Selection of Reference Genes for RT-qPCR Analysis in Coccinella septempunctata to Assess Un-intended Effects of RNAi Transgenic Plants

文献类型： 外文期刊

第一作者： Dai, Liangying

作者： Dai, Liangying;Yang, Chunxiao;Liu, Yong;Yang, Chunxiao;Zhang, Hongjun;Pan, Huipeng;Zhou, Xuguo;Preisser, Evan L.;Zhang, Hongjun;Pan, Huipeng

作者机构：

关键词： Coccinella septempunctata;RT-qPCR;reference gene;RNAi transgenic plants;environmental risk assessment;plant incorporated protectant

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：5.753； 五年影响因子：6.612 ）

ISSN： 1664-462X

年卷期： 2016 年 7 卷

页码：

收录情况： SCI

摘要： The development of genetically engineered plants that employ RNA interference (RNAi) to suppress invertebrate pests opens up new avenues for insect control. While this biotechnology shows tremendous promise, the potential for both non target and off-target impacts, which likely manifest via altered mRNA expression in the exposed organisms, remains a major concern. One powerful tool for the analysis of these un-intended effects is reverse transcriptase-quantitative polymerase chain reaction, a technique for quantifying gene expression using a suite of reference genes for normalization. The seven-spotted ladybeetle Coccinella septempunctata, a commonly used predator in both classical and augmentative biological controls, is a model surrogate species used in the environmental risk assessment (ERA) of plant incorporated protectants (PIPs). Here, we assessed the suitability of eight reference gene candidates for the normalization and analysis of C. septempunctata v-ATPase A gene expression under both biotic and abiotic conditions. Five computational tools with distinct algorisms, geNorm, Norrnfinder, BestKeeper, the Delta C-t method, and RefFinder, were used to evaluate the stability of these candidates. As a result, unique sets of reference genes were recommended, respectively, for experiments involving different developmental stages, tissues, and ingested dsRNAs. By providing a foundation for standardized RT-qPCR analysis in C. septempunctata, our work improves the accuracy and replicability of the ERA of PIPs involving RNAi transgenic plants.

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