Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Integrated metabolomics and phytochemical genomics approaches for studies on rice

GigaScience20165:11

DOI: 10.1186/s13742-016-0116-7

Received: 15 September 2015

Accepted: 6 February 2016

Published: 2 March 2016

Abstract

Metabolomics is widely employed to monitor the cellular metabolic state and assess the quality of plant-derived foodstuffs because it can be used to manage datasets that include a wide range of metabolites in their analytical samples. In this review, we discuss metabolomics research on rice in order to elucidate the overall regulation of the metabolism as it is related to the growth and mechanisms of adaptation to genetic modifications and environmental stresses such as fungal infections, submergence, and oxidative stress. We also focus on phytochemical genomics studies based on a combination of metabolomics and quantitative trait locus (QTL) mapping techniques. In addition to starch, rice produces many metabolites that also serve as nutrients for human consumers. The outcomes of recent phytochemical genomics studies of diverse natural rice resources suggest there is potential for using further effective breeding strategies to improve the quality of ingredients in rice grains.

Keywords

Rice Metabolomics Metabolism Mass spectrometry Phytochemical genomics Quantitative trait loci

Background

Rice (Oryza sativa L.) is one of the most important crops worldwide and in Asian countries in particular. It serves not only as an energy source, but also as a source of nutrition. A recent report on the genomic sequencing of rice revealed that japonica rice was first domesticated from a population of its closely related wild ancestor (Oryza rufipogon) in the south part of China. Then, indica rice was developed by subsequent crossings of japonica populations with the local species of wild rice as the use of japonica varieties spread into southeast and south Asia [1]. As a result, there are now various rice landraces with different tastes, flavors, and tolerance to environmental stresses such as pests, drought, temperature, and nutrition limitation [2]. The metabolism of these landraces is closely related to the above mentioned traits. Thus, there have been many studies on the physiology of rice that were based on metabolomics, an approach that can be used to analyze a wide range of metabolites in each sample. Such data could greatly increase the efficacy of using the biodiversity of rice cultivars and landraces [3, 4]. Metabolomics combined with other high-throughput technology such as transcriptomics and proteomics is referred to as integrated metabolomics and is sometimes used in studies aiming to understand the metabolism as a phenotype of genome function [5, 6].

In this short review, we discuss two topics. The first is the application of metabolomics to studies aiming to provide an understanding of the association between the metabolism and certain biological events, or the metabolic changes that occur in response to interventions such as stress treatment or gene modification. The second topic is phytochemical genomics approaches to rice research. Phytochemical genomics is a recently emerging concept, the focus of which is understanding the genetic basis of phytochemical biosynthesis [7]. Rice accumulates various types of rice-specific metabolites [8], and the biosynthetic pathways that produce them are mostly still unknown. We discuss studies of rice metabolite biosynthesis based on metabolomics as a key research tool, and describe recent papers discussing metabolite quantitative trait locus (QTL) analysis.

Review

Use of metabolomics for the investigation of metabolism in rice

Metabolomics has often been applied to the investigation of the response to biotic or abiotic stresses in rice. For example, a metabolomic analysis of rice leaves infected with the fungus Magnaporthe grisea, which causes rice blast disease, presented a model for how this biotrophic/hemi-biotrophic pathogen succeeds in suppressing the host’s defenses and takes up the nutrients required to propagate in plant tissue [9] (Table 1). In this study, metabolomic analysis revealed a modification of the shikimate pathway (an increase in quinate and the accumulation of non-polymerized lignin precursors) that resulted in a reduction in lignified papillae formation and an increase of the mannitol content of susceptible hosts [9]. Since mannitol was proposed to be an important carbohydrate for fungal growth [10], its increased concentration in susceptible hosts suggests the active metabolic re-programming of infected plants by pathogens [9]. In addition, RNA-Seq and high-throughput SuperSAGE analysis based on next-generation sequencing recently revealed upregulation of quinate permease upon infection [11], which supports the data produced by the above-mentioned metabolomic study. Likewise, metabolomics integrated with transcriptomics was applied to the investigation of rice infected with Xanthomonas oryzae, the causal organism of bacterial leaf blight. This revealed many different metabolic responses between wild type and genetically modified rice with disease resistance [12] (The dataset for [12] is open and available at: [13, 14].
Table 1

Metabolomic research in rice

Category

Research materials

Analytical method

Analytes

Other omics tools

Year

Reference

Biotic stress

Leaves infected with fungal pathogen (Magnaporthe grisea)

Infusion-MS, GC-MS

Mainly primary metabolites, lignin monomers

 

2009

[9]

Biotic stress

Leaves infected with pathogenic bacteria (Xanthomonas oryzae pv. oryzae)

LC-MS, GC-MS

Mainly primary metabolites

Transcriptomics

2010

[12]

Biotic stress

Leaves of rice infected with rice brown spot fungi (Bipolaris oryzae)

HPLC, LC-MS

Specialized metabolites

 

2008

[15]

Biotic stress

Rice plants inoculated with symbiotic rhizobacterium

LC-MS

Specialized metabolites

 

2013

[22]

Abiotic stress

Leaves of rice challenged with submergence

1H NMR

Mainly primary metabolites

 

