- Research article
- Open Access
A deep transcriptomic analysis of pod development in the vanilla orchid (Vanilla planifolia)
© Rao et al.; licensee BioMed Central Ltd. 2014
- Received: 28 April 2014
- Accepted: 28 October 2014
- Published: 7 November 2014
Pods of the vanilla orchid (Vanilla planifolia) accumulate large amounts of the flavor compound vanillin (3-methoxy, 4-hydroxy-benzaldehyde) as a glucoside during the later stages of their development. At earlier stages, the developing seeds within the pod synthesize a novel lignin polymer, catechyl (C) lignin, in their coats. Genomic resources for determining the biosynthetic routes to these compounds and other flavor components in V. planifolia are currently limited.
Using next-generation sequencing technologies, we have generated very large gene sequence datasets from vanilla pods at different times of development, and representing different tissue types, including the seeds, hairs, placental and mesocarp tissues. This developmental series was chosen as being the most informative for interrogation of pathways of vanillin and C-lignin biosynthesis in the pod and seed, respectively. The combined 454/Illumina RNA-seq platforms provide both deep sequence coverage and high quality de novo transcriptome assembly for this non-model crop species.
The annotated sequence data provide a foundation for understanding multiple aspects of the biochemistry and development of the vanilla bean, as exemplified by the identification of candidate genes involved in lignin biosynthesis. Our transcriptome data indicate that C-lignin formation in the seed coat involves coordinate expression of monolignol biosynthetic genes with the exception of those encoding the caffeoyl coenzyme A 3-O-methyltransferase for conversion of caffeoyl to feruloyl moieties. This database provides a general resource for further studies on this important flavor species.
- Vanilla planifolia
- RNA sequencing
Vanilla is the most popular flavoring material in the United State . Of the approximately 110 Vanilla species (Orchidaceae, monocot) that have been identified, only two are important in terms of commerce and cultivation; Vanilla planifolia Andrews and V. tahitensis JW Moore . Among these, V. planifolia is the most valued for its flavor qualities and is therefore widely cultivated and used for the production of food additives [2, 3]. The fully-grown mature fruits of vanilla, also called beans or pods, develop characteristic aromatic properties by the process of curing. The cured beans are referred to as vanilla , and the major flavor compound is vanillin (3-methoxy, 4-hydroxy-benzaldehyde).
In spite of its economic importance, there is little genetic diversity and few genetic or genomic resources in V. planifolia. The flavor industry generally disfavors genetically modified crops, so there have been virtually no attempts to modify the quantity or quality of the flavor of the vanilla bean through biotechnological approaches. Nevertheless, a better understanding of the genetic complement of V. planifolia could provide information on the still-disputed biosynthetic route to vanillin , support mechanistic studies on other areas of the novel biochemistry of this species, such as the biosynthesis of novel seed coat lignins , and provide molecular markers to advance non-transgenic breeding programs targeting flavor quality, yield or disease resistance.
The chromosome number for V. planifolia has been reported as 2n = 32. Most V. planifolia accessions are considered to be diploid with a 2C-value of 5.03 pg , but due to the large size and complexity of the V. planifolia genome, limited sequence resources are currently available.
The rapid development of next-generation sequencing (NGS) technologies has enabled the efficient and economical high-throughput sequencing of entire genomes or transcriptomes [7, 8]. NGS systems are typically represented by Roche’s 454 GS FLX and Illumina/Solexa Genome Analyzer. In general, Roche’s sequencing technology produces long reads and is advantageous for assembly of sequences into longer contigs; however, the number of reads generated in each run is lower than that of other platforms and not enough to reach deep coverage for low-abundance genes. The Illumina technology provides a high number of reads for deeper coverage, which is beneficial for gene discovery [9, 10]. Although its short read length limits de novo contig assembly efficiency [9, 10], various strategies, algorithms and software have been developed for short-read assembly, especially for de novo assembly in the absence of a reference genome .
Using the above technologies, we have generated very large gene sequence datasets from vanilla pods at different times of development, and representing different tissue types, including the seeds, hairs, and placental and mesocarp tissues within the pod. Based on previous studies [5, 12, 13], this developmental series was chosen as reflecting the times of synthesis and accumulation of vanillin and catechyl lignin in the pod and seed, respectively. The annotated sequence data provide a foundation for understanding multiple aspects of the biochemistry and development of the vanilla bean, as exemplified by the identification of candidate genes involved in lignin biosynthesis and interrogation of their expression patterns in the developing pod and seed in relation to the accumulation of the C-lignin polymer in the seed coat .
