Relationship between histone modifications and transcription factor binding is protein family specific

DNA Methylation in the Adaptive Response to Exercise

Emerging evidence published over the past decade has highlighted the role of DNA methylation in skeletal muscle function and health, including as an epigenetic transducer of the adaptive response to exercise. In this review, we aim to synthesize the latest findings in this field to highlight: (1) the shifting understanding of the genomic localization of altered DNA methylation in response to acute and chronic aerobic and resistance exercise in skeletal muscle (e.g., promoter, gene bodies, enhancers, intergenic regions, un-annotated regions, and genome-wide methylation); (2) how these global/regional methylation changes relate to transcriptional activity following exercise; and (3) the factors (e.g., individual demographic or genetic features, dietary, training history, exercise parameters, local epigenetic characteristics, circulating hormones) demonstrated to alter both the pattern of DNA methylation after exercise, and the relationship between DNA methylation and gene expression. Finally, we discuss the changes in non-CpG methylation and 5-hydroxymethylation after exercise, as well as the importance of emerging single-cell analyses to future studies-areas of increasing focus in the field of epigenetics. We anticipate that this review will help generate a framework for clinicians and researchers to begin developing and testing exercise interventions designed to generate targeted changes in DNA methylation as part of a personalized exercise regimen.

ChIP-DIP: A multiplexed method for mapping hundreds of proteins to DNA uncovers diverse regulatory elements controlling gene expression

Gene expression is controlled by the dynamic localization of thousands of distinct regulatory proteins to precise regions of DNA. Understanding this cell-type specific process has been a goal of molecular biology for decades yet remains challenging because most current DNA-protein mapping methods study one protein at a time. To overcome this, we developed ChIP-DIP (ChIP Done In Parallel), a split-pool based method that enables simultaneous, genome-wide mapping of hundreds of diverse regulatory proteins in a single experiment. We demonstrate that ChIP-DIP generates highly accurate maps for all classes of DNA-associated proteins, including histone modifications, chromatin regulators, transcription factors, and RNA Polymerases. Using these data, we explore quantitative combinations of protein localization on genomic DNA to define distinct classes of regulatory elements and their functional activity. Our data demonstrate that ChIP-DIP enables the generation of 'consortium level', context-specific protein localization maps within any molecular biology lab.

G-quadruplex resolution: From molecular mechanisms to physiological relevance

Guanine-rich DNA sequences can fold into stable four-stranded structures called G-quadruplexes or G4s. Research in the past decade demonstrated that G4 structures are widespread in the genome and prevalent in regulatory regions of actively transcribed genes. The formation of G4s has been tightly linked to important biological processes including regulation of gene expression and genome maintenance. However, they can also pose a serious threat to genome integrity especially by impeding DNA replication, and G4-associated somatic mutations have been found accumulated in the cancer genomes. Specialised DNA helicases and single stranded DNA binding proteins that can resolve G4 structures play a crucial role in preventing genome instability. The large variety of G4 unfolding proteins suggest the presence of multiple G4 resolution mechanisms in cells. Recently, there has been considerable progress in our detailed understanding of how G4s are resolved, especially during DNA replication. In this review, we first discuss the current knowledge of the genomic G4 landscapes and the impact of G4 structures on DNA replication and genome integrity. We then describe the recent progress on the mechanisms that resolve G4 structures and their physiological relevance. Finally, we discuss therapeutic opportunities to target G4 structures.

Dynamic 3D genome reorganization during senescence: defining cell states through chromatin

Cellular senescence, a cell state characterized by growth arrest and insensitivity to growth stimulatory hormones, is accompanied by a massive change in chromatin organization. Senescence can be induced by a range of physiological signals and pathological stresses and was originally thought to be an irreversible state, implicated in normal development, wound healing, tumor suppression and aging. Recently cellular senescence was shown to be reversible in some cases, with exit being triggered by the modulation of the cell's transcriptional program by the four Yamanaka factors, the suppression of p53 or H3K9me3, PDK1, and/or depletion of AP-1. Coincident with senescence reversal are changes in chromatin organization, most notably the loss of senescence-associated heterochromatin foci (SAHF) found in oncogene-induced senescence. In addition to fixed-cell imaging, chromatin conformation capture and multi-omics have been used to examine chromatin reorganization at different spatial resolutions during senescence. They identify determinants of SAHF formation and other key features that differentiate distinct types of senescence. Not surprisingly, multiple factors, including the time of induction, the type of stress experienced, and the type of cell involved, influence the global reorganization of chromatin in senescence. Here we discuss how changes in the three-dimensional organization of the genome contribute to the regulation of transcription at different stages of senescence. In particular, the distinct contributions of heterochromatin- and lamina-mediated interactions, changes in gene expression, and other cellular control mechanisms are discussed. We propose that high-resolution temporal and spatial analyses of the chromatin landscape during senescence will identify early markers of the different senescence states to help guide clinical diagnosis.

