Operational Environments

La Tronche, France

Operational Environments

La Tronche, France
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Chaillou T.,Operational Environments | Malgoyre A.,Operational Environments | Banzet S.,Operational Environments | Chapot R.,Operational Environments | And 5 more authors.
Physiological Genomics | Year: 2011

Quantifying target mRNA using real-time quantitative reverse transcription-polymerase chain reaction requires an accurate normalization method. Determination of normalization factors (NFs) based on validated reference genes according to their relative stability is currently the best standard method in most usual situations. This method controls for technical errors, but its physiological relevance requires constant NF values for a fixed weight of tissue. In the functional overload model, the increase in the total RNA concentration must be considered in determining the NF values. Here, we pointed out a limitation of the classical geNorm-derived normalization. geNorm software selected reference genes despite that the NF values extensively varied under experiment. Only the NF values calculated from four intentionally selected genes were constant between groups. However, a normalization based on these genes is questionable. Indeed, three out of four genes belong to the same functional class (negative regulator of muscle mass), and their use is physiological nonsense in a hypertrophic model. Thus, we proposed guidelines for optimizing target mRNA normalization and quantification, useful in models of muscle mass modulation. In our study, the normalization method by multiple reference genes was not appropriate to compare target mRNA levels between overloaded and control muscles. A solution should be to use an absolute quantification of target mRNAs per unit weight of tissue, without any internal normalization. Even if the technical variations will stay present as a part of the intergroup variations, leading to less statistical power, we consider this method acceptable because it will not generate misleading results. Copyright © 2011 the American Physiological Society.


Pugniere P.,Genomic Core Facility | Banzet S.,Operational Environments | Chaillou T.,Operational Environments | Mouret C.,Genomic Core Facility | Peinnequin A.,Genomic Core Facility
Analytical Biochemistry | Year: 2011

A large part of the reliability of reverse transcription quantitative polymerase chain reaction (RT-qPCR) data depends on technical variations. Such variations are mainly attributable to the reverse transcription step. Standardization is a key factor in decreasing the intersample variability. However, an ideal standardization is not always possible, and compromises must be found. Due to technical requirements, the current consensus is that a constant amount of total RNA should be used for the RT step (CA-RT). Because RNA isolation yields are variable, such a practice requires the use of variable volumes of nucleic acid extracts in RT reaction. We demonstrate that some RNA extracts contain both exogenous and endogenous inhibitors. These inhibitors induce a decrease in RT efficiency that significantly impairs the reliability of RT-qPCR data. Conversely, these inhibitors have a slight effect on the qPCR step. To overcome such drawbacks, we proposed to carry out the RT reaction with a constant volume of RNA extract by preserving a constant RNA amount through the supplementation of yeast transfer RNA (CV-RT). We show that CV-RT, compared with the usual CA-RT, allows us to decrease the RT-qPCR variability induced by intersample differences. Such a decrease is a prerequisite for the reliability of messenger RNA quantification. © 2011 Elsevier Inc. All rights reserved.


PubMed | Operational environments
Type: Journal Article | Journal: Physiological genomics | Year: 2011

Quantifying target mRNA using real-time quantitative reverse transcription-polymerase chain reaction requires an accurate normalization method. Determination of normalization factors (NFs) based on validated reference genes according to their relative stability is currently the best standard method in most usual situations. This method controls for technical errors, but its physiological relevance requires constant NF values for a fixed weight of tissue. In the functional overload model, the increase in the total RNA concentration must be considered in determining the NF values. Here, we pointed out a limitation of the classical geNorm-derived normalization. geNorm software selected reference genes despite that the NF values extensively varied under experiment. Only the NF values calculated from four intentionally selected genes were constant between groups. However, a normalization based on these genes is questionable. Indeed, three out of four genes belong to the same functional class (negative regulator of muscle mass), and their use is physiological nonsense in a hypertrophic model. Thus, we proposed guidelines for optimizing target mRNA normalization and quantification, useful in models of muscle mass modulation. In our study, the normalization method by multiple reference genes was not appropriate to compare target mRNA levels between overloaded and control muscles. A solution should be to use an absolute quantification of target mRNAs per unit weight of tissue, without any internal normalization. Even if the technical variations will stay present as a part of the intergroup variations, leading to less statistical power, we consider this method acceptable because it will not generate misleading results.

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