Year: 2018
Type: preprint
Abstract: 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 outperforms previous approaches defining enhancer classes based on DNA methylation.
DOI: 10.1101/262212
Authors: Emanuele Libertini, Rifat A. Hamoudi, Simon Heath, Lee Lancashire, Arcadio Rubio Garcia, Luigi Grassi, Kate Downes, Willem H. Ouwehand, Biola-Maria Javierre, Jonathan Cairns, Steven Wingett, Dirk S. Paul, Marta Gut, Ivo G. Gut, Joost H. A. Martens, Alexandr Ivliev, Hendrik G. Stunnenberg, Mattia Frontini, Mikhail Spivakov, Peter Fraser, Antony Cutler, Chris Wallace, Stephan Beck
Institutions: University College London, University of Sharjah, Centro Nacional de Análisis Genómico, Parc Científic de Barcelona, University of Cambridge, National Institute for Health and Care Research, MRC Biostatistics Unit, Addenbrooke's Hospital, NIHR Cambridge Biomedical Research Centre, Wellcome Trust, Cancer Research UK, Wellcome Sanger Institute, Cancer Research UK Cambridge Center, Babraham Institute, Radboud University Nijmegen, National Health Service, British Heart Foundation
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Funders: NHS Blood and Transplant, Wellcome Trust, National Institute for Health and Care Research, British Heart Foundation, European Commission, Directorate for Biological Sciences, Medical Research Council, Cambridge Institute for Medical Research, University of Cambridge, NIHR Cambridge Biomedical Research Centre
Awards: G1093531541, G119964413, Novel immunotherapies for type 1 diabetes, G1349290491, Partitioning autoimmune diseases on their molecular basis., G2325242462, G2473632591, G2473961061, Diabetes and Inflammation Laboratory., A BLUEPRINT of Haematopoietic Epigenomes, G3029103535, Epigenetic Training Network - Innovative techniques and models to understand epigenetic regulation in the pathogenesis of common diseases, G3202681502, G3207356259, Epigenetics towards systems biology, G3813812808, Genes and Pathways in Type 1 Diabetes., G393177772, Megakarocyte and Platelet Biology and Genomics, G4273598029, Cellular Medicine at the Cambridge Institute for Medical Research., G5257833011, G5702163051, G5725558014, G6609318545, G6991716568, G7644601275, G7658546326, G7992221752, Research Excellence, G84888450, G969978795
OpenAlex ID: W2795208195