae .
The regulation of precursor genes of the MEP pathway by wounding, hormones or elicitors has been demonstrated in various plant species [49–54]. Similarly, evidence for the regulation of precursor biosynthesis of the mevalonate (MVA) pathway is also abundant [55–60]. For example, HMGR enzyme activity and protein level were shown to increase by fungal infection in potato tubers and sweet potato root . Furthermore, HMGR1 expression was induced by treatment with MeJA in potato, whereas HMGR2 expression was reduced . In response to caterpillar herbivory, transcripts of HMGR1 were reduced in alfalfa . Our results show that in tomato stem trichomes HMGR1 and HMGR3 were induced by JA treatment approximately 2-fold, whereas expression of HMGR2 remained unaltered (Table 4). None of the prenyl diphosphate synthases were induced in tomato trichomes by JA treatment, whereas two seemed to be downregulated (FPS, SGN-U578686; and GGPS, SGN-U573348; Table 4). We did not find any transcripts for GGPS1 (SGN-U574849) in our stem trichome database, although it has been shown to be induced in tomato leaves by JA-treatment . Finally, from the very recently identified cis-prenyltransferases only CPT5, that produces medium-length chain polyisoprenoids , was upregulated by JA, 1.7-fold (Table 4).
Identification of transcription factors involved in regulating terpene synthases in tomato trichomes
Our primary aim was to identify transcription factor(s) that regulate terpene biosynthesis. Based on the annotated contigs, 2.7% of the transcripts in the tomato stem trichomes encode transcription factors. For comparison, in Arabidopsis thaliana ~6% of the genes in all tissues encode TFs (TAIR10 genome release, ). Since JA is essential for establishing indirect defense responses in tomato [34, 35] and the induction of terpene synthases in trichomes [16, 21], we hypothesized that TFs involved in the regulation of terpene biosynthesis would also be JA-inducible genes. Most of the transcription factors known to be involved in regulation of terpenoid pathways are jasmonate-inducible and of the APETALA2, WRKY or MYC class [25–30, 33]. However, in Arabidopsis it was recently shown that two MYC transcription factors (AtMYC3 and AtMYC4), which act additively with AtMYC2 in the activation of JA responses, are, in contrast to AtMYC2, only marginally induced by JA treatment . Based on all the above, the initial selection of transcription factors to be analyzed from our quantitative stem trichome database was limited to TFs of the AP2, WRKY and MYC class that showed a 2-fold or higher induction by JA treatment (2.2-fold was the induction rate of control gene SlMTS1; , renamed SlTPS5; Table 5). None of the MYC transcription factors of our database showed induction higher than 2, so for further analysis the closest homolog of AtMYC2  was selected, as it has been shown to activate the AtTPS11 and AtTPS21 promoters . After discarding TFs that were not trichome-specific or did not show highest expression in trichomes, the list was narrowed down to four candidate transcription factors. According to the Q-RT-PCR data however, none of these TFs was significantly induced by JA treatment (Figure 2). Since the numbers of sequence reads of these genes is very low both in the Control and JA samples (Table 6), the fold-induction in the Illumina experiments must have been overestimated.
SlMYC1 and SlWRKY73 transactivate terpene synthase promoters in planta
SlMYC1 showed strongest transactivation of SlTPS5 and SlTPS3 and to a lesser extent of SlTPS7 and SlTPS9 but no transactivation of SlTPS8 promoter (Figure 3b), although SlMYC1 is also expressed in the root, albeit not strongly (Figure 2). As shown in Figure 5SlMYC1 is expressed (at different levels) in every plant tissue and SlMYC1 is able to activate all the terpene synthase promoters tested except one, so it seems to be a regulator of multiple TPSs, in contrast to SlEOT1 that is only expressed in the glandular trichomes and can specifically transactivate the SlTPS5 promoter and none of the other TPS promoters tested (Figure 5, ). The other two selected TFs (SlWRKY78 and SlWRKY28; Additional file 1: Figure S3) were not able to significantly transactivate any of the tested terpene synthase promoters. However it cannot be excluded that these TFs were not expressed in the transient assay.
One question that arises is, of course, where SlWRKY73 and SlMYC1 bind on these terpene synthase promoters. In the promoter sequence of SlTPS5, SlTPS3 and SlTPS7 there are five, four and one W-boxes (TGAC(C/T)) respectively (PLACE; , Additional file 1: Table S2), which could serve as potential binding site(s) for SlWRKY73. Furthermore, SlTPS5 promoter contains two G-box-like elements (CACATG instead of the canonical CACGTG), one T/G-box element (AACGTG) and one T/G-box-like element (TACGTG) (Additional file 1: Table S2), which could potentially be the binding site(s) of SlMYC1. The promoter of SlTPS3, with which SlMYC1 interacts less strongly, contains one G-box-like element and one T/G-box element (Additional file 1: Table S2). The SlTPS7 promoter, which SlMYC can also activate, contains one T/G-box (Additional file 1: Table S2). The SlTPS9 promoter  however, does not contain any of these elements, which could indicate the existence of an uncharacterized motif to which SlMYC1 binds. When using the motif search program MEME  with all four promoters that SlMYC1 can activate, one 8 bp motif was identified in the plus or minus (for SlTPS9) orientation: CTAGG(T/A)(A/G)G. The validation of a (putative) regulatory element as the binding site for these TFs would require extensive further experimentation. However, since our transactivation assays do not indicate direct binding, the TF-TPS promoter interactions observed in the ATTAs, could take place through an additional protein. To address the issue of which terpene synthases (and possibly other genes as well) these TFs regulate, we are currently starting the more laborious but more conclusive approach of creating stably transformed silenced and overexpressing plants.
