多组学系统分析生物育种产品非期望效应的研究进展

doi:10.19586/j.2095-2341.2026.0025

生物技术进展 ›› 2026, Vol. 16 ›› Issue (4): 732-743.DOI: 10.19586/j.2095-2341.2026.0025

多组学系统分析生物育种产品非期望效应的研究进展

高宇¹(), 韩天意¹, 贺晓云¹, 程楠¹, 刘威辰², 付伟³, 黄昆仑¹(), 陈红³()

^1.中国农业大学食品科学与营养工程学院，北京 100083
^2.中国质量检验检测科学研究院，北京 100176
^3.农业农村部科技发展中心，北京 100176

收稿日期:2026-02-06 接受日期:2026-03-03 出版日期:2026-07-25 发布日期:2026-09-11
通信作者: 黄昆仑,陈红
作者简介:高宇 E-mail: 1340461357@qq.com
基金资助:
农业生物育种重大专项(2023ZD0406304)

Research Progress on Multi-omics Systematic Analysis of Unintended Effects in Biotechnology-derived Breeding Products

Yu GAO¹(), Tianyi HAN¹, Xiaoyun HE¹, Nan CHENG¹, Weichen LIU², Wei FU³, Kunlun HUANG¹(), Hong CHEN³()

^1.College of Food Science and Nutritional Engineering，China Agricultural University，Beijing 100083，China
^2.Chinese Academy of Quality and Inspection & Testing，Beijing 100176，China
^3.Development Center for Science and Technology，Ministry of Agriculture and Rural Affairs，Beijing 100176，China

Received:2026-02-06 Accepted:2026-03-03 Online:2026-07-25 Published:2026-09-11
Contact: Kunlun HUANG,Hong CHEN

摘要/Abstract

摘要：

随着转基因（genetically modified，GM）与基因编辑（genome editing，GE）作物在全球农业贸易中的商业化进程加速，如何精准捕捉生物育种产品中潜在的非期望效应（unintended effects，UEs），已经成为生物安全评价的核心议题之一。长期以来，评价体系主要依据“实质等同性”原则，因此靶向检测技术在常规理化指标评估中发挥着重要作用。然而，传统检测手段难以捕捉复杂的分子变异，且传统的“混合多组学（bulk multi-omics）”分析往往会屏蔽组织细胞的异质性，导致在微观层面上无法探测到稀有变异及其复杂的微环境互作。综述了近年来利用多组学技术评估生物育种产品非期望效应的研究进展，重点探讨了单细胞组学与空间组学等前沿技术在解析组织特异性非期望效应中的应用潜力，深入分析了非靶向多组学（基因组、转录组、蛋白质组及代谢组）如何通过全谱扫描实现全层级分子变异分析，并对多组学数据整合分析和人工智能在未来生物安全监管中的挑战与前景进行了展望。综上，整合多维度组学数据的系统生物学分析策略，并结合机器学习与人工智能，是未来实现生物育种产品全面、精准的食用安全及非期望效应评价的重要趋势。

关键词: 生物育种产品, 非期望效应, 多组学, 单细胞组学, 空间组学, 生物安全评价

Abstract:

With the accelerating commercialization of genetically modified （GM） and gene-edited （GE） crops within global agricultural trade， the accurate identification of potential unintended effects （UEs） in biotechnology-derived breeding products has become a central issue in biosafety assessment. For a long duration， the evaluation system has relied largely on the principle of "substantial equivalence"， wherein targeted detection technologies have played a significant role in assessing conventional physicochemical indicators. However， traditional methods struggle to capture complex molecular variations. Moreover， conventional "bulk multi-omics" analysis tends to mask cellular heterogeneity， failing to detect rare variants and their intricate microenvironmental interactions at the microscopic level. This review summarized recent advances in the application of multi-omics technologies for evaluating unintended effects in biotech breeding products. It focused on the potential of emerging technologies such as single-cell omics and spatial omics in deciphering tissue-specific unintended effects. Furthermore， the article provided an in-depth analysis of how non-targeted multi-omics—encompassing genomics， transcriptomics， proteomics， and metabolomics—enable the comprehensive profiling of multi-layered molecular variations through full-spectrum scanning. Finally， the article explored the challenges and prospects of integrated multi-omics data analysis and artificial intelligence （AI） in future biosafety regulatory frameworks. Therefore， a systems biology strategy integrating multi-dimensional omics data， combined with machine learning and artificial intelligence， represents a crucial trend toward achieving comprehensive and precise food safety and unintended effects assessment of biotech breeding products.

