Large-scale epigenomic projects have mapped hundreds of thousands of potential regulatory sites in the human genome, but only a small proportion of these elements are proximal to transcription start sites. It is believed that the majority of these sequences are remote promoter-activating genomic sites scattered within several hundreds of kilobases from their cognate promoters and referred to as enhancers. It is still unclear what principles, aside from relative closeness in the linear genome, determine which promoter(s) is controlled by a given enhancer; however, this understanding is of great fundamental and clinical relevance. In recent years, C-methods (chromosome conformation capture-based methods) have become a powerful tool for the identification of enhancer–promoter spatial contacts that, in most cases, reflect their functional link. Here, we describe a new hybridisation-based promoter Capture-C protocol that makes use of biotinylated dsDNA probes generated by PCR from a custom pool of long oligonucleotides. The described protocol allows high-resolution promoter interactome description, providing a flexible and cost-effective alternative to the existing promoter Capture-C modifications. Based on the obtained data, we propose several tips on probe design that could potentially improve the results of future experiments. A hypothesis assuming that genomic enhancers physically approach target genes, which can be located as far as several hundreds of kilobases in the linear genomic sequence, was first proposed in the 1980s5; however, compelling experimental evidence of such looping interactions were obtained only in the last 20 years following the introduction of chromosome conformation capture (3C) method in 20026. Based on the proximity ligation principle, which had been originally applied in a more simplistic procedure called nuclear ligation assay7, 3C gave birth to a family of methods collectively referred to as C-methods (reviewed in Denker and de Laat8).Spatial proximity between mammalian enhancers and corresponding cognate promoters is thought to be a prerequisite for their regulatory activity. It is widely accepted that the majority of remote regulatory elements come into spatial interactions with promoters of regulated genes9. Generally, enhancers are located within the distance of a few tens of thousands of base pairs from the cognate promoters; moreover, it is the closest promoter in the genome sequence that tends to be targeted by the given enhancer. However, this simple rule has many exceptions, and at least some enhancers bypass the nearest genes to activate more distant targets. Furthermore, it is quite common that enhancers regulate transcription of several different genes11, thus it is impossible to precisely predict regulatory relationships between enhancers and promoters based only on their relative genomic position.While there is major variability in promoter–enhancer distance, many enhancers are in fact adjacent to the promoters of their target genes12 and Nishizaki and Boyle13). A large-scale search for the genetic underpinnings of such diseases with GWASs revealed thousands of associated genomic loci. Surprisingly, multiple lines of evidence suggest that causal genetic polymorphisms in these GWAS loci are mostly located within non-coding regions of the genome, presumably in promoters and remote regulatory sequences15. Hence, prediction of genes that could be regulated by enhancers containing potential disease-causal variants has become one of the key directions of post-GWAS studies, which aim to understand the biological mechanisms of common diseases from the genetic perspective19.Apart from insight into mammalian transcription regulation processes, description of functional enhancer–promoter landscape appeared to be indispensable for understanding the genetic causes of various common diseases : ‘circular 3C’23 or ‘3C-on-chip’24. 4C has low scalability. In addition, PCR amplification in 4C is thought to introduce bias, which cannot be accounted for bioinformatically due to the fixed position of read ends on the anchor restriction fragment. The recent introduction of unique molecular identifiers in the 4C protocol addressed the bias problem, but nevertheless, this method is still inconvenient if one wishes to study interactions of more than a few promoters25.In recent years, several approaches, both experimental and computational, aimed to better predict links between regulatory elements and specific promoters have been proposed. One of them is based on the estimation of promoter–enhancer spatial proximity using C-methods. It turned out that exceptionally deep sequencing is necessary to reliably detect promoter–enhancer loops in mammalian genomes using completely untargeted, genome-wide C-methods such as Hi-C 22. This group of protocols relies on the in-solution hybridisation of Hi-C or 3C-seq libraries with pools of promoter-targeted biotinylated probes. Library fragments on which biotinylated probes have annealed, are then pulled-down on streptavidin beads, PCR-amplified and sequenced. Post-hybridisation libraries are substantially enriched in the ligation products of probe-targeted promoters with the rest of the genome . Targeted region in Capture-C is readily scaled from several promoters of interest26 to genome-wide sets containing thousands of promoters27. Simultaneously, Capture-C allows the study of the promoter spatial interactome at an exceptionally high resolution currently attainable with Hi-C and other all-versus-all techniques only when they are combined with extremely deep sequencing28. These features make Capture-C the first choice for data-driven prediction of functional enhancer–promoter pairs.A much more scalable hybridisation-based strategy is embodied in promoter Capture-C methods29. Here, we describe a modification of the promoter Capture-C technique, which is based on 3C-seq library hybridisation with PCR-generated dsDNA probes. We show that such a strategy allows affordable, in-depth interactome profiling for hundreds of promoters in one experiment. We assume that this method could be useful in post-GWAS studies in which the regulatory landscape around GWAS-detected hits is interrogated in relevant cellular contexts.In all published promoter Capture-C protocols, single-stranded DNA or RNA probes are used for enrichment, making experiments costly or in some cases more labour-intensive30. We excluded 747 genes from the 5 Mbp MHC region on the chromosome 6 because otherwise the list of the target genes would be skewed towards the genes from the single GWAS hit, almost all of which are apparently irrelevant for schizophrenia biology. The borders of the regions were extended by 250 kb from both sides  and 5′-biotinylated forward (5′ bio-CTGGGATCGCGCCCATAACTC 3′) and reverse (5′ bio-CGTGGAAAGGCGGACCCTCAG 3′) primers. As low molecular weight, non-specific PCR products were generated in PCR, we used two rounds of amplification with SPRI bead clean-up in between. Initially, 100 ng of synthetic oligonucleotides was amplified with 19 PCR cycles, then DNA was cleaned up with 1.2 volumes of AMPureXP beads  to get rid of low molecular weight, non-specific PCR-products. Six additional PCR cycles were then applied to 20 ng of the purified template, which generated approximately 1 μg of 182 bp dsDNA probes per reaction. Obtained probes were purified with 1.2 × AMPureXP beads  and eluted into 10 mM Tris–HCl, pH 8.0.2 in a humidified atmosphere. Approximately 5 million cells were taken in each experiment. Two biological replicates (HeLa.1.1 and HeLa.1.2) of 3C-seq libraries were prepared according to Golov et al.31. A DpnII restriction enzyme was used for chromatin digestion. 