Allele-specific epigenome maps reveal sequence-dependent stochastic switching at regulatory loci

AIMER: A SNP-independent software for identifying imprinting-like allelic methylated regions from DNA methylome

Genomic imprinting is essential for mammalian growth and embryogenesis. High-throughput bisulfite sequencing accompanied with parental haplotype-specific information allows analysis of imprinted genes and imprinting control regions (ICRs) on a large scale. Currently, although several allelic methylated regions (AMRs) detection software were developed, methods for detecting imprinted AMRs is still limited. Here, we developed a SNP-independent statistical approach, AIMER, to detect imprinting-like AMRs. By using the mouse frontal cortex methylome as input, we demonstrated that AIMER performs very well in detecting known germline ICRs compared with other methods. Furthermore, we found the putative parental AMRs AIMER detected could be distinguished from sequence-dependent AMRs. Finally, we found a novel germline imprinting-like AMR using WGBS data from 17 distinct mouse tissue samples. The results indicate that AIMER is a good choice for detecting imprinting-like (parent-of-origin-dependent) AMRs. We hope this method will be helpful for future genomic imprinting studies. The Python source code for our project is now publicly available on both GitHub (https://github.com/ZhaoLab-TMU/AIMER) and Gitee (https://gitee.com/zhaolab_tmu/AIMER).

Antipsychotic-induced epigenomic reorganization in frontal cortex of individuals with schizophrenia

Genome-wide association studies have revealed >270 loci associated with schizophrenia risk, yet these genetic factors do not seem to be sufficient to fully explain the molecular determinants behind this psychiatric condition. Epigenetic marks such as post-translational histone modifications remain largely plastic during development and adulthood, allowing a dynamic impact of environmental factors, including antipsychotic medications, on access to genes and regulatory elements. However, few studies so far have profiled cell-specific genome-wide histone modifications in postmortem brain samples from schizophrenia subjects, or the effect of antipsychotic treatment on such epigenetic marks. Here, we conducted ChIP-seq analyses focusing on histone marks indicative of active enhancers (H3K27ac) and active promoters (H3K4me3), alongside RNA-seq, using frontal cortex samples from antipsychotic-free (AF) and antipsychotic-treated (AT) individuals with schizophrenia, as well as individually matched controls (n=58). Schizophrenia subjects exhibited thousands of neuronal and non-neuronal epigenetic differences at regions that included several susceptibility genetic loci, such as NRG1, DISC1, and DRD3. By analyzing the AF and AT cohorts separately, we identified schizophrenia-associated alterations in specific transcription factors, their regulatees, and epigenomic and transcriptomic features that were reversed by antipsychotic treatment; as well as those that represented a consequence of antipsychotic medication rather than a hallmark of schizophrenia in postmortem human brain samples. Notably, we also found that the effect of age on epigenomic landscapes was more pronounced in frontal cortex of AT-schizophrenics, as compared to AF-schizophrenics and controls. Together, these data provide important evidence of epigenetic alterations in the frontal cortex of individuals with schizophrenia, and remark for the first time on the impact of age and antipsychotic treatment on chromatin organization.

Epigenomic insights into common human disease pathology

The epigenome-the chemical modifications and chromatin-related packaging of the genome-enables the same genetic template to be activated or repressed in different cellular settings. This multi-layered mechanism facilitates cell-type specific function by setting the local sequence and 3D interactive activity level. Gene transcription is further modulated through the interplay with transcription factors and co-regulators. The human body requires this epigenomic apparatus to be precisely installed throughout development and then adequately maintained during the lifespan. The causal role of the epigenome in human pathology, beyond imprinting disorders and specific tumour suppressor genes, was further brought into the spotlight by large-scale sequencing projects identifying that mutations in epigenomic machinery genes could be critical drivers in both cancer and developmental disorders. Abrogation of this cellular mechanism is providing new molecular insights into pathogenesis. However, deciphering the full breadth and implications of these epigenomic changes remains challenging. Knowledge is accruing regarding disease mechanisms and clinical biomarkers, through pathogenically relevant and surrogate tissue analyses, respectively. Advances include consortia generated cell-type specific reference epigenomes, high-throughput DNA methylome association studies, as well as insights into ageing-related diseases from biological 'clocks' constructed by machine learning algorithms. Also, 3rd-generation sequencing is beginning to disentangle the complexity of genetic and DNA modification haplotypes. Cell-free DNA methylation as a cancer biomarker has clear clinical utility and further potential to assess organ damage across many disorders. Finally, molecular understanding of disease aetiology brings with it the opportunity for exact therapeutic alteration of the epigenome through CRISPR-activation or inhibition.

Epigenomic states contribute to coordinated allelic transcriptional bursting in iPSC reprogramming

Two alleles of a gene can be transcribed independently or coordinatedly, which can lead to temporal expression heterogeneity with potentially distinct impacts on cell fate. Here, we profiled genome-wide allelic transcriptional burst kinetics during the reprogramming of MEF to induced pluripotent stem cells. We show that the degree of coordination of allelic bursting differs among genes, and alleles of many reprogramming-related genes burst in a highly coordinated fashion. Notably, we show that the chromatin accessibility of the two alleles of highly coordinated genes is similar, unlike the semi-coordinated or independent genes, suggesting the degree of coordination of allelic bursting is linked to allelic chromatin accessibility. Consistently, we show that many transcription factors have differential binding affinity between alleles of semi-coordinated or independent genes. We show that highly coordinated genes are enriched with chromatin accessibility regulators such as H3K4me3, H3K4me1, H3K36me3, H3K27ac, histone variant H3.3, and BRD4. Finally, we demonstrate that enhancer elements are highly enriched in highly coordinated genes. Our study demonstrates that epigenomic states contribute to coordinated allelic bursting to fine-tune gene expression during induced pluripotent stem cell reprogramming.

Phased genomics reveals hidden somatic mutations and provides insight into fruit development in sweet orange

Although revisiting the discoveries and implications of genetic variations using phased genomics is critical, such efforts are still lacking. Somatic mutations represent a crucial source of genetic diversity for breeding and are especially remarkable in heterozygous perennial and asexual crops. In this study, we focused on a diploid sweet orange (Citrus sinensis) and constructed a haplotype-resolved genome using high fidelity (HiFi) reads, which revealed 10.6% new sequences. Based on the phased genome, we elucidate significant genetic admixtures and haplotype differences. We developed a somatic detection strategy that reveals hidden somatic mutations overlooked in a single reference genome. We generated a phased somatic variation map by combining high-depth whole-genome sequencing (WGS) data from 87 sweet orange somatic varieties. Notably, we found twice as many somatic mutations relative to a single reference genome. Using these hidden somatic mutations, we separated sweet oranges into seven major clades and provide insight into unprecedented genetic mosaicism and strong positive selection. Furthermore, these phased genomics data indicate that genomic heterozygous variations contribute to allele-specific expression during fruit development. By integrating allelic expression differences and somatic mutations, we identified a somatic mutation that induces increases in fruit size. Applications of phased genomics will lead to powerful approaches for discovering genetic variations and uncovering their effects in highly heterozygous plants. Our data provide insight into the hidden somatic mutation landscape in the sweet orange genome, which will facilitate citrus breeding.

