Transcriptome analysis of Cymbidium sinense and its application to the identification of genes associated with floral development
© Zhang et al.; licensee BioMed Central Ltd. 2013
Received: 31 October 2012
Accepted: 15 April 2013
Published: 24 April 2013
Cymbidium sinense belongs to the Orchidaceae, which is one of the most abundant angiosperm families. C. sinense, a high-grade traditional potted flower, is most prevalent in China and some Southeast Asian countries. The control of flowering time is a major bottleneck in the industrialized development of C. sinense. Little is known about the mechanisms responsible for floral development in this orchid. Moreover, genome references for entire transcriptome sequences do not currently exist for C. sinense. Thus, transcriptome and expression profiling data for this species are needed as an important resource to identify genes and to better understand the biological mechanisms of floral development in C. sinense.
In this study, de novo transcriptome assembly and gene expression analysis using Illumina sequencing technology were performed. Transcriptome analysis assembles gene-related information related to vegetative and reproductive growth of C. sinense. Illumina sequencing generated 54,248,006 high quality reads that were assembled into 83,580 unigenes with an average sequence length of 612 base pairs, including 13,315 clusters and 70,265 singletons. A total of 41,687 (49.88%) unique sequences were annotated, 23,092 of which were assigned to specific metabolic pathways by the Kyoto Encyclopedia of Genes and Genomes (KEGG). Gene Ontology (GO) analysis of the annotated unigenes revealed that the majority of sequenced genes were associated with metabolic and cellular processes, cell and cell parts, catalytic activity and binding. Furthermore, 120 flowering-associated unigenes, 73 MADS-box unigenes and 28 CONSTANS-LIKE (COL) unigenes were identified from our collection. In addition, three digital gene expression (DGE) libraries were constructed for the vegetative phase (VP), floral differentiation phase (FDP) and reproductive phase (RP). The specific expression of many genes in the three development phases was also identified. 32 genes among three sub-libraries with high differential expression were selected as candidates connected with flower development.
RNA-seq and DGE profiling data provided comprehensive gene expression information at the transcriptional level that could facilitate our understanding of the molecular mechanisms of floral development at three development phases of C. sinense. This data could be used as an important resource for investigating the genetics of the flowering pathway and various biological mechanisms in this orchid.
KeywordsFloral development Flowering time Digital gene expression Transcriptome Cymbidium sinense
The Orchidaceae is one of the largest and most widespread families of flowering plants, with more than 250000 species . Cymbidium, a very important economically flowering genus with the Orchidaceae, has nearly 55 species throughout the world and is widespread in tropical and subtropical Asia, reaching as far south as Papua New Guinea and Australia . Cymbidium sinense is a perennial terrestrial species native to China and has more than a thousand years of cultural history. It holds a strong position in traditional flower markets in China, Japan, Korea, and Southeast Asia. It is of great horticultural value as an ornamental plant because of its elegance, its upright leaves, and beautiful and fragrant flowers. C. sinense blooms in winter from January to March and is usually regarded as a Spring Festival flower. Although C. sinense is a valuable orchid, its flowering pathway is not clearly understood. Variation in environmental conditions, including light, temperature and hormones, is likely to regulate flowering time and flower quality. Flowering regulation technology for C. sinense is currently scarce on the market, which seriously hinders the development of the orchid industry and also reduces the economic value of C. sinense.
