Demystifying the secret mission of enhancers: linking distal regulatory elements to target genes

Type-2 diabetes epigenetic biomarkers: present status and future directions for global and Indigenous health

Type-2 diabetes is a systemic condition with rising global prevalence, disproportionately affecting Indigenous communities worldwide. Recent advances in epigenomics methods, particularly in DNA methylation detection, have enabled the discovery of associations between epigenetic changes and Type-2 diabetes. In this review, we summarise DNA methylation profiling methods, and discuss how these technologies can facilitate the discovery of epigenomic biomarkers for Type-2 diabetes. We critically evaluate previous DNA methylation biomarker studies, particularly those using microarray platforms, and advocate for a shift towards sequencing-based approaches to improve genome-wide coverage. Furthermore, we emphasise the need for biomarker studies that include genetically diverse populations, especially Indigenous communities who are significantly impacted by Type-2 diabetes. We discuss research approaches and ethical considerations that can better facilitate Type-2 diabetes biomarker development to ensure that future genomics-based precision medicine efforts deliver equitable health outcomes. We propose that by addressing these gaps, future research can better capture the genetic and environmental complexities of Type-2 diabetes among populations at disproportionate levels of risk, ultimately leading to more effective diagnostic and therapeutic strategies.

Derepression of transposable elements in mouse prefrontal cortex disrupts social behavior

Here, we present a synthetic biology approach to assess the social behavioral consequences of altered function of the Krüppel-associated box zinc finger protein (KZFP) interacting protein TRIM28 within the prefrontal cortex (PFC) of male and female mice. We reprogrammed natural TRIM28 WT by replacing the transcriptionally repressive domain with an enhanced transcriptional activation domain VP64-p65-Rta (TRIM28 VPR ), or by excising the transcriptional regulatory domain (TRIM28 NFD ). In vitro validation confirmed that TRIM28 WT represses, and TRIM28 VPR activates, the expression of a KZFP-regulated luciferase reporter gene. Upon intra-PFC viral-mediated delivery of TRIM28 variants, we observed that inversion of TRIM28 transcriptional control via HSV-TRIM28 VPR reduced the salience of novel social interaction for male and female mice while not affecting non-social behaviors. RNA-sequencing revealed HSV-TRIM28 VPR promoted transcriptional escape of all classes of TEs, particularly those located within intronic and distal enhancer regions of downregulated immune genes. HSV-TRIM28 VPR -driven social deficits were reversible by intra-PFC repletion of interferon cytokines. These novel data point to PFC KZFP-TRIM28 interactions as necessary to stabilize TEs to enable cis-regulation of key immune gene expression and enhance organismal capacity for complex, pro-social behaviors.

BmNPV interacts with super-enhancer regions of the host chromatin to hijack cellular transcription machinery

Effective transcriptional activation relies on the spatial interaction between specific DNA elements. DNA interactions have also been observed between DNA viruses and their hosts, with limited understanding of the involved details. Baculovirus is a representative species of DNA virus and has been reported to interact with the host genome in our previous study. However, the biological significance of the baculovirus-host trans-species DNA interaction and its underlying mechanisms remain elusive. Here, using Bombyx mori nucleopolyhedrovirus (BmNPV) as the model virus, we combine epigenome, transcriptome, and biochemical assays to investigate the baculovirus-host DNA interaction. Our data show that BmNPV hijacks the transcriptional regulatory capacity of host super-enhancers (SEs) by physically interacting with these regions on the host genome. This results in the usurpation of the activating capacity of an SE-binding transcription factor GATA by the virus, thereby impairing the SE-induced specific transcriptional activation of the target antiviral genes. Moreover, the hijacked regulatory capacity is spread on BmNPV genome through cis-interaction of viral DNA, leading to enhanced viral gene expression. Overall, our results provide novel insights into the intricate interplay of viruses with host gene expression regulatory networks and broaden the vision in the mechanisms of viral exploitation on cellular machinery.

Complete F9 Gene Deletion, Duplication, and Triplication Rearrangements: Implications for Factor IX Expression and Clinical Phenotypes

BACKGROUND

 Factor IX (FIX) plays a critical role in blood coagulation. Complete deletion of F9 results in severe hemophilia B, whereas the clinical implications of complete F9 duplication and triplication remain understudied.

OBJECTIVE

 To investigate the rearrangement mechanisms underlying complete F9 deletion (cases 1 and 2), duplication (cases 3 and 4), and triplication (case 5), and to explore their association with FIX expression levels and clinical impacts.

METHODS

 Plasma FIX levels were detected using antigen and activity assays. CNVplex technology, optical genome mapping, and long-distance polymerase chain reaction were employed to characterize the breakpoints of the chromosomal rearrangements.

RESULTS

 Cases 1 and 2 exhibited FIX activities below 1%. Case 3 displayed FIX activities within the reference range. However, cases 4 and 5 showed a significant increase in FIX activities. Alu-mediated nonallelic homologous recombination was identified as the cause of F9 deletion in case 1; FoSTeS/MMBIR (Fork Stalling and Template Switching/microhomology-mediated break-induced replication) contributed to both F9 deletion and tandem duplication observed in cases 2 and 3; BIR/MMBIR (break-induced replication/microhomology-mediated break-induced replication) mediated by the same pair of low-copy repeats results in similar duplication-triplication/inversion-duplication (DUP-TRP/INV-DUP) rearrangements in cases 4 and 5, leading to complete F9 duplication and triplication, respectively.

CONCLUSION

 Large deletions involving the F9 gene exhibit no apparent pattern, and the extra-hematologic clinical phenotypes require careful analysis of other genes within the deletion. The impact of complete F9 duplication and triplication on FIX expression might depend on the integrity of the F9 upstream sequence and the specific rearrangement mechanisms. Notably, DUP-TRP/INV-DUP rearrangements significantly elevate FIX activity and are closely associated with thrombotic phenotypes.

Refine localizations of functional variants affecting eggshell color of Lueyang black-boned chicken in the SLCO1B3

Table eggs with color-uniformity shell are visually attractive for consumers. Lueyang black-boned chicken (LBC) lays colorful eggs, which is undesirable for sale of table eggs, but provides a segregating population for mapping functional variants affecting eggshell color. SLCO1B3 was identified as the causative gene for blue eggs in the Dongxiang and Araucana chickens. The aim of this study is to map functional variants associated with chicken eggshell color in the SLCO1B3. Eggshell color of LBC (n = 383) was measured using the L*a*b color space. SLCO1B3 was resequencing using a subset (n = 30) of 383 samples. Linkage disequilibrium among 139 SNP was analyzed. Association of 16 SNP in the SLCO1B3 and 8 in CPOX, ALAS1, and ABCG2 genes with L*a*b were tested by a polygenic model (LMM) and a polygenic/oligogenic mixed model (BSLMM). Chromatin state annotations were retrieved from the UCSC database. Effect of SLCO1B3 variants distributed in mapping and upstream 1.6-kb regions on promoter activities were analyzed using dual-luciferase reporter assay. One hundred and thirty-nine variants maintained low linkage disequilibrium with 80% of r2 less than 0.226. Fifteen SLCO1B3 variants were significantly associated with a*, of which 1B3_SNP108 was showed the strongest association and the largest effect on a*. In the BSLMM, 1B3_SNP108 alone appeared in the Markov chain Monte Carlo as major variants in 100% of posterior inclusion probability. None of variants in CPOX, ALAS1, and ABCG2 were significantly associated with color indexes except that 2 ALAS1 variants were associated with L*. 1B3_SNP108 distributes in the Intron4 where 6 active enhancers and 1 ATAC island were enriched. However, 1B3_SNP108-containing constructs showed negligible activities in the reporter assay. No significant differences of activities between haplotypes were found for five 5'-deleted promoter constructs. The data recognizes 1B3_SNP108 as a valuable marker for breeding of eggshell color. Functional variants are localized in the region adjacent to the 1B3_SNP108 due to low linkage disequilibrium in the LBC. Our findings extend the role of SLCO1B3 from a causative gene for blue eggs to a major regulator driving continuous variation of LBC eggshell color.

