Research Fellow, Harvard University, Computational epigenomics (2010)
PhD, Max Planck Institute for Informatics, Computational epigenomics (2017)
Doctor of Philosophy, Universitat Des Saarlandes (2017)
Master of Science, Universitat Des Saarlandes (2009)
Bachelor of Science, Universitat Des Saarlandes (2008)
DNA Methylation Dynamics of Human Hematopoietic Stem Cell Differentiation
CELL STEM CELL
2016; 19 (6): 808-822
Hematopoietic stem cells give rise to all blood cells in a differentiation process that involves widespread epigenome remodeling. Here we present genome-wide reference maps of the associated DNA methylation dynamics. We used a meta-epigenomic approach that combines DNA methylation profiles across many small pools of cells and performed single-cell methylome sequencing to assess cell-to-cell heterogeneity. The resulting dataset identified characteristic differences between HSCs derived from fetal liver, cord blood, bone marrow, and peripheral blood. We also observed lineage-specific DNA methylation between myeloid and lymphoid progenitors, characterized immature multi-lymphoid progenitors, and detected progressive DNA methylation differences in maturing megakaryocytes. We linked these patterns to gene expression, histone modifications, and chromatin accessibility, and we used machine learning to derive a model of human hematopoietic differentiation directly from DNA methylation data. Our results contribute to a better understanding of human hematopoietic stem cell differentiation and provide a framework for studying blood-linked diseases.
View details for DOI 10.1016/j.stem.2016.10.019
View details for Web of Science ID 000389474900017
View details for PubMedID 27867036
View details for PubMedCentralID PMC5145815
- Epigenomic Profiling of Human CD4(+) T Cells Supports a Linear Differentiation Model and Highlights Molecular Regulators of Memory Development IMMUNITY 2016; 45 (5): 1148-1161
Epigenetic Homogeneity Within Colorectal Tumors Predicts Shorter Relapse-Free and Overall Survival Times for Patients With Locoregional Cancer
2016; 151 (5): 961-972
There are few validated biomarkers that can be used to predict outcomes for patients with colorectal cancer. Part of the challenge is the genetic and molecular heterogeneity of colorectal tumors not only among patients, but also within tumors. We have explored intratumor heterogeneity at the epigenetic level, due to its dynamic nature. We analyzed DNA methylation profiles of the digestive tract surface and the central bulk and invasive front regions of colorectal tumors.We determined the DNA methylation profiles of >450,000 CpG sites in 3 macrodissected regions of 79 colorectal tumors and 23 associated liver metastases, obtained from 2 hospitals in Spain. We also analyzed samples for KRAS and BRAF mutations, 499,170 single nucleotide polymorphisms, and performed immunohistochemical analyses.We observed differences in DNA methylation among the 3 tumor sections; regions of tumor-host interface differed the most from the other tumor sections. Interestingly, tumor samples collected from areas closer to the gastrointestinal transit most frequently shared methylation events with metastases. When we calculated individual coefficients to quantify heterogeneity, we found that epigenetic homogeneity was significantly associated with short time of relapse-free survival (log-rank P = .037) and short time of overall survival (log-rank P = .026) in patients with locoregional colorectal cancer.In an analysis of 79 colorectal tumors, we found significant heterogeneity in patterns of DNA methylation within each tumor; the level of heterogeneity correlates with times of relapse-free and overall survival.
View details for DOI 10.1053/j.gastro.2016.08.001
View details for Web of Science ID 000390954600035
View details for PubMedID 27521480
Epigenetic dynamics of monocyte-to-macrophage differentiation
EPIGENETICS & CHROMATIN
Monocyte-to-macrophage differentiation involves major biochemical and structural changes. In order to elucidate the role of gene regulatory changes during this process, we used high-throughput sequencing to analyze the complete transcriptome and epigenome of human monocytes that were differentiated in vitro by addition of colony-stimulating factor 1 in serum-free medium.Numerous mRNAs and miRNAs were significantly up- or down-regulated. More than 100 discrete DNA regions, most often far away from transcription start sites, were rapidly demethylated by the ten eleven translocation enzymes, became nucleosome-free and gained histone marks indicative of active enhancers. These regions were unique for macrophages and associated with genes involved in the regulation of the actin cytoskeleton, phagocytosis and innate immune response.In summary, we have discovered a phagocytic gene network that is repressed by DNA methylation in monocytes and rapidly de-repressed after the onset of macrophage differentiation.