2012

[25]

Abiotic stress

Developing caryopses grown under high night temperature

CE-MS

Primary metabolites

Transcriptomics

2010

[28]

Abiotic stress

Leaves of rice cultivars grown under high night temperature

GC-MS

Primary metabolites

 

2015

[29]

Abiotic stress

Floral organs of rice cultivars under heat stress

GC-MS

Primary metabolites

 

2015

[33]

Abiotic stress

Leaves of rice challenged with drought stress

GC-MS

Mainly primary metabolites

Transcriptomics

2013

[30]

Abiotic stress

Leaves of rice challenged with drought stress

GC-MS

Mainly primary metabolites

Transcriptomics, proteomics

2011

[31]

Abiotic stress

Aerial parts of rice treated with cold and drought stress

GC-MS, CE-MS, LC-MS

Mainly primary metabolites

Transcriptomics

2014

[32]

Abiotic stress

Rice challenged with salt stress

GC-MS

Mainly primary metabolites

 

2007

[34]

Abiotic stress

Leaves of rice treated with ozone

CE-MS

Mainly primary metabolites

Transcriptomics, proteomics

2008

[35]

Abiotic stress/genetic modification

Suspension cells over-expressing cell death suppressor (BI-1)

CE-MS

Water-soluble primary metabolites

 

2010

[36]

Abiotic stress/ genetic modification

Leaf blade, leaf sheath, and roots of plant disrupted in glutamate synthase

GC-MS

Mainly primary metabolites

 

2011

[38]

Genetic modification

Grains of a double mutant rice deficient in starch synthase genes

GC-MS, LC-MS

Primary metabolites and lipids

 

2016

[40]

Genetic modification

High-tryptophan rice where anthranilate synthesis-related pathway is modified

LC-MS, CE-MS

Primary and specialized metabolites

Transcriptomics

2007, 2011

[41, 42]

Genetic modification

Leaves of rice expressing a moss Na+ transporter

GC-MS

Primary metabolites

Ionomics

2007

[43]

Genetic modification

Leaves of rice expressing NAD kinase

CE-MS

Primary metabolites

 

2010

[44]

Genetic modification

Leaves of rice over-expressing rice full-length cDNA

GC-MS

Mainly primary metabolites

 

2010

[45]

Natural variation

Grains of rice diversity research set

GC-MS, CE-MS, LC-MS

Primary and specialized metabolites, and lipids

 

2011

[47]

Natural variation

3 commercial rice cultivars in Laos

1H NMR, GC-MS, GC-MS (volatile), ICP-MS

Primary and specialized metabolites, volatiles, minerals

Genomics, ionomics

2012

[48]

Natural variation

Cooked grains of 10 rice cultivars

LC-MS

Primary and specialized metabolites

Genomics

2010

[49]

Natural variation

Grains of 51 japonica and 49 indica cultivars

LC-MS, GC-MS

Primary and specialized metabolites.

 

2014

[50]

Natural variation

Grains of 68 world rice core collection

GC-MS

Mainly primary metabolites

 

2007

[51]

Natural variation

Grains of knockout mutant disrupted in starch synthesis-related genes

GC-MS, CE-MS, LC-MS

Primarily and specialized metabolites, lipids

 

2012

[52]

Natural variation

Leaves of 38 rice varieties

LC-MS

Primary and specialized metabolites

 

2013

[62]

Natural variation

Grains of BILs

GC-MS, CE-MS, LC-MS

Primarily and specialized metabolites, lipids

Genomics

2012

[63]

Natural variation

Flag leaves and grains of 210 RILs

LC-MS

Primary and specialized metabolites

Genomics

2013

[64]

Natural variation

Leaves of 529 rice accessions

LC-MS

Primary and specialized metabolites

Genomics

2014

[66]

Natural variation

Leaves of 175 Japanese rice cultivars

LC-MS

Primary and specialized metabolites

Genomics

2015

[67]

Natural variation

Flag leaf, culm, panicle, grain, and root of 24 Chinese cultivated rice germsperm

LC-MS

Primary and specialized metabolites

Genomics

2015

[68]

Natural variation

Leaves of 322 RILs

LC-MS

Specialized metabolites

Genomics

2015

[69]

Abbreviation: MS mass spectrometry, GC gas chromatography, LC liquid chromatography, HPLC high-performance liquid chromatography, NMR nuclear magnetic resonance, CE capillary electrophoresis, ICP inductively coupled plasma, BIL backcross inbred line, RIL recombinant inbred line

Biotic stress or interaction with plants and other organisms greatly affect plant metabolism and sometimes the activation of specialized (secondary) metabolism can be implemented in the defense reaction against biotic stress. Metabolic profiling of rice leaves infected with rice brown spot fungi (Bipolaris oryzae) revealed the accumulation of serotonin with its amide conjugated with hydroxycinnamic acids [15]. The serotonin-biosynthesis-deficient mutant of rice (sl, Sekiguchi lesion) showed increased susceptibility to B. oryzae [15]. Serotonin is derived from the tryptophan pathway, which is often involved in the production of defensive specialized metabolites in gramineous plants (e.g., Benzoxazinone in maize, wheat, and rye [1618]; avenanthramides in oats [19, 20]). This suggests the shared importance of the tryptophan pathway in defense-related reactions in gramineous plants [21]. Metabolic profiling has also been used to monitor strain-dependent differences in the response of specialized metabolism in rice infected with the symbiotic rhizobacterium Azospirillum [22].

