We describe Cleavage Under Targets and Release Using Nuclease (CUT&RUN), a chromatin profiling strategy in which antibody-targeted controlled cleavage by micrococcal nuclease releases specific protein-DNA complexes into the supernatant for paired-end DNA sequencing. Unlike Chromatin Immunoprecipitation (ChIP), which fragments and solubilizes total chromatin, CUT&RUN is performed in situ, allowing for both quantitative high-resolution chromatin mapping and probing of the local chromatin environment. When applied to yeast and human nuclei, CUT&RUN yielded precise transcription factor profiles while avoiding crosslinking and solubilization issues. CUT&RUN is simple to perform and is inherently robust, with extremely low backgrounds requiring only ~1/10th the sequencing depth as ChIP, making CUT&RUN especially cost-effective for transcription factor and chromatin profiling. When used in conjunction with native ChIP-seq and applied to human CTCF, CUT&RUN mapped directional long range contact sites at high resolution. We conclude that in situ mapping of protein-DNA interactions by CUT&RUN is an attractive alternative to ChIP-seq.DOI:http://dx.doi.org/10.7554/eLife.21856.001 The DNA in a person’s skin cell will contain the same genes as the DNA in their muscle or brain cells. However, these cells have different identities because different genes are active in skin, muscle and brain cells. Proteins called transcription factors dictate the patterns of gene activation in the different kinds of cells by binding to DNA and switching nearby genes on or off. Transcription factors interact with other proteins such as histones that help to package DNA into a structure known as chromatin. Together, transcription factors, histones and other chromatin-associated proteins determine whether or not nearby genes are active.Sometimes transcription factors and other chromatin-associated proteins bind to the wrong sites on DNA; this situation can lead to diseases in humans, such as cancer. This is one of the many reasons why researchers are interested in working out where specific DNA-binding proteins are located in different situations. A technique called chromatin immunoprecipitation (or ChIP for short) can be used to achieve this goal, yet despite being one of the most widely used techniques in molecular biology, ChIP is hampered by numerous problems. As such, many researchers are keen to find alternative approaches.Skene and Henikoff have now developed a new method, called CUT&RUN (which is short for “Cleavage Under Targets & Release Using Nuclease”) to map specific interactions between protein and DNA in a way that overcomes some of the problems with ChIP. In CUT&RUN, unlike in ChIP, the DNA in the starting cells does not need to be broken up first; this means that protein-DNA interactions are more likely to be maintained in their natural state. With CUT&RUN, as in ChIP, a specific antibody identifies the protein of interest. But in CUT&RUN, this antibody binds to the target protein in intact cells and cuts out the DNA that the protein is bound to, releasing the DNA fragment from the cell. This new strategy allows the DNA fragments to be sequenced and identified more efficiently than is currently possible with ChIP.Skene and Henikoff showed that their new method could more accurately identify where transcription factors bind to DNA from yeast and human cells. CUT&RUN also identified a specific histone that is rarely found in yeast chromatin and the technique can be used with a small number of starting cells. Given the advantages that CUT&RUN offers over ChIP, Skene and Henikoff anticipate that the method will be viewed as a cost-effective and versatile alternative to ChIP. In future, the method could be automated so that multiple analyses can be performed at once.DOI:http://dx.doi.org/10.7554/eLife.21856.002 The action of transcription factors (TFs) at their binding sites on DNA drives gene expression patterns, and so genome-wide TF mapping has become a central goal both for individual researchers and large-scale infrastructural projects. TF profiling is most commonly carried out using chromatin immunoprecipitation (ChIP), a protocol that has changed little since it was first introduced over 30 years ago . Cells aChIP can also be performed without crosslinking, using ionic conditions that do not disrupt electrostatic contacts . ‘NativeEscherichia coli Dam methyltransferase is tethered to the TF and catalyzes N6-methylation of adenine at GATC sites in vivo. Sites can be mapped genome-wide using an N6-methyl-directed restriction enzyme. However, as the resolution of DamID is limited by the distribution of GATC sites, DamID cannot obtain the high resolution that is potentially attainable using a sequencing read-out  method, crude nuclei from crosslinked cells are first treated with a TF-specific antibody, followed by addition of a chimeric fusion between Protein A and MNase (pA-MN) and activation by calcium . ProteinChromatin Immuno-Cleavage (ChIC) has the advantage of using TF-specific antibodies to tether MNase and cleave only at binding sites. To adapt ChIC for deep sequencing, it was necessary to reduce the representation of background breaks in DNA that otherwise dominate deep sequencing libraries. The CUT&RUN modifications of ChIC were motivated by the observation that light MNase treatment of nuclei liberates mononucleosomes and TF-DNA complexes, leaving behind oligonucleosomes . We expe++ on ice to initiate the cleavage reaction then stopped seconds-to-minutes later by chelation, and (4) centrifuged to recover the supernatant containing the released TF-DNA complexes. DNA is then extracted from the supernatant and used directly for sequencing library preparation.Based on this rationale, we developed a simple CUT&RUN protocol . UnfixedWe first performed CUT&RUN using crude yeast nuclei. To rigorously compare CUT&RUN and ChIP-seq, we used the same FLAG-tagged TF strains, the same nuclear preparation protocol, the same mouse anti-FLAG monoclonal antibody and the same procedure for Illumina library preparation and paired-end sequencing . As mousSaccharomyces cerevisiae strain expressing 3XFLAG-tagged histone H2A, which would be expected to release nucleosomal fragments genome-wide. Indeed, over a 100-fold digestion time course at 0°C, we observed gradual cleavage and release of fragments down to mononucleosomal size, completely dependent on the presence of the primary antibody , and rDNA enhancer binding protein 1 (Reb1), obtaining ~2–3 million mapped paired-end reads per sample. We found that the size distributions of mapped fragments were virtually superimposable below ~150 bp for time points between 4 s and 128 s  histone variant in place of H3, is that it is part of the multi-megadalton kinetochore complex throughout the cell cycle , which rTo confirm that the differences we observed between the CUT&RUN supernatant and total DNA were due to differential solubility of kinetochore chromatin, we split the samples before digestion, and for one aliquot we stopped the cleavage reaction with 2 M NaCl and recovered the supernatant for sequencing. We obtained similar results for the high-salt fraction as for total DNA . The uneExamination of the ≥150 bp profiles  reveals Having established proof-of-principle in a simple well-studied genome, we next applied CUT&RUN to CCCTC-binding factor (CTCF) in human K562 cells. To directly compare the efficiency of various methods, we randomly selected 10 million reads for each technique and plotted the raw scores as an indication of information content per sequenced read. As was the case for yeast TFs, CTCF CUT&RUN showed higher dynamic range than other profiling methods, including standard X-ChIP-seq and ChIP-exo . When alCTCF has 11 zinc fingers and therefore may represent an unusually stable protein–DNA interaction. We therefore tested CUT&RUN using Myc and Max which are basic-loop-helix proteins that bind to a short E-box motif and have b residence times . CUT&RUNWe also considered the possibility that antibody-tethered MNase may be excluded from highly compacted heterochromatic regions in higher eukaryotes and as such CUT&RUN might be limited to analysis of protein-DNA interactions in euchromatic regions. We therefore performed CUT&RUN for the repressive histone mark H3K27me3. By analyzing 10 million reads from CUT&RUN and ENCODE X-ChIP-seq, we observed similar H3K27me3 landscapes, but at a much higher dynamic range for CUT&RUN, which demonstrates that Protein A-MNase is able to access compacted chromatin . FurtherAs nucleosome-sized fragments adjacent to TFs are released together with TF-containing fragments, we wondered whether 3D adjacencies might also be subject to cleavage and release. Chromosome-Conformation-Capture (3C) methods, such as Hi-C and ChIA-PET , are theall high-scoring ChIA-PET fragments overlap with direct and indirect sites , Cse4 (SBY5146), Abf1 and Reb1 under the control of their respective endogenous promoters were previously described  were rapidly mixed with a thawed nuclei aliquot and held at room temperature (RT) ≥5 min, placed on a magnet stand to clear (<1 min), and decanted on a magnet stand. The beads were then incubated 5 min RT in HNT supplemented with protease inhibitors (Roche Complete tablets) and 1 mM phenylmethylsulfonyl fluoride (PMSF) (=HNT-PPi) containing 3% bovine serum albumen (BSA) and 2 mM EDTA pH 8, then incubated 5’ with HNT-PPi +0.1% BSA (blocking buffer), using the magnet stand to decant. The beads were incubated 2 hr at 4°C with mouse anti-FLAG antibody (1:200–1:350), decanted, washed once in HNT + PMSF, then incubated 1 hr at 4°C with rabbit anti-mouse IgG antibody (1:200) in blocking buffer. The beads were washed once in HNT + PMSF, then incubated 1 hr at 4°C with pA-MN  in blocking buffer. The beads were washed twice in HNT + PMSF and once in 20 mM HEPES pH 7.5, 100 mM NaCl (Digestion buffer), optionally including 10% polyethylene glycol 8000 for Sth1 and Mot1. The beads were brought up in 1.2 ml Digestion buffer, divided into 8 × 150 µL aliquots, equilibrated to 0°C, then quickly mixed with CaCl2, stopping the reaction with 150 µL 2XSTOP . After incubating at 37°C for 20 min, the beads were centrifuged 5 min at 13,000 rpm at 4°C, the supernatant was removed on a magnet stand and mixed with 3 µL 10% SDS and 2 µL Proteinase K , incubated at 70°C 10 min, then extracted at room temperature once with buffered phenol-chloroform-isoamyl alcohol , transferred to a phase-lock tube , re-extracted with one vol CHCl3, transferred to a fresh tube containing 2 µL 2 mg/ml glycogen, precipitated by addition of 2–2.5 vol ethanol, chilled in ice and centrifuged 10 min at 13,000 rpm at 4°C. The pellet was rinsed with 100% ethanol, air-dried and dissolved in 25 µL 0.1 x TE8 .CUT&RUN begins with crude nuclei prepared according to published procedures. The following protocol is provided in step-by-step format (Appendix 2). Nuclei from ~5 × 10escribed , dividedTo extend CUT&RUN for high-salt extraction, digestions were performed in a 50 µL volume, stopped with 50 µL 2XSTOP, omitting RNase and substituting the standard 200 mM NaCl with 4 M NaCl. After 20 min at 37°C, 200 µL 67 µg/ml RNase A was added, incubated 20 min, then centrifuged 13,000 rpm to clarify the supernatant.2; 0.1% Triton X-100; 20% Glycerol) for 5 min on ice followed by centrifugation as above.  