Molecular Co-occupancy Identifies Transcription Factor Binding Cooperativity In Vivo.

Gene activation requires the cooperative activity of multiple transcription factors at cis-regulatory elements (CREs). Yet, most transcription factors have short residence time, questioning the requirement of their physical co-occupancy on DNA to achieve cooperativity. Here, we present a DNA footprinting method that detects individual molecular interactions of transcription factors and nucleosomes with DNA in vivo. We apply this strategy to quantify the simultaneous binding of multiple transcription factors on single DNA molecules at mouse CREs. Analysis of the binary occupancy patterns at thousands of motif combinations reveals that high DNA co-occupancy occurs for most types of transcription factors, in the absence of direct physical interaction, at sites of competition with nucleosomes. Perturbation of pairwise interactions demonstrates the function of molecular co-occupancy in binding cooperativity. Our results reveal the interactions regulating CREs at molecular resolution and identify DNA co-occupancy as a widespread cooperativity mechanism used by transcription factors to remodel chromatin.

Selective 40S Footprinting Reveals Cap-Tethered Ribosome Scanning in Human Cells.

Translation regulation occurs largely during the initiation phase. Here, we develop selective 40S footprinting to visualize initiating 40S ribosomes on endogenous mRNAs in vivo. This reveals the positions on mRNAs where initiation factors join the ribosome to act and where they leave. We discover that in most human cells, most scanning ribosomes remain attached to the 5' cap. Consequently, only one ribosome scans a 5' UTR at a time, and 5' UTR length affects translation efficiency. We discover that eukaryotic initiation factor 3B (eIF3B,) eIF4G1, and eIF4E remain bound to 80S ribosomes as they begin translating, with a decay half-length of ∼12 codons. Hence, ribosomes retain these initiation factors while translating short upstream open reading frames (uORFs), providing an explanation for how ribosomes can reinitiate translation after uORFs in humans. This method will be of use for studying translation initiation mechanisms in vivo.

Disome and Trisome Profiling Reveal Genome-wide Targets of Ribosome Quality Control.

The ribosome-associated protein quality control (RQC) system that resolves stalled translation events is activated when ribosomes collide and form disome, trisome, or higher-order complexes. However, it is unclear whether this system distinguishes collision complexes formed on defective mRNAs from those with functional roles on endogenous transcripts. Here, we performed disome and trisome footprint profiling in yeast and found collisions were enriched on diverse sequence motifs known to slow translation. When 60S recycling was inhibited, disomes accumulated at stop codons and could move into the 3' UTR to reinitiate translation. The ubiquitin ligase and RQC factor Hel2/ZNF598 generally recognized collisions but did not induce degradation of endogenous transcripts. However, loss of Hel2 triggered the integrated stress response, via phosphorylation of eIF2α, thus linking these pathways. Our results suggest that Hel2 has a role in sensing ribosome collisions on endogenous mRNAs, and such events may be important for cellular homeostasis.

The native cistrome and sequence motif families of the maize ear.

Elucidating the transcriptional regulatory networks that underlie growth and development requires robust ways to define the complete set of transcription factor (TF) binding sites. Although TF-binding sites are known to be generally located within accessible chromatin regions (ACRs), pinpointing these DNA regulatory elements globally remains challenging. Current approaches primarily identify binding sites for a single TF (e.g. ChIP-seq), or globally detect ACRs but lack the resolution to consistently define TF-binding sites (e.g. DNAse-seq, ATAC-seq). To address this challenge, we developed MNase-defined cistrome-Occupancy Analysis (MOA-seq), a high-resolution (< 30 bp), high-throughput, and genome-wide strategy to globally identify putative TF-binding sites within ACRs. We used MOA-seq on developing maize ears as a proof of concept, able to define a cistrome of 145,000 MOA footprints (MFs). While a substantial majority (76%) of the known ATAC-seq ACRs intersected with the MFs, only a minority of MFs overlapped with the ATAC peaks, indicating that the majority of MFs were novel and not detected by ATAC-seq. MFs were associated with promoters and significantly enriched for TF-binding and long-range chromatin interaction sites, including for the well-characterized FASCIATED EAR4, KNOTTED1, and TEOSINTE BRANCHED1. Importantly, the MOA-seq strategy improved the spatial resolution of TF-binding prediction and allowed us to identify 215 motif families collectively distributed over more than 100,000 non-overlapping, putatively-occupied binding sites across the genome. Our study presents a simple, efficient, and high-resolution approach to identify putative TF footprints and binding motifs genome-wide, to ultimately define a native cistrome atlas.