Metastable epialleles in humans

First identified in isogenic mice, metastable epialleles (MEs) are loci where the extent of DNA methylation (DNAm) is variable between individuals but correlates across tissues derived from different germ layers within a given individual. This property, termed systemic interindividual variation (SIV), is attributed to stochastic methylation establishment before germ layer differentiation. Evidence suggests that some putative human MEs are sensitive to environmental exposures in early development. In this review we introduce key concepts pertaining to human MEs, describe methods used to identify MEs in humans, and review their genomic features. We also highlight studies linking DNAm at putative human MEs to early environmental exposures and postnatal (including disease) phenotypes.

Transition of allele-specific DNA hydroxymethylation at regulatory loci is associated with phenotypic variation in monozygotic twins discordant for psychiatric disorders

BACKGROUND

Major psychiatric disorders such as schizophrenia (SCZ) and bipolar disorder (BPD) are complex genetic mental illnesses. Their non-Mendelian features, such as those observed in monozygotic twins discordant for SCZ or BPD, are likely complicated by environmental modifiers of genetic effects. 5-Hydroxymethylcytosine (5hmC) is an important epigenetic mark in gene regulation, and whether it is linked to genetic variants that contribute to non-Mendelian features remains largely unexplored.

METHODS

We combined the 5hmC-selective chemical labeling method (5hmC-seq) and whole-genome sequencing (WGS) analysis of peripheral blood DNA obtained from monozygotic (MZ) twins discordant for SCZ or BPD to identify allelic imbalances in hydroxymethylome maps, and examined association of allele-specific hydroxymethylation (AShM) transition with disease susceptibility based on Bayes factors (BF) derived from the Bayesian generalized additive linear mixed model. We then performed multi-omics integrative analysis to determine the molecular pathogenic basis of those AShM sites. We finally employed luciferase reporter, CRISPR/Cas9 technology, electrophoretic mobility shift assay (EMSA), chromatin immunoprecipitation (ChIP), PCR, FM4-64 imaging analysis, and RNA sequencing to validate the function of interested AShM sites in the human neuroblastoma SK-N-SH cells and human embryonic kidney 293T (HEK293T) cells.

RESULTS

We identified thousands of genetic variants associated with AShM imbalances that exhibited phenotypic variation-associated AShM changes at regulatory loci. These AShM marks showed plausible associations with SCZ or BPD based on their effects on interactions among transcription factors (TFs), DNA methylation levels, or other epigenomic marks and thus contributed to dysregulated gene expression, which ultimately increased disease susceptibility. We then validated that competitive binding of POU3F2 on the alternative allele at the AShM site rs4558409 (G/T) in PLLP-enhanced PLLP expression, while the hydroxymethylated alternative allele, which alleviated the POU3F2 binding activity at the rs4558409 site, might be associated with the downregulated PLLP expression observed in BPD or SCZ. Moreover, disruption of rs4558409 promoted neural development and vesicle trafficking.

CONCLUSION

Our study provides a powerful strategy for prioritizing regulatory risk variants and contributes to our understanding of the interplay between genetic and epigenetic factors in mediating SCZ or BPD susceptibility.

DNA methylation differences between stick insect ecotypes

Epigenetic mechanisms, such as DNA methylation, can influence gene regulation and affect phenotypic variation, raising the possibility that they contribute to ecological adaptation. Beginning to address this issue requires high-resolution sequencing studies of natural populations to pinpoint epigenetic regions of potential ecological and evolutionary significance. However, such studies are still relatively uncommon, especially in insects, and are mainly restricted to a few model organisms. Here, we characterize patterns of DNA methylation for natural populations of Timema cristinae adapted to two host plant species (i.e. ecotypes). By integrating results from sequencing of whole transcriptomes, genomes and methylomes, we investigate whether environmental, host and genetic differences of these stick insects are associated with methylation levels of cytosine nucleotides in the CpG context. We report an overall genome-wide methylation level for T. cristinae of ~14%, with methylation being enriched in gene bodies and impoverished in repetitive elements. Genome-wide DNA methylation variation was strongly positively correlated with genetic distance (relatedness), but also exhibited significant host-plant effects. Using methylome-environment association analysis, we pinpointed specific genomic regions that are differentially methylated between ecotypes, with these regions being enriched for genes with functions in membrane processes. The observed association between methylation variation and genetic relatedness, and with the ecologically important variable of host plant, suggests a potential role for epigenetic modification in T. cristinae adaptation. To substantiate such adaptive significance, future studies could test whether methylation can be transmitted across generations and the extent to which it responds to experimental manipulation in field and laboratory studies.

Simultaneous sequencing of genetic and epigenetic bases in DNA

DNA comprises molecular information stored in genetic and epigenetic bases, both of which are vital to our understanding of biology. Most DNA sequencing approaches address either genetics or epigenetics and thus capture incomplete information. Methods widely used to detect epigenetic DNA bases fail to capture common C-to-T mutations or distinguish 5-methylcytosine from 5-hydroxymethylcytosine. We present a single base-resolution sequencing methodology that sequences complete genetics and the two most common cytosine modifications in a single workflow. DNA is copied and bases are enzymatically converted. Coupled decoding of bases across the original and copy strand provides a phased digital readout. Methods are demonstrated on human genomic DNA and cell-free DNA from a blood sample of a patient with cancer. The approach is accurate, requires low DNA input and has a simple workflow and analysis pipeline. Simultaneous, phased reading of genetic and epigenetic bases provides a more complete picture of the information stored in genomes and has applications throughout biomedicine.

Allele-specific RNA N 6-methyladenosine modifications reveal functional genetic variants in human tissues

An intricate network of cis- and trans-elements acts on RNA N 6-methyladenosine (m6A), which in turn may affect gene expression and, ultimately, human health. A complete understanding of this network requires new approaches to accurately measure the subtle m6A differences arising from genetic variants, many of which have been associated with common diseases. To address this gap, we developed a method to accurately and sensitively detect transcriptome-wide allele-specific m6A (ASm6A) from MeRIP-seq data and applied it to uncover 12,056 high-confidence ASm6A modifications from 25 human tissues. We also identified 1184 putative functional variants for ASm6A regulation, a subset of which we experimentally validated. Importantly, we found that many of these ASm6A-associated genetic variants were enriched for common disease-associated and complex trait-associated risk loci, and verified that two disease risk variants can change m6A modification status. Together, this work provides a tool to detangle the dynamic network of RNA modifications at the allelic level and highlights the interplay of m6A and genetics in human health and disease.

MethNet: a robust approach to identify regulatory hubs and their distal targets in cancer

Aberrations in the capacity of DNA/chromatin modifiers and transcription factors to bind non-coding regions can lead to changes in gene regulation and impact disease phenotypes. However, identifying distal regulatory elements and connecting them with their target genes remains challenging. Here, we present MethNet, a pipeline that integrates large-scale DNA methylation and gene expression data across multiple cancers, to uncover novel cis regulatory elements (CREs) in a 1Mb region around every promoter in the genome. MethNet identifies clusters of highly ranked CREs, referred to as 'hubs', which contribute to the regulation of multiple genes and significantly affect patient survival. Promoter-capture Hi-C confirmed that highly ranked associations involve physical interactions between CREs and their gene targets, and CRISPRi based scRNA Perturb-seq validated the functional impact of CREs. Thus, MethNet-identified CREs represent a valuable resource for unraveling complex mechanisms underlying gene expression, and for prioritizing the verification of predicted non-coding disease hotspots.

