Open Access

Transcriptomic profiling of mature embryo from an elite super-hybrid rice LYP9 and its parental lines

Contributed equally
BMC Plant Biology20088:114

DOI: 10.1186/1471-2229-8-114

Received: 20 June 2008

Accepted: 11 November 2008

Published: 11 November 2008

Abstract

Background

The mature embryo of rice (Oryza sativa, L.) is a synchronized and integrated tissue mass laying the foundation at molecular level for its growth, development, and differentiation toward a developing and ultimately a mature plant. We carried out an EST (expressed-sequence-tags)-based transcriptomic study, aiming at gaining molecular insights into embryonic development of a rice hybrid triad–an elite hybrid rice LYP9 and its parental lines (93-11 and PA64s)–and possible relatedness to heterosis.

Results

We generated 27,566 high-quality ESTs from cDNA libraries made from mature rice embryos. We classified these ESTs into 7,557 unigenes (2,511 contigs and 5,046 singletons) and 7,250 (95.9%) of them were annotated. We noticed that the high-abundance genes in mature rice embryos belong to two major functional categories, stress-tolerance and preparation-for-development, and we also identified 191 differentially-expressed genes (General Chi-squared test, P-value <= 0.05) between LYP9 and its parental lines, representing typical expression patterns including over-dominance, high- and low-parent dominance, additivity, and under-dominance. In LYP9, the majority of embryo-associated genes were found not only abundantly and specifically enriched but also significantly up-regulated.

Conclusion

Our results suggested that massively strengthening tissue-(or stage-) characteristic functions may contribute to heterosis rather than a few simple mechanistic explanations at the individual gene level. In addition, the large collection of rice embryonic ESTs provides significant amount of data for future comparative analyses on plant development, especially for the important crops of the grass family.

Background

Heterosis is a phenomenon that particular inbred lines can produce progenies with favourable phenotypes over their parents, such as stronger tolerance to stresses and higher yields. Two fundamental hypotheses for heterosis were defined in classical genetic studies, including genome-wide dominance complementation and locus-specific over-dominant effects [13]. Evidence showed that both play roles in heterosis with involvement of epistasis [46]. Another theory suggested that heterosis might result from the loss of control of metabolism among heterozygotes [7]. Along with the development of genome-wide expression technology such as EST, SAGE, and microarrays, current technological advancements allow scientists to gain comprehensive information and knowledge on gene expression profiles of particular tissues from hybrids and their parental lines [8]. As it is obvious that differentially-expressed genes (DEGs) among hybrid triads may offer straightforward molecular clues to characterize phenotypic differences and genes responsible for favourable phenotypes, high-throughput gene expression profiling methods have been used for identifying DEGs in recent years. Various DEGs and their expression patterns in hybrids and their parental lines have been defined and analyzed, including over-dominance, high-parent dominance, additivity, low-parent dominance, and under-dominance [9, 10]. The distribution of DEGs varies among different samples, which not only reflects the complexity of rice transcriptomes but also points to the possibility where intricate molecular mechanisms may be involved in heterosis.

As both an important cereal crop and a model plant, the rice genome has been sequenced multiple times for its two major subspecies [11, 12]. We have been studying rice genome with a particular interest on the molecular mechanism of heterosis as one of the specific aims proposed for the Super-hybrid Rice Genome Project (SRGP). The project focuses on an elite super-hybrid (Liang-You-Pei-Jiu, LYP9; which can yield about 20–30% more grains; [13]) and its parental lines (93-11 and PA64s). Several datasets of DEGs as potential heterosis-associated genes as well as possible molecular mechanisms have been collected from major tissues, such as leaves, roots, and panicles [10, 14]. The mature embryo is an important developmental stage in the rice life cycle, as it is a synchronized, undeveloped miniature plant that consists of precursor tissues for root, leaf, and stem. It was reported that its long-lived mRNA reserved in dry plant seeds might, in general, contain a valuable molecular record of embryogenic development and primary biological process [15]. A recent study suggested that the embryo of heterotic F1 possesses certain advantages at a very early developmental stage [16]. Therefore, it is important to know whether the mature embryo deposits heterotic potential for further development, as a genome-wide transcriptomic study for the mature rice embryo of a hybrid triad has not yet been reported to this date.

In this study, we constructed cDNA libraries for mature embryos from 93-11, PA64s and LYP9, and randomly sequenced approximately 10,000 ESTs from each library, resulting in 27,566 high-quality ESTs and 7,557 unigenes. We analyzed these ESTs and identified 191 DEGs between LYP9 and its parental lines (General Chi-squared test, P-value <= 0.05). Assigning the DEGs into functional categories and expression patterns, we realized that multiple modes are at work for heterosis. All our EST sequences were submitted to the NCBI dbEST database under accession number FG943106 to FG970671.

Results

EST acquisition

We acquired approximately 10,000 clones from each library, which yielded 28,555 high-quality ESTs after removal of vector and low-quality sequences. The collection was further refined by removing sequences that are not anchored in the Oryza sativa Gene Index database (r17) and predicted gene sequences of the 93-11 genome [11], resulted in 27,566 high-quality ESTs that include 9,084, 9,027, and 9,455 ESTs for 93-11, PA64s, and LYP9, respectively (Table 1). We assembled the ESTs into 7,557 clusters, including 2,511 contigs and 5,046 singletons. The three datasets contain 3117, 3938, and 3017 unigenes from 93-11, PA64s, and LYP9, respectively, with a similar cluster-size distribution (Figure 1). Only 635 (8.4% of total) unigenes were found universally-expressed as they are shared by all three libraries; there are about a third of them are shared by two libraries and more than 50% unigenes in each library are unique (Figure 2). Since most of the unique genes were detected as single copies, we believe that the transcriptome of a mature rice embryo is rather complex and the current sampling is not deep enough to discover low-abundance genes as most of the genes should be shared among the cDNA libraries rather than truly unique to each. Therefore, we focused our analysis on unigenes expressed in relatively high abundance.
Table 1

Summary of EST sequences, contigs, and singletons in three rice cDNA libraries

Library ID

No. of reads

No. of high-quality EST

No. of contigs

No. of singletons

No. of unigenes

93-11

10875

9084

810

2307

3117

PA64s

10751

9027

1127

2811

3938

LYP9

11172

9455

784

2233

3017

Total

32798

27566

2511

5046

7557

https://static-content.springer.com/image/art%3A10.1186%2F1471-2229-8-114/MediaObjects/12870_2008_Article_322_Fig1_HTML.jpg
Figure 1

Unigenes from three libraries sorted by cluster-size. ESTs from all libraries were clustered into unigenes (contigs and singletons) and the number of unigenes in each size-based cluster bins was plotted.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2229-8-114/MediaObjects/12870_2008_Article_322_Fig2_HTML.jpg
Figure 2

Venn diagrams of unigenes shared among libraries based on unigene counts.

