The International Human Epigenome Consortium: A Blueprint for Scientific Collaboration and Discovery

An ATAC-seq atlas of chromatin accessibility in mouse tissues

The Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) is a fundamental epigenomics approach and has been widely used in profiling the chromatin accessibility dynamics in multiple species. A comprehensive reference of ATAC-seq datasets for mammalian tissues is important for the understanding of regulatory specificity and developmental abnormality caused by genetic or environmental alterations. Here, we report an adult mouse ATAC-seq atlas by producing a total of 66 ATAC-seq profiles from 20 primary tissues of both male and female mice. The ATAC-seq read enrichment, fragment size distribution, and reproducibility between replicates demonstrated the high quality of the full dataset. We identified a total of 296,574 accessible elements, of which 26,916 showed tissue-specific accessibility. Further, we identified key transcription factors specific to distinct tissues and found that the enrichment of each motif reflects the developmental similarities across tissues. In summary, our study provides an important resource on the mouse epigenome and will be of great importance to various scientific disciplines such as development, cell reprogramming, and genetic disease.

Partially methylated domains are hypervariable in breast cancer and fuel widespread CpG island hypermethylation

Global loss of DNA methylation and CpG island (CGI) hypermethylation are key epigenomic aberrations in cancer. Global loss manifests itself in partially methylated domains (PMDs) which extend up to megabases. However, the distribution of PMDs within and between tumor types, and their effects on key functional genomic elements including CGIs are poorly defined. We comprehensively show that loss of methylation in PMDs occurs in a large fraction of the genome and represents the prime source of DNA methylation variation. PMDs are hypervariable in methylation level, size and distribution, and display elevated mutation rates. They impose intermediate DNA methylation levels incognizant of functional genomic elements including CGIs, underpinning a CGI methylator phenotype (CIMP). Repression effects on tumor suppressor genes are negligible as they are generally excluded from PMDs. The genomic distribution of PMDs reports tissue-of-origin and may represent tissue-specific silent regions which tolerate instability at the epigenetic, transcriptomic and genetic level.

Disruption of the Sensory System Affects Sterile Cutaneous Inflammation In Vivo

Increasing evidence suggests that nerve fibers responding to noxious stimuli (nociceptors) modulate immunity in a variety of tissues, including the skin. Yet, the role of nociceptors in regulating sterile cutaneous inflammation remains unexplored. To address this question, we have developed a detailed description of the sterile inflammation caused by overexposure to UVB irradiation (i.e., sunburn) in the mouse plantar skin. Using this model, we observed that chemical depletion of nociceptor terminals did not alter the early phase of the inflammatory response to UVB, but it caused a significant increase in the number of dendritic cells and αβ⁺ T cells as well as enhanced extravasation during the later stages of inflammation. Finally, we showed that such regulation was driven by the nociceptive neuropeptide calcitonin gene-related peptide. In conclusion, we propose that nociceptors not only play a crucial role in inflammation through avoidance reflexes and behaviors, but can also regulate sterile cutaneous immunity in vivo.

Does the Presence of Transposable Elements Impact the Epigenetic Environment of Human Duplicated Genes?

Epigenetic modifications have an important role to explain part of the intra- and inter-species variation in gene expression. They also have a role in the control of transposable elements (TEs) whose activity may have a significant impact on genome evolution by promoting various mutations, which are expected to be mostly deleterious. A change in the local epigenetic landscape associated with the presence of TEs is expected to affect the expression of neighboring genes since these modifications occurring at TE sequences can spread to neighboring sequences. In this work, we have studied how the epigenetic modifications of genes are conserved and what the role of TEs is in this conservation. For that, we have compared the conservation of the epigenome associated with human duplicated genes and the differential presence of TEs near these genes. Our results show higher epigenome conservation of duplicated genes from the same family when they share similar TE environment, suggesting a role for the differential presence of TEs in the evolutionary divergence of duplicates through variation in the epigenetic landscape.

RnBeads 2.0: comprehensive analysis of DNA methylation data

DNA methylation is a widely investigated epigenetic mark with important roles in development and disease. High-throughput assays enable genome-scale DNA methylation analysis in large numbers of samples. Here, we describe a new version of our RnBeads software - an R/Bioconductor package that implements start-to-finish analysis workflows for Infinium microarrays and various types of bisulfite sequencing. RnBeads 2.0 (https://rnbeads.org/) provides additional data types and analysis methods, new functionality for interpreting DNA methylation differences, improved usability with a novel graphical user interface, and better use of computational resources. We demonstrate RnBeads 2.0 in four re-runnable use cases focusing on cell differentiation and cancer.

Identification of rheumatoid arthritis causal genes using functional genomics

Over the past decade, genome-wide association studies have contributed a wealth of knowledge to our understanding of polygenic disorders such as rheumatoid arthritis. As the size of sample cohorts has improved so too have the computational and experimental methods used to robustly define variants associated with disease susceptibility. The challenge now remains to translate these findings into improved understanding of disease aetiology and patient care. Whilst much of the focus of translating the findings of genome-wide association studies has been on global analysis of all variants identified, careful functional study of individual disease susceptibility loci will be required in order to refine our understanding of how individual variants contribute to disease risk. Here, we present the argument behind such an approach and describe some of the novel tools being used to investigate risk loci. This includes the use of chromosomal conformation capture techniques and modifications of the CRISPR-Cas9 system, with several examples of their implementation being described.

Chromatin capture links the metabolic enzyme AHCY to stem cell proliferation

Profiling the chromatin-bound proteome (chromatome) in a simple, direct, and reliable manner might be key to uncovering the role of yet uncharacterized chromatin factors in physiology and disease. Here, we have designed an experimental strategy to survey the chromatome of proliferating cells by using the DNA-mediated chromatin pull-down (Dm-ChP) technology. Our approach provides a global view of cellular chromatome under normal physiological conditions and enables the identification of chromatin-bound proteins de novo. Integrating Dm-ChP with genomic and functional data, we have discovered an unexpected chromatin function for adenosylhomocysteinase, a major one-carbon pathway metabolic enzyme, in gene activation. Our study reveals a new regulatory axis between the metabolic state of pluripotent cells, ribosomal protein production, and cell division during the early phase of embryo development, in which the metabolic flux of methylation reactions is favored in a local milieu.

