Open Access

Comparative expression profiling in grape (Vitis vinifera) berries derived from frequency analysis of ESTs and MPSS signatures

  • Alberto Iandolino1, 3,
  • Kan Nobuta2,
  • Francisco Goes da Silva1,
  • Douglas R Cook1 and
  • Blake C Meyers2Email author
BMC Plant Biology20088:53

DOI: 10.1186/1471-2229-8-53

Received: 26 June 2007

Accepted: 12 May 2008

Published: 12 May 2008

Abstract

Background

Vitis vinifera (V. vinifera) is the primary grape species cultivated for wine production, with an industry valued annually in the billions of dollars worldwide. In order to sustain and increase grape production, it is necessary to understand the genetic makeup of grape species. Here we performed mRNA profiling using Massively Parallel Signature Sequencing (MPSS) and combined it with available Expressed Sequence Tag (EST) data. These tag-based technologies, which do not require a priori knowledge of genomic sequence, are well-suited for transcriptional profiling. The sequence depth of MPSS allowed us to capture and quantify almost all the transcripts at a specific stage in the development of the grape berry.

Results

The number and relative abundance of transcripts from stage II grape berries was defined using Massively Parallel Signature Sequencing (MPSS). A total of 2,635,293 17-base and 2,259,286 20-base signatures were obtained, representing at least 30,737 and 26,878 distinct sequences. The average normalized abundance per signature was ~49 TPM (Transcripts Per Million). Comparisons of the MPSS signatures with available Vitis species' ESTs and a unigene set demonstrated that 6,430 distinct contigs and 2,190 singletons have a perfect match to at least one MPSS signature. Among the matched sequences, ESTs were identified from tissues other than berries or from berries at different developmental stages. Additional MPSS signatures not matching to known grape ESTs can extend our knowledge of the V. vinifera transcriptome, particularly when these data are used to assist in annotation of whole genome sequences from Vitis vinifera.

Conclusion

The MPSS data presented here not only achieved a higher level of saturation than previous EST based analyses, but in doing so, expand the known set of transcripts of grape berries during the unique stage in development that immediately precedes the onset of ripening. The MPSS dataset also revealed evidence of antisense expression not previously reported in grapes but comparable to that reported in other plant species. Finally, we developed a novel web-based, public resource for utilization of the grape MPSS data [1].

Background

Grapes species (Vitis spp.) represent the most widely cultivated and economically important fruit crop in the world [2]. The use of grape berries includes the production of juice, fresh and dried fruit, and distilled liquor, although wine produced from cultivars of V. vinifera has the highest economic value of grape products. Grapevine berries are non-climacteric fruits with a characteristic double sigmoid growth curve. The initial phase of exponential berry growth (stage I) is followed by a lag phase (stage II), with growth resuming after the onset of ripening or "veraison" (stage III). Berry development is characterized by changes in numerous biological processes, including cell division and enlargement, primary and secondary metabolism, and resistance or susceptibility to abiotic or biotic stresses [3, 4]. The importance of this plant species to agriculture has made the development of genomic resources a high priority. Among these resources, transcriptional profiling of important grape tissues is a practical option that may reveal transcriptional complexity and changes in this dynamic developmental system.

Massively parallel signature sequencing technology (MPSS) [5, 6] is a sequence-based method for measuring gene expression. The depth of sampling provided by MPSS can identify a nearly complete inventory of transcripts in a given sample. The method is based on a unique process for parallel sequencing, which starts with the cloning of a cDNA library on 5 μm diameter microbeads; one transcript from the original RNA sample is represented on a single bead [5]. From each bead, a sequence of the 'signature' of 17 or more nucleotides is obtained by successive round of sequencing reactions [57]. These signatures are derived from and include the most 3' occurrence of a specific restriction enzyme site in a transcript (most often DpnII, producing signatures that start with GATC) [5, 6]. The output of the method is conceptually similar to a possibly more familiar method called Serial Analysis of Gene Expression (SAGE) [8]. However, the MPSS technology permits the simultaneous sequencing of millions of signatures from a given library [5]. By matching these signatures to the genome to identify specific genes, the abundance of each signature represents and measures the gene expression levels in the sample tissue. Among several published applications of this technology, we have previously conducted comprehensive transcriptional analyses of the reference plant species Arabidopsis thaliana and rice [7, 9]. While MPSS, SAGE, and expressed sequence tags (ESTs) are all sequence-based technologies for transcriptional profiling, MPSS provides more thorough qualitative and quantitative description of gene expression due to its tremendous depth. While novel sequencing technologies, such as sequence-by-synthesis (SBS) and 454, offer deeper sequencing and longer read lengths, none have yet demonstrated consistently better results than MPSS for mRNA profiling [10].

In this report, we have measured gene expression in developing grape berries using MPSS, compared this expression profile with that provided by the current Vitis Unigene set [4], and we developed a novel web-based resource for utilization of the grape MPSS data. As a result of this analysis, we were able to annotate thousands of signatures matching predicted genes, quantify the expression level of these genes in the developing berries, compare the expression profiles derived from ESTs and MPSS signature frequencies, and expand the coverage of known transcripts in an important grapevine organ at a specific developmental stage. Because these data are based on sequences, they comprise a resource that will be useful for the annotation of any grape genomic sequence produced in the future.

Results

Analysis of the V. viniferaberry MPSS dataset and signature annotation

An MPSS library was constructed using RNA extracted from stage II berries (green, hard) that were sampled from field-grown V. vinifera cv. Cabernet Sauvignon. After cloning of the cDNA library onto beads, 17-base and 20-base signatures were generated by MPSS sequencing [5, 6]. We note that these are not independent samples, in that 20-base signatures are obtained by extending previously recorded 17-base signatures by three nucleotides; due to a low failure rate at each additional base of sequencing, the raw count of sequences is lower for the 20-base data. A total of 2,635,293 17-base and 2,259,286 20-base signatures were produced that corresponded to 30,737 and 26,878 distinct sequences, respectively (Table 1A–C). This represents a discovery rate or average raw abundance value of approximately one distinctive sequence for every ~49 sequenced cDNA tags.
Table 1

Summary statistics of raw 17- and 20-base MPSS signatures from grape berries.