Nuclei were briefly washed in 1.5 ml Buffer 1  and then washed in 1.5 ml Buffer 2 . Nuclei were resuspended in 500 µl Buffer 2 and 10 µl antibody was added and incubated at 4°C for 2 hr. Nuclei were washed 3 x in 1 ml Buffer 2 to remove unbound antibody. Nuclei were resupended in 300 µl Buffer 2 and 5 µl pA-MN added and incubated at 4°C for 1 hr. Nuclei were washed 3 x in 0.5 ml Buffer 2 to remove unbound pA-MN. Tubes were placed in a metal block in ice-water and quickly mixed with 100 mM CaCl2 to a final concentration of 2 mM. The reaction was quenched by the addition of EDTA and EGTA to a final concentration of 10 mM and 20 mM respectively and 1 ng of mononucleosome-sized DNA fragments from Drosophila DNA added as a spike-in. Cleaved fragments were liberated into the supernatant by incubating the nuclei at 4°C for 1 hr, and nuclei were pelleted by centrifugation as above. DNA fragments were extracted from the supernatant and used for the construction of sequencing libraries. We have also adapted this protocol for use with magnetic beads (Appendix 3).Human K562 cells were purchased from ATCC . CUT&RUN was performed using a centrifugation-based protocol. Ten million cells were harvested by centrifugation  and washed in ice cold phosphate-buffered saline (PBS). Nuclei were isolated by hypotonic lysis in 1 ml NE1  and amplifying for eight or more cycles. To deplete total DNA samples of large fragments originating from insoluble chromatin, samples were mixed with ½ volume of Agencourt AMPure XP beads, held 5–10 min, placed on a magnet stand, and the supernatant was retained, discarding the beads. To reduce the representation of the remaining large fragments, the number of PCR cycles using the KAPA polymerase library preparation method was increased to 14 cycles and adapter concentrations were increased accordingly: Increasing the number of PCR cycles favors exponential amplification of shorter fragments over linear amplification of fragments that are too long for polymerase to completely transit. Libraries were sequenced for 25 cycles in paired-end mode on the Illumina HiSeq 2500 platform at the Fred Hutchinson Cancer Research Center Genomics Shared Resource. Paired-end fragments were mapped to the sacCer3/V64 genome and build and to release r5.51 (May 2013) of the D. melanogaster genomic sequence obtained from FlyBase using Novoalign (Novocraft) as described to generate SAM files. For human samples, paired-end fragments were mapped to hg19 using Bowtie2. Custom scripts for data processing are provided in Supplementary Software and can be downloaded from https://github.com/peteskene. For comparative analyses, publicly available datasets downloaded from the NCBI SRA archive were: ERR718799 (Abf1), SRR2568522 (Reb1), GSM749690 , and the CTCF ChIP-exo BAM file was kindly provided by Frank Pugh.Sequencing libraries were prepared from DNA fragments as described  but withS. cerevisiae genome to identify sites with significant log-odds motif scores. This identified 1899 Abf1 sites and 1413 Reb1 sites. Following previous studies, we use correspondence of a yeast TF binding site to the motif for that TF to be the ‘gold-standard’ for a true-positive call . Track screen shots were produced using IGV  for MAST searching of the ive call . MEME waive call . Peak-casing IGV Sequencing data have been deposited in GEO at National Center for Biotechnology Information under the accession number GSE84474. In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been favorably evaluated by Jessica Tyler (Senior Editor) and three reviewers, one of whom is a member of our Board of Reviewing Editors. The reviewers have opted to remain anonymous.Thank you for submitting your article "CUT&RUN: An efficient alternative strategy for high-resolution mapping of DNA binding sites" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.The method described is interesting and compared with conventional ChIP-seq results in higher resolution, lower background, and greater versatility, where mapping of long range interaction showed near base-pair resolution. A limitation/concern is that the method was used to analyze the binding of CTCF (and the centromeric histone variant CenH3). We request that the authors analyze another factor with limited binding sites in the genome such as a pioneer factor that binds to inaccessible, thus regions resistant to light MNAse treatment. Additionally, an explanation as to how the authors can tell direct from indirect cuts due to topology is necessary.Reviewer #1:In the manuscript by Skene and Henikoff, the authors developed Cleavage Under Targets and Release Using Nuclease (CUT & RUN), a chromatin immunocleavage (ChIC) based genome-wide mapping strategy. Compared with conventional ChIP-seq, this new method results in higher resolution, lower background, and greater versatility. In this method, native nuclei were immobilized and incubated with antibody against the protein of interest, and treated with protein A-MNase fusion protein for a very short time at 4o C. DNA released from MNase digestion was subjected to deep-sequencing. The authors tested this method with transcription factors including CTCF and with centromeric histone variant CenH3, and reported data with high resolution and sensitivity. Subsequent long-range interaction mapping by this method showed near base-pair resolution. Overall, the study provided a useful option for genome-wide mapping. There are few points that need to be addressed:1) It could be problematic to use budding yeast to study insoluble DNA-binding protein complexes. Yeast is a relatively simple model system, and more importantly, most of the yeast genome is actively transcribed, which is very different from the mammalian genome. The mammalian genome is more complex and has large regions of heterochromatin. It is not clear if those regions are accessible to the CUT & RUN methodology. The authors should test the applicability of their methodology to mammalian cells. At the very least, the authors must state in the text the potential limitations of this new methodology.2) It is unclear if the method can be used to probe chromatin states, including a variety of histone modifications. These features are very topical in the field of chromatin biology. Most current genome-wide profiling approaches can provide useful information on chromatin modifications. It would be useful for the authors to test if CUT & RUN can provide data on genome-wide histone modifications. If not, the authors should discuss this deficiency.3) In the second paragraph of the subsection CUT&RUN robustly maps yeast TF binding sites in situ at high resolution”, how did the authors define "true positives for both TFs"?Reviewer #2:The authors describe a technique for quantitative genome-wide mapping of chromatin-bound factors using antibodies and MNase in nucleo, without requiring fixation or sonication. They show efficient and accurate mapping of two yeast transcription factors at high resolution, precise mapping of the centromeric nucleosome, which is rare in abundance and trapped in an insoluble complex, and mapping of CTCF in human cells, including direct binding sites as well as indirect 3D contact points.The technique is ingenious and the study is very well presented. I particularly want to applaud the authors for including clear and detailed supplementary protocols, which is rarely seen. If the technology worked as well as claimed and were a true alternative for ChIP-seq, I would highly recommend this manuscript for publication. However, I have a major concern about the ability of CUT&RUN to distinguish direct from indirect contacts and additional worries about specificity and versatility that would need to be addressed experimentally.A) 3D contacts: the authors present the ability of CUT&RUN to identify distal indirect contacts of CTCF as an asset, but to me it appears to be a major limitation. If I understand correctly, of the ~20k sites identified by CUT&RUN, only those showing native ChIP signal (~10%) are direct binding sites; the others are indirect. That would imply that native ChIP must be performed in parallel to CUT&RUN to identify direct binding sites. That seem a major limitation that disqualifies the technique as a full replacement for ChIP-seq. Is this limited to CTCF or they see evidence of indirect sites also in their yeast data? Are these indirect contacts captured by conventional X-ChIP? Is there any way to distinguish direct from indirect bioinformatically or by modifications to the CUT&RUN protocol?B) Specificity: the authors show convincingly that CUT&RUN is as sensitive or more sensitive than IP methods. They also show that the background noise is lower e.g.  than in B1) the authors perform CUT&RUN experiments in cells lacking the targeted protein.B2) the authors perform detailed analyses to determine false positive rates, including calling peaks for Abf1 and Reb1 and showing that the majority of the top peaks are within motifs.B3) the authors analyze enrichment over DNaseI hypersensitive sites for all the CUT&RUN experiments and compare it with enrichment in native ChIP and X-ChIP as a measure of false positive rate.C) Versatility: to claim that CUT&RUN is a "suitable replacement" (Abstract) or even just "an attractive alternative" (Discussion) to ChIP, the authors should show that it can be applied to a similarly broad array of chromatin-binding proteins. Specifically, my concern is that CUT&RUN works best when the targeted protein resides in a nucleosome free region, and the three proteins targeted here belong to that category. The experiment on Cse4 helps but a more broadly distributed heterochromatic protein would be more convincing.D) Scales: in several points it is hard to follow the comparisons because scales are omitted or vague (e.g. "low" to "high") in genome browser snapshots and heatmaps. They must be included for all plots and the values represented fully described in the text or legends.Reviewer #3:The authors build on their previously developed method to dig deeper into the possibilities offered by ChIP variants to precisely map the genomic location of DNA binding proteins. The technique has the potential to be scalable and transferable and thus impactful.As the authors point out, the preferred method to analyze genomic occupancy is ChIP-seq because of its relatively simplicity and adaptability to different proteins. Other methods that tag specific enzymes to binding sites are inherently more tedious to apply to multiple proteins because they require the construction of protein fusions. Additionally, they sometimes lack resolution or depth. However, they have the advantage of working in native conditions or even in vivo. The presented method has the advantages of ChIP-seq since it requires only an antibody to the protein of interest without the need of generating a transgenic cell lines. Thus, it can be complemented and compared directly to ChIP-seq. It also gains in resolution over conventional ChIP-seq by relying on an enzymatic digestion of the DNA under native conditions. Additionally, by releasing MNase accessible regions, the method reduced background reads. Finally, it allows the investigation of the local accessibility state. Thus, with minor controls it can be a valuable method for the large community studying how specific proteins interact with the genome.The described robust performance across digestions time is a very positive attribute of this protocol. Other protocols that require MNase digestion are extremely sensitive to its activity and thus very hard to set up. The overlap on size distribution of The authors compare Cut&Run with ORGANIC ChIP-seq, a previous generation method from their latest improved method. How does this method compare to their latest method to map TF binding at single base-pair resolution published last year?Cut&Run relies on MNase digestion. How does Cut&Run perform for pioneer transcription factors that bind to inaccessible, thus resistant regions to light MNAse treatment? Thus, should be discussed and perhaps tested (see below).Related, and in light of this comment "When aligned to CTCF motifs found within DNaseI hypersensitive 245 sites, CUT&RUN and X-ChIP-seq CTCF heat maps show strong concordance": How does it compare to whole genome ChIP-seq? How does the >150bs run compares to an accessibility experiments? Would a MNase experiment be a better background model for peak calling? If true, then the impact of this method should be reworded.CTCF is an atypical TFs in terms of its ability to ChIP. Few novel protocols have