HOCOMOCO: expansion and enhancement of the collection of transcription factor binding sites models

DNA methylation clocks for estimating biological age in Chinese cohorts

Epigenetic clocks are accurate predictors of human chronological age based on the analysis of DNA methylation (DNAm) at specific CpG sites. However, a systematic comparison between DNA methylation data and other omics datasets has not yet been performed. Moreover, available DNAm age predictors are based on datasets with limited ethnic representation. To address these knowledge gaps, we generated and analyzed DNA methylation datasets from two independent Chinese cohorts, revealing age-related DNAm changes. Additionally, a DNA methylation aging clock (iCAS-DNAmAge) and a group of DNAm-based multi-modal clocks for Chinese individuals were developed, with most of them demonstrating strong predictive capabilities for chronological age. The clocks were further employed to predict factors influencing aging rates. The DNAm aging clock, derived from multi-modal aging features (compositeAge-DNAmAge), exhibited a close association with multi-omics changes, lifestyles, and disease status, underscoring its robust potential for precise biological age assessment. Our findings offer novel insights into the regulatory mechanism of age-related DNAm changes and extend the application of the DNAm clock for measuring biological age and aging pace, providing the basis for evaluating aging intervention strategies.

A weighted two-stage sequence alignment framework to identify motifs from ChIP-exo data

In this study, we introduce TESA (weighted two-stage alignment), an innovative motif prediction tool that refines the identification of DNA-binding protein motifs, essential for deciphering transcriptional regulatory mechanisms. Unlike traditional algorithms that rely solely on sequence data, TESA integrates the high-resolution chromatin immunoprecipitation (ChIP) signal, specifically from ChIP-exonuclease (ChIP-exo), by assigning weights to sequence positions, thereby enhancing motif discovery. TESA employs a nuanced approach combining a binomial distribution model with a graph model, further supported by a "bookend" model, to improve the accuracy of predicting motifs of varying lengths. Our evaluation, utilizing an extensive compilation of 90 prokaryotic ChIP-exo datasets from proChIPdb and 167 H. sapiens datasets, compared TESA's performance against seven established tools. The results indicate TESA's improved precision in motif identification, suggesting its valuable contribution to the field of genomic research.

HOCOMOCO in 2024: a rebuild of the curated collection of binding models for human and mouse transcription factors

We present a major update of the HOCOMOCO collection that provides DNA binding specificity patterns of 949 human transcription factors and 720 mouse orthologs. To make this release, we performed motif discovery in peak sets that originated from 14 183 ChIP-Seq experiments and reads from 2554 HT-SELEX experiments yielding more than 400 thousand candidate motifs. The candidate motifs were annotated according to their similarity to known motifs and the hierarchy of DNA-binding domains of the respective transcription factors. Next, the motifs underwent human expert curation to stratify distinct motif subtypes and remove non-informative patterns and common artifacts. Finally, the curated subset of 100 thousand motifs was supplied to the automated benchmarking to select the best-performing motifs for each transcription factor. The resulting HOCOMOCO v12 core collection contains 1443 verified position weight matrices, including distinct subtypes of DNA binding motifs for particular transcription factors. In addition to the core collection, HOCOMOCO v12 provides motif sets optimized for the recognition of binding sites in vivo and in vitro, and for annotation of regulatory sequence variants. HOCOMOCO is available at https://hocomoco12.autosome.org and https://hocomoco.autosome.org.

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.

Blimp-1 and c-Maf regulate Il10 and negatively regulate common and unique proinflammatory gene networks in IL-12 plus IL-27-driven T helper-1 cells

Background

CD4 + Th1 cells producing IFN-γ are required to eradicate intracellular pathogens, however if uncontrolled these cells can cause immunopathology. The cytokine IL-10 is produced by multiple immune cells including Th1 cells during infection and regulates the immune response to minimise collateral host damage. In this study we aimed to elucidate the transcriptional network of genes controlling the expression of Il10 and proinflammatory cytokines, including Ifng in Th1 cells differentiated from mouse naive CD4 + T cells.

Methods

We applied computational analysis of gene regulation derived from temporal profiling of gene expression clusters obtained from bulk RNA sequencing (RNA-seq) of flow cytometry sorted naïve CD4 + T cells from mouse spleens differentiated in vitro into Th1 effector cells with IL-12 and IL-27 to produce Ifng and Il10, compared to IL-27 alone which express Il10 only , or IL-12 alone which express Ifng and no Il10, or medium control driven-CD4 + T cells which do not express effector cytokines . Data were integrated with analysis of active genomic regions from these T cells using an assay for transposase-accessible chromatin with sequencing (ATAC)-seq, integrated with literature derived-Chromatin-immunoprecipitation (ChIP)-seq data and the RNA-seq data, to elucidate the transcriptional network of genes controlling expression of Il10 and pro-inflammatory effector genes in Th1 cells. The co-dominant role for the transcription factors, Prdm1 (encoding Blimp-1) and Maf (encoding c-Maf) , in cytokine gene regulation in Th1 cells, was confirmed using T cells obtained from mice with T-cell specific deletion of these transcription factors.

Results

We show that the transcription factors Blimp-1 and c-Maf each have unique and common effects on cytokine gene regulation and not only co-operate to induce Il10 gene expression in IL-12 plus IL-27 differentiated mouse Th1 cells, but additionally directly negatively regulate key proinflammatory cytokines including Ifng, thus providing mechanisms for reinforcement of regulated Th1 cell responses.

Conclusions

These data show that Blimp-1 and c-Maf positively and negatively regulate a network of both unique and common anti-inflammatory and pro-inflammatory genes to reinforce a Th1 response in mice that will eradicate pathogens with minimum immunopathology.

CEMIG: prediction of the cis-regulatory motif using the de Bruijn graph from ATAC-seq

Sequence motif discovery algorithms enhance the identification of novel deoxyribonucleic acid sequences with pivotal biological significance, especially transcription factor (TF)-binding motifs. The advent of assay for transposase-accessible chromatin using sequencing (ATAC-seq) has broadened the toolkit for motif characterization. Nonetheless, prevailing computational approaches have focused on delineating TF-binding footprints, with motif discovery receiving less attention. Herein, we present Cis rEgulatory Motif Influence using de Bruijn Graph (CEMIG), an algorithm leveraging de Bruijn and Hamming distance graph paradigms to predict and map motif sites. Assessment on 129 ATAC-seq datasets from the Cistrome Data Browser demonstrates CEMIG's exceptional performance, surpassing three established methodologies on four evaluative metrics. CEMIG accurately identifies both cell-type-specific and common TF motifs within GM12878 and K562 cell lines, demonstrating its comparative genomic capabilities in the identification of evolutionary conservation and cell-type specificity. In-depth transcriptional and functional genomic studies have validated the functional relevance of CEMIG-identified motifs across various cell types. CEMIG is available at https://github.com/OSU-BMBL/CEMIG, developed in C++ to ensure cross-platform compatibility with Linux, macOS and Windows operating systems.