The transition from vegetative growth to flowering is very importance because flowering is the first step of sexual reproduction . In Arabidopsis thaliana, flower initiation takes place via four (gibberellin, autonomous, vernalization, light-dependent) genetic pathways. These processes are integrated by the function of the FLOWERING LOCUS D (FD), FLOWERING LOCUS E (FE), FLOWERING WAGENINGEN (FWA), PROTODERMAL FACTOR2 (PDF2), SUPPRESSOR OF OVEREXPRESSION OF CO 1(SOC1), and FLOWERING LOCUS T (FT) genes. The integrated signal of floral induction is transmitted to the floral meristem identity genes LEATY (LFY) and APETALA1 (AP1), after which floral morphogenesis takes place . Currently only a few flowering genes have been cloned from orchids. DOH1 and DOMADS1 were isolated and identified from Dendrobium Madame. DOH1 plays a negative regulatory role in floral formation while DOMADS1 is a marker gene specifically expressed in the shoot apical meristem during floral transition. DOH1 is a possible upstream regulator of DOMADS1 and inhibits its expression [5, 6]. Some MADS-box function genes were also isolated from Dendrobium crumenatum. They include APETALA2 (AP2), PISTILLATA (PI)/GLOBOSA (GLO), APETALA3 (AP3) /DEFIENCE (DEF)-like, AGAMOUS (AG) and SEPALLATA (SEP) genes, which play an important role in the early regulation of the conversion to flowers . OMADS3 was isolated and characterized from Oncidium Gower Ramsey with a function similar to a functional gene regulating flower formation as well as floral initiation . OMADS1 from O. Gower Ramsey promoted flower transition and formation by acting as an activator for FT and SOC1 in Arabidopsis. OMADS1 was able to strongly interact with OMADS3, which influenced flower formation and floral initiation .
Compared with other orchids, very little research exists on the role of flowering genes in the regulation of the vegetative-to-flowering transition and flower initiation in Cymbidium. Few reports investigating the functions of flowering time genes of Cymbidium exist. Genomic resources available for the species are also scarce. Together with the nucleotide sequences obtained by NCBI searches, 60 Cymbidium expressed sequence tags (ESTs) are, however, available. Nevertheless, this genetic data is insufficient for elucidating the molecular mechanism of floral regulation in C. sinense.
In recent years, Illumina sequencing techniques have provided fascinating opportunities in life sciences and dramatically improved the efficiency of gene discovery. However, the entire transcriptome of C. sinense has not been sequenced. In this study, RNA-seq and digital gene expression (DGE) were performed using Illumina technology. Illumina sequencing provided comprehensive information about gene expression at the transcriptional level that could facilitate our understanding of the molecular mechanisms of C. sinense floral development. Such data for C. sinense could also be used as an important resource to investigate the flowering pathway and various other biological mechanisms in other orchid species.
Illumina sequencing and sequence assembly
Summary for C. sinense transcriptome
Total number of raw reads
Total number of clean reads
Total clean nucleotides (nt)
Average read length
Total number of contigs
Mean length of contigs
Total number of unigenes
Mean length of unigenes
Annotation of predicted proteins
Statistics of annotation results for C. sinense unigenes
GO classification for unigenes
Metabolic pathway assignment by KEGG
The pathways and the products involved in the pathway of plant hormone signal transduction
Cysteine and methionine metabolism
Indole alkaloid biosynthesis
α-Linolenic acid metabolism
Identifying C. sinenseflowering time-associated genes and MADS-box genes
C. sinense unigenes that share homology with flowering time genes
Floral integrator pathway
Floral meristem identity
Identifying C. sinense CONSTANS-like gene family
CO plays a central regulatory role in the photoperiod pathway, and is regulated by the circadian clock and light [11, 12]. To determine the COL gene family of C. sinense, we analyzed the transcriptome database generated by this study and found 28 unigenes annotated as zinc finger protein CONSTANS. As shown in Additional file 3, they showed homology with 12 Arabidopsis COL genes.
C. sinense unigenes that share homology with CONSTANS-like genes of Arabidopsis
Length of gene (bp)
Arabidopsis thaliana COL1
Arabidopsis thaliana COL2
Arabidopsis thaliana COL6
Arabidopsis thaliana COL9
Arabidopsis thaliana COL9
Arabidopsis thaliana COL13
Arabidopsis thaliana COL14
DGE library sequencing and evaluation
DGE sequencing quality evaluation and alignment statistics
Only adaptor reads
Only adaptor reads%
Containing N reads
Containing N reads%
Low quality reads
Low quality reads%
Total clean reads
Total clean reads%
Total base pairs
Total mapped reads to gene
Total% of mapped reads
≤2 bp mismatch
≤2 bp mismatch%
Total unmapped reads
Total unmapped reads%
Variation in gene expression among three developmental stages
To identify the differentially expressed genes during three developmental stages, the number of clean tags for each gene was calculated, and the genes that were differentially expressed between the two samples were identified according to the method described by Audic and Claverie .
