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Dor, Y.

Publications and source records attributed to Dor, Y..

17 recordsLinked to original sources

Systematic errors in enzymatic conversion limit cell-free DNA methylation specificity

Enzymatic methylation sequencing (EM-seq) converts unmethylated cytosines to uracils while preserving DNA integrity, making it attractive for liquid biopsies. Here we report a reproducible fragment-level over-conversion error in EM-seq, which is not observed in bisulfite-based conversion or in Oxford Nanopore sequencing. While bisulfite and nanopore errors occur at sporadic CpGs, EM-seq generates molecules that appear fully unmethylated, introducing a false-positive background signal that severely limits deconvolution specificity in cfDNA analysis.

genomics↗

Cell-to-cell variability and gain of methylation at polycomb CpG islands as a hallmark of aging

Aging is a complex multifactorial process that affects cellular function and tissue homeostasis over time. Despite extensive research, the molecular mechanisms driving cellular aging remain poorly understood1,2. Many studies have focused on changes in DNA methylation as an indicator of aging3. In particular, the degree of methylation at polycomb CpG islands has been shown to be predictive of phenotypic changes associated with aging4,5. Since many age-related pathological processes, are thought to be of single-cell origin (e.g. cancer), we questioned whether polycomb DNA methylation also occurs preferentially in a subset of cells within the overall population. Using single-cell whole-genome methylation data from multiple ages and tissues, we identify Average Polycomb CpG Methylation as a hallmark of cellular aging. This revealed that aging occurs at varying rates within specific cells, with faster proliferating cells showing accelerated levels. Gene expression analysis in "young" and "old" single cells identified changes in immune response, translation regulation, tumorigenesis, neurodegeneration and other cellular processes associated with aging. These results challenge traditional models of homogeneous cellular aging and suggest that aging itself is a highly individualized process at the single-cell level that may be driven by programmed changes in polycomb CpG island DNA methylation.

genomics↗

The genetic basis for DNA methylation variation across tissues and development

The mechanisms by which genetic variation shapes the epigenome across cell types and developmental stages have remained elusive. Here, we define a unifying developmental framework for DNA methylation programming, grounded in genome-wide methylation and genetic variation data from both mouse and human. In mice, we identify thousands of differentially methylated regions (DMRs) linked to sequence polymorphisms that disrupt transcription factor binding. These DMRs are programmed either during implantation or later in organogenesis, revealing two distinct periods of epigenetic regulation. Extending this logic to humans, we analyze our atlas of over 200 WGBS samples from 39 purified cell types and map 33,574 regions where common SNPs control allele-specific methylation. These include both early-established and cell-type-specific loci, many of which colocalize with eQTLs, enhancers, silencers, and disease-associated variants. Our results uncover a widespread mechanism by which genetic variation influences the regulatory landscape, linking sequence, methylation, and transcription across tissues. This cross-species atlas of sequence-dependent methylation not only clarifies the logic and timing of epigenetic programming, but also provides a foundational resource for deciphering non-coding variants in development, complex disease, and regenerative medicine.

genomics↗

Time is encoded by methylation changes at clustered CpG sites

Age-dependent changes in DNA methylation allow chronological and biological age inference, but the underlying mechanisms remain unclear. Using ultra-deep sequencing of >300 blood samples from healthy individuals, we show that age-dependent DNA methylation changes are regional and occur at multiple adjacent CpG sites, either stochastically or in a coordinated block-like manner. Deep learning analysis of single-molecule patterns in two genomic loci achieved accurate age prediction with a median error of 1.46-1.7 years on held-out human blood samples, dramatically improving current epigenetic clocks. Factors such as gender, BMI, smoking and other measures of biological aging do not affect chronological age inference. Longitudinal 10-year samples revealed that early deviations from epigenetic age are maintained throughout life and subsequent changes faithfully record time. Lastly, the model inferred chronological age from as few as 50 DNA molecules, suggesting that age is encoded by individual cells. Overall, DNA methylation changes in clustered CpG sites illuminate the principles of time measurement by cells and tissues, and facilitate medical and forensic applications. O_FIG O_LINKSMALLFIG WIDTH=174 HEIGHT=200 SRC="FIGDIR/small/626674v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@c4dd52org.highwire.dtl.DTLVardef@9e6e62org.highwire.dtl.DTLVardef@160f8c3org.highwire.dtl.DTLVardef@16be7a3_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