Rewired Metabolism Caused by the Oncogenic Deregulation of MYC as an Attractive Therapeutic Target in Cancers

MYC is one of the most deregulated oncogenes on multiple levels in cancer. As a node transcription factor, MYC plays a diverse regulatory role in many cellular processes, including cell cycle and metabolism, both in physiological and pathological conditions. The relentless growth and proliferation of tumor cells lead to an insatiable demand for energy and nutrients, which requires the rewiring of cellular metabolism. As MYC can orchestrate all aspects of cellular metabolism, its altered regulation plays a central role in these processes, such as the Warburg effect, and is a well-established hallmark of cancer development. However, our current knowledge of MYC suggests that its spatial- and concentration-dependent contribution to tumorigenesis depends more on changes in the global or relative expression of target genes. As the direct targeting of MYC is proven to be challenging due to its relatively high toxicity, understanding its underlying regulatory mechanisms is essential for the development of tumor-selective targeted therapies. The aim of this review is to comprehensively summarize the diverse forms of MYC oncogenic deregulation, including DNA-, transcriptional- and post-translational level alterations, and their consequences for cellular metabolism. Furthermore, we also review the currently available and potentially attractive therapeutic options that exploit the vulnerability arising from the metabolic rearrangement of MYC-driven tumors.

Genomic and epigenomic determinants of heat stress-induced transcriptional memory in Arabidopsis

BACKGROUND

Transcriptional regulation is a key aspect of environmental stress responses. Heat stress induces transcriptional memory, i.e., sustained induction or enhanced re-induction of transcription, that allows plants to respond more efficiently to a recurrent HS. In light of more frequent temperature extremes due to climate change, improving heat tolerance in crop plants is an important breeding goal. However, not all heat stress-inducible genes show transcriptional memory, and it is unclear what distinguishes memory from non-memory genes. To address this issue and understand the genome and epigenome architecture of transcriptional memory after heat stress, we identify the global target genes of two key memory heat shock transcription factors, HSFA2 and HSFA3, using time course ChIP-seq.

RESULTS

HSFA2 and HSFA3 show near identical binding patterns. In vitro and in vivo binding strength is highly correlated, indicating the importance of DNA sequence elements. In particular, genes with transcriptional memory are strongly enriched for a tripartite heat shock element, and are hallmarked by several features: low expression levels in the absence of heat stress, accessible chromatin environment, and heat stress-induced enrichment of H3K4 trimethylation. These results are confirmed by an orthogonal transcriptomic data set using both de novo clustering and an established definition of memory genes.

CONCLUSIONS

Our findings provide an integrated view of HSF-dependent transcriptional memory and shed light on its sequence and chromatin determinants, enabling the prediction and engineering of genes with transcriptional memory behavior.

Prediction of mammalian tissue-specific CLOCK-BMAL1 binding to E-box DNA motifs

The Brain and Muscle ARNTL-Like 1 protein (BMAL1) forms a heterodimer with either Circadian Locomotor Output Cycles Kaput (CLOCK) or Neuronal PAS domain protein 2 (NPAS2) to act as a master regulator of the mammalian circadian clock gene network. The dimer binds to E-box gene regulatory elements on DNA, activating downstream transcription of clock genes. Identification of transcription factor binding sites and genomic features that correlate to DNA binding by BMAL1 is a challenging problem, given that CLOCK-BMAL1 or NPAS2-BMAL1 bind to several distinct binding motifs (CANNTG) on DNA. Using three different types of tissue-specific machine learning models with features based on (1) DNA sequence, (2) DNA sequence plus DNA shape, and (3) DNA sequence and shape plus histone modifications, we developed an interpretable predictive model of genome-wide BMAL1 binding to E-box motifs and dissected the mechanisms underlying BMAL1-DNA binding. Our results indicated that histone modifications, the local shape of the DNA, and the flanking sequence of the E-box motif are sufficient predictive features for BMAL1-DNA binding. Our models also provide mechanistic insights into tissue specificity of DNA binding by BMAL1.

A multi-omics integrative analysis based on CRISPR screens re-defines the pluripotency regulatory network in ESCs

A comprehensive and precise definition of the pluripotency gene regulatory network (PGRN) is crucial for clarifying the regulatory mechanisms in embryonic stem cells (ESCs). Here, after a CRISPR/Cas9-based functional genomics screen and integrative analysis with other functional genomes, transcriptomes, proteomes and epigenome data, an expanded pluripotency-associated gene set is obtained, and a new PGRN with nine sub-classes is constructed. By integrating the DNA binding, epigenetic modification, chromatin conformation, and RNA expression profiles, the PGRN is resolved to six functionally independent transcriptional modules (CORE, MYC, PAF, PRC, PCGF and TBX). Spatiotemporal transcriptomics reveal activated CORE/MYC/PAF module activity and repressed PRC/PCGF/TBX module activity in both mouse ESCs (mESCs) and pluripotent cells of early embryos. Moreover, this module activity pattern is found to be shared by human ESCs (hESCs) and cancers. Thus, our results provide novel insights into elucidating the molecular basis of ESC pluripotency.