NCBoost classifies pathogenic non-coding variants in Mendelian diseases through supervised learning on purifying selection signals in humans

State-of-the-art methods assessing pathogenic non-coding variants have mostly been characterized on common disease-associated polymorphisms, yet with modest accuracy and strong positional biases. In this study, we curated 737 high-confidence pathogenic non-coding variants associated with monogenic Mendelian diseases. In addition to interspecies conservation, a comprehensive set of recent and ongoing purifying selection signals in humans is explored, accounting for lineage-specific regulatory elements. Supervised learning using gradient tree boosting on such features achieves a high predictive performance and overcomes positional bias. NCBoost performs consistently across diverse learning and independent testing data sets and outperforms other existing reference methods.

Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage

Tissue fibrosis is a major cause of mortality that results from the deposition of matrix proteins by an activated mesenchyme. Macrophages accumulate in fibrosis, but the role of specific subgroups in supporting fibrogenesis has not been investigated in vivo. Here we used single-cell RNA sequencing (scRNA-seq) to characterize the heterogeneity of macrophages in bleomycin-induced lung fibrosis in mice. A novel computational framework for the annotation of scRNA-seq by reference to bulk transcriptomes (SingleR) enabled the subclustering of macrophages and revealed a disease-associated subgroup with a transitional gene expression profile intermediate between monocyte-derived and alveolar macrophages. These CX3CR1⁺SiglecF⁺ transitional macrophages localized to the fibrotic niche and had a profibrotic effect in vivo. Human orthologues of genes expressed by the transitional macrophages were upregulated in samples from patients with idiopathic pulmonary fibrosis. Thus, we have identified a pathological subgroup of transitional macrophages that are required for the fibrotic response to injury.

Comprehensive Analysis of Chromatin States in Atypical Teratoid/Rhabdoid Tumor Identifies Diverging Roles for SWI/SNF and Polycomb in Gene Regulation

Biallelic inactivation of SMARCB1, encoding a member of the SWI/SNF chromatin remodeling complex, is the hallmark genetic aberration of atypical teratoid rhabdoid tumors (ATRT). Here, we report how loss of SMARCB1 affects the epigenome in these tumors. Using chromatin immunoprecipitation sequencing (ChIP-seq) on primary tumors for a series of active and repressive histone marks, we identified the chromatin states differentially represented in ATRTs compared with other brain tumors and non-neoplastic brain. Re-expression of SMARCB1 in ATRT cell lines enabled confirmation of our genome-wide findings for the chromatin states. Additional generation of ChIP-seq data for SWI/SNF and Polycomb group proteins and the transcriptional repressor protein REST determined differential dependencies of SWI/SNF and Polycomb complexes in regulation of diverse gene sets in ATRTs.

GENCODE reference annotation for the human and mouse genomes

The accurate identification and description of the genes in the human and mouse genomes is a fundamental requirement for high quality analysis of data informing both genome biology and clinical genomics. Over the last 15 years, the GENCODE consortium has been producing reference quality gene annotations to provide this foundational resource. The GENCODE consortium includes both experimental and computational biology groups who work together to improve and extend the GENCODE gene annotation. Specifically, we generate primary data, create bioinformatics tools and provide analysis to support the work of expert manual gene annotators and automated gene annotation pipelines. In addition, manual and computational annotation workflows use any and all publicly available data and analysis, along with the research literature to identify and characterise gene loci to the highest standard. GENCODE gene annotations are accessible via the Ensembl and UCSC Genome Browsers, the Ensembl FTP site, Ensembl Biomart, Ensembl Perl and REST APIs as well as https://www.gencodegenes.org.

Reveal cell type-specific regulatory elements and their characterized histone code classes via a hidden Markov model

BACKGROUND

With the maturity of next generation sequencing technology, a huge amount of epigenomic data have been generated by several large consortia in the last decade. These plenty resources leave us the opportunity about sufficiently utilizing those data to explore biological problems.

RESULTS

Here we developed an integrative and comparative method, CsreHMM, which is based on a hidden Markov model, to systematically reveal cell type-specific regulatory elements (CSREs) along the whole genome, and simultaneously recognize the histone codes (mark combinations) charactering them. This method also reveals the subclasses of CSREs and explicitly label those shared by a few cell types. We applied this method to a data set of 9 cell types and 9 chromatin marks to demonstrate its effectiveness and found that the revealed CSREs relates to different kinds of functional regulatory regions significantly. Their proximal genes have consistent expression and are likely to participate in cell type-specific biological functions.

CONCLUSIONS

These results suggest CsreHMM has the potential to help understand cell identity and the diverse mechanisms of gene regulation.

A likelihood approach to testing hypotheses on the co-evolution of epigenome and genome

Central questions to epigenome evolution include whether interspecies changes of histone modifications are independent of evolutionary changes of DNA, and if there is dependence whether they depend on any specific types of DNA sequence changes. Here, we present a likelihood approach for testing hypotheses on the co-evolution of genome and histone modifications. The gist of this approach is to convert evolutionary biology hypotheses into probabilistic forms, by explicitly expressing the joint probability of multispecies DNA sequences and histone modifications, which we refer to as a class of Joint Evolutionary Model for the Genome and the Epigenome (JEMGE). JEMGE can be summarized as a mixture model of four components representing four evolutionary hypotheses, namely dependence and independence of interspecies epigenomic variations to underlying sequence substitutions and to underlying sequence insertions and deletions (indels). We implemented a maximum likelihood method to fit the models to the data. Based on comparison of likelihoods, we inferred whether interspecies epigenomic variations depended on substitution or indels in local genomic sequences based on DNase hypersensitivity and spermatid H3K4me3 ChIP-seq data from human and rhesus macaque. Approximately 5.5% of homologous regions in the genomes exhibited H3K4me3 modification in either species, among which approximately 67% homologous regions exhibited local-sequence-dependent interspecies H3K4me3 variations. Substitutions accounted for less local-sequence-dependent H3K4me3 variations than indels. Among transposon-mediated indels, ERV1 insertions and L1 insertions were most strongly associated with H3K4me3 gains and losses, respectively. By initiating probabilistic formulation on the co-evolution of genomes and epigenomes, JEMGE helps to bring evolutionary biology principles to comparative epigenomic studies.

