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Transcription of tRNA genes by RNA Polymerase III (RNAPIII) is tightly regulated by signaling cascades in response to nutrient availability. The emerging notion of differential tRNA gene regulation implies the existence of additional regulatory mechanisms. However, tRNA gene-specific regulatory factors have not been described. For that reason, we decoded the proteome of a single native tRNA gene locus in yeast. We observed dynamic reprogramming of the core RNAPIII transcription machinery upon nutrient perturbation. In addition, we identified Fpt1, a protein of unknown function. Fpt1 uniquely occupied tRNA genes but its occupancy varied and correlated with the efficiency of RNAPIII eviction upon nutrient perturbation. Decoding the proteome of a tRNA gene in the absence of Fpt1 revealed that Fpt1 promotes eviction of RNAPIII. Cells without Fpt1 also showed impaired shutdown of ribosome biogenesis genes upon nutrient perturbation. Our findings provide support for a chromatin-associated mechanism required for RNAPIII eviction from tRNA genes and for tuning an integrated physiological response to changing metabolic demands.Abstract Figure

The supplementary data contain Supplementary Materials and Methods, Supplementary Figures 1 through 8 with legends, and Supplementary Tables 1 to 5. Supplementary Fig. 1. YW3-56 inhibits growth of p53 wild type and mutant cells. Supplementary Fig. 2. PAD inhibitor YW3-56 inhibits PAD4 in HL-60 derived granulocytic cells. Supplementary Fig. 3. Effects of YW3-56 on the ATF4 and AMPK gene networks. Supplementary Fig. 4. ATF4 directly binds to the SESN2 and DDIT4 promoters. Supplementary Fig. 5. ChIP-exo analyses of ATF4 binding to its target sites. Supplementary Figure 6. mTOR inhibition following thapsigargin induced ER stress. Supplementary Figure 7. Effects of ATF4, SESN2, and DDIT4 depletion on cell growth and YW3-56 mediated cell killing. Supplementary Figure 8. Features of YW3-56 induced cell death. Supplementary Table 1. List of qRT-PCR and ChIP-qPCR primers used in this research work. Supplementary Table 2. Genes with altered expression after YW3-56 treatment. (see Excel spreadsheet) Supplementary Table 3. List of ATF4 associated genes. (see Excel spreadsheet) Supplementary Table 4. List of CEBPB associated genes. (see Excel spreadsheet) Supplementary Table 5. Expression changes of ATF4 and CEBPB candidate genes after YW3-56 treatment.

YW3-56 microarray data

CEBPB associated genes

We previously reported that a pan-PAD inhibitor, YW3-56, activates p53 target genes to inhibit cancer growth. However, the p53-independent anticancer activity and molecular mechanisms of YW3-56 remain largely elusive. Here, gene expression analyses found that ATF4 target genes involved in endoplasmic reticulum (ER) stress response were activated by YW3-56. Depletion of ATF4 greatly attenuated YW3-56–mediated activation of the mTORC1 regulatory genes SESN2 and DDIT4. Using the ChIP-exo method, high-resolution genomic binding sites of ATF4 and CEBPB responsive to YW3-56 treatment were generated. In human breast cancer cells, YW3-56–mediated cell death features mitochondria depletion and autophagy perturbation. Moreover, YW3-56 treatment effectively inhibits the growth of triple-negative breast cancer xenograft tumors in nude mice. Taken together, we unveiled the anticancer mechanisms and therapeutic potentials of the pan-PAD inhibitor YW3-56. Mol Cancer Ther; 14(4); 877–88. ©2015 AACR.

ATF4 associated genes

Confidence in experimental results is critical for discovery. As the scale of data generation in genomics has grown exponentially, experimental error has likely kept pace despite the best efforts of many laboratories. Technical mistakes can and do occur at nearly every stage of a genomics assay (i.e., cell line contamination, reagent swapping, tube mislabelling, etc.) and are often difficult to identify post-execution. However, the DNA sequenced in genomic experiments contains certain markers (e.g., indels) encoded within and can often be ascertained forensically from experimental datasets. We developed the Genotype validation Pipeline (GenoPipe), a suite of heuristic tools that operate together directly on raw and aligned sequencing data from individual high-throughput sequencing experiments to characterize the underlying genome of the source material. We demonstrate how GenoPipe validates and rescues erroneously annotated experiments by identifying unique markers inherent to an organism’s genome (i.e., epitope insertions, gene deletions, and SNPs).

Genome-wide, little is understood about how proteins organize at inducible promoters before and after induction and to what extent inducible and constitutive architectures depend on cofactors. We report that sequence-specific transcription factors and their tethered cofactors (e.g., SAGA [Spt–Ada–Gcn5–acetyltransferase], Mediator, TUP, NuA4, SWI/SNF, and RPD3-L) are generally bound to promoters prior to induction (“poised”), rather than recruited upon induction, whereas induction recruits the preinitiation complex (PIC) to DNA. Through depletion and/or deletion experiments, we show that SAGA does not function at constitutive promoters, although a SAGA-independent Gcn5 acetylates +1 nucleosomes there. When inducible promoters are poised, SAGA catalyzes +1 nucleosome acetylation but not PIC assembly. When induced, SAGA catalyzes acetylation, deubiquitylation, and PIC assembly. Surprisingly, SAGA mediates induction by creating a PIC that allows TFIID (transcription factor II-D) to stably associate, rather than creating a completely TFIID-independent PIC, as generally thought. These findings suggest that inducible systems, where present, are integrated with constitutive systems.