been established and tested with CTCF but fail when repeated with other TFs. Thus, I highly recommend to perform and report a Cut&Run experiment with another TF such as homeodomain. This to me is an important and the only wet experiment the authors should perform. The rest of my comments could be addressed by re-analysis of the data.The claim about recovering 3D interactions is weak and to me there is not enough support to include this claim in the manuscript. First, it needs the MNase background model. The MNase is not covalently bound to CTCF, thus some of the release fragments could be released at similar frequencies in both conditions, while CTCF-interacting elements will be resealed at higher frequencies. Secondly, no technique is perfect, but discussion about transient binding and its implications for the digestion of certain regions should be suggested. Finally, the most important information from 3C techniques is that they identify the binding partners. Cut&Run even in the best case scenario will identify regions that could be in contact with one of the many CTCF binding sites in the genome. The method described is interesting and compared with conventional ChIP-seq results in higher resolution, lower background, and greater versatility, where mapping of long range interaction showed near base-pair resolution. A limitation/concern is that the method was used to analyze the binding of CTCF (and the centromeric histone variant CenH3). We request that the authors analyze another factor with limited binding sites in the genome such as a pioneer factor that binds to inaccessible, thus regions resistant to light MNAse treatment. Additionally, an explanation as to how the authors can tell direct from indirect cuts due to topology is necessary.We thank the reviewers and editors for their enthusiasm for the method and for their many helpful comments and suggestions, which have greatly improved the manuscript. We now provide new data and analyses showing that CUT&RUN performs better than conventional ChIP-seq for a broad range of other factors, now directly addressing issues of DNA accessibility, background and versatility. As for the second issue, we now show that direct and indirect site detection is the same for CUT&RUN and X-ChIP-seq, and that in either case, a method such as our new native ChIP protocol is needed to decide. We have also addressed each specific comment in our point-by-point response.Reviewer #1:[…]1) It could be problematic to use budding yeast to study insoluble DNA-binding protein complexes. Yeast is a relatively simple model system, and more importantly, most of the yeast genome is actively transcribed, which is very different from the mammalian genome. The mammalian genome is more complex and has large regions of heterochromatin. It is not clear if those regions are accessible to the CUT & RUN methodology. The authors should test the applicability of their methodology to mammalian cells. At the very least, the authors must state in the text the potential limitations of this new methodology.We agree that the concern that tethered MNase cannot break through mammalian heterochromatin needs to be addressed. Accordingly, we have performed CUT&RUN using an antibody against H3K27me3, which is a marker for facultative heterochromatin. The resulting profiles match ChIP-seq profiles by the ENCODE project with much better dynamic range using the same number of reads (new 2) It is unclear if the method can be used to probe chromatin states, including a variety of histone modifications. These features are very topical in the field of chromatin biology. Most current genome-wide profiling approaches can provide useful information on chromatin modifications. It would be useful for the authors to test if CUT & RUN can provide data on genome-wide histone modifications. If not, the authors should discuss this deficiency.We agree that users of CUT&RUN will want to apply the method to histone modifications. As explained in response to point 1, we have addressed this concern by showing that CUT&RUN provides H3K27me3 profiles that are better than the current standard: ChIP-seq datasets published by the ENCODE project. Therefore, CUT&RUN provides an attractive alternative to ChIP for histone modifications.3) In the second paragraph of the subsection "CUT&RUN robustly maps yeast TF binding sites in situ at high resolution”, how did the authors define "true positives for both TFs"?We now explicitly define true positives in a “Motif identification” subsection in Methods as sites with statistically significant motifs determined by MAST searching of the yeast genome, using motifs found by MEME with ChIP-seq and CUT&RUN peak calls. The close correspondence between motifs determined using both CUT&RUN and ChIP-seq  providesReviewer #2:The authors describe a technique for quantitative genome-wide mapping of chromatin-bound factors using antibodies and MNase in nucleo, without requiring fixation or sonication. They show efficient and accurate mapping of two yeast transcription factors at high resolution, precise mapping of the centromeric nucleosome, which is rare in abundance and trapped in an insoluble complex, and mapping of CTCF in human cells, including direct binding sites as well as indirect 3D contact points.The technique is ingenious and the study is very well presented. I particularly want to applaud the authors for including clear and detailed supplementary protocols, which is rarely seen. If the technology worked as well as claimed and were a true alternative for ChIP-seq, I would highly recommend this manuscript for publication. However, I have a major concern about the ability of CUT&RUN to distinguish direct from indirect contacts and additional worries about specificity and versatility that would need to be addressed experimentally.We thank Reviewer 2 for raising these important issues, which we now address with additional data, analyses and clarifications.A) 3D contacts: the authors present the ability of CUT&RUN to identify distal indirect contacts of CTCF as an asset, but to me it appears to be a major limitation. If I understand correctly, of the ~20k sites identified by CUT&RUN, only those showing native ChIP signal (~10%) are direct binding sites; the others are indirect. That would imply that native ChIP must be performed in parallel to CUT&RUN to identify direct binding sites. That seem a major limitation that disqualifies the technique as a full replacement for ChIP-seq. Is this limited to CTCF or they see evidence of indirect sites also in their yeast data? Are these indirect contacts captured by conventional X-ChIP?Yes, the inability to distinguish direct from indirect sites is common to both X-ChIP-seq and CUT&RUN. The evidence for this is presented in the heat maps in the new Is there any way to distinguish direct from indirect bioinformatically or by modifications to the CUT&RUN protocol?We have not found an experimental way to separate the long-range cleavages from those nearby, as they occur nearly simultaneously during the digestion time-course.B) Specificity: the authors show convincingly that CUT&RUN is as sensitive or more sensitive than IP methods. They also show that the background noise is lower e.g.  than in As detailed below, we now show that there is no bias towards accessible chromatin regions. The peaks referred to are instead attributable to indirect contact sites, which are also present in X-ChIP-seq but not native datasets, as explained above.The authors partially deal with this in B1) the authors perform CUT&RUN experiments in cells lacking the targeted protein.We thank reviewer 2 for raising this critical issue, which we had not adequately addressed. Nearly all of the proteins that we profiled in this study are essential, so this exact control is problematic at best. However, the underlying concern raised is that accessible chromatin regions might be a source of false positive peaks, and we address this issue with a rigorous negative control: In our yeast experiments we leave out the primary anti-FLAG antibody thus identically treating an aliquot of the same nuclei with IgG (rabbit anti-mouse) and pA-MN in parallel. After maximum digestion time for an experiment, we recover low levels of background cleavage fragments from throughout the genome. In this way, the cells are not disrupted, and the single negative control serves for all yeast proteins in the study. We now include representative negative control tracks in B2) the authors perform detailed analyses to determine false positive rates, including calling peaks for Abf1 and Reb1 and showing that the majority of the top peaks are within motifs.We have now performed this analysis for Abf1 and Reb1. To compare CUT&RUN and ORGANIC motif recovery, peak-call thresholds were adjusted to report similar numbers of peaks. We find that the majority of peaks called using a threshold algorithm have a significant motif for both CUT&RUN and ORGANIC, and the relative performance is similar for both a stringent threshold  and a relaxed threshold . By calling peaks using a simple threshold algorithm that makes no assumptions concerning peak features, and varying the threshold for high and low stringency, we believe that this performance comparison is robust. Indeed, consistent with the differences in sensitivity and specificity seen in the occupancy heat maps displayed in B3) the authors analyze enrichment over DNaseI hypersensitive sites for all the CUT&RUN experiments and compare it with enrichment in native ChIP and X-ChIP as a measure of false positive rate.As pointed out above, the concern about accessible DNA is addressed with our no primary antibody negative controls both for yeast and for human. As for detection of false positives that might arise for any reason, we find that a sensitive criterion is to ask whether an Abf1 peak is found at a Reb1 site and vice-versa, such as is shown graphically in C) Versatility: to claim that CUT&RUN is a "suitable replacement" (Abstract) or even just "an attractive alternative" (Discussion) to ChIP, the authors should show that it can be applied to a similarly broad array of chromatin-binding proteins. Specifically, my concern is that CUT&RUN works best when the targeted protein resides in a nucleosome free region, and the three proteins targeted here belong to that category. The experiment on Cse4 helps but a more broadly distributed heterochromatic protein would be more convincing.We agree that it is important to demonstrate versatility beyond transcription factors and nucleosomes, and we have addressed this in two ways. First, as described in response to reviewer 1, our addition of H3K27me3 shows that CUT&RUN outperforms ChIP-seq for heterochromatin. Second, we now address the question of whether CUT&RUN can profile dynamic components of the chromatin landscape. Myc and Max are considered to be especially dynamic TFs, and as explained below and shown in the new D) Scales: in several points it is hard to follow the comparisons because scales are omitted or vague (e.g. "low" to "high") in genome browser snapshots and heatmaps. They must be included for all plots and the values represented fully described in the text or legends.We have corrected this issue throughout the manuscript, indicating the pixel values for the heat maps, and for the browser snapshots, we indicate the Y-axis values in the figure where peak autoscaling was done, and in the legend where the tracks in each panel are identically scaled.Reviewer #3:The authors build on their previously developed method to dig deeper into the possibilities offered by ChIP variants to precisely map the genomic location of DNA binding proteins. The technique has the potential to be scalable and transferable and thus impactful.As the authors point out, the preferred method to analyze genomic occupancy is ChIP-seq because of its relatively simplicity and adaptability to different proteins. Other methods that tag specific enzymes to