A generative model for the behavior of RNA polymerase

Atlas of nascent RNA transcripts reveals tissue-specific enhancer to gene linkages

Gene transcription is controlled and modulated by regulatory regions, including enhancers and promoters. These regions are abundant in non-coding bidirectional transcription that results in generally unstable RNA. Using nascent RNA transcription data across hundreds of human samples, we identified over 800,000 regions containing bidirectional transcription. We then identify tissue specific, highly correlated transcription between bidirectional and gene regions. The identified correlated pairs, a bidirectional region and a gene, are enriched for disease associated SNPs and often supported by independent 3D data. We present these resources as a database called DBNascent ( https://nascent.colorado.edu/ ) which serves as a resource for future studies into gene regulation, enhancer associated RNAs, and transcription factors.

LIET model: capturing the kinetics of RNA polymerase from loading to termination

Transcription by RNA polymerases is an exquisitely regulated step of the central dogma. Transcription is the primary determinant of cell-state, and most cellular perturbations impact transcription by altering polymerase activity. Thus, detecting changes in polymerase activity yields insight into most cellular processes. Nascent run-on sequencing provides a direct readout of polymerase activity, but no tools exist to model all aspects of this activity at genes. We focus on RNA polymerase II-responsible for transcribing protein-coding genes. We present the first model to capture the complete process of gene transcription. For individual genes, this model parameterizes each distinct stage of transcription-loading, initiation, elongation, and termination, hence LIET-in a biologically interpretable Bayesian mixture, which is applied to nascent run-on data. Our improved modeling of loading/initiation demonstrates these stages are characteristically different between sense and antisense strands. Applying LIET to 24 human cell-types, our analysis indicates the position of dissociation (the last step of termination) appears to be highly consistent, indicative of a tightly regulated process. Furthermore, by applying LIET to perturbation experiments, we demonstrate its ability to detect specific changes in pausing (5' end), strand-bias, and dissociation location (3' end)-opening the door to differential assessment of transcription at individual stages of individual genes.

TF Profiler: a transcription factor inference method that broadly measures transcription factor activity and identifies mechanistically distinct networks

TF Profiler is a method of inferring transcription factor (TF) regulatory activity, i.e., when a TF is present and actively participating in the regulation of transcription, directly from nascent sequencing assays such as PRO-seq and GRO-seq. While ChIP assays have measured DNA localization, they fall short of identifying when and where the effector domain of a transcription factor is active. Our method uses RNA polymerase activity to infer TF effector domain activity across hundreds of data sets and transcription factors. TF Profiler is broadly applicable, providing regulatory insights on any PRO-seq sample for any transcription factor with a known binding motif.

Enhancer RNA transcription pinpoints functional genetic variants linked to asthma

Bidirectional enhancer RNA (eRNA) transcription is a widespread response to environmental signals and glucocorticoids. We investigated whether single nucleotide polymorphisms (SNPs) within dynamically regulated eRNA-transcribing regions contribute to genetic variation in asthma. Through applying multivariate regression modeling with permutation-based significance thresholding to a large clinical cohort, we identified novel associations between asthma and 35 SNPs located in eRNA-transcribing regions implicated in regulating cellular processes relevant to asthma, including rs258760 (mean allele frequency = 0.34, asthma odds ratio = 0.95; P = 5.04E-03). We show that rs258760 disrupts an active aryl hydrocarbon receptor (AHR) response element linked to transcriptional regulation of the glucocorticoid receptor gene by AHR ligands, which are commonly found in combusted air pollution. The role of rs258760 as a protective variant for asthma was independently validated using UK Biobank data. Our findings establish eRNA signatures as a tool for discovery of functional genetic variants and define a novel association between air pollution, glucocorticoid signaling and asthma.

Cell type-dependent directional transcription at enhancers

Enhancers are noncoding regulatory regions in the genome that play essential roles in modulating gene expression. Previous work showed that enhancers are not transcriptionally silent but are characterized by bidirectional expression of short capped noncoding RNAs. Balanced bidirectional expression has therefore been used as a key feature for the detection of enhancers from transcriptome data. Instead, by analyzing FANTOM5 and other deep cap analysis gene expression transcriptome datasets, we find enhancer transcription preferentially in one direction in individual cell types. As the preferred direction of transcription of an enhancer can switch between cell types, balanced bidirectional enhancer expression may appear if transcriptome data are aggregated over cell types. 5' single-cell RNA sequencing data showed that enhancers were almost exclusively expressed unidirectionally in a single cell. Reporter assay data demonstrated that the regulatory function of an enhancer does not depend on its preference for unidirectional or bidirectional expression. We conclude that requiring balanced bidirectional transcription for enhancer detection may discard most valid enhancers when applied to transcriptome data of a single cell type.

TFIIH kinase CDK7 drives cell proliferation through a common core transcription factor network

How cyclin-dependent kinase 7 (CDK7) coordinately regulates the cell cycle and RNA polymerase II transcription remains unclear. Here, high-resolution cryo-electron microscopy revealed how two clinically relevant inhibitors block CDK7 function. In cells, CDK7 inhibition rapidly suppressed transcription, but constitutively active genes were disproportionately affected versus stimulus-responsive. Distinct transcription factors (TFs) regulate constitutive versus stimulus-responsive genes. Accordingly, stimulus-responsive TFs were refractory to CDK7 inhibition whereas constitutively active "core" TFs were repressed. Core TFs (n = 78) are predominantly promoter associated and control cell cycle and proliferative gene expression programs across cell types. Mechanistically, rapid suppression of core TF function can occur through CDK7-dependent phosphorylation changes in core TFs and RB1. Moreover, CDK7 inhibition depleted core TF protein levels within hours, consistent with durable target gene suppression. Thus, a major but unappreciated biological function for CDK7 is regulation of a TF cohort that drives proliferation, revealing an apparent universal mechanism by which CDK7 coordinates RNAPII transcription with cell cycle CDK regulation.

