- Research article
- Open Access
Transcriptome profiling of fruit development and maturation in Chinese white pear (Pyrus bretschneideri Rehd)
- Min Xie†1,
- Ying Huang†1,
- Yanping Zhang1,
- Xin Wang1,
- Hua Yang2,
- Oliver Yu2,
- Wenhao Dai3 and
- Congbing Fang1, 2Email author
© Xie et al.; licensee BioMed Central Ltd. 2013
- Received: 9 August 2013
- Accepted: 20 November 2013
- Published: 23 November 2013
Pear (Pyrus spp) is an important fruit species worldwide; however, its genetics and genomic information is limited. Combining the Solexa/Illumina RNA-seq high-throughput sequencing approach (RNA-seq) with Digital Gene Expression (DGE) analysis would be a powerful tool for transcriptomic study. This paper reports the transcriptome profiling analysis of Chinese white pear (P. bretschneideri) using RNA-seq and DGE to better understand the molecular mechanisms in fruit development and maturation of Chinese white pear.
De novo transcriptome assembly and gene expression analysis of Chinese white pear were performed in an unprecedented depth (5.47 gigabase pairs) using high-throughput Illumina RNA-seq combined with a tag-based Digital Gene Expression (DGE) system. Approximately, 60.77 million reads were sequenced, trimmed, and assembled into 90,227 unigenes. These unigenes comprised 17,619 contigs and 72,608 singletons with an average length of 508 bp and had an N50 of 635 bp. Sequence similarity analyses against six public databases (Uniprot, NR, and COGs at NCBI, Pfam, InterPro, and KEGG) found that 61,636 unigenes can be annotated with gene descriptions, conserved protein domains, or gene ontology terms. By BLASTing all 61,636 unigenes in KEGG, a total of 31,215 unigenes were annotated into 121 known metabolic or signaling pathways in which a few primary, intermediate, and secondary metabolic pathways are directly related to pear fruit quality. DGE libraries were constructed for each of the five fruit developmental stages. Variations in gene expression among all developmental stages of pear fruit were significantly different in a large amount of unigenes.
Extensive transcriptome and DGE profiling data at five fruit developmental stages of Chinese white pear have been obtained from a deep sequencing, which provides comprehensive gene expression information at the transcriptional level. This could facilitate understanding of the molecular mechanisms in fruit development and maturation. Such a database can also be used as a public information platform for research on molecular biology and functional genomics in pear and other related species.
- Fruit Development
- Digital Gene Expression
- Pear Fruit
- Digital Gene Expression Library
- Fruit Developmental Stage
The genus Pyrus is one of the most important genera in Rosaceae family for fruit production. Pyrus species are widely used for commercial fruit production in 76 countries or regions around the world (http://faostat.fao.org) and their economic importance has been well recognized . There are four major edible species in Pyrus: P. communis L. is mainly cultivated in Europe, North America, South America, Africa, and Australia. The other three species, P. bretschneideri Rehd., P. ussuriensis Maxim., and P. pyrifolia (Burm.) Nakai., are grown in East Asian [1, 2]. The world production of pear has doubled in the past 16 years. China is the largest pear producer. In 2010, China produced 15.23 million tons (Mt) of pear fruits that accounted of 67.26% of world pear production (22.64 Mt) (FAOSTAT, 2012).
As of July 2013, 26,696 nucleotide sequences, 4,413 expressed sequence tags (ESTs), 52 genome survey sequences (GSS), and 2,636 proteins from the Pyrus genus have been deposited in GenBank. These sequences mostly derived from cDNA cloning and EST sequencing [3–7] provide useful information for transcriptional analysis, candidate gene discovery, and gene functional analysis; however, a comprehensive description of genes that expressed in pear fruit during the fruit development and maturation period remains unavailable.