binding sites are inherently more tedious to apply to multiple proteins because they require the construction of protein fusions. Additionally, they sometimes lack resolution or depth. However, they have the advantage of working in native conditions or even in vivo. The presented method has the advantages of ChIP-seq since it requires only an antibody to the protein of interest without the need of generating a transgenic cell lines. Thus, it can be complemented and compared directly to ChIP-seq. It also gains in resolution over conventional ChIP-seq by relying on an enzymatic digestion of the DNA under native conditions. Additionally, by releasing MNase accessible regions, the method reduced background reads. Finally, it allows the investigation of the local accessibility state. Thus, with minor controls it can be a valuable method for the large community studying how specific proteins interact with the genome.targeted.We thank reviewer 3 for these positive comments, but there appears to be a misunderstanding concerning the supposed use of our method to investigate local accessibility states. Here and below, reviewer 3 seems to be assuming that tethered MNase will have the same biases as free MNase, but our data show that this is not the case at all. This was a point that we made in describing how yeast kinetochores, the cores of which are the most AT-rich regions in the genome, remain fully intact in our Cse4 and H2A CUT&RUN profiles. However, the well-known preference for cleaving AT-rich DNA  has led to the understandable assumption that tethered MNase would have the same DNA accessibility artifact. In stark contrast to this expectation, our study shows that CUT&RUN has remarkably little bias either for or against accessible or inaccessible regions genome-wide. We now demonstrate this fact more directly by showing that our No Antibody negative controls show no difference between NDRs and flanking nucleosomes for both yeast new  and humaThe described robust performance across digestions time is a very positive attribute of this protocol. Other protocols that require MNase digestion are extremely sensitive to its activity and thus very hard to set up. The overlap on size distribution of not critical at all. We now make this point in the text.Our intention in showing a doubling digestion time series over >2 orders of magnitude was to vividly illustrate how we get a limit digestion, so that digestion time is The authors compare Cut&Run with ORGANIC ChIP-seq, a previous generation method from their latest improved method. How does this method compare to their latest method to map TF binding at single base-pair resolution published last year?We agree, and now show CTCF MNase X-ChIP-seq data. Results are comparable to results using CUT&RUN.Cut&Run relies on MNase digestion. How does Cut&Run perform for pioneer transcription factors that bind to inaccessible, thus resistant regions to light MNAse treatment? Thus, should be discussed and perhaps tested (see below).Applying CUT&RUN to a designated pioneer transcription factor is not a suitable way to test whether tethered MNase can “break through” inaccessible chromatin for at least three reasons: 1) By definition the pioneer factors open chromatin when they bind, and so any site that is bound by a pioneer factor when the antibody is added to the nuclei is already opened up. 2) Using ChIP-seq, CTCF binding was shown to inhibit binding of the classical pioneer factor, FoxA1 (PMID: 21151129); this finding implies that CTCF is more penetrant than pioneer factors in breaking through inaccessible chromatin. 3) In the December 1, 2016 issue of Cell, CTCF binding sites were shown to be directly centered over sites of nucleosome occupancy mapped using chemical cleavage, and the authors went on to argue that CTCF is itself a pioneer factor (PMID:27889238).To address the question of whether CUT&RUN applies to inaccessible regions, we profiled heterochromatin that is marked with H3K27me3. As described in response to reviewer 1, we find that CUT&RUN provides excellent profiles of H3K27me3 that correspond closely to H3K27me3 ChIP but with better dynamic range for the same number of reads new , definitRelated, and in light of this comment "When aligned to CTCF motifs found within DNaseI hypersensitive 245 sites, CUT&RUN and X-ChIP-seq CTCF heat maps show strong concordance": How does it compare to whole genome ChIP-seq?We now present a direct comparison between CUT&RUN and ENCODE ChIP-seq at all previously identified ENCODE CTCF peaks new .How does the >150bs run compares to an accessibility experiments?This question seems to arise from the misunderstanding concerning targeted versus free MNase referred to above. That is, CUT&RUN does not provide accessibility measurements. Being tethered, MNase specifically releases neighboring nucleosomes. In contrast, MNase-seq gives DNA accessibility, TF occupancy and nucleosome positions in a single experiment. To illustrate this distinction, we now compare CUT&RUN size fractions to MNase-seq data at Abf1 and Reb1 sites (compare Would a MNase experiment be a better background model for peak calling? If true, then the impact of this method should be reworded.free MNase has a strong AT-rich cleavage bias, but from the results of introduce an AT-bias into CUT&RUN profiles where no such bias exists. Also, as can be seen in No. This question seems to assume that tethered MNase should be cleaving preferentially within accessible regions, whereas in our no-antibody controls, we show that there is no such preference. Again, perhaps what has been confusing is the fact that CTCF is an atypical TFs in terms of its ability to ChIP. Few novel protocols have been established and tested with CTCF but fail when repeated with other TFs. Thus, I highly recommend to perform and report a Cut&Run experiment with another TF such as homeodomain. This to me is an important and the only wet experiment the authors should perform. The rest of my comments could be addressed by re-analysis of the data.To address this concern, we have now added CUT&RUN data for c-Myc and Max transcription factors  which haThe claim about recovering 3D interactions is weak and to me there is not enough support to include this claim in the manuscript. First, it needs the MNase background model. The MNase is not covalently bound to CTCF, thus some of the release fragments could be released at similar frequencies in both conditions, while CTCF-interacting elements will be resealed at higher frequencies. Secondly, no technique is perfect, but discussion about transient binding and its implications for the digestion of certain regions should be suggested. Finally, the most important information from 3C techniques is that they identify the binding partners. Cut&Run even in the best case scenario will identify regions that could be in contact with one of the many CTCF binding sites in the genome.There is no question about the reality of the two classes of sites, as >90% of all high-scoring CTCF ChIA-PET sites have high-scoring CUT&RUN sites. This correspondence to 3D interaction maps was an unexpected discovery that can explain the nature of the sites that are not seen using Native ChIP, but are also seen using X-ChIP new . Regardl


Stem cells are defined by their capacities to self-renew and generate progeny of multiple lineages. The transcription factor SOX2 has key roles in the regulation of stem cell characteristics, but whether SOX2 achieves these functions through similar mechanisms in distinct stem cell populations is not known. To address this question, we performed RNA-seq and SOX2 ChIP-seq on embryonic mouse cortex, spinal cord, stomach and lung/esophagus. We demonstrate that, although SOX2 binds a similar motif in the different cell types, its target regions are primarily cell-type-specific and enriched for the distinct binding motifs of appropriately expressed interacting co-factors. Furthermore, cell-type-specific SOX2 binding in endodermal and neural cells is most often found around genes specifically expressed in the corresponding tissue. Consistent with this, we demonstrate that SOX2 target regions can act as cis-regulatory modules capable of directing reporter expression to appropriate tissues in a zebrafish reporter assay. In contrast, SOX2 binding sites found in both endodermal and neural tissues are associated with genes regulating general stem cell features, such as proliferation. Notably, we provide evidence that SOX2 regulates proliferation through conserved mechanisms and target genes in both germ layers examined. Together, these findings demonstrate how SOX2 simultaneously regulates cell-type-specific, as well as core transcriptional programs in neural and endodermal stem cells. The fine-tuned activities of stem cells are essential for embryonic development and organ maintenance. All types of stem cells share the abilities to self-renew and differentiate into specific cell-types, though the stem cells of different organs have distinct gene expression profiles and competences. Despite these unifying properties, to what extent diverse populations of stem cells utilize similar regulatory mechanisms remains unclear. To address this issue, we map the binding pattern of the key stem cell transcription factor SOX2 in the developing cortex, spinal cord, stomach and lung/esophagus. We find that, even though the core DNA-sequence it targets is similar in all tissues, its binding location is highly divergent and reflects the gene expression profile of each cell type. Moreover, DNA-regions targeted by SOX2 are enriched for binding motifs of distinct co-factors in each tissue, which we demonstrate can physically and functionally interact with SOX2. Consistent with these findings, DNA-regions bound specifically in cells of one germ layer are capable of driving fluorescent reporter gene expression in corresponding zebrafish tissues. Finally, we demonstrate that proliferation genes are bound by SOX2 in all examined tissues, and that cell division is controlled via a common mechanism in neural and endodermal cells. These findings provide a striking example of how a single transcription factor regulates both core and specific stem cell processes in multiple cellular contexts. Stem cells are essential for the formation of organs during development, as well as for their homeostasis and maintenance throughout life. Stem cells are defined by their capacity for self-renewal and their ability to generate progeny that differentiate into one or more definitive cell types. However, whether the common characteristics of stem cells are regulated through conserved transcriptional mechanisms in different stem cell populations is not well understood.The transcription factor SOX2 is expressed in an array of stem cell subtypes, from pluripotent stem cells in the early pre-implantation embryo to adult organ specific stem cells . Gain- aThe binding pattern of SOX2 is influenced by several regulatory mechanisms. While the capacity of SOX2 to target its binding motifs can be influenced by the local status of chromatin compaction ,12, its To examine how specific and shared gene expression patterns are regulated in different stem cell populations, we have analyzed SOX2 binding in stem cells of the CNS and two endoderm derived organs; the developing lung/esophagus and stomach. We demonstrate that SOX2 targeted DNA-regions are mainly cell-type-specific and enriched for the unique binding motifs of specific co-factors. Moreover, we provide evidence that SOX2 targeted regions can function as cis-regulatory modules (CRMs) that can drive reporter gene expression in corresponding regions of transgenic zebrafish embryos. While cell-type-specific SOX2 binding is enriched around genes with corresponding cell-type-specific expression and function, sites that are bound by SOX2 in both neural and endodermal cells are more often associated with genes that regulate general stem cell features, such as stem cell