Functional annotation

We annotated 7,250 (95.9%) out of a total of 7,557 unigenes in our dataset based on sequence similarity to those in public databases (Table 2). Among 307 un-annotated unigenes, 13 have significant similarity to the repeat sequence collection (TIGR_Oryza_Repeats.v3.3) and one to a non-coding RNA sequence from the mouse genome http://biobases.ibch.poznan.pl/ncRNA/. The remaining 293 unannotated unigenes are rather short but have open reading frames more than 30 amino acids in length, and they may represent novel protein-coding genes or non-coding RNAs as they are expressed in rice embryos.
Table 2

Annotations of unigenes based on different public databases

Database

No. of matched unigenes

Percentage of total unigenes (%)

TIGR OGI(ver17)

7126

94.3

TIGR PseudoMolecule(ver5)

6151

81.4

NCBI UNIGENE(ver62)

6714

88.8

NCBI nr protein database

5831

77.2

93-11 BGI_Scan

5854

77.5

Uniprot protein database

3628

48.0

TIGR to GO

4565

60.4

KEGG Automatic Annotation Server

945

12.5

Alignments were performed by using blast (e-value = 1.00e-05), and 7,250 (95.9%) unigenes were annotated.

We aligned the best hits to TIGR OGI database with the Gene Index to GO mapping database at TIGR and assigned at least one GO term to 4,565 (60.4%) transcripts. The GO-annotated unigenes are 1,995 (64.0%), 2,500 (63.5%), and 1,908 (63.2%) for 93-11, PA64s, and LYP9, respectively. We assigned most of the unigenes into several functional categories, including cellular process, metabolism process, multicellular organism development, stress tolerance, transport, cell, binding, catalytic activity, transcription regulator activity, translation regulator activity, and transporter activity (Figure 3).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2229-8-114/MediaObjects/12870_2008_Article_322_Fig3_HTML.jpg
Figure 3

Functional classification of unigenes. Functional classifications are based on GO terms (A, unigenes; B, ESTs) and KEGG categories (C, unigenes; D, ESTs).

We also annotated our datasets using KEGG Automatic Annotation Server http://www.genome.jp/kegg/kaas/ for pathway information (BBH method). We were able to offer 945 (12.5%) unigenes KO numbers, including 429 (13.8%), 560 (14.2%), and 509 (16.9%) in the libraries of 93-11, PA64s, and LYP9, respectively. The categories of metabolism, genetic information processing (GIP), environmental information processing (EIP), and cellular process involved 64.1%, 20.7%, 6.1%, and 9.1% of the total annotated unigenes, respectively. As summarized in Figure 3, carbon (25.9% of metabolism), energy (15.2% of metabolism), and amino acid (22.3% of metabolism) metabolisms are major contributors among the subsets of metabolism. In the category of GIP, translation (43.4% of GIP) and sorting (39.0% of GIP) are the majority as opposed to transcription (11.1% of GIP). Signal transduction (58.1% of EIP) and membrane transport (41.9% of EIP) constitute the majority of EIP. Other than these major categories, the rest of cellular processes showed lower abundance although defence systems (endocrine and immune systems) were found more abundant among the low-abundance categories.

Expression abundance

We calculated the expression abundance for each unigene using their cluster sizes (a sum of ESTs from all three libraries) and sorted them into three abundance levels: high-abundance (>= 100 ESTs), medium-abundance (>= 13 EST; this cutoff value was chosen as it represents 0.5‰ of the total 27,566 ESTs and is at a turning point when the number of ESTs per unigene was plotted in our redundancy analysis), and low-abundance (Table 3). The top 32 bins of high-abundance genes constitute only 0.42% of total unigenes in kind but 33.47% of the total EST counts (Table 4). The entire collection and its annotated details are listed in Additional file 1: Summary of unigenes. Their sequences are supplemented in Additional file 2: A full list of unigene sequences.
Table 3

Classification of unigenes by expression abundance

Category

Standard

Number of genes

Number of ESTs

High

>= 100

32(0.42%)

9227 (33.47%)

Medium

>= 13

183(2.42%)

5315 (19.28%)

Low

< 13

7342(97.16%)

13024 (47.25%)

Alignments were performed by using blast (e-value = 1.00e-05), and 7,250 (95.9%) unigenes were annotated.

Table 4

High-abundance genes and functional annotations

Unigene ID

Abundance

TPMa

Functional annotation

Unigene_0001

2739

99354

Cytochrome P450 monooxygenaseb

Unigene_0002

1121

40663

rRNA intron-encoded homing endonuclease

Unigene_0003

443

16069

Os11g0211800

Unigene_0004

379

13748

Em-proteinc

Unigene_0005

272

9867

Ramy1

Unigene_0006

256

9286

Lectinb

Unigene_0007

249

9032

LEA3b

Unigene_0008

238

8633

RAB21b

Unigene_0009

219

7944

Thaumatin-like proteinb

Unigene_0010

210

7618

HSP16.9b

Unigene_0011

209

7581

HSP70b

Unigene_0012

203

7364

HSP17.4b

Unigene_0013

197

7146

NAD-/NADP-dependent oxidoreductase

Unigene_0014

171

6203

Late embryogenesis abundant protein D-34b

Unigene_0015

164

5949

Senescence-associated proteinb

Unigene_0016

163

5913

Glyceraldehyde-3-phosphate dehydrogenase

Unigene_0017

161

5840

RAB24b

Unigene_0018

151

5477

E2

Unigene_0019

149

5405

EF-1 alpha

Unigene_0020

144

5223

Os03g0723400

Unigene_0021

139

5042

Rab16bb

Unigene_0022

138

5006

Ec proteinc

Unigene_0023

136

4933

Seed maturation proteinb

Unigene_0024

117

4244

Hypothetical protein APECO1_2466

Unigene_0025

111

4026

Os04g0589800

Unigene_0026

111

4026

Bowman Birk trypsin inhibitorb

Unigene_0027

110

3990

RAB25b

Unigene_0028

110

3990

HSP90(81-2)b

Unigene_0029

109

3954

Multiple stress-responsive zinc-finger proteinb

Unigene_0030

105

3809

Embryogenic-cell protein 40c

Unigene_0031

103

3736

DnaJ proteinb

Unigene_0032

100

3627

Plasma membrane associated protein

aTranscript per million,b stress-tolerance associated genes, and c development-associated genes.