An encyclopedia of enhancer-gene regulatory interactions in the human genome

Identifying transcriptional enhancers and their target genes is essential for understanding gene regulation and the impact of human genetic variation on disease1-6. Here we create and evaluate a resource of >13 million enhancer-gene regulatory interactions across 352 cell types and tissues, by integrating predictive models, measurements of chromatin state and 3D contacts, and largescale genetic perturbations generated by the ENCODE Consortium7. We first create a systematic benchmarking pipeline to compare predictive models, assembling a dataset of 10,411 elementgene pairs measured in CRISPR perturbation experiments, >30,000 fine-mapped eQTLs, and 569 fine-mapped GWAS variants linked to a likely causal gene. Using this framework, we develop a new predictive model, ENCODE-rE2G, that achieves state-of-the-art performance across multiple prediction tasks, demonstrating a strategy involving iterative perturbations and supervised machine learning to build increasingly accurate predictive models of enhancer regulation. Using the ENCODE-rE2G model, we build an encyclopedia of enhancer-gene regulatory interactions in the human genome, which reveals global properties of enhancer networks, identifies differences in the functions of genes that have more or less complex regulatory landscapes, and improves analyses to link noncoding variants to target genes and cell types for common, complex diseases. By interpreting the model, we find evidence that, beyond enhancer activity and 3D enhancer-promoter contacts, additional features guide enhancerpromoter communication including promoter class and enhancer-enhancer synergy. Altogether, these genome-wide maps of enhancer-gene regulatory interactions, benchmarking software, predictive models, and insights about enhancer function provide a valuable resource for future studies of gene regulation and human genetics.

Molecular mechanisms underpinning quantitative resistance to Phytophthora sojae in Glycine max using a systems genomics approach

Expression of quantitative disease resistance in many host-pathogen systems is controlled by genes at multiple loci, each contributing a small effect to the overall response. We used a systems genomics approach to study the molecular underpinnings of quantitative disease resistance in the soybean-Phytophthora sojae pathosystem, incorporating expression quantitative trait loci (eQTL) mapping and gene co-expression network analysis to identify the genes putatively regulating transcriptional changes in response to inoculation. These findings were compared to previously mapped phenotypic (phQTL) to identify the molecular mechanisms contributing to the expression of this resistance. A subset of 93 recombinant inbred lines (RILs) from a Conrad × Sloan population were inoculated with P. sojae isolate 1.S.1.1 using the tray-test method; RNA was extracted, sequenced, and the normalized read counts were genetically mapped from tissue collected at the inoculation site 24 h after inoculation from both mock and inoculated samples. In total, more than 100,000 eQTLs were mapped. There was a switch from predominantly cis-eQTLs in the mock treatment to an almost entirely nonoverlapping set of predominantly trans-eQTLs in the inoculated treatment, where greater than 100-fold more eQTLs were mapped relative to mock, indicating vast transcriptional reprogramming due to P. sojae infection occurred. The eQTLs were organized into 36 hotspots, with the four largest hotspots from the inoculated treatment corresponding to more than 70% of the eQTLs, each enriched for genes within plant-pathogen interaction pathways. Genetic regulation of trans-eQTLs in response to the pathogen was predicted to occur through transcription factors and signaling molecules involved in plant-pathogen interactions, plant hormone signal transduction, and MAPK pathways. Network analysis identified three co-expression modules that were correlated with susceptibility to P. sojae and associated with three eQTL hotspots. Among the eQTLs co-localized with phQTLs, two cis-eQTLs with putative functions in the regulation of root architecture or jasmonic acid, as well as the putative master regulators of an eQTL hotspot nearby a phQTL, represent candidates potentially underpinning the molecular control of these phQTLs for resistance.

Toward a comprehensive catalog of regulatory elements

Regulatory elements are the genomic regions that interact with transcription factors to control cell-type-specific gene expression in different cellular environments. A precise and complete catalog of functional elements encoded by the human genome is key to understanding mammalian gene regulation. Here, we review the current state of regulatory element annotation. We first provide an overview of assays for characterizing functional elements, including genome, epigenome, transcriptome, three-dimensional chromatin interaction, and functional validation assays. We then discuss computational methods for defining regulatory elements, including peak-calling and other statistical modeling methods. Finally, we introduce several high-quality lists of regulatory element annotations and suggest potential future directions.

Systematic mapping and modeling of 3D enhancer-promoter interactions in early mouse embryonic lineages reveal regulatory principles that determine the levels and cell-type specificity of gene expression

Mammalian embryogenesis commences with two pivotal and binary cell fate decisions that give rise to three essential lineages, the trophectoderm (TE), the epiblast (EPI) and the primitive endoderm (PrE). Although key signaling pathways and transcription factors that control these early embryonic decisions have been identified, the non-coding regulatory elements via which transcriptional regulators enact these fates remain understudied. To address this gap, we have characterized, at a genome-wide scale, enhancer activity and 3D connectivity in embryo-derived stem cell lines that represent each of the early developmental fates. We observed extensive enhancer remodeling and fine-scale 3D chromatin rewiring among the three lineages, which strongly associate with transcriptional changes, although there are distinct groups of genes that are irresponsive to topological changes. In each lineage, a high degree of connectivity or "hubness" positively correlates with levels of gene expression and enriches for cell-type specific and essential genes. Genes within 3D hubs also show a significantly stronger probability of coregulation across lineages, compared to genes in linear proximity or within the same contact domains. By incorporating 3D chromatin features, we build a novel predictive model for transcriptional regulation (3D-HiChAT), which outperformed models that use only 1D promoter or proximal variables in predicting levels and cell-type specificity of gene expression. Using 3D-HiChAT, we performed genome-wide in silico perturbations to nominate candidate functional enhancers and hubs in each cell lineage, and with CRISPRi experiments we validated several novel enhancers that control expression of one or more genes in their respective lineages. Our study comprehensively identifies 3D regulatory hubs associated with the earliest mammalian lineages and describes their relationship to gene expression and cell identity, providing a framework to understand lineage-specific transcriptional behaviors.

