Abstract
The global incidence of obesity has led to an increasing need for understanding the molecular mechanisms that drive this epidemic and its comorbidities. Quantitative real-time RT-PCR (RT-qPCR) is the most reliable and widely used method for gene expression analysis. The selection of suitable reference genes (RGs) is critical for obtaining accurate gene expression information. The current study aimed to identify optimal RGs to perform quantitative transcriptomic analysis based on RT-qPCR for obesity and diabetes research, employing in vitro and mouse models, and human tissue samples. Using the ReFinder program we evaluated the stability of a total of 15 RGs. The impact of choosing the most suitable RGs versus less suitable RGs on RT-qPCR results was assessed. Optimal RGs differed between tissue and cell type, species, and experimental conditions. By employing different sets of RGs to normalize the mRNA expression of peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC1α), we show that sub-optimal RGs can markedly alter the PGC1α gene expression profile. Our study demonstrates the importance of validating RGs prior to normalizing transcriptional expression levels of target genes and identifies optimal RG pairs for reliable RT-qPCR normalization in cells and in human and murine muscle and adipose tissue for obesity/diabetes research.
Highlights
The epidemic of obesity has led to a world in which more people are obese than underweight[1]
In addition to utilizing cultured cells and mouse models of obesity, we examined reference genes (RGs) in atrial appendage (AA) and subcutaneous adipose tissue (SAT) from humans with body mass index (BMI) ranging from normal to class III obesity
Assessment of nucleic acid quality and Quantitative Polymerase Chain Reaction (qPCR) validation, which are key parameters to guarantee a successful qPCR assay based on MIQE guidelines, was performed for all samples and RGs employed in this study (Fig. S1)
Summary
The epidemic of obesity has led to a world in which more people are obese than underweight[1]. To examine the pathophysiology and molecular mechanisms of obesity, type 2 diabetes, and other obesity-related comorbidities, the scientific community employs a variety of tools and techniques including metabolomic, proteomic, transcriptomic, and novel DNA sequencing strategies[5, 6]. Quantitative real-time RT-PCR (RT-qPCR) is the premier molecular method for quantifying gene transcript levels due to its high sensitivity, accuracy, and specificity[7]. To obtain accurate gene expression information based on qPCR, it is imperative to complete a number of complex technical steps and adequately address a range of quality control issues previously described in the “Minimum Information for Publication of Quantitative Real-Time PCR Experiments” (MIQE) guidelines[9]. The current study aimed to identify suitable RGs to perform quantitative transcriptomic analysis based on RT-qPCR for obesity and diabetes research, employing in vitro models, mouse models and human tissue samples
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