Spatiotemporal kinetics of CAF-1-dependent chromatin maturation ensures transcription fidelity during S-phase.

Proper maintenance of epigenetic information after replication is dependent on the rapid assembly and maturation of chromatin. Chromatin Assembly Complex 1 (CAF-1) is a conserved histone chaperone that deposits (H3-H4)2 tetramers as part of the replication-dependent chromatin assembly process. Loss of CAF-1 leads to a delay in chromatin maturation, albeit with minimal impact on steady-state chromatin structure. However, the mechanisms by which CAF-1 mediates the deposition of (H3-H4)2 tetramers and the phenotypic consequences of CAF-1-associated assembly defects are not well understood. We used nascent chromatin occupancy profiling to track the spatiotemporal kinetics of chromatin maturation in both wild-type (WT) and CAF-1 mutant yeast cells. Our results show that loss of CAF-1 leads to a heterogeneous rate of nucleosome assembly, with some nucleosomes maturing at near WT kinetics and others showing significantly slower maturation kinetics. The slow-to-mature nucleosomes are enriched in intergenic and poorly transcribed regions, suggesting that transcription-dependent assembly mechanisms can reset the slow-to-mature nucleosomes following replication. Nucleosomes with slow maturation kinetics are also associated with poly(dA:dT) sequences, which implies that CAF-1 deposits histones in a manner that counteracts resistance from the inflexible DNA sequence, promoting the formation of histone octamers as well as ordered nucleosome arrays. In addition, we show that the delay in chromatin maturation is accompanied by a transient and S-phase-specific loss of gene silencing and transcriptional regulation, revealing that the DNA replication program can directly shape the chromatin landscape and modulate gene expression through the process of chromatin maturation.

Profiling the quantitative occupancy of myriad transcription factors across conditions by modeling chromatin accessibility data.

Over a thousand different transcription factors (TFs) bind with varying occupancy across the human genome. Chromatin immunoprecipitation (ChIP) can assay occupancy genome-wide, but only one TF at a time, limiting our ability to comprehensively observe the TF occupancy landscape, let alone quantify how it changes across conditions. We developed TF occupancy profiler (TOP), a Bayesian hierarchical regression framework, to profile genome-wide quantitative occupancy of numerous TFs using data from a single chromatin accessibility experiment (DNase- or ATAC-seq). TOP is supervised, and its hierarchical structure allows it to predict the occupancy of any sequence-specific TF, even those never assayed with ChIP. We used TOP to profile the quantitative occupancy of hundreds of sequence-specific TFs at sites throughout the genome and examined how their occupancies changed in multiple contexts: in approximately 200 human cell types, through 12 h of exposure to different hormones, and across the genetic backgrounds of 70 individuals. TOP enables cost-effective exploration of quantitative changes in the landscape of TF binding.

Linking the dynamics of chromatin occupancy and transcription with predictive models.

Though the sequence of the genome within each eukaryotic cell is essentially fixed, it exists within a complex and changing chromatin state. This state is determined, in part, by the dynamic binding of proteins to the DNA. These proteins-including histones, transcription factors (TFs), and polymerases-interact with one another, the genome, and other molecules to allow the chromatin to adopt one of exceedingly many possible configurations. Understanding how changing chromatin configurations associate with transcription remains a fundamental research problem. We sought to characterize at high spatiotemporal resolution the dynamic interplay between transcription and chromatin in response to cadmium stress. Whereas gene regulatory responses to environmental stress in yeast have been studied, how the chromatin state changes and how those changes connect to gene regulation remain unexplored. By combining MNase-seq and RNA-seq data, we found chromatin signatures of transcriptional activation and repression involving both nucleosomal and TF-sized DNA-binding factors. Using these signatures, we identified associations between chromatin dynamics and transcriptional regulation, not only for known cadmium response genes, but across the entire genome, including antisense transcripts. Those associations allowed us to develop generalizable models that predict dynamic transcriptional responses on the basis of dynamic chromatin signatures.

Local nucleosome dynamics and eviction following a double-strand break are reversible by NHEJ-mediated repair in the absence of DNA replication.

