BACKGROUND: Aging is a major risk factor for a variety of neurobiological diseases leading to variations of transcriptional expression in affected tissues. Reverse transcription of RNA followed by quantitative PCR is a powerful technique for detection and quantification of specific transcripts differentially expressed. An essential prerequisite for accurate interpretation of quantitative PCR data obtained from expression studies is an appropriate normalization process. Therefore we validated the expression of the most frequently used reference genes consisting of Gapdh and Actb as well as Hmbs, Hprt1 and Gusb in an animal model of mice in respect to two major influence factors, aging and ischemia. In the experimental settings we intended to reflect variations in both, the local and systemic immune response. RESULTS: The consistency in gene expression of the tested transcripts was quantified based on standard deviation, correlation analysis and two algorithms available as Visual Basic Applications (VBA) termed GeNorm and Normfinder. Overall, the results of the study proofed the suitability of Actb in combination with Gapdh and with tissue-specific limitations Hmbs in brain and Gusb in white blood cells as the most stable transcripts for accurate normalization. We clearly demonstrated that both, the aging process per se and aging in combination with ischemia are confounding factors with respect to the expression stability of Hprt1. CONCLUSIONS: The present study emphasizes the urgent need to validate the expression stability also from bona fide unvaried transcripts under specific conditions of investigation to ensure adequate normalization of qPCR data. Based on the expression stability, the use of Gapdh and Actb as highly abundant transcripts for normalization of qPCR data under conditions of aging and ischemia in a mouse model was evaluated. However, for low abundant genes the use of Hmbs in brain and Gusb in white blood cells is recommended. Copyright (c) 2008 Elsevier Inc. All rights reserved.
BACKGROUND: Aging is a major risk factor for a variety of neurobiological diseases leading to variations of transcriptional expression in affected tissues. Reverse transcription of RNA followed by quantitative PCR is a powerful technique for detection and quantification of specific transcripts differentially expressed. An essential prerequisite for accurate interpretation of quantitative PCR data obtained from expression studies is an appropriate normalization process. Therefore we validated the expression of the most frequently used reference genes consisting of Gapdh and Actb as well as Hmbs, Hprt1 and Gusb in an animal model of mice in respect to two major influence factors, aging and ischemia. In the experimental settings we intended to reflect variations in both, the local and systemic immune response. RESULTS: The consistency in gene expression of the tested transcripts was quantified based on standard deviation, correlation analysis and two algorithms available as Visual Basic Applications (VBA) termed GeNorm and Normfinder. Overall, the results of the study proofed the suitability of Actb in combination with Gapdh and with tissue-specific limitations Hmbs in brain and Gusb in white blood cells as the most stable transcripts for accurate normalization. We clearly demonstrated that both, the aging process per se and aging in combination with ischemia are confounding factors with respect to the expression stability of Hprt1. CONCLUSIONS: The present study emphasizes the urgent need to validate the expression stability also from bona fide unvaried transcripts under specific conditions of investigation to ensure adequate normalization of qPCR data. Based on the expression stability, the use of Gapdh and Actb as highly abundant transcripts for normalization of qPCR data under conditions of aging and ischemia in a mouse model was evaluated. However, for low abundant genes the use of Hmbs in brain and Gusb in white blood cells is recommended. Copyright (c) 2008 Elsevier Inc. All rights reserved.
Authors: James R Perkins; John M Dawes; Steve B McMahon; David L H Bennett; Christine Orengo; Matthias Kohl Journal: BMC Genomics Date: 2012-07-02 Impact factor: 3.969
Authors: Matthias W Sieber; Nadine Jaenisch; Martin Brehm; Madlen Guenther; Bettina Linnartz-Gerlach; Harald Neumann; Otto W Witte; Christiane Frahm Journal: PLoS One Date: 2013-01-03 Impact factor: 3.240
Authors: Rodney M Ritzel; Sarah J Pan; Rajkumar Verma; John Wizeman; Joshua Crapser; Anita R Patel; Richard Lieberman; Royce Mohan; Louise D McCullough Journal: Mol Vis Date: 2016-06-04 Impact factor: 2.367