Atlas of imprinted and allele-specific DNA methylation in the human body

Allele-specific DNA methylation, determined genetically or epigenetically, is involved in gene regulation and underlies multiple pathologies. Yet, our knowledge of this phenomenon is partial, and largely limited to blood lineages. Here, we present a comprehensive atlas of allele-specific DNA methylation, using deep whole-genome sequencing across 39 normal human cell types. We identified 325k genomic regions, covering 6% of the genome and containing 11% of all CpG sites, that show a bimodal distribution of methylated and unmethylated molecules. In 34K of these regions, genetic variations at individual alleles segregate with methylation patterns, thus validating allele-specific methylation. We also identified 460 regions showing parentally-imprinted methylation, the majority of which were not previously reported. Surprisingly, sequence-dependent and parent-dependent methylation patterns are often restricted to specific cell types, revealing unappreciated variation in the human allele-specific methylation across the human body. The atlas provides a resource for studying allele-specific methylation and regulatory mechanisms underlying imprinted expression in specific human cell types. HighlightsO_LIA comprehensive atlas of allele-specific methylation in primary human cell types C_LIO_LI325k genomic regions show a bimodal pattern of of hyper- and hypo-methylation of DNA C_LIO_LIAllele-specific methylation at 34k genomic regions C_LIO_LITissue-specific effects at known imprinting control regions (ICRs) C_LIO_LI100s of novel loci exhibiting parentally-imprinted methylation C_LIO_LIParentally-imprinting methylation is often cell-type-specific C_LI

genomics↗

Epigenetic liquid biopsies reveal elevated vascular endothelial cell turnover and erythropoiesis in asymptomatic COVID-19 patients

The full spectrum of tissues affected by SARS-CoV-2 infection is crucial for deciphering the heterogenous clinical course of COVID-19. Here, we analyzed DNA methylation and histone modification patterns in circulating chromatin to assess cell type-specific turnover in severe and asymptomatic COVID-19 patients, in relation to clinical outcome. Patients with severe COVID-19 had a massive elevation of circulating cell-free DNA (cfDNA) levels, which originated in lung epithelial cells, cardiomyocytes, vascular endothelial cells and erythroblasts, suggesting increased cell death or turnover in these tissues. The immune response to infection was reflected by elevated B cell and monocyte/macrophage cfDNA levels, and by evidence of an interferon response in cells prior to cfDNA release. Strikingly, monocyte/macrophage cfDNA levels (but not monocyte counts), as well as lung epithelium cfDNA and vascular endothelial cfDNA, predicted clinical deterioration and duration of hospitalization. Asymptomatic patients had elevated levels of immune-derived cfDNA but did not show evidence of pulmonary or cardiac damage. Surprisingly, these patients showed elevated levels of vascular endothelial cell and erythroblast cfDNA, suggesting that sub-clinical vascular and erythrocyte turnover are universal features of COVID-19, independent of disease severity. Epigenetic liquid biopsies provide non-invasive means of monitoring COVID-19 patients, and reveal sub-clinical vascular damage and red blood cell turnover.

microbiology↗

What fraction of cellular DNA turnover becomes cfDNA?

