Metabolomics Research Strategies of Rice Based on GC-MS

doi:10.3969/j.issn.1006-8082.2015.06.001

Abstract

Abstract: With the continuous development of modern molecular biology techniques, the use of systems biology methods in plant research is promoted in recent years. Taking rice grain as materials, this study based on GC - MS metabonomics method, combining statistics and bioinformatics analysis （heat map, PCA, S - the Plot and MetPA, etc.）, puts forward a set of methods of extracting for rice metabolites, discusses the applicable to rice metabolomics research strategy. This study provides a technical support for revealing the mechanism of plant growth and the molecular mechanism to adapt to adversity in the future.

Key words: rice, grain, metabonomics, metabolic network

摘要： 近年来，随着现代分子生物学技术的不断发展，推动了系统生物学方法在植物研究中的运用。本研究以水稻籽粒为材料，基于GC-MS的代谢组学方法，结合统计学与生物信息学分析手段（热点图、PCA、S-Plot和MetPA等），提出了一套适合水稻代谢物的提取方法，探讨了适用于水稻代谢组学研究的策略。本研究为未来揭示植物发育机理及适应逆境的分子机制提供了技术支持。

关键词: 水稻, 籽粒, 代谢组学, 代谢网络

CLC Number:

S511

CHEN Dong-Mei-＃, LI Zhong-＃, ZHANG Zhi-Xing, LIN Wen-Xiong-*. Metabolomics Research Strategies of Rice Based on GC-MS[J]. , 2015, 21(6): 1-6.

陈冬梅＃, 李忠＃, 张志兴, 林文雄*. 基于GC-MS分析的水稻代谢组学研究策略[J]. 中国稻米, 2015, 21(6): 1-6.

References

[1]    Oliver S G, Winson M K, Kell D B, et al. Systematic functional analysis of the yeast genome[J]. Trends Biotechnol, 1998, 16（9）: 373-378.

[2]    Fiehn O. Metabolomics–the link between genotypes and phenotypes[J]. Plant Mol Biol, 2002, 48（1-2）: 155-171.

[3]    Dixon R A, Strack D. Phytochemistry meets genome analysis, and beyond[J]. Phytochemistry, 2003, 62（6）: 815-816.

[4]    林文雄，李忠，陈军，等. 水稻籽粒灌浆的发育遗传与分子生态特性研究[J]. 中国生态农业学报，2012，19（6）：1 237-1 242.

[5]    Zhu G, Ye N, Yang J, et al. Regulation of expression of starch synthesis genes by ethylene and ABA in relation to the development of rice inferior and superior spikelets[J]. J Exp Bot, 2011, 62（11）: 3907-3916.

[6]    Peng T, Lv Q, Zhang J, et al. Differential expression of the microRNAs in superior and inferior spikelets in rice （Oryza sativa） [J]. J Exp Bot, 2011, 62（14）: 4 943-4 954.

[7]    Zhang Z, Chen J, Li Z, et al. Differential proteomic expressions between superior and inferior spikelets of rice in response to varied nitrogen treatments[J]. Australian Journal of Crop Science, 2012, 6（2）: 316-325.

[8]    Schauer N, Semel Y, Roessner U, et al. Comprehensive metabolic profiling and phenotyping of interspecific introgression lines for tomato improvement[J]. Nat Biotechnol, 2006, 24（4）: 447-454.

[9]    Pongsuwan W, Bamba T, Yonetani T, et al. Quality prediction of Japanese green tea using pyrolyzer coupled GC/MS based metabolic fingerprinting[J]. J Agric Food Chem, 2008, 56（3）: 744-750.

[10] Shu X L, Frank T, Shu Q Y, et al. Metabolite profiling of germinating rice seeds[J]. J Agric Food Chem, 2008, 56（24）: 11 612-11 620.

[11] Lisec J, Schauer N, Kopka J, et al. Gas chromatography mass spectrometry–based metabolite profiling in plants[J]. Nat Protoc, 2006, 1（1）: 387-396.

[12] Trygg J, Holmes E, Lundstedt T. Chemometrics in metabonomics[J]. J Proteome Res, 2007, 6（2）: 469-479.

[13] Weckwerth W, Wenzel K, Fiehn O. Process for the integrated extraction, identification and quantification of metabolites, proteins and RNA to reveal their co‐regulation in biochemical networks[J]. Proteomics, 2004, 4（1）: 78-83.

[14] Nikolau B J, Wurtele E S. Concepts in plant metabolomics [M]. Springer, 2007.

[15] Tohge T, Fernie A R. Web-based resources for mass-spectrometry-based metabolomics: a user’s guide[J]. Phytochemistry, 2009, 70（4）: 450-456.

[16] Dieterle F, Ross A, Schlotterbeck G, et al. Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics[J]. Anal Chem, 2006, 78（13）: 4 281-4 290.