%0 Journal Article
%T Validation of internal reference genes for quantitative real-time PCR in a non-model organism, the yellow-necked mouse, Apodemus flavicollis
%A Jan Axtner
%A Simone Sommer
%J BMC Research Notes
%D 2009
%I BioMed Central
%R 10.1186/1756-0500-2-264
%X Although the three programs used different algorithms the ranking order of reference genes was significantly concordant and geNorm differed in only one, NormFinder in two positions compared to BestKeeper. The genes ordered by their mean rank from the most to the least stable gene were: Rps18, Sdha, Canx, Actg1, Pgk1, Ubc, Rpl13a and Actb. Analyses of the normalization factor revealed best results when the five most stable genes were included for normalization.We established a SYBR green qPCR assay for liver samples of wild A. flavicollis and conclude that five genes should be used for appropriate normalization. Our study provides the basis to investigate differential expression of genes under selection under natural selection conditions in liver samples of A. flavicollis. This approach might also be applicable to other non-model organisms.Quantitative real-time RT PCR (qPCR) has become a tool with a broad spectrum of use in molecular biology [1]. By quantifying mRNA levels it allows valuable insights into the variation of gene expression between certain individuals or different treatment groups. The most common practice in qPCR is the relative measurement of the expression of a gene of interest after normalization to an internal reference gene. These formerly called house-keeping genes were thought to be constantly expressed in every cell or every tissue and were supposed to be neither up nor down regulated. This assumption has proven false by a growing number of studies [2-4]. All genes seem to be regulated under some conditions and there seems to be no universal reference gene with a constant expression in all tissues [5-9]. But still the relative quantification against an internal reference gene is the most accurate way to detect expression differences especially in low copy mRNA because it controls for artificial variation, e.g. due to differences in the amount of sample, RNA extraction or reverse transcription efficiency [10]. Thus, a careful validation of the
%U http://www.biomedcentral.com/1756-0500/2/264