GmoDetector: An accurate and efficient GMO identification approach and its applications

Lihong Chen, Junfei Zhou, Tiantian Li, Zhiwei Fang, Lun Li, Gang Huang, Lifen Gao, Xiaobo Zhu, Xusheng Zhou, Huafeng Xiao, Jing Zhang, Qi Jie Xiong, Jianan Zhang, Aijin Ma, Wenxue Zhai, Weixiong Zhang, Hai Peng

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

The rapid increase of genetically modified organisms (GMOs) entering the food and feed markets, and the contamination of donor (micro)organisms of transgenic elements make it more challenging for the existing GMO detection. In this study, we developed a high-throughput and contamination-removal GMO detection approach named as GmoDetector. GmoDetector targeted 64 common transgenic elements and 76 GMO-specific events collected from 251 singular GM events, and combined with next generation sequencing (NGS) and target enrichment technology to detect various GMOs. As a result, GmoDetector was able to exclude the donor (micro)organism contamination, and detect the authorized and unauthorized GMOs (UGMOs) in any forms of food or feed, such as processed or unprocessed. The sensitivity of GmoDetector is as low as 0.1% (GMO content), which has met the GMO labeling threshold for all countries. Therefore, GmoDetector is a robust tool for accurate and efficient detection of the authorized and UGMOs.

Original languageEnglish
Article number110662
JournalFood Research International
Volume149
DOIs
StatePublished - Nov 2021

Keywords

  • GMO characteristic fragment
  • GMO identification
  • GmoDetector
  • Next generation sequencing
  • Probe capture

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