IC4R012-Metabolomics-2012-22229385

Project Title

• Dissection of genotype–phenotype associations in rice grains using metabolome quantitative trait loci analysis

The Background of This Project

• As metabolic state is modulated by heritable factors,the genetic control of metabolic traits has been a major focus in the field of plant metabolic research. However, the relationship between genotype and metabolomic traits(m-traits) is not straightforward, because m-traits are under complex influences of quantitative trait loci (QTL) (Fernie and Schauer, 2009; Kliebenstein, 2009; Peleg et al., 2009).Furthermore, the metabolic composition of plant tissues is dynamically affected by environmental factors through post-translational interactions involving entire metabolic networks (Chan et al., 2010; Kerwin et al., 2011). Recent progress in metabolomics and QTL mapping techniques has made it possible to investigate the effect of genetic background on m-trait levels for a wide variety of metabolites.Metabolome QTL (mQTL) analyses have been performed for Arabidopsis and tomato (Solanum lycopersicum) using metabolomic techniques such as GC–MS and LC–MS, from which a better understanding of the genetics of plant metabolism has emerged (Keurentjes et al., 2006; Schauer et al., 2006, 2008; Lisec et al., 2008, 2009; Rowe et al., 2008).
• In this study, an mQTL analysis was performed for rice grains. An understanding of the genetic background of m-traits in rice (Oryza sativa) grain would provide a foundation for improvement of the nutritional quality of one of the world’s most important food crops.To address these issues, a large-scale metabolome dataset, including a wide variety of rice metabolites, was obtained using an analytical platform that covers primary and secondary metabolites. Based on this dataset, the genetic backgrounds representing natural variations in rice metabolism were investigated by analysis of broad-sense heritability, metabolite–metabolite correlation, QTL mapping and identification of putative causative genes from a candidate mQTL region.

Plant Culture & Treatment

• The plant population consisted of 85 back-crossed inbred lines derived from the cross Sasanishiki/Habataki//Sasanishiki///Sasanishiki (Sasanishiki · Habataki) (Nagata et al., 2002b). Seeds from the experimental lines were grown in a paddy field at the National Institute of Agrobiological Sciences (Tsukuba, Japan) in 2005 and 2007, employing similar cultivation schedules. The seeds of the 2005 and 2007 harvests were used for metabolome analysis. One hundred dehulled seeds obtained from whole seeds harvested from 10 independent plants were ground to a fine powder using an MM300 mixer mill (Retsch, http://www.retsch.com/) at 20 Hz for 2 min in a stainless steel grinding vessel. The powder was divided between small sample tubes (50–100 mg) under nitrogen, and the samples were stored at )80�C until analysis.

Research Findings

• Finally, a metabolome dataset containing m-trait data for 759 metabolite signals from 85 experimental lines for the 2005 and 2007 harvests was obtained. Among the metabolite signals,a total of 93 were identified by comparing their chromatographic behaviour with mass spectra of the standard compounds, and 38 metabolites were structurally annotated using MS and MS/MS spectral data (Matsuda et al., 2011)(Figure 1 and Table S1).

'Figure 1. Rice grain metabolites annotated in this study.
Rice grain metabolites annotated in this study are represented in the simplified metabolic pathway. The colours of the nodes represent the methods used for analysis of that metabolite, i.e. GC-TOF-MS (yellow), CE-TOF-MS (blue), LC-IT-TOF-MS (green) and LC-Q-TOF-MS (red). The shape of the node represents the reliability of metabolite annotation. Rectangles indicate metabolites whose structure was identified by comparing their chromatographic behaviour and mass spectra with standard compounds. Diamonds indicate metabolites whose structure was determined by MS and MS/MS spectral data. Abbreviations for metabolites are defined in Table S1.'

• In this study, H2 was estimated using ANOVA by considering variation between the harvests of 2005 and 2007 as phenotypic variance derived from environmental factors. If quantitative traits are not affected by environmental factors, H2 should be close to 1. The distributions across all m-traits indicated that more than half of the m-traits have relatively low broad-sense heritability(H2 < 0.6) (Figure 2).

'Figure 2. Distribution of levels of broad-sense heritability of metabolic traits.Broad-sense heritability (H2) was estimated using one-way ANOVA by considering variations between the 2005 and 2007 harvests as phenotypic variance derived from environmental factors.'

• The annotated metabolite data showed that several groups of metabolites exhibited high heritability. Examples of such metabolites include lysophosphatidyl cholines,oryzanolsand flavone glycosides (Figure 3).

'Figure 3. Broad-sense heritability of rice grain metabolites.
The levels of broad-sense heritability (H2) determined for each metabolite are represented by the colours of the nodes, from white (H2 = 0.3) to red (H2 = 1.0). High broad-sense heritability indicates that the m-trait is mainly governed by genetic rather than environmental (non-heritable) factors. Abbreviations for metabolites are defined in Table S1.'

• Figure 4 shows the genome-wide distribution of mQTLs. It is clear that mQTLs are unevenly distributed throughout the rice genome, and that there are several regions in which mQTLs are densely clustered.

'Figure 4. Genome-wide distribution of mQTLs.Each row represents QTL mapping of single metabolic traits (m-traits). The results for a total of 758 m-traits are shown. Regions indicated in red or blue represent 1 ) LOD support intervals of detected mQTLs that positively affect the m-traits of Sasanishiki (red) and Habataki (blue) genotypes. The colours on the right of the panels indicate the various metabolome analysis pipelines, that is, CE-TOF-MS (blue), GC-TOF-MS (yellow), LC-IT-TOF-MS (green) and LC-Q-TOF-MS (red), used for analysis of metabolites.'

Labs working on this Project

• RIKEN Plant Science Center, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Japan,
• QTL Genomics Research Center, National Institute of Agrobiological Sciences, Kannondai 2-1-2, Tsukuba, Ibaraki, Japan, and
• Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, Japan

Corresponding Author

• Kazuki Saito：ksaito@psc.riken.jp