On the incongruence of genotype-phenotype and fitness landscapes

The mapping from genotype to phenotype to fitness typically involves multiple nonlinearities that can transform the effects of mutations. For example, mutations may contribute additively to a phenotype, but their effects on fitness may combine non-additively because selection favors a low or intermediate value of that phenotype. This can cause incongruence between the topographical properties of a fitness landscape and its underlying genotype-phenotype landscape. Yet, genotype-phenotype landscapes are often used as a proxy for fitness landscapes to study the dynamics and predictability of evolution. Here, we use theoretical models and empirical data on transcription factor-DNA interactions to systematically study the incongruence of genotype-phenotype and fitness landscapes when selection favors a low or intermediate phenotypic value. Using the theoretical models, we prove a number of fundamental results. For example, selection for low or intermediate phenotypic values does not change simple sign epistasis into reciprocal sign epistasis, implying that genotype-phenotype landscapes with only simple sign epistasis motifs will always give rise to single-peaked fitness landscapes under such selection. More broadly, we show that such selection tends to create fitness landscapes that are more rugged than the underlying genotype-phenotype landscape, but this increased ruggedness typically does not frustrate adaptive evolution because the local adaptive peaks in the fitness landscape tend to be nearly as tall as the global peak. Many of these results carry forward to the empirical genotype-phenotype landscapes, which may help to explain why low- and intermediate-affinity transcription factor-DNA interactions are so prevalent in eukaryotic gene regulation.

How do mutations change phenotypic traits and organismal fitness? This question is often addressed in the context of a classic metaphor of evolutionary theory—the fitness landscape. A fitness landscape is akin to a physical landscape, in which genotypes define spatial coordinates, and fitness defines the elevation of each coordinate. Evolution then acts like a hill-climbing process, in which populations ascend fitness peaks as a consequence of mutation and selection. It is becoming increasingly common to construct such landscapes using experimental data from high-throughput sequencing technologies and phenotypic assays, in systems such as macromolecules and gene regulatory circuits. Although these landscapes are typically defined by molecular phenotypes, and are therefore more appropriately referred to as genotype-phenotype landscapes, they are often used to study evolutionary dynamics. This requires the assumption that the molecular phenotype is a reasonable proxy for fitness, which need not be the case. For example, selection may favor a low or intermediate phenotypic value, causing incongruence between a fitness landscape and its underlying genotype-phenotype landscape. Here, we study such incongruence using a diversity of theoretical models and experimental data from gene regulatory systems. We regularly find incongruence, in that fitness landscapes tend to comprise more peaks than their underlying genotype-phenotype landscapes. However, using evolutionary simulations, we show that this increased ruggedness need not impede adaptation.

RNA polymerase efficiently transcribes DNA-scaffolded, cooperative bacteriophage repressor complexes

DNA can act as a scaffold for the cooperative binding of protein oligomers. For example, the phage 186 CI repressor forms a wheel of seven dimers wrapped in DNA with specific binding sites, while phage λ CI repressor dimers bind to two well-separated sets of operators, forming a DNA loop. Atomic force microscopy was used to measure transcription elongation by E. coli RNA polymerase through these protein complexes. 186 CI, or λ CI, bound along unlooped DNA negligibly interfered with transcription by RNAP. Wrapped and looped topologies induced by these scaffolded, cooperatively bound repressor oligomers did not form significantly better roadblocks to transcription. Thus, despite binding with high affinity, these repressors are not effective roadblocks to transcription.

A novel convolution attention model for predicting transcription factor binding sites by combination of sequence and shape

The discovery of putative transcription factor binding sites (TFBSs) is important for understanding the underlying binding mechanism and cellular functions. Recently, many computational methods have been proposed to jointly account for DNA sequence and shape properties in TFBSs prediction. However, these methods fail to fully utilize the latent features derived from both sequence and shape profiles and have limitation in interpretability and knowledge discovery. To this end, we present a novel Deep Convolution Attention network combining Sequence and Shape, dubbed as D-SSCA, for precisely predicting putative TFBSs. Experiments conducted on 165 ENCODE ChIP-seq datasets reveal that D-SSCA significantly outperforms several state-of-the-art methods in predicting TFBSs, and justify the utility of channel attention module for feature refinements. Besides, the thorough analysis about the contribution of five shapes to TFBSs prediction demonstrates that shape features can improve the predictive power for transcription factors-DNA binding. Furthermore, D-SSCA can realize the cross-cell line prediction of TFBSs, indicating the occupancy of common interplay patterns concerning both sequence and shape across various cell lines. The source code of D-SSCA can be found at https://github.com/MoonLord0525/.

iRegNet: an integrative Regulatory Network analysis tool for Arabidopsis thaliana

