Stable Internal Control Genes Research Articles

Validation of reference genes for quantitative measurement of immune gene expression in shrimp

To accurately measure the relative expression of a target gene, mRNA expression data is routinely normalized with reference to an internal control gene. We examined the transcriptional stability of four internal control genes, β-actin, glyceroldehyde-3 phosphate dehydrogenase (GAPDH), elongation factor1-alpha (EF1-α), and 18S ribosomal RNA (18S rRNA) while measuring the mRNA expression of a gene encoding a pattern recognition protein, lipopolysaccharide and glucan binding protein (LGBP) gene, in healthy and white spot syndrome virus (WSSV) infected shrimp ( Penaeus stylirostris) before and after (4, 8, 16 and 32 h) challenge using real-time RT-PCR. Here, we describe a method to rank the internal control genes based on a linear regression analysis. This analysis enables us to analyze the multivariate data set, e.g. time course study samples with control and treatment groups. Using the linear regression analysis and the WSSV-challenged time course samples, GAPDH was found to be the most stable internal control gene followed by the genes EF1-α, 18S rRNA and β-actin. Using the program geNorm, GAPDH was also found to be the most stable gene followed by the genes EF1-α, β-actin and 18S rRNA. Using the program NormFinder, the ranking of the internal control genes were in the order of EF1-α > GAPDH > 18S rRNA > β-actin. The ability to compare the healthy and WSSV infected samples in parallel by the regression analysis makes this method a very useful approach while identifying the optimal reference gene for gene expression analysis.

Internal Controls for Quantitative Polymerase Chain Reaction of Swine Mammary Glands During Pregnancy and Lactation

High-throughput microarray analysis is an efficient means of obtaining a genome-wide view of transcript profiles across physiological states. However, quantitative PCR (qPCR) remains the chosen method for high-precision mRNA abundance analysis. Essential for reliability of qPCR data is normalization using appropriate internal control genes (ICG), which is now, more than ever before, a fundamental step for accurate gene expression profiling. We mined mammary tissue microarray data on >13,000 genes at −34, −14, 0, 7, 14, 21, and 28 d relative to parturition in 27 crossbred primiparous gilts to identify suitable ICG. Initial analysis revealed TBK1, PCSK2, PTBP1, API5, VAPB, QTRT1, TRIM41, TMEM24, PPP2R5B, and AP1S1 as the most stable genes (sample/reference=1±0.2). We also included 9 genes previously identified as ICG in bovine mammary tissue. Gene network analysis of the 19 genes identified AP1S1, API5, MTG1, VAPB, TRIM41, MRPL39, and RPS15A as having no known co-regulation. In addition, UXT and ACTB were added to this list, and mRNA abundance of these 9 genes was measured by qPCR. Expression of all 9 of these genes was decreased markedly during lactation. In a previous study with bovine mammary tissue, mRNA of stably expressed genes decreased during lactation due to a dilution effect brought about by large increases in expression of highly abundant genes. To verify this effect, highly abundant mammary genes such as CSN1S2, SCD, FABP3, and LTF were evaluated by qPCR. The tested ICG had a negative correlation with these genes, demonstrating a dilution effect in the porcine mammary tissue. Gene stability analysis identified API5, VABP, and MRPL39 as the most stable ICG in porcine mammary tissue and indicated that the use of those 3 genes was most appropriate for calculating a normalization factor. Overall, results underscore the importance of proper validation of internal controls for qPCR and highlight the limitations of using absence of time effects as the criteria for selection of appropriate ICG. Further, we showed that use of the same ICG from one organism might not be suitable for qPCR normalization in other species.