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Quantitative polymerase chain reaction and microchip electrophoresis to detect major rearrangements of the low-density lipoprotein receptor gene causing familial hypercholesterolemia.

作者信息

Cantafora Alfredo, Blotta Ida, Pino Elisabetta, Pisciotta Livia, Calandra Sebastiano, Bertolini Stefano

机构信息

Department of Hematology, Oncology and Molecular Medicine, Istituto Superiore di Sanita, Rome, Italy.

出版信息

Electrophoresis. 2004 Nov;25(21-22):3882-9. doi: 10.1002/elps.200406064.

Abstract

A variety of rearrangements in the low-density lipoprotein receptor (LDLR) gene cause severe forms of familial hypercholesterolemia (FH). However, current methods for searching these abnormalities in FH samples, e.g., Southern and Northern Blot, are labor-intensive and not routinely used by diagnostic laboratories. We developed a simpler approach based on the quantitative polymerase chain reaction (PCR) amplification of part or all gene's coding sequences by a series of multiplex amplifications comprising three nonadjacent gene sections plus a fourth section used as an internal reference. Thereafter, the analysis of these PCR products by microchip electrophoresis revealed either deletions or duplications in the investigated gene sections through the simple comparison of electropherograms obtained from mutant and control samples. This required primers leading to well-resolved peaks with minimal size differences among coamplified products and PCR conditions allowing a linear quantitative response to template amount variations as those caused by duplication or deletion of specific gene sections. Also, the inclusion of exon 17 amplification product as an internal reference in each multiplex PCR allowed the normalization of quantitative results by dividing the area of each amplified section by the area of exon 17. The comparison of these ratios calculated from 10 carriers of 6 LDLR known rearrangements with those obtained from 14 control samples showed that gross deletions roughly halved and duplications doubled the ratio values of exons involved in the mutation. This allowed to distinguish gross mutations from sample-to-sample differences that reached at maximum 8% variation over mean values.

摘要

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