proliferation. Thus, we use functional experiments to show that the mechanisms by which SOX2 controls the proliferation rate of stem cells in the cortex are conserved in stem cells of the embryonic stomach and spinal cord.Sox2-Gfp knock-in mice  and the foregut, in E11.5 -in mice . In the  mice  in lung/esophagus, 962 in stomach, 6357 in cortex and 2313 in spinal cord , numbersspecific . Althougspecific , only a specific . In contspecific . Moreovespecific . Finallyspecific Consistent with these findings, principal component analysis (PCA) exposed a close relationship between the binding patterns of SOX2 in embryonic endodermal cells and that previously described in the adult stomach  Fig 1G)Fig 1G). The binding stability and specificity of SOX2 is dependent on its interactions with heterodimerizing partner factors . For insin vitro, which raises the possibility that additional undefined factors may facilitate the detected interaction between these transcription factors.The selective enrichment of these binding motifs in the various SOX2 peak-sets raised the question if their corresponding transcription factors could functionally interact with SOX2. To address this question, we focused on OTX1, FOXA1 and ZEB1, which represent transcription factors targeting motifs with distinct spacing to SOX2 motifs , and whoTo examine whether these factors could interact at the functional level, CRMs specifically or commonly bound by SOX2 in neural and endodermal tissues, were isolated and inserted into luciferase (luc) reporter vectors. Luc-reporters containing CRMs bound by SOX2 specifically in the CNS were activated in an additive fashion by SOX2 and OTX1 misexpression in mouse embryonic carcinoma P19 cells . Similar+ cells isolated from the cortex, spinal cord, stomach and lung/esophagus of Sox2-Gfp mice showed high internal concordance  [SoxB1 mRNAs [+ transfected cells by approximately 50% in the spinal cord and the stomach endoderm  ,24 in thendoderm . Hence, GBM) [Interestingly, high levels of Sox2 have previously been shown to reduce cortical proliferation by facilitating TCF/LEF, the Wnt-signaling mediating transcription factors, repression of pro-proliferative genes in a GRO/TLE co-repressor dependent manner . ConsistGBM)  (S6B andGBM)  (S6B andGBM) .SOX2 has key regulatory roles in many different stem cell populations at both embryonic and adult stages. However, whether SOX2 utilizes similar mechanisms to control common cellular processes in different stem cell populations is not understood. To address this issue, we have analyzed SOX2 binding and gene expression, at a genome wide scale, in embryonic mouse cortex, spinal cord, stomach and lung/esophagus. Together, these data reveal a core SOX2 regulated gene network centered on the regulation of stem cell proliferation and differentiation.regulation of stem cell proliferation” , with high background regions removed (available upon request). Then, we filtered these peaks for only those that were also called in both individual duplicate experiments in each tissue . The center of each peak was extended by ±100bp, and these were used for default overlap analysis in Galaxy v.16.10.rc1 [As input to the ChIP-seq experiments, which were performed in duplicate according to , 40–60 E6.10.rc1 , with al−4 for all experiments, and again used only peaks that overlapped in peak calls from both individual ChIP-seq repeats, as well as in the merged files. For lungs/esophaguses, Run00207_L3_1_131128_SN893_0207_AC2LG9ACXX_ATCACG_AR001.fastq and Run00183_L3_1_130418_SN893_0183_AD1Y1UACXX_ATCACG_AR001.fastq were used as duplicates.In order to confirm that our peak calling approach had revealed true SOX2 binding sites, we repeated the peak calling using MACS14 at a p-value cutoff of <10Sox2-Gfp embryos (Jackson mouse stock number 017592) were photographed fresh upon dissection. BrdU treatments were performed with a single treatment of 100ug/g body weight BrdU for one hour. Antibody stains were performed using homemade rabbit anti-SOX2 , mouse anti-Ki67 (Millipore MAB4190), rabbit anti-BrdU (Rockland 600-401-C29), and FITC conjugated goat anti-GFP (Abcam ab6662). BrdU stain denaturing was performed according to [E11.5 SOX motif central enrichment was assessed using CentriMo v.4.11.2 . NearestToppcluster  was usedThe PCA plots were created with DiffBind. The plots are based on affinity (read count) data. A binding matrix was calculated with scores based on read counts for every sample within the binding site intervals (peaks). For the DHS PCA plots the peaks of the corresponding SOX2-ChIP were used.https://github.com/dariober/bioinformatics-cafe/tree/master/fastaRegexFinder). When the motifs were identified within the same peak, their positions were compared and assembled to find the number of occurrences for specific spacing’s. These results are presented as rolling averages of 5bp in . 63892 cortex, 59193 spinal cord, 72151 stomach and 40074 lung/esophagus cells were then FACS sorted into triplicates on a BD Influx machine, with plots shown in Overlap enrichment between different gene sets was calculated by taking the number of overlapping genes between the gene sets and dividing this by the multiple of the number of genes in the two gene sets (# overlapping genes/(# genes in set 1 x # genes in set 2)). Significance was calculated by using Rstudio prop.test between two overlapping gene sets.Common and specific SOX2 bound enhancers were selected based on their cell-type-specific binding profiles, proximity to specifically expressed genes and conservation scores, cloned into the Tol2-e1b-GFP vector and injected into one or two cell zebrafish embryos according to .Hoxa2 +397bp , Tm7sf2 -710bp , Dnajc19 -115kb , Pax3 +25kb , Foxa2 +78kb  and Foxp1 +11kb  into pGL3-TKMax luciferase constructs. ZEB1-flag, OTX1-flag and FOXA1-flag constructs were made by PCR amplifying the respective open reading frames from cDNA, and ligating them into pCIG expression plasmids. All other expression plasmids were reported in [P19 cell luciferase assays were performed and processed as described in . LuciferIn ovo electroporation was performed as previously described in [ex vivo electroporation of E13.5 stomachs was performed as described with intestine in [ribed in , while estine in .+ cells was divided to the nuclear background staining of at least 50 SOX2- cells.To compare SOX2 high and SOX2 low cells, each SOX2 image had the average nuclear staining intensity of at least 50 cells assayed in ImageJ . SOX higCo-immunoprecipitation was performed as previously described . BrieflyS1 FigA) Percentage of iI67+ cells that are SOX2+ in the E11.5 cortex, spinal cord, stomach and lung/esophagus. (B) FACS plots of SOX2+ cells (encircled) sorted from dissected E11.5 SOX2-GFP cortices, spinal cords, stomachs and lung/esophagus. (C) Seqminer heat maps showing raw reads from all SOX2 ChIP-seqs (singlets or merged replicates) within peaks called from cortex, spinal cord, stomach or lung/esophagus SOX2 ChIP-seqs and the corresponding percentage of peaks bound in all tissues based on clustering. (D) The average proportion of SOX2 ChIP-seq reads derived from cortex, spinal cord, stomach and lung/esophagus experiments within the different SOX2 peak sets.((TIF)Click here for additional data file.S2 FigA) Diffbind heat map and dendogram of the correlations between cortex, spinal cord, stomach and lung/esophagus SOX2 ChIP-seq replicates. (B) Central enrichment of SOX2 binding motifs in cortex, spinal cord, stomach and lung/esophagus SOX2 ChIP-seq peaks called by MACS14. The percentage of peaks that the given motifs are found centrally enriched, and their p-values are listed. (C) Venn diagram showing overlap between sites bound in the different SOX2 ChIP-seq experiments, as called by MACS14. 17 peaks overlapped in all four tissues. (D) Overlap between peaks called by MACS14 and SISSRS, with the percentage overlap with the smaller group listed.((TIF)Click here for additional data file.S3 FigA) Seqminer heat maps showing alignment of SOX2 ChIP-seq reads from ESCs and merged replicates from cortex, spinal cord, stomach and lung/esophagus to SOX2 peaks in ESCs. The bar graph shows the percentage of ESC peaks bound in each tissue. (B) The percentage of common, CNS common, cortex specific, spinal cord specific, endoderm common, stomach specific and lung/esophagus specific SOX2 ChIP-seq peaks that overlap with SOX2 peaks in ESCs. (C) Seqminer read density-clustering heatmap of merged replicate SOX2 ChIP-seqs within peak regions called in ESCs. Stippled lines separate three clusters of ESC SOX2 peaks that are bound in all tissues (white), specifically in the CNS (red) or specifically in the endoderm (blue). (D) Fold enrichment and p-value scores from Panther of selected GO terms for genes within 500kb of ESC ChIP-seq peak clusters, from S3C Fig, bound in all tissues (white), specifically in the CNS (red) or specifically in the endoderm (blue).((TIF)Click here for additional data file.S4 FigA) Top five HOMER de novo transcription factor binding motifs enriched in specific and common SOX2 ChIP-seq peak sets. Mammalian transcription factors with consensus sites matching the motif, p-values for motif enrichment and the percentage of peaks the motifs are found in are inset next to each motif. (B) 5bp rolling averages of distance between SOX2 motifs and those of OTX1 in cortex peaks, FOXA1 in stomach and lung/esophagus peaks and ZEB1 in all peak sets. The median distance and most common spacing are labelled on each graph. (C) Co-immunoprecipitation using Flag-tagged transcription factors, identified in D) PCA and hierarchical clustering of all RNA-seq replicates from E11.5 SOX2-GFP cortices, spinal cords, stomachs and lung/esophagus based on the most variable genes expressed above RPKM 1. (E) Venn diagram showing specific and overlapping gene expression based on pair-wise Deseq2 analysis padj<0.01 and fold change >2. (F) Fold enrichment and p-value scores from Panther of selected GO terms for genes specifically expressed in (E). (G) Bar graph showing the average expression of genes bound and not bound by SOX2 in the cortex, spinal cord, stomach and lung/esophagus. P-values are calculated with two sided, unpaired t-tests .((TIF)Click here for additional data file.S5 FigA-C) SOX2 ChIP-seq tracks with read scale maximum values inset top left  and reporter expression of regulatory regions commonly bound by SOX2 in both CNS and endoderm (A), specifically bound in CNS (B) or specifically bound in endoderm (C), as well as the chromosomal location of each region and statistics for the number of GFP+ fish out of total injected survivors. Arrows indicate endodermal GFP reporter expression from regions bound by SOX2 commonly and endoderm specifically.((TIF)Click here for additional data file.S6 FigA) SOX2 ChIP-seq tracks of reads in cortex (red), spinal cord (green), stomach (blue) and lung/esophagus (yellow) around the Ccnd1 promoter region with read scale maximum values inset to left. Inset is a zoom on the upstream promoter region to highlight binding to this region in the four tissues. The Ccnd1-409 promoter luciferase construct is repressed in a dose dependent fashion by SOX2 in P19 cells. (B) Percentage of electroporated cells in the 72 hpf chick spinal cord labelled by a 30 minute pulse of BrdU following epistatic manipulation of SOX2 and Wnt pathway activity. Blocking canonical Wnt-activation by B-catenin using Tcf4-dominant negative (TCF4DN) decreases proliferation, while blocking repression by TLE/GRG using Lef1-Grg binding mutant (LEF1GBM) increases proliferation regardless of SOX2 activity. (C) Percentage of electroporated cells in E13.5 stomach explants labelled by a 30 minute pulse of BrdU following overexpression of GFP, SOX2/LEF1GBM or dnSOXB1/TCF4DN. All error bars represent standard deviations between experiments and p-values are calculated with two sided, unpaired t-tests .((TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.