Recent years, use of RNA-seq, the next generation sequencing technology, has generated over one billion bp of high-quality DNA sequence per analysis/experiment  and has dramatically improved the efficiency of gene discovery and functional analysis [9, 10], which largely facilitates the investigation of the functional complexity of transcriptomes [11, 12]. Illumina sequencing of transcriptomes for yeast [13, 14], Arabidopsis, mouse [16, 17], and human cells [18, 19] has confirmed that next generation sequencing is well-suited for surveying transcriptome profiles in eukaryotes. Recently Illumina RNA-seq system has been used to identify genes related to bud dormancy in pear (P. pyrifolia) . RNA-seq is not limited to detecting transcripts for organisms with existing genomic sequences; it also can be used to sequence non-model organisms that lack of genomic information [21–25]. DGE is a tag-based transcriptome sequencing approach in which the expression level of all genes in a sample is measured by counting the number of individual mRNA molecules produced from each gene, which enables the DGE protocol more suitable and affordable for comparative gene expression studies [25–31]. Despite its obvious potential, the next generation sequencing has not yet been applied to pear research.
In this study, 5.47 gigabase pairs of high quality DNA sequence were generated using Illumina technology to survey the poly (A)+ transcriptome of P. bretschneideri. A total of 90,227 unigenes were assembled. All known homologous genes in major metabolic pathways related to fruit development and maturation were identified. Furthermore, five DGE libraries were constructed and gene expression profiles at different fruit developmental stages were analyzed. These annotated transcriptome sequences and gene expression profiles provide useful information for identification of genes involved in quality trait development during fruit development and maturation in pear species.
Plant material and RNA extraction
cDNA library construction, Illumina sequencing and De novoassembly
To obtain a complete gene expression profile, RNA from each tissue sample was pooled. The poly (A)+ RNA was isolated from 20 μg of the pooled RNA using Dynal oligo(dT) 25 beads (Invitrogen) according to the Illumina manufacturer’s instructions. The mRNA was then cleaved into short sequences using divalent cations at 70°C for 5 min. The cleaved RNA fragments were used for first strand cDNA synthesis using SuperScript III reverse transcriptase (Invitrogen) and N6 random hexamers (Takara). Second strand cDNA synthesis was performed using RNase H (Invitrogen) and DNA polymerase (Invitrogen). Subsequently, cDNA fragments were ligated to adapters after an end repair process. These products were purified and enriched using the QIAquick PCR Purification Kit to develop a cDNA library.
The cDNA library was sequenced from both 5′ and 3′ ends on the Illumina sequencing platform (HiSeqTM 2000). Image deconvolution and Q-value (quality value assigned to each base) calculation were carried out in the Illumina data processing pipeline (version 1.6). Before assembly, adaptor sequences, empty reads, low quality sequences with N percentage (i.e., the percentage of nucleotides in read which could not be sequenced) over 5%, and those containing more than 20% bases with Q-value ≤ 10 were removed. De novo assembly of the cleaned reads was performed using SOAPdenovo software (version 1.03, http://soap.genomics.org.cn). The de Bruijn graph was firstly used to generate contigs . The reads were then mapped back to the contigs, and the paired-end reads and contigs from the same transcript were further joined into scaffolds. The complete scaffolds were subsequently obtained after the paired-end reads were again used for gap fillings . To reduce any sequence redundancy, the scaffolds were further assembled into unigenes using TGICL (copyright(c), http://www.tigr.org/tdb/tgi) . Among them, the scaffolds with more than 70% similarity after multiple alignments were grouped into clusters, and others that cannot reach the threshold set and fall into any assembly should remain as a list of singletons. The assembled unigenes (larger than 200 bp) were deposited in the Transcriptome Shotgun Assembly Sequence Database (TSA) at NCBI with the accession number from JR595427 to JR673747.
Functional annotation and classification
Unigenes that were larger than 150 bp were used for BLAST search and annotation against the NCBI nonredundant (nr) database with the BLASTX algorithm (http://www.ncbi.nlm.nih.gov/) using an E-value cut-off of 10-5. Functional annotation by gene ontology terms (GO; http://www.geneontology.org) was carried out based on the best hits from nr annotation using Blast2go software (http://www.blast2go.de/) , and the resultant GO id were further used for GO classification by WEGO  (http://wego.genomics.org.cn/cgi-bin/wego/index.pl). Annotation of COG and KEGG pathways was performed by sequence comparisons using BLASTX algorithm against Cluster of Orthologous Groups database and Kyoto Encyclopedia of Genes and Genomes database with an E-value threshold of 10-5. Annotations of all assembled sequences were deposited in the Transcriptome Shotgun Assembly Sequence Database (TSA) at NCBI and can be searched using the Gene-ID listed in Additional file 1.