Gene expression is delicately controlled via multilayered genetic and/or epigenetic regulatory mechanisms. Rapid development of the high-throughput sequencing (HTS) technology and its derivative methods including chromatin immunoprecipitation sequencing (ChIP-seq) and DNA affinity purification sequencing (DAP-seq) have generated a large volume of data on DNA-protein interactions (DPIs) and histone modifications on a genome-wide scale. However, the ability to comprehensively retrieve empirically validated upstream regulatory networks of genes of interest (GOIs) and genomic regions of interest (ROIs) remains limited. Here, we present integrative Regulatory Network (iRegNet), a web application that analyzes the upstream regulatory network for user-queried GOIs or ROIs in the Arabidopsis (Arabidopsis thaliana) genome. iRegNet covers the largest empirically proven DNA-binding profiles of Arabidopsis transcription factors (TFs) and non-TF proteins, and histone modifications obtained from all currently available Arabidopsis ChIP-seq and DAP-seq data. iRegNet not only catalogs upstream regulomes and epigenetic chromatin states for single-query gene/genomic region but also suggests significantly overrepresented upstream genetic regulators and epigenetic chromatin states of user-submitted multiple query genes/genomic regions. Furthermore, gene-to-gene coexpression index and protein-protein interaction information were also integrated into iRegNet for a more reliable identification of upstream regulators and realistic regulatory networks. Thus, iRegNet will help discover upstream regulators as well as molecular regulatory networks of GOI(s) and/or ROI(s), and is freely available at http://chromatindynamics.snu.ac.kr:8082/iRegNet_main.

Transcription factors in neurodevelopmental and associated psychiatric disorders: A potential convergence for genetic and environmental risk factors

Neurodevelopmental disorders (NDDs) are a heterogeneous and highly prevalent group of psychiatric conditions marked by impairments in the nervous system. Their onset occurs during gestation, and the alterations are observed throughout the postnatal life. Although many genetic and environmental risk factors have been described in this context, the interactions between them challenge the understanding of the pathways associated with NDDs. Transcription factors (TFs)-a group of over 1,600 proteins that can interact with DNA, regulating gene expression through modulation of RNA synthesis-represent a point of convergence for different risk factors. In addition, TFs organize critical processes like angiogenesis, blood-brain barrier formation, myelination, neuronal migration, immune activation, and many others in a time and location-dependent way. In this review, we summarize important TF alterations in NDD and associated disorders, along with specific impairments observed in animal models, and, finally, establish hypotheses to explain how these proteins may be critical mediators in the context of genome-environment interactions.

H3K27Ac modification and gene expression in psoriasis

BACKGROUND

Numerous alterations in gene expression have been described in psoriatic lesions compared to uninvolved or healthy skin. However, the mechanisms which induce this altered expression remain unclear. Epigenetic modifications play a key role in regulating genes' expression. Only three studies compared the whole-genome DNA methylation of psoriasis versus healthy skin. The present is the first study of genome-wide comparison of histone modifications between psoriatic to healthy skins.

OBJECTIVE

Our objective was to explore the pattern of H3K27Ac modifications in psoriatic lesions compared to uninvolved psoriatic and healthy skin, in order to identify new genes involved in the pathogenesis of psoriasis.

METHOD

Using ChIP-seq with anti H3K27Ac we compared the acetylation of lysine 27 on histone 3 (H3K27Ac) modification between psoriatic to healthy skins, combined with mRNA array.

RESULTS

We found a differential H3K27Ac pattern between psoriatic compared to uninvolved or healthy skins. We found that many of the overexpressed and H3K27Ac enriched genes in psoriasis, harbor a putative GRHL transcription factor-binding site.

CONCLUSIONS

In the most overexpressed genes in psoriasis, there is an enrichment of H3K27Ac. However, the loss of H3K27 acetylation modification does not correlate with decreased gene expression. GRHL appears to play an important role in the pathogenesis of psoriasis and therefore, might be a new target for psoriasis therapeutics.

Genetic Variants in Transcription Factor Binding Sites in Humans: Triggered by Natural Selection and Triggers of Diseases

Variants of transcription factor binding sites (TFBSs) constitute an important part of the human genome. Current evidence demonstrates close links between nucleotides within TFBSs and gene expression. There are multiple pathways through which genomic sequences located in TFBSs regulate gene expression, and recent genome-wide association studies have shown the biological significance of TFBS variation in human phenotypes. However, numerous challenges remain in the study of TFBS polymorphisms. This article aims to cover the current state of understanding as regards the genomic features of TFBSs and TFBS variants; the mechanisms through which TFBS variants regulate gene expression; the approaches to studying the effects of nucleotide changes that create or disrupt TFBSs; the challenges faced in studies of TFBS sequence variations; the effects of natural selection on collections of TFBSs; in addition to the insights gained from the study of TFBS alleles related to gout, its associated comorbidities (increased body mass index, chronic kidney disease, diabetes, dyslipidemia, coronary artery disease, ischemic heart disease, hypertension, hyperuricemia, osteoporosis, and prostate cancer), and the treatment responses of patients.

