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Statistical differences resulting from selection of stable reference genes after hypoxia and hypothermia in the neonatal rat brain

Real-time reverse transcription PCR (qPCR) normalized to an internal reference gene (RG), is a frequently used method for quantifying gene expression changes in neuroscience. Although RG expression is assumed to be constant independent of physiological or experimental conditions, several studies hav...

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Autores principales: Bustelo, Martín, Bruno, Martín A., Loidl, César F., Rey-Funes, Manuel, Steinbusch, Harry W. M., Gavilanes, Antonio W. D., van den Hove, D. L. A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7241816/
https://www.ncbi.nlm.nih.gov/pubmed/32437382
http://dx.doi.org/10.1371/journal.pone.0233387
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author Bustelo, Martín
Bruno, Martín A.
Loidl, César F.
Rey-Funes, Manuel
Steinbusch, Harry W. M.
Gavilanes, Antonio W. D.
van den Hove, D. L. A.
author_facet Bustelo, Martín
Bruno, Martín A.
Loidl, César F.
Rey-Funes, Manuel
Steinbusch, Harry W. M.
Gavilanes, Antonio W. D.
van den Hove, D. L. A.
author_sort Bustelo, Martín
collection PubMed
description Real-time reverse transcription PCR (qPCR) normalized to an internal reference gene (RG), is a frequently used method for quantifying gene expression changes in neuroscience. Although RG expression is assumed to be constant independent of physiological or experimental conditions, several studies have shown that commonly used RGs are not expressed stably. The use of unstable RGs has a profound effect on the conclusions drawn from studies on gene expression, and almost universally results in spurious estimation of target gene expression. Approaches aimed at selecting and validating RGs often make use of different statistical methods, which may lead to conflicting results. Based on published RG validation studies involving hypoxia the present study evaluates the expression of 5 candidate RGs (Actb, Pgk1, Sdha, Gapdh, Rnu6b) as a function of hypoxia exposure and hypothermic treatment in the neonatal rat cerebral cortex–in order to identify RGs that are stably expressed under these experimental conditions–using several statistical approaches that have been proposed to validate RGs. In doing so, we first analyzed RG ranking stability proposed by several widely used statistical methods and related tools, i.e. the Coefficient of Variation (CV) analysis, GeNorm, NormFinder, BestKeeper, and the ΔCt method. Using the Geometric mean rank, Pgk1 was identified as the most stable gene. Subsequently, we compared RG expression patterns between the various experimental groups. We found that these statistical methods, next to producing different rankings per se, all ranked RGs displaying significant differences in expression levels between groups as the most stable RG. As a consequence, when assessing the impact of RG selection on target gene expression quantification, substantial differences in target gene expression profiles were observed. Altogether, by assessing mRNA expression profiles within the neonatal rat brain cortex in hypoxia and hypothermia as a showcase, this study underlines the importance of further validating RGs for each individual experimental paradigm, considering the limitations of the statistical methods used for this aim.
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spelling pubmed-72418162020-06-03 Statistical differences resulting from selection of stable reference genes after hypoxia and hypothermia in the neonatal rat brain Bustelo, Martín Bruno, Martín A. Loidl, César F. Rey-Funes, Manuel Steinbusch, Harry W. M. Gavilanes, Antonio W. D. van den Hove, D. L. A. PLoS One Research Article Real-time reverse transcription PCR (qPCR) normalized to an internal reference gene (RG), is a frequently used method for quantifying gene expression changes in neuroscience. Although RG expression is assumed to be constant independent of physiological or experimental conditions, several studies have shown that commonly used RGs are not expressed stably. The use of unstable RGs has a profound effect on the conclusions drawn from studies on gene expression, and almost universally results in spurious estimation of target gene expression. Approaches aimed at selecting and validating RGs often make use of different statistical methods, which may lead to conflicting results. Based on published RG validation studies involving hypoxia the present study evaluates the expression of 5 candidate RGs (Actb, Pgk1, Sdha, Gapdh, Rnu6b) as a function of hypoxia exposure and hypothermic treatment in the neonatal rat cerebral cortex–in order to identify RGs that are stably expressed under these experimental conditions–using several statistical approaches that have been proposed to validate RGs. In doing so, we first analyzed RG ranking stability proposed by several widely used statistical methods and related tools, i.e. the Coefficient of Variation (CV) analysis, GeNorm, NormFinder, BestKeeper, and the ΔCt method. Using the Geometric mean rank, Pgk1 was identified as the most stable gene. Subsequently, we compared RG expression patterns between the various experimental groups. We found that these statistical methods, next to producing different rankings per se, all ranked RGs displaying significant differences in expression levels between groups as the most stable RG. As a consequence, when assessing the impact of RG selection on target gene expression quantification, substantial differences in target gene expression profiles were observed. Altogether, by assessing mRNA expression profiles within the neonatal rat brain cortex in hypoxia and hypothermia as a showcase, this study underlines the importance of further validating RGs for each individual experimental paradigm, considering the limitations of the statistical methods used for this aim. Public Library of Science 2020-05-21 /pmc/articles/PMC7241816/ /pubmed/32437382 http://dx.doi.org/10.1371/journal.pone.0233387 Text en © 2020 Bustelo et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Bustelo, Martín
Bruno, Martín A.
Loidl, César F.
Rey-Funes, Manuel
Steinbusch, Harry W. M.
Gavilanes, Antonio W. D.
van den Hove, D. L. A.
Statistical differences resulting from selection of stable reference genes after hypoxia and hypothermia in the neonatal rat brain
title Statistical differences resulting from selection of stable reference genes after hypoxia and hypothermia in the neonatal rat brain
title_full Statistical differences resulting from selection of stable reference genes after hypoxia and hypothermia in the neonatal rat brain
title_fullStr Statistical differences resulting from selection of stable reference genes after hypoxia and hypothermia in the neonatal rat brain
title_full_unstemmed Statistical differences resulting from selection of stable reference genes after hypoxia and hypothermia in the neonatal rat brain
title_short Statistical differences resulting from selection of stable reference genes after hypoxia and hypothermia in the neonatal rat brain
title_sort statistical differences resulting from selection of stable reference genes after hypoxia and hypothermia in the neonatal rat brain
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7241816/
https://www.ncbi.nlm.nih.gov/pubmed/32437382
http://dx.doi.org/10.1371/journal.pone.0233387
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