Sequence-to-expression approach to identify etiological non-coding DNA variations in P53 and cMYC-driven diseases

Background and methods

Disease risk prediction based on DNA sequence and transcriptional profile can improve disease screening, prevention, and potential therapeutic approaches by revealing contributing genetic factors and altered regulatory networks. Despite identifying many disease-associated DNA variants through genome-wide association studies, distinguishing deleterious non-coding DNA variations remains poor for most common diseases. We previously reported that non-coding variations disrupting cis-overlapping motifs (CisOMs) of opposing transcription factors significantly affect enhancer activity. We designed in vitro experiments to uncover the significance of the co-occupancy and competitive binding and inhibition between P53 and cMYC on common target gene expression.

Results

Analyzing publicly available ChIP-seq data for P53 and cMYC in human embryonic stem cells and mouse embryonic cells showed that ~ 344-366 genomic regions are co-occupied by P53 and cMYC. We identified, on average, two CisOMs per region, suggesting that co-occupancy is evolutionarily conserved in vertebrates. Our data showed that treating U2OS cells with doxorubicin increased P53 protein level while reducing cMYC level. In contrast, no change in protein levels was observed in Raji cells. ChIP-seq analysis illustrated that 16-922 genomic regions were co-occupied by P53 and cMYC before and after treatment, and substitutions of cMYC signals by P53 were detected after doxorubicin treatment in U2OS. Around 187 expressed genes near co-occupied regions were altered at mRNA level according to RNA-seq data. We utilized a computational motif-matching approach to determine that changes in predicted P53 binding affinity by DNA variations in CisOMs of co-occupied elements significantly correlate with alterations in reporter gene expression. We performed a similar analysis using SNPs mapped in CisOMs for P53 and cMYC from ChIP-seq data in U2OS and Raji, and expression of target genes from the GTEx portal.

Conclusions

We found a significant correlation between change in motif-predicted cMYC binding affinity by SNPs in CisOMs and altered gene expression. Our study brings us closer to developing a generally applicable approach to filter etiological non-coding variations associated with P53 and cMYC-dependent diseases.

The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models

Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × ∼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.

DNA methylation entropy is associated with DNA sequence features and developmental epigenetic divergence

Epigenetic information defines tissue identity and is largely inherited in development through DNA methylation. While studied mostly for mean differences, methylation also encodes stochastic change, defined as entropy in information theory. Analyzing allele-specific methylation in 49 human tissue sample datasets, we find that methylation entropy is associated with specific DNA binding motifs, regulatory DNA, and CpG density. Then applying information theory to 42 mouse embryo methylation datasets, we find that the contribution of methylation entropy to time- and tissue-specific patterns of development is comparable to the contribution of methylation mean, and methylation entropy is associated with sequence and chromatin features conserved with human. Moreover, methylation entropy is directly related to gene expression variability in development, suggesting a role for epigenetic entropy in developmental plasticity.

Genome-wide evaluation of the effect of short tandem repeat variation on local DNA methylation

Short tandem repeats (STRs) contribute significantly to genetic diversity in humans, including disease-causing variation. Although the effect of STR variation on gene expression has been extensively assessed, their impact on epigenetics has been poorly studied and limited to specific genomic regions. Here, we investigated the hypothesis that some STRs act as independent regulators of local DNA methylation in the human genome and modify risk of common human traits. To address these questions, we first analyzed two independent data sets comprising PCR-free whole-genome sequencing (WGS) and genome-wide DNA methylation levels derived from whole-blood samples in 245 (discovery cohort) and 484 individuals (replication cohort). Using genotypes for 131,635 polymorphic STRs derived from WGS using HipSTR, we identified 11,870 STRs that associated with DNA methylation levels (mSTRs) of 11,774 CpGs (Bonferroni P < 0.001) in our discovery cohort, with 90% successfully replicating in our second cohort. Subsequently, through fine-mapping using CAVIAR we defined 585 of these mSTRs as the likely causal variants underlying the observed associations (fm-mSTRs) and linked a fraction of these to previously reported genome-wide association study signals, providing insights into the mechanisms underlying complex human traits. Furthermore, by integrating gene expression data, we observed that 12.5% of the tested fm-mSTRs also modulate expression levels of nearby genes, reinforcing their regulatory potential. Overall, our findings expand the catalog of functional sequence variants that affect genome regulation, highlighting the importance of incorporating STRs in future genetic association analysis and epigenetics data for the interpretation of trait-associated variants.

MC profiling: a novel approach to analyze DNA methylation heterogeneity in genome-wide bisulfite sequencing data

DNA methylation is an epigenetic mark implicated in crucial biological processes. Most of the knowledge about DNA methylation is based on bulk experiments, in which DNA methylation of genomic regions is reported as average methylation. However, average methylation does not inform on how methylated cytosines are distributed in each single DNA molecule. Here, we propose Methylation Class (MC) profiling as a genome-wide approach to the study of DNA methylation heterogeneity from bulk bisulfite sequencing experiments. The proposed approach is built on the concept of MCs, groups of DNA molecules sharing the same number of methylated cytosines. The relative abundances of MCs from sequencing reads incorporates the information on the average methylation, and directly informs on the methylation level of each molecule. By applying our approach to publicly available bisulfite-sequencing datasets, we individuated cell-to-cell differences as the prevalent contributor to methylation heterogeneity. Moreover, we individuated signatures of loci undergoing imprinting and X-inactivation, and highlighted differences between the two processes. When applying MC profiling to compare different conditions, we identified methylation changes occurring in regions with almost constant average methylation. Altogether, our results indicate that MC profiling can provide useful insights on the epigenetic status and its evolution at multiple genomic regions.

Genome-Wide DNA Methylation Profile Indicates Potential Epigenetic Regulation of Aging in the Rhesus Macaque Thymus

We analyzed whole-genome bisulfite sequencing (WGBS) and RNA sequencing data of two young (1 year old) and two adult (9 years old) rhesus macaques (Macaca mulatta) to characterize the genomic DNA methylation profile of the thymus and explore the molecular mechanism of age-related changes in the thymus. Combining the two-omics data, we identified correlations between DNA methylation and gene expression and found that DNA methylation played an essential role in the functional changes of the aging thymus, especially in immunity and coagulation. The hypomethylation levels of C3 and C5AR2 and the hypermethylation level of C7 may lead to the high expressions of these genes in adult rhesus macaque thymuses, thus activating the classical complement pathway and the alternative pathway and enhancing their innate immune function. Adult thymuses had an enhanced coagulation pathway, which may have resulted from the hypomethylation and upregulated expressions of seven coagulation-promoting factor genes (F13A1, CLEC4D, CLEC4E, FCN3, PDGFRA, FGF2 and FGF7) and the hypomethylation and low expression of CPB2 to inhibit the degradation of blood clots. Furthermore, the functional decline in differentiation, activation and maturation of T cells in adult thymuses was also closely related to the changes in methylation levels and gene expression levels of T cell development genes (CD3G, GAD2, ADAMDEC1 and LCK) and the thymogenic hormone gene TMPO. A comparison of the age-related methylated genes among four mammal species revealed that most of the epigenetic clocks were species-specific. Furthermore, based on the genomic landscape of allele-specific DNA methylation, we identified several age-related clustered sequence-dependent allele-specific DNA methylated (cS-ASM) genes. Overall, these DNA methylation patterns may also help to assist with understanding the mechanisms of the aging thymus with the epigenome.

Without mechanisms, theories and models in insect epigenetics remain a black box

Insect epigenetics must confront the remarkable diversity of epigenomic systems in various lineages and use mechanistic approaches to move beyond vague functional explanations based on predictions and inferences. To accelerate progress, what is required now is a convergence of genomic data with biochemical and single-cell-type analyses in selected species representing contrasting evolutionary solutions in epigenetics.