ΔNp63 is a pioneer factor that binds inaccessible chromatin and elicits chromatin remodeling

BACKGROUND

ΔNp63 is a master transcriptional regulator playing critical roles in epidermal development and other cellular processes. Recent studies suggest that ΔNp63 functions as a pioneer factor that can target its binding sites within inaccessible chromatin and induce chromatin remodeling.

METHODS

In order to examine if ΔNp63 can bind to inaccessible chromatin and to determine if specific histone modifications are required for binding, we induced ΔNp63 expression in two p63-naïve cell lines. ΔNp63 binding was then examined by ChIP-seq and the chromatin at ΔNp63 targets sites was examined before and after binding. Further analysis with competitive nucleosome binding assays was used to determine how ΔNp63 directly interacts with nucleosomes.

RESULTS

Our results show that before ΔNp63 binding, targeted sites lack histone modifications, indicating ΔNp63's capability to bind at unmodified chromatin. Moreover, the majority of the sites that are bound by ectopic ΔNp63 expression exist in an inaccessible state. Once bound, ΔNp63 induces acetylation of the histone and the repositioning of nucleosomes at its binding sites. Further analysis with competitive nucleosome binding assays reveal that ΔNp63 can bind directly to nucleosome edges with significant binding inhibition occurring within 50 bp of the nucleosome dyad.

CONCLUSION

Overall, our results demonstrate that ΔNp63 is a pioneer factor that binds nucleosome edges at inaccessible and unmodified chromatin sites and induces histone acetylation and nucleosome repositioning.

CpG content-dependent associations between transcription factors and histone modifications

Understanding the factors that underlie the epigenetic regulation of genes is crucial to understand the gene regulatory machinery as a whole. Several experimental and computational studies examined the relationship between different factors involved. Here we investigate the relationship between transcription factors (TFs) and histone modifications (HMs), based on ChIP-seq data in cell lines. As it was shown that gene regulation by TFs differs depending on the CpG class of a promoter, we study the impact of the CpG content in promoters on the associations between TFs and HMs. We suggest an approach based on sparse linear regression models to infer associations between TFs and HMs with respect to CpG content. A study of the partial correlation of HMs for the two classes of high and low CpG content reveals possible CpG dependence and potential candidates for confounding factors in our models. We show that the models are accurate, inferred associations reflect known biological relationships, and we give new insight into associations with respect to CpG content. Moreover, analysis of a ChIP-seq dataset in HepG2 cells of the HM H3K122ac, an HM about little is known, reveals novel TF associations and supports a previously established link to active transcription.

Integrated requirement of non-specific and sequence-specific DNA binding in Myc-driven transcription

Eukaryotic transcription factors recognize specific DNA sequence motifs, but are also endowed with generic, non-specific DNA-binding activity. How these binding modes are integrated to determine select transcriptional outputs remains unresolved. We addressed this question by site-directed mutagenesis of the Myc transcription factor. Impairment of non-specific DNA backbone contacts caused pervasive loss of genome interactions and gene regulation, associated with increased intra-nuclear mobility of the Myc protein in murine cells. In contrast, a mutant lacking base-specific contacts retained DNA-binding and mobility profiles comparable to those of the wild-type protein, but failed to recognize its consensus binding motif (E-box) and could not activate Myc-target genes. Incidentally, this mutant gained weak affinity for an alternative motif, driving aberrant activation of different genes. Altogether, our data show that non-specific DNA binding is required to engage onto genomic regulatory regions; sequence recognition in turn contributes to transcriptional activation, acting at distinct levels: stabilization and positioning of Myc onto DNA, and-unexpectedly-promotion of its transcriptional activity. Hence, seemingly pervasive genome interaction profiles, as detected by ChIP-seq, actually encompass diverse DNA-binding modalities, driving defined, sequence-dependent transcriptional responses.

An interpretable bimodal neural network characterizes the sequence and preexisting chromatin predictors of induced transcription factor binding

BACKGROUND

Transcription factor (TF) binding specificity is determined via a complex interplay between the transcription factor's DNA binding preference and cell type-specific chromatin environments. The chromatin features that correlate with transcription factor binding in a given cell type have been well characterized. For instance, the binding sites for a majority of transcription factors display concurrent chromatin accessibility. However, concurrent chromatin features reflect the binding activities of the transcription factor itself and thus provide limited insight into how genome-wide TF-DNA binding patterns became established in the first place. To understand the determinants of transcription factor binding specificity, we therefore need to examine how newly activated transcription factors interact with sequence and preexisting chromatin landscapes.

RESULTS

Here, we investigate the sequence and preexisting chromatin predictors of TF-DNA binding by examining the genome-wide occupancy of transcription factors that have been induced in well-characterized chromatin environments. We develop Bichrom, a bimodal neural network that jointly models sequence and preexisting chromatin data to interpret the genome-wide binding patterns of induced transcription factors. We find that the preexisting chromatin landscape is a differential global predictor of TF-DNA binding; incorporating preexisting chromatin features improves our ability to explain the binding specificity of some transcription factors substantially, but not others. Furthermore, by analyzing site-level predictors, we show that transcription factor binding in previously inaccessible chromatin tends to correspond to the presence of more favorable cognate DNA sequences.