Epigenetic modifications play a significant role in gene regulations and thus heavily influence phenotypic outcomes. Whereas cross-species epigenomic comparisons have been fruitful in revealing the function of epigenetic modifications, it still remains unclear how the epigenome changes across species. A central question in epigenome evolution studies is whether interspecies epigenomic variations rely on genomic changes in cis and, if partially yes, whether different genomic changes have distinct impacts. To tackle this question, we initiated a likelihood-based approach, in which different hypotheses related to the co-evolution of the genome and the epigenome could be converted into probabilistic models. By fitting the models to actual data, each model yielded a likelihood, and the hypothesis corresponded to the largest likelihood was selected as most supported by observed data. In this work, we focused on the influence of two types of underlying sequence changes: substitutions, and insertions and deletions (indels). We quantitatively assessed the dependence of H3K4me3 variations on substitutions and indels between human and rhesus, and separated their relative impacts within each genomic region with H3K4me3. The methodology presented here provides a framework for modeling the epigenome together with the genome and a quantitative approach to test different evolutionary hypotheses.

Identification of differentially methylated cell types in epigenome-wide association studies

An outstanding challenge of epigenome-wide association studies (EWASs) performed in complex tissues is the identification of the specific cell type(s) responsible for the observed differential DNA methylation. Here we present a statistical algorithm called CellDMC ( https://github.com/sjczheng/EpiDISH ), which can identify differentially methylated positions and the specific cell type(s) driving the differential methylation. We validated CellDMC on in silico mixtures of DNA methylation data generated with different technologies, as well as on real mixtures from epigenome-wide association and cancer epigenome studies. CellDMC achieved over 90% sensitivity and specificity in scenarios where current state-of-the-art methods did not identify differential methylation. By applying CellDMC to an EWAS performed in buccal swabs, we identified smoking-associated differentially methylated positions occurring in the epithelial compartment, which we validated in smoking-related lung cancer. CellDMC may be useful in the identification of causal DNA-methylation alterations in disease.

Integrative analysis of single-cell expression data reveals distinct regulatory states in bidirectional promoters

Background

Bidirectional promoters (BPs) are prevalent in eukaryotic genomes. However, it is poorly understood how the cell integrates different epigenomic information, such as transcription factor (TF) binding and chromatin marks, to drive gene expression at BPs. Single-cell sequencing technologies are revolutionizing the field of genome biology. Therefore, this study focuses on the integration of single-cell RNA-seq data with bulk ChIP-seq and other epigenetics data, for which single-cell technologies are not yet established, in the context of BPs.

Results

We performed integrative analyses of novel human single-cell RNA-seq (scRNA-seq) data with bulk ChIP-seq and other epigenetics data. scRNA-seq data revealed distinct transcription states of BPs that were previously not recognized. We find associations between these transcription states to distinct patterns in structural gene features, DNA accessibility, histone modification, DNA methylation and TF binding profiles.

Conclusions

Our results suggest that a complex interplay of all of these elements is required to achieve BP-specific transcriptional output in this specialized promoter configuration. Further, our study implies that novel statistical methods can be developed to deconvolute masked subpopulations of cells measured with different bulk epigenomic assays using scRNA-seq data.

Electronic supplementary material

The online version of this article (10.1186/s13072-018-0236-7) contains supplementary material, which is available to authorized users.

Genome-wide association study implicates immune dysfunction in the development of Hodgkin lymphoma

To further our understanding of inherited susceptibility to Hodgkin lymphoma (HL), we performed a meta-analysis of 7 genome-wide association studies totaling 5325 HL cases and 22 423 control patients. We identify 5 new HL risk loci at 6p21.31 (rs649775; P = 2.11 × 10-10), 6q23.3 (rs1002658; P = 2.97 × 10-8), 11q23.1 (rs7111520; P = 1.44 × 10-11), 16p11.2 (rs6565176; P = 4.00 × 10-8), and 20q13.12 (rs2425752; P = 2.01 × 10-8). Integration of gene expression, histone modification, and in situ promoter capture Hi-C data at the 5 new and 13 known risk loci implicates dysfunction of the germinal center reaction, disrupted T-cell differentiation and function, and constitutive NF-κB activation as mechanisms of predisposition. These data provide further insights into the genetic susceptibility and biology of HL.

Deciphering the Emerging Complexities of Molecular Mechanisms at GWAS Loci

Genome-wide association studies (GWASs) have identified thousands of loci associated with hundreds of complex diseases and traits, and progress is being made toward elucidating the causal variants and genes underlying these associations. Functional characterization of mechanisms at GWAS loci is a multi-faceted challenge. Challenges include linkage disequilibrium and allelic heterogeneity at each locus, the noncoding nature of most loci, and the time and cost needed for experimentally evaluating the potential mechanistic contributions of genes and variants. As GWAS sample sizes increase, more loci are identified, and the complexities of individual loci emerge. Loci can consist of multiple association signals, each of which can reflect the influence of multiple variants, inseparable by association analyses. Each signal within a locus can influence the same or different target genes. Experimental studies of genes and variants can differ on the basis of cell type, cellular environment, or other context-specific variables. In this review, we describe the complexity of mechanisms at GWAS loci-including multiple signals, multiple variants, and/or multiple genes-and the implications these complexities hold for experimental study design and interpretation of GWAS mechanisms.

What's in the "fold"?

Complexity in genome architecture determines how gene expression programs are established, maintained, and modified from early developmental stages to normal adult phenotypes. Large scale and hierarchical organization of the genome impacts various aspects of cell functions, ranging from X-chromosome inactivation, stem-cell fate determination to transcription, DNA replication, and cellular repair. While chromatin loops and topologically-associated domains represent a basic structural or fundamental unit of chromatin organization, spatio-temporal organization of the genome further creates a complex network of interacting genome patterns, forming chromosomal compartments and chromosome territories. The understanding of human diseases, including cancers, auto-immune disorders, Alzheimer's, and cardiovascular diseases, relies on the associated molecular and epigenetic mechanisms. There is a growing interest in the impact of three-dimensional chromatin folding upon the genome structure and function, which gives rise to the question "What's in the fold?" and is the main focus of this review. Here we discuss the principles determining the spatial and regulatory relationships between gene regulation and three-dimensional chromatin landscapes, and how changes in chromatin-folding could influence the outcome of genome function in healthy and disease states.