Digital gene expression (DGE) library construction and sequencing
Total RNA was extracted from fruit samples collected at five fruit developmental stages. The tag library was prepared using the Illumina gene expression sample prep kit. Briefly, the poly(A)+ RNA was purified from 6 μg of RNA using oligo(dT) magnetic beads. Double-stranded cDNA was directly synthesized on the beads and subsequently digested with NlaIII. All cDNA fragments with 3′ ends were purified with magnetic beads before their 5′ ends were ligated to Illumina adapter 1. A 21 bp tag with the adaptor 1 was produced after digested with MmeI (an enzyme that recognizes the junction of the Illumina adaptor 1 and the CATG site). The Illumina adapter 2 was then ligated to the 3′ ends of the tag. The library was amplified by PCR for 15 cycles and 90 bp strips were purified by PAGE gel electrophoresis. These strips were then digested and single-chain molecules were attached to the Illumina sequencing chip for sequencing. All raw tag data are available in Gene Expression Omnibus (GEO) at NCBI with the accession number: GSE33388.
Analysis and mapping of DGE tags
To map DGE tags, sequencing-received raw image data were filtered to remove low quality tags (tags with unknown sequences ‘N’), empty tags (sequence with only the adaptors but no tags), and tags with only one copy number (probable sequencing error). For annotation, cleaned tags with CATG and the 21 bp tag sequence were mapped to our transcriptome reference database with no more than 1 nucleotide mismatch. All tags that mapped to reference sequences of multiple genes were filtered out and the remaining tags were designated as unambiguous tags for gene expression analysis. The number of unambiguous tags of each gene was calculated and then normalized to TPM (number of transcripts per million clean tags). The differentially expressed tags were used for mapping and annotation. A complete list of all differentially expressed genes is shown in Additional file 2, Additional file 3, Additional file 4 and Additional file 5.
Evaluation of DGE libraries
To compare the gene expression in different fruit developmental stages, the frequency of each tag in different DGE libraries was statistically analyzed using the method of Audic et al.. The false discovery rate (FDR) was used to determine the threshold P-value in multiple tests. The threshold determining the significance of differentially expressed genes was set at FDR ≤ 0.001 and log2 foldchange ≥ 2.
Pathway enrichment analysis
Here N is the number of all genes that with KEGG annotation, n is the number of DEGs in N, M is the number of all genes that were annotated to specific pathways, and m is number of DEGs in M.
Illumina sequencing and de novoassembly
Summary of sequence assembly of P. bretschneideri mRNA pooled from ten different tissues samples
Base pairs (bp)
Mean length (bp)
Raw sequencing reads
Contigs (≥100 bp)
Clusters (≥150 bp)
Singletons (≥150 bp)
Total unigenes (≥150 bp)
Annotation of predicted proteins
Summary of annotations of the P. bretschneideri unigenes
All assembled unigenes
Gene annotations against NR plant proteins
Gene annotations against NR Arabidopsis proteins
Unique gene annotations against NR
Gene annotations against UniProt
Gene annotations against InterPro
Gene annotations against Pfam
Gene annotations against COG
Gene annotations against KEGG
GO annotations for NR protein hits
3 main categories, 44 sub-categories
All annotated Unigenes
Conserved domain annotation
Thirty most frequently occurring InterPro domains/families in the P. bretschneideri unigenes
Protein kinase, catalytic domain
Serine/threonine-protein kinase-like domain
Serine/threonine-protein kinase domain
Serine/threonine-protein kinase, active site
Tyrosine-protein kinase, catalytic domain
Protein kinase, ATP binding site
RNA recognition motif domain
WD40 repeat, subgroup
Zinc finger, RING-type
WD40 repeat 2
Zinc finger, C3HC4 RING-type
Leucine-rich repeat-containing N-terminal, type 2
HTH transcriptional regulator, Myb-type, DNA-binding
SANT domain, DNA binding
F-box domain, cyclin-like
Cytochrome P450, E-class, group I
EF-Hand 1, calcium-binding site
WD40 repeat, conserved site
Ankyrin repeat-containing domain
Helicase, superfamily 1/2, ATP-binding domain
Gene ontology (GO), clusters of orthologous groups (COG) and Kyoto Encyclopedia of genes and genomes (KEGG) ontology (KO) classification
A total of 90,227 annotated sequences were mapped to canonical pathways in Kyoto Encyclopedia of Genes and Genomes (KEGG)  to identify active pathways in white pear. Of which, 24,169 sequences were assigned to 121 KEGG pathways (Additional file 9). The most represented pathways by the unique sequences were metabolic pathways (5,488 members), biosynthesis of secondary metabolites (2,904 members), plant-pathogen interaction (2,389 members), and spliceosome (1,121 members). These annotations provide a valuable resource for investigating specific processes, functions, and pathways in pear growth and development.
