Eugene J H Wee1, Sakandar Rauf1, Muhammad J A Shiddiky1, Alexander Dobrovic2, Matt Trau3. 1. Centre for Personalized Nanomedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), and. 2. Translational Genomics & Epigenomics Laboratory, Ludwig Institute for Cancer Research, Olivia Newton-John Cancer & Wellness Centre, Heidelberg, Victoria, Australia; Department of Pathology, University of Melbourne, Parkville, Victoria, Australia. 3. Centre for Personalized Nanomedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), and School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia, Queensland, Australia; m.trau@uq.edu.au.
Abstract
BACKGROUND: DNA methylation is a potential source of disease biomarkers. Typically, methylation levels are measured at individual cytosine/guanine (CpG) sites or over a short region of interest. However, regions of interest often show heterogeneous methylation comprising multiple patterns of methylation (epialleles) on individual DNA strands. Heterogeneous methylation is largely ignored because digital methods are required to deconvolute these usually complex patterns of epialleles. Currently, only single-molecule approaches, such as next generation sequencing (NGS), can provide detailed epiallele information. Because NGS is not yet feasible for routine practice, we developed a single-molecule-like approach, named for epiallele quantification (EpiQ). METHODS: EpiQ uses DNA ligases and the enhanced thermal instability of short (≤19 bases) mismatched DNA probes for the relative quantification of epialleles. The assay was developed using fluorescent detection on a gel and then adapted for electrochemical detection on a microfabricated device. NGS was used to validate the analytical accuracy of EpiQ. RESULTS: In this proof of principle study, EpiQ detected with 90%-95% specificity each of the 8 possible epialleles for a 3-CpG cluster at the promoter region of the CDKN2B (p15) tumor suppressor gene. EpiQ successfully profiled heterogeneous methylation patterns in clinically derived samples, and the results were cross-validated with NGS. CONCLUSIONS: EpiQ is a potential alternative tool for characterizing heterogeneous methylation, thus facilitating its use as a biomarker. EpiQ was developed on a gel-based assay but can also easily be adapted for miniaturized chip-based platforms.
BACKGROUND: DNA methylation is a potential source of disease biomarkers. Typically, methylation levels are measured at individual cytosine/guanine (CpG) sites or over a short region of interest. However, regions of interest often show heterogeneous methylation comprising multiple patterns of methylation (epialleles) on individual DNA strands. Heterogeneous methylation is largely ignored because digital methods are required to deconvolute these usually complex patterns of epialleles. Currently, only single-molecule approaches, such as next generation sequencing (NGS), can provide detailed epiallele information. Because NGS is not yet feasible for routine practice, we developed a single-molecule-like approach, named for epiallele quantification (EpiQ). METHODS: EpiQ uses DNA ligases and the enhanced thermal instability of short (≤19 bases) mismatched DNA probes for the relative quantification of epialleles. The assay was developed using fluorescent detection on a gel and then adapted for electrochemical detection on a microfabricated device. NGS was used to validate the analytical accuracy of EpiQ. RESULTS: In this proof of principle study, EpiQ detected with 90%-95% specificity each of the 8 possible epialleles for a 3-CpG cluster at the promoter region of the CDKN2B (p15) tumor suppressor gene. EpiQ successfully profiled heterogeneous methylation patterns in clinically derived samples, and the results were cross-validated with NGS. CONCLUSIONS: EpiQ is a potential alternative tool for characterizing heterogeneous methylation, thus facilitating its use as a biomarker. EpiQ was developed on a gel-based assay but can also easily be adapted for miniaturized chip-based platforms.
Authors: Mario Menschikowski; Carsten Jandeck; Markus Friedemann; Susan Richter; Dana Thiem; Björn Sönke Lange; Meinolf Suttorp Journal: Cancer Genomics Proteomics Date: 2018 Jul-Aug Impact factor: 4.069
Authors: Annette M Lim; Ida Lm Candiloro; Nicholas Wong; Marnie Collins; Hongdo Do; Elena A Takano; Christopher Angel; Richard J Young; June Corry; David Wiesenfeld; Stephen Kleid; Elizabeth Sigston; Bernard Lyons; Danny Rischin; Benjamin Solomon; Alexander Dobrovic Journal: Clin Epigenetics Date: 2014-12-09 Impact factor: 6.551
Authors: Nicholas C Wong; Bernard J Pope; Ida L Candiloro; Darren Korbie; Matt Trau; Stephen Q Wong; Thomas Mikeska; Xinmin Zhang; Mark Pitman; Stefanie Eggers; Stephen R Doyle; Alexander Dobrovic Journal: BMC Bioinformatics Date: 2016-02-24 Impact factor: 3.169