MethMotif: an integrative cell specific database of transcription factor binding motifs coupled with DNA methylation profiles

Leveraging the MethMotif Toolkit to Characterize Context-Specific Features and Roles of Methylation Sensitive Transcription Factors

This article presents a comprehensive guide for using the MethMotif platform, which includes the MethMotif database, the TFregulomeR R package, and a new R library, Forked-TF, designed specifically for analyzing leucine-zipper transcription factors (TFs) that bind DNA as dimers. The MethMotif platform integrates transcription factor binding site (TFBS) motifs with DNA methylation profiles, providing an in-depth analysis of how methylation modulates TF binding across different cell types and conditions. The protocols are organized into three main workflows: (1) Exploration of transcription factor dimerization partners, (2) visualization of methylation-specific TF motifs using TFregulomeR, and (3) characterization of leucine-zipper TF binding patterns with a focus on dimerization. Using the platform's MethMotif database, users can retrieve ChIP-seq and DNA methylation data, intersect TFBS peak regions, and generate TFBS-methylation-informed motif logos. A case study of CEBPB in K562 cells is included to demonstrate the use of the platform, showing how to identify TF dimers, analyze their co-binding behavior, and visualize the impact of DNA methylation on binding specificity. The protocols also provide step-by-step instructions for software installation, data input formats, and interpretation of results, making it accessible to researchers with varying levels of computational expertise. Through these protocols, users can uncover how DNA methylation and TF dimerization influence gene regulatory networks, with a focus on leucine-zipper TFs in a cell-type-specific context. © 2025 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Exploration of transcription factor dimerization partners Support Protocol 1: Software installation Support Protocol 2: Docker installation Support Protocol 3: Verifying installation Basic Protocol 2: Visualization of alternative cofactors Basic Protocol 3: Characterization of bZIP partners/cofactors Basic Protocol 4: Context-independent and context-dependent analysis.

Using methylation data to improve transcription factor binding prediction

Modelling the regulatory mechanisms that determine cell fate, response to external perturbation, and disease state depends on measuring many factors, a task made more difficult by the plasticity of the epigenome. Scanning the genome for the sequence patterns defined by Position Weight Matrices (PWM) can be used to estimate transcription factor (TF) binding locations. However, this approach does not incorporate information regarding the epigenetic context necessary for TF binding. CpG methylation is an epigenetic mark influenced by environmental factors that is commonly assayed in human cohort studies. We developed a framework to score inferred TF binding locations using methylation data. We intersected motif locations identified using PWMs with methylation information captured in both whole-genome bisulfite sequencing and Illumina EPIC array data for six cell lines, scored motif locations based on these data, and compared with experimental data characterizing TF binding (ChIP-seq). We found that for most TFs, binding prediction improves using methylation-based scoring compared to standard PWM-scores. We also illustrate that our approach can be generalized to infer TF binding when methylation information is only proximally available, i.e. measured for nearby CpGs that do not directly overlap with a motif location. Overall, our approach provides a framework for inferring context-specific TF binding using methylation data. Importantly, the availability of DNA methylation data in existing patient populations provides an opportunity to use our approach to understand the impact of methylation on gene regulatory processes in the context of human disease.

DNA Methylation in the CYP3A Distal Regulatory Region (DRR) Is Associated with the Expression of CYP3A5 and CYP3A7 in Human Liver Samples

CYP3As are important drug-metabolizing enzymes in the liver. The causes for large inter-person variability in CYP3A expression/activity remain poorly understood. DNA methylation broadly regulates gene expression and the developmental transition from fetal CYP3A7 to adult CYP3A4, and CpG methylation upstream of the CYP3A4 promoter is associated with its expression. However, because non-promoter CYP3A regulatory regions remain largely uncharacterized, how DNA methylation influences CYP3A expression has yet to be fully explored. We recently identified a distal regulatory region (DRR) that controls the expression of CYP3A4, CYP3A5, and CYP3A7. Here, we investigated the relationship between CYP3A expression and the methylation status of 16 CpG sites within the DRR in 70 liver samples. We found significant associations between DRR methylation and the expression of CYP3A5 and CYP3A7 but not CYP3A4, indicating differential CYP3A regulation by the DRR. Also, we observed a dynamic reduction in DRR DNA methylation during the differentiation of induced pluripotent stem cells to hepatocytes, which correlated with increased CYP3A expression. We then evaluated the relative contribution of genetic variants, TFs, and DRR DNA methylation on CYP3A expression in liver samples. Our results reinforce the DRR as a CYP3A regulator and suggest that DNA methylation may impact CYP3A-mediated drug metabolism.

MethylSeqLogo: DNA methylation smart sequence logos

BACKGROUND

Some transcription factors, MYC for example, bind sites of potentially methylated DNA. This may increase binding specificity as such sites are (1) highly under-represented in the genome, and (2) offer additional, tissue specific information in the form of hypo- or hyper-methylation. Fortunately, bisulfite sequencing data can be used to investigate this phenomenon.

METHOD

We developed MethylSeqLogo, an extension of sequence logos which includes new elements to indicate DNA methylation and under-represented dimers in each position of a set binding sites. Our method displays information from both DNA strands, and takes into account the sequence context (CpG or other) and genome region (promoter versus whole genome) appropriate to properly assess the expected background dimer frequency and level of methylation. MethylSeqLogo preserves sequence logo semantics-the relative height of nucleotides within a column represents their proportion in the binding sites, while the absolute height of each column represents information (relative entropy) and the height of all columns added together represents total information RESULTS: We present figures illustrating the utility of using MethylSeqLogo to summarize data from several CpG binding transcription factors. The logos show that unmethylated CpG binding sites are a feature of transcription factors such as MYC and ZBTB33, while some other CpG binding transcription factors, such as CEBPB, appear methylation neutral.

CONCLUSIONS

Our software enables users to explore bisulfite and ChIP sequencing data sets-and in the process obtain publication quality figures.

The role of epigenetics in women's reproductive health: the impact of environmental factors

This paper explores the significant role of epigenetics in women's reproductive health, focusing on the impact of environmental factors. It highlights the crucial link between epigenetic modifications-such as DNA methylation and histones post-translational modifications-and reproductive health issues, including infertility and pregnancy complications. The paper reviews the influence of pollutants like PM2.5, heavy metals, and endocrine disruptors on gene expression through epigenetic mechanisms, emphasizing the need for understanding how dietary, lifestyle choices, and exposure to chemicals affect gene expression and reproductive health. Future research directions include deeper investigation into epigenetics in female reproductive health and leveraging gene editing to mitigate epigenetic changes for improving IVF success rates and managing reproductive disorders.