Epialleles and epiallelic heterogeneity in hematological malignancies

DNA methylation has a well-established role in the pathogenesis, prognosis, and response to treatment in all the spectra of hematological malignancies. However, most of the data reported involve average DNA methylation observed in a sample. The emergence of bisulfite sequencing methods such as enhanced reduced representation that permit analyze adjacent CpGs led to exciting findings. Among these are the epialleles shift and the resulting epigenetic heterogeneity observed in leukemias and lymphomas. Epialleles seem to have an influential role as the cause of mutations that characterize leukemias, may stratify groups with different prognosis and response to treatment, and may be redistributed in the genome at different time points of the disease promoting activation of alternate transcriptional networks. Epiallelic shift may be responsible for the intratumor heterogeneity observed within the cells of the same tumor which increases with disease aggressiveness. It may also responsible for the interpatient heterogeneity explaining why blood cancers exhibit different behavior among different patients. Understanding better epiallelic conformation and the consequent chromatin conformational changes and the pathways that may be affected will permit deeper understanding of hematological malignancies pathogenesis and treatment.

Computational methods for single-cell DNA methylomes

Dissecting intercellular epigenetic differences is key to understanding tissue heterogeneity. Recent advances in single-cell DNA methylome profiling have presented opportunities to resolve this heterogeneity at the maximum resolution. While these advances enable us to explore frontiers of chromatin biology and better understand cell lineage relationships, they pose new challenges in data processing and interpretation. This review surveys the current state of computational tools developed for single-cell DNA methylome data analysis. We discuss critical components of single-cell DNA methylome data analysis, including data preprocessing, quality control, imputation, dimensionality reduction, cell clustering, supervised cell annotation, cell lineage reconstruction, gene activity scoring, and integration with transcriptome data. We also highlight unique aspects of single-cell DNA methylome data analysis and discuss how techniques common to other single-cell omics data analyses can be adapted to analyzing DNA methylomes. Finally, we discuss existing challenges and opportunities for future development.

SpecHap: a diploid phasing algorithm based on spectral graph theory

Haplotype phasing plays an important role in understanding the genetic data of diploid eukaryotic organisms. Different sequencing technologies (such as next-generation sequencing or third-generation sequencing) produce various genetic data that require haplotype assembly. Although multiple diploid haplotype phasing algorithms exist, only a few will work equally well across all sequencing technologies. In this work, we propose SpecHap, a novel haplotype assembly tool that leverages spectral graph theory. On both in silico and whole-genome sequencing datasets, SpecHap consumed less memory and required less CPU time, yet achieved comparable accuracy with state-of-art methods across all the test instances, which comprises sequencing data from next-generation sequencing, linked-reads, high-throughput chromosome conformation capture, PacBio single-molecule real-time, and Oxford Nanopore long-reads. Furthermore, SpecHap successfully phased an individual Ambystoma mexicanum, a species with gigantic diploid genomes, within 6 CPU hours and 945MB peak memory usage, while other tools failed to yield results either due to memory overflow (40GB) or time limit exceeded (5 days). Our results demonstrated that SpecHap is scalable, efficient, and accurate for diploid phasing across many sequencing platforms.

Allele-specific DNA methylation maps in monozygotic twins discordant for psychiatric disorders reveal that disease-associated switching at the EIPR1 regulatory loci modulates neural function

The non-Mendelian features of phenotypic variations within monozygotic twins are likely complicated by environmental modifiers of genetic effects that have yet to be elucidated. Here, we performed methylome and genome analyses of blood DNA from psychiatric disorder-discordant monozygotic twins to study how allele-specific methylation (ASM) mediates phenotypic variations. We identified that thousands of genetic variants with ASM imbalances exhibit phenotypic variation-associated switching at regulatory loci. These ASMs have plausible causal associations with psychiatric disorders through effects on interactions between transcription factors, DNA methylations, and other epigenomic markers and then contribute to dysregulated gene expression, which eventually increases disease susceptibility. Moreover, we also experimentally validated the model that the rs4854158 alternative C allele at an ASM switching regulatory locus of EIPR1 encoding endosome-associated recycling protein-interacting protein 1, is associated with demethylation and higher RNA expression and shows lower TF binding affinities in unaffected controls. An epigenetic ASM switching induces C allele hypermethylation and then recruits repressive Polycomb repressive complex 2 (PRC2), reinforces trimethylation of lysine 27 on histone 3 and inhibits its transcriptional activity, thus leading to downregulation of EIPR1 in schizophrenia. Moreover, disruption of rs4854158 induces gain of EIPR1 function and promotes neural development and vesicle trafficking. Our study provides a powerful framework for identifying regulatory risk variants and contributes to our understanding of the interplay between genetic and epigenetic variants in mediating psychiatric disorder susceptibility.

Upregulation of Thymidylate Synthase Induces Pemetrexed Resistance in Malignant Pleural Mesothelioma

Malignant pleural mesothelioma (MPM) is an invasive malignancy that develops in the pleural cavity, and antifolates are used as chemotherapeutics for treating. The majority of antifolates, including pemetrexed (PMX), inhibit enzymes involved in purine and pyrimidine synthesis. MPM patients frequently develop drug resistance in clinical practice, however the associated drug-resistance mechanism is not well understood. This study was aimed to elucidate the mechanism underlying resistance to PMX in MPM cell lines. We found that among the differentially expressed genes associated with drug resistance (determined by RNA sequencing), TYMS expression was higher in the established resistant cell lines than in the parental cell lines. Knocking down TYMS expression significantly reduced drug resistance in the resistant cell lines. Conversely, TYMS overexpression significantly increased drug resistance in the parental cells. Metabolomics analysis revealed that the levels of dTMP were higher in the resistant cell lines than in the parental cell lines; however, resistant cells showed no changes in dTTP levels after PMX treatment. We found that the nucleic acid-biosynthetic pathway is important for predicting the efficacy of PMX in MPM cells. The results of chromatin immunoprecipitation-quantitative polymerase chain reaction (ChIP-qPCR) assays suggested that H3K27 acetylation in the 5′-UTR of TYMS may promote its expression in drug-resistant cells. Our findings indicate that the intracellular levels of dTMP are potential biomarkers for the effective treatment of patients with MPM and suggest the importance of regulatory mechanisms of TYMS expression in the disease.

Semicoordinated allelic-bursting shape dynamic random monoallelic expression in pregastrulation embryos

Recently, allele-specific single-cell RNA-seq analysis has demonstrated widespread dynamic random monoallelic expression of autosomal genes (aRME) in different cell types. However, the prevalence of dynamic aRME during pregastrulation remains unknown. Here, we show that dynamic aRME is widespread in different lineages of pregastrulation embryos. Additionally, the origin of dynamic aRME remains elusive. It is believed that independent transcriptional bursting from each allele leads to dynamic aRME. Here, we show that allelic burst is not perfectly independent; instead it happens in a semicoordinated fashion. Importantly, we show that semicoordinated allelic bursting of genes, particularly with low burst frequency, leads to frequent asynchronous allelic bursting, thereby contributing to dynamic aRME. Furthermore, we found that coordination of allelic bursting is lineage specific and genes regulating the development have a higher degree of coordination. Altogether, our study provides significant insights into the prevalence and origin of dynamic aRME and their developmental relevance during early development.

