|Gambetta, Gregory -|
|Matthews, Mark -|
Submitted to: Physiologia Plantarum
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: November 23, 2012
Publication Date: July 1, 2013
Repository URL: http://onlinelibrary.wiley.com/doi/10.1111/ppl.12014/pdf
Citation: Gambetta, G.A., Mcelrone, A.J., Matthews, M.A. 2013. Genomic DNA-based absolute quantification of gene expression in Vitis. Physiologia Plantarum. 148(3):334-343. Technical Abstract: Many studies in which gene expression is quantified by polymerase chain reaction represent the expression of a gene of interest (GOI) relative to that of a reference gene (RG). Relative expression is founded on the assumptions that RG expression is stable across samples, treatments, organs, etc., and that reaction efficiencies of the GOI and RG are equal; assumptions which are often faulty. The true variability in RG expression and actual reaction efficiencies are seldom determined experimentally. Here we present a rapid and robust method for absolute quantification of expression in Vitis where varying concentrations of genomic DNA were used to construct GOI standard curves. This methodology was utilized to absolutely quantify and determine the variability of the previously validated RG ubiquitin (VvUbi) across three test studies in three different tissues (roots, leaves and berries). In addition, in each study a GOI was absolutely quantified. Data sets resulting from relative and absolute methods of quantification were compared and the differences were striking. VvUbi expression was significantly different in magnitude between test studies and variable among individual samples. Absolute quantification consistently reduced the coefficients of variation of the GOIs by more than half, often resulting in differences in statistical significance and in some cases even changing the fundamental nature of the result. Utilizing genomic DNA-based absolute quantification is fast and efficient. Through eliminating error introduced by assuming RG stability and equal reaction efficiencies between the RG and GOI this methodology produces less variation, increased accuracy and greater statistical power.