The metabolome profile is also very sensitive to abiotic stresses. Rice often suffers from submergence, a major constraint of rice production in south and southeast Asia [23]. Adaptation to submergence in deep water is facilitated by SUB1A, a protein that encodes an ethylene-responsive transcription factor that restricts growth under flooding conditions [23, 24]. The metabolic profiling of the crossbred line M202 (Sub1) that has a higher tolerance to deep flood conditions compared to wild type M202 demonstrated that the presence of SUB1A in M202 led to the suppression of carbohydrate metabolism in shoot tissues [25]. This finding suggests that in the crossbred line M202 (Sub1) with SUB1A, the carbohydrate metabolism is reconstituted in a manner that suppresses elongation growth when the plant is submerged, thereby reducing energy loss under unfavorable conditions [25].

High night temperature is also a severe stress that declines yield [26] and often affects grain quality in rice [27]. Metabolomic analysis of rice grown under high night temperature conditions has been applied to find the dysregulation of central metabolism in developing caryopses (grains) [28], as well as differences in metabolic profiles among 12 cultivars with differing sensitivity to this stress during the vegetative stage [29]. In addition, metabolomic studies of rice subjected to abiotic stresses including drought [3033], heat [33], cold [32], salts [34] and oxidative stress caused by treatment of ozone [35] and menadione (a synthetic vitamin K analog) [36] suggest that various adaptive responses could be conferred to rice via metabolic reprogramming.

Metabolomics has been also used to characterize the in vivo functions of metabolic rice genes. Rice possesses 3 cytosolic glutamate synthase genes that are essential to nitrogen assimilation. One of them, OsGS1;1, is known to be crucial to normal growth and grain filling [37], although how these isozymes diverged in the context of nitrogen assimilation process and regulation of metabolic pathways has not been well investigated. A metabolomics analysis of a mutant disrupted in GS1;1 revealed that the disruption has pleiotropic effects on the metabolism of this mutant, which suggests that this enzyme is of physiological importance in the balancing of the metabolic network [38]. Metabolomics has also been useful in the analysis of an autophagy-deficient rice mutant Osatg7 [39], a double mutant rice deficient in starch synthase genes SSIIIa and SSIVb (ss3a ss4b) [40], a high-tryptophan rice in which the anthranilate synthesis-related pathway is modified [41, 42], rice expressing a moss Na+ transporter [43], rice over-expressing Arabidopsis NAD kinase [44], and in a mutant screen for modified metabolic profiles [45, 46]. Metabolomics was also used to investigate the genetic background of quality traits in rice [4752], the metabolic changes triggered by light and dark cycles [53, 54], and biomarkers that represent the developmental period of rice [55] (The dataset for [48] is open and available at: [56].

Phytochemical genomics in rice

Plants synthesize many kinds of so-called specialized or secondary metabolites called phytochemicals, many of which are beneficial to humans as drugs and other health-promoting compounds. Conversely, some phytochemicals are harmful to humans and methods are required for reducing the levels of these compounds in foodstuffs. To understand the genetic basis of phytochemical biosynthesis, metabolomics is often employed in combination with QTL analysis of inbred lines and natural variants [5760]. In this case, relatively large numbers of samples should be analyzed in order to identify the exact loci associated with such metabolic traits. Indeed, a widely targeted metabolomic approach based on a mode available in triple-quadrupole mass spectrometers called selected reaction monitoring, is likely to be a good method for assessing representative metabolites in a high-throughput manner [61, 62].

An analysis of the metabolome QTLs (mQTLs) in rice was conducted using backcross inbred lines of ‘Sasanishiki’ (high-quality japonica rice) and ‘Hatabaki’ (high-yield indica rice) to understand the genetic backgrounds associated with metabolite profile in rice grains [63]. In this study, metabolomic analysis using 4 different metabolic profiling platforms detected about 760 metabolite signals from the grains and QTL analysis identified about 800 mQTLs distributed within the rice genomes. The mQTLs acquired from datasets of 2 different harvest years clearly showed significant QTL-environment interactions in primary metabolites. In contrast, the mQTLs of specialized metabolites were detected with higher reproducibility. In the strong mQTLs, some candidate genes could also be identified via in silico analysis. An mQTL analysis of rice grain metabolites and flag leaves was also conducted using recombinant inbred lines derived from ‘ZS97’ and ‘MH63’, the parents of a cultivar widely grown in China [64]. This research also detected many metabolic traits and mQTLs by which the metabolic pathways, especially those for flavonoid biosynthesis, were elucidated in greater detail. Reconstitution of the corresponding metabolic pathways using genetic modification clearly demonstrated the effectiveness of mQTL analysis in the identification of unknown metabolic genes [64].