Protocol for using single-cell sequencing to study the heterogeneity of NF1 nerve sheath tumors from clinical biospecimens

Single-cell sequencing is a powerful technology to understand the heterogeneity of clinical biospecimens. Here, we present a protocol for obtaining single-cell suspension from neurofibromatosis type 1-associated nerve sheath tumors for transcriptomic profiling on the 10x platform. We describe steps for clinical sample collection, generation of single-cell suspension, and cell capture and sequencing. We then detail methods for integrative analysis, developmental Schwann cell trajectory building using bioinformatic tools, and comparative analysis. This protocol can be adapted for single-cell sequencing using mouse nerve tumors. For complete details on the use and execution of this protocol, please refer to Zhang et al. (2022).¹.

Enhancer variants on chromosome 2p14 regulating SPRED2 and ACTR2 act as a signal amplifier to protect against rheumatoid arthritis

Genome-wide association studies (GWASs) have repeatedly reported multiple non-coding single-nucleotide polymorphisms (SNPs) at 2p14 associated with rheumatoid arthritis (RA), but their functional roles in the pathological mechanisms of RA remain to be explored. In this study, we integrated a series of bioinformatics and functional experiments and identified three intronic RA SNPs (rs1876518, rs268131, and rs2576923) within active enhancers that can regulate the expression of SPRED2 directly. At the same time, SPRED2 and ACTR2 influence each other as a positive feedback signal amplifier to strengthen the protective role in RA by inhibiting the migration and invasion of rheumatoid fibroblast-like synoviocytes (FLSs). In particular, the transcription factor CEBPB preferentially binds to the rs1876518-T allele to increase the expression of SPRED2 in FLSs. Our findings decipher the molecular mechanisms behind the GWAS signals at 2p14 for RA and emphasize SPRED2 as a potential candidate gene for RA, providing a potential target and direction for precise treatment of RA.

Spatial multiomics map of trophoblast development in early pregnancy

The relationship between the human placenta-the extraembryonic organ made by the fetus, and the decidua-the mucosal layer of the uterus, is essential to nurture and protect the fetus during pregnancy. Extravillous trophoblast cells (EVTs) derived from placental villi infiltrate the decidua, transforming the maternal arteries into high-conductance vessels1. Defects in trophoblast invasion and arterial transformation established during early pregnancy underlie common pregnancy disorders such as pre-eclampsia2. Here we have generated a spatially resolved multiomics single-cell atlas of the entire human maternal-fetal interface including the myometrium, which enables us to resolve the full trajectory of trophoblast differentiation. We have used this cellular map to infer the possible transcription factors mediating EVT invasion and show that they are preserved in in vitro models of EVT differentiation from primary trophoblast organoids3,4 and trophoblast stem cells5. We define the transcriptomes of the final cell states of trophoblast invasion: placental bed giant cells (fused multinucleated EVTs) and endovascular EVTs (which form plugs inside the maternal arteries). We predict the cell-cell communication events contributing to trophoblast invasion and placental bed giant cell formation, and model the dual role of interstitial EVTs and endovascular EVTs in mediating arterial transformation during early pregnancy. Together, our data provide a comprehensive analysis of postimplantation trophoblast differentiation that can be used to inform the design of experimental models of the human placenta in early pregnancy.

De novo human brain enhancers created by single-nucleotide mutations

Advanced human cognition is attributed to increased neocortex size and complexity, but the underlying evolutionary and regulatory mechanisms are largely unknown. Using human and macaque embryonic neocortical H3K27ac data coupled with a deep learning model of enhancers, we identified ~4000 enhancer gains in humans, which, per our model, can often be attributed to single-nucleotide essential mutations. Our analyses suggest that functional gains in embryonic brain development are associated with de novo enhancers whose putative target genes exhibit increased expression in progenitor cells and interneurons and partake in critical neural developmental processes. Essential mutations alter enhancer activity through altered binding of key transcription factors (TFs) of embryonic neocortex, including ISL1, POU3F2, PITX1/2, and several SOX TFs, and are associated with central nervous system disorders. Overall, our results suggest that essential mutations lead to gain of embryonic neocortex enhancers, which orchestrate expression of genes involved in critical developmental processes associated with human cognition.

Mechanisms underlying WNT-mediated priming of human embryonic stem cells

Embryogenesis is guided by a limited set of signaling pathways dynamically expressed in different places. How a context-dependent signaling response is generated has been a central question of developmental biology, which can now be addressed with in vitro models of human embryos that are derived from embryonic stem cells (hESCs). Our previous work demonstrated that during early stages of hESC differentiation, cells chronicle signaling hierarchy. Only cells that have been exposed (primed) by WNT signaling can respond to subsequent activin exposure and differentiate to mesendodermal (ME) fates. Here, we show that WNT priming does not alter SMAD2 binding nor its chromatin opening but, instead, acts by inducing the expression of the SMAD2 co-factor EOMES. Expression of EOMES is sufficient to replace WNT upstream of activin-mediated ME differentiation, thus unveiling the mechanistic basis for priming and cellular memory in early development.

Single-cell sequencing reveals activation of core transcription factors in PRC2-deficient malignant peripheral nerve sheath tumor

Loss-of-function mutations in the polycomb repressive complex 2 (PRC2) occur frequently in malignant peripheral nerve sheath tumor, an aggressive sarcoma that arises from NF1-deficient Schwann cells. To define the oncogenic mechanisms underlying PRC2 loss, we use engineered cells that dynamically reassemble a competent PRC2 coupled with single-cell sequencing from clinical samples. We discover a two-pronged oncogenic process: first, PRC2 loss leads to remodeling of the bivalent chromatin and enhancer landscape, causing the upregulation of developmentally regulated transcription factors that enforce a transcriptional circuit serving as the cell's core vulnerability. Second, PRC2 loss reduces type I interferon signaling and antigen presentation as downstream consequences of hyperactivated Ras and its cross talk with STAT/IRF transcription factors. Mapping of the transcriptional program of these PRC2-deficient tumor cells onto a constructed developmental trajectory of normal Schwann cells reveals that changes induced by PRC2 loss enforce a cellular profile characteristic of a primitive mesenchymal neural crest stem cell.

gDesigner: computational design of synthetic gRNAs for Cas12a-based transcriptional repression in mammalian cells

Synthetic networks require complex intertwined genetic regulation often relying on transcriptional activation or repression of target genes. CRISPRi-based transcription factors facilitate the programmable modulation of endogenous or synthetic promoter activity and the process can be optimised by using software to select appropriate gRNAs and limit non-specific gene modulation. Here, we develop a computational software pipeline, gDesigner, that enables the automated selection of orthogonal gRNAs with minimized off-target effects and promoter crosstalk. We next engineered a Lachnospiraceae bacterium Cas12a (dLbCas12a)-based repression system that downregulates target gene expression by means of steric hindrance of the cognate promoter. Finally, we generated a library of orthogonal synthetic dCas12a-repressed promoters and experimentally demonstrated it in HEK293FT, U2OS and H1299 cells lines. Our system expands the toolkit of mammalian synthetic promoters with a new complementary and orthogonal CRISPRi-based system, ultimately enabling the design of synthetic promoter libraries for multiplex gene perturbation that facilitate the understanding of complex cellular phenotypes.

dbEmbryo multi-omics database for analyses of synergistic regulation in early mammalian embryo development

During early mammalian embryo development, different epigenetic marks undergo reprogramming and play crucial roles in the mediation of gene expression. Currently, several databases provide multi-omics information on early embryos. However, how interconnected epigenetic markers function together to coordinate the expression of the genetic code in a spatiotemporal manner remains difficult to analyze, markedly limiting scientific and clinical research. Here, we present dbEmbryo, an integrated and interactive multi-omics database for human and mouse early embryos. dbEmbryo integrates data on gene expression, DNA methylation, histone modifications, chromatin accessibility, and higher-order chromatin structure profiles for human and mouse early embryos. It incorporates customized analysis tools, such as "multi-omics visualization," "Gene&Peak annotation," "ZGA gene cluster," "cis-regulation," "synergistic regulation," "promoter signal enrichment," and "3D genome." Users can retrieve gene expression and epigenetic profile patterns to analyze synergistic changes across different early embryo developmental stages. We showed the uniqueness of dbEmbryo among extant databases containing data on early embryo development and provided an overview. Using dbEmbryo, we obtained a phase-separated model of transcriptional control during early embryo development. dbEmbryo offers web-based analytical tools and a comprehensive resource for biologists and clinicians to decipher molecular regulatory mechanisms of human and mouse early embryo development.