Biochemistry of greenhouse-grown vanilla beans
To determine if the green vanilla beans obtained from greenhouse-grown material retain the biosynthetic capabilities of plants grown in nature, we measured the vanillin content of the greenhouse grown vanilla bean. Without acid hydrolysis, only a very low amount of vanillin could be detected in the green beans. After acid hydrolysis, 0.53 g of vanillin per100g fresh green bean was measured in the six-month old vanilla beans, indicating that the beans used in this study maintain the biosynthetic capability of naturally-grown beans as regards glucovanillin accumulation. The ability of greenhouse-grown plants to accumulate C-lignin in the seed coats has been reported previously .
Next generation sequencing and assembly of reads
To obtain transcript expression information in each tissue or organ, each of 26 vanilla Illumina libraries was assembled separately using a combination of the programs Velvet  and Oases . Velvet currently takes short reads, removes errors, and then produces high quality unique contigs. Oases uploads a preliminary assembly produced by Velvet, and then clusters the contigs into small groups, called loci. It then exploits the paired-end read and long read information, when available, to construct transcript isoforms [14, 15]. Using these approaches, transcripts per library ranged from 44,403 to 94,637 with mean transcript length ranging from 1,694 to 2,159 bp (Additional file 3). To demonstrate the quality of assembled transcripts, the size distribution of four selected samples is shown in Additional file 4.
For quality control, we mapped our results to another recently produced V. planifolia sequence dataset that did not include multiple tissue types. This consisted of 372 M bases of short 454 reads generated by the University of Copenhagen and deposited at [NCBI: SRX286672]. The combined assembled transcripts were mapped using Blast, and the results showed excellent consistency between the 454 reads in the public domain and the vanilla transcripts generated in this project. A total of 96.6% of the 454 reads from [NCBI: SRX286672] were able to be mapped back to 82,595 combined assembled transcripts. For further quality control analysis, we randomly selected 35 contigs and designed primers for RT-PCR amplification. In this analysis, 32 out of the 35 primer sets successfully resulted in a band of the expected size (Additional file 5), and the identities of all the resulting cloned sequences were confirmed by Sanger sequencing.
Mapping of transcripts to reference databases
An integrated digital gene expression database
To estimate transcript expression levels in each of the 26 Illumina libraries, the reads were mapped back to the assembled transcript sequences using Bowtie2 2.0.0 . The number of reads mapped to each transcript were then counted and used to calculate the reads per kilobase of transcript per million reads mapped (RPKM) for every transcript in each library (Additional files 7, 8 and 9).
In the integrated gene expression database, the expression levels of a total of 301,459 transcripts are presented in 26 different tissues or organs. Most of these genes (183,724) showed above 1 RPKM value in at least one tissue or organ. Furthermore, 37,538 transcripts showed above 10 RPKM value in at least one tissue or organ, and 2,156 transcripts were above 100 RPKM value in at least one tissue or organ.
For further validation of reference genes in Vanilla, two frequently used housekeeping genes, Actin1 and ubiquitin (Ubi), were selected from the stably expressed genes list, and their expression levels were evaluated in early stage seeds and in stem by quantitative real-time RT-PCR. Actin1 showed a similar constitutive expression pattern to that of Ubi, but with higher expression level (Additional file 11). The data are consistent with our digital gene expression database, and Actin1 was chosen as the reference gene for further gene expression analyses. Another group also recently identified Actin and EF1 to be the most stably expressed reference genes during pod development in V. planifolia.
Annotation of tissue-specifically expressed genes
After sorting the tissue-specifically expressed genes into Gene Ontology (GO) categories using Plant GOslim ancestor terms (http://www.geneontology.org/)  and the KEGG pathway database (http://www.genome.jp/kegg/) [25, 26], we found that genes involved in primary metabolic processes, biosynthesis processes and cellular metabolic processes were highly expressed in 6 week old seed (Additional file 13A). The 6 week seed specific gene set was rich in genes annotated as having involvement in starch and sucrose metabolism pathways in the KEGG database (Additional file 13, under KEGG enzyme code EC:220.127.116.11, EC:18.104.22.168, EC:22.214.171.124, EC:126.96.36.199 and EC:188.8.131.52). Genes with transport, trans-membrane transport and response to stimulus functions in the GO categories were found to be more highly expressed in the aerial root-specific gene group (Additional file 13B). In the leaf, most genes were annotated as encoding photosystem I and II proteins.
There were only a few specific marker genes for the major organ systems. More tissue-specific markers were found for roots and seeds than for leaves, perhaps reflecting the more specialized nature of these organ systems . These subsets of organ-specific genes will be a useful tool for research into biological processes during Vanilla pod development.