SlMYC1 acts synergistically with SlEOT1 in the transactivation of the SlTPS5promoter
Interestingly, SlEOT1 and SlMYC1 acted synergistically in the transactivation of the SlTPS5 promoter (Figure 4). Combinatorial control of transcriptional regulation is commonly found in plants and other eukaryotes . For example, in abscisic acid (ABA) signaling, the 67 bp promoter region of the dehydration-responsive gene rd22 contains a MYC and a MYB recognition site, where AtMYC2 and AtMYB2 can bind, respectively. In Arabidopsis leaf protoplasts it was shown that these TFs could individually activate transcription of β-glucuronidase driven by this 67 bp promoter region of rd22 and that the transient activation was stronger when AtMYC2 and AtMYB2 were combined . Transgenic plants overexpressing these TFs each showed ABA hypersensitivity but the effect was more profound in plants overexpressing both TFs .
Given the fact that SlMYC1 and SlEOT1 are not induced by JA (Figure 2, ) and yet the proteins can transactivate the JA-inducible SlTPS5 promoter indicates that they could be regulating the steady-state transcription of SlTPS5. These TFs might however also be involved in the enhanced SlTPS5 expression by interacting with other, inducible TF(s). From the well-studied cases of transcriptional regulation in Catharanthus roseus[25–27] and Arabidopsis [32, 33, 64] it has becomes clear that it usually involves a network of TFs. In Solanum lycopersicum we are only just starting to unravel the complexity of transcriptional regulation of terpene biosynthesis.
Hormone treatment and RNA isolation
Tomato plants (Solanum lycopersicum cultivar Moneymaker) were grown in soil in a greenhouse with day/night temperatures of 23°C/18°C and a 16/8 h light/dark regime for four weeks. They were then sprayed either with JA solution (1 mM JA; Duchefa, NL, in tap water + 0,05% SilwetL-77; GE Silicones, VA, USA) or with control solution (0,05% SilwetL-77 in tap water). Stem pieces were collected 30 min, 2 h, 8 h and 24 h later for pyrosequencing or 24 h later for expression analyses and trichomes were isolated by shaking the stems in liquid nitrogen. Total RNA was isolated using TRIzol (Invitrogen, Paisley, UK) according to the manufacturer’s instructions. Equal amount of trichome RNA from the different time points was pooled creating the control (C) and JA samples. RNA used for pyrosequencing was then purified on a RNeasy Plant column (Qiagen, Valencia, CA, USA).
Transcriptome database construction
RNA quality was determined with the Agilent RNA pico chip (Agilent Technologies, Waldbronn, Germany). Synthesis and amplification of cDNA was performed using the SMART PCR cDNA Synthesis and Advantage 2 PCR kits (Clontech Inc., CA, USA) according to the manufacturer’s instructions with some modifications of adapters to eliminate 3′ poly(A)-stretches prior to sequencing. cDNA quality was determined with the Agilent DNA 7500 chip (Agilent Technologies, Waldbronn, Germany) or on an 1% agarose/EtBr gel. Normalization of the cDNA was carried out using the Evrogen TRIMMER kit (Evrogen, Moscow, Russia) according to the manufacturer’s protocol. The normalization efficiency was determined both on an agarose/EtBr gel (1%) and with an Agilent DNA 7500 chip. The cDNA was purified and concentrated using the Qiaquick PCR purification kit (Qiagen, Valencia, CA, USA). cDNA shearing and FLX Titanium library preparation was carried out using the Roche GS FLX Titanium General Library Preparation Method kit (Roche Diagnostics, Mannheim, Germany) according to the manufacturer’s protocol. The size range of the fragments was determined with an Agilent DNA 1000 chip (Agilent Technologies, Waldbronn, Germany). Exclusion of smaller-sized fragments was performed using the double SPRI method as described in the Roche GS FLX Titanium General Library Preparation protocol (Roche Diagnostics, Mannheim, Germany). End-polishing, small fragment removal, library immobilization, fill-in reaction and single-stranded library isolation was performed using the GS FLX Titanium General Library Preparation Method kit (454 Life Sciences, Roche Diagnostics, Mannheim, Germany) according to manufacturer’s instructions.
