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

Cell-Based Therapy and Genome Editing as Emerging Therapeutic Approaches to Treat Rheumatoid Arthritis

Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inflammation of the joints. Although much remains unknown about the pathogenesis of RA, there is evidence that impaired immune tolerance and the development of RA are related. And it is precisely the restoration of immune tolerance at the site of the inflammation that is the ultimate goal of the treatment of RA. Over the past few decades, significant progress has been made in the treatment of RA, with higher rates of disease remission and improved long-term outcomes. Unfortunately, despite these successes, the proportion of patients with persistent, difficult-to-treat disease remains high, and the task of improving our understanding of the basic mechanisms of disease development and developing new ways to treat RA remains relevant. This review focuses on describing new treatments for RA, including cell therapies and gene editing technologies that have shown potential in preclinical and early clinical trials. In addition, we discuss the opportunities and limitations associated with the use of these new approaches in the treatment of RA.

Interspecies regulatory landscapes and elements revealed by novel joint systematic integration of human and mouse blood cell epigenomes

Knowledge of locations and activities of cis-regulatory elements (CREs) is needed to decipher basic mechanisms of gene regulation and to understand the impact of genetic variants on complex traits. Previous studies identified candidate CREs (cCREs) using epigenetic features in one species, making comparisons difficult between species. In contrast, we conducted an interspecies study defining epigenetic states and identifying cCREs in blood cell types to generate regulatory maps that are comparable between species, using integrative modeling of eight epigenetic features jointly in human and mouse in our Validated Systematic Integration (VISION) Project. The resulting catalogs of cCREs are useful resources for further studies of gene regulation in blood cells, indicated by high overlap with known functional elements and strong enrichment for human genetic variants associated with blood cell phenotypes. The contribution of each epigenetic state in cCREs to gene regulation, inferred from a multivariate regression, was used to estimate epigenetic state Regulatory Potential (esRP) scores for each cCRE in each cell type, which were used to categorize dynamic changes in cCREs. Groups of cCREs displaying similar patterns of regulatory activity in human and mouse cell types, obtained by joint clustering on esRP scores, harbored distinctive transcription factor binding motifs that were similar between species. An interspecies comparison of cCREs revealed both conserved and species-specific patterns of epigenetic evolution. Finally, we showed that comparisons of the epigenetic landscape between species can reveal elements with similar roles in regulation, even in the absence of genomic sequence alignment.

Synovial Sarcoma Chromatin Dynamics Reveal a Continuum in SS18:SSX Reprograming

Synovial sarcoma (SyS) is an aggressive soft-tissue malignancy characterized by a pathognomonic chromosomal translocation leading to the formation of the SS18::SSX fusion oncoprotein. SS18::SSX associates with mammalian BAF complexes suggesting deregulation of chromatin architecture as the oncogenic driver in this tumour type. To examine the epigenomic state of SyS we performed comprehensive multi-omics analysis on 52 primary pre-treatment human SyS tumours. Our analysis revealed a continuum of epigenomic states across the cohort at fusion target genes independent of rare somatic genetic lesions. We identify cell-of-origin signatures defined by enhancer states and reveal unexpected relationships between H2AK119Ub1 and active marks. The number of bivalent promoters, dually marked by the repressive H3K27me3 and activating H3K4me3 marks, has strong prognostic value and outperforms tumor grade in predicting patient outcome. Finally, we identify SyS defining epigenomic features including H3K4me3 expansion associated with striking promoter DNA hypomethylation in which SyS displays the lowest mean methylation level of any sarcoma subtype. We explore these distinctive features as potential vulnerabilities in SyS and identify H3K4me3 inhibition as a promising therapeutic strategy.

Transient loss of Polycomb components induces an epigenetic cancer fate

Although cancer initiation and progression are generally associated with the accumulation of somatic mutations1,2, substantial epigenomic alterations underlie many aspects of tumorigenesis and cancer susceptibility3-6, suggesting that genetic mechanisms might not be the only drivers of malignant transformation7. However, whether purely non-genetic mechanisms are sufficient to initiate tumorigenesis irrespective of mutations has been unknown. Here, we show that a transient perturbation of transcriptional silencing mediated by Polycomb group proteins is sufficient to induce an irreversible switch to a cancer cell fate in Drosophila. This is linked to the irreversible derepression of genes that can drive tumorigenesis, including members of the JAK-STAT signalling pathway and zfh1, the fly homologue of the ZEB1 oncogene, whose aberrant activation is required for Polycomb perturbation-induced tumorigenesis. These data show that a reversible depletion of Polycomb proteins can induce cancer in the absence of driver mutations, suggesting that tumours can emerge through epigenetic dysregulation leading to inheritance of altered cell fates.

3D epigenomics and 3D epigenopathies

Mammalian genomes are intricately compacted to form sophisticated 3-dimensional structures within the tiny nucleus, so called 3D genome folding. Despite their shapes reminiscent of an entangled yarn, the rapid development of molecular and next-generation sequencing technologies (NGS) has revealed that mammalian genomes are highly organized in a hierarchical order that delicately affects transcription activities. An increasing amount of evidence suggests that 3D genome folding is implicated in diseases, giving us a clue on how to identify novel therapeutic approaches. In this review, we will study what 3D genome folding means in epigenetics, what types of 3D genome structures there are, how they are formed, and how the technologies have developed to explore them. We will also discuss the pathological implications of 3D genome folding. Finally, we will discuss how to leverage 3D genome folding and engineering for future studies. [BMB Reports 2024; 57(5): 216-231].

Advances in the multimodal analysis of the 3D chromatin structure and gene regulation

Recent studies have demonstrated that the three-dimensional conformation of the chromatin plays a crucial role in gene regulation, with aberrations potentially leading to various diseases. Advanced methodologies have revealed a link between the chromatin conformation and biological function. This review divides these methodologies into sequencing-based and imaging-based methodologies, tracing their development over time. We particularly highlight innovative techniques that facilitate the simultaneous mapping of RNAs, histone modifications, and proteins within the context of the 3D architecture of chromatin. This multimodal integration substantially improves our ability to establish a robust connection between the spatial arrangement of molecular components in the nucleus and their functional roles. Achieving a comprehensive understanding of gene regulation requires capturing diverse data modalities within individual cells, enabling the direct inference of functional relationships between these components. In this context, imaging-based technologies have emerged as an especially promising approach for gathering spatial information across multiple components in the same cell.

