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Japanese pear (Pyrus pyrifolia) is a major fruit tree in the family Rosaceae and is bred for fruit production. To promote the development of breeding strategies and molecular research for Japanese pear, we sequenced the transcripts of Japanese pear variety ‘Hosui’. To exhaustively collect information of total gene expression, RNA samples from various organs and stages of Japanese pear were sequenced by three technologies, single-molecule real-time (SMRT) sequencing, 454 pyrosequencing, and Sanger sequencing. Using all those reads, we determined comprehensive reference sequences of Japanese pear. Then, their protein sequences were predicted, and biological functional annotations were assigned. Finally, we developed a web database, TRANSNAP (http://plantomics.mind.meiji.ac.jp/nashi), which is the first web resource of Japanese pear omics information. This database provides highly reliable information via a user-friendly web interface: the reference sequences, gene functional annotations, and gene expression profiles from microarray experiments. In addition, based on sequence comparisons among Japanese, Chinese and European pears, similar protein sequences among the pears and species-specific proteins in Japanese pear can be quickly and efficiently identified. TRANSNAP will aid molecular research and breeding in Japanese pear, and its information is available for comparative analysis among other pear species and families.

Watercore is a physiological disorder of Japanese pear fruits, in which a watery translucent area appears in the flesh of fruits on the tree. The disorder leads to the loss of commercial value, and resistance to watercore is one of the most important features to be studied in the breeding of Japanese pear. Even in susceptible cultivars such as ‘Hosui’ (syn. ‘Housui’), the degree of watercore development varies markedly from year to year, which makes it difficult to analyze the characteristics of this phenomenon prior to visible watercore development. We used two F1 sib pairs from ‘Hosui’ × ‘Hougetsu’ and ‘Akiakari’ × ‘Taihaku’ crosses. Each pair contains a susceptible sib and a resistant sib. In these pairs, susceptible sibs developed severe watercore in all the fruits examined in each year, whereas no watercore was found even in overmature fruits of resistant sibs. To search for genes related to watercore, we used a custom pear microarray and studied differential gene expression in susceptible and resistant sibs prior to visible watercore development. We identified 115 differentially expressed genes including sugar metabolism–, hormone–, and cell wall–related genes. Some of these genes were differentially expressed only in immature fruits prior to watercore development. These genes would be useful as molecular markers to predict watercore at early developmental stages or in pear breeding programs.

Analysis of a gene expression network (GEN), which is constructed based on similarity of gene expression profiles, is a widely used approach to gain clues for new biological insights. The recent abundant availability of transcriptome data in public databases is enabling GEN analysis under various experimental conditions, and even comparative GEN analysis across species. To provide a platform to gain biological insights from public transcriptome data, valuable databases have been created and maintained. This chapter introduces the web database OryzaExpress, providing omics information on Oryza sativa (rice). The integrated database Plant Omics Data Center, supporting a wide variety of plant species, is also described to compare omics information among multiple plant species.

Steroidal glycoalkaloids (SGAs) are cholesterol-derived specialized metabolites produced in species of the Solanaceae. Here, we report that a group of jasmonate-responsive transcription factors of the ETHYLENE RESPONSE FACTOR (ERF) family (JREs) are close homologs of alkaloid regulators in Cathranthus roseus and tobacco, and regulate production of SGAs in tomato. In transgenic tomato, overexpression and dominant suppression of JRE genes caused drastic changes in SGA accumulation and in the expression of genes for metabolic enzymes involved in the multistep pathway leading to SGA biosynthesis, including the upstream mevalonate pathway. Transactivation and DNA-protein binding assays demonstrate that JRE4 activates the transcription of SGA biosynthetic genes by binding to GCC box-like elements in their promoters. These JRE-binding elements occur at significantly higher frequencies in proximal promoter regions of the genes regulated by JRE genes, supporting the conclusion that JREs mediate transcriptional co-ordination of a series of metabolic genes involved in SGA biosynthesis.

For proper control of biological activity, some key genes are highly expressed in a particular spatiotemporal domain. Mining of such spatiotemporally expressed genes using large-scale gene expression data derived from a broad range of experimental sources facilitates our understanding of genome-scale functional gene networks. However, comprehensive information on spatiotemporally expressed genes is lacking in plants. To collect such information, we devised a new index, Δdmax, which is the maximum difference in relative gene expression levels between sample runs which are neighboring when sorted by the levels. Employing this index, we comprehensively evaluated transcripts using large-scale RNA sequencing (RNA-Seq) data stored in the Sequence Read Archive for eight plant species: Arabidopsis thaliana (Arabidopsis), Solanum lycopersicum (tomato), Solanum tuberosum (potato), Oryza sativa (rice), Sorghum bicolor (sorghum), Vitis vinifera (grape), Medicago truncatula (Medicago), and Glycine max (soybean). Based on the frequency distribution of the Δdmax values, approximately 70,000 transcripts showing 0.3 or larger Δdmax values were extracted for the eight species. Information on these genes including the Δdmax values, functional annotations, conservation among species, and experimental conditions where the genes show high expression levels is provided in a new database, CATchUP (http://plantomics.mind.meiji.ac.jp/CATchUP). The CATchUP database assists in identifying genes specifically expressed under particular conditions with powerful search functions and an intuitive graphical user interface.