A Statistical Method for Observing Personal Diploid Methylomes and Transcriptomes with Single-Molecule Real-Time Sequencing

We address the problem of observing personal diploid methylomes, CpG methylome pairs of homologous chromosomes that are distinguishable with respect to phased heterozygous variants (PHVs), which is challenging due to scarcity of PHVs in personal genomes. Single molecule real-time (SMRT) sequencing is promising as it outputs long reads with CpG methylation information, but a serious concern is whether reliable PHVs are available in erroneous SMRT reads with an error rate of ∼15%. To overcome the issue, we propose a statistical model that reduces the error rate of phasing CpG site to 1%, thereby calling CpG hypomethylation in each haplotype with >90% precision and sensitivity. Using our statistical model, we examined GNAS complex locus known for a combination of maternally, paternally, or biallelically expressed isoforms, and observed allele-specific methylation pattern almost perfectly reflecting their respective allele-specific expression status, demonstrating the merit of elucidating comprehensive personal diploid methylomes and transcriptomes.

TFmapper: A Tool for Searching Putative Factors Regulating Gene Expression Using ChIP-seq Data

Background: Next-generation sequencing coupled to chromatin immunoprecipitation (ChIP-seq), DNase I hypersensitivity (DNase-seq) and the transposase-accessible chromatin assay (ATAC-seq) has generated enormous amounts of data, markedly improved our understanding of the transcriptional and epigenetic control of gene expression. To take advantage of the availability of such datasets and provide clues on what factors, including transcription factors, epigenetic regulators and histone modifications, potentially regulates the expression of a gene of interest, a tool for simultaneous queries of multiple datasets using symbols or genomic coordinates as search terms is needed.

Results: In this study, we annotated the peaks of thousands of ChIP-seq datasets generated by ENCODE project, or ChIP-seq/DNase-seq/ATAC-seq datasets deposited in Gene Expression Omnibus (GEO) and curated by Cistrome project; We built a MySQL database called TFmapper containing the annotations and associated metadata, allowing users without bioinformatics expertise to search across thousands of datasets to identify factors targeting a genomic region/gene of interest in a specified sample through a web interface. Users can also visualize multiple peaks in genome browsers and download the corresponding sequences.

Conclusion: TFmapper will help users explore the vast amount of publicly available ChIP-seq/DNase-seq/ATAC-seq data and perform integrative analyses to understand the regulation of a gene of interest. The web server is freely accessible at http://www.tfmapper.org/.

A Cosine Similarity-Based Method to Infer Variability of Chromatin Accessibility at the Single-Cell Level

Cellular identity between generations of developing cells is propagated through the epigenome particularly via the accessible parts of the chromatin. It is now possible to measure chromatin accessibility at single-cell resolution using single-cell assay for transposase accessible chromatin (scATAC-seq), which can reveal the regulatory variation behind the phenotypic variation. However, single-cell chromatin accessibility data are sparse, binary, and high dimensional, leading to unique computational challenges. To overcome these difficulties, we developed PRISM, a computational workflow that quantifies cell-to-cell chromatin accessibility variation while controlling for technical biases. PRISM is a novel multidimensional scaling-based method using angular cosine distance metrics coupled with distance from the spatial centroid. PRISM takes differences in accessibility at each genomic region between single cells into account. Using data generated in our lab and publicly available, we showed that PRISM outperforms an existing algorithm, which relies on the aggregate of signal across a set of genomic regions. PRISM showed robustness to noise in cells with low coverage for measuring chromatin accessibility. Our approach revealed the previously undetected accessibility variation where accessible sites differ between cells but the total number of accessible sites is constant. We also showed that PRISM, but not an existing algorithm, can find suppressed heterogeneity of accessibility at CTCF binding sites. Our updated approach uncovers new biological results with profound implications on the cellular heterogeneity of chromatin architecture.

Epigenetic prediction of response to anti-PD-1 treatment in non-small-cell lung cancer: a multicentre, retrospective analysis

BACKGROUND

Anti-programmed death-1 (PD-1) treatment for advanced non-small-cell lung cancer (NSCLC) has improved the survival of patients. However, a substantial percentage of patients do not respond to this treatment. We examined the use of DNA methylation profiles to determine the efficacy of anti-PD-1 treatment in patients recruited with current stage IV NSCLC.

METHODS

In this multicentre study, we recruited adult patients from 15 hospitals in France, Spain, and Italy who had histologically proven stage IV NSCLC and had been exposed to PD-1 blockade during the course of the disease. The study structure comprised a discovery cohort to assess the correlation between epigenetic features and clinical benefit with PD-1 blockade and two validation cohorts to assess the validity of our assumptions. We first established an epigenomic profile based on a microarray DNA methylation signature (EPIMMUNE) in a discovery set of tumour samples from patients treated with nivolumab or pembrolizumab. The EPIMMUNE signature was validated in an independent set of patients. A derived DNA methylation marker was validated by a single-methylation assay in a validation cohort of patients. The main study outcomes were progression-free survival and overall survival. We used the Kaplan-Meier method to estimate progression-free and overall survival, and calculated the differences between the groups with the log-rank test. We constructed a multivariate Cox model to identify the variables independently associated with progression-free and overall survival.

FINDINGS

Between June 23, 2014, and May 18, 2017, we obtained samples from 142 patients: 34 in the discovery cohort, 47 in the EPIMMUNE validation cohort, and 61 in the derived methylation marker cohort (the T-cell differentiation factor forkhead box P1 [FOXP1]). The EPIMMUNE signature in patients with stage IV NSCLC treated with anti-PD-1 agents was associated with improved progression-free survival (hazard ratio [HR] 0·010, 95% CI 3·29 × 10^-4-0·0282; p=0·0067) and overall survival (0·080, 0·017-0·373; p=0·0012). The EPIMMUNE-positive signature was not associated with PD-L1 expression, the presence of CD8+ cells, or mutational load. EPIMMUNE-negative tumours were enriched in tumour-associated macrophages and neutrophils, cancer-associated fibroblasts, and senescent endothelial cells. The EPIMMUNE-positive signature was associated with improved progression-free survival in the EPIMMUNE validation cohort (0·330, 0·149-0·727; p=0·0064). The unmethylated status of FOXP1 was associated with improved progression-free survival (0·415, 0·209-0·802; p=0·0063) and overall survival (0·409, 0·220-0·780; p=0·0094) in the FOXP1 validation cohort. The EPIMMUNE signature and unmethylated FOXP1 were not associated with clinical benefit in lung tumours that did not receive immunotherapy.

