Transcriptomic analysis of cut tree peony with glucose supply using the RNA-Seq technique

ORIGINAL PAPER

Transcriptomic analysis of cut tree peony with glucose supplyusing the RNA-Seq technique

Chao Zhang • Yanjie Wang • Jianxin Fu •

Li Dong • Shulin Gao • Danni Du

Received: 27 May 2013 / Revised: 17 September 2013 / Accepted: 23 September 2013 / Published online: 17 October 2013

� Springer-Verlag Berlin Heidelberg 2013

Abstract

Key message Several unigenes encoding ACS and ERF

involved in ethylene biosynthesis and signal transduc-

tion were greatly down-regulated in the petal tran-

scriptome of cut tree peony ‘Luoyang Hong’ with

glucose treatment. Glucose also repressed stress-related

transcription factor genes DREB, CBF, NAC, WRKY

and bHLH.

Abstract Tree peony (Paeonia suffruticosa Andrews) is a

famous traditional flower in China. Glucose supply pro-

longing vase life of cut tree peony flowers is associated

with its role in the suppression of sensitivity to ethylene

and ethylene production, but the regulation mechanism of

sugar on ethylene biosynthesis and signaling is unclear. In

the present work, a normalized cDNA pool was constructed

as the reference transcriptome from mixed petals of dif-

ferent developmental cut tree peony ‘Luoyang Hong’ and

sequenced using the Illumina HiSeqTM 2000 platform. We

obtained 33,117 unigenes annotated with public protein

databases. In addition, the transcriptome change in petals

of cut tree peony with glucose supply and the control

treatment was investigated. With non-redundant annota-

tion, 173 differentially expressed genes were identified,

with 41 up-regulated genes and 132 down-regulated genes.

According to RNA-Seq data and real-time quantitative

polymerase chain reaction validation, one unigene encod-

ing ACS, a key ethylene synthetic enzyme, and four

unigenes encoding ERF, which is involved in ethylene

signal transduction was greatly down-regulated with glu-

cose treatment. Furthermore, stress-related transcription

factor genes DREB, CBF, NAC, WRKY and bHLH were

also repressed with glucose supply, as well as several other

stress-responsive and stress-tolerance genes, indicating that

glucose supply probably releases the effects induced by

various environmental stress. All the results and analysis

are valuable resources for better understanding of the

beneficial influence of exogenous sugars on cut tree peony.

Keywords Cut flower � Ethylene � Illumina

sequencing � Paeonia suffruticosa � Stress � Sugar

Abbreviations

ACC 1-Aminocyclopropane-1-carboxylate

ACO ACC oxidase

ACS ACC synthase

bHLH Helix-loop-helix

CBF C-repeat binding factor

DEG Differentially expressed gene

DREB Dehydration-responsive element-binding

protein

EBF EIN3-binding f-box protein

EIN Ethylene-insensitive

ERF Ethylene-responsive transcription factor

NAC NAM/ATAF1/CUC

RNA-Seq RNA sequencing

RPKM Reads per kb per million reads

RT-qPCR Real-time quantitative polymerase chain

reaction

Communicated by H. Judelson.

Electronic supplementary material The online version of thisarticle (doi:10.1007/s00299-013-1516-0) contains supplementarymaterial, which is available to authorized users.

C. Zhang � Y. Wang � J. Fu � L. Dong (&) � S. Gao � D. Du

College of Landscape Architecture, National Flower

Engineering Technology Research Center, Beijing Forestry

University, Beijing 100083, People’s Republic of China

e-mail: [email protected]

123

Plant Cell Rep (2014) 33:111–129

DOI 10.1007/s00299-013-1516-0

http://dx.doi.org/10.1007/s00299-013-1516-0

Introduction

Carbohydrates serve as energy source and play essential

roles in plant growth and development (Gibson 2000, 2005;

Smeekens 2000; Rolland et al. 2006; Dekkers et al. 2008;

Zhu et al. 2009). Photosynthesis provides plants with

sugars in the plant life cycle. Since cuttings from orna-

mental plants, particularly cut flowers, can hardly produce

carbohydrates through photosynthesis, the amount of car-

bohydrate in cut flowers is limited. Application of exoge-

nous sugar to cut carnation (Dianthus caryophyllus)

flowers generally delays visible senescence and prolongs

vase life (Mayak and Dilley 1976), so lack of sugar has

been regarded as one of the important reasons for petal

senescence of cut flowers (Borochov and Woodson 1989).

For a long time, it was believed that the effects of sugars on

delaying flower senescence are attributed to their roles in

providing respiration substrate and structural materials and

maintaining osmotic pressure (Halevy and Mayak 1979).

According to a review in 2003 (Pun and Ichimura 2003),

decreasing the sensitivity to ethylene and delaying the

climacteric ethylene biosynthesis are regarded as the two

additional important roles of sugars in the senescence of

cut flowers beside those mentioned above. In addition,

many researches and reviews have revealed that sugars

have vital hormone-like functions as primary messengers

in signal transduction (Jang and Sheen 1994; Sheen et al.

1999; Rolland et al. 2002, 2006; Leon and Sheen 2003;

Moore et al. 2003; Yanagisawa et al. 2003; Ramon et al.

2008; Smeekens et al. 2010). In cut carnation, exogenous

sugar reduces ethylene production of the petals, which is

accompanied by delay of 1-aminocyclopropane-1-carbox-

ylate (ACC) oxidase (ACO) and ACC synthase (ACS)

mRNA accumulation (Verlinden and Garcia 2004).

Besides, sucrose was reported to act like silver thiosulphate

(STS, an ethylene action inhibitor) and inhibit the expres-

sion of genes involved in ethylene biosynthesis, as well as

ethylene-insensitive3-like (EIL) genes, the important regu-

lators in the ethylene signal transduction pathway (Hoe-

berichts et al. 2007). Sugar treatment also affects mRNA

accumulation of genes encoding ethylene-responsive tran-

scription factors (ERFs) in petunia (Petunia hybrida) (Liu

et al. 2011). Overall, compared with other effects, the

regulation of sugars on ethylene sensitivity may be even

more essential in extending the vase life of ethylene-sen-

sitive cut flowers (van Doorn and Woltering 2008).

Tree peony (Paeonia suffruticosa Andrews), which

belongs to the genus Paeonia of the Paeoniaceae family, is a

famous traditional flower in China for its esthetic value and

unique culture symbolization. However, short vase life has

greatly restricted the development of cut tree peony mar-

keting (Jia et al. 2008). Previous study reveals that flower

opening and senescence process of most cut tree peony

cultivars are associated with ethylene production (Jia et al.

2008). Moreover, display life of cut and potted tree peony is

greatly shortened by exogenous ethylene, but is extended by

1-methylcyclopropene, an ethylene inhibitor (Zhou et al.

2009; Zhang et al. 2012b), suggesting that ethylene probably

plays an important role in the regulation of flower senescence

in tree peony. Although ethylene biosynthesis and signaling

pathway in Arabidopsis has been well defined (Yang and

Hoffman 1984; Kende 1989; Guo and Ecker 2004; Chen

et al. 2005; Lin et al. 2009), only a few genes involved in

ethylene biosynthesis or signal transduction, like PsACS1,

PsACO1, CTR1s, ethylene resistant 1 (ETR1) and ethylene-

insensitive3 (EIN3)/EILs, have been isolated and character-

ized in tree peony (Zhou et al. 2010, 2013; Gao et al. 2011;

Wang et al. 2013). Among these genes, PsACS1 and PsEIL3

are proven to play important roles in ethylene-mediated petal

senescence in cut tree peony (Wang et al. 2013; Zhou et al.

