基于代谢组学的转基因水稻生物安全评价方法研究

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

摘要/Abstract

摘要：

代谢组学因其全面无偏向性、非靶标轮廓分析的技术优势,成为目前转基因生物安全评价技术研究的热点。以转基因抗虫水稻及其非转基因受体为研究对象,采用基于超高效液相色谱-四级杆-飞行时间串联质谱（UPLC-QTOF/MS）分析的转基因作物代谢组评价技术流程,对代谢组提取方法、数据处理方法、分析平台等进行了探索。结果表明,应用80%甲醇水能有效提取水稻苗期叶片中的代谢物;经UPLC分离、QTOF/MS分析、正交校正的偏最小二乘辨别分析（OPLS-DA）等计算方法,能够有效发现转基因水稻及其受体之间的差异代谢物。本研究建立的完整的转基因水稻代谢组分析方法具有分辨率高、易掌握的优点。本研究丰富了现有的转基因生物安全评价的内容和方法,为转基因生物安全管理提供技术支撑。

关键词: 转基因抗虫水稻, 代谢组学与代谢物, 转基因生物安全评价, UPLC-QTOF/MS分析

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

兰青阔, 赵新, 沈晓玲, 魏静娜, 刘双, 陈锐, 檀建新, 王永. 基于代谢组学的转基因水稻生物安全评价方法研究[J]. 生物技术通报, 2020, 36(11): 222-229.

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.

图/表 8

表1 UPLC流动相洗脱梯度

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

图1 不同浓度提取溶剂下BPI图

图2 转基因水稻及其受体苗期叶片的PCA分析图 ■ 转基因,▲ 非转基因;A：正离子模式,B：负离子模式

图3 转基因水稻及其受体苗期叶片的OPLS-DA分析图 ■ 转基因,▲ 非转基因;A：正离子模式,B：负离子模式

图4 49个差异代谢物在两组样本中的分布情况 ■ 蓝色阴影-转基因,■ 紫色阴影-非转基因;A：正离子模式,B：负离子模式

图5 转基因抗虫水稻苗期叶片代谢物相关性分析 a-e依次代表P值为0.05、0.01、0.001、1e-04、1e-05

图6 转基因抗虫水稻苗期叶片代谢物欧氏距离分析 a-e依次代表P值为0.05、0.01、0.001、1e-04、1e-05

图7 差异代谢物在两组样本中的分布情况

参考文献 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.