Comprehensive analysis of the REST transcription factor regulatory networks in IDH mutant and IDH wild-type glioma cell lines and tumors

The RE1-silencing transcription factor (REST) acts either as a repressor or activator of transcription depending on the genomic and cellular context. REST is a key player in brain cell differentiation by inducing chromatin modifications, including DNA methylation, in a proximity of its binding sites. Its dysfunction may contribute to oncogenesis. Mutations in IDH1/2 significantly change the epigenome contributing to blockade of cell differentiation and glioma development. We aimed at defining how REST modulates gene activation and repression in the context of the IDH mutation-related phenotype in gliomas. We studied the effects of REST knockdown, genome wide occurrence of REST binding sites, and DNA methylation of REST motifs in IDH wild type and IDH mutant gliomas. We found that REST target genes, REST binding patterns, and TF motif occurrence proximal to REST binding sites differed in IDH wild-type and mutant gliomas. Among differentially expressed REST targets were genes involved in glial cell differentiation and extracellular matrix organization, some of which were differentially methylated at promoters or gene bodies. REST knockdown differently impacted invasion of the parental or IDH1 mutant glioma cells. The canonical REST-repressed gene targets showed significant correlation with the GBM NPC-like cellular state. Interestingly, results of REST or KAISO silencing suggested the interplay between these TFs in regulation of REST-activated and repressed targets. The identified gene regulatory networks and putative REST cooperativity with other TFs, such as KAISO, show distinct REST target regulatory networks in IDH-WT and IDH-MUT gliomas, without concomitant DNA methylation changes. We conclude that REST could be an important therapeutic target in gliomas.

CancerMHL: the database of integrating key DNA methylation, histone modifications and lncRNAs in cancer

The discovery of key epigenetic modifications in cancer is of great significance for the study of disease biomarkers. Through the mining of epigenetic modification data relevant to cancer, some researches on epigenetic modifications are accumulating. In order to make it easier to integrate the effects of key epigenetic modifications on the related cancers, we established CancerMHL (http://www.positionprediction.cn/), which provide key DNA methylation, histone modifications and lncRNAs as well as the effect of these key epigenetic modifications on gene expression in several cancers. To facilitate data retrieval, CancerMHL offers flexible query options and filters, allowing users to access specific key epigenetic modifications according to their own needs. In addition, based on the epigenetic modification data, three online prediction tools had been offered in CancerMHL for users. CancerMHL will be a useful resource platform for further exploring novel and potential biomarkers and therapeutic targets in cancer. Database URL: http://www.positionprediction.cn/.

Less-is-more: selecting transcription factor binding regions informative for motif inference

Numerous statistical methods have emerged for inferring DNA motifs for transcription factors (TFs) from genomic regions. However, the process of selecting informative regions for motif inference remains understudied. Current approaches select regions with strong ChIP-seq signal for a given TF, assuming that such strong signal primarily results from specific interactions between the TF and its motif. Additionally, these selection approaches do not account for non-target motifs, i.e. motifs of other TFs; they presume the occurrence of these non-target motifs infrequent compared to that of the target motif, and thus assume these have minimal interference with the identification of the target. Leveraging extensive ChIP-seq datasets, we introduced the concept of TF signal 'crowdedness', referred to as C-score, for each genomic region. The C-score helps in highlighting TF signals arising from non-specific interactions. Moreover, by considering the C-score (and adjusting for the length of genomic regions), we can effectively mitigate interference of non-target motifs. Using these tools, we find that in many instances, strong ChIP-seq signal stems mainly from non-specific interactions, and the occurrence of non-target motifs significantly impacts the accurate inference of the target motif. Prioritizing genomic regions with reduced crowdedness and short length markedly improves motif inference. This 'less-is-more' effect suggests that ChIP-seq region selection warrants more attention.

Pancreatic cancer acquires resistance to MAPK pathway inhibition by clonal expansion and adaptive DNA hypermethylation

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer with poor prognosis. It is marked by extraordinary resistance to conventional therapies including chemotherapy and radiation, as well as to essentially all targeted therapies evaluated so far. More than 90% of PDAC cases harbor an activating KRAS mutation. As the most common KRAS variants in PDAC remain undruggable so far, it seemed promising to inhibit a downstream target in the MAPK pathway such as MEK1/2, but up to now preclinical and clinical evaluation of MEK inhibitors (MEKi) failed due to inherent and acquired resistance mechanisms. To gain insights into molecular changes during the formation of resistance to oncogenic MAPK pathway inhibition, we utilized short-term passaged primary tumor cells from ten PDACs of genetically engineered mice. We followed gain and loss of resistance upon MEKi exposure and withdrawal by longitudinal integrative analysis of whole genome sequencing, whole genome bisulfite sequencing, RNA-sequencing and mass spectrometry data.

RESULTS

We found that resistant cell populations under increasing MEKi treatment evolved by the expansion of a single clone but were not a direct consequence of known resistance-conferring mutations. Rather, resistant cells showed adaptive DNA hypermethylation of 209 and hypomethylation of 8 genomic sites, most of which overlap with regulatory elements known to be active in murine PDAC cells. Both DNA methylation changes and MEKi resistance were transient and reversible upon drug withdrawal. Furthermore, MEKi resistance could be reversed by DNA methyltransferase inhibition with remarkable sensitivity exclusively in the resistant cells.

CONCLUSION

Overall, the concept of acquired therapy resistance as a result of the expansion of a single cell clone with epigenetic plasticity sheds light on genetic, epigenetic and phenotypic patterns during evolvement of treatment resistance in a tumor with high adaptive capabilities and provides potential for reversion through epigenetic targeting.

Modeling methyl-sensitive transcription factor motifs with an expanded epigenetic alphabet

BACKGROUND

Transcription factors bind DNA in specific sequence contexts. In addition to distinguishing one nucleobase from another, some transcription factors can distinguish between unmodified and modified bases. Current models of transcription factor binding tend not to take DNA modifications into account, while the recent few that do often have limitations. This makes a comprehensive and accurate profiling of transcription factor affinities difficult.

RESULTS

Here, we develop methods to identify transcription factor binding sites in modified DNA. Our models expand the standard A/C/G/T DNA alphabet to include cytosine modifications. We develop Cytomod to create modified genomic sequences and we also enhance the MEME Suite, adding the capacity to handle custom alphabets. We adapt the well-established position weight matrix (PWM) model of transcription factor binding affinity to this expanded DNA alphabet. Using these methods, we identify modification-sensitive transcription factor binding motifs. We confirm established binding preferences, such as the preference of ZFP57 and C/EBPβ for methylated motifs and the preference of c-Myc for unmethylated E-box motifs.

CONCLUSIONS

Using known binding preferences to tune model parameters, we discover novel modified motifs for a wide array of transcription factors. Finally, we validate our binding preference predictions for OCT4 using cleavage under targets and release using nuclease (CUT&RUN) experiments across conventional, methylation-, and hydroxymethylation-enriched sequences. Our approach readily extends to other DNA modifications. As more genome-wide single-base resolution modification data becomes available, we expect that our method will yield insights into altered transcription factor binding affinities across many different modifications.

MethMotif.Org 2024: a database integrating context-specific transcription factor-binding motifs with DNA methylation patterns

MethMotif (https://methmotif.org) is a publicly available database that provides a comprehensive repository of transcription factor (TF)-binding profiles, enriched with DNA methylation patterns. In this release, we have enhanced the platform, expanding our initial collection to over 700 position weight matrices (PWM), all of which include DNA methylation profiles. One of the key advancements in this release is the segregation of TF-binding motifs based on their cofactors and DNA methylation status. We have previously demonstrated that gene ontology (GO) enriched terms associated with TF target genes may differ based on their association with alternative cofactors and DNA methylation status. MethMotif provides precomputed GO annotations for each human TF of interest, as well as for TF-co-TF complexes, enabling a comprehensive analysis of TF functions in the context of their co-factors. Additionally, MethMotif has been updated to encompass data for two new species, Mus musculus and Arabidopsis thaliana, widening its applicability to a broader community. MethMotif stands out as the first and only TF-binding motifs database to incorporate context-specific PWM coupled with epigenetic information, thereby enlightening context-specific TF functions. This enhancement allows the community to explore and gain deeper insights into the regulatory mechanisms governing transcriptional processes.