2013). Furthermore, exogenous glucose supply is found to

delay tree peony ‘Luoyang Hong’ cut flower senescence and

extend flower vase life related to its effect in the suppression

of sensitivity to ethylene and ethylene production (Zhang

et al. 2012a), whereas detailed information about how

exogenous sugar works is still unclear, since little is known

of ethylene biosynthesis and signaling genes in tree peony,

which hinders the further study on mechanism of ethylene-

induced senescence, not to mention revealing the regulation

mechanism of sugar on ethylene signaling.

Next-generation sequencing technology in recent years

has provided new opportunities to the field of RNA

sequencing (RNA-Seq). RNA-Seq is not limited to

detecting transcripts that correspond to existing genomic

sequence, which makes RNA-Seq particularly attractive for

non-model organisms without genomic sequences (Wang

et al. 2009b, 2012a; Zhang et al. 2012c). It is widely used

in both well-studied model organisms and non-model

organisms in various studies, including transcript profiling,

single nucleotide polymorphisms discovery and the iden-

tification of genes that are differentially expressed between

samples (Wang et al. 2010b; Gai et al. 2012; Trick et al.

2012; Xu et al. 2012). For instance, a cDNA library from

flower buds of Paeonia ostii during chilling fulfillment was

established and sequenced on the Roche 454 GS FLX

platform, and 23,652 contigs and singletons were obtained

and 2,253 potential simple sequence repeat loci were

identified (Gai et al. 2012). However, RNA-Seq technology

has not been used to analyze transcriptome dynamics of

petal in cut tree peony flower.

Characterization of complex network of biochemical

and cellular processes responsible for the exogenous sugar

regulation in cut tree peony is important and necessary in

revealing the mechanism of postharvest senescence. Here,

we used RNA-Seq technology on the Illumina HiSeqTM

2000 platform to investigate the effect of glucose on

112 Plant Cell Rep (2014) 33:111–129

123

transcriptome profiling in petals of cut tree peony ‘Luoy-

ang Hong’. First, we constructed and sequenced a tran-

scriptome library from mixed petal samples of different

developmental stages of cut tree peony with and without

glucose treatment. Then, using the transcriptome database

as reference, we conducted a comprehensive analysis of

transcriptome changes between glucose-treated library and

the control library. Based on the expression of differential

expression genes (DEGs) involved in ethylene biosynthesis

and signal transduction, we attempted to reveal how glu-

cose suppressed the sensitivity to ethylene and ethylene

production of ‘Luoyang Hong’. To the best of our knowl-

edge, this study is the first exploration to characterize the

petal transcriptome of cut tree peony with RNA-Seq

technology. In addition to providing valuable sequence

resource of tree peony, our objective was to figure out the

effects of glucose on ethylene biosynthesis and signal

transduction at the transcription level.

Materials and methods

Plant materials

Tree peony (P. suffruticosa ‘Luoyang Hong’) was grown

under field conditions in the peony garden of Luoyang

Flowers and Trees Company, Henan, China. Flowers were

harvested in the afternoon at stage 1; flower opening stages

were described by Guo et al. (2004): stage 1, soft bud;

stage 2, pre-opening; stage 3, initial opening; stage 4, half

opening; stage 5, full opening. After transport to the Lab of

Flower Physiology and Application in Beijing Forestry

University, Beijing, China, flowers were trimmed to 25 cm

in stem length and all leaves were removed.

These cut tree peonies were placed with their stem end in

60 g L-1 glucose solution or distilled water. Petal samples

were collected from flowers with glucose supply and control

treatment at each development stage, respectively. All

samples were pooled together for reference transcriptome

analysis. Meanwhile, petal samples of control and glucose-

treated upon 6 h (stage 2) were collected for RNA-Seq,

respectively; the samples of control and glucose-treated

upon 6 to 72 h (stage 5) were used for real-time quantitative

polymerase chain reaction (RT-qPCR) analysis, respec-

tively. All samples were immediately frozen in liquid

nitrogen and stored at -80 �C until used for RNA extraction.

RNA extraction, cDNA library preparation

and sequencing

Library preparation and sequencing for reference tran-

scriptome analysis and RNA-Seq analysis were carried out

as follows: total RNA was extracted by the modified

cetyltrimethylammonium bromide method (Chang et al.

1993) and then treated with RQ1 RNase-free DNase

(Promega, USA) according to manufacturer’s protocols.

RNA quality was verified using 2100 Bioanalyzer RNA

Nanochip (Agilent, Santa Clara, CA, USA).

Briefly, mRNA was purified from 20 lg of total RNA

using Oligo (dT) magnetic beads. After purification, the

mRNA was cut into short fragments in a fragmentation

buffer. Taking these short fragments as templates, the first-

strand cDNA was synthesized using random hexamer-

primers, and the second-strand cDNA was further synthe-

sized. Then, these cDNA fragments were purified with a

QiaQuick PCR extraction kit (Qiagen, Valencia, CA,

USA), sent through an end repair process and ligated to

sequencing adaptors. These products were purified using

agarose gel electrophoresis and enriched with PCR

amplification to create the final cDNA library. Finally, the

cDNA library was sequenced at the Beijing Genomics

Institute (Shenzhen, China) in accordance with the manu-

facturer’s instructions (Illumina, San Diego, CA, USA)

using the Illumina HiSeqTM 2000 sequencing system.

Data filtering, de novo assembly and gene annotation

of reference transcriptome

Before data analysis, the raw reads were cleaned by

removing adaptor sequences, empty reads and reads with

unknown or low-quality bases to get clean reads. The

sequencing data of clean reads are deposited in NCBI

Sequence Read Archive (http://www.ncbi.nlm.nih.gov/

Traces/sra) with accession number SRR953481. Tran-

scriptome de novo assembly was carried out with short

reads assembling program Trinity (release 20120608)

(Grabherr et al. 2011). Contigs without ambiguous bases

were obtained by combining reads with certain length of

overlap. Then, the reads were mapped back to contigs

using Trinity to construct unigenes with the paired-end

information. The program detected contigs from the same

transcript as well as the distances between these contigs.

Next, the contigs were connected with Trinity, and

sequences that could not be extended on either end were

obtained. Such sequences are defined as unigenes. Unigene

sequences were aligned by Blastx (E value \1e-5) to the

protein databases like NCBI non-redundant (Nr) protein

database (http://www.ncbi.nlm.nih.gov), Swiss-Prot pro-

tein database (http://www.expasy.ch/sprot), the cluster of

orthologous groups (COG) database (http://www.ncbi.nlm.

nih.gov/COG) and the kyoto encyclopedia of genes and

genomes (KEGG) pathway database (http://www.genome.

jp/kegg). The unigenes were tentatively annotated accord-

ing to the known sequences with the highest sequence

similarity, and the annotated unigenes direction and coding

sequences were identified by the best alignment results. If

Plant Cell Rep (2014) 33:111–129 113

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http://www.ncbi.nlm.nih.gov/Traces/sra

http://www.ncbi.nlm.nih.gov/Traces/sra

http://www.ncbi.nlm.nih.gov

http://www.expasy.ch/sprot

http://www.ncbi.nlm.nih.gov/COG

http://www.ncbi.nlm.nih.gov/COG

http://www.genome.jp/kegg

http://www.genome.jp/kegg

the results of different databases conflicted with each other,

a priority order of Nr, Swiss-Prot, KEGG and COG should

be followed when deciding the sequence direction of

unigenes. When a unigene happened to be unaligned to

none of the abovementioned databases, a software named

ESTscan (Iseli et al. 1999) was used to predict its coding

regions as well as to decide its sequence direction.

