Validation of endogenous reference genes for qRT-PCR analysis of human visceral adipose samples

Abstract

Background: Given the epidemic proportions of obesity worldwide and the concurrent prevalence of metabolic syndrome, there is an urgent need for better understanding the underlying mechanisms of metabolic syndrome, in particular, the gene expression differences which may participate in obesity, insulin resistance and the associated series of chronic liver conditions. Real-time PCR (qRT-PCR) is the standard method for studying changes in relative gene expression in different tissues and experimental conditions. However, variations in amount of starting material, enzymatic efficiency and presence of inhibitors can lead to quantification errors. Hence the need for accurate data normalization is vital. Among several known strategies for data normalization, the use of reference genes as an internal control is the most common approach. Recent studies have shown that both obesity and presence of insulin resistance influence an expression of commonly used reference genes in omental fat. In this study we validated candidate reference genes suitable for qRT-PCR profiling experiments using visceral adipose samples from obese and lean individuals.

Results: Cross-validation of expression stability of eight selected reference genes using three popular algorithms, GeNorm, NormFinder and BestKeeper found ACTB and RPII as most stable reference genes.

Conclusions: We recommend ACTB and RPII as stable reference genes most suitable for gene expression studies of human visceral adipose tissue. The use of these genes as a reference pair may further enhance the robustness of qRT-PCR in this model system.

Figure 1

Gene expression stability M of candidate reference genes in visceral adipose tissue calculated by GeNorm software. The program proceeds with stepwise exclusion of genes with relatively higher variable expression among the samples. The expression stability measure (M) is the average of the stability values of the remaining genes. The lower the M, the more stable the gene in the subset. a) n = 9, b) n = 19.

Figure 2

Determination of optimal number of reference genes for normalization by pairwise variation analysis using GeNorm software. Bar values indicate the magnitude of the change in normalization factor after the inclusion of an additional reference gene. A large variation indicates that the added gene has a significant effect and should probably be included for calculation of the normalization factor. a) n = 9, b) n = 19

Figure 3

Determination of the most stable reference genes using NormFinder. Two groups considered were - lean and obese patient tissues. Bars represent inter-group variances, while error bars representing the average of intra-group variance. Ideal reference gene has inter-group variation as close to zero as possible and error bars as small as possible. a) n = 9, b) n = 19