Cell-free DNA (cfDNA) tests use small amounts of DNA in the bloodstream as biomarkers. While it is thought that cfDNA is largely released by dying cells, the proportion of dying cells DNA that reaches the bloodstream is unknown. Here, we integrate estimates of cellular turnover rates to calculate the expected amount of cfDNA. By comparing this to the actual amount of cell type-specific cfDNA, we estimate the proportion of DNA reaching plasma as cfDNA. We demonstrate that <10% of the DNA from dying cells is detectable in plasma, and the ratios of measured to expected cfDNA levels vary a thousand-fold among cell types, often reaching well below 0.1%. The analysis suggests that local clearance, presumably via phagocytosis, takes up most of the dying cells DNA. Insights into the underlying mechanism may help to understand the physiological significance of cfDNA and improve the sensitivity of liquid biopsies.

systems biology↗

Accurate age prediction from blood using of small set of DNA methylation sites and a cohort-based machine learning algorithm

Chronological age prediction from DNA methylation sheds light on human aging, indicates poor health and predicts lifespan. Current clocks are mostly based on linear models from hundreds of methylation sites, and are not suitable for sequencing-based data. We present GP-age, an epigenetic clock for blood, that uses a non-linear cohort-based model of 11,910 blood methylomes. Using 30 CpG sites alone, GP-age outperforms state-of-the-art models, with a median accuracy of ~2 years on held-out blood samples, for both array and sequencing-based data. We show that aging-related changes occur at multiple neighboring CpGs, with far-reaching implications on aging research at the cellular level. By training three independent clocks, we show consistent deviations between predicted and actual age, suggesting individual rates of biological aging. Overall, we provide a compact yet accurate alternative to array-based clocks for blood, with future applications in longitudinal aging research, forensic profiling, and monitoring epigenetic processes in transplantation medicine and cancer. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=158 HEIGHT=200 SRC="FIGDIR/small/524874v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@14eb626org.highwire.dtl.DTLVardef@98aecdorg.highwire.dtl.DTLVardef@1fc2ca1org.highwire.dtl.DTLVardef@d6138f_HPS_FORMAT_FIGEXP M_FIG C_FIG O_LIMachine learning analysis of a large cohort (~12K) of DNA methylomes from blood C_LIO_LIA 30-CpG regression model achieves a 2.1-year median error in predicting age C_LIO_LIImproved accuracy ([&ge;]1.75 years) from sequencing data, using neighboring CpGs C_LIO_LIPaves the way for easy and accurate age prediction from blood, using NGS data C_LI MotivationEpigenetic clocks that predict age from DNA methylation are a valuable tool in the research of human aging, with additional applications in forensic profiling, disease monitoring, and lifespan prediction. Most existing epigenetic clocks are based on linear models and require hundreds of methylation sites. Here, we present a compact epigenetic clock for blood, which outperforms state-of-the-art models using only 30 CpG sites. Finally, we demonstrate the applicability of our clock to sequencing-based data, with far reaching implications for a better understanding of epigenetic aging.

bioinformatics↗

Disrupted RNA editing in beta cells mimics early stage type 1 diabetes

A major hypothesis for the etiology of type 1 diabetes (T1D) postulates initiation by viral infection, leading to double-stranded RNA (dsRNA)-mediated interferon response; however, a causal virus has not been identified. Here we use a mouse model, corroborated with human data, to demonstrate that endogenous dsRNA in beta-cells can lead to a diabetogenic immune response, thus identifying a virus-independent mechanism for T1D initiation. We found that disruption of the RNA editing enzyme ADAR in beta-cells triggers a massive interferon response, islet inflammation and beta-cell failure, with features bearing striking similarity to early-stage human T1D. Glycolysis via calcium enhances the interferon response, suggesting an actionable vicious cycle of inflammation and increased beta-cell workload. One sentence summaryAdar inactivation in beta-cells triggers a glucose-dependent interferon response causing insulitis and diabetes

cell biology↗

Species-specific roles for the MAFA and MAFB transcription factors in regulatingislet β cell identity

