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The clinical success of combined androgen deprivation therapy (ADT) and radiation therapy (RT) in prostate cancer (PCa) created interest in understanding the mechanistic links between androgen receptor (AR) signaling and the DNA damage response (DDR). Convergent data have led to a model where AR both regulates, and is regulated by, the DDR. Integral to this model is that the AR regulates the transcription of DDR genes both at steady state and in response to ionizing radiation (IR). In this study, we sought to determine which immediate transcriptional changes are induced by IR in an AR-dependent manner. Using PRO-seq to quantify changes in nascent RNA transcription in response to IR, the AR antagonist enzalutamide, or the combination of the two, we find that enzalutamide treatment significantly decreased expression of canonical AR target genes but had no effect on DDR gene sets in PCa cells. Surprisingly, we also found that the AR is not a primary regulator of DDR genes either in response to IR or at steady state in asynchronously growing PCa cells. Our data indicate that the clinical benefit of ADT and RT is not due to the direct regulation of DDR gene transcription by AR.

Precision genomic run-on assays (PRO-seq) quantify nascent RNA at single nucleotide resolution with strand specificity. Here we deconstruct a recently published genomic nascent RNA processing pipeline (PEPPRO) into its components and link the analyses to the underlying molecular biology. PRO-seq experiments are evolving and variations can be found throughout the literature. The analyses are presented as individual code chunks with comprehensive details so that users can modify the framework to accommodate different protocols. We present the framework to quantify the following quality control metrics: library complexity, nascent RNA purity, nuclear run-on efficiency, alignment rate, sequencing depth, and RNA degradation.

Chromatin accessibility assays have revolutionized the field of transcription regulation by providing single-nucleotide resolution measurements of regulatory features such as promoters and transcription factor binding sites. ATAC-seq directly measures how well the Tn5 transpose accesses chromatinized DNA. Tn5 has a complex sequence bias that is not effectively scaled with traditional bias-correction methods. We model this complex bias using a rule ensemble machine learning approach that integrates information from many input k-mers proximal to the ATAC sequence reads. We effectively characterize and correct single-nucleotide sequence biases and regional sequence biases of the Tn5 enzyme. Correction of enzymatic sequence bias is an important step in interpreting chromatin accessibility assays that aim to infer transcription factor binding and regulatory activity of elements in the genome.

Adipocytes contribute to metabolic disorders such as obesity, diabetes, and atherosclerosis. Prior characterizations of the transcriptional network driving adipogenesis have overlooked transiently acting transcription factors (TFs), genes, and regulatory elements that are essential for proper differentiation. Moreover, traditional gene regulatory networks provide neither mechanistic details about individual regulatory element–gene relationships nor temporal information needed to define a regulatory hierarchy that prioritizes key regulatory factors. To address these shortcomings, we integrate kinetic chromatin accessibility (ATAC-seq) and nascent transcription (PRO-seq) data to generate temporally resolved networks that describe TF binding events and resultant effects on target gene expression. Our data indicate which TF families cooperate with and antagonize each other to regulate adipogenesis. Compartment modeling of RNA polymerase density quantifies how individual TFs mechanistically contribute to distinct steps in transcription. The glucocorticoid receptor activates transcription by inducing RNA polymerase pause release, whereas SP and AP-1 factors affect RNA polymerase initiation. We identifyTwist2as a previously unappreciated effector of adipocyte differentiation. We find that TWIST2 acts as a negative regulator of 3T3-L1 and primary preadipocyte differentiation. We confirm thatTwist2knockout mice have compromised lipid storage within subcutaneous and brown adipose tissue. Previous phenotyping ofTwist2knockout mice and Setleis syndromeTwist2−/−patients noted deficiencies in subcutaneous adipose tissue. This network inference framework is a powerful and general approach for interpreting complex biological phenomena and can be applied to a wide range of cellular processes.

