March 2012, Vol.5(2):442–451, doi:10.1093/mp/ssr101
Open Archive, Elsevier user license
RESEARCH ARTICLES
Deciphering Starch Quality of Rice Kernels Using Metabolite Profiling and Pedigree Network Analysis
Miyako Kusano a,b,,
Atsushi Fukushima a
Naoko Fujita c
Yozo Okazaki a
Makoto Kobayashia
Naoko Fujita Oitome c
Kaworu Ebana d
Kazuki Saito a,e
aRIKEN Plant Science Center, Tsurumi, Yokohama 230-0045, Japan
bKIHARA Institute for Biological Research, Yokohama City University, Totsuka, Yokohama 244-0813, Japan
cDepartment of Biological Production, Akita Prefectural University, Akita 010-0195, Japan
dNational Institute of Agrobiological Sciences, 2–1–2 Kannondai, Tsukuba, Ibaraki 305-8602, Japan
eGraduate School of Pharmaceutical Sciences, Chiba University, Chiba 260-8675, Japan
ABSTRACT
The physiological properties of rice grains are immediately obvious to consumers. High-coverage metabolomic characterization of the rice diversity research set predicted a negative correlation between fatty acid and lipid levels and amylose/total starch ratio (amylose ratio), but the reason for this is unclear. To obtain new insight into the relationships among the visual phenotypes of rice kernels, starch granule structures, amylose ratios, and metabolite changes, we investigated the metabolite changes of five Japonica cultivars with various amylose ratios and two knockout mutants (e1, aStarch synthase IIIa(SSIIIa)-deficient mutant and theSSIIIa/starch branching enzyme(BE) double-knockout mutant4019) by using mass spectrometry-based metabolomics techniques. Scanning electron microscopy clearly showed that the two mutants had unusual starch granule structures. The metabolomic compositions of two cultivars with high amylose ratios (Hoshiyutaka and Yumetoiro) exhibited similar patterns, while that of the double-knockout mutant, which has an extremely high amylose ratio, differed. Rice pedigree network analysis of the cultivars and the mutants provided insight into the association between metabolic-trait properties and their underlying genetic basis in rice breeding in Japan. Multidimensional scaling analysis revealed that the Hoshiyutaka and Yumetoiro cultivars were Indica-like, yet they are classified as Japonica subpopulations. Exploring metabolomic traits is a powerful way to follow rice genetic traces and breeding history.
Key words
starch quality
traits
amylose ratio
metabolite profiling
pedigree networks
rice
INTRODUCTION
Rice is an important cereal of the human diet because it is a good source of carbohydrates. Improvement in starch quality in rice grains is directly connected to rice-grain eating and cooking quality because 80–90% of the dry matter of rice grains is composed of starch (Duan and Sun, 2005). Starch granules in both rice and other cereals and crops consist of amylose and amylopectin. Starch quality is affected by many factors, including growth conditions and variety/cultivar differences. Of these, the amylose/total starch ratio (amylose ratio) is one of the most important factors for predicting the physicochemical properties of starches (Blaszczak et al., 2003).
Metabolomics can give a snapshot of biochemical status in cells, body fluids, tissues, and organs in organisms (Fukushima et al., 2009; Albinsky et al., 2010; Kusano et al., 2011). Metabolomics has provided great contributions to both phenotyping and diagnostic analyses of plants (Nunes-Nesi et al., 2010). Gas chromatography–mass spectrometry was applied for the detection of primary metabolites such as fatty acids, sugars, amino acids, and organic acids in metabolomics (Saito and Matsuda, 2010; Kusano et al., 2011). Lipidomic analysis including phospholipids was performed using liquid chromatography–mass spectrometry (LC–MS) (Welti et al., 2007; Murphy and Gaskell, 2011; Okazaki et al., 2011). An association analysis among metabolite composition, genotype, and phenotype revealed a close link between biomass and a specific combination of metabolites including starch in recombinant inbred lines (Meyer et al., 2007) and accessions in Arabidopsis (Sulpice et al., 2009). Trait–metabolite association analysis using the rice diversity research set (RDRS) predicted that amylose ratio and fatty acids and other metabolites such as glycerol, phosphate, and putrescine are negatively correlated among rice cultivars, while the two mutants, e1, a Starch synthase IIIa (SSIIIa)-deficient mutant (amylose ratio, approximately 30%; Nipponbare background (Fujita et al., 2007)), and the SSIIIa/starch branching enzyme (BE) double-knockout mutant 4019 (amylose ratio, approximately 36%; Nipponbare/Kinmaze background), did not show the same trend (Redestig et al., 2011). However, it is unclear why there was an association between amylose ratio and the metabolites in rice kernels of the cultivars but not in the mutants.