Type 2 diabetes (T2D) is associated with compromised identity of insulin-producing pancreatic islet beta ({beta}) cells, characterized by inappropriate production of other islet cell-enriched hormones. Here we examined how hormone misexpression was influenced by the MAFA and MAFB transcription factors, closely related proteins that maintain islet cell function. Mice specifically lacking MafA in {beta} cells demonstrated broad, population-wide changes in hormone gene expression with an overall gene signature closely resembling islet gastrin (Gast)-positive cells generated under conditions of chronic hyperglycemia and obesity. A human {beta} cell line deficient in MAFB, but not one lacking MAFA, also produced a gastrin (GAST)-positive gene expression pattern. In addition, GAST was detected in human T2D {beta} cells with low levels of MAFB. Moreover, evidence is provided that human MAFB can directly repress GAST gene transcription. These results support a novel, species-specific role for MafA and MAFB in maintaining adult mouse and human {beta} cell identity, respectively, by repressing expression of Gast/GAST and other non-{beta} cell hormones.

cell biology↗

Megakaryocyte and erythroblast DNA in plasma and platelets

Circulating cell-free DNA (cfDNA) fragments are a biological analyte with extensive utility in diagnostic medicine. Understanding the source of cfDNA and mechanisms of release is crucial for designing and interpreting cfDNA-based liquid biopsy assays. Using cell type-specific methylation markers as well as genome-wide methylation analysis, we determined that megakaryocytes, the precursors of anuclear platelets, are major contributors to cfDNA ([~]26%), while erythroblasts contribute 1-4% of cfDNA in healthy individuals. Surprisingly, we discovered that platelets contain genomic DNA fragments originating in megakaryocytes, contrary to the general understanding that platelets lack genomic DNA. Megakaryocyte-derived cfDNA is increased in pathologies involving increased platelet production (Essential Thrombocythemia, Idiopathic Thrombocytopenic Purpura) and decreased upon reduced platelet production due to chemotherapy-induced bone marrow suppression. Similarly, erythroblast cfDNA is reflective of erythrocyte production and is elevated in patients with Thalassemia. Megakaryocyte- and erythroblast-specific DNA methylation patterns can thus serve as novel biomarkers for pathologies involving increased or decreased thrombopoiesis and erythropoiesis, which can aid in determining the etiology of aberrant levels of erythrocytes and platelets. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/510502v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@164e8borg.highwire.dtl.DTLVardef@7eb5e1org.highwire.dtl.DTLVardef@d6ff73org.highwire.dtl.DTLVardef@11cf846_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

Development of methylation-based biomarkers for breast cancer detection by model training and validation in synthetic cell-free DNA

Circulating tumour-derived DNA (ctDNA) carries the genetic and epigenetic characteristics of the tumour from which it is derived and can give information about the biology and tissue origins of the underlying tumour. DNA methylation is an epigenetic mark that is specific to individual tissues and, as methylation profiles are disrupted in tumours, they can indicate the tissue of origin and cancer type of ctDNA. We have developed a set of methylation biomarkers for detecting breast cancer in plasma cell-free DNA (cfDNA). First, we mined publicly available methylation datasets to create synthetic methylation profiles that were modelled to reflect cfDNA from healthy subjects and cancer patients. These profiles were restricted to the most differentially methylated CpGs between breast tumour samples and haematopoietic cells. Regularised logistic regression models were trained using 10-fold cross-validation on synthetic cfDNA datasets with distinct fractions of breast tumour DNA spiked in silico into healthy cfDNA with the addition of 10% of a mix of different tissues. Initial validation with synthetic cfDNA permitted detection of breast cancer-derived DNA with as little as 0.25% tumour DNA spiked in silico into healthy subject cfDNA with an area under ROC curve (AUC) of 0.63. Performances of classifiers increased with increased fractions of spike-in tumour DNA (AUCs 0.77 and 0.93 at tumour DNA fractions 0.5% and 1% respectively). We then combined the most discriminative CpG markers from our models with methylation markers of breast cancer that had already been published to obtain a single marker set. In vitro testing of MCF-7 breast cancer cell line DNA spiked into leukocyte DNA showed highly significant correlation for individual markers between laboratory-measured and published methylation data for MCF-7 and leukocytes (R > 0.89, P < 2.2 x 10-16). These preliminary data indicate promising results for detection of breast cancer cell line DNA using this methylation marker set, which now require testing in cfDNA from breast cancer patients and healthy controls.