With the Nr annotation, gene ontology (GO) annotations

of unigenes were obtained using Blast2GO software (ver-

sion 2.3.5, http://www.blast2go.de/) (Conesa et al. 2005)

(E value \1e-5). Then, WEGO software (http://wego.

genomics.org.cn/cgi-bin/wego/index.pl) (Ye et al. 2006)

was used to perform GO functional classification (biolog-

ical process, molecular function and cellular component)

for all unigenes and to understand the distribution of gene

functions of the species from the macro level.

Data filtering, de novo assembly and gene expression

analysis of RNA-Seq

Clean reads, obtained with the same method in data fil-

tering of reference transcriptome, were mapped to refer-

ence sequences using SOAPaligner/soap2 (Li et al. 2009).

Mismatches no more than two bases were allowed in the

alignment to the prepared reference transcriptome data-

base. Reads per kb per million reads (RPKM) were used to

show the expression quantity, thus avoiding the influence

of sequencing length and differences. The gene expression

level was calculated by the numbers of reads mapped to the

reference sequence and every gene using RPKM method

(Mortazavi et al. 2008). Gene coverage is the percentage of

a gene covered by reads. This value is equal to the ratio of

the base number in a gene covered by unique mapping

reads to the total bases number of that gene. To identify the

DEGs in the two samples, the protocol of Audic and

Claverie (1997) was used. The false discovery rate (FDR)

was used to determine the threshold of the P value in

multiple tests and, for the analysis, a threshold of

FDR B0.001 and an absolute value of log2ratio C1 were

used to judge the significance of the gene expression dif-

ferences (Benjamini and Yekutieli 2001).

RT-qPCR analysis

Total RNA used for RT-qPCR was extracted from petals

of control flowers and glucose-treated flowers for 6 and

72 h using three biological replicates. Total RNA

extraction and genomic DNA removal were performed as

described above, and total RNA concentration was

measured. The first-strand cDNA was synthesized from

1 lg of DNA-free RNA using a PrimeScript� RT

reagent Kit (Takara, Japan) according to the manufac-

turer’s instructions, and the cDNA with tenfold dilution

with nuclease-free water was used as the template for

RT-qPCR analysis. Primer sequences (designed using

Primer Premier 5) are listed in Supplementary Table 1.

RT-qPCR was performed on a Miniopticon Real-Time

PCR instrument (Bio-Rad, USA) using SYBR Green to

detect dsDNA synthesis. The reaction mixture (20-lL

total volume) contained 10 lL of SYBR� Premix Ex

TaqTM (TaKaRa, Japan), 0.4 lL of each primer (10 lM),

2 lL of diluted cDNA and 7.2 lL of PCR-grade water.

The two-step RT-qPCR program began with 30 s at

95 �C, followed by 40 cycles of 95 �C for 5 s and 60 �C

for 20 s and completed with a melting curve analysis

with a temperature ramp from 60 to 95 �C. No-template

controls for each primer set were included in each run.

Expression levels were normalized relative to that of P.

suffruticosa ubiquitin (Wang et al. 2012b).

Results

Illumina sequencing and de novo assembly

of transcriptome

In order to obtain an overview of the transcriptional

information in petals of cut tree peony and prepare the

reference database for the following RNA-Seq analysis, a

normalized cDNA pool was constructed from mix petals of

flowers with glucose treatment and the control treatment at

different development stages and sequenced using the

Illumina HiSeqTM 2000 platform. At first, we obtained a

total of about 57.2 million raw reads with an average length

of 90 nt from sequencing (Supplementary Table 2). After

data filtering and quality checks, more than 53.7 million

clean reads (93.98 % of the raw reads) are generated with a

Q20 (base quality more than 20) percentage of 97.78 %

and a GC content of 46.20 % from the transcriptome

library. The total length of the reads is more than 4.8 Gb

(Supplementary Table 2). All clean reads were assembled

de novo by Trinity, and a total of 121,125 contigs with

average length of 279 nt were obtained (Supplementary

Table 2). To join further sequences and remove any

redundant sequences, contigs were connected to yield

50,829 unigenes (including 36,063 distinct singletons) with

average length of 585 nt. Of these, 18,809 unigenes

(37.00 %) are longer than 500 bp, while 14,676 contigs

(12.12 %) are longer than 500 bp, and 7,490 unigenes

(14.74 %) are longer than 1,000 bp (Supplementary

Fig. 1).

Annotation against public databases

After a large number of distinct sequences were obtained,

all assembled unigenes were aligned by Blastx to the Nr

114 Plant Cell Rep (2014) 33:111–129

123

http://www.blast2go.de/

http://wego.genomics.org.cn/cgi-bin/wego/index.pl

http://wego.genomics.org.cn/cgi-bin/wego/index.pl

database for annotation, as well as to the Swiss-Prot,

KEGG and COG, with a cut-off E value of 1e-5. Results

indicated that out of 50,829 unigenes, a total of 33,117

(65.15 %) unigenes in the transcriptome library are anno-

tated, most of which are annotated against Nr database

(32,220; 63.39 %) (Supplementary Table 3), and a total of

18,615 (36.62 %) unigenes are annotated against SwissProt

database: 17,404 (34.24 %) against KEGG database and

10,231 (20.13 %) against COG database. Besides, 7,553

(14.86 %) unigenes match all four databases (Supplemen-

tary Table 3).

In Supplementary Fig. 2a, the E value distribution of the

top hits in the Nr database showed that 22.44 % of the

mapped sequences have strong homology (smaller than

1e-100), while 77.56 % of the homolog sequences range

between 1e-5 and 1e-100. The similarity distribution has

a comparable pattern with 32.43 % of the sequences having

a similarity higher than 80 %, and 42.00 % of the hits have

a similarity ranging from 60 to 80 % (Supplementary

Fig. 2b). For species distribution, more than half of the

distinct sequences have top matches with sequences from

Vitis vinifera, followed by Ricinus communis (11.87 %)

and Populus trichocarpa (11.06 %) (Supplementary

Fig. 2c). Besides, there are 48 distinct sequences with the

highest homology to genes from herbaceous peony

(Paeonia lactiflora), another famous flower of the genus

Paeonia.

Functional annotation of all non-redundant unigenes

Based on Nr annotations, 24,857 unigenes were assigned

with GO to provide a controlled vocabulary to facilitate

high-quality functional gene annotation (Lomax 2005). All

GO terms are allocated into three main GO categories

(biological process, molecular function and cellular com-

ponent) including 58 functional groups (Fig. 1). Under the

hugest category of biological process, ‘cellular process’

(16,888; 67.94 %) is the largest group, followed by ‘met-

abolic process’ (16,172; 65.06 %) and ‘response to stim-

ulus’ (8,682; 34.93 %). Among the 16 functional groups of

molecular function, GO terms are predominantly associ-

ated with ‘binding’ (12,908; 51.93 %), ‘catalytic activity’

(12,674; 50.99 %) and ‘transporter activity’ (1,755;

7.06 %). And ‘cell’ (16,835; 67.73 %) and ‘cell part’

(16,829; 67.70 %) terms are dominant in the category of

cellular component. But few genes are categorized into

terms of ‘sulfur utilization’, ‘metallochaperone activity’

and ‘channel regulator activity’ (Fig. 1).

