BLUEPRINT to decode the epigenetic signature written in blood
David J. Adams,Lucia Altucci,Stylionos E. Antonarakis,Juan Ballesteros,Stephan Beck,Adrian Bird,Christoph Bock,Bernhard O. Boehm,Elias Campo,Andrea Caricasole,Frederik Dahl,Emmanouil T. Dermitzakis,Tariq Enver,Manel Esteller,Xavier Estivill,Anne C. Ferguson-Smith,Jude Fitzgibbon,Paul Flicek,Claudia Giehl,Thomas Graf,Frank Grosveld,Roderic Guigó,Ivo Gut,Kristian Helin,Jonas Jarvius,Ralf Küppers,Hans Lehrach,Thomas Lengauer,Åke Lernmark,David Leslie,Markus Loeffler,Elizabeth Macintyre,Antonello Mai,Joost H.A. Martens,Saverio Minucci,Willem H. Ouwehand,Pier Giuseppe Pelicci,Hèléne Pendeville,Bo T. Porse,Vardham Rakyan,Wolf Reik,Martin Schrappe,Dirk Schübeler,Martin Seifert,Reiner Siebert,David P Simmons,Nicole Soranzo,Salvatore Spicuglia,Michael R. Stratton,Hendrik G. Stunnenberg,Amos Tanay,David Torrents,Alfonso Valencia,Edo Vellenga,Martin Vingron,Jörn Walter,Spike Willcocks +56 more
TLDR
The EU-funded BLUEPRINT consortium that will generate epigenomic maps of at least 100 different blood cell types aims to bridge the gap in current knowledge between individual components of the epigenome and their functional dynamics through state-of-the-art analysis in a defined set of primarily human hematopoietic cells from healthy and diseased individuals.Abstract:
volume 30 number 3 march 2012 nature biotechnology To the Editor: Last October, scientists gathered in Amsterdam to celebrate the start of BLUEPRINT (http://www.blueprintepigenome.eu/), an EU-funded consortium that will generate epigenomic maps of at least 100 different blood cell types. With this initiative, Europe has pledged a substantial contribution to the ultimate goal of the International Human Epigenome Consortium (IHEC) to map 1,000 human epigenomes. Here, we provide a brief background to the scientific questions that prompted the formation of BLUEPRINT, summarize the overall goals of BLUEPRINT and detail the specific areas in which the consortium will focus its initial efforts and resources. In mammals, nucleated cells share the same genome but have different epigenomes depending on the cell type and many other factors, resulting in an astounding diversity in phenotypic plasticity with respect to morphology and function. This diversity is defined by cell-specific patterns of gene expression, which are controlled through regulatory sites in the genome to which transcription factors bind. In eukaryotes, access to these sites is orchestrated via chromatin, the complex of DNA, RNA and proteins that constitutes the functional platform of the genome. In contrast with DNA, chromatin is not static but highly dynamic, particularly through modifications of histones at nucleosomes and cytosines at the DNA level that together define the epigenome, the epigenetic state of the cell. Advances in new genomics technologies, particularly next-generation sequencing, allow the epigenome to be studied in a holistic fashion, leading to a better understanding of chromatin function and functional annotation of the genome. Yet little is known about how epigenetic characteristics vary between different cell types, in health and disease or among individuals. This lack of a quantitative framework for the dynamics of the epigenome and its determinants is a major hurdle for the translation of epigenetic observations into regulatory models, the identification of associations between epigenotypes and diseases, and the subsequent development of new classes of compounds for disease prevention and treatment. The task, however, is daunting as each of the several hundred cell types in the human body is expected to show specific epigenomic features that are further expected to respond to environmental inputs in time and space. The research community has realized these limitations and the need for concerted action. The IHEC was founded to coordinate large-scale international efforts toward the goal of a comprehensive human epigenome reference atlas (http://www.ihec-epigenomes. org/). The IHEC will coordinate epigenomic mapping and characterization worldwide to avoid redundant research efforts, implement high data quality standards, coordinate data storage, management and analysis, and provide free access to the epigenomes produced. The maps generated