Based on the GO functional classification, most of the differential expression gene sets demonstrated down-regulated expression in the FDP library, and these genes were mainly correlated to membrane, vesicle and cellular component organization (Additional file 5). In the KEGG classification, 6 gene sets were significantly enriched, and most of these genes were mainly down-regulated in the FDP library and correlated to plant hormone signal transduction (Additional file 5).
Meanwhile, we compared the variations in gene expression between VP and RP. A total of 14795 genes, including 14125 up-regulated and 761 down-regulated genes, were identified in RP compared to VP (Figure 7). 1408 genes showed specific expression in RP and 38 genes showed specific expression in VP. Those genes that showed specific expression were listed in Additional file 6.
Based on the GO functional classification, most of the gene sets demonstrated up-regulated expression in the RP library, and these genes were mainly correlated to hydrolase activity, binding and organelle parts (Additional file 7). In the KEGG classification, 13 gene sets were significantly enriched, and most of these genes were mainly up-regulated in the FDP library and correlated to the spliceosome and ribosome (Additional file 7).
A comparison between FDP and RP revealed more variations in gene expression. The results revealed 20572 genes with significant differential expression levels between FDP and RP. Among them, 20040 and 532 genes were up-regulated and down-regulated, respectively (Figure 7). 2535 genes showed specific expression in RP and 25 genes showed specific expression in FDP. Those genes that showed specific expression are listed in Additional file 8.
Based on the GO functional classification, most of the gene sets demonstrated up-regulated expression in the RP library, and these genes were mainly correlated to binding, membrane-bounded organelles and organelle parts (Additional file 9). In the KEGG classification, 12 gene sets were significantly enriched, and most of these genes were mainly up-regulated in the RP library and correlated to the spliceosome and RNA transport (Additional file 9).
Illumina sequencing and sequence annotation
C. sinense is a very popular and traditional orchid in China. However, little is known about the mechanisms responsible for floral development and genomic information for C. sinense is currently unavailable. The aims of this project were to generate a large amount of cDNA sequence data that would facilitate more detailed studies in C. sinense, and to identify the genes controlling flowering time. The availability of transcriptome data for C. sinense will meet the initial information needs for functional studies of this species and its relatives. In this study, a combination of RNA-seq and three DGE analyses were performed using Illumina sequencing, which generated 54,248,006 high quality reads that were assembled into 83580 unigenes with an average sequence length of 612 base pairs. These unigenes were used for BLASTX and annotation against protein databases like nr, SwissPort, COG, KEGG and GO. In total, 41687 sequences were identified through BLAST searches and 50.1% unigenes had no homologues in the NCBI database. This may indicate that C. sinense vegetative and reproductive growth contains many unique processes and pathways.
Identifying C. sinenseflowering time-associated genes
The photoperiod and vernalization pathways are two important genetic networks of flowering control . In our study, the sequences associated with both pathways could be identified (Table 4). The photoperiod pathway comprises three parts: photoreceptors, a circadian clock and an output pathway from the clock specific to flowering . Light signals are first received by two photoreceptors, phytochromes (PhyA, PhyB, PhyC, PhyD, PhyE) and cryptochromes (Cryl, Cry2) [16–20], which process the physical signals and produce a circadian clock [21–23]. The circadian clock provides a seasonal signal that adjusts plant development and regulates flowering time. It is controlled by a group of gene complexes that are composed of TOC1, CCA1 and LHY and regulate transcriptional–translational negative feedback loops [24–26]. ELF3 affects light input to the oscillator [27, 28]. The processed signal is transmitted to the GI gene and the resultant signal activates the CO gene . GI is another clock-associated protein known to regulate circadian oscillation and flowering time of Arabidopsis[29, 30]. The gi mutants are defective for the expression of CCA1 and LHY genes. The circadian clock controls the expression rhythm of CO through GI. CO is a transcription factor that promotes flowering by inducing the expression of the direct downstream genes FT and SOC1. COL genes have been identified in some plant species, each of which seems to have a large family of these genes . In Arabidopsis, there are 17 COL genes . There are at least 16 COL genes in the rice genome . These COL proteins contain two B-box domains at the N-terminus and a CCT domain at the C-terminus [35, 36]. In this study, 28 C. sinense COL unigenes containing the conserved CCT domain were identified (Additional file 3). Seven unigenes were used to perform the phylogenetic analysis. They were classified into three conserved COL subgroups as defined previously . COL proteins belonging to different subgroups are expected to perform distinct biological roles, but only several COL genes controlling flowering time have been studied in detail . We presume that seven unigenes in C. sinense are expected to perform distinct biological roles. Sequence homologues for other genes involved in regulation of the circadian clock described above (GI, EIF3, PIF3, LHY, CHS, CCA1) could be found in our database (Table 4). Furthermore, detailed metabolic pathways for the circadian rhythm and 222 related unigenes, such as phytochromes (PhyA, PhyB, PhyC, PhyD, PhyE) and cryptochromes (Cryl, Cry2) were found (Figure 5). Thus, these photoperiod pathways may be concerned with the regulation of flowering time in C. sinense.
