Biosafety Assessment Technology Research for Genetically Modified Rice Based on Metabolomics

doi:10.13560/j.cnki.biotech.bull.1985.2020-0198

Abstract

Abstract:

Currently metabolomics is becoming the hotspot in safety assessment research of genetically modified organism（GMO）because of its unbiased and untargeted analysis. Here metabolome extraction method,multivariate processing methods,and data analysis platform were explored while transgenic insect-resistant rice and its non-transgenic receptor were taken as the research object and technical process of metabolomics evaluation of genetically modified crops was used based on the ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry（UPLC-QTOF/MS）. The results showed that metabolites in the leaves of rice seedlings were effectively extracted with extraction buffer containing 80% methanol. By UPLC separation,QTOF/MS analysis,and orthogonal partial least squares-discriminate analysis（OPLS-DA）,the differential metabolites between genetically modified rice and its non-genetically modified rice were effectively discovered. The systematic analysis method of transgenic rice metabolome established in this study demonstrates the advantages of high resolution and easy to operate. This study enriches the current contents and methods of safe assessment for GMO as well as provides technical support for the safety management of GMO.

Key words: transgenic insect-resistant rice, metabolomics and metabolites, GMO safety assessment, UPLC-QTOF/MS analysis

LAN Qing-kuo, ZHAO Xin, SHEN Xiao-ling, WEI Jing-na, LIU Shuang, CHEN Rui, TAN Jian-xin, WANG Yong. Biosafety Assessment Technology Research for Genetically Modified Rice Based on Metabolomics[J]. Biotechnology Bulletin, 2020, 36(11): 222-229.