Analysis of metabolic pathway genes using P. bretschneideri unigenes
The overall quality of pear fruits is closely related to flavor, grit (stone cell) content, flesh texture, skin russet, and appearance [1, 46]. During fruit development and maturation, pear fruits undergo a series of physiological and biochemical changes including expansion of size, accumulation of soluble solids, change of pigments, and formation of aromatic volatiles [1, 2]. Most of these traits are inherited in a complex polygenic manner and controlled by multiple QTLs, which poses a significant challenge to traditional breeding [2, 47]. In order to better understand the genetic and molecular basis of these changes related to quality formation, a few pathways including several primary, intermediate, and secondary metabolic pathways that are related to pear quality development were analyzed. A total of 61,636 annotated unigenes were used to analyze metabolic pathway genes with simple keyword searches. Each search result was confirmed with a BLAST search.
Digital gene expression (DGE) library sequencing
Statistics of P. bretschneideri DGE sequencing
Total number of raw tags
Total number of distinct raw tags
Total number of clean tags
Total number of distinct clean tags
Clean tag/Raw tag
Total number of tags mapping to genes
% of tags mapping to genes
Total number of distinct tags mapping to gene
% of distinct tags mapping to gene
Number of all tag-mapped genes
% of all tag-mapped genes
Number of unambiguous tags mapping to gene
% of unambiguous tags mapping to gene
Number of distinct unambiguous tags mapping to gene
% of distinct unambiguous tags mapping to gene
Number of unambiguous tag-mapped genes
% of unambiguous tag-mapped genes
Number of unknown tags
% of unknown tags
Number of distinct unknown tags
% of distinct unknown tags
Tag sequences in the five DGE libraries were mapped to the transcriptome reference database generated in the above-mentioned Illumina sequencing. The reference database contains 162,456 distinct sequences with 142,331 unambiguous reference tags. Among the distinct tags (68,916 to 77,039) generated from the Illumina sequencing in the five libraries of fruit developmental stages, 32,705 to 36,335 distinct tags were mapped to individual genes in the reference database (Table 4). Tags mapped to a unique sequence are the most critical subset of the DGE libraries as they can explicitly identify a transcript. Up to 31.2% (28,175) of the sequences in the transcriptome reference tag database could be unequivocally identified by a unique tag (Table 4).
Gene expression profile changes between developmental stages
To profile the gene expression pattern during different developmental stages of pear fruit, the number of clean tags for each gene was calculated and its differentially expressed tags between two samples of two adjacent stages were identified using an algorithm developed by Audic et al..