Antibodies offer a powerful means to interrogate specific proteins in a complex milieu. However, antibody availability and reliability can be problematic, whereas epitope tagging can be impractical in many cases. To address these limitations, the Protein Capture Reagents Program (PCRP) generated over a thousand renewable monoclonal antibodies (mAbs) against human presumptive chromatin proteins. However, these reagents have not been widely field-tested. We therefore performed a screen to test their ability to enrich genomic regions via chromatin immunoprecipitation (ChIP) and a variety of orthogonal assays. Eight hundred eighty-seven unique antibodies against 681 unique human transcription factors (TFs) were assayed by ultra-high-resolution ChIP-exo/seq, generating approximately 1200 ChIP-exo data sets, primarily in a single pass in one cell type (K562). Subsets of PCRP mAbs were further tested in ChIP-seq, CUT&RUN, STORM super-resolution microscopy, immunoblots, and protein binding microarray (PBM) experiments. About 5% of the tested antibodies displayed high-confidence target (i.e., cognate antigen) enrichment across at least one assay and are strong candidates for additional validation. An additional 34% produced ChIP-exo data that were distinct from background and thus warrant further testing. The remaining 61% were not substantially different from background, and likely require consideration of a much broader survey of cell types and/or assay optimizations. We show and discuss the metrics and challenges to antibody validation in chromatin-based assays.

The genome-wide architecture of chromatin-associated proteins that maintains chromosome integrity and gene regulation is not well defined. Here we use chromatin immunoprecipitation, exonuclease digestion and DNA sequencing (ChIP–exo/seq)1,2 to define this architecture in Saccharomyces cerevisiae. We identify 21 meta-assemblages consisting of roughly 400 different proteins that are related to DNA replication, centromeres, subtelomeres, transposons and transcription by RNA polymerase (Pol) I, II and III. Replication proteins engulf a nucleosome, centromeres lack a nucleosome, and repressive proteins encompass three nucleosomes at subtelomeric X-elements. We find that most promoters associated with Pol II evolved to lack a regulatory region, having only a core promoter. These constitutive promoters comprise a short nucleosome-free region (NFR) adjacent to a +1 nucleosome, which together bind the transcription-initiation factor TFIID to form a preinitiation complex. Positioned insulators protect core promoters from upstream events. A small fraction of promoters evolved an architecture for inducibility, whereby sequence-specific transcription factors (ssTFs) create a nucleosome-depleted region (NDR) that is distinct from an NFR. We describe structural interactions among ssTFs, their cognate cofactors and the genome. These interactions include the nucleosomal and transcriptional regulators RPD3-L, SAGA, NuA4, Tup1, Mediator and SWI–SNF. Surprisingly, we do not detect interactions between ssTFs and TFIID, suggesting that such interactions do not stably occur. Our model for gene induction involves ssTFs, cofactors and general factors such as TBP and TFIIB, but not TFIID. By contrast, constitutive transcription involves TFIID but not ssTFs engaged with their cofactors. From this, we define a highly integrated network of gene regulation by ssTFs. A ChIP–exo method is used to define the genome-wide positional organization of proteins associated with gene transcription, DNA replication, centromeres, subtelomeres and transposons, revealing distinct protein assemblies for constitutive and inducible gene expression.

When detected at single-base-pair resolution, the genome-wide location, occupancy level, and structural organization of DNA-binding proteins provide mechanistic insights into genome regulation. Here we use ChIP-exo to provide a near-base-pair resolution view of the epigenomic organization of the Escherichia coli transcription machinery and nucleoid structural proteins at the time when cells are growing exponentially and upon rapid reprogramming (acute heat shock). We examined the site specificity of three sigma factors (RpoD/σ70, RpoH/σ32, and RpoN/σ54), RNA polymerase (RNAP or RpoA, -B, -C), and two nucleoid proteins (Fis and IHF). We suggest that DNA shape at the flanks of cognate motifs helps drive site specificity. We find that although RNAP and sigma factors occupy active cognate promoters, RpoH and RpoN can occupy quiescent promoters without the presence of RNAP. Thus, promoter-bound sigma factors can be triggered to recruit RNAP by a mechanism that is distinct from an obligatory cycle of free sigma binding RNAP followed by promoter binding. These findings add new dimensions to how sigma factors achieve promoter specificity through DNA sequence and shape, and further define mechanistic steps in regulated genome-wide assembly of RNAP at promoters in E. coli.

