XL-DNase-Seq: Footprinting Analysis of Dynamic Transcription Factors.

We have developed a novel method for genomic footprinting of transcription factors (TFs) that detects potential gene regulatory relationships from DNase-seq data at the nucleotide level. We introduce an assay termed cross-link (XL)-DNase-seq, designed to capture chromatin interactions of dynamic TFs. A mild cross-linking step in XL-DNase-seq improves the detection of DNase-based footprints of dynamic TFs. The footprint strengths and detectability depend on an optimal cross-linking procedure. This method may help extract novel gene regulatory circuits involving previously undetectable TFs. The XL-DNase-seq method is illustrated here for activated mouse macrophage-like cells, which share several features with inflammatory macrophages.

DNA-binding factor footprints and enhancer RNAs identify functional non-coding genetic variants.

BACKGROUND: Genome-wide association studies (GWAS) have revealed a multitude of candidate genetic variants affecting the risk of developing complex traits and diseases. However, the highlighted regions are typically in the non-coding genome, and uncovering the functional causative single nucleotide variants (SNVs) is challenging. Prioritization of variants is commonly based on genomic annotation with markers of active regulatory elements, but current approaches still poorly predict functional variants. To address this, we systematically analyze six markers of active regulatory elements for their ability to identify functional variants. RESULTS: We benchmark against molecular quantitative trait loci (molQTL) from assays of regulatory element activity that identify allelic effects on DNA-binding factor occupancy, reporter assay expression, and chromatin accessibility. We identify the combination of DNase footprints and divergent enhancer RNA (eRNA) as markers for functional variants. This signature provides high precision, but with a trade-off of low recall, thus substantially reducing candidate variant sets to prioritize variants for functional validation. We present this as a framework called FINDER-Functional SNV IdeNtification using DNase footprints and eRNA. CONCLUSIONS: We demonstrate the utility to prioritize variants using leukocyte count trait and analyze variants in linkage disequilibrium with a lead variant to predict a functional variant in asthma. Our findings have implications for prioritizing variants from GWAS, in development of predictive scoring algorithms, and for functionally informed fine mapping approaches.

GeF-seq: A Simple Procedure for Base-Pair Resolution ChIP-seq.

Nucleotide sequences recognized and bound by DNA-binding proteins (DBPs) are critical to controlling and maintaining gene expression, replication, chromosome segregation, cell division, and nucleoid structure in bacterial cells. Therefore, determination of the binding sequences of DBPs is important not only to study DBP recognition mechanisms but also to understand the fundamentals of cell homeostasis. While ChIP-seq analysis appears to be an effective way to determine DBP binding sites on the genome, the resolution is sometimes not sufficient to identify the sites precisely. Here we introduce a simple and effective method named Genome Footprinting with high-throughput sequencing (GeF-seq) to determine binding sites of DBPs with single base-pair resolution. GeF-seq detects binding sites of DBPs as sharp peaks and thus makes it possible to identify the recognition sequence in each "binding peak" more easily and accurately compared to the common ChIP-seq.

Transcriptional regulation of the yersiniabactin receptor fyuA gene by the ferric uptake regulator in Klebsiella pneumoniae NTUH-K2044.

The ferric uptake regulator (Fur) is a global regulator that influences the expression of virulence genes in Klebsiella pneumoniae. Bioinformatics analysis suggests Fur may involve in iron acquisition via the identified regulatory box upstream of the yersiniabactin receptor gene fyuA. To observe the impact of the gene fyuA on the virulence of K. pneumoniae, the gene fyuA knockout strain and complementation strain were constructed and then conducted a series of phenotypic experiments including chrome azurol S (CAS) detection, crystal violet staining, and wax moth virulence experiment. To examine the regulatory relationship between Fur and the gene fyuA, green fluorescent protein (GFP) reporter gene fusion assay, real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR), gel migration assay (EMSA), and DNase I footprinting assay were used to clarify the regulatory mechanism of Fur on fyuA. CAS detection revealed that the gene fyuA could affect the generation of iron carriers in K. pneumoniae. Crystal violet staining experiment showed that fyuA could positively influence biofilm formation. Wax moth virulence experiment indicated that the deletion of the fyuA could weaken bacterial virulence. GFP reporter gene fusion experiment and RT-qPCR analysis revealed that Fur negatively regulated the expression of fyuA in iron-sufficient environment. EMSA experiment demonstrated that Fur could directly bind to the promoter region of fyuA, and DNase I footprinting assay further identified the specific binding site sequences. The study showed that Fur negatively regulated the transcriptional expression of fyuA by binding to upstream of the gene promoter region, and then affected the virulence of K. pneumoniae.