Genome-wide profiling of chromatin accessibility by DNase-seq or ATAC-seq has been widely used to identify regulatory DNA elements and transcription factor binding sites. However, enzymatic DNA cleavage exhibits intrinsic sequence biases that confound chromatin accessibility profiling data analysis. Existing computational tools are limited in their ability to account for such intrinsic biases and not designed for analyzing single-cell data. Here, we present Simplex Encoded Linear Model for Accessible Chromatin (SELMA), a computational method for systematic estimation of intrinsic cleavage biases from genomic chromatin accessibility profiling data. We demonstrate that SELMA yields accurate and robust bias estimation from both bulk and single-cell DNase-seq and ATAC-seq data. SELMA can utilize internal mitochondrial DNA data to improve bias estimation. We show that transcription factor binding inference from DNase footprints can be improved by incorporating estimated biases using SELMA. Furthermore, we show strong effects of intrinsic biases in single-cell ATAC-seq data, and develop the first single-cell ATAC-seq intrinsic bias correction model to improve cell clustering. SELMA can enhance the performance of existing bioinformatics tools and improve the analysis of both bulk and single-cell chromatin accessibility sequencing data.

Nascent transcript measurements derived from run-on sequencing experiments are critical for the investigation of transcriptional mechanisms and regulatory networks. However, conventional gene annotations specify the boundaries of mRNAs, which significantly differ from the boundaries of primary transcripts. Moreover, transcript isoforms with distinct transcription start and end coordinates can vary between cell types. Therefore, new primary transcript annotations are needed to accurately interpret run-on data. We developed the primaryTranscriptAnnotation R package to infer the transcriptional start and termination sites of annotated genes from genomic run-on data. We then used these inferred co-ordinates to annotate transcriptional units identified de novo. Hence, this package provides the novel utility to integrate data-driven primary transcript annotations with transcriptional unit coordinates identified in an unbiased manner. Our analyses demonstrated that this new methodology increases the sensitivity for detecting differentially expressed transcripts and provides more accurate quantification of RNA polymerase pause indices, consistent with the importance of using accurate primary transcript coordinates for interpreting genomic nascent transcription data.Availabilityhttps://github.com/WarrenDavidAnderson/genomicsRpackage/tree/master/primaryTranscriptAnnotation

SummaryMany lncRNAs have been discovered using transcriptomic data, however, it is broadly unclear what fraction of lncRNAs are functional and what structural properties affect their phenotype. MUNC lncRNA, also known as DRReRNA, stimulates skeletal muscle differentiation. The prevailing hypothesis is that MUNC stimulates the Myod1 gene in cis as an enhancer RNA and stimulates expression of several other promyogenic genes in trans by recruiting the cohesin complex to their promoters. Experimental probing of the RNA structure revealed that MUNC contains multiple structural domains not detected by RNA structure prediction algorithms in the absence of experimental information. We discovered that these specific and structurally distinct domains are required for induction of different promyogenic genes, for binding at different genomic sites to regulate the expression of adjacent genes, and for binding the cohesin complex. Moreover, we found that induction of Myod1 or interaction with cohesin comprise only a subset of the broad regulatory impact of this lncRNA. Our study thus reveals unexpectedly complex, structure-driven functions for the MUNC lncRNA and emphasizes the importance of experimentally determined structures for understanding structure-function relationships in lncRNAs.

Many lncRNAs have been discovered using transcriptomic data; however, it is unclear what fraction of lncRNAs is functional and what structural properties affect their phenotype. MUNC lncRNA (also known as

Nascent RNA profiling is growing in popularity; however, there is no standard analysis pipeline to uniformly process the data and assess quality. Here, we introduce PEPPRO, a comprehensive, scalable workflow for GRO-seq, PRO-seq, and ChRO-seq data. PEPPRO produces uniformly processed output files for downstream analysis and assesses adapter abundance, RNA integrity, library complexity, nascent RNA purity, and run-on efficiency. PEPPRO is restartable and fault-tolerant, records copious logs, and provides a web-based project report. PEPPRO can be run locally or using a cluster, providing a portable first step for genomic nascent RNA analysis. Supplementary Information The online version contains supplementary material available at (10.1186/s13059-021-02349-4).