The adapted Activity-By-Contact model for enhancer-gene assignment and its application to single-cell data

MOTIVATION

Identifying regulatory regions in the genome is of great interest for understanding the epigenomic landscape in cells. One fundamental challenge in this context is to find the target genes whose expression is affected by the regulatory regions. A recent successful method is the Activity-By-Contact (ABC) model which scores enhancer-gene interactions based on enhancer activity and the contact frequency of an enhancer to its target gene. However, it describes regulatory interactions entirely from a gene's perspective, and does not account for all the candidate target genes of an enhancer. In addition, the ABC model requires two types of assays to measure enhancer activity, which limits the applicability. Moreover, there is neither implementation available that could allow for an integration with transcription factor (TF) binding information nor an efficient analysis of single-cell data.

RESULTS

We demonstrate that the ABC score can yield a higher accuracy by adapting the enhancer activity according to the number of contacts the enhancer has to its candidate target genes and also by considering all annotated transcription start sites of a gene. Further, we show that the model is comparably accurate with only one assay to measure enhancer activity. We combined our generalized ABC model with TF binding information and illustrated an analysis of a single-cell ATAC-seq dataset of the human heart, where we were able to characterize cell type-specific regulatory interactions and predict gene expression based on TF affinities. All executed processing steps are incorporated into our new computational pipeline STARE.

AVAILABILITY AND IMPLEMENTATION

The software is available at https://github.com/schulzlab/STARE.

CONTACT

marcel.schulz@em.uni-frankfurt.de.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Aberrant promoter methylation contributes to LRIG1 silencing in basal/triple-negative breast cancer

BACKGROUND

LRIG1, the founding member of the LRIG (leucine-rich repeat and immunoglobulin-like domain) family of transmembrane proteins, is a negative regulator of receptor tyrosine kinases and a tumour suppressor. Decreased LRIG1 expression is consistently observed in cancer, across diverse tumour types, and is linked to poor patient prognosis. However, mechanisms by which LRIG1 is repressed are not fully understood. Silencing of LRIG1 through promoter CpG island methylation has been reported in colorectal and cervical cancer but studies in breast cancer remain limited.

METHODS

In silico analysis of human breast cancer patient data were used to demonstrate a correlation between DNA methylation and LRIG1 silencing in basal/triple-negative breast cancer, and its impact on patient survival. LRIG1 gene expression, protein abundance, and methylation enrichment were examined by quantitative reverse-transcription PCR, immunoblotting, and methylation immunoprecipitation, respectively, in breast cancer cell lines in vitro. We examined the impact of global demethylation on LRIG1 expression and methylation enrichment using 5-aza-2'-deoxycytidine. We also examined the effects of targeted demethylation of the LRIG1 CpG island, and transcriptional activation of LRIG1 expression, using the RNA guided deadCas9 transactivation system.

RESULTS

Across breast cancer subtypes, LRIG1 expression is lowest in the basal/triple-negative subtype so we investigated whether differential methylation may contribute to this. Indeed, we find that LRIG1 CpG island methylation is most prominent in basal/triple-negative cell lines and patient samples. Use of the global demethylating agent 5-aza-2'-deoxycytidine decreases methylation leading to increased LRIG1 transcript expression in basal/triple-negative cell lines, while having no effect on LRIG1 expression in luminal/ER-positive cell lines. Using a CRISPR/deadCas9 (dCas9)-based targeting approach, we demonstrate that TET1-mediated demethylation (Tet1-dCas9) along with VP64-mediated transcriptional activation (VP64-dCas9) at the CpG island, increased endogenous LRIG1 expression in basal/triple-negative breast cancer cells, without transcriptional upregulation at predicted off-target sites. Activation of LRIG1 by the dCas9 transactivation system significantly increased LRIG1 protein abundance, reduced site-specific methylation, and reduced cancer cell viability. Our findings suggest that CRISPR-mediated targeted activation may be a feasible way to restore LRIG1 expression in cancer.

CONCLUSIONS

Our study contributes novel insight into mechanisms which repress LRIG1 in triple-negative breast cancer and demonstrates for the first time that targeted de-repression of LRIG1 in cancer cells is possible. Understanding the epigenetic mechanisms associated with repression of tumour suppressor genes holds potential for the advancement of therapeutic approaches.

Integrative analysis of epigenetics data identifies gene-specific regulatory elements

Understanding how epigenetic variation in non-coding regions is involved in distal gene-expression regulation is an important problem. Regulatory regions can be associated to genes using large-scale datasets of epigenetic and expression data. However, for regions of complex epigenomic signals and enhancers that regulate many genes, it is difficult to understand these associations. We present StitchIt, an approach to dissect epigenetic variation in a gene-specific manner for the detection of regulatory elements (REMs) without relying on peak calls in individual samples. StitchIt segments epigenetic signal tracks over many samples to generate the location and the target genes of a REM simultaneously. We show that this approach leads to a more accurate and refined REM detection compared to standard methods even on heterogeneous datasets, which are challenging to model. Also, StitchIt REMs are highly enriched in experimentally determined chromatin interactions and expression quantitative trait loci. We validated several newly predicted REMs using CRISPR-Cas9 experiments, thereby demonstrating the reliability of StitchIt. StitchIt is able to dissect regulation in superenhancers and predicts thousands of putative REMs that go unnoticed using peak-based approaches suggesting that a large part of the regulome might be uncharted water.

Chromatin accessibility and regulatory vocabulary across indicine cattle tissues

BACKGROUND

Spatiotemporal changes in the chromatin accessibility landscape are essential to cell differentiation, development, health, and disease. The quest of identifying regulatory elements in open chromatin regions across different tissues and developmental stages is led by large international collaborative efforts mostly focusing on model organisms, such as ENCODE. Recently, the Functional Annotation of Animal Genomes (FAANG) has been established to unravel the regulatory elements in non-model organisms, including cattle. Now, we can transition from prediction to validation by experimentally identifying the regulatory elements in tropical indicine cattle. The identification of regulatory elements, their annotation and comparison with the taurine counterpart, holds high promise to link regulatory regions to adaptability traits and improve animal productivity and welfare.

RESULTS

We generate open chromatin profiles for liver, muscle, and hypothalamus of indicine cattle through ATAC-seq. Using robust methods for motif discovery, motif enrichment and transcription factor binding sites, we identify potential master regulators of the epigenomic profile in these three tissues, namely HNF4, MEF2, and SOX factors, respectively. Integration with transcriptomic data allows us to confirm some of their target genes. Finally, by comparing our results with Bos taurus data we identify potential indicine-specific open chromatin regions and overlaps with indicine selective sweeps.

CONCLUSIONS

Our findings provide insights into the identification and analysis of regulatory elements in non-model organisms, the evolution of regulatory elements within two cattle subspecies as well as having an immediate impact on the animal genetics community in particular for a relevant productive species such as tropical cattle.