To identify embryo-characteristic genes, we carried out a hierarchical clustering analysis based on EST abundance, rice gene annotation in the unigene database at NCBI, and comparative analysis with representative libraries of other rice tissues selected from the Digital Differential Display (DDD) database (Table 5). We also did a parallel analysis utilizing SAGE data of different tissues (leaf, root, and panicle), which were generated from the same hybrid triad lines [10] based on annotations in our database that contains predicted gene sequences from the 93-11 genome. The results are rather consistent; not only is there a significant amount of high-abundance genes unique to the mature embryo (Figure 4A and 4B) but also most of the medium-abundance genes are rather characteristic for the embryo (Additional file 3: Hierarchical clustering of medium-abundance genes.). In addition, we compared embryo-associated genes against all predicted genes from the parental genomes (93-11 and PA64s) based on GO functional classification, and observed the enrichment of genes in several categories, such as response to stimulus and stress in the category Biological Processes (Figure 4C).
Table 5

Libraries for Digital-Differential-Display analysis

Tissue

Lib ID

EST

Clustered EST

Unigene

Callus

19058

53637

50361

10713

Flower

19057

46187

43264

10451

9 leaf-stage leaf

19096

13230

12891

681

Drought stress (leaf)

9652

5615

4154

1344

Panicle

19050

13571

13060

3634

Root of seedlings

19053

17250

16565

4445

Seed

19082

9792

9408

4228

Mature rice embryo

local data

28555

27566

7557

Data from this study are highlighted in bold and italic.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2229-8-114/MediaObjects/12870_2008_Article_322_Fig4_HTML.jpg
Figure 4

Hierarchical clustering and GO analyses of embryo-associated genes. We summarized our hierarchical clustering analyses (A, compared to NCBI DDD database; B, compared to SAGE data generated from the same hybrid line) of high-abundance genes based on data from the embryo and other tissues (also see Table 5). The expression abundance was normalized to transcript per million. The frequencies of unigenes from different libraries are indicated by color intensity (red), and blank was adjusted to be black. A GO classification of embryo-associated genes in comparison to the genome total (C) was also showed to emphasize the fact that the enrichment of stress- and development-associated genes in the mature embryo.

Based on GO functional classification, we found that (1) high-abundance genes are enriched in cellular and metabolic processes, response to stress, cell, binding, catalytic activity, and translation regulator activity; (2) medium-abundance genes are those for embryo development, response to external stimulus, transport, enzyme regulator activity, signal transducer activity, structural molecule activity, transcription regulator activity, and transporter activity; and (3) low-abundance genes are involved in anatomical structure morphogenesis, death, growth, reproduction, response to biotic stimulus, and motor activity (Figure 5).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2229-8-114/MediaObjects/12870_2008_Article_322_Fig5_HTML.jpg
Figure 5

Functional classification by expression abundance. GO classification and KEGG pathway analyses based on gene expression abundance. GO classes (A) and KEGG pathways (B) of all unigenes sorted by expression abundance described in Table 3 were shown. The high-abundance genes represent the most important functional categories and pathways in the mature embryo.

We also analyzed the genes classified as different pathways. We found that (1) high-abundance genes concentrate on carbohydrate metabolism, folding/sorting/degradation, membrane transport, signal transduction, and defence (endocrine and immune) systems, and (2) medium-abundance genes are mainly involved in the pathways of energy metabolism, lipid metabolism, amino acid metabolism, transcription and translation (Figure 5). High-abundance genes were found in the pathways of glycolysis, pores ion channels, protein folding and associated processing, ubiquitin-mediated proteolysis, MAPK signalling, progesterone-mediated oocyte maturation, and antigen processing/presentation; all appear performing important functions in keeping embryonic status and rapid growth recovery.

Abundant embryo-characteristic gene families

We discovered four major gene families expressed in high-abundance in the rice embryo (Table 6). The most abundant gene family belongs to cytochrome P450 monooxygenases, making up 10% of total ESTs. They are universally-expressed and senescence-associated in all eukaryotic species. Among plants, these enzymes are important for the biosynthesis of hormones, defensive compounds, and fatty acids. The second group is composed of HSPs (heat shock proteins), and they are multifunctional molecular chaperones and many show over-expression during embryonic development and are inducible by desiccation and other stress types. We identified four low-molecular-weight (LMW, 17–30 KD) HSPs in our dataset; they are HSP16.9, HSP17.4, HSP22, and HSP26 as well as four classical HSPs: HSP40, HSP70, HSP90, and HSP101. In numbers, there are 16% of the abundantly-expressed genes and 3.3% of the total ESTs belonging to the HSP family. The third protein family is the LEA (late embryogenesis abundant; 3.0% of the total ESTs) proteins that are a group of proteins accumulating in high abundance during last stages of seed formation and periods of the water shortage in vegetative organs. Ample evidence bsuggested that LEA proteins, especially its subgroup 3 members, are involved in desiccation resistance through a variety of machineries, including water retention, ion sequestration, and direct protein protection [17, 18]. The fourth group is the RAB family, which are membrane-associated small GTP-binding proteins localized to discrete subcellular compartments and involved in signal transduction, cytoskeleton organization, and vesicle trafficking. They are also stress-inducible. We identified its four members whose ESTs make up 2.6% of the total collection: RAB16 (b, c, d), RAB21, RAB24, and RAB25.
Table 6

Abundant gene families detected in the dataset

Gene family

Subfamily

Expression abundance

Unigene ID

   

