Current challenges and future potential of tomato breeding using omics approaches

Abstract

As tomatoes are one of the most important vegetables in the world, improvements in the quality and yield of tomato are strongly required. For this purpose, omics approaches such as metabolomics and transcriptomics are used not only for basic research to understand relationships between important traits and metabolism but also for the development of next generation breeding strategies of tomato plants, because an increase in the knowledge improves the taste and quality, stress resistance and/or potentially health-beneficial metabolites and is connected to improvements in the biochemical composition of tomatoes. Such omics data can be applied to network analyses to potentially reveal unknown cellular regulatory networks in tomato plants. The high-quality tomato genome that was sequenced in 2012 will likely accelerate the application of omics strategies, including next generation sequencing for tomato breeding. In this review, we highlight the current studies of omics network analyses of tomatoes and other plant species, in particular, a gene coexpression network. Key applications of omics approaches are also presented as case examples to improve economically important traits for tomato breeding.

Keywords: Solanum lycopersicum; biochemical trait; coexpression analysis; metabolomics; multinetwork; tomato; transcriptomics.

Fig. 1

To-date coverage of the tomato metabolome using the MS-based metabolomics platform in PRIMe (Platform for RIKEN Metabolomics, http://prime.psc.riken.jp/). Principal component analysis was performed using the physicochemical properties of the metabolomic dataset obtained from Kusano et al. (2011a) and those from the LycoCyc database (http://solgenomics.net/tools/solcyc/) (Mazourek et al. 2009). We used the latest version of the metabolite information in our custom database and ChemSpider (http://www.chemspider.com/). PC, principal component.

Fig. 2

The current status of (A) experimental information and (B) tissues in 23 tomato microarray datasets. We used a collection of 393 Affymetrix tomato GeneChip data from the NCBI GEO, ArrayExpress and TFGD (Fei et al. 2011) public databases for this calculation (collection date, July 2012).

Fig. 3

An example of a tomato multinetwork. (A) We constructed a tomato multinetwork that consisted of 10209 nodes and 85352 links. Information for the tomato metabolic pathway was obtained from the LycoCyc database (http://solgenomics.net/tools/solcyc/) (Mazourek et al. 2009), coexpression information (Fukushima et al. 2012), protein-protein interactions from interolog (Yu et al. 2004) and miRNA-gene relationships from PMRD (Zhang et al. 2010). (B) An expansion of the glutamine synthetase (GS)-related network in the multinetwork. Because the genes annotated as GS1 (SGN-U313258), GTS1 (SGN-U313257) and CGS (SGN-U314517) have many links to other genes, these genes are thought to be ‘hub’ gene candidates. PPI, protein-protein interaction; miRNA, micro RNA; GTS, putative GS; CGS, chloroplastic GS.