 

17-base signatures

20-base signatures

    

A. Summary of sequencing results.

Sequencing frame

Absolute

% of Total

Absolute

% of Total

    

2-step run

1,194,288

45.3%

1,002,346

44.4%

    

4-step run

1,441,005

54.7%

1,256,940

55.6%

    

B. Single filter results.

 

17-base signatures

20-base signatures

    

Reliability Filter a

Count

Proportion

Count

Proportion

    

R

17,976

58.5%

15,699

58.4%

    

nR

12,761

41.5%

11,179

41.6%

    

Significance Filter a

        

S

16,029

52.1%

13,817

51.4%

    

nS

14,708

47.9%

13,061

48.6%

    

C. Combined filter results.

 

17-base Total

17-base Distinct

20-base Total

20-base Distinct

Filter categories a

Absolute

% of Total

Count

% of Total

Absolute

% of Total

Count

% of Total

RS

2,550,024

96.8%

13,586

44.2%

2,187,369

96.8%

11,874

44.2%

RnS

17,862

0.7%

4,390

14.3%

14,958

0.7%

3,825

14.2%

nRS

47,346

1.7%

2443

7.9%

39,338

1.7%

1943

7.2%

nRnS

20,061

0.8%

10,318

33.6%

17,621

0.8%

9,236

34.4%

TOTAL

2,635,293

100.0%

30,737

100.0%

2,259,286

100.0%

26,878

100.0%

"Absolute" indicates the total number of successful sequencing reads, while "count" or "distinct" indicates the number of different sequences.

a R = reliable, nR = non-reliable, S = significant, nS = non-significant, RS = reliable and significant, RnS = reliable and non-significant, nRS = non-reliable and significant, nRnS = non-reliable and non-significant.

Initially, to link the MPSS signatures to predicted gene annotations, all sites ("GATC") that could potentially produce an MPSS signature were identified from the available Vitis Unigene dataset in public databases. This comprised 14,658 contigs (1,307 from non-vinifera Vitis species) and 14,931 singletons (1,080 from non-vinifera Vitis species). All potential signatures starting with the GATC anchor sequence were extracted from both sense and antisense directions of the grape sequences. A total of 84,834 and 48,490 distinct 17-base potential signatures were identified, respectively, in contigs and singletons of this version of the Vitis cDNA data. When both datasets were combined, the total number of unique genomic signatures equaled 123,563. The total number of in silico-extracted distinct MPSS signatures is approximately six-fold lower than the 753,894 distinct "genomic" MPSS signatures reported for the completed Arabidopsis sequence [11], reflecting the incomplete nature of the grape EST dataset and the lack of intergenic and intron sequences.

Observed MPSS signatures were classified based on the output of "reliability" and "significance" filters [11]. The purpose of these filters is to separate high quality data, which is represented by signatures encountered above specified frequency thresholds, from background signal generated by very low abundance MPSS signatures. As with other MPSS datasets, the grape library was generated from four sequencing runs representing two sequencing frames [11]. There were two runs for each of the "two-step" and "four-step" sequencing frames. The reliability filter asks whether a signature is present in more than one sequencing run (of the four total runs); signatures observed in more than one run are considered "reliable". The significance filter identifies as "significant" only those signatures with a normalized abundance greater than three transcripts per million (TPM). The classifications of 17- and 20-base expressed signatures in terms of reliability and significance are shown in Tables 1A–C and 2; 96.8% of all MPSS signatures corresponded to the "reliable" and "significant" category, consistent with an extremely low abundance for signatures not passing the filters. This value is similar to the 97.5% reported for the Arabidopsis MPSS dataset [11]. Among MPSS signatures with exact sequence matches to EST contigs (Table 2A–B) and singletons (Table 2C–D), unique "reliable" and "significant" signatures represented the largest category (more than 60% of the unique signatures).
Table 2

Distinct MPSS signatures matching EST contigs or singletons classified based on "reliability" and "significance" filters.

 

Significant

Non-significant

A. Filter results for 11,345 17-base signatures matching EST contigs.

 

8,012 (66.2%)

4,094 (33.8%)

Reliable

7,686 (63.5%)

1,734 (14.3%)

9,420 (77.8%)

6,519 contigs

1,235 contigs

Non-reliable

326 (2.7%)

2,360 (19.5%)

2,686 (22.2%)

632 contigs

2,276 contigs

B. Filter results for 10,179 20-base signatures matching EST contigs.

 

Significant

Non-significant

 

7,053 (65.3%)

3,751 (34.7%)

Reliable

6,698 (61.1%)

1,473 (13.6%)

8,171 (75.6%)

5,830 contigs

1,490 contigs

Non-reliable

355 (3.3%)

2,278 (21.1%)

2,633 (24.4%)

386 contigs

2,155 contigs

C. Filter results for 3,889 17-base signatures matching EST singletons.

 

Significant

Non-significant

 

2,700 (64.6%)

1,477 (35.4%)

Reliable

2,587 (61.9%)

638 (15.3%)

3,225 (77.2%)

2,423 singletons

663 singletons

Non-reliable

113 (2.7%)

839 (20.1%)

952 (22.8%)

127 singletons

857 singletons

D. Filter results for 3,367 20-base signatures matching EST singletons.

 

Significant

Non-significant

 

2,318 (64.4%)

1,282 (35.6%)

Reliable

2,207 (61.3%)

499 (13.9%)

2,706 (75.2%)

2,096 singletons

511 singletons

Non-reliable

111 (3.1%)

783 (21.8%)

894 (24.8%)

128 singletons

780 singletons

Percentages refer to the total distinct signatures. Filters are described in the text, and are as defined in Meyers et al., [11]. In parts A and B, the number of EST contigs matched was out of a total of 6,430 unique contigs matched by 17-base signatures or 5,831 by 20-base signatures; signatures from different filter categories may match to the same contig (see Additional file 1A1B and Table 3 for details). In parts C and D, the number of EST singletons matched was out of a total of 2,190 unique singletons matched by 17-base signatures or 2,097 by 20-base signatures; signatures from different filter categories may match to the same singleton (see Additional file 1A1B and Table 3 for details).