View details for DOI 10.1186/s13072-016-0079-z
View details for Web of Science ID 000381738500001
View details for PubMedID 27478504
View details for PubMedCentralID PMC4967341
- A general concept for consistent documentation of computational analyses DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2015
Dissecting the role of aberrant DNA methylation in human leukaemia
Chronic myeloid leukaemia (CML) is a myeloproliferative disorder characterized by the genetic translocation t(9;22)(q34;q11.2) encoding for the BCR-ABL fusion oncogene. However, many molecular mechanisms of the disease progression still remain poorly understood. A growing body of evidence suggests that the epigenetic abnormalities are involved in tyrosine kinase resistance in CML, leading to leukaemic clone escape and disease propagation. Here we show that, by applying cellular reprogramming to primary CML cells, aberrant DNA methylation contributes to the disease evolution. Importantly, using a BCR-ABL inducible murine model, we demonstrate that a single oncogenic lesion triggers DNA methylation changes, which in turn act as a precipitating event in leukaemia progression.
View details for DOI 10.1038/ncomms8091
View details for Web of Science ID 000355531400017
View details for PubMedID 25997600
View details for PubMedCentralID PMC4443494
- Epigenetic Dysregulation in the Prefrontal Cortex of Suicide Completers CYTOGENETIC AND GENOME RESEARCH 2015; 146 (1): 19-27
Comprehensive analysis of DNA methylation data with RnBeads.
2014; 11 (11): 1138-1140
RnBeads is a software tool for large-scale analysis and interpretation of DNA methylation data, providing a user-friendly analysis workflow that yields detailed hypertext reports (http://rnbeads.mpi-inf.mpg.de/). Supported assays include whole-genome bisulfite sequencing, reduced representation bisulfite sequencing, Infinium microarrays and any other protocol that produces high-resolution DNA methylation data. Notable applications of RnBeads include the analysis of epigenome-wide association studies and epigenetic biomarker discovery in cancer cohorts.
View details for DOI 10.1038/nmeth.3115
View details for PubMedID 25262207
View details for PubMedCentralID PMC4216143
- DNA methylation signatures link prenatal famine exposure to growth and metabolism NATURE COMMUNICATIONS 2014; 5
Regulation of DNA Methylation Patterns by CK2-Mediated Phosphorylation of Dnmt3a
2014; 8 (3): 743-753
DNA methylation is a central epigenetic modification that is established by de novo DNA methyltransferases. The mechanisms underlying the generation of genomic methylation patterns are still poorly understood. Using mass spectrometry and a phosphospecific Dnmt3a antibody, we demonstrate that CK2 phosphorylates endogenous Dnmt3a at two key residues located near its PWWP domain, thereby downregulating the ability of Dnmt3a to methylate DNA. Genome-wide DNA methylation analysis shows that CK2 primarily modulates CpG methylation of several repeats, most notably of Alu SINEs. This modulation can be directly attributed to CK2-mediated phosphorylation of Dnmt3a. We also find that CK2-mediated phosphorylation is required for localization of Dnmt3a to heterochromatin. By revealing phosphorylation as a mode of regulation of de novo DNA methyltransferase function and by uncovering a mechanism for the regulation of methylation at repetitive elements, our results shed light on the origin of DNA methylation patterns.
View details for DOI 10.1016/j.celrep.2014.06.048
View details for Web of Science ID 000341572200012
View details for PubMedID 25066127
Aberrant DNA methylation reprogramming during induced pluripotent stem cell generation is dependent on the choice of reprogramming factors.
Cell regeneration (London, England)
2014; 3 (1): 4-?
The conversion of somatic cells into pluripotent stem cells via overexpression of reprogramming factors involves epigenetic remodeling. DNA methylation at a significant proportion of CpG sites in induced pluripotent stem cells (iPSCs) differs from that of embryonic stem cells (ESCs). Whether different sets of reprogramming factors influence the type and extent of aberrant DNA methylation in iPSCs differently remains unknown. In order to help resolve this critical question, we generated human iPSCs from a common fibroblast cell source using either the Yamanaka factors (OCT4, SOX2, KLF4 and cMYC) or the Thomson factors (OCT4, SOX2, NANOG and LIN28), and determined their genome-wide DNA methylation profiles. In addition to shared DNA methylation aberrations present in all our iPSCs, we identified Yamanaka-iPSC (Y-iPSC)-specific and Thomson-iPSC (T-iPSC)-specific recurrent aberrations. Strikingly, not only were the genomic locations of the aberrations different but also their types: reprogramming with Yamanaka factors mainly resulted in failure to demethylate CpGs, whereas reprogramming with Thomson factors mainly resulted in failure to methylate CpGs. Differences in the level of transcripts encoding DNMT3b and TET3 between Y-iPSCs and T-iPSCs may contribute partially to the distinct types of aberrations. Finally, de novo aberrantly methylated genes in Y-iPSCs were enriched for NANOG targets that are also aberrantly methylated in some cancers. Our study thus reveals that the choice of reprogramming factors influences the amount, location, and class of DNA methylation aberrations in iPSCs. These findings may provide clues into how to produce human iPSCs with fewer DNA methylation abnormalities.
