Reference gene selection for real-time quantitative PCR analysis of the mouse uterus in the peri-implantation period

• Published in: PloS one Vol. 8; no. 4; p. e62462
• Main Authors: Lin, Pengfei, Lan, Xiangli, Chen, Fenglei, Yang, Yanzhou, Jin, Yaping, Wang, Aihua
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 24.04.2013; Public Library of Science (PLoS)
• Subjects: Analysis; Animal models; Animals; Biology; Biotechnology; Computational Biology - methods; Embryo Implantation - genetics; Female; Gene expression; Gene Expression Profiling; Gene Expression Regulation; Genes; Glyceraldehyde-3-phosphate dehydrogenase; Identification methods; Implantation; Laboratories; Male; Medical research; Mice; Molecular modelling; Polymerase chain reaction; Pregnancy; Pseudopregnancy; Real time; Studies; Tissues; Uterus; Uterus - metabolism; Veterinary colleges; Veterinary medicine; Veterinary Science; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0062462

The study of uterine gene expression patterns is valuable for understanding the biological and molecular mechanisms that occur during embryo implantation. Real-time quantitative RT-PCR (qRT-PCR) is an extremely sensitive technique that allows for the precise quantification of mRNA abundance; however, selecting stable reference genes suitable for the normalization of qRT-PCR data is required to avoid the misinterpretation of experimental results and erroneous analyses. This study employs several mouse models, including an early pregnancy, a pseudopregnancy, a delayed implantation and activation, an artificial decidualization and a hormonal treatment model; ten candidate reference genes (PPIA, RPLP0, HPRT1, GAPDH, ACTB, TBP, B2M, 18S, UBC and TUBA) that are found in uterine tissues were assessed for their suitability as internal controls for relative qRT-PCR quantification. GeNorm(PLUS), NormFinder, and BestKeeper were used to evaluate these candidate reference genes, and all of these methods identified RPLP0 and GAPDH as the most stable candidates and B2M and 18S as the least stable candidates. However, when the different models were analyzed separately, the reference genes exhibited some variation in their expression levels.