To further evaluate the effectiveness of the annotation

process and predict possible functions of unigenes, we

searched the annotated sequences for genes involved in

COG classifications. In total, out of 32,220 Nr hits, 18,568

sequences have a COG classification (Fig. 2). Among the

25 COG categories, the cluster for ‘general function pre-

diction’ (3,078; 16.58 %) represents the largest group,

followed by ‘transcription’ (1,630; 8.78 %), ‘replication,

recombination and repair’ (1,506; 8.11 %), ‘posttransla-

tional modification, protein turnover and chaperones’

(1,468; 7.91 %), ‘translation, ribosomal structure and bio-

genesis’ (1,315; 7.08 %), ‘signal transduction mechanisms’

(1,162; 6.26 %), and ‘carbohydrate transport and metabo-

lism’ (1,123; 6.05 %) (Fig. 2), while the categories

‘extracellular structures’ and ‘nuclear structure’ represent

the smallest groups.

Pathway-based analysis can help to further understand

the biological functions and interactions of genes. KEGG

was always employed as a reference database of pathway

Fig. 1 Histogram presentation of gene ontology (GO) classification.

The results are summarized in three main categories: biological

process, molecular function and cellular component. The y-axis

indicates the percentage of a specific category of genes in that main

category

Plant Cell Rep (2014) 33:111–129 115

123

networks for integration and interpretation of large-scale

datasets generated by high-throughput sequencing tech-

nology (Xie et al. 2012; Wang et al. 2010a). To better

understand the function of sequenced genes in cut tree

peony, a Blastx search against KEGG protein database with

a cutoff E value of 1e-5 was made on the assembled

unigenes. Overall, 17,404 unigenes are assigned to 128

KEGG pathways (Supplementary Table 4). Among them,

the largest two pathway groups are ‘metabolic pathways’

(3,788; 21.77 %) and ‘biosynthesis of secondary metabo-

lites’ (1,877; 10.78 %). The pathways with the least rep-

resentation by the unique sequences are ‘lipoic acid

metabolism’ (6; 0.03 %) and ‘betalain biosynthesis’ (2;

0.01 %) (Supplementary Table 4).

A total of 56 assembled genes were identified to be

involved in ethylene biosynthesis or its signal transduc-

tion (Table 1). Among them, 5, 17 and 5 unigenes are

annotated with predicted function as S-adenosylmethio-

nine (SAM) synthase (SAMS), ACO and ACS, respec-

tively, and 13 unigenes are predicted as constitutive

triple response 1 (CTR1) genes, while more than one

unigene is aligned to ETR, ethylene response sensor

(ERS), EIN4, EIN2, EIN3/EIL and ERF involved in the

ethylene signaling (Table 1). Among these unigenes,

several unigenes representing ACS and ACO have high

sequence similarity to those that have already been

reported (GenBank accession No. DQ337250 and

DQ337251). In addition, 2 of 13 unigenes representing

CTR1 show high sequence similarity to the reported

CTR1 genes PsCTR1 and PsCTR3 (Gao and Dong 2010)

and all five unigenes encoding EIN3/EIL match with the

three EIN3/EIL genes that have already been reported

(GenBank accession number JQ771469, JQ771470 and

JQ771471).

RNA-Seq Illumina sequencing and mapping

to the reference transcriptome database

RNA-Seq method generates absolute information and

avoids many of the inherent limitations of microarray

analysis (Xu et al. 2012). This method was employed to

analyze variations in gene expression of petals in cut tree

peony with glucose treatment. We sequenced two cDNA

libraries: G (glucose treatment) and C (the control

treatment). Then, 14.8 and 14.4 million sequence reads

were generated for each of the two libraries (Supple-

mentary Table 5). The sequence reads were aligned to

the reference transcriptome database in the above-men-

tioned Illumina sequencing using SOAPaligner/soap2

software (set to allow two base mismatches). Read

Fig. 2 Histogram presentation

of clusters of orthologous

groups (COG) classification

Table 1 Unigenes involved in ethylene biosynthesis and signal

transduction in the reference transcriptome

Predicted function Number of unigenes

Ethylene biosynthesis

SAMS 5

ACO 17

ACS 5

Ethylene signal transduction

ETR 4

ERS 2

EIN4 2

CTR1 13

EIN2 3

EIN3/EIL 5

ERF 5

116 Plant Cell Rep (2014) 33:111–129

123

number and percentage of mapped reads in the two

libraries are similar, of the mapped reads, more than 10.2

and 9.9 million reads in the two libraries match to a

unique sequence in the reference database (Supplemen-

tary Table 5).

The distribution of genes’ coverage was used to evaluate

the normality of our RNA-Seq data. As shown in Supple-

mentary Fig. 3, the distribution of distinct unigenes over

different read abundance categories shows similar patterns

for both two RNA-Seq libraries. More than 80 % coverage

are 33 % (15,187) and 32 % (14,989) in the two libraries,

and more than 60 % coverage are 69 % (32,087) and 68 %

(31,915) (Supplementary Fig. 3), which could be further

applied to gene expression calculation and others data

analysis.

Functional analysis of DEGs based on RNA-Seq data

Differentially expressed genes were screened out by com-

parison between the two samples. FDR B0.001 and an

absolute value of log2ratio C1 were used as the threshold

to judge the significance of the DEGs, which resulted in a

set of 299 DEGs. With Nr annotation, 173 DEGs are

identified, with 41 up-regulated genes and 132 down-reg-

ulated genes (Table 2). The Transcriptome Shotgun

Assembly project of 173 DEGs has been deposited at

GenBank under the accession GANK00000000.

GO analysis were used to classify the functions of the

annotated DEGs with glucose treatment. Based on sequence

homology, 173 DEGs can be categorized into 44 functional

groups (Fig. 3). In the three main categories (biological

process, molecular function and cellular component) of the

GO classification, there are 22, 10 and 12 functional groups,

respectively (Fig. 3). Among these groups, the terms ‘met-

abolic process’ and ‘catalytic activity’ are dominant in bio-

logical process and molecular function categories,

respectively, while ‘cell’ and ‘cell part’ are dominant in the

category of cellular component. We also noticed that,

although the number of down-regulated genes is more than

that of up-regulated genes in most functional groups, the

number of up-regulated genes is dominant in the functional

groups of ‘rhythmic process’, ‘growth’, ‘molecular trans-

ducer activity’, ‘receptor activity’, ‘membrane-enclosed

lumen’, ‘symplast’ and ‘cell junction’ (Fig. 3).

