Simplified ChIP-exo assays

ChIP-exo and CRISPRi/a illuminate the role of Pdr1 and Yap1 in acetic acid tolerance in Saccharomyces cerevisiae

Budding yeast Saccharomyces cerevisiae has great potential as a host organism for various biorefinery applications. Nevertheless, the utilization of renewable plant biomass as feedstock for yeast in industrial applications remains a bottleneck, largely due to the presence of inhibitory substances such as acetic acid that are released in the biomass pretreatment processes. Exposure to acetic acid leads to different cellular stress mechanisms, several of which are directed by transcription factors. In this work, the role of the transcription factors Pdr1 and Yap1 in acetic acid tolerance was investigated using ChIP-exo and CRISPR interference/activation (CRISPRi/a). Pdr1 is the main regulator of the pleiotropic drug response, whereas Yap1 governs the oxidative stress response. CRISPRa targeting YAP1 for overexpression conferred a higher specific growth rate of S. cerevisiae, whereas CRISPRi-based downregulation of PDR1 proved to be beneficial for growth in medium containing acetic acid. ChIP-exo experiments showed increased binding of Pdr1 or Yap1 to their target promoters in the presence of acetic acid, and a large number of promoters were bound by either transcription factor. Promoters of genes involved in amino acid synthesis or encoding ABC transporters had the highest level of binding enrichment in the presence of acetic acid. The results highlight the potential for developing more acetic acid-tolerant yeast by altering the expression of transcription factor-encoding genes and demonstrate how expression can be fine-tuned by CRISPRi/a.IMPORTANCEBiotechnological conversion of plant biomass into a variety of commodity chemicals and specialty molecules is an important step towards a bioeconomy. This study highlights the importance of two transcription factors, Pdr1 and Yap1, in the tolerance of Saccharomyces cerevisiae to acetic acid, a common inhibitor in bioprocesses using lignocellulosic biomass. CRISPR interference/activation and ChIP-exo were used to manipulate the expression and binding of these transcription factors in response to acetic acid stress. The study provides new insights into adaptation to acetic acid and suggests ways to improve yeast performance in industrial applications.

Genomic regions occupied by both RARα and VDR are involved in the convergence and cooperation of retinoid and vitamin D signaling pathways

Retinoic acid receptors (RARs) and the vitamin D receptor (VDR) regulate distinct but overlapping gene sets in multiple cell types. The abundance and characteristics of regulatory regions, occupied by both RARs and VDR are largely unexplored. We used global approaches (ChIP-seq, RNA-seq, and ATAC-seq) and bioinformatics tools to map and characterize common binding regions of RARα and VDR in differentiated human THP-1 cells. We found that the cistromes of ligand-activated RARα and VDR largely overlapped, and their agonists (AM580 and calcitriol) co-regulated several genes, often cooperatively. Common binding regions were frequently (but not exclusively) annotated with co-regulated genes and exhibited increased MED1 occupancy upon ligand stimulation, suggesting their involvement in gene regulation. Chromatin accessibility was typically higher in the common regions than in regions occupied exclusively by RARα or VDR. DNA response elements for RARα (DR1/2/5) and VDR (DR3) were enriched in the common regions, albeit the co-occurrence of the two types of canonical motifs was low (8.4%), suggesting that "degenerate" DR1/2/5 and DR3 motifs or other sequences could mediate the binding. In summary, common binding regions of RARα and VDR are at the crossroads of the retinoid and vitamin D pathways, playing important roles in their convergence and cooperation.

ChIP-mini: a low-input ChIP-exo protocol for elucidating DNA-binding protein dynamics in intracellular pathogens

Genome-wide identification of binding profiles for DNA-binding proteins from the limited number of intracellular pathogens in infection studies is crucial for understanding virulence and cellular processes but remains challenging, as the current ChIP-exo is designed for high-input bacterial cells (>1010). Here, we developed an optimized ChIP-mini method, a low-input ChIP-exo utilizing a 5,000-fold reduced number of initial bacterial cells and an analysis pipeline, to identify genome-wide binding dynamics of DNA-binding proteins in host-infected pathogens. Applying ChIP-mini to intracellular Salmonella Typhimurium, we identified 642 and 1,837 binding sites of H-NS and RpoD, respectively, elucidating changes in their binding position and binding intensity during infection. Post-infection, we observed 21 significant reductions in H-NS binding at intergenic regions, exposing the promoter region of virulence genes, such as those in Salmonella pathogenicity islands-2, 3 and effectors. Furthermore, we revealed the crucial phenomenon that novel and significantly increased RpoD bindings were found within regions exhibiting diminished H-NS binding, thereby facilitating substantial upregulation of virulence genes. These findings markedly enhance our understanding of how H-NS and RpoD simultaneously coordinate the transcription initiation of virulence genes within macrophages. Collectively, this work demonstrates a broadly adaptable tool that will enable the elucidation of DNA-binding protein dynamics in diverse intracellular pathogens during infection.

Coordinated repression of totipotency-associated gene loci by histone methyltransferase EHMT2 through binding to LINE-1 regulatory elements

Mouse embryonic stem cells (mESCs) and other naïve pluripotent stem cells can reverse typical developmental trajectories and, at low frequency, de-differentiate into 2-cell-like cells (2CLCs) that resemble the mammalian embryo during zygotic genome activation (ZGA). This affords the opportunity to reveal molecular principles that govern the pre-implantation stages of mammalian development. We leveraged a multipurpose allele for acute protein depletion and efficient immunoprecipitation to dissect the molecular functions of the chromatin repressor EHMT2, a candidate antagonist of the mESC-to-2CLC transition. This allowed us to define categories of EHMT2 target genes characterized by distinct modes of EHMT2 chromatin engagement and repression. Most notably, EHMT2 directly represses large clusters of co-regulated gene loci that comprise a significant fraction of the 2CLC-specific transcriptome by initiating H3K9me2 spreading from distal LINE-1 elements. EHMT2 counteracts the recruitment of the activator DPPA2/4 to promoter-proximal endogenous retroviral elements (ERVs) at 2CLC genes. EHMT2 depletion elevates the expression of ZGA-associated transcripts in 2CLCs and synergizes with spliceosome inhibition and retinoic acid signaling in facilitating the mESC-to-2CLC transition. In contrast to ZGA-associated genes, repression of germ layer-associated transcripts by EHMT2 occurs outside of gene clusters in collaboration with ZFP462 and entails binding to non-repeat enhancers. Our observations show that EHMT2 attenuates the bidirectional differentiation potential of mouse pluripotent stem cells and define molecular modes for locus-specific transcriptional repression by this essential histone methyltransferase.