We interrogated at nucleotide resolution the spatiotemporal order of chromatin changes that occur immediately following a site-specific double-strand break (DSB) upstream of the PHO5 locus and its subsequent repair by nonhomologous end joining (NHEJ). We observed the immediate eviction of a nucleosome flanking the break and the repositioning of adjacent nucleosomes away from the break. These early chromatin events were independent of the end-processing Mre11-Rad50-Xrs2 (MRX) complex and preceded the MRX-dependent broad eviction of histones and DNA end-resectioning that extends up to ∼8 kb away from the break. We also examined the temporal dynamics of NHEJ-mediated repair in a G1-arrested population. Concomitant with DSB repair by NHEJ, we observed the redeposition and precise repositioning of nucleosomes at their originally occupied positions. This re-establishment of the prelesion chromatin landscape suggests that a DNA replication-independent mechanism exists to preserve epigenome organization following DSB repair.

RoboCOP: jointly computing chromatin occupancy profiles for numerous factors from chromatin accessibility data.

Chromatin is a tightly packaged structure of DNA and protein within the nucleus of a cell. The arrangement of different protein complexes along the DNA modulates and is modulated by gene expression. Measuring the binding locations and occupancy levels of different transcription factors (TFs) and nucleosomes is therefore crucial to understanding gene regulation. Antibody-based methods for assaying chromatin occupancy are capable of identifying the binding sites of specific DNA binding factors, but only one factor at a time. In contrast, epigenomic accessibility data like MNase-seq, DNase-seq, and ATAC-seq provide insight into the chromatin landscape of all factors bound along the genome, but with little insight into the identities of those factors. Here, we present RoboCOP, a multivariate state space model that integrates chromatin accessibility data with nucleotide sequence to jointly compute genome-wide probabilistic scores of nucleosome and TF occupancy, for hundreds of different factors. We apply RoboCOP to MNase-seq and ATAC-seq data to elucidate the protein-binding landscape of nucleosomes and 150 TFs across the yeast genome, and show that our model makes better predictions than existing methods. We also compute a chromatin occupancy profile of the yeast genome under cadmium stress, revealing chromatin dynamics associated with transcriptional regulation.

Genome-wide chromatin footprinting reveals changes in replication origin architecture induced by pre-RC assembly.

Start sites of DNA replication are marked by the origin recognition complex (ORC), which coordinates Mcm2-7 helicase loading to form the prereplicative complex (pre-RC). Although pre-RC assembly is well characterized in vitro, the process is poorly understood within the local chromatin environment surrounding replication origins. To reveal how the chromatin architecture modulates origin selection and activation, we "footprinted" nucleosomes, transcription factors, and replication proteins at multiple points during the Saccharomyces cerevisiae cell cycle. Our nucleotide-resolution protein occupancy profiles resolved a precise ORC-dependent footprint at 269 origins in G2. A separate class of inefficient origins exhibited protein occupancy only in G1, suggesting that stable ORC chromatin association in G2 is a determinant of origin efficiency. G1 nucleosome remodeling concomitant with pre-RC assembly expanded the origin nucleosome-free region and enhanced activation efficiency. Finally, the local chromatin environment restricts the loading of the Mcm2-7 double hexamer either upstream of or downstream from the ARS consensus sequence (ACS).

Glucocorticoid receptor recruits to enhancers and drives activation by motif-directed binding.

Glucocorticoids are potent steroid hormones that regulate immunity and metabolism by activating the transcription factor (TF) activity of glucocorticoid receptor (GR). Previous models have proposed that DNA binding motifs and sites of chromatin accessibility predetermine GR binding and activity. However, there are vast excesses of both features relative to the number of GR binding sites. Thus, these features alone are unlikely to account for the specificity of GR binding and activity. To identify genomic and epigenetic contributions to GR binding specificity and the downstream changes resultant from GR binding, we performed hundreds of genome-wide measurements of TF binding, epigenetic state, and gene expression across a 12-h time course of glucocorticoid exposure. We found that glucocorticoid treatment induces GR to bind to nearly all pre-established enhancers within minutes. However, GR binds to only a small fraction of the set of accessible sites that lack enhancer marks. Once GR is bound to enhancers, a combination of enhancer motif composition and interactions between enhancers then determines the strength and persistence of GR binding, which consequently correlates with dramatic shifts in enhancer activation. Over the course of several hours, highly coordinated changes in TF binding and histone modification occupancy occur specifically within enhancers, and these changes correlate with changes in the expression of nearby genes. Following GR binding, changes in the binding of other TFs precede changes in chromatin accessibility, suggesting that other TFs are also sensitive to genomic features beyond that of accessibility.