Open chromatin in grapevine marks candidate CREs and with other chromatin features correlates with gene expression

Vitis vinifera is an economically important crop and a useful model in which to study chromatin dynamics. In contrast to the small and relatively simple genome of Arabidopsis thaliana, grapevine contains a complex genome of 487 Mb that exhibits extensive colonization by transposable elements. We used Hi-C, ChIP-seq and ATAC-seq to measure how chromatin features correlate to the expression of 31 845 grapevine genes. ATAC-seq revealed the presence of more than 16 000 open chromatin regions, of which we characterize nearly 5000 as possible distal enhancer candidates that occur in intergenic space > 2 kb from the nearest transcription start site (TSS). A motif search identified more than 480 transcription factor (TF) binding sites in these regions, with those for TCP family proteins in greatest abundance. These open chromatin regions are typically within 15 kb from their nearest promoter, and a gene ontology analysis indicated that their nearest genes are significantly enriched for TF activity. The presence of a candidate cis-regulatory element (cCRE) > 2 kb upstream of the TSS, location in the active nuclear compartment as determined by Hi-C, and the enrichment of H3K4me3, H3K4me1 and H3K27ac at the gene are correlated with gene expression. Taken together, these results suggest that regions of intergenic open chromatin identified by ATAC-seq can be considered potential candidates for cis-regulatory regions in V. vinifera. Our findings enhance the characterization of a valuable agricultural crop, and help to clarify the understanding of unique plant biology.

Advances in promoter engineering: novel applications and predefined transcriptional control

Synthetic biology continues to progress by relying on more robust tools for transcriptional control, of which promoters are the most fundamental component. Numerous studies have sought to characterize promoter function, determine principles to guide their engineering, and create promoters with stronger expression or tailored inducible control. In this review, we will summarize promoter architecture and highlight recent advances in the field, focusing on the novel applications of inducible promoter design and engineering towards metabolic engineering and cellular therapeutic development. Additionally, we will highlight how the expansion of new, machine learning techniques for modeling and engineering promoter sequences are enabling more accurate prediction of promoter characteristics. This article is protected by copyright. All rights reserved.

Computational prediction of CRISPR-impaired non-coding regulatory regions

Genome-wide CRISPR screens are becoming more widespread and allow the simultaneous interrogation of thousands of genomic regions. Although recent progress has been made in the analysis of CRISPR screens, it is still an open problem how to interpret CRISPR mutations in non-coding regions of the genome. Most of the tools concentrate on the interpretation of mutations introduced in gene coding regions. We introduce a computational pipeline that uses epigenomic information about regulatory elements for the interpretation of CRISPR mutations in non-coding regions. We illustrate our analysis protocol on the analysis of a genome-wide CRISPR screen in hTERT-RPE1 cells and reveal novel regulatory elements that mediate chemoresistance against doxorubicin in these cells. We infer links to established and to novel chemoresistance genes. Our analysis protocol is general and can be applied on any cell type and with different CRISPR enzymes.

Genomic editing of intronic enhancers unveils their role in fine-tuning tissue-specific gene expression in Arabidopsis thaliana

Enhancers located in introns are abundant and play a major role in the regulation of gene expression in mammalian species. By contrast, the functions of intronic enhancers in plants have largely been unexplored and only a handful of plant intronic enhancers have been reported. We performed a genome-wide prediction of intronic enhancers in Arabidopsis thaliana using open chromatin signatures based on DNase I sequencing. We identified 941 candidate intronic enhancers associated with 806 genes in seedling tissue and 1,271 intronic enhancers associated with 1,069 genes in floral tissue. We validated the function of 15 of 21 (71%) of the predicted intronic enhancers in transgenic assays using a reporter gene. We also created deletion lines of three intronic enhancers associated with two different genes using CRISPR/Cas. Deletion of these enhancers, which span key transcription factor binding sites, did not abolish gene expression but caused varying levels of transcriptional repression of their cognate genes. Remarkably, the transcriptional repression of the deletion lines occurred at specific developmental stages and resulted in distinct phenotypic effects on plant morphology and development. Clearly, these three intronic enhancers are important in fine-tuning tissue- and development-specific expression of their cognate genes.

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.

Using functional genomics to advance the understanding of psoriatic arthritis

Psoriatic arthritis (PsA) is a complex disease where susceptibility is determined by genetic and environmental risk factors. Clinically, PsA involves inflammation of the joints and the skin, and, if left untreated, results in irreversible joint damage. There is currently no cure and the few treatments available to alleviate symptoms do not work in all patients. Over the past decade, genome-wide association studies (GWAS) have uncovered a large number of disease-associated loci but translating these findings into functional mechanisms and novel targets for therapeutic use is not straightforward. Most variants have been predicted to affect primarily long-range regulatory regions such as enhancers. There is now compelling evidence to support the use of chromatin conformation analysis methods to discover novel genes that can be affected by disease-associated variants. Here, we will review the studies published in the field that have given us a novel understanding of gene regulation in the context of functional genomics and how this relates to the study of PsA and its underlying disease mechanism.

CRISPR genome engineering for retinal diseases

Novel gene therapy treatments for inherited retinal diseases have been at the forefront of translational medicine over the past couple of decades. Since the discovery of CRISPR mechanisms and their potential application for the treatment of inherited human conditions, it seemed inevitable that advances would soon be made using retinal models of disease. The development of CRISPR technology for gene therapy and its increasing potential to selectively target disease-causing nucleotide changes has been rapid. In this chapter, we discuss the currently available CRISPR toolkit and how it has been and can be applied in the future for the treatment of inherited retinal diseases. These blinding conditions have until now had limited opportunity for successful therapeutic intervention, but the discovery of CRISPR has created new hope of achieving such, as we discuss within this chapter.

Promoter-interacting expression quantitative trait loci are enriched for functional genetic variants

Expression quantitative trait loci (eQTLs) studies provide associations of genetic variants with gene expression but fall short of pinpointing functionally important eQTLs. Here, using H3K27ac HiChIP assays, we mapped eQTLs overlapping active cis-regulatory elements that interact with their target gene promoters (promoter-interacting eQTLs, pieQTLs) in five common immune cell types (Database of Immune Cell Expression, Expression quantitative trait loci and Epigenomics (DICE) cis-interactome project). This approach allowed us to identify functionally important eQTLs and show mechanisms that explain their cell-type restriction. We also devised an approach to eQTL discovery that relies on HiChIP-based promoter interaction maps as a structural framework for deciding which SNPs to test for association with gene expression, and observe ultra-long-distance pieQTLs (>1 megabase away), including several disease-risk variants. We validated the functional role of pieQTLs using reporter assays, CRISPRi, dCas9-tiling guides and Cas9-mediated base-pair editing. In this article we present a method for functional eQTL discovery and provide insights into relevance of noncoding variants for cell-specific gene regulation and for disease association beyond conventional eQTL mapping.