The comparative analysis between fruit stage 3 (FS3) and FS2 libraries revealed that 2,135 genes showed significant expression changes. Among these genes, 923 and 1,212 genes were up-regulated and down-regulated, respectively, in FS3 compared to FS2 (Figure 10, Additional file 3). Among the ten most up-regulated and ten most down-regulated genes, seven annotated genes were up-regulated in FS3: two prunin 2 precursor [Prunus dulcis], one unnamed protein product [Vitis vinifera], one dehydration-responsive protein RD22 [Prunus persica], one mitochondrial carrier protein [Ricinus communis], one conserved hypothetical protein [Ricinus communis], and one tonoplast intrinsic protein (putative) [Ricinus communis]. Seven functionally defined genes were down-regulated in FS3, including a flavonoid 3-hydroxylase gene [Ricinus communis], a RNA binding protein (putative) [Ricinus communis], an APETALA2 (AP2) domain class transcription factor [Malus x domestica], a CBF/DREB1 transcription factor [Malus baccata], an anthocyanidin synthase gene [Pyrus communis], an Auxin-binding protein ABP19a, and an NADH dehydrogenase subunit 6 [Nicotiana tabacum]. Six genes were also blasted without annotation (Additional file 12). Based on the GO functional classification, almost all genes were involved in cellular components, i.e., extracellular region, membrane part, anchored to membrane and external encapsulating structure. Among genes enriched in KO, significant changes were observed in genes of metabolic pathways, such as biosynthesis of phenylpropanoids, biosynthesis of plant hormones, flavonoid biosynthesis, and starch and sucrose metabolism (Additional file 14).
Between the FS4 and FS3 libraries, 1,824 genes demonstrated significant changes. In the FS4 library, 797 and 1,027 genes were up-regulated and down-regulated, respectively, in comparison with the FS3 library (Figure 10, Additional file 4). Nine genes among the ten most differentially up- and down-regulated genes showed no similarity (Additional file 12). Among the up-regulated genes, five were hypothetical protein from Vitis vinifera and Populus trichocarpa, in which only one matched a flavonoid 3-hydroxylase gene from Ricinus communis, two down-regulated genes were predicted to encode proteins found in Ricinus communis and Populus trichocarpa, an unnamed protein product [Vitis vinifera], a laccase 1a [Populus trichocarpa] and an Nonspecific lipid-transfer protein precursor [Ricinus communis] (Additional file 12). Almost all gene sets enriched in GO were correlated to plastid, plastid part, thylakoid, and chloroplast, while the gene sets enriched in KO were related to metabolic pathways (Additional file 15).
The comparison between the FS5 and FS4 libraries also revealed significant variations in gene expression. A total of 1,411 genes, including 484 up-regulated and 927 down-regulated, were identified in FS5 compared to FS4 (Figure 10, Additional file 5). Eight genes among the ten most differentially up- and down-regulated genes showed no similarity (Additional file 12). Among the ten up-regulated genes, one aligned with a beta-D-xylosidase gene [Pyrus pyrifolia], one with an AP2 domain class transcription factor [Malus x domestica], and two genes were unnamed protein product [Vitis vinifera] and conserved hypothetical protein [Ricinus communis]. Among the ten down-regulated genes, three aligned with dehydration-responsive protein RD22, two with prunin precursors, one with a delta-12 oleate desaturase [Gossypium hirsutum], and two were unnamed protein product or hypothetical protein. Most genes enriched in GO were related (or functional in) chloroplast stroma and photosystem. Genes in enriched KO showing significant changes between the FS5 and FS4 libraries were related to cysteine and methionine metabolism, photosynthesis-antenna proteins and isoquinoline alkaloid biosynthesis (Additional file 16).
Expression profiling during ripening
In this study, differences in the number and expression profile of DEGs were observed at five developmental stages of pear fruit. 28,743 unigenes were expressed during fruit ripening, with 810, 2,135, 1,824, and 1,411 showing differential expression between 25 and 55 DPA, 55 and 85 DPA, 85 and 115 DPA, and 115 and 145 DPA, respectively. It showed that fewer genes expressed in the cell division stage (25 to 55 DPA) than in the period of cell expansion (55 to 145 DPA) and fruit ripening (115 to 145 DPA) (Figures 1 and 10).
To characterize the levels of gene expression, the number of unambiguous tags of each gene was calculated and normalized to the number of TPM. Based on this analysis, the gene expression levels in the five fruit developmental stages were categorized to rare (TPM < 5), low (TPM > 5–50), moderate (TPM > 50–100), and high (TPM >100) (Additional file 17). The largest portion of transcripts (20,431 out of 28,744, 71.08%) exhibited rare expression, and followed by low expression (6,483 out of 28,744, 22.55%). Only a small fraction of transcripts was expressed at moderate (3.21%) and high (3.15%) levels.