Mapping nucleosome positions using DNase-seq.

Although deoxyribonuclease I (DNase I) was used to probe the structure of the nucleosome in the 1960s and 1970s, in the current high-throughput sequencing era, DNase I has mainly been used to study genomic regions devoid of nucleosomes. Here, we reveal for the first time that DNase I can be used to precisely map the (translational) positions of in vivo nucleosomes genome-wide. Specifically, exploiting a distinctive DNase I cleavage profile within nucleosome-associated DNA--including a signature 10.3 base pair oscillation that corresponds to accessibility of the minor groove as DNA winds around the nucleosome--we develop a Bayes-factor-based method that can be used to map nucleosome positions along the genome. Compared to methods that require genetically modified histones, our DNase-based approach is easily applied in any organism, which we demonstrate by producing maps in yeast and human. Compared to micrococcal nuclease (MNase)-based methods that map nucleosomes based on cuts in linker regions, we utilize DNase I cuts both outside and within nucleosomal DNA; the oscillatory nature of the DNase I cleavage profile within nucleosomal DNA enables us to identify translational positioning details not apparent in MNase digestion of linker DNA. Because the oscillatory pattern corresponds to nucleosome rotational positioning, it also reveals the rotational context of transcription factor (TF) binding sites. We show that potential binding sites within nucleosome-associated DNA are often centered preferentially on an exposed major or minor groove. This preferential localization may modulate TF interaction with nucleosome-associated DNA as TFs search for binding sites.

Synergistic and tunable human gene activation by combinations of synthetic transcription factors.

Mammalian genes are regulated by the cooperative and synergistic actions of many transcription factors. In this study we recapitulate this complex regulation in human cells by targeting endogenous gene promoters, including regions of closed chromatin upstream of silenced genes, with combinations of engineered transcription activator-like effectors (TALEs). These combinations of TALE transcription factors induced substantial gene activation and allowed tuning of gene expression levels that will broadly enable synthetic biology, gene therapy and biotechnology.

Branching process deconvolution algorithm reveals a detailed cell-cycle transcription program.

Due to cell-to-cell variability and asymmetric cell division, cells in a synchronized population lose synchrony over time. As a result, time-series measurements from synchronized cell populations do not reflect the underlying dynamics of cell-cycle processes. Here, we present a branching process deconvolution algorithm that learns a more accurate view of dynamic cell-cycle processes, free from the convolution effects associated with imperfect cell synchronization. Through wavelet-basis regularization, our method sharpens signal without sharpening noise and can remarkably increase both the dynamic range and the temporal resolution of time-series data. Although applicable to any such data, we demonstrate the utility of our method by applying it to a recent cell-cycle transcription time course in the eukaryote Saccharomyces cerevisiae. Our method more sensitively detects cell-cycle-regulated transcription and reveals subtle timing differences that are masked in the original population measurements. Our algorithm also explicitly learns distinct transcription programs for mother and daughter cells, enabling us to identify 82 genes transcribed almost entirely in early G1 in a daughter-specific manner.

ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia.

Chromatin immunoprecipitation (ChIP) followed by high-throughput DNA sequencing (ChIP-seq) has become a valuable and widely used approach for mapping the genomic location of transcription-factor binding and histone modifications in living cells. Despite its widespread use, there are considerable differences in how these experiments are conducted, how the results are scored and evaluated for quality, and how the data and metadata are archived for public use. These practices affect the quality and utility of any global ChIP experiment. Through our experience in performing ChIP-seq experiments, the ENCODE and modENCODE consortia have developed a set of working standards and guidelines for ChIP experiments that are updated routinely. The current guidelines address antibody validation, experimental replication, sequencing depth, data and metadata reporting, and data quality assessment. We discuss how ChIP quality, assessed in these ways, affects different uses of ChIP-seq data. All data sets used in the analysis have been deposited for public viewing and downloading at the ENCODE (http://encodeproject.org/ENCODE/) and modENCODE (http://www.modencode.org/) portals.