VRN2 is a key gene involved in the vernalization pathway . It promotes flowering by repressing the expression of FLOWERING LOCUS C (FLC). VRN2 codes for a protein with homology to PcG proteins . Thus, VRN2 may function to keep the FLC-chromatin state for down-regulation. Sequence homologs for VRN2 could be found in our database (Table 4). Furthermore, temperature obviously affects the flowering time of C. sinense. We speculate that the vernalization pathway is related to the regulation of flowering time in C. sinense.
Identifying C. sinenseflowering integration genes and floral meristem identity genes
Flowering integration genes accept the signal from the genetic pathway, and then induce floral meristem identity (FMI) genes for flowering as a whole . At present, three integration genes, including SOC1/AGL20, FT and TFL, have been identified. SOC1 and FT are the direct target genes for CO. The expression of FT and SOC1 is controlled positively not only by the light pathway, but also by the autonomous pathway acting negatively through FLC. The vernalization signal increases SOC1 expression, presumably by reducing FLC levels, and SOC1 can also be up-regulated by a gibberellin pathway. Accordingly, SOC1 and FT act as the convergence of all four pathways . TFL1 codes for a protein with homology to FT. The mutations in TFL1 are semidominant and early flowering with a determinate inflorescence . Thus, TFL1 codes for a repressor of flowering. TFL2 functions as a negative repressor of FT expression. Sequence homologs for the floral integrator pathway genes related to FT, SOC1 and TFL were all identified in our C. sinense transcriptome database (Table 4).
The floral meristem is initiated by a set of FMI genes that include LFY, AP1, CAL, AP2, and UNUSUAL FLORAL ORGANS (UFO)[44–47]. Among them, LFY and AP1 play a central role in flower meristem identity [48–50]. LFY regulates inflorescence shape and controls flowering time [51, 52]. AP1 is the downriver target gene of LFY. LFY induces expression of AP1. LFY protein, combined with the AP1 promoter activates the transcription of AP1. The phenotype of the loss-of-function-type mutations in LFY or AP1 gene is as follows: Flowers either have vegetative characteristics or have been replaced by vegetative shoots . The phenotype of double mutations in lfy/ap1 is a scarce floral structure [49, 55], which elucidated the functional redundancies in AP1-LFY. Functional redundancies were detected in AP1-CAL, AP2-AP1 and AP2-LFY genes [56, 57]. AP1 and CAL belong to the MADS domain genes and UFO codes for an F-box protein which degrades other proteins through a ubiquitation pathway [46, 58, 59]. LFY is also thought to promote CAL and UFO expression [60, 61]. Therefore, these FMI genes cooperate together to promote the transition from vegetative growth to reproductive growth. Sequence homologs for FMI genes CAL, AP2, LFY and AP1 were all identified in our C. sinense transcriptome database. These information of flowering integration genes and FMI genes would facilitate more detailed studies on the mechanism of floral differentiation for C. sinense.
Differential expression of genes among three developmental phases
We have identified the putative homologs of COL and other key genes involved in controlling flowering time in C. sinense. More studies on their expression patterns and on their interactive relationships in the future could be used to elucidate the molecular mechanisms that regulate the floral transition and the flowering genetic pathway in C. sinense. To better understand the information related to gene expression obtained for C. sinense, we created three DEG libraries to analyze the gene expression patterns under three developmental phases. In the comparison between FDP with VP, most of the expressed genes were down-regulated and parts were up-regulated. In contrast, when RP was compared to VP and FDP, most of the expressed genes were up-regulated (Figure 7). Furthermore, a large amount of genes which showed specific expression in the three phases were likely involved in floral development.