Drug-microenvironment perturbations reveal resistance mechanisms and prognostic subgroups in CLL

The tumour microenvironment and genetic alterations collectively influence drug efficacy in cancer, but current evidence is limited and systematic analyses are lacking. Using chronic lymphocytic leukaemia (CLL) as a model disease, we investigated the influence of 17 microenvironmental stimuli on 12 drugs in 192 genetically characterised patient samples. Based on microenvironmental response, we identified four subgroups with distinct clinical outcomes beyond known prognostic markers. Response to multiple microenvironmental stimuli was amplified in trisomy 12 samples. Trisomy 12 was associated with a distinct epigenetic signature. Bromodomain inhibition reversed this epigenetic profile and could be used to target microenvironmental signalling in trisomy 12 CLL. We quantified the impact of microenvironmental stimuli on drug response and their dependence on genetic alterations, identifying interleukin 4 (IL4) and Toll-like receptor (TLR) stimulation as the strongest actuators of drug resistance. IL4 and TLR signalling activity was increased in CLL-infiltrated lymph nodes compared with healthy samples. High IL4 activity correlated with faster disease progression. The publicly available dataset can facilitate the investigation of cell-extrinsic mechanisms of drug resistance and disease progression.

The hypoxia-induced changes in miRNA-mRNA in RNA-induced silencing complexes and HIF-2 induced miRNAs in human endothelial cells

The cellular adaptive response to hypoxia relies on the expression of hypoxia-inducible factors (HIFs), HIF-1 and HIF-2. HIFs regulate global gene expression changes during hypoxia that are necessary for restoring oxygen homeostasis and promoting cell survival. In the early stages of hypoxia, HIF-1 is elevated, whereas at the later stages, HIF-2 becomes the predominant form. What governs the transition between the two HIFs (the HIF switch) and the role of miRNAs in this regulation are not completely clear. Genome-wide expression studies on the miRNA content of RNA-induced silencing complexes (RISC) in HUVECs exposed to hypoxia compared to the global miRNA-Seq analysis revealed very specific differences between these two populations. We analyzed the miRNA and mRNA composition of RISC at 2 h (mainly HIF-1 driven), 8 h (HIF-1 and HIF-2 elevated), and 16 h (mainly HIF-2 driven) in a gene ontology context. This allowed for determining the direct impact of the miRNAs in modulating the cellular signaling pathways involved in the hypoxic adaptive response. Our results indicate that the miRNA-mRNA RISC components control the adaptive responses, and this does not always rely on the miRNA transcriptional elevations during hypoxia. Furthermore, we demonstrate that the hypoxic levels of the vast majority of HIF-1-dependent miRNAs (including miR-210-3p) are also HIF-2 dependent and that HIF-2 governs the expression of 11 specific miRNAs. In summary, the switch from HIF-1 to HIF-2 during hypoxia provides an important level of miRNA-driven control in the adaptive pathways in endothelial cells.

Single-cell roadmap of human gonadal development

Gonadal development is a complex process that involves sex determination followed by divergent maturation into either testes or ovaries1. Historically, limited tissue accessibility, a lack of reliable in vitro models and critical differences between humans and mice have hampered our knowledge of human gonadogenesis, despite its importance in gonadal conditions and infertility. Here, we generated a comprehensive map of first- and second-trimester human gonads using a combination of single-cell and spatial transcriptomics, chromatin accessibility assays and fluorescent microscopy. We extracted human-specific regulatory programmes that control the development of germline and somatic cell lineages by profiling equivalent developmental stages in mice. In both species, we define the somatic cell states present at the time of sex specification, including the bipotent early supporting population that, in males, upregulates the testis-determining factor SRY and sPAX8s, a gonadal lineage located at the gonadal-mesonephric interface. In females, we resolve the cellular and molecular events that give rise to the first and second waves of granulosa cells that compartmentalize the developing ovary to modulate germ cell differentiation. In males, we identify human SIGLEC15+ and TREM2+ fetal testicular macrophages, which signal to somatic cells outside and inside the developing testis cords, respectively. This study provides a comprehensive spatiotemporal map of human and mouse gonadal differentiation, which can guide in vitro gonadogenesis.

Transcription factor-driven coordination of cell cycle exit and lineage-specification in vivo during granulocytic differentiation : In memoriam Professor Niels Borregaard

Differentiation of multipotent stem cells into mature cells is fundamental for development and homeostasis of mammalian tissues, and requires the coordinated induction of lineage-specific transcriptional programs and cell cycle withdrawal. To understand the underlying regulatory mechanisms of this fundamental process, we investigated how the tissue-specific transcription factors, CEBPA and CEBPE, coordinate cell cycle exit and lineage-specification in vivo during granulocytic differentiation. We demonstrate that CEBPA promotes lineage-specification by launching an enhancer-primed differentiation program and direct activation of CEBPE expression. Subsequently, CEBPE confers promoter-driven cell cycle exit by sequential repression of MYC target gene expression at the G1/S transition and E2F-meditated G2/M gene expression, as well as by the up-regulation of Cdk1/2/4 inhibitors. Following cell cycle exit, CEBPE unleashes the CEBPA-primed differentiation program to generate mature granulocytes. These findings highlight how tissue-specific transcription factors coordinate cell cycle exit with differentiation through the use of distinct gene regulatory elements.

DeepCAGE: Incorporating Transcription Factors in Genome-wide Prediction of Chromatin Accessibility

Although computational approaches have been complementing high-throughput biological experiments for the identification of functional regions in the human genome, it remains a great challenge to systematically decipher interactions between transcription factors (TFs) and regulatory elements to achieve interpretable annotations of chromatin accessibility across diverse cellular contexts. To solve this problem, we propose DeepCAGE, a deep learning framework that integrates sequence information and binding statuses of TFs, for the accurate prediction of chromatin accessible regions at a genome-wide scale in a variety of cell types. DeepCAGE takes advantage of a densely connected deep convolutional neural network architecture to automatically learn sequence signatures of known chromatin accessible regions and then incorporates such features with expression levels and binding activities of human core TFs to predict novel chromatin accessible regions. In a series of systematic comparisons with existing methods, DeepCAGE exhibits superior performance in not only the classification but also the regression of chromatin accessibility signals. In a detailed analysis of TF activities, DeepCAGE successfully extracts novel binding motifs and measures the contribution of a TF to the regulation with respect to a specific locus in a certain cell type. When applied to whole-genome sequencing data analysis, our method successfully prioritizes putative deleterious variants underlying a human complex trait and thus provides insights into the understanding of disease-associated genetic variants. DeepCAGE can be downloaded from https://github.com/kimmo1019/DeepCAGE.

