Selection of Reference Genes for the Normalization of RT-qPCR Data in Gene Expression Studies in Insects: A Systematic Review.
Selection of Reference Genes for the Normalization of RT-qPCR Data in Gene Expression Studies in Insects: A Systematic Review.
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DOI：
10.3389/fphys.2018.01560
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发表时间：
2018
影响因子：
4
通讯作者：
Pan H
中科院分区：
文献类型：
--
作者：
Lü J;Yang C;Zhang Y;Pan H
逆转录定量聚合酶链式反应(RT-qPCR)是一种可靠的技术，可以在临床和生物学研究的许多领域中定量检测靶基因在不同生物学过程中的表达水平。选择合适的参考基因进行RT-qPCR标准化是可靠测量基因表达水平的基本前提。在这里，通过分析2008至2017年间发表的数据集，我们总结了昆虫基因表达研究中参考基因选择的当前趋势，该方法使用最广泛使用的SYBR Green方法进行RT-qPCR归一化。我们整理了90篇有代表性的论文，主要发表在2013-2017年间，其中共进行了100次实验，调查了78种昆虫。此外，还确定了排名前五的期刊、排名前十的常用参考基因和排名前十的实验因素。用线性回归分析了参考基因数量、实验因子、分析工具与发表日期(年份)之间的关系。我们发现，这篇论文发表得越近，它倾向于探索的实验因素就越多，使用的分析工具也就越多。然而，线性回归分析没有显示参考基因的数量与研究发表日期之间的显著相关性。综上所述，这种荟萃分析将对计划昆虫基因表达研究的研究人员有很大帮助，因为它为表达研究提供了合适的参考基因的总结，考虑了参考基因的最佳数量，并审查了每项研究的平均实验因素和分析工具的数量。
Reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) is a reliable technique for quantifying expression levels of targeted genes during various biological processes in numerous areas of clinical and biological research. Selection of appropriate reference genes for RT-qPCR normalization is an elementary prerequisite for reliable measurements of gene expression levels. Here, by analyzing datasets published between 2008 and 2017, we summarized the current trends in reference gene selection for insect gene expression studies that employed the most widely used SYBR Green method for RT-qPCR normalization. We curated 90 representative papers, mainly published in 2013–2017, in which a total of 78 insect species were investigated in 100 experiments. Furthermore, top five journals, top 10 frequently used reference genes, and top 10 experimental factors have been determined. The relationships between the numbers of the reference genes, experimental factors, analysis tools on the one hand and publication date (year) on the other hand was investigated by linear regression. We found that the more recently the paper was published, the more experimental factors it tended to explore, and more analysis tools it used. However, linear regression analysis did not reveal a significant correlation between the number of reference genes and the study publication date. Taken together, this meta-analysis will be of great help to researchers that plan gene expression studies in insects, especially the non-model ones, as it provides a summary of appropriate reference genes for expression studies, considers the optimal number of reference genes, and reviews the average number of experimental factors and analysis tools per study.
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