Selection of reference genes for normalization of quantitative polymerase chain reaction data in mouse models of heart failure

Li,Qiaoling; Hu,Tingting; Chen,Liang; Sun,Jiayin; Xie,Jun; Li,Rang; Xu,Biao

doi:10.3892/mmr.2014.2750

Selection of reference genes for normalization of quantitative polymerase chain reaction data in mouse models of heart failure

Authors:
- Qiaoling Li
- Tingting Hu
- Liang Chen
- Jiayin Sun
- Jun Xie
- Rang Li
- Biao Xu
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Affiliations: Department of Cardiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu 210008, P.R. China, Department of Cardiology, Huai'an First People's Hospital, Huai'an, Jiangsu 223300, P.R. China
Published online on: October 23, 2014 https://doi.org/10.3892/mmr.2014.2750
Pages: 393-399

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Abstract

The accurate measurement of mRNA expression levels is crucially dependent on the use of relevant reference genes for the normalization of data. Currently, heart failure is a serious and widespread disease, and multiple mouse models are utilized for the study of this complex disease. Although mouse models are commonly used to study cardiovascular disease, various studies have not employed the appropriate selection strategies. The present study investigated the expression stability of eight candidate reference genes (GAPDH, ACTB, B2M, CycA, TBP, PBGD, HTRP 1 and 18S) in two mouse models of heart failure, including the transverse aortic arch constriction (TAC) model and the myocardial infarction (MI) model, using GeNorm software. The expression of BNP was normalized using different reference gene strategies, and it was demonstrated that its induction following heart failure was most profound with the optimal reference gene combination. The most stable genes were identified as follows: TBP and CycA in the MI model, and PBGD and GAPDH in the TAC model. The present study provides important information for reference gene selection in mouse models of heart failure, and will aid further investigations of the transcriptome in cardiovascular research.

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