The transcriptomes of peripheral blood cells in children with juvenile idiopathic arthritis (JIA) have distinct transcriptional aberrations that suggest impairment of transcriptional regulation. To gain a better understanding of this phenomenon, we studied known JIA genetic risk loci, the majority of which are located in non-coding regions, where transcription is regulated and coordinated on a genome-wide basis. We examined human neutrophils and CD4 primary T cells to identify genes and functional elements located within those risk loci.We analyzed RNA sequencing (RNA-Seq) data, H3K27ac and H3K4me1 chromatin immunoprecipitation-sequencing (ChIP-Seq) data, and previously published chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) data to characterize the chromatin landscapes within the known JIA-associated risk loci.HLA-DQA1, HLA-DQB2, TRAF1, and IRF1 were associated with the long-distance interacting regions within the LD regions as determined from ChIA-PET data.In both neutrophils and primary CD4+ T cells, the majority of the JIA-associated linkage disequilibrium (LD) blocks contained H3K27ac and/or H3K4me1 marks. These LD blocks were also binding sites for a small group of transcription factors, particularly in neutrophils. Furthermore, these regions showed abundant intronic and intergenic transcription in neutrophils. In neutrophils, none of the genes that were differentially expressed between untreated patients with JIA and healthy children were located within the JIA-risk LD blocks. In CD4+ T cells, multiple genes, including These findings suggest that genetic risk contributes to the aberrant transcriptional control observed in JIA. Furthermore, these findings demonstrate the challenges of identifying the actual causal variants within complex genomic/chromatin landscapes.The online version of this article (doi:10.1186/s13075-017-1260-x) contains supplementary material, which is available to authorized users. One of the significant findings to emerge from translational studies of polyarticular juvenile idiopathic arthritis (JIA) has been the observation that peripheral blood cells in children with this disease have distinctly abnormal expression profiles. These abnormalities can be observed in neutrophils , peripheprima facie evidence that the causal variants have anything to do with that particular gene [In addition to identifying transcriptional abnormalities, there has also been considerable progress in our ability to identify genetic risk in JIA , 9. Furtlar gene .We have previously demonstrated how the field might begin to make sense of the wealth of genetic data that are being generated by GWAS and fine mapping studies , and howOur earlier paper focused entirely on the novel risk regions identified in the Hinks study . In the http://www.broadinstitute.org/mpg/snap) [r2 threshhold: 0.9, Population Panel: CEU, Distance limit: 500. We selected the smallest number as our start location and the largest number as our stop location for each defined LD block.SNPs used in this query are listed in Table pg/snap)  to definWe queried neutrophil RNA-seq data from previously published RNA-seq studies , 20, 21 Complementary DNA was synthesized from total RNA using an iScript cDNA Synthesis kit (Bio-Rad). rt-PCR was performed using a HotStar Master PCR kit (Qiagen) with a Veriti thermocycler (Life Technologies) and included a control with no reverse transcription to exclude the possibility of an artifactual result due to contaminating genomic DNA. The temperature profile consisted of an initial step of 95 °C for 10 minutes, followed by 35 cycles of 95 °C for 30 seconds, 60 °C for 30 seconds and 72 °C for 30 seconds, and then a final step of 72 °C for 10 minutes. PCR products were resolved in 1.5% agarose gel. The nucleotide sequences of the primers were as follows: for ncTNFa , 5′-CCTAATTCTGGGTTTGGGTTTGGG-3′ (forward) and 5′-CCTACTTTCACCTCCATCCATCCT-3′ (reverse); for ncSTAT4 , 5′- GTCTTGTGCAACTTCTTCCTTTC-3′ (forward) and 5′- ACCCTGTGACTGTTTGAGATTAC-3′ (reverse).http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeRegTfbsClustered/). Only the TFBS information annotated for blood cells was used. The whole genome was binned to 100 bp bins and intersected with H3K4me1 or H3K27ac peak regions, which were used as background. Fisher’s exact test was applied to test the significance of the enrichment of TFBS of each transcription factor within H3K4me1 or H3K27ac peaks in LD blocks compared to other regions with H3K4me1 or H3K27ac peaks across the whole genome. We set the cutoff of the false discovery rate (FDR) to 0.05 for all the analyses.ENCODE TFBS data were downloaded from UCSC Genome Browser ENCODE data . For these analyses, we queried all known JIA-associated risk regions.Functional DNA elements such as enhancers do not always regulate the nearest gene. Indeed, in a recent study of chromatin conformation using HiC in T and B cell lines, Martin et al.  showed tOne means of using genetic risk data to provide mechanistic insights is to identify specific biological pathways encompassed by SNP-associated genes , 25. HowOur previous work had queried new SNPs recently identified in a genetic fine mapping study published by Hinks et al. . In the Review of RNA-Seq data using the UCSC Genome Browser suggested the presence of multiple non-coding RNA transcripts in neutrophils within the JIA-associated LD blocks. We used rtPCR to confirm the presence of two of these transcripts, i.e., those within the LD blocks containing the rs3821236 and rs1800629 SNPs. Figure For these analyses, we examined only those LD blocks not previously examined in our earlier paper  as notedp value <2.2e-16 for both H3K4me1 and H3K27ac marks).Our first analyses were in neutrophils. Of the 23 risk loci, 16 LD blocks contained H3K4me1 marks, while 14 LD blocks contained H3K27ac marks; 13 LD blocks contained both H3K4me1 and H3K27ac marks (Table MIF) gene, but this region is also rich in transcription factor (TF) binding regions and contains H3K4me1/H3K27ac histone marks. Elevated MIF has been identified in patients with all three major JIA subtypes [Representative screen shots from the UCSC genome browser within two of the LD regions of interest are shown in Fig. subtypes . At the Similarly, Additional file p value <2.2e-16 for both H3K27ac and H3K4me1 marks).We next examined H3K4me1/H3K27ac marked regions in CD4+ T cells in those same 23 LD blocks. We queried whether there are functional elements located in CD4+ T cells within these risk-conferring regions. Of the 23 LD blocks, 20 contained H3K4me1 marks and 15 contained H3K27ac marks. All of the 15 LD blocks that contained H3K27ac marks also contained H3K4me1 marks  of TSS) within these LD blocks. TFs that were particularly enriched included POLR2A, CHD1 and TAF1 Fig. . We noteFor CD4+ T cells, we observed a limited number of TFBS that were significantly enriched in the LD blocks conferring risk of JIA that co-localized with H3K27ac/H3K4me1 histone marks Fig. . These rWe used the 6520 long-distance chromatin interactions from a previously-reported ChIA-PET data analysis of CD4+ T cells  to definWe next analyzed the ChIA-PET data in light of transcriptomes from CD4+ T cells derived from healthy children. We identified several genes within the CHIA-PET interacting regions, including ILB, DDX39B, RASSF5 and JAK1, which demonstrated high levels of expression (average fragments per kilobase of transcript (FPKM) >100). We noted that there are multiple small RNA molecules  that are encoded within these regions, but the sequencing libraries were not prepared to detect their presence because the FPKM of those molecules is zero.Among the 42 long-range interactions, 66 regions contained both H3K4me1 and H3K27ac marks, while 2 regions contained only H3K27ac marks and 11 contained only H3K4me1 marks; only 5 of the interacting regions contained neither H3K4me1 nor H3K27ac marks. There were in all 61 genes associated with these long-range interacting regions. The genes associated with regions containing both H3K4me1 and H3K27ac marks have average higher FPKM than those genes that contained only H3K4me1 marks or those with no histone marks , these same loci are also rich in other functional elements that regulate transcription on a global basis. For example, Fig. PTPN2 gene. While PTPN2 is generally considered a regulator of T cell responses, this protein tyrosine phosphatase is broadly expressed in innate immune cells, including in neutrophils, where it serves as a negative regulator of cytokine signaling [We also found a large degree of complexity around rs2900180 Fig. , and rs2ignaling . Howeverignaling , 39. Thiignaling . Using HThis idea is corroborated by our analysis of the published CD4+ T cells ChIA-PET dataset. ChIA-PET uses crosslinked DNA to identify physical interactions between regions of DNA, which may either be in close proximity or many Mb apart on a specific chromosome (or even regions that are on different chromosomes) , 40. TheENCODE also revealed that much of the genome is transcribed, and that many (if not most) of these non-coding RNA species are functional. In rheumatology, miRNA are the best-known of these non-coding RNA families, as numerous papers have shown links between specific miRNA molecules and specific immunologic features of the rheumatic diseases . Our owThe findings we report here provide an explanation for an observation that we have recently reported: differentially expressed genes in JIA are not situated within the LD blocks that confer genetic risk . Using wThis paper adds to the mounting evidence that suggests that the field of pediatric rheumatology may require a fundamental re-assessment of theories on the pathogenesis of JIA. Long accepted as an “autoimmune disease” triggered by the recognition of a “self” peptide by the adaptive immune system, new evidence from the fields of genetics and genomics ought to prompt a reconsideration of this view. We propose, instead, that JIA emerges because of genetic and epigenetically mediated alterations in leukocyte genomes. These alterations, we propose, involve both innate and adaptive immune systems and lead to expression of inflammatory mediators in the absence of the external signals normally required to initiate or sustain an inflammatory response. Our work in both whole blood buffy coats  and neutWe demonstrated that the known genetic risk loci for JIA are enriched for multiple functional elements within the non-coding genome of human neutrophils. These functional elements include rich TF binding sites, H3K4me1 and H3K27ac-marked enhancers, and non-coding RNAs. We believe that these data and newer understanding of genetic risk and genome function invite new understanding of JIA pathogenesis, and may provide the basis for radical new approaches to therapy.