2 μg of each post-PCR 3C-seq library were taken in the hybridisation reactions. hybridisation with subsequent washes and PCRs was conducted as described earlier31 with the following minor modifications:Per hybridisation reaction, one combined mix (instead of two) was prepared in a PCR tube before DNA melting. Each mix contained 1 ug of sheared salmon DNA , 10 pmol of Illumina Uni oligo (5′ AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTC 3′), 10 pmol of Illumina PE 2.0 primer (5′ CAAGCAGAAGACGGCATACGA 3′), 1 μg of 3C-seq library and 500 ng of probes in a total volume of 60 μl.After DNA melting at 95 °C, the hybridisation mix was cooled to 70 °C, then an equal volume of pre-warmed 2 × hybridisation buffer  was added to each reaction. Tubes were incubated at 70 °C for an additional 45 min, then the temperature was lowered to 65 °C and hybridisation took place for 20–30 h.Human HeLa cells were cultured in a DMEM medium supplemented with 10% FBS, 100 U/ml penicillin and 100 U/ml streptomycin at 37 °C in 5% COA 2% formaldehyde solution was used for chromatin fixation instead of 1.5%.Hybridisation reactions were conducted at 60 °C instead of 65 °C.Streptavidin beads were washed with an HS washing buffer  at 55 °C instead of 65 °C.We also made two biological replicates of promoter Capture-C using a modified protocol (referred to as HeLa.2.1 and HeLa.2.2). Several important changes were introduced in this protocol:All four post-capture libraries were subjected to 150 bp paired-end sequencing on an Illumina HiSeq 4000 platform. Between 60 and 85 million pairs of reads were generated for each experiment  and Bowtie 2.2.333. For annotation of the promoter interacting regions (PIRs), we used the CHiCAGO R package (version 1.1.8).Raw Capture-C sequencing reads were mapped on the hg19 reference human genome and filtered using the HiCUP pipeline v0.6.134. CHiCAGO relies on building data-driven two-component convolution background model with distance dependent “Brownian” component and random “technical noise” component. PIRs are identified by comparison of the number of promoter-ligated reads in each genomic bin with the number expected according to the generated model. Final “CHiCAGO score” is assigned to each pair “promoter—genomic bin” by re-weighting of generated p values in a procedure which specifically takes into account that larger numbers of tests are performed at promoter–distal genomic regions, where progressively smaller numbers of interactions are expected. Bins with CHiCAGO scores ≥ 5 are considered to be PIRs. The “Brownian” component of the background model in CHiCAGO at least partially accounts for the differences in the local chromatin packing inside nuclei of various cell types as well as for the potential differences in the fixation procedure. Thus, detection of PIRs in CHiCAGO is robust to variations in shape of the scaling plot profiles in different experiments.CHiCAGO  is a publicly available pipeline for a robust interaction calling in Capture-C datahttps://bitbucket.org/chicagoTeam/chicago/src/master/chicagoTools/). Chinput files are tab-delimited text files that contain basic information about all pairs promoter—“other end” for which at least one ligation product was detected. Each line of the chinput file describes one promoter—“other end” pair, this description includes genomic coordinates of the promoter and the “other end” as well as the number of detected ligation products between this particular pair of bins.BAM files were converted to a CHiCAGO-compatible chinput format with bam2chicago script  containing all probes, which were roughly equivalent to 867 promoters used during probe design . The mean GC-content of these two probes were assigned to each of the selected baits as “probe average GC-content” .In situ Hi-C raw data for four experiments on HeLa cells  were obtained from the database of Genotypes and Phenotypes (dbGaP) through dbGaP accession number phs00064035 to analyse localization of detected Capture-C interactions relative to TAD boundaries. To assess the expected number of inter-TAD loops we performed simulations, in which we randomly assigned orientation (forward or backward relative to the reference genome) to the set of intra-chromosomal interactions, which we had described . Then we called simulated interactions inter-TAD or intra-TAD depending on whether they crossed a TAD boundary or not. We repeated the simulation 100 times and counted the median number of inter-TAD interactions among all simulations, this figure we called “the expected number of inter-TAD interactions”.We used currently unpublished data on topologically associated domain (TAD) boundaries positions in HeLa cellshttps://www.encodeproject.org/files/ENCFF280NBC/@@download/ENCFF280NBC.bed.gz) with PIRs from either of the datasets (“bedtools intersect -wao” command in bedtools v2.26.0). We then deleted all duplicated promoter–regulatory element interactions. Lists of discovered loops are available in Supplementary Tables For identification of potentially functional promoter-centred loops in Hi-C and promoter Capture-C data, we intersected genomic regions annotated as enhancers or CTCF-bound sites (in Registry of candidate cis-Regulatory Elements of ENCODE project 36 we searched for CTCF motifs in promoters and PIRs of selected loops (annotatePeaks.pl command). We discarded all loops that do not contain at least one CTCF motif in each of the bases. If a promoter or a PIR contained several motifs we assigned to it a motif with the best score.For analysis of CTCF motif orientation in loop bases we first selected only those interactions which contain CTCF ChIP-seq peaks (intersection was performed with bedtools v2.26.0) in both promoter and PIR. Then, using HOMER package v.4.11https://www.encodeproject.org/files/ENCFF280NBC/@@download/ENCFF280NBC.bed.gz). For similar analysis . In Fig. 37 (https://starklab.org/data/muerdter_boryn_2017/peaks_inhibitor_correctedEnrichment4_supp.table3.tsv). DHS sites were overlaid with STARR-seq peaks using bedtools v2.26.0.To compute PIRs’ enrichment with specific types of regulatory sites and chromatin states, we used the “peakEnrichment4Features” command in the CHiCAGO package. ChIP-seq-predicted positions of four types of regulatory elements  in HeLa cells were taken from the Registry of candidate cis-Regulatory Elements of ENCODE project (https://epigenomegateway.wustl.edu/legacy/) to visualise the detected interactions within the context of relevant functional genomics annotations.We utilised WashU EpiGenome Browser  ligation products (three replicates for each cell type) were mapped on the hg19 reference human genome and filtered using the HiCUP pipeline v0.6.132 . Nine chinput files were generated based on CHiCAGO design files downloaded from https://osf.io/u8tzp/. One thousand baits covered with more than 10,000 unique reads in each of the nine analysed experiments were randomly chosen. Only the rows pertaining to these baits were selected and written in nine modified chinput files. These modified files were analysed using the CHiCAGO R package (version 1.1.8) to generate “full data” sets of PIRs for each cell type. Only non-bait bins falling within a 1 Mb distance from a promoter with a CHiCAGO score higher than 5 were called PIRs. The ligation products from the modified chinput files were then subsampled to obtain chinput files with exactly 10,000 unique ligation products for each bait. These nine once downsampled chinput files were then used as a starting point in 29 rounds of random subsampling. In the first round, 9667 ligation products per promoter were subsampled. In each subsequent round, chinput files with 333 less unique ligation products per promoter than in the previous round were generated. To discover PIRs for each downsampling round, groups of three chinput files for the same cell type and with the same per promoter coverage were combined in the CHiCAGO analysis, which yielded 30 sets of PIRs for each studied cell type. These 90 sets of PIRs and three “full data” sets were overlaid against regulatory data, downloaded from ENCODE. We used three types of regulatory annotations: CTCF-bound sites, enhancers without CTCF and enhancers with CTCF. CTCF-bound sites were retrieved from ChIP-seq data for corresponding cell types , while enhancers were annotated according to cis-Regulatory Elements from the ENCODE project . Enhancers, which are located within 5 kb from the closest CTCF peak, were denoted “enhancers with CTCF” (intersection was performed with bedtools v2.26.0). Promoter–regulatory element pairs conserved between “full data” and 30 k-downsampled datasets were referred to as standard pools of interaction. Finally, we repeated the described analysis for two other sets of 1000 randomly-chosen baits and computed the average retainment of regulatory loops from corresponding standard pools during 29 rounds of downsampling per cell type. Loops with a size more than 0.25 Mb or 