Figures
Abstract
Background
Hypericum perforatum L. (St. John’s wort) is a medicinal plant with pharmacological properties that are antidepressant, anti-inflammatory, antiviral, anti-cancer, and antibacterial. Its major active metabolites are hypericins, hyperforins, and melatonin. However, little genetic information is available for this species, especially that concerning the biosynthetic pathways for active ingredients.
Methodology/Principal Findings
Using de novo transcriptome analysis, we obtained 59,184 unigenes covering the entire life cycle of these plants. In all, 40,813 unigenes (68.86%) were annotated and 2,359 were assigned to secondary metabolic pathways. Among them, 260 unigenes are involved in the production of hypericin, hyperforin, and melatonin. Another 2,291 unigenes are classified as potential Type III polyketide synthase. Our BlastX search against the AGRIS database reveals 1,772 unigenes that are homologous to 47 known Arabidopsis transcription factor families. Further analysis shows that 10.61% (6,277) of these unigenes contain 7,643 SSRs.
Conclusion
We have identified a set of putative genes involved in several secondary metabolism pathways, especially those related to the synthesis of its active ingredients. Our results will serve as an important platform for public information about gene expression, genomics, and functional genomics in H. perforatum.
Citation: He M, Wang Y, Hua W, Zhang Y, Wang Z (2012) De Novo Sequencing of Hypericum perforatum Transcriptome to Identify Potential Genes Involved in the Biosynthesis of Active Metabolites. PLoS ONE 7(7): e42081. https://doi.org/10.1371/journal.pone.0042081
Editor: Frederick C. C. Leung, University of Hong Kong, China
Received: March 1, 2012; Accepted: July 2, 2012; Published: July 30, 2012
Copyright: © He et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work benefited from financial support from the “Twelve-Five technology support program funding (2011BAI06B06)” and the “Graduate student Innovation Fund of Shaanxi Normal University (2008CXB014), Shaanxi, P. R. China”. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Hypericum perforatum L. (common St. John’s wort) is a widely known medicinal herb used mostly as a remedy for depression [1]. It also has other broad pharmacological activities, such as anti-tumor, anti-inflammatory, antiviral, antioxidant, anti-cancer, and antibacterial properties [2], [3]. Human health is benefited because of this diversity of active ingredients within various chemical groups. Its major active metabolites – hypericins, hyperforins, and melatonin – belong to the naphthodianthrones, phloroglucinols, and alkaloids, respectively. Xanthones and flavonoids have also been identified in extracts from this plant [4].
H. perforatum has significant amounts of hypericin and hyperforin, which are considered to be most promising naturally occurring agents because of their important biological properties. Hypericins are the characteristic compounds of the genus Hypericum (Hypericaceae). Hyperforin has been found in significant amounts only in H. perforatum [5], whereas other Hypericum species contain only low levels of that compound [6]. Consequently, H. perforatum fascinates the researchers, and reveals huge market demand. Although the biosynthesis pathway leading to hypericins and hyperforins is still poorly understood, it is presumed that the type III polyketide synthase (PKS) is involved [7], [8]. This PKS family of enzyme complexes produces various polyketides in plants, including naphthodianthrones, phloroglucinols, xanthones, and flavonoids [4], [7], [8]. Type III PKSs catalyze the condensation between specific CoAs, such as acetyl-CoA and malonyl-CoA [9]. Based on their mechanisms of cyclization, these PKSs in higher plants are classified into three groups: chalcone synthase (CHS-type), stilbene synthase (STS-type), and coumaroyltriacetic acid synthase (CTAS-type) [9]. All have diverse functions that vary according to substrate preference, the amount of condensed malonyl-CoA, and the mechanism of cyclization reactions [10], [11].
(A) and gaps (B) for unigenes from Hypericum perforatum.
Melatonin (N-acetyl-5-methoxytryptamine), a hormone secreted by the pineal gland in animal brains, helps regulate other hormones and maintain the body’s circadian rhythm [12]. It is also present in the plant kingdom [13], where it is considered an antioxidant or growth promoter [14]. Although its biosynthetic pathway is poorly understood, it is thought to be derived from tryptophan and serotonin [15]. Much current research has been focused on the detection, function, and biosynthesis of melatonin in H. perforatum because those plants produce significantly larger amounts of that hormone compared with other species [13].