INTERPRETATION

Our study shows that the epigenetic milieu of NSCLC tumours indicates which patients are most likely to benefit from nivolumab or pembrolizumab treatments. The methylation status of FOXP1 could be associated with validated predictive biomarkers such as PD-L1 staining and mutational load to better select patients who will experience clinical benefit with PD-1 blockade, and its predictive value should be evaluated in prospective studies.

FUNDING

"Obra Social" La Caixa, Cellex Foundation, and the Health and Science Departments of the Generalitat de Catalunya.

Non-coding RNAs in cardiovascular diseases: diagnostic and therapeutic perspectives

Recent research has demonstrated that the non-coding genome plays a key role in genetic programming and gene regulation during development as well as in health and cardiovascular disease. About 99% of the human genome do not encode proteins, but are transcriptionally active representing a broad spectrum of non-coding RNAs (ncRNAs) with important regulatory and structural functions. Non-coding RNAs have been identified as critical novel regulators of cardiovascular risk factors and cell functions and are thus important candidates to improve diagnostics and prognosis assessment. Beyond this, ncRNAs are rapidly emgerging as fundamentally novel therapeutics. On a first level, ncRNAs provide novel therapeutic targets some of which are entering assessment in clinical trials. On a second level, new therapeutic tools were developed from endogenous ncRNAs serving as blueprints. Particularly advanced is the development of RNA interference (RNAi) drugs which use recently discovered pathways of endogenous short interfering RNAs and are becoming versatile tools for efficient silencing of protein expression. Pioneering clinical studies include RNAi drugs targeting liver synthesis of PCSK9 resulting in highly significant lowering of LDL cholesterol or targeting liver transthyretin (TTR) synthesis for treatment of cardiac TTR amyloidosis. Further novel drugs mimicking actions of endogenous ncRNAs may arise from exploitation of molecular interactions not accessible to conventional pharmacology. We provide an update on recent developments and perspectives for diagnostic and therapeutic use of ncRNAs in cardiovascular diseases, including atherosclerosis/coronary disease, post-myocardial infarction remodelling, and heart failure.

Epigenomic annotation-based interpretation of genomic data: from enrichment analysis to machine learning

Motivation

One of the goals of functional genomics is to understand the regulatory implications of experimentally obtained genomic regions of interest (ROIs). Most sequencing technologies now generate ROIs distributed across the whole genome. The interpretation of these genome-wide ROIs represents a challenge as the majority of them lie outside of functionally well-defined protein coding regions. Recent efforts by the members of the International Human Epigenome Consortium have generated volumes of functional/regulatory data (reference epigenomic datasets), effectively annotating the genome with epigenomic properties. Consequently, a wide variety of computational tools has been developed utilizing these epigenomic datasets for the interpretation of genomic data.

Results

The purpose of this review is to provide a structured overview of practical solutions for the interpretation of ROIs with the help of epigenomic data. Starting with epigenomic enrichment analysis, we discuss leading tools and machine learning methods utilizing epigenomic and 3D genome structure data. The hierarchy of tools and methods reviewed here presents a practical guide for the interpretation of genome-wide ROIs within an epigenomic context.

Contact

mikhail.dozmorov@vcuhealth.org.

Supplementary information

Supplementary data are available at Bioinformatics online.

A novel cell-type deconvolution algorithm reveals substantial contamination by immune cells in saliva, buccal and cervix

Aim: An outstanding challenge in epigenome studies is the estimation of cell-type proportions in complex epithelial tissues. Materials & methods: Here, we construct and validate a DNA methylation reference and algorithm for complex tissues that contain epithelial, immune and nonimmune stromal cells. Results: Using this reference, we show that easily accessible tissues such as saliva, buccal and cervix exhibit substantial variation in immune cell (IC) contamination. We further validate our reference in the context of oral cancer, where it correctly predicts an increased IC infiltration in cancer but suppressed in patients with highest smoking exposure. Finally, our method can improve the specificity of differentially methylated CpG calls in epithelial cancer. Conclusion: The degree and variation of IC contamination in complex epithelial tissues is substantial. We provide a valuable resource and tool for assessing the epithelial purity and IC contamination of samples and for identifying differential methylation in such complex tissues.

Blood-based analyses of cancer: Circulating myeloid-derived suppressor cells - is a new era coming?

Progress in cancer treatment made by the beginning of the 21st century has shifted the paradigm from one-size-fits-all to tailor-made treatment. The popular vision, to study solid tumors through the relatively noninvasive sampling of blood, is one of the most thrilling and rapidly advancing fields in global cancer diagnostics. From this perspective, immune-cell analysis in cancer could play a pivotal role in oncology practice. This approach is driven both by rapid technological developments, including the analysis of circulating myeloid-derived suppressor cells (cMDSCs), and by the increasing application of (immune) therapies, the success or failure of which may depend on effective and timely measurements of relevant biomarkers. Although the implementation of these powerful noninvasive diagnostic capabilities in guiding precision cancer treatment is poised to change the ways in which we select and monitor cancer therapy, challenges remain. Here, we discuss the challenges associated with the analysis and clinical aspects of cMDSCs and assess whether the problems in implementing tumor-evolution monitoring as a global tool in personalized oncology can be overcome.

Mammary molecular portraits reveal lineage-specific features and progenitor cell vulnerabilities

Casey et al. integrate epigenomic, transcriptomic, and proteomic profiling of primary basal and luminal mammary cells to identify master epigenetic regulators of the mammary epithelium and uncover stem and progenitor cell vulnerabilities. They develop a pipeline to identify drugs that abrogate progenitor cell activity in normal and high-risk breast cancer patient samples in vitro and in vivo.

