Ferrocene addition caused accumulation of iron within slices and loss of oligodendrocytes, an effect that was partially inhibited when ferrocene and ER stressor tunicamycin (Tm) were added together. An upward trend  in the expression of UPR transcripts in response to ferrocene was demonstrated using real-time PCR, while a significant upregulation of mRNA for B cell immunoglobulin-binding protein (BiP) and C/EBP homologous binding protein (CHOP) occurred following exposure to Tm. In silico analysis revealed consensus DNA-binding sequences for UPR-associated transcription factors within the promoter regions of eight iron-regulatory genes. In addition, dual-staining for CHOP and oligodendrocyte transcription factor 2 (OLIG2) or Ionized calcium binding adaptor molecule 1 (Iba1) showed nuclear expression of CHOP in some oligodendrocyte-lineage cells in response to Tm or Tm+ferrocene, but CHOP was rarely found in microglia. Co-expression of UPR-associated activated transcription factor 6 (ATF6) was detected in the nuclei of some oligodendrocyte-lineage cells exposed to Tm alone, or to Tm and ferrocene, but rarely in microglia. These data highlight the therapeutic potential of targeting UPR-associated proteins when developing novel treatments for chronic brain disorders that are affected by dysregulated iron.The accumulation of iron within the brain occurs in many chronic disorders including Alzheimer’s and Parkinson’s disease and multiple sclerosis. Outside the CNS, a link between levels of iron and the unfolded protein response has already been established. To determine if such a relationship operates in within the brain, we used our  Occasional but infrequent hints have documented an interplay between the UPR and iron metabolism . Howeverain [see  for compain [see  of the aThe research described was approved by the Animal Care Research Ethics Committee Filing ID 12/FEB/04) and was carried out in accordance with European directive 2010/63/EU.Neonatal hippocampal slices were maintained as previously described .Five μg/ml tunicamycin  exposure was used to activate a UPR response, while iron loading was induced by a 12-h treatment with 1 μM ferrocene, a membrane-permeable compound that enables uptake of iron by cells . Slices were harvested immediately after iron loading.®Fast Optical 96-Well Reaction Plate and Fast SYPR green master mix  and the Step One real-time PCR System (Thermo-Fisher).RNA was isolated from control and treated slice cultures using TRIreagent  as per the manufacturer’s instructions. Following DNAse I treatment  and cDNA synthesis using manufacturer protocol , real-time PCR was accomplished using Applied Biosystems 4346906 MicroAmpUnless otherwise indicated, oligonucleotide primer sequences  used were designed using Primer3. Primer sequence details are summarized in the Supplementary Table Tissue viability was measured as described in Tissue was fixed and stained as previously described . Briefly1 was used to determine if any UPR transcription factor consensus DNA binding sequences were present within the promoter regions of genes that encode iron homeostasis regulators. Only hits that had a dissimilarity index <15% and a random expectation <0.5 were included in this analysis.The ALGGEN PROMO algorithmt-test, Mann-Whitney test or an ANOVA with Dunnet post-test, as appropriate, and as indicated in figure legends. Differences were considered statistically significant if P<0.05.Data was analyzed using Prism 6 (GraphPad Software). All measurements were expressed as mean ± standard error of the mean (SEM) unless otherwise indicated. Normality was assessed using the Shapiro-Wilk test. Statistical analyses were carried out using Student’s Real-time PCR analysis of changes in the transcripts for B-cell immunoglobulin binding protein (BiP), calreticulin (CRT), X-box binding protein 1 (XBP1), activated transcription factor 6 (ATF6), activated transcription factor and C/EBP homologous binding protein (CHOP) revealed an approximate 1.4–2.1-fold increase, which was not found to be statistically significant following three replicate experiments Figure .The ability of cultured hippocampal slices to mount a response to ER stress was confirmed when a significant increase in BiP and CHOP transcription (approximately 30-fold for both), was detected following a 12-h treatment with 5 μg/ml tunicamycin  DNA binding sequences, we identified a number of putative binding sites for UPR-associated transcription within the promoter regions of iron homeostasis genes Table . None ofIn an attempt to elucidate whether or not cross-talk between iron-regulatory proteins and ER stress, we exploited our rat hippocampal slice-based model of iron accumulation. Following slice exposure to Tm, F or both, real-time PCR and fluorescent immunohistochemistry yielded the following primary findings: (1) Tm exposure does not affect the level of iron in hippocampal slices, but it triggers a UPR that appears to prevent iron overload and possibly ameliorates toxic effects of iron on oligodendrocytes and microglia; (2) iron overload causes an upward trend in the levels of transcripts encoding the major markers of the UPR, although this was not statistically significant; (3) the observation that Tm treatment led to no significant change in the expression of FTL or FTH transcripts was surprising, since the promoter regions of eight genes that encode iron-regulatory proteins, including FTL and FTH, contain putative binding sites for ER stress-associated transcription factors; (4) transcription factor ATF6 is activated following exposure of cultured hippocampal slices to Tm, F and TmF; (5) Activated ATF6 is detectable within the Tm- and TmF-treated oligodendrocycte population but rarely in similarly treated microglia.While intriguing, it remains unclear how concurrent iron accumulation and UPR activation, demonstrated here, leads to the ameliorated accumulation of iron. Outside the brain, the main description of the intersection between the UPR and iron metabolism is the action of CHOP on hepcidin (via modulation of C/EBPα binding and/or through CREBH) in the liver and spleen  althoughOur observation that CHOP immunofluorescence appears to be increased in Olig-2 positive cells in slices that were co-exposed to Tm and Ferrocene compared with only Tm is consistent with these studies and it hints at the potential therapeutic benefit of UPR proteins induced by Tm to protect against iron-induced toxicity within oligodendrocyte lineage cells. However, further work is required to elucidate or reasonably speculate on such a mechanism.SH carried out the PCR. SH and JM completed the tissue staining. JM and UF conceived and oversaw research described. SH, JM, and UF wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Plasmodium falciparum employs a broad range of mechanisms to regulate gene transcription throughout the organism’s complex life cycle. To better understand this regulatory machinery, we assembled a rich collection of genomic and epigenomic data sets, including information about transcription factor (TF) binding motifs, patterns of covalent histone modifications, nucleosome occupancy, GC content, and global 3D genome architecture. We used these data to train machine learning models to discriminate between high-expression and low-expression genes, focusing on three distinct stages of the red blood cell phase of the Plasmodium life cycle. Our results highlight the importance of histone modifications and 3D chromatin architecture in Plasmodium transcriptional regulation and suggest that AP2 transcription factors may play a limited regulatory role, perhaps operating in conjunction with epigenetic factors.Empirical evidence suggests that the malaria parasite  Plasmodium falciparum, employs a variety of mechanisms to modify the expression of its genes throughout its complex life cycle. In this work, we gather a rich collection of data describing various aspects of the gene regulatory apparatus in P. falciparum, and we use a machine learning approach to help understand the relative importance of each potential regulatory mechanism. Our results highlight the importance of two particular mechanisms: patterns of biochemical modifications on the histone proteins that form the primary scaffold for DNA in the cell and the three-dimensional conformation of DNA in the nucleus.The parasite responsible for the most lethal form of malaria,  Plasmodium falciparum is the deadliest species of malaria parasite, responsible for 445,000 deaths in 2016 Plasmodiu4  or humdatasets . This didatasets . It is pPlasmodium AP2 genes are conserved proteins containing putative DNA-binding domains, homologous to the plant Apetela2/Ethylene Response Factor (AP2/ERF) DNA-binding proteins, the second largest class of transcription