0.5 Mb were filtered out from all obtained datasets for additional analysis , reads were mapped on the hg19 reference human genome and filtered using the HiCUP pipeline v0.6.1 . Chinput files were generated and information pertaining 100 promoters with the highest coverage was selected for downsampling. In this case in the first round data were downsampled to 20,000 unique ligation products per promoter totally . This dataset was stepwise downsampled to 1000 ligation products per promoter. The downsampling procedure was performed with the same set of 100 promoters three times. For PIR regulatory annotation we used CTCF and H3K27ac (as the marker of enhancers) ChIP-seq peaks in primary human osteoblasts from ENCODE .We also downsampled our Capture-C data and data generated by Chesi et al.https://www.encodeproject.org/files/ENCFF280NBC/@@download/ENCFF280NBC.bed.gz).For downsampling of HeLa Capture-C data generated in this study we selected 187 promoters with the coverage higher than 15,000 unique ligation products per bait. In this case we started downsampling from 15,000 reads and used for functional loop annotation regions that were called enhancers or CTCF-bound sites in Registry of candidate cis-Regulatory Elements of ENCODE project  inserts corresponding to continuous fragments from genomes of studied species. We wondered whether PCR products targeting discontinuous genomic regions can be used as probes in a similar manner. We were particularly interested in studying promoters, for which several alternative Capture-C enrichment strategies have been developed22. To address the feasibility of using dsDNA probes in promoter Capture-C experiments, we purchased a pool of single-stranded synthetic DNA oligonucleotides targeting 861 human promoter regions located inside the genomic regions of schizophrenia GWAS hits or in their immediate vicinity  was PCR amplified with a pair of 5′-biotinylated primers to obtain dsDNA probes, which were then used in two subsequent hybridisation reactions with 3C-seq libraries generated from HeLa cells. We used HeLa cells to validate our novel experimental strategy as there are plenty of epigenomic data sets for these cells; furthermore, the high-resolution Hi-C32. Unique valid pairs were afterwards assigned to targeted promoters. A substantial portion (26–66%) of the unique valid read pairs mapped to 861 targeted promoters , it gives substantially worse per promoter coverage than Capture-C . Indeed, this modification increased the cis–trans ratio to a level optimal for C-methods45 but also led to a decrease in restriction digestion efficiency, which finally manifested in a lower proportion of library fragments containing meaningful ligation junctions  using 1.5% formaldehyde for cell cross-linking. We noticed that the libraries obtained had low cis–trans read ratio, indicating the high proportion of spurious ligation events, which reduce the overall capacity to detect functionally relevant interactionsons Fig. B,C.46. In addition, we found that many promoters with low coverage in our primary experiments had both their probes with comparatively low GC-content  are trimmed as PCR-duplicates, which indicates a low complexity of the obtained library and means that the quality of generated data could not be improved by deeper sequencing. This result is potentially due to the use of higher temperatures during enrichment (e.g. for probe hybridisation and streptavidin bead washing) than were originally applied in protocols in which short DNA probes were used for targeted enrichmentent Fig. D,E. Thisent Fig. C. On theent Fig. D. This eent Fig. E. In the27, we analysed interaction profiles of 861 studied promoters with the CHiCAGO R package, which is designed to identify promoter-interacting genomic regions (PIRs) in promoter Capture-C data34. When combining data from all four conducted experiments, we were able to identify 5,241 PIRs, the vast majority of which resided within the same topologically associated domain (TAD) as the corresponding anchor promoter .In our Capture-C data we also detected 65 loops, which have CTCF ChIP-seq peaks and motifs for CTCF binding on both anchors of the loop. In agreement with previous publicationsTo further confirm superiority of our modified protocol in comparison with the original one we annotated promoter–enhancer and promoter–CTCF loops detected in data from either of the protocols in separate analysis. Despite slightly lower median per promoter coverage in data obtained with the modified protocol , whereas coverage of others is much higher . Simultaneously, comparison between Capture-C results and Hi-C results suggests that coverage could significantly influence completeness of promoter-centred interaction annotation , meaning that we seemingly cannot detect a significant fraction of loops for the other half. We believe that, apart from increasing the number of replicates and depth of sequencing, the quality of obtained data could be improved by modifications in probe design, some of which will be described below.49. Meanwhile, we noticed that some researchers have already used dsDNA to enrich sequencing libraries in fragments from regions of interest50 and recently introduced C-TALE—novel hybridisation-based many-versus-all C-method in which dsDNA probes are exploited31. We showed that the comparative inefficiency of dsDNA probes could be compensated with an additional round of hybridisation without introducing significant biases in final contact maps.Single-stranded RNA or DNA probes are commonly used to enrich 3C-seq and Hi-C libraries with specific types of ligation products. The double-stranded probes are thought to be not as efficient as single stranded ones since they tend to reanneal on each other during hybridisation. On the other hand, the usage of PCR to amplify synthetic DNA oligo-precursors of probes is very convenient since this allows the generation of vast amounts of probes for many hybridisation reactions from oligos purchased once. Some protocols have already taken advantage of this and exploited PCR to generate DNA templates for subsequent in vitro transcription, which produce biotinylated ssRNA probes for targeted enrichment of Hi-C librariesSynthesis of custom pools of long oligonucleotides is considerably cheaper than purchase of commercial kits for targeted enrichment. Thus PCR-generated dsDNA probes make Capture-C experiments less expensive, especially in those cases when the study is focused on a small set of promoters  since in such experiments the fraction of expenditures associated with sequencing is comparatively small and the price of probes becomes the crucial factor. In projects where interactions of the same set of promoters should be analysed in various different cellular models the usage of PCR-generated dsDNS probes seems even more beneficial because, in contrast to probes from the commercial kits, they can be generated in almost unlimited amounts from precursors synthetised once.34). Moreover, enrichments of CTCF-binding sites and enhancer-specific chromatin marks in the PIRs (~ 2.7X for CTCF and ~ 1.88X for enhancers), which we detected using our new protocol, were similar to the enrichments described in the finest specimens of Capture-C data published to date (2–3X enrichment)27. Overall, we conclude that the described version of Capture-C is able to detect promoter–enhancer and promoter–CTCF loops on a par with pre-existing variants of the Capture-C procedure. Our protocol may be especially useful in experiments aiming to study regulatory interactions in and around genomic regions annotated as genetically associated with specific traits by GWASs. We hope the Capture-C protocol described here will be included in the toolbox of methods available for in-depth study of promoter-centred spatial interactions.We show that dsDNA probes generated from a pool of long synthetic oligonucleotides could be successfully used in promoter Capture-C experiments, resulting in high resolution maps of spatial interactions for sets of several hundred promoters. This version of the probe preparation protocol is reasonably simple and makes Capture-C experiments more affordable and flexible. Computational analysis of the obtained data showed that the number of promoter-centred interactions detected under described Capture-C protocol is