Heat shock transcription factor 1 (HSF1) orchestrates cellular stress protection by activating or repressing gene transcription in response to protein misfolding, oncogenic cell proliferation, and other environmental stresses. HSF1 is tightly regulated via intramolecular repressive interactions, post-ranslational modifications, and protein-protein interactions. How these HSF1 regulatory protein interactions are altered in response to acute and chronic stress is largely unknown. To elucidate the profile of HSF1 protein interactions under normal growth and chronic and acutely stressful conditions, quantitative proteomics studies identified interacting proteins in the response to heat shock or in the presence of a poly-glutamine aggregation protein cell-based model of Huntington's disease. These studies identified distinct protein interaction partners of HSF1 as well as changes in the magnitude of shared interactions as a function of each stressful condition. Several novel HSF1-interacting proteins were identified that encompass a wide variety of cellular functions, including roles in DNA repair, mRNA processing, and regulation of RNA polymerase II. One HSF1 partner, CTCF, interacted with HSF1 in a stress-inducible manner and functions in repression of specific HSF1 target genes. Understanding how HSF1 regulates gene repression is a crucial question, given the dysregulation of HSF1 target genes in both cancer and neurodegeneration. These studies expand our understanding of HSF1-mediated gene repression and provide key insights into HSF1 regulation via protein-protein interactions.

Inducible degron systems are widely used to specifically and rapidly deplete proteins of interest in cell lines and organisms. An advantage of inducible degradation is that the biological system under study remains intact and functional until perturbation, a feature that necessitates that the endogenous levels of the protein are maintained. However, endogenous tagging of genes with auxin-inducible degrons (AID) can result in chronic, auxin-independent proteasome-mediated degradation. The ARF-AID (auxin-response factor-auxin-inducible degron) system is a re-engineered auxin-inducible protein degradation system. The additional expression of the ARF-PB1 domain prevents chronic, auxin-independent degradation of AID-tagged proteins while preserving rapid auxin-induced degradation of tagged proteins. Here, we describe the protocol for engineering human cell lines to implement the ARF-AID system for specific and inducible protein degradation. These methods are adaptable and can be extended from cell lines to organisms. © 2020 The Authors. Basic Protocol 1: Generation of ARF-P2A-TIR1 progenitor cells Basic Protocol 2: Designing, cloning, and testing of a gene-specific sgRNA Basic Protocol 3: Design and amplification of a homology-directed repair construct (C-terminal tagging) Alternate Protocol 1: Design and amplification of a homology-directed repair construct (N-terminal tagging) Basic Protocol 4: Tagging of a gene of interest with AID Alternate Protocol 2: Establishment of an ARF-AID clamp system Basic Protocol 5: Testing of auxin-mediated degradation of the AID-tagged protein.

The ARF-AID (Auxin Response Factor-Auxin Inducible Degron) system is a re-engineered auxin-inducible protein degradation system. Inducible degron systems are widely used to specifically and rapidly deplete proteins of interest in cell lines and organisms. An advantage of inducible degradation is that the biological system under study remains intact and functional until perturbation. This feature necessitates that the endogenous levels of the protein are maintained. However, endogenous tagging of genes with AID can result in chronic, auxin-independent proteasome-mediated degradation. The additional expression of the ARF-PB1 domain in the re-engineered ARF-AID system prevents chronic degradation of AID-tagged proteins while preserving rapid degradation of tagged proteins. Here we describe the protocol for engineering human cell lines to implement the ARF-AID system for specific and inducible protein degradation. These methods are adaptable and can be extended from cell lines to organisms.

The next step in developing the ARF-AID system is to tag the gene of interest with AID. The ARF-AID system requires full-length AID (Figure 1) because the characterized interaction domains of AID with ARF are domains III and IV. Domains I and II are involved in the interaction with TIR1. The mini-AID lacks domains III and IV and will not interact with ARF to stabilize the protein in the absence of auxin (Sathyan et al., 2019). Note that the antibiotic selection marker (HygroR) is co-transcribed with AID and the protein products are separated during translation. Therefore, the resistance marker will be expressed at levels comparable to the target protein.

Please refer to the description section of the protocol collection.