factors in Arapidopsis thaliana  is suboptimal. Alternately, it is possible that AP2 DNA binding requires both a TF-specific motif and a permissive epigenetic state at a given locus. DNA accessibility and epigenetic state is known to play a role in restricting TF binding in eukaryotes generally [Plasmodium specifically, detailed study of one TF found that the presence of a consensus motif was neither strictly necessary nor sufficient for TF binding [Plasmodium and would clarify if AP2 factors truly play a limited role in erythrocytic transcriptional regulation. Initial Chip-seq results against AP2-G2 and AP2-I, transcription factors thought to be involved in sexual development and cell invasion respectively, suggest that AP2 may interact with some promotors to either act as a repressor for AP2-G2  and associated with low expression. In contrast, scores assigned to this feature were close to zero for both the Logistic and XGBoost models. H2Az signal was previously reported to be almost completely absent from gene coding sequence , which mAn inherent limitation of our analysis is that, given these data, we cannot easily separate correlations from causative relationships. This is particularly important when modeling transcription using epigenetic data, given previous evidence that some histone marks (H3K36 and H3K79 methylation) are deposited directly as an effect of Pol II elongation, rather than preceding transcriptional activation . In the Plasmodium life cycle where experimental dissection of epigentic function would be most informative. For instance, H4K20me3 is not predictive of expression the ring stage, but is consistently associated with transcriptional repression in the schizont stage  in all three stages, 2162 were either “high” and “intermediate” or “low” and “intermediate”, and the remaining 182 were labeled as “high” and “low” expression at least once. Plasmodium displayed peak enrichment at the start codons of Plasmodium genes, while only one displayed the highest enrichment at transcription start sites [Plasmodium lacks a strongly positioned +1 nucleosome at the TSS, but that clearly positioned nucleosomes are observed at the start and end of coding sequences [During model development and feature refinement, we came to the surprising discovery that placing the promoter/gene body division using start codon position was more effective than using transcription start sites . This obrt sites . Additioequences , 9. In tS1 FigComparison of test AUCs for motif-only models using 500 bp, 1kb, or 2kb promoter windows.(PDF)Click here for additional data file.S1 TableGRO-seq values, normalized for GC content, gene length and the parasitemia factor of a stage .(XLSX)Click here for additional data file.


As any living organisms, plants must respond to a wide variety of environmental stimuli. Plant hormones regulate almost all aspects of plant growth and development. Among all the plant hormones, ethylene is the only gaseous plant hormone that plays pleiotropic roles in plant growth, plant development and stress responses. Transcription regulation is one main mechanism by which a cell orchestrates gene activity. This control allows the cell or organism to respond to a variety of intra- and extracellular signals and thus mount a response. Here we review the progress of transcription regulation in the ethylene response. Like all living organisms, plants must respond to a wide variety of environmental stimuli. Plant hormones, produced in response to environmental stimuli, regulate almost all aspects of plant growth and development. Ethylene is a gaseous plant hormone that plays pleiotropic roles in plant growth, plant development, and stress responses. Histone acetylation, which is modulated through ethylene-mediated signaling, regulates dynamic changes in chromatin structure that result in transcriptional regulation in responses to ethylene.RESPONSIVE-TO-ANTAGONIST 1 (RAN1)(Ethylene is perceived by a family of receptors bound to the endoplasmic reticulum (ER) membrane . Each re 1 (RAN1). In the  1 (RAN1). CTR1 is 1 (RAN1), prevent 1 (RAN1).In the presence of ethylene both ethylene receptors and CTR1 are inactivated, and the C-terminal end of EIN2 is dephosphorylated and cleaved by unknown mechanisms. The cleaved C-terminal end of EIN2 translocates to the nucleus  where itEIN3 gene encodes a nuclear-localized protein that is essential to the response to ethylene , such as ERF3 and ERF4, which are known to function as active repressors in vitro and in vivo, have been shown to physically interact with AtSAP18, which in turn interacts and forms a repression complex with AtHDA19  and p300, the mammalian family of HAT domain containing transcriptional coactivators, play pleotropic roles in plant growth and development (hac1hac5 double mutant was found to have a constitutive triple response phenotype (hac1hac5 double mutant due to the reduction of histone acetylation levels; however, similar to that in gcn5 mutant, the downstream ethylene responsive genes are elevated in hac1hac5 double mutant (A Number of studies have shown a tight link between histone acetylation and plant hormone responses . In the pression . In Arabelopment . The hachenotype . It was e mutant , suggestHAD6 and HDA19, are specifically elevated by ethylene treatment. The expression of ethylene responsive gene ERF1 is anti-correlated with the levels of histone H3 acetylation in 35S:HDA19 transgenic plants, showing that HDA19 indirectly influences ERF1 gene expression (Expression of two HDACs, pression . It is ppression . JAZ propression . This prH3K14Ac and H3K23Ac, but not H3K9Ac, H3K18Ac, or H3K27Ac, are elevated by ethylene treatment at the molecular level . InteresTaken together, currently available data suggest that in the absence of ethylene ENAP1 binds to histones to keep chromatin in a relaxed state poised for a rapid response to ethylene . In the Plants must respond accurately and quickly to hormones, and this necessitates a flexible and rapid way to control gene expression. The acetylation of histone tails by HATs neutralizes positive charges on these proteins that would otherwise interact with negatively charged DNA, facilitating nucleosome unwrapping for rapid transcription activation. How plants utilize a limited number of HATs and HDACs to specifically regulate responses to different hormones is largely unknown. Presumably, the specificity relies on the partners of HATs and HDACs. Identification of the HAT- and HDAC-containing complexes upon ethylene treatment will reveal details of the molecular mechanisms that underlie the ethylene response. Recent studies have clearly shown that different tissues respond to plant hormones differently . Most avBoth authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


While genetic variation at chromatin loops is relevant for human disease, the relationships between contact propensity , genetics, and gene regulation are unclear. We quantitatively interrogate these relationships by comparing Hi-C and molecular phenotype data across cell types and haplotypes. While chromatin loops consistently form across different cell types, they have subtle quantitative differences in contact frequency that are associated with larger changes in gene expression and H3K27ac. For the vast majority of loci with quantitative differences in contact frequency across haplotypes, the changes in magnitude are smaller than those across cell types; however, the proportional relationships between contact propensity, gene expression, and H3K27ac are consistent. These findings suggest that subtle changes in contact propensity have a biologically meaningful role in gene regulation and could be a mechanism by which regulatory genetic variants in loop anchors mediate effects on expression. It is currently unclear how quantitative changes in chromatin loop propensity contribute to differential gene regulation. Here, the authors use phased Hi-C, RNA-seq, and ChIP-seq to show that subtle changes in loop propensity associate with differential gene regulation across cell types and haplotypes. These colocalized regions, also known as loop anchors, are preferentially enriched for disease associated distal regulatory variation and expression quantitative trait loci (eQTLs)22. While it has been shown that the physical 3D distance between looped loci can vary25, previous studies examining cell type and haplotype differences in looping have considered loops to be either present or absent, rather than a quantitative phenotype. Thus, the extent to which quantitative differences between chromatin loops exist, and whether they are associated with differences in gene expression and regulation, has yet to be explored.Chromatin loops colocalize regulatory elements with their targets26. Together, this