The research material used in the mQTL analysis varies from inbred lines to natural variants because the identification of single nucleotide polymorphism markers is becoming increasingly feasible thanks to the wider availability of high-throughput DNA sequencing technology [65]. Recently, a genome-wide association study (GWAS) was conducted using ~6.4 million SNPs obtained from 529 diverse rice accessions [66] and revealed substantial metabolic diversity conferred by variations in rice genomes. In this research, the contributions of 5 new genes associated with the metabolism of rice were confirmed. This also demonstrates the potential of mQTL analysis to be used as a tool in phytochemical genomics. The GWAS study also dissected the genetic architecture for generating the natural variation seen in the specialized metabolism in Japanese rice cultivars [67]. Similar approaches were also applied to determine the spatiotemporal distribution of phenolamides in rice plants and metabolome GWAS analysis identified 2 spermidine hydroxycinnamoyltransferase genes [68].

mQTL analysis has also been used to investigate the genetic background of the metabolic response of rice to stress. Metabolic profiling revealed that rice contains a non-protein amino acid, (R)-β-tyrosine, the concentration of which can increase in germinated seeds, leaves, roots and even exudates upon jasmonic acid treatment [69]. Genetic mapping of the β-tyrosine QTL identified the causal gene that encodes a tyrosine aminomutase. A bioassay of β-tyrosine using several dicot plants suggested that this compound plays an allelopathic role in rice [69]. These findings suggest that the investigation of biodiversity in rice cultivars and landraces could help elucidate naturally developed mechanisms for the survival of rice in various environments.

As described in rice, phytochemical genomics has been mainly used to elucidate the genes that encode biosynthetic enzymes of metabolites in leaves and grains grown under good field conditions [63, 64, 6669]. These metabolome datasets acquired in the optimal or sub-optimal growth conditions have done well to identify many mQTLs, but many chances to understand the ecological relevance of various rice phytochemicals might have been lost because some metabolic pathways can only be activated in response to biotic and abiotic stress. More in-depth mQTL analysis of rice grown under various stress conditions would reveal the hidden functions of rice genomes in the adaptation to various growth conditions, although this would not be a trivial task. A combination of mQTLs and information in databases of QTLs regarding various agronomic traits [70] could serves as a reference for further studies on the ecological relevance of various rice phytochemicals.

Identification of the function of genes related to the metabolite biosynthesis is still difficult and time-consuming. Introduction of genes of interest into rice itself or other model plants [71] and reverse genetics [7274] have been used to confirm the gene functions in vivo. A technology for targeted gene mutagenesis in plants including rice is rapidly developing [75, 76], suggesting that the precise elimination of gene function in rice will be more facile in the future. In addition, rapid and space-saving rice breeding systems that enable researchers to drastically shorten the life cycle of some cultivars have been developed [77]. A combination of these technologies will help to accelerate the phytochemical genomics in rice.

Metabolomics has provided irreplaceable information on rice metabolism. The techniques for data recording and processing of metabolomics are more sophisticated than ever. Thus, it may be possible to focus efforts on validating various hypotheses elucidated from existing metabolomics research. Metabolomics has long functioned as a “hypothesis generator” [78] and these hypotheses remain to be assessed in further studies.

Declarations

Acknowledgments

This work was supported in part by grant aids from the Strategic International Research Cooperative Program of Japan Science and Technology Agency (Metabolomics for a Low Carbon Society, JST-NSF) and the Competitive Program for Creative Science and Technology of RIKEN (Integrated Lipidology).

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
RIKEN Center for Sustainable Resource Science
(2)
Kihara Institute for Biological Research, Yokohama City University
(3)
Graduate School of Pharmaceutical Sciences, Chiba University