Expression profiling database construction
Starting from the same total RNA samples (C and JA, see above), mRNA was amplified and purified using the MessageAmp II aRNA Amplification kit (Applied Biosystems/Ambion, CA, USA) according to manufacturer’s instructions. RNA quality was determined with the Agilent RNA pico chip (Agilent Technologies, Waldbronn, Germany). Synthesis of cDNA was performed using the MessageAmp II aRNA Amplification kit (Applied Biosystems/Ambion, CA, USA) according to manufacturer’s instructions with modifications of the adapters to enable sequencing of 3′ cDNA ends. cDNA was purified with the Qiaquick PCR purification kit (Qiagen, Valencia, CA, USA). cDNA quality was determined with the Agilent DNA 7500 chip (Agilent Technologies, Waldbronn, Germany) or on an 1% agarose/EtBr gel. Shearing and ligation was carried out using standard Illumina PE adapters containing a specific sample ID tag. Adapter-ligated cDNA fragments were column purified with the Qiaquick PCR purification kit (Qiagen, Valencia, CA, USA). The size range of the fragments was determined with an Agilent DNA 1000 chip (Agilent Technologies, Waldbronn, Germany). Exclusion of smaller-sized fragments was performed using a single SPRI procedure as described in the Agencourt Ampure PCR Purification protocol (Agencourt Bioscience Corporation, MA, USA). The size range of single-stranded fragments was determined with an Agilent RNA pico 6000 chip (Agilent Technologies, Waldbronn, Germany). Expression profiling was performed using the Illumina Genome Analyzer II System (Illumina, USA).
Databases assembly, EST annotation and homology searches
The 454 sequencing reads (Control and JA combined) were assembled into contigs de novo by Vertis Biotechnologie AG, Germany using the CLCbio software . Nucleotide sequences of the contigs were then blasted against the SGN unigenes v2 tomato database (ftp.solgenomics.net/unigene_builds/combined_species_assemblies/tomato_species) for annotation, using a local Eblast tool (E value 1e–9). The GA II reads (Control and JA separately) were mapped to the annotated contigs of the 454 sequencing trichome database by Vertis Biotechnologie AG, Germany.
The resulting contigs were also imported in the bioinformatics tool Blast2GO v.2.5.0  and were blasted against the National Center for Biotechnology Information (NCBI) non-redundant protein database BLASTX (E value 1e–3). Further analyses with this tool included functional annotation by Gene Ontology (GO) terms and Enzyme Commission numbers (EC code), InterPro terms (InterProScan; ) and metabolic pathways (Kyoto Encyclopedia of Genes and Genomes, KEGG; ).
cDNA synthesis and quantitative-real time-PCR
DNA was removed from RNA with DNAse (Ambion, Huntingdon, UK) according to the manufacturer’s instuctions and cDNA was synthesized from 1.5 μg RNA using M-MuLV H− Reverse Transcriptase (Fermentas, St. Leon-Rot, Germany). For Q-RT-PCR, cDNA equivalent to 100 ng total RNA was used as template in 20 μl volume and reactions were performed in the ABI 7500 Real-Time PCR System (Applied Biosystems) using the Platinum SYBR Green qPCR SuperMix-UDG kit (Invitrogen, Paisley, UK) with the following cycling program: 2 min 50°C, 7 min 95°C, 45 cycles of 15 sec at 95°C and 1 min at 60°C, followed by a melting curve analysis. Primer pairs were tested for amplification kinetics and linearity with a standard cDNA dilution curve and new primers were designed if necessary. Expression levels were normalized using ACTIN (SGN-U579547) mRNA levels. Effects of JA on gene expression were analyzed in three biological replicates by T-test using PASW Statistics 17.0 . The homogeneity of variance was tested by Levene’s test.
Cloning, construct design and ATTAs
TFs SlMYC1 (KF430611; sequence of the full-length ORF obtained from the 454 trichome database), SlWRKY28 and SlWRKY73 (; Additional file 1: Figure S4) were cloned between restriction sites NcoI (at the ATG) and SacI (at the 3′ end of the sequence) in front of the Nos terminator in vector pKG1662 (KeyGene, Wageningen, NL; for a map of the vector see patent nr US2011/0113512A1) driven by the CaMV 35S promoter. TF SlWRKY78 (; Additional file 1: Figure S4) was cloned downstream of the CaMV 35S promoter in vector pJVII, a pMON999-based vector (Monsanto, St. Louis, MO) with a modified multiple cloning site (MCS), between restriction sites XbaI (at the ATG) and BsrGI (at the 3′ end of the sequence). All constructs were verified by sequencing and then the expression cassettes containing 35S promoter, cDNA of interest and nos terminator were transferred to the MCS of the binary vector pBINplus  between HindIII and SmaI restriction sites. The final constructs were transformed to Agrobacterium tumefaciens GV3101 (pMP90). The promoter:GUS constructs used in the transient transactivation assay have been described elsewhere . The A. tumefaciens transient transactivation assay (ATTA) was performed as described in Spyropoulou et al., .
Expression of terpenoids 1
Solanaceae genomics network
We thank Raymond Hulzink (KeyGene, Wageningen, NL) for the preparation of the transcriptome and expression profiling libraries, Carlos A. Villarroel Figueroa for help with the bioinformatics, Juan M. Alba Cano for help with the statistical analyses and Harold Lemeris, Ludeck Tikovsky and Thijs Hendrix for the excellent care of the plants. This work was supported by grant 1C002RP of the Technological Top Institute-Green Genetics with KeyGene as project leader.
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