Cell subtype-specific effects of genetic variation in the Alzheimer's disease brain

The relationship between genetic variation and gene expression in brain cell types and subtypes remains understudied. Here, we generated single-nucleus RNA sequencing data from the neocortex of 424 individuals of advanced age; we assessed the effect of genetic variants on RNA expression in cis (cis-expression quantitative trait loci) for seven cell types and 64 cell subtypes using 1.5 million transcriptomes. This effort identified 10,004 eGenes at the cell type level and 8,099 eGenes at the cell subtype level. Many eGenes are only detected within cell subtypes. A new variant influences APOE expression only in microglia and is associated with greater cerebral amyloid angiopathy but not Alzheimer's disease pathology, after adjusting for APOEε4, providing mechanistic insights into both pathologies. Furthermore, only a TMEM106B variant affects the proportion of cell subtypes. Integration of these results with genome-wide association studies highlighted the targeted cell type and probable causal gene within Alzheimer's disease, schizophrenia, educational attainment and Parkinson's disease loci.

Molecular pathological approach to cancer epigenomics and its clinical application

Careful microscopic observation of histopathological specimens, accumulation of large numbers of high-quality tissue specimens, and analysis of molecular pathology in relation to morphological features are considered to yield realistic data on the nature of multistage carcinogenesis. Since the morphological hallmark of cancer is disruption of the normal histological structure maintained through cell-cell adhesiveness and cellular polarity, attempts have been made to investigate abnormalities of the cadherin-catenin cell adhesion system in human cancer cells. It has been shown that the CDH1 tumor suppressor gene encoding E-cadherin is silenced by DNA methylation, suggesting that a "double hit" involving DNA methylation and loss of heterozygosity leads to carcinogenesis. Therefore, in the 1990s, we focused on epigenomic mechanisms, which until then had not received much attention. In chronic hepatitis and liver cirrhosis associated with hepatitis virus infection, DNA methylation abnormalities were found to occur frequently, being one of the earliest indications that such abnormalities are present even in precancerous tissue. Aberrant expression and splicing of DNA methyltransferases, such as DNMT1 and DNMT3B, was found to underlie the mechanism of DNA methylation alterations in various organs. The CpG island methylator phenotype in renal cell carcinoma was identified for the first time, and its therapeutic targets were identified by multilayer omics analysis. Furthermore, the DNA methylation profile of nonalcoholic steatohepatitis (NASH)-related hepatocellular carcinoma was clarified in groundbreaking studies. Since then, we have developed diagnostic markers for carcinogenesis risk in NASH patients and noninvasive diagnostic markers for upper urinary tract cancer, as well as developing a new high-performance liquid chromatography-based diagnostic system for DNA methylation diagnosis. Research on the cancer epigenome has revealed that DNA methylation alterations occur from the precancerous stage as a result of exposure to carcinogenic factors such as inflammation, smoking, and viral infections, and continuously contribute to multistage carcinogenesis through aberrant expression of cancer-related genes and genomic instability. DNA methylation alterations at the precancerous stages are inherited by or strengthened in cancers themselves and determine the clinicopathological aggressiveness of cancers as well as patient outcome. DNA methylation alterations have applications as biomarkers, and are expected to contribute to diagnosis, as well as preventive and preemptive medicine.

Aberrant non-canonical NF-κB signalling reprograms the epigenome landscape to drive oncogenic transcriptomes in multiple myeloma

In multiple myeloma, abnormal plasma cells establish oncogenic niches within the bone marrow by engaging the NF-κB pathway to nurture their survival while they accumulate pro-proliferative mutations. Under these conditions, many cases eventually develop genetic abnormalities endowing them with constitutive NF-κB activation. Here, we find that sustained NF-κB/p52 levels resulting from such mutations favours the recruitment of enhancers beyond the normal B-cell repertoire. Furthermore, through targeted disruption of p52, we characterise how such enhancers are complicit in the formation of super-enhancers and the establishment of cis-regulatory interactions with myeloma dependencies during constitutive activation of p52. Finally, we functionally validate the pathological impact of these cis-regulatory modules on cell and tumour phenotypes using in vitro and in vivo models, confirming RGS1 as a p52-dependent myeloma driver. We conclude that the divergent epigenomic reprogramming enforced by aberrant non-canonical NF-κB signalling potentiates transcriptional programs beneficial for multiple myeloma progression.

EpiVar Browser: advanced exploration of epigenomics data under controlled access

MOTIVATION

Human epigenomic data has been generated by large consortia for thousands of cell types to be used as a reference map of normal and disease chromatin states. Since epigenetic data contains potentially identifiable information, similarly to genetic data, most raw files generated by these consortia are stored in controlled-access databases. It is important to protect identifiable information, but this should not hinder secure sharing of these valuable datasets.

RESULTS

Guided by the Framework for responsible sharing of genomic and health-related data from the Global Alliance for Genomics and Health (GA4GH), we have developed an approach and a tool to facilitate the exploration of epigenomics datasets' aggregate results, while filtering out identifiable information. Specifically, the EpiVar Browser allows a user to navigate an epigenetic dataset from a cohort of individuals and enables direct exploration of genotype-chromatin phenotype relationships. Because individual genotypes and epigenetic signal tracks are not directly accessible, and rather aggregated in the portal output, no identifiable data is released, yet the interface allows for dynamic genotype-epigenome interrogation. This approach has the potential to accelerate analyses that would otherwise require a lengthy multi-step approval process and provides a generalizable strategy to facilitate responsible access to sensitive epigenomics data.

AVAILABILITY AND IMPLEMENTATION

Online portal: https://computationalgenomics.ca/tools/epivar; EpiVar Browser source code: https://github.com/c3g/epivar-browser; bw-merge-window tool source code: https://github.com/c3g/bw-merge-window.

Adaptive Selection of Cis-regulatory Elements in the Han Chinese

Cis-regulatory elements have an important role in human adaptation to the living environment. However, the lag in population genomic cohort studies and epigenomic studies, hinders the research in the adaptive analysis of cis-regulatory elements in human populations. In this study, we collected 4,013 unrelated individuals and performed a comprehensive analysis of adaptive selection of genome-wide cis-regulatory elements in the Han Chinese. In total, 12.34% of genomic regions are under the influence of adaptive selection, where 1.00% of enhancers and 2.06% of promoters are under positive selection, and 0.06% of enhancers and 0.02% of promoters are under balancing selection. Gene ontology enrichment analysis of these cis-regulatory elements under adaptive selection reveals that many positive selections in the Han Chinese occur in pathways involved in cell-cell adhesion processes, and many balancing selections are related to immune processes. Two classes of adaptive cis-regulatory elements related to cell adhesion were in-depth analyzed, one is the adaptive enhancers derived from neanderthal introgression, leads to lower hyaluronidase level in skin, and brings better performance on UV-radiation resistance to the Han Chinese. Another one is the cis-regulatory elements regulating wound healing, and the results suggest the positive selection inhibits coagulation and promotes angiogenesis and wound healing in the Han Chinese. Finally, we found that many pathogenic alleles, such as risky alleles of type 2 diabetes or schizophrenia, remain in the population due to the hitchhiking effect of positive selections. Our findings will help deepen our understanding of the adaptive evolution of genome regulation in the Han Chinese.