Accurate de novo transcription unit annotation from run-on and sequencing data

Functional element annotations are critical tools used to provide insight into the molecular processes governing cell development, differentiation, and disease. Run-on and sequencing assays measure the production of nascent RNAs and can provide an effective data source for discovering functional elements. However, the accurate inference of functional elements from run-on sequencing data remains an open problem because the signal is noisy and challenging to model. Here we investigated computational approaches that convert run-on and sequencing data into annotations representing transcription units, including genes and non-coding RNAs. We developed a convolutional neural network, called c onvolutional discovery of g ene a natomy using P RO-seq (CGAP), trained to identify different anatomical features of a transcription unit, which were then stitched together into transcript annotations using a hidden Markov model (HMM). Comparison with existing methods showed a significant performance improvement using our novel CGAP-HMM approach. We developed a voting system that ensembles the top three annotation strategies, resulting in large and significant improvements in transcription unit annotation accuracy over the best performing individual method. Finally, we also report a conditional generative adversarial network (cGAN) as a generative approach to transcription unit annotation that shows promise for further development. Collectively our work provides novel tools for de novo transcription unit annotation from run-on and sequencing data that are accurate enough to be useful in many applications.

Mediator kinase inhibition suppresses hyperactive interferon signaling in Down syndrome

Hyperactive interferon (IFN) signaling is a hallmark of Down syndrome (DS), a condition caused by Trisomy 21 (T21); strategies that normalize IFN signaling could benefit this population. Mediator-associated kinases CDK8 and CDK19 drive inflammatory responses through incompletely understood mechanisms. Using sibling-matched cell lines with/without T21, we investigated Mediator kinase function in the context of hyperactive IFN in DS over a 75 min to 24 hr timeframe. Activation of IFN-response genes was suppressed in cells treated with the CDK8/CDK19 inhibitor cortistatin A (CA), via rapid suppression of IFN-responsive transcription factor (TF) activity. We also discovered that CDK8/CDK19 affect splicing, a novel means by which Mediator kinases control gene expression. To further probe Mediator kinase function, we completed cytokine screens and metabolomics experiments. Cytokines are master regulators of inflammatory responses; by screening 105 different cytokine proteins, we show that Mediator kinases help drive IFN-dependent cytokine responses at least in part through transcriptional regulation of cytokine genes and receptors. Metabolomics revealed that Mediator kinase inhibition altered core metabolic pathways in cell type-specific ways, and broad upregulation of anti-inflammatory lipid mediators occurred specifically in kinase-inhibited cells during hyperactive IFNγ signaling. A subset of these lipids (e.g. oleamide, desmosterol) serve as ligands for nuclear receptors PPAR and LXR, and activation of these receptors occurred specifically during hyperactive IFN signaling in CA-treated cells, revealing mechanistic links between Mediator kinases, lipid metabolism, and nuclear receptor function. Collectively, our results establish CDK8/CDK19 as context-specific metabolic regulators, and reveal that these kinases control gene expression not only via TFs, but also through metabolic changes and splicing. Moreover, we establish that Mediator kinase inhibition antagonizes IFN signaling through transcriptional, metabolic, and cytokine responses, with implications for DS and other chronic inflammatory conditions.

ZNF143 binds DNA and stimulates transcription initiation to activate and repress direct target genes

Transcription factors bind to sequence motifs and act as activators or repressors. Transcription factors interface with a constellation of accessory cofactors to regulate distinct mechanistic steps to regulate transcription. We rapidly degraded the essential and pervasively expressed transcription factor ZNF143 to determine its function in the transcription cycle. ZNF143 facilitates RNA polymerase initiation and activates gene expression. ZNF143 binds the promoter of nearly all its activated target genes. ZNF143 also binds near the site of genic transcription initiation to directly repress a subset of genes. Although ZNF143 stimulates initiation at ZNF143-repressed genes (i.e. those that increase transcription upon ZNF143 depletion), the molecular context of binding leads to cis repression. ZNF143 competes with other more efficient activators for promoter access, physically occludes transcription initiation sites and promoter-proximal sequence elements, and acts as a molecular roadblock to RNA polymerases during early elongation. The term context specific is often invoked to describe transcription factors that have both activation and repression functions. We define the context and molecular mechanisms of ZNF143-mediated cis activation and repression.

Cellular zinc status alters chromatin accessibility and binding of p53 to DNA

Zn2+ is an essential metal required by approximately 850 human transcription factors. How these proteins acquire their essential Zn2+ cofactor and whether they are sensitive to changes in the labile Zn2+ pool in cells remain open questions. Using ATAC-seq to profile regions of accessible chromatin coupled with transcription factor enrichment analysis, we examined how increases and decreases in the labile zinc pool affect chromatin accessibility and transcription factor enrichment. We found 685 transcription factor motifs were differentially enriched, corresponding to 507 unique transcription factors. The pattern of perturbation and the types of transcription factors were notably different at promoters versus intergenic regions, with zinc-finger transcription factors strongly enriched in intergenic regions in elevated Zn2+ To test whether ATAC-seq and transcription factor enrichment analysis predictions correlate with changes in transcription factor binding, we used ChIP-qPCR to profile six p53 binding sites. We found that for five of the six targets, p53 binding correlates with the local accessibility determined by ATAC-seq. These results demonstrate that changes in labile zinc alter chromatin accessibility and transcription factor binding to DNA.

