4.4 Article

Normalization of gene expression using SYBR green qPCR: A case for paraoxonase 1 and 2 in Alzheimer's disease brains

Journal

JOURNAL OF NEUROSCIENCE METHODS
Volume 200, Issue 1, Pages 14-19

Publisher

ELSEVIER SCIENCE BV
DOI: 10.1016/j.jneumeth.2011.05.026

Keywords

qPCR; Reference gene; Normalization; Paraoxonase 1; Paraoxonase 2; Alzheimer's disease

Funding

  1. Canadian Institutes for Health Research
  2. Natural Sciences and Engineering Research Council of Canada
  3. Fonds de la Recherche en Sante du Quebec
  4. Alcan Corporation

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Validating the expression stability of reference genes is crucial for reliable normalization of real-time quantitative PCR (qPCR) data, but relatively few studies have investigated this issue in brain human tissues. The present study thus aimed at identifying in human post-mortem brain tissues a set of suitable endogenous reference genes (ERG) for the expression analysis of potential candidate genes associated with Alzheimer's disease (AD). The mRNA levels of ten common ERGs (ACTB, GAPDH, GPS1,GUSB,M-RIP, PGK1, POL2RF, PPIA, UBE2D2, and YES1) were determined in the frontal cortex of autopsy-confirmed AD and non-demented control cases (n = 20) using SYBR Green technology. Then, these levels were ranked according to their expression stability using three software applications: geNorm, NormFinder and Best-Keeper. Whereas PPIA and UBE2D2 were among the ERGs with the most reliable expression. ACTS was the worst. Subsequently, using PPIA and UBE2D2 as ERGs for normalization, the mRNA levels of paraoxonase 1 (PON1) and paraoxonase 2 (PON2) were quantified in the frontal cortex of AD and control cases (n = 80) and analyzed using the REST 2009 program. Our results indicate that both paraoxonases are expressed in the human frontal cortex and that PON2 but not PON1 mRNA levels are up-regulated in AD relative to non-demented controls. However, re-analysis of the results by ANCOVA indicated that the significance of the difference between AD and control groups depended upon the ERG used for normalization. The use of a computational method allowing the inclusion of possible confounding factors is thus recommended for the analysis of data. (C) 2011 Published by Elsevier B.V.

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