An Osteoporosis Susceptibility Allele at 11p15 Regulates SOX6 Expression by Modulating TCF4 Chromatin Binding

Osteoporosis is an age-related complex disease clinically diagnosed with bone mineral density (BMD). Although several genomewide association studies (GWASs) have discovered multiple noncoding genetic variants at 11p15 influencing osteoporosis risk, the functional mechanisms of these variants remain unknown. Through integrating bioinformatics and functional experiments, a potential functional single-nucleotide polymorphism (SNP; rs1440702) located in an enhancer element was identified and the A allele of rs1440702 acted as an allelic specificities enhancer to increase its distal target gene SOX6 (~600 Kb upstream) expression, which plays a key role in bone formation. We also validated this long-range regulation via conducting chromosome conformation capture (3C) assay. Furthermore, we demonstrated that SNP rs1440702 with a risk allele (rs1440702-A) could increase the activity of the enhancer element by altering the binding affinity of the transcription factor TCF4, resulting in the upregulation expression of SOX6 gene. Collectively, our integrated analyses revealed how the noncoding genetic variants (rs1440702) affect osteoporosis predisposition via long-range gene regulatory mechanisms and identified its target gene SOX6 for downstream biomarker and drug development. © 2022 American Society for Bone and Mineral Research (ASBMR).

FABIAN-variant: predicting the effects of DNA variants on transcription factor binding

While great advances in predicting the effects of coding variants have been made, the assessment of non-coding variants remains challenging. This is especially problematic for variants within promoter regions which can lead to over-expression of a gene or reduce or even abolish its expression. The binding of transcription factors to the DNA can be predicted using position weight matrices (PWMs). More recently, transcription factor flexible models (TFFMs) have been introduced and shown to be more accurate than PWMs. TFFMs are based on hidden Markov models and can account for complex positional dependencies. Our new web-based application FABIAN-variant uses 1224 TFFMs and 3790 PWMs to predict whether and to which degree DNA variants affect the binding of 1387 different human transcription factors. For each variant and transcription factor, the software combines the results of different models for a final prediction of the resulting binding-affinity change. The software is written in C++ for speed but variants can be entered through a web interface. Alternatively, a VCF file can be uploaded to assess variants identified by high-throughput sequencing. The search can be restricted to variants in the vicinity of candidate genes. FABIAN-variant is available freely at https://www.genecascade.org/fabian/.

Single-cell chromatin profiling of the primitive gut tube reveals regulatory dynamics underlying lineage fate decisions

Development of the gastrointestinal system occurs after gut tube closure, guided by spatial and temporal control of gene expression. However, it remains unclear what forces regulate these spatiotemporal gene expression patterns. Here we perform single-cell chromatin profiling of the primitive gut tube to reveal organ-specific chromatin patterns that reflect the anatomical patterns of distinct organs. We generate a comprehensive map of epigenomic changes throughout gut development, demonstrating that dynamic chromatin accessibility patterns associate with lineage-specific transcription factor binding events to regulate organ-specific gene expression. Additionally, we show that loss of Sox2 and Cdx2, foregut and hindgut lineage-specific transcription factors, respectively, leads to fate shifts in epigenomic patterns, linking transcription factor binding, chromatin accessibility, and lineage fate decisions in gut development. Notably, abnormal expression of Sox2 in the pancreas and intestine impairs lineage fate decisions in both development and adult homeostasis. Together, our findings define the chromatin and transcriptional mechanisms of organ identity and lineage plasticity in development and adult homeostasis.

Temporal Gene Expression Profiles Reflect the Dynamics of Lymphoid Differentiation

Understanding the emergence of lymphoid committed cells from multipotent progenitors (MPP) is a great challenge in hematopoiesis. To gain deeper insight into the dynamic expression changes associated with these transitions, we report the quantitative transcriptome of two MPP subsets and the common lymphoid progenitor (CLP). While the transcriptome is rather stable between MPP2 and MPP3, expression changes increase with differentiation. Among those, we found that pioneer lymphoid genes such as Rag1, Mpeg1, and Dntt are expressed continuously from MPP2. Others, such as CD93, are CLP specific, suggesting their potential use as new markers to improve purification of lymphoid populations. Notably, a six-transcription factor network orchestrates the lymphoid differentiation program. Additionally, we pinpointed 24 long intergenic-non-coding RNA (lincRNA) differentially expressed through commitment and further identified seven novel forms. Collectively, our approach provides a comprehensive landscape of coding and non-coding transcriptomes expressed during lymphoid commitment.

ID3 promotes homologous recombination via non-transcriptional and transcriptional mechanisms and its loss confers sensitivity to PARP inhibition

The inhibitor of DNA-binding 3 (ID3) is a transcriptional regulator that limits interaction of basic helix-loop-helix transcription factors with their target DNA sequences. We previously reported that ID3 loss is associated with mutational signatures linked to DNA repair defects. Here we demonstrate that ID3 exhibits a dual role to promote DNA double-strand break (DSB) repair, particularly homologous recombination (HR). ID3 interacts with the MRN complex and RECQL helicase to activate DSB repair and it facilitates RAD51 loading and downstream steps of HR. In addition, ID3 promotes the expression of HR genes in response to ionizing radiation by regulating both chromatin accessibility and activity of the transcription factor E2F1. Consistently, analyses of TCGA cancer patient data demonstrate that low ID3 expression is associated with impaired HR. The loss of ID3 leads to sensitivity of tumor cells to PARP inhibition, offering new therapeutic opportunities in ID3-deficient tumors.

Epigenetic reprogramming of airway macrophages promotes polarization and inflammation in muco-obstructive lung disease

Lung diseases, such as cystic fibrosis and COPD, are characterized by mucus obstruction and chronic airway inflammation, but their mechanistic link remains poorly understood. Here, we focus on the function of the mucostatic airway microenvironment on epigenetic reprogramming of airway macrophages (AM) and resulting transcriptomic and phenotypical changes. Using a mouse model of muco-obstructive lung disease (Scnn1b-transgenic), we identify epigenetically controlled, differentially regulated pathways and transcription factors involved in inflammatory responses and macrophage polarization. Functionally, AMs from Scnn1b-transgenic mice have reduced efferocytosis and phagocytosis, and excessive inflammatory responses upon lipopolysaccharide challenge, mediated through enhanced Irf1 function and expression. Ex vivo stimulation of wild-type AMs with native mucus impairs efferocytosis and phagocytosis capacities. In addition, mucus induces gene expression changes, comparable with those observed in AMs from Scnn1b-transgenic mice. Our data show that mucostasis induces epigenetic reprogramming of AMs, leading to changes favoring tissue damage and disease progression. Targeting these altered AMs may support therapeutic approaches in patients with muco-obstructive lung diseases.