Figures/Tables 8

References 26

[1]	国际农业生物技术应用服务组织. 2018年全球生物技术/转基因作物商业化发展态势[J]. 中国生物工程杂志, 2019,39(8):1-6.
	International Service for the Acquisition of Agri-biotech Applications China Biotechnology. Development trend of global biotechnology/GENETICALLY modified crops commercialization in 2018[J]. China Biotechnology, 2019,39(8):1-6.
[2]	Goodman RE. Biosafety:evaluation and regulation of genetically modified(GM)crops in the United States[J]. Journal of Huazhong Agricultural University, 2014,33(6):109-114.
[3]	Sanvido O, Romeis J, Gathmann A, et al. Evaluating environmental risks of genetically modified crops:ecological harm criteria for regulatory decision-making[J]. Environmental Science & Policy, 2012,15(1):82-91.
[4]	Zepeda J, Cohen J. Biosafety regulation of genetically modified orphan crops in developing countries:A way forward[J]. Regulating Agricultural Biotechnology:Economics and Policy, 2006: 509-533.
[5]	Development. Environment Directorate, Development. Development. Directorate for Science, Development. Group of National Experts on Safety. Safety evaluation of foods derived by modern biotechnology:concepts and principles[M]. Organization for Economic, 1993.
[6]	Cellini F, Chesson A, Colquhoun I, et al. Unintended effects and their detection in genetically modified crops[J]. Food and Chemical Toxicology, 2004,42(7):1089-1125. doi: 10.1016/j.fct.2004.02.003 URL pmid: 15123383
[7]	张焕春, 陈笑芸, 汪小福, 等. 转基因作物的非预期效应及其检测[J]. 浙江农业学报, 2012,24(1):125-132.
	Zhang HC, Chen XY, Wang XF, et al. Unintended effects and their detection in genetically modified crops[J]. Acta Agriculturae Zhejiangensis, 2020,24(1):125-132.
[8]	Simó C, Ibáez C, Valdés A, et al. Metabolomics of genetically modified crops[J]. International Journal of Molecular Sciences, 2014,15(10):18941-18966. URL pmid: 25334064
[9]	Davies H. A role for “omics” technologies in food safety assessment[J]. Food Control, 2010,21(12):1601-1610. doi: 10.1016/j.foodcont.2009.03.002 URL
[10]	Christ B, Pluskal T, Aubry S, et al. Contribution of untargeted metabolomics for future assessment of biotech crops[J]. Trends in Plant Science, 2018,23(12):1047-1056. doi: 10.1016/j.tplants.2018.09.011 URL pmid: 30361071
[11]	Wang X, Zhang X, Yang J, et al. Effect on transcriptome and metabolome of stacked transgenic maize containing insecticidal cry and glyphosate tolerance epsps genes[J]. Plant Journal, 2018,93(6):1007-1016. doi: 10.1111/tpj.13825 URL pmid: 29356248
[12]	Benevenuto RF, Agapitotenfen SZ, Vilperte V, et al. Molecular responses of genetically modified maize to abiotic stresses as determined through proteomic and metabolomic analyses[J]. PLoS One, 2017,12(2):e0173069. doi: 10.1371/journal.pone.0173069 URL pmid: 28245233
[13]	Li R, Quan S, Yan X, et al. Molecular characterization of genetically-modified crops:Challenges and strategies[J]. Biotechnology Advances, 2017,35(2):302-309. doi: 10.1016/j.biotechadv.2017.01.005 URL pmid: 28131814
[14]	Tohge T, Fernie AR. Web-based resources for mass-spectrometry-based metabolomics:a user’s guide[J]. Phytochemistry, 2009,70(4):450-456. doi: 10.1016/j.phytochem.2009.02.004 URL pmid: 19285697
[15]	Harrigan GG, Chassy B. Challenges for metabolomics as a tool in safety assessments[M]. INTECH Open Access Publisher, 2012.
[16]	Fernández AF, Llorach R, Andrés LC, Perera, A. An R package to process LC/MS metabolomic data:MAIT(Metabolite Automatic Identification Toolkit)[J]. Bioinformatics. 2014,30(13):1937-1939. doi: 10.1093/bioinformatics/btu136 URL pmid: 24642061
[17]	王丽娜, 王步军. 基于UPLC-QTOF/MS的小麦发芽代谢组学分析方法[J]. 作物学报, 2019,45(12):1899-1904. doi: 10.3724/SP.J.1006.2019.91017 URL
	Wang LN, Wang BJ. Analysis method of wheat germination metabolomics based on UPLC-QTOF/MS[J]. Acta Agronomica Sinica, 2019,45(12):1899-1904. doi: 10.3724/SP.J.1006.2019.91017 URL
[18]	Peng C, Ding L, Hu C, et al. Effect on metabolome of the grains of transgenic rice containing insecticidal cry and glyphosate tolerance epsps genes[J]. Plant Growth Regulation, 2019,88(1):1-7. doi: 10.1007/s10725-019-00482-6 URL
[19]	Mesnage R, Arno M, Seralini G, et al. Transcriptome and metabolome analysis of liver and kidneys of rats chronically fed NK603 Roundup-tolerant genetically modified maize[J]. Environmental Sciences Europe, 2017,29(1):6. URL pmid: 28239534
[20]	Kruger NJ, Troncoso-Ponce MA, Ratcliffe RG. 1H NMR metabolite fingerprinting and metabolomic analysis of perchloric acid extracts from plant tissues[J]. Nature Protocols, 2008,3(6):1001-1012. doi: 10.1038/nprot.2008.64 URL pmid: 18536647
[21]	Frank T, Röhlig RM, Davies HV, et al. Metabolite profiling of maize kernels-- genetic modification versus environmental influence[J]. Journal of Agricultural and Food Chemistry, 2012,60(12):3005-3012. doi: 10.1021/jf204167t URL pmid: 22375597
[22]	Boonchaisri S, Stevenson TW, Dias DA, et al. Utilization of GC-MS untargeted metabolomics to assess the delayed response of glufosinate treatment of transgenic herbicide resistant(HR)buffalo grasses(Stenotaphrum secundatum L. )[J]. Metabolomics, 2020,16(2):1-25.
[23]	Chang Y, Zhao C, Zhu Z, et al. Metabolic profiling based on LC/MS to evaluate unintended effects of transgenic rice with cry1Ac and sck genes[J]. Plant Molecular Biology, 2012,78(4-5):477-487. doi: 10.1007/s11103-012-9876-3 URL pmid: 22271304
[24]	Leon C, Rodriguez-Meizoso I, Lucio M, et al. Metabolomics of transgenic maize combining Fourier transform-ion cyclotron resonance-mass spectrometry, capillary electrophoresis-mass spectrometry and pressurized liquid extraction[J]. Journal of Chromatography A, 2009,1216(43):7314-7323. doi: 10.1016/j.chroma.2009.04.092 URL pmid: 19477454
[25]	Levandi T, Leon C, Kaljurand M, et al. Capillary electrophoresis time-of-flight mass spectrometry for comparative metabolomics of transgenic versus conventional maize[J]. Analytical Chemistry, 2008,80(16):6329-6335. doi: 10.1021/ac8006329 URL pmid: 18613645
[26]	Boonchaisri S, Rochfort S, Stevenson TW, et al. Recent developments in metabolomics-based research in understanding transgenic grass metabolism[J]. Metabolomics, 2019,15(4):1-19.

步骤	时间/min	流速/（mL/min）	流动相A/%	流动相B/%
1	Initial	0.40	90.0	10.0
2	6.00	0.40	80.0	20.0
3	7.00	0.40	30.0	70.0
4	14.00	0.40	0.0	100.0

步骤	时间/min	流速/（mL/min）	流动相A/%	流动相B/%
1	Initial	0.40	90.0	10.0
2	6.00	0.40	80.0	20.0
3	7.00	0.40	30.0	70.0
4	14.00	0.40	0.0	100.0