RESUMO
BACKGROUND: Traumatic brain injury models are widely studied, especially through gene expression, either to further understand implied biological mechanisms or to assess the efficiency of potential therapies. A large number of biological pathways are affected in brain trauma models, whose elucidation might greatly benefit from transcriptomic studies. However the suitability of reference genes needed for quantitative RT-PCR experiments is missing for these models. RESULTS: We have compared five potential reference genes as well as total cDNA level monitored using Oligreen reagent in order to determine the best normalizing factors for quantitative RT-PCR expression studies in the early phase (0-48 h post-trauma (PT)) of a murine model of diffuse brain injury. The levels of 18S rRNA, and of transcripts of beta-actin, glyceraldehyde-3P-dehydrogenase (GAPDH), beta-microtubulin and S100beta were determined in the injured brain region of traumatized mice sacrificed at 30 min, 3 h, 6 h, 12 h, 24 h and 48 h post-trauma. The stability of the reference genes candidates and of total cDNA was evaluated by three different methods, leading to the following rankings as normalization factors, from the most suitable to the less: by using geNorm VBA applet, we obtained the following sequence: cDNA(Oligreen); GAPDH > 18S rRNA > S100beta > beta-microtubulin > beta-actin; by using NormFinder Excel Spreadsheet, we obtained the following sequence: GAPDH > cDNA(Oligreen) > S100beta > 18S rRNA > beta-actin > beta-microtubulin; by using a Confidence-Interval calculation, we obtained the following sequence: cDNA(Oligreen) > 18S rRNA; GAPDH > S100beta > beta-microtubulin > beta-actin. CONCLUSION: This work suggests that Oligreen cDNA measurements, 18S rRNA and GAPDH or a combination of them may be used to efficiently normalize qRT-PCR gene expression in mouse brain trauma injury, and that beta-actin and beta-microtubulin should be avoided. The potential of total cDNA as measured by Oligreen as a first-intention normalizing factor with a broad field of applications is highlighted. Pros and cons of the three methods of normalization factors selection are discussed. A generic time- and cost-effective procedure for normalization factor validation is proposed.