Differential gene expression in an elite hybrid rice cultivar (Oryza sativa, L) and its parental lines based on SAGE data
© Song et al; licensee BioMed Central Ltd. 2007
Received: 27 March 2007
Accepted: 19 September 2007
Published: 19 September 2007
It was proposed that differentially-expressed genes, aside from genetic variations affecting protein processing and functioning, between hybrid and its parents provide essential candidates for studying heterosis or hybrid vigor. Based our serial analysis of gene expression (SAGE) data from an elite Chinese super-hybrid rice (LYP9) and its parental cultivars (93-11 and PA64s) in three major tissue types (leaves, roots and panicles) at different developmental stages, we analyzed the transcriptome and looked for candidate genes related to rice heterosis.
By using an improved strategy of tag-to-gene mapping and two recently annotated genome assemblies (93-11 and PA64s), we identified 10,268 additional high-quality tags, reaching a grand total of 20,595 together with our previous result. We further detected 8.5% and 5.9% physically-mapped genes that are differentially-expressed among the triad (in at least one of the three stages) with P-values less than 0.05 and 0.01, respectively. These genes distributed in 12 major gene expression patterns; among them, 406 up-regulated and 469 down-regulated genes (P < 0.05) were observed. Functional annotations on the identified genes highlighted the conclusion that up-regulated genes (some of them are known enzymes) in hybrid are mostly related to enhancing carbon assimilation in leaves and roots. In addition, we detected a group of up-regulated genes related to male sterility and 442 down-regulated genes related to signal transduction and protein processing, which may be responsible for rice heterosis.
We improved tag-to-gene mapping strategy by combining information from transcript sequences and rice genome annotation, and obtained a more comprehensive view on genes that related to rice heterosis. The candidates for heterosis-related genes among different genotypes provided new avenue for exploring the molecular mechanism underlying heterosis.
Heterosis is defined as advantageous quantitative and qualitative traits of offspring over their parents, and the utilization of heterosis principles has been a major practice for increasing productivity of plants and animals . A considerable amount of efforts have been invested in unraveling genetic basis of heterosis in rice (Oryza sativa, L) and it was explained mainly by mechanisms such as dominance  and epistasis . Although many investigators favored one hypothesis over another, biological mechanisms of rice heterosis may not be fully characterized based on genetic approaches alone, especially based on classical genetic concepts.
Recently, it has been reported that differentially-expressed genes between hybrids and their parental inbreeds are correlated with heterosis [4, 5]. In wheat, a variety of differentially-expressed genes including transcription factors and genes involved in metabolism, signal transduction, disease resistance, and retrotransposons were detected responsible for heterosis by using a differential display technique [6, 7]. Even ribosomal proteins have been scrutinized since they are indicators of translation activities and plastid biogenesis . Various techniques have been applied to pin down genes involved in heterosis, such as a variety of sequence-based and hybridization-based methods; some have yielded interesting candidates and others proposed expression patterns of these candidates [5, 9]. For instance, a hybrid-specific expressed gene AG5 (a RNA-binding protein) in wheat was identified . Another study on gene generated expression profiles of an elite rice hybrid and its parents at three stages of young panicle development by using a cDNA microarray consisting of 9,198 ESTs and the result pointed to a significant mid-parent heterosis . Nevertheless, it is necessary to generate more data in large-scale, taking the advantage of the fast advancing genomic technology.
SAGE technology is a sequence-based approach for investigating gene expression in large-scale and allows much deeper sampling than EST (expressed sequence tag)-based approaches. It has proven to be a very powerful method for large-scale discovery of new transcripts, acquisition of quantitative information of expressed transcripts, and the quantitative comparison between libraries [12–14]. The technique has been used extensively in animal systems including human and mouse, and more particular in cancer research where several hundred libraries and nearly 7 million SAGE tags have been obtained [13, 15]. In plant, several studies have employed this methodology for transcript profiling in Arabidopsis [16, 17] and rice [18, 19]. However, a bottleneck of SAGE is tag-to-gene mapping, which refers to the unambiguous determination of the gene represented by a SAGE tag. Other limitations include lack of accurate genomic sequences and adequate amount SAGE data. Therefore, encouragements should be given to studies that generated publicly available data since heterosis is not simply a manifestation of a few seemingly important genes but many.
We have been studying the rice genome with a particular interest in the molecular mechanism of heterosis as part of the Super-hybrid Rice Genome Project (SRGP), focusing on an elite super-hybrid (Liang-You-Pei-Jiu, LYP9 ) and its parental lines, using gene expression technology, including EST and SAGE techniques. The objective of our current work was to recover more sequence tags (gene expression information) from our previous SAGE study . In our new analysis, SAGE tags were mapped to two newly annotated genome assemblies, paternal cultivar (93-11) and maternal cultivar (Pei-Ai 64s, PA64s) (BGI unpublished data) [22, 23]; the latter was not available when we carried out the first analysis. Prefect matches of SAGE tags to their own genome sequences allowed us to map more tags in a very significant way: twice as much tags were mapped as compared to the previous result. We also used three types of transcripts, including full-length cDNA (FL-cDNA) , expressed sequence tags (ESTs) [25, 26], and UniGene data as well as a new strategy in the current analysis.
