オミクス科学を活用した作物の分子育種に向けて

抄録

The increase in quality and productivity from crop cultivation has been offset by the narrowing of the crop genetic base (domestication syndrome) which has led to greater susceptibility to environmental stress. However, complete genome information for major crop species facilitates another approach to the breeding strategy for crop breeding. The approach of metabolomics-assisted breeding is a systematic strategy for introducing high quality traits on the basis of information on natural variance and metabolic polymorphisms. In order to perform this strategy, we need to understand not only metabolites displaying natural variation but also their underlying biosynthetic pathways to find the key genes for the production of target metabolites. Plant specialized (secondary) metabolites, widely diversified in their chemical structure, developed during the long evolutionary period of plant adaptation to environmental niches with gene duplication and convergent evolution of some key enzymatic genes. Recent technical developments allow for affordable whole genome sequencing as well as omics studies. Availability of several resources such as knockout mutant libraries, QTL lines and wild accessions, has resulted in a dramatic increase in the number of approaches for functional genomics, including metabolic phenotype screening, network analysis, mQTL and GWAS studies. We integrated these approaches with metabolomic analysis to refine the biosynthetic structures of phenolic secondary metabolism including natural variance and tissue and species specificity. Tomato represents an important, readily available model crop and its diploid nature renders genetics relatively easy. A mass spectrometry (MS) based global metabolite profiling and microarray analysis were combined to allow comparisons between the relative metabolic levels of leaves and fruits of S. lycopersicum and seven wild tomato species. Based on the results from metabolite profiling, we performed pathway-reconstruction for finding novel genes involved in tomato secondary metabolism by integration analysis with transcriptome data. Our metabolomics-assisted functional genomics approach, which has resulted in discovery of key genes involved in chemical diversity in model plant and tomato species, is described.