View details for DOI 10.1186/2045-9769-3-4
View details for PubMedID 25408883
View details for PubMedCentralID PMC4230737
A Prognostic DNA Methylation Signature for Stage I Non-Small-Cell Lung Cancer
JOURNAL OF CLINICAL ONCOLOGY
2013; 31 (32): 4140-?
Non-small-cell lung cancer (NSCLC) is a tumor in which only small improvements in clinical outcome have been achieved. The issue is critical for stage I patients for whom there are no available biomarkers that indicate which high-risk patients should receive adjuvant chemotherapy. We aimed to find DNA methylation markers that could be helpful in this regard.A DNA methylation microarray that analyzes 450,000 CpG sites was used to study tumoral DNA obtained from 444 patients with NSCLC that included 237 stage I tumors. The prognostic DNA methylation markers were validated by a single-methylation pyrosequencing assay in an independent cohort of 143 patients with stage I NSCLC.Unsupervised clustering of the 10,000 most variable DNA methylation sites in the discovery cohort identified patients with high-risk stage I NSCLC who had shorter relapse-free survival (RFS; hazard ratio [HR], 2.35; 95% CI, 1.29 to 4.28; P = .004). The study in the validation cohort of the significant methylated sites from the discovery cohort found that hypermethylation of five genes was significantly associated with shorter RFS in stage I NSCLC: HIST1H4F, PCDHGB6, NPBWR1, ALX1, and HOXA9. A signature based on the number of hypermethylated events distinguished patients with high- and low-risk stage I NSCLC (HR, 3.24; 95% CI, 1.61 to 6.54; P = .001).The DNA methylation signature of NSCLC affects the outcome of stage I patients, and it can be practically determined by user-friendly polymerase chain reaction assays. The analysis of the best DNA methylation biomarkers improved prognostic accuracy beyond standard staging.
View details for DOI 10.1200/JCO.2012.48.5516
View details for Web of Science ID 000330533200017
View details for PubMedID 24081945
Charting a dynamic DNA methylation landscape of the human genome
2013; 500 (7463): 477-481
DNA methylation is a defining feature of mammalian cellular identity and is essential for normal development. Most cell types, except germ cells and pre-implantation embryos, display relatively stable DNA methylation patterns, with 70-80% of all CpGs being methylated. Despite recent advances, we still have a limited understanding of when, where and how many CpGs participate in genomic regulation. Here we report the in-depth analysis of 42 whole-genome bisulphite sequencing data sets across 30 diverse human cell and tissue types. We observe dynamic regulation for only 21.8% of autosomal CpGs within a normal developmental context, most of which are distal to transcription start sites. These dynamic CpGs co-localize with gene regulatory elements, particularly enhancers and transcription-factor-binding sites, which allow identification of key lineage-specific regulators. In addition, differentially methylated regions (DMRs) often contain single nucleotide polymorphisms associated with cell-type-related diseases as determined by genome-wide association studies. The results also highlight the general inefficiency of whole-genome bisulphite sequencing, as 70-80% of the sequencing reads across these data sets provided little or no relevant information about CpG methylation. To demonstrate further the utility of our DMR set, we use it to classify unknown samples and identify representative signature regions that recapitulate major DNA methylation dynamics. In summary, although in theory every CpG can change its methylation state, our results suggest that only a fraction does so as part of coordinated regulatory programs. Therefore, our selected DMRs can serve as a starting point to guide new, more effective reduced representation approaches to capture the most informative fraction of CpGs, as well as further pinpoint putative regulatory elements.
View details for DOI 10.1038/nature12433
View details for Web of Science ID 000323316100039
View details for PubMedID 23925113
View details for PubMedCentralID PMC3821869
RRBSMAP: a fast, accurate and user-friendly alignment tool for reduced representation bisulfite sequencing
2012; 28 (3): 430-432
Reduced representation bisulfite sequencing (RRBS) is a powerful yet cost-efficient method for studying DNA methylation on a genomic scale. RRBS involves restriction-enzyme digestion, bisulfite conversion and size selection, resulting in DNA sequencing data that require special bioinformatic handling. Here, we describe RRBSMAP, a short-read alignment tool that is designed for handling RRBS data in a user-friendly and scalable way. RRBSMAP uses wildcard alignment, and avoids the need for any preprocessing or post-processing steps. We benchmarked RRBSMAP against a well-validated MAQ-based pipeline for RRBS read alignment and observed similar accuracy but much improved runtime performance, easier handling and better scaling to large sample sets. In summary, RRBSMAP removes bioinformatic hurdles and reduces the computational burden of large-scale epigenome association studies performed with RRBS.http://rrbsmap.computational-epigenetics.org/ http://email@example.comSupplementary data are available at Bioinformatics online.