The ChIP-exo assay precisely delineates protein-DNA crosslinking patterns by combining chromatin immunoprecipitation with 5′ to 3′ exonuclease digestion. Within a regulatory complex, the physical distance of a regulatory protein to DNA affects crosslinking efficiencies. Therefore, the spatial organization of a protein-DNA complex could potentially be inferred by analyzing how crosslinking signatures vary between the subunits of a regulatory complex. Here, we present a computational framework that aligns ChIP-exo crosslinking patterns from multiple proteins across a set of coordinately bound regulatory regions, and which detects and quantifies protein-DNA crosslinking events within the aligned profiles. By producing consistent measurements of protein-DNA crosslinking strengths across multiple proteins, our approach enables characterization of relative spatial organization within a regulatory complex. We demonstrate that our approach can recover aspects of regulatory complex spatial organization when applied to collections of ChIP-exo data that profile regulatory machinery at yeast ribosomal protein genes and yeast tRNA genes. We also demonstrate the ability to quantify changes in protein-DNA complex organization across conditions by applying our approach to data profiling Drosophila Pol II transcriptional components. Our results suggest that principled analyses of ChIP-exo crosslinking patterns enable inference of spatial organization within protein-DNA complexes.

When detected at single-base-pair resolution, the genome-wide location, occupancy level, and structural organization of DNA-binding proteins provide mechanistic insights into genome regulation. Here we use ChIP-exo to provide a near-base-pair resolution view of the epigenomic organization of the

How co-transcriptional RNA quality control is regulated remains poorly understood. Here, we report that in S. cerevisiae premature transcription termination of mRNAs is regulated by the evolutionarily conserved ATP-dependent chromatin remodeling INO80 complex. Loss of INO80 leads to an increase in promoter-proximally paused RNA Polymerase II and defective progression into the gene body. We show that promoter-proximal transcriptional pausing correlates with loading of RNA surveillance and transcription termination factors to mRNA transcripts. Cells lacking INO80 are defective for the Nrd1-Nab3-Sen1 (NNS)-dependent pathway for transcription termination at snRNA genes and promoter-proximally sites of mRNA genes. We demonstrate that INO80 promotes the association of the RNA surveillance and termination factor Nab2 with short promoter-proximal mRNA transcripts. We provide evidence that co-transcriptional recruitment of Nab2 to chromatin is regulated by INO80, which enables the interaction of Nab2 with the histone variant H2A.Z. Our work suggests a chromatin mechanism for premature transcription termination at promoter-proximally pausing sites, linking RNA quality control to the transcriptional process.

The ability to aggregate experimental data analysis and results into a concise and interpretable format is a key step in evaluating the success of an experiment. This critical step determines baselines for reproducibility and is a key requirement for data dissemination. However, in practice it can be difficult to consolidate data analyses that encapsulates the broad range of datatypes available in the life sciences. We present STENCIL, a web templating engine designed to organize, visualize, and enable the sharing of interactive data visualizations. STENCIL leverages a flexible web framework for creating templates to render highly customizable visual front ends. This flexibility enables researchers to render small or large sets of experimental outcomes, producing high-quality downloadable and editable figures that retain their original relationship to the source data. REST API based back ends provide programmatic data access and supports easy data sharing. STENCIL is a lightweight tool that can stream data from Galaxy, a popular bioinformatic analysis web platform. STENCIL has been used to support the analysis and dissemination of two large scale genomic projects containing the complete data analysis for over 2,400 distinct datasets. Code and implementation details are available on GitHub: https://github.com/CEGRcode/stencil

In Saccharomyces cerevisiae, RNA polymerase II (Pol II) selects transcription start sites (TSSs) by a unidirectional scanning process. During scanning, a preinitiation complex (PIC) assembled at an upstream core promoter initiates at select positions within a window ~40–120 bp downstream. Several lines of evidence indicate that Ssl2, the yeast homolog of XPB and an essential and conserved subunit of the general transcription factor (GTF) TFIIH, drives scanning through its DNA-dependent ATPase activity, therefore potentially controlling both scanning rate and scanning extent (processivity). To address questions of how Ssl2 functions in promoter scanning and interacts with other initiation activities, we leveraged distinct initiation-sensitive reporters to identify novel ssl2 alleles. These ssl2 alleles, many of which alter residues conserved from yeast to human, confer either upstream or downstream TSS shifts at the model promoter ADH1 and genome-wide. Specifically, tested ssl2 alleles alter TSS selection by increasing or narrowing the distribution of TSSs used at individual promoters. Genetic interactions of ssl2 alleles with other initiation factors are consistent with ssl2 allele classes functioning through increasing or decreasing scanning processivity but not necessarily scanning rate. These alleles underpin a residue interaction network that likely modulates Ssl2 activity and TFIIH function in promoter scanning. We propose that the outcome of promoter scanning is determined by two functional networks, the first being Pol II activity and factors that modulate it to determine initiation efficiency within a scanning window, and the second being Ssl2/TFIIH and factors that modulate scanning processivity to determine the width of the scanning widow., eLife digest In eukaryotic organisms such as yeast, the process of converting genes into proteins begins with the transcription of DNA sequences into mRNA molecules. An enzyme called RNA Polymerase II (Pol II) is responsible for creating new strands of mRNA, but a variety of other so called transcription factors is also needed to kickstart the transcription process. These transcription factors are delivered to genes, where they attach to specific sequences, or promoters, which sit at the beginning of each gene. Once these transcription factors are in place, the double stranded DNA is unzipped to provide access to the DNA that will serve as the template for transcription. In budding yeast, Pol II and another specific transcription factor, known as TFIIH, work together to scan these promoter sequences to find the appropriate start sites of mRNA synthesis. However, several aspects of this process, such as how TFIIH works in promoter scanning, how far its scanning functions can extend, and how its activity is controlled, are currently poorly understood. Zhao et al. have investigated these questions in budding yeast. Using a range of genetic and genomic techniques, Zhao et al. found that certain sections of TFIIH were involved in choosing specific transcription start sites of mRNA synthesis during promoter scanning. These sections were identical in different eukaryotic organisms from yeast to humans, suggesting that these regions may be important for tuning or controlling the activity of TFIIH. Moreover, in yeast, the activity of TFIIH determines how far the scanning unit was able to move along the promoter DNA. Finally, Zhao et al. found that the initiation by promoter scanning was regulated by two distinct networks. The first network controlled how well mRNA synthesis could be initiated at individual transcription start sites; and the second network – driven by TFIIH – controlled which promoter sequences could be scanned to initiate transcription. This research provides an in-depth look into the early steps of the process of converting DNA into mRNA. The biological machinery used to initiate and control this action is highly conserved between yeast and humans, suggesting that the mechanisms for controlling the activity of these factors could be similar, even if their initiation processes may differ.