Previous studies on H. perforatum have mainly involved its active ingredients and their pharmacological activities. Although much effort has been devoted to cloning and identifying the key enzymes for secondary metabolism in that species [16]–[19], only limited genomic information has been submitted to the National Center for Biotechnology Information (NCBI), i.e., 70 nucleotide sequences and 3 ESTs. Only a few of its genes function in secondary metabolism, and most studies have concentrated primarily on the Hyp-1 enzyme, which catalyzes hypericin biosynthesis. This is because traditional methods for gene cloning and sequencing are time-consuming, expensive, and produce only a little genetic information.
By contrast, RNA-Seq is a recently developed approach for profiling transcriptomes. It has many advantages because it is cost-effective, highly sensitive, more accurate, and has a large dynamic range [20]. It is now widely used to analyze gene expression and discover novel transcripts, SNPs, splice junctions, and fusion transcripts [21]–[23]. Here, we describe the utilization of Illumina/Solexa paired-end technology for de novo transcriptome analysis of H. perforatum throughout its life cycle. We obtained 2.2 GB of nucleotides and discovered almost all of the known genes for hypericin, hyperforin, and melatonin biosynthesis. The work presented here is the first to profile the genetic information of H. perforatum. Then it also provides an insight into the secondary metabolic pathways in that species, our results could be used for further genetic manipulation to improve its yield of active metabolites.
(A), COG Function Classification of transcriptome. A total of 11,209 unigenes showing significant homology to COGs database at NCBI (E-value ≤1.0e−5) had COG classification among 24 categories. (B), H. perforatum unigenes with GO annotations based on Arabidopsis protein hits from NR. Right y-axis, percentage of genes; left y-axis, number of genes.
Results and Discussion
Short-read De novo Sequencing and Assembly
To obtain an overview of the H. perforatum gene expression profile over its entire growing cycle, cDNA samples from different developmental stages (vegetative stage, floral budding stage, and fresh fruiting stages) were prepared and RNA-seq was performed via Illumina HiSeq™ 2000. After trimming the adapter sequences and sequences that were less than 90 bases long, we obtained 24,429,306 clean paired-end reads with a total of 2,198,637,540 (2.2 GB) nucleotides. The Q20 percentage (sequencing error rate <1%) and GC percentage were 94.62% and 50.45%, respectively, and each read length was 90 bp×2. All reads were deposited in the NCBI and can be accessed in the Short Read Archive (SRA) under accession number SRA050246.2. We then applied SOAP2 de novo software [24] for assembling those short reads through a step-wise strategy. Eventually 59,184 unigenes (≥200 bp) were obtained, with an average length of 422 bp and an N50 of 532 bp (Table 1). Analysis of size distributions (Figure 1A) revealed that 69.47% fell within the range of 300 bp to 1,000 bp. Moreover, 98.98% unigenes showed no gap (Figure 1B).
Functional Annotation and Gene Ontology Classification
To orient the unigenes derived from RNA-seq, we performed BlastX (version 2.2.21) alignment (e value <1.00E−05) against several protein databases: GenBank non-redundant (NR), Swiss-Prot, Clusters of Orthologous Groups (COG), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG). The best aligning results were used to decide the sequence direction of these unigenes. Here, 40,813 (68.86%) unigenes were oriented (Table 2).
COG (http://www.ncbi.nlm.nih.gov/COG/) is delineated by comparing protein sequences encoded in complete genomes and representing major phylogenetic lineages. Such an analysis provided us with function predictions and classifications. Some transcripts had multiple COG functions. Altogether, 11,207 unigenes were clustered into 24 functional categories (Figure 2A). Among them, the “General function prediction only” cluster was the largest (26.9%), followed by “Transcription” (16.11%). Another 698 unigenes (6.23%) belonged to the “Secondary metabolites biosynthesis” group. In that group, Cytochrome P450 had the most abundant sequences, with a total of 187 unigenes being involved in various pathways.