TRACE: transcription factor footprinting using chromatin accessibility data and DNA sequence.

Transcription is tightly regulated by cis-regulatory DNA elements where transcription factors (TFs) can bind. Thus, identification of TF binding sites (TFBSs) is key to understanding gene expression and whole regulatory networks within a cell. The standard approaches used for TFBS prediction, such as position weight matrices (PWMs) and chromatin immunoprecipitation followed by sequencing (ChIP-seq), are widely used but have their drawbacks, including high false-positive rates and limited antibody availability, respectively. Several computational footprinting algorithms have been developed to detect TFBSs by investigating chromatin accessibility patterns; however, these also have limitations. We have developed a footprinting method to predict TF footprints in active chromatin elements (TRACE) to improve the prediction of TFBS footprints. TRACE incorporates DNase-seq data and PWMs within a multivariate hidden Markov model (HMM) to detect footprint-like regions with matching motifs. TRACE is an unsupervised method that accurately annotates binding sites for specific TFs automatically with no requirement for pregenerated candidate binding sites or ChIP-seq training data. Compared with published footprinting algorithms, TRACE has the best overall performance with the distinct advantage of targeting multiple motifs in a single model.

A common pattern of DNase I footprinting throughout the human mtDNA unveils clues for a chromatin-like organization.

Human mitochondrial DNA (mtDNA) is believed to lack chromatin and histones. Instead, it is coated solely by the transcription factor TFAM. We asked whether mtDNA packaging is more regulated than once thought. To address this, we analyzed DNase-seq experiments in 324 human cell types and found, for the first time, a pattern of 29 mtDNA Genomic footprinting (mt-DGF) sites shared by ∼90% of the samples. Their syntenic conservation in mouse DNase-seq experiments reflect selective constraints. Colocalization with known mtDNA regulatory elements, with G-quadruplex structures, in TFAM-poor sites (in HeLa cells) and with transcription pausing sites, suggest a functional regulatory role for such mt-DGFs. Altered mt-DGF pattern in interleukin 3-treated CD34+ cells, certain tissue differences, and significant prevalence change in fetal versus nonfetal samples, offer first clues to their physiological importance. Taken together, human mtDNA has a conserved protein-DNA organization, which is likely involved in mtDNA regulation.

CUT&RUN detects distinct DNA footprints of RNA polymerase II near the transcription start sites.

CUT&RUN is a powerful tool to study protein-DNA interactions in vivo. DNA fragments cleaved by the targeted micrococcal nuclease identify the footprints of DNA-binding proteins on the chromatin. We performed CUT&RUN on human lung carcinoma cell line A549 maintained in a multi-well cell culture plate to profile RNA polymerase II. Long (> 270 bp) DNA fragments released by CUT&RUN corresponded to the bimodal peak around the transcription start sites, as previously seen with chromatin immunoprecipitation. However, we found that short (< 120 bp) fragments identify a well-defined peak localised at the transcription start sites. This distinct DNA footprint of short fragments, which constituted only about 5% of the total reads, suggests the transient positioning of RNA polymerase II before promoter-proximal pausing, which has not been detected in the physiological settings by standard chromatin immunoprecipitation. We showed that the positioning of the large-size-class DNA footprints around the short-fragment peak was associated with the directionality of transcription, demonstrating the biological significance of distinct CUT&RUN footprints of RNA polymerase II.

Accurate and sensitive quantification of protein-DNA binding affinity.

Transcription factors (TFs) control gene expression by binding to genomic DNA in a sequence-specific manner. Mutations in TF binding sites are increasingly found to be associated with human disease, yet we currently lack robust methods to predict these sites. Here, we developed a versatile maximum likelihood framework named No Read Left Behind (NRLB) that infers a biophysical model of protein-DNA recognition across the full affinity range from a library of in vitro selected DNA binding sites. NRLB predicts human Max homodimer binding in near-perfect agreement with existing low-throughput measurements. It can capture the specificity of the p53 tetramer and distinguish multiple binding modes within a single sample. Additionally, we confirm that newly identified low-affinity enhancer binding sites are functional in vivo, and that their contribution to gene expression matches their predicted affinity. Our results establish a powerful paradigm for identifying protein binding sites and interpreting gene regulatory sequences in eukaryotic genomes.