CONCLUSIONS

Bichrom thus provides a framework for modeling, interpreting, and visualizing the joint sequence and chromatin landscapes that determine TF-DNA binding dynamics.

Genome-wide discovery of OsHOX24-binding sites and regulation of desiccation stress response in rice

OsHOX24 mediates regulation of desiccation stress response via complex regulatory network as indicated by its binding to several target genes including transcription factors in rice. HD-ZIP I subfamily of homeobox transcription factors (TFs) are involved in abiotic stress responses and plant development. Previously, we demonstrated the role of OsHOX24, a member of HD-ZIP I subfamily, in abiotic stress responses. In this study, we identified downstream targets of OsHOX24 under control and desiccation stress conditions via chromatin immunoprecipitation-sequencing (ChIP-seq) approach in wild-type and OsHOX24 over-expression transgenic in rice. OsHOX24-binding sites in each sample and differential binding sites between the samples were detected at various genomic locations, including genic and intergenic regions. Gene ontology enrichment analysis revealed that OsHOX24 direct target genes were involved in several biological processes, including plant development, ABA-mediated signalling pathway, ubiquitin-dependent protein catabolic process, ion transport, abiotic and biotic stress responses besides transcriptional and translational regulation. The enrichment of several cis-regulatory motifs representing binding sites of other TFs, such as ABFs, ERF1, MYB1, LTREs and SORLIP2, suggested the involvement of OsHOX24 in a complex regulatory network. These findings indicate that OsHOX24-mediated desiccation stress regulation involves modulation of a plethora of target genes, which participate in diverse pathways in rice.

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.

Landscape of DNA binding signatures of myocyte enhancer factor-2B reveals a unique interplay of base and shape readout

Myocyte enhancer factor-2B (MEF2B) has the unique capability of binding to its DNA target sites with a degenerate motif, while still functioning as a gene-specific transcriptional regulator. Identifying its DNA targets is crucial given regulatory roles exerted by members of the MEF2 family and MEF2B's involvement in B-cell lymphoma. Analyzing structural data and SELEX-seq experimental results, we deduced the DNA sequence and shape determinants of MEF2B target sites on a high-throughput basis in vitro for wild-type and mutant proteins. Quantitative modeling of MEF2B binding affinities and computational simulations exposed the DNA readout mechanisms of MEF2B. The resulting binding signature of MEF2B revealed distinct intricacies of DNA recognition compared to other transcription factors. MEF2B uses base readout at its half-sites combined with shape readout at the center of its degenerate motif, where A-tract polarity dictates nuances of binding. The predominant role of shape readout at the center of the core motif, with most contacts formed in the minor groove, differs from previously observed protein-DNA readout modes. MEF2B, therefore, represents a unique protein for studies of the role of DNA shape in achieving binding specificity. MEF2B-DNA recognition mechanisms are likely representative for other members of the MEF2 family.

Cancer Treatment: An Epigenetic View

Cancer can be identified as an uncontrolled growth and reproduction of cell. Accumulation of genetic aberrations (mutations of oncogenes and tumor-suppressor genes and epigenetic modifications) is one of the characteristics of cancer cell. Increasing number of studies highlighted importance of the epigenetic alterations in cancer treatment and prognosis. Now, cancer epigenetics have a huge importance for developing novel biomarkers and therapeutic target for cancer. In this review, we will provide a summary of the major epigenetic changes involved in cancer and preclinical results of epigenetic therapeutics.

Sequence and chromatin determinants of transcription factor binding and the establishment of cell type-specific binding patterns

Transcription factors (TFs) selectively bind distinct sets of sites in different cell types. Such cell type-specific binding specificity is expected to result from interplay between the TF's intrinsic sequence preferences, cooperative interactions with other regulatory proteins, and cell type-specific chromatin landscapes. Cell type-specific TF binding events are highly correlated with patterns of chromatin accessibility and active histone modifications in the same cell type. However, since concurrent chromatin may itself be a consequence of TF binding, chromatin landscapes measured prior to TF activation provide more useful insights into how cell type-specific TF binding events became established in the first place. Here, we review the various sequence and chromatin determinants of cell type-specific TF binding specificity. We identify the current challenges and opportunities associated with computational approaches to characterizing, imputing, and predicting cell type-specific TF binding patterns. We further focus on studies that characterize TF binding in dynamic regulatory settings, and we discuss how these studies are leading to a more complex and nuanced understanding of dynamic protein-DNA binding activities. We propose that TF binding activities at individual sites can be viewed along a two-dimensional continuum of local sequence and chromatin context. Under this view, cell type-specific TF binding activities may result from either strongly favorable sequence features or strongly favorable chromatin context.