GO (http://www.geneontology.org/) is an international classification system for standardized gene functions, offering a controlled vocabulary and a strictly defined conceptualization for comprehensively describing the properties of genes and their products within any organism. The three main, independent GO categories are biological processes, molecular functions, and cellular components. With NR annotation, we used the Blast2GO (version 2.3.5) (http://www.blast2go.org/). Program [25] to obtain GO annotations and were able to map 15,145 (25.59%) unigenes to GO terms. Because a transcript sometimes had multiple terms, 15,145 of our unigenes could be summarized into the three main GO categories and then into 44 sub-categories. Of these, 10,562 (69.74%) comprised the largest category, molecular function, followed by cellular component (10,318, 68.13%) and biological process (8,721, 57.58%) (Figure 2B). Within the biological process group, the great majority was related to metabolic process (GO: 0008152, 70.85%) and cellular processes (GO: 0009987, 66.19%). Within cellular component, the largest proportion were assigned to cells (GO: 0005623, 98.73%), cell parts (GO: 0044464, 89.19%), and organelles (GO: 0043226, 70.01%). Approximately 70.85%, of those for cellular components pertained to catalytic activity (GO: 0003824), followed by binding (GO: 0005488, 63.42%).
Using the KEGG (http://www.genome.ad.jp/kegg/) database, we mapped 20,548 unigenes to 121 pathways. Among them, 4,148 were involved in metabolic pathways. Another 2,359, related to secondary metabolism, were mapped to 35 pathways (Figure 3). For example, the “Purine metabolism pathway” (ID: ko00230) and “phenylpropanoid biosynthesis pathway” (ID: ko00940) were the largest groups, containing 397 unigenes. The latter is the upstream pathway for flavonoid biosynthesis. We also found 242 and 115 unigenes in the “flavonoid biosynthesis pathway” (ID: ko00941) and “flavone and flavonol biosynthesis pathway” (ID: ko0094), respectively.
Genes Related to Major Secondary Metabolism
Biosynthesis of hypericin begins with the condensation of one molecule of acetyl-CoA with seven molecules of malonyl-CoA. The octaketide chain that forms subsequently undergoes cyclization and decarboxylation, leading to the formation of emodin anthrone (Figure 4A) [7]. For hypericin biosynthesis, Hyp-1 (Hypericum perforatum phenolic oxidative coupling protein) plays an important role in catalyzing that condensation reaction from emodin to hypericin [16]. Our annotated databases revealed 12 unigenes homologous to Hyp-1 (Table 3).
Dashed box within (B) occurs in animals. Hyp-1, Hypericum perforatum phenolic oxidative coupling protein; MEP pathway, non-mevalonate pathway; MAT, dimethylallyltranstransferase; AS I, anthranilate synthase I; AS II, anthranilate synthase II; PAT, phosphoribosylanthranilate transferase; PAI, phosphoribosylanthranilate isomerase; IGPS, indole-3-glycerol phosphate synthase; TSA, Tryptophan synthase alpha chain; TSB, Tryptophan synthase beta chain; TDC, tryptophan decarboxylase; TPH, tryptophan hydroxylase; ORCA3, octadecanoid-derivative responsive Catharanthus AP2-domain protein 3; DXS, 1-D-deoxyxylulose 5-phosphate synthase; DXR, 1-deoxy-D-xylulose 5-phosphate reductoisomerase; CMS, 4-diphosphocytidyl-2-C-methyl-D-erythritol synthase; CMK, 4-(cytidine 5′-diphospho)-2-C-methyl-D-erythritol kinase; MCS, 2-C-methyl-D-erythritol 2,4-cyclodiphosphate synthase; HDS, (E)-4-hydroxy-3-methylbut-2-enyl-diphosphate synthase; IDS, isopentenyl-diphosphate:NAD(P)+ oxidoreductase; IPI, isopentenylpyrophosphate isomerase; GPPS, geranylgeranyl pyrophosphate synthase; DMAPP, dimethylallyl diphosphate; IPP, isopentenyl diphosphate; GPP, geranyl diphosphate; MEP, 2-C-methyl-Derythritol-4-phosphate; CDP-ME, 4-(cytidine-5′-diphospho)-2-C-methyl-Derythritol; CDP-MEP, 2-phospho-4-(cytidine-5′-diphospho)-2-C- methyl-Derythritol; Me-cPP, 2-C-methyl-D-erythritol-2,4, cyclodiphosphate; HMBPP, 1-hydroxy-2-methyl- 2-(E)-butenyl 4-diphosphate.