Sasquatch: predicting the impact of regulatory SNPs on transcription factor binding from cell- and tissue-specific DNase footprints.

In the era of genome-wide association studies (GWAS) and personalized medicine, predicting the impact of single nucleotide polymorphisms (SNPs) in regulatory elements is an important goal. Current approaches to determine the potential of regulatory SNPs depend on inadequate knowledge of cell-specific DNA binding motifs. Here, we present Sasquatch, a new computational approach that uses DNase footprint data to estimate and visualize the effects of noncoding variants on transcription factor binding. Sasquatch performs a comprehensive k-mer-based analysis of DNase footprints to determine any k-mer's potential for protein binding in a specific cell type and how this may be changed by sequence variants. Therefore, Sasquatch uses an unbiased approach, independent of known transcription factor binding sites and motifs. Sasquatch only requires a single DNase-seq data set per cell type, from any genotype, and produces consistent predictions from data generated by different experimental procedures and at different sequence depths. Here we demonstrate the effectiveness of Sasquatch using previously validated functional SNPs and benchmark its performance against existing approaches. Sasquatch is available as a versatile webtool incorporating publicly available data, including the human ENCODE collection. Thus, Sasquatch provides a powerful tool and repository for prioritizing likely regulatory SNPs in the noncoding genome.

High-resolution digital profiling of the epigenome.

The widespread adoption of short-read DNA sequencing as a digital epigenomic readout platform has motivated the development of genome-wide tools that achieve base-pair resolution. New methods for footprinting and affinity purification of nucleosomes, RNA polymerases, chromatin remodellers and transcription factors have increased the resolution of epigenomic profiling by two orders of magnitude, leading to new insights into how the chromatin landscape affects gene regulation. These digital epigenomic tools have also been applied to directly profile both turnover kinetics and transcription in situ. In this Review, we describe how these new genome-wide tools allow interrogation of diverse aspects of the epigenome.

The Ferric Uptake Regulator Represses Type VI Secretion System Function by Binding Directly to the clpV Promoter in Salmonella enterica Serovar Typhimurium.

Type VI secretion systems (T6SSs) are highly conserved and complex protein secretion systems that deliver effector proteins into eukaryotic hosts or other bacteria. T6SSs are regulated precisely by a variety of regulatory systems, which enables bacteria to adapt to varied environments. A T6SS within Salmonella pathogenicity island 6 (SPI-6) is activated during infection, and it contributes to the pathogenesis, as well as interbacterial competition, of Salmonella enterica serovar Typhimurium (S. Typhimurium). However, the regulation of the SPI-6 T6SS in S. Typhimurium is not well understood. In this study, we found that the SPI-6 T6SS core gene clpV was significantly upregulated in response to the iron-depleted condition and during infection. The global ferric uptake regulator (Fur) was shown to repress the clpV expression in the iron-replete medium. Moreover, electrophoretic mobility shift and DNase I footprinting assays revealed that Fur binds directly to the clpV promoter region at multiple sites spanning the transcriptional start site. We also observed that the relieving of Fur-mediated repression on clpV contributed to the interbacterial competition activity and pathogenicity of S. Typhimurium. These findings provide insights into the direct regulation of Fur in the expression and functional activity of SPI-6 T6SS in S. Typhimurium and thus help to elucidate the mechanisms of bacterial adaptability and virulence.

Most brain disease-associated and eQTL haplotypes are not located within transcription factor DNase-seq footprints in brain.