Use of chromatin immunoprecipitation with exonuclease treatment (ChIP–exo) determines the positional organization of hundreds of chromosomal proteins throughout the Saccharomyces cerevisiae genome. The resulting ultra-high-resolution map provides insight into the regulation of genes, enhancers, replication origins, centromeres, subtelomeres and transposons. Ultra-high-resolution map of protein–chromatin interactions of the Saccharomyces cerevisiae genome.

In Saccharomyces cerevisiae, RNA Polymerase II (Pol II) selects transcription start sites (TSS) by a unidirectional scanning process. During scanning, a preinitiation complex (PIC) assembled at an upstream core promoter initiates at select positions within a window ∼40-120 basepairs downstream. Several lines of evidence indicate that Ssl2, the yeast homolog of XPB and an essential and conserved subunit of the general transcription factor (GTF) TFIIH, drives scanning through its DNA-dependent ATPase activity, therefore potentially controlling both scanning rate and scanning extent (processivity). To address questions of how Ssl2 functions in promoter scanning and interacts with other initiation activities, we leveraged distinct initiation-sensitive reporters to identify novel ssl2 alleles. These ssl2 alleles, many of which alter residues conserved from yeast to human, confer either upstream or downstream TSS shifts at the model promoter ADH1 and genome-wide. Specifically, tested ssl2 alleles alter TSS selection by increasing or narrowing the distribution of TSSs used at individual promoters. Genetic interactions of ssl2 alleles with other initiation factors are consistent with ssl2 allele classes functioning through increasing or decreasing scanning processivity but not necessarily scanning rate. These alleles underpin a residue interaction network that likely modulates Ssl2 activity and TFIIH function in promoter scanning. We propose that the outcome of promoter scanning is determined by two functional networks, the first being Pol II activity and factors that modulate it to determine initiation efficiency within a scanning window, and the second being Ssl2/TFIIH and factors that modulate scanning processivity to determine the width of the scanning widow.

Although we have made significant progress, we still possess a limited understanding of how genomic and epigenomic information directs gene expression programs through sequence-specific transcription factors (TFs). Extensive research has settled on three general classes of TF targets in metazoans: promoter accessibility via chromatin regulation (e.g., SAGA), assembly of the general transcription factors on promoter DNA (e.g., TFIID), and recruitment of RNA polymerase (Pol) II (e.g., Mediator) to establish a transcription pre-initiation complex (PIC). Here we discuss TFs and their targets. We also place this in the context of our current work with Saccharomyces (yeast), where we find that promoters typically lack an architecture that supports TF function. Moreover, yeast promoters that support TF binding also display interactions with cofactors like SAGA and Mediator, but not TFIID. It is unknown to what extent all genes in metazoans require TFs and their cofactors.

There has been a rapid development in genome sequencing, including high-throughput next generation sequencing (NGS) technologies, automation in biological experiments, new bioinformatics tools and utilization of high-performance computing and cloud computing. ChIP-based NGS technologies, e.g. ChIP-seq and ChIP-exo, are widely used to detect the binding sites of DNA-interacting proteins in the genome and help us to have a deeper mechanistic understanding of genomic regulation. As sequencing data is generated at an unprecedented pace from the ChIP-based NGS pipelines, there is an urgent need for a metadata management system. To meet this need, we developed the Platform for Eukaryotic Genomic Regulation (PEGR), a web service platform that logs metadata for samples and sequencing experiments, manages the data processing workflows, and provides reporting and visualization. PEGR links together people, samples, protocols, DNA sequencers and bioinformatics computation. With the help of PEGR, scientists can have a more integrated understanding of the sequencing data and better understand the scientific mechanisms of genomic regulation. In this paper, we present the architecture and the major functionalities of PEGR. We also share our experience in developing this application and discuss the future directions.