Molecular basis of global promoter sensing and nucleosome capture by the SWR1 chromatin remodeler

The SWR1 chromatin remodeling complex is recruited to +1 nucleosomes downstream of transcription start sites of eukaryotic promoters, where it exchanges histone H2A for the specialized variant H2A.Z. Here, we use cryoelectron microscopy (cryo-EM) to resolve the structural basis of the SWR1 interaction with free DNA, revealing a distinct open conformation of the Swr1 ATPase that enables sliding from accessible DNA to nucleosomes. A complete structural model of the SWR1-nucleosome complex illustrates critical roles for Swc2 and Swc3 subunits in oriented nucleosome engagement by SWR1. Moreover, an extended DNA-binding α helix within the Swc3 subunit enables sensing of nucleosome linker length and is essential for SWR1-promoter-specific recruitment and activity. The previously unresolved N-SWR1 subcomplex forms a flexible extended structure, enabling multivalent recognition of acetylated histone tails by reader domains to further direct SWR1 toward the +1 nucleosome. Altogether, our findings provide a generalizable mechanism for promoter-specific targeting of chromatin and transcription complexes.

Leveraging the power of long reads for targeted sequencing

Long-read sequencing technologies have improved the contiguity and, as a result, the quality of genome assemblies by generating reads long enough to span and resolve complex or repetitive regions of the genome. Several groups have shown the power of long reads in detecting thousands of genomic and epigenomic features that were previously missed by short-read sequencing approaches. While these studies demonstrate how long reads can help resolve repetitive and complex regions of the genome, they also highlight the throughput and coverage requirements needed to accurately resolve variant alleles across large populations using these platforms. At the time of this review, whole-genome long-read sequencing is more expensive than short-read sequencing on the highest throughput short-read instruments; thus, achieving sufficient coverage to detect low-frequency variants (such as somatic variation) in heterogenous samples remains challenging. Targeted sequencing, on the other hand, provides the depth necessary to detect these low-frequency variants in heterogeneous populations. Here, we review currently used and recently developed targeted sequencing strategies that leverage existing long-read technologies to increase the resolution with which we can look at nucleic acids in a variety of biological contexts.

Benchmarking and building DNA binding affinity models using allele-specific and allele-agnostic transcription factor binding data

BACKGROUND

Transcription factors (TFs) bind to DNA in a highly sequence-specific manner. This specificity manifests itself in vivo as differences in TF occupancy between the two alleles at heterozygous loci. Genome-scale assays such as ChIP-seq currently are limited in their power to detect allele-specific binding (ASB) both in terms of read coverage and representation of individual variants in the cell lines used. This makes prediction of allelic differences in TF binding from sequence alone desirable, provided that the reliability of such predictions can be quantitatively assessed.

RESULTS

We here propose methods for benchmarking sequence-to-affinity models for TF binding in terms of their ability to predict allelic imbalances in ChIP-seq counts. We use a likelihood function based on an over-dispersed binomial distribution to aggregate evidence for allelic preference across the genome without requiring statistical significance for individual variants. This allows us to systematically compare predictive performance when multiple binding models for the same TF are available. To facilitate the de novo inference of high-quality models from paired-end in vivo binding data such as ChIP-seq, ChIP-exo, and CUT&Tag without read mapping or peak calling, we introduce an extensible reimplementation of our biophysically interpretable machine learning framework named PyProBound. Explicitly accounting for assay-specific bias in DNA fragmentation rate when training on ChIP-seq yields improved TF binding models. Moreover, we show how PyProBound can leverage our threshold-free ASB likelihood function to perform de novo motif discovery using allele-specific ChIP-seq counts.

CONCLUSION

Our work provides new strategies for predicting the functional impact of non-coding variants.

Systematic identification of post-transcriptional regulatory modules

In our cells, a limited number of RNA binding proteins (RBPs) are responsible for all aspects of RNA metabolism across the entire transcriptome. To accomplish this, RBPs form regulatory units that act on specific target regulons. However, the landscape of RBP combinatorial interactions remains poorly explored. Here, we perform a systematic annotation of RBP combinatorial interactions via multimodal data integration. We build a large-scale map of RBP protein neighborhoods by generating in vivo proximity-dependent biotinylation datasets of 50 human RBPs. In parallel, we use CRISPR interference with single-cell readout to capture transcriptomic changes upon RBP knockdowns. By combining these physical and functional interaction readouts, along with the atlas of RBP mRNA targets from eCLIP assays, we generate an integrated map of functional RBP interactions. We then use this map to match RBPs to their context-specific functions and validate the predicted functions biochemically for four RBPs. This study provides a detailed map of RBP interactions and deconvolves them into distinct regulatory modules with annotated functions and target regulons. This multimodal and integrative framework provides a principled approach for studying post-transcriptional regulatory processes and enriches our understanding of their underlying mechanisms.

aChIP is an efficient and sensitive ChIP-seq technique for economically important plant organs

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is crucial for profiling histone modifications and transcription factor binding throughout the genome. However, its application in economically important plant organs (EIPOs) such as seeds, fruits and flowers is challenging due to their sturdy cell walls and complex constituents. Here we present advanced ChIP (aChIP), an optimized method that efficiently isolates chromatin from plant tissues while simultaneously removing cell walls and cellular constituents. aChIP precisely profiles histone modifications in all 14 tested EIPOs and identifies transcription factor and chromatin-modifying enzyme binding sites. In addition, aChIP enhances ChIP efficiency, revealing numerous novel modified sites compared with previous methods in vegetative tissues. aChIP reveals the histone modification landscape for rapeseed dry seeds, highlighting the intricate roles of chromatin dynamics during seed dormancy and germination. Altogether, aChIP is a powerful, efficient and sensitive approach for comprehensive chromatin profiling in virtually all plant tissues, especially in EIPOs.