The temporal dynamics of lncRNA Firre-mediated epigenetic and transcriptional regulation

Numerous studies have now demonstrated that lncRNAs can influence gene expression programs leading to cell and organismal phenotypes. Typically, lncRNA perturbations and concomitant changes in gene expression are measured on the timescale of many hours to days. Thus, we currently lack a temporally grounded understanding of the primary, secondary, and tertiary relationships of lncRNA-mediated transcriptional and epigenetic regulation-a prerequisite to elucidating lncRNA mechanisms. To begin to address when and where a lncRNA regulates gene expression, we genetically engineer cell lines to temporally induce the lncRNA Firre. Using this approach, we are able to monitor lncRNA transcriptional regulatory events from 15 min to four days. We observe that upon induction, Firre RNA regulates epigenetic and transcriptional states in trans within 30 min. These early regulatory events result in much larger transcriptional changes after 12 h, well before current studies monitor lncRNA regulation. Moreover, Firre-mediated gene expression changes are epigenetically remembered for days. Overall, this study suggests that lncRNAs can rapidly regulate gene expression by establishing persistent epigenetic and transcriptional states.

Characterizing primary transcriptional responses to short term heat shock in Down syndrome

Heat shock stress induces genome-wide changes in transcription regulation, activating a coordinated cellular response to enable survival. We noticed many heat shock genes are up-regulated in blood samples from individuals with trisomy 21. We characterized the immediate transcriptional response to heat shock of two lymphoblastoid cell lines derived from brothers with and without trisomy 21. The trisomy 21 cells displayed a more robust heat shock response after just one hour at 42°C than the matched disomic cells.

ZNF143 binds DNA and stimulates transcripstion initiation to activate and repress direct target genes

Transcription factors bind to sequence motifs and act as activators or repressors. Transcription factors interface with a constellation of accessory cofactors to regulate distinct mechanistic steps to regulate transcription. We rapidly degraded the essential and ubiquitously expressed transcription factor ZNF143 to determine its function in the transcription cycle. ZNF143 facilitates RNA Polymerase initiation and activates gene expression. ZNF143 binds the promoter of nearly all its activated target genes. ZNF143 also binds near the site of genic transcription initiation to directly repress a subset of genes. Although ZNF143 stimulates initiation at ZNF143-repressed genes (i.e. those that increase expression upon ZNF143 depletion), the molecular context of binding leads to cis repression. ZNF143 competes with other more efficient activators for promoter access, physically occludes transcription initiation sites and promoter-proximal sequence elements, and acts as a molecular roadblock to RNA Polymerases during early elongation. The term context specific is often invoked to describe transcription factors that have both activation and repression functions. We define the context and molecular mechanisms of ZNF143-mediated cis activation and repression.

Model-based characterization of the equilibrium dynamics of transcription initiation and promoter-proximal pausing in human cells

In metazoans, both transcription initiation and the escape of RNA polymerase (RNAP) from promoter-proximal pausing are key rate-limiting steps in gene expression. These processes play out at physically proximal sites on the DNA template and appear to influence one another through steric interactions. Here, we examine the dynamics of these processes using a combination of statistical modeling, simulation, and analysis of real nascent RNA sequencing data. We develop a simple probabilistic model that jointly describes the kinetics of transcription initiation, pause-escape, and elongation, and the generation of nascent RNA sequencing read counts under steady-state conditions. We then extend this initial model to allow for variability across cells in promoter-proximal pause site locations and steric hindrance of transcription initiation from paused RNAPs. In an extensive series of simulations, we show that this model enables accurate estimation of initiation and pause-escape rates. Furthermore, we show by simulation and analysis of real data that pause-escape is often strongly rate-limiting and that steric hindrance can dramatically reduce initiation rates. Our modeling framework is applicable to a variety of inference problems, and our software for estimation and simulation is freely available.

Cellular zinc status alters chromatin accessibility and binding of transcription factor p53 to genomic sites

Zinc (Zn2+) is an essential metal required by approximately 2500 proteins. Nearly half of these proteins act on DNA, including > 850 human transcription factors, polymerases, DNA damage response factors, and proteins involved in chromatin architecture. How these proteins acquire their essential Zn2+ cofactor and whether they are sensitive to changes in the labile Zn2+ pool in cells remain open questions. Here, we examine how changes in the labile Zn2+ pool affect chromatin accessibility and transcription factor binding to DNA. We observed both increases and decreases in accessibility in different chromatin regions via ATAC-seq upon treating MCF10A cells with elevated Zn2+ or the Zn2+-specific chelator tris(2-pyridylmethyl)amine (TPA). Transcription factor enrichment analysis was used to correlate changes in chromatin accessibility with transcription factor motifs, revealing 477 transcription factor motifs that were differentially enriched upon Zn2+ perturbation. 186 of these transcription factor motifs were enriched in Zn2+ and depleted in TPA, and the majority correspond to Zn2+ finger transcription factors. We selected TP53 as a candidate to examine how changes in motif enrichment correlate with changes in transcription factor occupancy by ChIP-qPCR. Using publicly available ChIP-seq and nascent transcription datasets, we narrowed the 50,000+ ATAC-seq peaks to 2164 TP53 targets and subsequently selected 6 high-probability TP53 binding sites for testing. ChIP-qPCR revealed that for 5 of the 6 targets, TP53 binding correlates with the local accessibility determined by ATAC-seq. These results demonstrate that changes in labile zinc directly alter chromatin accessibility and transcription factor binding to DNA.