93-11

PA64s

LYP9

Total

 

P450a

Cytochrome P450 Monooxygenase

1111

348

1280

2739

Unigene_0001

HSPb

LMW

Hsp16.9

29

102

79

210

Unigene_0010

  

Hsp17.4

28

74

101

203

Unigene_0012

  

Hsp22

3

3

13

19

Unigene_0141

  

Hsp26

4

6

40

50

Unigene_0055

 

Classical HSP protein

Hsp40

29

45

29

103

Unigene_0031

  

Hsp70

71

50

88

209

Unigene_0011

  

Hsp90

44

34

32

110

Unigene_0027

  

Hsp101

1

4

11

16

Unigene_0170

LEAc

LEA

LEA3

90

61

98

249

Unigene_0007

  

LEA D-34

80

16

75

171

Unigene_0014

  

LEA

21

7

27

55

Unigene_0052

  

LEA

8

17

13

38

Unigene_0072

  

LEA

12

3

19

34

Unigene_0081

 

Seed maturation protein

PM

61

19

56

136

Unigene_0023

  

PM24

8

6

9

23

Unigene_0123

  

PM

4

2

11

17

Unigene_0151

  

PM

4

4

7

15

Unigene_0180

  

PM

3

5

4

12

Unigene_0228

  

PM

3

1

0

4

Unigene_0848

  

PM23

1

1

1

3

Unigene_0999

  

PM36

2

1

0

3

Unigene_1204

RABd

RAB21

 

85

52

101

238

Unigene_0008

 

RAB24

 

64

29

68

161

Unigene_0017

 

RAB16

RAB16b

46

27

66

139

Unigene_0021

  

RAB16c

27

11

26

64

Unigene_0047

  

RAB16d

1

4

6

11

Unigene_0247

 

RAB25

 

45

13

52

110

Unigene_0028

aCytochrome P450 family, bheat shock protein, clate embryogenesis abundant protein, and d RAB protein.

DEGs between LYP9and its parents

Based on P-values of 0.05 and 0.01 from general Chi-squared test, we defined 191 (2.53%) and 89 (1.12%) DEGs, respectively, between LYP9 and its parental lines (Table 7). The full list of all DEGs is in Additional file 4: DEGs and their expression patterns. Most of these DEGs belong to high- and medium-abundance genes, 84.3% and 59.2%, respectively. We focus our discussion on the larger group (P-value 0.05) unless specified. The DEGs cover almost all expression patterns with rather unequal distributions. First, the majority of DEGs (84.3%, 161) exhibit either high- or low-parent dominances rather than deviate significantly from both (15.7%, 30). Second, of the 161 genes, 72.7% (117) show differences between PA64s and LYP9, whereas only 27.3% (44) exhibit differences between 93-11 and LYP9. This result indicated that the gene expression pattern of LYP9 is more similar to 93-11 than to PA64s at this developmental stage. Third, there are slightly less high-parent dominant genes (76) than low-parent dominant ones (85) in embryo both between 93-11 and LYP9 (18/26) and between PA64s and LYP9 (58/59), whereas over-dominant genes (20) are more than under-dominant genes (6). Finally, there are slightly more up-regulated genes than down-regulated genes in general. When we used the more stringent P-value (0.01), most of the observed rules did not change except that the up-regulated genes between PA64s and LYP9 were found slightly more than down-regulated genes (36/28). In general, the distribution pattern of DEGs in the embryo is similar to that of the panicle as compared to our previous SAGE study [10]. When we displayed the fold change of DEGs (LYP9/[(93-11+PA64s)/2]) in a two-dimensional plot (Figure 6), the consistency remains.
Table 7

Differentially-expressed genes between F1 hybrid (LYP9) and its parental lines (93-11 and PA64s)

Genetic class

Subclass

Expression pattern

P-value(total number of DEGs)

   

0.05 (191)

0.01 (89)

   

#

%

#

%

Up-regulated

Over-dominance

L > N > P

5

2.6

3

3.4

  

L > P > N

0

0

0

0

  

L > N = P

15

7.9

8

9

 

High-parent dominance

L = P > N

18

9.4

6

6.7

  

L = N > P

58

30.4

36

40.4

Additivity

Additivity

N < L < P

1

0.5

0

0

  

P < L < N

3

1.6

0

0

Down-regulated

Low-parent dominance

L = P < N

26

13.6

7

7.9

  

L = N < P

59

30.9

28

31.5

 

Under-dominance

L < N < P

2

1

0

0

  

L < P < N

0

0

0

0

  

L < P = N

4

2.1

1

1.1

N, P, and L stand for paternal lines (93-11), maternal lines (PA64s), and F1 hybrids (LYP9), respectively.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2229-8-114/MediaObjects/12870_2008_Article_322_Fig6_HTML.jpg
Figure 6

Distribution of DEGs by fold changes. A two-dimensional display of expression profiling and fold changes of DEGs in mature rice embryos. N, L, and P stand for 93-11, LYP9, and PA64S, respectively. 12 different expression projections are displayed clock-wise according to their fold changes. The radius at which a gene is plotted represents fold changes L/[(P+N)/2]. Spots falling on the horizontal and vertical lines exhibit pure additivity and over- (or under-) dominance, respectively.

Integrated analysis with proteomic and SAGE data

We compared our DEGs to those identified from proteomic data generated from the same material (Weiwei Wang, personal communication). The proteomic data were acquired from the same material as what we used in this study, mature embryo of 93-11, PA64s, and LYP9. Over 1,300 2-D gel spots (putative proteins) were analyzed and 54 differentially-expressed proteins were identified in the study. We found that most of the high-abundance genes also highly expressed in the proteomic data, but the expression patterns were not always consistent. There were only nine genes found consistent in both datasets, and three of them were not (Table 8, when there are several protein spots for one gene and one of the spots are consistent with the EST data, we classified this gene as consistent). Seven of the consistent DEGs were found expressed higher in LYP9 except early embryogenesis protein and glutelin; both are down-regulated. Most of the DEGs in the protein data fell into three general categories: additivity, over-dominance, and under-dominance, whereas most of the DEGs generated from the EST data are in other groups: high- and low-parent dominance. We also compared our EST data to those of our SAGE experiments generated from the same hybrid triad. The SAGE data were generated from nine SAGE libraries made from three tissues: root, leaf, and panicle of the same hybrid triad, 93-11, PA64s, and LYP9. Roughly ten-thousand SAGE tags from each library were obtained and 20,595 unique tags were annotated, and among them, 1,216 DEGs (p < 0.01) were detected [19]. In this study, we identified 22 DEGs between the two datasets, which are shared by at least one tissue (library). The expression patterns in both dataset were summarized in Additional file 5: DEGs shared with SAGE data. These genes are more likely to be associated with heterosis, especially when some of the DEGs have consistent expression pattern in all tissues.
Table 8