Learning protein-DNA interaction landscapes by integrating experimental data through computational models.

MOTIVATION: Transcriptional regulation is directly enacted by the interactions between DNA and many proteins, including transcription factors (TFs), nucleosomes and polymerases. A critical step in deciphering transcriptional regulation is to infer, and eventually predict, the precise locations of these interactions, along with their strength and frequency. While recent datasets yield great insight into these interactions, individual data sources often provide only partial information regarding one aspect of the complete interaction landscape. For example, chromatin immunoprecipitation (ChIP) reveals the binding positions of a protein, but only for one protein at a time. In contrast, nucleases like MNase and DNase can be used to reveal binding positions for many different proteins at once, but cannot easily determine the identities of those proteins. Currently, few statistical frameworks jointly model these different data sources to reveal an accurate, holistic view of the in vivo protein-DNA interaction landscape. RESULTS: Here, we develop a novel statistical framework that integrates different sources of experimental information within a thermodynamic model of competitive binding to jointly learn a holistic view of the in vivo protein-DNA interaction landscape. We show that our framework learns an interaction landscape with increased accuracy, explaining multiple sets of data in accordance with thermodynamic principles of competitive DNA binding. The resulting model of genomic occupancy provides a precise mechanistic vantage point from which to explore the role of protein-DNA interactions in transcriptional regulation. AVAILABILITY AND IMPLEMENTATION: The C source code for compete and Python source code for MCMC-based inference are available at http://www.cs.duke.edu/∼amink. CONTACT: amink@cs.duke.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Stability selection for regression-based models of transcription factor-DNA binding specificity.

MOTIVATION: The DNA binding specificity of a transcription factor (TF) is typically represented using a position weight matrix model, which implicitly assumes that individual bases in a TF binding site contribute independently to the binding affinity, an assumption that does not always hold. For this reason, more complex models of binding specificity have been developed. However, these models have their own caveats: they typically have a large number of parameters, which makes them hard to learn and interpret. RESULTS: We propose novel regression-based models of TF-DNA binding specificity, trained using high resolution in vitro data from custom protein-binding microarray (PBM) experiments. Our PBMs are specifically designed to cover a large number of putative DNA binding sites for the TFs of interest (yeast TFs Cbf1 and Tye7, and human TFs c-Myc, Max and Mad2) in their native genomic context. These high-throughput quantitative data are well suited for training complex models that take into account not only independent contributions from individual bases, but also contributions from di- and trinucleotides at various positions within or near the binding sites. To ensure that our models remain interpretable, we use feature selection to identify a small number of sequence features that accurately predict TF-DNA binding specificity. To further illustrate the accuracy of our regression models, we show that even in the case of paralogous TF with highly similar position weight matrices, our new models can distinguish the specificities of individual factors. Thus, our work represents an important step toward better sequence-based models of individual TF-DNA binding specificity. AVAILABILITY: Our code is available at http://genome.duke.edu/labs/gordan/ISMB2013. The PBM data used in this article are available in the Gene Expression Omnibus under accession number GSE47026.

Global control of cell-cycle transcription by coupled CDK and network oscillators.

A significant fraction of the Saccharomyces cerevisiae genome is transcribed periodically during the cell division cycle, indicating that properly timed gene expression is important for regulating cell-cycle events. Genomic analyses of the localization and expression dynamics of transcription factors suggest that a network of sequentially expressed transcription factors could control the temporal programme of transcription during the cell cycle. However, directed studies interrogating small numbers of genes indicate that their periodic transcription is governed by the activity of cyclin-dependent kinases (CDKs). To determine the extent to which the global cell-cycle transcription programme is controlled by cyclin-CDK complexes, we examined genome-wide transcription dynamics in budding yeast mutant cells that do not express S-phase and mitotic cyclins. Here we show that a significant fraction of periodic genes are aberrantly expressed in the cyclin mutant. Although cells lacking cyclins are blocked at the G1/S border, nearly 70% of periodic genes continued to be expressed periodically and on schedule. Our findings reveal that although CDKs have a function in the regulation of cell-cycle transcription, they are not solely responsible for establishing the global periodic transcription programme. We propose that periodic transcription is an emergent property of a transcription factor network that can function as a cell-cycle oscillator independently of, and in tandem with, the CDK oscillator.