An IDH-independent mechanism of DNA hypermethylation upon VHL inactivation in cancer

Hypermethylation of tumour suppressors and other aberrations of DNA methylation in tumours play a significant role in cancer progression. DNA methylation can be affected by various environmental conditions, including hypoxia. The response to hypoxia is mainly achieved through activation of the transcriptional program associated with HIF1A transcription factor. Inactivation of Von Hippel-Lindau Tumour Suppressor gene (VHL) by genetic or epigenetic events, which also induces aberrant activation of HIF1A, is the most common driver event for renal cancer. With whole-genome bisulphite sequencing and LC-MS, we demonstrated that VHL inactivation induced global genome hypermethylation in human kidney cancer cells under normoxic conditions. This effect was reverted by exogenous expression of wild-type VHL. We showed that global genome hypermethylation in VHL mutants can be explained by transcriptional changes in MDH and L2HGDH genes that cause the accumulation of 2-hydroxyglutarate - a metabolite that inhibits DNA demethylation by TET enzymes. Unlike the known cases of DNA hypermethylation in cancer, 2-hydroxyglutarate was accumulated in the cells with the wild-type isocitrate dehydrogenases.

Altered regulation of DPF3, a member of the SWI/SNF complexes, underlies the 14q24 renal cancer susceptibility locus

Our study investigated the underlying mechanism for the 14q24 renal cell carcinoma (RCC) susceptibility risk locus identified by a genome-wide association study (GWAS). The sentinel single-nucleotide polymorphism (SNP), rs4903064, at 14q24 confers an allele-specific effect on expression of the double PHD fingers 3 (DPF3) of the BAF SWI/SNF complex as assessed by massively parallel reporter assay, confirmatory luciferase assays, and eQTL analyses. Overexpression of DPF3 in renal cell lines increases growth rates and alters chromatin accessibility and gene expression, leading to inhibition of apoptosis and activation of oncogenic pathways. siRNA interference of multiple DPF3-deregulated genes reduces growth. Our results indicate that germline variation in DPF3, a component of the BAF complex, part of the SWI/SNF complexes, can lead to reduced apoptosis and activation of the STAT3 pathway, both critical in RCC carcinogenesis. In addition, we show that altered DPF3 expression in the 14q24 RCC locus could influence the effectiveness of immunotherapy treatment for RCC by regulating tumor cytokine secretion and immune cell activation.

Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences

Chromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. We develop a computational method, chromatin interaction neural network (ChINN), to predict chromatin interactions between open chromatin regions using only DNA sequences. ChINN predicts CTCF- and RNA polymerase II-associated and Hi-C chromatin interactions. ChINN shows good across-sample performances and captures various sequence features for chromatin interaction prediction. We apply ChINN to 6 chronic lymphocytic leukemia (CLL) patient samples and a published cohort of 84 CLL open chromatin samples. Our results demonstrate extensive heterogeneity in chromatin interactions among CLL patient samples.

An Intronic Risk SNP rs12454712 for Central Obesity Acts As an Allele-Specific Enhancer To Regulate BCL2 Expression

Genome-wide association studies (GWAS) have reproducibly associated the single nucleotide polymorphism (SNP) rs12454712 with waist-to-hip ratio adjusted for BMI (WHRadjBMI), but the functional role underlying this intronic variant is unknown. Integrative genomic and epigenomic analyses supported rs12454712 as a functional independent variant. We further demonstrated that rs12454712 acted as an allele-specific enhancer regulating expression of its located gene BCL2 by using dual-luciferase reporter assays and clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9. Specifically, the rs12454712-C allele can bind transcription factor ZNF329, which efficiently elevates the enhancer activity and increases BCL2 expression. Knocking down Bcl2 in 3T3-L1 cells led to the downregulation of adipogenic differentiation marker genes and increased cell apoptosis. A significant negative correlation between BCL2 expression in subcutaneous adipose tissues and obesity was observed. Our findings illustrate the molecular mechanisms behind the intronic SNP rs12454712 for central obesity, which would be a potential and promising target for developing appropriate therapies.

Sequence-Based Deep Learning Frameworks on Enhancer-Promoter Interactions Prediction

Enhancer-promoter interactions (EPIs) in the human genome are of great significance to transcriptional regulation, which tightly controls gene expression. Identification of EPIs can help us better decipher gene regulation and understand disease mechanisms. However, experimental methods to identify EPIs are constrained by funds, time, and manpower, while computational methods using DNA sequences and genomic features are viable alternatives. Deep learning methods have shown promising prospects in classification and efforts that have been utilized to identify EPIs. In this survey, we specifically focus on sequence-based deep learning methods and conduct a comprehensive review of the literature. First, we briefly introduce existing sequence- based frameworks on EPIs prediction and their technique details. After that, we elaborate on the dataset, pre-processing means, and evaluation strategies. Finally, we concluded with the challenges these methods are confronted with and suggest several future opportunities. We hope this review will provide a useful reference for further studies on enhancer-promoter interactions.

A Functional Variant Rs492554 Associated With Congenital Heart Defects Modulates SESN2 Expression Through POU2F1

Hypoxia exposure is responsible for the high incidence of congenital heart defects (CHDs) in high-altitude areas, which is nearly 20 times higher than that in low-altitude areas. However, the genetic factors involved are rarely reported. Sestrin2 (SESN2), a hypoxia stress-inducible gene, protects cardiomyocyte viability under stress; thus, SESN2 polymorphism may be a potential risk factor for CHD. We performed an association study of the SESN2 polymorphisms with CHD risk in two independent groups of the Han Chinese population from two different altitude areas. The allele-specific effects of lead single-nucleotide polymorphisms (SNPs) were assessed by expression quantitative trait locus, electrophoretic mobility shift, and luciferase reporter assays. The molecular mechanism of Sesn2 action against hypoxia-induced cell injury was investigated in embryonic rat-heart-derived H9c2 cells treated with or without hypoxia-mimetic cobalt chloride. SNP rs492554 was significantly associated with reduced CHD risk in the high-altitude population, but not in the low-altitude population. The protective T allele of rs492554 was correlated with higher SESN2 expression and showed a preferential binding affinity to POU2F1. We then identified SNP rs12406992 in strong linkage disequilibrium with rs492554 and mapped it within the binding motif of POU2F1. The T-C haplotype of rs492554-rs12406992 could increase luciferase expression, whereas POU2F1 knockdown effectively suppressed it. Mechanistically, increased Sesn2 protects against oxidative stress and cell apoptosis and maintains cell viability and proliferation. In summary, CHD-associated SNP rs492554 acts as an allele-specific distal enhancer to modulate SESN2 expression via interaction with POU2F1, which might provide new mechanistic insights into CHD pathogenesis.

Transcription factor enrichment analysis (TFEA) quantifies the activity of multiple transcription factors from a single experiment

Detecting changes in the activity of a transcription factor (TF) in response to a perturbation provides insights into the underlying cellular process. Transcription Factor Enrichment Analysis (TFEA) is a robust and reliable computational method that detects positional motif enrichment associated with changes in transcription observed in response to a perturbation. TFEA detects positional motif enrichment within a list of ranked regions of interest (ROIs), typically sites of RNA polymerase initiation inferred from regulatory data such as nascent transcription. Therefore, we also introduce muMerge, a statistically principled method of generating a consensus list of ROIs from multiple replicates and conditions. TFEA is broadly applicable to data that informs on transcriptional regulation including nascent transcription (eg. PRO-Seq), CAGE, histone ChIP-Seq, and accessibility data (e.g., ATAC-Seq). TFEA not only identifies the key regulators responding to a perturbation, but also temporally unravels regulatory networks with time series data. Consequently, TFEA serves as a hypothesis-generating tool that provides an easy, rigorous, and cost-effective means to broadly assess TF activity yielding new biological insights.