DEGs shared by EST and protein data

Unigene ID (description)

EST(copy number)

Protein (vol%)

 

93-11

PA64s

LYP9

93-11

PA64s

LYP9

Consistent

      

Unigene_0007 (LEA3)

90

61

98

0.962

-

0.326

Unigene_0012 (sHSP)

28

74

101

0.323

0.721

0.782

Unigene_0025 (Bowman Birk trypsin inhibitor)

49

13

49

0.413

0.571

0.856

    

2.64

0.643

0.808

    

0.835

0.217

0.203

Unigene_0054 (Lipoprotein)

12

4

35

0.896

1.18

2.33

    

0.321

0.741

-

    

0.462

1.14

0.751

Unigene_0006 (Lectin)

106

53

97

0.952

0.544

0.211

    

0.121

0.158

0.405

Unigene_0299 (Enolase)

0

5

4

0.421

0.595

0.736

Unigene_0048 (Embryo-specific protein)

21

9

33

0.624

0.883

1.67

Unigene_0073 (Early embryogenesis protein)

26

3

9

0.145

0.136

0.298

    

0.286

-

0.197

    

0.392

0.067

0.073

Unigene_0442 (Glutelin)

1

6

0

0.154

0.319

0.824

    

0.202

0.732

0.261

    

0.242

0.38

-

Not Consistent

      

Unigene_0093 (PreproMP73)

8

5

17

0.184

0.146

-

    

0.061

0.303

0.104

Unigene_0011 (HSP70)

71

50

88

0.421

0.923

0.332

Unigene_0253 (Superoxide dismutase)

1

8

2

0.434

-

0.301

Alignments of DEGs to genome sequences

To understand the regulatory mechanism of DEGs, we traced their genomic sequences in 93-11 and PA64s, and identified the regulatory and transcript sequences among orthologous gene pairs. We scrutinized the genomic sequences (3 kb upstream from the translational start codon including 5'UTR, 3 kb downstream from the translational stop codon including 3'UTR, and coding region) from the genome sequences of 93-11 and PA64s for the 22 DEGs that are confirmed with our SAGE data. We found that 9 (41%) genes have almost identical sequences in all regions (without deletion/insertions more than 3 bp in length), and we believed that these genes might be regulated by distant trans-regulatory mechanisms other than cis-elements in their immediate promoters. Among the 13 (59%) genes that showed significant sequence deviations, 5 (23%), 10 (45%), and 11 (50%) genes have differences in coding, 5'UTR, and 3' UTR regions, respectively. We also analyzed well-defined regulatory sequence motifs and found that 14 (64%) genes have obvious motif variations. Our results were summarized in Table 9.
Table 9

DEGs in parental genomes

93-11

PA64s

5'UTR

Coding

3'UTR

Motif

9311_Chr01_0275

Pa64_Chr01_0330

N

N

N

Y

9311_Chr02_0049

Pa64_Chr02_0052

N

N

N

N

9311_Chr02_2243

Pa64_Chr02_2903

N

N

N

N

9311_Chr03_0335

Pa64_Chr03_0342

N

N

N

N

9311_Chr03_1553

Pa64_Chr02_1741

N

N

N

N

9311_Chr03_2170

Pa64_Chr02_1375

N

N

N

N

9311_Chr05_2916

Pa64_Chr05_2748

N

N

N

N

9311_Chr05_2972

Pa64_Chr05_2805

N

N

N

N

9311_Chr07_1714

Pa64_Chr07_1619

N

N

N

N

9311_Chr01_4347

Pa64_Chr01_4085

N

N

Y

Y

9311_Chr03_0234

Pa64_Chr03_0241

N

N

Y

Y

9311_Chr10_1957

Pa64_Chr10_1617

N

N

Y

Y

9311_Chr01_2124

Pa64_Chr01_2174

Y

N

N

Y

9311_Chr11_1172

Pa64_Chr11_1013

Y

N

N

Y

9311_Chr05_0084

Pa64_Chr05_0081

Y

N

Y

Y

9311_Chr07_0583

Pa64_Chr07_0573

Y

N

Y

Y

9311_Chr11_0059

Pa64_Chr11_0066

Y

N

Y

Y

9311_Chr01_0387

Pa64_Chr01_0451

Y

Y

Y

Y

9311_Chr03_0613

Pa64_Chr03_0602

Y

Y

Y

Y

9311_Chr03_1065

Pa64_Chr03_1056

Y

Y

Y

Y

9311_Chr04_0532

Pa64_Chr03_0450

Y

Y

Y

Y

9311_Chr06_3185

Pa64_Chr06_2801

Y

Y

Y

Y

N stands for insignificant differences (less than 3 bp in the sequence) and Y stands for significant differences (more than 3 bp in the sequence or motif differences).

Quantitative PCR-based Experimental validation of DEGs

We chose eight stress-tolerance-associated and four development-associated genes–both expressed in high-abundance and functionally important to mature rice embryo–to validate their expression patterns, using quantitative real-time PCR or qRT-PCR. The stress-tolerance-associated proteins are LEA3, HSP17.4, HSP70, tonoplast intrinsic protein, ubiquitin E2, thaumatin-like protein, Bowman-Birk trypsin inhibitor, and cold-regulated protein (gi| 115464405|). The four development-associated genes included one embryo-specific protein and three metabolism-associated proteins (succinate dehydrogenase, NADP-dependent oxidoreductase and S-adenosylmethionine decarboxylase). Almost all the results showed consistent trends with their corresponding EST data (Figure 7). We listed the primer sequences used in the experiment in Additional file 6: Primers for qRT-PCR.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2229-8-114/MediaObjects/12870_2008_Article_322_Fig7_HTML.jpg
Figure 7

qRT-PCR verification. The quantitative real-time PCR results confirmed the expression variations among samples. The EST section showed the fold changes detected by cluster size.