Key words: biotechnology-derived breeding products, unintended effects, multi-omics, single-cell omics, spatial omics, biosafety assessment

中图分类号:

Q3-3

高宇, 韩天意, 贺晓云, 程楠, 刘威辰, 付伟, 黄昆仑, 陈红. 多组学系统分析生物育种产品非期望效应的研究进展[J]. 生物技术进展, 2026, 16(4): 732-743.

Yu GAO, Tianyi HAN, Xiaoyun HE, Nan CHENG, Weichen LIU, Wei FU, Kunlun HUANG, Hong CHEN. Research Progress on Multi-omics Systematic Analysis of Unintended Effects in Biotechnology-derived Breeding Products[J]. Current Biotechnology, 2026, 16(4): 732-743.

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链接本文: https://swjsjz.magtechjournal.com/CN/10.19586/j.2095-2341.2026.0025

https://swjsjz.magtechjournal.com/CN/Y2026/V16/I4/732

图/表 6

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转基因植物	预期性状	检测方法	非期望效应结论	参考文献
转基因杨树	抗虫性、抗逆性	NGS、转录组学（RNA-seq）、表型测定	观察到非期望效应，杨树的树高和胸径变小，生长迟缓	[15]
转基因水稻	抗叶螟与黄螟	双端测序（paired-end sequencing，PE）	观察到非期望效应，在TT51-1转基因水稻10号和4号染色体上出现额外的非预期插入	[16]
转基因大豆	抗病性	全基因组测序（whole-genome sequencing，WGS）	未观察到显著的非期望效应	[17]
转基因木瓜	抗木瓜环斑病毒	WGS	未观察到显著的非期望效应	[18]
转基因白菜	谷胱甘肽S-转移酶基因表达下调	全基因组亚硫酸盐测序(whole-genome bisulfite sequencing，WGBS)	观察到非期望效应，转基因株系中DNA的甲基化水平发生显著变化	[19]
转基因玉米	耐低氮性	纳米孔单分子测序	未观察到显著的非期望效应	[20]
基因编辑小麦	白粉病抗性	WGS、RNA测序(RNA-seq)	观察到非期望效应，其4B染色体大片断缺失，导致TaTMT3B基因被异常激活	[21]

转基因植物	预期性状	检测方法	非期望效应结论	参考文献
转基因杨树	抗虫性、抗逆性	NGS、转录组学（RNA-seq）、表型测定	观察到非期望效应，杨树的树高和胸径变小，生长迟缓	[15]
转基因水稻	抗叶螟与黄螟	双端测序（paired-end sequencing，PE）	观察到非期望效应，在TT51-1转基因水稻10号和4号染色体上出现额外的非预期插入	[16]
转基因大豆	抗病性	全基因组测序（whole-genome sequencing，WGS）	未观察到显著的非期望效应	[17]
转基因木瓜	抗木瓜环斑病毒	WGS	未观察到显著的非期望效应	[18]
转基因白菜	谷胱甘肽S-转移酶基因表达下调	全基因组亚硫酸盐测序(whole-genome bisulfite sequencing，WGBS)	观察到非期望效应，转基因株系中DNA的甲基化水平发生显著变化	[19]
转基因玉米	耐低氮性	纳米孔单分子测序	未观察到显著的非期望效应	[20]
基因编辑小麦	白粉病抗性	WGS、RNA测序(RNA-seq)	观察到非期望效应，其4B染色体大片断缺失，导致TaTMT3B基因被异常激活	[21]