View details for DOI 10.1093/bioinformatics/btr668
View details for Web of Science ID 000300043200023
View details for PubMedID 22155871
View details for PubMedCentralID PMC3268241
Analyzing epigenome data in context of genome evolution and human diseases.
Methods in molecular biology (Clifton, N.J.)
2012; 856: 431-467
This chapter describes bioinformatic tools for analyzing epigenome differences between species and in diseased versus normal cells. We illustrate the interplay of several Web-based tools in a case study of CpG island evolution between human and mouse. Starting from a list of orthologous genes, we use the Galaxy Web service to obtain gene coordinates for both species. These data are further analyzed in EpiGRAPH, a Web-based tool that identifies statistically significant epigenetic differences between genome region sets. Finally, we outline how the use of the statistical programming language R enables deeper insights into the epigenetics of human diseases, which are difficult to obtain without writing custom scripts. In summary, our tutorial describes how Web-based tools provide an easy entry into epigenome data analysis while also highlighting the benefits of learning a scripting language in order to unlock the vast potential of public epigenome datasets.
View details for DOI 10.1007/978-1-61779-585-5_18
View details for PubMedID 22399470
Genomic Distribution and Inter-Sample Variation of Non-CpG Methylation across Human Cell Types
2011; 7 (12)
DNA methylation plays an important role in development and disease. The primary sites of DNA methylation in vertebrates are cytosines in the CpG dinucleotide context, which account for roughly three quarters of the total DNA methylation content in human and mouse cells. While the genomic distribution, inter-individual stability, and functional role of CpG methylation are reasonably well understood, little is known about DNA methylation targeting CpA, CpT, and CpC (non-CpG) dinucleotides. Here we report a comprehensive analysis of non-CpG methylation in 76 genome-scale DNA methylation maps across pluripotent and differentiated human cell types. We confirm non-CpG methylation to be predominantly present in pluripotent cell types and observe a decrease upon differentiation and near complete absence in various somatic cell types. Although no function has been assigned to it in pluripotency, our data highlight that non-CpG methylation patterns reappear upon iPS cell reprogramming. Intriguingly, the patterns are highly variable and show little conservation between different pluripotent cell lines. We find a strong correlation of non-CpG methylation and DNMT3 expression levels while showing statistical independence of non-CpG methylation from pluripotency associated gene expression. In line with these findings, we show that knockdown of DNMTA and DNMT3B in hESCs results in a global reduction of non-CpG methylation. Finally, non-CpG methylation appears to be spatially correlated with CpG methylation. In summary these results contribute further to our understanding of cytosine methylation patterns in human cells using a large representative sample set.
View details for DOI 10.1371/journal.pgen.1002389
View details for Web of Science ID 000299167900009
View details for PubMedID 22174693
View details for PubMedCentralID PMC3234221
- Learning from Past Treatments and Their Outcome Improves Prediction of In Vivo Response to Anti-HIV Therapy STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY 2011; 10 (1)
Quantitative comparison of genome-wide DNA methylation mapping technologies
2010; 28 (10): 1106-U196
DNA methylation plays a key role in regulating eukaryotic gene expression. Although mitotically heritable and stable over time, patterns of DNA methylation frequently change in response to cell differentiation, disease and environmental influences. Several methods have been developed to map DNA methylation on a genomic scale. Here, we benchmark four of these approaches by analyzing two human embryonic stem cell lines derived from genetically unrelated embryos and a matched pair of colon tumor and adjacent normal colon tissue obtained from the same donor. Our analysis reveals that methylated DNA immunoprecipitation sequencing (MeDIP-seq), methylated DNA capture by affinity purification (MethylCap-seq), reduced representation bisulfite sequencing (RRBS) and the Infinium HumanMethylation27 assay all produce accurate DNA methylation data. However, these methods differ in their ability to detect differentially methylated regions between pairs of samples. We highlight strengths and weaknesses of the four methods and give practical recommendations for the design of epigenomic case-control studies.
View details for DOI 10.1038/nbt.1681
View details for Web of Science ID 000282870500033
View details for PubMedID 20852634
View details for PubMedCentralID PMC3066564