References

  1. Huang X, Kurata N, Wei X, Wang Z-X, Wang A, Zhao Q, et al. A map of rice genome variation reveals the origin of cultivated rice. Nature. 2012;490:497–501.View ArticlePubMedGoogle Scholar
  2. Fitzgerald MA, McCouch SR, Hall RD. Not just a grain of rice: the quest for quality. Trends Plant Sci. 2009;14:133–9.View ArticlePubMedGoogle Scholar
  3. Kusano M, Yang Z, Okazaki Y, Nakabayashi R, Fukushima A, Saito K. Using metabolomic approaches to explore chemical diversity in rice. Mol Plant. 2015;8:58–67.View ArticlePubMedGoogle Scholar
  4. Oikawa A, Matsuda F, Kusano M, Okazaki Y, Saito K. Rice metabolomics. Rice. 2008;1:63–71.View ArticleGoogle Scholar
  5. Nakabayashi R, Saito K. Integrated metabolomics for abiotic stress responses in plants. Curr Opin Plant Biol. 2015;24:10–6.View ArticlePubMedGoogle Scholar
  6. Watson BS, Bedair MF, Urbanczyk-Wochniak E, Huhman DV, Yang DS, Allen SN, et al. Integrated metabolomics and transcriptomics reveal enhanced specialized metabolism in Medicago truncatula root border cells. Plant Physiol. 2015;167:1699–716.PubMed CentralView ArticlePubMedGoogle Scholar
  7. Saito K. Phytochemical genomics — a new trend. Curr Opin Plant Biol. 2013;16:373–80.View ArticlePubMedGoogle Scholar
  8. Yang Z, Nakabayashi R, Okazaki Y, Mori T, Takamatsu S, Kitanaka S, et al. Toward better annotation in plant metabolomics: isolation and structure elucidation of 36 specialized metabolites from Oryza sativa (rice) by using MS/MS and NMR analyses. Metabolomics. 2014;10:543–55.View ArticlePubMedGoogle Scholar
  9. Parker D, Beckmann M, Zubair H, Enot DP, Caracuel-Rios Z, Overy DP, et al. Metabolomic analysis reveals a common pattern of metabolic re-programming during invasion of three host plant species by Magnaporthe grisea. Plant J. 2009;59:723–37.View ArticlePubMedGoogle Scholar
  10. Solomon PS, Waters ODC, Oliver RP. Decoding the mannitol enigma in filamentous fungi. Trends Microbiol. 2007;15:257–62.View ArticlePubMedGoogle Scholar
  11. Soanes DM, Chakrabarti A, Paszkiewicz KH, Dawe AL, Talbot NJ. Genome-wide transcriptional profiling of appressorium development by the rice blast fungus Magnaporthe oryzae. PLoS Pathog. 2012;8:e1002514.PubMed CentralView ArticlePubMedGoogle Scholar
  12. Sana TR, Fischer S, Wohlgemuth G, Katrekar A, Jung K-H, Ronald PC, et al. Metabolomic and transcriptomic analysis of the rice response to the bacterial blight pathogen Xanthomonas oryzae pv. oryzae. Metabolomics. 2010;6:451–65.PubMed CentralView ArticlePubMedGoogle Scholar
  13. Sana TR, Fischer S, Wohlgemuth G, Katrekar A, Jung K-H, Ronald PC et al. Supplementary Materials 3 and 4. Metabolomics. 2010. http://link.springer.com/article/10.1007%2Fs11306-010-0218-7. Accessed 3 Feb 2016
  14. Kind T. Rice Infection Study. MetabolomeXchange. 2013. http://metabolomexchange.org/site/#/dataset/mwbs/ST000007. Accessed 3 Feb 2016
  15. Ishihara A, Hashimoto Y, Tanaka C, Dubouzet JG, Nakao T, Matsuda F, et al. The tryptophan pathway is involved in the defense responses of rice against pathogenic infection via serotonin production. Plant J. 2008;54:481–95.View ArticlePubMedGoogle Scholar
  16. Frey M, Chomet P, Glawischnig E, Stettner C, Grun S, Winklmair A, et al. Analysis of a chemical plant defense mechanism in grasses. Science. 1997;277(5326):696–9.View ArticlePubMedGoogle Scholar
  17. Nomura T, Ishihara A, Imaishi H, Endo TR, Ohkawa H, Iwamura H. Molecular characterization and chromosomal localization of cytochrome P450 genes involved in the biosynthesis of cyclic hydroxamic acids in hexaploid wheat. Mol Genet Genomics. 2002;267:210–7.View ArticlePubMedGoogle Scholar
  18. Nomura T, Ishihara A, Imaishi H, Ohkawa H, Endo TR, Iwamura H. Rearrangement of the genes for the biosynthesis of benzoxazinones in the evolution of Triticeae species. Planta. 2003;217:776–82.View ArticlePubMedGoogle Scholar
  19. Mayama S, Tani T, Matsuura Y, Ueno T, Fukami H. The production of phytoalexins by oat in response to crown rust, Puccinia coronata f. sp. avenae. Physiol Plant Pathol. 1981;19:217–26.View ArticleGoogle Scholar
  20. Miyagawa H, Ishihara A, Nishimoto T, Ueno T, Mayama S. Induction of avenanthramides in oat leaves inoculated with crown rust fungus, Puccinia coronata f. sp. avenae. Biosci Biotechnol Biochem. 1995;59:2305–6.View ArticleGoogle Scholar
  21. Ishihara A, Matsukawa T, Nomura T, Sue M, Oikawa A, Okazaki Y, et al. Involvement of tryptophan-pathway-derived secondary metabolism in the defence responses of grasses. In: D’Mello JPF, editor. Amino acids in higher plants. Oxfordshire: CABI; 2015. p. 362–89.Google Scholar