Antibodies offer a powerful means to interrogate specific proteins in a complex milieu. However, antibody availability and reliability are problematic and epitope tagging can be impractical in many cases. In an effort to improve this situation, the Protein Capture Reagents Program (PCRP) generated over a thousand renewable monoclonal antibodies (mAbs) against human-presumptive chromatin proteins. However, these reagents have not been widely field-tested. We therefore performed a screen to test their ability to enrich genomic regions via chromatin immunoprecipitation (ChIP) and a variety of orthogonal assays. 887 unique antibodies against 681 unique human transcription factors (TFs), were assayed by ultra-high resolution ChIP-exo/seq, primarily in a single pass in one cell type (K562). Deep systematic analyses of the resulting ∼1,200 ChIP-exo datasets can be found at www.PCRPvalidation.org. Subsets of PCRP mAbs were further tested in ChIP-seq, CUT&RUN, STORM super-resolution microscopy, immunoblots, and protein binding microarray (PBM) experiments. About 5% of the tested antibodies displayed target (i.e., cognate antigen) enrichment across at least one assay and are strong candidates for additional validation. An additional 34% produced ChIP-exo data that was distinct from background and thus warrant further testing. The remaining 61% were not substantially different from background, and likely require consideration of a much broader survey of cell types and/or assay optimizations. We demonstrate and discuss the metrics and challenges to antibody validation in chromatin-based assays.

Background The majority of eukaryotic promoters utilize multiple transcription start sites (TSSs). How multiple TSSs are specified at individual promoters across eukaryotes is not understood for most species. In Saccharomyces cerevisiae, a pre-initiation complex (PIC) comprised of Pol II and conserved general transcription factors (GTFs) assembles and opens DNA upstream of TSSs. Evidence from model promoters indicates that the PIC scans from upstream to downstream to identify TSSs. Prior results suggest that TSS distributions at promoters where scanning occurs shift in a polar fashion upon alteration in Pol II catalytic activity or GTF function. Results To determine the extent of promoter scanning across promoter classes in S. cerevisiae, we perturb Pol II catalytic activity and GTF function and analyze their effects on TSS usage genome-wide. We find that alterations to Pol II, TFIIB, or TFIIF function widely alter the initiation landscape consistent with promoter scanning operating at all yeast promoters, regardless of promoter class. Promoter architecture, however, can determine the extent of promoter sensitivity to altered Pol II activity in ways that are predicted by a scanning model. Conclusions Our observations coupled with previous data validate key predictions of the scanning model for Pol II initiation in yeast, which we term the shooting gallery. In this model, Pol II catalytic activity and the rate and processivity of Pol II scanning together with promoter sequence determine the distribution of TSSs and their usage.

Additional file 5. Review history.

The constitutive androstane receptor (CAR; NR1I3) is a nuclear receptor orchestrating complex roles in cell and systems biology. Species differences in CAR's effector pathways remain poorly understood, including its role in regulating liver tumor promotion. We developed transgenic mouse models to assess genome-wide binding of mouse and human CAR, following receptor activation in liver with direct ligands and with phenobarbital, an indirect CAR activator. Genomic interaction profiles were integrated with transcriptional and biological pathway analyses. Newly identified CAR target genes included Gdf15 and Foxo3, important regulators of the carcinogenic process. Approximately 1000 genes exhibited differential binding interactions between mouse and human CAR, including the proto-oncogenes, Myc and Ikbke, which demonstrated preferential binding by mouse CAR as well as mouse CAR-selective transcriptional enhancement. The ChIP-exo analyses also identified distinct binding motifs for the respective mouse and human receptors. Together, the results provide new insights into the important roles that CAR contributes as a key modulator of numerous signaling pathways in mammalian organisms, presenting a genomic context that specifies species variation in biological processes under CAR's control, including liver cell proliferation and tumor promotion.

General regulatory factors (GRFs), such as Reb1, Abf1, Rap1, Mcm1, and Cbf1, positionally organize yeast chromatin through interactions with a core consensus DNA sequence. It is assumed that sequence recognition via direct base readout suffices for specificity and that spurious nonfunctional sites are rendered inaccessible by chromatin. We tested these assumptions through genome-wide mapping of GRFs in vivo and in purified biochemical systems at near–base pair (bp) resolution using several ChIP-exo–based assays. We find that computationally predicted DNA shape features (e.g., minor groove width, helix twist, base roll, and propeller twist) that are not defined by a unique consensus sequence are embedded in the nonunique portions of GRF motifs and contribute critically to sequence-specific binding. This dual source specificity occurs at GRF sites in promoter regions where chromatin organization starts. Outside of promoter regions, strong consensus sites lack the shape component and consequently lack an intrinsic ability to bind cognate GRFs, without regard to influences from chromatin. However, sites having a weak consensus and low intrinsic affinity do exist in these regions but are rendered inaccessible in a chromatin environment. Thus, GRF site-specificity is achieved through integration of favorable DNA sequence and shape readouts in promoter regions and by chromatin-based exclusion from fortuitous weak sites within gene bodies. This study further revealed a severe G/C nucleotide cross-linking selectivity inherent in all formaldehyde-based ChIP assays, which includes ChIP-seq. However, for most tested proteins, G/C selectivity did not appreciably affect binding site detection, although it does place limits on the quantitativeness of occupancy levels.