suggests that the contact frequency measured in a pool of cells reflects the proportion of cells in which a contact is occurring, or the probability for the contact to occur (contact propensity) across all cells in the sample. Investigating contact frequency as measured by Hi-C, in combination with molecular phenotypes, may reveal if contact propensity between looped loci varies across cell types and haplotypes, and if this variation is associated with differential regulation of gene expression.Bulk chromatin conformation assays  were designed to measure physical contact frequency between two pieces of colocalized  DNA in a pool of cells. While a recent single-cell Hi-C study found that contacts occur within single cells at loops called from bulk data, there was variability in the contact profiles of looped loci between cells2 created and phased the GM12878 Hi-C map (which is the highest-resolution map currently available) to study differences in looping across haplotypes, and did not observe differences between the paternal and maternal haplotypes outside of imprinted regions. Other more recent studies employing CTCF ChIA-PET5 and H3K27ac Hi-ChIP27 have reported that allelic imbalance in chromatin looping occurs throughout the genome. These contradictory results are likely due to the experimental design and types of effects examined in these studies. Rao et al.2 used Hi-C data to look for large differences across haplotypes, and thus may have missed smaller effects. The studies using ChIA-PET and Hi-ChIP sought to identify allelic imbalance of all sizes, but employed experimental approaches that may be biased as they simultaneously measure either CTCF binding or regulatory region activity and chromatin looping, thereby conflating the allelic bias of the two phenotypes. A genome-wide quantitative analysis into allele-specific chromatin looping using phased Hi-C would enable the unbiased estimation of the magnitude at which contact propensity varies across haplotypes at all types of chromatin loops (rather than only those at promoters and/or enhancers). Additionally, integrating this data with phased gene expression and H3K27ac data could provide evidence that contact propensity plays a role in long-range gene expression regulation, and provide insight into how regulatory genetic variants may influence chromatin structure.If contact propensity between looped loci does in fact play a role in gene regulation, a genetic variant that affects contact propensity would likely have a downstream effect on gene expression. Therefore, the association between contact propensity and gene expression would exist not only across cell types, but also across haplotypes. Recent studies examining whether chromatin loops vary across haplotypes, and the functional consequences of this variation, have come to conflicting conclusions. Rao et al.In this study, we generate a resource of phased, high resolution Hi-C chromatin maps from induced pluripotent stem cells (iPSCs) and iPSC-derived cardiomyocytes (iPSC-CMs) from seven individuals in a three-generation family for whom we have 50× whole-genome sequence (WGS), and phase gene expression (RNA-seq) and enhancer activity (H3K27ac ChIP-seq) data generated from the same iPSCs and iPSC-CMs. We identify chromatin loops, quantitatively characterize cell-type-associated looping, and find that while loops tend to be present in both cell types, some loops exhibit significantly increased contact propensity within one cell type. We show that these quantitatively-identified cell-type-associated loops (CTALs) recapitulate known biology discovered through previous qualitative comparisons of cell-type-specific loops, including being enriched for differentially expressed genes and regulatory regions, becoming more specialized throughout differentiation, and connecting distal eQTLs to their target gene. Additionally, our quantitative analyses reveal that small magnitude changes in contact propensity are proportionally associated with large changes in molecular phenotypes: an association that could not be identified by qualitative comparisons. We next examine allelic differences in contact propensity by phasing our Hi-C data, and find that haplotype-associated chromatin loops (HTALs) are highly enriched for imprinted regions or for being associated with copy-number variation, but not for eQTLs, suggesting that regulatory genetic variants do not exert large effects on chromatin contact propensity. Finally, we examine the association between differential contact propensity and differential gene expression and H3K27ac over a range of magnitudes across both cell types and haplotypes by quantitatively associating the phenotypes in aggregate across the genome. These analyses reveal a genome-wide proportional relationship between differential contact propensity and differential expression and H3K27ac that is consistent across cell types and haplotypes. Our study therefore suggests that the cellular context of a chromatin loop  affects the propensity for an interaction at a loop to occur, and that these small changes in contact propensity are associated with large functional effects. This model suggests that regulatory genetic variation could mediate its effects on gene expression through subtle modification of contact propensity at chromatin loops.28  from seven individuals in a three-generation family from iPSCORE (the iPSC collection for Omics REsearch)8 Figure . Fibrobl33  in iPSCs and iPSC-CMs (or any other iPSC-derived cell type) to date, and were comparable in resolution to the Hi-C map in GM128782  at multiple resolutions, identifying 17,567 loops in iPSCs , and 19,003 iPSC-CM loops  intra-chromosomal contacts after aligning and filtering ~6 billion Hi-C read pairs across all 24 Hi-C samples  near loop anchors. In both cell types, we found enrichments for active and bivalent chromatin states  with CTCF motifs at both anchors had inward facing CTCF motifs. Further, 63.3% and 65.3% loops in iPSC and iPSC-CMs, respectively, were within 25 kb of a CTCF ChIA-PET interaction from GM128785. We next examined the types of chromatin states that were statistically significantly paired together (Fisher’s exact p < 0.05) and found two subnetworks, one with active chromatin states and the other with repressed or bivalent chromatin, which were discrete in iPSC-CMs  and found 28.7% and 33.5% of iPSC and iPSC-CM loops to be within 25 kb of a pHiC interaction in these cell types, respectively. Together, these results indicate that the identified chromatin loops include those with active regulatory interactions , those with repressive interactions , those that are structural (CTCF-CTCF), and those with a variety of other types of chromatin states (that were not significantly enriched for being paired together) at their anchors.To characterize the types of chromatin loops that comprised the loop sets, we examined the distribution of H3K27ac and ATAC peaks, CTCF motifs, and ROADMAP chromatin states from the most epigenetically similar cell typetes Fig. , H3K27actes Fig. , and chrtes Fig.  from thetes Fig.  right. ACMs Fig.  and crosCMs Fig. . This cr34. We found a large proportion of loops which were differentially called, but visually appeared to consistently form across cell types  of the interactions between both cell types. We observed that the majority of loops that were called in both cell types . These CTALs were identified across a range of logCPM levels and were distinct from those called within each cell type . We also observed that iPSC-CTAL anchors which overlapped iPSC bivalent enhancers were more likely to overlap fetal heart bivalent enhancers  to the genes that they were statistically associated with (eGenes) had stronger contact intensities within iPSCs than iPSC-CMs. We found a strong enrichment (Mann–Whitney U test p ~1 × 10−293) for increased iPSC:iPSC-CM contact frequency ratio above non-eQTL-eGene loops  in the upregulated cell type remained within this set of loops, though the extent of the differences in chromatin looping were smaller  between the two phenotypes; however, the magnitudes at which the phenotypes varied were quite different, with gene expression varying up to 250-fold, and the middle three quantiles of chromatin looping varying less than threefold. For these analyses, we pooled data across the genome to measure the association between contact frequency and gene expression, independent of a particular locus; therefore, this analysis compares the relationship between contact propensity and gene expression in aggregate across the genome. As each pair of fold change measurements between contact frequency and gene expression are from the same locus in two different cell types, locus specific biases based on the linear genome which affect