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

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 suitabl...

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Autores principales:	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
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a44ca9d6535b468986ac3dcbda46d949
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spelling	oai:doaj.org-article:a44ca9d6535b468986ac3dcbda46d9492021-12-02T11:53:10ZValidation of optimal reference genes for quantitative real time PCR in muscle and adipose tissue for obesity and diabetes research10.1038/s41598-017-03730-92045-2322https://doaj.org/article/a44ca9d6535b468986ac3dcbda46d9492017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-03730-9https://doaj.org/toc/2045-2322Abstract 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.Lester J. PerezLiliam RiosPurvi TrivediKenneth D’SouzaAndrew CowieCarine NziroreraDuncan WebsterKeith BruntJean-Francois LegareAnsar HassanPetra C. KienesbergerThomas PulinilkunnilNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-13 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q 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 Validation of optimal reference genes for quantitative real time PCR in muscle and adipose tissue for obesity and diabetes research
description	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.
format	article
author	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
author_facet	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
author_sort	Lester J. Perez
title	Validation of optimal reference genes for quantitative real time PCR in muscle and adipose tissue for obesity and diabetes research
title_short	Validation of optimal reference genes for quantitative real time PCR in muscle and adipose tissue for obesity and diabetes research
title_full	Validation of optimal reference genes for quantitative real time PCR in muscle and adipose tissue for obesity and diabetes research
title_fullStr	Validation of optimal reference genes for quantitative real time PCR in muscle and adipose tissue for obesity and diabetes research
title_full_unstemmed	Validation of optimal reference genes for quantitative real time PCR in muscle and adipose tissue for obesity and diabetes research
title_sort	validation of optimal reference genes for quantitative real time pcr in muscle and adipose tissue for obesity and diabetes research
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/a44ca9d6535b468986ac3dcbda46d949
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_version_	1718394884594860032

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

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