cancer biology↗

A human DNA methylation atlas reveals principles of cell type-specific methylation and identifies thousands of cell type-specific regulatory elements

DNA methylation is a fundamental epigenetic mark that governs chromatin organization, cell identity, and gene expression. Here we describe a human methylome atlas, based on deep whole-genome bisulfite sequencing allowing fragment-level analysis across thousands of unique markers for 39 cell types sorted from 207 healthy tissue samples. Replicates of the same cell-type are >99.5% identical, demonstrating robustness of cell identity programs to genetic variation and environmental perturbation. Unsupervised clustering of the atlas recapitulates key elements of tissue ontogeny, and identifies methylation patterns retained since gastrulation. Loci uniquely unmethylated in an individual cell type often reside in transcriptional enhancers and contain DNA binding sites for tissue-specific transcriptional regulators. Uniquely hyper-methylated loci are rare and are enriched for CpG islands, polycomb targets, and CTCF binding sites, suggesting a novel role in shaping cell type-specific chromatin looping. The atlas provides an essential resource for interpretation of disease-associated genetic variants, and a wealth of potential tissue-specific biomarkers for use in liquid biopsies. Summary paragraphDNA methylation, a fundamental epigenetic mark, governs chromatin organization and gene expression1, thus defining the molecular identity of cells and providing a window into developmental processes with wide-ranging physiologic and clinical ramifications. Current DNA methylation datasets have limitations, typically including only a fraction of methylation sites, many from cell lines that underwent massive changes in culture or from tissues containing unspecified mixtures of cells2-6. We present a human methylome atlas based on deep whole-genome bisulfite sequencing of 39 sorted, primary cell types and use this dataset to address fundamental questions in developmental biology, physiology and pathology. Biological replicates are >99.5% identical, demonstrating unappreciated robustness to genetic variation and environmental perturbations. Clustering recapitulates key elements of tissue ontogeny, identifying methylation patterns retained since gastrulation. Loci uniquely unmethylated in individual cell types identify novel transcriptional enhancers and are enriched for tissue-specific transcription factors binding motifs. In contrast, loci uniquely hyper-methylated in specific cell types are rare, enriched for CpG islands and polycomb targets, and overlap CTCF binding sites, suggesting a novel role in shaping cell-type-specific chromatin looping. Finally, the atlas facilitates fragment-level deconvolution of tissue and plasma methylomes across thousands of cell-type specific regions to quantify their individual components at unprecedented resolution. The human cell-type-specific methylation atlas provides an essential resource for studying gene regulation by defining cell-type-specific distal enhancers and regulators of 3D organization, for identifying pathological changes in DNA methylation, and for the interpretation of methylation-based liquid biopsies. O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY O_LIA deep methylation atlas of 39 human cell types, sorted from healthy samples C_LIO_LIMethylomes record developmental history of cells C_LIO_LIThousands of novel cell type-specific methylation markers C_LIO_LIHypo-methylation uncovers cell type-specific regulatory map of distal enhancers C_LIO_LIHyper-methylation across CTCF sites C_LIO_LICell type-specific biomarkers facilitate fragment-level deconvolution of tissues and cfDNA C_LI

genomics↗

Remote immune processes revealed by immune-derived circulating cell-free DNA

Blood cell counts often fail to report on immune processes occurring in remote tissues. Here we use immune cell type-specific methylation patterns in circulating cell-free DNA (cfDNA) for studying human immune cell dynamics. We characterized cfDNA released from specific immune cell types in healthy individuals (N=242), cross sectionally and longitudinally. Immune cfDNA levels had no individual steady state as opposed to blood cell counts, suggesting that cfDNA concentration reflects adjustment of cell survival to maintain homeostatic cell numbers. We also observed selective elevation of immune-derived cfDNA upon perturbations of immune homeostasis. Following influenza vaccination (N=92), B-cell-derived cfDNA levels increased prior to elevated B-cell counts and predicted efficacy of antibody production. Patients with Eosinophilic Esophagitis (N=21) and B-cell lymphoma (N=27) showed selective elevation of eosinophil and B-cell cfDNA respectively, which were undetectable by cell counts in blood. Immune-derived cfDNA provides a novel biomarker for monitoring immune responses to physiological and pathological processes that are not accessible using conventional methods.