PEREGRINE: A genome-wide prediction of enhancer to gene relationships supported by experimental evidence

Enhancers are powerful and versatile agents of cell-type specific gene regulation, which are thought to play key roles in human disease. Enhancers are short DNA elements that function primarily as clusters of transcription factor binding sites that are spatially coordinated to regulate expression of one or more specific target genes. These regulatory connections between enhancers and target genes can therefore be characterized as enhancer-gene links that can affect development, disease, and homeostatic cellular processes. Despite their implication in disease and the establishment of cell identity during development, most enhancer-gene links remain unknown. Here we introduce a new, publicly accessible database of predicted enhancer-gene links, PEREGRINE. The PEREGRINE human enhancer-gene links interactive web interface incorporates publicly available experimental data from ChIA-PET, eQTL, and Hi-C assays across 78 cell and tissue types to link 449,627 enhancers to 17,643 protein-coding genes. These enhancer-gene links are made available through the new Enhancer module of the PANTHER database and website where the user may easily access the evidence for each enhancer-gene link, as well as query by target gene and enhancer location.

ZFARED: A Database of the Antioxidant Response Elements in Zebrafish

Background:

Antioxidant Response Elements (ARE) play a key role in the expression of Nrf2 target genes by regulating the Keap1-Nrf2-ARE pathway, which offers protection against toxic agents and oxidative stress-induced diseases.

Objective:

To develop a database of putative AREs for all the genes in the zebrafish genome. This database will be helpful for researchers to investigate Nrf2 regulatory mechanisms in detail.

Methods:

To facilitate researchers functionally characterize zebrafish AREs, we have developed a database of AREs, Zebrafish Antioxidant Response Element Database (ZFARED), for all the protein-coding genes including antioxidant and mitochondrial genes in the zebrafish genome. The front end of the database was developed using HTML, JavaScript, and CSS and tested in different browsers. The back end of the database was developed using Perl scripts and Perl-CGI and Perl- DBI modules.

Results:

ZFARED is the first database on the AREs in zebrafish, which facilitates fast and efficient searching of AREs. AREs were identified using the in-house developed Perl algorithms and the database was developed using HTML, JavaScript, and Perl-CGI scripts. From this database, researchers can access the AREs based on chromosome number (1 to 25 and M for mitochondria), strand (positive or negative), ARE pattern and keywords. Users can also specify the size of the upstream/promoter regions (5 to 30 kb) from transcription start site to access the AREs located in those specific regions.

Conclusion:

ZFARED will be useful in the investigation of the Keap1-Nrf2-ARE pathway and its gene regulation. ZFARED is freely available at http://zfared.buc.edu.in/.

Transcriptional enhancers: from prediction to functional assessment on a genome-wide scale

Enhancers are cis-regulatory sequences located distally to target genes. These sequences consolidate developmental and environmental cues to coordinate gene expression in a tissue-specific manner. Enhancer function and tissue specificity depend on the expressed set of transcription factors, which recognize binding sites and recruit cofactors that regulate local chromatin organization and gene transcription. Unlike other genomic elements, enhancers are challenging to identify because they function independently of orientation, are often distant from their promoters, have poorly defined boundaries, and display no reading frame. In addition, there are no defined genetic or epigenetic features that are unambiguously associated with enhancer activity. Over recent years there have been developments in both empirical assays and computational methods for enhancer prediction. We review genome-wide tools, CRISPR advancements, and high-throughput screening approaches that have improved our ability to both observe and manipulate enhancers in vitro at the level of primary genetic sequences, chromatin states, and spatial interactions. We also highlight contemporary animal models and their importance to enhancer validation. Together, these experimental systems and techniques complement one another and broaden our understanding of enhancer function in development, evolution, and disease.

Erythropoietin Regulates Transcription and YY1 Dynamics in a Pre-established Chromatin Architecture

The three-dimensional architecture of the genome plays an essential role in establishing and maintaining cell identity. However, the magnitude and temporal kinetics of changes in chromatin structure that arise during cell differentiation remain poorly understood. Here, we leverage a murine model of erythropoiesis to study the relationship between chromatin conformation, the epigenome, and transcription in erythroid cells. We discover that acute transcriptional responses induced by erythropoietin (EPO), the hormone necessary for erythroid differentiation, occur within an invariant chromatin topology. Within this pre-established landscape, Yin Yang 1 (YY1) occupancy dynamically redistributes to sites in proximity of EPO-regulated genes. Using HiChIP, we identify chromatin contacts mediated by H3K27ac and YY1 that are enriched for enhancer-promoter interactions of EPO-responsive genes. Taken together, these data are consistent with an emerging model that rapid, signal-dependent transcription occurs in the context of a pre-established chromatin architecture.

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EPO induces rapid RNA Pol II response at a key subset of genes

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YY1 is redistributed in the genome following 1 h EPO stimulation

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CTCF and YY1 bind different locations pre and post 1 h EPO stimulation

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E-P loops mediated by H3K27ac are largely invariant in response to EPO

Integrating distal and proximal information to predict gene expression via a densely connected convolutional neural network

MOTIVATION

Interactions among cis-regulatory elements such as enhancers and promoters are main driving forces shaping context-specific chromatin structure and gene expression. Although there have been computational methods for predicting gene expression from genomic and epigenomic information, most of them neglect long-range enhancer-promoter interactions, due to the difficulty in precisely linking regulatory enhancers to target genes. Recently, HiChIP, a novel high-throughput experimental approach, has generated comprehensive data on high-resolution interactions between promoters and distal enhancers. Moreover, plenty of studies suggest that deep learning achieves state-of-the-art performance in epigenomic signal prediction, and thus promoting the understanding of regulatory elements. In consideration of these two factors, we integrate proximal promoter sequences and HiChIP distal enhancer-promoter interactions to accurately predict gene expression.

RESULTS

We propose DeepExpression, a densely connected convolutional neural network, to predict gene expression using both promoter sequences and enhancer-promoter interactions. We demonstrate that our model consistently outperforms baseline methods, not only in the classification of binary gene expression status but also in regression of continuous gene expression levels, in both cross-validation experiments and cross-cell line predictions. We show that the sequential promoter information is more informative than the experimental enhancer information; meanwhile, the enhancer-promoter interactions within ±100 kbp around the TSS of a gene are most beneficial. We finally visualize motifs in both promoter and enhancer regions and show the match of identified sequence signatures with known motifs. We expect to see a wide spectrum of applications using HiChIP data in deciphering the mechanism of gene regulation.

AVAILABILITY AND IMPLEMENTATION

DeepExpression is freely available at https://github.com/wanwenzeng/DeepExpression.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Predicting target genes of non-coding regulatory variants with IRT

SUMMARY

Interpreting genetic variants of unknown significance (VUS) is essential in clinical applications of genome sequencing for diagnosis and personalized care. Non-coding variants remain particularly difficult to interpret, despite making up a large majority of trait associations identified in genome-wide association studies (GWAS) analyses. Predicting the regulatory effects of non-coding variants on candidate genes is a key step in evaluating their clinical significance. Here, we develop a machine-learning algorithm, Inference of Connected expression quantitative trait loci (eQTLs) (IRT), to predict the regulatory targets of non-coding variants identified in studies of eQTLs. We assemble datasets using eQTL results from the Genotype-Tissue Expression (GTEx) project and learn to separate positive and negative pairs based on annotations characterizing the variant, gene and the intermediate sequence. IRT achieves an area under the receiver operating characteristic curve (ROC-AUC) of 0.799 using random cross-validation, and 0.700 for a more stringent position-based cross-validation. Further evaluation on rare variants and experimentally validated regulatory variants shows a significant enrichment in IRT identifying the true target genes versus negative controls. In gene-ranking experiments, IRT achieves a top-1 accuracy of 50% and top-3 accuracy of 90%. Salient features, including GC-content, histone modifications and Hi-C interactions are further analyzed and visualized to illustrate their influences on predictions. IRT can be applied to any VUS of interest and each candidate nearby gene to output a score reflecting the likelihood of regulatory effect on the expression level. These scores can be used to prioritize variants and genes to assist in patient diagnosis and GWAS follow-up studies.