The biosynthesis of hyperforins can be divided into two phases of formation – carbon skeletons and prenyl side chains (Figure 4B) [8], [26], [27]. This skeleton starts from one molecule of isobutyryl-CoA and three molecules of malonyl-CoA that undergo a condensation reaction catalyzed by type III PKS (known as isobutyrophenone synthase, or BUS). Prenylation of that skeleton accepts the prenyl from an isoprenoid, which is biosynthesized via the non-mevalonate pathway (MEP pathway) [25]. Three molecules of dimethylallyl pyrophosphate and one molecule of geranyl diphosphate join in modifying those prenyl side chains to yield hyperforin. We identified more than 91 unigenes from our database and determined that they are involved in the entire MEP pathway. This is the first time all of these genes have been identified in H. perforatum (Table 3). We also identified 91 unigenes homologous to dimethylallyltranstransferase (MAT) gene from our database.
Although tryptophan biosynthesis has been clearly described in Arabidopsis [28], the pathway from tryptophan to melatonin is still unclear. In mammals, yeast, and bacteria, melatonin is synthesized from tryptophan via 5-hydroxytryptophan, tryptamine, and serotonin [29]. In H. perforatum, melatonin is synthesized from tryptophan via 5-hydroxytryptophan and serotonin [15]. We drew a putative melatonin biosynthetic pathway for that species as well (Figure 4C). In our database, we found 66 unigenes encoding nine enzymes involved in melatonin biosynthesis, including anthranilate synthase (AS) I and II, phosphoribosylanthranilate transferase (PAT), phosphoribosylanthranilate isomerase (PAI), indole-3-glycerol phosphate synthase (IGPS), tryptophan synthase (TSA and TSB), tryptophan decarboxylase (TDC), and tryptophan hydroxylase (TPH) (Table 3). This is first time that any of these have been identified in H. perforatum.
Prediction of Type III Polyketide Synthase
Type III PKS is a class of enzymes that catalyzes the synthesis of polyketides, such as CHS, BUS, and STS. In higher plants, the CHS-type shows >80% similarity with chalcone synthases and >70% similarity with non-chalcone synthases, or STS- and CTAS-types [30].
Only five type III PKS proteins – benzophenone synthase, octaketide synthase, chalcone synthase, HyPKS1, and HyPKS2– have previously been reported from H. perforatum [17]. Here, we used PKSIIIexplorer (http://type3pks.in/tsvm/pks3/index.php) to predict unigenes encoding such proteins [31], and obtained 2,291 (3.87%) unigenes (Table S1). In that species, polyketides may have dual functions during biotic stress: 1) as antioxidants to protect cells from oxidative damage, and 2) as phytoalexins to inhibit the growth of pathogens [32], [33]. Our results provide an overview of type III PKSs in H. perforatum that will facilitate further studies.
Identification and Analysis of Transcription Factors
Transcription factors (TFs) affect metabolic flux by regulating related encoding enzymes gene expression. Their identification offers information for manipulating secondary metabolism in medicinal plants. Here, BlastX was performed to search against the AGRIS (Arabidopsis Gene Regulatory Information Server) database [34]. In all, 1,772 unigenes were annotated in 744 independent coding sequences of Arabidopsis TFs (identity >77%) that belong to 47 known TF families (Table 4). For example, 212, 189, 144, 132, 120, 106, and 95 unigenes were annotated to NAC, C2H2, AP2-EREBP, C3H, Homeobox, bHLH, and MYB families, respectively.
AP2/EREBP, bHLH, and MYB are important TF families regulating secondary metabolism in plants, playing an important role in the control of indole alkaloid and tryptophan biosynthesis [35]–[37]. The octadecanoid-derivative responsive Catharanthus AP2-domain protein 3 (ORCA3) activates the expression of several genes that encode enzymes involved in indole alkaloid biosynthesis and MEP pathway, e.g., AS I, TDC, and 1-D-deoxyxylulose 5-phosphate synthase (DXS). Altered tryptophan regulation 1 (ATR1) – a MYB factor – and altered tryptophan regulation 2 (ATR2) – a bHLH factor – activate genes that function in tryptophan biosynthesis and metabolism in Arabidopsis [36], [37].
