Identification of optimal reference genes for RT-qPCR in the rat hypothalamus and intestine for the study of obesity

Abstract

BACKGROUND Obesity has a complicated metabolic pathology, and defining the underlying mechanisms of obesity requires integrative studies with molecular endpoints. Real time quantitative PCR (RT-qPCR) is a powerful tool that has been widely utilized. However, the importance of using carefully validated reference genes in RT-qPCR seems to be overlooked in obesity-related research. The objective of this study was to select a set of reference genes with stable expressions to be used for RT-qPCR normalization in rats under fasted vs. re-fed and chow vs. high fat diet (HFD) conditions. DESIGN Male Long-Evans rats were treated under four conditions, chow/fasted, chow/re-fed, HFD/fasted, and HFD/re-fed. Expression stabilities of the13 candidate reference genes were evaluated in the rat hypothalamus, duodenum, jejunum, and ileum using ReFinder software program. The optimal number of reference genes needed for RT-qPCR analyses was determined using geNorm. RESULTS Using geNorm analysis, we found that it was sufficient to use the two most stably expressed genes as references in RT-qPCR analyses for each tissue under specific experimental conditions. Unique subsets of reference genes out of the 13 candidate genes were identified, each of which is specific for one type of rat tissue (hypothalamus, duodenum, jejunum, or ileum) under a different combination of diet and feeding condition. CONCLUSIONS Our study demonstrates that gene expression levels of reference genes commonly used in obesity-related studies, such as ACTB, or RPS18, are altered by changes in acute or chronic energy status. These findings underline the importance of using reference genes that are stable in expression across experimental conditions when studying the rat hypothalamus and intestine, because these tissues play an integral role in regulation of energy homeostasis. It is our hope that this study will raise awareness among obesity researchers on the essential need for reference gene validation in gene expression studies.

Determination of qPCR Reference Genes Suitable for Normalizing Gene Expression in a Canine Model of Duchenne Muscular Dystrophy

“…The accuracy of this technique is criticallydependent on good normalization: even with identical starting material, slight differences in the efficiency of RNA isolation or cDNA synthesis can significantly affect subsequent quantitation. Effective normalization requires appropriate reference genes, and indeed efforts to identify, validate and publicize such genes are becoming more common in a variety of disease states [20][21][22][23][24] and model organisms [25][26][27][28][29][30]. A review of the literature specifically within the DMD field however reveals a considerable number of candidates: selected examples in dystrophic dogs include GAPDH [31][32][33], RPS18 [34], HPRT1 [35,36], 18S [37]; in humans, TBP and GUSB [13]; and in mice GAPDH [33,38], ActB [39], 18S [40].…”

Identification of suitable reference genes for real-time quantitative PCR analysis of hydrogen peroxide-treated human umbilical vein endothelial cells

“…This technique normalizes the gene of interest against an endogenous control whose expression remains unaltered in the samples under analysis [21]. The concept of validating reference genes used for normalization in qRT-PCR analysis before use was initially suggested in 2002 [22], and has been realized in various scientific disciplines such as plant sciences [23, 24], cancer [25, 26], stem cells [27, 28], and cardiovascular research [14, 29, 30]. Considering that an algorithm is one-sided for evaluating the expression stability of reference genes, many statistical approaches are usually integrated to determine the optimal reference genes under different experimental conditions [17].…”

“…However, despite validation, the positive control has no significant influence on the stability evaluation by the calculation software. Thus, many studies [14, 36–39] evaluated suitable reference genes by calculation software without the positive control.…”

“…Unfortunately, increasing data have shown that no single gene is expressed constantly across all cell types or under all physiological/pathological conditions [11–13]. Therefore, to obtain accurate gene expression information, it is imperative that stable reference genes be chosen for the specific type of tissue and experimental condition [14]. GeNorm [13] and NormFinder [15] are the most commonly used methods to evaluate reference genes, but different statistical algorithms are known to cause inconsistent rankings.…”

Validation of Reference Genes for qPCR Analysis of Resistance Training and Androgenic Anabolic Steroids on Hypothalamus, Adrenal Gland and Fat Tissue

“…In other study, also did not find stable results for Actb gene from hypothalamus of an obesity rat model [45]. Actb and Gapdh genes were rejected in muscle tissue by qBase, software that uses M-Value to analyze reference genes [26].…”