Illumina sequencing and sequence assembly

To obtain an overview of the C. sinense transcriptome, a cDNA library was generated from an equal mixture of RNA isolated from all organs, and pair end sequenced using the Illumina Hiseq™2000 platform. After cleaning and quality checks, 54 million 90-bp reads were assembled into 147162 contigs with a mean length of 326 bp (Table 1). Using paired-end reads, these contigs were further assembled into 83580 unigenes by Trinity, including 13315 clusters and 70265 singletons, with a mean length of 612 bp. The size distribution of these contigs and unigenes are shown in Figures 1 and 2. The assembly produced a substantial number of large contigs: 11852 contigs were >1,000 bp in length and 26698 contigs were >500 bp, although most contigs were between 200 and 300 bp in length (Figure 1). 17644 unigenes were >1,000 bp in length (Figure 2).

Annotation of predicted proteins

Among the 41687 unigenes, approximately 36.1% could be annotated in COG based on sequence homologies. In the COG classification, 15041 unigenes were classified into 25 function classifications (Figure 3). ‘General function prediction’ was dominant. ‘Translation’, ‘replication, recombination and repair’ and ‘posttranslational’ also shared a high-percentage of genes among the categories, and only a few genes matched the terms ‘nuclear structure’ and ‘extracellular structures’. 2199 unigenes were annotated as the ‘signal transduction mechanisms’ category, which suggests that our study may allow for the identification of novel genes involved in signal transduction pathways. The COG analysis showed that the identified genes are involved in various biological processes.

GO classification for unigenes

We used GO assignments to classify the functions of the predicted C. sinense unigenes. 16565 annotated unigenes were further categorized into 44 functional groups (Figure 4). Metabolic and cellular processes were the most highly represented groups in the biological process category. 5986 unigenes were annotated as the ‘metabolic process’ category, which suggests that our study may allow for the identification of novel genes involved in secondary metabolite synthesis pathways. Cell and cell parts were dominant groups in the “cellular component function” category. In the “molecular function” category, a high percentage of genes came from the ‘binding’ (41.1%) and ‘catalytic activity’ (44.6%) groups. In the ‘nitrogen utilization’, ‘virion’ and ‘translation regulator activity’ groups, only a single unigene was annotated for each (Figure 4).

Metabolic pathway assignment by KEGG

The circadian clock is an important factor controlling plant physiology, and is also an important part of the photoperiod pathway. It regulates physiological activities by controlling the circadian rhythm [10]. A circadian rhythm was found in the KEGG pathway involving 222 (0.97%) unigenes. The detailed metabolic pathway for the circadian rhythm is shown in Figure 5. Every gene in the pathway was associated with several unigenes. The pathway will be useful for further studies on the effect of the photoperiod pathway on C. sinense flowering-related processes.

Identifying C. sinenseflowering time-associated genes and MADS-box genes

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.

We selected seven unigenes which have two conserved B-box domains and a CCT domain to further examine their relationship with other COL genes from Arabidopsis, rice, Phalaenopsis and C. sinense. The seven unigenes are shown in Table 5. Their amino acid sequences were used to construct a phylogenetic tree using MEGA4 [13] to determine genetic distances. The phylogenetic tree was showed that all members could be divided into three divergent groups (Figure 6). Unigene9883 and CL11547.Contig1 were clustered with CsCO, PhalCOL as well as OsHd1 in group I. They are closely related to Arabidopsis AtCO/AtCOL1/AtCOL2. CL10349.Contig1, Unigene55660, CL1617.Contig1 and CL1617.Contig2 were clustered in group II. CL1617.Contig1 and CL1617.Contig2 are closely related to AtCOL9/AtCOL10. Unigene55650 is closely related to AtCOL13, and CL10349.Contig1 is closely related to AtCOL14/AtCOL15. Unigene10682 was assigned to group III and is closely related to AtCOL6/AtCOL16. These results are consistent with the data shown in Table 5.

DGE library sequencing and evaluation

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 [14].

The variations in gene expression were analyzed by comparing VP and FDP, VP and RP, and FDP and RP. The comparison between VP and FDP revealed significant variations in expression. A total of 5314 genes, including 613 up-regulated and 4701 down-regulated genes, were identified in FDP compared to VP (Figure 7). 34 genes showed specific expression in FDP and 107 genes showed specific expression in VP. These specific expression genes were listed in Additional file 4.

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).

We also focused on the genes related to flower development and chose some genes that showed significantly different expression among three developmental phases (Figure 8). The functional annotation for these unigenes was listed in Additional file 10. For example, Unigene40123 demonstrated higher expression levels in FDP and RP than in VP, Unigene33969 showed higher expression levels in RP than in VP and FDP, and Unigene65459 showed higher expression levels in VP and RP than in FDP, which were all homologous with Arabidopsis thaliana auxin response factor 2 (ARF2) and related to auxin metabolism. Other significant expression of different genes among the three developmental phases might be associated with circadian rhythm (Unigene4387, 33551, 35813, homolog with FD), the vernalization pathway (Unigene49193 and CL2992.Contig2, homologous with EMBRYONIC FLOWER2 (EMF2) and VERNALIZATION INSENSITIVE 3 (VIN3), respectively and so on. To validate DGE-tag profiling, 16 genes were selected randomly to examine the expression using RT-qPCR (Figure 9). The results were exhibited differential expression among three libraries and were identical to those obtained by DGE expression profiling. Thus, the data generated in this study is sufficient to be used as a tool to investigate some specific flowering genes which show comparative expression levels among different developmental phases.

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 [4]. 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 [15]. 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 [11]. 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[31]. CO is a transcription factor that promotes flowering by inducing the expression of the direct downstream genes FT[32] and SOC1[33]. COL genes have been identified in some plant species, each of which seems to have a large family of these genes [34]. In Arabidopsis, there are 17 COL genes [35]. There are at least 16 COL genes in the rice genome [34]. 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 [35]. 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 [34]. 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 [37]. It promotes flowering by repressing the expression of FLOWERING LOCUS C (FLC)[38]. VRN2 codes for a protein with homology to PcG proteins [39]. 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 [4]. 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[40]. 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 [41]. TFL1 codes for a protein with homology to FT[42]. The mutations in TFL1 are semidominant and early flowering with a determinate inflorescence [43]. 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[53]. 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 [54]. 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[40]. 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 [62]. 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 [63]. 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.

Plant materials and growth conditions

C. sinense ‘Qi Jian Bai Mo’ plants used in this study were grown and maintained in pots in a greenhouse of the South China Botanical Garden at a day/night temperature of 28/25°C with a 12-h period. The cDNA libraries were prepared from the entire plant of C. sinense at vegetative and reproductive stages. The vegetative phase (VP) samples were collected from a non-pseudobulb shoot (Figure 10A). The floral differentiation phase (FDP) samples were collected from the entire adult plant which had developed floral buds at the base of the pseudobulb (Figure 10B). The reproductive phase (RP) samples were collected from entire adult plants which had developed a peduncle with floral organs at the base of the pseudobulb (Figure 10C).

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 [64]. 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 [65] to obtain the GO annotation of unigenes. After obtaining the GO annotation for every unigene, we used WEGO software [66] to perform GO functional classification for all unigenes and to understand the distribution of gene functions. KEGG is a major public pathway-related database [67] 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 [68]. 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 [14]. 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 [69]. 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 [70] software and Java Treeview [71] 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.