Summary of mapped tags among nine libraries
Copy Number Distribution of Mapped Tags
Evaluation dataset, virtual tags, and mapped tags
Dataset for evaluating tag assignment
Mapped tags and supporting evidence
Mapped Tags (%)
Differentially-expressed genes among twelve distribution patterns
Differentially-expressed genes with significance a
P < 0.05
P < 0.01
Up/Down (>= 2)b
Up/Down (>= 2)b
N vs L
P vs L
From the overall distribution of differentially-expressed genes with higher P values (P < 0.01), we made several observations among the samples. First, gene distribution pattern in panicles is rather distinct and more biased than that in the other two tissues, in such a way that most of the down-regulated genes are very paternal-like (or almost identical to 93-11, N = L < P) and the up-regulated genes are rather dispersive (not focused along the solid line of N = L > P). The dispersiveness suggested that most of these genes are roughly paternal-like but their expression levels are approximating toward either the hybrid (LYP9) or the mid-parent in a quantitative manner. We speculate that this obviously restricted distribution in panicles may be either due to one or both the following possible biases. One bias may come from thermo-sensitive male sterility unique to the maternal cultivar, PA64s, where germline-related genes may be crippled in their overall gene expression though epigenetic mechanisms. The other possible bias may be resulted from incompatibility between alleles from the parental lines, which may cause a rather major regulatory effect for the majority of genes, such as DNA methylation in germline tissues. Second, the distribution of genes in leaves and roots are somewhat similar, especially among the down-regulated genes, and fold changes of these down-regulated genes are not as apparent as those in panicles. However, the distributions of up-regulated genes in the two tissues are rather distinct, where the up-regulated genes in leaves are biased toward over-dominant expression albeit a minority of the genes is found spreading toward mid-parent. In roots, the up-regulated genes, though they are rather smaller in number as compared to panicles and leaves (101 genes, Table 4), are mostly over-dominant. Finally, in the process of summarizing gene distributions in the twelve patterns, we found that a minority of the differentially-expressed genes (25 to 45%) exhibited additive expression (P > L > N and N > L > P; genes that were plotted on the horizontal lines), whereas the majority of the genes, 380 (55%), 408 (72%), and 309 (75%), are non-additive in panicles, leaves, and roots, respectively. Among the sum of these non-additive genes in all three tissues, 552 genes showed over-dominant expression, and a smaller amount, 394 genes, were found under-dominantly expressed. In addition, 115 and 32 genes are expressed at the same level as their paternal line (93-11) and maternal line (PA64s), respectively; these genes are classified as dominant expression.
Functional analyses of differentially-expressed genes
There were many other functionally annotated genes found to be significantly up-regulated, including rapid alkalinization factor, proteinase inhibitor, and MADS-box transcription factors; all appeared to be relative to the traits for photoperiod sensitive genic male sterility, male fertility restoration, and pollen fertility, according to the quantitative trait loci (QTL) database (Gramene ; see Additional file 7). Among them, the MADS-box (9311_Chr06_3092 and 9311_Chr01_4641) and rapid alkalinization factor (9311_Chr12_1510) genes were found highly expressed in the hybrid as compared to its parental lines despite the fact that the expression of these genes are already higher in its paternal line 93-11 than in its maternal line PA64s. This result indicated that these genes may play important roles directly or indirectly in flower morphogenesis and fertility of hybrid LYP9.
We also identified a large number of down-regulated genes that were not obvious in the previous analysis, largely due to more mapped tags and subtleties in data analysis protocols. These expression-suppressed genes belong to different functional categories among the three tissues; most of them are involved in energy metabolism, lipid metabolism, and glycan biosynthesis and metabolism in panicles, amino acid metabolism and protein processing in leaves, and biosynthesis of secondary metabolites in roots (Figure 4). The top-one down-regulated genes in panicles, leaves, and roots are metallothionein, peptidase M48, and glutathione S-transferase respectively. Metallothioneins are cysteine-rich proteins that can bind to heavy metals and scavenging reactive oxygen to protect plants from oxidative damage. Although it is the most down-regulated gene in panicle, it is up-regulated in root which plays an important role in assimilating, filtrating, and concentrating metal irons especially in screening heavy metal irons. Peptidase M48 is a family of proteins that function in protein degradation. We also found some other down-regulated genes related protein degradation, such as ubiquitin and ubiquitin-conjugating enzyme. Glutathione S-transferase is an enzyme to metabolize toxic exogenous compound that utilizes glutathione in the detoxification, for chemical defense in plants. We speculate that both of these up- and down-regulated genes represent a significant fraction of the genes regulating panicle development, rapid growth, stress tolerance, and grain yield in LYP9. Obviously, further verification and functional examination of these differentially-expressed genes are of essence in understanding their precise roles in heterosis.