The mammary epithelium depends on specific lineages and their stem and progenitor function to accommodate hormone-triggered physiological demands in the adult female. Perturbations of these lineages underpin breast cancer risk, yet our understanding of normal mammary cell composition is incomplete. Here, we build a multimodal resource for the adult gland through comprehensive profiling of primary cell epigenomes, transcriptomes, and proteomes. We define systems-level relationships between chromatin–DNA–RNA–protein states, identify lineage-specific DNA methylation of transcription factor binding sites, and pinpoint proteins underlying progesterone responsiveness. Comparative proteomics of estrogen and progesterone receptor–positive and –negative cell populations, extensive target validation, and drug testing lead to discovery of stem and progenitor cell vulnerabilities. Top epigenetic drugs exert cytostatic effects; prevent adult mammary cell expansion, clonogenicity, and mammopoiesis; and deplete stem cell frequency. Select drugs also abrogate human breast progenitor cell activity in normal and high-risk patient samples. This integrative computational and functional study provides fundamental insight into mammary lineage and stem cell biology.

Distinct epigenomic patterns are associated with haploinsufficiency and predict risk genes of developmental disorders

Haploinsufficiency is a major mechanism of genetic risk in developmental disorders. Accurate prediction of haploinsufficient genes is essential for prioritizing and interpreting deleterious variants in genetic studies. Current methods based on mutation intolerance in population data suffer from inadequate power for genes with short transcripts. Here we show haploinsufficiency is strongly associated with epigenomic patterns, and develop a computational method (Episcore) to predict haploinsufficiency leveraging epigenomic data from a broad range of tissue and cell types by machine learning methods. Based on data from recent exome sequencing studies on developmental disorders, Episcore achieves better performance in prioritizing likely-gene-disrupting (LGD) de novo variants than current methods. We further show that Episcore is less-biased by gene size, and complementary to mutation intolerance metrics for prioritizing LGD variants. Our approach enables new applications of epigenomic data and facilitates discovery and interpretation of novel risk variants implicated in developmental disorders.

Predicting haploinsufficient genes helps to understand the genetic risk underlying developmental disorders. Here, the authors develop a Random Forest-based method that uses epigenomic data to predict haploinsufficiency, Episcore, which is complementary to methods based on mutation intolerance scores.

DeepBlueR: large-scale epigenomic analysis in R

Motivation

While large amounts of epigenomic data are publicly available, their retrieval in a form suitable for downstream analysis is a bottleneck in current research. The DeepBlue Epigenomic Data Server provides a powerful interface and API for filtering, transforming, aggregating and downloading data from several epigenomic consortia.

Results

To make public epigenomic data conveniently available for analysis in R, we developed an R/Bioconductor package that connects to the DeepBlue Epigenomic Data Server, enabling users to quickly gather and transform epigenomic data from selected experiments for analysis in the Bioconductor ecosystem.

Availability and Implementation

http://deepblue.mpi-inf.mpg.de/R.

Requirements

R 3.3, Bioconductor 3.4.

Supplementary information

Supplementary data are available at Bioinformatics online.

DNA Methylation Patterns in Normal Tissue Correlate more Strongly with Breast Cancer Status than Copy-Number Variants

Normal tissue at risk of neoplastic transformation is characterized by somatic mutations, copy-number variation and DNA methylation changes. It is unclear however, which type of alteration may be more informative of cancer risk. We analyzed genome-wide DNA methylation and copy-number calls from the same DNA assay in a cohort of healthy breast samples and age-matched normal samples collected adjacent to breast cancer. Using statistical methods to adjust for cell type heterogeneity, we show that DNA methylation changes can discriminate normal-adjacent from normal samples better than somatic copy-number variants. We validate this important finding in an independent dataset. These results suggest that DNA methylation alterations in the normal cell of origin may offer better cancer risk prediction and early detection markers than copy-number changes.

Epigenetic Applications in Adverse Outcome Pathways and Environmental Risk Evaluation

BACKGROUND

The epigenome may be an important interface between environmental chemical exposures and human health. However, the links between epigenetic modifications and health outcomes are often correlative and do not distinguish between cause and effect or common-cause relationships. The Adverse Outcome Pathway (AOP) framework has the potential to demonstrate, by way of an inference- and science-based analysis, the causal relationship between chemical exposures, epigenome, and adverse health outcomes.

OBJECTIVE

The objective of this work is to discuss the epigenome as a modifier of exposure effects and risk, perspectives for integrating toxicoepigenetic data into an AOP framework, tools for the exploration of epigenetic toxicity, and integration of AOP-guided epigenetic information into science and risk-assessment processes.

DISCUSSION

Organizing epigenetic information into the topology of a qualitative AOP network may help describe how a system will respond to epigenetic modifications caused by environmental chemical exposures. However, understanding the biological plausibility, linking epigenetic effects to short- and long-term health outcomes, and including epigenetic studies in the risk assessment process is met by substantive challenges. These obstacles include understanding the complex range of epigenetic modifications and their combinatorial effects, the large number of environmental chemicals to be tested, and the lack of data that quantitatively evaluate the epigenetic effects of environmental exposure.

CONCLUSION

We anticipate that epigenetic information organized into AOP frameworks can be consistently used to support biological plausibility and to identify data gaps that will accelerate the pace at which epigenetic information is applied in chemical evaluation and risk-assessment paradigms. https://doi.org/10.1289/EHP2322.

Cell-Specific PEAR1 Methylation Studies Reveal a Locus that Coordinates Expression of Multiple Genes

Chromosomal interactions connect distant enhancers and promoters on the same chromosome, activating or repressing gene expression. PEAR1 encodes the Platelet-Endothelial Aggregation Receptor 1, a contact receptor involved in platelet function and megakaryocyte and endothelial cell proliferation. PEAR1 expression during megakaryocyte differentiation is controlled by DNA methylation at its first CpG island. We identified a PEAR1 cell-specific methylation sensitive region in endothelial cells and megakaryocytes that showed strong chromosomal interactions with ISGL20L2, RRNAD1, MRLP24, HDGF and PRCC, using available promoter capture Hi-C datasets. These genes are involved in ribosome processing, protein synthesis, cell cycle and cell proliferation. We next studied the methylation and expression profile of these five genes in Human Umbilical Vein Endothelial Cells (HUVECs) and megakaryocyte precursors. While cell-specific PEAR1 methylation corresponded to variability in expression for four out of five genes, no methylation change was observed in their promoter regions across cell types. Our data suggest that PEAR1 cell-type specific methylation changes may control long distance interactions with other genes. Further studies are needed to show whether such interaction data might be relevant for the genome-wide association data that showed a role for non-coding PEAR1 variants in the same region and platelet function, platelet count and cardiovascular risk.