Identification of genes potentially involved in lignin and vanillin biosynthesis
The digital expression database contains transcript level values for all potential lignin pathway genes in additional pod tissues such as hairs and placenta at later stages of pod development. This represents the timing and location of the accumulation of glucovanillin . The vanillin pathway likely shares several reactions with the monolignol pathway ; genes specifically dedicated to vanillin biosynthesis remain to be described, although forms of PAL  and O-methyltransferase  have been suggested be involved in the process. The present work will facilitate the documentation of genes important for the formation of glucovanillin and other flavor compounds, and such results will be reported elsewhere.
Next-generation sequencing and computational data mining tools have been used for high-throughput transcriptomic profiling of developing vanilla beans. The combined 454/Illumina RNA-seq platforms provide both deep sequence coverage and high quality de novo transcriptome assembly for this non-model crop species. The annotated sequence data provide a foundation for understanding multiple aspects of the biochemistry and development of the vanilla bean, as exemplified by the identification of candidate genes involved in an unusual lignin biosynthesis pathway in vanilla seed. Our transcriptome data indicate that C-lignin formation in the seed coat involves coordinate expression of monolignol biosynthetic genes with the exception of those encoding the caffeoyl coenzyme A 3-O-methyltransferase for conversion of caffeoyl to feruloyl moieties. This database provides a general resource for further studies on this important flavor species.
Plant materials and RNA isolation
Vanilla (Vanilla planifolia) vines were grown in the greenhouse in MetroMix 350 soil mix at 20–23°C (24–28°C in summer) with 45-60% relative humidity with a 16 h day from 6:00 h to 22:00 h, 150 μmol/m2/s light intensity, facilitated by supplementary lighting. Plants were watered daily with weekly addition of Scotts peat-lite 20-10-20 fertilizer at 100 ppm N. The flowers were hand pollinated in March and April of 2011. Green vanilla beans were harvested periodically after pollination. The selected 26 different tissue samples varied from early stages of pod development (seed, placental laminae (“light” for short) and mesocarp (“dark” for short) tissues at 6 , 8 and 10 weeks post-pollination), mature pods (seed, V-shaped hair cells (“hair” for short), placental laminae, and mesocarp tissues at 3, 5 and 6 months post-pollination), leaf and stem from mature plants harvested at 6 months and 1 year post-pollination of the flowers, and aerial roots harvested from one year old stems. Before tissue separation, the beans were cut into 2 cm sections to facilitate the isolation of seeds from mesocarp and placental tissue. At the later stages (after 3 months), the hair cells were also isolated from the placentae cells. The isolated vanilla pod tissues and seeds were immediately frozen in liquid nitrogen and ground to a fine powder with a mortar and pestle, and then stored at −80°C until RNA isolation using TRI Reagent (Molecular Research Center, Inc. Cincinnati, OH) according to the manufacturer’s protocol. Briefly, about 100 mg of tissues were mixed with 1 ml of TRI reagent in 2.0 ml Eppendorf tubes, and the samples allowed to stand for 10 min at room temperature. Chloroform (0.2 ml) was added, and the tubes were capped and shaken vigorously by vortexing followed by centrifugation at 12,000 × g for 10 min at 4°C. The upper aqueous phases containing RNA were transferred to fresh tubes and 0.5 ml of isopropanol was added and mixed. The samples were allowed to stand for 10 min at room temperature and were then centrifuged at 12,000 × g for 10 min at 4°C to precipitate the RNA. The supernatant was removed and the RNA pellet washed with 1 ml of 75% ethanol and centrifuged at 10,000 × g for 5 min. The RNA was briefly dried under air and re-dissolved in water.
Determination of vanillin content in greenhouse-grown vanilla bean
Vanillin content of mature vanilla beans obtained from the greenhouse was measured using HPLC after acid hydrolysis. Briefly, the six month old green vanilla beans were ground to a fine powder under liquid nitrogen. The samples (three replicates) were then extracted with 70% methanol for 4 h at room temperature. After removing the solid residue by centrifugation, the methanolic solutions were dried under nitrogen gas and hydrolysed with 20% (V/V) HCl at 100°C for 1 h and subject directly to HPLC. The HPLC system consisted of a Thermo UltiMate 3000 HPLC system (Thermo Scientific, Sunnyvale, CA) and a Phenomenax C 18 column (250 × 4.6 mm 5 μ) (Phenomenex Inc, Torrance, CA). The mobile phase used was A: 1% phosphoric acid, B: acetonitrile. Gradient: 8% B for 5 min, 8% B to 35% B over 45 min, then 100% for 10 min. Commercial vanillin from Sigma-Aldrich (St. Louis, MO) was used as a standard for identification and construction of a standard curve.