Drosophila circadian oscillator controls daily rhythms in physiology, metabolism and behavior via transcriptional feedback loops. CLOCK-CYCLE (CLK-CYC) heterodimers initiate feedback loop function by binding E-box elements to activate per and tim transcription. PER-TIM heterodimers then accumulate, bind CLK-CYC to inhibit transcription, and are ultimately degraded to enable the next round of transcription. The timing of transcriptional events in this feedback loop coincide with, and are controlled by, rhythms in CLK-CYC binding to E-boxes. PER rhythmically binds CLK-CYC to initiate transcriptional repression, and subsequently promotes the removal of CLK-CYC from E-boxes. However, little is known about the mechanism by which CLK-CYC is removed from DNA. Previous studies demonstrated that the transcription repressor CLOCKWORK ORANGE (CWO) contributes to core feedback loop function by repressing per and tim transcription in cultured S2 cells and in flies. Here we show that CWO rhythmically binds E-boxes upstream of core clock genes in a reciprocal manner to CLK, thereby promoting PER-dependent removal of CLK-CYC from E-boxes, and maintaining repression until PER is degraded and CLK-CYC displaces CWO from E-boxes to initiate transcription. These results suggest a model in which CWO co-represses CLK-CYC transcriptional activity in conjunction with PER by competing for E-box binding once CLK-CYC-PER complexes have formed. Given that CWO orthologs DEC1 and DEC2 also target E-boxes bound by CLOCK-BMAL1, a similar mechanism may operate in the mammalian clock.The  Drosophila, the CLOCK-CYCLE (CLK-CYC) activator complex binds E-box regulatory sequences to initiate transcription of hundreds of effector genes including their own repressors, PERIOD (PER) and TIMELESS (TIM), which feed back to repress CLK-CYC until they are degraded, thus allowing another cycle of CLK-CYC activation. Although the repression process is critical for the stability and accuracy of circadian timekeeping, how PER-TIM complexes maintain a transcriptionally repressed state for many hours is not well understood. Here we demonstrate that the transcription factor CLOCKWORK ORANGE (CWO) antagonizes CLK-CYC E-box binding, thus enhancing the removal of CLK-CYC from E-boxes to maintain transcriptional repression. This process requires PER, which suggests that PER-TIM and CWO cooperate to maintain a transcriptionally repressed state by removing CLK-CYC from E-boxes. These results demonstrate that PER-TIM requires CWO to effectively repress circadian transcription, and given that circadian transcriptional regulators are well conserved, this mechanism may function to repress transcription in other animals including humans.Circadian clocks control daily rhythms in animal, plant and fungal physiology, metabolism and behavior via transcriptional feedback loops. In  Cyanobacteria to humans have internal circadian clocks that drive daily rhythms in physiology, metabolism and behavior, thereby synchronizing internal processes with the external environment. In eukaryotes, the circadian clock keeps time via one or more transcriptional feedback loops [Drosophila, a heterodimer formed by CLOCK (CLK) and CYCLE (CYC) binds E-box sequence activates transcription to initiate clock function. In the core loop, CLK-CYC activates period (per) and timeless (tim) transcription during mid-day, effecting a rise in per and tim mRNA levels that peaks during the early evening. PER and TIM proteins then accumulate, form a dimer, and move into the nucleus to bind CLK-CYC during the night, thereby inhibiting their transcriptional activity until PER and TIM are degraded early in the morning [vrille (vri) and PAR-domain protein 1ɛ (Pdp1ɛ), which bind D-boxes to repress and activate transcription, respectively, and drive RNA cycling of Clk and other output genes in the opposite phase as per, tim, vri and Pdp1ɛ [Almost all organisms from ck loops . In Dros morning ,3. Anothnd Pdp1ɛ –6.in vitro [per circadian regulatory sequence (CRS) and the tim upstream sequence [per and tim promoters in per01 flies [Drosophila Schneider 2 (S2) cells [PER was previously found inhibit CLK-CYC binding to E-boxes in vitro , which ssequence . However01 flies , indicat2) cells , suggestDrosophila Schneider 2 (S2) cells, overexpression of CWO reduces the basal transcription of per, tim, vri and Pdp1ɛ promoter-driven luciferase reporter genes [cwo mutants or cwo RNAi knockdown flies, the levels of per, tim, vri and Pdp1ɛ mRNAs are increased during the early to mid-morning [Previous studies demonstrated that CWO, a basic helix-loop-helix (bHLH)-ORANGE transcriptional factor , is a dier genes ,12,13. Fer genes . In cwo -morning ,12. Thes-morning ,12. Howein vivo, which suggests that CWO competes with CLK to bind E-boxes. We also show that CWO acts to decrease CLK binding to tim E-boxes during early morning, when PER binds CLK-CYC to reduce its binding to DNA [tim promoter in Clkout flies (i.e. comparable to binding at ZT2 in wild-type) and constant low CWO binding in per01 flies (i.e. comparable to binding at ZT14 in wild-type) supports our model for CWO repression. As a whole, these results suggest that CWO co-represses CLK-CYC activity with PER by competing with CLK-CYC-PER complexes for E-box binding, therefore promoting the transition to off-DNA repression.In this study we demonstrate that CWO and CLK bind core clock gene E-boxes in a reciprocal pattern across the circadian cycle g to DNA , but notcwo mRNA cycles in phase with per, tim, vri, and Pdp1, but with a higher basal level, and thus lower amplitude [cwo mRNA levels cycle, it is possible that constant CWO levels result from post-transcriptional regulation or a long half-life.Earlier studies demonstrated that mplitude ,12–14. Tmplitude . Given tin vivo, and whether the binding intensity changes throughout the day. To test these possibilities, ChIP assays were carried out on wild-type flies collected in the early morning (ZT2) and in the early night (ZT14) using CWO and CLK antisera. Fragments containing upstream E-boxes from tim, per, Pdp1 and vri, which are necessary for high-amplitude mRNA cycling in vitro or in vivo [tim, vri and Pdp1 E-box containing fragments were two to threefold more abundant at ZT2 than at ZT14  mutant (mE1-E2), an E-box 2 (E2) mutant (E1-mE2), an E1 and E2 double mutant (mE1-mE2) or a control with wild-type E-boxes (E1-E2) were generated, inserted into the pHPdestGFP vector [The reciprocal binding pattern of CLK and CWO implies that these transcription factors compete for E-box binding. If so, both CLK and CWO must occupy the same E-boxes. To test this possibility, we determined how mutating E-boxes upstream of es apart ,26, a stes apart . Both ofS2 cells , suggestP vector , and tarGFP mRNA levels in flies collected every 4-h during an LD cycle. Quantification of GFP mRNA levels in flies with WT tim promoter shows a ~10-fold diurnal rhythm with a peak at ZT14 and a trough at ZT2 to ZT6 (per and tim mRNA cycling in wild-type flies [tim mRNA peak (ZT14), mE1-E2, E1-mE2 and mE1-mE2 flies express little or no eGFP mRNA  to monitor 2 to ZT6 , consistpe flies ,30. HoweGFP mRNA , indicatS2 cells .tim promoter, CWO binding intensity was drastically reduced in mE1-E2, E1-mE2 and mE1-mE2 flies compared to WT  and peak (ZT14) times of CLK-CYC target gene transcription and mRNA abundance in LD. Although cwo5703 mutants lengthen the period of activity rhythms by 2–3h in DD [cwo5703 mutants and wild-type flies in LD [tim E-boxes with a robust rhythm in wild-type flies and a lower amplitude rhythm in the cwo5703 mutant . Recentt livers . This rein vitro revealed that both DEC1 and DEC2 bind to E-box motifs targeted by CLK-BMAL1 [Dec1, Dec2 or both Dec1 and Dec2 are combined with that for Per1 [The mammalian orthologs of CWO, called DEC1 and DEC2 , suppress CLOCK-BMAL1-induced activation –50. Gel LK-BMAL1 –49, and LK-BMAL1 . In addifor Per1 . In contfor Per1 . Taken ttim circadian enhancer were used to construct GFP-reporter transgenes. These 136bp fragments extend from -578 to -714 relative to the tim transcription start site, and contain “E1-E2” E-box motifs that are wild-type (E1-E2), E1 mutant (mE1-E2), E2 mutant (E1-mE2) or E1-E2 mutant (mE1-mE2). These wild-type and mutant E-box fragments were generated by PCR amplification using the following primer sets: E1-E2, 5’-CACCTTTGGCAAATAAACGTGCGGCA-3’ and 5’-TGCCGGCGTTTGTGCGAA-3’; mE1-E2, 5’-CACCTTTGGCAAATAAACGTGCGGCACGTTGTGATTAAGATCTAGCCGAT-3’ and 5’-TGCCGGCGTTTGTGCGAA-3’; E1-mE2, 5’-CACCTTTGGCAAATAAGATCTCGGAGATTTGTGATTACACGTGAGCCGAT-3’ and 5’-TGCCGGCGTTTGTGCGAA-3’; mE1-mE2, 5’-CACCTTTGGCAAATAAGATCTCGGAGATTTGTGATTAAGATCTAGCCGAT-3’ and 5’-TGCCGGCGTTTGTGCGAA-3’. The PCR products were inserted into the pENTR/D-TOPO vector using pENTR Directional TOPO cloning kit (Invitrogen), and then subcloned into the pHPdesteGFP vector, which expresses Green Fluorescent Protein (GFP) according to the enhancer sequence inserted [tim circadian enhancer GFP (tim-CEG) flies [DNA fragments containing wild-type or mutant E-boxes from the upstream inserted , using GG) flies –54.3VO4, and 1 mM NaF. This homogenate was sonicated 3 to 5 times for 10 s each time, using a Misonix XL2000 model sonicator at a setting of 3 and then centrifuged at 20,000 g for 10 min. The supernatant was collected as RIPA S extract, and protein concentration was determined by the Bradford assay. Equal amounts of RIPA S extract were run, transferred, and probed with guinea pig anti-CWO (GP-27), 1:5,000 and mouse anti-beta-actin (Abcom), 1:20,000. Horseradish peroxidase-conjugated secondary antibodies (Sigma) against guinea pig and mouse were diluted 1:5,000. Immunoblots were visualized using ECL plus (GE) reagent. Protein levels were measured by placing a rectangle of the same size over each CWO, ß-Actin or non-specific (NS) protein band on films used to visualize the immunoblots, and quantifying the signal within each rectangle via densitometric analysis using the ImageJ program. The levels of CWO were calculated as a CWO:ß-Actin or CWO:NS ratio, and CWO abundance at each time point was plotted relative to wild-type at ZT2.Flies were entrained in a 12-h light/12-h dark (LD) incubator for at least 3 days, collected at the indicated time points, and frozen. Isolated frozen fly heads were homogenized in radioimmunoprecipitation assay (RIPA) buffer  containing 0.5 mM PMSF (phenylmethylsulfonyl fluoride), 10 μg/ml aprotinin, 10 μg/ml leupeptin, 2 μg/ml pepstatin A, 1 mM Natim, per, vri, and Pdp1 in wild-type flies and the circadian enhancer in tim-CEG flies were first quantified via qPCR, and the resulting values were corrected for nonspecific binding to an intergenic region on chromosome 3R (nucleotides 29576172 to 29576303). The primers used for qPCR were as follows: for tim E-boxes, 5’-ACACTGACCGAAACACCCACTC-3’ and 5’-GCGGCACGTTGTGATTACACG-3’; for per E-boxes, 5’-GGGTGAGTAATGCCGTTGCGAAAT-3’ and 5’-ATTTGCTGGCCAAGTCACGCAGTT-3’; for vri E-boxes, 5’-CTGGTGCCTCACATTCCACG-3’ and 5’- CAGCAGTCAAGTTATAGCAGCGC-3’; for Pdp1 E-boxes, 5’-GCACTCTCATTCTCTCTGTCGC-3’ and 5’-ACTTGGGGGACTGGAACTG-3’; for tim-CEG, 5’-GCCCCCTTCACCTTTGGCAAATA-3’ and 5’-TACAAGAAAGCTGGGTCGGCG-3’; and for the intergenic region, 5’-CAGGAGTCGVAGGACCAACC-3’ and 5’-GTCCTGAGAGGCTGAGAGGC-3’. PCR amplification using each pair of primers produced a single band of the expected size. The tim-CEG primers target vector sequences that flank the genomic tim E-box insert, and thus do not amplify endogenous tim genomic sequences.Chromatin IP (ChIP) assays and qPCR quantification were performed as previously described . CLK andGFP mRNA levels. Total RNA was isolated from frozen fly heads using Trizol (Invitrogen), and treated with a Turbo DNase DNA-free kit (Ambion) to eliminate genomic DNA contamination. DNA-free total RNA (1.0 μg) was reverse transcribed using oligo(dT) 12–28 primers (Invitrogen) and Superscript II (Invitrogen). The reverse transcription (RT) product was amplified with SsoFast qPCR Supermix (Bio-Rad) in a Bio-Rad CFX96 Real-Time PCR System using primers to GFP (5’-TACGGCAAGCTGACCCTGAAGT-3’ and 5’-CGCACCATCTTCTTCAAGGACG-3’) and ribosomal protein 49 (rp49) (5’-TACAGGCCCAAGATCGTGAA-3’ and 5’-GCACTCTGTTGTCGATACCC-3’). For each sample, mRNA quantity was determined using the standard curve for each gene analyzed. To determine the relative levels of GFP mRNA over a diurnal cycle, GFP mRNA levels were divided by rp49 mRNA levels for each time point and plotted as the GFP/rp49 mRNA ratio. To quantify GFP mRNA in different tim-CEG strains at the wild-type (E1-E2) peak, GFP/rp49 values were normalized to the E1-E2 value at ZT14.Quantitative RT-PCR was performed as described ,56, withS1 Fig(A) Quantitative PCR (qPCR) was performed to measure GFP mRNA levels in E1-E2 tim circadian enhancer flies collected at the indicated times in LD. Relative GFP mRNA values were generated by dividing the GFP mRNA signal by that of ribosomal protein 49 (RP49), which is expressed at constant levels. Error bars represent the SEM (n = 3). (B) Quantification of GFP mRNA levels in E1-E2, mE1-E2, E1-mE2 and mE1-mE2 tim circadian enhancer transgenic flies collected at ZT14 as described in panel A. Relative GFP mRNA levels were normalized to the E1-E2 tim circadian enhancer fly value, which was designated as 1.0, then the means of each data set were calculated and plotted. Error bars represent the SEM (n = 3).(TIF)Click here for additional data file.