AVAILABILITY AND IMPLEMENTATION

Codes and data used in this work are available at https://github.com/miaecle/eQTL_Trees.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Transcription factors: building hubs in the 3D space

The hierarchical three-dimensional folding of the mammalian genome constitutes an important regulatory layer of gene expression and cell fate control during processes such as development and tumorigenesis. Accumulating evidence supports the existence of complex topological assemblies in which multiple genes and regulatory elements are frequently interacting with each other in the 3D nucleus. Here, we will discuss the nature, organizational principles, and potential function of such assemblies, including the recently reported enhancer “hubs,” “cliques,” and FIREs (frequently interacting regions) as well as multi-contact hubs. We will also review recent studies that investigate the role of transcription factors (TFs) in driving the topological genome reorganization and hub formation in the context of cell fate transitions and cancer. Finally, we will highlight technological advances that enabled these studies, current limitations, and future directions necessary to advance our understating in the field.

EpiRegio: analysis and retrieval of regulatory elements linked to genes

A current challenge in genomics is to interpret non-coding regions and their role in transcriptional regulation of possibly distant target genes. Genome-wide association studies show that a large part of genomic variants are found in those non-coding regions, but their mechanisms of gene regulation are often unknown. An additional challenge is to reliably identify the target genes of the regulatory regions, which is an essential step in understanding their impact on gene expression. Here we present the EpiRegio web server, a resource of regulatory elements (REMs). REMs are genomic regions that exhibit variations in their chromatin accessibility profile associated with changes in expression of their target genes. EpiRegio incorporates both epigenomic and gene expression data for various human primary cell types and tissues, providing an integrated view of REMs in the genome. Our web server allows the analysis of genes and their associated REMs, including the REM's activity and its estimated cell type-specific contribution to its target gene's expression. Further, it is possible to explore genomic regions for their regulatory potential, investigate overlapping REMs and by that the dissection of regions of large epigenomic complexity. EpiRegio allows programmatic access through a REST API and is freely available at https://epiregio.de/.

Rare variant association testing in the non-coding genome

The development of next-generation sequencing technologies has opened-up some new possibilities to explore the contribution of genetic variants to human diseases and in particular that of rare variants. Statistical methods have been developed to test for association with rare variants that require the definition of testing units and, in these testing units, the selection of qualifying variants to include in the test. In the coding regions of the genome, testing units are usually the different genes and qualifying variants are selected based on their functional effects on the encoded proteins. Extending these tests to the non-coding regions of the genome is challenging. Testing units are difficult to define as the non-coding genome organisation is still rather unknown. Qualifying variants are difficult to select as the functional impact of non-coding variants on gene expression is hard to predict. These difficulties could explain why very few investigators so far have analysed the non-coding parts of their whole genome sequencing data. These non-coding parts yet represent the vast majority of the genome and some studies suggest that they could play a major role in disease susceptibility. In this review, we discuss recent experimental and statistical developments to gain knowledge on the non-coding genome and how this knowledge could be used to include rare non-coding variants in association tests. We describe the few studies that have considered variants from the non-coding genome in association tests and how they managed to define testing units and select qualifying variants.

HiChIP-Peaks: a HiChIP peak calling algorithm

MOTIVATION

HiChIP is a powerful tool to interrogate 3D chromatin organization. Current tools to analyse chromatin looping mechanisms using HiChIP data require the identification of loop anchors to work properly. However, current approaches to discover these anchors from HiChIP data are not satisfactory, having either a very high false discovery rate or strong dependence on sequencing depth. Moreover, these tools do not allow quantitative comparison of peaks across different samples, failing to fully exploit the information available from HiChIP datasets.

RESULTS

We develop a new tool based on a representation of HiChIP data centred on the re-ligation sites to identify peaks from HiChIP datasets, which can subsequently be used in other tools for loop discovery. This increases the reliability of these tools and improves recall rate as sequencing depth is reduced. We also provide a method to count reads mapping to peaks across samples, which can be used for differential peak analysis using HiChIP data.

AVAILABILITY AND IMPLEMENTATION

HiChIP-Peaks is freely available at https://github.com/ChenfuShi/HiChIP_peaks.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Functional effects of variation in transcription factor binding highlight long-range gene regulation by epromoters

Identifying DNA cis-regulatory modules (CRMs) that control the expression of specific genes is crucial for deciphering the logic of transcriptional control. Natural genetic variation can point to the possible gene regulatory function of specific sequences through their allelic associations with gene expression. However, comprehensive identification of causal regulatory sequences in brute-force association testing without incorporating prior knowledge is challenging due to limited statistical power and effects of linkage disequilibrium. Sequence variants affecting transcription factor (TF) binding at CRMs have a strong potential to influence gene regulatory function, which provides a motivation for prioritizing such variants in association testing. Here, we generate an atlas of CRMs showing predicted allelic variation in TF binding affinity in human lymphoblastoid cell lines and test their association with the expression of their putative target genes inferred from Promoter Capture Hi-C and immediate linear proximity. We reveal >1300 CRM TF-binding variants associated with target gene expression, the majority of them undetected with standard association testing. A large proportion of CRMs showing associations with the expression of genes they contact in 3D localize to the promoter regions of other genes, supporting the notion of 'epromoters': dual-action CRMs with promoter and distal enhancer activity.

CRISPR Interference-Potential Application in Retinal Disease

The treatment of dominantly inherited retinal diseases requires silencing of the pathogenic allele. RNA interference to suppress gene expression suffers from wide-spread off-target effects, while CRISPR-mediated gene disruption creates permanent changes in the genome. CRISPR interference uses a catalytically inactive 'dead' Cas9 directed by a guide RNA to block transcription of chosen genes without disrupting the DNA. It is highly specific and potentially reversible, increasing its safety profile as a therapy. Pre-clinical studies have demonstrated the versatility of CRISPR interference for gene silencing both in vivo and in ex vivo modification of iPSCs for transplantation. Applying CRISPR interference techniques for the treatment of autosomal dominant inherited retinal diseases is promising but there are few in vivo studies to date. This review details how CRISPR interference might be used to treat retinal diseases and addresses potential challenges for clinical translation.