CCIVR2 facilitates comprehensive identification of both overlapping and non-overlapping antisense transcripts within specified regions

Pairs of sense and antisense transcriptions that are adjacent at their 5' and 3' regions are called divergent and convergent transcription, respectively. However, the structural properties of divergent/convergent transcription in different species or RNA biotypes are poorly characterized. Here, we developed CCIVR2, a program that facilitates identification of both overlapping and non-overlapping antisense transcripts produced from divergent/convergent transcription whose transcription start sites (TSS) or transcript end sites (TES) are located within a specified region. We used CCIVR2 to analyze antisense transcripts starting around the sense TSS (from divergent transcription) or ending around the sense TES (from convergent transcription) in 11 different species and found species- and RNA biotype-specific features of divergent/convergent transcription. Furthermore, we confirmed that CCIVR2 enables the identification of multiple sense/antisense transcript pairs from divergent transcription, including those with known functions in processes such as embryonic stem cell differentiation and TGFβ stimulation. CCIVR2 is therefore a valuable bioinformatics tool that facilitates the characterization of divergent/convergent transcription in different species and aids the identification of functional sense/antisense transcript pairs from divergent transcription in specified biological processes.

FT-6876, a Potent and Selective Inhibitor of CBP/p300, is Active in Preclinical Models of Androgen Receptor-Positive Breast Cancer

BACKGROUND

Patients with triple-negative breast cancer (TNBC) expressing the androgen receptor (AR) respond poorly to neoadjuvant chemotherapy, although AR antagonists have shown promising clinical activity, suggesting these tumors are AR-dependent. cAMP responsive element binding protein (CREB)-binding protein (CBP) and p300 are transcriptional co-activators for the AR, a key driver of AR+ breast and prostate cancer, and may provide a novel therapeutic target in AR+ TNBC.

OBJECTIVES

The aim of this study was to determine the therapeutic potential of FT-6876, a new CBP/p300 bromodomain inhibitor, in breast cancer models with a range of AR levels in vitro and in vivo.

METHODS

Effects of FT-6876 on the CBP/p300 pathway were determined by combining chromatin immunoprecipitation (ChIP) with precision run-on sequencing (PRO-seq) complemented with H3K27 acetylation (Ac) and transcriptional profiling. The antiproliferative effect of FT-6876 was also measured in vitro and in vivo.

RESULTS

We describe the discovery of FT-6876, a potent and selective CBP/p300 bromodomain inhibitor. The combination of ChIP and PRO-seq confirmed the reduction in H3K27Ac at specific promoter sites concurrent with a decrease in CBP/p300 on the chromatin and a reduction in nascent RNA and enhancer RNA. This was associated with a time- and concentration-dependent reduction in H3K37Ac associated with a decrease in AR and estrogen receptor (ER) target gene expression. This led to a time-dependent growth inhibition in AR+ models, correlated with AR expression. Tumor growth inhibition was also observed in AR+ tumor models of TNBC and ER+ breast cancer subtypes with consistent pharmacokinetics and pharmacodynamics.

CONCLUSION

Our findings demonstrate FT-6876 as a promising new CBP/p300 bromodomain inhibitor, with efficacy in preclinical models of AR+ breast cancer.

Integrated genomics approaches identify transcriptional mediators and epigenetic responses to Afghan desert particulate matter in small airway epithelial cells

Military Deployment to Southwest Asia and Afghanistan and exposure to toxic airborne particulates have been associated with an increased risk of developing respiratory disease, collectively termed deployment-related respiratory diseases (DRRDs). Our knowledge about how particulates mediate respiratory disease is limited, precluding the appropriate recognition or management. Central to this limitation is the lack of understanding of how exposures translate into dysregulated cell identity with dysregulated transcriptional programs. The small airway epithelium is involved in both the pathobiology of DRRD and fine particulate matter deposition. To characterize small airway epithelial cell epigenetic and transcriptional responses to Afghan desert particulate matter (APM) and investigate the functional interactions of transcription factors that mediate these responses, we applied two genomics assays, the assay for transposase accessible chromatin with sequencing (ATAC-seq) and Precision Run-on sequencing (PRO-seq). We identified activity changes in a series of transcriptional pathways as candidate regulators of susceptibility to subsequent insults, including signal-dependent pathways, such as loss of cytochrome P450 or P53/P63, and lineage-determining transcription factors, such as GRHL2 loss or TEAD3 activation. We further demonstrated that TEAD3 activation was unique to APM exposure despite similar inflammatory responses when compared with wood smoke particle exposure and that P53/P63 program loss was uniquely positioned at the intersection of signal-dependent and lineage-determining transcriptional programs. Our results establish the utility of an integrated genomics approach in characterizing responses to exposures and identifying genomic targets for the advanced investigation of the pathogenesis of DRRD.

Artificial Intelligence in Biological Sciences

Artificial intelligence (AI), currently a cutting-edge concept, has the potential to improve the quality of life of human beings. The fields of AI and biological research are becoming more intertwined, and methods for extracting and applying the information stored in live organisms are constantly being refined. As the field of AI matures with more trained algorithms, the potential of its application in epidemiology, the study of host–pathogen interactions and drug designing widens. AI is now being applied in several fields of drug discovery, customized medicine, gene editing, radiography, image processing and medication management. More precise diagnosis and cost-effective treatment will be possible in the near future due to the application of AI-based technologies. In the field of agriculture, farmers have reduced waste, increased output and decreased the amount of time it takes to bring their goods to market due to the application of advanced AI-based approaches. Moreover, with the use of AI through machine learning (ML) and deep-learning-based smart programs, one can modify the metabolic pathways of living systems to obtain the best possible outputs with the minimal inputs. Such efforts can improve the industrial strains of microbial species to maximize the yield in the bio-based industrial setup. This article summarizes the potentials of AI and their application to several fields of biology, such as medicine, agriculture, and bio-based industry.