Expressed signatures were mapped to grape EST contigs and singletons based on exact matches to the in silico extracted "potential signatures" (see above). A total of 5,794 and 5,407 contigs were matched by expressed reliable and significant 17-base and 20-base MPSS signatures, respectively (see Additional file 1A1B). This represented, on average, more than 40% of all known Vitis sp. genes. On the other hand, only 14% of singletons in the Vitis sp. EST set were matched by MPSS signatures (Table 2C and 2D). The vast majority of the unmatched Vitis sp sequences had in silico potential signatures that were not detected in the MPSS data. It is possible that the corresponding genes were not expressed in this sample; alternatively, unmatched contig and singleton EST sequences may represent 5' reads of cDNA clones, and thus fail to represent 3' regions where the majority of MPSS signatures originate. The disproportionate representation of singleton ESTs among the unmatched set is consistent with this later interpretation, because singleton ESTs in the Vitis dataset are more often the product of 5' sequencing reactions.

Most signatures matched a single contig or singleton, while ~40% matched two or more [see Additional file 1A1B]. In excess of 70% of matched contigs and singletons showed a one-to-one assignment to a reliable and significant MPSS signature (Figure 1) [see Additional file 2]. The remaining sequences had one-to-many assignments of up to a maximum of 16 different signatures to a single contig [see Additional file 3]. Sequences of 17–20 bp are rarely duplicated by chance in unrelated genes [7] [see Additional file 4]. Instead, biological factors involving gene duplication or transcript processing may complicate the unambiguous assignment of signatures to transcripts. Thus, gene family members with high sequence similarity are likely to yield distinct transcripts containing the same signature, while the use of multiple polyadenylation sites or alternative splice site selection can yield multiple signatures from the same transcription unit. To estimate the frequency of alternative termination, a subset of 5,145 contigs was properly aligned in their 5' to 3' orientation. From this subset, 975 contigs matched by at least two MPSS signatures were identified. The abundance counts of 17-nucleotide significant and reliable MPSS signatures were transformed to relative frequency values and the location of each signature was plotted along the 3'-to-5' axis for each of the 975 contigs (Figure 2). The signature frequency per contig decreased exponentially from the 3'-to-5' direction. On average, ~70% of all signatures originate from the 3' most GATC site, while only ~29% and ~14% of signatures originate from the second and third 3' most positions (further 5'), respectively. Therefore, most of the transcripts matched by MPSS are the product of polyadenylation at the most distal of all recorded 3' sites. It is possible, however, that the MPSS signatures that did not match ESTs (contigs or singletons) are derived from longer 3' ends for which transcript sequence was not available.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2229-8-53/MediaObjects/12870_2007_Article_261_Fig1_HTML.jpg
Figure 1

Frequency distribution of grape ESTs matched by filtered MPSS signatures. Reliable and significant MPSS signatures were matched to EST contigs and EST singletons. Up to 16 and 10 MPSS signatures matched to one EST contig and singleton, respectively. The proportion of the number of MPSS signatures matching to (A) EST contigs and (B) EST singletons are represented by the bar graph.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2229-8-53/MediaObjects/12870_2007_Article_261_Fig2_HTML.jpg
Figure 2

Frequency of reliable and significant 17-mer MPSS signatures in a subset of 5'-to-3' oriented contigs. Signatures were mapped based on their location relative to the 3' end of the EST contigs. Most signatures were found at the 3'-most DpnII site, indicated as position #1 on the x-axis. However, expressed MPSS signatures were found as far 5' as eighth DpnII site from the 3' end of the contig.

Analysis of sense-antisense expression

Approximately 15% and 11% of the EST contigs and singletons, respectively, were matched by MPSS signatures in both sense and antisense orientations (Tables 3A–B). The MPSS signature frequencies were much higher on the sense strand for some sequences, while other sequences had higher MPSS abundances on the antisense strand [see Additional file 5]. Contigs matched in both orientations represented ~12% of the known berry transcriptome (of a total of 7,828 including contigs derived from EST sequenced and cloned from cDNA libraries other than green stage II), with the 2,891 MPSS signatures matching these contigs representing ~52% of the total MPSS abundance. It is possible that the sense-antisense transcript pairs are an important transcriptional feature which could provide a mechanism for post-transcriptional gene silencing [12] during this dynamic phase of berry development. Functional categorization of these contigs showed no particular overrepresented category (Figure 3). Moreover, none of these contigs had significant identifiable tBLASTx hits in both reading frame orientations, suggesting protein coding is a property of only one strand. It is possible that anti-sense transcripts could result from overlapping 3'UTRs of adjacent genes, or from transcription of an overlapping non-coding RNA.
Table 3

Matched and un-matched Vitis EST contig and singletons.

 

17-mer

20-mer

A. EST contigs.

Category a

Count

Freq

Count

Freq

5' and 3'

942

6.4%

736

5.0%

5' or 3'

5,488

37.4%

5,095

34.8%

Subtotal

6,430

43.9%

5,831

39.8%

With tag but no match

6,875

46.9%

7,474

51.0%

Without tags

1,353

9.2%

1,353

9.2%

Subtotal

8,228

56.1%

8,827

60.2%

Total

14,658

100.0%

14,658

100.0%

B. EST singletons.

 

17-mer

20-mer

Category a

Count

Freq

Count

Freq

5' and 3'

238

1.6%

263

1.8%

5' or 3'

1,952

13.3%

1,834

12.5%

Subtotal

2,190

14.9%

2,097

14.3%

With tag but no match

9,074

61.8%

9,167

62.4%

Without tags

3,667

25.0%

3,667

25.0%

Subtotal

12,741

86.8%

12,834

87.4%

Total

14,931

100.0%

14,931

100.0%

a Categories are as follows: "5' and 3"' indicates contigs matched by reliable and significant (RS) tags in both sense and antisense orientation simultaneously; "5' or 3"' indicates contigs matched in their sense or antisense orientation but not both; "with tag but no match" indicates contigs with in silico identified GATC tag sites but without sequenced signatures (the presence of a DpnII site but no matching MPSS tag, see materials and methods for details); and "without tags" indicates contigs lacking GATC sites.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2229-8-53/MediaObjects/12870_2007_Article_261_Fig3_HTML.jpg
Figure 3

Functional categorization of transcripts with both sense and anti-sense MPSS signatures. EST contigs, which have both sense and anti-sense MPSS signatures, were categorized based on GO (Gene Ontology) annotation and the proportion of each category is displayed in pie-chart: (A) Cellular component, (B) Molecular function, and (C) Biological process.