Independent Transposon Exaptation Is a Widespread Mechanism of Redundant Enhancer Evolution in the Mammalian Genome

Many regulatory networks appear to involve partially redundant enhancers. Traditionally, such enhancers have been hypothesized to originate mainly by sequence duplication. An alternative model postulates that they arise independently, through convergent evolution. This mechanism appears to be counterintuitive to natural selection: Redundant sequences are expected to either diverge and acquire new functions or accumulate mutations and become nonfunctional. Nevertheless, we show that at least 31% of the redundant enhancer pairs in the human genome (and 17% in the mouse genome) indeed originated in this manner. Specifically, for virtually all transposon-derived redundant enhancer pairs, both enhancer partners have evolved independently, from the exaptation of two different transposons. In addition to conferring robustness to the system, redundant enhancers could provide an evolutionary advantage by fine-tuning gene expression. Consistent with this hypothesis, we observed that the target genes of redundant enhancers exhibit higher expression levels and tissue specificity as compared with other genes. Finally, we found that although enhancer redundancy appears to be an intrinsic property of certain mammalian regulatory networks, the corresponding enhancers are largely species-specific. In other words, the redundancy in these networks is most likely a result of convergent evolution.

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.

Mechanistic Dissection of RNA-Binding Proteins in Regulated Gene Expression at Chromatin Levels

Eukaryotic genomes are known to prevalently transcribe diverse classes of RNAs, virtually all of which, including nascent RNAs from protein-coding genes, are now recognized to have regulatory functions in gene expression, suggesting that RNAs are both the products and the regulators of gene expression. Their functions must enlist specific RNA-binding proteins (RBPs) to execute their regulatory activities, and recent evidence suggests that nearly all biochemically defined chromatin regions in the human genome, whether defined for gene activation or silencing, have the involvement of specific RBPs. Interestingly, the boundary between RNA- and DNA-binding proteins is also melting, as many DNA-binding proteins traditionally studied in the context of transcription are able to bind RNAs, some of which may simultaneously bind both DNA and RNA to facilitate network interactions in three-dimensional (3D) genome. In this review, we focus on RBPs that function at chromatin levels, with particular emphasis on their mechanisms of action in regulated gene expression, which is intended to facilitate future functional and mechanistic dissection of chromatin-associated RBPs.

Lessons from eRNAs: understanding transcriptional regulation through the lens of nascent RNAs

Nascent transcription assays, such as global run-on sequencing (GRO-seq) and precision run-on sequencing (PRO-seq), have uncovered a myriad of unstable RNAs being actively produced from numerous sites genome-wide. These transcripts provide a more complete and immediate picture of the impact of regulatory events. Transcription factors recruit RNA polymerase II, effectively initiating the process of transcription; repressors inhibit polymerase recruitment. Efficiency of recruitment is dictated by sequence elements in and around the RNA polymerase loading zone. A combination of sequence elements and RNA binding proteins subsequently influence the ultimate stability of the resulting transcript. Some of these transcripts are capable of providing feedback on the process, influencing subsequent transcription. By monitoring RNA polymerase activity, nascent assays provide insights into every step of the regulated process of transcription.

The Five Dimensions of Parkinson's Disease Genetic Risk

Genome-wide association studies of Parkinson's disease have revealed polymorphic variants associated with closely mapped genes of interest. We propose here that those genes may only represent the tip of an iceberg of regulatory effects and do not necessary reflect disease relevance. To usefully interpret a risk locus, one needs to consider 5 dimensions of information, which represent the three-dimensional structure of chromatin (dimensions #1- 3), which is locally variable across time (dimension #4), and, most importantly, dependent on cell type and context (dimension #5).

Coordinated transcriptional regulation by thyroid hormone and glucocorticoid interaction in adult mouse hippocampus-derived neuronal cells

The hippocampus is a well-known target of thyroid hormone (TH; e.g., 3,5,3'-triiodothyronine-T3) and glucocorticoid (GC; e.g., corticosterone-CORT) action. Despite evidence that TH and GC play critical roles in neural development and function, few studies have identified genes and patterns of gene regulation influenced by the interaction of these hormones at a genome-wide scale. In this study we investigated gene regulation by T3, CORT, and T3 + CORT in the mouse hippocampus-derived cell line HT-22. We treated cells with T3, CORT, or T3 + CORT for 4 hr before cell harvest and RNA isolation for microarray analysis. We identified 9 genes regulated by T3, 432 genes by CORT, and 412 genes by T3 + CORT. Among the 432 CORT-regulated genes, there were 203 genes that exhibited an altered CORT response in the presence of T3, suggesting that T3 plays a significant role in modulating CORT-regulated genes. We also found 80 genes synergistically induced, and 73 genes synergistically repressed by T3 + CORT treatment. We performed in silico analysis using publicly available mouse neuronal chromatin immunoprecipitation-sequencing datasets and identified a considerable number of synergistically regulated genes with TH receptor and GC receptor peaks mapping within 1 kb of chromatin marks indicative of hormone-responsive enhancer regions. Functional annotation clustering of synergistically regulated genes reveal the relevance of proteasomal-dependent degradation, neuroprotective effect of growth hormones, and neuroinflammatory responses as key pathways to how TH and GC may coordinately influence learning and memory. Taken together, our transcriptome data represents a promising exploratory dataset for further study of common molecular mechanisms behind synergistic TH and GC gene regulation, and identify specific genes and their role in processes mediated by cross-talk between the thyroid and stress axes in a mammalian hippocampal model system.

HACER: an atlas of human active enhancers to interpret regulatory variants

Recent studies have shown that disease-susceptibility variants frequently lie in cell-type-specific enhancer elements. To identify, interpret, and prioritize such risk variants, we must identify the enhancers active in disease-relevant cell types, their upstream transcription factor (TF) binding, and their downstream target genes. To address this need, we built HACER (http://bioinfo.vanderbilt.edu/AE/HACER/), an atlas of Human ACtive Enhancers to interpret Regulatory variants. The HACER atlas catalogues and annotates in-vivo transcribed cell-type-specific enhancers, as well as placing enhancers within transcriptional regulatory networks by integrating ENCODE TF ChIP-Seq and predicted/validated chromatin interaction data. We demonstrate the utility of HACER in (i) offering a mechanistic hypothesis to explain the association of SNP rs614367 with ER-positive breast cancer risk, (ii) exploring tumor-specific enhancers in selective MYC dysregulation and (iii) prioritizing/annotating non-coding regulatory regions targeting CCND1. HACER provides a valuable resource for studies of GWAS, non-coding variants, and enhancer-mediated regulation.

Exploring the genetic basis of human population differences in DNA methylation and their causal impact on immune gene regulation

BACKGROUND

DNA methylation is influenced by both environmental and genetic factors and is increasingly thought to affect variation in complex traits and diseases. Yet, the extent of ancestry-related differences in DNA methylation, their genetic determinants, and their respective causal impact on immune gene regulation remain elusive.