MCM2-7 loading-dependent ORC release ensures genome-wide origin licensing

Origin recognition complex (ORC)-dependent loading of the replicative helicase MCM2-7 onto replication origins in G1-phase forms the basis of replication fork establishment in S-phase. However, how ORC and MCM2-7 facilitate genome-wide DNA licensing is not fully understood. Mapping the molecular footprints of budding yeast ORC and MCM2-7 genome-wide, we discovered that MCM2-7 loading is associated with ORC release from origins and redistribution to non-origin sites. Our bioinformatic analysis revealed that origins are compact units, where a single MCM2-7 double hexamer blocks repetitive loading through steric ORC binding site occlusion. Analyses of A-elements and an improved B2-element consensus motif uncovered that DNA shape, DNA flexibility, and the correct, face-to-face spacing of the two DNA elements are hallmarks of ORC-binding and efficient helicase loading sites. Thus, our work identified fundamental principles for MCM2-7 helicase loading that explain how origin licensing is realised across the genome.

A continuum of zinc finger transcription factor retention on native chromatin underlies dynamic genome organization

Transcription factor (TF) residence on chromatin translates into quantitative transcriptional or structural outcomes on genome. Commonly used formaldehyde crosslinking fixes TF-DNA interactions cumulatively and compromises the measured occupancy level. Here we mapped the occupancy level of global or individual zinc finger TFs like CTCF and MAZ, in the form of highly resolved footprints, on native chromatin. By incorporating reinforcing perturbation conditions, we established S-score, a quantitative metric to proxy the continuum of CTCF or MAZ retention across different motifs on native chromatin. The native chromatin-retained CTCF sites harbor sequence features within CTCF motifs better explained by S-score than the metrics obtained from other crosslinking or native assays. CTCF retention on native chromatin correlates with local SUMOylation level, and anti-correlates with transcriptional activity. The S-score successfully delineates the otherwise-masked differential stability of chromatin structures mediated by CTCF, or by MAZ independent of CTCF. Overall, our study established a paradigm continuum of TF retention across binding sites on native chromatin, explaining the dynamic genome organization.

Effects of super-enhancers in cancer metastasis: mechanisms and therapeutic targets

Metastasis remains the principal cause of cancer-related lethality despite advancements in cancer treatment. Dysfunctional epigenetic alterations are crucial in the metastatic cascade. Among these, super-enhancers (SEs), emerging as new epigenetic regulators, consist of large clusters of regulatory elements that drive the high-level expression of genes essential for the oncogenic process, upon which cancer cells develop a profound dependency. These SE-driven oncogenes play an important role in regulating various facets of metastasis, including the promotion of tumor proliferation in primary and distal metastatic organs, facilitating cellular migration and invasion into the vasculature, triggering epithelial-mesenchymal transition, enhancing cancer stem cell-like properties, circumventing immune detection, and adapting to the heterogeneity of metastatic niches. This heavy reliance on SE-mediated transcription delineates a vulnerable target for therapeutic intervention in cancer cells. In this article, we review current insights into the characteristics, identification methodologies, formation, and activation mechanisms of SEs. We also elaborate the oncogenic roles and regulatory functions of SEs in the context of cancer metastasis. Ultimately, we discuss the potential of SEs as novel therapeutic targets and their implications in clinical oncology, offering insights into future directions for innovative cancer treatment strategies.

Recent advances in exploring transcriptional regulatory landscape of crops

Crop breeding entails developing and selecting plant varieties with improved agronomic traits. Modern molecular techniques, such as genome editing, enable more efficient manipulation of plant phenotype by altering the expression of particular regulatory or functional genes. Hence, it is essential to thoroughly comprehend the transcriptional regulatory mechanisms that underpin these traits. In the multi-omics era, a large amount of omics data has been generated for diverse crop species, including genomics, epigenomics, transcriptomics, proteomics, and single-cell omics. The abundant data resources and the emergence of advanced computational tools offer unprecedented opportunities for obtaining a holistic view and profound understanding of the regulatory processes linked to desirable traits. This review focuses on integrated network approaches that utilize multi-omics data to investigate gene expression regulation. Various types of regulatory networks and their inference methods are discussed, focusing on recent advancements in crop plants. The integration of multi-omics data has been proven to be crucial for the construction of high-confidence regulatory networks. With the refinement of these methodologies, they will significantly enhance crop breeding efforts and contribute to global food security.