Oxidative stress and metabolism meet epigenetic modulation in physical exercise

Physical exercise is established as an important factor of health and generally is recommended for its positive effects on several tissues, organs, and systems. These positive effects come from metabolic adaptations that also include oxidative eustress, in which physical activity increases ROS production and antioxidant mechanisms, although this depends on the intensity of the exercise. Muscle metabolism through mechanisms such as aerobic and anaerobic glycolysis, tricarboxylic acid cycle, and oxidative lipid metabolism can produce metabolites and co-factors which directly impact the epigenetic machinery. In this review, we clearly reinforce the evidence that exercise regulates several epigenetic mechanisms and explain how these mechanisms can be regulated by metabolic products and co-factors produced during exercise. In fact, recent evidence has demonstrated the importance of epigenetics in the gene expression changes implicated in metabolic adaptation after exercise. Importantly, intermediates of the metabolism generated by continuous, acute, moderate, or strenuous exercise control the activity of epigenetic enzymes, therefore turning on or turning off the gene expression of specific programs which can lead to physiological adaptations after exercise.

Exploring noncoding variants in genetic diseases: from detection to functional insights

Previous studies on genetic diseases predominantly focused on protein-coding variations, overlooking the vast noncoding regions in the human genome. The development of high-throughput sequencing technologies and functional genomics tools has enabled the systematic identification of functional noncoding variants. These variants can impact gene expression, regulation, and chromatin conformation, thereby contributing to disease pathogenesis. Understanding the mechanisms that underlie the impact of noncoding variants on genetic diseases is indispensable for the development of precisely targeted therapies and the implementation of personalized medicine strategies. The intricacies of noncoding regions introduce a multitude of challenges and research opportunities. In this review, we introduce a spectrum of noncoding variants involved in genetic diseases, along with research strategies and advanced technologies for their precise identification and in-depth understanding of the complexity of the noncoding genome. We will delve into the research challenges and propose potential solutions for unraveling the genetic basis of rare and complex diseases.

FORGEdb: a tool for identifying candidate functional variants and uncovering target genes and mechanisms for complex diseases

The majority of disease-associated variants identified through genome-wide association studies are located outside of protein-coding regions. Prioritizing candidate regulatory variants and gene targets to identify potential biological mechanisms for further functional experiments can be challenging. To address this challenge, we developed FORGEdb ( https://forgedb.cancer.gov/ ; https://forge2.altiusinstitute.org/files/forgedb.html ; and https://doi.org/10.5281/zenodo.10067458 ), a standalone and web-based tool that integrates multiple datasets, delivering information on associated regulatory elements, transcription factor binding sites, and target genes for over 37 million variants. FORGEdb scores provide researchers with a quantitative assessment of the relative importance of each variant for targeted functional experiments.

Single-cell functional genomics reveals determinants of sensitivity and resistance to natural killer cells in blood cancers

Cancer cells can evade natural killer (NK) cell activity, thereby limiting anti-tumor immunity. To reveal genetic determinants of susceptibility to NK cell activity, we examined interacting NK cells and blood cancer cells using single-cell and genome-scale functional genomics screens. Interaction of NK and cancer cells induced distinct activation and type I interferon (IFN) states in both cell types depending on the cancer cell lineage and molecular phenotype, ranging from more sensitive myeloid to less sensitive B-lymphoid cancers. CRISPR screens in cancer cells uncovered genes regulating sensitivity and resistance to NK cell-mediated killing, including adhesion-related glycoproteins, protein fucosylation genes, and transcriptional regulators, in addition to confirming the importance of antigen presentation and death receptor signaling pathways. CRISPR screens with a single-cell transcriptomic readout provided insight into underlying mechanisms, including regulation of IFN-γ signaling in cancer cells and NK cell activation states. Our findings highlight the diversity of mechanisms influencing NK cell susceptibility across different cancers and provide a resource for NK cell-based therapies.

The Retinal Ganglion Cell Repopulation, Stem Cell Transplantation, and Optic Nerve Regeneration Consortium

Purpose

The Retinal Ganglion Cell (RGC) Repopulation, Stem Cell Transplantation, and Optic Nerve Regeneration (RReSTORe) consortium was founded in 2021 to help address the numerous scientific and clinical obstacles that impede development of vision-restorative treatments for patients with optic neuropathies. The goals of the RReSTORe consortium are: (1) to define and prioritize the most critical challenges and questions related to RGC regeneration; (2) to brainstorm innovative tools and experimental approaches to meet these challenges; and (3) to foster opportunities for collaborative scientific research among diverse investigators.

Design and Participants

The RReSTORe consortium currently includes > 220 members spanning all career stages worldwide and is directed by an organizing committee comprised of 15 leading scientists and physician-scientists of diverse backgrounds.

Methods

Herein, we describe the structure and organization of the RReSTORe consortium, its activities to date, and the perceived impact that the consortium has had on the field based on a survey of participants.

Results

In addition to helping propel the field of regenerative medicine as applied to optic neuropathies, the RReSTORe consortium serves as a framework for developing large collaborative groups aimed at tackling audacious goals that may be expanded beyond ophthalmology and vision science.

Conclusions

The development of innovative interventions capable of restoring vision for patients suffering from optic neuropathy would be transformative for the ophthalmology field, and may set the stage for functional restoration in other central nervous system disorders. By coordinating large-scale, international collaborations among scientists with diverse and complementary expertise, we are confident that the RReSTORe consortium will help to accelerate the field toward clinical translation.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

A novel approach to risk exposure and epigenetics-the use of multidimensional context to gain insights into the early origins of cardiometabolic and neurocognitive health

BACKGROUND

Each mother-child dyad represents a unique combination of genetic and environmental factors. This constellation of variables impacts the expression of countless genes. Numerous studies have uncovered changes in DNA methylation (DNAm), a form of epigenetic regulation, in offspring related to maternal risk factors. How these changes work together to link maternal-child risks to childhood cardiometabolic and neurocognitive traits remains unknown. This question is a key research priority as such traits predispose to future non-communicable diseases (NCDs). We propose viewing risk and the genome through a multidimensional lens to identify common DNAm patterns shared among diverse risk profiles.