Cross-referencing SAGE data to Microarray-based results
Differentially-expressed genes from 93-11 leaf libraries confirmed by microarray data
Up-Regulated Tags (≥2-fold)
Major intrinsic protein
EPSP synthase (3-phosphoshikimate 1-carboxyvinyltransferase)
Thiamine biosynthesis Thi4 protein
Protein of unknown function DUF250
Down-Regulated Tags (>2-fold)
Mitochondrial substrate carrier
Photosystem I reaction centre subunit IV/PsaE
Glycine hydroxymethyl transferase
Cellular retinaldehyde-binding)/triple function, C-terminal
Rieske [2Fe-2S] region
Photosystem II manganese-stabilizing protein PsbO
Photosystem I reaction centre, subunit XI PsaL
Lipase, class 3
Glutamine synthetase, beta-Grasp
Heat shock protein DnaJ, N-terminal
Tag-to-gene mapping procedures
SAGE and related sequencing-based techniques are very effective for studying gene expression in organisms where well-characterized genome sequences are available, and they have been applied to a number of eukaryotic species [17, 19, 32] and the merits and success have been discussed very recently by Marco Marra and his colleagues with ample experimental data , albeit pitfalls do exist . In our previous SAGE study, we utilized the available FL-cDNA sequences  for tag-to-gene mapping , as these FL-cDNA sequences best represent the rice transcriptome albeit in a rather limited amount. However, a large proportion (83%) of the SAGE tags was not found in this cDNA data collection that is known not covering all the genes of the rice genome. To overcome this limit, we utilized a new strategy for tag-to-gene mapping based on newly annotated genes of the two rice genome assemblies and other transcript sequences (FL-cDNA, UniGene, and ESTs). This process led to a significant improvement in gene identification, resulting in 10,268 additional tags and 68.85% extra differentially-expressed genes at a higher P value (P < 0.01), as compared to the previous collection.
Aside from the success of mapping SAGE tags to annotated genes in the genome, there are a couple of important points that are worthy of further discussion. First, we always have tags that are mapped to ambiguous positions, and they may belong to multiple loci (such as gene families and splicing variants) in the genome sequence, especially when the length of SAGE tags is as short as 14 bp. There were 4,014 (20%) such tags in our case, we assigned these tags to the genomes and used them for functional analysis. For example, despite the fact that a tag with a sequence of "AACAAGCTCA" was assigned to two different loci (9311_Chr04_1718 and 9311_Chr05_1829), the two were evidenced by two different FL-cDNA sequences (AK0ah71547 and AK061050), allowing us to identify them as members of the fructose-bisphosphate aldolase gene family. These two genes were found down-regulated in roots of the hybrid line, and they are involved in glycolysis/Gluconeogenesis pathways. Therefore, it is critical to map these seemingly ambiguous genes, especially when they are differentially regulated in the hybrid. It is possible to design experiments to distinguish these genes with locus-specific primers since most of these duplicated (or closely related) genes may be not identical in their UTR and genomic sequence, especially when genome sequences are readily available. As we have reported previously, the rice genome has enormous number of duplicated genes  that some of them may actually hold pivotal information in hybrid vigor.
The second point has to do with the fact that a fraction (often more than 40%) of the experimental tags remains unassigned to genes so we need to figure out the possible reasons. When comparing unassigned tags to virtual tags based on predicted NlaIII sites in the nuclear and organellar (mitochondrial and chloroplast) genome sequences, we found that 2,500 tags out of 47,867 (5%) were absent in the genome sequence assembly of 93-11, and 342 tags (0.6%) were derived from either the mitochondrial (491 kb) or chloroplast genomes (134 kb). These unassigned tags are most likely due to sequencing errors, sequences interrupted by introns, un-assembled sequences (including those in the sequence gaps), and organelle-specific sequences. In addition, we have technically implemented an artificial 300-bp UTRs for predicted genes without transcript-based evidence and only extracted the 3' most (canonical position) tags from virtual transcripts. This procedure is certainly incapable of including all UTR length variants, largely due to the absence of canonical polyadenylation signal for the accurate determination of the 3' UTR length in plant genomes . To estimate the result of such a procedure, we compared the remaining total unassigned tags to a cumulative virtual tag dataset constructed by varying the artificial UTR lengths in a 100-bp interval, from 100 to 500 bp, resulting in a further assignment of 3,119 (6.5%) additional tags. However, these tags were considered unreliable and were not included in this analysis. Nevertheless, the UTR-derived anomaly seems contributing to the impaired tag assignment in a similar way as the sequence anomaly. Other obvious factors resulting in unassigned tags, such as experimental artifacts (incomplete enzyme digestions and ligations, as well as inefficient cloning procedures), are not discussed here in details.