Genomic resources for dissecting the role of non-protein coding variation in gene-environment interactions

The majority of single nucleotide variants (SNVs) identified in Genome Wide Association Studies (GWAS) fall within non-protein coding DNA and have the potential to alter gene expression. Non-protein coding DNA can control gene expression by acting as transcription factor (TF) binding sites or by regulating the organization of DNA into chromatin. SNVs in non-coding DNA sequences can disrupt TF binding and chromatin structure and this can result in pathology. Further, environmental health studies have shown that exposure to xenobiotics can disrupt the ability of TFs to regulate entire gene networks and result in pathology. However, there is a large amount of interindividual variability in exposure-linked health outcomes. One explanation for this heterogeneity is that genetic variation and exposure combine to disrupt gene regulation, and this eventually manifests in disease. Many resources exist that annotate common variants from GWAS and combine them with conservation, functional genomics, and TF binding data. These annotation tools provide clues regarding the biological implications of an SNV, as well as lead to the generation of hypotheses regarding potentially disrupted target genes, epigenetic markers, pathways, and cell types. Collectively this information can be used to predict how SNVs can alter an individual's response to exposure and disease risk. A basic understanding of the regulatory information contained within non-protein coding DNA is needed to predict the biological consequences of SNVs, and to determine how these SNVs impact exposure-related disease. We hope that this review will aid in the characterization of disease-associated genetic variation in the non-protein coding genome.

A preliminary computational outputs versus experimental results: Application of sTRAP, a biophysical tool for the analysis of SNPs of transcription factor-binding sites

Background

In the human genome, the transcription factors (TFs) and transcription factor‐binding sites (TFBSs) network has a great regulatory function in the biological pathways. Such crosstalk might be affected by the single‐nucleotide polymorphisms (SNPs), which could create or disrupt a TFBS, leading to either a disease or a phenotypic defect. Many computational resources have been introduced to predict the TFs binding variations due to SNPs inside TFBSs, sTRAP being one of them.

Methods

A literature review was performed and the experimental data for 18 TFBSs located in 12 genes was provided. The sequences of TFBS motifs were extracted using two different strategies; in the size similar with synthetic target sites used in the experimental techniques, and with 60 bp upstream and downstream of the SNPs. The sTRAP (http://trap.molgen.mpg.de/cgi-bin/trap_two_seq_form.cgi) was applied to compute the binding affinity scores of their cognate TFs in the context of reference and mutant sequences of TFBSs. The alternative bioinformatics model used in this study was regulatory analysis of variation in enhancers (RAVEN; http://www.cisreg.ca/cgi-bin/RAVEN/a). The bioinformatics outputs of our study were compared with experimental data, electrophoretic mobility shift assay (EMSA).

Results

In 6 out of 18 TFBSs in the following genes COL1A1, Hb ḉᴪ, TF, FIX, MBL2, NOS2A, the outputs of sTRAP were inconsistent with the results of EMSA. Furthermore, no p value of the difference between the two scores of binding affinity under the wild and mutant conditions of TFBSs was presented. Nor, were any criteria for preference or selection of any of the measurements of different matrices used for the same analysis.

Conclusion

Our preliminary study indicated some paradoxical results between sTRAP and experimental data. However, to link the data of sTRAP to the biological functions, its optimization via experimental procedures with the integration of expanded data and applying several other bioinformatics tools might be required.

Transcription Factor Binding in Embryonic Stem Cells Is Constrained by DNA Sequence Repeat Symmetry

Transcription factor (TF) recognition is dictated by the underlying DNA motif sequence specific for each TF. Here, we reveal that DNA sequence repeat symmetry plays a central role in defining TF-DNA-binding preferences. In particular, we find that different TFs bind similar symmetry patterns in the context of different developmental layers. Most TFs possess dominant preferences for similar DNA repeat symmetry types. However, in some cases, preferences of specific TFs are changed during differentiation, suggesting the importance of information encoded outside of known motif regions. Histone modifications also exhibit strong preferences for similar DNA repeat symmetry patterns unique to each type of modification. Next, using an in vivo reporter assay, we show that gene expression in embryonic stem cells can be positively modulated by the presence of genomic and computationally designed DNA oligonucleotides containing identified nonconsensus-repetitive sequence elements. This supports the hypothesis that certain nonconsensus-repetitive patterns possess a functional ability to regulate gene expression. We also performed a solution NMR experiment to probe the stability of double-stranded DNA via imino proton resonances for several double-stranded DNA sequences characterized by different repetitive patterns. We suggest that such local stability might play a key role in determining TF-DNA binding preferences. Overall, our findings show that despite the enormous sequence complexity of the TF-DNA binding landscape in differentiating embryonic stem cells, this landscape can be quantitatively characterized in simple terms using the notion of DNA sequence repeat symmetry.