under the umbrella of the IHEC contain detailed information on DNA methylation, histone modification, nucleosome occupancy, and corresponding coding and noncoding RNA expression in different normal and diseased cell types. This will allow integration of different layers of epigenetic information for a wide variety of distinct cell types and thus provide a resource for both basic and applied research. BLUEPRINT aims to bridge the gap in our current knowledge between individual components of the epigenome and their functional dynamics through state-of-the-art analysis in a defined set of primarily human hematopoietic cells from healthy and diseased individuals. Mammalian blood formation or hematopoiesis is one of the best-studied systems of stem cell biology. Blood formation can be viewed as a hierarchical process, and classically, differentiation is defined to occur along the myeloid and lymphoid lineages. The identity of cellular intermediates and the geometry of branch points are still under intense investigation and therefore provide a paradigm for delineation of fundamental principles of cell fate determination and regulation of proliferation and lifespan, which differ considerably between different types of blood cells. BLUEPRINT will generate reference epigenomes of at least 50 specific blood cell types and their malignant counterparts and aim to provide high-quality reference epigenomes of primary cells from >60 individuals with detailed genetic and, where appropriate, medical records. To account for and quantify the impact of DNA sequence variation on epigenome differences, BLUEPRINT will work whenever possible on samples of known genetic variation, including samples from the Cambridge BioResource (Cambridge, UK), the International Cancer Genome Consortium and the British Diabetic Twin Study for disease-discordant monozygotic twin samples. The Wellcome Trust Sanger Institute (Hinxton, UK) will also provide full genomic sequencing for up to 100 samples. BLUEPRINT will harness existing proven technologies to generate reference epigenomes, including RNA-Seq for transcriptome analysis, bisulfite sequencing for methylome analysis, DNaseI-Seq for analysis of hypersensitive sites and ChIPSeq for analysis of at least six histone marks. Moreover, BLUEPRINT aims to develop new technologies to enhance high-throughput epigenome mapping, particularly when using few cells. BLUEPRINT is initially focusing on four main areas. One main goal of the project is to comprehensively analyze diverse epigenomic maps and make them available as an integrated BLUEPRINT-IHEC resource to the scientific community. Integration is envisioned for related projects within species (e.g., the 1000 Genomes Project) and between species (e.g., modENCODE) to better understand functional aspects (e.g., shared pathways) and the evolution of cell lineage development. Analysis of the BLUEPRINT data is expected to catalyze a better understanding of the relationship between epigenetic and genomic information and will form the basis for generation of new methods (e.g., epigenetic imputation) for prediction of epigenetic states from epigenomic profiles. Such prediction methods will facilitate a move toward a more quantitative knowledge and modeling of epigenetic mechanisms. As a result, such models could in the future assist in ‘reverse engineering’ of regulatory networks to repair or restore epigenetic codes that have been perturbed by disease. A second goal of BLUEPRINT is to systematically link epigenetic variation with phenotypic plasticity in health and disease. This will be attempted in three ways. First, genetic and epigenetic varation in two blood cell types from 100 healthy individuals will be analyzed. These measurements will be combined with whole-genome and transcriptome sequencing to dissect the interplay between common DNA sequence BLUEPRINT to decode the epigenetic signature written in blood CORRESPONDENCEread more
Citations
More filters
Journal ArticleDOI
The Ensembl Variant Effect Predictor.
William M. McLaren,Laurent Gil,Sarah E. Hunt,Harpreet Singh Riat,Graham R. S. Ritchie,Anja Thormann,Paul Flicek,Fiona Cunningham +7 more
TL;DR: The Ensembl Variant Effect Predictor can simplify and accelerate variant interpretation in a wide range of study designs.