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Dynamic aRME is widespread in different lineages of pregastrulation embryos

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Semicoordinated bursting of genes with low burst frequency leads to dynamic aRME

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Degree of coordination of allelic bursting is lineage specific

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Developmental genes have higher degree of coordination of allelic bursting

Recovering genotypes and phenotypes using allele-specific genes

With the recent increase in RNA sequencing efforts using large cohorts of individuals, surveying allele-specific gene expression is becoming increasingly frequent. Here, we report that, despite not containing explicit variant information, a list of genes known to be allele-specific in an individual is enough to recover key variants and link the individuals back to their genotypes and phenotypes. This creates a privacy conundrum.

Calling differentially methylated regions from whole genome bisulphite sequencing with DMRcate

Whole genome bisulphite sequencing (WGBS) permits the genome-wide study of single molecule methylation patterns. One of the key goals of mammalian cell-type identity studies, in both normal differentiation and disease, is to locate differential methylation patterns across the genome. We discuss the most desirable characteristics for DML (differentially methylated locus) and DMR (differentially methylated region) detection tools in a genome-wide context and choose a set of statistical methods that fully or partially satisfy these considerations to compare for benchmarking. Our data simulation strategy is both biologically informed-employing distribution parameters derived from large-scale consortium datasets-and thorough. We report DML detection ability with respect to coverage, group methylation difference, sample size, variability and covariate size, both marginally and jointly, and exhaustively with respect to parameter combination. We also benchmark these methods on FDR control and computational time. We use this result to backend and introduce an expanded version of DMRcate: an existing DMR detection tool for microarray data that we have extended to now call DMRs from WGBS data. We compare DMRcate to a set of alternative DMR callers using a similarly realistic simulation strategy. We find DMRcate and RADmeth are the best predictors of DMRs, and conclusively find DMRcate the fastest.

Perspectives on Allele-Specific Expression

Diploidy has profound implications for population genetics and susceptibility to genetic diseases. Although two copies are present for most genes in the human genome, they are not necessarily both active or active at the same level in a given individual. Genomic imprinting, resulting in exclusive or biased expression in favor of the allele of paternal or maternal origin, is now believed to affect hundreds of human genes. A far greater number of genes display unequal expression of gene copies due to cis-acting genetic variants that perturb gene expression. The availability of data generated by RNA sequencing applied to large numbers of individuals and tissue types has generated unprecedented opportunities to assess the contribution of genetic variation to allelic imbalance in gene expression. Here we review the insights gained through the analysis of these data about the extent of the genetic contribution to allelic expression imbalance, the tools and statistical models for gene expression imbalance, and what the results obtained reveal about the contribution of genetic variants that alter gene expression to complex human diseases and phenotypes.

Starvation and Climate Change—How to Constrain Cancer Cell Epigenetic Diversity and Adaptability to Enhance Treatment Efficacy

Advanced metastatic cancer is currently not curable and the major barrier to eliminating the disease in patients is the resistance of subpopulations of tumor cells to drug treatments. These resistant subpopulations can arise stochastically among the billions of tumor cells in a patient or emerge over time during therapy due to adaptive mechanisms and the selective pressures of drug therapies. Epigenetic mechanisms play important roles in tumor cell diversity and adaptability, and are regulated by metabolic pathways. Here, I discuss knowledge from ecology, evolution, infectious disease, species extinction, metabolism and epigenetics to synthesize a roadmap to a clinically feasible approach to help homogenize tumor cells and, in combination with drug treatments, drive their extinction. Specifically, cycles of starvation and hyperthermia could help synchronize tumor cells and constrain epigenetic diversity and adaptability by limiting substrates and impairing the activity of chromatin modifying enzymes. Hyperthermia could also help prevent cancer cells from entering dangerous hibernation-like states. I propose steps to a treatment paradigm to help drive cancer extinction that builds on the successes of fasting, hyperthermia and immunotherapy and is achievable in patients. Finally, I highlight the many unknowns, opportunities for discovery and that stochastic gene and allele level epigenetic mechanisms pose a major barrier to cancer extinction that warrants deeper investigation.

Genetic impacts on DNA methylation: research findings and future perspectives

Multiple recent studies highlight that genetic variants can have strong impacts on a significant proportion of the human DNA methylome. Methylation quantitative trait loci, or meQTLs, allow for the exploration of biological mechanisms that underlie complex human phenotypes, with potential insights for human disease onset and progression. In this review, we summarize recent milestones in characterizing the human genetic basis of DNA methylation variation over the last decade, including heritability findings and genome-wide identification of meQTLs. We also discuss challenges in this field and future areas of research geared to generate insights into molecular processes underlying human complex traits.

Epigenomically Bistable Regions across Neuron-Specific Genes Govern Neuron Eligibility to a Coding Ensemble in the Hippocampus

Sensory inputs activate sparse neuronal ensembles in the dentate gyrus of the hippocampus, but how eligibility of individual neurons to recruitment is determined remains elusive. We identify thousands of largely bistable (CpG methylated or unmethylated) regions within neuronal gene bodies, established during mouse dentate gyrus development. Reducing DNA methylation and the proportion of the methylated epialleles at bistable regions compromises novel context-induced neuronal activation. Conversely, increasing methylation and the frequency of the methylated epialleles at bistable regions enhances intrinsic excitability. Single-nucleus profiling reveals enrichment of specific epialleles related to a subset of primarily exonic, bistable regions in activated neurons. Genes displaying both differential methylation and expression in activated neurons define a network of proteins regulating neuronal excitability and structural plasticity. We propose a model in which bistable regions create neuron heterogeneity and constellations of exonic methylation, which may contribute to cell-specific gene expression, excitability, and eligibility to a coding ensemble.

Odell et al. show regions within neuronal genes with bistable DNA methylation states that are associated with gene expression, excitability, and activation in the dentate gyrus of the hippocampus. These data suggest that the methylation state of bistable regions dictates, via modulating gene expression, neuron eligibility to a coding ensemble.

Epigenetically regulated digital signaling defines epithelial innate immunity at the tissue level

To prevent damage to the host or its commensal microbiota, epithelial tissues must match the intensity of the immune response to the severity of a biological threat. Toll-like receptors allow epithelial cells to identify microbe associated molecular patterns. However, the mechanisms that mitigate biological noise in single cells to ensure quantitatively appropriate responses remain unclear. Here we address this question using single cell and single molecule approaches in mammary epithelial cells and primary organoids. We find that epithelial tissues respond to bacterial microbe associated molecular patterns by activating a subset of cells in an all-or-nothing (i.e. digital) manner. The maximum fraction of responsive cells is regulated by a bimodal epigenetic switch that licenses the TLR2 promoter for transcription across multiple generations. This mechanism confers a flexible memory of inflammatory events as well as unique spatio-temporal control of epithelial tissue-level immune responses. We propose that epigenetic licensing in individual cells allows for long-term, quantitative fine-tuning of population-level responses.

A transient heritable memory regulates HIV reactivation from latency

Reactivation of human immunodeficiency virus 1 (HIV-1) from latently infected T cells is a critical barrier to cure patients. It remains unknown whether reactivation of individual latent cells occurs stochastically in response to latency reversal agents (LRAs) or is a deterministic outcome of an underlying cell state. To characterize these single-cell responses, we leverage the classical Luria-Delbrück fluctuation test where single cells are isolated from a clonal population and exposed to LRAs after colony expansion. Data show considerable colony-to-colony fluctuations with the fraction of reactivating cells following a skewed distribution. Modeling systematic measurements of fluctuations over time uncovers a transient heritable memory that regulates HIV-1 reactivation, where single cells are in an LRA-responsive state for a few weeks before switching back to an irresponsive state. These results have enormous implications for designing therapies to purge the latent reservoir and further utilize fluctuation-based assays to uncover hidden transient cellular states underlying phenotypic heterogeneity.