JASPAR 2024: 20th anniversary of the open-access database of transcription factor binding profiles

JASPAR (https://jaspar.elixir.no/) is a widely-used open-access database presenting manually curated high-quality and non-redundant DNA-binding profiles for transcription factors (TFs) across taxa. In this 10th release and 20th-anniversary update, the CORE collection has expanded with 329 new profiles. We updated three existing profiles and provided orthogonal support for 72 profiles from the previous release's UNVALIDATED collection. Altogether, the JASPAR 2024 update provides a 20% increase in CORE profiles from the previous release. A trimming algorithm enhanced profiles by removing low information content flanking base pairs, which were likely uninformative (within the capacity of the PFM models) for TFBS predictions and modelling TF-DNA interactions. This release includes enhanced metadata, featuring a refined classification for plant TFs' structural DNA-binding domains. The new JASPAR collections prompt updates to the genomic tracks of predicted TF binding sites (TFBSs) in 8 organisms, with human and mouse tracks available as native tracks in the UCSC Genome browser. All data are available through the JASPAR web interface and programmatically through its API and the updated Bioconductor and pyJASPAR packages. Finally, a new TFBS extraction tool enables users to retrieve predicted JASPAR TFBSs intersecting their genomic regions of interest.

E4F1 and ZNF148 are transcriptional activators of the -57A > C and wild-type TERT promoter

Point mutations within the TERT promoter are the most recurrent somatic noncoding mutations identified across different cancer types, including glioblastoma, melanoma, hepatocellular carcinoma, and bladder cancer. They are most abundant at -146C > T and -124C > T, and rarer at -57A > C, with the latter originally described as a familial case, but subsequently shown also to occur somatically. All three mutations create de novo E26-specific (ETS) binding sites and result in activation of the TERT gene, allowing cancer cells to achieve replicative immortality. Here, we used a systematic proteomics screen to identify transcription factors preferentially binding to the -146C > T, -124C > T, and -57A > C mutations. Although we confirmed binding of multiple ETS factors to the mutant -146C > T and -124C > T sequences, we identified E4F1 as a -57A > C-specific binder and ZNF148 as a TERT wild-type (WT) promoter binder that showed reduced interaction with the -124C > T allele. Both proteins are activating transcription factors that bind specifically to the -57A > C and WT (at position 124) TERT promoter sequence in corresponding cell lines, and up-regulate TERT transcription and telomerase activity. Our work describes new regulators of TERT gene expression with possible roles in cancer.

Widespread effects of DNA methylation and intra-motif dependencies revealed by novel transcription factor binding models

Several studies suggested that transcription factor (TF) binding to DNA may be impaired or enhanced by DNA methylation. We present MeDeMo, a toolbox for TF motif analysis that combines information about DNA methylation with models capturing intra-motif dependencies. In a large-scale study using ChIP-seq data for 335 TFs, we identify novel TFs that show a binding behaviour associated with DNA methylation. Overall, we find that the presence of CpG methylation decreases the likelihood of binding for the majority of methylation-associated TFs. For a considerable subset of TFs, we show that intra-motif dependencies are pivotal for accurately modelling the impact of DNA methylation on TF binding. We illustrate that the novel methylation-aware TF binding models allow to predict differential ChIP-seq peaks and improve the genome-wide analysis of TF binding. Our work indicates that simplistic models that neglect the effect of DNA methylation on DNA binding may lead to systematic underperformance for methylation-associated TFs.

DNA methylation alterations at RE1-silencing transcription factor binding sites and their flanking regions in cancer

BACKGROUND

DNA methylation changes, frequent early events in cancer, can modulate the binding of transcription factors. RE1-silencing transcription factor (REST) plays a fundamental role in regulating the expression of neuronal genes, and in particular their silencing in non-neuronal tissues, by inducing chromatin modifications, including DNA methylation changes, not only in the proximity of its binding sites but also in the flanking regions. REST has been found aberrantly expressed in brain cancer and other cancer types. In this work, we investigated DNA methylation alterations at REST binding sites and their flanking regions in a brain cancer (pilocytic astrocytoma), two gastrointestinal tumours (colorectal cancer and biliary tract cancer) and a blood cancer (chronic lymphocytic leukemia).

RESULTS

Differential methylation analyses focused on REST binding sites and their flanking regions were conducted between tumour and normal samples from our experimental datasets analysed by Illumina microarrays and the identified alterations were validated using publicly available datasets. We discovered distinct DNA methylation patterns between pilocytic astrocytoma and the other cancer types in agreement with the opposite oncogenic and tumour suppressive role of REST in glioma and non-brain tumours.

CONCLUSIONS

Our results suggest that these DNA methylation alterations in cancer may be associated with REST dysfunction opening the enthusiastic possibility to develop novel therapeutic interventions based on the modulation of this master regulator in order to restore the aberrant methylation of its target regions into a normal status.

The Interplay between Dysregulated Metabolism and Epigenetics in Cancer

Cellular metabolism (or energetics) and epigenetics are tightly coupled cellular processes. It is arguable that of all the described cancer hallmarks, dysregulated cellular energetics and epigenetics are the most tightly coregulated. Cellular metabolic states regulate and drive epigenetic changes while also being capable of influencing, if not driving, epigenetic reprogramming. Conversely, epigenetic changes can drive altered and compensatory metabolic states. Cancer cells meticulously modify and control each of these two linked cellular processes in order to maintain their tumorigenic potential and capacity. This review aims to explore the interplay between these two processes and discuss how each affects the other, driving and enhancing tumorigenic states in certain contexts.

Integrative genomic analyses in adipocytes implicate DNA methylation in human obesity and diabetes

DNA methylation variations are prevalent in human obesity but evidence of a causative role in disease pathogenesis is limited. Here, we combine epigenome-wide association and integrative genomics to investigate the impact of adipocyte DNA methylation variations in human obesity. We discover extensive DNA methylation changes that are robustly associated with obesity (N = 190 samples, 691 loci in subcutaneous and 173 loci in visceral adipocytes, P < 1 × 10-7). We connect obesity-associated methylation variations to transcriptomic changes at >500 target genes, and identify putative methylation-transcription factor interactions. Through Mendelian Randomisation, we infer causal effects of methylation on obesity and obesity-induced metabolic disturbances at 59 independent loci. Targeted methylation sequencing, CRISPR-activation and gene silencing in adipocytes, further identifies regional methylation variations, underlying regulatory elements and novel cellular metabolic effects. Our results indicate DNA methylation is an important determinant of human obesity and its metabolic complications, and reveal mechanisms through which altered methylation may impact adipocyte functions.