Hi-C read depth 45 are held constant. Overall, these results suggest that small magnitude changes in contact propensity may be functional as they are associated with large magnitude changes in gene expression across cell types.Next, we examined the quantitative association between contact propensity and differential expression, as well as the quantitative association between contact propensity and differential H3K27ac, across cell types. We tested whether the fold change in contact frequency across cell types was in the direction of the cell type with higher differential expression or H3K27ac. We found that across the union loop set, anchors overlapping DE genes with higher expression in iPSCs had significantly greater contact frequency in iPSCs, and anchors overlapping DE with higher expression in iPSC-CMs had significantly higher iPSC-CM contact frequency; similar patterns were found for DE H3K27ac peaks  within each individual using a binomial test. We identified a total of 189 ASE peaks  in iPSCs and 618 ASE peaks  in iPSC-CMs, and 2582 ASE genes  in iPSCs and 2214 ASE genes  in iPSC-CMs.To enable the functional characterization of haplotype-specific chromatin looping, we phased the Hi-C, H3K27ac, and RNA-seq data to obtain haplotype-associated phenotype data . We next examined whether the HTALs were truly cell type specific or if the sparsity of the Hi-C data statistically limited our ability to detect allelic loop imbalance present in both cell types. For each of seven individuals, we determined the individual’s maternal allele ratio for each of the 27 iPSC HTALs using the iPSC Hi-C data, as well as the maternal allele ratio using the iPSC-CM Hi-C data , which suggests that loop imbalance was consistent across both cell types. As we observed that the maternal allele frequencies were highly correlated across cell types, to increase power for these analyses, for each of the 26,679 chromatin loops in the union set, we pooled contacts for each individual across their corresponding iPSCs and iPSC-CMs. We observed a median of 50 informative contacts per individual per loop, which corresponds to 100% power to identify HTALs with an allelic imbalance ratio of 70% or higher with α = 0.02 in an individual , slightly higher than the statistically expected 5% by chance; however, only a mean of 0.1% (26.6) were significant under FDR q < 0.05 in each individual , showing that even with the increased power by using the combined cell type data, the majority of loops had small allelic differences  with the WASP analysis; however, the majority  were found in both sets. These results and power indicate that while we may not detect all small haplotype differences , large haplotype differences in chromatin looping occur infrequently.To characterize haplotype-specific chromatin looping, we performed a genome-wide analysis to identify haplotype-associated chromatin loops with consistent significant allelic imbalance  across individuals. Within each cell type, for each individual, we assigned informative Hi-C contacts carrying a phased allele to each haplotype Fig.  and examata Fig. . We thenata Fig. . For botual Fig. . To idences Fig. . In comp5 and somatic and inherited CNVs47; Supplementary Table p > 0.05 for all tests; Fig. q > 0.05); together, these results indicate that loops which varied between haplotypes were not more likely to be a specific type of loop. We next compared the distribution of genomic features known to cause large allelic differences within HTALs and the union loop set , and somatic  and inherited  CNVs previously identified in these samples32. To examine whether these trends held across all levels of imbalance significance, we quantified the extent of association of each genomic feature with chromatin loop allelic imbalance as a function of HTAL p-value. For imprinted genes, as the p-value threshold increased, the odds ratio increased almost log-linearly, whereas CNV overlap increased but to a lesser extent , we found no significant enrichment within HTALs. Thus, while CNV type was not associated with allelic imbalance, loop detection may be affected by CNV presence. The observed pattern of enrichment in deletions is consistent with linearly closer loci having increased Hi-C contact propensity (as deletions reduce the linear space between loci) thereby increasing contact frequency and loop detection power; conversely, duplications increase linear distance and thus decrease contact frequency and loop detection power. Thus, it is unclear how much of this enrichment is due to a technical artefact induced by increased power at deletions. Overall, these results confirm previous reports which suggested that genetic imprinting5 may be a strong driver allelic imbalance, and suggest that CNVs may have smaller effects on allelic imbalance in chromatin looping.We next examined whether the 114 genome-wide significant HTALs were statistically more likely to be a specific type of loop, or overlap genomic features previously shown to be associated with differential chromatin looping  or eQTL-eGene loop. We found ASE peaks to be enriched at HTAL anchors, and also being a PE loop to be enriched  or higher across cell types, whereas only ~5% of loops showed a similar fold change across haplotypes . As we observed a consistent relationship between the two, we believe these data indicate that contact propensity is a mechanism involved in regulating gene expression, similar to enhancer activity or transcription factor binding strength. Notably, as we identified a non-directional correlation, contact propensity may either affect, or be affected by, gene expression and/or regulation.51 suggest that differential contact propensity could result from changes in spatial proximity. The fact that CTCF and Pol2 ChIA-PET show similar profiles to Hi-C data5 suggest that differences in protein binding near loop loci could also affect contact propensity. Finally, as we found associations between contact propensity and H3K27ac, regulatory chromatin activity could modulate contact propensity. Future studies examining these mechanisms could provide insights into the biological processes underlying differential contact propensity and gene regulation.While the mechanisms underlying changes in contact propensity are currently unknown, there are several reasonable hypotheses. Previous studies showing that the physical 3D structure of the genome can be reconstructed from contact frequency via polymer physics modelsThe identification of specific causal variants associated with differential contact propensity is likely to be challenging, as we did not find a large number of HTALs with strong effects outside of imprinted and copy-number variable regions. As the effects of imprinting are parental in nature, rather than genetic, it is necessary to search outside of these regions for causal regulatory variants. In non-imprinted regions, if we interpolate the association between gene expression and contact propensity, the linear model would suggest that a gene with 98% ASE would be expected to be associated with a loop imbalance of only ~52%. This minute difference in loop imbalance provides a possible explanation for why we did not observe HTALs associated with gene regulation or ASE, but found a quantitative association between Hi-C signal and functional phenotypes overall. Additionally, it suggests that high coverage would be needed to identify HTALs outside imprinted regions. Thus, for the validation of specific variants, or identifying loop QTLs, future studies should consider using an unbiased targeted loop capture assay with higher sensitivity and targeted coverage than Hi-C, such as sequence-based pHi-C, and perform quantitative analyses using these data.55. It has been established that the creation of new chromatin loops can alter gene expression56, however, is has been less clear whether altering gene expression results in meaningful changes in chromatin loops57. Evaluating whether chromatin loop changes are meaningful requires an understanding of the scale at which functional changes in chromatin loops occur. As our findings suggest that subtle changes are functional, we believe these discordant interpretations could have arisen from studies either not being sufficiently powered to detect small effects, or from discounting small changes as nonfunctional. Our work therefore provides a foundation for future studies to quantitatively examine how changes in contact propensity elicit changes in expression (or vice versa) and suggests that studies designed to detect small magnitude changes in chromatin loop variability may be needed to delineate the relationship between chromatin loop imbalance and gene expression.Finally, our work provides some insight into the ongoing question of whether changes