To obtain new insight about the relationships among visual phenotypes including rice kernels, starch structure, amylose ratio, and metabolite changes including phospholipids, we investigated the metabolite changes of Japonica cultivars with various amylose ratios and two knockout mutants (Fujita et al., 2007) by combing metabolite profile data obtained from gas chromatography–time-of-flight–mass spectrometry (GC–MS) and from LC–ion-trap–time-of-flight–MS (IT–MS) analyses. We also conducted rice pedigree network analysis of the cultivars and the mutants to estimate the extent of the association between metabolite-trait properties and their underlying genetic basis in rice breeding in Japan.
RESULTS
Visual Phenotypes and Morphological Traits of the Rice Seeds and Kernels
We observed the visual phenotypes of the rice seeds and kernels of the five cultivars (Nipponbare, Kinmaze, Soft158, Yumetoiro, and Hoshiyutaka) and the two knockout mutants (e1 and 4019). Cultivar Yumetoiro and Hoshiyutaka are known to have high amylose ratios, while Soft158 has a low amylose ratio (Sakai et al., 1989; Ohta et al., 2004; Redestig et al., 2011). Rice seeds and kernels of the two high-amylose cultivars showed long-grain phenotypes (Figure 1D and 1E, and Table 1). The rice seeds of other cultivars and the mutants looked similar (Figure 1). However, the kernels of the e1 mutant had white cores (Fujita et al., 2007) while the 4019 kernels looked almost completely opaque (Figure 1F and 1G). The weight of 100 kernels of the high-amylose cultivars and 4019 was low compared to that of Nipponbare (Table 1).
Figure 1. Visual Phenotypes of Rice Seeds and Brown Rice of the Five Cultivars and the Two Knockout Mutants.
Values are presented as the mean ± standard deviation (SD). Ten grains of each biological replicate were used (number of biological replicates, n = 3).
Differences between Nipponbare and each cultivar or mutant analyzed using Welch's t-test were statistically significant.
*
p < 0.05;
**
p < 0.005;
***
p < 0.0005
a
Size was tentatively calculated by multiplying length and width of each seed or kernel.
Starch Granule Structures of the Cultivars and the Knockout Mutants
To obtain insight about the relationships between amylose ratio and starch granule structure, we conducted starch granule imaging using scanning electron microscopy (SEM) (Figure 2). SEM images of the starch granule structures of the cultivars looked similar (Figure 2A–2E), while that of e1 showed relatively small starch granules, some of which were round (Figure 2F and 2H). Kernels of the amylose-hyperaccumulating mutant 4019 had uniquely shaped starch granules, namely large and spherical or wormlike (Figure 2G and 2I). The SEM imaging results suggested that there is probably no correlation between amylose ratio and starch granule structure, at least among the assayed cultivars.
Figure 2. Scanning Electron Microscopy (SEM) of the Starch Granules.
SEM images are magnified 1000× for starch granules of (A) Nipponbare, (B) Kinmaze, (C)Soft158, (D) Yumetoiro, (E) Hoshiyutaka, (F)e1, and (G)4019. SEM images are magnified 4000× for (H)e1 and (I)4019.
Scale bar (white) = 10 μm at a magnification of 1000× and 5 μm at a magnification of 4000×.
We further investigated how many starch granules are packed in rice kernels of e1, 4019, and Nipponbare by using SEM (Figure 3). The result of the cross-sections of endosperm of e1 and 4019 clearly showed that the starch granules of the mutants were loosely packed in rice kernels.
Figure 3. Cross-Sections of Endosperm in Rice Kernels.
