Quantification of Endothelin Receptor mRNA by Competitive RT-PCR
Part of the
Methods in Molecular Biology™
book series (MIMB, volume 206)
The potent effects of the endothelins (ETs), including their vasoconstrictor, positive inotropic and co-mitogenic actions, are mediated by at least two distinct ET receptor subtypes, ETA (1) and ETB (2). The ETA receptor is selective for ET-1, with binding affinity ET-1>ET-2≫ET-3, while the ETB receptor is nonisoform selective. Both subtypes are structurally similar, having seven transmembrane domains characteristic of the G-protein-coupled superfamily (1,2). In several pathophysiological conditions, including myocardial infarction (3), congestive heart failure (4), and renal failure (5), there is altered expression of ET receptors. These changes further implicate ET in the pathogenesis of such conditions and provide additional characterization of the disease process. It is, therefore, essential to have an accurate and reliable means of measuring ET receptor expression. Traditional Northern analysis has the disadvantage of low sensitivity, and while the reverse transcription-polymerase chain reaction (RT-PCR) offers 1000–10,000-fold greater sensitivity, the exponential nature of its amplification kinetics makes it difficult to obtain truly quantitative information. Competitive RT-PCR obviates this problem by co-amplifying the gene of interest with a known concentration of mutant cDNA, which as the name suggests, competes for primer binding and PCR substrates. The subsequent PCR products from wild-type and mutant are distinguished by size or the presence or absence of a restriction enzyme site. By constructing plots of the ratio of wild-type to competitor densities vs molar concentration of competitor cRNA, the starting concentration of the wild-type RNA can be calculated.
KeywordsKlenow Fragment Starting Concentration Mutant cDNA Moloney Murine Leukemia Virus Reverse Transcriptase Competitor Density
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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