Summary Nuclear actin has been elusive due to the lack of knowledge about molecular mechanisms. From actin-containing chromatin remodeling complexes, we discovered an arginine mono-methylation mark on an evolutionarily conserved R256 residue of actin (R256me1). Actin R256 mutations in yeast affect nuclear functions and cause diseases in human. Interestingly, we show that an antibody specific for actin R256me1 preferentially stains nuclear actin over cytoplasmic actin in yeast, mouse, and human cells. We also show that actin R256me1 is regulated by protein arginine methyl transferase-5 (PRMT5) in HEK293 cells. A genome-wide survey of actin R256me1 mark provides a landscape for nuclear actin correlated with transcription. Further, gene expression and protein interaction studies uncover extensive correlations between actin R256me1 and active transcription. The discovery of actin R256me1 mark suggests a fundamental mechanism to distinguish nuclear actin from cytoplasmic actin through post-translational modification (PTM) and potentially implicates an actin PTM mark in transcription and human diseases.

SUMMARY In multicellular eukaryotes, RNA polymerase (Pol) II pauses transcription ~30–50 bp after initiation. While the budding yeast Saccharomyces has its transcription mechanisms mostly conserved with other eukaryotes, it appears to lack this fundamental promoter-proximal pausing. However, we now report that nearly all yeast genes, including constitutive and inducible genes, manifest two distinct transcriptional stall sites that are brought on by acute environmental signaling (e.g., peroxide stress). Pol II first stalls at the pre-initiation stage before promoter clearance, but after DNA melting and factor acquisition, and may involve inhibited dephosphorylation. The second stall occurs at the +2 nucleosome. It acquires most, but not all, elongation factor interactions. Its regulation may include Bur1/Spt4/5. Our results suggest that a double Pol II stall is a mechanism to downregulate essentially all genes in concert., Graphical Abstract, In Brief Unlike metazoans, transcription in budding yeast proceeds rapidly from start to end. However, Badjatia et al. now show that acute stress causes Pol II to stall at two primary locations at the 5′ ends of most yeast genes. Stalling may facilitate rapid gene silencing, which promotes stress-induced gene-specific reprogramming.

Transcription of protein-coding and noncoding DNA occurs pervasively throughout the mammalian genome. Their sites of initiation are generally inferred from transcript 5′ ends and are thought to be either locally dispersed or focused. How these two modes of initiation relate is unclear. Here, we apply permanganate treatment and chromatin immunoprecipitation (PIP-seq) of initiation factors to identify the precise location of melted DNA separately associated with the preinitiation complex (PIC) and the adjacent paused complex (PC). This approach revealed the two known modes of transcription initiation. However, in contrast to prevailing views, they co-occurred within the same promoter region: initiation originating from a focused PIC, and broad nucleosome-linked initiation. PIP-seq allowed transcriptional orientation of Pol II to be determined, which may be useful near promoters where sufficient sense/anti-sense transcript mapping information is lacking. PIP-seq detected divergently oriented Pol II at both coding and noncoding promoters, as well as at enhancers. Their occupancy levels were not necessarily coupled in the two orientations. DNA sequence and shape analysis of initiation complex sites suggest that both sequence and shape contribute to specificity, but in a context-restricted manner. That is, initiation sites have the locally “best” initiator (INR) sequence and/or shape. These findings reveal a common core to pervasive Pol II initiation throughout the human genome.

Mediator is a highly conserved transcriptional coactivator organized into four modules, namely Tail, Middle, Head, and Kinase (CKM). Previous work suggests regulatory roles for Tail and CKM, but an integrated model for these activities is lacking. Here, we analyzed the genome-wide distribution of Mediator subunits in wild-type and mutant yeast cells in which RNA polymerase II promoter escape is blocked, allowing detection of transient Mediator forms. We found that although all modules are recruited to upstream activated regions (UAS), assembly of Mediator within the pre-initiation complex is accompanied by the release of CKM. Interestingly, our data show that CKM regulates Mediator-UAS interaction rather than Mediator-promoter association. In addition, although Tail is required for Mediator recruitment to UAS, Tailless Mediator nevertheless interacts with core promoters. Collectively, our data suggest that the essential function of Mediator is mediated by Head and Middle at core promoters, while Tail and CKM play regulatory roles.

We report a mechanism through which the transcription machinery directly controls topoisomerase 1 (TOP1) activity to adjust DNA topology throughout the transcription cycle. By comparing TOP1 occupancy using chromatin immunoprecipitation sequencing (ChIP-seq) versus TOP1 activity using topoisomerase 1 sequencing (TOP1-seq), a method reported here to map catalytically engaged TOP1, TOP1 bound at promoters was discovered to become fully active only after pause-release. This transition coupled the phosphorylation of the carboxyl-terminal-domain (CTD) of RNA polymerase II (RNAPII) with stimulation of TOP1 above its basal rate, enhancing its processivity. TOP1 stimulation is strongly dependent on the kinase activity of BRD4, a protein that phosphorylates Ser2-CTD and regulates RNAPII pause-release. Thus the coordinated action of BRD4 and TOP1 overcame the torsional stress opposing transcription as RNAPII commenced elongation but preserved negative supercoiling that assists promoter melting at start sites. This nexus between transcription and DNA topology promises to elicit new strategies to intercept pathological gene expression.