  22. Chamam A, Sanguin H, Bellvert F, Meiffren G, Comte G, Wisniewski-Dyé F, et al. Plant secondary metabolite profiling evidences strain-dependent effect in the Azospirillum-Oryza sativa association. Phytochemistry. 2013;87:65–77.View ArticlePubMedGoogle Scholar
  23. Xu K, Xu X, Fukao T, Canlas P, Maghirang-Rodriguez R, Heuer S, et al. Sub1A is an ethylene-response-factor-like gene that confers submergence tolerance to rice. Nature. 2006;442:705–8.View ArticlePubMedGoogle Scholar
  24. Bailey-Serres J, Fukao T, Gibbs DJ, Holdsworth MJ, Lee SC, Licausi F, et al. Making sense of low oxygen sensing. Trends Plant Sci. 2012;17:129–38.View ArticlePubMedGoogle Scholar
  25. Barding GA, Fukao T, Béni S, Bailey-Serres J, Larive CK. Differential metabolic regulation governed by the rice SUB1A gene during submergence stress and identification of alanylglycine by 1H NMR spectroscopy. J Proteome Res. 2012;11:320–30.View ArticlePubMedGoogle Scholar
  26. Peng S, Huang J, Sheehy JE, Laza RC, Visperas RM, Zhong X, et al. Rice yields decline with higher night temperature from global warming. Proc Natl Acad Sci U S A. 2004;101:9971–5.PubMed CentralView ArticlePubMedGoogle Scholar
  27. Hakata M, Kuroda M, Miyashita T, Yamaguchi T, Kojima M, Sakakibara H, et al. Suppression of α-amylase genes improves quality of rice grain ripened under high temperature. Plant Biotechnol J. 2012;10:1110–7.View ArticlePubMedGoogle Scholar
  28. Yamakawa H, Hakata M. 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. Plant Cell Physiol. 2010;51:795–809.PubMed CentralView ArticlePubMedGoogle Scholar
  29. Glaubitz U, Erban A, Kopka J, Hincha DK, Zuther E. High night temperature strongly impacts TCA cycle, amino acid and polyamine biosynthetic pathways in rice in a sensitivity-dependent manner. J Exp Bot. 2015;66:6385–97.PubMed CentralView ArticlePubMedGoogle Scholar
  30. Degenkolbe T, Do PT, Kopka J, Zuther E, Hincha DK, Köhl KI. Identification of drought tolerance markers in a diverse population of rice cultivars by expression and metabolite profiling. PLoS One. 2013;8:e63637.PubMed CentralView ArticlePubMedGoogle Scholar
  31. Shu L, Lou Q, Ma C, Ding W, Zhou J, Wu J, et al. Genetic, proteomic and metabolic analysis of the regulation of energy storage in rice seedlings in response to drought. Proteomics. 2011;11:4122–38.View ArticlePubMedGoogle Scholar
  32. Maruyama K, Urano K, Yoshiwara K, Morishita Y, Sakurai N, Suzuki H, et al. Integrated analysis of the effects of cold and dehydration on rice metabolites, phytohormones, and gene transcripts. Plant Physiol. 2014;164:1759–71.PubMed CentralView ArticlePubMedGoogle Scholar
  33. Li X, Lawas LMF, Malo R, Glaubitz U, Erban A, Mauleon R, et al. Metabolic and transcriptomic signatures of rice floral organs reveal sugar starvation as a factor in reproductive failure under heat and drought stress. Plant Cell Environ. 2015;38:2171–92.View ArticlePubMedGoogle Scholar
  34. Zuther E, Koehl K, Kopka J. Comparative metabolome analysis of the salt response in breeding cultivars of rice. In: Jenks M, Hasegawa P, Jain SM, editors. Advances in molecular breeding toward drought and salt tolerant crops. Dordrecht: Springer Netherlands; 2007. p. 285–315.View ArticleGoogle Scholar
  35. Cho K, Shibato J, Agrawal GK, Jung Y-H, Kubo A, Jwa N-S, et al. Integrated transcriptomics, proteomics, and metabolomics analyses to survey ozone responses in the leaves of rice seedling. J Proteome Res. 2008;7:2980–98.View ArticlePubMedGoogle Scholar
  36. Ishikawa T, Takahara K, Hirabayashi T, Matsumura H, Fujisawa S, Terauchi R, et al. Metabolome analysis of response to oxidative stress in rice suspension cells overexpressing cell death suppressor Bax inhibitor-1. Plant Cell Physiol. 2010;51:9–20.View ArticlePubMedGoogle Scholar
  37. Tabuchi M, Sugiyama K, Ishiyama K, Inoue E, Sato T, Takahashi H, et al. Severe reduction in growth rate and grain filling of rice mutants lacking OsGS1;1, a cytosolic glutamine synthetase1;1. Plant J. 2005;42:641–51.View ArticlePubMedGoogle Scholar
  38. Kusano M, Tabuchi M, Fukushima A, Funayama K, Diaz C, Kobayashi M, et al. Metabolomics data reveal a crucial role of cytosolic glutamine synthetase 1;1 in coordinating metabolic balance in rice. Plant J. 2011;66:456–66.View ArticlePubMedGoogle Scholar
  39. Kurusu T, Koyano T, Hanamata S, Kubo T, Noguchi Y, Yagi C, et al. OsATG7 is required for autophagy-dependent lipid metabolism in rice postmeiotic anther development. Autophagy. 2014;10:878–88.View ArticlePubMedGoogle Scholar
