Reference gene selection for reverse transcription quantitative polymerase chain reaction in chicken hypothalamus under different feeding status
Variation in gene expression data can originate from two distinguishable sources. One is true biological variation caused by genotype, tissue differences and individual different response to experimental conditions. The second one is related to technical factors. Experimenters introduce variance into qPCR results by the cumulative effect of differences in the starting material, extraction yield, RNA quantity and quality, reverse transcription and during assay setup. Efforts should be made to correct for technical variability to reliably measure the gene expression changes. Among these, ensuring similar amounts for RNA isolation, and transcribing similar amount of RNA, using internal controls, called reference genes (formally called housekeeping genes) can help to reduce technical variance. The use of internal reference genes (internal controls) is a preferred way to normalize qPCR results, because their expression levels are affected by all sources of introduced variation during qPCR workflow, the same way as the expression of target genes (Huggett, Dheda, Bustin, & Zumla, 2005; Kozera & Rapacz, 2013). However, several studies concluded that no universal reference gene exist (Chervoneva et al., 2010; Nascimento et al., 2015; Olias, Adam, Meyer, Scharff, & Gruber, 2014). The Minimum Information for Publication of Quantitative Real‐Time PCR Experiments (MIQE) recommends that the justification of number and choice of reference genes is essential before publication of quantitative RT‐qPCR experiments to avoid misleading results (Bustin et al., 2009). There are already some examples for this type of publications in various farm animal species (Cappelli et al., 2008; Ji et al., 2013; Macabelli et al., 2014; Park et al., 2015; Zhu, Lin, Liao, & Wang, 2015). In the case of chicken, studies investigated the stability of reference genes during inflammation in circulating leukocytes (De Boever, Vangestel, De Backer, Croubels, & Sys, 2008) between pectoralis major, biceps femoris, liver and abdominal fat (Bagés, Estany, Tor, & Pena, 2015), between lymphoid organs (Borowska, Rothwell, Bailey, Watson, & Kaiser, 2016), fibroblasts infected with different viruses (Kuchipudi et al., 2012; Yang, Lei, Rodriguez‐Palacios, Tang, & Yue, 2013; Yin et al., 2011; Yue, Lei, Yang, Li, & Tang, 2010) and in pectoralis major under the effect of different lysine supplementation and post‐hatch periods (Nascimento et al., 2015).
In animal production, feed intake has a great significance, as it determines growth and performance of livestock, consequently the efficiency of production. Therefore, searching and studying feeding‐related molecular markers has a great interest in poultry species as well. Ad libitum feeding, fasting, and refeeding of chicken are typically used to study not only the expression of neuropeptides involved in feed intake regulation (Sintubin et al., 2014) but also the carbohydrate metabolism (Coudert et al., 2015), fat metabolism (Honda et al., 2016), and muscle physiology as well (Nakashima & Ishida, 2015). Feed intake and feeding behaviour of animals are primarily regulated by the function of hypothalamus in the central nervous system. It receives signals from peripheral organs and other parts of the brain and integrates them. The result is the expressional differences in orexigenic and anorexigenic neuropeptides encoding genes affecting feed intake (Denbow & Cline, 2012). There are currently no studies investigating the reference gene expression stability in chicken hypothalamus under different feed intake.