comparable with numbers reported for other variants of the Capture-C  in our dataset do not comply with the above-mentioned threshold value, and we are unable to detect some interactions of these genes. Furthermore, comparatively low coverage of many promoters in our data prompted us to analyse promoter interactions in 2 kb genomic bins. It is believed that such binning can potentially introduce biases associated with uneven distribution of restriction sites among different bins42, we noticed that probes with a high GC-content generally perform worse, at least in the hybridisation conditions we ultimately used; however, there are multiple properties of each specific sequence, which we cannot account for and which can potentially lead to poor performance of the probe. For this reason, several probes are usually designed for each promoter in Capture-C as a fail-safe measure, and in all likelihood, more probes will give better and more uniform per promoter coverage40.In our experience, several factors make major contributions to the final coverage of a given promoter in Capture-C analysis, the most obvious being the sequencing depth. Although at some point, one cannot improve coverage with further sequencing as the complexity of the final Capture-C library becomes a limiting factor. This complexity is known to be partially determined by the complexity of the initial 3C-seq (Hi-C) library. As Hi-C libraries prepared from the same amount of starting materials are typically much less complex than 3C-seq libraries (due to inefficiency of proximity ligation in Hi-C), we usually opt to work with 3C-seq protocol, even though we eventually obtain a much worse proportion of valid pairs in sequencing output. Complexity of the initial library and choice of 3C-seq protocol could become crucial when dealing with small numbers of cells (less than 1 mln). Another major contributor to the complexity of Capture-C libraries is enrichment procedure itself. Due to inefficiency of the hybridisation reaction the most part of the targeted restriction fragments do not end up binding to the streptavidin beads. Hybridisation with double-stranded probes is seemingly less effective; therefore, we started with a large amount of 3C-seq library  in the first round of enrichment. Besides, we observed that harsh washing conditions can substantially decrease the overall complexity of Capture-C libraries in case of exploitation of short DNA probes and opted to use milder washing conditions. Finally, per promoter coverage in Capture-C is heavily influenced by probe design, specifically the number of probes per promoter, probe GC-content and probe sequence. Differences in the capability of probes to capture targeted restriction fragments enhances the disparity in per promoter coverage in Capture-C data. Like other authors40. We anticipate that designing four to ten probes per each promoter for Capture-C experiments on promoter sets of such size  will significantly improve coverage level and uniformity while only slightly changing overall expenditures.We believe that the shortcomings of probe design in our current experimental settings are major reasons leading to unsatisfactory coverage of substantial part of promoters. Several potential improvements could be introduced to mitigate these shortcomings. Based on obtained data, we suppose that per promoter coverage could benefit from decreasing probe GC-content since GC-rich probes perform worse under the described experimental conditions. Another reasonable optimisation strategy is increasing the number of probes per targeted promoter. We used two probes per promoter, but the usage of a 4-cutter restriction enzyme at the 3C stage makes it possible to engage significantly more restriction fragment ends for each promoter during the probe designSupplementary Figures.Supplementary Information.Supplementary Tables.


Drosophila heart. Binding sites for Suppressor of Hairless [Su(H)], an integral transcription factor in the Notch signaling pathway, are enriched in the enhancers of PC-specific genes. Here we show three distinct mechanisms regulating the expression of two different PC-specific genes, Holes in muscle (Him), and Zn finger homeodomain 1 (zfh1). Him transcription is activated in PCs in a permissive manner by Notch signaling: in the absence of Notch signaling, Su(H) forms a repressor complex with co-repressors and binds to the Him enhancer, repressing its transcription; upon alleviation of this repression by Notch signaling, Him transcription is activated. In contrast, zfh1 is transcribed by a Notch-instructive mechanism in most PCs, where mere alleviation of repression by preventing the binding of Su(H)-co-repressor complex is not sufficient to activate transcription. Our results suggest that upon activation of Notch signaling, the Notch intracellular domain associates with Su(H) to form an activator complex that binds to the zfh1 enhancer, and that this activator complex is necessary for bringing about zfh1 transcription in these PCs. Finally, a third, Notch-independent mechanism activates zfh1 transcription in the remaining, even skipped-expressing, PCs. Collectively, our data show how the same feature, enrichment of Su(H) binding sites in PC-specific gene enhancers, is utilized by two very distinct mechanisms, one permissive, the other instructive, to contribute to the same overall goal: the specification and differentiation of a cardiac cell subtype by activation of the pericardial gene program. Furthermore, our results demonstrate that the zfh1 enhancer drives expression in two different domains using distinct Notch-instructive and Notch-independent mechanisms.The development of a complex organ involves the specification and differentiation of diverse cell types constituting that organ. Two major cell subtypes, contractile cardial cells (CCs) and nephrocytic pericardial cells (PCs), comprise the  The remarkable cellular diversity observed in metazoan organs raises an important developmental question: how are the specification and differentiation of the many distinct cell types comprising such an organ achieved?Drosophila heart provides a particularly amenable system for addressing this question. An organ that pumps hemolymph throughout the body cavity, the Drosophila heart consists of two groups of cells: an inner linear tube of paired contractile cardial cells (CCs) that express Myocyte enhancer factor 2 (Mef2), and an external sheath of nephrocytic pericardial cells (PCs) that express both Zn finger homeodomain 1 (zfh1) and Holes in muscle (Him) .–9.DrosopHim suggested that Su(H) forms a repressor complex that binds to this enhancer, with Notch signaling alleviating the repression in PCs to activate Him transcription . Sim4 driver ; S4 Fig Him enhancer in CCs, then a knockdown of any one of these co-repressors should also recapitulate the ectopic reporter activity in CCs detected when Su(H) binding to the Him enhancer was disrupted. We found that RNAi-mediated individual knockdowns of gro, H, or CtBP targeted to the cardiac mesoderm exhibits ectopic reporter activity by the HimWT enhancer in CCs similar that observed in the case of the HimSu(H) enhancer or the Su(H) RNAi (Him.Since Su(H) had previously been shown to form transcriptional repressor complexes with the co-repressors Groucho (Gro), Hairless (H), and C-terminal Binding Protein (CtBP) –35, we e(H) RNAi , consistzfh1 enhancer (zfh1Su(H)) (zfh1Su(H) eliminates reporter activity in the Odd-PCs and Tin-PCs but not in the Eve-PCs  sites in the zfh1 enhancer does not result in any ectopic reporter activity in CCs. Knocking down the co-repressors Gro, H, or CtBP also fail to produce any ectopic reporter activity from the zfh1 enhancer; the reporter is expressed in the same pattern as in an otherwise wild-type embryo (zfh1Su(H) enhancer—absence of reporter activity in CCs, Odd-PCs and Tin-PCs, but continued reporter activity in Eve-PCs—is also detected from the wild-type zfh1 enhancer (zfh1WT) when Su(H) levels in the cardiac mesoderm are depleted by RNAi (zfh1 enhancer requires Su(H) binding for activation in the majority of PCs, indicating that Su(H) functions as an activator of gene activity in this cardiac regulatory element, in contrast to its role in the Him enhancer where it serves as a repressor in CCs.In contrast, we find that mutation of