Selected genes with different expression trends in five fruit developmental stages
TPM (transcript per million clean tag)
Sucrose phosphate synthase
Auxin influx transport protein
COL domain class transcription factor
UDPG flavonoid 7-O-glucosyltransferase
NAD-dependent sorbitol dehydrogenase
NAD-dependent sorbitol dehydrogenase
S6 ribosomal protein
Structural molecule activity
DEAD-box RNA helicase-like protein
Large ribosomal subunit
Plasma membrane ATPase
Plasma membrane intrinsic protein
Plasma intrinsic protein
BHLH domain class transcription factor
60S ribosomal protein L35a
Ribosomal protein S21e
Illumina sequencing and function annotation
In this study, one ‘Dangshansuli’ pear cDNA library and five DGE libraries (from fruit samples collected at 25, 55, 85, 115 and 145 DPA) were constructed using RNA-Seq technology. These constructed libraries were subjected to comparative gene expression studies. As a result, we obtained 90,227 unigenes including 17,619 clusters and 72,608 singletons. 62,077 out of 90,227 assembled unigenes were annotated. Of which, 61,624 were annotated to the non-redundant (nr) NCBI database and 28,114, 33,205, and 24,196 were annotated to GO, COG and KEGG databases, respectively. For the remaining 28,150 unigenes (31.2% of all 90,227 assembled unigenes), the absence of significant homology to existing genes could be caused by several factors. Obviously, the length and completeness of the assembled unigenes were one main factor, which can be seen from the increasing proportion of sequences with matches in the NR database as the length of uingenes increased (Figure 3A). However, the assembled unigenes shorter than 500 bp added up to 64,812 (71.8% of all 90,227 assembled unigenes) (Figure 2B), and some of them were too short to allow statistically meaningful matches. In addition, for some unigenes, lack of homologous sequences in the public databases may indicate specific roles for them in P. bretschneideri.
Unigenes identified through conserved domain annotation showed that most abundant InterPro domains/families were Protein kinase and its subcategories that are known to regulate the majority of cellular pathways. Cytochrome P450, Myb-DNA-binding families (MYB), and “WD40-repeat” domains were also very abundant. MYB and WD40-repeat proteins contributed to the extensive modification and regulation of various secondary compounds in many pathways have been validated to be key roles in anthocyanin biosynthesis by regulating anthocyanin structural genes . MYB proteins also appear to be versatile in regulating secondary metabolism, cellular morphogenesis, meristem formation, and the cell cycle . Recent research showed that Cytochrome P450 encoded enzymes that are responsible for the conversion of the mogroside backbone to various mogrosides .
ESTs sequences had been intensively used for transcriptional analysis, candidate gene discovery, and gene functional analysis. However, since RNA-Seq technology was developed, only 4,413 expressed sequence tags (ESTs) of Pyrus plants have been deposited in GenBank due to the fact that EST analyses can only identify limited number of candidate genes that are involved in complex biosynthetic pathways. Using RNA-seq, over one billion nucleotides of high-quality DNA sequence per analysis/experiment can be generated , which has dramatically improved the efficiency of gene discovery and functional analysis [9, 10]. Furthermore, RNA-Seq was less expensive, more efficient, and allowed faster gene discovery than traditional EST analysis. RNA-seq recently has been widely used for transcriptome profiling analysis in many plant species [15, 26, 41, 48–52].
Genes related to metabolic pathway
In the present study, a total of 31,215 unigenes were assigned to 121 KEGG pathways, among which 2,417 unigenes were identified to be associated with carbohydrate metabolisms. A total of 34.8% and 29.2% of all 2,417 unigenes are involved in flavonoid biosynthesis and starch and sucrose metabolism, respectively. Invertase (25 unigenes), SS (24 unigenes), ADP glucose pyrophosphorylase (24 unigenes), SPS (23 unigenes), and starch synthase (23 unigenes) were ranked top five enzymes in 18 key enzymes (see Additional file 10) involved in starch and sucrose metabolism pathways.