Expression profiles determined by EST and MPSS abundances

To quantify gene expression levels, we used the relative abundance of the 7,686 reliable and significant 17-base MPSS signatures from the stage II berry library. These signatures represent the most robust subset of the MPSS expression data. Although the remaining 1,734 reliable but not significant signatures were not considered in this analysis, prior analysis suggests that these signatures are likely to represent genuine transcripts expressed at very low levels [11]. The transcripts represented by these signatures may be expressed at higher levels in different specific cells or tissue layers that were not sampled.

The MPSS sequences provide an inventory of the transcript population in a given organ or tissue that can be sorted based on abundance. This data is particularly powerful when aligned with EST data from related tissues, as it allows sorting based on abundance and predicted gene function. The MPSS-matched set of 5,791 grape EST contigs are derived from a series of cDNA libraries that survey several stages of plant development, as well as responses to biotic and abiotic stress [4]. Off these, 4,753 contigs contained ESTs derived from one or more grape berry tissues, while 1,038 contigs were composed of ESTs from other grape tissues but not from berries (Table 4A). A total of 1,242 EST contigs matched by MPSS signatures were from ESTs found in only a single grape tissue; of these, 555 corresponded to berry-specific EST contigs. The remaining contigs were exclusively derived from leaves, flowers, petioles, stems, buds and even roots. The remaining 4,548 cDNA contigs and sequences were detected in two or more grape organs (Table 4A). Only three MPSS-matched EST contigs were found in all seven of the grape cDNA libraries. In a similar analysis of the EST singletons, the vast majority corresponded to transcripts previously observed exclusively in berry cDNA libraries, but only 207 were stage II berries (Table 4B). Among the contigs and singletons not previously associated with berry libraries were those derived from flower and leaf cDNA libraries. MPSS signatures provided valuable information to confirm the presence and relative transcriptional levels of transcripts. Many of these transcripts may have been previously mistakenly identified as tissue-specific based on EST data only because EST sequencing was not deep enough to detect these low abundance transcripts in different tissues. The MPSS data demonstrate that the inventory of genes in a given tissue is complex and there may be substantially more overlap in diverse tissues than previously characterized, and this can be identified only by sequencing ESTs at a very deep level.
Table 4

Grape ESTs derived from distinct tissue types matched by MPSS signatures (only from Vitis vinifera).

Categories

Contigs

A. EST contigs.

Berry pre-veraison

79

Berry veraison

117

Berry post-veraison

359

Subtotal berry

555

Compound bud

87

Flower

132

Seed

1

Petiole

63

Stem

67

Leaf

332

Roots

5

Subtotal from single organ library (excluding berry libraries)

687

Subtotal from single organ library a

1,242

2-Organs

1,520

3-Organs

1,084

4-Organs

763

5-Organs

491

6-Organs

312

7-Organs

181

8-Organs

135

9-Organs

46

All 10 sampled organs

10

Subtotal from multiple organ libraries b

4,542

Subtotal non-ESTs

6

Total matched contigs and non-EST sequences

5,791

Total matched contigs not previously associated with any berry libraryc

1,038

Total matched contigs previously associated with berry librariesf

4,753

   A. Total matched contigs from other grape berry librariesd

3,439

   B. Total matched contigs with ≥1 EST derived from stage II berry librariese

1,314

Total unmatched contigs and non-EST sequences

7,567

   C. Total unmatched contigs from all libraries except (D)

6,990

   D. Total unmatched contigs corresponding to UCD stage II berry librariesh

577

B. EST singletons.

Categories

Contigs

Berry pre-veraison

177

Berry veraison

343

Berry post-veraison

389

Subtotal berry

909

Compound bud

45

Flower

231

Seed

16

Petiole

34

Stem

89

Leaf

414

Roots

49

Subtotal from other libraries (excluding berry libraries)

878

Total matched singletons

1,787

   A. Total matched singletons with ≥ 1 EST derived from stage II berry libraries

207

Total unmatched singletons

9,754

   B. Total unmatched singletons from all libraries except (D)

8,947

   C. Total unmatched singletons corresponding to UCD stage II berry libraries

807

Only 7,686 distinct, reliable and significant, 17-base MPSS signatures were used in this analysis.

a Matched contigs with at least one EST derived from berry libraries excluding those from UCD. Berry libraries in this category include multiple developmental stages and growing conditions [4].

b Matched contigs with ESTs found in cDNA libraries originated from at least two or more different grapevine organs. It may or may not simultaneously include berry libraries.

cIndicates matched contigs assembled with ESTs not previously associated with berry libraries

dIndicates matched contigs assembled with ESTs not previously associated with the UCD flower-berry libraries. This number includes contigs with at least one EST derived from flower-berry libraries reported by other groups.

e Indicates contigs containing at least one EST produced from the flower-berry UCD libraries [4].

fCorresponds to the sum of A and B.

gTotal number of contigs and non-ESTs sequences [4] not matched by 17-mer reliable and significant tags. This value corresponds to the sum of C and D.

h Contigs with at least one EST derived from SII berry libraries [4] but not matched by 17-mer reliable and significant tags.

One advantage of tag-based transcriptional profiling technologies such as ESTs, SAGE and MPSS is that the targets are not preselected prior to analysis. While the discovery rate of new transcripts using ESTs-based approaches is limited by the extent of sequencing effort and redundancy within a given cDNA library, unmatched or low abundance MPSS signatures could be used as primers for PCR based methods to expand the current set of known genes for Vitis [13]. There were 18,631 distinct 17-base MPSS signatures that did not match known grape EST sequences, of which 5,900 were both significant and reliable; these are most likely to represent novel genes not previously identified as transcribed or transcriptional variants. We tested this hypothesis by using available sequence of the grape genome, composed of 57,662 contigs containing 487,125,096 base pairs [14]. In total, 20,661 17-mer and 17,867 20-mer distinct MPSS signatures matched to genome contig sequences. Among these, there were 9,125 and 7,771 distinct 17-mer and 20-mer MPSS signatures that matched only genomic contigs and not ESTs. Taking the 17-mer signatures as the benchmark, the MPSS data reveal 44% more transcript diversity than recorded in the existing public EST resource.