SEM images are magnified 3000× for (A) and (C) Nipponbare, (B)e1, and (D)4019. The mutant kernels of e1 and those of Nipponbare were harvested on 2005, while 4019 and the corresponding Nipponbare were on 2008.
Scale bar (white) = 5 μm at a magnification of 4000×.
Metabolite Profiling of the Cultivars and the Knockout Mutants by Using GC–MS and IT–MS
We next performed metabolite profiling to investigate the extent of the metabolite changes in kernels of the cultivars and the knockout mutants by using GC–MS and IT–MS (Supplemental Data 1 and 2). Differentially changed metabolites compared to the control Nipponbare were visualized using a heatmap generated using hierarchical cluster analysis (HCA) (Figure 4). The metabolite changes found in the metabolite profiles of the double-knockout mutant 4019 showed a unique pattern (class III in Figure 4). On the other hand, the single knockout mutant e1 was grouped into class I. Two high-amylose cultivars (Hoshiyutaka and Yumetoiro) had similar patterns according to changes in their metabolite profiles (class II in Figure 4). Subsequently, we assessed the extent of metabolite-level changes of the cultivars and the mutants at the chosen statistical threshold (5% false discovery rate and log2-fold change > |1|). A Venn diagram was used to find the significantly changed metabolites in common or those that differed in Hoshiyutaka and Yumetoiro (Figure 5 and Table 2). Metabolites that were differentially changed in e1 and 4019 were compared to investigate how much the lack of SSIIIa affects metabolic alternations in rice kernels (Figure 5 and Table 2). In Hoshiyutaka and Yumetoiro, levels of eight of the metabolites were commonly decreased (Figure 5 and Table 2). The level of 18:0-LPC was increased in the amylose-rich cultivars (Table 2). Between e1 and 4019, the levels of many metabolites were commonly increased such as fructose-6-phosphate, fructose, glucose, 18:0-LPC, and intermediates in the TCA cycle (Figure 5 and Table 2), while the phytol level was significantly decreased (Table 2). Approximately 50% of the metabolites were commonly changed in e1 and 4019 (Figure 5).
Figure 4. Heatmap with Cluster Dendrogram of the Differentially Changed Metabolites by Hierarchical Cluster Analysis (HCA).
Annotated or identified metabolites using gas chromatography–mass spectrometry (GC–MS) (99 metabolites) and ion trap–mass spectrometry (IT–MS) (10 metabolites) appear in the rows and the samples appear in the columns. HCA was performed using the matrix (log2-fold change; cultivars or mutants versus control NB). The complete list of differentially changed metabolites is available in Supplemental Data 1 and 2. Abbreviations: HCA, hierarchical cluster analysis; NB, Nipponbare; Kin, Kinmaze; Hoshi, Hoshiyutaka; Yume, Yumetoiro.
Figure 5. Venn Diagram of Significantly Changed Metabolites in Common or Those that Differ in (A) Hoshiyutaka and Yumetoiro and (B) e1 and 4019.
The Venn diagram in (A) shows the number of commonly decreased metabolites in Hoshiyutaka and Yumetoiro compared to Nipponbare (NB), while the Venn diagram in (B) represents the number of commonly increased metabolites in e1 and 4019.
Significant levels were set at false discovery rate (FDR) < 0.05 discovery rate and log2-fold change (FC) > |1|. See also Table 2 for more detail.
Table 2. Common Changed Metabolites between Hoshiyutaka and Yumetoiro and Between e1and 4019.
(A) Common changed metabolites between Hoshiyutaka and Yumetoiro.