Sugar is one of the most important biochemical components that determine fruit quality. A series of enzymes control sucrose metabolism during fruit development and maturation. Invertase, SS, and SPS are three key enzymes that are deeply involved in fruit sucrose matabolism. For example, invertase (β-D-fructofuranosidase) cleaves sucrose to glucose and fructose irreversible, while soluble acid invertase (AIV) presumably hydrolyzes sucrose to hexose for cell growth and development [53, 54]. A decrease of soluble AIV activity was correlated with a rapid increase of sucrose during fruit maturation in Japanese pear [55, 56]. SS is involved in the movement of sucrose to diverse pathways important for metabolic structure and storage functions in plant cells . It has been reported that SS plays a role in sucrose cleavage rather than sucrose synthesis [58, 59]; however, one report suggested that SS was also involved in sucrose synthesis in mature peach fruit where a high level of sucrose was accumulated . In pear fruit, SS also appears to be involved in sucrose synthesis since SS activity increased along with a sucrose accumulation in pear fruit . Recent research in potato also showed that SS strongly determines the intracellular levels of UDP-glucose, ADP-glucose, and starch, and total yield in potato tubers . SPS also plays a major role in sucrose biosynthesis. Both SPS and SS are two important determinants of sucrose accumulation in Asian pear fruit. In 23 pear cultivars, the activity of SS was closely correlated with sucrose content, while SPS showed a weak correlation .
In the family Rosaceae, sorbitol is a major carbohydrate of translocated photosynthates. Sorbitol is converted into other sugars in the fruit by sorbitol-metabolizing enzymes, in which sorbitol oxidase and NAD-dependent sorbitol dehydrogenase are two major players [62, 63]. Research also revealed that three gene families, sorbitol transport (SOT), sorbitol dehydrogenase (SDH), and sorbitol-6-phosphate dehydrogenase (S6PDH), showed more impacts on sugar metabolism in pear than in non-rosaceous species . Wu et al. (2013)  identified four S6PDH genes through genome sequencing; however, no unigenes coding S6PDH were detected in this study. This may be caused by the difference in gene assembly or annotation. Yamaki and Moriguchi (1989) reported that NAD-dependent sorbitol dehydrogenase that converts sorbitol to fructose showed a high activity throughout the entire fruit developing period in Japanese pear fruit. However, the activity of sorbitol oxidase activity that is about one-tenth of NAD-dependent sorbitol dehydrogenase, was high in immature fruit, but decreased during the fruit expansion period and increased again during the fruit maturation stage . In peach fruit, sorbitol oxidase activity was relatively high, but other sorbitol-related enzymes were barely detectable .
In this study, two important enzymes, ADP glucose pyrophosphorylase (24 unigenes) and starch synthase (23 unigenes) that are involved in starch metabolic pathways were identified. In plants, regulation of starch metabolism is complex. In synthesis of starch, ADP-glucose was the glucosyl donor for the elongation of α-1, 4-glucosidic chains . The first committed step is ADP-glucose synthesis that is catalyzed by ADP-glucose pyrophosphorylase (ADPGlc PPase) . As a glucose donor, ADPG molecule is transferred to non-reducing end of the (1–4) glucan primer by starch synthase catalyses, thus formation of a long-chain amylase. Ghosh and Preiss (1966) showed that the reaction catalyzed by ADPGlc PPase is a regulation step in starch synthesis in higher plants as well as in the cyanobacteria [68–70]. Most of the plant ADPGlc PPases are allosterically regulated by 3-PGA and inorganic orthophosphate (Pi) [69, 70]. Some reports also suggested that in higher plants the enzyme activity can also be regulated by its reductive state [71–73].