According to the predicted functions of 173 DEGs, they

are categorized into nine functional classifications associ-

ated with petal senescence and ‘other function’ (Table 2). In

the functional classification of ‘signal sense and transduc-

tion’, several unigenes (Unigene18592, Unigene18590,

Unigene27744, Unigene18589 and Unigene26806) encod-

ing leucine-rich repeat receptor-like protein kinase are

induced with glucose supply, as well as Unigene15653,

Unigene23974, Unigene15155 and Unigene16711,

respectively, encoding putative abscisic acid receptor pyr-

abactin resistance-like (PYL), auxin-induced protein 5NG4,

ARR21-like, SAUR family protein and gibberellin receptor

gibberellin insensitive dwarf 1 (GID1) involved in hormone

signal perception and transduction. Furthermore, glucose is

found to down-regulate unigenes encoding mitogen-acti-

vated protein kinase kinase kinase, calcium-binding protein,

protein phosphatase 2c and diacylglycerol kinase. Most

DEGs in the functional classification of ‘transcription factor’

are down-regulated, including several unigenes putatively

encoding transcription factors with well-known roles in

stress response or senescence, such as ERF, dehydration-

responsive element-binding protein (DREB), WRKY,

C-repeat binding factor (CBF) and NAM/ATAF1/CUC

(NAC). Similarly, many DEGs in ‘stress defense’ are down-

regulated with glucose supply, such as late embryogenesis

abundant protein (LEA) (Unigene10532 and CL8450.Con-

tig1), nudix hydrolase (CL1519.Contig1), glutathione

S-transferase (GST) (CL2255.Contig2), plasma membrane

intrinsic protein (PIP) (Unigene21708) and so on. Uni-

gene10826 and Unigene1496 are up-regulated and, respec-

tively, encode monosaccharide transporter and

6-phosphogluconolactonase, while other unigenes encoding

phosphoglycerate kinase, O-fucosyltransferase-like protein,

cellulose synthase and glyceraldehyde-3-phosphate dehy-

drogenase are repressed with glucose supply. ‘Ions remobi-

lization and transport’ classification includes three down-

regulated unigenes, respectively, encoding phosphate

transporter (Unigene17205), anion exchanger family protein

(Unigene27119) and boron transporter (CL3690.Contig1).

As to ‘amino acid synthesis, protein metabolism and deg-

radation’, it is complicated that different unigenes encoding

‘f-box protein’ and ‘U-box domain-containing protein’ show

different responses (up-regulated or down-regulated) to the

glucose treatment. Unigenes related to ein3-binding f-box

protein (EBF) and serine carboxypeptidase are up-regulated,

while unigenes encoding E3 ubiquitin ligase, metalloendo-

proteinase and protein binding protein are down-regulated.

In the functional classification of ‘cell wall compounds

degradation’, CL5522.Contig2 encoding pectate lyase is

induced with glucose treatment, and Unigene20328 and

Unigene28931, respectively, encoding exopolygalacturon-

ase and xyloglucan endotransglucosylase/hydrolase are

repressed.

To further understand the functions of the DEGs, we

mapped the genes to terms in the KEGG database and

compared this with the whole transcriptome background,

with a view to search for genes involved in metabolic or

signal transduction pathways that were significantly enri-

ched. Among those genes with a KEGG pathway annota-

tion, 132 DEGs are identified. Notably, specific enrichment

of genes is observed for pathways involved in ‘metabolic

pathways’, ‘biosynthesis of secondary metabolites’,

Plant Cell Rep (2014) 33:111–129 117

123

Ta

ble

2Id

enti

fica

tio

no

f1

73

dif

fere

nti

ally

exp

ress

edg

enes

in2

RN

A-s

eqd

atas

ets

Fu

nct

ion

clas

sifi

cati

on

Gen

eID

Ca-

RP

KM

Gb-

RP

KM

log

2

rati

oP

val

ue

FD

RS

equen

cean

no

tati

on

Up

-reg

ula

ted

gen

es(4

1)

Tra

nsc

rip

tio

nfa

cto

r(2

)U

nig

ene2

525

41

6.7

37

.77

1.1

82

.68

E-

21

5.3

0E

-1

9R

ING

-H2

fin

ger

pro

tein

CL

29

67

.Co

nti

g1

12

.17

24

.71

1.0

21

.63

E-

08

1.0

5E

-06

WD

-rep

eat

pro

tein

Ions

rem

obil

izat

ion

and

tran

sport

(1)

Unig

ene8

12

15.3

836.9

51.2

61.2

8E

-2

83

.52E

-26

Hea

vy-m

etal

-ass

oci

ated

dom

ain-c

onta

inin

gpro

tein

Su

gar

met

abo

lism

and

tran

spo

rt(4

)U

nig

ene2

470

85

.51

12

.96

1.2

31

.19

E-

06

5.7

5E

-0

5B

eta-

glu

cosi

das

e1

2-l

ike

CL

72

87

.Co

nti

g1

6.0

91

3.3

41

.13

4.4

4E

-1

13

.99E

-0

9U

DP

-gly

cosy

ltra

nsf

eras

e8

5A

3is

ofo

rm1

Un

igen

e10

82

69

.99

21

.31

.09

1.7

6E

-0

56

.59E

-0

4M

on

osa

cch

arid

etr

ansp

ort

er

Un

igen

e14

96

14

.08

28

.83

1.0

31

.30

E-

06

6.2

5E

-0

56

-ph

osp

hog

luco

no

lact

on

ase

4

Nucl

eic

acid

synth

esis

,m

odifi

cati

on

and

bre

akdow

n(2

)U

nig

ene1

9336

3.7

512.1

21.6

92.2

1E

-0

58

.06E

-0

4D

icer

-lik

ep

rote

in

CL

88

0.C

on

tig

37

4.7

91

63

.88

1.1

31

.45

E-

15

22

.67E

-1

49

Met

hy

ltra

nsf

eras

eP

MT

8

Lip

idsy

nth

esis

and

bre

akd

ow

n(3

)C

L7

83

9.C

on

tig

11

.88

8.2

42

.14

5.9

6E

-1

36

.63E

-11

2-h

ydro

xy-6

-oxononat

rien

edio

ate

hydro

lase

CL

46

43

.Co

nti

g1

5.3

51

2.5

61

.23

1.3

1E

-0

77

.40E

-06

GD

SL

este

rase

/lip

ase

1-l

ike

CL

35

39

.Co

nti

g1

75

.97

16

2.1

91

.09

6.7

5E

-2

06

1.6

1E

-2

02

Lip

ox

yg

enas

e

Cel

lw

all

com

po

un

ds

deg

rad

atio

n(1

)C

L5

52

2.C

on

tig

21

5.8

33

.72

1.0

93

.53

E-

16

5.0

1E

-1

4P

ecta

tely

ase

Str

ess

def

ense

(3)

CL

34

94

.Co

nti

g1

16

.84

39

.36

1.2

39

.48

E-

13

1.0

3E

-1

0T

hio

red

oxin

H2

-lik

e

CL

57

64

.Co

nti

g3

9.6

82

1.3

41

.14

4.2

6E

-0

82

.60E

-0

6P

ath

ogen

esis

-rel

ated

pro

tein

ST

H-2

Un

igen

e21

68

01

2.3

32

5.4

31

.04

6.4

8E

-0

94

.45E

-07

GS

T-l

ike

pro

tein

Sig

nal

sen

sean

dtr

ansd

uct

ion

(10

)U

nig

ene1

859

27

.19

27

.77

1.9

55

.71

E-

12

5.6

9E

-10

Leu

cine-

rich

repea

tre

cepto

r-li

ke

pro

tein

kin

ase

Un

igen

e18

59

09

.75

31

.51

1.6

91

.13

E-

11

1.0

9E

-09

Leu

cine-

rich

repea

tre

cepto

r-li

ke

pro

tein

kin

ase

Un

igen

e27

74

49

.68

31

.08

1.6

82

.17

E-

13

2.5

3E

-11

Leu

cine-

rich

repea

tre

cepto

r-li

ke

pro

tein

kin

ase

Un

igen

e18

58

96

.36

19

.67

1.6

32

.92

E-

06

1.3

0E

-04

Leu

cine-

rich

repea

tre

cepto

r-li

ke

pro

tein

kin

ase

Un

igen

e15

65

31

0.6

52

5.9

61

.29

2.3

3E

-0

71

.28E

-05

Absc

isic

acid

rece

pto

rP

YL

Un

igen

e23

97

43

2.6

67

7.2

71

.24

1.4

2E

-5

17

.51E

-4

9A

ux

in-i

nd

uce

dp

rote

in5

NG

4

Un

igen

e26

80

69

.02

21

.12

1.2

37

.54

E-

06

3.0

8E

-04

Leu

cine-

rich

repea

tre

cepto

r-li

ke

pro

tein

kin

ase

Un

igen

e15

15

54

.36

9.7

71

.16

6.1

4E

-0

73

.12E

-0

5A

RR

21-l

ike

Un

igen

e16

71

11

9.5

24

3.5

11

.16

6.5

2E

-0

83

.88E

-0

6S

AU

Rfa

mil

yp

rote

in

Un

igen

e12

48

13

0.2

76

2.8

1.0

52

.68

E-

09

1.9

4E

-07

Gib

ber

elli

nre

cepto

rG

ID1

Am

ino

acid

syn

thes

isan

dp

rote

inm

etab

oli

sm/d

egra

dat

ion

(6)

