Keywords

Large-Scale Collection of Genes Expressed in Common Wheat

Wheat is characteristically polyploidic in nature and harbors large complex genomes. Therefore, accumulation of expressed sequence tags (ESTs) for wheat is particularly important for enabling functional genomics and molecular breeding studies. We obtained large collections of ESTs from various tissues in the wheat life cycle and from tissues subjected to stresses. Since full-length cDNAs are indispensable for certifying the ESTs collected and for annotating genes present in the genome, we performed a systematic survey of and sequencing of full-length cDNA clones. The strategy for the collection of ESTs in common wheat is shown in Fig. 10.1. First, total RNAs were extracted from the ten tissues over the course of the wheat life cycle. Subsequently, RNAs were extracted from biotic and abiotic-stressed tissues. The cDNA libraries were constructed from these RNAs by using standard methods. Colonies were randomly picked and sequenced from both ends. At present, 894,756 unbiased ESTs are available. More than 1.2 million wheat ESTs are registered in the NCBI EST database, 70 % of which were contributed from Japan. For full-length cDNAs, the cDNA library was constructed with the CAP-trapper method (Kawaura et al. 2009). After one-path sequencing of cDNA clones, independent clones were selected three times, and the inserts of the cDNA clones were verified. Finally, 22,519 sequence-verified full-length cDNAs were obtained.

Fig. 10.1
figure 1

MUGEST: Wheat EST project in Japan

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Functional Analysis of ESTs in Common Wheat

About one million ESTs comprising 125.3 Mb nucleotides were collected from the 51 cDNA libraries constructed from various tissues (including biotic and abiotic tissues) and organs under a range of conditions (Manickavelu et al. 2012). ESTs were assembled with stringent parameters so as to produce 37,138 contigs and 215,199 singlets, 10.6 % of which had no homology with those in the public databases. Using these EST data, we developed the correspondence analysis (CA) method (Yano et al. 2006), as shown in Fig. 10.2. The CA method enables identification and comparisons of significant gene expression with the specific library. This method was applied for comparison of gene expression analysis of susceptive and resistant near-isogenic lines in common wheat infected by Puccinia triticina (Manickavelu et al. 2010). In Fig. 10.2, four libraries developed for powdery mildew and leaf rust (two susceptible and two resistant) have been compared. Using this method, common and specific genes related to treatments or the library were easily selected.

Fig. 10.2
figure 2

Correspondence analysis for characterization of gene expression profiles in four libraries. Four cDNA libraries were developed for powdery mildew and leaf rust (two susceptible and two resistant). Each corner refers to each library; the significant genes related to specific libraries are positioned near, and in the same color as, the corresponding library

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Based on the gene ontology, characteristic proteins were classified according to molecular functions, cellular localization, and biological processes. Furthermore, the unigenes were classified into susceptible and resistant classes based on the EST members assembled from the respective libraries. Several genes showing specific expression in the resistant and susceptible lines could be selected. The molecular pathogenicity of leaf rust after infection in wheat was evaluated, and the EST data were supplied for future studies.

Full-Length cDNA Collection in Common Wheat

For construction of full-length cDNAs, total RNAs were extracted from the 17 tissues studied (Kawaura et al. 2009). Tese RNAs were mixed together to construct a cDNA library with the CAP-trapper method. We used three rounds of cDNA selection. At first, 19,968 clones were supplied for one-path sequencing from both ends. These sequences were clustered and non-redundant groups were selected. In this step, 6,162 sequence-verified full-length cDNAs were obtained after using the primer-walked Sanger sequencing method. Subsequently, data were obtained for an additional 10,645 cDNAs. After the second round, the already sequenced clones were subtracted from the library. An additional 5,712 redundant cDNAs were sequenced with the 454 FLx + instrument. Finally, 22,519 sequence-verified full-length cDNAs are now available. These FLcDNAs showed a wide size distribution with a range of 64–8,983 bp, and the mean size of these FLcDNAs was 1,848 bp larger than those of barley (1,711 bp; Matsumoto et al. 2011) and rice (1,746 bp; Kikuchi et al. 2003).