The pharmacoepigenomics informatics pipeline defines a pathway of novel and known warfarin pharmacogenomics variants

AIM

'Pharmacoepigenomics' methods informed by omics datasets and pre-existing knowledge have yielded discoveries in neuropsychiatric pharmacogenomics. Now we evaluate the generality of these methods by discovering an extended warfarin pharmacogenomics pathway.

MATERIALS & METHODS

We developed the pharmacoepigenomics informatics pipeline, a scalable multi-omics variant screening pipeline for pharmacogenomics, and conducted an experiment in the genomics of warfarin.

RESULTS

We discovered known and novel pharmacogenomics variants and genes, both coding and regulatory, for warfarin response, including adverse events. Such genes and variants cluster in a warfarin response pathway consolidating known and novel warfarin response variants and genes.

CONCLUSION

These results can inform a new warfarin test. The pharmacoepigenomics informatics pipeline may be able to discover new pharmacogenomics markers in other drug-disease systems.

Pathways to understanding the genomic aetiology of osteoarthritis

Osteoarthritis is a common, complex disease with no curative therapy. In this review, we summarize current knowledge on disease aetiopathogenesis and outline genetics and genomics approaches that are helping catalyse a much-needed improved understanding of the biological underpinning of disease development and progression.

ChIP-ping the branches of the tree: functional genomics and the evolution of eukaryotic gene regulation

Advances in the methods for detecting protein-DNA interactions have played a key role in determining the directions of research into the mechanisms of transcriptional regulation. The most recent major technological transformation happened a decade ago, with the move from using tiling arrays [chromatin immunoprecipitation (ChIP)-on-Chip] to high-throughput sequencing (ChIP-seq) as a readout for ChIP assays. In addition to the numerous other ways in which it is superior to arrays, by eliminating the need to design and manufacture them, sequencing also opened the door to carrying out comparative analyses of genome-wide transcription factor occupancy across species and studying chromatin biology in previously less accessible model and nonmodel organisms, thus allowing us to understand the evolution and diversity of regulatory mechanisms in unprecedented detail. Here, we review the biological insights obtained from such studies in recent years and discuss anticipated future developments in the field.

Epigenome-based cancer risk prediction: rationale, opportunities and challenges

The incidence of cancer is continuing to rise and risk-tailored early diagnostic and/or primary prevention strategies are urgently required. The ideal risk-predictive test should: integrate the effects of both genetic and nongenetic factors and aim to capture these effects using an approach that is both biologically stable and technically reproducible; derive a score from easily accessible biological samples that acts as a surrogate for the organ in question; and enable the effectiveness of risk-reducing measures to be monitored. Substantial evidence has accumulated suggesting that the epigenome and, in particular, DNA methylation-based tests meet all of these requirements. However, the development and implementation of DNA methylation-based risk-prediction tests poses considerable challenges. In particular, the cell type specificity of DNA methylation and the extensive cellular heterogeneity of the easily accessible surrogate cells that might contain information relevant to less accessible tissues necessitates the use of novel methods in order to account for these confounding issues. Furthermore, the engagement of the scientific community with health-care professionals, policymakers and the public is required in order to identify and address the organizational, ethical, legal, social and economic challenges associated with the routine use of epigenetic testing.

The 4D nucleome project

The 4D Nucleome Network aims to develop and apply approaches to map the structure and dynamics of the human and mouse genomes in space and time with the goal of gaining deeper mechanistic insights into how the nucleus is organized and functions. The project will develop and benchmark experimental and computational approaches for measuring genome conformation and nuclear organization, and investigate how these contribute to gene regulation and other genome functions. Validated experimental technologies will be combined with biophysical approaches to generate quantitative models of spatial genome organization in different biological states, both in cell populations and in single cells.

Identifying co-opted transposable elements using comparative epigenomics

The human genome gives rise to different epigenomic landscapes that define each cell type and can be deregulated in disease. Recent efforts by ENCODE, the NIH Roadmap and the International Human Epigenome Consortium (IHEC) have made significant advances towards assembling reference epigenomic maps of various tissues. Notably, these projects have found that approximately 80% of human DNA was biochemically active in at least one epigenomic assay while only approximately 10% of the sequence displayed signs of purifying selection. Given that transposable elements (TEs) make up at least 50% of the human genome and can be actively transcribed or act as regulatory elements either for their own purposes or be co-opted for the benefit of their host; we are interested in exploring their overall contribution to the "functional" genome. Traditional methods used to identify functional DNA have relied on comparative genomics, conservation analysis and low throughput validation assays. To discover co-opted TEs, and distinguish them from noisy genomic elements, we argue that comparative epigenomic methods will also be important.

The European Bioinformatics Institute in 2017: data coordination and integration

The European Bioinformatics Institute (EMBL-EBI) supports life-science research throughout the world by providing open data, open-source software and analytical tools, and technical infrastructure (https://www.ebi.ac.uk). We accommodate an increasingly diverse range of data types and integrate them, so that biologists in all disciplines can explore life in ever-increasing detail. We maintain over 40 data resources, many of which are run collaboratively with partners in 16 countries (https://www.ebi.ac.uk/services). Submissions continue to increase exponentially: our data storage has doubled in less than two years to 120 petabytes. Recent advances in cellular imaging and single-cell sequencing techniques are generating a vast amount of high-dimensional data, bringing to light new cell types and new perspectives on anatomy. Accordingly, one of our main focus areas is integrating high-quality information from bioimaging, biobanking and other types of molecular data. This is reflected in our deep involvement in Open Targets, stewarding of plant phenotyping standards (MIAPPE) and partnership in the Human Cell Atlas data coordination platform, as well as the 2017 launch of the Omics Discovery Index. This update gives a birds-eye view of EMBL-EBI's approach to data integration and service development as genomics begins to enter the clinic.

Systems-epigenomics inference of transcription factor activity implicates aryl-hydrocarbon-receptor inactivation as a key event in lung cancer development

Background

Diverse molecular alterations associated with smoking in normal and precursor lung cancer cells have been reported, yet their role in lung cancer etiology remains unclear. A prominent example is hypomethylation of the aryl hydrocarbon-receptor repressor (AHRR) locus, which is observed in blood and squamous epithelial cells of smokers, but not in lung cancer.