CanMethdb: a database for genome-wide DNA methylation annotation in cancers

MOTIVATION

DNA methylation within gene body and promoters in cancer cells is well documented. An increasing number of studies showed that cytosine-phosphate-guanine (CpG) sites falling within other regulatory elements could also regulate target gene activation, mainly by affecting transcription factors (TFs) binding in human cancers. This led to the urgent need for comprehensively and effectively collecting distinct cis-regulatory elements and TF-binding sites (TFBS) to annotate DNA methylation regulation.

RESULTS

We developed a database (CanMethdb, http://meth.liclab.net/CanMethdb/) that focused on the upstream and downstream annotations for CpG-genes in cancers. This included upstream cis-regulatory elements, especially those involving distal regions to genes, and TFBS annotations for the CpGs and downstream functional annotations for the target genes, computed through integrating abundant DNA methylation and gene expression profiles in diverse cancers. Users could inquire CpG-target gene pairs for a cancer type through inputting a genomic region, a CpG, a gene name, or select hypo/hypermethylated CpG sets. The current version of CanMethdb documented a total of 38 986 060 CpG-target gene pairs (with 6 769 130 unique pairs), involving 385 217 CpGs and 18 044 target genes, abundant cis-regulatory elements and TFs for 33 TCGA cancer types. CanMethdb might help biologists perform in-depth studies of target gene regulations based on DNA methylations in cancer.

AVAILABILITY AND IMPLEMENTATION

The main program is available at https://github.com/chunquanlipathway/CanMethdb.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Context-dependent CpG methylation directs cell-specific binding of transcription factor ZBTB38

DNA methylation on CpGs regulates transcription in mammals, both by decreasing the binding of methylation-repelled factors and by increasing the binding of methylation-attracted factors. Among the latter, zinc finger proteins have the potential to bind methylated CpGs in a sequence-specific context. The protein ZBTB38 is unique in that it has two independent sets of zinc fingers, which recognize two different methylated consensus sequences in vitro. Here, we identify the binding sites of ZBTB38 in a human cell line, and show that they contain the two methylated consensus sequences identified in vitro. In addition, we show that the distribution of ZBTB38 sites is highly unusual: while 10% of the ZBTB38 sites are also bound by CTCF, the other 90% of sites reside in closed chromatin and are not bound by any of the other factors mapped in our model cell line. Finally, a third of ZBTB38 sites are found upstream of long and active CpG islands. Our work therefore validates ZBTB38 as a methyl-DNA binder in vivo and identifies its unique distribution in the genome.

SEMplMe: a tool for integrating DNA methylation effects in transcription factor binding affinity predictions

MOTIVATION

Aberrant DNA methylation in transcription factor binding sites has been shown to lead to anomalous gene regulation that is strongly associated with human disease. However, the majority of methylation-sensitive positions within transcription factor binding sites remain unknown. Here we introduce SEMplMe, a computational tool to generate predictions of the effect of methylation on transcription factor binding strength in every position within a transcription factor's motif.

RESULTS

SEMplMe uses ChIP-seq and whole genome bisulfite sequencing to predict effects of methylation within binding sites. SEMplMe validates known methylation sensitive and insensitive positions within a binding motif, identifies cell type specific transcription factor binding driven by methylation, and outperforms SELEX-based predictions for CTCF. These predictions can be used to identify aberrant sites of DNA methylation contributing to human disease.

AVAILABILITY AND IMPLEMENTATION

SEMplMe is available from https://github.com/Boyle-Lab/SEMplMe .

[VIPR1 promoter methylation promotes transcription factor AP-2α binding to inhibit VIPR1 expression and promote hepatocellular carcinoma cell growth in vitro]

OBJECTIVE

To explore the transcriptional regulation mechanism and biological function of low expression of vasoactive intestinal peptide receptor 1 (VIPR1) in hepatocellular carcinoma (HCC).

METHODS

We constructed plasmids carrying wild-type VIPR1 promoter or two mutant VIPR1 promoter sequences for transfection of the HCC cell lines Hep3B and Huh7, and examined the effect of AP-2α expression on VIPR1 promoter activity using dual-luciferase reporter assay. Pyrosequencing was performed to detect the changes in VIPR1 promoter methylation level in HCC cells treated with a DNA methyltransferase inhibitor (DAC). Chromatin immunoprecipitation was used to evaluate the binding ability of AP-2α to VIPR1 promoter. Western blotting was used to assess the effect of AP-2α knockdown on VIPR1 expression and examine the differential expression of VIPR1 in the two cell lines. The effects of VIPR1 overexpression and knockdown on the proliferation, cell cycle and apoptosis of HCC cells were analyzed using CCK8 assay and flow cytometry. We also observed the growth of HCC xenograft with lentivirus-mediated over-expression of VIPR1 in nude mice.

RESULTS

Compared with the wild-type VIPR1 promoter group, co-transfection with the vector carrying two promoter mutations and the AP-2α-over-expressing plasmid obviously restored the luciferase activity in HCC cells (P < 0.05). DAC treatment of the cells significantly decreased the methylation level of VIPR1 promoter and inhibited the binding of AP-2α to VIPR1 promoter (P < 0.01). The HCC cells with AP-2α knockdown showed increased VIPR1 expression, which was lower in Huh7 cells than in Hep3B cells. VIPR1 overexpression in HCC cells caused significant cell cycle arrest in G2/M phase (P < 0.01), promoted cell apoptosis (P < 0.001), and inhibited cell proliferation (P < 0.001), while VIPR1 knockdown produced the opposite effects. In the tumor-bearing nude mice, VIPR1 overexpression in the HCC cells significantly suppressed the increase of tumor volume (P < 0.001) and weight (P < 0.05).

CONCLUSION

VIPR1 promoter methylation in HCC promotes the binding of AP-2α and inhibits VIPR1 expression, while VIPR1 overexpression causes cell cycle arrest, promotes cell apoptosis, and inhibits cell proliferation and tumor growth.

DNA methylation dynamics and dysregulation delineated by high-throughput profiling in the mouse

We have developed a mouse DNA methylation array that contains 296,070 probes representing the diversity of mouse DNA methylation biology. We present a mouse methylation atlas as a rich reference resource of 1,239 DNA samples encompassing distinct tissues, strains, ages, sexes, and pathologies. We describe applications for comparative epigenomics, genomic imprinting, epigenetic inhibitors, patient-derived xenograft assessment, backcross tracing, and epigenetic clocks. We dissect DNA methylation processes associated with differentiation, aging, and tumorigenesis. Notably, we find that tissue-specific methylation signatures localize to binding sites for transcription factors controlling the corresponding tissue development. Age-associated hypermethylation is enriched at regions of Polycomb repression, while hypomethylation is enhanced at regions bound by cohesin complex members. Apc ^Min/+ polyp-associated hypermethylation affects enhancers regulating intestinal differentiation, while hypomethylation targets AP-1 binding sites. This Infinium Mouse Methylation BeadChip (version MM285) is widely accessible to the research community and will accelerate high-sample-throughput studies in this important model organism.