Uncovering uncharacterized binding of transcription factors from ATAC-seq footprinting data.

Transcription factors (TFs) are crucial epigenetic regulators, which enable cells to dynamically adjust gene expression in response to environmental signals. Computational procedures like digital genomic footprinting on chromatin accessibility assays such as ATACseq can be used to identify bound TFs in a genome-wide scale. This method utilizes short regions of low accessibility signals due to steric hindrance of DNA bound proteins, called footprints (FPs), which are combined with motif databases for TF identification. However, while over 1600 TFs have been described in the human genome, only ~ 700 of these have a known binding motif. Thus, a substantial number of FPs without overlap to a known DNA motif are normally discarded from FP analysis. In addition, the FP method is restricted to organisms with a substantial number of known TF motifs. Here we present DENIS (DE Novo motIf diScovery), a framework to generate and systematically investigate the potential of de novo TF motif discovery from FPs. DENIS includes functionality (1) to isolate FPs without binding motifs, (2) to perform de novo motif generation and (3) to characterize novel motifs. Here, we show that the framework rediscovers artificially removed TF motifs, quantifies de novo motif usage during an early embryonic development example dataset, and is able to analyze and uncover TF activity in organisms lacking canonical motifs. The latter task is exemplified by an investigation of a scATAC-seq dataset in zebrafish which covers different cell types during hematopoiesis.

Shortened CRISPR-Cas9 arrays enable multiplexed gene targeting in bacteria from a smaller DNA footprint.

CRISPR technologies comprising a Cas nuclease and a guide RNA (gRNA) can utilize multiple gRNAs to enact multi-site editing or regulation in the same cell. Nature devised a highly compact means of encoding gRNAs in the form of CRISPR arrays composed of conserved repeats separated by targeting spacers. However, the capacity to acquire new spacers keeps the arrays longer than necessary for CRISPR technologies. Here, we show that CRISPR arrays utilized by the Cas9 nuclease can be shortened without compromising and sometimes even enhancing targeting activity. Using multiplexed gene repression in E. coli, we found that each region could be systematically shortened to varying degrees before severely compromising targeting activity. Surprisingly, shortening some spacers yielded enhanced targeting activity, which was linked to folding of the transcribed array prior to processing. Overall, shortened CRISPR-Cas9 arrays can facilitate multiplexed editing and gene regulation from a smaller DNA footprint across many bacterial applications of CRISPR technologies.

Identification of important genomic footprints using eight different selection signature statistics in domestic cattle breeds.

In the present study, the population genomic data of different cattle breeds were explored to decipher the genomic regions affected due to selective events and reflected in the productive, reproductive, thermo-tolerance, and health-related traits. To find out these genomic deviations due to selective sweeps, we used eight different statistical tools (Tajima's D, Fu & Li's D*, CLR, ROH, iHS, FST, FLK, and hapFLK) on seven indigenous and five exotic cattle breeds. We further performed composite analysis by comparing their covariance matrix. Several candidate genes were found to be related to milk production (ADARB, WDR70, and CA8), reproductive (PARN, FAM134B2, and ZBTB20), and health-related traits (SP110, CXCL2, CLXCL3, CXCL5, IRF8, and MYOM1). The outcome of this investigation provides a basis for detecting selective sweeps that explain the genetic variation of traits. They may possess functional importance for multiple cattle breeds in different subcontinents. However, further studies are required to improve the findings using high-density arrays or whole-genome sequencing with higher resolution and greater sample sizes.