The first procedure for implementing the ARF-AID system is to establish ARF-TIR1 progenitor cells, as shown in Figure 2A. All the plasmids for integrating ARF-TIR1 into the AAVS1 locus in human cells are available from Addgene. The choice of transfection method varies depending on the cell type; lipofectamine 3000 works efficiently for HEK293T cells.

Experiments that profile nascent RNA are growing in popularity; however, there is no standard analysis pipeline to uniformly process the data and assess quality. Here, we introduce PEPPRO, a comprehensive, scalable workflow for GRO-seq, PRO-seq, and ChRO-seq data. PEPPRO produces uniform processed output files for downstream analysis, including alignment files, signal tracks, and count matrices. Furthermore, PEPPRO simplifies downstream analysis by using a standard project definition format which can be read using metadata APIs in R and Python. For quality control, PEPPRO provides several novel statistics and plots, including assessments of adapter abundance, RNA integrity, library complexity, nascent RNA purity, and run-on efficiency. PEPPRO is restartable and fault-tolerant, records copious logs, and provides a web-based project report for navigating results. It can be run on local hardware or using any cluster resource manager, using either native software or our provided modular Linux container environment. PEPPRO is thus a robust and portable first step for genomic nascent RNA analysis. Availability BSD2-licensed code and documentation: https://peppro.databio.org .

SUMMARY Although aneuploidy is found in the majority of tumors, the degree of aneuploidy varies widely. It is unclear how cancer cells become aneuploid or how highly aneuploid tumors are different from those of more normal ploidy. We developed a simple computational method that measures the degree of aneuploidy or structural rearrangements of large chromosome regions of 522 human breast tumors from The Cancer Genome Atlas (TCGA). Highly aneuploid tumors overexpress activators of mitotic transcription and the genes encoding proteins that segregate chromosomes. Overexpression of three mitotic transcriptional regulators, E2F1, MYBL2, and FOXM1, is sufficient to increase the rate of lagging anaphase chromosomes in a non-transformed vertebrate tissue, demonstrating that this event can initiate aneuploidy. Highly aneuploid human breast tumors are also enriched in TP53 mutations. TP53 mutations co-associate with the overexpression of mitotic transcriptional activators, suggesting that these events work together to provide fitness to breast tumors., In Brief Pfister et al. analyzed TCGA sequence data to identify drivers of aneuploidy in breast tumors. TP53 is mutated in most aneuploid tumors, and a large number of genes that control mitosis are overexpressed. The oncogenes E2F1, MYBL2, and FOXM1 that regulate mitotic transcription drive the overexpression of mitotic proteins to lower the fidelity of chromosome segregation.

Heat Shock Factor 1 (Hsf1) is the master transcriptional regulator of molecular chaperones and binds to the same cis-acting element - Heat Shock Element (HSE) - across the eukaryotic lineage. In budding yeast, Hsf1 drives transcription of ~20 genes essential to maintain proteostasis under basal conditions, yet its specific targets and extent of inducible binding during heat shock remain unclear. Here we combine Hsf1 ChIP-seq, nascent RNA-seq and Hsf1 nuclear depletion to quantify Hsf1 binding and transcription across the yeast genome. Hsf1 binds 74 loci during acute heat shock, 46 of which are linked to genes with strong Hsf1-dependent transcription. Most of these targets show detectable Hsf1 binding under basal conditions, but basal occupancy and heat shock-inducible binding both vary over two orders of magnitude. Notably, Hsf1’s induced DNA binding leads to a disproportionate (up to 50-fold) increase in nascent transcription. While variation in basal Hsf1 occupancy poorly correlates with the strength of the HSE, promoters with high basal Hsf1 occupancy have nucleosome-depleted regions due to the presence of ‘pioneer’ factors. Such accessible chromatin may be critical for Hsf1 occupancy of its genomic sites as the activator is incapable of binding HSEs embedded within a stable nucleosome in vitro. In response to heat shock, however, Hsf1 is able to gain access to nucleosomal sites and promotes chromatin remodeling with the RSC complex playing a key role. We propose that the interplay between nucleosome occupancy, HSE strength and active Hsf1 levels allows cells to precisely tune expression of the proteostasis network.