METHODS

We identified multifactorial Maternal Risk Profiles (MRPs) generated from population-based data (n = 15,454, Avon Longitudinal Study of Parents and Children (ALSPAC)). Using cord blood HumanMethylation450 BeadChip data, we identified genome-wide patterns of DNAm that co-vary with these MRPs. We tested the prospective relation of these DNAm patterns (n = 914) to future outcomes using decision tree analysis. We then tested the reproducibility of these patterns in (1) DNAm data at age 7 and 17 years within the same cohort (n = 973 and 974, respectively) and (2) cord DNAm in an independent cohort, the Generation R Study (n = 686).

RESULTS

We identified twenty MRP-related DNAm patterns at birth in ALSPAC. Four were prospectively related to cardiometabolic and/or neurocognitive childhood outcomes. These patterns were replicated in DNAm data from blood collected at later ages. Three of these patterns were externally validated in cord DNAm data in Generation R. Compared to previous literature, DNAm patterns exhibited novel spatial distribution across the genome that intersects with chromatin functional and tissue-specific signatures.

CONCLUSIONS

To our knowledge, we are the first to leverage multifactorial population-wide data to detect patterns of variability in DNAm. This context-based approach decreases biases stemming from overreliance on specific samples or variables. We discovered molecular patterns demonstrating prospective and replicable relations to complex traits. Moreover, results suggest that patterns harbour a genome-wide organisation specific to chromatin regulation and target tissues. These preliminary findings warrant further investigation to better reflect the reality of human context in molecular studies of NCDs.

Accelerated DNA methylation age plays a role in the impact of cardiovascular risk factors on the human heart

BACKGROUND

DNA methylation (DNAm) age acceleration (AgeAccel) and cardiac age by 12-lead advanced electrocardiography (A-ECG) are promising biomarkers of biological and cardiac aging, respectively. We aimed to explore the relationships between DNAm age and A-ECG heart age and to understand the extent to which DNAm AgeAccel relates to cardiovascular (CV) risk factors in a British birth cohort from 1946.

RESULTS

We studied four DNAm ages (AgeHannum, AgeHorvath, PhenoAge, and GrimAge) and their corresponding AgeAccel. Outcomes were the results from two publicly available ECG-based cardiac age scores: the Bayesian A-ECG-based heart age score of Lindow et al. 2022 and the deep neural network (DNN) ECG-based heart age score of Ribeiro et al. 2020. DNAm AgeAccel was also studied relative to results from two logistic regression-based A-ECG disease scores, one for left ventricular (LV) systolic dysfunction (LVSD), and one for LV electrical remodeling (LVER). Generalized linear models were used to explore the extent to which any associations between biological cardiometabolic risk factors (body mass index, hypertension, diabetes, high cholesterol, previous cardiovascular disease [CVD], and any CV risk factor) and the ECG-based outcomes are mediated by DNAm AgeAccel. We derived the total effects, average causal mediation effects (ACMEs), average direct effects (ADEs), and the proportion mediated [PM] with their 95% confidence intervals [CIs]. 498 participants (all 60-64 years) were included, with the youngest ECG heart age being 27 and the oldest 90. When exploring the associations between cardiometabolic risk factors and Bayesian A-ECG cardiac age, AgeAccelPheno appears to be a partial mediator, as ACME was 0.23 years [0.01, 0.52] p = 0.028 (i.e., PM≈18%) for diabetes, 0.34 [0.03, 0.74] p = 0.024 (i.e., PM≈15%) for high cholesterol, and 0.34 [0.03, 0.74] p = 0.024 (PM≈15%) for any CV risk factor. Similarly, AgeAccelGrim mediates ≈30% of the relationship between diabetes or high cholesterol and the DNN ECG-based heart age. When exploring the link between cardiometabolic risk factors and the A-ECG-based LVSD and LVER scores, it appears that AgeAccelPheno or AgeAccelGrim mediate 10-40% of these associations.

CONCLUSION

By the age of 60, participants with accelerated DNA methylation appear to have older, weaker, and more electrically impaired hearts. We show that the harmful effects of CV risk factors on cardiac age and health, appear to be partially mediated by DNAm AgeAccelPheno and AgeAccelGrim. This highlights the need to further investigate the potential cardioprotective effects of selective DNA methyltransferases modulators.

ChromGene: gene-based modeling of epigenomic data

Various computational approaches have been developed to annotate epigenomes on a per-position basis by modeling combinatorial and spatial patterns within epigenomic data. However, such annotations are less suitable for gene-based analyses. We present ChromGene, a method based on a mixture of learned hidden Markov models, to annotate genes based on multiple epigenomic maps across the gene body and flanks. We provide ChromGene assignments for over 100 cell and tissue types. We characterize the mixture components in terms of gene expression, constraint, and other gene annotations. The ChromGene method and annotations will provide a useful resource for gene-based epigenomic analyses.

Prospective Blood Transcriptomics Study in a Motor Vehicle Collision Cohort Identified a Protective Function of the SAMD15 Gene Against Chronic Pain

Traumatic brain injuries following motor vehicle collisions (MVCs) are ubiquitous. Surprisingly, there are no correlates between concussion impact force and long-term pain outcomes. To study the molecular underpinnings of chronic pain after MVC, we assembled a prospective cohort of 36 subjects that experienced MVC and suffered documented mild traumatic brain injuries. For each participant, a first blood sample was drawn within 72 hours of the collision, then a second one at the 6-month mark. Pain was also assessed at the second blood draw to determine if pain became chronic or resolved. Blood samples enabled transcriptomics analyses for immune cells. At the transcriptome-wide level, we found that Sterile Alpha Motif Domain Containing 15 (SAMD15) mRNA was significantly upregulated with time in subjects who resolved their pain whereas unregulated in those with persistent pain. Using several large publicly available datasets, such as the UK Biobank and the GTeX portal, we then linked elevated SAMD15 gene expression, elevated neutrophils cell counts, and decreased risk for chronic pain to increased dosage of the T allele at SNP rs4903580, situated within SAMD15's gene locus. The causality between the components of our model was established and supported by Mendelian randomization. Overall, our results support the role of SAMD15 as a potential gene effector for neutrophil-dependent chronic pain development. PERSPECTIVE: This article highlights the potential protective role of the SAMD15 gene against chronic pain following a mild traumatic brain injury. The expression of the gene is associated with a SNP rs4903580, which is itself associated with neutrophils counts as well as chronic pain in large genetic studies.

EpiVar Browser: advanced exploration of epigenomics data under controlled access

Motivation

Human epigenomic data has been generated by large consortia for thousands of cell types to be used as a reference map of normal and disease chromatin states. Since epigenetic data contains potentially identifiable information, similarly to genetic data, most raw files generated by these consortia are stored in controlled-access databases. It is important to protect identifiable information, but this should not hinder secure sharing of these valuable datasets.

