Sqstm1, which encodes for p62 protein. p62 plays an important role in maintaining cellular homeostasis through selective autophagy and activating signal transduction pathways, such as NF-κB to support cell survival and caspase-8 to initiate apoptosis. FXR regulation of Sqstm1 may serve as a protective mechanism.Farnesoid X Receptor (FXR) is a member of the nuclear receptor superfamily and is a ligand-activated transcription factor essential for maintaining liver and intestinal homeostasis. FXR is protective against carcinogenesis and inflammation in liver and intestine as demonstrated by the development of inflammation and tumors in the liver and intestine of FXR knock-out mice. However, mechanisms for the protective effects of FXR are not completely understood. This study reports a novel role of FXR in regulating expression of Sqstm1 gene in both mouse livers and ileums as determined by chromatin immunoprecipitation. In addition, FXR activation enhanced transcriptional activation of Sqstm1 in vitro. However, wild-type mice treated with GW4064, a synthetic FXR ligand, showed that FXR activation induced mRNA and protein expression of Sqstm1/p62 in ileum, but not in liver. Interestingly, FXR-transgenic mice showed induced mRNA expression of Sqstm1 in both liver and ileum compared to wild-type mice.This study showed that FXR bound to the Our current study has identified a novel role of FXR in regulating the expression of p62, a key factor in protein degradation and cell signaling. Regulation of p62 by FXR indicates tissue-specific and gene-dosage effects. Furthermore, FXR-mediated induction of p62 may implicate a protective mechanism of FXR. Sqstm1), which encodes for p62 protein. p62 is often found in cellular protein aggregates because it interacts with ubiquitinated proteins through its C-terminal ubiquitin associated (UBA) domain Autophagy was strictly thought of as a bulk protein degradation pathway until the discovery that it also performs selective degradation of polyubiquitinated proteins via sequestosome-1 is a ligand-activated transcription factor and a member of the nuclear receptor superfamily. FXR is highly expressed in liver and intestine In addition to its function in maintaining bile-acid homeostasis, FXR regulates lipid metabolism Sqstm1 gene in liver and ileum by genome-wide analysis Sqstm1 gene. If this hypothesis is verified, it may represent a mechanism by which FXR maintains tissue homeostasis and regulates inflammation. Therefore, the purpose of this study was to determine if binding of FXR to the Sqstm1 gene in the liver and ileum produces a functional binding site capable of inducing transcriptional activation of the Sqstm1 gene. Our findings indicate that FXR binds to the Sqstm1 gene in both liver and ileum. However, activation of FXR only induces Sqstm1 expression in the ileum but not in the liver, suggesting complex regulation of Sqstm1 gene transcription in a tissue-specific manner. In addition, FXR-mediated induction of p62 may be a potential protective mechanism of FXR.A possible new role for FXR has been revealed through our discovery of a novel binding site within the Animals for Chromatin Immunoprecipitation (ChIP) studies were treated as previously described Sqstm1 gene in liver and ileum were generated by loading sequencing BAR files into Affymetrix Integrated Genome Browser (IGB) Chromatin immunoprecipitation (ChIP) followed by massive parallel sequencing (ChIP-seq) analysis was performed as previously reported Nr0b2 and Ostβ genes, which are positive control regions for FXR binding, as well as for the novel FXR binding site in the Sqstm1 gene. A novel FXR binding site identified by ChIP-seq analysis was located 13.1 kb downstream of the Sqstm1 transcription start site (TSS). This site was amplified by ChIP qPCR analysis using primers: Nr0b2 3′ binding site F: 5′-CAGTCCACGCCCTCAGCCC-3′ and R: 5′-GGCAGGAGGAGGTCTGAAAGC-3′, Ostβ F: 5′-CCGCAATGGCAGATCATAC-3′ and R: 5′-GTGAATGACCCCACGAATG-3′, and Sqstm1 F: 5′-CACTGCACATGTGTGTTTCTGTGT-3′ and R: 5′-AGGGTGTGGACAGTGTTGAAGACA-3′. ChIP-qPCR results were normalized to input and expressed as fold over IgG negative controls.ChIP was performed as previously described 5′-CGATCCTCTTCAACCCAGATG-3′ and R: 5′-AGGGCTCCAAGCATTCACACA-3′, Ibabp F: 5′-GGTCTTCCAGGAGACGTGAT-3′ and R: 5′-ACATTCTTTGCCAATGGTGA-3′, and Sqstm1/p62 F: 5'-AGAATGTGGGGAGAGTGTG-3′ and R: 5'-TCGTCTCCTCCTGAGCAGTT-3′. Real-time qPCR results were normalized to 18 s and expressed as fold over WT vehicle control.RNA was isolated using TRI Reagent  according to the manufacturer’s instructions, and RNA concentration was determined by spectrophotometry. cDNA was generated using standard RT-PCR protocols, and qPCR was performed using SYBR Green chemistry. The following primers were used for Real-Time qPCR: Shp F: Sqstm1/p62 gene containing a FXR response element, which