Summary Identification of functional transcription factors that regulate a given gene set is an important problem in gene regulation studies. Conventional approaches for identifying transcription factors, such as DNA sequence motif analysis, are unable to predict functional binding of specific factors and not sensitive enough to detect factors binding at distal enhancers. Here, we present binding analysis for regulation of transcription (BART), a novel computational method and software package for predicting functional transcription factors that regulate a query gene set or associate with a query genomic profile, based on more than 6000 existing ChIP-seq datasets for over 400 factors in human or mouse. This method demonstrates the advantage of utilizing publicly available data for functional genomics research. Availability and implementation BART is implemented in Python and available at http://faculty.virginia.edu/zanglab/bart. Supplementary information Supplementary data are available at Bioinformatics online.

Rapid perturbation of protein function permits the ability to define primary molecular responses while avoiding down-stream cumulative effects of protein dysregulation. The auxin-inducible degron (AID) system was developed as a tool to achieve rapid and inducible protein degradation in non-plant systems. However, tagging proteins at their endogenous loci results in chronic, auxin-independent degradation by the proteasome. To correct this deficiency, we expressed the Auxin Response Transcription Factor (ARF) in an improved inducible degron system. ARF is absent from previously engineered AID systems, but ARF is a critical component of native auxin signaling. In plants, ARF directly interacts with AID in the absence of auxin and we found that expression of the ARF Phox and Bem1 (PB1) domain suppresses constitutive degradation of AID-tagged proteins. Moreover, the rate of auxin-induced AID degradation is substantially faster in the ARF-AID system. To test the ARF-AID system in a quantitative and sensitive manner, we measured genome-wide changes in nascent transcription after rapidly depleting the ZNF143 transcription factor. Transciptional profiling indicates that ZNF143 activates transcription in cis and ZNF143 regulates promoter-proximal paused RNA Polymerase density. Rapidly inducible degradation systems that preserve the target protein’s native expression levels and patterns will revolutionize the study of biological systems by enabling specific and temporally defined protein dysregulation.

A key challenge in quantitative ChIP combined with high-throughput sequencing (ChIP-seq) is the normalization of data in the presence of genome-wide changes in occupancy. Analysis-based normalization methods were developed for transcriptomic data and these are dependent on the underlying assumption that total transcription does not change between conditions. For genome-wide changes in transcription factor (TF) binding, these assumptions do not hold true. The challenges in normalization are confounded by experimental variability during sample preparation, processing and recovery. We present a novel normalization strategy utilizing an internal standard of unchanged peaks for reference. Our method can be readily applied to monitor genome-wide changes by ChIP-seq that are otherwise lost or misrepresented through analytical normalization. We compare our approach to normalization by total read depth and two alternative methods that utilize external experimental controls to study TF binding. We successfully resolve the key challenges in quantitative ChIP-seq analysis and demonstrate its application by monitoring the loss of Estrogen Receptor-alpha (ER) binding upon fulvestrant treatment, ER binding in response to estrodiol, ER mediated change in H4K12 acetylation and profiling ER binding in patient-derived xenographs. This is supported by an adaptable pipeline to normalize and quantify differential TF binding genome-wide and generate metrics for differential binding at individual sites.

A key challenge in quantitative ChIP-seq is the normalisation of data in the presence of genome-wide changes in occupancy. Analysis-based normalisation methods were developed for transcriptomic data and these are dependent on the underlying assumption that total transcription does not change between conditions. For genome-wide changes in transcription factor binding, these assumptions do not hold true. The challenges in normalisation are confounded by experimental variability during sample preparation, processing, and recovery. We present a novel normalisation strategy utilising an internal standard of unchanged peaks for reference. Our method can be readily applied to monitor genome- wide changes by ChIP-seq that are otherwise lost or misrepresented through analytical normalisation. We compare our approach to normalisation by total read depth and two alternative methods that utilise external experimental controls to study transcription factor binding. We successfully resolve the key challenges in quantitative ChIP-seq analysis and demonstrate its application by monitoring the loss of Estrogen Receptor-alpha (ER) binding upon fulvestrant treatment, ER binding in response to estrodiol, ER mediated change in H4K12 acetylation and profiling ER binding in Patient-Derived Xenographs. This is supported by an adaptable pipeline to normalise and quantify differential transcription factor binding genome- wide and generate metrics for differential binding at individual sites.