Dense genotyping approaches have revealed much about the genetic architecture both of gene expression and disease susceptibility. However, assigning causality to genetic variants associated with a transcriptomic or phenotypic trait presents a far greater challenge. The development of epigenomic resources by ENCODE, the Epigenomic Roadmap and others has led to strategies that seek to infer the likely functional variants underlying these genome-wide association signals. It is known, for example, that such variants tend to be located within areas of open chromatin, as detected by techniques such as DNase-seq and FAIRE-seq. We aimed to assess what proportion of variants associated with phenotypic or transcriptomic traits in the human brain are located within transcription factor binding sites. The bioinformatic tools, Wellington and HINT, were used to infer transcription factor footprints from existing DNase-seq data derived from central nervous system tissues with high spatial resolution. This dataset was then employed to assess the likely contribution of altered transcription factor binding to both expression quantitative trait loci (eQTL) and genome-wide association study (GWAS) signals. Surprisingly, we show that most haplotypes associated with GWAS or eQTL phenotypes are located outside of DNase-seq footprints. This could imply that DNase-seq footprinting is too insensitive an approach to identify a large proportion of true transcription factor binding sites. Importantly, this suggests that prioritising variants for genome engineering studies to establish causality will continue to be frustrated by an inability of footprinting to identify the causative variant within a haplotype.

Genomic footprinting.

The advent of DNA footprinting with DNase I more than 35 years ago enabled the systematic analysis of protein-DNA interactions, and the technique has been instrumental in the decoding of cis-regulatory elements and the identification and characterization of transcription factors and other DNA-binding proteins. The ability to analyze millions of individual genomic cleavage events via massively parallel sequencing has enabled in vivo DNase I footprinting on a genomic scale, offering the potential for global analysis of transcription factor occupancy in a single experiment. Genomic footprinting has opened unique vistas on the organization, function and evolution of regulatory DNA; however, the technology is still nascent. Here we discuss both prospects and challenges of genomic footprinting, as well as considerations for its application to complex genomes.

Genome-wide footprinting: ready for prime time?

High-throughput sequencing technologies have allowed many gene locus-level molecular biology assays to become genome-wide profiling methods. DNA-cleaving enzymes such as DNase I have been used to probe accessible chromatin. The accessible regions contain functional regulatory sites, including promoters, insulators and enhancers. Deep sequencing of DNase-seq libraries and computational analysis of the cut profiles have been used to infer protein occupancy in the genome at the nucleotide level, a method introduced as 'digital genomic footprinting'. The approach has been proposed as an attractive alternative to the analysis of transcription factors (TFs) by chromatin immunoprecipitation followed by sequencing (ChIP-seq), and in theory it should overcome antibody issues, poor resolution and batch effects. Recent reports point to limitations of the DNase-based genomic footprinting approach and call into question the scope of detectable protein occupancy, especially for TFs with short-lived chromatin binding. The genomics community is grappling with issues concerning the utility of genomic footprinting and is reassessing the proposed approaches in terms of robust deliverables. Here we summarize the consensus as well as different views emerging from recent reports, and we describe the remaining issues and hurdles for genomic footprinting.

Analysis of computational footprinting methods for DNase sequencing experiments.

DNase-seq allows nucleotide-level identification of transcription factor binding sites on the basis of a computational search of footprint-like DNase I cleavage patterns on the DNA. Frequently in high-throughput methods, experimental artifacts such as DNase I cleavage bias affect the computational analysis of DNase-seq experiments. Here we performed a comprehensive and systematic study on the performance of computational footprinting methods. We evaluated ten footprinting methods in a panel of DNase-seq experiments for their ability to recover cell-specific transcription factor binding sites. We show that three methods--HINT, DNase2TF and PIQ--consistently outperformed the other evaluated methods and that correcting the DNase-seq signal for experimental artifacts significantly improved the accuracy of computational footprints. We also propose a score that can be used to detect footprints arising from transcription factors with potentially short residence times.

Hydroxyl-radical footprinting combined with molecular modeling identifies unique features of DNA conformation and nucleosome positioning.

Nucleosomes are the most abundant protein-DNA complexes in eukaryotes that provide compaction of genomic DNA and are implicated in regulation of transcription, DNA replication and repair. The details of DNA positioning on the nucleosome and the DNA conformation can provide key regulatory signals. Hydroxyl-radical footprinting (HRF) of protein-DNA complexes is a chemical technique that probes nucleosome organization in solution with a high precision unattainable by other methods. In this work we propose an integrative modeling method for constructing high-resolution atomistic models of nucleosomes based on HRF experiments. Our method precisely identifies DNA positioning on nucleosome by combining HRF data for both DNA strands with the pseudo-symmetry constraints. We performed high-resolution HRF for Saccharomyces cerevisiae centromeric nucleosome of unknown structure and characterized it using our integrative modeling approach. Our model provides the basis for further understanding the cooperative engagement and interplay between Cse4p protein and the A-tracts important for centromere function.