The differentially-expressed genes in multiple expression patterns
Over the years, differential gene expression between the hybrid and its parental cultivars has been hypothesized to attribute to heterosis [5, 34]. As having partitioned the differentially-expressed genes into twelve patterns as conventionally done, we found only 25% to 45% or minorities of the genes were additively expressed in the rice hybrid; this result contradicted what was reported for a similar study in hybrid maize, where additively expressed genes were found as a major trend, 77.7% . The reason for such a disparity may be complex as it may be related to operational pollination strategies and differences in epigenetic regulations. Meyer et al. (2004) have shown that alternative pollination methods (hand-vs. self-pollination) have significant effects on seed size and early seedling growth rate in Arabidopsis. The patterns of gene expression altered obviously in cross-fertilized kernel as compared to self-fertilized kernel, both qualitatively and quantitatively , largely due to cis-transcriptional variations in maize inbred lines that lead to additive expression patterns in the F1 hybrids . For the involvement of possible epigenetic mechanisms, we refer to the difference in transposon density between the two species as the maize genome is more heavily bombarded by active repeats and we speculate that a more vigorous methylation tactic might be used in gene regulation in maize. Among non-additively expressed genes, both over-dominant and under-dominant genes are rather abundant, supporting in part the over-dominance hypothesis for rice heterosis .
Among all differentially-expressed genes, we identified up to 70% of them (P < 0.01) exhibiting paternal-like expression (PLE) profiles, especially in panicles, which are at least in part attributable to two plausible mechanisms – molecular imprinting and defective expressions of the maternal alleles – as often observed in panicles harvested at the pollen maturing stage, where thermo-sensitive male sterility of the maternal line (PA64s) may be relevant . For instance, two MADS-box transcription factors related to pollen fertility have been consistently observed as up-regulated in the hybrid, but they do not express in the male-sterility plant [39, 40]. The rapid alkalinization factor, a polypeptide hormone that was suggested to be related to nuclear sterility and development , was observed to be up-regulated and located in photoperiod-sensitive and genic male sterility trait based on our QTL analysis. Although we have not been able to plot plausible functional scenarios on the precise roles of these genes, the findings undoubtedly provide useful clues for future molecular studies.
Putative regulation mechanisms of differentially-expressed genes
Differential gene expression in plants is known to be mainly regulated by two forms of mechanisms – cis- and trans-regulations at transcription levels as well as epigenetic and post-transcription modulations . For instance, differential methylation in CpG or CNG islands [9, 42] and allele-dependent mechanisms of gene regulation  have been demonstrated between hybrid and its parents in rice and maize. However, variations among cis-regulatory elements are hard to study but trans-regulatory factors are easier to identify based on gene expression data. We have indeed found over 48 transcription factors, annotated as differentially-expressed genes, including MADS-box genes, TFIIE, bZIP, and Jumonji; these genes have been found involved in various aspects of development and differentiation in land plants. Some of the MADS-box genes function in floral tissues as "molecular architects" of flower morphogenesis. TFIIE is an essential component of the RNA polymerase II transcription machinery , playing important roles at two distinct but sequential steps in transcription: pre-initiation complex formation-activation (open complex formation) and the transition from initiation to elongation . Although the possible contributions of these transcription factors, all-purpose or members of multiple gene families, to hybrid vigor may not be easily demonstrated, their presence and regulated expression are initial clues for in-depth molecular and genetic studies.
An increasing number of studies have reported that functional divergence in duplicated gene is accompanied by gene expression change although the evolution mechanism behind this process remains unclear. There was a report that 7% of duplicated gene pairs co-express in yeast , and we know that gene and chromosomal segment duplications widely exist in the rice genome, including an ancient whole genome duplication, recent segmental duplications, and massive ongoing individual gene duplications that cover 65.7% of the genome . We found 7 of our 698 ambiguous assigned tags are mapped to the duplicated gene pairs, which we suspected the duplication with a high homology may affect gene expression including silencing and up- or down-regulation of one of the duplicated genes after hybridization . When looking into the possible molecular assays in distinguishing the different alleles, we found that it is actually possible to design allele-specific primers to detect the expression level of duplication pairs.
We improved the tag-to-gene mapping strategy by combining information from transcript sequences and rice genome annotation and obtained over 10,000 new tags for a more comprehensive view of genes that related to rice heterosis. These heterotic expression genes among different genotypes provided new avenues for exploring the molecular mechanisms underlying heterosis, including variable gene expression patterns.
We constructed a PCUE database for rice (Oryza sativa) on the basis of available genomic resources that contain (1) the improved whole genome shot-gun sequence assemblies of 93-11 [GenBank: AAAA02000000] and PA64s as well as their annotations , (2) a collection of 19,079 non-redundant FL-cDNAs (nr-FL-cDNAs;  from KOME , and (3) 51,336 UniGenes (UniGene Build #59) and 1,183,931 ESTs from NCBI .
We aligned the collected transcript sequences to the two genome sequences by using BLAT  to obtain a dataset for tag annotations. The threshold parameters set for aligned transcripts are (1) at least 90% identical to their genomic sequences and (2) covering ≥ 90% transcript sequences. When a transcript has more than one hit to genomic sequences, the longest consensus was selected as the best-aligned (true) locus. We further selected sequences that span the 3' end of a predicted gene but do not extend to the next with ≥ 100-bp overlapping sequences. As a result, our predicted genes were partitioned into two sets: supported by one or more transcripts and without supporting data.
