Locus-specific proteome decoding reveals Fpt1 as a chromatin-associated negative regulator of RNA polymerase III assembly.

Transcription of tRNA genes by RNA polymerase III (RNAPIII) is tuned by signaling cascades. The emerging notion of differential tRNA gene regulation implies the existence of additional regulatory mechanisms. However, tRNA gene-specific regulators have not been described. Decoding the local chromatin proteome of a native tRNA gene in yeast revealed reprogramming of the RNAPIII transcription machinery upon nutrient perturbation. Among the dynamic proteins, we identified Fpt1, a protein of unknown function that uniquely occupied RNAPIII-regulated genes. Fpt1 binding at tRNA genes correlated with the efficiency of RNAPIII eviction upon nutrient perturbation and required the transcription factors TFIIIB and TFIIIC but not RNAPIII. In the absence of Fpt1, eviction of RNAPIII was reduced, and the shutdown of ribosome biogenesis genes was impaired upon nutrient perturbation. Our findings provide support for a chromatin-associated mechanism required for RNAPIII eviction from tRNA genes and tuning the physiological response to changing metabolic demands.

GenoPipe: identifying the genotype of origin within (epi)genomic datasets.

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).

Joint sequence & chromatin neural networks characterize the differential abilities of Forkhead transcription factors to engage inaccessible chromatin.

The DNA-binding activities of transcription factors (TFs) are influenced by both intrinsic sequence preferences and extrinsic interactions with cell-specific chromatin landscapes and other regulatory proteins. Disentangling the roles of these binding determinants remains challenging. For example, the FoxA subfamily of Forkhead domain (Fox) TFs are known pioneer factors that can bind to relatively inaccessible sites during development. Yet FoxA TF binding also varies across cell types, pointing to a combination of intrinsic and extrinsic forces guiding their binding. While other Forkhead domain TFs are often assumed to have pioneering abilities, how sequence and chromatin features influence the binding of related Fox TFs has not been systematically characterized. Here, we present a principled approach to compare the relative contributions of intrinsic DNA sequence preference and cell-specific chromatin environments to a TF's DNA-binding activities. We apply our approach to investigate how a selection of Fox TFs (FoxA1, FoxC1, FoxG1, FoxL2, and FoxP3) vary in their binding specificity. We over-express the selected Fox TFs in mouse embryonic stem cells, which offer a platform to contrast each TF's binding activity within the same preexisting chromatin background. By applying a convolutional neural network to interpret the Fox TF binding patterns, we evaluate how sequence and preexisting chromatin features jointly contribute to induced TF binding. We demonstrate that Fox TFs bind different DNA targets, and drive differential gene expression patterns, even when induced in identical chromatin settings. Despite the association between Forkhead domains and pioneering activities, the selected Fox TFs display a wide range of affinities for preexiting chromatin states. Using sequence and chromatin feature attribution techniques to interpret the neural network predictions, we show that differential sequence preferences combined with differential abilities to engage relatively inaccessible chromatin together explain Fox TF binding patterns at individual sites and genome-wide.

GenoPipe: identifying the genotype of origin within (epi)genomic datasets.

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).

An integrated SAGA and TFIID PIC assembly pathway selective for poised and induced promoters.

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.

Genome-wide promoter assembly in E. coli measured at single-base resolution.

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.

PEGR: a flexible management platform for reproducible epigenomic and genomic research.

Reproducibility is a significant challenge in (epi)genomic research due to the complexity of experiments composed of traditional biochemistry and informatics. Recent advances have exacerbated this as high-throughput sequencing data is generated at an unprecedented pace. Here, we report the development of a Platform for Epi-Genomic Research (PEGR), a web-based project management platform that tracks and quality controls experiments from conception to publication-ready figures, compatible with multiple assays and bioinformatic pipelines. It supports rigor and reproducibility for biochemists working at the bench, while fully supporting reproducibility and reliability for bioinformaticians through integration with the Galaxy platform.

STENCIL: A web templating engine for visualizing and sharing life science datasets.

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.

Ssl2/TFIIH function in transcription start site scanning by RNA polymerase II in Saccharomyces cerevisiae.

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.

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.

A ChIP-exo screen of 887 Protein Capture Reagents Program transcription factor antibodies in human cells.

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.

A high-resolution protein architecture of the budding yeast genome.

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.

What do Transcription Factors Interact With?

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.

Acute stress drives global repression through two independent RNA polymerase II stalling events in Saccharomyces.

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.

Actin R256 Mono-methylation Is a Conserved Post-translational Modification Involved in Transcription.

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.

Alignment and quantification of ChIP-exo crosslinking patterns reveal the spatial organization of protein-DNA complexes.

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 its subunits. 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. Applying our approach to collections of ChIP-exo data, we demonstrate that it can recover aspects of regulatory complex spatial organization 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 analyze 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.

Universal promoter scanning by Pol II during transcription initiation in Saccharomyces cerevisiae.

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.

Phase separation directs ubiquitination of gene-body nucleosomes.

The conserved yeast E3 ubiquitin ligase Bre1 and its partner, the E2 ubiquitin-conjugating enzyme Rad6, monoubiquitinate histone H2B across gene bodies during the transcription cycle1. Although processive ubiquitination might-in principle-arise from Bre1 and Rad6 travelling with RNA polymerase II2, the mechanism of H2B ubiquitination across genic nucleosomes remains unclear. Here we implicate liquid-liquid phase separation3 as the underlying mechanism. Biochemical reconstitution shows that Bre1 binds the scaffold protein Lge1, which possesses an intrinsically disordered region that phase-separates via multivalent interactions. The resulting condensates comprise a core of Lge1 encapsulated by an outer catalytic shell of Bre1. This layered liquid recruits Rad6 and the nucleosomal substrate, which accelerates the ubiquitination of H2B. In vivo, the condensate-forming region of Lge1 is required to ubiquitinate H2B in gene bodies beyond the +1 nucleosome. Our data suggest that layered condensates of histone-modifying enzymes generate chromatin-associated 'reaction chambers', with augmented catalytic activity along gene bodies. Equivalent processes may occur in human cells, and cause neurological disease when impaired.

ChExMix: A Method for Identifying and Classifying Protein-DNA Interaction Subtypes.

Regulatory proteins can employ multiple direct and indirect modes of interaction with the genome. The ChIP-exo mixture model (ChExMix) provides a principled approach to detecting multiple protein-DNA interaction modes in a single ChIP-exo experiment. ChExMix discovers and characterizes binding event subtypes in ChIP-exo data by leveraging both protein-DNA cross-linking signatures and DNA motifs. In this study, we present a summary of the major features and applications of ChExMix. We demonstrate that ChExMix does not require high-resolution protein-DNA binding assay data to detect binding event subtypes. Specifically, we apply ChExMix to analyze 393 ChIP-seq data profiles in K562 cells. Similar binding event subtypes are discovered across multiple proteins, suggesting the existence of colocalized regulatory protein modules that are recruited to DNA through a particular sequence-specific transcription factor. Our results thus suggest that ChExMix can characterize protein-DNA binding interaction modes using data from multiple types of protein-DNA interaction assays.

PEGR: a management platform for ChIP-based next generation sequencing pipelines.

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.