Rce1, a novel transcriptional repressor, regulates cellulase gene expression by antagonizing the transactivator Xyr1 in Trichoderma reesei.

Cellulase gene expression in the model cellulolytic fungus Trichoderma reesei is supposed to be controlled by an intricate regulatory network involving multiple transcription factors. Here, we identified a novel transcriptional repressor of cellulase gene expression, Rce1. Disruption of the rce1 gene not only facilitated the induced expression of cellulase genes but also led to a significant delay in terminating the induction process. However, Rce1 did not participate in Cre1-mediated catabolite repression. Electrophoretic mobility shift (EMSA) and DNase I footprinting assays in combination with chromatin immunoprecipitation (ChIP) demonstrated that Rce1 could bind directly to a cbh1 (cellobiohydrolase 1-encoding) gene promoter region containing a cluster of Xyr1 binding sites. Furthermore, competitive binding assays revealed that Rce1 antagonized Xyr1 from binding to the cbh1 promoter. These results indicate that intricate interactions exist between a variety of transcription factors to ensure tight and energy-efficient regulation of cellulase gene expression in T. reesei. This study also provides important clues regarding increased cellulase production in T. reesei.

Functional footprinting of regulatory DNA.

Regulatory regions harbor multiple transcription factor (TF) recognition sites; however, the contribution of individual sites to regulatory function remains challenging to define. We describe an approach that exploits the error-prone nature of genome editing-induced double-strand break repair to map functional elements within regulatory DNA at nucleotide resolution. We demonstrate the approach on a human erythroid enhancer, revealing single TF recognition sites that gate the majority of downstream regulatory function.

DeFCoM: analysis and modeling of transcription factor binding sites using a motif-centric genomic footprinter.

Motivation: Identifying the locations of transcription factor binding sites is critical for understanding how gene transcription is regulated across different cell types and conditions. Chromatin accessibility experiments such as DNaseI sequencing (DNase-seq) and Assay for Transposase Accessible Chromatin sequencing (ATAC-seq) produce genome-wide data that include distinct 'footprint' patterns at binding sites. Nearly all existing computational methods to detect footprints from these data assume that footprint signals are highly homogeneous across footprint sites. Additionally, a comprehensive and systematic comparison of footprinting methods for specifically identifying which motif sites for a specific factor are bound has not been performed. Results: Using DNase-seq data from the ENCODE project, we show that a large degree of previously uncharacterized site-to-site variability exists in footprint signal across motif sites for a transcription factor. To model this heterogeneity in the data, we introduce a novel, supervised learning footprinter called Detecting Footprints Containing Motifs (DeFCoM). We compare DeFCoM to nine existing methods using evaluation sets from four human cell-lines and eighteen transcription factors and show that DeFCoM outperforms current methods in determining bound and unbound motif sites. We also analyze the impact of several biological and technical factors on the quality of footprint predictions to highlight important considerations when conducting footprint analyses and assessing the performance of footprint prediction methods. Finally, we show that DeFCoM can detect footprints using ATAC-seq data with similar accuracy as when using DNase-seq data. Availability and Implementation: Python code available at https://bitbucket.org/bryancquach/defcom. Contact: bquach@email.unc.edu or tsfurey@email.unc.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

[Ultrasonic Footprinting].

Ligand binding influences the dynamics of the DNA helix in both the binding site and adjacent regions. This, in particular, is reflected in the changing pattern of cleavage of complexes under the action of ultrasound. The specificity of ultrasound-induced cleavage of the DNA sugar-phosphate backbone was studied in actinomycin D (AMD) complexes with double-stranded DNA restriction fragments. After antibiotic binding, the cleavage intensity of phosphodiester bonds between bases was shown to decrease at the chromophore intercalation site and to increase in adjacent positions. The character of cleavage depended on the sequences flanking the binding site and the presence of other AMD molecules bound in the close vicinity. A comparison of ultrasonic and DNase I cleavage patterns of AMD-DNA complexes provided more detail on the local conformation and dynamics of the DNA double helix in both binding site and adjacent regions. The results pave the way for developing a novel approach to studies of the nucleotide sequence dependence of DNA conformational dynamics and new techniques to identify functional genome regions.