RESULTS

We report extensive population differences in DNA methylation between 156 individuals of African and European descent, detected in primary monocytes that are used as a model of a major innate immunity cell type. Most of these differences (~ 70%) are driven by DNA sequence variants nearby CpG sites, which account for ~ 60% of the variance in DNA methylation. We also identify several master regulators of DNA methylation variation in trans, including a regulatory hub nearby the transcription factor-encoding CTCF gene, which contributes markedly to ancestry-related differences in DNA methylation. Furthermore, we establish that variation in DNA methylation is associated with varying gene expression levels following mostly, but not exclusively, a canonical model of negative associations, particularly in enhancer regions. Specifically, we find that DNA methylation highly correlates with transcriptional activity of 811 and 230 genes, at the basal state and upon immune stimulation, respectively. Finally, using a Bayesian approach, we estimate causal mediation effects of DNA methylation on gene expression in ~ 20% of the studied cases, indicating that DNA methylation can play an active role in immune gene regulation.

CONCLUSION

Using a system-level approach, our study reveals substantial ancestry-related differences in DNA methylation and provides evidence for their causal impact on immune gene regulation.

FastSKAT: Sequence kernel association tests for very large sets of markers

The sequence kernel association test (SKAT) is widely used to test for associations between a phenotype and a set of genetic variants that are usually rare. Evaluating tail probabilities or quantiles of the null distribution for SKAT requires computing the eigenvalues of a matrix related to the genotype covariance between markers. Extracting the full set of eigenvalues of this matrix (an n × n matrix, for n subjects) has computational complexity proportional to n3 . As SKAT is often used when n > 10 4 , this step becomes a major bottleneck in its use in practice. We therefore propose fastSKAT, a new computationally inexpensive but accurate approximations to the tail probabilities, in which the k largest eigenvalues of a weighted genotype covariance matrix or the largest singular values of a weighted genotype matrix are extracted, and a single term based on the Satterthwaite approximation is used for the remaining eigenvalues. While the method is not particularly sensitive to the choice of k, we also describe how to choose its value, and show how fastSKAT can automatically alert users to the rare cases where the choice may affect results. As well as providing faster implementation of SKAT, the new method also enables entirely new applications of SKAT that were not possible before; we give examples grouping variants by topologically associating domains, and comparing chromosome-wide association by class of histone marker.

Nascent RNA sequencing analysis provides insights into enhancer-mediated gene regulation

BACKGROUND

Enhancers are distal cis-regulatory elements that control gene expression. Despite an increasing appreciation of the importance of enhancers in cellular function and disease, our knowledge of enhancer-regulated transcription is very limited. Nascent RNA sequencing technologies, such as global nuclear run-on sequencing (GRO-seq) and precision run-on sequencing (PRO-seq), not only provide a direct and reliable measurement of enhancer activity, but also allow for quantifying transcription of enhancers and target genes simultaneously, making these technologies extremely useful for exploring enhancer-mediated regulation.

RESULTS

Nascent RNA sequencing analysis (NRSA) provides a comprehensive view of enhancer-mediated gene regulation. NRSA not only outperforms existing methods for enhancer identification, but also enables annotation and quantification of active enhancers, and prediction of their target genes. Furthermore, NRSA identifies functionally important enhancers by integrating 1) nascent transcriptional changes in enhancers and their target genes and 2) binding profiles from regulator(s) of interest. Applied to wildtype and histone deacetylase 3 (Hdac3) knockout mouse livers, NRSA showed that HDAC3 regulates RNA polymerase recruitment through both proximal (promoter) and distal (enhancer) regulatory elements. Integrating ChIP-seq with PRO-seq data, NRSA prioritized enhancers based on their potential contribution to mediating HDAC3 regulation.

CONCLUSIONS

NRSA will greatly facilitate the usage of nascent RNA sequencing techniques and accelerate the study of enhancer-mediated regulation.

The accessible chromatin landscape during conversion of human embryonic stem cells to trophoblast by bone morphogenetic protein 4

Human embryonic stem cells (hESCs) exposed to the growth factor bone morphogenetic protein 4 (BMP4) in the absence of FGF2 have been used as a model to study the development of placental development. However, little is known about the cis-regulatory mechanisms underlying this important process. In this study, we used the public available chromatin accessibility data of hESC H1 cells and BMP4-induced trophoblast (TB) cell lines to identify DNase I hypersensitive sites (DHSs) in the two cell lines, as well as the transcription factor (TF) binding sites within the DHSs. By comparing read profiles in H1 and TB, we identified 17 472 TB-specific DHSs. The TB-specific DHSs are enriched in terms of "blood vessel" and "trophectoderm," consisting of TF motifs family: Leucine Zipper, Helix-Loop-Helix, GATA, and ETS. To validate differential expression of the TFs binding to these motifs, we analyzed public available RNA-seq and microarray data in the same context. Finally, by integrating the protein-protein interaction data, we constructed a TF network for placenta development and identified top 20 key TFs through centrality analysis in the network. Our results indicate BMP4-induced TB system provided an invaluable model for the study of TB development and highlighted novel candidate genes in placenta development in human.

Identification of cis-regulatory elements by chromatin structure

The systematic identification of cis-regulatory elements (CREs) in plant genomes is critically important in understanding transcriptional regulation during development and in response to environmental cues. Several genome-wide structure-based methods have been successfully applied to plant genomes in the past few years. Here, we review recent results on the identification and characterization of CREs in multiple plant species and in different biological processes and discuss future applications of chromatin accessibility data to understand the mechanism, function and evolution of transcriptional regulation networks.

Enhancer RNA profiling predicts transcription factor activity

Transcription factors (TFs) exert their regulatory influence through the binding of enhancers, resulting in coordination of gene expression programs. Active enhancers are often characterized by the presence of short, unstable transcripts termed enhancer RNAs (eRNAs). While their function remains unclear, we demonstrate that eRNAs are a powerful readout of TF activity. We infer sites of eRNA origination across hundreds of publicly available nascent transcription data sets and show that eRNAs initiate from sites of TF binding. By quantifying the colocalization of TF binding motif instances and eRNA origins, we derive a simple statistic capable of inferring TF activity. In doing so, we uncover dozens of previously unexplored links between diverse stimuli and the TFs they affect.

BRG1 interacts with SOX10 to establish the melanocyte lineage and to promote differentiation

Mutations in SOX10 cause neurocristopathies which display varying degrees of hypopigmentation. Using a sensitized mutagenesis screen, we identified Smarca4 as a modifier gene that exacerbates the phenotypic severity of Sox10 haplo-insufficient mice. Conditional deletion of Smarca4 in SOX10 expressing cells resulted in reduced numbers of cranial and ventral trunk melanoblasts. To define the requirement for the Smarca4 -encoded BRG1 subunit of the SWI/SNF chromatin remodeling complex, we employed in vitro models of melanocyte differentiation in which induction of melanocyte-specific gene expression is closely linked to chromatin alterations. We found that BRG1 was required for expression of Dct, Tyrp1 and Tyr, genes that are regulated by SOX10 and MITF and for chromatin remodeling at distal and proximal regulatory sites. SOX10 was found to physically interact with BRG1 in differentiating melanocytes and binding of SOX10 to the Tyrp1 distal enhancer temporally coincided with recruitment of BRG1. Our data show that SOX10 cooperates with MITF to facilitate BRG1 binding to distal enhancers of melanocyte-specific genes. Thus, BRG1 is a SOX10 co-activator, required to establish the melanocyte lineage and promote expression of genes important for melanocyte function.