In silico expression profiles resulting from EST (Table 5) and MPSS signature frequencies (Table 6) showed both differences and commonalities in the relative abundance of the top-ranked genes. For example, a common feature of both datasets is the relative high abundance of several chitinases, metallothionein-like and storage proteins, as well as a putative transcription factor and an elongation factor 1-α. On the other hand, two hexameric polyubiquitins and a plasma membrane aquaporin were among the top ranked genes based on MPSS signatures but not based on EST counts, and the opposite was true (present among top ESTs, not among MPSS signatures) for a non-specific lipid transfer protein A. A similar pattern emerges from the analysis of singleton ESTs that matched abundant MPSS signatures (Table 7). Among such singleton ESTs, there were transcripts related to cell wall modification (xyloglucan-specific fungal endoglucanase inhibitor protein and an extensin-like protein), abiotic/biotic stress factors (catalase and hydroperoxide oxidase), a eukaryotic translation initiation factor and several poorly annotated transcripts.
Table 5

Most highly expressed grape EST contigs in the grape berry stage II libraries, based on MPSS signature abundance.

Contig ID

Signature abundancea

ESTsb

ESTsc

Total ESTsd

BlastX definition [species of closest EST match]

1027142, 1026885, 1027113

108049

31

81

112

2S albumin precursor [Vitis vinifera]

1027101

67435

52

66

118

AF449424_1 11S globulin-like protein [Corylus avellana]

1027135, 1027103, 1027117

60308

17

36

53

Storage protein [Corylus avellana, Coffea arabica and Prunus dulcis]

1027226

46226

49

31

80

conglutin gamma [Lupinus albus]

1027222

18313

34

8

42

AF240006_1 7S globulin [Sesamum indicum]

1027379, 1026935

17310

7

8

15

plasma membrane aquaporins

1027444, 1027445, 1027446

13927

185

187

372

Endochitinase and class Ib chitinase [Galega orientalis]

1027477

13748

2

4

6

hexameric polyubiquitin

1027684, 1027685

10450

3

17

20

expressed protein [Arabidopsis thaliana]

1027454

6505

12

7

19

AF121261_1 elongation factor 1-alpha 1; EF-1-alpha1 [Lilium longiflorum]

1027543, 1027547, 1027548, 1027553, 1027554

5994

3

14

17

T06239 probable glutathione transferase (EC 2.5.1.18), 2,4-D inducible – soybean

1026907

5460

4

 

4

T10174 hypothetical protein – castor bean

1027486

5403

3

2

5

hexameric polyubiquitin

1027455

4782

 

1

1

AF121261_1 elongation factor 1-alpha 1; EF-1-alpha1 [Lilium longiflorum]

1027495

4688

2

4

6

ARF_ORYSA ADP-ribosylation factor

1027181

4668

2

1

3

TCTP_HEVBR Translationally controlled tumor protein homolog (TCTP)

1026972, 1027353, 1027133

4642

14

28

42

metallothionein-like protein type 2 [Persea americana]

1027119

4419

1

6

7

ATFP3 [Arabidopsis thaliana]

1027883

4390

1

 

1

LTCOR11 [Lavatera thuringiaca]

1026987

4208

36

22

58

AF281656_1 putative transcription factor [Vitis vinifera]

The expression level was determined based on the signature with the maximum normalized abundance.

a The sum of abundance of matching 17-base MPSS signatures from the grape library.

b ESTs derived from stage II green, hard berries

c ESTs derived from stage II green, soft berries

d Total signatures matching contigs present in the UC Davis berry stage II libraries.

Table 6

Most highly expressed grape EST contigs in the grape berry stage II libraries based on EST frequency.

Contig ID

Signature abundancea

ESTsb

ESTsc

Total ESTsd

BlastX definition [species of closest EST match]

1027444

13927

183

186

369

endochitinase

1027108

0

81

152

233

NLTA_RICCO NONSPECIFIC LIPID-TRANSFER PROTEIN A (NS-LTP A)

1027101

67435

52

66

118

AF449424_1 11S globulin-like protein [Corylus avellana]

1027142

108049

28

78

106

2S albumin precursor [Vitis vinifera]

1027226

46226

49

31

80

conglutin gamma [Lupinus albus]

1027166

0

30

44

74

putative metallothionein-like protein [Vitis vinifera]

1027085

0

13

48

61

putative metallothionein-like protein [Vitis vinifera]

1027135

60308

17

34

51

AF449424_1 11S globulin-like protein [Corylus avellana]

1027222

18313

34

8

42

AF240006_1 7S globulin [Sesamum indicum]

1026987

4208

23

14

37

AF281656_1 putative transcription factor [Vitis vinifera]

1026972

4642

10

13

23

metallothionein-like protein type 2 [Persea americana]

1027300

0

14

7

21

Transcript Antisense to Ribosomal RNA; Tar1p [Saccharomyces cerevisiae]

1027410

4208

13

8

21

AF281656_1 putative transcription factor [Vitis vinifera]

1027454

6505

12

7

19

AF121261_1 elongation factor 1-alpha 1; EF-1-alpha1 [Lilium longiflorum]

1027053

120

4

14

18

No hit

1027353

4642

4

14

18

No hit

1027684

10450

3

15

18

expressed protein [Arabidopsis thaliana]

1027078

0

3

14

17

THIH_RICCO Thioredoxin H-type (TRX-H)

1028129

3736

2

15

17

AF192486_1 omega-6 fatty acid desaturase [Sesamum indicum]

1027302

4075

13

3

16

No hit

a The sum of abundance of matching 17-base MPSS signatures from the grape library.

b ESTs derived from stage II green, hard berries.

c ESTs derived from stage II green, soft berries.

d Total signatures matching contigs present in the UC Davis berry stage II libraries.

Table 7

Top 20 grape EST singletons based on MPSS signature abundance.

