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High Throughput MicroRNA Profiling: Optimized Multiplex qRT-PCR at Nanoliter Scale on the Fluidigm Dynamic ArrayTM IFCs
Published on: August 3, 2011
Optimizing RT-qPCR for sex-specific analysis of iron-induced retinal gene expression
Joan-Feliu Calafat1, Miquel Garcia2, Gemma Manich1
1Department of Morphological Sciences, Unit of Human Anatomy and Embryology, Faculty of Medicine, Universitat Autònoma de Barcelona, Bellaterra, Spain.
None:
The study of iron and iron-handling proteins in the retina is crucial due to their involvement in age-related diseases. In this study, we aimed to evaluate common housekeeping genes to identify the most appropriate reference genes for retinal gene expression analysis in a context of iron excess, considering sex-specific variations. Eight reference genes were selected for RT-qPCR analysis in 20-week-old male and female mice, intraperitoneally injected with iron-dextran versus controls. RefFinder, a web-based tool integrating geNorm, NormFinder, DeltaCq and BestKeeper algorithms, was used to generate a list of candidate genes according to their stability. The top-ranked candidate genes were validated by using them to normalize RT-qPCR expression data from the target genes transferrin receptor (Tfrc) and divalent metal transporter 1 (Dmt1), across all conditions. Four out of eight candidate reference genes presented sex-dependent expression levels and one iron-dependent expression levels. The stability rank resulted in Rpl13a/Ube2d2 and Rplp0/Actb as the most stable candidates in female and male samples, respectively. Mixed male and female samples resulted in Ube2d2/Actb as the most stable candidate genes. In the three analysis, the least stable candidate reference gene was Rn18s. After normalization with the top ranked candidate genes, Tfrc mRNA reductions were validated in the iron administered male, female and sex-mixed groups. Reductions in Dmt1 mRNA levels were only observed in male retinal samples. Therefore, sex significantly influences mRNA levels of both candidate reference genes and iron-dependent genes. This highlights the importance of accounting for sex-specific variations in RT-qPCR analysis using mouse retinas.

