The surge in the number of gene expression studies and tendencies to increase the quality of analysis have necessitated the identification of stable reference genes. Although rabbits are classical experimental model animals, stable reference genes have not been identified for normalization. The aims of this study were to compare the expression profiles of the widely used reference genes in rabbit oocytes and preimplantation stage embryos, and to select and validate stable ones to use as reference.
Quantitative real time PCR method was used to evaluate 13 commonly used references (Actb, Gapdh, Hprt1, H2afz, Ubc, Ppia, Eef1e1, Polr2a, Tbp, G6pdx, B2m, Pgk1, and Ywhaz) and POU5F1 (Oct4) genes. Expressions of these genes were examined in multiple individual embryos of seven different preimplantation developmental stages and embryo types (in vivo and in vitro). Initial analysis identified three genes (Ubc, Tbp, and B2m) close to the detection limit with irregular expression between the different stages. As variability impedes the selection of stable genes, these were excluded from further analysis. The expression levels of the remaining ten genes, varied according to developmental stage and embryo types. These genes were ranked using the geNorm software and finally the three most stable references (H2afz, Hprt1, and Ywhaz) were selected. Normalization factor was calculated (from the geometric averages of the three selected genes) and used to normalize the expressions of POU5F1 gene. The results showed the expected expression patterns of the POU5F1 during development.
Compared to the earlier studies with similar objectives, the comparison of large number of genes, the use of multiple individual embryos as compared to pools, and simultaneous analyses of in vitro and in vivo derived embryo samples were unique approaches in our study. Based on quantification, pattern and geNorm analyses, we found the three genes (H2afz, Hprt1, and Ywhaz) to be the most stable across developmental stages and embryo types, and the geometric averages of these genes can be used for appropriate normalization.