A refined, rapid and reproducible high resolution melt (HRM)-based method suitable for quantification of global LINE-1 repetitive element methylation

被引:37
作者
Tse M.Y. [1 ]
Ashbury J.E. [2 ]
Zwingerman N. [2 ]
King W.D. [2 ]
Taylor S.A.M. [3 ,4 ]
Pang S.C. [1 ]
机构
[1] Department of Anatomy and Cell Biology, Queen's University, Kingston, ON
[2] Department of Community Health and Epidemiology, Queen's University, Kingston, ON
[3] Department of Laboratory Medicine, Saint John Regional Hospital, Horizon Health Network, Halifax, NS
[4] Department of Pathology, Dalhousie University, Halifax, NS
基金
加拿大创新基金会;
关键词
Methylation Level; Single Nucleotide Polymorphism; High Resolution Melting; High Resolution Melting Analysis; Percent Methylation;
D O I
10.1186/1756-0500-4-565
中图分类号
学科分类号
摘要
Background: The methylation of DNA is recognized as a key mechanism in the regulation of genomic stability and evidence for its role in the development of cancer is accumulating. LINE-1 methylation status represents a surrogate measure of genome-wide methylation. Findings. Using high resolution melt (HRM) curve analysis technology, we have established an in-tube assay that is linear (r > 0.9986) with a high amplification efficiency (90-105%), capable of discriminating between partcipant samples with small differences in methylation, and suitable for quantifying a wide range of LINE-1 methylation levels (0-100%) - including the biologically relevant range of 50-90% expected in human DNA. We have optimized this procedure to perform using 2 g of starting DNA and 2 ng of bisulfite-converted DNA for each PCR reaction. Intra- and inter-assay coefficients of variation were 1.44% and 0.49%, respectively, supporting the high reproducibility and precision of this approach. Conclusions: In summary, this is a completely linear, quantitative HRM PCR method developed for the measurement of LINE-1 methylation. This cost-efficient, refined and reproducible assay can be performed using minimal amounts of starting DNA. These features make our assay suitable for high throughput analysis of multiple samples from large population-based studies. © 2011 Tse et al; licensee BioMed Central Ltd.
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