Uncovering the RNA-binding protein landscape in the pluripotency network of human embryonic stem cells

Embryonic stem cell (ESC) self-renewal and cell fate decisions are driven by a broad array of molecular signals. While transcriptional regulators have been extensively studied in human ESCs (hESCs), the extent to which RNA-binding proteins (RBPs) contribute to human pluripotency remains unclear. Here, we carry out a proteome-wide screen and identify 810 proteins that bind RNA in hESCs. We reveal that RBPs are preferentially expressed in hESCs and dynamically regulated during early stem cell differentiation. Notably, many RBPs are affected by knockdown of OCT4, a master regulator of pluripotency, several dozen of which are directly targeted by this factor. Using cross-linking and immunoprecipitation (CLIP-seq), we find that the pluripotency-associated STAT3 and OCT4 transcription factors interact with RNA in hESCs and confirm the binding of STAT3 to the conserved NORAD long-noncoding RNA. Our findings indicate that RBPs have a more widespread role in human pluripotency than previously appreciated.

Induction of the PPARγ (Peroxisome Proliferator-Activated Receptor γ)-GCM1 (Glial Cell Missing 1) Syncytialization Axis Reduces sFLT1 (Soluble fms-Like Tyrosine Kinase 1) in the Preeclamptic Placenta

[Figure: see text].

Statistical mechanics meets single-cell biology

Single-cell omics is transforming our understanding of cell biology and disease, yet the systems-level analysis and interpretation of single-cell data faces many challenges. In this Perspective, we describe the impact that fundamental concepts from statistical mechanics, notably entropy, stochastic processes and critical phenomena, are having on single-cell data analysis. We further advocate the need for more bottom-up modelling of single-cell data and to embrace a statistical mechanics analysis paradigm to help attain a deeper understanding of single-cell systems biology.

Evolution of DNA methylation in the human brain

DNA methylation is a critical regulatory mechanism implicated in development, learning, memory, and disease in the human brain. Here we have elucidated DNA methylation changes during recent human brain evolution. We demonstrate dynamic evolutionary trajectories of DNA methylation in cell-type and cytosine-context specific manner. Specifically, DNA methylation in non-CG context, namely CH methylation, has increased (hypermethylation) in neuronal gene bodies during human brain evolution, contributing to human-specific down-regulation of genes and co-expression modules. The effects of CH hypermethylation is particularly pronounced in early development and neuronal subtypes. In contrast, DNA methylation in CG context shows pronounced reduction (hypomethylation) in human brains, notably in cis-regulatory regions, leading to upregulation of downstream genes. We show that the majority of differential CG methylation between neurons and oligodendrocytes originated before the divergence of hominoids and catarrhine monkeys, and harbors strong signal for genetic risk for schizophrenia. Remarkably, a substantial portion of differential CG methylation between neurons and oligodendrocytes emerged in the human lineage since the divergence from the chimpanzee lineage and carries significant genetic risk for schizophrenia. Therefore, recent epigenetic evolution of human cortex has shaped the cellular regulatory landscape and contributed to the increased vulnerability to neuropsychiatric diseases.

Enhancer pleiotropy, gene expression, and the architecture of human enhancer-gene interactions

Enhancers are often studied as noncoding regulatory elements that modulate the precise spatiotemporal expression of genes in a highly tissue-specific manner. This paradigm has been challenged by recent evidence of individual enhancers acting in multiple tissues or developmental contexts. However, the frequency of these enhancers with high degrees of “pleiotropy” out of all putative enhancers is not well understood. Consequently, it is unclear how the variation of enhancer pleiotropy corresponds to the variation in expression breadth of target genes. Here, we use multi-tissue chromatin maps from diverse human tissues to investigate the enhancer–gene interaction architecture while accounting for 1) the distribution of enhancer pleiotropy, 2) the variations of regulatory links from enhancers to target genes, and 3) the expression breadth of target genes. We show that most enhancers are tissue-specific and that highly pleiotropy enhancers account for <1% of all putative regulatory sequences in the human genome. Notably, several genomic features are indicative of increasing enhancer pleiotropy, including longer sequence length, greater number of links to genes, increasing abundance and diversity of encoded transcription factor motifs, and stronger evolutionary conservation. Intriguingly, the number of enhancers per gene remains remarkably consistent for all genes (∼14). However, enhancer pleiotropy does not directly translate to the expression breadth of target genes. We further present a series of Gaussian Mixture Models to represent this organization architecture. Consequently, we demonstrate that a modest trend of more pleiotropic enhancers targeting more broadly expressed genes can generate the observed diversity of expression breadths in the human genome.

Polymorphism rs368234815 of interferon lambda 4 gene and spontaneous clearance of hepatitis C virus in haemodialysis patients: a case-control study

BACKGROUND

In non-uremic subjects, IFNL4 rs368234815 predicts HCV clearance. We investigated whether rs368234815 is associated with spontaneous HCV clearance in haemodialysis patients and whether it is a stronger predictor of HCV resolution than the IFNL polymorphisms already associated with HCV clearance in dialysis subjects. We also evaluated an association of rs368234815 with patients` survival and alterations in transcription factor binding sites (TFBS) caused by IFNL polymorphisms.

METHODS

Among 161 haemodialysis patients with positive anti-HCV antibodies, 68 (42.2%) spontaneously resolved HCV infection, whereas 93 remained HCV RNA positive. Patients were tested for near IFNL3 rs12980275, IFNL3 rs4803217, IFNL4 rs12979860, IFNL4 rs368234815, and near IFNL4 rs8099917. IFNL4 rs368234815 polymorphism (TT/TT, ΔG/TT, ΔG/ΔG) was genotyped by restriction fragment length polymorphism analysis; other IFNL polymorphisms - by high resolution melting curve analysis. We used the Kaplan-Meier method with the log-rank test for survival analysis. In silico analysis included the use of ENCODE TFBS ChIP-seq data, HOCOMOCO, JASPAR CORE, and CIS-BP databases, and FIMO software.

RESULTS

The probability (OR, 95%CI, P) of spontaneous HCV clearance for rs368234815 TT/TT patients was higher than for the ΔG allele carriers (2.63, 1.38-5.04, 0.003). This probability for other major homozygotes varied between 2.80, 1.45-5.43, 0.002 for rs12980275 and 2.44, 1.27-4.69, 0.007 for rs12979860. In the additive model, rs368234815 TT/TT was the strongest predictor of HCV clearance (6.38, 1.69-24.2, 0.003). Survival analysis suggested an association of the ΔG allele with mortality due to neoplasms (log-rank P = 0.005). The rs368234815 ∆G allele caused TFBS removal for PLAGL1.

CONCLUSIONS

In haemodialysis patients, the association of rs368234815 with the spontaneous HCV clearance is better than that documented for other IFNL3/IFNL4 polymorphisms only in the additive mode of inheritance. However, identifying the homozygosity in the variant ∆G allele of rs368234815 means a more potent prediction of persistent HCV infection in haemodialysis subjects that we observe in the case of the variant homozygosity of other tested IFNL3/IFNL4 polymorphisms. Removal of PLAGL1 TFBS in subjects harbouring the rs368234815 ∆G allele may contribute to cancer susceptibility. The association of rs368234815 with cancer-related mortality needs further studies in HCV-exposed subjects.

