Thomas Kristen C, Zheng Xi Fiona, Garces Suarez Francia, Raftery Joanna M, Quinlan Kate G R, Yang Nan, North Kathryn N, Houweling Peter J
Institute for Neuroscience and Muscle Research, Children's Hospital at Westmead, Sydney, NSW, Australia.
Institute for Neuroscience and Muscle Research, Children's Hospital at Westmead, Sydney, NSW, Australia ; Discipline of Paediatrics and Child Health, Faculty of Medicine, University of Sydney, Sydney, NSW, Australia.
PLoS One. 2014 Feb 11;9(2):e88653. doi: 10.1371/journal.pone.0088653. eCollection 2014.
The ability to obtain accurate and reproducible data using quantitative real-time Polymerase Chain Reaction (RT-qPCR) is limited by the process of data normalization. The use of 'housekeeping' or 'reference' genes is the most common technique used to normalize RT-qPCR data. However, commonly used reference genes are often poorly validated and may change as a result of genetic background, environment and experimental intervention. Here we present an analysis of 10 reference genes in mouse skeletal muscle (Actb, Aldoa, Gapdh, Hprt1, Ppia, Rer1, Rn18s, Rpl27, Rpl41 and Rpl7L1), which were identified as stable either by microarray or in the literature. Using the MIQE guidelines we compared wild-type (WT) mice across three genetic backgrounds (R129, C57BL/6j and C57BL/10) as well as analyzing the α-actinin-3 knockout (Actn3 KO) mouse, which is a model of the common null polymorphism (R577X) in human ACTN3. Comparing WT mice across three genetic backgrounds, we found that different genes were more tightly regulated in each strain. We have developed a ranked profile of the top performing reference genes in skeletal muscle across these common mouse strains. Interestingly the commonly used reference genes; Gapdh, Rn18s, Hprt1 and Actb were not the most stable. Analysis of our experimental variant (Actn3 KO) also resulted in an altered ranking of reference gene suitability. Furthermore we demonstrate that a poor reference gene results in increased variability in the normalized expression of a gene of interest, and can result in loss of significance. Our data demonstrate that reference genes need to be validated prior to use. For the most accurate normalization, it is important to test several genes and use the geometric mean of at least three of the most stably expressed genes. In the analysis of mouse skeletal muscle, strain and intervention played an important role in selecting the most stable reference genes.
使用定量实时聚合酶链反应（RT-qPCR）获取准确且可重复数据的能力受到数据标准化过程的限制。使用“管家”基因或“参照”基因是用于标准化RT-qPCR数据的最常见技术。然而，常用的参照基因往往验证不足，并且可能因遗传背景、环境和实验干预而发生变化。在此，我们对小鼠骨骼肌中的10个参照基因（Actb、Aldoa、Gapdh、Hprt1、Ppia、Rer1、Rn18s、Rpl27、Rpl41和Rpl7L1）进行了分析，这些基因通过微阵列或文献被鉴定为稳定基因。我们依据MIQE指南，比较了三种遗传背景（R129、C57BL/6j和C57BL/10）下的野生型（WT）小鼠，并分析了α-辅肌动蛋白-3基因敲除（Actn3 KO）小鼠，该小鼠是人类ACTN3中常见无效多态性（R577X）的模型。比较三种遗传背景下的WT小鼠，我们发现不同基因在每个品系中的调控更为严格。我们针对这些常见小鼠品系，制定了骨骼肌中表现最佳的参照基因的排名概况。有趣的是，常用的参照基因Gapdh、Rn18s、Hprt1和Actb并非最稳定的。对我们的实验变体（Actn3 KO）的分析也导致了参照基因适用性排名的改变。此外，我们证明使用不佳的参照基因会导致目标基因标准化表达的变异性增加，并可能导致显著性丧失。我们的数据表明，参照基因在使用前需要进行验证。为了实现最准确的标准化，测试多个基因并使用至少三个表达最稳定基因的几何平均值非常重要。在小鼠骨骼肌分析中，品系和干预在选择最稳定的参照基因方面发挥了重要作用。