both Su(H) binding sites in the h1Su(H))  abolisheh1Su(H)) . A more  Eve-PCs . Howevere embryo . Consist by RNAi . Thus, tHim and zfh1 enhancers raised the question of whether both these enhancers are regulated by the Notch signaling pathway in the CCs, Tin-PCs, and Odd-PCs. The processed intracellular domain of Notch (Nicd) has previously been shown to associate with Su(H) to form a complex that both outcompetes the Su(H)-co-repressor complex for binding sites in the enhancers of target genes, thereby alleviating repression, and functions additionally as an activator complex . Note that this ectopic expression of Nicd also reduces Mef2 expression in CCs, suggesting that in a wild-type embryo, Notch signaling may also serve to inhibit Mef2 expression, and thus also the CC-specific myogenic gene program that Mef2 activates, in PCs. Additionally, since Him has previously been shown to act as a direct antagonist of Mef2 function [Him in PCs likely serves as yet another mechanism for repressing the CC-specific myogenic gene program in this subtype.Collectively, our results here demonstrate three distinct mechanisms by which these PC-specific genes have their transcription restricted to pericardial cells and are prevented from being expressed in cardial cells: one involving Su(H), an integral component of the Notch signaling pathway, repressing function , the traicd from the cell membrane. Nicd then enters the nucleus and associates with Su(H) to form a Nicd-Su(H) activator complex that competes dynamically with the Su(H)-co-repressor complex for Su(H) binding motifs on the enhancers. However, Nicd both enhances the recruitment of the Nicd-Su(H) activator complex and its residence time at the enhancer, thereby favoring its presence over the Su(H)-co-repressor complex. This signaling pathway allows the activation of target genes to occur by one of two mechanisms: (i) a Notch-permissive process in which alleviating repression through the competitive displacement of the Su(H)-co-repressor complex is sufficient to initiate transcription of certain target genes due to the presence of other local TF activators that are then free to exert their positive effects, or (ii) a Notch-instructive process, which necessitates the inductive activating effect of the Nicd-Su(H) complex for transcription initiation to occur [Su(H) has previously been shown to associate with co-repressors in the absence of Notch signaling to form a repressor complex that binds dynamically and rapidly on and off the enhancers of target genes to prevent their transcription –40. Notcto occur –40.Him enhancer is a Notch-permissive enhancer, while the zfh1 enhancer belongs to the class of Notch-instructive enhancers. In the absence of Notch signaling in CCs, co-repressors associate with Su(H) and the resulting repressor complex prevents transcription of PC-specific genes such as Him by a Notch-permissive mechanism  activator complex that prevents transcription initiation in CCs (icd fragment associating with Su(H) to form an activator complex that dynamically outcompetes the repressor complex in binding to the enhancers. For the Notch-permissive PC enhancers, the resulting displacement of the repressor complex by competition in PCs is sufficient to initiate transcription due to the presence of other local TF activators (icd-Su(H) complex in PCs which initiates transcription of PC genes  levels are depleted. When the binding of the Su(H)-co-repressor complex to the Him Notch-permissive enhancer is prevented by either mutating the Su(H) motifs or knocking down Su(H) levels via cardiac mesoderm-targeted RNAi, reporter expression is de-repressed in CCs  complex in wild-type embryos, mutating the Su(H) binding sites, or knocking down Su(H) or a co-repressor in our experiments, is sufficient to ensure transcription -co-repressor complex is not sufficient to activate expression; in this case, transcriptional activation requires the binding of the Nicd-Su(H) activator complex. Thus, when Su(H) binding is disrupted, the inability of the Su(H)-co-repressor complex to bind to the zfh1 enhancer is not enough to bring about ectopic transcription in CCs (icd-Su(H) activator complex can now also no longer bind to the enhancer in PCs, resulting in its inactivation in a subset of those cells  activator complex and its binding to the zfh1 enhancer in PCs, resulting in its similar inactivation in the same PC subset -co-repressor complex, and the latter due to direct activation by the Nicd-Su(H) complex. This prediction is borne out by our experiments revealing that ectopic expression of both Him and zfh1 occurs in CCs when the Nicd is targeted to both CCs and PCs , the mutation of the Su(H) motif in the enhancer, or the ectopic expression of Nicd argues strongly in favor of a Notch-permissive mechanism regulating Him, our results do not preclude additional modes of regulation. Su(H) could, in addition to being involved in the Notch-permissive mechanism described above, also activate a separate repressor of the Him enhancer. The presence of such a Su(H)-activated repressor might explain why, even when the Notch-permissive mechanism is activated by the ectopic Hand-GAL4-driven expression of Nicd, the reporter activity from the HimWT enhancer in both CCs and PCs, while present, is lower than that in PCs of otherwise wild-type embryos , which has previously been shown to promote the pericardial fate in differentiating cardiac mesoderm cells [Drosophila orthologs  of Zinc finger and BTB domain containing 14 (ZBTB14)—binding site motifs for these TFs were identified as positively weighted sequence features in machine learning-mediated discrimination of PC enhancers and subsequently validated empirically [Him and zfh1 PC enhancers (This reduced level of reporter activity (compared to that in PCs of otherwise wild-type embryos), despite the ectopic presence of reporter activity in CCs, is seen from both the elopment –43. Our rm cells –57. Yet nhancers  Files [5Notch or its ligand Delta result in the overproduction of CCs at the expense of PCs [numb, a gene encoding an antagonist of Notch, leads to the production of additional PCs at the expense of CCs [Him and zfh1: both genes are expressed specifically in PCs, not in CCs, and both are associated with enhancers that are enriched for Su(H) binding sites and drive transcription in the pericardial cells. However, we show in this study that this very feature, the enrichment of Su(H) binding sites in enhancers of the PC-specific genes, is utilized by two strikingly distinct regulatory mechanisms, Notch-permissive in the case of Him and Notch-instructive in the case of zfh1, to achieve ultimately the same result: specific expression of both these genes in PCs. Our results emphasize how very similar developmental outcomes can arise from identical inputs despite using very different regulatory mechanisms, thereby underscoring the importance of examining in detail even those aspects of developmental phenomena that superficially appear to be the same.Regardless, mutations that eliminate the function of either e of PCs –43, while of CCs , 28, 60.icd-Su(H) activator complex  for a TF expressed exclusively in Eve-PCs in addition to the two Su(H) binding sites, and that the binding of this Eve-PC-specific TF to the enhancer is sufficient to initiate transcription (icd-Su(H) activator complex to the Notch-instructive zfh1 enhancer is prevented by mutating the Su(H) motifs (icd-Su(H) complex and the Eve-PC-specific TF prevents transcription from being activated  motifs , using R) motifs , or usin) motifs , bindingctivated . Note thzfh1 in Eve-PCs is Eve itself since it is expressed exclusively in the Eve-PCs of the heart. Furthermore, in anterior corner cell (aCC) motor neurons, Eve transcriptionally activates the GATA TF Grain (Grn), and both Eve and Grn activate zfh1 expression [zfh1 PC enhancer sequence Click here for additional data file.S2 FiglacZ reporter gene activity  driven by the HimWT enhancers in appropriate genotypes of stage 16 embryos. All CCs express Mef2 (red) while PCs are marked by Zfh1 (blue). Scale bar: 10 μm. Representative images of HimWT–driven reporter activity in embryos containing one copy of the TinD-GAL4 driver (A-A´´´), one copy of the UAS-RNAi construct Su(H)HMS05748 (B-B´´´), one copy of the UAS-RNAi construct Su(H)HM05110 (C-C´´´), one copy of the UAS-RNAi construct groKK108953 (D-D´´´), one copy of the UAS-RNAi construct HGD1458 (E-E´´´), and one copy of the UAS-RNAi construct CtBPKK108401 (F-F´´´). Note that the reporter activity is similar to that from HimWT enhancers in otherwise wild-type embryos.