We performed RNAseq analysis of diploid ancestors to assign these FLcDNAs to three genomes, namely A, B, and D. Total RNAs were extracted from seedlings, roots and spikes of Triticum urartu Tum., Aegilops speltoides Tausch, and Aegilops tauschii Coss. These RNAs were utilized for RNAseq by using the Illumina Hiseq 2000 system. For each RNA sample, more than 50 Gb were read. These RNA data were grouped to generate contig clusters for each species. In this case, we used data for 16,807 FLcDNAs. These FLcDNA data were compared against contigs from each species. Top hits were selected based on homology and SNP patterns amongst genes from the three genomes. Thus, 4,759 cDNAs were assigned to the A genome, 2,849 to B, and 5,343 to the D genome. In total, 77 % of genes could be assigned. The remaining 23 % had no counterpart in the diploid RNAseq data pool. The B genome had a lesser number of expressed genes than the other genomes. This finding suggests that the lesser sequence homology of genes between the B genome of common wheat and the S genome of Ae. speltoides might disturb, to some extent, the ability to assign groups.

Identification of Standard Transcripts in Common Wheat

We attempted to identify standard transcripts in common wheat from the EST data; our data for 805,544 ESTs, including those for FLcDNAs, were assembled in each of the ten strains of common wheat. After assembly with MIRS (http://chevreux.org/projects_mira.html), 191,988 contigs and 326,851 singlets were obtained. The resultant contigs and singlets were clustered by the CD HIT software by setting the criteria at an identity of 99.5 % to obtain homoeologs (6×), and 95 % to obtain homologs (2×). Consequently, 162,874 gene clusters (2×) were grouped. Out of these clusters, 81,694 clusters were matched to expressed genes of barley (Matsumoto et al. 2011), Brachypodium (Mochida et al. 2013), and rice (http://ricegaas.dna.affrc.go.jp/rgadb/). By removing redundant results, 27,943 gene clusters that had homologies with Poaceae transcripts could be established at the diploid level; 81,180 clusters had no homologs among cereal genomes. Therefore, we conducted a “blat search” against 5× draft wheat genome sequences (Brenchley et al. 2012); 54,446 clusters were found to have homologies with the wheat genome sequences. By grouping these clusters at 80 % identity, 13,060 wheat-specific genes were obtained. From these analyses, 41,003 gene groups were clustered in wheat. Out of the 41,003 genes, 27,943 wheat genes had common homologies to Poaceae-expressed genes.

Chromosome Assignment of Expressed Genes in Common Wheat

We assigned these expressed homoeologs to each chromosome based on the “Survey sequence” organized by the International Wheat Genome Sequence Consortium (http://www.wheatgenome.org/). From this rough assignment, we can estimate the number of expressed genes from each chromosome and/or chromosome arm (Fig. 10.3). Chromosome 3B and 5BL were highly expressed. Conversely, genes located on 6B, which is the Japanese sequencing target, were less commonly expressed.

Fig. 10.3
figure 3

Chromosome assignments of wheat-expressed genes by using the Chinese Wheat “Survey-sequences” of IWGSC

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Conclusion

Snapshots of gene expression profiles in the wheat life cycle and/or in response to environmental stresses were developed; 22,519 sequence-verified full-length cDNAs were obtained. Of these, 77.4 % were assigned into the A, B, and D genomes with the RNAseq data from their diploid ancestors. We obtained 284,822 homoeo-clusters (6×) from 805,544 ESTs of common wheat. These homoeo-clusters were classified into 27,943 (Poaceae common) and 13,060 (wheat specific) gene groups (2×). In total, 41,003 wheat transcripts (2×) were classified; 284,822 homoeo-clusters were assigned into 21 chromosomes using “Survey-sequences” from the IWGSC.

Thus, the words of Prof. Kihara (1946), “The history of the earth is recorded in the layers of its crust. The history of all organisms is inscribed in the chromosomes” hold true even now, in the genomics era.