Discussion

Genes in abundance: stress tolerance and development

High-abundance genes are known to characterize tissue- or organ-specificity since they are dominant in manifestation of major functions of the tissue or organ. We found that the embryo shares 3,649, 3,670, and 2,768 genes with leaf, panicle, and root, respectively, and there are 2,342 genes common to all tissues. All high-abundance and most medium-abundance genes in our dataset are either specifically expressed or unique to their tissues of origin whereas only 47 genes in medium-abundance expressed universally at similar expression level, as compared to the SAGE data, distributing among the most basic GO categories (Additional file 7: GO classification of universally-expressed genes among embryo and other tissues from SAGE data.). In an attempt to define functional characteristics of the mature rice embryo based on its high- and medium-abundance genes, we found that two major functional categories stood out: stress tolerance and development, which are composed of 56.3% and 9.4% of high-abundance genes, respectively. Furthermore, there are still large amount of genes in the universally expressed categories such as metabolism and cellular process which expressed specifically in high-abundance in mature embryo.

We identified a large number of genes that are in the category of anti-stress via various mechanisms, which include a complete water-deficit tolerance system and genes involved in resistance in oxidation damage and fungal pathogen infection (Table 6 and Table 10). Water deficit is a major threatening factor to embryos, and a water-deficit tolerance system in plants was reported, including membrane proteins, chaperones (mainly HSPs and LEAs), water channel proteins, protease inhibitors, proteases, and the ubiqutin system [17]. The three high-abundance gene families (LEA, HSP and RAB) that we categorized in this study all have important functions in this system [17, 18]. Our proteomic study with the same material also provided a footnote to this conclusion (Weiwei Wang, personal communication).
Table 10

Stress-associated genes

Water-deficit Tolerance Genes

Protection

Name (Unigene ID)

Expression level

   

93-11

PA64s

LYP9

Total

 

WSI

WSI18 (Unigene_0040)

28

6

43

77

  

WSI76 (Unigene_0061)

12

6

22

40

 

Tonoplast intrinsic protein

1.1 (Unigene_0034)

50

2

45

97

  

3.1 (Unigene_0074)

16

5

16

37

  

1.2 (Unigene_0378)

1

1

5

7

 

Aquaporin PIP2.1 (Unigene_0260)

4

1

6

11

 

Water channel protein (Unigene_0100)

12

10

7

29

 

Bowman Birk trypsin inhibitor (Unigene_0025)

49

13

49

111

 

Lectin (Unigene_0006)

106

53

97

256

Degradation

Ubiquitin E2 (Unigene_0018)

41

88

22

151

 

Cysteine proteinase (Unigene_0051)

17

16

24

57

 

Aspartic proteinase oryzasin-1 (Unigene_0176)

8

5

2

15

 

Aspartic protease (Unigene_0161)

5

6

5

16

 

Asparaginyl endopeptidase REP-2 (Unigene_0083)

8

20

5

33

Other Abundantly Expressed Stress-associated Genes

Protection

Name(Unigene ID)

Expression level

   

93-11

PA64s

LYP9

Total

 

Thaumatin-like protein (Unigene_0009)

111

22

86

219

 

Gytochrome P450 monooxygenase (Unigene_0001)

1111

348

1280

2739

 

NAD-/NADP-dependent oxidoreductase (Unigene_0013)

61

25

111

197

 

Heavy metal transport/detoxification protein (Unigene_0107)

4

9

14

27

 

Universal stress protein (Unigene_0238 and Unigene_0234)

4

4

4

12

   

7

2

3

12

Three gene families–Em, Ec, and germin proteins–have been suggested to regulate the development of rice embryo to seedling [15]. The Em and Ec proteins are encoded by conserved mRNAs stored in mature embryos but germin transcribes after growth recovery. The Em protein is involved in the process of seed desiccation, and the Ec protein is the only bona fide Zn metallothionein yet found in higher plants [15]; both may play important roles in ion homeostasis during embryogenesis since Em and Ec proteins are expressed as the most abundantly expressed genes in our dataset with 379 and 138 ESTs, respectively. In addition, we identified one embryogenic cell protein 40 in the high-abundance gene class as well as other development-associated proteins including embryo-specific protein, differentiation embryo protein 31, and early embryogenesis protein in medium-abundance genes.

Pathway analysis on the mRNA repertoire provided a better glance on the basic physiology of rice mature embryos detected by pathway analysis, which showed that the embryo is well-prepared for rapid development. First, genes in the pathways of metabolism, especially the subsets of carbon, energy, and amino acid metabolisms, are stored in high abundance. Material utilization and energy production, such as glycolysis (10.4% of carbon metabolism), TCA cycle, starch and sucrose metabolism, oxidative phosphorylation, and carbon fixation are of essence for the embryo. Second, genes participating in translation (43.4% of GIP) and sorting (39.0% of GIP) are in high abundance to ensure rapid translation of the reserved RNA. Third, in the category of EIP, signal transduction (58.1% of EIP) and membrane transport (41.9% of EIP) stood out obviously to guarantee rapid signal transduction in the recovery phase, especially the MAPK signal pathway that is important in regulating cell cycle. Fourth, the defence (endocrine and immune) system is the highly-expressed gene category in our dataset for cellular processes, which ensures hormonal regulation in development and protection.

Our detailed analyses indicated that several important pathways active in mature plant tissues are almost absent in embryos. For example, we only identified nine ESTs involved in photosynthesis and none involved in respiration from our dataset. Although present at low level, transcription (11.1% of GIP) and replication/repair (6.4% of GIP) genes are important for growth recovery as compared to the complete absence of genes in the categories of signal molecules and interaction as well as the low level of cellular process genes in our dataset. The reservoir of the reserved mRNAs in the mature embryo appeared well-prepared for basic metabolism, response to environmental stimulus, and signal transduction to ensure rapid recovery. In the contrary, secondary metabolism, cellular process, and transcription constitute a relatively small portion of the total, and most of these functions must depend upon new mRNA syntheses.