MIAOME: Human Microbiome Affect The Host Epigenome

Besides the genetic factors having tremendous influences on the regulations of the epigenome, the microenvironmental factors have recently gained extensive attention for their roles in affecting the host epigenome. There are three major types of microenvironmental factors: microbiota-derived metabolites (MDM), microbiota-derived components (MDC) and microbiota-secreted proteins (MSP). These factors can regulate host physiology by modifying host gene expression through the three highly interconnected epigenetic mechanisms (e.g. histone modifications, DNA modifications, and non-coding RNAs). However, no database was available to provide the comprehensive factors of these types. Herein, a database entitled 'Human Microbiome Affect The Host Epigenome (MIAOME)' was constructed. Based on the types of epigenetic modifications confirmed in the literature review, the MIAOME database captures 1068 (63 genus, 281 species, 707 strains, etc.) human microbes, 91 unique microbiota-derived metabolites & components (16 fatty acids, 10 bile acids, 10 phenolic compounds, 10 vitamins, 9 tryptophan metabolites, etc.) derived from 967 microbes; 50 microbes that secreted 40 proteins; 98 microbes that directly influence the host epigenetic modification, and provides 3 classifications of the epigenome, including (1) 4 types of DNA modifications, (2) 20 histone modifications and (3) 490 ncRNAs regulations, involved in 160 human diseases. All in all, MIAOME has compiled the information on the microenvironmental factors influence host epigenome through the scientific literature and biochemical databases, and allows the collective considerations among the different types of factors. It can be freely assessed without login requirement by all users at: http://miaome.idrblab.net/ttd/

Long-term effects of wildfire smoke exposure during early life on the nasal epigenome in rhesus macaques

BACKGROUND

Wildfire smoke is responsible for around 20% of all particulate emissions in the U.S. and affects millions of people worldwide. Children are especially vulnerable, as ambient air pollution exposure during early childhood is associated with reduced lung function. Most studies, however, have focused on the short-term impacts of wildfire smoke exposures. We aimed to identify long-term baseline epigenetic changes associated with early-life exposure to wildfire smoke. We collected nasal epithelium samples for whole genome bisulfite sequencing (WGBS) from two groups of adult female rhesus macaques: one group born just before the 2008 California wildfire season and exposed to wildfire smoke during early-life (n = 8), and the other group born in 2009 with no wildfire smoke exposure during early-life (n = 14). RNA-sequencing was also performed on a subset of these samples.

RESULTS

We identified 3370 differentially methylated regions (DMRs) (difference in methylation ≥ 5%, empirical p < 0.05) and 1 differentially expressed gene (FLOT2) (FDR < 0.05, fold of change ≥ 1.2). The DMRs were annotated to genes significantly enriched for synaptogenesis signaling, protein kinase A signaling, and a variety of immune processes, and some DMRs significantly correlated with gene expression differences. DMRs were also significantly enriched within regions of bivalent chromatin (top odds ratio = 1.46, q-value < 3 × 10^-6) that often silence key developmental genes while keeping them poised for activation in pluripotent cells.

CONCLUSIONS

These data suggest that early-life exposure to wildfire smoke leads to long-term changes in the methylome over genes impacting the nervous and immune systems. Follow-up studies will be required to test whether these changes influence transcription following an immune/respiratory challenge.

ChIP-AP: an integrated analysis pipeline for unbiased ChIP-seq analysis

Chromatin immunoprecipitation coupled with sequencing (ChIP-seq) is a technique used to identify protein–DNA interaction sites through antibody pull-down, sequencing and analysis; with enrichment ‘peak’ calling being the most critical analytical step. Benchmarking studies have consistently shown that peak callers have distinct selectivity and specificity characteristics that are not additive and seldom completely overlap in many scenarios, even after parameter optimization. We therefore developed ChIP-AP, an integrated ChIP-seq analysis pipeline utilizing four independent peak callers, which seamlessly processes raw sequencing files to final result. This approach enables (1) better gauging of peak confidence through detection by multiple algorithms, and (2) more thoroughly surveys the binding landscape by capturing peaks not detected by individual callers. Final analysis results are then integrated into a single output table, enabling users to explore their data by applying selectivity and sensitivity thresholds that best address their biological questions, without needing any additional reprocessing. ChIP-AP therefore presents investigators with a more comprehensive coverage of the binding landscape without requiring additional wet-lab observations.

DiseaseMeth version 3.0: a major expansion and update of the human disease methylation database

DNA methylation has a growing potential for use as a biomarker because of its involvement in disease. DNA methylation data have also substantially grown in volume during the past 5 years. To facilitate access to these fragmented data, we proposed DiseaseMeth version 3.0 based on DiseaseMeth version 2.0, in which the number of diseases including increased from 88 to 162 and High-throughput profiles samples increased from 32 701 to 49 949. Experimentally confirmed associations added 448 pairs obtained by manual literature mining from 1472 papers in PubMed. The search, analyze and tools sections were updated to increase performance. In particular, the FunctionSearch now provides for the functional enrichment of genes from localized GO and KEGG annotation. We have also developed a unified analysis pipeline for identifying differentially DNA methylated genes (DMGs) from the original data stored in the database. 22 718 DMGs were found in 99 diseases. These DMGs offer application in disease evaluation using two self-developed online tools, Methylation Disease Correlation and Cancer Prognosis & Co-Methylation. All query results can be downloaded and can also be displayed through a box plot, heatmap or network module according to whichever search section is used. DiseaseMeth version 3.0 is freely available at http://diseasemeth.edbc.org/.

Hippocampal epigenetic and insulin-like growth factor alterations in noninvasive versus invasive mechanical ventilation in preterm lambs

BACKGROUND

The brain of chronically ventilated preterm human infants is vulnerable to collateral damage during invasive mechanical ventilation (IMV). Damage is manifest, in part, by learning and memory impairments, which are hippocampal functions. A molecular regulator of hippocampal development is insulin-like growth factor 1 (IGF1). A gentler ventilation strategy is noninvasive respiratory support (NRS). We tested the hypotheses that NRS leads to greater levels of IGF1 messenger RNA (mRNA) variants and distinct epigenetic profile along the IGF1 gene locus in the hippocampus compared to IMV.

METHODS

Preterm lambs were managed by NRS or IMV for 3 or 21 days. Isolated hippocampi were analyzed for IGF1 mRNA levels and splice variants for promoter 1 (P1), P2, and IGF1A and 1B, DNA methylation in P1 region, and histone covalent modifications along the gene locus.

RESULTS

NRS had significantly greater levels of IGF1 P1 (predominant transcript), and 1A and 1B mRNA variants compared to IMV at 3 or 21 days. NRS also led to more DNA methylation and greater occupancy of activating mark H3K4 trimethylation (H3K4me3), repressive mark H3K27me3, and elongation mark H3K36me3 compared to IMV.

CONCLUSIONS

NRS leads to distinct IGF1 mRNA variant levels and epigenetic profile in the hippocampus compared to IMV.

IMPACT

Our study shows that 3 or 21 days of NRS of preterm lambs leads to distinct IGF1 mRNA variant levels and epigenetic profile in the hippocampus compared to IMV. Preterm infant studies suggest that NRS leads to better neurodevelopmental outcomes later in life versus IMV. Also, duration of IMV is directly related to hippocampal damage; however, molecular players remain unknown. NRS, as a gentler mode of respiratory management of preterm neonates, may reduce damage to the immature hippocampus through an epigenetic mechanism.