In this study, multiple unigenes in the main flavonoid biosynthesis pathways were annotated according to KEGG (Figure 6B). Flavonoids including phenylpropanoids, flavones, flavonols, and anthocyanins are important secondary metabolites that are directly involved in the development of fruit quality, such as color, flavor, and health beneficial ingredients [1, 46, 74]. These compounds are also involved in the formation of undesirable brown pigments in fruits following bruising or cutting and/or storage . Information on phenylpropanoid metabolism in pear is limited. Nishitani et al. (2010) have studied the importance of phenylpropanoid metabolism in pear fruit ripening using oligoarray analysis . In present study, a large number (497) of unigenes involved in phenylpropanoid biosynthesis were detected. Lignin biosynthesis in the phenylpropanoid pathway is one of the important factors for pear fruit quality. Lignin is the primary component of stone cells in pear fruit  and its synthesis has direct influence on formation and content of stone cells, ultimately influencing pear fruit quality . Anthocyanin biosynthesis is essential for fruit coloration. Red coloration is determined by the content and composition of anthocyanins in the fruit skin . Most enzymes in the anthocyanin biosynthetic pathway in pears are well studied . For example, Feng et al. (2010) reported that anthocyanin biosynthesis in pears is regulated by a R2R3-MYB transcription factor PyMYB10 .
Digital gene expression (DGE) at different stages of pear fruits
Analyses of KEGG pathways showed that DEGs were observed in several different pathways including some metabolic pathways, such as biosynthesis of phenylpropanoids, plant hormones, and flavonoids; starch and sucrose metabolism; cysteine and methionine metabolism; biosynthesis of photosynthesis-antenna proteins and isoquinoline alkaloid. These are closely related to fruit development in pear and other species [60, 61, 66, 74, 79].
Comparative analysis showed that dehydration-responsive protein RD22 was significantly up-regulated in pear fruit between FS3 and FS2 and significantly down-regulated between the FS3 and FS2 libraries. This showed that this gene involved in the whole expansion stage of pear fruit, so we infer that it may play important roles in the process of pear fruit growth and development. Although some research reported that dehydration-responsive protein RD22 was related to stress/defense response [80–82]. The role of this gene in pear fruit development remains unknown. AP2 was well known for its roles in floral organ identity and develop. In this study, we found that AP2 domain class transcription factor was significantly down-regulated and up-regulated in the fruit developmental stage of FS3/FS2 and FS5/FS4, respectively, which indicated that the AP2 domain class transcription factor was involved in fruit growth and development in pear. Recent research showed that AP2 was involved in many aspects of fruit development in other species. For example, Chung et al. (2010) proved that a tomato (Solanum lycopersicum) APETALA2/ERF gene (SlAP2a) is a negative regulator of fruit ripening . AP2 genes are also involved in seed mass and yield development via regulation of embryo cell number and cell size . Rohrmann et al. (2011) reported that some genes in the AP2-EREBP family responsive to ethylene also showed the altered expression from the green fruit developmental stage onwards . Ripoll et al. (2011) found that AP2 acts to prevent overgrowth of replum by negatively regulating BP and RPL, two genes that normally act to promote replum formation in Arabidopsis. AP2 also acts to prevent overgrowth of the valve margin by repressing the expression of the valve margin identity gene . These studies indicated that AP2 domain class transcription factor is an important regulatory factor in fruit development. Other genes identified in this study, such as metallothionein-like protein type 3, nonspecific lipid-transfer protein precursor, cytochrome P450, zinc finger protein, prunin 2 precursor, flavonoid 3-hydroxylase gene, CBF/DREB1 transcription factor, anthocyanidin synthase gene, etc. may have their own roles in the process of pear fruit growth and development. These roles need further verification.
In this study, the transcriptome profile of Chinese white pear (P. bretschneideri) was investigated using Solexa/Illumina RNA-seq and DGE deep sequencing technologies. A total of 90,227 unigenes were assembled and 62,077 unigenes were annotated. The results demonstrated the feasibility of using Illumina sequencing-based DGE system for gene expression profiling and provided new directions for functional analysis of genes involved in pear fruit development. These findings provide a substantial contribution to existing sequence resources of pear species and will certainly valuable for elucidation of molecular mechanisms in fruit development and maturation in pear or related species. Therefore, this study clearly evidenced that Illumina sequencing technology could be applied as a rapid and cost-effective method for de novo transcriptome analysis of non-model plant species that lack of prior genome annotation.
The assembled unigenes (larger than 200 bp) were deposited in the Transcriptome Shotgun Assembly Sequence Database (TSA) at NCBI, with accession numbers from JR595427 to JR673747.
The project was funded by the National Natural Science Foundation of China (grants no. 31071756 and 31201583), and the Program for Innovative Research Team in Anhui Agricultural University.
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