IC4R011-Metabolomics-2015-25267402

Project Title

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

The Background of This Project

• 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.

Plant Culture & Treatment

• 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.

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).

'Figure 1. Combined metabolomics networks of rice. Each node represents one metabolite signal. The molecular MS/MS network on MS/MS spectral similarity is shown as blue edges. Red edges represent the metabolite co-accumulation network on metabolites with similar accumulation patterns observed among the 175 rice cultivars. Interpretable networks were obtained by employing a threshold of similarity score above 0.7 for both networks. Clusters mentioned in the text are presented by circles. The structures of representative metabolites in each cluster are also shown. Metabolite names by the bold numbers are presented in Table S4. Nodes of metabolites with relatively large broad-sense heritability (H2 > 0.5) and significantly distorted from the normal distribution by Kolmogorov-Smirnov test (P < 0.01) are shown in orange color. Green nodes are metabolites with H2 > 0.5 and P > 0.01 (See legend of Figure 6b).'

• 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).

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'Figure 2. Tandem mass (MS/MS) spectra of rice metabolites.(a) Apigenin-6-C-a-L-arabinosyl-8-C-a-L-arabinoside 6 (peak ID 33368), (b) apigenin-C-hexosideC-pentoside 7 (ID38198), and (c) unknown metabolite (ID 11261) . MS2T ID indicates the code of the representative MS/MS spectral tag of each metabolite in the RIKEN PRIME MS2T library.'

• As shown in Figure 3(a), 323 significant associations among 143 SNPs and 89 metabolites were observed when employing a relatively strict threshold (a = 1.0 9 10�5, false discovery ratio: 3.4%, Table S6). Red lines in Figure 3(b) show the associations among the SNPs and the metabolites (aligned on the upper and lower boundaries in the figure, respectively). We found that one polymorphism tends to affect the levels of multiple metabolites, as 113 of 143 SNPs were significantly associated with more than two metabolites (Figure 3b).

'Figure 3. Genetic architecture of rice secondary metabolism.(a) Manhattan plot for genome-wide association mapping of rice metabolic phenotypes. SNPs significantly associated with some metabolite levels were plotted on the rice genome(a = 1.0 9 10�3).(b) Associations between 3168 SNPs aligned on the upper boundary and 342 metabolites aligned on the lower boundary. Positions of SNPs correspond to the above panel. Red, blue,and gray lines represent significant associations between SNPs and metabolites with threshold levels of a = 1.0 9 10�5, 5.0 9 10�5, and 1.0 9 10�3, respectively. Positions of metabolite clusters and representative metabolites are also represented (Table S4 for metabolite names).'

• The GWAS clearly showed that there are several hotspots of significantly associated SNPs. Among these genetic hubs, one of the most prominent hotspot regions is located around the short arm of chromosome 6, where the SNP genotype NIAS_Os_ac06000458 with G/A alleles was tightly associated with the levels of various flavone-C-glycosides (Figure 4a). For instance, the SNP genotype explained 68.6% of the total variation of the levels of apigenin-di-C-arabinoside 6 for 175 cultivars. Near the SNP marker, there were OsCGT gene encoding flavone C-glucosyltransferase that functions in the selective formation of 6C-glucosylflavone (Brazier-Hicks et al., 2009) and its two homologous UGT genes (Os06g0289200 and Os06g0289900, Figure 4b).

'Figure 4. GWAS of 31 metabolites in flavone-Cglycoside cluster.(a) Manhattan plot for 31 metabolites in flavone-C-glycoside cluster (a = 1.0 9 10�3). Position of SNPs associated with 6C-arabinosylation of flavone (NIAS_Os_ac06000458) is indicated by grey arrow.(b) Rice genome region around the SNP marker, NIAS_Os_ac06000458 on chromosome 6.(c) Associations between genotypes of NIAS_Os_ac06000458 and apigenin-di-C-arabinoside levels.

Labs working on this Project

• 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

Corresponding Author

• Kazuki Saito:ksaito@faculty.chiba-u.jp