Pitfalls in target mRNAs quantification for real-time quantitative RT-PCR in overload

[Mike Bikov]

Physiol Genomics. 2010 Dec 14. [Epub ahead of print]
Pitfalls in target mRNAs quantification for real-time quantitative RT-PCR in overload-induced skeletal muscle hypertrophy.

Chaillou T, Malgoyre A, Banzet S, Chapot R, Koulmann N, Pugniere P, Beaudry M, Bigard X, Peinnequin A.

IRBA.
Abstract

Quantifying target mRNA using real-time quantitative reverse transcription-polymerase chain reaction (RT-qPCR) 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 a 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 inter-group variations, leading to a less statistical power, we consider this method acceptable because it will not generate misleading results.

PMID: 21156833 [PubMed - as supplied by publisher]​

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