Results

Guided by the Framework for responsible sharing of genomic and health-related data from the Global Alliance for Genomics and Health (GA4GH), we have developed a tool to facilitate the exploration of epigenomics datasets' aggregate results, while filtering out identifiable information. Specifically, the EpiVar Browser allows a user to navigate an epigenetic dataset from a cohort of individuals and enables direct exploration of genotype-chromatin phenotype relationships. Because the information about individual genotypes is not accessible and aggregated in the output that is made available, no identifiable data is released, yet the interface allows for dynamic genotype - epigenome interrogation. This approach has the potential to accelerate analyses that would otherwise require a lengthy multi-step approval process and provides a generalisable strategy to facilitate responsible access to sensitive epigenomics data.

Availability and implementation

Online portal instance: https://computationalgenomics.ca/tools/epivarSource code: https://github.com/c3g/epivar-browser.

Interpreting non-coding disease-associated human variants using single-cell epigenomics

Genome-wide association studies (GWAS) have linked hundreds of thousands of sequence variants in the human genome to common traits and diseases. However, translating this knowledge into a mechanistic understanding of disease-relevant biology remains challenging, largely because such variants are predominantly in non-protein-coding sequences that still lack functional annotation at cell-type resolution. Recent advances in single-cell epigenomics assays have enabled the generation of cell type-, subtype- and state-resolved maps of the epigenome in heterogeneous human tissues. These maps have facilitated cell type-specific annotation of candidate cis-regulatory elements and their gene targets in the human genome, enhancing our ability to interpret the genetic basis of common traits and diseases.

A meta-analysis of immune-cell fractions at high resolution reveals novel associations with common phenotypes and health outcomes

BACKGROUND

Changes in cell-type composition of tissues are associated with a wide range of diseases and environmental risk factors and may be causally implicated in disease development and progression. However, these shifts in cell-type fractions are often of a low magnitude, or involve similar cell subtypes, making their reliable identification challenging. DNA methylation profiling in a tissue like blood is a promising approach to discover shifts in cell-type abundance, yet studies have only been performed at a relatively low cellular resolution and in isolation, limiting their power to detect shifts in tissue composition.

METHODS

Here we derive a DNA methylation reference matrix for 12 immune-cell types in human blood and extensively validate it with flow-cytometric count data and in whole-genome bisulfite sequencing data of sorted cells. Using this reference matrix, we perform a directional Stouffer and fixed effects meta-analysis comprising 23,053 blood samples from 22 different cohorts, to comprehensively map associations between the 12 immune-cell fractions and common phenotypes. In a separate cohort of 4386 blood samples, we assess associations between immune-cell fractions and health outcomes.

RESULTS

Our meta-analysis reveals many associations of cell-type fractions with age, sex, smoking and obesity, many of which we validate with single-cell RNA sequencing. We discover that naïve and regulatory T-cell subsets are higher in women compared to men, while the reverse is true for monocyte, natural killer, basophil, and eosinophil fractions. Decreased natural killer counts associated with smoking, obesity, and stress levels, while an increased count correlates with exercise and sleep. Analysis of health outcomes revealed that increased naïve CD4 + T-cell and N-cell fractions associated with a reduced risk of all-cause mortality independently of all major epidemiological risk factors and baseline co-morbidity. A machine learning predictor built only with immune-cell fractions achieved a C-index value for all-cause mortality of 0.69 (95%CI 0.67-0.72), which increased to 0.83 (0.80-0.86) upon inclusion of epidemiological risk factors and baseline co-morbidity.

CONCLUSIONS

This work contributes an extensively validated high-resolution DNAm reference matrix for blood, which is made freely available, and uses it to generate a comprehensive map of associations between immune-cell fractions and common phenotypes, including health outcomes.

Integrative epigenomic and functional characterization assay based annotation of regulatory activity across diverse human cell types

We introduce ChromActivity, a computational framework for predicting and annotating regulatory activity across the genome through integration of multiple epigenomic maps and various functional characterization datasets. ChromActivity generates genomewide predictions of regulatory activity associated with each functional characterization dataset across many cell types based on available epigenomic data. It then for each cell type produces (1) ChromScoreHMM genome annotations based on the combinatorial and spatial patterns within these predictions and (2) ChromScore tracks of overall predicted regulatory activity. ChromActivity provides a resource for analyzing and interpreting the human regulatory genome across diverse cell types.

Single-cell analysis reveals transcriptomic and epigenomic impacts on the maternal-fetal interface following SARS-CoV-2 infection

During pregnancy the maternal-fetal interface plays vital roles in fetal development. Its disruption is frequently found in pregnancy complications. Recent studies show increased incidences of adverse pregnancy outcomes in patients with COVID-19; however, the mechanism remains unclear. Here we analysed the molecular impacts of SARS-CoV-2 infection on the maternal-fetal interface. Generating bulk and single-nucleus transcriptomic and epigenomic profiles from patients with COVID-19 and control samples, we discovered aberrant immune activation and angiogenesis patterns in distinct cells from patients. Surprisingly, retrotransposons were also dysregulated in specific cell types. Notably, reduced enhancer activities of LTR8B elements were functionally linked to the downregulation of pregnancy-specific glycoprotein genes in syncytiotrophoblasts. Our findings revealed that SARS-CoV-2 infection induced substantial changes to the epigenome and transcriptome at the maternal-fetal interface, which may be associated with pregnancy complications.

Exploring the genetic basis of coronary artery disease using functional genomics

Genome-wide Association Studies (GWAS) have identified more than 300 loci associated with coronary artery disease (CAD), defining the genetic risk map of the disease. However, the translation of the association signals into biological-pathophysiological mechanisms constitute a major challenge. Through a group of examples of studies focused on CAD, we discuss the rationale, basic principles and outcomes of the main methodologies implemented to prioritize and characterize causal variants and their target genes. Additionally, we highlight the strategies as well as the current methods that integrate association and functional genomics data to dissect the cellular specificity underlying the complexity of disease mechanisms. Despite the limitations of existing approaches, the increasing knowledge generated through functional studies helps interpret GWAS maps and opens novel avenues for the clinical usability of association data.

scAnno: a deconvolution strategy-based automatic cell type annotation tool for single-cell RNA-sequencing data sets