Un

igen

e32

66

51

.53

11

.41

2.9

4.0

8E

-0

61

.77E

-0

4f-

Bo

xfa

mil

yp

rote

in

Un

igen

e17

70

00

.98

6.8

62

.81

.32

E-

05

5.1

2E

-0

4E

in3

-bin

din

gf-

bo

xp

rote

in

Un

igen

e15

48

7.7

52

0.4

1.4

1.0

4E

-0

76

.00E

-0

6U

-bo

xd

om

ain-c

on

tain

ing

pro

tein

Un

igen

e20

09

09

.23

23

.92

1.3

71

.42

E-

08

9.2

9E

-0

7S

erin

eca

rbo

xy

pep

tid

ase

Un

igen

e20

08

91

3.1

29

.69

1.1

81

.10

E-

08

7.3

8E

-0

7S

erin

eca

rbo

xy

pep

tid

ase-

lik

e

118 Plant Cell Rep (2014) 33:111–129

123

Ta

ble

2co

nti

nu

ed

Fu

nct

ion

clas

sifi

cati

on

Gen

eID

Ca-

RP

KM

Gb-

RP

KM

log

2

rati

oP

val

ue

FD

RS

equen

cean

no

tati

on

Oth

erfu

nct

ions

(9)

Un

igen

e87

97

8.8

91

8.9

11

.09

2.6

7E

-0

71

.45E

-0

5f-

Bo

xp

rote

in-l

ike

Unig

ene2

3657

334.6

62608.2

22.9

60

0C

hal

cone

synth

ase

Un

igen

e30

20

41

.49

10

.16

2.7

71

.53

E-

06

7.2

0E

-0

5P

leio

tro

pic

dru

gre

sist

ance

pro

tein

CL

41

87

.Co

nti

g1

2.4

31

0.0

32

.04

2.5

9E

-0

59

.36E

-0

4P

rog

este

ron

e5

-bet

a-re

du

ctas

e

Un

igen

e23

77

85

.79

18

.36

1.6

65

.18

E-

19

9.1

5E

-1

7H

yp

oth

etic

alp

rote

inA

RA

LY

DR

AF

T

CL

72

3.C

on

tig

15

.51

4.0

51

.35

1.8

2E

-1

01

.54E

-0

8M

TN

3-l

ike

pro

tein

Un

igen

e22

68

34

5.6

79

8.1

01

.13

.88

E-

25

9.2

2E

-23

Har

pin

-induce

d1

Un

igen

e46

97

8.6

71

8.4

11

.09

1.0

2E

-0

75

.92E

-0

6C

yto

chro

me

P4

50

73

4A

1-l

ike

Un

igen

e16

13

14

.95

10

.51

1.0

88

.43

E-

08

4.9

3E

-0

6F

erre

do

xin

–n

itri

tere

du

ctas

e

CL

24

82

.Co

nti

g1

4.8

99

.94

21

.02

1.4

6E

-0

66

.92E

-0

5H

yp

oth

etic

alp

rote

inV

ITIS

V

Do

wn

-reg

ula

ted

gen

es(1

32

)

Tra

nsc

rip

tio

nfa

cto

r(1

5)

Un

igen

e13

85

51

0.5

70

.28

-5

.24

6.9

8E

-1

16

.12E

-0

9E

RF

tran

scri

pti

on

fact

or

Un

igen

e16

47

55

.83

0.3

8-

3.9

65

.94

E-

08

3.5

5E

-0

6D

RE

Btr

ansc

rip

tio

nfa

cto

r

CL

19

50

.Co

nti

g1

7.7

0.5

2-

3.8

82

.68

E-

20

5.0

9E

-1

8W

RK

Ytr

ansc

rip

tio

nfa

cto

r

Un

igen

e10

41

21

.61

2.7

9-

2.9

67

.81

E-

12

7.6

3E

-1

0Z

inc

fin

ger

fam

ily

pro

tein

CL

68

97

.Co

nti

g1

9.1

92

.29

-2

.01

4.4

2E

-1

03

.52E

-0

8b

HL

Htr

ansc

rip

tio

nfa

cto

r

Un

igen

e21

98

32

1.2

45

.51

-1

.95

2.1

6E

-1

73

.41E

-1

5C

BF

1tr

ansc

rip

tio

nfa

cto

r

Un

igen

e16

97

12

6.0

88

.73

-1

.58

9.2

4E

-1

07

.11E

-0

8N

AC

do

mai

n-c

on

tain

ing

pro

tein

Un

igen

e21

45

81

1.2

43

.87

-1

.54

6.4

2E

-0

62

.65E

-0

4Z

inc

fin

ger

pro

tein

Un

igen

e20

37

31

5.6

25

.79

-1

.43

8.0

8E

-0

95

.47E

-0

7R

ing

fin

ger

pro

tein

Un

igen

e21

59

36

1.8

72

4.9

3-

1.3

13

.27

E-

30

9.2

8E

-2

8E

RF

tran

scri

pti

on

fact

or

Un

igen

e27

38

53

9.6

81

7.5

8-

1.1

71

.75

E-

25

4.1

7E

-2

3R

ING

-H2

fin

ger

pro

tein

Un

igen

e64

76

50

.33

23

.37

-1

.11

5.4

5E

-1

57

.08E

-1

3E

RF

tran

scri

pti

on

fact

or

CL

56

11

.Co

nti

g1

12

.59

5.9

-1

.09

2.1

4E

-0

81

.37E

-0

6N

AC

do

mai

ncl

ass

tran

scri

pti

on

fact

or

Un

igen

e79

42

24

.49

11

.79

-1

.05

1.3

3E

-0

55

.16E

-0

4Z

inc

fin

ger

fam

ily

pro

tein

Un

igen

e16

97

47

6.0

53

7.7

1-

1.0

11

.30

E-

28

3.5

8E

-2

6E

RF

tran

scri

pti

on

fact

or

Ions

rem

obil

izat

ion

and

tran

sport

(3)