Uncovering co-regulatory modules and gene regulatory networks in the heart through machine learning-based analysis of large-scale epigenomic data

The availability of large-scale epigenomic data from various cell types and conditions has yielded valuable insights for evaluating and learning features predicting the co-binding of transcription factors (TF). However, prior attempts to develop models predicting motif co-occurrence lacked scalability for globally analyzing any motif combination or making cross-species predictions. Moreover, mapping co-regulatory modules (CRM) to gene regulatory networks (GRN) is crucial for understanding underlying function. Currently, no comprehensive pipeline exists for large-scale, rapid, and accurate CRM and GRN identification. In this study, we analyzed and evaluated different TF binding characteristics facilitating biologically significant co-binding to identify all potential clusters of co-binding TFs. We curated the UniBind database, containing ChIP-Seq data from over 1983 samples and 232 TFs, and implemented two machine learning models to predict CRMs and the potential regulatory networks they operate on. Two machine learning models, Convolution Neural Networks (CNN) and Random Forest Classifier(RFC), used to predict co-binding between TFs, were compared using precision-recall Receiver Operating Characteristic (ROC) curves. CNN outperformed RFC (AUC 0.94 vs. 0.88) and achieved higher F1 scores (0.938 vs. 0.872). The CRMs generated by the clustering algorithm were validated against ChipAtlas and MCOT, revealing additional motifs forming CRMs. We predicted 200k CRMs for 50k+ human genes, validated against recent CRM prediction methods with 100% overlap. Further, we narrowed our focus to study heart-related regulatory motifs, filtering the generated CRMs to report 1784 Cardiac CRMs containing at least four cardiac TFs. Identified cardiac CRMs revealed potential novel regulators like ARID3A and RXRB for SCAD, including known TFs like PPARG for F11R. Our findings highlight the importance of the NKX family of transcription factors in cardiac development and provide potential targets for further investigation in cardiac disease.

Cbp1 and Cren7 form chromatin-like structures that ensure efficient transcription of long CRISPR arrays

CRISPR arrays form the physical memory of CRISPR adaptive immune systems by incorporating foreign DNA as spacers that are often AT-rich and derived from viruses. As promoter elements such as the TATA-box are AT-rich, CRISPR arrays are prone to harbouring cryptic promoters. Sulfolobales harbour extremely long CRISPR arrays spanning several kilobases, a feature that is accompanied by the CRISPR-specific transcription factor Cbp1. Aberrant Cbp1 expression modulates CRISPR array transcription, but the molecular mechanisms underlying this regulation are unknown. Here, we characterise the genome-wide Cbp1 binding at nucleotide resolution and characterise the binding motifs on distinct CRISPR arrays, as well as on unexpected non-canonical binding sites associated with transposons. Cbp1 recruits Cren7 forming together 'chimeric' chromatin-like structures at CRISPR arrays. We dissect Cbp1 function in vitro and in vivo and show that the third helix-turn-helix domain is responsible for Cren7 recruitment, and that Cbp1-Cren7 chromatinization plays a dual role in the transcription of CRISPR arrays. It suppresses spurious transcription from cryptic promoters within CRISPR arrays but enhances CRISPR RNA transcription directed from their cognate promoters in their leader region. Our results show that Cbp1-Cren7 chromatinization drives the productive expression of long CRISPR arrays.

Mesoplasma florum: a near-minimal model organism for systems and synthetic biology

Mesoplasma florum is an emerging model organism for systems and synthetic biology due to its small genome (∼800 kb) and fast growth rate. While M. florum was isolated and first described almost 40 years ago, many important aspects of its biology have long remained uncharacterized due to technological limitations, the absence of dedicated molecular tools, and since this bacterial species has not been associated with any disease. However, the publication of the first M. florum genome in 2004 paved the way for a new era of research fueled by the rise of systems and synthetic biology. Some of the most important studies included the characterization and heterologous use of M. florum regulatory elements, the development of the first replicable plasmids, comparative genomics and transposon mutagenesis, whole-genome cloning in yeast, genome transplantation, in-depth characterization of the M. florum cell, as well as the development of a high-quality genome-scale metabolic model. The acquired data, knowledge, and tools will greatly facilitate future genome engineering efforts in M. florum, which could next be exploited to rationally design and create synthetic cells to advance fundamental knowledge or for specific applications.

Pycallingcards: an integrated environment for visualizing, analyzing, and interpreting Calling Cards data

MOTIVATION

Unraveling the transcriptional programs that control how cells divide, differentiate, and respond to their environments requires a precise understanding of transcription factors' (TFs) DNA-binding activities. Calling cards (CC) technology uses transposons to capture transient TF binding events at one instant in time and then read them out at a later time. This methodology can also be used to simultaneously measure TF binding and mRNA expression from single-cell CC and to record and integrate TF binding events across time in any cell type of interest without the need for purification. Despite these advantages, there has been a lack of dedicated bioinformatics tools for the detailed analysis of CC data.

RESULTS

We introduce Pycallingcards, a comprehensive Python module specifically designed for the analysis of single-cell and bulk CC data across multiple species. Pycallingcards introduces two innovative peak callers, CCcaller and MACCs, enhancing the accuracy and speed of pinpointing TF binding sites from CC data. Pycallingcards offers a fully integrated environment for data visualization, motif finding, and comparative analysis with RNA-seq and ChIP-seq datasets. To illustrate its practical application, we have reanalyzed previously published mouse cortex and glioblastoma datasets. This analysis revealed novel cell-type-specific binding sites and potential sex-linked TF regulators, furthering our understanding of TF binding and gene expression relationships. Thus, Pycallingcards, with its user-friendly design and seamless interface with the Python data science ecosystem, stands as a critical tool for advancing the analysis of TF functions via CC data.

AVAILABILITY AND IMPLEMENTATION

Pycallingcards can be accessed on the GitHub repository: https://github.com/The-Mitra-Lab/pycallingcards.