Identification of DNA motifs that regulate DNA methylation

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

Intrinsically disordered proteins and phenotypic switching: Implications in cancer

It is now well established that intrinsically disordered proteins (IDPs) that constitute a large part of the proteome across the three kingdoms, play critical roles in several biological processes including phenotypic switching. However, dysregulated expression of IDPs that engage in promiscuous interactions can lead to pathological states. In this chapter, using cancer as a paradigm, we discuss how IDP conformational dynamics and the resultant conformational noise can modulate phenotypic switching. Thus, contrary to the prevailing wisdom that phenotypic switching is highly deterministic (has a genetic underpinning) in cancer, emerging evidence suggests that non-genetic mechanisms, at least in part due to the conformational noise, may also be a confounding factor in phenotypic switching.

Identifying Transcriptional Regulatory Modules Among Different Chromatin States in Mouse Neural Stem Cells

Gene expression regulation is a complex process involving the interplay between transcription factors and chromatin states. Significant progress has been made toward understanding the impact of chromatin states on gene expression. Nevertheless, the mechanism of transcription factors binding combinatorially in different chromatin states to enable selective regulation of gene expression remains an interesting research area. We introduce a nonparametric Bayesian clustering method for inhomogeneous Poisson processes to detect heterogeneous binding patterns of multiple proteins including transcription factors to form regulatory modules in different chromatin states. We applied this approach on ChIP-seq data for mouse neural stem cells containing 21 proteins and observed different groups or modules of proteins clustered within different chromatin states. These chromatin-state-specific regulatory modules were found to have significant influence on gene expression. We also observed different motif preferences for certain TFs between different chromatin states. Our results reveal a degree of interdependency between chromatin states and combinatorial binding of proteins in the complex transcriptional regulatory process. The software package is available on Github at - https://github.com/BSharmi/DPM-LGCP.

Histone H3 Serine 10 Phosphorylation Facilitates Endothelial Activation in Diabetic Kidney Disease

The posttranslational histone modifications that epigenetically affect gene transcription extend beyond conventionally studied methylation and acetylation patterns. By examining the means by which podocytes influence the glomerular endothelial phenotype, we identified a role for phosphorylation of histone H3 on serine residue 10 (phospho-histone H3Ser10) in mediating endothelial activation in diabetes. Culture media conditioned by podocytes exposed to high glucose caused glomerular endothelial vascular cell adhesion protein 1 (VCAM-1) upregulation and was enriched for the chemokine CCL2. A neutralizing anti-CCL2 antibody prevented VCAM-1 upregulation in cultured glomerular endothelial cells, and knockout of the CCL2 receptor CCR2 diminished glomerular VCAM-1 upregulation in diabetic mice. CCL2/CCR2 signaling induced glomerular endothelial VCAM-1 upregulation through a pathway regulated by p38 mitogen-activated protein kinase, mitogen- and stress-activated protein kinases 1/2 (MSK1/2), and phosphorylation of H3Ser10, whereas MSK1/2 inhibition decreased H3Ser10 phosphorylation at the VCAM1 promoter. Finally, increased phospho-histone H3Ser10 levels were observed in the kidneys of diabetic endothelial nitric oxide synthase knockout mice and in the glomeruli of humans with diabetic kidney disease. These findings demonstrate the influence that histone protein phosphorylation may have on gene activation in diabetic kidney disease. Histone protein phosphorylation should be borne in mind when considering epigenetic targets amenable to therapeutic manipulation in diabetes.

A distinct class of antioxidant response elements is consistently activated in tumors with NRF2 mutations

NRF2 is a redox-responsive transcription factor that regulates expression of cytoprotective genes via its interaction with DNA sequences known as antioxidant response elements (AREs). NRF2 activity is induced by oxidative stress, but oxidative stress is not the only context in which NRF2 can be activated. Mutations that disrupt the interaction between NRF2 and KEAP1, an inhibitor of NRF2, lead to NRF2 hyperactivation and promote oncogenesis. The mechanisms underlying NRF2's oncogenic properties remain unclear, but likely involve aberrant expression of select NRF2 target genes. We tested this possibility using an integrative genomics approach to get a precise view of the direct NRF2 target genes dysregulated in tumors with NRF2 hyperactivating mutations. This approach revealed a core set of 32 direct NRF2 targets that are consistently upregulated in NRF2 hyperactivated tumors. This set of NRF2 "cancer target genes" includes canonical redox-related NRF2 targets, as well as target genes that have not been previously linked to NRF2 activation. Importantly, NRF2-driven upregulation of this gene set is largely independent of the organ system where the tumor developed. One key distinguishing feature of these NRF2 cancer target genes is that they are regulated by high affinity AREs that fall within genomic regions possessing a ubiquitously permissive chromatin signature. This implies that these NRF2 cancer target genes are responsive to oncogenic NRF2 in most tissues because they lack the regulatory constraints that restrict expression of most other NRF2 target genes. This NRF2 cancer target gene set also serves as a reliable proxy for NRF2 activity, and high NRF2 activity is associated with significant decreases in survival in multiple cancer types. Overall, the pervasive upregulation of these NRF2 cancer targets across multiple cancers, and their association with negative outcomes, suggests that these will be central to dissecting the functional implications of NRF2 hyperactivation in several cancer contexts.