A comparison of experimental assays and analytical methods for genome-wide identification of active enhancers

Mounting evidence supports the idea that transcriptional patterns serve as more specific identifiers of active enhancers than histone marks; however, the optimal strategy to identify active enhancers both experimentally and computationally has not been determined. Here, we compared 13 genome-wide RNA sequencing (RNA-seq) assays in K562 cells and show that nuclear run-on followed by cap-selection assay (GRO/PRO-cap) has advantages in enhancer RNA detection and active enhancer identification. We also introduce a tool, peak identifier for nascent transcript starts (PINTS), to identify active promoters and enhancers genome wide and pinpoint the precise location of 5' transcription start sites. Finally, we compiled a comprehensive enhancer candidate compendium based on the detected enhancer RNA (eRNA) transcription start sites (TSSs) available in 120 cell and tissue types, which can be accessed at https://pints.yulab.org . With knowledge of the best available assays and pipelines, this large-scale annotation of candidate enhancers will pave the way for selection and characterization of their functions in a time- and labor-efficient manner.

Quantifying RNA synthesis at rate-limiting steps of transcription using nascent RNA-sequencing data

Nascent RNA-sequencing tracks transcription at nucleotide resolution. The genomic distribution of engaged transcription complexes, in turn, uncovers functional genomic regions. Here, we provide analytical steps to (1) identify transcribed regulatory elements de novo genome-wide, (2) quantify engaged transcription complexes at enhancers, promoter-proximal regions, divergent transcripts, gene bodies, and termination windows, and (3) measure distribution of transcription machineries and regulatory proteins across functional genomic regions. This protocol tracks engaged transcription complexes across functional genomic regions demonstrated in human K562 erythroleukemia cells.

For complete details on the use and execution of this protocol, please refer to Vihervaara et al. (2021).

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Identification of transcribed regulatory elements de novo genome-wide

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Quantification of engaged transcription complexes at functional genomic regions

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Measuring distribution of transcription regulators across the functional genomic regions

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Revealing functional genomic regions from nascent transcription data

Protocol variations in run-on transcription dataset preparation produce detectable signatures in sequencing libraries

Background

A variety of protocols exist for producing whole genome run-on transcription datasets. However, little is known about how differences between these protocols affect the signal within the resulting libraries.

Results

Using run-on transcription datasets generated from the same biological system, we show that a variety of GRO- and PRO-seq preparation methods leave identifiable signatures within each library. Specifically we show that the library preparation method results in differences in quality control metrics, as well as differences in the signal distribution at the 5 ^′ end of transcribed regions. These shifts lead to disparities in eRNA identification, but do not impact analyses aimed at inferring the key regulators involved in changes to transcription.

Conclusions

Run-on sequencing protocol variations result in technical signatures that can be used to identify both the enrichment and library preparation method of a particular data set. These technical signatures are batch effects that limit detailed comparisons of pausing ratios and eRNAs identified across protocols. However, these batch effects have only limited impact on our ability to infer which regulators underlie the observed transcriptional changes.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12864-022-08352-8).

Deconvolution of multiplexed transcriptional responses to wood smoke particles defines rapid aryl hydrocarbon receptor signaling dynamics

The heterogeneity of respirable particulates and compounds complicates our understanding of transcriptional responses to air pollution. Here, we address this by applying precision nuclear run-on sequencing and the assay for transposase-accessible chromatin sequencing to measure nascent transcription and chromatin accessibility in airway epithelial cells after wood smoke particle (WSP) exposure. We used transcription factor enrichment analysis to identify temporally distinct roles for ternary response factor-serum response factor complexes, the aryl hydrocarbon receptor (AHR), and NFκB in regulating transcriptional changes induced by WSP. Transcription of canonical targets of the AHR, such as CYP1A1 and AHRR, was robustly increased after just 30 min of WSP exposure, and we discovered novel AHR-regulated pathways and targets including the DNA methyltransferase, DNMT3L. Transcription of these genes and associated enhancers rapidly returned to near baseline by 120 min after exposure. The kinetics of AHR- and NFκB-regulated responses to WSP were distinguishable based on the timing of both transcriptional responses and chromatin remodeling, with induction of several cytokines implicated in maintaining NFκB-mediated responses through 120 min of exposure. In aggregate, our data establish a direct and primary role for AHR in mediating airway epithelial responses to WSP and identify crosstalk between AHR and NFκB signaling in controlling proinflammatory gene expression. This work also defines an integrated genomics-based strategy for deconvoluting multiplexed transcriptional responses to heterogeneous environmental exposures.

Spt4 facilitates the movement of RNA polymerase II through the +2 nucleosomal barrier

Spt4 is a transcription elongation factor with homologs in organisms with nucleosomes. Structural and in vitro studies implicate Spt4 in transcription through nucleosomes, and yet the in vivo function of Spt4 is unclear. Here, we assess the precise position of Spt4 during transcription and the consequences of the loss of Spt4 on RNA polymerase II (RNAPII) dynamics and nucleosome positioning in Saccharomyces cerevisiae. In the absence of Spt4, the spacing between gene-body nucleosomes increases and RNAPII accumulates upstream of the nucleosomal dyad, most dramatically at nucleosome +2. Spt4 associates with elongating RNAPII early in transcription, and its association dynamically changes depending on nucleosome positions. Together, our data show that Spt4 regulates early elongation dynamics, participates in co-transcriptional nucleosome positioning, and promotes RNAPII movement through the gene-body nucleosomes, especially the +2 nucleosome.