ChEC-Seq: A Comprehensive Guide for Scalable and Cost-Efficient Genome-Wide Profiling in Saccharomyces cerevisiae

Chromatin endogenous cleavage coupled with high-throughput sequencing (ChEC-seq) is a profiling method for protein-DNA interactions that can detect binding locations in vivo, does not require antibodies or fixation, and provides genome-wide coverage at near nucleotide resolution.The core of this method is an MNase fusion of the target protein, which allows it, when triggered by calcium exposure, to cut DNA at its binding sites and to generate small DNA fragments that can be readily separated from the rest of the genome and sequenced.Improvements since the original protocol have increased the ease, lowered the costs, and multiplied the throughput of this method to enable a scale and resolution of experiments not available with traditional methods such as ChIP-seq. This method describes each step from the initial creation and verification of the MNase-tagged yeast strains, over the ChEC MNase activation and small fragment purification procedure to the sequencing library preparation. It also briefly touches on the bioinformatic steps necessary to create meaningful genome-wide binding profiles.

An Optimized High-Resolution Mapping Method for Glucocorticoid Receptor-DNA Binding in Mouse Primary Macrophages

ChIP-exo is a powerful tool for achieving enhanced sensitivity and single-base-pair resolution of transcription factor (TF) binding, which utilizes a combination of chromatin immunoprecipitation (ChIP) and lambda exonuclease digestion (exo) followed by high-throughput sequencing. ChIP-nexus (chromatin immunoprecipitation experiments with nucleotide resolution through exonuclease, unique barcode, and single ligation) is an updated and simplified version of the original ChIP-exo method, which has reported an efficient adapter ligation through the DNA circularization step. Building upon an established method, we present a protocol for generating NGS (next-generation sequencing) ready and high-quality ChIP-nexus library for glucocorticoid receptor (GR). This method is specifically optimized for bone marrow-derived macrophage (BMDM) cells. The protocol is initiated by the formation of DNA-protein cross-links in intact cells. This is followed by chromatin shearing, chromatin immunoprecipitation, ligation of sequencing adapters, digestion of adapter-ligated DNA using lambda exonuclease, and purification of single-stranded DNA for circularization and library amplification.

Benchmarking DNA binding affinity models using allele-specific transcription factor binding data

Transcription factors (TFs) bind to DNA in a highly sequence-specific manner. This specificity can manifest itself in vivo at heterozygous loci as a difference in TF occupancy between the two alleles. When applied on a genomic scale, functional genomic assays such as ChIP-seq typically lack the statistical power to detect allele-specific binding (ASB) at the level of individual variants. To address this, we propose a framework for benchmarking sequence-to-affinity models for TF binding in terms of their ability to predict allelic imbalances in ChIP-seq counts. We show that a likelihood function based on an over-dispersed binomial distribution can aggregate evidence for allelic preference across the genome without requiring statistical significance for individual variants. This allows us to systematically compare predictive performance when multiple binding models for the same TF are available. We introduce PyProBound, an easily extensible reimplementation of the ProBound biophysically interpretable machine learning framework. Configuring PyProBound to explicitly account for a confounding sequence-specific bias in DNA fragmentation rate yields improved TF binding models when training on ChIP-seq data. We also show how our likelihood function can be leveraged to perform de novo motif discovery on the raw allele-aware ChIP-seq counts.

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.

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.

Systematic mapping and modeling of 3D enhancer-promoter interactions in early mouse embryonic lineages reveal regulatory principles that determine the levels and cell-type specificity of gene expression

Mammalian embryogenesis commences with two pivotal and binary cell fate decisions that give rise to three essential lineages, the trophectoderm (TE), the epiblast (EPI) and the primitive endoderm (PrE). Although key signaling pathways and transcription factors that control these early embryonic decisions have been identified, the non-coding regulatory elements via which transcriptional regulators enact these fates remain understudied. To address this gap, we have characterized, at a genome-wide scale, enhancer activity and 3D connectivity in embryo-derived stem cell lines that represent each of the early developmental fates. We observed extensive enhancer remodeling and fine-scale 3D chromatin rewiring among the three lineages, which strongly associate with transcriptional changes, although there are distinct groups of genes that are irresponsive to topological changes. In each lineage, a high degree of connectivity or "hubness" positively correlates with levels of gene expression and enriches for cell-type specific and essential genes. Genes within 3D hubs also show a significantly stronger probability of coregulation across lineages, compared to genes in linear proximity or within the same contact domains. By incorporating 3D chromatin features, we build a novel predictive model for transcriptional regulation (3D-HiChAT), which outperformed models that use only 1D promoter or proximal variables in predicting levels and cell-type specificity of gene expression. Using 3D-HiChAT, we performed genome-wide in silico perturbations to nominate candidate functional enhancers and hubs in each cell lineage, and with CRISPRi experiments we validated several novel enhancers that control expression of one or more genes in their respective lineages. Our study comprehensively identifies 3D regulatory hubs associated with the earliest mammalian lineages and describes their relationship to gene expression and cell identity, providing a framework to understand lineage-specific transcriptional behaviors.

Mll1 pioneers histone H3K4me3 deposition and promotes formation of CD8 + T stem cell memory

CD8 + T cells with stem cell-like properties (T SCM ) sustain adaptive immunity to intracellular pathogens and tumors. However, the developmental origins and chromatin regulatory factors (CRFs) that establish their differentiation are unclear. Using an RNA interference screen of all CRFs we discovered the histone methylase Mll1 was required during T cell receptor (TCR) stimulation for development of a T SCM precursor state and mature memory (T MEM ) cells, but not short-lived or transitory effector cell-like states, in response to viral infections and tumors. Mll1 was essential for widespread de novo deposition of histone H3 lysine 4 trimethylation (H3K4me3) upon TCR stimulation, which accounted for 70% of all activation-induced sites in mature T MEM cells. Mll1 promoted both H3K4me3 deposition and reduced TCR-induced Pol II pausing at genes whose single-cell transcriptional dynamics explained trajectories into nascent T SCM precursor states during viral infection. Our results suggest Mll1-dependent control of Pol II elongation and H3K4me3 establishes and maintains differentiation of CD8 + T SCM cell states.