in chromatin looping cause changes in gene expression, or if changes in gene expression cause changes in looping28. We have complied with all relevant ethical regulations for work with human participants, and informed consent was obtained. iPSCORE recruitment was approved by the Institutional Review Boards of the University of California, San Diego and The Salk Institute (Project no. 110776ZF), and consent forms were received from each subject. Subject information including sex, age, and ethnicity were collected during recruitment 29 following the manufacturer’s protocol. Each independent reprogramming resulted in one or more iPSC clones of the subject. At passages 12–13, genomic integrity of at least one iPSC clone per subject was assessed using Illumina HumanCoreExome arrays, and pluripotency of iPSCs was assessed for most clones in this study by flow cytometry of the pluripotency markers SSEA4 and TRA-1-8128. iPSCs of each clone were harvested between passages 12 and 40, resulting in a total of 38 iPSC samples used in this study  to determine integrity. Concentration was measured by Nanodrop. Illumina Truseq Stranded mRNA libraries were prepared and sequenced on HiSeq2500, to an average of 40 M 100 bp paired-end reads per sample. RNA-seq reads were aligned using STAR59 with a splice junction database built from the Gencode v19 gene annotation60. Transcript and gene-based expression values were quantified using the RSEM package (1.2.20)61 and normalized to transcript per million bp (TPM).RNA-seq data was obtained from Benaglio et al.31. For H3K27ac, 2 × 106 fixed cells were lysed in 60 µl of MAGnify™ Chromatin Immunoprecipitation System Lysis Buffer (Thermo Scientific) and sonicated using Bioruptor 200 (Diagenode) for 35–45 min of 30 s on/30 s off cycles. H3K27ac antibodies  or GR184333-2 (1 µg)) were coupled for 2 h to ProteinG Dynabeads (Thermo Scientific), and used for overnight chromatin immunoprecipitation in IP buffer ). Beads were washed five times with washing buffer  and once with TE buffer. DNA was eluted and reverse crosslinked overnight in elution buffer  at 65 °C. DNA was purified using Qiagen MinElute PCR Purification kit, quantified by Qubit (Thermo Scientific) and submitted to library preparation and barcoding using KAPA Hyper Library preparation kit (KAPA Biosystems). Libraries were sequenced on an Illumina HiSeq2500 or a HiSeq4000 to an average of 35 M 100 bp paired-end reads per sample.H3K27ac data was obtained from Benaglio et al.62. Duplicate reads, reads mapping to blacklisted regions from ENCODE, reads not mapping to chromosomes chr1-chr22, chrX, chrY, and read pairs with mapping quality Q < 30 were filtered. Peak calling was performed using MACS263  using pooled BAM files from all iPSC or iPSC-CM samples and with reads derived from sonicated chromatin not subjected to IP  from a pool of samples used as a negative control.ChIP-seq reads were mapped to the hg19 reference using BWA31. Specifically, the ATAC-seq protocol has been adapted from Buenrostro et al.64. Frozen nuclear pellets of 5 × 104 cells each were thawed on ice, suspended in 50 μL transposition reaction mix , and incubated for 30 min at 37 °C. Reactions were purified using Qiagen MinElute kit, eluted in 10 μL water and amplified using the KAPA real-time library amplification kit (KAPA Biosystems) with barcoded adaptors. PCR reactions were terminated after 10–13 cycles and purified using AmPure XP beads (Beckman Coulter). Samples were size selected using SPRIselect beads (Beckman Coulter) to a size range of 150–850 kbp and sequenced on an Illumina HiSeq2500 to an average depth of 30 M 100 bp paired-end reads.ATAC-seq data was obtained from Benaglio et al.q < 0.01).ATAC-seq reads were aligned using STAR to hg19 and filtered using the same protocol as for ChIP-seq. In addition, to restrict the analysis to regions spanning only one nucleosome, we required an insert size no larger than 140 bp, as we observed that this improved sensitivity to call peaks and reduced noise. Peak calling was performed using MACS2 on merged BAM files of iPSC and iPSC-CM meta-samples with the command ‘macs2 callpeak --nomodel --nolambda --keep-dup all --call-summits -f BAMPE -g hs’, and peaks were filtered by enrichment score , read pairs within the same restriction enzyme fragment, low mapping quality read pairs (MAPQ < 30), and duplicate reads. Subsequently, read pairs that were less than 2 kb apart were removed to avoid self-ligated fragments. These filtered read pairs (contacts) were subsequently used to generate chromatin contact maps for each sample via Juicer. To create Hi-C contact maps on a per individual basis, contacts were pooled across all samples of a particular cell type for each individual, and to create maps of iPSC and iPSC-CM, contacts were pooled across individuals within the respective cell type. These processes resulted in a set of binary.hic files, which were utilized to obtain raw and Knight-Ruiz (KR)66 normalized counts as well as normalization vectors of contact frequency matrices via Juicebox command line tools67 at various resolutions used throughout this study.For each sample, Hi-C reads were first aligned to human reference genome hg19 using BWA-MEM (version 0.7.15)67. The contact matrices were then vectorized in order to calculate Pearson correlation between each of the samples in R. Hierarchical clustering analyses of the Pearson correlation were performed in R using hclust with default settings and (1 − Pearson correlation) as dissimilarity height. HiCRep was run using the default parameters on chromosome 22 as suggested by the documentation33.The KR normalized contact matrices of each sample were retrieved from the.hic files at 1 Mb using Juicebox68 and HICCUPS67 as summarized in Supplementary Figure q < 0.01) were identified through jointly modeling the contact probability using raw contact frequencies and KR normalization vectors with the Fit-Hi-C algorithm (Step 1). Next, the output of Fit-Hi-C was pruned by requiring that: (1) the interaction itself was significant; and (2) for each anchor of the interaction, three of the five immediately upstream or downstream bins from the opposing anchor were significant (Step 2). We then merged high-confidence interactions within 20 kb using pgltools69 (Step 3), discarded interactions that did not have any other interactions within 20 kb, and retained the most significant call at each interaction event (Step 4).Chromatin loops in iPSC and iPSC-CM were called using both Fit-Hi-Cp) and window size (i) were used to call loops at 5 and 10 kb resolutions provided by HICCUPS67, and parameters for other resolutions were chosen by linearly scaling the parameters with respect to the resolution chosen. Specifically, for 6, 7, 8, and 9 kb resolutions, the values of these two parameters were interpolated from the 5 and 10 kb values, and rounded to the closest integer. For resolutions >10 kb, the default 10 kb parameters were used. Following loop calling, as performed by Rao et al.2, for resolutions from 5 to 10 kb, loops within 20 kb were merged using pgltools. For resolutions above 10 kb, loops within twice the size of the anchor were merged using pgltools. At each merging event, the loop call with the most statistical significance provided from HICCUPS output was retained.For HICCUPS, loops were called using fixed-size bin resolutions from 5 to 25 kb at 1 kb bin-size intervals using parameters summarized in Supplementary Table 34. We found many loop calls from both Fit-Hi-C and HICCUPS that were located at random points throughout the Hi-C matrix far off the diagonal  RNA-seq, (2) H3K27ac ChIP-seq, and (3) ATAC-seq aligned to each of the 1-Mb bins from all available samples for each cell type, averaged the read counts across all samples for each assay in each cell type, and assigned the compartment with higher average read counts from all the three assays as the active compartment (A) and the other compartment as inactive compartment (B). While most of the time all three assays had consistent compartment activity calls, chr21 of iPSC and chr22 of iPSC-CM had inconsistent calls, where we assigned the compartment activity based on the majority of assays.To create the union loop set, we used pgltools merge to find all loops from the iPSC call set and iPSC-CM call set with both anchors within 20 kb. This process led to merge events of 1, 2, or 3 loops, which were resolved as follows: (1) if there was only one loop present within 20 kb , this loop was retained, (2) if there were two loops present within 20 kb, the loops were merged by pgltools merge, (3) if there were three loops present, pgltools closest was used to identify which two loops were closest together; these two loops were merged, and the third loop was retained as its original