BANP opens chromatin and activates CpG-island-regulated genes

The majority of gene transcripts generated by RNA polymerase II in mammalian genomes initiate at CpG island (CGI) promoters^1,2, yet our understanding of their regulation remains limited. This is in part due to the incomplete information that we have on transcription factors, their DNA-binding motifs and which genomic binding sites are functional in any given cell type^3-5. In addition, there are orphan motifs without known binders, such as the CGCG element, which is associated with highly expressed genes across human tissues and enriched near the transcription start site of a subset of CGI promoters^6-8. Here we combine single-molecule footprinting with interaction proteomics to identify BTG3-associated nuclear protein (BANP) as the transcription factor that binds this element in the mouse and human genome. We show that BANP is a strong CGI activator that controls essential metabolic genes in pluripotent stem and terminally differentiated neuronal cells. BANP binding is repelled by DNA methylation of its motif in vitro and in vivo, which epigenetically restricts most binding to CGIs and accounts for differential binding at aberrantly methylated CGI promoters in cancer cells. Upon binding to an unmethylated motif, BANP opens chromatin and phases nucleosomes. These findings establish BANP as a critical activator of a set of essential genes and suggest a model in which the activity of CGI promoters relies on methylation-sensitive transcription factors that are capable of chromatin opening.

Computational methods and next-generation sequencing approaches to analyze epigenetics data: Profiling of methods and applications

Epigenetics is mainly comprised of features that regulate genomic interactions thereby playing a crucial role in a vast array of biological processes. Epigenetic mechanisms such as DNA methylation and histone modifications influence gene expression by modulating the packaging of DNA in the nucleus. A plethora of studies have emphasized the importance of analyzing epigenetics data through genome-wide studies and high-throughput approaches, thereby providing key insights towards epigenetics-based diseases such as cancer. Recent advancements have been made towards translating epigenetics research into a high throughput approach such as genome-scale profiling. Amongst all, bioinformatics plays a pivotal role in achieving epigenetics-related computational studies. Despite significant advancements towards epigenomic profiling, it is challenging to understand how various epigenetic modifications such as chromatin modifications and DNA methylation regulate gene expression. Next-generation sequencing (NGS) provides accurate and parallel sequencing thereby allowing researchers to comprehend epigenomic profiling. In this review, we summarize different computational methods such as machine learning and other bioinformatics tools, publicly available databases and resources to identify key modifications associated with epigenetic machinery. Additionally, the review also focuses on understanding recent methodologies related to epigenome profiling using NGS methods ranging from library preparation, different sequencing platforms and analytical techniques to evaluate various epigenetic modifications such as DNA methylation and histone modifications. We also provide detailed information on bioinformatics tools and computational strategies responsible for analyzing large scale data in epigenetics.

The Tiger Rattlesnake genome reveals a complex genotype underlying a simple venom phenotype

Variation in gene regulation is ubiquitous, yet identifying the mechanisms producing such variation, especially for complex traits, is challenging. Snake venoms provide a model system for studying the phenotypic impacts of regulatory variation in complex traits because of their genetic tractability. Here, we sequence the genome of the Tiger Rattlesnake, which possesses the simplest and most toxic venom of any rattlesnake species, to determine whether the simple venom phenotype is the result of a simple genotype through gene loss or a complex genotype mediated through regulatory mechanisms. We generate the most contiguous snake-genome assembly to date and use this genome to show that gene loss, chromatin accessibility, and methylation levels all contribute to the production of the simplest, most toxic rattlesnake venom. We provide the most complete characterization of the venom gene-regulatory network to date and identify key mechanisms mediating phenotypic variation across a polygenic regulatory network.

Deciphering the genetic code of DNA methylation

DNA methylation plays crucial roles in many biological processes and abnormal DNA methylation patterns are often observed in diseases. Recent studies have shed light on cis-acting DNA elements that regulate locus-specific DNA methylation, which involves transcription factors, histone modification and DNA secondary structures. In addition, several recent studies have surveyed DNA motifs that regulate DNA methylation and suggest potential applications in diagnosis and prognosis. Here, we discuss the current biological foundation for the cis-acting genetic code that regulates DNA methylation. We review the computational models that predict DNA methylation with genetic features and discuss the biological insights revealed from these models. We also provide an in-depth discussion on how to leverage such knowledge in clinical applications, particularly in the context of liquid biopsy for early cancer diagnosis and treatment.

GTRD: an integrated view of transcription regulation

The Gene Transcription Regulation Database (GTRD; http://gtrd.biouml.org/) contains uniformly annotated and processed NGS data related to gene transcription regulation: ChIP-seq, ChIP-exo, DNase-seq, MNase-seq, ATAC-seq and RNA-seq. With the latest release, the database has reached a new level of data integration. All cell types (cell lines and tissues) presented in the GTRD were arranged into a dictionary and linked with different ontologies (BRENDA, Cell Ontology, Uberon, Cellosaurus and Experimental Factor Ontology) and with related experiments in specialized databases on transcription regulation (FANTOM5, ENCODE and GTEx). The updated version of the GTRD provides an integrated view of transcription regulation through a dedicated web interface with advanced browsing and search capabilities, an integrated genome browser, and table reports by cell types, transcription factors, and genes of interest.

Cord blood DNA methylome in newborns later diagnosed with autism spectrum disorder reflects early dysregulation of neurodevelopmental and X-linked genes

BACKGROUND

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with complex heritability and higher prevalence in males. The neonatal epigenome has the potential to reflect past interactions between genetic and environmental factors during early development and influence future health outcomes.

METHODS

We performed whole-genome bisulfite sequencing of 152 umbilical cord blood samples from the MARBLES and EARLI high-familial risk prospective cohorts to identify an epigenomic signature of ASD at birth. Samples were split into discovery and replication sets and stratified by sex, and their DNA methylation profiles were tested for differentially methylated regions (DMRs) between ASD and typically developing control cord blood samples. DMRs were mapped to genes and assessed for enrichment in gene function, tissue expression, chromosome location, and overlap with prior ASD studies. DMR coordinates were tested for enrichment in chromatin states and transcription factor binding motifs. Results were compared between discovery and replication sets and between males and females.

RESULTS

We identified DMRs stratified by sex that discriminated ASD from control cord blood samples in discovery and replication sets. At a region level, 7 DMRs in males and 31 DMRs in females replicated across two independent groups of subjects, while 537 DMR genes in males and 1762 DMR genes in females replicated by gene association. These DMR genes were significantly enriched for brain and embryonic expression, X chromosome location, and identification in prior epigenetic studies of ASD in post-mortem brain. In males and females, autosomal ASD DMRs were significantly enriched for promoter and bivalent chromatin states across most cell types, while sex differences were observed for X-linked ASD DMRs. Lastly, these DMRs identified in cord blood were significantly enriched for binding sites of methyl-sensitive transcription factors relevant to fetal brain development.

CONCLUSIONS

At birth, prior to the diagnosis of ASD, a distinct DNA methylation signature was detected in cord blood over regulatory regions and genes relevant to early fetal neurodevelopment. Differential cord methylation in ASD supports the developmental and sex-biased etiology of ASD and provides novel insights for early diagnosis and therapy.