Deep-learning optimized DEOCSU suite provides an iterable pipeline for accurate ChIP-exo peak calling

Recognizing binding sites of DNA-binding proteins is a key factor for elucidating transcriptional regulation in organisms. ChIP-exo enables researchers to delineate genome-wide binding landscapes of DNA-binding proteins with near single base-pair resolution. However, the peak calling step hinders ChIP-exo application since the published algorithms tend to generate false-positive and false-negative predictions. Here, we report the development of DEOCSU (DEep-learning Optimized ChIP-exo peak calling SUite), a novel machine learning-based ChIP-exo peak calling suite. DEOCSU entails the deep convolutional neural network model which was trained with curated ChIP-exo peak data to distinguish the visualized data of bona fide peaks from false ones. Performance validation of the trained deep-learning model indicated its high accuracy, high precision and high recall of over 95%. Applying the new suite to both in-house and publicly available ChIP-exo datasets obtained from bacteria, eukaryotes and archaea revealed an accurate prediction of peaks containing canonical motifs, highlighting the versatility and efficiency of DEOCSU. Furthermore, DEOCSU can be executed on a cloud computing platform or the local environment. With visualization software included in the suite, adjustable options such as the threshold of peak probability, and iterable updating of the pre-trained model, DEOCSU can be optimized for users' specific needs.

NetProphet 3: a machine learning framework for transcription factor network mapping and multi-omics integration

MOTIVATION

Many methods have been proposed for mapping the targets of transcription factors (TFs) from gene expression data. It is known that combining outputs from multiple methods can improve performance. To date, outputs have been combined by using either simplistic formulae, such as geometric mean, or carefully hand-tuned formulae that may not generalize well to new inputs. Finally, the evaluation of accuracy has been challenging due to the lack of genome-scale, ground-truth networks.

RESULTS

We developed NetProphet3, which combines scores from multiple analyses automatically, using a tree boosting algorithm trained on TF binding location data. We also developed three independent, genome-scale evaluation metrics. By these metrics, NetProphet3 is more accurate than other commonly used packages, including NetProphet 2.0, when gene expression data from direct TF perturbations are available. Furthermore, its integration mode can forge a consensus network from gene expression data and TF binding location data.

AVAILABILITY AND IMPLEMENTATION

All data and code are available at https://zenodo.org/record/7504131#.Y7Wu3i-B2x8.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

NetTIME: a Multitask and Base-pair Resolution Framework for Improved Transcription Factor Binding Site Prediction

Motivation

Machine learning models for predicting cell-type-specific transcription factor (TF) binding sites have become increasingly more accurate thanks to the increased availability of next-generation sequencing data and more standardized model evaluation criteria. However, knowledge transfer from data-rich to data-limited TFs and cell types remains crucial for improving TF binding prediction models because available binding labels are highly skewed towards a small collection of TFs and cell types. Transfer prediction of TF binding sites can potentially benefit from a multitask learning approach; however, existing methods typically use shallow single-task models to generate low-resolution predictions. Here, we propose NetTIME, a multitask learning framework for predicting cell-type-specific TF binding sites with base-pair resolution.

Results

We show that the multitask learning strategy for TF binding prediction is more efficient than the single-task approach due to the increased data availability. NetTIME trains high-dimensional embedding vectors to distinguish TF and cell-type identities. We show that this approach is critical for the success of the multitask learning strategy and allows our model to make accurate transfer predictions within and beyond the training panels of TFs and cell types. We additionally train a linear-chain conditional random field (CRF) to classify binding predictions and show that this CRF eliminates the need for setting a probability threshold and reduces classification noise. We compare our method’s predictive performance with two state-of-the-art methods, Catchitt and Leopard, and show that our method outperforms previous methods under both supervised and transfer learning settings.

Availability and implementation

NetTIME is freely available at https://github.com/ryi06/NetTIME and the code is also archived at https://doi.org/10.5281/zenodo.6994897.

Supplementary information

Supplementary data are available at Bioinformatics online.

Elucidating the biology of transcription factor-DNA interaction for accurate identification of cis-regulatory elements

Transcription factors (TFs) play a critical role in determining cell fate decisions by integrating developmental and environmental signals through binding to specific cis-regulatory modules and regulating spatio-temporal specificity of gene expression patterns. Precise identification of functional TF binding sites in time and space not only will revolutionize our understanding of regulatory networks governing cell fate decisions but is also instrumental to uncover how genetic variations cause morphological diversity or disease. In this review, we discuss recent advances in mapping TF binding sites and characterizing the various parameters underlying the complexity of binding site recognition by TFs.

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.

Whole-genome methods to define DNA and histone accessibility and long-range interactions in chromatin

Defining the genome-wide chromatin landscape has been a goal of experimentalists for decades. Here we review highlights of these efforts, from seminal experiments showing discontinuities in chromatin structure related to gene activation to extensions of these methods elucidating general features of chromatin related to gene states by exploiting deep sequencing methods. We also review chromatin conformational capture methods to identify patterns in long-range interactions between genomic loci.

scDALI: modeling allelic heterogeneity in single cells reveals context-specific genetic regulation

While it is established that the functional impact of genetic variation can vary across cell types and states, capturing this diversity remains challenging. Current studies using bulk sequencing either ignore this heterogeneity or use sorted cell populations, reducing discovery and explanatory power. Here, we develop scDALI, a versatile computational framework that integrates information on cellular states with allelic quantifications of single-cell sequencing data to characterize cell-state-specific genetic effects. We apply scDALI to scATAC-seq profiles from developing F1 Drosophila embryos and scRNA-seq from differentiating human iPSCs, uncovering heterogeneous genetic effects in specific lineages, developmental stages, or cell types.

