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Braz. j. med. biol. res ; 36(12): 1629-1641, Dec. 2003. ilus, tab
Article in English | LILACS | ID: lil-350451

ABSTRACT

The reverse transcription-polymerase chain reaction (RT-PCR) is the most sensitive method used to evaluate gene expression. Although many advances have been made since quantitative RT-PCR was first described, few reports deal with the mathematical bases of this technique. The aim of the present study was to develop and standardize a competitive PCR method using standard-curves to quantify transcripts of the myogenic regulatory factors MyoD, Myf-5, Myogenin and MRF4 in chicken embryos. Competitor cDNA molecules were constructed for each gene under study using deletion primers, which were designed to maintain the anchorage sites for the primers used to amplify target cDNAs. Standard-curves were prepared by co-amplification of different amounts of target cDNA with a constant amount of competitor. The content of specific mRNAs in embryo cDNAs was determined after PCR with a known amount of competitor and comparison to standard-curves. Transcripts of the housekeeping á-actin gene were measured to normalize the results. As predicted by the model, most of the standard-curves showed a slope close to 1, while intercepts varied depending on the relative efficiency of competitor amplification. The sensitivity of the RT-PCR method permitted the detection of as few as 60 MyoD/Myf-5 molecules per reaction but approximately 600 molecules of MRF4/Myogenin mRNAS were necessary to produce a measurable signal. A coefficient of variation of 6 to 19 percent was estimated for the different genes analyzed (6 to 9 repetitions). The competitive RT-PCR assay described here is sensitive, precise and allows quantification of up to 9 transcripts from a single cDNA sample.


Subject(s)
Animals , Chick Embryo , Models, Theoretical , Myogenic Regulatory Factors , Reverse Transcriptase Polymerase Chain Reaction , DNA, Complementary , Gene Expression , Reproducibility of Results , RNA, Messenger , Sensitivity and Specificity , Transcription, Genetic
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