Deconvolution of Expression for Nascent RNA sequencing data (DENR) highlights pre-RNA isoform diversity in human cells

Motivation

Quantification of isoform abundance has been extensively studied at the mature RNA level using RNA-seq but not at the level of precursor RNAs using nascent RNA sequencing.

Results

We address this problem with a new computational method called Deconvolution of Expression for Nascent RNA-sequencing data (DENR), which models nascent RNA-sequencing read-counts as a mixture of user-provided isoforms. The baseline algorithm is enhanced by machine-learning predictions of active transcription start sites and an adjustment for the typical ‘shape profile’ of read-counts along a transcription unit. We show that DENR outperforms simple read-count-based methods for estimating gene and isoform abundances, and that transcription of multiple pre-RNA isoforms per gene is widespread, with frequent differences between cell types. In addition, we provide evidence that a majority of human isoform diversity derives from primary transcription rather than from post-transcriptional processes.

Availability and implementation

DENR and nascentRNASim are freely available at https://github.com/CshlSiepelLab/DENR (version v1.0.0) and https://github.com/CshlSiepelLab/nascentRNASim (version v0.3.0).

Supplementary information

Supplementary data are available at Bioinformatics online.

The Δ40p53 isoform inhibits p53-dependent eRNA transcription and enables regulation by signal-specific transcription factors during p53 activation

The naturally occurring Δ40p53 isoform heterotetramerizes with wild-type p53 (WTp53) to regulate development, aging, and stress responses. How Δ40p53 alters WTp53 function remains enigmatic because their co-expression causes tetramer heterogeneity. We circumvented this issue with a well-tested strategy that expressed Δ40p53:WTp53 as a single transcript, ensuring a 2:2 tetramer stoichiometry. Human MCF10A cell lines expressing Δ40p53:WTp53, WTp53, or WTp53:WTp53 (as controls) from the native TP53 locus were examined with transcriptomics (precision nuclear run-on sequencing [PRO-seq] and RNA sequencing [RNA-seq]), metabolomics, and other methods. Δ40p53:WTp53 was transcriptionally active, and, although phenotypically similar to WTp53 under normal conditions, it failed to induce growth arrest upon Nutlin-induced p53 activation. This occurred via Δ40p53:WTp53-dependent inhibition of enhancer RNA (eRNA) transcription and subsequent failure to induce mRNA biogenesis, despite similar genomic occupancy to WTp53. A different stimulus (5-fluorouracil [5FU]) also showed Δ40p53:WTp53-specific changes in mRNA induction; however, other transcription factors (TFs; e.g., E2F2) could then drive the response, yielding similar outcomes vs. WTp53. Our results establish that Δ40p53 tempers WTp53 function to enable compensatory responses by other stimulus-specific TFs. Such modulation of WTp53 activity may be an essential physiological function for Δ40p53. Moreover, Δ40p53:WTp53 functional distinctions uncovered herein suggest an eRNA requirement for mRNA biogenesis and that human p53 evolved as a tetramer to support eRNA transcription.

How does Δ40p53, a naturally occurring isoform of p53 that is linked to accelerated aging, alter WTp53 function? Using an innovative approach, this study reveals that Δ40p53 suppresses enhancer RNA transcription and allows other stimulus-specific transcription factors to modulate the p53 transcriptional response.

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.

Stress-induced transcriptional memory accelerates promoter-proximal pause release and decelerates termination over mitotic divisions

Heat shock instantly reprograms transcription. Whether gene and enhancer transcription fully recover from stress and whether stress establishes a memory by provoking transcription regulation that persists through mitosis remained unknown. Here, we measured nascent transcription and chromatin accessibility in unconditioned cells and in the daughters of stress-exposed cells. Tracking transcription genome-wide at nucleotide-resolution revealed that cells precisely restored RNA polymerase II (Pol II) distribution at gene bodies and enhancers upon recovery from stress. However, a single heat exposure in embryonic fibroblasts primed a faster gene induction in their daughter cells by increasing promoter-proximal Pol II pausing and by accelerating the pause release. In K562 erythroleukemia cells, repeated stress refined basal and heat-induced transcription over mitotic division and decelerated termination-coupled pre-mRNA processing. The slower termination retained transcripts on the chromatin and reduced recycling of Pol II. These results demonstrate that heat-induced transcriptional memory acts through promoter-proximal pause release and pre-mRNA processing at transcription termination.

Combining signal and sequence to detect RNA polymerase initiation in ATAC-seq data

The assay for transposase-accessible chromatin followed by sequencing (ATAC-seq) is an inexpensive protocol for measuring open chromatin regions. ATAC-seq is also relatively simple and requires fewer cells than many other high-throughput sequencing protocols. Therefore, it is tractable in numerous settings where other high throughput assays are challenging to impossible. Hence it is important to understand the limits of what can be inferred from ATAC-seq data. In this work, we leverage ATAC-seq to predict the presence of nascent transcription. Nascent transcription assays are the current gold standard for identifying regions of active transcription, including markers for functional transcription factor (TF) binding. We combine mapped short reads from ATAC-seq with the underlying peak sequence, to determine regions of active transcription genome-wide. We show that a hybrid signal/sequence representation classified using recurrent neural networks (RNNs) can identify these regions across different cell types.