The evaluation dataset
In order to evaluate the accuracy of tag-to-gene mapping methodology, we built a test dataset that contains 2,480 FL-cDNA sequences that satisfied all five criteria: (1) ORF length > 300 bp, (2) with poly(A) signal (AATAAA/ATTAAA) or poly(A) tails (with a minimal number of five A) , (3) alignable to a unique predicated gene with homolog (based on 50% protein sequence similarity or 100 residues) to Arabidopsis, (4) a unique CATG tag and experimental data, and (5) alignable to a unique predicted gene and corresponding UniGenes or ESTs. We further divided this dataset into three categories: UniGene, EST, and predicted gene. In the Unigene and EST categories, we have twelve subsets. Eight of those were sequences with poly(A) signal (Uni-S and EST-S), with poly(A) tails (Uni-A and EST-A), with both poly(A) signal and tail (Uni-B and EST-B), without poly(A) signal and tails (Uni-N and EST-N). The other four subsets contained the longest and the best transcripts that were best validated by either UniGenes or ESTs (Unibest or ESTbest). To know the length of 3'-UTR, we used 19,079 non-redundant FL-cDNA to determine the length distribution and found that 95% of these genes have UTR length shorter than 1280 bp, with an average size of 422 bp and a median of 295 bp. We therefore added five different lengths (100-, 200-, 300-, 400-, and 500-bp) to construct virtual UTRs for the predicted genes. We finally built virtual tags from each of the above-mentioned subsets by extracting a 10-bp tag from the immediate downstream sequence of the last (3'-most) NlaIII (CATG) site. We evaluated the success rates of virtual tags that match the test set.
Virtual tags and tag-to-gene mapping
Since predicted genes do not have UTRs, we extracted consecutive exons together to form gene models from the two genome assemblies and added to them either UTR sequences based on information from known transcripts or artificial UTRs in a length of 300 bp. We obtained four groups of tag data, including those based on cDNA, Unimax, ESTmax, and predicted genes (P-300). We mapped 68,462 unique empirical tags from our data  to the four groups of virtual tags after filtering cloning linkers, vectors, and simple repeats. We excluded 47,867 tags from further processing and their outcomes from our analysis protocol were summarized (see Additional file 8). These tags were regarded as unmapped tags although 45,025 of them were actually mapped to the nuclear genome but in unexpected range of correct positions of exon and UTR sequences. Most of them were believed to fragmented mRNAs that were co-processed during library construction procedures.
We annotated all our SAGE tags based on InterPro/Network and KEGG for protein families, domains, and functions. We chose the best scoring primary (sequence similarity-based) annotations from family-type categories first, followed by domain-type and others. If the gene had no primary annotation then we used a network-based annotation . P values between copy numbers among libraries were calculated based on Audic-Claverie (or AC) statistics  by using IDEG6 software [53, 54]. The significance of the differentially-expressed genes was defined with P values less than 0.05 or 0.01. Ratios of up-regulated and down-regulated genes were calculated according to ratio = L/[(P+N)/2] (≥ 2) and [(P+N)/2]/L (<2), respectively.
Microarray and QTL data
We used microarray data from the leaf tissue at the milky stage, which were generated in our laboratory. The microarray contains 60,727 oligonucleotide probes representing all predicted genes from the genome sequence of 93-11 . We physically mapped the oligonucleotides to the most up-to-date version of the genome assembly  with the threshold that each oligonucleotide must match to one unique gene with 90% or higher sequence identity. We also used rice QTL data with physical position on TIGR4 genome from Gramene and mapped differentially-expressed genes to nine QTL categories.
Serial analysis of gene expression
Quantitative trait locus
non-redundant full-length cDNAs
We are grateful to our microarray team members for providing the leaf data and to Drs. Xiangjun Tian and Weihua Chen for critical reading of the manuscript and many constructive discussions. This work received financial support from Chinese Academy of Science (KSCX1-SW-03) and the Ministry of Science and Technology (2005AA235110) to JY.