Possible biological mechanisms of heterosis

When displaying all DEGs into two-dimension space, we found a composite expression pattern that suggests multiple modes of action as they are classified into 5 patterns: over-dominance, high-parent dominance, additivity, low-parent dominance and under-dominance (Table 7). Although up- and down-regulated DEGs are almost equal in numbers (Figure 6), up-regulated genes are dominant in the most abundant gene classes including cellular, metabolic, response to stress, embryo development, enzyme regulator activity, transcription regulator activity, and transporter activity (figure 8).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2229-8-114/MediaObjects/12870_2008_Article_322_Fig8_HTML.jpg
Figure 8

GO classification of DEGs.

Strengthened stress-tolerance system in F1

The majority of stress-tolerance genes in our dataset exhibited differential expressions among the hybrid triad, and especially showed up-regulated expressions in LYP9 (Table 6 and Table 10). In the GO term, five out of seven (71.4%) DEGs are in the category of response to stress and up-regulated in F1. In the gene families related to stress tolerance (HSP, LEA, and RAB), 70.4% were differentially expressed whereas 89% of them are up-regulated together with 5.5% down-regulated and 5.5% additivity in F1. And as shown in Table 10, among 65% of the DEGs, 77% of them are up-regulated (protective genes) as opposed to 23% down-regulated (the degradation genes). Functionally, stress-tolerance genes are classified into two groups: protective and injury-induced genes. Taking the water-deficit-tolerance related genes as examples, we know that protective genes, including chaperones (mainly LEAs and HSPs), protease inhibitors, water-deficit induced membrane proteins, and water channel proteins, are counterparts of the protein-degrading mechanism, promoting survivability under water-deficit conditions. Injury-induced genes include proteases and ubiquitin system, which are involved in the degradation of proteins that are denatured during cellular water loss (Figure 9). In LYP9, most of the protective genes are up-regulated as compared to the down-regulated injury-induced genes. Protective genes that participate in other stress-related functions are also up-regulated in the F1. Proteomic data also added footnotes to this notion, where the most functionally important and representative genes, including LEA3, sHSP, Bowman-Birk trypsin inhibitor, Lipoprotein and lectin, thaumatin-like protein were found as up-regulated DEGs. In addition, our SAGE data also pointed out a universal strengthening in the ability of stress-tolerance in the F1, where heavy metal transport/detoxification protein, senescence-associated protein and RAB21 protein were found up-regulated albeit in other tissues of LYP9. We validated some of these results with qRT-PCR.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2229-8-114/MediaObjects/12870_2008_Article_322_Fig9_HTML.jpg
Figure 9

DEGs in water-deficit tolerance system. Note that genes playing protective roles are all up-regulated whereas those injury-induced are down-regulated.

Better preparation for growth recovery

DEG analysis suggested that the F1 appears better prepared for growth recovery as compared to its parental lines. First, genes participating in material and energy utilization are up-regulated and genes participating in anabolism are down-regulated (Additional file 8: Summary of DEGs in pathway analysis). In general, the up-regulated DEGs were found in carbon metabolism with an exception of the oxidative phosphorylation of a NADP-dependent malic enzyme in energy metabolism. In contrast, genes in carbon, nitrogen fixation, and nucleotide metabolism are mostly down-regulated. In amino acid metabolism, we identified genes both up-regulated and down-regulated, but all genes related to amino acid biosynthesis are down-regulated. This indicated that LYP9 up-regulate material and energy generation and utilization to ensure its embryo to be active immediately when being re-hydrated as material anabolism is normally down-regulated. Second, in the category of GIP, genes involved in protein folding and associated processing are up-regulated and genes of ubiquitin mediated proteolysis and proteasome are down-regulated. Genes involved in transcription are down-regulated but those involved in translation are not obviously biased, where both down- and up-regulated genes were found. Third, in EIP, genes in membrane transport and MAPK signal transduction are up-regulated but calcium signalling pathway and phosphatidylinositol signalling systems are down-regulated; the result suggested that MAPK signalling pathway may be the major signal pathway triggered as development starts after re-hydration. Finally, DEGs involved in cellular process but not in the response (immune) system appeared down-regulated. Some of these genes and their trends of expressions were found consistent in our SAGE and proteomic data. For example, the up-regulation of an important enolase was confirmed in the protein data. And the up-regulation of succinate dehydrogenase was validated based on the qRT-PCR test. Another instance is β-amylase that is a key enzyme for starch hydrolysis, which is not only found up-regulated in the embryo but also in the leaf and panicle of LYP9.

Complementation and over-dominance

We found that embryo-characteristic genes among DEGs are primarily up-regulated and showed high-parent dominance (79.2%) with the addition of over-dominance (20.8%). The high-parent dominance genes often occur in gene families. For example, in the HSP family, the expression of LMW HSPs has the tendency of PA64s, higher than that of 93-11, or expressed in an over-dominance fashion. In contrast, the expression level of classical HSPs resembles that of 93-11, which is higher than that of PA64s, exhibiting over-dominance. In the LEA and RAB family, as well as other stress-tolerance genes, all the DEGs show high-parent dominance with expression levels similar to 93-11. More than two third of the DEGs differ between PA64s and LYP9 (72.7%), whereas less than one third show differences between 93-11 and LYP9 (27.3%). The result is consistent with the fact that LYP9 is more like 93-11 than PA64s in their phenotypic appearances (Lihuang Zhu and Qingzhong Xue, personal communications). Therefore, it appears that the hybrid vigour of LYP9 may be contributed by a composite of beneficial alleles from both parents, complementing the relatively weaker alleles in a global fashion, and over-dominance is an essential strategy for stress tolerance and metabolism.

Strengthening tissue-characteristic functions may contribute to heterosis

Analyses on DEGs have indicated that the mode of action for heterosis is most likely multi-fold and at multiple levels. If we have to point out a single mode of action, we believe that the strengthening of embryo-characteristic genes in LYP9 is most noticeable. For instance, the F1, LYP9, is predicted to have a significantly better stress-tolerance system than its parental cultivars, supported by up-regulated protective genes and down-regulated injury-induced genes. In addition, LYP9 keeps strong responses to environmental signals and potentially high-level activities after growth recovery. Therefore, when seeds re-hydrate, the F1 embryo may perform better than its parental lines. This phenomenon can be cautiously generalized based on similar analyses among other tissues of the same hybrid line. For example, in leaf, it was detected that up-regulated genes in hybrid are dominated by DEGs related to enhancing carbon assimilation whereas those related to decomposition of carbohydrates are down-regulated [10], resulting in an increased efficiency of photosynthesis or energy production.