Unig

ene1

7205

12.5

52.9

-2

.11

2.0

4E

-1

73

.24E

-1

5P

ho

sph

ate

tran

spo

rter

PH

O1

-lik

e

Un

igen

e27

11

91

06

.24

39

.13

-1

.44

1.6

0E

-3

55

.31E

-3

3A

nio

nex

chan

ger

fam

ily

pro

tein

CL

36

90

.Co

nti

g1

10

.02

4.7

9-

1.0

66

.80

E-

10

5.3

0E

-0

8B

oro

ntr

ansp

ort

er4

-lik

e

Su

gar

met

abo

lism

and

tran

spo

rt(8

)U

nig

ene2

883

71

1.0

32

.17

-2

.34

5.3

6E

-2

41

.21E

-2

1P

ho

sph

og

lyce

rate

kin

ase

Un

igen

e20

84

21

9.9

76

.65

-1

.59

4.7

6E

-0

62

.03E

-0

4O

-fu

cosy

ltra

nsf

eras

e-li

ke

pro

tein

Un

igen

e22

06

52

6.5

51

0.6

8-

1.3

13

.19

E-

16

4.5

5E

-14

Cel

lulo

sesy

nth

ase-

like

CL

28

16

.Co

nti

g1

10

.27

4.2

9-

1.2

61

.39

E-

05

5.3

7E

-0

4O

-fu

cosy

ltra

nsf

eras

e-li

ke

pro

tein

Un

igen

e16

43

52

9.7

91

3.7

9-

1.1

11

.05

E-

11

1.0

2E

-09

Cel

lulo

sesy

nth

ase

A

Un

igen

e15

55

97

6.8

33

7.4

8-

1.0

42

.26

E-

09

1.6

5E

-0

7B

eta-

glu

cosi

das

e

CL

81

34

.Co

nti

g1

80

.12

39

.48

-1

.02

1.1

8E

-5

56

.77E

-5

3U

DP

-gly

cosy

ltra

nsf

eras

e

Plant Cell Rep (2014) 33:111–129 119

123

Ta

ble

2co

nti

nu

ed

Fu

nct

ion

clas

sifi

cati

on

Gen

eID

Ca-

RP

KM

Gb-

RP

KM

log

2

rati

oP

val

ue

FD

RS

equen

cean

no

tati

on

Nucl

eic

acid

synth

esis

,m

odifi

cati

on

and

bre

akdow

n(4

)U

nig

ene1

2813

22.7

411.3

5-

13

.73E

-0

71

.98

E-

05

Gly

cera

ldeh

yde-

3-p

hosp

hat

edeh

ydro

gen

ase

CL

38

98

.Co

nti

g2

9.0

41

.12

-3

.01

2.1

8E

-0

58

.00E

-04

Arg

inin

e/se

rine-

rich

-spli

cing

fact

or

RS

P31-l

ike

Un

igen

e51

70

23

.65

3.9

3-

2.5

93

.53

E-

10

2.8

4E

-0

8F

lap

end

onu

clea

seG

EN

-lik

e

Un

igen

e20

86

22

0.2

68

.36

-1

.28

1.8

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123

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122 Plant Cell Rep (2014) 33:111–129

123

‘glycerophospholipid metabolism’ and ‘endocytosis’

(Supplementary Table 6).

RT-qPCR validation

To validate the results of RNA-Seq data, 13 differentially

expressed unigenes were analyzed by quantitative real-time

PCR, including 1 gene (CL7248.Contig1) encoding ACS, 1

gene (Unigene17700) encoding EBF and 11 putative

transcription factor genes that, respectively, encode protein

ERF (Unigene13855, Unigene21593, Unigene6476 and

Unigene16974), DREB (Unigene16475), CBF (Uni-

gene21983), NAC (Unigene16971 and CL5611.Contig1),

WRKY (CL1950.Contig1), helix-loop-helix (bHLH)

(CL6897.Contig1) and WD-repeat protein (CL2967.Con-

tig1). With the exception of CL2967.Contig1, most

examined unigenes exhibit down-regulated expression with

6-h glucose treatment (Fig. 4) and expressions of all these

unigenes are identical to those obtained from RNA-Seq

data, suggesting that the data generated from RNA-Seq

assay of this study are sufficient to be used to investigate

glucose-induced transcriptomic changes in cut tree peony.

Besides, the expressions of these 13 unigenes in control

and 72-h glucose-treated flowers were also analyzed and it

was found that after 72-h glucose treatment, among the 13

unigenes, the expressions of those related to ERF, DREB,

CBF, WRKY and ACS are still greatly repressed (Fig. 4).

Discussion

Illumina sequencing, one of next-generation sequencing

technologies, is a powerful tool for gene discovery and

global analysis of molecular mechanisms during different

development stages or under various stimuli. With this

technology, more than 57.2 million raw data were pro-

duced in our study (Supplementary Table 2). Furthermore,

33,117 out of 50,829 unigenes were annotated against

public databases (Supplementary Table 3). Unigene

sequences obtained in our study would provide more

valuable gene information and supplement the transcrip-

tome studies in this well-known flower. According to the

result of GO classification in this study (Fig. 1), ‘cellular

process’, ‘binding’ and ‘cell’ are the largest groups in three

main GO categories of biological process, molecular

function and cellular component, respectively. The present

study is the first exploration to gain insight into the petal

transcriptome of cut tree peony, although two studies have

been carried out to get global understanding of flower bud

formation and development in tree peony (Shu et al. 2009;

Gai et al. 2012). One of these two studies, which has

constructed and sequenced a cDNA library from mix buds

of tree peony (P. ostii ‘Feng Dan’) potted flowers duringTa

ble

2co

nti

nu

ed

Funct

ion

clas

sifi

cati

on

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eID

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cean

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on

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igen

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igen

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40

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erk

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erm

illi

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read

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rary

of

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als

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ow

ers

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hth

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the

lib

rary

of

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eatm

ent

Plant Cell Rep (2014) 33:111–129 123

123

the period of bud dormancy release using the Roche 454

GS FLX platform, generated 625,342 raw reads and 12,345

expressed sequence tags annotated against public databases

(Gai et al. 2012). Sequencing data in this study of flower

buds (Gai et al. 2012) are significantly less than those in

our study. What is more, the result of GO classification in

this study (Gai et al. 2012) is quite different from that in

our study. For instance, ‘cellular process’, ‘binding’ and

‘cell’ are not dominant in the three GO categories in the

study of Gai et al. (2012). In addition, ‘metallochaperone

activity’ is dominant in the category of molecular function

in the study of tree peony buds, but genes in the term of

‘metallochaperone activity’ are fewer in our study (Fig. 1).

Transcriptome is very dynamic and alters dramatically both

during development and in response to environmental

signals such as biotic and abiotic stress (Zenoni et al. 2010;

Lee et al. 2012; Xu et al. 2012). The difference of GO

classification between the study of tree peony buds and our

study is probably related to different tissue sampling, since

there are enormous changes of gene transcripts from the

bud stage to flower opening period. Besides, higher output

in this study with new sequencing platform provides more

gene sequence information, including a series of genes with

low abundant transcripts, which might be another reason to

influence the result of GO classification.

Cut flowers, detached from the mother plant, are con-

stantly being exposed to abiotic stresses such as ethylene,

dehydration and nutrient deficiency. Ethylene plays

important roles in diverse aspects of plant growth and

development, especially in regulating the senescence of

many species of cut flowers, including the cut tree peony.

In this study, the cut tree peony reference transcriptome

database contains multiple unigenes encoding each of these

three ethylene biosynthesis enzymes (Table 1). The num-

ber of unigenes related with SAMS and ACS is similar

with that in the study of carnation, but number of unigenes

related with ACO is more than four times than that in

carnation (Tanase et al. 2012). In our previous study, we

have confirmed that the glucose treatment depresses the

endogenous ethylene production during the postharvest

Fig. 3 Functional gene

ontology (GO) classification of

173 annotated differentially

expressed genes (DEGs).