call.42, normalized to remove library size bias using trimmed mean of M values (TMM), and compared between the 11 iPSC and 13 iPSC-CM samples using quasi-likelihood F-test. By comparing Hi-C read coverages at the same genome loci in two cell types, the linear genome biases that are known to affect Hi-C are held constant 45. The significant differential loops were determined by FDR adjusted q < 0.01.After filtering contacts with Juicer, raw contact frequencies for union loops were obtained by intersecting the filtered read pairs from the 11 iPSC and 13 iPSC-CM Hi-C samples with the union loop set using pgltools coverage. These raw contact frequencies were used as input in edgeRhttps://genome.ucsc.edu/) and updating the loop positions according to this no-gap-genome; (2) sliding the loop positions on the no-gap-genome for a consistent random distance d such that 2 Mb < d < chromosome size - 2 Mb for each null set; and (3) gap regions were added back to the genome, null loop positions were updated back to hg19. In step 2, when loop positions moved beyond the chromosome size after rotation, loops were instead moved to the beginning of the chromosome. Null loops with anchors overlapping a gap region were removed (an average of 0.5% loops were removed in each cell type).As chromatin loops, and genome annotations such as chromatin states, are highly structured and depend on genomic distance both between their own anchors and other chromatin loops, we used permutation to test for functional enrichment within chromatin loops and at loop anchors. We generated 1000 null loop sets for both the iPSC called and iPSC-CM called loop sets to use for statistical analysis, as genome-wide background levels of genomic traits may not accurately represent a true random distribution of paired-genomic loci. The null loops were generated for each chromosome by (1) removing the gap regions on the human reference genome obtained from UCSC genome browser (The findMotifsGenome.pl script from HOMER (v4.7) was used to determine enriched motifs at loop anchors, using the entire size of the anchor as the search space. The HOMER script annotatePeaks.pl was used to identify the distribution frequencies of CTCF motifs, H3K27ac ChIP-seq reads, or ATAC-seq reads in each set of loop anchors with a bin size of 500 bp and a window size of 50 kb using all bam files for the respective molecular phenotype simultaneously.71, the core 15-chromatin-state models were obtained as BED format from http://egg2.wustl.edu/roadmap/web_portal/chr_state_learning.html#core_15state, and the states were separated into their original 200 bp bins. To determine the enrichment of each chromatin state at a loop anchor, we compared the proportion of 200 bp bins in the state of interest on the loop anchor, to the genome-wide background level of the bins via Fisher’s exact test. A significance level of p < (0.05/15) was considered significant.For each of the ROADMAP tissues72 to obtain the number of reads for each assay from each gene as annotated in gencode v19, or from each peak identified by merging all the H3K27ac data together. Next, we used DEseq2 v1.10.173 with default parameters to identify differential peaks and genes with a log2(fold change) > 2 and an FDR corrected q-value < 0.05.To identify differential H3K27ac peaks and genes, we first used featureCountsTo determine if cell type specific regulatory regions were enriched at CTALs, for each cell type, we first split the union loop set into CTALs and non-CTALs. Next, we examined whether the proportion of CTALs overlapping a cell type specific regulatory region was statistically larger than the proportion of non-CTALs. For example, to test whether iPSC-CTALs were more likely to harbor an iPSC-specific active promoter, we restricted the analysis to loops overlapping an iPSC active promoter, and tested whether the proportion of loops overlapping an iPSC-specific active promoter was higher within CTALs than non-CTALs. For all analyses, we used Roadmap E020 (iPSC) for iPSCs, and Roadmap E083  for iPSC-CMs. We defined an anchor as overlapping a cell type specific regulatory region as an anchor which overlapped the region in the tested cell type (E020 for iPSC-CTALs and E083 for CM-CTALs), but did not overlap the region in the other cell type .74. Next, as Haploseq only identifies heterozygous sites, we filled in missing genotype data with unphased genotypes from iPSCORE WGS variant calls for these individuals . We first determined the initial phased genotypes for each individual, at each site at least one individual was heterozygous, by analyzing the HiC data with HaploseqZ score via a binomial approximation to a normal distribution from the greater and lesser allele counts, always using the greater allele as the test variable, and then calculated a p-value from a half-normal distribution for each person to account for the imbalance values being > 0.5 by definition. When comparing Hi-C counts across haplotypes, biases known to affect Hi-C read depth are held constant as the genomic loci are held constant (see Methods section ‘‘Identification of CTALs’’ for details). To obtain a single p-value for imbalance of each loop, we use Fisher’s method to obtain a meta-p-value across all seven individuals. Finally, to identify genome-wide significant HTALs, we use the Benjamini-Hochberg FDR correction to obtain a q-value, and identified loops with a q-value <0.05 as genome-wide significant HTALs.To identify haplotype-associated chromatin loops (HTALs), we phased contacts from each chromatin loop in the union loop set across cell types, and identified allelic imbalance that was statistically significant at a genome-wide threshold. We first identified all contacts within 25 kb of a loop, kept those containing at least one heterozygous SNV, and discarded those with no heterozygous SNVs. Next, using all BAM files for each individual , we assigned contacts to their matching haplotype when all heterozygous SNVs matched a single haplotype, and discarded other contacts. We did not remap reads with WASP as (1) the alignment scores from the single end bams do not reflect the true mapping scores of the Hi-C contact due to the highly chimeric nature of Hi-C reads, and (2) the insert size that appears from normal paired-end mapping of Hi-C reads, and thus cannot be filtered by WASP. At each loop, we then calculate a p-values for each individual separately from combined_test.py with the option –as_only. These p-values were combined via Fisher’s method and both the combined and raw p-values were used for downstream analyses. This analysis resulted in the identification of 89 HTALs, 83 of which were contained in the half-normal HTAL set (93%). We repeated the analyses from Fig. To identify HTALs with a beta-binomial test, we utilized the combined haplotype scripts from WASP. First, we created a CHT input file using the haplotype counts for each loop. Next, we passed these files to fit_as_coefficients.py to calculate the binomial overdispersion parameters. Finally, we obtained Z scores as above using parameters for numbers of contacts (ranging from 5 to 100 in steps of 5), allelic imbalance fractions (from 0.55 to 0.95 in steps of 0.05). We then calculated the power from a half-normal distribution using alpha thresholds ranging from 1 × 10x− to 9 × 10x− for any integer 2 ≤ x ≤ 6 within each individual. We then calculated the alpha threshold from a meta-p-value obtained from combining seven individuals displaying the same imbalance via Fisher’s method.To determine the power to identify chromatin loop imbalance at different allelic imbalance fractions, we calculated p-values.To examine whether any pairs of chromatin states were enriched at opposing HTAL anchors, we annotated all HTAL anchors with the chromatin states they overlapped  via pgltools intersect1D. Next, we used a Fisher’s exact test for each pair of states  to compare the proportion of HTALs with the states at their anchors to the proportion of non-HTALs. Finally, to correct for multiple testing, we performed FDR correction on the To examine whether any loop sets  were enriched for HTALs, we annotated HTALs by the loops they overlapped via pgltools intersect. Next, we used a Fisher’s exact test for each loop set to test for enrichment of the loop set within HTALs relative to non-HTALs.75 to reduce reference allele mapping bias, used MBASED46 (R package version 1.4.0) to obtain allelic ratios and p-values for each gene and peak for each individual, and identified significant genes or peaks as those with an FDR corrected q-value < 0.05.To identify genes and peaks exhibiting genome-wide significant allele-specific expression (ASE) from RNA-seq or ChIP-seq data, within each cell type, for each individual, we pooled all samples by cell