Erythroid development and differentiation from multiprogenitor cells into red blood cells requires precise transcriptional regulation. Key erythroid transcription factors, GATA1 and TAL1, cooperate, along with other proteins, to regulate many aspects of this process. How GATA1 and TAL1 are juxtaposed along the DNA and their cognate DNA binding site across the mouse genome remains unclear. We applied high-resolution ChIP-exo (chromatin immunoprecipitation followed by 5'-to-3' exonuclease treatment and then massively parallel DNA sequencing) to GATA1 and TAL1 to study their positional organization across the mouse genome during GATA1-dependent maturation. Two complementary methods, MultiGPS and peak pairing, were used to determine high-confidence binding locations by ChIP-exo. We identified ∼10,000 GATA1 and ∼15,000 TAL1 locations, which were essentially confirmed by ChIP-seq (chromatin immunoprecipitation followed by massively parallel DNA sequencing). Of these, ∼4,000 locations were bound by both GATA1 and TAL1. About three-quarters of them were tightly linked to a partial E-box located 7 or 8 bp upstream of a WGATAA motif. Both TAL1 and GATA1 generated distinct characteristic ChIP-exo peaks around WGATAA motifs that reflect their positional arrangement within a complex. We show that TAL1 and GATA1 form a precisely organized complex at a compound motif consisting of a TG 7 or 8 bp upstream of a WGATAA motif across thousands of genomic locations.

ChIP-seq and ChIP-exo identify where proteins bind along any genome in vivo. Although ChIP-seq is widely adopted in academic research, it has inherently high noise. In contrast, ChIP-exo has relatively low noise and achieves near-base pair resolution. Consequently, and unlike other genomic assays, ChIP-exo provides structural information on genome-wide binding proteins. Construction of ChIP-exo libraries is technically difficult. Here we describe greatly simplified ChIP-exo methods, each with use-specific advantages. This is achieved through assay optimization and use of Tn5 tagmentation and/or single-stranded DNA ligation. Greater library yields, lower processing time, and lower costs are achieved. In comparing assays, we reveal substantial limitations in other ChIP-based assays. Importantly, the new ChIP-exo assays allow high-resolution detection of some protein-DNA interactions in organs and in as few as 27,000 cells. It is suitable for high-throughput parallelization. The simplicity of ChIP-exo now makes it a highly appropriate substitute for ChIP-seq, and for broader adoption., While ChIP-exo is low noise and highly informative regarding genome-wide binding proteins, libraries are difficult to construct. Here the authors present a simplified ChIP-exo method for high-resolution detection of interactions.

Motivation Regulatory proteins associate with the genome either by directly binding cognate DNA motifs or via protein–protein interactions with other regulators. Each recruitment mechanism may be associated with distinct motifs and may also result in distinct characteristic patterns in high-resolution protein–DNA binding assays. For example, the ChIP-exo protocol precisely characterizes protein–DNA crosslinking patterns by combining chromatin immunoprecipitation (ChIP) with 5′ → 3′ exonuclease digestion. Since different regulatory complexes will result in different protein–DNA crosslinking signatures, analysis of ChIP-exo tag enrichment patterns should enable detection of multiple protein–DNA binding modes for a given regulatory protein. However, current ChIP-exo analysis methods either treat all binding events as being of a uniform type or rely on motifs to cluster binding events into subtypes. Results To systematically detect multiple protein–DNA interaction modes in a single ChIP-exo experiment, we introduce the ChIP-exo mixture model (ChExMix). ChExMix probabilistically models the genomic locations and subtype memberships of binding events using both ChIP-exo tag distribution patterns and DNA motifs. We demonstrate that ChExMix achieves accurate detection and classification of binding event subtypes using in silico mixed ChIP-exo data. We further demonstrate the unique analysis abilities of ChExMix using a collection of ChIP-exo experiments that profile the binding of key transcription factors in MCF-7 cells. In these data, ChExMix identifies possible recruitment mechanisms of FoxA1 and ERα, thus demonstrating that ChExMix can effectively stratify ChIP-exo binding events into biologically meaningful subtypes. Availability and implementation ChExMix is available from https://github.com/seqcode/chexmix. Supplementary information Supplementary data are available at Bioinformatics online.