When FDP was compared to VP, TFL1 showed specific expression in VP. TFL1 codes for a flowering repressor that can function to control inflorescence architecture and extend the phases of shoot meristems in Arabidopsis. Our sequence was consistent with this theory. Among all specific expression of annotated genes in RP compared to VP or FDP, auxin response factors (ARF2) were identified in RP. Auxin signaling is important in the regulation of developmental transitions such as seed germination, induction of flowering, leaf senescence and shedding of senescent organs. ARFs are transcription factors that mediate responses to the plant hormone auxin . For example, ARF2 promotes the transition between multiple stages of Arabidopsis development, including initiation of flowering, rosette leaf senescence, floral organ abscission and silique ripening. In contrast, ARF1 is a transcriptional repressor and the arf1 mutation increases the transcription of Aux/IAA genes in Arabidopsis flowers . Moreover, most of the specific gene expression was not annotated as a relative function, which could assist in the search for some new genes associated with floral development.
Although the molecular functions of C. sinense genes and the associated floral genetic pathways remain unknown, the present transcriptome analysis provides valuable information regarding C. sinense development, which could facilitate further investigations of the detailed floral development mechanisms of this culturally important orchid.
The combination of RNA-seq and DGE analysis based on Illumina sequencing technology provided comprehensive information on gene expression. Candidate genes on flowering time genes, MADS-box genes and COL genes were rapidly identified by this approach. This data could be used as a tool to investigate the flowering pathway and various other biological pathways in C. sinense.
Plant materials and growth conditions
Fresh samples were used to extract total RNA immediately.
cDNA library preparation and Illumina sequencing for transcriptome analysis
Total RNA was extracted using Column Plant RNAout2.0 (Tiandz, Inc. Beijing, China) according to the manufacturer’s protocol. To obtain complete gene expression information, a pooled RNA sample including roots, leaves, pseudobulbs, flower buds, young and mature inflorescences was used for transcriptome sequencing analysis. According to the Illumina manufacturer’s instructions, poly(A)+ RNA was purified from 20 mg of pooled total RNA using oligo(dT) magnetic beads. Fragmentation buffer was added to interrupt mRNA to short fragments. Using these short fragments as templates, a random hexamer-primer was used to synthesize the first-strand cDNA. Second-strand cDNA was synthesized using 10×buffer, 25 mM dNTPs, 20-60 U/μl RNaseH and 5 U/μl DNA polymerase I. Short fragments were purified with the QiaQuick PCR extraction kit and resolved with EB buffer for end reparation and for adding poly(A). Thereafter, the short fragments were connected with sequencing adapters. After agarose gel electrophoresis, suitable fragments were selected for PCR amplification as templates. Finally, the library was sequenced using Illumina HiSeq™ 2000. All raw transcriptome data were deposited in the GeneBank Short Read Archive (Accession SRA058042).
Analysis of Illumina sequencing results
Raw reads from the image data output from the sequencing machine were generated by base calling. After filtering raw reads, de novo assembly of the transcriptome was carried out with a short reads assembling program – Trinity . Trinity connects the contigs and obtains sequences defined as unigenes.
The generated unigenes were used for BLASTX and annotation against protein databases, including non-redundant (nr), SwissPort, COG, KEGG, and GO protein database, with a cut-off E-value of 0.00001. GO (http://www.geneontology.org) has three ontologies: molecular function, cellular component and biological process. With nr annotation, we used the Blast2GO program  to obtain the GO annotation of unigenes. After obtaining the GO annotation for every unigene, we used WEGO software  to perform GO functional classification for all unigenes and to understand the distribution of gene functions. KEGG is a major public pathway-related database  that is able to analyze a gene product during a metabolic process and related gene function in cellular processes. With the help of the KEGG database, we can further study genes’ biological complex behaviors, and by KEGG annotation we can obtain pathway annotation for unigenes. The KEGG pathways annotation was performed using Blastall software against the KEGG database.