TFIID Enables RNA Polymerase II Promoter-Proximal Pausing

RNA polymerase II (RNAPII) transcription is governed by the pre-initiation complex (PIC), which contains TFIIA, TFIIB, TFIID, TFIIE, TFIIF, TFIIH, RNAPII, and Mediator. After initiation, RNAPII enzymes pause after transcribing less than 100 bases; precisely how RNAPII pausing is enforced and regulated remains unclear. To address specific mechanistic questions, we reconstituted human RNAPII promoter-proximal pausing in vitro, entirely with purified factors (no extracts). As expected, NELF and DSIF increased pausing, and P-TEFb promoted pause release. Unexpectedly, the PIC alone was sufficient to reconstitute pausing, suggesting RNAPII pausing is an inherent PIC function. In agreement, pausing was lost upon replacement of the TFIID complex with TATA-binding protein (TBP), and PRO-seq experiments revealed widespread disruption of RNAPII pausing upon acute depletion (t = 60 min) of TFIID subunits in human or Drosophila cells. These results establish a TFIID requirement for RNAPII pausing and suggest pause regulatory factors may function directly or indirectly through TFIID.

Nascent transcript analysis of glucocorticoid crosstalk with TNF defines primary and cooperative inflammatory repression

The glucocorticoid receptor (NR3C1, also known as GR) binds to specific DNA sequences and directly induces transcription of anti-inflammatory genes that contribute to cytokine repression, frequently in cooperation with NF-kB. Whether inflammatory repression also occurs through local interactions between GR and inflammatory gene regulatory elements has been controversial. Here, using global run-on sequencing (GRO-seq) in human airway epithelial cells, we show that glucocorticoid signaling represses transcription within 10 min. Many repressed regulatory regions reside within "hyper-ChIPable" genomic regions that are subject to dynamic, yet nonspecific, interactions with some antibodies. When this artifact was accounted for, we determined that transcriptional repression does not require local GR occupancy. Instead, widespread transcriptional induction through canonical GR binding sites is associated with reciprocal repression of distal TNF-regulated enhancers through a chromatin-dependent process, as evidenced by chromatin accessibility and motif displacement analysis. Simultaneously, transcriptional induction of key anti-inflammatory effectors is decoupled from primary repression through cooperation between GR and NF-kB at a subset of regulatory regions. Thus, glucocorticoids exert bimodal restraints on inflammation characterized by rapid primary transcriptional repression without local GR occupancy and secondary anti-inflammatory effects resulting from transcriptional cooperation between GR and NF-kB.

Identification and dynamic quantification of regulatory elements using total RNA

The spatial and temporal regulation of transcription initiation is pivotal for controlling gene expression. Here, we introduce capped-small RNA-seq (csRNA-seq), which uses total RNA as starting material to detect transcription start sites (TSSs) of both stable and unstable RNAs at single-nucleotide resolution. csRNA-seq is highly sensitive to acute changes in transcription and identifies an order of magnitude more regulated transcripts than does RNA-seq. Interrogating tissues from species across the eukaryotic kingdoms identified unstable transcripts resembling enhancer RNAs, pri-miRNAs, antisense transcripts, and promoter upstream transcripts in multicellular animals, plants, and fungi spanning 1.6 billion years of evolution. Integration of epigenomic data from these organisms revealed that histone H3 trimethylation (H3K4me3) was largely confined to TSSs of stable transcripts, whereas H3K27ac marked nucleosomes downstream from all active TSSs, suggesting an ancient role for posttranslational histone modifications in transcription. Our findings show that total RNA is sufficient to identify transcribed regulatory elements and capture the dynamics of initiated stable and unstable transcripts at single-nucleotide resolution in eukaryotes.

Discovering Transcriptional Regulatory Elements From Run-On and Sequencing Data Using the Web-Based dREG Gateway

Transcription is a chromatin mark that can be used effectively to identify the location of active enhancers and promoters, collectively known as transcriptional regulatory elements (TREs). We recently introduced dREG, a tool for the identification of TREs using run-on and sequencing (RO-seq) assays, including global run-on and sequencing (GRO-seq), precision run-on and sequencing (PRO-seq), and chromatin run-on and sequencing (ChRO-seq). In this protocol, we present step-by-step instructions for running dREG on an arbitrary run-on and sequencing dataset. Users provide dREG with bigWig files (in which each read is represented by a single base) representing the location of RNA polymerase in a cell or tissue sample of interest, and dREG returns a list of genomic regions that are predicted to be active TREs. Finally, we demonstrate the use of dREG regions in discovering transcription factors controlling response to a stimulus and predicting their target genes. Together, this protocol provides detailed instructions for running dREG on arbitrary run-on and sequencing data. © 2018 by John Wiley & Sons, Inc.

Identification of regulatory elements from nascent transcription using dREG

Our genomes encode a wealth of transcription initiation regions (TIRs) that can be identified by their distinctive patterns of actively elongating RNA polymerase. We previously introduced dREG to identify TIRs using PRO-seq data. Here, we introduce an efficient new implementation of dREG that uses PRO-seq data to identify both uni- and bidirectionally transcribed TIRs with 70% improvement in accuracy, three- to fourfold higher resolution, and >100-fold increases in computational efficiency. Using a novel strategy to identify TIRs based on their statistical confidence reveals extensive overlap with orthogonal assays, yet also reveals thousands of additional weakly transcribed TIRs that were not identified by H3K27ac ChIP-seq or DNase-seq. Novel TIRs discovered by dREG were often associated with RNA polymerase III initiation, bound by pioneer transcription factors, or located in broad domains marked by repressive chromatin modifications. Our results suggest that transcription initiation can be a powerful tool for expanding the catalog of functional elements.

