The validation of Short Interspersed Nuclear Elements (SINEs) as a RT-qPCR normalization strategy in a rodent model...

In gene expression studies via RT-qPCR many conclusions are inferred by using reference genes. However, it is generally known that also reference genes could be differentially expressed between various tissue types, experimental conditions and animal models. An increasing amount of studies have been performed to validate the stability of reference genes. In this study, two rodent-specific Short Interspersed Nuclear Elements (SINEs), which are located throughout the transcriptome, were validated and assessed against nine reference genes in a model of Temporal Lobe Epilepsy (TLE). Two different brain regions (i.e. hippocampus and cortex) and two different disease stages (i.e. acute phase and chronic phase) of the systemic kainic acid rat model for TLE were analyzed by performing expression analyses with the geNorm and NormFinder algorithms. Finally, we performed a rank aggregation analysis and validated the reference genes and the rodent-specific SINEs (i.e. B elements) individually via Gfap gene expression.

GeNorm ranked Hprt1, Pgk1 and Ywhaz as the most stable genes in the acute phase, while Gusb and B2m were ranked as the most unstable, being significantly upregulated. The two B elements were ranked as most stable for both brain regions in the chronic phase by geNorm. In contrast, NormFinder ranked the B1 element only once as second best in cortical tissue for the chronic phase. Interestingly, using only one of the two algorithms would have led to skewed conclusions. Finally, the rank aggregation method indicated the use of the B1 element as the best option to normalize target genes, independent of the disease progression and brain region. This result was supported by the expression profile of Gfap.

In this study, we demonstrate the potential of implementing SINEs -notably the B1 element- as a stable normalization factor in a rodent model of TLE, independent of brain region or disease progression.