检测和量化转基因生物(GMO)的新方法Novel approaches for detection and quantification of genetically modified organisms (GMOs) |
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课程网址: | http://videolectures.net/ipssc2017_bogozalec_kosir_gmos/ |
主讲教师: | Alexandra Bogožalec Košir |
开课单位: | 国家生物研究所 |
开课时间: | 2017-05-23 |
课程语种: | 英语 |
中文简介: | 食品和饲料产品中的转基因生物(GMO)的标签或耐受阈值通常需要量化。今天,检测来自转基因生物(GMO)的特定核酸序列的黄金标准是实时定量PCR(qPCR)。然而,随着转基因生物数量的增加,qPCR几乎不具备时间和成本效益。此外,qPCR对抑制剂的敏感性可能会受到限制,这些抑制剂通常可以与复杂基质中的核酸共同提取,并且当目标物在高水平非目标核酸背景下以低浓度存在时,存在显著的偏差。 为了解决这个问题,已经开发了四种液滴数字PCR(ddPCR)多重分析方法,其中两种在欧盟授权的转基因玉米系[1]中定量12种,另两种在欧盟授权的转基因大豆系[2]中定量11种。能够在四个反应中直接量化12个玉米和11个大豆品系。对关键参数的性能进行了评估,包括检测和量化限制、真实性、重复性和鲁棒性。真实性是通过一系列熟练程度计划和真实样本确定的。此外,还证明了成本效率的潜在显著提高。 随着授权转基因生物(GMO)的使用增加,尤其是在饲料产品中,有意或无意出现未经授权或未知转基因生物(UGMOs)的可能性也在增加。因此,一种新的GW技术与NGS相结合,被称为线性富集片段扩增(ALF)[3]。该方法在一个复杂的样本上进行了测试,该样本含有四种不同浓度的转基因生物,即使在浓度较低的情况下也能识别转基因生物。ALF消除了以往GW策略的缺点,如随机启动DNA扩增和半巢式PCR。NGS非常适合对混合物中的所有扩增片段进行测序。此外,还开发了一个自动化的、基于网络的分析管道,用于识别含有已知筛选元素的UGMO。为了证明设计的管道识别UGMO的能力,样本中一个GMO的完整序列未知,模仿UGMO。样本中的所有四种转基因生物均已鉴定,从而证明检测到UGMO。 |
课程简介: | Quantification of genetically modified organisms (GMOs) in food and feed products is often required for their labelling or tolerance thresholds. Today the golden standard for testing of specific nucleic-acid sequences derived from genetically modified organisms (GMOs) is real-time quantitative PCR (qPCR). However, with the increasing number of GMOs, qPCR is becoming barely time and cost-effective. Furthermore, qPCR can be limited by its sensitivity to the inhibitors that can be frequently co-extracted with the nucleic acids from complex matrices and by a significant bias when the target is present at low concentrations in a background of high levels of non-target nucleic acids. To tackle this issues, four droplet digital PCR (ddPCR) multiplex assays, two quantifying 12 in EU authorised GM maize lines [1] and two quantifying 11 in EU authorised GM soybean lines [2], have been developed. Enabling direct quantification of 12 maize and 11 soybean lines in just four reactions. Performance was assessed for the critical parameters, including limits of detection and quantification, trueness, repeatability, and robustness. Trueness was determined on a number of proficiency programme and real-life samples. Moreover, potentially significant improvement in cost efficiency was demonstrated. With the increased use of authorized genetically modified organisms (GMOs), especially in feed products, the possibility of intentional or unintentional presence of unauthorized or unknown GMOs (UGMOs) is also on the rise. Thus, a novel GW technology coupled with NGS called amplification of linear-enriched fragments (ALF) [3], has been developed. The approach was tested on a complex sample, containing four GMOs of different concentrations, allowing the identification of GMOs even when present in a low concentration. ALF eliminates drawbacks, such as random start of DNA amplification and semi-nested PCR, of previous GW strategies. NGS is ideally suited for sequencing all amplified fragments in a mixture. Furthermore, a first outline of an automated, web-based analysis pipeline for identification of UGMOs containing known screening elements has been developed. To prove the power of the designed pipeline to identify UGMOs, a complete sequence of one GMO in the sample was unknown, mimicking a UGMO. All four GMOs in the sample were identified, thus proving the detection of UGMO. |
关 键 词: | 转基因生物; 特定核酸序列; 扩增片段 |
课程来源: | 视频讲座网 |
数据采集: | 2022-03-27:zkj |
最后编审: | 2022-03-27:zkj |
阅读次数: | 89 |