DNA methylation impact on Fabry disease

BACKGROUND

Fabry disease (FD) is a rare X-linked disease caused by mutations in GLA gene with consequent lysosomal accumulation of globotriaosylceramide (Gb3). Women with FD often show highly heterogeneous symptoms that can manifest from mild to severe phenotype.

MAIN BODY

The phenotypic variability of the clinical manifestations in heterozygous women with FD mainly depends on the degree and direction of inactivation of the X chromosome. Classical approaches to measure XCI skewness might be not sufficient to explain disease manifestation in women. In addition to unbalanced XCI, allele-specific DNA methylation at promoter of GLA gene may influence the expression levels of the mutated allele, thus impacting the onset and the outcome of FD. In this regard, analyses of DNA methylation at GLA promoter, performed by approaches allowing distinction between mutated and non-mutated allele, may be much more informative. The aim of this review is to critically evaluate recent literature articles addressing the potential role of DNA methylation in the context of FD. Although up to date relatively few works have addressed this point, reviewing all pertinent studies may help to evaluate the importance of DNA methylation analysis in FD and to develop new research and technologies aimed to predict whether the carrier females will develop symptoms.

CONCLUSIONS

Relatively few studies have addressed the complexity of DNA methylation landscape in FD that remains poorly investigated. The hope for the future is that ad hoc and ultradeep methylation analyses of GLA gene will provide epigenetic signatures able to predict whether pre-symptomatic female carriers will develop symptoms thus helping timely interventions.

MethylationToActivity: a deep-learning framework that reveals promoter activity landscapes from DNA methylomes in individual tumors

Although genome-wide DNA methylomes have demonstrated their clinical value as reliable biomarkers for tumor detection, subtyping, and classification, their direct biological impacts at the individual gene level remain elusive. Here we present MethylationToActivity (M2A), a machine learning framework that uses convolutional neural networks to infer promoter activities based on H3K4me3 and H3K27ac enrichment, from DNA methylation patterns for individual genes. Using publicly available datasets in real-world test scenarios, we demonstrate that M2A is highly accurate and robust in revealing promoter activity landscapes in various pediatric and adult cancers, including both solid and hematologic malignant neoplasms.

PTSD is associated with increased DNA methylation across regions of HLA-DPB1 and SPATC1L

Posttraumatic stress disorder (PTSD) is characterized by intrusive thoughts, avoidance, negative alterations in cognitions and mood, and arousal symptoms that adversely affect mental and physical health. Recent evidence links changes in DNA methylation of CpG cites to PTSD. Since clusters of proximal CpGs share similar methylation signatures, identification of PTSD-associated differentially methylated regions (DMRs) may elucidate the pathways defining differential risk and resilience of PTSD. Here we aimed to identify epigenetic differences associated with PTSD. DNA methylation data profiled from blood samples using the MethylationEPIC BeadChip were used to perform a DMR analysis in 187 PTSD cases and 367 trauma-exposed controls from the Grady Trauma Project (GTP). DMRs were assessed with R package bumphunter. We identified two regions that associate with PTSD after multiple test correction. These regions were in the gene body of HLA-DPB1 and in the promoter of SPATC1L. The DMR in HLA-DPB1 was associated with PTSD in an independent cohort. Both DMRs included CpGs whose methylation associated with nearby sequence variation (meQTL) and that associated with expression of their respective genes (eQTM). This study supports an emerging literature linking PTSD risk to genetic and epigenetic variation in the HLA region.

Rare genetic variation at transcription factor binding sites modulates local DNA methylation profiles

Although DNA methylation is the best characterized epigenetic mark, the mechanism by which it is targeted to specific regions in the genome remains unclear. Recent studies have revealed that local DNA methylation profiles might be dictated by cis-regulatory DNA sequences that mainly operate via DNA-binding factors. Consistent with this finding, we have recently shown that disruption of CTCF-binding sites by rare single nucleotide variants (SNVs) can underlie cis-linked DNA methylation changes in patients with congenital anomalies. These data raise the hypothesis that rare genetic variation at transcription factor binding sites (TFBSs) might contribute to local DNA methylation patterning.

In this work, by combining blood genome-wide DNA methylation profiles, whole genome sequencing-derived SNVs from 247 unrelated individuals along with 133 predicted TFBS motifs derived from ENCODE ChIP-Seq data, we observed an association between the disruption of binding sites for multiple TFs by rare SNVs and extreme DNA methylation values at both local and, to a lesser extent, distant CpGs. While the majority of these changes affected only single CpGs, 24% were associated with multiple outlier CpGs within ±1kb of the disrupted TFBS. Interestingly, disruption of functionally constrained sites within TF motifs lead to larger DNA methylation changes at nearby CpG sites. Altogether, these findings suggest that rare SNVs at TFBS negatively influence TF-DNA binding, which can lead to an altered local DNA methylation profile. Furthermore, subsequent integration of DNA methylation and RNA-Seq profiles from cardiac tissues enabled us to observe an association between rare SNV-directed DNA methylation and outlier expression of nearby genes.

In conclusion, our findings not only provide insights into the effect of rare genetic variation at TFBS on shaping local DNA methylation and its consequences on genome regulation, but also provide a rationale to incorporate DNA methylation data to interpret the functional role of rare variants.

One of the major challenges for human genetics in the post-genomic era is to interpret the functional relevance of genetic variation. Quantitative trait locus (QTL) analyses have associated an important fraction of genetic variants with a wide range of molecular phenotypes including gene expression (eQTL) and DNA methylation (meQTL), providing insights into the mechanisms by which genetic variation can contribute to health and disease. Although QTL mapping represents an excellent approach to identify biologically relevant functional variants, these studies have been mainly focused on common variants and do not include low-frequency and rare variants. Here, we observed that rare regulatory variants, i.e, single nucleotide variants (SNVs) that disrupt transcription factor binding sites (TFBSs), are associated with changes in DNA methylation at both local and, to a lesser extent, broader locations, most likely, by altering the DNA-binding affinity of transcription factors (TFs). Interestingly, we have also shown that this change in DNA methylation can alter expression levels of nearby genes. Overall, these data suggest a role of rare regulatory SNVs in shaping DNA methylation, and suggest that the incorporation of DNA methylation data may help to interpret the functional consequences of human genetic variation.

Detection of haplotype-dependent allele-specific DNA methylation in WGBS data

In heterozygous genomes, allele-specific measurements can reveal biologically significant differences in DNA methylation between homologous alleles associated with local changes in genetic sequence. Current approaches for detecting such events from whole-genome bisulfite sequencing (WGBS) data perform statistically independent marginal analysis at individual cytosine-phosphate-guanine (CpG) sites, thus ignoring correlations in the methylation state, or carry-out a joint statistical analysis of methylation patterns at four CpG sites producing unreliable statistical evidence. Here, we employ the one-dimensional Ising model of statistical physics and develop a method for detecting allele-specific methylation (ASM) events within segments of DNA containing clusters of linked single-nucleotide polymorphisms (SNPs), called haplotypes. Comparisons with existing approaches using simulated and real WGBS data show that our method provides an improved fit to data, especially when considering large haplotypes. Importantly, the method employs robust hypothesis testing for detecting statistically significant imbalances in mean methylation level and methylation entropy, as well as for identifying haplotypes for which the genetic variant carries significant information about the methylation state. As such, our ASM analysis approach can potentially lead to biological discoveries with important implications for the genetics of complex human diseases.