Background Eukaryotic transcription is regulated through two complexes, the general transcription factor IID (TFIID) and the coactivator Spt–Ada–Gcn5 acetyltransferase (SAGA). Recent findings confirm that both TFIID and SAGA contribute to the synthesis of nearly all transcripts and are recruited genome-wide in yeast. However, how this broad recruitment confers selectivity under specific conditions remains an open question. Results Here we find that the SAGA/TREX-2 subunit Sus1 associates with upstream regulatory regions of many yeast genes and that heat shock drastically changes Sus1 binding. While Sus1 binding to TFIID-dominated genes is not affected by temperature, its recruitment to SAGA-dominated genes and RP genes is significantly disturbed under heat shock, with Sus1 relocated to environmental stress-responsive genes in these conditions. Moreover, in contrast to recent results showing that SAGA deubiquitinating enzyme Ubp8 is dispensable for RNA synthesis, genomic run-on experiments demonstrate that Sus1 contributes to synthesis and stability of a wide range of transcripts. Conclusions Our study provides support for a model in which SAGA/TREX-2 factor Sus1 acts as a global transcriptional regulator in yeast but has differential activity at yeast genes as a function of their transcription rate or during stress conditions., VG-M was supported by the FPU program from MEC (AP2009-0917). This study was supported by funds from MINECO (BFU2011-23418 and BFU2014-57636 to SR-N, BIO2012-40244 to AC and BFU2016-77728-C3-3-P to JEP-O) and GV (PROMETEO/2012/061, PROMETEO/2016/093 and ACOMP/2014/061 to SR-N, and PROMETEOII/2015/006, ACOMP/2014/253 to JEP-O). The contributions of PF-T and AC were supported by the FP7 Project STATegra 306000. BFP was supported by NIH GM059055. BFP has a fnancial interest in Peconic, LLC, which utilizes the ChIP-exo technology implemented in this study and could potentially beneft from the outcomes of this research.

Recent advances in experimental and computational methodologies are enabling ultra-high resolution genome-wide profiles of protein-DNA binding events. For example, the ChIP-exo protocol precisely characterizes protein-DNA cross-linking patterns by combining chromatin immunoprecipitation (ChIP) with 5' → 3' exonuclease digestion. Similarly, deeply sequenced chromatin accessibility assays (e.g. DNase-seq and ATAC-seq) enable the detection of protected footprints at protein-DNA binding sites. With these techniques and others, we have the potential to characterize the individual nucleotides that interact with transcription factors, nucleosomes, RNA polymerases and other regulatory proteins in a particular cellular context. In this review, we explain the experimental assays and computational analysis methods that enable high-resolution profiling of protein-DNA binding events. We discuss the challenges and opportunities associated with such approaches.

The Ccr4 (carbon catabolite repression 4)-Not complex is a major regulator of stress responses that controls gene expression at multiple levels, from transcription to mRNA decay. Ccr4, a “core” subunit of the complex, is the main cytoplasmic deadenylase in Saccharomyces cerevisiae; however, its mRNA targets have not been mapped on a genome-wide scale. Here, we describe a genome-wide approach, RNA immunoprecipitation (RIP) high-throughput sequencing (RIP-seq), to identify the RNAs bound to Ccr4, and two proteins that associate with it, Dhh1 and Puf5. All three proteins were preferentially bound to lowly abundant mRNAs, most often at the 3′ end of the transcript. Furthermore, Ccr4, Dhh1, and Puf5 are recruited to mRNAs that are targeted by other RNA-binding proteins that promote decay and mRNA transport, and inhibit translation. Although Ccr4-Not regulates mRNA transcription and decay, Ccr4 recruitment to mRNAs correlates better with decay rates, suggesting it imparts greater control over transcript abundance through decay. Ccr4-enriched mRNAs are refractory to control by the other deadenylase complex in yeast, Pan2/3, suggesting a division of labor between these deadenylation complexes. Finally, Ccr4 and Dhh1 associate with mRNAs whose abundance increases during nutrient starvation, and those that fluctuate during metabolic and oxygen consumption cycles, which explains the known genetic connections between these factors and nutrient utilization and stress pathways.

Gene expression is controlled by a variety of proteins that interact with the genome. Their precise organization and mechanism of action at every promoter remains to be worked out. To better understand the physical interplay among genome-interacting proteins, we examined the temporal binding of a functionally diverse subset of these proteins: nucleosomes (H3), H2AZ (Htz1), SWR (Swr1), RSC (Rsc1, Rsc3, Rsc58, Rsc6, Rsc9, Sth1), SAGA (Spt3, Spt7, Ubp8, Sgf11), Hsf1, TFIID (Spt15/TBP and Taf1), TFIIB (Sua7), TFIIH (Ssl2), FACT (Spt16), Pol II (Rpb3), and Pol II carboxyl-terminal domain (CTD) phosphorylation at serines 2, 5, and 7. They were examined under normal and acute heat shock conditions, using the ultrahigh resolution genome-wide ChIP-exo assay in Saccharomyces cerevisiae. Our findings reveal a precise positional organization of proteins bound at most genes, some of which rapidly reorganize within minutes of heat shock. This includes more precise positional transitions of Pol II CTD phosphorylation along the 5′ ends of genes than previously seen. Reorganization upon heat shock includes colocalization of SAGA with promoter-bound Hsf1, a change in RSC subunit enrichment from gene bodies to promoters, and Pol II accumulation within promoter/+1 nucleosome regions. Most of these events are widespread and not necessarily coupled to changes in gene expression. Together, these findings reveal protein–genome interactions that are robustly reprogrammed in precise and uniform ways far beyond what is elicited by changes in gene expression.