转基因作物	预期性状	检测方法	非期望效应结论	参考文献
转基因水稻	抗黑条矮缩病毒	转录组学（RNA-seq）、代谢组学（HPLC-MS）	未观察到显著的非期望效应	[28]
转基因玉米	抗绿盲蝽	转录组学（RNA-seq）、代谢组学（HPLC-MS）	未观察到显著的非期望效应	[24]
转基因杨树	抗虫性	转录组学（RNA-seq）	未观察到显著的非期望效应	[23]
转基因玉米	耐草甘膦	转录组学（RNA-Seq）	观察到非期望效应，丁酸酯代谢及缬氨酸、亮氨酸和异亮氨酸降解等多个生物学过程和代谢通路发生改变	[25]

转基因作物	预期性状	检测方法	非期望效应结论	参考文献
转基因水稻	抗黑条矮缩病毒	转录组学（RNA-seq）、代谢组学（HPLC-MS）	未观察到显著的非期望效应	[28]
转基因玉米	抗绿盲蝽	转录组学（RNA-seq）、代谢组学（HPLC-MS）	未观察到显著的非期望效应	[24]
转基因杨树	抗虫性	转录组学（RNA-seq）	未观察到显著的非期望效应	[23]
转基因玉米	耐草甘膦	转录组学（RNA-Seq）	观察到非期望效应，丁酸酯代谢及缬氨酸、亮氨酸和异亮氨酸降解等多个生物学过程和代谢通路发生改变	[25]

转基因作物	预期性状	检测方法	非期望效应结论	参考文献
转基因水稻	抗虫性	蛋白组学（LC-MS/MS）	未观察到显著的非期望效应	[33]
转基因玉米	抗虫性、耐除草剂	蛋白质组学（DIA）、代谢组学（LC-MS）	未观察到显著的非期望效应	[32]
转基因玉米	植酸酶高表达	蛋白质组学（TOF/TOF-MS）	未观察到显著的非期望效应	[34]
转基因菜豆	抗金黄色花叶病毒	蛋白质组学（MALDI-TOF/TOF-MS）	未观察到显著的非期望效应	[30]

多组学系统分析生物育种产品非期望效应的研究进展

Research Progress on Multi-omics Systematic Analysis of Unintended Effects in Biotechnology-derived Breeding Products

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转基因作物	预期性状	检测方法	非期望效应结论	参考文献
转基因玉米	抗双斑叶甲	转录组学（RNA-seq）、代谢组学（UPLC-MS/MS）	未观察到显著的非期望效应	[40]
转基因水稻	抗虫性	转录组学（RNA-seq）、代谢组学（UPLC-MS/MS）	未观察到显著的非期望效应	[41]
转基因烟草细胞	增强绿原酸的生物合成	代谢组学（UHPLC-MS）	观察到非期望效应，绿原酸的合成并未增强，而导致了苯甲酸类化合物的积累	[42]
转基因玉米	抗虫性、草甘膦耐受性	转录组学（RNA-seq）、代谢组学（UPLC-MS）	未观察到显著的非期望效应	[43]

组学	转基因作物	预期性状	检测方法	非期望效应结论	参考文献
空间组学	转基因水稻	樱花素高表达	液相色谱串联质谱（LC-MS/MS）、基质辅助激光解吸/电离质谱成像（MALDI-MSI）	未观察到显著的非期望效应	[60]
	转基因大豆	耐除草剂	基质辅助激光解吸/电离质谱成像（MALDI-MSI）结合LC-MS/MS脂质组学分析	观察到非期望效应，鉴定出18种脂质在表达丰度与空间分布上存在显著差异	[61]
	转基因大豆	耐除草剂	基质辅助激光解吸/电离质谱成像（MALDI-MSI）结合LC-Q-TOF质谱技术	观察到非期望效应，在与营养存储相关和胁迫响应与防御相关蛋白中出现丰度和空间分布的差异	[62]
其他新兴组学	转基因大豆	耐草甘膦	离子组学（ICP-MS）和脂质组学（¹H NMR）	差异存在，但未观察到显著的非期望效应	[63]
	转基因水稻	抗虫性	近红外光谱（NIR）与太赫兹光谱（THz）成像技术	观察到非期望效应，脂质与有机酸类代谢物组学出现显著差异	[64]
	转基因大豆	高油酸	蛋白质组学（TMT）和脂质组学（LC-MS/MS）	观察到非期望效应，脂氧合酶和油质蛋白代谢物组发生改变	[65-66]

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