Sensitivity of transcription factors to DNA methylation

Dynamic binding of transcription factors (TFs) to regulatory elements controls transcriptional states throughout organism development. Epigenetics modifications, such as DNA methylation mostly within cytosine-guanine dinucleotides (CpGs), have the potential to modulate TF binding to DNA. Although DNA methylation has long been thought to repress TF binding, a more recent model proposes that TF binding can also inhibit DNA methylation. Here, we review the possible scenarios by which DNA methylation and TF binding affect each other. Further in vivo experiments will be required to generalize these models.

TET2 deficiency reprograms the germinal center B cell epigenome and silences genes linked to lymphomagenesis

The TET2 DNA hydroxymethyltransferase is frequently disrupted by somatic mutations in diffuse large B cell lymphomas (DLBCLs), a tumor that originates from germinal center (GC) B cells. Here, we show that TET2 deficiency leads to DNA hypermethylation of regulatory elements in GC B cells, associated with silencing of the respective genes. This hypermethylation affects the binding of transcription factors including those involved in exit from the GC reaction and involves pathways such as B cell receptor, antigen presentation, CD40, and others. Normal GC B cells manifest a typical hypomethylation signature, which is caused by AID, the enzyme that mediates somatic hypermutation. However, AID-induced demethylation is markedly impaired in TET2-deficient GC B cells, suggesting that AID epigenetic effects are partially dependent on TET2. Last, we find that TET2 mutant DLBCLs also manifest the aberrant TET2-deficient GC DNA methylation signature, suggesting that this epigenetic pattern is maintained during and contributes to lymphomagenesis.

TFBSshape: an expanded motif database for DNA shape features of transcription factor binding sites

TFBSshape (https://tfbsshape.usc.edu) is a motif database for analyzing structural profiles of transcription factor binding sites (TFBSs). The main rationale for this database is to be able to derive mechanistic insights in protein-DNA readout modes from sequencing data without available structures. We extended the quantity and dimensionality of TFBSshape, from mostly in vitro to in vivo binding and from unmethylated to methylated DNA. This new release of TFBSshape improves its functionality and launches a responsive and user-friendly web interface for easy access to the data. The current expansion includes new entries from the most recent collections of transcription factors (TFs) from the JASPAR and UniPROBE databases, methylated TFBSs derived from in vitro high-throughput EpiSELEX-seq binding assays and in vivo methylated TFBSs from the MeDReaders database. TFBSshape content has increased to 2428 structural profiles for 1900 TFs from 39 different species. The structural profiles for each TFBS entry now include 13 shape features and minor groove electrostatic potential for standard DNA and four shape features for methylated DNA. We improved the flexibility and accuracy for the shape-based alignment of TFBSs and designed new tools to compare methylated and unmethylated structural profiles of TFs and methods to derive DNA shape-preserving nucleotide mutations in TFBSs.

Complex impact of DNA methylation on transcriptional dysregulation across 22 human cancer types

Accumulating evidence has demonstrated that transcriptional regulation is affected by DNA methylation. Understanding the perturbation of DNA methylation-mediated regulation between transcriptional factors (TFs) and targets is crucial for human diseases. However, the global landscape of DNA methylation-mediated transcriptional dysregulation (DMTD) across cancers has not been portrayed. Here, we systematically identified DMTD by integrative analysis of transcriptome, methylome and regulatome across 22 human cancer types. Our results revealed that transcriptional regulation was affected by DNA methylation, involving hundreds of methylation-sensitive TFs (MethTFs). In addition, pan-cancer MethTFs, the regulatory activity of which is generally affected by DNA methylation across cancers, exhibit dominant functional characteristics and regulate several cancer hallmarks. Moreover, pan-cancer MethTFs were found to be affected by DNA methylation in a complex pattern. Finally, we investigated the cooperation among MethTFs and identified a network module that consisted of 43 MethTFs with prognostic potential. In summary, we systematically dissected the transcriptional dysregulation mediated by DNA methylation across cancer types, and our results provide a valuable resource for both epigenetic and transcriptional regulation communities.

Epigenomic Convergence of Neural-Immune Risk Factors in Neurodevelopmental Disorder Cortex

Neurodevelopmental disorders (NDDs) affect 7-14% of all children in developed countries and are one of the leading causes of lifelong disability. Epigenetic modifications are poised at the interface between genes and environment and are predicted to reveal insight into NDD etiology. Whole-genome bisulfite sequencing was used to examine DNA cytosine methylation in 49 human cortex samples from 3 different NDDs (autism spectrum disorder, Rett syndrome, and Dup15q syndrome) and matched controls. Integration of methylation changes across NDDs with relevant genomic and genetic datasets revealed differentially methylated regions (DMRs) unique to each type of NDD but with shared regulatory functions in neurons and microglia. NDD DMRs were enriched within promoter regions and for transcription factor binding sites with identified methylation sensitivity. DMRs from all 3 disorders were enriched for ontologies related to nervous system development and genes with disrupted expression in brain from neurodevelopmental or neuropsychiatric disorders. Genes associated with NDD DMRs showed expression patterns indicating an important role for altered microglial function during brain development. These findings demonstrate an NDD epigenomic signature in human cortex that will aid in defining therapeutic targets and early biomarkers at the interface of genetic and environmental NDD risk factors.

Identification and characterization of methylation-mediated transcriptional dysregulation dictate methylation roles in preeclampsia

Background

Preeclampsia (PE) is a heterogeneous, hypertensive disorder of pregnancy, with no robust biomarkers or effective treatments. PE increases the risk of poor outcomes for both the mother and the baby. Methylation-mediated transcriptional dysregulation motifs (methTDMs) could contribute the PE development. However, precise functional roles of methTDMs in PE have not been globally described.

Methods

Here, we develop a comprehensive and computational pipeline to identify PE-specific methTDMs following TF, gene, methylation expression profile, and experimentally verified TF-gene interactions.

Results

The regulation patterns of methTDMs are multiple and complex in PE and contain relax inhibition, intensify inhibition, relax activation, intensify activation, reverse activation, and reverse inhibition. A core module is extracted from global methTDM network to further depict the mechanism of methTDMs in PE. The common and specific features of any two kinds of regulation pattern are also analyzed in PE. Some key methylation sites, TFs, and genes such as IL2RG are identified in PE. Functional analysis shows that methTDMs are associated with immune-, insulin-, and NK cell-related functions. Drug-related network identifies some key drug repurposing candidates such as NADH.

Conclusion

Collectively, the study highlighted the effect of methylation on the transcription process in PE. MethTDMs could contribute to identify specific biomarkers and drug repurposing candidates for PE.