  40. Toyosawa Y, Kawagoe Y, Matsushima R, Crofts N, Ogawa M, Fukuda M, et al. Deficiency of starch synthase IIIa and IVb alters starch granule morphology from polyhedral to spherical in rice endosperm. Plant Physiol. 2016. doi:10.1104/pp.15.01232.PubMedGoogle Scholar
  41. Dubouzet JG, Ishihara A, Matsuda F, Miyagawa H, Iwata H, Wakasa K. Integrated metabolomic and transcriptomic analyses of high-tryptophan rice expressing a mutant anthranilate synthase alpha subunit. J Exp Bot. 2007;58:3309–21.View ArticlePubMedGoogle Scholar
  42. Saika H, Oikawa A, Matsuda F, Onodera H, Saito K, Toki S. Application of gene targeting to designed mutation breeding of high-tryptophan rice. Plant Physiol. 2011;156:1269–77.PubMed CentralView ArticlePubMedGoogle Scholar
  43. Jacobs A, Lunde C, Bacic A, Tester M, Roessner U. The impact of constitutive heterologous expression of a moss Na + transporter on the metabolomes of rice and barley. Metabolomics. 2007;3:307–17.View ArticleGoogle Scholar
  44. Takahara K, Kasajima I, Takahashi H, Hashida SN, Itami T, Onodera H, et al. Metabolome and photochemical analysis of rice plants overexpressing Arabidopsis NAD kinase gene. Plant Physiol. 2010;152:1863–73.PubMed CentralView ArticlePubMedGoogle Scholar
  45. Albinsky D, Kusano M, Higuchi M, Hayashi N, Kobayashi M, Fukushima A, et al. Metabolomic screening applied to rice FOX Arabidopsis lines leads to the identification of a gene-changing nitrogen metabolism. Mol Plant. 2010;3:125–42.View ArticlePubMedGoogle Scholar
  46. Suzuki M, Kusano M, Takahashi H, Nakamura Y, Hayashi N, Kobayashi M, et al. Rice-Arabidopsis FOX line screening with FT-NIR-based fingerprinting for GC-TOF/MS-based metabolite profiling. Metabolomics. 2009;6:137–45.View ArticleGoogle Scholar
  47. Redestig H, Kusano M, Ebana K, Kobayashi M, Oikawa A, Okazaki Y, et al. Exploring molecular backgrounds of quality traits in rice by predictive models based on high-coverage metabolomics. BMC Sys Biol. 2011;5:176.View ArticleGoogle Scholar
  48. Calingacion MN, Boualaphanh C, Daygon VD, Anacleto R, Hamilton RS, Biais B, et al. A genomics and multi-platform metabolomics approach to identify new traits of rice quality in traditional and improved varieties. Metabolomics. 2012;8:771–83.View ArticleGoogle Scholar
  49. Heuberger AL, Lewis MR, Chen M-H, Brick MA, Leach JE, Ryan EP. Metabolomic and functional genomic analyses reveal varietal differences in bioactive compounds of cooked rice. PLoS One. 2010;5:e12915.PubMed CentralView ArticlePubMedGoogle Scholar
  50. Hu C, Shi J, Quan S, Cui B, Kleessen S, Nikoloski Z, et al. Metabolic variation between japonica and indica rice cultivars as revealed by non-targeted metabolomics. Sci Rep. 2014;4:5067.PubMedGoogle Scholar
  51. Kusano M, Fukushima A, Kobayashi M, Hayashi N, Jonsson P, Moritz T, et al. Application of a metabolomic method combining one-dimensional and two-dimensional gas chromatography-time-of-flight/mass spectrometry to metabolic phenotyping of natural variants in rice. J Chromatogr B Analyt Technol Biomed Life Sci. 2007;855:71–9.View ArticlePubMedGoogle Scholar
  52. Kusano M, Fukushima A, Fujita N, Okazaki Y, Kobayashi M, Oitome NF, et al. Deciphering starch quality of rice kernels using metabolite profiling and pedigree network analysis. Mol Plant. 2012;5:442–51.View ArticlePubMedGoogle Scholar
  53. Sato S, Arita M, Soga T, Nishioka T, Tomita M. Time-resolved metabolomics reveals metabolic modulation in rice foliage. BMC Sys Biol. 2008;2:51.View ArticleGoogle Scholar
  54. Sato S, Soga T, Nishioka T, Tomita M. Simultaneous determination of the main metabolites in rice leaves using capillary electrophoresis mass spectrometry and capillary electrophoresis diode array detection. Plant J. 2004;40:151–63.View ArticlePubMedGoogle Scholar
  55. Tarpley L, Duran AL, Kebrom TH, Sumner LW. Biomarker metabolites capturing the metabolite variance present in a rice plant developmental period. BMC Plant Biol. 2005;5:8.PubMed CentralView ArticlePubMedGoogle Scholar
  56. Jacob D. Rice - MetaPhor. MetabolomeXchange. 2011. http://metabolomexchange.org/site/#/dataset/meryb/R06001. Accessed 3 Feb 2016.
  57. Kliebenstein D. Advancing genetic theory and application by metabolic quantitative trait loci analysis. Plant Cell. 2009;21:1637–46.PubMed CentralView ArticlePubMedGoogle Scholar
