Difference between revisions of "IC4R011-Metabolomics-2015-25267402"

Revision as of 06:46, 22 July 2016

Metabolome-genome-wide association study dissects genetic architecture for generating natural variation in rice secondary metabolism

Recently, metabolomics studies revealed that the composition of secondary metabolites in plants is an inherently variable phenotype, as genetic polymorphisms cause large qualitative and quantitative variations in metabolic phenotypes (metabolotypes) among cultivars and ecotypes (Chan et al., 2010a; Saito and Matsuda, 2010; Weigel, 2012; Carreno-Quintero et al., 2013).Recently, metabolomics studies revealed that the composition of secondary metabolites in plants is an inherently variable phenotype, as genetic polymorphisms cause large qualitative and quantitative variations in metabolic phenotypes (metabolotypes) among cultivars and ecotypes (Chan et al., 2010a; Saito and Matsuda, 2010; Weigel, 2012; Carreno-Quintero et al., 2013).
In this study, GWAS was conducted by analyzing the aerial part of 175 Japanese diverse rice (Oryza sativa) cultivar seedlings using liquid chromatography-tandem mass spectrometry (LC-MS/MS) for the non-targeted analysis of known and unknown metabolites (Bottcher et al., 2008;Matsuda et al., 2009). The analysis revealed that there are two types of genetic architectures responsible for the natural variations in the composition of secondary metabolites in the rice population. While the small number of mQTLs tightly associated with levels of one-third of analyzed metabolites, levels of other one-third of metabolites were under the smaller effect of multiple QTL.

A Japanese rice collection of 175 accessions were used in this study (Table S1) (Yonemaru et al., 2012). The Sasanishiki/Habataki chromosome segment substitution lines (CSSLs, 39 accessions) were also used (Ando et al., 2008). Seeds were sterilized in 10% sodium hypochloric acid solution by vacuum infiltration for 1 h,and then immersed in aqueous 2% PPMTM solution (Nacalai Tesque, Kyoto, Japan, http://www.nacalai.co.jp/) at 28°C for 1 day in darkness. Seeds were sown in wet commercial fertilized soil (Bonsol II; Sumitomo Chemical, Tokyo, Japan, http://www.sumitomochem.co.jp/), and maintained under a 12-h light (28°C)/12-h dark(20°C) cycle for germination. Plants were kept under constant subirrigation conditions with tap water. After 2 weeks of growth, the entire aboveground (or aerial) part of one seedling was collected,weighed, and frozen in liquid nitrogen for analysis. Samples were stored at �80°C until analysis.

A metabolome dataset composed of 342 metabolite signals(peaks) in 668 samples was obtained using liquid chromatography-mass spectrometry (LC/MS) (Tables S1–S3) (Matsuda et al., 2009, 2010). Metabolite annotation successfully characterized the structures of 91 metabolites, demonstrating that phytochemicals produced in rice cultivars were more diverse than previously reported (Figure 1 and Table S4) (Besson et al., 1985; Mohanlal et al., 2011).For further characterization of metabolite structure, a molecular MS/MS network was constructed by connecting two metabolite signals (nodes) that had similar MS/MS spectra (See Experimental Procedures, blue edges in Figure 1).

Among the metabolite signals, 6 and 32 metabolite signals were ‘annotated’ and ‘identified’, respectively, on the basis of comparisons of MS/MS spectra, an exact mass number,and retention time with those of standards (Figure 2) (Yang et al., 2014).

RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Japan,
Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, Japan,
National Institute of Agrobiological Sciences, 2-1-2 Kannondai, Tsukuba, Ibaraki, Japan, and
Graduate School of Pharmaceutical Sciences, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba, Japan

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 ==Research Findings==
+*A metabolome dataset composed of 342 metabolite signals(peaks) in 668 samples was obtained using liquid chromatography-mass spectrometry (LC/MS) (Tables S1–S3) (Matsuda et al., 2009, 2010). Metabolite annotation successfully characterized the structures of 91 metabolites, demonstrating that phytochemicals produced in rice cultivars were more diverse than previously reported (Figure 1 and Table S4) (Besson et al., 1985; Mohanlal et al., 2011).For further characterization of metabolite structure, a molecular MS/MS network was constructed by connecting two metabolite signals (nodes) that had similar MS/MS spectra (See Experimental Procedures, blue edges in Figure 1).
+<br>
+*Among the metabolite signals, 6 and 32 metabolite signals were ‘annotated’ and ‘identified’, respectively, on the basis of comparisons of MS/MS spectra, an exact mass number,and retention time with those of standards (Figure 2) (Yang et al., 2014).
+<br>
 ==Labs working on this Project==