Evidence based selection of commonly used RT-qPCR reference genes for the analysis of mouse skeletal muscle

Kristen C Thomas; Xi Fiona Zheng; Francia Garces Suarez; Joanna M Raftery; Kate G R Quinlan; Nan Yang; Kathryn N North; Peter J Houweling

doi:10.1371/journal.pone.0088653

Evidence based selection of commonly used RT-qPCR reference genes for the analysis of mouse skeletal muscle

PLoS One. 2014 Feb 11;9(2):e88653. doi: 10.1371/journal.pone.0088653. eCollection 2014.

Authors

Kristen C Thomas¹, Xi Fiona Zheng², Francia Garces Suarez², Joanna M Raftery¹, Kate G R Quinlan², Nan Yang², Kathryn N North³, Peter J Houweling⁴

Affiliations

¹ Institute for Neuroscience and Muscle Research, Children's Hospital at Westmead, Sydney, NSW, Australia.
² 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.
³ 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 ; Murdoch Childrens Research Institute (MCRI), The Royal Children's Hospital, Melbourne, VIC, Australia ; Department of Paediatrics, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, VIC, Australia.
⁴ 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 ; Murdoch Childrens Research Institute (MCRI), The Royal Children's Hospital, Melbourne, VIC, Australia.

Abstract

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Animals
Gene Expression Profiling / methods
Genetic Techniques*
Genetic Variation
Mice
Mice, Inbred C57BL
Mice, Knockout
Muscle, Skeletal / metabolism*
Oligonucleotide Array Sequence Analysis
Real-Time Polymerase Chain Reaction / methods*
Reference Standards
Reproducibility of Results
Species Specificity

Grants and funding

This project was funded in part by a grant from the National Health and Medical Research Council of Australia (1002033). XFZ is supported by a Dora Lush Biomedical Scholarship from the National Health and Medical Research Council of Australia (1038991). KGRQ is supported by an Early Career Fellowship from the NHMRC of Australia (0511981). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.