![]() However, consistent conclusions have not been reached owing to constant changes in fat accumulation of adipose tissue and associated cell size with the development of obesity, different analyzing methods used, etc. In recent years, several research groups have evaluated stability of reference genes for qPCR in human and mouse adipose tissue by different methods of mathematical algorithms ( 7, 13– 17). Therefore, it is essential to validate potential reference genes to establish whether they are appropriate for a specific experimental purpose. However, increasing evidence suggests that the expression of reference genes often varies considerably with differences in subjects, animal species, experimental models, disease conditions, tissue types, etc. During the past decades, β-actin (ACTB), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), 18S ribosomal RNA (18S), ribosomal protein large P0 (RPLP0), and TATA box-binding protein (TBP) have been used extensively as reference genes in physiological status and diseases including obesity ( 7– 10). Normalizing to a reference gene, whose expression has to be stable and independent of the experimental conditions, is a key step for internally controlling for error in qPCR ( 4– 6). However, the MIQE standards have not been embraced more widely in practice. In 2009, the minimum information for the publication of quantitative real-time PCR experiments (MIQE) was published to provide the scientific community with a consistent workflow and key considerations to perform qPCR experiments ( 4). However, there remain a number of problems associated with qPCR use, including variability of sample preparation, extraction and storage, RNA isolation and purification, RT, poor choice of primers, and inappropriate reference targets ( 3– 5). To investigate the underlying mechanisms, a variety of tools and techniques including metabolic, proteomic, transcriptomic, and novel DNA sequencing strategies have been employed, among which quantitative PCR (qPCR) and reverse transcription (RT)-qPCR are the most accurate and sensitive techniques for quantifying mRNA in biological samples and have become accessible to virtually all research labs ( 3– 5). The rapidly increasing prevalence of obesity worldwide and its associated metabolic complications, such as non-alcoholic fatty liver, dyslipidemia, and type 2 diabetes, have become a threat for human health ( 1, 2). These results suggest that PPIA, RPLP0, or YWHAZ may be more appropriate to be used as reference gene than ACTB and GAPDH in the adipose tissue and liver of mice during the process of high-fat diet-induced obesity. In addition, the mostly used genes ACTB and GAPDH were more unstable in the fat and liver, the ACTB mRNA levels were increased in four adipose tissues, and the GAPDH mRNA levels were decreased in four adipose tissues and liver after HFD feeding. The data were analyzed by the GeNorm, NormFinder, BestKeeper, and Delta-Ct method, and the results showed that the most stable reference genes were different for a specific organ or tissue in a specific time point however, PPIA, RPLP0, and YWHAZ were the top three most stable reference genes in qPCR experiments on adipose, hepatic tissues, and muscles of mice in diet-induced obesity. In this study, using the high-fat diet (HFD)-induced obese mouse model, we assessed the expression of 10 commonly used reference genes to validate gene-expression stability in adipose tissue, liver, and muscle across different time points (4, 8, 12, and 16 weeks after HFD feeding) during the process of obesity. Quantitative PCR (qPCR), the most accurate and sensitive technique for quantifying mRNA expression, and choice of appropriate reference genes for internal error controlling in qPCR are essential to understanding the molecular mechanisms that drive the obesity epidemic and its comorbidities. 1Laboratory of Nutrition and Development, Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.Xiuqin Fan 1 Hongyang Yao 1 Xuanyi Liu 1 Qiaoyu Shi 1 Liang Lv 2 Ping Li 1 Rui Wang 1 Tiantian Tang 1 Kemin Qi 1 *
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