ChIP-Seq Occupancy Mapping of the Archaeal Transcription Machinery

Genome-wide occupancy studies for RNA polymerases and their basal transcription factors deliver information about transcription dynamics and the recruitment of transcription elongation and termination factors in eukaryotes and prokaryotes. The primary method to determine genome-wide occupancies is chromatin immunoprecipitation combined with deep sequencing (ChIP-seq). Archaea possess a transcription machinery that is evolutionarily closer related to its eukaryotic counterpart but it operates in a prokaryotic cellular context. Studies on archaeal transcription brought insight into the evolution of transcription machineries and the universality of transcription mechanisms. Because of the limited resolution of ChIP-seq, the close spacing of promoters and transcription units found in archaeal genomes pose a challenge for ChIP-seq and the ensuing data analysis. The extreme growth temperature of many established archaeal model organisms necessitates further adaptations. This chapter describes a version of ChIP-seq adapted for the basal transcription machinery of thermophilic archaea and some modifications to the data analysis.

Bacterial metabolism and pathogenesis intimate intertwining: time for metabolic modelling to come into action

We take a snapshot of the recent understanding of bacterial metabolism and the bacterial-host metabolic interplay during infection, and highlight key outcomes and challenges for the practical implementation of bacterial metabolic modelling computational tools in the pathogenesis field.

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.

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.

FACT is recruited to the +1 nucleosome of transcribed genes and spreads in a Chd1-dependent manner

The histone chaperone FACT occupies transcribed regions where it plays prominent roles in maintaining chromatin integrity and preserving epigenetic information. How it is targeted to transcribed regions, however, remains unclear. Proposed models include docking on the RNA polymerase II (RNAPII) C-terminal domain (CTD), recruitment by elongation factors, recognition of modified histone tails, and binding partially disassembled nucleosomes. Here, we systematically test these and other scenarios in Saccharomyces cerevisiae and find that FACT binds transcribed chromatin, not RNAPII. Through a combination of high-resolution genome-wide mapping, single-molecule tracking, and mathematical modeling, we propose that FACT recognizes the +1 nucleosome, as it is partially unwrapped by the engaging RNAPII, and spreads to downstream nucleosomes aided by the chromatin remodeler Chd1. Our work clarifies how FACT interacts with genes, suggests a processive mechanism for FACT function, and provides a framework to further dissect the molecular mechanisms of transcription-coupled histone chaperoning.

Resolving diverse protein-DNA footprints from exonuclease-based ChIP experiments

MOTIVATION

High-throughput chromatin immunoprecipitation (ChIP) sequencing-based assays capture genomic regions associated with the profiled transcription factor (TF). ChIP-exo is a modified protocol, which uses lambda exonuclease to digest DNA close to the TF-DNA complex, in order to improve on the positional resolution of the TF-DNA contact. Because the digestion occurs in the 5'-3' orientation, the protocol produces directional footprints close to the complex, on both sides of the double stranded DNA. Like all ChIP-based methods, ChIP-exo reports a mixture of different regions associated with the TF: those bound directly to the TF as well as via intermediaries. However, the distribution of footprints are likely to be indicative of the complex forming at the DNA.

RESULTS

We present ExoDiversity, which uses a model-based framework to learn a joint distribution over footprints and motifs, thus resolving the mixture of ChIP-exo footprints into diverse binding modes. It uses no prior motif or TF information and automatically learns the number of different modes from the data. We show its application on a wide range of TFs and organisms/cell-types. Because its goal is to explain the complete set of reported regions, it is able to identify co-factor TF motifs that appear in a small fraction of the dataset. Further, ExoDiversity discovers small nucleotide variations within and outside canonical motifs, which co-occur with variations in footprints, suggesting that the TF-DNA structural configuration at those regions is likely to be different. Finally, we show that detected modes have specific DNA shape features and conservation signals, giving insights into the structure and function of the putative TF-DNA complexes.

AVAILABILITY AND IMPLEMENTATION

The code for ExoDiversity is available on https://github.com/NarlikarLab/exoDIVERSITY.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

CaBagE: A Cas9-based Background Elimination strategy for targeted, long-read DNA sequencing

A substantial fraction of the human genome is difficult to interrogate with short-read DNA sequencing technologies due to paralogy, complex haplotype structures, or tandem repeats. Long-read sequencing technologies, such as Oxford Nanopore's MinION, enable direct measurement of complex loci without introducing many of the biases inherent to short-read methods, though they suffer from relatively lower throughput. This limitation has motivated recent efforts to develop amplification-free strategies to target and enrich loci of interest for subsequent sequencing with long reads. Here, we present CaBagE, a method for target enrichment that is efficient and useful for sequencing large, structurally complex targets. The CaBagE method leverages the stable binding of Cas9 to its DNA target to protect desired fragments from digestion with exonuclease. Enriched DNA fragments are then sequenced with Oxford Nanopore's MinION long-read sequencing technology. Enrichment with CaBagE resulted in a median of 116X coverage (range 39-416) of target loci when tested on five genomic targets ranging from 4-20kb in length using healthy donor DNA. Four cancer gene targets were enriched in a single reaction and multiplexed on a single MinION flow cell. We further demonstrate the utility of CaBagE in two ALS patients with C9orf72 short tandem repeat expansions to produce genotype estimates commensurate with genotypes derived from repeat-primed PCR for each individual. With CaBagE there is a physical enrichment of on-target DNA in a given sample prior to sequencing. This feature allows adaptability across sequencing platforms and potential use as an enrichment strategy for applications beyond sequencing. CaBagE is a rapid enrichment method that can illuminate regions of the 'hidden genome' underlying human disease.