- Budak H: Understanding of Heterosis. KSU J Science and Engineering. 2002, 5 (2): 69-75.Google Scholar
- Xiao J, Li J, Yuan L, Tanksley SD: Dominance is the major genetic basis of heterosis in rice as revealed by QTL analysis using molecular markers. Genetics. 1995, 140: 745-754.PubMedPubMed CentralGoogle Scholar
- Yu SB, Li JX, Xu GC, Tan YF, Gao YJ, Li XH, Zhang QF, Maroof MAS: Importance of epistasis as the genetic basis of heterosis in an elite rice hybrid. Proc Natl Acad Sci USA. 1997, 94: 9226-9231. 10.1073/pnas.94.17.9226.PubMedPubMed CentralView ArticleGoogle Scholar
- Birchler JA, Auger DL, Riddle NC: In search of the molecular basis of heterosis. Plant Cell. 2003, 15 (10): 2236-2239. 10.1105/tpc.151030.PubMedPubMed CentralView ArticleGoogle Scholar
- Sun Q, Wu L, Ni Z, Meng F, Wang Z, Lin Z: Differential gene expression patterns in leaves between hybrids and their parental inbreds are correlated with heterosis in a wheat diallel cross. Plant Science (Oxford). 2004, 166 (3): 651-657.View ArticleGoogle Scholar
- Yao Y, Ni Z, Zhang Y, Chen Y, Ding Y, Han Z, Liu Z, Sun Q: Identification of differentially expressed genes in leaf and root between wheat hybrid and its parental inbreds using PCR-based cDNA subtraction. Plant Mol Biol. 2005, 58 (3): 367-384. 10.1007/s11103-005-5102-x.PubMedView ArticleGoogle Scholar
- Wu LM, Ni ZF, Meng FR, Lin Z, Sun QX: Cloning and characterization of leaf cDNAs that are differentially expressed between wheat hybrids and their parents. MGG Molecular Genetics and Genomics. 2003, 270 (3): 281-286. 10.1007/s00438-003-0919-y.PubMedView ArticleGoogle Scholar
- Yao Y, Ni Z, Du J, Wang X, Wu H, Sun Q: Isolation and characterization of 15 genes encoding ribosomal proteins in wheat (Triticum aestivum L.). Plant Science (Oxford). 2006, 170 (3): 579-586.View ArticleGoogle Scholar
- Xiong LZ, Xu CG, Saghai Maroof MA, Zhang Q: Patterns of cytosine methylation in an elite rice hybrid and its parental lines, detected by a methylation-sensitive amplification polymorphism technique. Mol Gen Genet. 1999, 261 (3): 439-446. 10.1007/s004380050986.PubMedView ArticleGoogle Scholar
- Ni Z, Sun Q, Liu Z, Wu L, Wang X: Identification of a hybrid-specific expressed gene encoding novel RNA-binding protein in wheat seedling leaves using differential display of mRNA. Mol Gen Genet. 2000, 263 (6): 934-938. 10.1007/PL00008693.PubMedView ArticleGoogle Scholar
- Huang Y, Zhang L, Zhang J, Yuan D, Xu C, Li X, Zhou D, Wang S, Zhang Q: Heterosis and polymorphisms of gene expression in an elite rice hybrid as revealed by a microarray analysis of 9198 unique ESTs. Plant Mol Biol. 2006, 62 (4–5): 579-591. 10.1007/s11103-006-9040-z.PubMedView ArticleGoogle Scholar
- Khattra J, Delaney AD, Zhao Y, Siddiqui A, Asano J, McDonald H, Pandoh P, Dhalla N, Prabhu AL, Ma K, Lee S, Ally A, Tam A, Sa D, Rogers S, Charest D, Stott J, Zuyderduyn S, Varhol R, Eaves C, Jones S, Holt R, Hirst M, Hoodless PA, Marra MA: Large-scale production of SAGE libraries from microdissected tissues, flow-sorted cells, and cell lines. Genome Res. 2007, 17 (1): 108-116. 10.1101/gr.5488207.PubMedPubMed CentralView ArticleGoogle Scholar
- Wang SM: Understanding SAGE data. Trends Genet. 2007, 23 (1): 42-50. 10.1016/j.tig.2006.11.001.PubMedView ArticleGoogle Scholar
- Chen J, Sun M, Lee S, Zhou G, Rowley JD, Wang SM: Identifying novel transcripts and novel genes in the human genome by using novel SAGE tags. Proc Natl Acad Sci USA. 2002, 99 (19): 12257-12262. 10.1073/pnas.192436499.PubMedPubMed CentralView ArticleGoogle Scholar
- Boon K, Osorio EC, Greenhut SF, Schaefer CF, Shoemaker J, Polyak K, Morin PJ, Buetow KH, Strausberg RL, De Souza SJ, Riggins GJ: An anatomy of normal and malignant gene expression. Proc Natl Acad Sci USA. 2002, 99 (17): 11287-11292. 10.1073/pnas.152324199.PubMedPubMed CentralView ArticleGoogle Scholar
- Lee JY, Lee DH: Use of serial analysis of gene expression technology to reveal changes in gene expression in Arabidopsis pollen undergoing cold stress. Plant Physiol. 2003, 132 (2): 517-529. 10.1104/pp.103.020511.PubMedPubMed CentralView ArticleGoogle Scholar