An Integrative Framework for Combining Sequence and Epigenomic Data to Predict Transcription Factor Binding Sites Using Deep Learning

Knowing the transcription factor binding sites (TFBSs) is essential for modeling the underlying binding mechanisms and follow-up cellular functions. Convolutional neural networks (CNNs) have outperformed methods in predicting TFBSs from the primary DNA sequence. In addition to DNA sequences, histone modifications and chromatin accessibility are also important factors influencing their activity. They have been explored to predict TFBSs recently. However, current methods rarely take into account histone modifications and chromatin accessibility using CNN in an integrative framework. To this end, we developed a general CNN model to integrate these data for predicting TFBSs. We systematically benchmarked a series of architecture variants by changing network structure in terms of width and depth, and explored the effects of sample length at flanking regions. We evaluated the performance of the three types of data and their combinations using 256 ChIP-seq experiments and also compared it with competing machine learning methods. We find that contributions from these three types of data are complementary to each other. Moreover, the integrative CNN framework is superior to traditional machine learning methods with significant improvements.

Profiling of H3K27Ac Reveals the Influence of Asthma on the Epigenome of the Airway Epithelium

Background

Asthma is a chronic airway disease driven by complex genetic–environmental interactions. The role of epigenetic modifications in bronchial epithelial cells (BECs) in asthma is poorly understood.

Methods

We piloted genome-wide profiling of the enhancer-associated histone modification H3K27ac in BECs from people with asthma (n = 4) and healthy controls (n = 3).

Results

We identified n = 4,321 (FDR < 0.05) regions exhibiting differential H3K27ac enrichment between asthma and health, clustering at genes associated predominately with epithelial processes (EMT). We identified initial evidence of asthma-associated Super-Enhancers encompassing genes encoding transcription factors (TP63) and enzymes regulating lipid metabolism (PTGS1). We integrated published datasets to identify epithelium-specific transcription factors associated with H3K27ac in asthma (TP73) and identify initial relationships between asthma-associated changes in H3K27ac and transcriptional profiles. Finally, we investigated the potential of CRISPR-based approaches to functionally evaluate H3K27ac-asthma landscape in vitro by identifying guide-RNAs capable of targeting acetylation to asthma DERs and inducing gene expression (TLR3).

Conclusion

Our small pilot study validates genome-wide approaches for deciphering epigenetic mechanisms underlying asthma pathogenesis in the airways.

A High-Resolution Map of Human Enhancer RNA Loci Characterizes Super-enhancer Activities in Cancer

Although enhancers play critical roles in cancer, quantifying enhancer activities in clinical samples remains challenging, especially for super-enhancers. Enhancer activities can be inferred from enhancer RNA (eRNA) signals, which requires enhancer transcription loci definition. Only a small proportion of human eRNA loci has been precisely identified, limiting investigations of enhancer-mediated oncogenic mechanisms. Here, we characterize super-enhancer regions using aggregated RNA sequencing (RNA-seq) data from large cohorts. Super-enhancers usually contain discrete loci featuring sharp eRNA expression peaks. We identify >300,000 eRNA loci in ∼377 Mb super-enhancer regions that are regulated by evolutionarily conserved, well-positioned nucleosomes and are frequently dysregulated in cancer. The eRNAs provide explanatory power for cancer phenotypes beyond that provided by mRNA expression through resolving intratumoral heterogeneity with enhancer cell-type specificity. Our study provides a high-resolution map of eRNA loci through which super-enhancer activities can be quantified by RNA-seq and a user-friendly data portal, enabling a broad range of biomedical investigations.

A holistic view of mouse enhancer architectures reveals analogous pleiotropic effects and correlation with human disease

BACKGROUND

Efforts to elucidate the function of enhancers in vivo are underway but their vast numbers alongside differing enhancer architectures make it difficult to determine their impact on gene activity. By systematically annotating multiple mouse tissues with super- and typical-enhancers, we have explored their relationship with gene function and phenotype.

RESULTS

Though super-enhancers drive high total- and tissue-specific expression of their associated genes, we find that typical-enhancers also contribute heavily to the tissue-specific expression landscape on account of their large numbers in the genome. Unexpectedly, we demonstrate that both enhancer types are preferentially associated with relevant 'tissue-type' phenotypes and exhibit no difference in phenotype effect size or pleiotropy. Modelling regulatory data alongside molecular data, we built a predictive model to infer gene-phenotype associations and use this model to predict potentially novel disease-associated genes.

CONCLUSION

Overall our findings reveal that differing enhancer architectures have a similar impact on mammalian phenotypes whilst harbouring differing cellular and expression effects. Together, our results systematically characterise enhancers with predicted phenotypic traits endorsing the role for both types of enhancers in human disease and disorders.

Discovery of regulatory noncoding variants in individual cancer genomes by using cis-X

We developed cis-X, a computational method for discovering regulatory noncoding variants in cancer by integrating whole-genome and transcriptome sequencing data from a single cancer sample. cis-X first finds aberrantly cis-activated genes that exhibit allele-specific expression accompanied by an elevated outlier expression. It then searches for causal noncoding variants that may introduce aberrant transcription factor binding motifs or enhancer hijacking by structural variations. Analysis of 13 T-lineage acute lymphoblastic leukemias identified a recurrent intronic variant predicted to cis-activate the TAL1 oncogene, a finding validated in vivo by chromatin immunoprecipitation sequencing of a patient-derived xenograft. Candidate oncogenes include the prolactin receptor PRLR activated by a focal deletion that removes a CTCF-insulated neighborhood boundary. cis-X may be applied to pediatric and adult solid tumors that are aneuploid and heterogeneous. In contrast to existing approaches, which require large sample cohorts, cis-X enables the discovery of regulatory noncoding variants in individual cancer genomes.

Stable H3K4me3 is associated with transcription initiation during early embryo development

MOTIVATION

During development of the mammalian embryo, histone modification H3K4me3 plays an important role in regulating gene expression and exhibits extensive reprograming on the parental genomes. In addition to these dramatic epigenetic changes, certain unchanging regulatory elements are also essential for embryonic development.

RESULTS

Using large-scale H3K4me3 chromatin immunoprecipitation sequencing data, we identified a form of H3K4me3 that was present during all eight stages of the mouse embryo before implantation. This 'stable H3K4me3' was highly accessible and much longer than normal H3K4me3. Moreover, most of the stable H3K4me3 was in the promoter region and was enriched in higher chromatin architecture. Using in-depth analysis, we demonstrated that stable H3K4me3 was related to higher gene expression levels and transcriptional initiation during embryonic development. Furthermore, stable H3K4me3 was much more active in blood tumor cells than in normal blood cells, suggesting a potential mechanism of cancer progression.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification and Analysis of p53-Regulated Enhancers in Hepatic Carcinoma