Metabolite changes
Metabolite name
log2-FC in Hoshi/NB
FDR
log2-FC in Yume/NB
FDR
Increased
18:0-lysoPC
1.0
0.00
0.7
0.00
Decreased
Serine
−1.5
0.00
−3.2
0.00
Alanine, beta-
−1.7
0.00
−3.3
0.00
Aspartic acid
−1.1
0.00
−1.1
0.00
Propane, 1,3-diamino-
−1.4
0.00
−1.5
0.00
Glutamine
−2.2
0.00
−4.7
0.00
Fructose
−1.4
0.00
−1.6
0.00
M000000_A217004–101_MST_2174.6_EITTMS_
−2.1
0.00
−2.9
0.00
Pyroglutamate
−1.5
0.00
−3.1
0.00
(B) Common changed metabolites between e1 and 4019
Metabolite changes
Metabolite name
log2-FC in e1/NB
FDR
log2-FC in 4019/NB
FDR
Increased
Homoserine
1.3
0.00
1.4
0.00
Aspartic acid
2.0
0.00
3.8
0.00
Arabinose
2.2
0.00
3.1
0.00
Shikimic acid
1.7
0.00
3.3
0.00
Fructose
2.6
0.00
2.5
0.00
N-Acetyl-d-glucosamine
1.1
0.00
1.1
0.00
M000000_A217004–101_MST_2174.6_EITTMS_
2.0
0.00
2.5
0.00
Fructose-6-phosphate
4.8
0.00
9.9
0.00
M000000_A237002–101_MST_2370.2_EITTMS_
1.3
0.00
2.0
0.00
M000000_A250001–101_MST_2495.5_EITTMS_
2.0
0.00
1.6
0.00
PR_MST_Polyol (Hexitol)_2539.5
3.2
0.00
4.2
0.00
Glucose
2.0
0.00
2.6
0.00
Glycerol
2.7
0.00
4.1
0.00
Citrate
1.2
0.00
2.7
0.00
Isocitrate
1.2
0.00
2.4
0.00
18:0-lysoPC
1.3
0.00
2.4
0.00
Decreased
Phytol
−1.6
0.00
−3.6
0.00
Significant levels were set at FDR < 0.05 discovery rate and log2-fold change (FC) > |1|. FC, fold change; FDR, false discovery rate; NB, Nipponbare; Kin, Kinmaze; Hoshi, Hoshiyutaka; Yume, Yumetoiro.
We investigated changes in the metabolite levels of fatty acids, phosphatidylcholines (PCs), and LPCs to validate our prediction of amylose ratio and fatty acid and lipid levels in our previous study (Supplemental Tables 1 and 3). The linoleate level was negatively correlated to amylose ratio among the traditional cultivars (Redestig et al., 2011). However, there are no correlation relationships among other fatty acids, PCs, LPCs, and amylose ratios in the assayed cultivars. The levels of three fatty acids (linoleate, oleate, and palmitate), two LPCs (16:0-lysoPC and 18:0-lysoPC), and six PCs were significantly increased in rice kernels of 4019 (Supplemental Table 3).
Rice Pedigree Network Analysis Reflected the Origin of the Metabolite Profile Patterns of the Cultivars
Metabolite profiling analysis clearly showed that the metabolite profiles of e1, Kinmaze, and Soft158 have similar patterns, while those of Hoshiyutaka and Yumetoiro are similar (Figure 4). We investigated the network of rice breeding history for the five cultivars using a rice characteristic database (Ohta et al., 2004) and the Plant Genetic Resources Search System in the NIASGBdb to obtain insight into the origins of metabolite profile alternations throughout rice relations in Japan (see ‘Methods’) (Takeya et al., 2011). A total of 171 cultivars were involved in the breeding history of generating the cultivars and the mutants (Figure 6). Hoshiyutaka and Yumetoiro are progenies of common Indica cultivars such as IR8, Peta, and Taichungnative1 (TN1).
Figure 6. Rice Pedigree Network Analysis Based on Information of Rice Relationships in Japan.
Each node represents a cultivar. The orange node shows the cultivars used for the study, while the blue node displays single (e1 and be) or double (4019) knockout mutants. The green nodes are the common ancestors of Hoshiyutaka and Yumetoiro. Two Indica cultivars, Mudgo and O. nivara, are shown as pink nodes. Edges represent pedigrees in rice breeding history. The green edge shows a parent–child relation (pcr), while the pink double line shows a pair relationship. The blue line and purple broken edges show mutations and selections, respectively. TN1, Taichungnative1.
Soft158 and Nipponbare were generated by the crossing of Japonica cultivars. Kinmaze is an old cultivar in Japan (Ohta et al., 2004) that exists in the middle of the pedigree network (Figure 6).
The double-knockout 4019 was generated by crossing e1 in the Nipponbare background with be, which is lacking a BE, in the Kinmaze background (Figure 6), although metabotype of the mutant 4019 was very specific (Figure 5).