Sequences with Blastx matches

were assigned GO terms and

classified into the following

functional categories: biological

process, molecular function and

cellular component

Fig. 4 RT-qPCR validation of 13 differentially expressed unigenes

(DEGs) in petals of cut tree peony with and without 60 g L-1 glucose

treatment. Data from RT-qPCR were normalized relative to P.

suffruticosa ubiquitin (Wang et al. 2012b), and presented as mean

with standard errors (SE) of three biological replications

c

124 Plant Cell Rep (2014) 33:111–129

123

Plant Cell Rep (2014) 33:111–129 125

123

development of cut tree peony flowers (Zhang et al.

2012a). In the preliminary experiment for this study, we

also found that 6-h glucose treatment (flowers were at stage

2) has already started depressing the ethylene production

(data not published). That is why the 6-h treatment samples

were used to understand the transcriptome change in petals

of cut tree peony with glucose supply, to reveal the regu-

lation of sugar on ethylene metabolism. After treated for

6 h, glucose is found to greatly down-regulate one unigene

(CL7248.Contig1) encoding ACS (Table 2; Fig. 4). With

the attempt to understand the pattern of the effect of glu-

cose at different developmental stages, the expressions of

the concerned DEGs were also examined in the full open

flowers upon 72-h vasing and the expression of

CL7248.Contig1 was found to increase significantly,

compared with that at the 6-h vasing, while glucose treat-

ment still represses its expression compared with the con-

trol (Fig. 4). With sequences alignment, the sequence of

CL7248.Contig1 was identified to be the same with partial

sequence of PsACS1 (GenBank accession number

DQ337250), which is confirmed to be an important gene

involved in ethylene biosynthesis during cut flower open-

ing and senescence of tree peony ‘Luoyang Hong’ (Zhou

et al. 2013). Down-regulation of PsACS1 expression can

explain decreased endogenous ethylene biosynthesis of

‘Luoyang Hong’ cut flowers with glucose treatment in our

previous study (Zhang et al. 2012a). Similarly, sugar

loading could decrease ethylene production and ACS

expression in cut carnation (Verlinden and Garcia 2004;

Hoeberichts et al. 2007). Additionally, based on reference

transcriptome database, more than one unigene each for

receptors (ETR, ERS and EIN4), Raf-like serine/threonine

kinase CTR1, positive regulator EIN2 and transcription

factor (EIN3/EIL and ERF) were obtained and further

analyzed. With further sequence alignment, we found that

genes corresponding to CTR1, EIN3/EIL and ERF exist in

tree peony genome as multi-gene family. Although several

unigenes corresponding to ETR, ERS, EIN4 and EIN2

were obtained, it is still hard to determine whether they

belong to the same gene (gene family member), since their

sequences are without certain overlapped sequences. Gene

expression analysis of RNA-Seq reveals that four unigenes

(Unigene13855, Unigene21593, Unigene6476 and Uni-

gene16974) encoding transcription factor ERF are

down-regulated with glucose treatment (Table 2; Fig. 4).

Similarly, microarray analysis of glucose-regulated tran-

scription network in Arabidopsis reveals that aspects of

ethylene biosynthesis and responses are modulated by

glucose, since two genes encoding ACO and ERF are

repressed by glucose (Li et al. 2006). Exogenous sugar was

reported in carnation to prevent the expression of EIN3/EIL

genes and other senescence-associated genes (Hoeberichts

et al. 2007). However, unigenes encoding EIN3/EIL,

another key transcription factor involved in ethylene signal

transduction, did not respond to glucose treatment in our

study, which is likely related with the time length of glu-

cose treatment. Furthermore, it was interesting to find that

Unigene17700 encoding EBF was greatly up-regulated

with 6-h glucose supply in RNA-Seq analysis (Table 2)

and RT-qPCR validation (Fig. 4). EBF was reported to be

involved in the degradation of EIN3 protein in Arabidopsis

(Potuschak et al. 2003; An et al. 2010). Based on the

above-mentioned results, we guess that glucose induces the

transcription of EBF gene, which may affect the degrada-

tion of EIN3 in cut tree peony. The transcript levels of ERF

genes are decreased with glucose supply, so the ethylene

response is supposed to be affected, which could explain

the inhibition of exogenous sugar on ethylene-induced

senescence. At present, further studies are needed to prove

the hypothesis and gain more novel insight into the regu-

lation of sugar on ethylene signaling.

Environmental stress factors affect flower opening and

senescence, and plants trigger the activation of stress-

responsive and stress-tolerance genes to respond to such

challenges. Among these stress-related genes, genes

encoding transcription factors are considered to be the most

interesting group because of their potential involvement in

regulating the expression of downstream genes. Besides

ERF, several transcription factor genes like DREB (Agar-

wal et al. 2006), CBF (Akhtar et al. 2012), NAC (Olsen

et al. 2005), WRKY (Ryu et al. 2006) and bHLH (Wang

et al. 2007) play important roles in plant senescence and/or

stress responses. However, fewer studies were carried out

to figure out the function of mentioned transcription factor

genes of cut flower response to environmental stimuli.

Exogenous ethylene and ABA accelerates the transcription

of most ERF gene family members of cut petunia (Liu et al.

2011). In cut rose, the expressions of both Rh-DREB1A and

Rh-DREB1B are induced by water deficit stress (Wang

et al. 2009a). The expression of NAC gene is induced with

ethylene treatment in cut carnation, but is repressed with

STS (Hoeberichts et al. 2007). In the present study, DREB,

CBF, NAC, WRKY and bHLH expressions are repressed

with glucose treatment (Table 2; Fig. 4), according to the

results of RNA-Seq and RT-qPCR validation. Similarly,

NAC gene was also found to be down-regulated by sugar

treatment in cut carnation (Hoeberichts et al. 2007).

More detailed investigation of DEGs reveals the inter-

esting finding that several glucose-repressed DEGs are

related with stress-response or stress-tolerance (Table 2),

such as LEA (Dalal et al. 2009), nudix hydrolase (Ogawa

et al. 2005), GST (Marrs 1996), PIP (Ma et al. 2008) and so

on. Generally, glucose supply to cut tree peony would help

provide substrates for respiration, increase the osmotic

concentration in petal and stem and represses ethylene

biosynthesis and signal transduction. Compared with the

126 Plant Cell Rep (2014) 33:111–129

123

control flowers, flowers with glucose treatment indeed

showed lower expression of stress-related genes, suggest-

ing that glucose probably releases the effects induced by

various disadvantage environmental stress. To some extent,

additional supply of glucose in vase solution could be

thought to improve vase condition for cut flower.

In summary, we presented a rapid and cost-effective

method for transcriptome and RNA-Seq analysis using

Illumina sequencing technology. A total of 50,829 assem-

bled unigenes were obtained with 33,117 sequences having

an above cut off Blastx result. With glucose treatment,

some unigenes encoding ACS and ERF involved in eth-

ylene biosynthesis and signal transduction were greatly

down-regulated. A hypothesis that glucose may enhance

the degradation of EIN3 through increasing the transcrip-

tion of EBF gene was presented in this study. Furthermore,

stress-related transcription factor genes DREB, CBF, NAC,

WRKY and bHLH were also repressed with glucose supply,

as well as several other stress-responsive and stress-toler-

ance genes. We believe that all the results and analyses are

valuable resources for better understanding of the benefi-

cial influence of exogenous sugars on cut tree peony and

also useful for other functional study in this flower, even in

other species in the family of Paeoniaceae.

Acknowledgments This work was supported by the National Nat-

ural Science Foundation of China (30972030).

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