type, applied WASP76 intersect function to obtain all loops containing the genomic characteristic. To identify ASE genes overlapping chromatin loop anchors, we utilized pgltools intersect1D function. To identify eQTLs polymorphic in the family with eGenes connected by a chromatin loop, we created a set of all eQTL-eGene pairs with empirical p < 0.05 from DeBoever et al.32 in the PGL format, and utilized pgltools intersect to find loops within 20 kb of the eQTL-eGene pair. For each genomic feature, we performed a Fisher’s exact test across multiple chromatin loop imbalance p-value thresholds to determine if the genomic feature was enriched in HTALs over the union loop set. To obtain a p-value threshold HTAL set, we filtered all chromatin loops to those exhibiting allelic imbalance with a p-value less than or equal to the threshold.To identify chromatin loops containing imprinted genes or CNVs, we utilized the pgltools findLoops function to create a bed file from the union loop set, and then used bedtools32 present in these individuals (1767 deletions and 1045 duplications). We then identified all loops which contained CNVs of the same type using pgltools findloops and intersect1D. Finally, to obtain p-values, we used a binomial approximation to a normal distribution, and tested for an enrichment in duplications above the genome-wide rate (µ = 0.37: the fraction of detected CNVs that were duplications).To measure enrichment of CNV types within union loops and HTALs, we identified all CNVs from DeBoever et al.75 to reduce reference allele mapping bias, and used MBASED to obtain major allele frequencies of each gene/peak. We then identified the most imbalanced SNV in each gene/peak, and used the SNV’s phase to determine the maternal allele frequency of the gene/peak. We then converted maternal allele frequencies to fold changes by dividing the maternal allele frequency by the paternal allele frequency for both molecular phenotypes, and the chromatin loop data.To examine the relationship between molecular phenotype (RNA-seq and H3K27ac ChIP-seq) allelic imbalance and chromatin loop imbalance, we compared allelic differences in molecular phenotype data to chromatin loop imbalance frequencies in iPSC-CM data. We first removed chromatin loops containing imprinted genes or CNVs. Next, for each union chromatin loop, we utilized the aforementioned allelic imbalance data; for each molecular phenotype, we pooled the iPSC-CM reads from all samples for each individual, applied WASPSupplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14Supplementary Data 15Source Data


APC gene occurs in a high percentage of colorectal tumors and is a central event driving tumor initiation in the large intestine. The APC protein performs multiple tumor suppressor functions including negative regulation of the canonical WNT signaling pathway by both cytoplasmic and nuclear mechanisms. Published reports that APC interacts with β-catenin in the chromatin fraction to repress WNT-activated targets have raised the possibility that chromatin-associated APC participates more broadly in mechanisms of transcriptional control. This screening study has used chromatin immunoprecipitation and next-generation sequencing to identify APC-associated genomic regions in colon cancer cell lines. Initial target selection was performed by comparison and statistical analysis of 3,985 genomic regions associated with the APC protein to whole transcriptome sequencing data from APC-deficient and APC-wild-type colon cancer cells, and two types of murine colon adenomas characterized by activated Wnt signaling.Mutation of the 289 transcripts altered in expression following APC loss in human cells were linked to APC-associated genomic regions. High-confidence targets additionally validated in mouse adenomas included 16 increased and 9 decreased in expression following APC loss, indicating that chromatin-associated APC may antagonize canonical WNT signaling at both WNT-activated and WNT-repressed targets. Motif analysis and comparison to ChIP-seq datasets for other transcription factors identified a prevalence of binding sites for the TCF7L2 and AP-1 transcription factors in APC-associated genomic regions. Our results indicate that canonical WNT signaling can collaborate with or antagonize the AP-1 transcription factor to fine-tune the expression of shared target genes in the colorectal epithelium. Future therapeutic strategies for APC-deficient colorectal cancers might be expanded to include agents targeting the AP-1 pathway. APC mutations initiate the development of a high percentage of colorectal cancers [PC encodes a multi-purpose protein whose functions include negative regulation of the canonical WNT signaling pathway [MYC, AXIN2, DKK1 and SP5 [Biallelic  cancers , 2. APC  pathway . The APC pathway , a licen pathway . APC int pathway –8, while pathway –11 and β pathway . Interac and SP5 , 13, fouThe contribution of APC loss to gene expression has been assumed to be exclusively β-catenin-mediated, although this has not been broadly addressed experimentally. This study was designed to identify a more comprehensive list of genes transcriptionally regulated by chromatin-associated APC and to determine whether or not APC mediates their transcriptional repression exclusively through displacement of β-catenin from TCF/LEF family transcription factor complexes.siRNA targeting APC and were compared to ChIP-seq data to identify candidate genes controlled by chromatin-associated APC. High-confidence candidate genes were likely shared targets of canonical WNT signaling and surprisingly included both genes increased in expression following APC loss and decreased in expression following APC loss.Chromatin immunoprecipitation of APC and next-generation sequencing were performed from HCT-116 colon cancer cells, which express wild-type APC yet can model the APC loss observed in the majority of colorectal cancers following transient siRNA-based silencing. Gene expression data were collected from HCT-116 cells in the presence or absence of APC-associated genomic sequences identified in our initial screening step exhibited enrichment of validated transcription factor binding sites for both TCF7L2 and AP-1, and co-occurrence of these transcription factors within many of these same genomic regions. These results indicate that AP-1 modulation should be investigated as a potential therapeutic strategy for targeting the expression of a large subset of canonical WNT target genes.MYC, AXIN2, SP5 and DKK1) regulated by chromatin-associated APC [p < 0.00001) were identified in both replicates relative to their respective inputs , including two out of four positive control transcripts  . These represent candidate target genes altered by canonical WNT signaling. 31 genes increased while 18 genes decreased in APC/Apc-deficient cells and adenomas. An additional filtering criterion focused on potential transcriptional regulatory elements identified by APC ChIP-seq and located within 10 kb upstream of transcription start sites or within first introns. This further reduced the number of candidate genes to 16 increased and 9 decreased following APC loss  Figure . These tMatInspector (from the Genomatix Software Suite) [MEME-ChIP [Regulatory Sequence Analysis Tools (RSAT) [MEME-ChIP and MatInspector only  , MEME-ChEME-ChIP  and Regus (RSAT)  algorithy Figure . Enrichep-values for ChIP-seq enrichment  and TCFpGL3-promoter reporter vector upstream of the firefly luciferase gene, similar to positive (TOPFLASH) and negative control (FOPFLASH) reporter vectors containing six repeats of either wild-type or mutant TCF7L2 (formerly known as TCF4) binding sites [APC knockdown enhances the ability of the TOPFLASH (TCF7L2) positive control to drive luciferase expression by two-fold, while other transcription factor binding motifs show little or no sensitivity to the presence or absence of APC , USF1, SP1 and EGR-1. Data were available from the laboratory of Dr. Richard Myers at the HudsonAlpha Institute for Biotechnology through the NCBI Gene Expression Omnibus , and fros Figure . APC peas Figure .p-values due to the smaller dataset . A similar co-occurrence was observed between TCF7L2 and SP1 binding sites (vs. 2D). Genomic sequences associated with genes transcriptionally repressed following APC loss contained fewer binding sites for TCF7L2, AP-1 and SP1. These data indicate that TCF7L2 and AP-1 or SP1 transcription factors might bind neighboring genomic regions and converge on the same gene promoter to coordinate transcriptional regulation.Co-occurrence of TCF7L2 and AP-1 binding sites was examined in order to test the hypothesis that these two transcription factor binding sites might coordinately regulate shared target genes. Frequencies were examined in a