Digital gene expression (DGE) library preparation and sequencing
Total RNA was extracted from VP, FDP and RP using Column Plant RNAout2.0. mRNA was enriched by using oligo(dT) magnetic beads. After adding the fragmentation buffer, the mRNA was interrupted to short fragments (about 200 bp). Then the first strand cDNA was synthesized by a random hexamer-primer using the mRNA fragments as templates. 10×buffer, 25 mM dNTPs, 20-60 U/μl RNaseH and 5 U/μl DNA polymerase I were added to synthesize the second strand. The double-stranded cDNA was purified with a QiaQuick PCR extraction kit and washed with EB buffer for end repair and single nucleotide A (adenine) addition. Finally, sequencing adaptors were ligated to the fragments. The required fragments were purified by agarose gel electrophoresis and enriched by PCR amplification. The library products were ready for sequencing analysis via Illumina HiSeq™ 2000. Three raw DGE data (VP, FDP and RP) were deposited in the GeneBank Short Read Archive (accession SRA058974, SRA058977, SRA058978, respectively).
Analysis and mapping of DGE tags
To map the DGE tags, the sequenced raw data were filtered to remove low quality tags (tags with an unknown nucleotide “N”), empty tags (no tag sequence between the adaptors) and tags with only one copy number (which might result from sequencing errors). For the annotation of tags, clean tags containing CATG and 21-bp tag sequences were mapped to our transcriptome reference database using SOAPaligner/soap2 . Mismatches of no more than two bases were allowed in the alignment.
Screening of differentially expressed genes (DEGs)
To compare the differences in gene expression at different developmental stages, the tag frequency in the different DGE libraries was statistically analyzed according to the method described by Audic and Claverie . The false discovery rate (FDR) was used to determine the threshold P-value in multiple tests. We used FDR < 0.001 and an absolute value of the log2 ratio >1 as the threshold to determine the significant difference in gene expression. The differentially expressed genes were used for GO and KEGG enrichment analyses according to a method similar to that described by Xue . GO terms, which take the corrected P-value ≤ 0.05 as a threshold, are significantly enriched in DEGs. KEGG pathways with a Q-value ≤ 0.05 are significantly enriched in DEGs.
Genes with similar expression patterns usually mean functional correlation. We perform cluster analysis of gene expression patterns with cluster  software and Java Treeview  software. In Figure 8, each column represents an experimental sample (e.g. VP, FDP and RP), each row represents a gene. Expression differences are shown in different colors. Red means high expression and green means low expression.
Quantitative real-time PCR (qRT-PCR) validation
Total RNA was extracted as described for the DGE library preparation and sequencing. Each RNA sample was treated with RNase-free DNase (Promega) following the manufacturer’s protocol in an effort to remove any residual genomic DNA (gDNA). DNase-treated RNA (2 mg) was subjected to reverse transcriptase reactions using oligo-dT primer and PrimeScript™ Reverse Transcriptase (Takara) according to the manufacturer’s protocol.
The sequences of the specific primer sets are listed in Additional file 11. The actin gene of C. sinense (ACC NO. GU181353) was used as an internal gene. qRT-PCR was performed using the SYBR Premix Ex Taq Kit (TaKaRa) according to the manufacturer’s protocol. The results were normalized to the expression level of the constitutive actin gene. A relative quantitative method (DDCt) was used to evaluate the quantitative variation.
Digital gene expression
Kyoto encyclopedia of genes and genomes
Circadian clock associated 1
Early flowering 3
Expressed sequence tag
Embryonic flower 2
Flowering locus D
Flowering locus E
Flowering locus T
Flowering locus C
Late elongated hypocotyl
Phytochrome interacting factor 3
Suppressor of overexpression of co 1
Terminal flower 1
Timing of cab expression 1
Vernalization insensitive 3
Unusual floral organs.
This study was supported by the National Natural Science Foundation of China (CNSF31101572 and CNSF31170654), the Science and Technology Project Foundation of Guangdong Province (2011A020201001 and 2012B091100255), Knowledge Innovation Program of the Chinese Academy Sciences (Y021121001) and the Foundation of Key Laboratory of Plant Resources Conservation and Sustainable Utilization, South China Botanical Garden, Chinese Academy of Sciences (201222ZS).
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