Singleton IDs

Signature abundancea

ESTsb

ESTsc

Total ESTsd

BlastX definition [species of closest EST match]

CB346285, CB348203, CB349257, CB349340, CB350305, CB979523

108049

1

5

6

Albumin seed storage protein precursor [Juglans regia]

CB346171, CB347912, CB349205, CB349357

67435

 

4

4

11S globulin

CB347682

46226

 

1

1

Xyloglucan-specific fungal endoglucanase inhibitor protein precursor [Lycopersicon esculentum]

CB346916, CB347117, CB347160, CB347210, CB347916, CB348271, CB348487, CB348646, CB349834, CB976447

33100

1

9

10

NLTA_RICCO Nonspecific lipid-transfer protein A (NS-LTP A) (phospholipid transfer protein) (PLTP)

CB346008, CB348119, CB348425, CB348509, CB348553, CB349918

13927

 

6

6

Chitinases [Arabis fecunda, Glycine max, Vitis vinifera, Fragaria × ananassa, chic pea]

CB346025, CB976380

5234

1

1

2

Chitinase [Oryza sativa (indica cultivar-group)]

CB347884

4642

 

1

1

A34131 metallothionein I homolog – spotted monkey flower

CB348305

4208

 

1

1

AF281656_1 putative transcription factor [Vitis vinifera]

CB978988

2805

1

 

1

AF236127_1 catalase [Vitis vinifera]

CB347891

2746

 

1

1

IF52_NICPL Eukaryotic translation initiation factor 5A-2 (eIF-5A) (eIF-4D)

CB347634

2724

 

1

1

Hydroperoxide lyase [Nicotiana attenuata]

CB348030

2219

 

1

1

S49422 11S globulin seed storage protein – prince's feather

CB346850, CB350175

2084

 

2

2

2S albumin [Vitis vinifera and Helianthus annus]

CB977561

1726

1

 

1

AF121261_1 elongation factor 1-alpha 1; EF-1-alpha1 [Lilium longiflorum]

CB978160

1714

1

 

1

expressed protein [Arabidopsis thaliana]

CB977027

1343

1

 

1

No Hit

CB976255

1267

1

 

1

Expressed protein; protein id: At3g52500.1, supported by cDNA: [Arabidopsis thaliana]

CB346104, CB347550

1049

 

2

2

endochitinase

CB347847

983

 

1

1

S54157 extensin-like protein – cowpea (fragment)

CB347925, CB349273

942

 

2

2

S51942 prunin 2 precursor – almond (fragment)

a The sum of abundance of matching 17-base MPSS signatures (reliable and significant) from the grape library.

b ESTs derived from stage II green, hard berries

c ESTs derived from stage II green, soft berries

d Total signatures matching contigs present in the UC Davis berry stage II libraries.

Significant differences were observed in the relative abundance of contigs from EST or MPSS signature counts. While a total of 195 contigs accounted for approximately 50% of the ESTs sequenced from the two berry SII libraries, only 10 contigs matched an identical proportion of the filtered MPSS signatures. The top 20 contigs ranked based on MPSS frequency accounted for 410,925 (56.7% of all sequences matching to EST contigs), suggesting a steeper curve and perhaps lower level of diversity in MPSS data. In contrast, the 20 most frequent contigs based on EST counts represented only 29.4% of the total EST for these two libraries.

As might be expected, MPSS signatures sequenced from V. vinifera berries stage II also matched several non-vinifera EST singletons and contigs in the Vitis Unigene set. Although the transcriptome of the non-vinifera species has been minimally characterized, a comparison of the top-ranked transcripts based on MPSS signature frequency (Tables 8 and 9) showed remarkable similarities between the different species.
Table 8

Most highly expressed grape EST contigs from non-vinifera libraries based on MPSS signature abundance.

Contig ID

Signature abundancea

Speciesb

BlastX definition [species of closest EST match]

1025631, 1026062

17310

Vae, Vru × Var

AF141899_1 putative aquaporin PIP1-3 [Vitis berlandieri × Vitis rupestris]

1025594

13748

Vae

UQFS ubiquitin precursor – common sunflower (fragment)

1025587

6505

Vae

AF121261_1 elongation factor 1-alpha 1; EF-1-alpha1 [Lilium longiflorum]

1025940

5994

Vru × Var

T06239 probable glutathione transferase (EC 2.5.1.18), 2,4-D inducible – soybean

1025641

5460

Vae

T10174 hypothetical protein – castor bean

1026041

5403

Vru × Var

T5J8.21 polyubiquitin (UBQ14) identical to GI:166795 [N. sylvestris]

1026403

5403

Vru × Var

C17L7.6 T32N4.13 score = 526.5, E = 1.9e-154, N = 3

1025620

4688

Vae

ARF_ORYSA ADP-ribosylation factor

1025855

4642

Vru × Var

MT1_CASGL Metallothionein-like protein 1 (MT-1)

1025856

4642

Vru × Var

No hit

1025842, 1025843

4208

Vru × Var

AF281656_1 putative transcription factor [Vitis vinifera]

1026742

3463

Vru × Var

F12F1.24 putative aspartic proteinase similar to GB:AAC49730

1026015

2805

Vru × Var

T12J5.2 M4E13.140 catalase

1026595

2746

Vru × Var

F16A14.17 F7A19.4 initiation factor 5A-4 identical to initiation factor 5A-4 [A. thaliana]

1025571, 1025572

2435

Vae

T09838 chlorophyll a/b binding protein precursor – upland cotton chloroplast

1026113

2355

Vru × Var

T23E18.12 dehydrin, putative similar to dehydrin GI:975646 from [Arabidopsis thaliana]

1025893

2164

Vru × Var

No hit

1025946

1877

Vru × Var

dormancy-associated protein -related [Arabidopsis thaliana]

1025914

1840

Vru × Var

cyclophilin [Ricinus communis]

1026217

1726

Vru × Var

AF121261_1 elongation factor 1-alpha 1; EF-1-alpha1 [Lilium longiflorum]

a The sum of abundance of matching 17-base MPSS signatures from the grape library.

b Species abbreviations are as follows: Vae = Vitis aestivalis; Vru × Var = D8909-15 (Vitis rupestris 'A. de Serres' × Vitis arizonica)

Table 9

Most highly expressed grape EST contigs from non-vinifera libraries based on MPSS signature abundance.