RNAseq library construction and next-generation sequencing
For each sample, 1 μg of total RNA was used for RNA-seq library construction using TruSeq RNA Sample Prep Kits v2 (Illumina Inc., San Diego, CA) according to the manufacturer’s instructions at the Genomics Core Facility at the Noble Foundation. Each library was indexed. Six libraries with different indexes each were pooled together to run on one Hiseq2000 lane targeting 100 bp paired reads. The Hiseq2000 run was conducted at the Genomics Core Facility of the Oklahoma Medical Research Foundation, Oklahoma City.
De novo transcript assembly and construction of a digital gene expression atlas
Processing of the 100 bp paired-end Illumina reads began by interleaving the read mates for each sample into a single file and trimming bases with quality scores of 20 or less from the end of each read. Reads less than 40 bp long after trimming were discarded along with their mates.
Each of the 26 vanilla Illumina libraries was assembled separately using a combination of Velvet  and Oases . Velvet was run using a k-mer length of 55 and an average insert length of 300 bp. Expected coverage and coverage cut-off levels were set to be automatically determined by the program. The results of the Velvet assemblies were then run through Oases using an insert length of 300 bp, a minimum transcript length of 200 bp, a coverage cut-off of 5, and an edge fraction cut-off of 0.25.
A combined assembly of all 26 Illumina samples (3,439,193,362 reads total) was produced using Velvet 1.2.03 with a k-mer length of 27, producing 6,371,646 contig sequences. A library of 1,678,293 454 reads (mean length =397.5 bp) was also produced and quality trimmed using a cut-off score of 20. The 454 reads and Illumina contigs were then assembled together with MIRA 3.2.1 , using the “normal” quality parameters for a de novo 454 EST assembly.
To estimate transcript expression levels in each of the 26 Illumina libraries, the reads were mapped back to the assembled transcript sequences using Bowtie2 2.0.0 (beta 7)  with the “sensitive” end-to-end alignment parameters. The number of reads mapped to each transcript was then counted and used to calculate the RPKM value for every transcript in each library.
Sequence analysis and gene annotation
The resulting 301,459 transcripts from de novo combined assembly were annotated via alignment with the NCBI NR database, the Conserved Domains Database, and the Arabidopsis, rice, and sorghum proteomes (ftp://ftp.ncbi.nlm.nih.gov/). Tissue specific genes were detected in R 3.0.1  with the package “rsgcc” . Tissue specific genes were further annotated with default parameter values using Blast2Go . After the Blast2Go mapping process, EC numbers from the KEGG pathway (http://www.genome.jp/kegg/) and GO terms were generated.
Real-time quantitative RT-PCR
Total RNA was isolated from seed, mesocarp and stem tissues as described above. cDNA synthesis was performed using DNase I-treated RNAs and Superscript III reverse transcriptase (Invitrogen). Primer pairs for real-time qRT-PCR were designed using GenScript Real-time PCR Primer Design software (https://www.genscript.com/ssl-bin/app/primer) with the default parameters (Additional file 15). qRT-PCR was performed in duplicate for each sample and at least two biological replicates were evaluated for each gene tested. The qRT-PCR reactions were performed in an optical 96-well plate with a PikoReal.
Real-Time PCR System (Thermo Scientific), using SYBR Green Master Mix reagent (Simga) with PCR program as previously described . Data were collected and analysed using PikoReal Software (Thermo Scientific). PCR efficiency was estimated using PikoReal software  and transcript levels were determined by relative quantification  using the actin gene as a reference.
The data sets supporting the results of this article are available in the NCBI Sequence Read Archive (SRA) repository, NCBI SRA accession No.: SRX627422, SRX634866, SRX634867, SRX634872, SRX634873, SRX634874, SRX634907, SRX634908, SRX634909, SRX648194, SRX648209, SRX648428, SRX648438, SRX648439, SRX648555, SRX648556, SRX648557, SRX648564, SRX648565, SRX648567, SRX648568, SRX648569, SRX648570, SRX648571, SRX648572, SRX648573, SRX648574.
We thank Jaime Barros-Rios and Luis Escamilla-Trevino for critical reading of the manuscript. The sequencing was supported by the Samuel Roberts Noble Foundation, and data analysis by the University of North Texas (start-up funds to RAD). Computational resources were provided by UNT’s High Performance Computing Services.
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