TFregulomeR reveals transcription factors' context-specific features and functions

Transcription factors (TFs) are sequence-specific DNA binding proteins, fine-tuning spatiotemporal gene expression. Since genomic occupancy of a TF is highly dynamic, it is crucial to study TF binding sites (TFBSs) in a cell-specific context. To date, thousands of ChIP-seq datasets have portrayed the genomic binding landscapes of numerous TFs in different cell types. Although these datasets can be browsed via several platforms, tools that can operate on that data flow are still lacking. Here, we introduce TFregulomeR (https://github.com/benoukraflab/TFregulomeR), an R-library linked to an up-to-date compendium of cistrome and methylome datasets, implemented with functionalities that facilitate integrative analyses. In particular, TFregulomeR enables the characterization of TF binding partners and cell-specific TFBSs, along with the study of TF’s functions in the context of different partnerships and DNA methylation levels. We demonstrated that TFs’ target gene ontologies can differ notably depending on their partners and, by re-analyzing well characterized TFs, we brought to light that numerous leucine zipper TFBSs derived from ChIP-seq experiments documented in current databases were inadequately characterized, due to the fact that their position weight matrices were assembled using a mixture of homodimer and heterodimer binding sites. Altogether, analyses of context-specific transcription regulation with TFregulomeR foster our understanding of regulatory network-dependent TF functions.

RSAT variation-tools: An accessible and flexible framework to predict the impact of regulatory variants on transcription factor binding

Gene regulatory regions contain short and degenerated DNA binding sites recognized by transcription factors (TFBS). When TFBS harbor SNPs, the DNA binding site may be affected, thereby altering the transcriptional regulation of the target genes. Such regulatory SNPs have been implicated as causal variants in Genome-Wide Association Study (GWAS) studies. In this study, we describe improved versions of the programs Variation-tools designed to predict regulatory variants, and present four case studies to illustrate their usage and applications. In brief, Variation-tools facilitate i) obtaining variation information, ii) interconversion of variation file formats, iii) retrieval of sequences surrounding variants, and iv) calculating the change on predicted transcription factor affinity scores between alleles, using motif scanning approaches. Notably, the tools support the analysis of haplotypes. The tools are included within the well-maintained suite Regulatory Sequence Analysis Tools (RSAT, http://rsat.eu), and accessible through a web interface that currently enables analysis of five metazoa and ten plant genomes. Variation-tools can also be used in command-line with any locally-installed Ensembl genome. Users can input personal collections of variants and motifs, providing flexibility in the analysis.

Contribution of DNA methylation in chronic stress-induced cardiac remodeling and arrhythmias in mice

It is recognized that stress can induce cardiac dysfunction, but the underlying mechanisms are not well understood. The present study aimed to test the hypothesis that chronic negative stress leads to alterations in DNA methylation of certain cardiac genes, which in turn contribute to pathologic remodeling of the heart. We found that mice that were exposed to chronic restraint stress (CRS) for 4 wk exhibited cardiac remodeling toward heart failure, as characterized by ventricular chamber dilatation, wall thinning, and decreased contractility. CRS also induced cardiac arrhythmias, including intermittent sinus tachycardia and bradycardia, frequent premature ventricular contraction, and sporadic atrioventricular conduction block. Circulating levels of stress hormones were elevated, and the cardiac expression of tyrosine hydroxylase, a marker of sympathetic innervation, was increased in CRS mice. Using reduced representation bisulfite sequencing, we found that although CRS did not lead to global changes in DNA methylation in the murine heart, it nevertheless altered methylation at specific genes that are associated with the dilated cardiomyopathy (DCM) (e.g., desmin) and adrenergic signaling of cardiomyocytes (ASPC) (e.g., adrenergic receptor-α1) pathways. We conclude that CRS induces cardiac remodeling and arrhythmias, potentially through altered methylation of myocardial genes associated with the DCM and ASPC pathways.-Zhang, P., Li, T., Liu, Y.-Q., Zhang, H., Xue, S.-M., Li, G., Cheng, H.-Y.M., Cao, J.-M. Contribution of DNA methylation in chronic stress-induced cardiac remodeling and arrhythmias in mice.

Identification of DNA motifs that regulate DNA methylation

DNA methylation is an important epigenetic mark but how its locus-specificity is decided in relation to DNA sequence is not fully understood. Here, we have analyzed 34 diverse whole-genome bisulfite sequencing datasets in human and identified 313 motifs, including 92 and 221 associated with methylation (methylation motifs, MMs) and unmethylation (unmethylation motifs, UMs), respectively. The functionality of these motifs is supported by multiple lines of evidence. First, the methylation levels at the MM and UM motifs are respectively higher and lower than the genomic background. Second, these motifs are enriched at the binding sites of methylation modifying enzymes including DNMT3A and TET1, indicating their possible roles of recruiting these enzymes. Third, these motifs significantly overlap with "somatic QTLs" (quantitative trait loci) of methylation and expression. Fourth, disruption of these motifs by mutation is associated with significantly altered methylation level of the CpGs in the neighbor regions. Furthermore, these motifs together with somatic mutations are predictive of cancer subtypes and patient survival. We revealed some of these motifs were also associated with histone modifications, suggesting a possible interplay between the two types of epigenetic modifications. We also found some motifs form feed forward loops to contribute to DNA methylation dynamics.

Global DNA Methylation Patterns in Human Gliomas and Their Interplay with Other Epigenetic Modifications

During the last two decades, several international consortia have been established to unveil the molecular background of human cancers including gliomas. As a result, a huge outbreak of new genetic and epigenetic data appeared. It was not only shown that gliomas share some specific DNA sequence aberrations, but they also present common alterations of chromatin. Many researchers have reported specific epigenetic features, such as DNA methylation and histone modifications being involved in tumor pathobiology. Unlike mutations in DNA, epigenetic changes are more global in nature. Moreover, many studies have shown an interplay between different types of epigenetic changes. Alterations in DNA methylation in gliomas are one of the best described epigenetic changes underlying human pathology. In the following work, we present the state of knowledge about global DNA methylation patterns in gliomas and their interplay with histone modifications that may affect transcription factor binding, global gene expression and chromatin conformation. Apart from summarizing the impact of global DNA methylation on glioma pathobiology, we provide an extract of key mechanisms of DNA methylation machinery.

The 26th annual Nucleic Acids Research database issue and Molecular Biology Database Collection

The 2019 Nucleic Acids Research (NAR) Database Issue contains 168 papers spanning molecular biology. Among them, 64 are new and another 92 are updates describing resources that appeared in the Issue previously. The remaining 12 are updates on databases most recently published elsewhere. This Issue contains two Breakthrough articles, on the Virtual Metabolic Human (VMH) database which links human and gut microbiota metabolism with diet and disease, and Vibrism DB, a database of mouse brain anatomy and gene (co-)expression with sophisticated visualization and session sharing. Major returning nucleic acid databases include RNAcentral, miRBase and LncRNA2Target. Protein sequence databases include UniProtKB, InterPro and Pfam, while wwPDB and RCSB cover protein structure. STRING and KEGG update in the section on metabolism and pathways. Microbial genomes are covered by IMG/M and resources for human and model organism genomics include Ensembl, UCSC Genome Browser, GENCODE and Flybase. Genomic variation and disease are well-covered by GWAS Catalog, PopHumanScan, OMIM and COSMIC, CADD being another major newcomer. Major new proteomics resources reporting here include iProX and jPOSTdb. The entire database issue is freely available online on the NAR website (https://academic.oup.com/nar). The NAR online Molecular Biology Database Collection has been updated, reviewing 506 entries, adding 66 new resources and eliminating 147 discontinued URLs, bringing the current total to 1613 databases. It is available at http://www.oxfordjournals.org/nar/database/c.