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Identification of Optimal Reference Genes for RT-Qpcr in the Rat

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Identification of Optimal Reference Genes for RT-Qpcr in the Rat International Journal of Obesity (2014) 38, 192–197 & 2014 Macmillan Publishers Limited All rights reserved 0307-0565/14 www.nature.com/ijo ORIGINAL ARTICLE Identification of optimal reference genes for RT-qPCR in the rat hypothalamus and intestine for the study of obesity B Li, EK Matter, HT Hoppert, BE Grayson, RJ Seeley and DA Sandoval BACKGROUND: Obesity has a complicated metabolic pathology, and defining the underlying mechanisms of obesity requires integrative studies with molecular end points. 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 have been 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 13 candidate reference genes were evaluated in the rat hypothalamus, duodenum, jejunum and ileum using the 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. B2M and RPLP0 in the hypothalamus, RPS18 and HMBS in the duodenum, RPLP2 and RPLP0 in the jejunum and RPS18 and YWHAZ in the ileum were the most suitable pairs for a normalization study when the four aforementioned experimental conditions were considered. 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 have an integral role in the 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. International Journal of Obesity (2014) 38, 192–197; doi:10.1038/ijo.2013.86 Keywords: reference gene; hypothalamus; intestine; RT-qPCR; rat INTRODUCTION isolation, reverse transcription and real-time PCR. To overcome The worldwide incidence of obesity has led to an increasing need these variables, a reference gene is used for normalizing gene for understanding the molecular mechanisms that drive this expression. However, this assumes that the expression of the epidemic. Obesity is the combined consequence of genetic, reference gene remains constant in all cell/tissue types and under behavioral and environmental factors that drive an imbalance specific experimental conditions. Unfortunately, increasing data between energy intake and energy expenditure toward an have shown that no single gene has constant expression across all 7–9 increase in adiposity.1 Hormones and peptides secreted from cell types or under all physiological/pathological conditions. adipose tissue and the gastrointestinal tract act as signals Therefore, to obtain accurate gene expression information, it is representing the current energy status to the central nervous imperative that stable reference genes be chosen for the specific system (CNS).2 The hypothalamus is a particularly important CNS type of tissue and experimental condition. Several algorithms, 9 10 11 region that is responsive to these peripheral signals to initiate including the geNorm, NormFinder, BestKeep and 12 changes in food intake and/or energy expenditure in order to comparative DCt (cycle thresholds) method, have been tightly regulate the overall energy balance.3 Gut-to-brain signaling developed for selection of suitable reference genes and are 13 is also thought to be important for lipid sensing4 and glucose widely used. Recently, ReFinder, a web-based program that sensing.5 Owing to the increase in the frequency and success of integrates four mathematical programs, that is, geNorm, bariatric surgery in the treatment of obesity, attention has turned NormFinder, BestKeeper and comparative DCt, was developed to understanding the mechanisms underlying the gut–brain axis to provide a convenient and adequate means for reference gene 12 in regulating energy homeostasis.6 evaluation. The use of at least three reference genes for the RT-qPCR has become a valuable method for gene expression correct normalization of RT-qPCR data has been proposed by 9 analysis and is used extensively in obesity research. In order to Vandesompele and is a recommended approach for normalizing 9,14 avoid measuring the absolute amount of messenger RNA within a RT-qPCR data. sample, data are analyzed relative to the control group. However, Rodents, such as mice and rats, have been commonly used in errors are introduced in the technique through the process of RNA studying diet-induced obesity and diabetes. Surprisingly, the Department of Internal Medicine, Division of Endocrinology University of Cincinnati College of Medicine, Cincinnati, OH, USA. Correspondence: Dr B Li, Division of Endocrinology/ Metabolism, Metabolic Diseases Institute, University of Cincinnati, 2170 E. Galbraith Rd., Cincinnati, OH 45237, USA. Email: [email