Enhancers can act as cis-regulatory elements to control transcriptional regulation by recruiting transcription factors (TFs) in a distance and orientation-independent manner. However, it is still unclear how p53 participates in the enhancer network as TF in hepatic carcinoma under the condition of DNA damage. A total of 14,286 active enhancers were identified through the integration of stable and unstable enhancer RNAs (eRNAs) captured by CAGE and GRO-seq, respectively. Furthermore, 218 p53-bound enhancers (Enh_p53) were identified by analyzing p53 ChIP-seq in HepG2 cells after DNA damage. The results showed that the enhancer expression and histone markers of enhancers (H3K4me1, H3K4me2, H3K4me3, H3K9ac, and H3K27ac) revealed significantly higher level on Enh_p53 than Enh_no−p53 which suggested that p53 participated in regulating enhancer activity and chromatin structure. By analyzing 124 TFs ChIP-seq from ENCODE, 93 TFs were found significantly enriched on Enh_p53 such as GATA4, YY1, and CTCF, indicating p53 may co-regulate enhancers with TFs participation. Moreover, significantly differentially expressed 438 miRNAs and 1,264 mRNAs were identified by analyzing small RNA-seq and RNA-seq, and 26 Enh_p53-miRNAs and 145 Enh_p53-mRNA interactions were identified by the integration of 3D genome data and genomic distance. The functional enrichment analysis showed that these miRNA targets and mRNAs were significantly involved in tumor biological processes and signaling pathways such as DNA replication, p53 signaling pathway, hepatitis B, focal adhesion, etc. The above results indicated that p53 participated in regulating enhancer network in hepatic carcinoma and Enh_p53 exhibited significantly different characteristics with Enh_no−p53.

GTRD: a database on gene transcription regulation-2019 update

The current version of the Gene Transcription Regulation Database (GTRD; http://gtrd.biouml.org) contains information about: (i) transcription factor binding sites (TFBSs) and transcription coactivators identified by ChIP-seq experiments for Homo sapiens, Mus musculus, Rattus norvegicus, Danio rerio, Caenorhabditis elegans, Drosophila melanogaster, Saccharomyces cerevisiae, Schizosaccharomyces pombe and Arabidopsis thaliana; (ii) regions of open chromatin and TFBSs (DNase footprints) identified by DNase-seq; (iii) unmappable regions where TFBSs cannot be identified due to repeats; (iv) potential TFBSs for both human and mouse using position weight matrices from the HOCOMOCO database. Raw ChIP-seq and DNase-seq data were obtained from ENCODE and SRA, and uniformly processed. ChIP-seq peaks were called using four different methods: MACS, SISSRs, GEM and PICS. Moreover, peaks for the same factor and peak calling method, albeit using different experiment conditions (cell line, treatment, etc.), were merged into clusters. To reduce noise, such clusters for different peak calling methods were merged into meta-clusters; these were considered to be non-redundant TFBS sets. Moreover, extended quality control was applied to all ChIP-seq data. Web interface to access GTRD was developed using the BioUML platform. It provides browsing and displaying information, advanced search possibilities and an integrated genome browser.

Functional Characteristics of the Mouse Il1b Promoter in Various Tissues Before and After Irradiation

Interleukin-1 beta (IL1B) is a key inducer of inflammation and an important factor in the regulation of hematopoietic stem cells and mesenchymal stromal progenitors. Irradiation of mice with ionizing radiation has been shown to induce a lasting increase in IL1B concentration in peripheral blood. One of the possible mechanisms may be demethylation of CpG cytosines in the Il1b promoter, which has not been characterized in detail for the mouse. In this study, the methylation level of CpGs located in a region between -3562 and -208 bp upstream of the start of transcription is studied in muscles, bones, liver, thymus, spleen, bone marrow, lymph nodes, lungs, and brain. The methylation level is compared to Il1b expression. Tissue-specific features of CpG methylation are established. It is demonstrated that the region between -2420 and -2406 bp is likely a part of the mouse Il1b promoter/enhancer and may determine the base level of Il1b expression in various tissues. Irradiation at a dose of 6 Gy does not change the methylation profile of most studied CpGs, and therefore, the cause of the stably increased IL1B level after irradiation is unlikely to be a change in the methylation of the studied CpGs in investigated tissues.

Integrative prediction of gene expression with chromatin accessibility and conformation data

BACKGROUND

Enhancers play a fundamental role in orchestrating cell state and development. Although several methods have been developed to identify enhancers, linking them to their target genes is still an open problem. Several theories have been proposed on the functional mechanisms of enhancers, which triggered the development of various methods to infer promoter-enhancer interactions (PEIs). The advancement of high-throughput techniques describing the three-dimensional organization of the chromatin, paved the way to pinpoint long-range PEIs. Here we investigated whether including PEIs in computational models for the prediction of gene expression improves performance and interpretability.

RESULTS

We have extended our [Formula: see text] framework to include DNA contacts deduced from chromatin conformation capture experiments and compared various methods to determine PEIs using predictive modelling of gene expression from chromatin accessibility data and predicted transcription factor (TF) motif data. We designed a novel machine learning approach that allows the prioritization of TFs binding to distal loop and promoter regions with respect to their importance for gene expression regulation. Our analysis revealed a set of core TFs that are part of enhancer-promoter loops involving YY1 in different cell lines.

CONCLUSION

We present a novel approach that can be used to prioritize TFs involved in distal and promoter-proximal regulatory events by integrating chromatin accessibility, conformation, and gene expression data. We show that the integration of chromatin conformation data can improve gene expression prediction and aids model interpretability.

PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data

Single-cell RNA sequencing is an increasingly used method to measure gene expression at the single cell level and build cell-type atlases of tissues. Hundreds of single-cell sequencing datasets have already been published. However, studies are frequently deposited as raw data, a format difficult to access for biological researchers due to the need for data processing using complex computational pipelines. We have implemented an online database, PanglaoDB, accessible through a user-friendly interface that can be used to explore published mouse and human single cell RNA sequencing studies. PanglaoDB contains pre-processed and pre-computed analyses from more than 1054 single-cell experiments covering most major single cell platforms and protocols, based on more than 4 million cells from a wide range of tissues and organs. The online interface allows users to query and explore cell types, genetic pathways and regulatory networks. In addition, we have established a community-curated cell-type marker compendium, containing more than 6000 gene-cell-type associations, as a resource for automatic annotation of cell types.

Inherited variants at 3q13.33 and 3p24.1 are associated with risk of diffuse large B-cell lymphoma and implicate immune pathways

We previously identified five single nucleotide polymorphisms (SNPs) at four susceptibility loci for diffuse large B-cell lymphoma (DLBCL) in individuals of European ancestry through a large genome-wide association study (GWAS). To further elucidate genetic susceptibility to DLBCL, we sought to validate two loci at 3q13.33 and 3p24.1 that were suggestive in the original GWAS with additional genotyping. In the meta-analysis (5662 cases and 9237 controls) of the four original GWAS discovery scans and three replication studies, the 3q13.33 locus (rs9831894; minor allele frequency [MAF] = 0.40) was associated with DLBCL risk [odds ratio (OR) = 0.83, P = 3.62 × 10-13]. rs9831894 is in linkage disequilibrium (LD) with additional variants that are part of a super-enhancer that physically interacts with promoters of CD86 and ILDR1. In the meta-analysis (5510 cases and 12 817 controls) of the four GWAS discovery scans and four replication studies, the 3p24.1 locus (rs6773363; MAF = 0.45) was also associated with DLBCL risk (OR = 1.20, P = 2.31 × 10-12). This SNP is 29 426-bp upstream of the nearest gene EOMES and in LD with additional SNPs that are part of a highly lineage-specific and tumor-acquired super-enhancer that shows long-range interaction with AZI2 promoter. These loci provide additional evidence for the role of immune function in the etiology of DLBCL, the most common lymphoma subtype.