Real-time PCR Analysis of Several Novel Transcripts
For a better understanding of metabolites, one must also evaluate the temporal and spatial expression profiles of key genes. Our BlastX alignment produced the best aligning results for HyAS I, HyAS II, and HyPAT. We then performed RT-PCR analysis to investigate the expression patterns of 12 novel transcripts (Figure 5; Table S2). Within the melatonin biosynthesis pathway, AS I, PAI, and TPH were highly expressed in the stems, whereas PAT, IGPS, and TSA were mainly expressed in the leaves. In addition, AS II was highly expressed in the leaves and seeds. It is generally accepted that tryptophan is biosynthesized in the chloroplasts [38], [39]. Our results are consistent with previous findings that the genes involved in tryptophan biosynthesis have high expression in the leaves and stems, both containing chloroplasts. We noted that the unigene44757 homolog of GmMYB75, an R2R3-MYB family member, was highly expressed in roots but very little in the seeds. To better known the function and expression pattern of unigene44757, further researches are needed.
ERROR BARs indicate standard deviation.
In the hyperforin biosynthesis pathway, MAT was expressed in all tissues, albeit at slightly higher levels in the leaves. PKS was mainly expressed in flowers but only minimally in the roots and seeds. These results support previous findings that polyketide is more abundant at the flowering stage. 4CL was expressed mainly in the leaves while PAL was highly expressed in the stems and roots. These two genes involved in the phenylpropanoid pathway showed different patterns that were not consistent with those of genes in other species [40], [41].
SSR Frequency and Distribution
Simple Sequence Repeats (SSRs) or microsatellites are ubiquitous in eukaryotic genomes. They are distributed in both coding and non-coding regions [42]. Their varied lengths affect the expression patterns of certain genes. SSRs are ideal for determining paternity, investigating population genetics, and recombination mapping, and they are considered the only molecular marker for providing clues about which alleles are more closely related [43].
In all, we identified 7,643 SSRs as potential molecular markers for genetics applications. Dinucleotide repeats (3,019) were the most common SSRs in our datasets. This distribution is consistent with that in most other dicotyledonous species, such as Arabidopsis, peanut, and grape [44]. The second major class was trinucleotide (1,990), followed by mononucleotide (1,317), hexanucleotide (586), pentanucleotide (482), and tetranucleotide (249) (Table 5). Di- and trinucleotides frequently showed five repeats while penta- and hexa- had two. The mononucleotide often appeared in 10 to 14 repeats. SSRs with five tandem repeats (30.37%) were the most common.
Table 6 presents the frequencies of these di- and trinucleotide repeats. In our database, the AG/CT motif was the dominant repeat motif (up to 33.95%), followed by AAG/CTT (8.18%); CG/GC (0.2%) occurred very infrequently. The results are consistent with those reported from other plant species [44]–[46].
Conclusions
Because Hypericum perforatum is the main natural source for extracted hypericin and hyperforin, research of this plant is on-going. Its pharmacological properties are gradually being revealed, including those that are anti-tumor, anti-inflammatory, antiviral, anti-cancer, and antibacterial. Likewise, some health-promoting compounds, such as melatonin, are products of secondary metabolism. Our study is the first to use Illumina/Solexa deep sequencing for identifying 59,184 unigenes within the H. perforatum gene pool. This enriched genetic information not only provides us with an insight into the molecular mechanisms of various metabolic pathways, but also enables us to improve our efforts in genetic manipulations and characterize species specific genes. This is an important public information platform for better understanding gene expression, genomics, and functional genomics in this valuable species.
Materials and Methods
Ethics Statement
No specific permits were required for the described field studies. No specific permissions were required for these locations and activities. The location is not privately-owned or protected in any way and the field studies did not involve endangered or protected species.
Plant Material
Seeds of Hypericum perforatum L. (Hypericaceae) were harvested from wild populations at Taibai Mountain (Shaanxi Province, China), and were identified by Dr. Ren Yi (Shaanxi Normal University). Plant voucher specimens (TB-Hp-001) have been conserved in the herbarium of College of Life Sciences at Shaanxi Normal University. The seeds were germinated in pots containing vermiculite. Conditions in the climate cabinet included a temperature of 25°C and a 16-h photoperiod (150 µmol·m−2·s−1). Plant materials available at each time point (i.e., roots, stems, leaves, and flowers) were collected at the vegetative stage (I, two-month-old seedlings), floral budding stage (II), and fresh fruiting stages (III). All samples were frozen immediately in liquid nitrogen and stored at −80°C.