subset of 549 genomic sequences chosen because of their enrichment by APC ChIP-seq and their proximity to 280 genes altered in expression following 16 cells . Neighbo16 cells . TCF7L2 ilencing vs. 2B. ng sites vs. 2D. AXIN2 and MYC loci served as positive controls. Analysis of transcription factor ChIP-seq data reveals overlap of four candidate regions with TCF7L2 and three candidate regions with β-catenin binding sites, so that canonical WNT signaling is clearly linked to ANGPT2 intron 1, the FAT1 enhancer, PHLDB2 intron 1, TSPAN5 intron 1, MALL intron 1 and TOM1L2 intron 1. AP-1 binding sites overlap with the PHLDB2 intron 1, MALL intron 1 and TOM1L2 intron 1 regions, while SP1 binding sites overlap with the FAT1 enhancer, PHLDB2 intron 1 and MALL intron 1 regions . ChIP antibodies targeted either β-catenin itself (CTNNB1 siRNA (AXIN2 intron 1 and the MYC promoter) and six out of ten candidate sequences . Silencing of CTNNB1 produced more subtle effects on APC ChIP enrichment of target sequences  and two of the candidate genomic sequences (from TSPAN5 intron 1 and the GPRC5A enhancer) exhibited loss of enrichment. Collectively, these knockdown data indicate that TSPAN5, GPRC5A, FAT1, PHLDB2, MALL and TOM1L2 are shared targets of APC and β-catenin, consistent with the widely accepted model that interaction with β-catenin mediates APC recruitment to, and function in, the chromatin fraction [KDM6B enhancer, NCL intron 1 and the MACROD1 promoter) are inconclusive. Six of the seven putative targets of APC and β-catenin/canonical WNT signaling overlap with TCF7L2 or β-catenin binding sites  decreased luciferase activity  and functional (RNA-seq) data, similar to other studies identifying WNT-activated target genes , 36. Theosophila , 29, chiosophila , mouse [osophila , 32 and osophila .MALL as well. We hypothesize that regardless of whether canonical WNT signaling activates or represses transcription of a particular gene, APC exerts an antagonistic effect by mediating β-catenin removal  methodologies are required for a more comprehensive identification of the genes targeted by APC-associated regions genome-wide.The chromatin-associated fraction of the APC tumor suppressor protein associates with a large number and variety of genomic regions, with patterns in genomic distribution and transcription factor binding site content that match ChIP-seq studies of other known components of the canonical WNT signaling pathway , 35. BinJUN promoter in a β-catenin-dependent manner [MYC and CCND1 [Mounting evidence links β-catenin , 37, 38 APC loss  or decreased  following APC silencing.It is as yet unclear what determines whether canonical WNT signaling activates or represses a particular target gene in human cells. Some correlation exists between the presence of TCF7L2 and AP-1 transcription factor binding sites and canonical WNT activation of transcription, as both types of binding site are more prevalent within the subset of genomic sequences associated with transcripts increased in expression following APC loss  vs. 2B. PHLDB2) have multiple associated peaks, some with either TCF7L2 or AP-1 binding sites, and some with both. These regulatory elements likely interact with one another and converge on the promoter to co-regulate transcription. According to this model, WNT-repressed targets . Probe-based sonication was performed at 4° C over a period of 40 minutes total by 30 pulses of 10-seconds each at 35% amplitude. Average fragment size was further reduced to ~1kb using micrococcal nuclease . Input material was pre-cleared by 2-hour incubation with 5 μg rabbit IgG antibody and 40 μL of pre-equilibrated Protein G Dynabead slurry (Thermo Fisher Scientific) at 4° C with rotation. 100 μL of the resulting supernatant was saved as “pre-cleared input”, and the remainder was used for α-APC ChIP in combination with 10 μg of α-APC antibody . ChIP was performed according to an established protocol , with thp-values and false discovery rates (FDR) for each peak. Peak score threshold was adjusted to lower the noise observed in input files while retaining internal positive control peaks in the AXIN2, DKK1 and MYC loci. The RefGene database was used to annotate peak regions, determine distances to transcription start sites and assign peaks to genes. The distribution of peak locations relative to transcription start sites is shown in https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE99264). Reviewers must use the secure token uzojsoqgddktvev. MEME-ChIP [Regulatory Sequence Analysis Tools (RSAT) [MatInspector [97–98% of reads for two ChIP-seq samples and the matching input samples passed quality control filters, and the Burrows-Wheeler Aligner generated bam alignment files using UCSC hg19. The Model-based Analysis of ChIP-Seq version 2 (MACS2) tool  performes (RSAT) , and Matnspector  algorithsiRNA (Dharmacon) targeting APC (L-003869-00-0005), scrambled sequence (D-001810-10-05), or neither (mock transfection). Cells were harvested 48 hours later by standard Trizol  isolation protocol. Single-read library preparation was performed using the TruSeq RNA Library Preparation Kit . Next-generation sequencing was performed by the OSU Genomics Shared Resource using an Illumina Genome Analyzer II instrument. The Cufflinks software program [q-values based on fold-changes and sample sizes and identified significantly significant differences between anti-APC siRNA and scrambled siRNA-transfected conditions (q < 0.05). RNA-seq datasets are available through the NCBI Gene Expression Omnibus (accession #GSE99264).HCT-116 cells were transfected on consecutive days with pooled  program  assignedCtnnb1 was confirmed by Sanger sequencing of cDNA from the adenomas from AOM/DSS-treated mice (Min/+Apc and AOM/DSS-treated mice relative to their respective non-adenoma colon tissue controls (using one-sample t-test with a significance cutoff of FDR < 0.05) in the same direction as was observed in human cells following APC silencing. For multiple test correction, fold-changes in expression for individual genes were ordered by p-value and ranked, and FDR was calculated by multiplying p-values by the total number of tests and dividing by their rank (Benjamini and Hochberg method). RNA-seq datasets are available through NCBI's Gene Expression Omnibus .RNA isolation, preparation of sequencing libraries and processing of these data were described previously . The preted mice . TranscrPublic ChIP-seq datasets were obtained through accession numbers GSM1010846 (EGR-1) GSM1010756 (FOSL1), GSM1010847 (JUND), GSM803474 (POLR2A), GSM1010902 (SP1), GSM782123 (TCF7L2), and GSM1010836 (USF1). The coordinates of β-catenin ChIP-seq peaks were obtained from published work from the laboratories of Dr. Shannon McWeeney and Dr. Gregory Yochum . All datpGL3-promoter vector upstream of the firefly luciferase gene. Restriction enzymes pairs varied depending on the target, with Kpn I and Bgl II used for the majority. Primer sets are listed in siRNA (Dharmacon) targeting APC (L-003869-00-0005), targeting CTNNB1 , or with scrambled sequence (D-001810-10-05) using Dharmafect 2 reagent (T-2002-01). 12 hours later, cells were trypsinized and re-seeded into 96-well plates at a density of 25,000 cells per well. Cells were co-transfected 12-hours post-plating with a firefly luciferase construct (9 ng per well) and a Renilla luciferase construct (1 ng per well). Luciferase assays were performed 24 hours later using the Promega Dual-Luciferase Reporter Assay System . The ratio of firefly luciferase to Renilla luciferase signal for each well was normalized to the average across all wells transfected with empty vector. Three independent experiments were performed and two-tailed Student's t-test was used to calculate p-values to test the hypothesis that each construct was APC-sensitive or β-catenin-sensitive, relative to its scrambled siRNA control. For multiple test correction, fold-changes in expression for individual constructs were ordered by p-value and ranked, and FDR was calculated by multiplying p-values by the total number of tests and dividing by their rank (Benjamini and Hochberg method).Genomic regions were PCR amplified, restriction digested and ligated into the The OSU Institutional Animal Care and Use Committee (IACUC) provided prior approval for all experiments involving mouse tissues (OLAW Assurance # A3261-01). IACUC approved the animal use protocol #2012A00000021, and animal work was conducted in accordance with its established criteria. Decisions to remove mice from the animal facility were made in conjunction with veterinary staff, and mice were observed and evaluated daily for predetermined criteria necessitating removal and euthanasia. Carbon dioxide inhalation followed by cervical dislocation was used to euthanize mice.