protected] Received 16 December 2012; revised 28 March 2013; accepted 28 April 2013; accepted article preview online 24 May 2013; advance online publication, 25 June 2013 Optimal reference genes for RT-qPCR for the study of obesity BLiet al 193 evaluation of reference genes in obesity research has received To measure the expression values of 13 candidate genes for all tissue types, little attention.15 In most gene expression studies, a single gene, a combination of 13 (genes) Â 4 (tissue types) plate-runs were executed. including ACTB and RPS18, is still commonly used without any The threshold value was manually set to 0.2 to guarantee the mention of whether these genes are affected by the experimental comparability between the Cts obtained from different genes and different conditions.16–18 In other fields, it has been realized that the runs. expression stability of reference genes is influenced by the conditions under which the studies are conducted.19–21 Therefore, Data analysis and statistics we studied 13 commonly used reference genes in the rat The gene expression stabilities of the 13 candidate reference genes from hypothalamus, duodenum, jejunum and ileum across different each tissue were determined by ReFinder. Upon input of Ct values, the diets and feeding schemes typically used in obesity research to ReFinder invokes four commonly used computational programs, geNorm, determine which reference genes would be most appropriate for NormFinder, BestKeeper and the comparative DCt method, to process these particular investigations. We found that the expression those data. The processed ranking results from each program were levels of several commonly used reference genes, such as RPS18 in aggregated by ReFinder to generate gene expression stability rank orders. the hypothalamus or ACTB in the intestine, fluctuated across the On the basis of their rankings from each program, ReFinder assigns an appropriate weight to each individual reference gene and then calculates diet and nutrition conditions we tested. These data underscore the geometric mean of the weights for each gene to reach its overall final the systematic validation of reference genes in obesity research. ranking, the comprehensive ranking order, among all 13 candidate genes. The details of the calculation procedures have been previously described.22 We used the comprehensive rank order in our results. MATERIALS AND METHODS The gene expression stabilities of the 13 candidate genes were analyzed Animals under four separate conditions for each tissue: (1) fasted vs re-fed under chow diet; (2) fasted vs re-fed under HFD; (3) chow vs HFD in fasted Male long-Evans rats were single-housed under controlled conditions condition; and (4) chow vs HFD in fasted then re-fed condition. Twelve Cts (12:12-h light–dark cycle, 50–60% humidity, 25 1C with free access to water from each individual gene were analyzed under each of the four and food except where noted) in the Metabolic Diseases Institute at the conditions described above for each tissue. We also did an overall University of Cincinnati. Rats were fed either a high-fat diet (HFD) (n 12) ¼ evaluation of gene expression stabilities in pooled conditions, in which 24 (Cat #: D03082706; 20% fat (butter fat), 16.6% protein and 52% Ct values of each candidate gene were analyzed for each tissue from carbohydrate, 4.54 kcal g À 1, Research Diets, New Brunswick, NJ, USA) or chow/fasted, chow/re-fed, HFD/fasted and HFD/re-fed animals. chow (Cat #: 7912; 5.8% fat (ether extract), 19.1% protein and 44.3% The optimal numbers of reference genes required for accurate normal- carbohydrate, 3.11 kcal g À 1, Harlan-Teklad, Indianapolis, IN, USA) (n 12) ¼ ization were determined by pairwise variation (V /V ) using geNorm. for 8 weeks before the experiments. All procedures for animal use were n n þ 1 A V value (n ¼ the number of reference genes desired) represents the approved by the University of Cincinnati Institutional Animal Care and Use n/n þ 1 pairwise variation between two sets of reference genes, with the second Committee. set containing an additional gene. A large variation means that the added gene has a significant effect and should be included in normalization Animal groups and tissue collection analyses. To calculate V values, Cts from all candidate genes were input into a geNorm excel-based program. An arbitrary cutoff value of 0.15 for Rats were assigned to one of the four treatment groups (n ¼ 6/group): V is adopted9 to assist the evaluation of optimal gene numbers.
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