(A-F´´´) (TIF)Click here for additional data file.S3 FiglacZ reporter gene activity  driven by the zfh1WT enhancers in appropriate genotypes of stage 16 embryos. All CCs express Mef2 (red) while PCs are marked by Zfh1 (blue). Scale bar: 10 μm. Representative images of zfh1WT–driven reporter activity in embryos containing one copy of the TinD-GAL4 driver (A-A´´´), one copy of the UAS-RNAi construct Su(H)HMS05748 (B-B´´´), one copy of the UAS-RNAi construct Su(H)HM05110 (C-C´´´), one copy of the UAS-RNAi construct groKK108953 (D-D´´´), one copy of the UAS-RNAi construct HGD1458 (E-E´´´), one copy of the UAS-RNAi construct CtBPKK108401 (F-F´´´), one copy of the UAS-RNAi construct NHMS00001 (G-G´´´), and one copy of the UAS-RNAi construct NHMS00009 (H-H´´´). Note that the reporter activity is similar to that from zfh1WT enhancers in otherwise wild-type embryos.(A-H´´´) (TIF)Click here for additional data file.S4 FigUAS-lacZ reporter gene activity  driven by TinD-GAL4 (A) and Hand-GAL4 (B) drivers in stage 16 embryos. All CCs express Mef2 (red) while PCs are marked by Zfh1 (blue). Note that all CCs and PCs express the reporter, albeit at somewhat different levels.(A-B´´´) (TIF)Click here for additional data file.S1 TableGenotypes of the embryos used to examine activity from transgenic reporter constructs via fluorescent immunohistochemistry, the number of embryos examined for each genotype as well as the number of embryos exhibiting specific phenotypes, and a list of the figures showing representative embryos of each genotype.(XLSX)Click here for additional data file.S1 FileThe Su(H) binding site is underscored and highlighted in yellow. Putative Pnt and Pdp1 binding sites based on protein binding microarray data are underscored and highlighted in green and cyan, respectively.(PDF)Click here for additional data file.S2 FileThe two Su(H) binding sites are underscored and highlighted in yellow. Putative Pnt, Pdp1, Eve and Grn binding sites based on protein binding microarray data are underscored and highlighted in green, cyan, magenta, and grey, respectively.(PDF)Click here for additional data file.S3 FileThe mutated Su(H) binding site is underscored and highlighted in yellow. The single nucleotide substitution used to create a binding site mutation that eliminates Su(H) binding is shown in red lowercase.(PDF)Click here for additional data file.S4 FileThe mutated Su(H) binding sites are underscored and highlighted in yellow. The single nucleotide substitutions used to create binding site mutations that eliminate Su(H) binding are shown in red lowercase.(PDF)Click here for additional data file.S5 FileThe methodology, primers, and results of the RT-qPCR analysis are provided.(PDF)Click here for additional data file.S6 FileThe complete sequence of this cloning vector, also available from GenBank with the accession number MT747949, is provided in GenBank flat file format.(PDF)Click here for additional data file.


PUMA but not p21. GLIS2 deficiency caused the elevation of H3K27ac and p53 binding on the PUMA enhancer, and promoted PUMA expression. It increased the rate of apoptosis, but not cell cycle. Moreover, GLIS2 represses H3K27ac level on enhancers, regulates the gene expression related with focal adhesion and promotes cell migration, through inhibiting p300. Big data analysis supports GLIS2 as an oncogene in colon cancer, and perhaps other cancers. Taken together, we have predicted candidates for p53 transcriptional regulators, and provided evidence for GLIS2 as an oncogene through repressing enhancer activation.Gene transcription is coordinately regulated by multiple transcription factors. However, a systematic approach is still lacking to identify co-regulators for transcription factors. Here, we performed ChIP-Seq analysis and predicted the regulators for p53-mediated transcription process, from which we confirmed the roles of GLIS2, MAZ and MEF2A in regulating p53 target genes. We revealed that GLIS2 selectively regulates the transcription of  The DNA binding motifs of transcription factors are critical for recognition of enhancer sequences. Recently, the development of epigenomics revealed the important roles of enhancer activation and silencing in the regulation for the downstream genes of signaling pathways7. Several histone modifications act as critical marks for enhancer activities. H3K4me1 is the mark for enhancer priming8; H3K27ac for active enhancers and H3K27me3 for silent enhancers5. Though the initial discovery was made with ChIP-Seq of mediator subunits, now H3K27ac in the intergenic chromatin is widely used for identification of active enhancers10. Therefore, whether the enhancer activity is regulated has become one of the emerged question for both signaling transduction and epigenetic fields.Epigenetic marks on chromatin are important signatures for cell identification, which co-operate with transcription factors to regulate transcription14. p53 is activated upon DNA damage or oncogene activation. It exerts its function through transcription activation of tumor suppressors, such as cyclin-dependent kinase inhibitor 1A (p21/CDKN1A) to arrest cell cycle and BCL2 binding component 3 (PUMA/BBC3) to induce apoptosis12. MDM2 proto-oncogene (MDM2), a ubiquitin E3 ligase and a p53 target gene, interacts with p53 and promotes its degradation, which forms a negative feedback loop to control p53 protein level in normal cells15. Till now, more than 100 genes were experimentally proved, and thousands were predicted as p53 target genes11. The accumulating results indicated that p53-activated genes varies in different cell lines, which raises the questions how these genes are selectively regulated in different cell lines, an open question in the transcription field for many years12.The tumor protein p53 is the most extensively studied transcription factor in mammals19, however, the utilization of these genome-wide profiling data is quite limited. Here, we performed p53 ChIP-Seq analysis upon two different stimuli, and predicted transcription regulators of p53-mediated transcription. We experimentally proved GLIS family zinc finger 2 (GLIS2) as a repressor for p53 target apoptotic gene PUMA. GLIS2 has been proposed as an oncogenic gene in leukemia, caused by the fusion of GLIS2 with CBFA2T3 which led to GLIS2 overexpression22. GLIS2 mutation has been shown to be related with nephronophthisis in human and mice23. But whether GLIS2 functions in other types of cancers and the underlying molecular mechanisms are not determined. Our ChIP-Seq data, together with transcriptome and enhancer analysis, indicated a role for GLIS2 in regulating enhancer activity, probably through repressing the expression of E1A binding protein p300 (p300).Selective transcription is regulated by many factors, which act synergistically according to the upstream signaling. With the development of functional genomics, the genome distribution profiles of many transcription factors, such as p53, have been determinedThe aim was to reveal the role and molecular mechanism for GLIS2 in promoting colorectal cancer. From a ChIP-Seq analysis carried out for p53, H3K27ac, and p300 in HCT116 cells, we found novel transcription regulators for p53 target genes. The candidates were then validated with siRNA knockdown and quantitative PCR of p53 target genes. GLIS2 was selected from the three confirmed genes was selected and the molecular mechanism was studied. The function for GLIS2 in colorectal cancer was studied with cell and animal models, and The correlation between GLIS2 and cancers were further analyzed with online big data. For all the deep sequencing analysis, two biological replicates were studied; and for all the other experiments, at least three biological replicates were studied.Antibodies recognizing GLIS2 , β-Actin , MDM2 (Abcam ab3110), p53 , P-p53(15S) (CST 9286), HA (Abcam ab9110), PUMA (CST 4976), Halo (Promega G921A), Flag (Sigma F1804), p300 (Abcam ab14984), H3K27ac (Abcam ab4729), H3K4me1 (CST 5326), H3K4me3 (Millipore 04-745), p21 (CST 2947), CHK2 (Epitomics 