- Fizames C, Munos S, Cazettes C, Nacry P, Boucherez J, Gaymard F, Piquemal D, Delorme V, Commes T, Doumas P, Cooke R, Marti J, Sentenac H, Gojon A: The Arabidopsis root transcriptome by serial analysis of gene expression. Gene identification using the genome sequence. Plant Physiol. 2004, 134 (1): 67-80. 10.1104/pp.103.030536.PubMedPubMed CentralView ArticleGoogle Scholar
- Matsumura H, Nirasawa S, Terauchi R: Technical advance: transcript profiling in rice (Oryza sativa L.) seedlings using serial analysis of gene expression (SAGE). Plant J. 1999, 20 (6): 719-726. 10.1046/j.1365-313X.1999.00640.x.PubMedView ArticleGoogle Scholar
- Gibbings JG, Cook BP, Oufault MR, Madden SL, Khurie S, Tumbull CJ, Dunwell JM: Globle transcript analysis of rice leaf and seed using SAGE technology. Plant Biotechnology Journal. 2003, 1: 271-285. 10.1046/j.1467-7652.2003.00026.x.PubMedView ArticleGoogle Scholar
- Wang Q, Zhang QD, Jiang GM, Lu CM, Kuang TY, Wu S, Li CQ, Jiao DM: Photosynthetic Characteristics of Two Superhigh-yield Hybrid Rice. Acta Botanica Sinica. 2000, 42 (12): 1285-1288.Google Scholar
- Bao J, Lee S, Chen C, Zhang X, Zhang Y, Liu S, Clark T, Wang J, Cao M, Yang H, Wang SM, Yu J: Serial analysis of gene expression study of a hybrid rice strain (LYP9) and its parental cultivars. Plant Physiol. 2005, 138 (3): 1216-1231. 10.1104/pp.105.060988.PubMedPubMed CentralView ArticleGoogle Scholar
- Yu J, Hu S, Wang J, Wong GK, Li S, Liu B, Deng Y, Dai L, Zhou Y, Zhang X, Cao M, Liu J, Sun J, Tang J, Chen Y, Huang X, Lin W, Ye C, Tong W, Cong L, Geng J, Han Y, Li L, Li W, Hu G, Huang X, Li W, Li J, Liu Z, Li L, et al: A draft sequence of the rice genome (Oryza sativa L. ssp. indica). Science. 2002, 296 (5565): 79-92. 10.1126/science.1068037.PubMedView ArticleGoogle Scholar
- Yu J, Wang J, Lin W, Li S, Li H, Zhou J, Ni P, Dong W, Hu S, Zeng C, Zhang J, Zhang Y, Li R, Xu Z, Li S, Li X, Zheng H, Cong L, Lin L, Yin J, Geng J, Li G, Shi J, Liu J, Lv H, Li J, Wang J, Deng Y, Ran L, Shi X, et al: The Genomes of Oryza sativa: a history of duplications. PLoS Biol. 2005, 3 (2): e38-10.1371/journal.pbio.0030038.PubMedPubMed CentralView ArticleGoogle Scholar
- Kikuchi S, Satoh K, Nagata T, Kawagashira N, Doi K, Kishimoto N, Yazaki J, Ishikawa M, Yamada H, Ooka H, Hotta I, Kojima K, Namiki T, Ohneda E, Yahagi W, Suzuki K, Li CJ, Ohtsuki K, Shishiki T, Otomo Y, Murakami K, Iida Y, Sugano S, Fujimura T, Suzuki Y, Tsunoda Y, Kurosaki T, Kodama T, Masuda H, Kobayashi M, et al: Collection, mapping, and annotation of over 28,000 cDNA clones from japonica rice. Science. 2003, 301 (5631): 376-379. 10.1126/science.1081288.PubMedView ArticleGoogle Scholar
- Wu J, Maehara T, Shimokawa T, Yamamoto S, Harada C, Takazaki Y, Ono N, Mukai Y, Koike K, Yazaki J, Fujii F, Shomura A, Ando T, Kono I, Waki K, Yamamoto K, Yano M, Matsumoto T, Sasaki T: A comprehensive rice transcript map containing 6591 expressed sequence tag sites. Plant Cell. 2002, 14 (3): 525-535. 10.1105/tpc.010274.PubMedPubMed CentralView ArticleGoogle Scholar
- Tang J, Xia H, Li D, Cao M, Tao Y, Tong W, Zhang X, Hu S, Wang J, Yu J, Yang H, Zhu L: Gene expression profiling in rice young panicle and vegetative organs and identification of panicle-specific genes through known gene functions. Mol Genet Genomics. 2005, 274 (5): 467-476. 10.1007/s00438-005-0043-2.PubMedView ArticleGoogle Scholar
- Gene Expression Omnibus. [http://www.ncbi.nlm.nih.gov/geo/]
- Audic S, Claverie JM: The significance of digital gene expression profiles. Genome Res. 1997, 7 (10): 986-995.PubMedGoogle Scholar
- Kyoto Encyclopedia of Genes and Genomes. [http://www.genome.jp/kegg/]
- InterPro. [http://www.ebi.ac.uk/interpro/]
- Gramene. [http://www.gramene.org]
- Pleasance ED, Marra MA, Jones SJ: Assessment of SAGE in transcript identification. Genome Res. 2003, 13 (6A): 1203-1215. 10.1101/gr.873003.PubMedPubMed CentralView ArticleGoogle Scholar
- Graber JH, Cantor CR, Mohr SC, Smith TF: In silico detection of control signals: mRNA 3'-end-processing sequences in diverse species. Proc Natl Acad Sci USA. 1999, 96 (24): 14055-14060. 10.1073/pnas.96.24.14055.PubMedPubMed CentralView ArticleGoogle Scholar
- Tsaftaris SA: Molecular aspects of heterosis in plants. Plant Physiol. 1995, 94: 362-370. 10.1111/j.1399-3054.1995.tb05324.x.View ArticleGoogle Scholar