A Survey of Rare Epigenetic Variation in 23,116 Human Genomes Identifies Disease-Relevant Epivariations and CGG Expansions

There is growing recognition that epivariations, most often recognized as promoter hypermethylation events that lead to gene silencing, are associated with a number of human diseases. However, little information exists on the prevalence and distribution of rare epigenetic variation in the human population. In order to address this, we performed a survey of methylation profiles from 23,116 individuals using the Illumina 450k array. Using a robust outlier approach, we identified 4,452 unique autosomal epivariations, including potentially inactivating promoter methylation events at 384 genes linked to human disease. For example, we observed promoter hypermethylation of BRCA1 and LDLR at population frequencies of ∼1 in 3,000 and ∼1 in 6,000, respectively, suggesting that epivariations may underlie a fraction of human disease which would be missed by purely sequence-based approaches. Using expression data, we confirmed that many epivariations are associated with outlier gene expression. Analysis of variation data and monozygous twin pairs suggests that approximately two-thirds of epivariations segregate in the population secondary to underlying sequence mutations, while one-third are likely sporadic events that occur post-zygotically. We identified 25 loci where rare hypermethylation coincided with the presence of an unstable CGG tandem repeat, validated the presence of CGG expansions at several loci, and identified the putative molecular defect underlying most of the known folate-sensitive fragile sites in the genome. Our study provides a catalog of rare epigenetic changes in the human genome, gives insight into the underlying origins and consequences of epivariations, and identifies many hypermethylated CGG repeat expansions.

Allele-specific open chromatin in human iPSC neurons elucidates functional disease variants

Most neuropsychiatric disease risk variants are in noncoding sequences and lack functional interpretation. Because regulatory sequences often reside in open chromatin, we reasoned that neuropsychiatric disease risk variants may affect chromatin accessibility during neurodevelopment. Using human induced pluripotent stem cell (iPSC)-derived neurons that model developing brains, we identified thousands of genetic variants exhibiting allele-specific open chromatin (ASoC). These neuronal ASoCs were partially driven by altered transcription factor binding, overrepresented in brain gene enhancers and expression quantitative trait loci, and frequently associated with distal genes through chromatin contacts. ASoCs were enriched for genetic variants associated with brain disorders, enabling identification of functional schizophrenia risk variants and their cis-target genes. This study highlights ASoC as a functional mechanism of noncoding neuropsychiatric risk variants, providing a powerful framework for identifying disease causal variants and genes.

CGGBP1-regulated cytosine methylation at CTCF-binding motifs resists stochasticity

Background

The human CGGBP1 binds to GC-rich regions and interspersed repeats, maintains homeostasis of stochastic cytosine methylation and determines DNA-binding of CTCF. Interdependence between regulation of cytosine methylation and CTCF occupancy by CGGBP1 remains unknown.

Results

By analyzing methylated DNA-sequencing data obtained from CGGBP1-depleted cells, we report that some transcription factor-binding sites, including CTCF, resist stochastic changes in cytosine methylation. By analysing CTCF-binding sites we show that cytosine methylation changes at CTCF motifs caused by CGGBP1 depletion resist stochastic changes. These CTCF-binding sites are positioned at locations where the spread of cytosine methylation in cis depends on the levels of CGGBP1.

Conclusion

Our findings suggest that CTCF occupancy and functions are determined by CGGBP1-regulated cytosine methylation patterns.

Allele-specific DNA methylation is increased in cancers and its dense mapping in normal plus neoplastic cells increases the yield of disease-associated regulatory SNPs

BACKGROUND

Mapping of allele-specific DNA methylation (ASM) can be a post-GWAS strategy for localizing regulatory sequence polymorphisms (rSNPs). The advantages of this approach, and the mechanisms underlying ASM in normal and neoplastic cells, remain to be clarified.

RESULTS

We perform whole genome methyl-seq on diverse normal cells and tissues and three cancer types. After excluding imprinting, the data pinpoint 15,112 high-confidence ASM differentially methylated regions, of which 1838 contain SNPs in strong linkage disequilibrium or coinciding with GWAS peaks. ASM frequencies are increased in cancers versus matched normal tissues, due to widespread allele-specific hypomethylation and focal allele-specific hypermethylation in poised chromatin. Cancer cells show increased allele switching at ASM loci, but disruptive SNPs in specific classes of CTCF and transcription factor binding motifs are similarly correlated with ASM in cancer and non-cancer. Rare somatic mutations affecting these same motif classes track with de novo ASM. Allele-specific transcription factor binding from ChIP-seq is enriched among ASM loci, but most ASM differentially methylated regions lack such annotations, and some are found in otherwise uninformative "chromatin deserts."

CONCLUSIONS

ASM is increased in cancers but occurs by a shared mechanism involving disruptive SNPs in CTCF and transcription factor binding sites in both normal and neoplastic cells. Dense ASM mapping in normal plus cancer samples reveals candidate rSNPs that are difficult to find by other approaches. Together with GWAS data, these rSNPs can nominate specific transcriptional pathways in susceptibility to autoimmune, cardiometabolic, neuropsychiatric, and neoplastic diseases.

Ultra-Deep DNA Methylation Analysis of X-Linked Genes: GLA and AR as Model Genes

Recessive X-linked disorders may occasionally evolve in clinical manifestations of variable severity also in female carriers. For some of such diseases, the frequency of the symptoms’ appearance during women’s life may be particularly relevant. This phenomenon has been largely attributed to the potential skewness of the X-inactivation process leading to variable phenotypes. Nonetheless, in many cases, no correlation with X-inactivation unbalance was demonstrated. However, methods for analyzing skewness have been mainly limited to Human Androgen Receptor methylation analysis (HUMARA). Recently, the X-inactivation process has been largely revisited, highlighting the heterogeneity existing among loci in the epigenetic state within inactive and, possibly, active X-chromosomes. We reasoned that gene-specific and ultra-deep DNA methylation analyses could greatly help to unravel details of the X-inactivation process and the roles of specific X genes inactivation in disease manifestations. We recently provided evidence that studying DNA methylation at specific autosomic loci at a single-molecule resolution (epiallele distribution analysis) allows one to analyze cell-to-cell methylation differences in a given cell population. We here apply the epiallele analysis at two X-linked loci to investigate whether females show allele-specific epiallelic patterns. Due to the high potential of this approach, the method allows us to obtain clearly distinct allele-specific epiallele profiles.

The influence of DNA methylation on monoallelic expression

Monoallelic gene expression occurs in diploid cells when only one of the two alleles of a gene is active. There are three main classes of genes that display monoallelic expression in mammalian genomes: (1) imprinted genes that are monoallelically expressed in a parent-of-origin dependent manner; (2) X-linked genes that undergo random X-chromosome inactivation in female cells; (3) random monoallelically expressed single and clustered genes located on autosomes. The heritability of monoallelic expression patterns during cell divisions implies that epigenetic mechanisms are involved in the cellular memory of these expression states. Among these, methylation of CpG sites on DNA is one of the best described modification to explain somatic inheritance. Here, we discuss the relevance of DNA methylation for the establishment and maintenance of monoallelic expression patterns among these three groups of genes, and how this is intrinsically linked to development and cellular states.