RNA Isolation and Sequencing
Total RNAs were extracted with an Omega Plant RNA Kit (with DNase I) according to the manufacturer’s instructions. A single RNA sample was pooled from Stages I, II, and III. An RNase inhibitor (RNAlong; BioTeke, Beijing, China) was added to the RNA samples before they were analyzed by BGI (Shenzhen, Guangdong, China). Afterward, a cDNA library was constructed and RNA-seq was performed via Illumina HiSeq™ 2000.
Total RNAs from various organs at Stage II (roots, stems, leaves, and flowers) were extracted separately, using an Omega Plant RNA Kit with DNase I. We also extracted total RNAs from the seeds, as described above. Single-stranded cDNAs for real-time PCR analysis were synthesized from RNAs, using a PrimeScriptTM 1st Strand cDNA Synthesis Kit (TaKaRa, Dalian, China).
De novo Transcriptome Assembly and Annotation
We obtained 25,666,478 raw reads. After removing those with only adaptors, with unknown nucleotides larger than 5%, or those that were of low quality, only clear reads remained. These were then assembled for unigene annotation so that we could classify them for gene functioning as we have previously described [24].
Prediction of Type III PKSs
PKSIIIexplorer is a web server based on the “Transductive Support Vector Machine” that allows for fast and reliable predictions of type III PKS proteins [9], [31]. As candidates, we used peptide sequences predicted from 40,813 unigenes in our blast results. Putative type III PKS or type III PKS -like proteins received positive scores; all others were scored negatively.
RT-PCR and Expression Analysis
RT-PCR analysis was used to evaluate the quality of the sequence assembly. Twelve transcripts were chosen for monitoring their expression patterns (Table S2). We utilized the IQ5 real-time PCR detection system (Bio-Rad) as we have previously described [24]. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH, GU014528) served as an internal reference gene, and relative expression was calculated per the 2−ΔΔCt method [47]. All quantitative PCR runs were repeated in three biological and three technical replications.
SSR Analysis and TF Identification
SSRs were detected among the 59,184 unigenes via MIcroSAtellite (MISA, http://pgrc.ipk-gatersleben.de/misa/). The parameters were adjusted in order to identify perfect mono-, di-, tri-, tetra-, penta-, and hexanucleotide motifs with a minimum of 10, 5, 5, 4, 3, and 3 repeats, respectively. A Blast search for all unigenes was conducted with AtTFDB (Arabidopsis transcription factor database) to find TFs with shared identities >77%.
Supporting Information
Table S1.
Putative Type III PKSs in Hypericum perforatum .
https://doi.org/10.1371/journal.pone.0042081.s001
(XLS)
Table S2.
Gene-specific primers used for gene expression analysis by quantitative real-time PCR.
https://doi.org/10.1371/journal.pone.0042081.s002
(DOC)
Acknowledgments
The authors sincerely thank Dr. Ren Yi for much assistance in taxonomically identifying our plant materials.
Author Contributions
Conceived and designed the experiments: MH WH YZ ZW. Performed the experiments: MH YW. Analyzed the data: MH WH. Contributed reagents/materials/analysis tools: ZW. Wrote the paper: MH ZW.
References
- 1. Butterweck V (2003) Mechanism of action of St John’s wort in depression: what is known? CNS Drugs 17: 539–562.
- 2. Caraci F, Crupi R, Drago F, Spina E (2011) Metabolic drug interactions between antidepressants and anticancer drugs: focus on selective serotonin reuptake inhibitors and hypericum extract. Curr Drug Metab 12: 570–577.
- 3. Birt DF, Widrlechner MP, Hammer KD, Hillwig ML, Wei J, et al. (2009) Hypericum in infection: Identification of anti-viral and anti-inflammatory constituents. Pharm Biol 47: 774–782.
- 4. Schröder J (1997) A family of plant-specific polyketide synthases facts and predictions. Trends Plant Sci 2: 373–378.
- 5. Umek A KS, Kartnig T, Heydel B (1999) Quantitative phytochemical analyses of six Hypericum species growing in Slovenia. Planta Med 65: 388–390.
- 6. Smelcerovic A, Spiteller M (2006) Phytochemical analysis of nine Hypericum L. species from Serbia and the F.Y.R. Macedonia. Pharmazie 61: 251–252.