Whole-genome bisulfite sequencing (WGBS) is the gold standard for studying landscape DNA methylation. Current computational methods for WGBS are mainly designed for gene regulatory regions with multiple under-methylated CpGs (UMCs), such as promoters and enhancers.To reliably predict the functional importance of single isolated UMCs across the genome, which is usually not achievable using traditional methods, we develop a multi-sample-based method. We identified 9421 sparse conserved under-methylated CpGs (scUMCs) from 31 high-quality methylomes, which are enriched in distal interacting anchor regions co-occupied by multiple chromatin-loop factors and are flanked by highly methylated CpGs. Moreover, cell lineage-specific scUMCs are associated with essential developmental genes, regulators of cell differentiation, and chromatin remodeling enzymes. Dynamic methylation levels of scUMCs correlate with the intensity of chromatin interactions and binding of looping factors as well as patterns of gene expression.We introduce an innovative computational method for the identification of scUMCs, which are novel epigenetic features associated with high-order chromatin structure, opening new directions in the study of the inter-relationships between DNA methylation and chromatin structure.The online version of this article (doi:10.1186/s13059-017-1296-x) contains supplementary material, which is available to authorized users. DNA methylation is an important epigenetic modification involved in both normal development and disease. In the whole genome, highly methylated CpG dinucleotides govern most of the methylome 70–80%) , 2, exce0–80% , 2Additional strategies for identifying gene transcriptional regulatory regions also include Dnase I Hypersensitive sites (DHSs)  as well PDGFRA [TAL1, and LMO2 [The genome possesses three-dimensional (3D) organization in nuclear space, which regulates transcription by facilitating interactions between gene promoters and distal regulatory elements within large topological domains –17. ChroPDGFRA , TAL1, aand LMO2 ) can be and LMO2 . Notablyand LMO2 . Two recand LMO2 , 27. Metand LMO2 , 29 haveHere, we developed a new method, which is based on the information from multiple samples, to identify functional UMCs. We define sparse conserved under-methylated UMC (scUMC) as CpG maintained at under-methylated levels and sparsely distributed in highly methylated background in multiple cell types. The scUMCs are found in distal anchor regions co-occupied by multiple chromatin-loop factors . Despite the fact that neighboring CpGs are highly methylated, the binding intensity of chromatin-loop factors and interaction frequencies associated with scUMC are comparable to those observed with conventional, long UMRs. Furthermore, cell-type-specific methylation of scUMCs (such as during cell lineage commitment) is concomitant with reduced chromatin interactions and chromatin-loop factor binding and altered gene expression programs. Overall, our results demonstrate that a new epigenetic feature, scUMC, is involved in cell-specific regulation of long-range chromatin interaction mediated by chromatin-looping factors.p < 0.01, only those UMCs conserved in ten or more methylomes are designated functional UMCs , the majority of single-base UMCs in a methylome population are sample-specific (~93%) Fig. . Thus, tIf 9421 scUMCs are functionally distinct from 43,996 conserved UMRs (see “Methods”), we reasoned they should differ in CpG density and proximity to gene promoters. In contrast to conserved UMRs, scUMCs are markedly CpG-poor Fig.  and genep value = 0.045; one-tailed t-test) by comparison and indistinguishable from other TFs such as POLR2A, MAX, MYC, YY1, and EP300  trending towards significance   of a gene, it was considered the scUMC-target gene. We used ChIA-PET experiments of Cohesin complex members RAD21  in GM128ppressor . In GM1205) Fig. . CollectPDGFRA in IDH mutant gliomas [In general, sparse UMCs are discarded by conventional approaches for predicting functional UMRs. Our multi-sample-based method identifies a novel epigenetic feature, scUMC, whose functionality is suggested by multiple lines of experimental evidence: DNA binding of chromatin-looping factor, chromatin-interaction intensity, and gene expression. We demonstrate evidence of functionality in both ESCs as well as blood lineage-committed cells and that differential methylation of scUMCs reflects their biological differences, being significantly enriched for genes involved in stem-cell differentiation and hematopoietic phenotypes. Despite the fact scUMCs represent individual unmethylated cytosines among a highly methylated background, scUMC gain of methylation is directly linked to both weakened binding of chromatin-loop factors as well as reduced chromatin interactions. In fact, much of the variation in CTCF binding has been linked to differential DNA methylation, concentrated at two critical positions within the CTCF recognition sequence . We are  gliomas . We obse gliomas . TherefoIn this study, we used a total of 51 datasets  found in at least one sample methylome. Next, we excluded UMCs lying in conventional UMRs from the subsequent analysis. In addition, the UMCs lying in SNPs are also removed. The result is candidate UMCs that may be scUMCs. Finally, we used the following formula to calculate the conservation score for a candidate UMC.X has a finite mean μ and finite non-zero variance σ2, then for any real number k > 1:It is expected the UMC with a higher conservation score (occurs in more samples) is more likely to be a functional region. Therefore, credible UMC detection is essentially the identification of “outlier” UMCs with significant conservation scores. Here, we used Chebyshev’s Inequality to detect the credible UMCs and merging regions within 300 bp to obtain scUMCs. Chebyshev’s Inequality is a non-parametric method to detect outliers [m and v as estimators of μ and σ2. For UMC conservation score, m and v are its mean and standard deviation in all the WGBS data. So, we have:Applying this into credible scUMC detection and using as v (standard deviation) larger than the m (mean), then the probability of finding a UMC which occurs with the same or more samples than this UMC in these 31 WGBS data is 1/(4.5**2) ≈ 0.05. This value is a kind of p value. Based on this criterion of p = 0.01, only the candidate UMCs conserved in ten or more samples would be detected as scUMCs. Non-conserved UMC is a subset of candidate UMCs based on non-significant p value (0.95) without scUMC.For a UMC, if its conservation score in all the WGBS data was 4.5 times of In order to compare scUMCs, we identified the conserved UMRs from all the UMRs detected from the 31 high-quality methylomes. We merged a total of 1,397,217 UMRs to 260,150 non-redundant UMRs. We then calculated the conservation score as described above for each UMC lying inside the non-redundant UMR. We defined 43,996 conserved UMRs with at least one UMC lying inside the UMR with the same conservation cutoff (not less than ten samples).To calculate the FDR for a cutoff of the minimal CpG number in a conventional UMR, we compared the UMR detected in the original methylome with a randomized methylome by HMM. For a given methylome, we performed a random shuffle for the methylation level of all the CpGs to destroy the spatial correlation in nearby CpGs and construct the randomized methylome. Thus, we detected the UMR in the randomized methylome by the same procedure. The resulting null distribution indicates the minimal CpG number required in classic UMR detection.p value cutoff of 1 × 10 − 5 determined by the total number of reads with respect to the theoretical mean coverage across the genome. Then, the remaining reads were analyzed with DANPOS v2.2.1 [The raw reads for ChIP-seq data were downloaded from Gene Expression Omnibus and the detail information about the data were listed in Additional file S v2.2.1  for readhttp://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) to trim the low-quality bases and the adapters. TopHat [Raw reads for GM12878  and H1  cells were downloaded from Gene Expression Omnibus . We used. TopHat  was used. TopHat .Additional file 1:A PDF file containing all supplementary figures. (DOCX 1444 kb)Additional file 2:A.docx file containing Tables S1–S4. (DOCX 282 kb)Additional file 3: Table S5.is an.xls file containing all datasets  used in this study. (XLS 36 kb)Additional file 4: Table S6.is an.xls file containing 9421 scUMCs detected in 31 WGBS. (XLS 1919 kb)