Indica–Japonica Differentiation by Metabotypes of Kinmaze, Soft158, Hoshiyutaka, and Yumetoiro, and those of the Cultivars in RDRS
Rice pedigree network analysis suggested that the specific metabolite changes found in Hoshiyutaka and Yumetoiro may reflect an Indica-like metabotype. To estimate the relationships between metabotype changes and the differences of the Indica or Japonica type in O. sativa, we conducted multidimensional scaling (MDS) using a metabolite profile dataset consisting of the normalized metabolite profiles of Kinmaze, Soft158, Hoshiyutaka, and Yumetoiro and those of the Indica and Japonica cultivars (non-glutinous rice) that were randomly chosen from the RDRS (see ‘Methods’). The coordinate plot of the MDS analysis demonstrated that the metabotypes of Hoshiyutaka and Yumetoiro were closer to those of the Indica cultivars, although both were classified as Japonica cultivars (Figure 7). Furthermore, the metabotype of Hoshiyutaka was located in the center of the plot.
Figure 7. The Coordinate Plot of Multidimensional Scaling (MDS) Analysis Using Normalized Metabolite Profiles of Four Cultivars and the Eight Representative Cultivars Chosen from the Rice Diversity Research Set (RDRS).
Euclidean distance and log2 ratio values compared to the metabolite profiles of Nipponbare were used (see ‘Methods’). I, Indica; J, Japonica.
DISCUSSION
Fatty Acids and LPCs Probably Pack Starch Granules in Rice Kernels
We expected that a negative correlation relationship between fatty acid/lipid levels and amylose ratio suggested by our prediction in our earlier study are probably required to maintain normal starch granules in rice kernels (Redestig et al., 2011). To verify this hypothesis, we observed the starch granule structures of the cultivars and the mutants by using SEM. As in our hypothesis, the starch granules of e1 and 4019, which show no significant correlation between fatty acid/lipid levels and amylose ratio, had unique structures (Figure 2). In particular, the shapes of the starch granules in 4019 were similar to those in high-amylose maize (Perez and Bertoft, 2010). Observations of the cross-sections of endosperm in these mutant kernels revealed that starch granules were loosely packed in both mutants (Figure 3).
Cereal starches contain free fatty acids and LPCs, and these compounds are associated with the amylose fraction (Morrison et al., 1984). Amylose and the longest linear branches of amylopectin develop inclusion complexes with fatty acids, monoglycerides, and LPCs (Toro-Vazquez et al., 2003). Addition of exogenous LPCs to maize starch paste can enhance its thermal stability, resulting in more stable starch quality against heat because of starch–LPC inclusion complex formation (Toro-Vazquez et al., 2003; Hernández-Hernández et al., 2011). The fold changes in the levels of oleate, palmitate, 16:0-LPC, and 18:0-LPC in e1 and 4019 were higher than those in the amylose-rich cultivars (Supplemental Table 3), while the levels of linoleate and palmitate showed a negative correlation with amylose ratio across the representative non-glutinous cultivars except for the mutants (Redestig et al., 2011). These results suggest that SSIIIa and BE are not only essential for maintenance of starch granule structure, but also affect the metabolite composition of rice kernels. We must emphasize that simple starch analyses cannot provide us with such detailed insights about an importance of starch biosynthesis-related genes for rice kernels.