Genome-wide quantification of transcription factor binding at single-DNA-molecule resolution using methyl-transferase footprinting.

Precise control of gene expression requires the coordinated action of multiple factors at cis-regulatory elements. We recently developed single-molecule footprinting to simultaneously resolve the occupancy of multiple proteins including transcription factors, RNA polymerase II and nucleosomes on single DNA molecules genome-wide. The technique combines the use of cytosine methyltransferases to footprint the genome with bisulfite sequencing to resolve transcription factor binding patterns at cis-regulatory elements. DNA footprinting is performed by incubating permeabilized nuclei with recombinant methyltransferases. Upon DNA extraction, whole-genome or targeted bisulfite libraries are prepared and loaded on Illumina sequencers. The protocol can be completed in 4-5 d in any laboratory with access to high-throughput sequencing. Analysis can be performed in 2 d using a dedicated R package and requires access to a high-performance computing system. Our method can be used to analyze how transcription factors cooperate and antagonize to regulate transcription.

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.

Genomic Footprinting Analyses from DNase-seq Data to Construct Gene Regulatory Networks.

Chromatin accessibility is directly linked with transcription in eukaryotes. Accessible regions associated with regulatory proteins are highly sensitive to DNase I digestion and are termed DNase I hypersensitive sites (DHSs). DHSs can be identified by DNase I digestion, followed by high-throughput DNA sequencing (DNase-seq). The single-base-pair resolution digestion patterns from DNase-seq allows identifying transcription factor (TF) footprints of local DNA protection that predict TF-DNA binding. The identification of differential footprinting between two conditions allows mapping relevant TF regulatory interactions. Here, we provide step-by-step instructions to build gene regulatory networks from DNase-seq data. Our pipeline includes steps for DHSs calling, identification of differential TF footprints between treatment and control conditions, and construction of gene regulatory networks. Even though the data we used in this example was obtained from Arabidopsis thaliana, the workflow developed in this guide can be adapted to work with DNase-seq data from any organism with a sequenced genome.

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.

Rapid and inexpensive preparation of genome-wide nucleosome footprints from model and non-model organisms.

MNase-seq (micrococcal nuclease sequencing) is used to map nucleosome positions in eukaryotic genomes to study the relationship between chromatin structure and DNA-dependent processes. Current protocols require at least two days to isolate nucleosome-protected DNA fragments. We have developed a streamlined protocol for S. cerevisiae and other fungi which takes only three hours. Modified protocols were developed for wild fungi and mammalian cells. This method for rapidly producing sequencing-ready nucleosome footprints from several organisms makes MNase-seq faster and easier, with less chemical waste.

Evolving origin-of-transfer sequences on staphylococcal conjugative and mobilizable plasmids-who's mimicking whom?

In Staphylococcus aureus, most multiresistance plasmids lack conjugation or mobilization genes for horizontal transfer. However, most are mobilizable due to carriage of origin-of-transfer (oriT) sequences mimicking those of conjugative plasmids related to pWBG749. pWBG749-family plasmids have diverged to carry five distinct oriT subtypes and non-conjugative plasmids have been identified that contain mimics of each. The relaxasome accessory factor SmpO, encoded by each conjugative plasmid, determines specificity for its cognate oriT. Here we characterized the binding of SmpO proteins to each oriT. SmpO proteins predominantly formed tetramers in solution and bound 5'-GNNNNC-3' sites within each oriT. Four of the five SmpO proteins specifically bound their cognate oriT. An F7K substitution in pWBG749 SmpO switched oriT-binding specificity in vitro. In vivo, the F7K substitution reduced but did not abolish self-transfer of pWBG749. Notably, the substitution broadened the oriT subtypes that were mobilized. Thus, this substitution represents a potential evolutionary intermediate with promiscuous DNA-binding specificity that could facilitate a switch between oriT specificities. Phylogenetic analysis suggests pWBG749-family plasmids have switched oriT specificity more than once during evolution. We hypothesize the convergent evolution of oriT specificity in distinct branches of the pWBG749-family phylogeny reflects indirect selection pressure to mobilize plasmids carrying non-cognate oriT-mimics.

