Validation of optimal reference genes for quantitative real time PCR in muscle and adipose tissue for obesity and diabetes research

Lester J. Perez, Liliam Rios, Purvi Trivedi, Kenneth D'Souza, Andrew Cowie, Carine Nzirorera, Duncan Webster, Keith Brunt, Jean Francois Legare, Ansar Hassan, Petra C. Kienesberger, Thomas Pulinilkunnil

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number3612
JournalScientific reports
Volume7
Issue number1
DOIs
StatePublished - Dec 1 2017
Externally publishedYes

ASJC Scopus subject areas

  • General

Fingerprint Dive into the research topics of 'Validation of optimal reference genes for quantitative real time PCR in muscle and adipose tissue for obesity and diabetes research'. Together they form a unique fingerprint.

Cite this