Emanuele Libertini , Rifat Hamoudi , Simon Heath , Lee Lancashire , Arcadio Rubio Garcia , Luigi Grassi , Kate Downes , Willem Ouwehand , Biola-Maria Javierre , Jonathan Cairns , Steven Wingett , Dirk Paul , Marta Gut , Ivo Gut , Joost Martens , Alexandr Ivliev , Hendrik Stunnenberg , Mattia Frontini , Mikhail Spivakov , Peter Fraser , Anthony Cutler , Chris Wallace , Stephan Beck
Understanding the regulatory landscape of human cells requires the integration of genomic and epigenomic maps, capturing combinatorial levels of cell type-specific and invariant activity states. Here, we segmented whole-genome bisulfite sequencing-derived methylomes into consecutive blocks of co-methylation (COMETs) to obtain spatial variation patterns of DNA methylation (DNAm oscillations) integrated with histone modifications and promoter-enhancer interactions derived from promoter capture Hi-C (PCHi-C) sequencing of the same purified blood cells. Mapping DNAm oscillations onto regulatory genome annotation revealed that enhancers are enriched for DNAm hyper-oscillations (>30-fold), where multiple machine learning models support DNAm as predictive of enhancer location. Based on this analysis, we report overall predictive power of 99% for DNAm oscillations, 77.3% for DNaseI, 41% for CGIs, 20% for UMRs and 0% for LMRs, demonstrating the power of DNAm oscillations over other methods for enhancer prediction. Methylomes of activated and non-activated CD4+ T cells indicate that DNAm oscillations exist in both states irrespective of activation; hence they can be used to determine the location of latent enhancers. Our approach advances the identification of tissue-specific regulatory elements and is a first demonstration of defining enhancer classes based on DNA methylation.