Chromatin run-on and sequencing maps the transcriptional regulatory landscape of glioblastoma multiforme

The human genome encodes a variety of poorly understood RNA species that remain challenging to identify using existing genomic tools. We developed chromatin run-on and sequencing (ChRO-seq) to map the location of RNA polymerase for almost any input sample, including samples with degraded RNA that are intractable to RNA sequencing. We used ChRO-seq to map nascent transcription in primary human glioblastoma (GBM) brain tumors. Enhancers identified in primary GBMs resemble open chromatin in the normal human brain. Rare enhancers that are activated in malignant tissue drive regulatory programs similar to the developing nervous system. We identified enhancers that regulate groups of genes that are characteristic of each known GBM subtype and transcription factors that drive them. Finally we discovered a core group of transcription factors that control the expression of genes associated with clinical outcomes. This study characterizes the transcriptional landscape of GBM and introduces ChRO-seq as a method to map regulatory programs that contribute to complex diseases.

Building a Science Gateway For Processing and Modeling Sequencing Data Via Apache Airavata

The amount of DNA sequencing data has been exponentially growing during the past decade due to advances in sequencing technology. Processing and modeling large amounts of sequencing data can be computationally intractable for desktop computing platforms. High performance computing (HPC) resources offer advantages in terms of computing power, and can be a general solution to these problems. Using HPCs directly for computational needs requires skilled users who know their way around HPCs and acquiring such skills take time. Science gateways acts as the middle layer between users and HPCs, providing users with the resources to accomplish compute-intensive tasks without requiring specialized expertise. We developed a web-based computing platform for genome biologists by customizing the PHP Gateway for Airavata (PGA) framework that accesses publicly accessible HPC resources via Apache Airavata. This web computing platform takes advantage of the Extreme Science and Engineering Discovery Environment (XSEDE) which provides the resources for gateway development, including access to CPU, GPU, and storage resources. We used this platform to develop a gateway for the dREG algorithm, an online computing tool for finding functional regions in mammalian genomes using nascent RNA sequencing data. The dREG gateway provides its users a free, powerful and user-friendly GPU computing resource based on XSEDE, circumventing the need of specialized knowledge about installation, configuration, and execution on an HPC for biologists. The dREG gateway is available at: https://dREG.dnasequence.org/.

Detecting Differential Transcription Factor Activity from ATAC-Seq Data

Transcription factors are managers of the cellular factory, and key components to many diseases. Many non-coding single nucleotide polymorphisms affect transcription factors, either by directly altering the protein or its functional activity at individual binding sites. Here we first briefly summarize high-throughput approaches to studying transcription factor activity. We then demonstrate, using published chromatin accessibility data (specifically ATAC-seq), that the genome-wide profile of TF recognition motifs relative to regions of open chromatin can determine the key transcription factor altered by a perturbation. Our method of determining which TFs are altered by a perturbation is simple, is quick to implement, and can be used when biological samples are limited. In the future, we envision that this method could be applied to determine which TFs show altered activity in response to a wide variety of drugs and diseases.

ER-positive breast cancer cells are poised for RET-mediated endocrine resistance

The RET tyrosine kinase signaling pathway is involved in the development of endocrine resistant ER+ breast cancer. However, we know little about how ER+ cells activate RET signaling and initiate an endocrine resistant phenotype. Here we show that both ER+ endocrine resistant and sensitive breast cancers have a functional RET tyrosine kinase signaling pathway, but that endocrine sensitive breast cancer cells lack RET ligands that are necessary to drive endocrine resistance. Transcription of one RET ligand, GDNF, is necessary and sufficient to confer resistance in the ER+ MCF-7 cell line. Endogenous GDNF produced by endocrine resistant cells is translated, secreted into the media, and activates RET signaling in nearby cells. In patients, RET ligand expression predicts responsiveness to endocrine therapies and correlates with survival. Collectively, our findings show that ER+ tumor cells are "poised" for RET mediated endocrine resistance, expressing all components of the RET signaling pathway, but endocrine sensitive cells lack high expression of RET ligands that are necessary to initiate the resistance phenotype.

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.

Connected Gene Communities Underlie Transcriptional Changes in Cornelia de Lange Syndrome

Cornelia de Lange syndrome (CdLS) is a complex multisystem developmental disorder caused by mutations in cohesin subunits and regulators. While its precise molecular mechanisms are not well defined, they point toward a global deregulation of the transcriptional gene expression program. Cohesin is associated with the boundaries of chromosome domains and with enhancer and promoter regions connecting the three-dimensional genome organization with transcriptional regulation. Here, we show that connected gene communities, structures emerging from the interactions of noncoding regulatory elements and genes in the three-dimensional chromosomal space, provide a molecular explanation for the pathoetiology of CdLS associated with mutations in the cohesin-loading factor NIPBL and the cohesin subunit SMC1A NIPBL and cohesin are important constituents of connected gene communities that are centrally positioned at noncoding regulatory elements. Accordingly, genes deregulated in CdLS are positioned within reach of NIPBL- and cohesin-occupied regions through promoter-promoter interactions. Our findings suggest a dynamic model where NIPBL loads cohesin to connect genes in communities, offering an explanation for the gene expression deregulation in the CdLS.