Two repeated motifs enriched within some enhancers and origins of replication are bound by SETMAR isoforms in human colon cells

Setmar is a gene specific to simian genomes. The function(s) of its isoforms are poorly understood and their existence in healthy tissues remains to be validated. Here we profiled SETMAR expression and its genome-wide binding landscape in colon tissue. We found isoforms V3 and V6 in healthy and tumour colon tissues as well as incell lines. In two colorectal cell lines SETMAR binds to several thousand Hsmar1 and MADE1 terminal ends, transposons mostly located in non-genic regions of active chromatin including in enhancers. It also binds to a 12-bp motifs similar to an inner motif in Hsmar1 and MADE1 terminal ends. This motif is interspersed throughout the genome and is enriched in GC-rich regions as well as in CpG islands that contain constitutive replication origins. It is also found in enhancers other than those associated with Hsmar1 and MADE1. The role of SETMAR in the expression of genes, DNA replication and in DNA repair are discussed.

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.

A heterochromatin inducing protein differentially recognizes self versus foreign genomes

Krüppel-associated box-domain zinc finger protein (KRAB-ZFP) transcriptional repressors recruit TRIM28/KAP1 to heterochromatinize the mammalian genome while also guarding the host by silencing invading foreign genomes. However, how a KRAB-ZFP recognizes target sequences in the natural context of its own or foreign genomes is unclear. Our studies on B-lymphocytes permanently harboring the cancer-causing Epstein-Barr virus (EBV) have shown that SZF1, a KRAB-ZFP, binds to several lytic/replicative phase genes to silence them, thereby promoting the latent/quiescent phase of the virus. As a result, unless SZF1 and its binding partners are displaced from target regions on the viral genome, EBV remains dormant, i.e. refractory to lytic phase-inducing triggers. As SZF1 also heterochromatinizes the cellular genome, we performed in situ footprint mapping on both viral and host genomes in physically separated B-lymphocytes bearing latent or replicative/active EBV genomes. By analyzing footprints, we learned that SZF1 recognizes the host genome through a repeat sequence-bearing motif near centromeres. Remarkably, SZF1 does not use this motif to recognize the EBV genome. Instead, it uses distinct binding sites that lack obvious similarities to each other or the above motif, to silence the viral genome. Virus mutagenesis studies show that these distinct binding sites are not only key to maintaining the established latent phase but also silencing the lytic phase in newly-infected cells, thus enabling the virus to establish latency and transform cells. Notably, these binding sites on the viral genome, when also present on the human genome, are not used by SZF1 to silence host genes during latency. This differential approach towards target site recognition may reflect a strategy by which the host silences and regulates genomes of persistent invaders without jeopardizing its own homeostasis.

Heterochromatin marks silenced portions of the human genome. Heterochromatin also serves as a defense strategy to silence foreign genomes. Yet, how the heterochromatin inducing KRAB-ZFP-TRIM28 machinery recognizes target sites on the native genome, whether self or foreign, is unclear. Using Epstein-Barr virus-infected cells in which a KRAB-ZFP, SZF1, silences lytic/replicative-phase genes of the virus, we performed in situ mapping of ZFP-footprints on cell and viral genomes. We find that while the ZFP uses a repeat sequence-bearing motif to target pericentromeric regions, it uses non-consensus sites to target viral genes. These findings point towards i) a mechanism for directing constitutive heterochromatin and ii) a strategy that allows the host to use the same heterochromatin machinery to regulate an invader without deregulating itself.

Runx proteins and transcriptional mechanisms that govern memory CD8 T cell development

Adaptive immunity to intracellular pathogens and tumors is mediated by antigen-experienced CD8 T cells. Individual naive CD8 T cells have the potential to differentiate into a diverse array of antigen-experienced subsets that exhibit distinct effector functions, life spans, anatomic positioning, and potential for regenerating an entirely new immune response during iterative pathogenic exposures. The developmental process by which activated naive cells undergo diversification involves regulation of chromatin structure and transcription but is not entirely understood. This review examines how alterations in chromatin structure, transcription factor binding, extracellular signals, and single-cell gene expression explain the differential development of distinct effector (T_EFF ) and memory (T_MEM ) CD8 T cell subsets. Special emphasis is placed on how Runx proteins function with additional transcription factors to pioneer changes in chromatin accessibility and drive transcriptional programs that establish the core attributes of cytotoxic T lymphocytes, subdivide circulating and non-circulating T_MEM cell subsets, and govern terminal differentiation. The discussion integrates the roles of specific cytokine signals, transcriptional circuits and how regulation of individual nucleosomes and RNA polymerase II activity can contribute to the process of differentiation. A model that integrates many of these features is discussed to conceptualize how activated CD8 T cells arrive at their fates.

Bcl6-Mediated Transcriptional Regulation of Follicular Helper T cells (TFH)

Follicular helper T cells (T_FH) are essential B cell-help providers in the formation of germinal centers (GCs), affinity maturation of GC B cells, differentiation of high-affinity antibody-producing plasma cells, and production of memory B cells. The transcription factor (TF) B cell lymphoma 6 (Bcl6) is at the center of gene regulation in T_FH biology, including differentiation and function, but how Bcl6 does this, and what additional TFs contribute, remain complex questions. This review focuses on advances in our understanding of Bcl6-mediated gene regulation of T_FH functions, and the modulation of T_FH by other TFs. These advances may have important implications in deciphering how repressor TFs can regulate many immunological cell types. An improved understanding of T_FH biology will likely provide insights into biomedically relevant diseases.

Regulation of Promoter Proximal Pausing of RNA Polymerase II in Metazoans

Regulation of transcription is a tightly choreographed process. The establishment of RNA polymerase II promoter proximal pausing soon after transcription initiation and the release of Pol II into productive elongation are key regulatory processes that occur in early elongation. We describe the techniques and tools that have become available for the study of promoter proximal pausing and their utility for future experiments. We then provide an overview of the factors and interactions that govern a multipartite pausing process and address emerging questions surrounding the mechanism of RNA polymerase II's subsequent advancement into the gene body. Finally, we address remaining controversies and future areas of study.

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.