genomics↗

ChIP-seq of plasma cell-free nucleosomes identifies cell-of-origin gene expression programs

AbstractGenomic DNA is packed by histone proteins that carry a multitude of post-translational modifications that reflect cellular transcriptional state. Cell-free DNA (cfDNA) is derived from fragmented chromatin in dying cells, and as such it retains the histones markings present in the cells of origin. Here, we pioneer chromatin immunoprecipitation followed by sequencing of cell-free nucleosomes (cfChIP-seq) carrying active chromatin marks. Our results show that cfChIP-seq provides multidimensional epigenetic information that recapitulates the epigenetic and transcriptional landscape in the cells of origin. We applied cfChIP-seq to 268 samples including samples from patients with heart and liver pathologies, and 135 samples from 56 metastatic CRC patients. We show that cfChIP-seq can detect pathology-related transcriptional changes at the site of the disease, beyond the information on tissue of origin. In CRC patients we detect clinically-relevant, and patient-specific information, including transcriptionally active HER2 amplifications. cfChIP-seq provides genome-wide information and requires low sequencing depth. Altogether, we establish cell-free chromatin immunoprecipitation as an exciting modality with potential for diagnosis and interrogation of physiological and pathological processes using a simple blood test. One Sentence SummaryChIP-seq of plasma-circulating nucleosomes (cfChIP-seq) from a simple blood test provides detailed information about gene expression programs in human organs, and cancer.

molecular biology↗

Comprehensive human cell-type methylation atlas reveals origins of circulating cell-free DNA in health and disease

Methylation patterns of circulating cell-free DNA (cfDNA) contain rich information about recent cell death events in the body. Here, we present an approach for unbiased determination of the tissue origins of cfDNA, using a reference methylation atlas of 25 human tissues and cell types. The method is validated using in silico simulations as well as in vitro mixes of DNA from different tissue sources at known proportions. We show that plasma cfDNA of healthy donors originates from white blood cells (55%), erythrocyte progenitors (30%), vascular endothelial cells (10%) and hepatocytes (1%). Deconvolution of cfDNA from patients reveals tissue contributions that agree with clinical findings in sepsis, islet transplantation, cancer of the colon, lung, breast and prostate, and cancer of unknown primary. We propose a procedure which can be easily adapted to study the cellular contributors to cfDNA in many settings, opening a broad window into healthy and pathologic human tissue dynamics.

molecular biology↗

Specific detection of cell-free DNA derived from intestinal epithelial cells using methylation patterns

Epithelial cells of the intestine undergo rapid turnover and are thought to be cleared via stool. Disruption of tissue architecture, as occurs in colorectal cancer (CRC), results in the release of material from dying intestinal epithelial cells to blood. This phenomenon could be utilized for diagnosis and monitoring of intestinal diseases, if circulating cell-free DNA (cfDNA) derived from intestinal cells could be specifically identified. Here we describe two genomic loci that are unmethylated specifically in intestinal epithelial cells, allowing for sensitive and specific detection of DNA derived from such cells. As expected, intestinal DNA is found in stool, but not in plasma, of healthy individuals. Patients with inflammatory bowel disease (IBD) have minimal amounts of intestinal cfDNA in the plasma, whereas patients with advanced CRC show a strong signal. The intestinal markers are not elevated in plasma samples from patients with pancreatic ductal adenocarcinoma (PDAC), and a combination of intestine- and pancreas-specific markers allowed for robust differentiation between plasma cfDNA derived from CRC and PDAC patients. Intestinal DNA markers provide a mutation-independent tool for monitoring intestinal dynamics in health and disease.

developmental biology↗