Genome-wide nucleosome and transcription factor responses to genetic perturbations reveal chromatin-mediated mechanisms of transcriptional regulation.

Epigenetic mechanisms contribute to gene regulation by altering chromatin accessibility through changes in transcription factor (TF) and nucleosome occupancy throughout the genome. Despite numerous studies focusing on changes in gene expression, the intricate chromatin-mediated regulatory code remains largely unexplored on a comprehensive scale. We address this by employing a factor-agnostic, reverse-genetics approach that uses MNase-seq to capture genome-wide TF and nucleosome occupancies in response to the individual deletion of 201 transcriptional regulators in Saccharomyces cerevisiae, thereby assaying nearly one million mutant-gene interactions. We develop a principled approach to identify and quantify chromatin changes genome-wide, observing differences in TF and nucleosome occupancy that recapitulate well-established pathways identified by gene expression data. We also discover distinct chromatin signatures associated with the up- and downregulation of genes, and use these signatures to reveal regulatory mechanisms previously unexplored in expression-based studies. Finally, we demonstrate that chromatin features are predictive of transcriptional activity and leverage these features to reconstruct chromatin-based transcriptional regulatory networks. Overall, these results illustrate the power of an approach combining genetic perturbation with high-resolution epigenomic profiling; the latter enables a close examination of the interplay between TFs and nucleosomes genome-wide, providing a deeper, more mechanistic understanding of the complex relationship between chromatin organization and transcription.

Single-cell multi-ome regression models identify functional and disease-associated enhancers and enable chromatin potential analysis.

We present a gene-level regulatory model, single-cell ATAC + RNA linking (SCARlink), which predicts single-cell gene expression and links enhancers to target genes using multi-ome (scRNA-seq and scATAC-seq co-assay) sequencing data. The approach uses regularized Poisson regression on tile-level accessibility data to jointly model all regulatory effects at a gene locus, avoiding the limitations of pairwise gene-peak correlations and dependence on peak calling. SCARlink outperformed existing gene scoring methods for imputing gene expression from chromatin accessibility across high-coverage multi-ome datasets while giving comparable to improved performance on low-coverage datasets. Shapley value analysis on trained models identified cell-type-specific gene enhancers that are validated by promoter capture Hi-C and are 11× to 15× and 5× to 12× enriched in fine-mapped eQTLs and fine-mapped genome-wide association study (GWAS) variants, respectively. We further show that SCARlink-predicted and observed gene expression vectors provide a robust way to compute a chromatin potential vector field to enable developmental trajectory analysis.

Drosophila ORC localizes to open chromatin and marks sites of cohesin complex loading.

The origin recognition complex (ORC) is an essential DNA replication initiation factor conserved in all eukaryotes. In Saccharomyces cerevisiae, ORC binds to specific DNA elements; however, in higher eukaryotes, ORC exhibits little sequence specificity in vitro or in vivo. We investigated the genome-wide distribution of ORC in Drosophila and found that ORC localizes to specific chromosomal locations in the absence of any discernible simple motif. Although no clear sequence motif emerged, we were able to use machine learning approaches to accurately discriminate between ORC-associated sequences and ORC-free sequences based solely on primary sequence. The complex sequence features that define ORC binding sites are highly correlated with nucleosome positioning signals and likely represent a preferred nucleosomal landscape for ORC association. Open chromatin appears to be the underlying feature that is deterministic for ORC binding. ORC-associated sequences are enriched for the histone variant, H3.3, often at transcription start sites, and depleted for bulk nucleosomes. The density of ORC binding along the chromosome is reflected in the time at which a sequence replicates, with early replicating sequences having a high density of ORC binding. Finally, we found a high concordance between sites of ORC binding and cohesin loading, suggesting that, in addition to DNA replication, ORC may be required for the loading of cohesin on DNA in Drosophila.

Identification of Tat-SF1 cellular targets by exon array analysis reveals dual roles in transcription and splicing.