- 7. Karioti A, Bilia AR (2010) Hypericins as potential leads for new therapeutics. Int J Mol Sci 11: 562–594.
- 8. Klingauf P, Beuerle T, Mellenthin A, El-Moghazy SA, Boubakir Z, et al. (2005) Biosynthesis of the hyperforin skeleton in Hypericum calycinum cell cultures. Phytochemistry 66: 139–145.
- 9. Flores-Sanchez IJ, Verpoorte R (2009) Plant polyketide synthases: A fascinating group of enzymes. Plant Physiol Biochem 47: 167–174.
- 10. Abe I, Morita H (2010) Structure and function of the chalcone synthase superfamily of plant type III polyketide synthases. Nat Prod Rep 27: 809–838.
- 11. Jez JM, Austin MB, Ferrer J, Bowman ME, Schröder J, et al. (2000) Structural control of polyketide formation in plant-specific polyketide synthases. Chem Biol 7: 919–930.
- 12. Altun A, Ugur-Altun B (2007) Melatonin: therapeutic and clinical utilization. Int J Clin Pract 61: 835–845.
- 13. Murch SJ, Simmons CB, Saxena PK (1997) Melatonin in feverfew and other medicinal plants. Lancet 350: 1598–1599.
- 14. Paredes SD, Korkmaz A, Manchester LC, Tan DX, Reiter RJ (2009) Phytomelatonin: a review. J Exp Bot 60: 57–69.
- 15. Murch SJ, Krishna R, Saxena PK (2000) Tryptophan is a precursor for melatonin and serotonin biosynthesis in in vitro regenerated St. John’s wort (Hypericum perforatum L. cv. Anthos) plants. Plant Cell Rep 19: 698–704.
- 16. Bais HP, Vepachedu R, Lawrence CB, Stermitz FR, Vivanco JM (2003) Molecular and biochemical characterization of an enzyme responsible for the formation of hypericin in St. John’s wort (Hypericum perforatum L.). J Biol Chem 278: 32413–32422.
- 17. Karppinen K, Hohtola A (2008) Molecular cloning and tissue-specific expression of two cDNAs encoding polyketide synthases from Hypericum perforatum. J Plant Physiol 165: 1079–1086.
- 18. Karppinen K, Hokkanen J, Mattila S, Neubauer P, Hohtola A (2008) Octaketide-producing type III polyketide synthase from Hypericum perforatum is expressed in dark glands accumulating hypericins. FEBS J 275: 4329–4342.
- 19. Liu B, Falkenstein-Paul H, Schmidt W, Beerhues L (2003) Benzophenone synthase and chalcone synthase from Hypericum androsaemum cell cultures: cDNA cloning, functional expression, and site-directed mutagenesis of two polyketide synthases. Plant J 34: 847–855.
- 20. Wang L, Si Y, Dedow LK, Shao Y, Liu P, et al. (2011) A low-cost library construction protocol and data analysis pipeline for Illumina-based strand-specific multiplex RNA-seq. PLoS One 6: e26426.
- 21. Crawford JE, Guelbeogo WM, Sanou A, Traoré A, Vernick KD, et al. (2010) De novo transcriptome sequencing in Anopheles funestus using Illumina RNA-seq technology. PLoS One 5: e14202.
- 22. Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10: 57–63.
- 23. Wickramasinghe S, Hua S, Rincon G, Islas-Trejo A, German JB, et al. (2011) Transcriptome profiling of bovine milk oligosaccharide metabolism genes using RNA-sequencing. PLoS One 25: e18895.
- 24. Hua WP, Zhang Y, Song J, Zhao LJ, Wang ZZ (2011) De novo transcriptome sequencing in Salvia miltiorrhiza to identify genes involved in the biosynthesis of active ingredients. Genomics 98: 272–279.
- 25. Conesa A GS, García-Gómez JM, Terol J, Talón M, Robles M (2005) Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. Bioinformatics 21: 3674–3676.
- 26. Boubakir Z, Beuerle T, Liu B, Beerhues L (2005) The first prenylation step in hyperforin biosynthesis. Phytochemistry 66: 51–57.
- 27. Adam P, Arigoni D, Bacher A, Eisenreich W (2002) Biosynthesis of hyperforin in Hypericum perforatum. J Med Chem 45: 4786–4793.