  58. Keurentjes JJ, Fu J, de Vos CH, Lommen A, Hall RD, Bino RJ, et al. The genetics of plant metabolism. Nat Genet. 2006;38:842–9.View ArticlePubMedGoogle Scholar
  59. Fernie AR, Schauer N. Metabolomics-assisted breeding: a viable option for crop improvement? Trends Genet. 2009;25:39–48.View ArticlePubMedGoogle Scholar
  60. Schauer N, Semel Y, Roessner U, Gur A, Balbo I, Carrari F, et al. Comprehensive metabolic profiling and phenotyping of interspecific introgression lines for tomato improvement. Nat Biotechnol. 2006;24:447–54.View ArticlePubMedGoogle Scholar
  61. Sawada Y, Akiyama K, Sakata A, Kuwahara A, Otsuki H, Sakurai T, et al. Widely targeted metabolomics based on large-scale MS/MS data for elucidating metabolite accumulation patterns in plants. Plant Cell Physiol. 2009;50:37–47.PubMed CentralView ArticlePubMedGoogle Scholar
  62. Chen W, Gong L, Guo Z, Wang W, Zhang H, Liu X, et al. A novel integrated method for large-scale detection, identification, and quantification of widely targeted metabolites: application in the study of rice metabolomics. Mol Plant. 2013;6:1769–80.View ArticlePubMedGoogle Scholar
  63. Matsuda F, Okazaki Y, Oikawa A, Kusano M, Nakabayashi R, Kikuchi J, et al. Dissection of genotype-phenotype associations in rice grains using metabolome quantitative trait loci analysis. Plant J. 2012;70:624–36.View ArticlePubMedGoogle Scholar
  64. Gong L, Chen W, Gao Y, Liu X, Zhang H, Xu C, et al. Genetic analysis of the metabolome exemplified using a rice population. Proc Natl Acad Sci U S A. 2013;110:20320–5.PubMed CentralView ArticlePubMedGoogle Scholar
  65. The 3,000 rice genomes project. The 3,000 rice genomes project. GigaScience. 2014;3:7.View ArticleGoogle Scholar
  66. Chen W, Gao Y, Xie W, Gong L, Lu K, Wang W, et al. Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism. Nat Genet. 2014;46:714–21.View ArticlePubMedGoogle Scholar
  67. Matsuda F, Nakabayashi R, Yang Z, Okazaki Y, Yonemaru J, Ebana K, et al. Metabolome-genome-wide association study dissects genetic architecture for generating natural variation in rice secondary metabolism. Plant J. 2015;81:13–23.PubMed CentralView ArticlePubMedGoogle Scholar
  68. Dong X, Gao Y, Chen W, Wang W, Gong L, Liu X, et al. Spatiotemporal distribution of phenolamides and the genetics of natural variation of hydroxycinnamoyl spermidine in rice. Mol Plant. 2015;8:111–21.View ArticlePubMedGoogle Scholar
  69. Yan J, Aboshi T, Teraishi M, Strickler SR, Spindel JE, Tung C-W, et al. The tyrosine aminomutase TAM1 is required for β-tyrosine biosynthesis in rice. Plant Cell. 2015;27:1265–78.View ArticlePubMedGoogle Scholar
  70. Yonemaru JI, Yamamoto T, Fukuoka S, Uga Y, Hori K, Yano M. Q-TARO: QTL annotation rice online database. Rice. 2010;3:194–203.View ArticleGoogle Scholar
  71. Hiei Y, Ohta S, Komari T, Kumashiro T. Efficient transformation of rice (Oryza sativa L.) mediated by Agrobacterium and sequence analysis of the boundaries of the T-DNA. Plant J. 1994;6:271–82.View ArticlePubMedGoogle Scholar
  72. Jeon JS, Lee S, Jung KH, Jun SH, Jeong DH, Lee J, et al. T-DNA insertional mutagenesis for functional genomics in rice. Plant J. 2000;22:561–70.View ArticlePubMedGoogle Scholar
  73. Till BJ, Cooper J, Tai TH, Colowit P, Greene E, Henikoff S, et al. Discovery of chemically induced mutations in rice by TILLING. BMC Plant Biol. 2007;7:19.PubMed CentralView ArticlePubMedGoogle Scholar
  74. Miyao A, Tanaka K, Murata K, Sawaki H, Takeda S, Abe K, et al. Target site specificity of the Tos17 retrotransposon shows a preference for insertion within genes and against insertion in retrotransposon-rich regions of the genome. Plant Cell. 2003;15:1771–80.PubMed CentralView ArticlePubMedGoogle Scholar
  75. Jiang W, Zhou H, Bi H, Fromm M, Yang B, Weeks DP. Demonstration of CRISPR/Cas9/sgRNA-mediated targeted gene modification in Arabidopsis, tobacco, sorghum and rice. Nucleic Acids Res. 2013;41:e188.PubMed CentralView ArticlePubMedGoogle Scholar
  76. Nishizawa-Yokoi A, Endo M, Ohtsuki N, Saika H, Toki S. Precision genome editing in plants via gene targeting and piggyBac-mediated marker excision. Plant J. 2015;81:160–8.PubMed CentralView ArticlePubMedGoogle Scholar
  77. Ohnishi T, Yoshino M, Yamakawa H, Kinoshita T. The biotron breeding system: A rapid and reliable procedure for genetic studies and breeding in rice. Plant Cell Physiol. 2011;52:1249–57.View ArticlePubMedGoogle Scholar
  78. Hall RD. Plant metabolomics: from holistic hope, to hype, to hot topic. New Phytol. 2006;169:453–68.View ArticlePubMedGoogle Scholar

Copyright

© Okazaki and Saito. 2016