DAMEfinder: a method to detect differential allele-specific methylation

Background

DNA methylation is a highly studied epigenetic signature that is associated with regulation of gene expression, whereby genes with high levels of promoter methylation are generally repressed. Genomic imprinting occurs when one of the parental alleles is methylated, i.e., when there is inherited allele-specific methylation (ASM). A special case of imprinting occurs during X chromosome inactivation in females, where one of the two X chromosomes is silenced, to achieve dosage compensation between the sexes. Another more widespread form of ASM is sequence dependent (SD-ASM), where ASM is linked to a nearby heterozygous single nucleotide polymorphism (SNP).

Results

We developed a method to screen for genomic regions that exhibit loss or gain of ASM in samples from two conditions (treatments, diseases, etc.). The method relies on the availability of bisulfite sequencing data from multiple samples of the two conditions. We leverage other established computational methods to screen for these regions within a new R package called DAMEfinder. It calculates an ASM score for all CpG sites or pairs in the genome of each sample, and then quantifies the change in ASM between conditions. It then clusters nearby CpG sites with consistent change into regions. In the absence of SNP information, our method relies only on reads to quantify ASM. This novel ASM score compares favorably to current methods that also screen for ASM. Not only does it easily discern between imprinted and non-imprinted regions, but also females from males based on X chromosome inactivation. We also applied DAMEfinder to a colorectal cancer dataset and observed that colorectal cancer subtypes are distinguishable according to their ASM signature. We also re-discover known cases of loss of imprinting.

Conclusion

We have designed DAMEfinder to detect regions of differential ASM (DAMEs), which is a more refined definition of differential methylation, and can therefore help in breaking down the complexity of DNA methylation and its influence in development and disease.

CloudASM: an ultra-efficient cloud-based pipeline for mapping allele-specific DNA methylation

SUMMARY

Methods for quantifying the imbalance in CpG methylation between alleles genome-wide have been described but their algorithmic time complexity is quadratic and their practical use requires painstaking attention to infrastructure choice, implementation and execution. To solve this problem, we developed CloudASM, a scalable, ultra-efficient, turn-key, portable pipeline on Google Cloud Platform (GCP) that uses a novel pipeline manager and GCP's serverless enterprise data warehouse.

AVAILABILITY AND IMPLEMENTATION

CloudASM is freely available in the GitHub repository https://github.com/TyckoLab/CloudASM and a sample dataset and its results are also freely available at https://console.cloud.google.com/storage/browser/cloudasm.

CONTACT

emmanuel.dumont@hmh-cdi.org.

Promoter-anchored chromatin interactions predicted from genetic analysis of epigenomic data

Promoter-anchored chromatin interactions (PAIs) play a pivotal role in transcriptional regulation. Current high-throughput technologies for detecting PAIs, such as promoter capture Hi-C, are not scalable to large cohorts. Here, we present an analytical approach that uses summary-level data from cohort-based DNA methylation (DNAm) quantitative trait locus (mQTL) studies to predict PAIs. Using mQTL data from human peripheral blood ([Formula: see text]), we predict 34,797 PAIs which show strong overlap with the chromatin contacts identified by previous experimental assays. The promoter-interacting DNAm sites are enriched in enhancers or near expression QTLs. Genes whose promoters are involved in PAIs are more actively expressed, and gene pairs with promoter-promoter interactions are enriched for co-expression. Integration of the predicted PAIs with GWAS data highlight interactions among 601 DNAm sites associated with 15 complex traits. This study demonstrates the use of mQTL data to predict PAIs and provides insights into the role of PAIs in complex trait variation.

The conserved DNMT1-dependent methylation regions in human cells are vulnerable to neurotoxicant rotenone exposure

BACKGROUND

Allele-specific DNA methylation (ASM) describes genomic loci that maintain CpG methylation at only one inherited allele rather than having coordinated methylation across both alleles. The most prominent of these regions are germline ASMs (gASMs) that control the expression of imprinted genes in a parent of origin-dependent manner and are associated with disease. However, our recent report reveals numerous ASMs at non-imprinted genes. These non-germline ASMs are dependent on DNA methyltransferase 1 (DNMT1) and strikingly show the feature of random, switchable monoallelic methylation patterns in the mouse genome. The significance of these ASMs to human health has not been explored. Due to their shared allelicity with gASMs, herein, we propose that non-traditional ASMs are sensitive to exposures in association with human disease.

RESULTS

We first explore their conservancy in the human genome. Our data show that our putative non-germline ASMs were in conserved regions of the human genome and located adjacent to genes vital for neuronal development and maturation. We next tested the hypothesized vulnerability of these regions by exposing human embryonic kidney cell HEK293 with the neurotoxicant rotenone for 24 h. Indeed,14 genes adjacent to our identified regions were differentially expressed from RNA-sequencing. We analyzed the base-resolution methylation patterns of the predicted non-germline ASMs at two neurological genes, HCN2 and NEFM, with potential to increase the risk of neurodegeneration. Both regions were significantly hypomethylated in response to rotenone.

CONCLUSIONS

Our data indicate that non-germline ASMs seem conserved between mouse and human genomes, overlap important regulatory factor binding motifs, and regulate the expression of genes vital to neuronal function. These results support the notion that ASMs are sensitive to environmental factors such as rotenone and may alter the risk of neurological disease later in life by disrupting neuronal development.

Transcriptional memory in skeletal muscle. Don't forget (to) exercise

Transcriptional memory describes an ancient and highly conserved form of cellular learning that enables cells to benefit from recent experience by retaining a mitotically inheritable but reversible memory of the initial transcriptional response when encountering an environmental or physiological stimulus. Herein, we will review recent progress made in the understanding of how cells can make use of diverse constituents of the epigenetic toolbox to retain a transcriptional memory of past states and perturbations. Specifically, we will outline how these mechanisms will help to improve our understanding of skeletal muscle plasticity in health and disease. We describe the epigenetic road map that allows skeletal muscle fibers to navigate through training-induced adaptation processes, and how epigenetic memory marks can preserve an autobiographical history of lifestyle behavior changes, pathological challenges, and aging. We will further consider some key findings in the field of exercise epigenomics to emphasize major challenges when interpreting dynamic changes in the chromatin landscape in response to acute exercise and training.

Metastable epialleles and their contribution to epigenetic inheritance in mammals

Many epigenetic differences between individuals are driven by genetic variation. Mammalian metastable epialleles are unusual in that they show variable DNA methylation states between genetically identical individuals. The occurrence of such states across generations has resulted in their consideration by many as strong evidence for epigenetic inheritance in mammals, with the classic A^vy and Axin^Fu mouse models - each products of repeat element insertions - being the most widely accepted examples. Equally, there has been interest in exploring their use as epigenetic biosensors given their susceptibility to environmental compromise. Here we review the classic murine metastable epialleles as well as more recently identified candidates, with the aim of providing a more holistic understanding of their biology. We consider the extent to which epigenetic inheritance occurs at metastable epialleles and explore the limited mechanistic insights into the establishment of their variable epigenetic states. We discuss their environmental modulation and their potential relevance in genome regulation. In light of recent whole-genome screens for novel metastable epialleles, we point out the need to reassess their biological relevance in multi-generational studies and we highlight their value as a model to study repeat element silencing as well as the mechanisms and consequences of mammalian epigenetic stochasticity.