- 28. Normanly J (2010) Approaching cellular and molecular resolution of auxin biosynthesis and metabolism. Cold Spring Harb Perspect Biol 2: 1–17.
- 29. Boutin JA, Audinot V, Ferry G, Delagrange P (2005) Molecular tools to study melatonin pathways and actions. Trends Pharmacol Sci 26: 412–419.
- 30. Mallika V, Sivakumar KC, Soniya EV (2011) Evolutionary implications and physicochemical analyses of selected proteins of type III polyketide synthase family. Evol Bioinform Online 7: 41–53.
- 31. Vijayan M, Chandrika SK, Vasudevan SE (2011) PKSIIIexplorer: TSVM approach for predicting type III polyketide synthase proteins. Bioinformation 6: 125–127.
- 32. Franklin G, Conceição LF, Kombrink E, Dias AC (2009) Xanthone biosynthesis in Hypericum perforatum cells provides antioxidant and antimicrobial protection upon biotic stress. Phytochemistry 70: 60–68.
- 33. Germ M, Stibilj V, Kreft S, Gaberščik A, Kreft I (2010) Flavonoid, tannin and hypericin concentrations in the leaves of St. John’s wort (Hypericum perforatum L.) are affected by UV-B radiation levels. Food Chem 122: 471–474.
- 34. Palaniswamy SK, James S, Sun H, Lamb RS, Davuluri RV, et al. (2006) AGRIS and AtRegNet, a platform to link cis-regulatory elements and transcription factors into regulatory networks. Plant Physiol 140: 818–829.
- 35. Gantet P, Memelink J (2002) Transcription factors: tools to engineer the production of pharmacologically active plant metabolites. Trends Pharmacol Sci 23: 563–569.
- 36. Bender J, Fink GR (1998) A Myb homologue, ATR1, activates tryptophan gene expression in Arabidopsis. Proc Natl Acad Sci U S A 95: 5655–5660.
- 37. Smolen GA, Pawlowski L, Wilensky SE, Bender J (2002) Dominant alleles of the basic helix-loop-helix transcription factor ATR2 activate stress-responsive genes in Arabidopsis. Genetics 161: 1235–1246.
- 38. Palombella AL, Dutcher SK (1998) Identification of the gene encoding the tryptophan synthase beta-subunit from Chlamydomonas reinhardtii. Plant Physiol 117: 455–464.
- 39. Li J, Chen S, Zhu L, Last RL (1995) Isolation of cDNAs encoding the tryptophan pathway enzyme indole-3-glycerol phosphate synthase from Arabidopsis thaliana. Plant Physiol 108: 877–878.
- 40. Song J, Wang ZZ (2009) Molecular cloning, expression and characterization of a phenylalanine ammonia-lyase gene (SmPAL1) from Salvia miltiorrhiza. Mol Biol Rep 36: 939–952.
- 41. Gui J, Shen J, Li L (2011) Functional characterization of evolutionarily divergent 4-coumarate:coenzyme a ligases in rice. Plant Physiol 157: 574–586.
- 42. Zhang L, Yuan D, Yu S, Li Z, Cao Y, et al. (2004) Preference of simple sequence repeats in coding and non-coding regions of Arabidopsis thaliana. Bioinformatics 20: 1081–1086.
- 43. Goldstein DB, Ruiz Linares A, Cavalli-Sforza LL, Feldman MW (1995) An evaluation of genetic distances for use with microsatellite loci. Genetics 139: 463–471.
- 44. Kumpatla SP, Mukhopadhyay S (2005) Mining and survey of simple sequence repeats in expressed sequence tags of dicotyledonous species. Genome 48: 985–998.
- 45. Wei W, Qi X, Wang L, Zhang Y, Hua W, et al. (2011) Characterization of the sesame (Sesamum indicum L.) global transcriptome using Illumina paired-end sequencing and development of EST-SSR markers. BMC Genomics 12: 451.
- 46. Wang Z, Fang B, Chen J, Zhang X, Luo Z, et al. (2010) De novo assembly and characterization of root transcriptome using Illumina paired-end sequencing and development of cSSR markers in sweet potato (Ipomoea batatas). BMC Genomics 11: 726.
- 47. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, et al. (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 3: RESEARCH0034.