Programs of gene expression are executed by a battery of transcription factors that coordinate divergent transcription from a pair of tightly linked core initiation regions of promoters and enhancers. Here, to investigate how divergent transcription is reprogrammed upon stress, we measured nascent RNA synthesis at nucleotide-resolution, and profiled histone H4 acetylation in human cells. Our results globally show that the release of promoter-proximal paused RNA polymerase into elongation functions as a critical switch at which a gene’s response to stress is determined. Highly transcribed and highly inducible genes display strong transcriptional directionality and selective assembly of general transcription factors on the core sense promoter. Heat-induced transcription at enhancers, instead, correlates with prior binding of cell-type, sequence-specific transcription factors. Activated Heat Shock Factor 1 (HSF1) binds to transcription-primed promoters and enhancers, and CTCF-occupied, non-transcribed chromatin. These results reveal chromatin architectural features that orient transcription at divergent regulatory elements and prime transcriptional responses genome-wide., Heat Shock Factor 1 (HSF1) is a regulator of stress-induced transcription. Here, the authors investigate changes to transcription and chromatin organization upon stress and find that activated HSF1 binds to transcription-primed promoters and enhancers, and to CTCF occupied, untranscribed chromatin.

Coupling molecular biology to high throughput sequencing has revolutionized the study of biology. Molecular genomics techniques are continually refined to provide higher resolution mapping of nucleic acid interactions and structure. Sequence preferences of enzymes can interfere with the accurate interpretation of these data. We developed seqOutBias to characterize enzymatic sequence bias from experimental data and scale individual sequence reads to correct intrinsic enzymatic sequence biases. SeqOutBias efficiently corrects DNase-seq, TACh-seq, ATAC-seq, MNase-seq, and PRO-seq data. We show that seqOutBias correction facilitates identification of true molecular signatures resulting from transcription factors and RNA polymerase interacting with DNA.

SUMMARY Genomic footprinting has emerged as an unbiased discovery method for transcription factor (TF) occupancy at cognate DNA in vivo. A basic premise of footprinting is that sequence-specific TF-DNA interactions are associated with localized resistance to nucleases, leaving observable signatures of cleavage within accessible chromatin. This phenomenon is interpreted to imply protection of the critical nucleotides by the stably bound protein factor. However, this model conflicts with previous reports of many TFs exchanging with specific binding sites in living cells on a timescale of seconds. We show that TFs with short DNA residence times have no footprints at bound motif elements. Moreover, the nuclease cleavage profile within a footprint originates from the DNA sequence in the factor-binding site, rather than from the protein occupying specific nucleotides. These findings suggest a revised understanding of TF footprinting and reveal limitations in comprehensive reconstruction of the TF regulatory network using this approach.

Proper gene regulation is essential for proper organismal development and appropriate responses to external stimuli. Specialized factors, termed master regulators, are often responsible for orchestrating the molecular events that result from signaling cascades. Master regulators coordinate the activation and repression of specific gene classes. Estrogen receptor α (ER) precipitates the signaling cascade that results from endogenous or exogenous estrogen hormones. ER is a classic transcriptional activator and the mechanisms by which ER coordinates gene activation are well characterized. However, it remains unclear how ER coordinates the immediate repression of genes. We integrated genomic transcription, chromosome looping, transcription factor binding, and chromatin structure data to analyze the molecular cascade that results from estradiol (E2)-induced signaling in human MCF-7 breast cancer cells and addressed the context-specific nature of gene regulation. We defined a class of genes that are immediately repressed upon estrogen stimulation, and we compared and contrasted the molecular characteristics of these repressed genes vs activated and unregulated genes. The most striking and unique feature of the repressed gene class is transient binding of ER at early time points after estrogen stimulation. We also found that p300, a coactivator and acetyltransferase, quantitatively redistributes from non-ER enhancers to ER enhancers after E2 treatment. These data support an extension of the classic physiological squelching model, whereby ER hijacks coactivators from repressed genes and redistributes the coactivators to ER enhancers that activate transcription.