- Swanson-Wagner RA, Jia Y, DeCook R, Borsuk LA, Nettleton D, Schnable PS: All possible modes of gene action are observed in a global comparison of gene expression in a maize F1 hybrid and its inbred parents. Proc Natl Acad Sci USA. 2006, 103 (18): 6805-6810. 10.1073/pnas.0510430103.PubMedPubMed CentralView ArticleGoogle Scholar
- Meng F, Ni Z, Wu L, Sun Q: Differential gene expression between cross-fertilized and self-fertilized kernels during the early stages of seed development in maize. Plant Science. 2005, 168: 23-28. 10.1016/j.plantsci.2004.07.011.View ArticleGoogle Scholar
- Stupar RM, Springer NM: Cis-transcriptional variation in maize inbred lines B73 and Mo17 leads to additive expression patterns in the F1 hybrid. Genetics. 2006, 173 (4): 2199-2210. 10.1534/genetics.106.060699.PubMedPubMed CentralView ArticleGoogle Scholar
- Song LQ, Fu TD, Tu JX, Ma CZ, Yang GS: Molecular validation of multiple allele inheritance for dominant genic male sterility gene in Brassica napus L. Theor Appl Genet. 2006, 113 (1): 55-62. 10.1007/s00122-006-0271-9.PubMedView ArticleGoogle Scholar
- Nagasawa N, Miyoshi M, Sano Y, Satoh H, Hirano H, Sakai H, Nagato Y: SUPERWOMAN1 and DROOPING LEAF genes control floral organ identity in rice. Development. 2003, 130 (4): 705-718. 10.1242/dev.00294.PubMedView ArticleGoogle Scholar
- Kang HG, Jeon JS, Lee S, An G: Identification of class B and class C floral organ identity genes from rice plants. Plant Mol Biol. 1998, 38 (6): 1021-1029. 10.1023/A:1006051911291.PubMedView ArticleGoogle Scholar
- Haruta M, Constabel CP: Rapid alkalinization factors in poplar cell cultures. Peptide isolation, cDNA cloning, and differential expression in leaves and methyl jasmonate-treated cells. Plant Physiol. 2003, 131 (2): 814-823. 10.1104/pp.014597.PubMedPubMed CentralView ArticleGoogle Scholar
- Dai Y, Ni Z, Dai J, Zhao T, Sun Q: Isolation and expression analysis of genes encoding DNA methyltransferase in wheat (Triticum aestivum L.). Biochimica et Biophysica Acta. 2005, 1729 (2): 118-125.PubMedView ArticleGoogle Scholar
- Hollick JB, Patterson GI, Asmundsson IM, Chandler VL: Paramutation alters regulatory control of the maize pl locus. Genetics. 2000, 154 (4): 1827-1838.PubMedPubMed CentralGoogle Scholar
- Maxon ME, Goodrich JA, Tjian R: Transcription factor IIE binds preferentially to RNA polymerase IIa and recruits TFIIH: a model for promoter clearance. Genes Dev. 1994, 8 (5): 515-524. 10.1101/gad.8.5.515.PubMedView ArticleGoogle Scholar
- Okamoto T, Yamamoto S, Watanabe Y, Ohta T, Hanaoka F, Roeder RG, Ohkuma Y: Analysis of the role of TFIIE in transcriptional regulation through structure-function studies of the TFIIEbeta subunit. J Biol Chem. 1998, 273 (31): 19866-19876. 10.1074/jbc.273.31.19866.PubMedView ArticleGoogle Scholar
- Zhang Z, Gu J, Gu X: How much expression divergence after yeast gene duplication could be explained by regulatory motif evolution?. Trends Genet. 2004, 20 (9): 403-407. 10.1016/j.tig.2004.07.006.PubMedView ArticleGoogle Scholar
- Adams KL, Wendel JF: Novel patterns of gene expression in polyploid plants. Trends Genet. 2005, 21 (10): 539-543. 10.1016/j.tig.2005.07.009.PubMedView ArticleGoogle Scholar
- Rice Information System. [http://rise.genomics.org.cn/rice/index2.jsp]
- Knowledge-based Oryza Molecular biological Encyclopedia. [http://cdna01.dna.affrc.go.jp/cDNA/]
- National Center for Biotechnology Information. [http://www.ncbi.nih.gov/]
- Kent WJ: BLAT – the BLAST-like alignment tool. Genome Res. 2002, 12 (4): 656-664. 10.1101/gr.229202. Article published online before March 2002.PubMedPubMed CentralView ArticleGoogle Scholar
- McDermott J, Bumgarner R, Samudrala R: Functional annotation from predicted protein interaction networks. Bioinformatics. 2005, 21 (15): 3217-3226. 10.1093/bioinformatics/bti514.PubMedView ArticleGoogle Scholar
- IDEG6. [http://telethon.bio.unipd.it/bioinfo/IDEG6/]
- Romualdi C, Bortoluzzi S, Danieli GA: Detecting differentially expressed genes in multiple tag sampling experiments: comparative evaluation of statistical tests. Hum Mol Genet. 2001, 10 (19): 2133-2141. 10.1093/hmg/10.19.2133.PubMedView ArticleGoogle Scholar
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.