Undoubtedly, single-cell RNA sequencing (scRNA-seq) has changed the research landscape by providing insights into heterogeneous, complex and rare cell populations. Given that more such data sets will become available in the near future, their accurate assessment with compatible and robust models for cell type annotation is a prerequisite. Considering this, herein, we developed scAnno (scRNA-seq data annotation), an automated annotation tool for scRNA-seq data sets primarily based on the single-cell cluster levels, using a joint deconvolution strategy and logistic regression. We explicitly constructed a reference profile for human (30 cell types and 50 human tissues) and a reference profile for mouse (26 cell types and 50 mouse tissues) to support this novel methodology (scAnno). scAnno offers a possibility to obtain genes with high expression and specificity in a given cell type as cell type-specific genes (marker genes) by combining co-expression genes with seed genes as a core. Of importance, scAnno can accurately identify cell type-specific genes based on cell type reference expression profiles without any prior information. Particularly, in the peripheral blood mononuclear cell data set, the marker genes identified by scAnno showed cell type-specific expression, and the majority of marker genes matched exactly with those included in the CellMarker database. Besides validating the flexibility and interpretability of scAnno in identifying marker genes, we also proved its superiority in cell type annotation over other cell type annotation tools (SingleR, scPred, CHETAH and scmap-cluster) through internal validation of data sets (average annotation accuracy: 99.05%) and cross-platform data sets (average annotation accuracy: 95.56%). Taken together, we established the first novel methodology that utilizes a deconvolution strategy for automated cell typing and is capable of being a significant application in broader scRNA-seq analysis. scAnno is available at https://github.com/liuhong-jia/scAnno.

A methylation clock model of mild SARS-CoV-2 infection provides insight into immune dysregulation

DNA methylation comprises a cumulative record of lifetime exposures superimposed on genetically determined markers. Little is known about methylation dynamics in humans following an acute perturbation, such as infection. We characterized the temporal trajectory of blood epigenetic remodeling in 133 participants in a prospective study of young adults before, during, and after asymptomatic and mildly symptomatic SARS-CoV-2 infection. The differential methylation caused by asymptomatic or mildly symptomatic infections was indistinguishable. While differential gene expression largely returned to baseline levels after the virus became undetectable, some differentially methylated sites persisted for months of follow-up, with a pattern resembling autoimmune or inflammatory disease. We leveraged these responses to construct methylation-based machine learning models that distinguished samples from pre-, during-, and postinfection time periods, and quantitatively predicted the time since infection. The clinical trajectory in the young adults and in a diverse cohort with more severe outcomes was predicted by the similarity of methylation before or early after SARS-CoV-2 infection to the model-defined postinfection state. Unlike the phenomenon of trained immunity, the postacute SARS-CoV-2 epigenetic landscape we identify is antiprotective.

A Multi-Tissue Gene Expression Atlas of Water Buffalo (Bubalus bubalis) Reveals Transcriptome Conservation between Buffalo and Cattle

We generated 73 transcriptomic data of water buffalo, which were integrated with publicly available data in this species, yielding a large dataset of 355 samples representing 20 major tissue categories. We established a multi-tissue gene expression atlas of water buffalo. Furthermore, by comparing them with 4866 cattle transcriptomic data from the cattle genotype-tissue expression atlas (CattleGTEx), we found that the transcriptomes of the two species exhibited conservation in their overall gene expression patterns, tissue-specific gene expression and house-keeping gene expression. We further identified conserved and divergent expression genes between the two species, with the largest number of differentially expressed genes found in the skin, which may be related to structural and functional differences in the skin of the two species. This work provides a source of functional annotation of the buffalo genome and lays the foundations for future genetic and evolutionary studies in water buffalo.

Acetylation of histone H2B marks active enhancers and predicts CBP/p300 target genes

Chromatin features are widely used for genome-scale mapping of enhancers. However, discriminating active enhancers from other cis-regulatory elements, predicting enhancer strength and identifying their target genes is challenging. Here we establish histone H2B N-terminus multisite lysine acetylation (H2BNTac) as a signature of active enhancers. H2BNTac prominently marks candidate active enhancers and a subset of promoters and discriminates them from ubiquitously active promoters. Two mechanisms underlie the distinct H2BNTac specificity: (1) unlike H3K27ac, H2BNTac is specifically catalyzed by CBP/p300; (2) H2A-H2B, but not H3-H4, are rapidly exchanged through transcription-induced nucleosome remodeling. H2BNTac-positive candidate enhancers show a high validation rate in orthogonal enhancer activity assays and a vast majority of endogenously active enhancers are marked by H2BNTac and H3K27ac. Notably, H2BNTac intensity predicts enhancer strength and outperforms current state-of-the-art models in predicting CBP/p300 target genes. These findings have broad implications for generating fine-grained enhancer maps and modeling CBP/p300-dependent gene regulation.

Multi-omics approaches to study platelet mechanisms

Platelets are small anucleate cell fragments (2-4 μm in diameter) in the blood, which play an essential role in thrombosis and hemostasis. Genetic or acquired platelet dysfunctions are linked to bleeding, increased risk of thromboembolic events and cardiovascular diseases. Advanced proteomic approaches may pave the way to a better understanding of the roles of platelets in hemostasis, and pathophysiological processes such as inflammation, metastatic spread and thrombosis. Further insights into the molecular biology of platelets are crucial to aid drug development and identify diagnostic markers of platelet activation. Platelet activation is known to be an extremely rapid process and involves multiple post-translational mechanisms at sub second time scale, including proteolysis and phosphorylation. Multi-omics technologies and biochemical approaches can be exploited to precisely probe and define these posttranslational pathways. Notably, the absence of a nucleus in platelets significantly reduces the number of present proteins, simplifying mass spectrometry-based proteomics and metabolomics approaches.

Transfer learning identifies sequence determinants of cell-type specific regulatory element accessibility

Dysfunction of regulatory elements through genetic variants is a central mechanism in the pathogenesis of disease. To better understand disease etiology, there is consequently a need to understand how DNA encodes regulatory activity. Deep learning methods show great promise for modeling of biomolecular data from DNA sequence but are limited to large input data for training. Here, we develop ChromTransfer, a transfer learning method that uses a pre-trained, cell-type agnostic model of open chromatin regions as a basis for fine-tuning on regulatory sequences. We demonstrate superior performances with ChromTransfer for learning cell-type specific chromatin accessibility from sequence compared to models not informed by a pre-trained model. Importantly, ChromTransfer enables fine-tuning on small input data with minimal decrease in accuracy. We show that ChromTransfer uses sequence features matching binding site sequences of key transcription factors for prediction. Together, these results demonstrate ChromTransfer as a promising tool for learning the regulatory code.

Hacking hematopoiesis - emerging tools for examining variant effects

Hematopoiesis is a continuous process of blood and immune cell production. It is orchestrated by thousands of gene products that respond to extracellular signals by guiding cell fate decisions to meet the needs of the organism. Although much of our knowledge of this process comes from work in model systems, we have learned a great deal from studies on human genetic variation. Considerable insight has emerged from studies on presumed monogenic blood disorders, which continue to provide key insights into the mechanisms critical for hematopoiesis. Furthermore, the emergence of large-scale biobanks and cohorts has uncovered thousands of genomic loci associated with blood cell traits and diseases. Some of these blood cell trait-associated loci act as modifiers of what were once thought to be monogenic blood diseases. However, most of these loci await functional validation. Here, we discuss the validation bottleneck and emerging methods to more effectively connect variant to function. In particular, we highlight recent innovations in genome editing, which have paved the path forward for high-throughput functional assessment of loci. Finally, we discuss existing barriers to progress, including challenges in manipulating the genomes of primary hematopoietic cells.