The coordinated regulation of gene expression at the transcriptional level is fundamental to organismal development and homeostasis. Inducible systems are invaluable when studying transcription because the regulatory process can be triggered instantaneously, allowing the tracking of ordered mechanistic events. Here, we use Precision Run-On sequencing (PRO-seq) to examine the genome-wide Heat Shock (HS) response inDrosophilaand the function of two key transcription factors on the immediate transcription activation or repression of all genes regulated by HS. We identify the primary HS response genes and the rate-limiting steps in the transcription cycle that GAGA-Associated Factor (GAF) and HS Factor (HSF) regulate. We demonstrate that GAF acts upstream of promoter-proximally paused RNA Polymerase II (Pol II) formation, likely at the step of chromatin opening, and that GAF-facilitated Pol II pausing is critical for HS activation. In contrast, HSF is dispensable for establishing or maintaining Pol II pausing, but is critical for the release of paused Pol II into the gene body at a subset of highly-activated genes. Additionally, HSF has no detectable role in the rapid HS-repression of thousands of genes.

Whereas the regulation of a gene is uniquely tailored to respond to specific biological needs, general transcriptional mechanisms are used by diversely regulated genes within and across species. The primary mode of regulation is achieved by modulating specific steps in the transcription cycle of RNA polymerase II (Pol II). Pol II “pausing” has recently been identified as a prevalent rate-limiting and regulated step in the transcription cycle. Many sequence-specific transcription factors (TFs) modulate the duration of the pause by directly or indirectly recruiting positive transcription elongation factor b (P-TEFb) kinase, which promotes escape of Pol II from the pause into productive elongation. These specialized TFs find their target-binding sites by discriminating between DNA sequence elements based on the chromatin context in which these elements reside and can result in productive changes in gene expression or nonfunctional “promiscuous” binding. The binding of a TF can precipitate drastic changes in chromatin architecture that can be both dependent and independent of active Pol II transcription. Here, we highlight heat-shock-mediated gene transcription as a model system in which to study common mechanistic features of gene regulation.

Previous studies have shown that GAGA Factor (GAF) is enriched on promoters with paused RNA Polymerase II (Pol II), but its genome-wide function and mechanism of action remain largely uncharacterized. We assayed the levels of transcriptionally-engaged polymerase using global run-on sequencing (GRO-seq) in control and GAF-RNAi Drosophila S2 cells and found promoter-proximal polymerase was significantly reduced on a large subset of paused promoters where GAF occupancy was reduced by knock down. These promoters show a dramatic increase in nucleosome occupancy upon GAF depletion. These results, in conjunction with previous studies showing that GAF directly interacts with nucleosome remodelers, strongly support a model where GAF directs nucleosome displacement at the promoter and thereby allows the entry Pol II to the promoter and pause sites. This action of GAF on nucleosomes is at least partially independent of paused Pol II because intergenic GAF binding sites with little or no Pol II also show GAF-dependent nucleosome displacement. In addition, the insulator factor BEAF, the BEAF-interacting protein Chriz, and the transcription factor M1BP are strikingly enriched on those GAF-associated genes where pausing is unaffected by knock down, suggesting insulators or the alternative promoter-associated factor M1BP protect a subset of GAF-bound paused genes from GAF knock-down effects. Thus, GAF binding at promoters can lead to the local displacement of nucleosomes, but this activity can be restricted or compensated for when insulator protein or M1BP complexes also reside at GAF bound promoters., Author Summary Transcriptional regulation is critical for proper gene expression in response to environmental changes and developmental programs. Eukaryotes have evolved multiple mechanisms by which transcription factors regulate transcription. One mechanism is the reorganization of chromatin to allow Pol II recruitment. Another is the release of promoter-proximal paused Pol II, where Pol II transcription that is halted 20–60 bases downstream of the transcription start site (TSS) is allowed to enter into productive elongation through the gene body. The Drosophila transcription factor GAF binds to genes that undergo pausing and interacts with nucleosome remodelers and the pausing factor NELF. Thus, GAF can regulate multiple points necessary for transcription, but its mechanistic role is not fully understood genome-wide. We depleted GAF from cells and examined the genome-wide changes in Pol II and nucleosome distributions across genes. We found that GAF depletion reduces polymerase density at genes where GAF binds just upstream of the TSS, and results in nucleosomes moving into the promoter region. Our results show that GAF is important for maintaining the promoter accessibility, allowing Pol II to be recruited to promoters and enter the pause sites downstream of the TSS. Thus, GAF is critical for providing the chromatin environment necessary for the proper control of gene expression.