Contig ID

Signature abundancea

Species

BlastX definition

CB518189

17310

Vci × Vru

AF141899_1 putative aquaporin PIP1–3 [Vitis berlandieri × Vitis rupestris]

CF205324

13748

Vru × Var

polyubiquitin [Elaeagnus umbellata]

CB288827

10450

Vae

expressed protein [Arabidopsis thaliana]

CF203205

5994

Vru × Var

GTXA_TOBAC PROBABLE GLUTATHIONE S-TRANSFERASE PARA (AUXIN-REGULATED PROTEIN PARA) (STR246C PROTEIN)

CB518203

4688

Vci × Vru

ADP-ribosylation factor [Arabidopsis thaliana]

CB518174

4668

Vci × Vru

TCTP_ORYSA Translationally controlled tumor protein homolog (TCTP)

CB518217

4642

Vci × Vru

MT1_CASGL Metallothionein-like protein 1 (MT-1)

CF206203

4208

Vru × Var

AF281656_1 putative transcription factor [Vitis vinifera]

CF202582

3036

Vru × Var

No hit

CF568957

2435

Vsh

T09838 chlorophyll a/b binding protein precursor – upland cotton chloroplast

CF204795

2164

Vru × Var

phase-change related protein [Quercus robur]

CF568866

2055

Vsh

DNJH_CUCSA DnaJ protein homolog (DNAJ-1)

CB518164

1726

Vci × Vru

AF121261_1 elongation factor 1-alpha 1; EF-1-alpha1 [Lilium longiflorum]

CF568912, CF568996

1354

Vsh

RBS_FAGCR Ribulose bisphosphate carboxylase small chain, chloroplast precursor (RuBisCO small subunit)

CB518167

913

Vci × Vru

60S ribosomal protein L26 (RPL26A) [Arabidopsis thaliana]

CB289025

866

Vae

seed specific protein Bn15D1B [Brassica napus]

CB602249

846

Vae

No hit

CB289590

802

Vae

60S ribosomal protein L27a [Panax ginseng]

CB288422

779

Vae

60S ribosomal protein L19 (RPL19B) [Arabidopsis thaliana]

CB518201

714

Vci × Vru

ARF_ORYSA ADP-ribosylation factor

a The sum of abundance of matching 17-base MPSS signatures from the grape library.

b Species abbreviations are as follows: Vae = Vitis aestivalis; Vru × Var = D8909-15 (Vitis rupestris 'A. de Serres' × Vitis arizonica), Vci × Vru = Vitis cinerea × Vitis rupestris, Vsh = Vitis shutthelworthii.

A website for access to the grape MPSS data

To facilitate public access and utilization of the MPSS data, we developed a database and web-based interface [15]. The database and interface is a customized version of a previously described website [16]. Unlike the Arabidopsis or rice MPSS sites which utilize the complete genomic sequence of these species, our grape database focuses on EST contigs. This required the development of specialized tools and methods. For example, the incomplete nature of ESTs required a BLAST tool that would allow the user to identify the closest grape sequence to their gene of interest. The MPSS data can be accessed by entering the grape contig identifier or EST code, the MPSS signature sequence, the grape sequence of interest, or a list of contig identifiers. The data on transcriptional activity that this website provides may be used as the starting point for analyses of individual genes or gene families in grape.

Discussion

We have explored expression patterns at a specific stage in grape berry development by comparing and combining two tag-based methods: ESTs and MPSS. Both approaches described similar patterns of transcripts abundances, although there were some clear differences perhaps associated with the methods themselves. In principle, due to deeper sequencing, the MPSS data should provide a more thorough and quantitative representation of the absolute transcript population in terms of representation and relative abundance than that from ESTs [7, 11]. This is particularly true when the number of cDNA clones sequenced from any given library is low or for genes expressed at only low levels in the sampled tissues. For the EST frequency to represent the absolute transcript frequency, sequencing efforts must be large and sampling must be unbiased. The goal of achieving saturation for libraries constructed from a specific tissue may be overcome by combining library information available in public domain databases, if those resources are large enough. However, the different protocols used for library construction and EST sequencing, the lack of complete control of growing conditions, genotype and even standardized guidelines to describe a particular stage in development, makes it difficult to achieve unbiased sampling. On the other hand, MPSS analysis is also subject to bias. For example, some highly transcribed genes (based on EST frequency analysis) were unmatched by any MPSS signatures, possibly due to either the lack of a GATC site in the sequence or a technological artifact. The lack of suitable DpnII sites in some Arabidopsis transcripts is one source of negative results in MPSS transcriptional profiles compared against other high-throughput technologies [17]. In addition, MPSS substantially underestimates expression for signatures either containing the recognition site for the Type IIS restriction endonuclease BbvI (used in MPSS sequencing), or signatures containing certain four-nucleotide words in the sequencing frames [11]. The formerly high cost of tag-based methods limited biological replication as part of the experimental approach; such data would be highly desirable to determine the degree of biological variation and technical noise derived from these technologies [7]. This may be more achievable with the next generation of technologies as costs are reduced. The combined application of multiple approaches for transcriptional profiling is likely to provide the most robust determination of transcript levels.

In the grape MPSS dataset, when multiple signatures matched to one contig, these usually varied significantly in abundance. However, these data were consistent with the most abundant MPSS signature derived from the predominant form of the transcript among the ESTs [1]. An assessment of alternative transcript polyadenylation based on MPSS in diverse tissues and treatments could provide insight into this mechanism of gene regulation by identifying differentially terminated transcripts. The annotation and analysis of signatures matching multiple contigs is a more difficult task, but validation of these data could be performed by using microarrays with specifically designed probes to determine the relative expression of all matched genes, or by repeating the MPSS experiment using a different "anchoring enzyme" such as NlaIII (CATG) instead of DpnII (GATC).

