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1.  Genome-Wide Screen for Differential DNA Methylation Associated with Neural Cell Differentiation in Mouse 
PLoS ONE  2011;6(10):e26002.
Cellular differentiation involves widespread epigenetic reprogramming, including modulation of DNA methylation patterns. Using Differential Methylation Hybridization (DMH) in combination with a custom DMH array containing 51,243 features covering more than 16,000 murine genes, we carried out a genome-wide screen for cell- and tissue-specific differentially methylated regions (tDMRs) in undifferentiated embryonic stem cells (ESCs), in in-vitro induced neural stem cells (NSCs) and 8 differentiated embryonic and adult tissues. Unsupervised clustering of the generated data showed distinct cell- and tissue-specific DNA methylation profiles, revealing 202 significant tDMRs (p<0.005) between ESCs and NSCs and a further 380 tDMRs (p<0.05) between NSCs/ESCs and embryonic brain tissue. We validated these tDMRs using direct bisulfite sequencing (DBS) and methylated DNA immunoprecipitation on chip (MeDIP-chip). Gene ontology (GO) analysis of the genes associated with these tDMRs showed significant (absolute Z score>1.96) enrichment for genes involved in neural differentiation, including, for example, Jag1 and Tcf4. Our results provide robust evidence for the relevance of DNA methylation in early neural development and identify novel marker candidates for neural cell differentiation.
PMCID: PMC3196508  PMID: 22028803
2.  Integrated Genetic and Epigenetic Analysis Identifies Haplotype-Specific Methylation in the FTO Type 2 Diabetes and Obesity Susceptibility Locus 
PLoS ONE  2010;5(11):e14040.
Recent multi-dimensional approaches to the study of complex disease have revealed powerful insights into how genetic and epigenetic factors may underlie their aetiopathogenesis. We examined genotype-epigenotype interactions in the context of Type 2 Diabetes (T2D), focussing on known regions of genomic susceptibility. We assayed DNA methylation in 60 females, stratified according to disease susceptibility haplotype using previously identified association loci. CpG methylation was assessed using methylated DNA immunoprecipitation on a targeted array (MeDIP-chip) and absolute methylation values were estimated using a Bayesian algorithm (BATMAN). Absolute methylation levels were quantified across LD blocks, and we identified increased DNA methylation on the FTO obesity susceptibility haplotype, tagged by the rs8050136 risk allele A (p = 9.40×10−4, permutation p = 1.0×10−3). Further analysis across the 46 kb LD block using sliding windows localised the most significant difference to be within a 7.7 kb region (p = 1.13×10−7). Sequence level analysis, followed by pyrosequencing validation, revealed that the methylation difference was driven by the co-ordinated phase of CpG-creating SNPs across the risk haplotype. This 7.7 kb region of haplotype-specific methylation (HSM), encapsulates a Highly Conserved Non-Coding Element (HCNE) that has previously been validated as a long-range enhancer, supported by the histone H3K4me1 enhancer signature. This study demonstrates that integration of Genome-Wide Association (GWA) SNP and epigenomic DNA methylation data can identify potential novel genotype-epigenotype interactions within disease-associated loci, thus providing a novel route to aid unravelling common complex diseases.
PMCID: PMC2987816  PMID: 21124985
3.  An Epigenetic Signature in Peripheral Blood Predicts Active Ovarian Cancer 
PLoS ONE  2009;4(12):e8274.
Recent studies have shown that DNA methylation (DNAm) markers in peripheral blood may hold promise as diagnostic or early detection/risk markers for epithelial cancers. However, to date no study has evaluated the diagnostic and predictive potential of such markers in a large case control cohort and on a genome-wide basis.
Principal Findings
By performing genome-wide DNAm profiling of a large ovarian cancer case control cohort, we here demonstrate that active ovarian cancer has a significant impact on the DNAm pattern in peripheral blood. Specifically, by measuring the methylation levels of over 27,000 CpGs in blood cells from 148 healthy individuals and 113 age-matched pre-treatment ovarian cancer cases, we derive a DNAm signature that can predict the presence of active ovarian cancer in blind test sets with an AUC of 0.8 (95% CI (0.74–0.87)). We further validate our findings in another independent set of 122 post-treatment cases (AUC = 0.76 (0.72–0.81)). In addition, we provide evidence for a significant number of candidate risk or early detection markers for ovarian cancer. Furthermore, by comparing the pattern of methylation with gene expression data from major blood cell types, we here demonstrate that age and cancer elicit common changes in the composition of peripheral blood, with a myeloid skewing that increases with age and which is further aggravated in the presence of ovarian cancer. Finally, we show that most cancer and age associated methylation variability is found at CpGs located outside of CpG islands.
Our results underscore the potential of DNAm profiling in peripheral blood as a tool for detection or risk-prediction of epithelial cancers, and warrants further in-depth and higher CpG coverage studies to further elucidate this role.
PMCID: PMC2793425  PMID: 20019873

Results 1-3 (3)