The appearance of rice kernels of the knockout mutants e1 and 4019 showed white cores and opacity, respectively (Figure 1F and 1G). Temperature is an important factor in the determination of rice grain quality, particularly during the grain-filling stage. When rice plants are grown at high temperature during this stage, the rice kernels have a chalky appearance and reduced weight (Tashiro and Wardlaw, 1991; Yamakawa et al., 2007). The endosperm of the chalky rice kernel ripened under high-temperature conditions were loosely packed with elliptical-shaped starch granules containing air spaces (Yamakawa et al., 2007). Furthermore, the levels of many genes and metabolites involved in starch biosynthesis and carbohydrate metabolism changed in the developing endosperm (Yamakawa and Hakata, 2010). Of these, SSIIIa is mainly expressed in developing rice endosperm (Hirose and Terao, 2004; Dian et al., 2005; Ohdan et al., 2005), and induction of SSIIIa in rice depends on temperature (Yamakawa et al., 2007; Yamakawa and Hakata, 2010). Kernels of the near-isogenic line CSSL50-1, which was derived from the original donor IR24 (Indica) in the largely Asominori background (Japonica), showed a chalky appearance and loosely packed endosperm granules with air spaces. Transcript profiling of the near isogenic line revealed differential changes in the expression levels of genes involving carbohydrate metabolism, signal transduction, and redox homeostasis compared to those in the control Asomonori (Liu et al., 2010). As the formation of grain chalkiness is influenced by multiple factors including starch synthesis, starch granule structure, and arrangement triggered by external stresses or down-regulation of genes involved in starch biosynthesis, investigations into fatty acid and LPC levels in developing rice grains of the mutants and other mutants with chalky or opaque phenotype should be completed soon to obtain detailed insight into the underlying mechanisms of starch granule packing in rice kernels.
Metabolite Profiling Is a Powerful Tool to Distinguish Cultivars Precisely
In this study, we used the Japonica cultivars and the mutants with the Japonica background as their direct parental lines. The Indica and Japonica cultivars have distinctive morphological and agronomic traits as well as differences at the molecular level, such as DNA restriction fragment length polymorphism (Ebana et al., 2005; Zhang et al., 2009), simple sequence repeats, and chloroplast sequence (McCouch et al., 2005). Traits including potassium chlorate resistance, drought resistance, apiculus hair length, cold sensitivity, and phenol reaction have been used often for Indica–Japonica differentiation, although the spectra of the variation of these traits overlap in the cultivars (Morishima and Oka, 1981). Seed lengths of many Indica cultivars exceed those of Japonica cultivars. However, the probability of misclassification using this trait was approximately 40% (Morishima and Oka, 1981).
Umemoto and colleagues reported that Japonica-type amylopectin tends to contain short-unit chains with a degree of polymerization (DP) ≤ 11 and long-unit chains with DP ≥ 25, while Indica-type amylopectin has a tendency to consist of intermediate-size chains with 12 ≤ DP ≤ 24 and long-unit chains with DP ≥ 25 (Umemoto et al., 1999). As the levels of the long-unit chains are similar in the two amylopectins, the specific characteristics of amylopectins depend on the presence of the short-unit or intermediate-sized chains in rice grains (Umemoto et al., 1999; Nakamura et al., 2002and Nakamura et al., 2002). On the basis of the chain length in the amylopectin clusters, starches of rice cultivars cultivated in Asia can be classified into two types: L (for Indica) and S (for Japonica) (Nakamura et al., 2002a). The metabolite changes of the cultivars showed similar patterns (Figure 4). Visible phenotypes of Hoshiyutaka and Yumetoiro have longer seeds and kernels than others (Figure 1 and Table 1). However, both cultivars belonged to S-type rice according to their DP values. Soft158 (low-amylose rice) and Nipponbare (medium-amylose rice) were also classified as S-type rice (Horibata et al., 2004). Use of a metabotype-based classification strategy can give us insight into the underlying status of seed composition in rice (Figure 7). Hoshiyutaka and Yumetoiro are indeed hybrid rice of the Indica and Japonica cultivars. According to the rice pedigree networks, ancestors of Hoshiyutaka are almost all Japonica cultivars except for Indica cultivars Mudgo and IR8 (Figure 6). Investigation of the extent of metabolite changes using a collection of backcross-recombinant inbred lines between Indica and Japonica cultivars will be performed in a future study. In summary, our results suggest that phenotype–metabotype associations between Hoshiyutaka and Yumetoiro are well coordinated across rice breeding history in Japan. Large-scale metabotyping of 171 cultivars in Figure 6would provide evidence to follow the traces of the rice pedigrees as a future study.
The five rice cultivars (Nipponbare, Kinmaze, Soft158, Hoshiyutaka, and Yumetoiro) and two knockout mutants (e1 and 4019) from RDRS were used for this study. Growth and harvesting were performed as previously described (Redestig et al., 2011).