The occurrence of genome-wide duplications may drive genome diversification and speciation in the plant kingdom [18]. Gene- and organ-specific silencing and unequal expression levels have been reported in upland cotton for homeologous genes resulting from whole genome polyploidization [1921] and a similar phenomenon may be the cause of yellow-seeded commercial soybean cultivars [22]. The extent to which duplication-associated changes in gene expression may be playing a role in grapevine phenotypes is largely unknown. Due to the ancestral polyploid nature of the grape genome [2325], duplication events leading to interactions or silencing among homeologous genes may have occurred. Evidence of extensive antisense expression was identified by comparing the ESTs and MPSS transcriptional profiling data. Initial whole transcriptome analysis in mammalian systems indicated that up to 20% of all transcripts formed sense-antisense (S/AS) pairs [2631]. Recent analysis derived from a large scale mouse cDNA sequencing project [32] and a high resolution transcriptional map of human chromosomes [33] revealed that S/AS pairs exists for up to 72% and 50% of all mouse and human transcripts, respectively. S/AS frequencies observed in the berry transcriptome are similar to those reported in Arabidopsis, where approximately 22% of all known genes have tissue specific natural antisense transcript pairs [7]. Considering the unequal contribution of different genes and regions in the genome to the formation of S/AS pairs [32], whole transcriptome analysis would certainly provide a more accurate description of the extent of the phenomena in grapes than the one determined with a limited coverage of the transcriptome in this study.

Two distinct sources of native antisense expression have been identified: cis- and trans-encoded antisense [2729]. The former correspond to transcripts derived from the opposite strand in the same genetic locus as the sense RNA. Cis-encoded antisense transcripts tend to have complete overlap with the sense strand forming long perfect match RNA duplexes [28]. Approximately 50% of sense-antisense pair categories in humans fell within this category [29]. Trans-encoded antisense transcripts derive from alternative loci and tend to have partial overlap with the sense strand of the original locus [27, 28]. The function of endogenous populations of dsRNA or small RNAs in grape remain to be elucidated with more detailed experiments, and this is best performed using short-read sequencing methods [34].

Tag-based transcriptional profiling approaches provide unique advantages for the discovery of novel expressed sequences. MPSS signatures derived from a specific stage in berry developmental revealed the existence of potentially 6,345 novel transcripts in grapes. These transcripts could be more fully identified to expand the set of known and experimentally verified Vitis genes either by PCR-based approaches [13], or ultimately aligning the signatures with grape genomic sequence. In the absence of full genome sequence information, PCR-based approaches may become particularly important for transcripts that are difficult to identify by means of EST-based approaches due to their low copy number or technical limitations of RNA-dependent cDNA synthesis. Whole genome sequencing of the V. vinifera genome, combined with data-rich tag-based (ESTs and MPSS signature frequencies) and microarray-based transcriptional data will greatly contribute to our understanding of the complex relationships between genome organization, transcriptional activity, and phenotypes. Because automated genome annotation systems are both error-prone and greatly improved with the incorporation of experimental data, the EST and MPSS data will prove invaluable in the coming years for gene discovery and the annotation of genomic sequences.

Conclusion

We have performed a complete transcriptional analysis of V. vinifera berries in transition to the ripening stage using MPSS combined with EST data. Approximately 30,000 distinct signatures, each representing a distinct transcript, were identified from the MPSS data and the signatures were mapped onto EST sequences. The number of MPSS signatures matching to one EST ranged from one to 16 and suggests the existence of numerous alternative transcripts in V. vinifera. In addition, a large set of MPSS signatures that matched to the anti-sense orientation ESTs was identified. Although the existence of antisense transcripts has been reported in many plant species, this is the first data to suggest the existence of antisense transcripts in V. vinifera. In addition to the signatures with EST matches, large numbers of MPSS signatures which do not match to ESTs were identified. While a small proportion could be due to sequencing errors, we believe the majority of these were mainly due to the low depth of sequence coverage in the current EST dataset; support for this interpretation derives from the fact that the proportion of signatures matching V. vinifera sequences was nearly doubled by incorporation of whole genome sequence data. High capacity, short read sequencing technologies, in particular next generation gigabase methods, have potential to contribute an important element to ongoing annotation of the genome sequence of V. vinifera. The grape MPSS data is accessible from University of Delaware MPSS website [1] and the EST data sets are available through UCDavis College of Agricultural and Environmental Sciences Genomics Facility (CGF) website [35].

Methods

Plant material and sampling procedures

The cDNA used for MPSS sequencing was constructed from stage II berries (green hard) sampled from field-grown V. vinifera cv. Cabernet Sauvignon, clone 8 vines located in the Tyree Teaching Vineyard, UC Davis, CA. Berries were sampled from multiple clusters and from different positions in individual clusters in order to ensure a representative sample. A sub-sample of berries at this stage was used to generate a cDNA library and expressed sequence tags (ESTs), as reported previously [4]. For additional details on sample handling and storage, see Goes da Silva et al., 2005.

MPSS data generation and analysis

All MPSS was performed essentially as described previously [5, 6], with the library produced and sequenced at Illumina, Inc. (formerly Solexa, Inc.; Hayward, CA). The raw and normalized MPSS data are available at University of Delaware MPSS website [1]. We compared MPSS signatures to the V. vinifera ESTs available at UC Davis CGF website [35] and assigned signatures to each sequence for which a perfect match was identified. The number of matches of a signature to the EST dataset was recorded as the "hits" for each signature. We merged the sequencing runs and calculate a single normalized abundance as reported earlier [11]. Contig orientation in the 5'-to-3' direction was performed using batch BLASTX search and the analysis of subject indexes of the first EST and last EST for each contig. Data analysis was conducted in MS Excel (Microsoft, Seattle, WA) and SAS V.8 statistical package (The SAS Institute, Cary, NC), or in a customized MySQL database [16] and figures in SigmaPlot version 8.0 (Systat Software Inc., San Jose, CA).

Declarations

Acknowledgements

We thank Huizhuan Wu and Mayumi Nakano for their work on the grape MPSS web interface, and Richi Gupta, Anna Leslie and Brian Chan for bioinformatics assistance. This work was supported by research grants from the NSF Plant Genome Research Program (awards #0110528 and #0321437 to B.C.M.), the USDA-ARS (SCA 58-5302-2-788 to D.R.C.), and the California Department of Food and Agriculture (Contract 02-0150 to D.R.C).

Authors’ Affiliations

(1)
Department of Plant Pathology and College of Agricultural and Environmental Sciences Genomics Facility, University of California
(2)
Department of Plant and Soil Sciences & Delaware Biotechnology Institute, University of Delaware
(3)
Monsanto

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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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