Tat specific factor 1 (Tat-SF1) interacts with components of both the transcription and splicing machineries and has been classified as a transcription-splicing factor. Although its function as an HIV-1 dependency factor has been investigated, relatively little is known about the cellular functions of Tat-SF1. To identify target genes of Tat-SF1, we utilized a combination of RNAi and exon-specific microarrays. These arrays, which survey genome-wide changes in transcript and individual exon levels, revealed 450 genes with transcript level changes upon Tat-SF1 depletion. Strikingly, 98% of these target genes were down-regulated upon depletion, indicating that Tat-SF1 generally activates gene expression. We also identified 89 genes that showed differential exon level changes after Tat-SF1 depletion. The 89 genes showed evidence of many different types of alternative exon use consistent with the regulation of transcription initiation sites and RNA processing. Minimal overlap between genes with transcript-level and exon-level changes suggests that Tat-SF1 does not functionally couple transcription and splicing. Biological processes significantly enriched with transcript- and exon-level targets include the cell cycle and nucleic acid metabolism; the insulin signaling pathway was enriched with Tat-SF1 transcript-level targets but not exon-level targets. Additionally, a hexamer, ATGCCG, was over-represented in the promoter region of genes showing changes in transcription initiation upon Tat-SF1 depletion. This may represent a novel motif that Tat-SF1 recognizes during transcription. Together, these findings suggest that Tat-SF1 functions independently in transcription and splicing of cellular genes.

Spatiotemporal kinetics of CAF-1-dependent chromatin maturation ensures transcription fidelity during S-phase.

Proper maintenance of epigenetic information after replication is dependent on the rapid assembly and maturation of chromatin. Chromatin Assembly Complex 1 (CAF-1) is a conserved histone chaperone that deposits (H3-H4)2 tetramers as part of the replication-dependent chromatin assembly process. Loss of CAF-1 leads to a delay in chromatin maturation, albeit with minimal impact on steady-state chromatin structure. However, the mechanisms by which CAF-1 mediates the deposition of (H3-H4)2 tetramers and the phenotypic consequences of CAF-1-associated assembly defects are not well understood. We used nascent chromatin occupancy profiling to track the spatiotemporal kinetics of chromatin maturation in both wild-type (WT) and CAF-1 mutant yeast cells. Our results show that loss of CAF-1 leads to a heterogeneous rate of nucleosome assembly, with some nucleosomes maturing at near WT kinetics and others exhibiting significantly slower maturation kinetics. The slow-to-mature nucleosomes are enriched in intergenic and poorly transcribed regions, suggesting that transcription-dependent assembly mechanisms can reset the slow-to-mature nucleosomes following replication. Nucleosomes with slow maturation kinetics are also associated with poly(dA:dT) sequences, which implies that CAF-1 deposits histones in a manner that counteracts resistance from the inflexible DNA sequence, promoting the formation of histone octamers as well as ordered nucleosome arrays. In addition, we demonstrate that the delay in chromatin maturation is accompanied by a transient and S-phase specific loss of gene silencing and transcriptional regulation, revealing that the DNA replication program can directly shape the chromatin landscape and modulate gene expression through the process of chromatin maturation.

An ensemble model of competitive multi-factor binding of the genome.

Hundreds of different factors adorn the eukaryotic genome, binding to it in large number. These DNA binding factors (DBFs) include nucleosomes, transcription factors (TFs), and other proteins and protein complexes, such as the origin recognition complex (ORC). DBFs compete with one another for binding along the genome, yet many current models of genome binding do not consider different types of DBFs together simultaneously. Additionally, binding is a stochastic process that results in a continuum of binding probabilities at any position along the genome, but many current models tend to consider positions as being either binding sites or not. Here, we present a model that allows a multitude of DBFs, each at different concentrations, to compete with one another for binding sites along the genome. The result is an "occupancy profile," a probabilistic description of the DNA occupancy of each factor at each position. We implement our model efficiently as the software package COMPETE. We demonstrate genome-wide and at specific loci how modeling nucleosome binding alters TF binding, and vice versa, and illustrate how factor concentration influences binding occupancy. Binding cooperativity between nearby TFs arises implicitly via mutual competition with nucleosomes. Our method applies not only to TFs, but also recapitulates known occupancy profiles of a well-studied replication origin with and without ORC binding. Importantly, the sequence preferences our model takes as input are derived from in vitro experiments. This ensures that the calculated occupancy profiles are the result of the forces of competition represented explicitly in our model and the inherent sequence affinities of the constituent DBFs.