Conclusion

We reported a comparative transcriptomic analysis based on ESTs acquired from mature embryos of a rice hybrid and its parental lines. Our study not only provided an initial characteristic description of expression profiles of rice embryos but also pointed out the enrichment of genes involved in stress-tolerance and preparation-for-recover (both development and physical activity). We also identified 191 DEGs between F1 and its parental lines. Functional analyses revealed clues for rice heterosis, such as multiple modes of actions and strengthening of embryo-characteristic function. Genes involved in stress-tolerance and preparation-for-recovery may play key roles for rice heterosis in mature embryos.

Methods

Plant materials and cDNA libraries

A fresh-harvest of mature seeds for LYP9 (F1 embryo harvested from the maternal plant,PA64s) and its parental lines 93-11 and PA64s were collected and stored at room temperature. The seeds were provided by the Chinese National R&D Center on Hybrid Rice, Changsha, China, and the plants were grown in its experimental rice paddies (28°30N, 113°40E, and altitude 40 m). Mature rice embryos were separated manually from the seeds by using scalpels and stored in liquid nitrogen immediately until RNA isolation.

We constructed cDNA libraries for each rice cultivar, using a combined priming method, i.e. used both oligo-dT- and random-priming to avoid potential cloning biases. Briefly, total RNA was extracted from dissected embryos with Trizol (Invitrogen), and mRNAs was purified with Oligotex mRNA Midi Kit (QIAGEN). Reverse transcription was carried out separately by using oligo-dT (with XhoI-linker sequence) and random primers in equal amount. For oligo-dT-primed cDNA, the second strand was synthesized and linked to an EcoRI-linker. After XhoI digestion, insert DNA was fractioned (1–2 Kb) with QIAquick Gel Extraction Kit (QIAGEN) and cloned into a directional pBluescript® II XR vector (Stratagene). For random-primed cDNA library, fractioned cDNA was cloned into a pUC18 vector.

EST assembly and annotation

We picked approximately 7,000 clones from each oligo-primed library and over 3,000 clones from each random-primed library for single-pass unidirectional sequencing from the 5'-end (MegaBase® 1000 sequencers). We used Phred [20, 21] for base-calling, rejected low-quality sequences according to default settings, and removed low-quality sequences shorter than 100 bp. Cross_match program was used to trim vector sequences. We also removed ESTs that do not have significant similarities [22] with the collection of Oryza sativa Gene Index database (release_17, it contains the largest assembly of publicly available rice ESTs) and the reference genomes including predicted genes from 93-11 [11]. We used Phrap (minmatch 50 and minscore 100) and Consed [23] to build consensus sequences and to view the assemblies. We further aligned all unigenes to NCBI nr-databases and clustered unigenes with the same gi-numbers to form non-redundant unigenes after manual inspections. In addition, we calculated the expression abundance for all unique sequences or unigenes based on ESTs.

We first annotated our data based on sequence similarity searches, using blast-based tools [22] (tblastx) against several databases, including NCBI non-redundant protein database, two public rice genome annotation databases (BGI rice genome annotations and TIGR rice pseudomolecule release 5), two major rice EST assembly databases (NCBI UNIGENE release 62 and TIGR OGI release 17) with an e-value cutoff of 1e-5. We then assigned functional categories for the unigenes according to GO functional classification using a web-based tool WEGO [24]http://wego.genomics.org.cn/cgi-bin/wego/index.pl and biological processes/pathways using the online KEGG Automatic Annotation Service http://www.genome.jp/kegg/kaas/ with the BBH option checked.

Defining specifically-expressed and differentially-expressed genes

We selected eight representative libraries from the NCBI Digital-Differential-Display (DDD) database for a comparative analysis (Table 5). We measured gene expression abundance as transcript per million (TPM) and sorted the expression abundance and annotation for every unigene among different tissues. Then do the hierarchical clustering analyses using default parameters. The parallel analysis with SAGE data was based on rice genome annotations with similar parameters. The GO classification analysis on both predicted genes from the parental (93-11 and PA64s) genomes and the mature embryo were performed and categories that has a larger percent of genes in embryo than genome were supposed to be enriched than genome general transcriptome.

DEGs among the three cultivars were determined by using IDEG6 [25, 26]. The cutoff value was set to p <= 0.05 for general Chi-squared test. All DEGs were classified into twelve offspring-parent distribution modes according to their expression patterns. Fold changes were detected based on the equation LYP9/[(93-11+PA64s)/2]. The radius at which a gene is plotted represents fold change and the position where each spot laid depends on the expression relationship among three libraries.

Quantitative Real-time PCR validation

We selected 12 functionally important and representative DEGs for validation using quantitative real-time PCR (qRT-PCR). Total RNA was digested with RNase-free DNase I to remove DNA contaminations and reverse-transcribed with a mixed primers (oligo-dT primer: random primer in a molar ratio of = 7:3). Gene-specific primers were designed for each DEG. and the rice β-actin gene was used as a control. qRT-PCR was performed by using a Quant SYBR Green PCR kit (Tiangen, china). The results were based on the average of three parallel experiments and analysed with the Opticon Monitor software. Melting curves and CT (cycle threshold) values for DEGs were used to measure expression levels. We calculated standard deviations for parallel samples, set cut-off standard to one, and quantified gene expression based on normalization with the control and self-normalization in parallel samples.

Notes

Declarations

Acknowledgements

Matured seeds were provided by Chinese National Hybrid Rice R&D Center. The work received financial support from Chinese Academy of Science (KSCX1-SW-03), the Ministry of Science and Technology (2005AA235110), and the National Basic Research Program of China (2006CB910404) awarded to JY. This project is also supported by a grant (30221004) from the National Natural Science Foundation of China.

Authors’ Affiliations

(1)
CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences
(2)
Graduate University of Chinese Academy of Sciences

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© Ge et al; licensee BioMed Central Ltd. 2008

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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