Using an L7Ae-Tethered, Hydroxyl Radical-Mediated Footprinting Strategy to Identify and Validate Kink-Turns in RNAs.

Kink-turns are important RNA structural modules that facilitate long-range tertiary interactions and form binding sites for members of the L7Ae family of proteins. Present in a wide variety of functional RNAs, kink-turns play key organizational roles in many RNA-based cellular processes, including translation, modification, and tRNA biogenesis. It is important to determine the contribution of kink-turns to the overall architecture of resident RNAs, as these modules dictate ribonucleoprotein (RNP) assembly and function. This chapter describes a site-directed, hydroxyl radical-mediated footprinting strategy that utilizes L7Ae-tethered chemical nucleases to experimentally validate computationally identified kink-turns in any RNA and under a wide variety of conditions. The work plan described here uses the catalytic RNase P RNA as an example to provide a blueprint for using this footprinting method to map RNA-protein interactions in other RNP complexes.

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.

In Vivo Footprinting Analysis in Trichoderma reesei.

The in vivo footprinting method identifies protein-targeted DNA regions under different conditions such as carbon sources. Dimethyl sulfate (DMS) generates methylated purine bases at DNA sites which are not bound by proteins or transcription factors. The DNA is cleaved by HCl, and the resulting DNA fragments are 5'-end [6-FAM]-labeled by a linker-mediated PCR (LM-PCR). Fluorescent fragments are separated and analyzed on a capillary sequencer, followed by automated data analysis using the software tool ivFAST.

ATAC-seq with unique molecular identifiers improves quantification and footprinting.

ATAC-seq (Assay for Transposase-Accessible Chromatin with high-throughput sequencing) provides an efficient way to analyze nucleosome-free regions and has been applied widely to identify transcription factor footprints. Both applications rely on the accurate quantification of insertion events of the hyperactive transposase Tn5. However, due to the presence of the PCR amplification, it is impossible to accurately distinguish independently generated identical Tn5 insertion events from PCR duplicates using the standard ATAC-seq technique. Removing PCR duplicates based on mapping coordinates introduces increasing bias towards highly accessible chromatin regions. To overcome this limitation, we establish a UMI-ATAC-seq technique by incorporating unique molecular identifiers (UMIs) into standard ATAC-seq procedures. UMI-ATAC-seq can rescue about 20% of reads that are mistaken as PCR duplicates in standard ATAC-seq in our study. We demonstrate that UMI-ATAC-seq could more accurately quantify chromatin accessibility and significantly improve the sensitivity of identifying transcription factor footprints. An analytic pipeline is developed to facilitate the application of UMI-ATAC-seq, and it is available at https://github.com/tzhu-bio/UMI-ATAC-seq .

Molecular Footprints of the Immune Assault on Pancreatic Beta Cells in Type 1 Diabetes.

Type 1 diabetes (T1D) is a chronic disease caused by the selective destruction of the insulin-producing pancreatic beta cells by infiltrating immune cells. We presently evaluated the transcriptomic signature observed in beta cells in early T1D and compared it with the signatures observed following in vitro exposure of human islets to inflammatory or metabolic stresses, with the aim of identifying "footprints" of the immune assault in the target beta cells. We detected similarities between the beta cell signatures induced by cytokines present at different moments of the disease, i.e., interferon-α (early disease) and interleukin-1ß plus interferon-γ (later stages) and the beta cells from T1D patients, identifying biological process and signaling pathways activated during early and late stages of the disease. Among the first responses triggered on beta cells was an enrichment in antiviral responses, pattern recognition receptors activation, protein modification and MHC class I antigen presentation. During putative later stages of insulitis the processes were dominated by T-cell recruitment and activation and attempts of beta cells to defend themselves through the activation of anti-inflammatory pathways (i.e., IL10, IL4/13) and immune check-point proteins (i.e., PDL1 and HLA-E). Finally, we mined the beta cell signature in islets from T1D patients using the Connectivity Map, a large database of chemical compounds/drugs, and identified interesting candidates to potentially revert the effects of insulitis on beta cells.

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.

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.