Journal ArticleDOI
methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles
Altuna Akalin,Matthias Kormaksson,Sheng Li,Francine E. Garrett-Bakelman,Maria E. Figueroa,Ari Melnick,Christopher E. Mason +6 more
TL;DR: An R package that rapidly analyzes genome-wide cytosine epigenetic profiles from high-throughput methylation and Hydroxymethylation sequencing experiments is described, which includes functions for clustering, sample quality visualization, differential methylation analysis and annotation features, thus automating and simplifying many of the steps for discerning statistically significant bases or regions of DNAmethylation.
Journal ArticleDOI
Epigenetic programming of monocyte-to-macrophage differentiation and trained innate immunity
Sadia Saeed,Jessica Quintin,Hindrik H. D. Kerstens,Nagesha A.S. Rao,Ali Aghajanirefah,Filomena Matarese,Shih-Chin Cheng,Jacqueline M. Ratter,Kim Berentsen,Martijn van der Ent,Nilofar Sharifi,Eva M. Janssen-Megens,Menno ter Huurne,Amit Mandoli,Tom van Schaik,Aylwin Ng,Aylwin Ng,Frances Burden,Kate Downes,Mattia Frontini,Vinod Kumar,Evangelos J. Giamarellos-Bourboulis,Willem H. Ouwehand,Jos W. M. van der Meer,Leo A. B. Joosten,Cisca Wijmenga,Joost H.A. Martens,Ramnik J. Xavier,Ramnik J. Xavier,Colin Logie,Mihai G. Netea,Hendrik G. Stunnenberg +31 more
TL;DR: The epigenetic and transcriptional programs of monocyte differentiation to macrophages that distinguish tolerant and trained macrophage phenotypes are uncovered, providing a resource to further understand and manipulate immune-mediated responses.
Journal ArticleDOI
Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data
TL;DR: Qualimap 2 represents a next step in the QC analysis of HTS data, along with comprehensive single-sample analysis of alignment data, and includes new modes that allow simultaneous processing and comparison of multiple samples.
Journal ArticleDOI
The UK10K project identifies rare variants in health and disease
Klaudia Walter,J L Min,Jie Huang,Lucy Crooks,Yasin Memari,Shane A. McCarthy,Perry Jrb.,ChangJiang Xu,Marta Futema,Daniel Lawson,Valentina Iotchkova,Stephan Schiffels,Audrey E. Hendricks,Petr Danecek,R Li,James A B Floyd,Louise V. Wain,Louise V. Wain,Inês Barroso,Steve E. Humphries,Matthew E. Hurles,Eleftheria Zeggini,Jeffrey C. Barrett,Vincent Plagnol,J. B. Richards,Greenwood Cmt.,Nicholas J. Timpson,Richard Durbin,Nicole Soranzo +28 more
TL;DR: In extensively phenotyped cohorts, insights from sequencing whole genomes or exomes of nearly 10,000 individuals from population-based and disease collections are described and population structure and functional annotation of rare and low-frequency variants are described.
References
More filters
Journal ArticleDOI
Chemoproteomics profiling of HDAC inhibitors reveals selective targeting of HDAC complexes
Marcus Bantscheff,Carsten Hopf,Mikhail M. Savitski,Antje Dittmann,Paola Grandi,Anne-Marie Michon,Judith Schlegl,Yann Abraham,Isabelle Becher,Giovanna Bergamini,Markus Boesche,Manja Delling,Birgit Dümpelfeld,Dirk Eberhard,Carola Huthmacher,Toby Mathieson,Daniel Poeckel,Valerie Reader,Katja Strunk,Gavain Sweetman,Ulrich Kruse,Gitte Neubauer,Nigel Ramsden,Gerard Drewes +23 more
TL;DR: This work revealed the selectivity with which 16 HDAC inhibitors target multiple HDAC complexes scaffolded by ELM-SANT domain subunits, including a novel mitotic deacetylase complex (MiDAC) and identified several non-HDAC targets for hydroxamate inhibitors.