Genome-wide association studies (GWAS) have discovered thousands loci associated with disease risk and quantitative traits, yet most of the variants responsible for risk remain uncharacterized. The majority of GWAS-identified loci are enriched for non-coding single-nucleotide polymorphisms (SNPs) and defining the molecular mechanism of risk is challenging. Many non-coding causal SNPs are hypothesized to alter transcription factor (TF) binding sites as the mechanism by which they affect organismal phenotypes. We employed an integrative genomics approach to identify candidate TF binding motifs that confer breast cancer-specific phenotypes identified by GWAS. We performed de novo motif analysis of regulatory elements, analyzed evolutionary conservation of identified motifs, and assayed TF footprinting data to identify sequence elements that recruit TFs and maintain chromatin landscape in breast cancer-relevant tissue and cell lines. We identified candidate causal SNPs that are predicted to alter TF binding within breast cancer-relevant regulatory regions that are in strong linkage disequilibrium with significantly associated GWAS SNPs. We confirm that the TFs bind with predicted allele-specific preferences using CTCF ChIP-seq data. We used The Cancer Genome Atlas breast cancer patient data to identify ANKLE1 and ZNF404 as the target genes of candidate TF binding site SNPs in the 19p13.11 and 19q13.31 GWAS-identified loci. These SNPs are associated with the expression of ZNF404 and ANKLE1 in breast tissue. This integrative analysis pipeline is a general framework to identify candidate causal variants within regulatory regions and TF binding sites that confer phenotypic variation and disease risk.

Transcription factor binding to DNA in vivo causes the recruitment of chromatin modifiers that can cause changes in chromatin structure, including the modification of histone tails. We previously showed that estrogen receptor (ER) target gene activation is facilitated by peptidylarginine deiminase 2 (PAD2)-catalyzed histone H3R26 deimination (H3R26Cit). Here we report that the genomic distributions of ER and H3R26Cit in breast cancer cells are strikingly coincident, linearly correlated, and observed as early as 2 minutes following estradiol treatment. The H3R26Cit profile is unlike that of previously described histone modifications and is characterized by sharp, narrow peaks. Paired-end MNase ChIP-seq indicates that the charge-neutral H3R26Cit modification facilitates ER binding to DNA by altering the fine structure of the nucleosome. Clinically, we find that PAD2 and H3R26Cit levels correlate with ER expression in breast tumors and that high PAD2 expression is associated with increased survival in ER+ breast cancer patients. These findings provide insight into how transcription factors gain access to nucleosomal DNA and implicate PAD2 as a novel therapeutic target for ER+ breast cancer., Author Summary Transcription factors bind to DNA to activate and repress gene transcription. Many transcription factors, particularly nuclear receptors, associate with their cognate DNA element in a highly dynamic manner in vivo. Highly acetylated histone tails and DNase sensitive chromatin are amenable to the initial binding of transcription factors. Upon binding to DNA, transcription factor binding recruits remodelers and coactivators that can cause a concomitant increase in accessibility and acetylation. Herein, we show that estrogen receptor recruitment of a histone deiminase causes the positively charged H3R26 residue to be neutralized. This modification changes the fine structure of the nucleosome particle and facilitates estrogen receptor binding. Lastly, we find that high deiminase expression is associated with increased survival in estrogen receptor-positive breast cancer patients.