Results

Using a novel systems-epigenomics algorithm, called SEPIRA, which leverages the power of a large RNA-sequencing expression compendium to infer regulatory activity from messenger RNA expression or DNA methylation (DNAm) profiles, we infer the landscape of binding activity of lung-specific transcription factors (TFs) in lung carcinogenesis. We show that lung-specific TFs become preferentially inactivated in lung cancer and precursor lung cancer lesions and further demonstrate that these results can be derived using only DNAm data. We identify subsets of TFs which become inactivated in precursor cells. Among these regulatory factors, we identify AHR, the aryl hydrocarbon-receptor which controls a healthy immune response in the lung epithelium and whose repressor, AHRR, has recently been implicated in smoking-mediated lung cancer. In addition, we identify FOXJ1, a TF which promotes growth of airway cilia and effective clearance of the lung airway epithelium from carcinogens.

Conclusions

We identify TFs, such as AHR, which become inactivated in the earliest stages of lung cancer and which, unlike AHRR hypomethylation, are also inactivated in lung cancer itself. The novel systems-epigenomics algorithm SEPIRA will be useful to the wider epigenome-wide association study community as a means of inferring regulatory activity.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-017-1366-0) contains supplementary material, which is available to authorized users.

Epigenetic and Transcriptional Variability Shape Phenotypic Plasticity

Epigenetic and transcriptional variability contribute to the vast diversity of cellular and organismal phenotypes and are key in human health and disease. In this review, we describe different types, sources, and determinants of epigenetic and transcriptional variability, enabling cells and organisms to adapt and evolve to a changing environment. We highlight the latest research and hypotheses on how chromatin structure and the epigenome influence gene expression variability. Further, we provide an overview of challenges in the analysis of biological variability. An improved understanding of the molecular mechanisms underlying epigenetic and transcriptional variability, at both the intra- and inter-individual level, provides great opportunity for disease prevention, better therapeutic approaches, and personalized medicine.

The Genetics of Multiple Sclerosis: From 0 to 200 in 50 Years

Multiple sclerosis (MS) is a common autoimmune disease that targets myelin in the central nervous system (CNS). Multiple genome-wide association studies (GWAS) over the past 10 years have uncovered more than 200 loci that independently contribute to disease pathogenesis. As with many other complex diseases, risk of developing MS is driven by multiple common variants whose biological effects are not immediately clear. Here, we present a historical perspective on the progress made in MS genetics and discuss current work geared towards creating a more complete model that accurately represents the genetic landscape of MS susceptibility. Such a model necessarily includes a better understanding of the individual contributions of each common variant to the cellular phenotypes, and interactions with other genes and with the environment. Future genetic studies in MS will likely focus on the role of rare variants and endophenotypes.

Statistical and integrative system-level analysis of DNA methylation data

Epigenetics plays a key role in cellular development and function. Alterations to the epigenome are thought to capture and mediate the effects of genetic and environmental risk factors on complex disease. Currently, DNA methylation is the only epigenetic mark that can be measured reliably and genome-wide in large numbers of samples. This Review discusses some of the key statistical challenges and algorithms associated with drawing inferences from DNA methylation data, including cell-type heterogeneity, feature selection, reverse causation and system-level analyses that require integration with other data types such as gene expression, genotype, transcription factor binding and other epigenetic information.

Characterization of the human thyroid epigenome

The thyroid gland, necessary for normal human growth and development, functions as an essential regulator of metabolism by the production and secretion of appropriate levels of thyroid hormone. However, assessment of abnormal thyroid function may be challenging suggesting a more fundamental understanding of normal function is needed. One way to characterize normal gland function is to study the epigenome and resulting transcriptome within its constituent cells. This study generates the first published reference epigenomes for human thyroid from four individuals using ChIP-seq and RNA-seq. We profiled six histone modifications (H3K4me1, H3K4me3, H3K27ac, H3K36me3, H3K9me3, H3K27me3), identified chromatin states using a hidden Markov model, produced a novel quantitative metric for model selection and established epigenomic maps of 19 chromatin states. We found that epigenetic features characterizing promoters and transcription elongation tend to be more consistent than regions characterizing enhancers or Polycomb-repressed regions and that epigenetically active genes consistent across all epigenomes tend to have higher expression than those not marked as epigenetically active in all epigenomes. We also identified a set of 18 genes epigenetically active and consistently expressed in the thyroid that are likely highly relevant to thyroid function. Altogether, these epigenomes represent a powerful resource to develop a deeper understanding of the underlying molecular biology of thyroid function and provide contextual information of thyroid and human epigenomic data for comparison and integration into future studies.

Genome-wide association studies of cancer: current insights and future perspectives

Genome-wide association studies (GWAS) provide an agnostic approach for investigating the genetic basis of complex diseases. In oncology, GWAS of nearly all common malignancies have been performed, and over 450 genetic variants associated with increased risks have been identified. As well as revealing novel pathways important in carcinogenesis, these studies have shown that common genetic variation contributes substantially to the heritable risk of many common cancers. The clinical application of GWAS is starting to provide opportunities for drug discovery and repositioning as well as for cancer prevention. However, deciphering the functional and biological basis of associations is challenging and is in part a barrier to fully unlocking the potential of GWAS.

Accurate and reproducible functional maps in 127 human cell types via 2D genome segmentation

The Roadmap Epigenomics Consortium has published whole-genome functional annotation maps in 127 human cell types by integrating data from studies of multiple epigenetic marks. These maps have been widely used for studying gene regulation in cell type-specific contexts and predicting the functional impact of DNA mutations on disease. Here, we present a new map of functional elements produced by applying a method called IDEAS on the same data. The method has several unique advantages and outperforms existing methods, including that used by the Roadmap Epigenomics Consortium. Using five categories of independent experimental datasets, we compared the IDEAS and Roadmap Epigenomics maps. While the overall concordance between the two maps is high, the maps differ substantially in the prediction details and in their consistency of annotation of a given genomic position across cell types. The annotation from IDEAS is uniformly more accurate than the Roadmap Epigenomics annotation and the improvement is substantial based on several criteria. We further introduce a pipeline that improves the reproducibility of functional annotation maps. Thus, we provide a high-quality map of candidate functional regions across 127 human cell types and compare the quality of different annotation methods in order to facilitate biomedical research in epigenomics.