Observation of Starch Granules Using SEM
Starch granules were prepared from polished rice by using the cold-alkali method (Yamamoto et al., 1973 and Yamamoto et al., 1981). Purified starch granules were coated with gold using a fine coater (JEOL JFC-1200) for 120 s. The morphology of the starch granules was examined by SEM (JEOL-5600, Tokyo, Japan). SEM was performed in secondary electron mode at 15 kV. For observation of cross-sections of endosperm, dried rice seeds were cut across the short axis with a razor blade. The surface was sputter coated with gold and observed using SEM.
Metabolite Profiling
Metabolite profiling using GC–MS and IT–MS was performed in accordance with the metabolomics metadata (Redestig et al., 2011). The data were log2 transformed for further analysis.
Statistical Data Analysis
Statistical analyses were performed using R v2.12.1 (www.r-project.org/) and Microsoft Office Excel 2007. Differences in the morphological traits of rice seeds and kernels between Nipponbare and each cultivar or mutant were determined using Welch’s t-test (p < 0.05). The fold changes of all cultivars and mutants were calculated by dividing by the mean value of Nipponbare. The differentially accumulated metabolites between a cultivar and Nipponbare and between a mutant and Nipponbare were detected using the LIMMA package (Smyth, 2004), which includes false discovery rate (FDR) correction for multiple testing (Benjamini and Hochberg, 1995). We identified metabolites with significant changes in metabolite levels (the log2-fold change > |1|) and the corresponding FDR-corrected p-values that were <0.05.
We used the log2-fold change matrix for HCA and MDS analysis. HCA was performed using Cluster 3.0 (de Hoon et al., 2004), and the results of HCA were visualized using Java TreeView v1.1.6 (http://jtreeview.sourceforge.net/). We applied Euclidean distance as similarity matrices for the metabolites and cultivars or mutants and the average linkage for clustering. Using Euclidean distance as implemented in the ‘cmdscale’ function of the R software, we performed MDS analysis, which tries to demonstrate the underlying structure of empirically acquired data. We also used the log2-fold change values for this analysis.
Rice Pedigree Network Analysis
Rice pedigree network analysis was performed using the following procedure. First, information about the rice pedigrees among Nipponbare, Kinmaze, Soft158, Hoshiyutaka, and Yumetoiro was collected from a rice characteristic database (http://ineweb.narcc.affrc.go.jp/) (Ohta et al., 2004) and NIASGBdb (www.gene.affrc.go.jp/databases-plant_search_en.php) (Takeya et al., 2011). The origins of e1 and 4019 were investigated on the basis of the literature (Fujita et al., 2007; Redestig et al., 2011). Second, we categorized the rice relationships into four categories: (1) parent–child relation (pcr); (2) pair relation (pair); (3) mutations induced by ethyl methanesulfonate mutation (EMS), by N-methyl-N-nitrosourea mutation (MNU), by endogenous retrotransposon Tos17 insertion (Tos17) (Agrawal et al., 2001), by γ-ray irradiation (gamma), and by natural mutation (mutation); and (4) natural selection (selection). The pedigree matrix was imported to Cytoscape 2.8.1 (www.cytoscape.org/), and then the rice pedigree network was visualized using a hierarchical layout algorithm. There were a total of 171 nodes and 317 edges in the network.
This work was partially supported by a Grant-in-Aid for Scientific Research (B) from the Japan Society for the Promotion of Science (19380007 to N.F.) and the Program for the Promotion of Basic and Applied Research for Innovations in Bio-oriented Industry to N.F.
ACKNOWLEDGMENTS
We thank Koji Takano at the RIKEN Plant Science Center for his technical assistance with the IT–MS analysis and Hiroki Asai at Akita Prefectural University for his help with the starch preparation. No conflict of interest declared.
GK Agrawal, M Yamazaki, M Kobayashi, R Hirochika, A Miyao, H Hirochika
Screening of the rice viviparous mutants generated by endogenous retrotransposon Tos17 insertion: tagging of a zeaxanthin epoxidase gene and a novel ostatc gene
Atlas of rice grain filling-related metabolism under high temperature: joint analysis of metabolome and transcriptome demonstrated inhibition of starch accumulation and induction of amino acid accumulation