Systemic interindividual epigenetic variation in humans is associated with transposable elements and under strong genetic control

BACKGROUND

Genetic variants can modulate phenotypic outcomes via epigenetic intermediates, for example at methylation quantitative trait loci (mQTL). We present the first large-scale assessment of mQTL at human genomic regions selected for interindividual variation in CpG methylation, which we call correlated regions of systemic interindividual variation (CoRSIVs). These can be assayed in blood DNA and do not reflect interindividual variation in cellular composition.

RESULTS

We use target-capture bisulfite sequencing to assess DNA methylation at 4086 CoRSIVs in multiple tissues from each of 188 donors in the NIH Gene-Tissue Expression (GTEx) program. At CoRSIVs, DNA methylation in peripheral blood correlates with methylation and gene expression in internal organs. We also discover unprecedented mQTL at these regions. Genetic influences on CoRSIV methylation are extremely strong (median R²=0.76), cumulatively comprising over 70-fold more human mQTL than detected in the most powerful previous study. Moreover, mQTL beta coefficients at CoRSIVs are highly skewed (i.e., the major allele predicts higher methylation). Both surprising findings are independently validated in a cohort of 47 non-GTEx individuals. Genomic regions flanking CoRSIVs show long-range enrichments for LINE-1 and LTR transposable elements; the skewed beta coefficients may therefore reflect evolutionary selection of genetic variants that promote their methylation and silencing. Analyses of GWAS summary statistics show that mQTL polymorphisms at CoRSIVs are associated with metabolic and other classes of disease.

CONCLUSIONS

A focus on systemic interindividual epigenetic variants, clearly enhanced in mQTL content, should likewise benefit studies attempting to link human epigenetic variation to the risk of disease.

Quantitative Comparison of Multiple Chromatin Immunoprecipitation-Sequencing (ChIP-seq) Experiments with spikChIP

The chromatin immunoprecipitation coupled with the next-generation sequencing (ChIP-seq) is a powerful technique that enables to characterize the genomic distribution of chromatin-associated proteins, histone posttranslational modifications, and histone variants. However, in the absence of a reference control for monitoring experimental and biological variations, the standard ChIP-seq scheme is unable to accurately assess changes in the abundance of chromatin targets across different experimental samples. To overcome this limitation, the combination of external spike-in material with the experimental chromatin is offered as an effective solution for quantitative comparison of ChIP-seq data across different conditions. Here, we detail (i) the experimental protocol for preparing quality control spike-in chromatin from Drosophila melanogaster cells and (ii) the computational protocol to compare ChIP-seq samples with spike-in based on the use of the spikChIP software.

Cell-Type Deconvolution of Bulk DNA Methylation Data with EpiSCORE

DNA methylation data generated from bulk tissue represents a mixture of many different cell types. Variation in the cell-type composition of tissues is thus a major confounder when inferring differential DNA methylation. Due to the high cost of single-cell methylome sequencing, computational methods that can dissect the cell-type heterogeneity of bulk DNA methylomes offer an efficient and cost-effective solution, especially in the context of large-scale EWAS. In this chapter, we present a step-by-step tutorial of Epigenetic cell-type deconvolution using Single-Cell Omic References (EpiSCORE), a reference-based method that leverages the high-resolution nature of single-cell RNA-Seq datasets to facilitate microdissection of bulk-tissue DNA methylomes.

Epigenetics, genomics imprinting and non-coding RNAs

Epigenetic traits are heritable phenotypes caused by alterations in chromosomes rather than DNA sequences. The actual epigenetic expression of the somatic cells of a species is identical, however, they may show distinct subtleties in various cell types in which they may be affected. Several recent studies demonstrated that the epigenetic system plays a very important role in regulating all biological natural processes in the body from birth to death. We outline the essential elements of epigenetics, genomic imprinting, and non-coding RNAs in this mini-review.

COOBoostR: An Extreme Gradient Boosting-Based Tool for Robust Tissue or Cell-of-Origin Prediction of Tumors

We present here COOBoostR, a computational method designed for the putative prediction of the tissue- or cell-of-origin of various cancer types. COOBoostR leverages regional somatic mutation density information and chromatin mark features to be applied to an extreme gradient boosting-based machine-learning algorithm. COOBoostR ranks chromatin marks from various tissue and cell types, which best explain the somatic mutation density landscape of any sample of interest. A specific tissue or cell type matching the chromatin mark feature with highest explanatory power is designated as a potential tissue- or cell-of-origin. Through integrating either ChIP-seq based chromatin data, along with regional somatic mutation density data derived from normal cells/tissue, precancerous lesions, and cancer types, we show that COOBoostR outperforms existing random forest-based methods in prediction speed, with comparable or better tissue or cell-of-origin prediction performance (prediction accuracy-normal cells/tissue: 76.99%, precancerous lesions: 95.65%, cancer cells: 89.39%). In addition, our results suggest a dynamic somatic mutation accumulation at the normal tissue or cell stage which could be intertwined with the changes in open chromatin marks and enhancer sites. These results further represent chromatin marks shaping the somatic mutation landscape at the early stage of mutation accumulation, possibly even before the initiation of precancerous lesions or neoplasia.

Parent-of-origin detection and chromosome-scale haplotyping using long-read DNA methylation sequencing and Strand-seq

Hundreds of loci in human genomes have alleles that are methylated differentially according to their parent of origin. These imprinted loci generally show little variation across tissues, individuals, and populations. We show that such loci can be used to distinguish the maternal and paternal homologs for all human autosomes without the need for the parental DNA. We integrate methylation-detecting nanopore sequencing with the long-range phase information in Strand-seq data to determine the parent of origin of chromosome-length haplotypes for both DNA sequence and DNA methylation in five trios with diverse genetic backgrounds. The parent of origin was correctly inferred for all autosomes with an average mismatch error rate of 0.31% for SNVs and 1.89% for insertions or deletions (indels). Because our method can determine whether an inherited disease allele originated from the mother or the father, we predict that it will improve the diagnosis and management of many genetic diseases.