3428), GAPDH , and LMNB1 (Abcam ab16048) were purchased from indicated commercial sources. Dynabeads MyOne streptavidin C1 were from Thermo-Fisher. Protein G-Sepharose beads were from GE Healthcare. PCR primers were custom synthesized by BGI and siRNAs by GenePharma. Nutlin-3a was purchased from Selleck and 5-FU from Sigma. HCT116, HL7702 and HepG2 Cell lines were purchased from Cell Bank of Chinese Academy. A549 and HeLa were purchased from ATCC. All the cell lines were cultured under recommended conditions according to the manufacturer’s instruction with 10% FBS.P-value was calculated using student’s t-test.Cells were scraped down and collected with centrifugation. Total RNA was extracted with RNA extraction kit (Aidlab) according to the manufacturer’s manual. Approximately 1 μg of total RNA was used for reverse transcription with a first strand cDNA synthesis kit (Toyobo). The resulted cDNA was then assayed with quantitative PCR. β-actin was used for normalization. The sequences of primers are in Supplementary Table 2, 10 mM NaCl, 1 mM DTT, proteinase inhibitors) was added to the cells, and incubated on ice for 10 min. Then 0.5 volumes of buffer B  was added to the cells, and incubated on ice for 1 min. The cell suspension was vortexed for 5 s and centrifuged at 800 × g for 5 min at 4 °C. The supernatant was collected as cytoplasm fraction. The above steps were repeated once more and the supernatant was discarded. The sediment was suspended in 10 volumes of PBS as the nuclear fraction. SDS loading buffer was added to the cell fractions for western blotting.Cells were harvested and spun down in cold PBS. Ten volumes of buffer A  and observed with fluorescent microscopy.24. Briefly, ~1 × 107 cells were cross-linked with 1% formaldehyde for 10 min, and quenched with 0.125 M glycine for 5 min. Cells were then washed three times with PBS and harvested in ChIP lysis buffer . DNA was digested to 150–300 bp by MNase (for histone modifications) or sonicated to 200–500 bp (for transcription factors) before extensive centrifugation. Four volume of ChIP dilution buffer  was added to the supernatant. The resulted lysate was then incubated with protein G beads and antibodies at 4 °C overnight. The beads were washed five times and DNA was eluted by ChIP elution buffer . The elution was incubated at 65 °C overnight and DNA was extracted with DNA purification kit (TIANGEN). The purified DNA was assayed by quantitative PCR. Assays were repeated at least three times. Data were shown as average values ± SD of at least three representative experiments and p-value was calculated using student’s t-test. The sequences of primers are in Supplementary Table ChIP assay was performed as previously described7 FB-EGFP or FB-GLIS2 stable cells were harvested, cross-linked with 1% formaldehyde for 10 min, and quenched with 0.125 M glycine for 5 min. Cells were lysed in 1 mL RIPA buffer , and rotated for 15 min at 4 °C. Cell lysates were centrifuged at 2300 × g for 5 min at 4 °C to isolate the nuclei. Nuclei were suspended in 500 μl of 0.5% SDS lysis buffer  and subjected for sonication to shear chromatin fragments to an average size between 200 and 500 bp. Fragmented chromatin was centrifuged at 16,100 × g for 10 min at 4 °C. Four hundred and fifty milliliter of supernatant was transferred to a new Eppendorf tube and NaCl solution was added to the final concentration of 300 mM. Supernatant was then incubated with 10 μl of Dynabeads MyOne streptavidin C1 (Thermo-Fisher Scientific) at 4 °C overnight. Then the Dynabeads were washed twice with 2% SDS, twice with RIPA buffer containg 0.5 M NaCl, twice with LiCl buffer , and twice with TE buffer . The chromatin was eluted in SDS elution buffer  followed by incubation at 65 °C overnight. The DNA was treated with RNase A (5 mg/ml) and protease K (0.2 mg/ml) at 37 °C for 30 min, and extracted with DNA purification kit (TIANGEN). The purified DNA was assayed with quantitative PCR or subjected for library construction.In total, 1 × 10Cells were harvested after digestion with 0.05% Trypsin-EDTA. The cells were then washed twice with PBS and fixed in ice-cold 70% ethanol overnight. Fixed cells were washed twice with PBS and stained in PBS containing propidium iodide  and RNase (100 μg/mL) for 30 min at 37 °C. Cell cycle analysis was performed on an Epics XL-MCL flow cytometer (Beckman Coulter) with System II (version 3.0) software (Beckman Coulter).p-value was calculated using student’s t-test.HCT116 cells were transfected with the indicated siRNAs for 12 h in 6-well plates. Then cells were seeded on the 96-well plate around 3000 cells per well, incubation for 24, 48, and 72 h respectively. Then the cells were added with 5 μl MTT (5 μg/μl) each well and incubated for 4 h at 37 °C. Then cells were added with 100 μl lysate buffer  each well and incubated for 4 h. Signals were collected by Microplate System. Assays were repeated at least three times. Data were shown as average values ± SD of at least three representative experiments and 25. Cells were cultured at 10,000 per well in CIM-Plate wells. The cell index signals were read by xCELLigence RTCA DP Analyzer (ACEA Bioscience Inc.).Cell proliferation was analyzed with RTCA instrument (ACEA Bioscience Inc.) according to the manufacturer’s instruction as described before5 HCT116 cells were plated in medium without serum or growth factors in the upper chamber with a Matrigel-coated membrane , and medium supplemented with 10% fetal bovine serum was used as a chemoattractant in the lower chamber. After 36 h of incubation, cells that did not invade through the membrane were removed by a cotton swab. Cells on the lower surface of the membrane were stained with crystal violet and counted. Assays were repeated at least three times. Data were shown as average values ± SD of at least three representative experiments and p-value was calculated using student’s t-test.In total, 1 × 105 HCT116 cells per site into the flank regions of the mice. Tumor volumes were measured twice a week using calipers. Tumor volumes were calculated as V = 0.5 × length × width2. After 24 days of injection, the tumors were harvested and weighed.The 5-week-old male BALB/C nude mice were purchased from Beijing HFK Bioscience Co. Ltd. Colon cancer model was established by injecting subcutaneously 8 × 10https://david.ncifcrf.gov).RNA-seq library was constructed by using Illumina TruSeq library construction kit. Five micrograms of total RNA was used for each sample according to the manufacturer’s instruction. The libraries were sequenced using HiSeq X Ten for 100 bp paired-end sequencing. Quality control of mRNA-seq data was performed using Fatsqc and low-quality bases were trimmed. All RNA-seq data were mapped to the human genome (hg19) by TopHat (version 2.1.1) and allow maximum 2 mismatch. The gene expression level was calculated by Cufflinks with default parameters and gene ontology analysis was performed using DAVID . After data filter, quality control of clean reads was performed by Fastqc again.26. Samtools was used to sort BAM file and filter duplicate reads27. Only unique mapped reads were accepted for further analysis. MACS1.4 was used for ChIP-Seq peaks calling with band width 500, model fold ranges from 5 to 50, p-value cutoff 1e10−828. Then HOMER annotatePeaks.pl was used to annotate ChIP-Seq peaks compared with reference genome hg38. AME in MEME Suite was used to find out significant enriched motif and Deeptools was used to display ChIP-Seq signal around TSS sites and peaks binding sites29.Bowtie2 (bowtie2 -q--phred33--end-to-end--sensitive -k 1 -p 5--fr--no-mixed--no-discordant -X 1000) was used for data mapping to human reference genome hg38To predict transcriptional co-regulators for p53, the 200 bp DNA sequences around p53 peak summit sites were used for AME analysis, ame --verbose 1 --oc ame-output-dir --control DMSO_P53_summits_200bp.fasta --bgformat 1 --scoring avg --method ranksum --pvalue-report-threshold 0.05 5Fu_P53_summits_200bp.fasta HOCOMOCOv11_full_HUMAN_mono_meme_format.meme. Then the motifs from 5-Fu or Nutlin-3a flank sequences were further compared with DMSO to filter the conserved and bias sites.7. Briefly, significant distal H3K27ac peaks (peak boundary 1.5 kb or peak center 3 kb away from gene TSS) were identified, and the peaks whose distance was shorter than 12.5 kb