Journal ArticleDOI
Identification of Type 1 Diabetes–Associated DNA Methylation Variable Positions That Precede Disease Diagnosis
Vardhman K. Rakyan,Huriya Beyan,Thomas A. Down,Mohammed I. Hawa,Siarhei Maslau,Deeqo Aden,Antoine Daunay,Florence Busato,Charles A. Mein,Burkhard J. Manfras,Kerith-Rae Dias,Christopher G. Bell,Jörg Tost,Bernhard O. Boehm,Stephan Beck,R. David Leslie +15 more
TL;DR: This EWAS of T1D represents an important contribution toward understanding the etiological role of epigenetic variation in type 1 diabetes, and it is also the first systematic analysis of the temporal origins of disease-associated epigenetic variations for any human complex disease.
Related Papers (5)
An integrated encyclopedia of DNA elements in the human genome
Integrative analysis of 111 reference human epigenomes
Anshul Kundaje,Wouter Meuleman,Wouter Meuleman,Jason Ernst,Misha Bilenky,Angela Yen,Angela Yen,Alireza Heravi-Moussavi,Pouya Kheradpour,Pouya Kheradpour,Zhizhuo Zhang,Zhizhuo Zhang,Jianrong Wang,Jianrong Wang,Michael J. Ziller,Viren Amin,John W. Whitaker,Matthew D. Schultz,Lucas D. Ward,Lucas D. Ward,Abhishek Sarkar,Abhishek Sarkar,Gerald Quon,Gerald Quon,Richard Sandstrom,Matthew L. Eaton,Matthew L. Eaton,Yi-Chieh Wu,Yi-Chieh Wu,Andreas R. Pfenning,Andreas R. Pfenning,Xinchen Wang,Xinchen Wang,Melina Claussnitzer,Melina Claussnitzer,Yaping Liu,Yaping Liu,Cristian Coarfa,R. Alan Harris,Noam Shoresh,Charles B. Epstein,Elizabeta Gjoneska,Elizabeta Gjoneska,Danny Leung,Wei Xie,R. David Hawkins,Ryan Lister,Chibo Hong,Philippe Gascard,Andrew J. Mungall,Richard A. Moore,Eric Chuah,Angela Tam,Theresa K. Canfield,R. Scott Hansen,Rajinder Kaul,Peter J. Sabo,Mukul S. Bansal,Mukul S. Bansal,Mukul S. Bansal,Annaick Carles,Jesse R. Dixon,Kai How Farh,Soheil Feizi,Soheil Feizi,Rosa Karlic,Ah Ram Kim,Ah Ram Kim,Ashwinikumar Kulkarni,Daofeng Li,Rebecca F. Lowdon,Ginell Elliott,Tim R. Mercer,Shane Neph,Vitor Onuchic,Paz Polak,Paz Polak,Nisha Rajagopal,Pradipta R. Ray,Richard C Sallari,Richard C Sallari,Kyle Siebenthall,Nicholas A Sinnott-Armstrong,Nicholas A Sinnott-Armstrong,Michael Stevens,Robert E. Thurman,Jie Wu,Bo Zhang,Xin Zhou,Arthur E. Beaudet,Laurie A. Boyer,Philip L. De Jager,Philip L. De Jager,Peggy J. Farnham,Susan J. Fisher,David Haussler,Steven J.M. Jones,Steven J.M. Jones,Wei Li,Marco A. Marra,Michael T. McManus,Shamil R. Sunyaev,Shamil R. Sunyaev,James A. Thomson,Thea D. Tlsty,Li-Huei Tsai,Li-Huei Tsai,Wei Wang,Robert A. Waterland,Michael Q. Zhang,Lisa Helbling Chadwick,Bradley E. Bernstein,Bradley E. Bernstein,Bradley E. Bernstein,Joseph F. Costello,Joseph R. Ecker,Martin Hirst,Alexander Meissner,Aleksandar Milosavljevic,Bing Ren,John A. Stamatoyannopoulos,Ting Wang,Manolis Kellis,Manolis Kellis +123 more
BEDTools: a flexible suite of utilities for comparing genomic features
Aaron R. Quinlan,Ira M. Hall +1 more