CRAG: de novo characterization of cell-free DNA fragmentation hotspots in plasma whole-genome sequencing

The fine-scale cell-free DNA fragmentation patterns in early-stage cancers are poorly understood. We developed a de novo approach to characterize the cell-free DNA fragmentation hotspots from plasma whole-genome sequencing. Hotspots are enriched in open chromatin regions, and, interestingly, 3'end of transposons. Hotspots showed global hypo-fragmentation in early-stage liver cancers and are associated with genes involved in the initiation of hepatocellular carcinoma and associated with cancer stem cells. The hotspots varied across multiple early-stage cancers and demonstrated high performance for the diagnosis and identification of tissue-of-origin in early-stage cancers. We further validated the performance with a small number of independent case-control-matched early-stage cancer samples.

Current challenges in understanding the role of enhancers in disease

Enhancers play a central role in the spatiotemporal control of gene expression and tend to work in a cell-type-specific manner. In addition, they are suggested to be major contributors to phenotypic variation, evolution and disease. There is growing evidence that enhancer dysfunction due to genetic, structural or epigenetic mechanisms contributes to a broad range of human diseases referred to as enhanceropathies. Such mechanisms often underlie the susceptibility to common diseases, but can also play a direct causal role in cancer or Mendelian diseases. Despite the recent gain of insights into enhancer biology and function, we still have a limited ability to predict how enhancer dysfunction impacts gene expression. Here we discuss the major challenges that need to be overcome when studying the role of enhancers in disease etiology and highlight opportunities and directions for future studies, aiming to disentangle the molecular basis of enhanceropathies.

CHD6 promotes broad nucleosome eviction for transcriptional activation in prostate cancer cells

Despite being a member of the chromodomain helicase DNA-binding protein family, little is known about the exact role of CHD6 in chromatin remodeling or cancer disease. Here we show that CHD6 binds to chromatin to promote broad nucleosome eviction for transcriptional activation of many cancer pathways. By integrating multiple patient cohorts for bioinformatics analysis of over a thousand prostate cancer datasets, we found CHD6 expression elevated in prostate cancer and associated with poor prognosis. Further comprehensive experiments demonstrated that CHD6 regulates oncogenicity of prostate cancer cells and tumor development in a murine xenograft model. ChIP-Seq for CHD6, along with MNase-Seq and RNA-Seq, revealed that CHD6 binds on chromatin to evict nucleosomes from promoters and gene bodies for transcriptional activation of oncogenic pathways. These results demonstrated a key function of CHD6 in evicting nucleosomes from chromatin for transcriptional activation of prostate cancer pathways.

Exploring the epitranscriptome by native RNA sequencing

Chemical RNA modifications, collectively referred to as the "epitranscriptome," are essential players in fine-tuning gene expression. Our ability to analyze RNA modifications has improved rapidly in recent years, largely due to the advent of high-throughput sequencing methodologies, which typically consist of coupling modification-specific reagents, such as antibodies or enzymes, to next-generation sequencing. Recently, it also became possible to map RNA modifications directly by sequencing native RNAs using nanopore technologies, which has been applied for the detection of a number of RNA modifications, such as N6-methyladenosine (m⁶A), pseudouridine (Ψ), and inosine (I). However, the signal modulations caused by most RNA modifications are yet to be determined. A global effort is needed to determine the signatures of the full range of RNA modifications to avoid the technical biases that have so far limited our understanding of the epitranscriptome.

scTAM-seq enables targeted high-confidence analysis of DNA methylation in single cells

Single-cell DNA methylation profiling currently suffers from excessive noise and/or limited cellular throughput. We developed scTAM-seq, a targeted bisulfite-free method for profiling up to 650 CpGs in up to 10,000 cells per experiment, with a dropout rate as low as 7%. We demonstrate that scTAM-seq can resolve DNA methylation dynamics across B-cell differentiation in blood and bone marrow, identifying intermediate differentiation states that were previously masked. scTAM-seq additionally queries surface-protein expression, thus enabling integration of single-cell DNA methylation information with cell atlas data. In summary, scTAM-seq is a high-throughput, high-confidence method for analyzing DNA methylation at single-CpG resolution across thousands of single cells.

Multiplexed functional genomic assays to decipher the noncoding genome

Linkage disequilibrium and the incomplete regulatory annotation of the noncoding genome complicates the identification of functional noncoding genetic variants and their causal association with disease. Current computational methods for variant prioritization have limited predictive value, necessitating the application of highly parallelized experimental assays to efficiently identify functional noncoding variation. Here, we summarize two distinct approaches, massively parallel reporter assays and CRISPR-based pooled screens and describe their flexible implementation to characterize human noncoding genetic variation at unprecedented scale. Each approach provides unique advantages and limitations, highlighting the importance of multimodal methodological integration. These multiplexed assays of variant effects are undoubtedly poised to play a key role in the experimental characterization of noncoding genetic risk, informing our understanding of the underlying mechanisms of disease-associated loci and the development of more robust predictive classification algorithms.

Computational methods to explore chromatin state dynamics

The human genome is marked by several singular and combinatorial histone modifications that shape the different states of chromatin and its three-dimensional organization. Genome-wide mapping of these marks as well as histone variants and open chromatin regions is commonly carried out via profiling DNA-protein binding or via chromatin accessibility methods. After the generation of epigenomic datasets in a cell type, statistical models can be used to annotate the noncoding regions of DNA and infer the combinatorial histone marks or chromatin states (CS). These methods involve partitioning the genome and labeling individual segments based on their CS patterns. Chromatin labels enable the systematic discovery of genomic function and activity and can label the gene body, promoters or enhancers without using other genomic maps. CSs are dynamic and change under different cell conditions, such as in normal, preneoplastic or tumor cells. This review aims to explore the available computational tools that have been developed to capture CS alterations under two or more cellular conditions.

Long-range phasing of dynamic, tissue-specific and allele-specific regulatory elements

Epigenomic maps identify gene regulatory elements by their chromatin state. However, prevailing short-read sequencing methods cannot effectively distinguish alleles, evaluate the interdependence of elements in a locus or capture single-molecule dynamics. Here, we apply targeted nanopore sequencing to profile chromatin accessibility and DNA methylation on contiguous ~100-kb DNA molecules that span loci relevant to development, immunity and imprinting. We detect promoters, enhancers, insulators and transcription factor footprints on single molecules based on exogenous GpC methylation. We infer relationships among dynamic elements within immune loci, and order successive remodeling events during T cell stimulation. Finally, we phase primary sequence and regulatory elements across the H19/IGF2 locus, uncovering primate-specific features. These include a segmental duplication that stabilizes the imprinting control region and a noncanonical enhancer that drives biallelic IGF2 expression in specific contexts. Our study advances emerging strategies for phasing gene regulatory landscapes and reveals a mechanism that overrides IGF2 imprinting in human cells.