were merged together. The enhancers were ranked by total signal of H3K27ac, and a plot was drawn to show the increased H3K27ac signal. Then a tangent line with slope 1 was found of the curve and the intersection point was determined as the tangency point. Enhancers above the tangency point were defined as super enhancers, meanwhile enhancers below it were defined as typical enhancers.Enhancers were identified by the algorithm developed by Richard A. Younghttp://gepia.cancer-pku.cn/). GEPIA uses log-rank test, also called the Mantel–Cox test, for the hypothesis evaluation. The cox proportional hazard ratio is based on Cox PH Model. The datasets of colon cancer are based on TCGA-COAD (Colon adenocarcinoma).The disease-free survival  and overall survival analysis were carried out via GEPIA performs 30. Overall survival (OS), progression-free survival (PFS), and disease-free survival (DFS) were evaluated by Kaplan–Meier survival analysis and log-rank test. On the basis of the GLIS2 expression level, cases were divided into two groups according to previously described methods32.RNA-seq data of colon cancer tissues, and survival information of patients in TCGA (The Cancer Genome Atlas) project were downloaded from the Genomic Data Commons (GDC) portal site  is a widely used chemotherapy drug to activate p53 signaling pathway. Nutlin-3a is an inhibitor of p53-MDM2 interaction and can activate the expression of p53 target genes. The activation of p53 target genes 11, 237 target genes were reported before and 286 were novel p53 target genes , and myocyte enhancer factor 2A (MEF2A), which were able to affect the expression of p53 target genes when knockdown , which supported co-localization of GLIS2 with p53 on p53 target genes (data not shown). Then we focused on GLIS2 to study its function in p53 signaling and tumorigenesis.To study regulation of p53 transcription, we utilized the 200 bp fragments around p53 peaks summit sites and predicted the potential bound transcription factors on these fragments. The analysis predicted many candidates, among which many have been reported by previous studies Fig. . We selelue Fig. . GLIS2 iGLIS2 was knocked down with two different siRNAs and 5-Fu was used to treat HCT116 cells. The results indicated that GLIS2 deficiency elevated the expression of PUMA, in both control and 5-Fu treated cells . Interestingly, p53 deficiency seems not to affect GLIS2 function, since in p53−/− cells, GLIS2 knockdown with siRNAs still elevated PUMA expression . Immunostaining of endogenous GLIS2 was performed with two different commercial antibodies. The one from LSBIO showed GLIS2 was localized in nucleus ; while that from Thermo-Fisher showed GLIS2 both in nucleus and cytoplasm (data not shown). Combined with the cell fractionation result, we thought GLIS2 should be a nuclear protein.To investigate the mechanism how GLIS2 regulates GLIS2 cDNA fused with Flag and biotin-acceptor-site (FB) tag, and established a stable cell line with the plasmid in HCT116 cells  and enhancer upon 5-Fu treatment . We then examined whether GLIS2 regulates the activity of p53 target enhancers. When ent Fig. . ChIP-Seent Fig. . The aveent Fig. . H3K27acent Fig. , which iown Fig. , suggestsed Fig. . Westernncy Fig. . These rPUMA and p21 are two major target genes for p53, one for apoptosis and the other for cell cycle arrest. When GLIS2 was knocked down in HCT116 cells, the percentage of apoptotic cell significantly increased , showed that GLIS2 is higher expressed in colon cancer compared with the normal tissues (Fig. GLIS2 is correlated with the tumor TNM stages (Fig. PUMA is negatively correlated with GLIS2 expression in colon and cancer tissues, supporting GLIS2’s role in repressing PUMA expression (Fig. To further study the relationship between GLIS2 and tumorigenesis, we injected Halo-tagged GLIS2 stable cells into nude mice. The results showed that GLIS2 expression in HCT116 enhanced the tumor number, weight and volume in xenograft experiment Fig. . We alsoues Fig. , suggestues Fig. , and theges Fig. . The expion Fig. . Furtherion Fig. . To sum 37; while MEF2A is tumor repressive and its mutation is associated with tumorigenesis39. This information will be useful to the study of p53-dependent transcription regulation and the methodology will be useful to study other transcription factors.In the current study, we applied a bioinformatics approach to identify the potential regulators of p53 target genes. We experimentally verified three novel transcription regulators, including GLIS2, MAZ, and MEF2A. MAZ is an oncogenic transcription factor associated with multiple cancersGLIS2 has been proposed as an oncogenic gene in leukemia, in which chromatin translocation causes the fusion of GLIS2 with CBFA2T3 and led to GLIS2 overexpression22, and its mutation is linked with nephronophthisis in human and mice23. But the underlying molecular mechanisms for GLIS2 in these diseases are not determined. Our study showed that GLIS2 is overexpressed in multiple cancers and may be useful for prognosis of colon cancer. GLIS2 regulates the expression of a subset of p53 target genes. It represses the expression of PUMA, but does not affect p21 and MDM2. Correspondingly, GLIS2 deficiency increased the rate of apoptosis induced by 5-Fu, but did not affect cell cycle distribution. Moreover, GLIS2 regulates the expression of genes related with focal adhesion, which is correlated with its ability of regulating cell migration. Thus, our data indicate that GLIS2 inhibits apoptosis through repressing PUMA expression, and promotes migration through enhancing the gene expression related with focal adhesion, which promotes tumorigenesis.PUMA enhancer, accompanied by increased p53 recruitment and PUMA expression. These demonstrated that GLIS2 functions as a repressor of PUMA enhancer. Our data further showed GLIS2 regulates enhancer activity globally, probably through transcriptionally modulating p300. However, we do not exclude the possibility that GLIS2 may regulate p300 recruitment to enhancers via other mechanisms. A previous study reported that GLIS2 interacts with HDAC3 and regulates gene expression in several kidney cancer cells34. It is possible GLIS2 represses enhancer activity through removing H3K27ac. Unfortunately, we failed to reproduce the experiment in our system. Meanwhile, we did not detect the interaction between GLIS2 and p300. However, GLIS2 may still modulate enhancer activity through other mechanisms. In our study, we found that GLIS2 knockdown affects the p53 binding to PUMA, but not p21, which is consistent with the mRNA expression. It suggests that GLIS2 is possibly involved in the regulation of p53 recruitment, but the detailed mechanisms require further studies.The regulation of enhancer activity emerged to be one of the critical question to be answered. H3K27ac, the target gene expression level and eRNA amount have been used to evaluate enhancer activity. However, eRNAs of many enhancers are too low to be reliably detected. As comparison, recruitment of transcription factors is much more reliable to be detected with ChIP-PCR or ChIP-Seq assay, which may serve as a better reporter. In our study, GLIS2 knockdown caused H3K27ac increase on We found one dataset of eGFP-GLIS2 ChIP-Seq in HEK293 on ENCODE. Though the dataset supported the localization of GLIS2 on p53 target genes, but the exact GLIS2 binding sites was far away from p53 sites of our study. It was maybe due to the different distribution of GLIS2 in different cells. So, we performed Capture-ChIP with HCT116 cells stable expressing FB-tagged GLIS2, which showed nice co-localization of GLIS2 with p53. Our data will be also useful to study the function of GLIS2 in other pathways.PUMA and genes related with focal adhesion. GLIS2 regulates transcription through repressing the expression of p300 and the activity of enhancers, including H3K27ac inhibition and prevention the binding of transcription factors. Taken together, we have proved GLIS2 as an oncogene in colon cancer through selectively regulating gene transcription and enhancer activity.To sum up, our study proved that GLIS2 acts as an oncogene in colorectal cancer repressing apoptosis and promoting cell migration. It transcriptionally represses the expression of the apoptotic gene Supplemental FiguresSupplemental datasets