is an inverted repeat separated by one nucleotide (IR1), was cloned into a PGL4-TK luciferase vector. This IR1 was located 13.1 kb downstream of the Sqstm1/p62 gene TSS. The cloned construct was confirmed by DNA sequencing, and the new plasmid was named PGL4-p62-TK luciferase vector.A 2 kb region of the 5′-GACAACTTTGAACACCACATCAGTGAACTTGGGGCCAACGTAGGATTGC-3′. These primers generated an IR1 mutant by changing the IR1 sequence CGGTCACTGACCT-3′5′- to the mutant sequence 5′-AGTTCACTGATGT-3′. The mutated base pairs are underlined in the original sequence. The mutation was confirmed by DNA sequencing, and the mutated plasmid was named p62-M.This IR1 was mutated in the PGL4-p62-TK vector using a QuikChange II XL Site-Directed Mutagenesis Kit  according to the manufacturer’s instructions utilizing the following primers: F:5′-GCAATCCTACGTTGGCCCCAAGTTCACTGATGTGGTGTTCAAAGTTGTC-3′ and R: 2 humidified atmosphere at 37°C. Cells were plated at a density of 5,000 cells per well in 200 µL medium in a 96-well plate and incubated overnight. Transient transfection was performed as previously described in vitro transfection reagent  according to the manufacturer’s instructions. The previously described PGL4-Shp-TK plasmid HepG2 cells, purchased from the American Type Culture Collection , were cultured in DMEM supplemented with 10% fetal bovine serum  and 1% penicillin/streptomycin in a 5% COCytoplasmic extracts from FXR −/− and WT mouse liver and ileum were isolated using a NE-PER kit  according to the manufacturer’s instructions. Protein concentration was measured using BCA assay . Western Blot was performed using 20 µg of protein separated on a 10% SDS/PAGE gel and transferred to a 0.45 µm PVDF membrane . The membrane was blocked with 5% non-fat milk in TBS before adding p62 antibody . A chloroquine-treated HeLa cell lysate sample was used as a positive control for p62 labeling (molecular weight 62 kDa), and β-Actin (molecular weight 42 kDa) was used as a loading control. Band density was determined using ImageJ software.t-test was used to determine statistical significance for samples that demonstrated equal variance. A Mann-Whitney Rank Sum test was used to determine statistical significance for samples that did not demonstrate equal variance. A p-value of <0.05 was considered statistically significant. A p-value of <0.05 is indicated by * and a p-value of <0.01 is indicated by **.A student’s Sqstm1 gene in the liver and ileum was discovered by our genome-wide ChIP-seq analysis Sqstm1 TSS on chromosome 11. Abundance of FXR binding to novel and known target genes in the liver and ileum in ChIP-seq results were interpreted by a binding peak value. The peak value of FXR binding to the Sqstm1 gene at the 13.1 kb site was 755 in liver and 815 in ileum  was 498, and the peak value of FXR binding to the Ostβ gene encoding organic solute transporter β (Ostβ) was 572 Sqstm1 gene by NUBIScan Binding of FXR to two regions at the 3′ end of the in ileum . The peain ileum . BindingSqstm1/p62 in both liver (126-fold) and ileum (18-fold) when compared to IgG controls. This binding was reduced to IgG control levels in liver and ileum for vehicle-treated FXR −/− mice  and to the Nr0b2/Shp gene  with GW4064 treatment for 4 hours. In ileum, 2-hour GW4064 treatment resulted in an increase in FXR binding to both Sqstm1/p62 and Ostβ genes .The binding results from ChIP-seq were confirmed by ChIP-qPCR as shown in −/− mice . Ostβ iscontrols . This bicontrols . Furthernd ileum . In liveSqstm1/p62 gene was functional in enhancing transcription. Activation of FXR by GW4064 increased the luciferase activity of p62 3 to 7 fold  found 13.1 kb downstream of the Sqstm1/p62 gene TSS compared to PGL4-TK vector control (5′-AGTTCACTGATGT-3′) reduced luciferase activity to levels similar to the PGL4-TK vector control . Shp is a classical target gene of FXR in the liver, and Ibabp is a direct target gene of FXR in the ileum. A significant GW4064-mediated induction of Shp mRNA was observed in both the 4- (2.8-fold) and 16-hour (2.4-fold) treatment groups for WT mouse livers (P<0.01), as shown in Sqstm1/p62 mRNA was seen for either time point in WT mouse livers .In contrast to results seen in the liver, a GW4064-mediated induction of both Ibabp and controls . TreatmeSqstm1 gene expression was due to FXR activation. As shown in Sqstm1/p62 baseline expression in mouse livers. For mouse ileums, a significant decrease in baseline Ibabp expression was seen in both vehicle- and GW4064-treated FXR −/− mice in the 4- and 16-hour treatment groups when compared to WT vehicle controls . Shp and Ibabp were used as positive controls and were also significantly increased in VP-FXR transgenic mouse liver and ileum, respectively, when compared to WT mice (p<0.05).We used VP-FXR transgenic mice to determine whether genetically constitutive activation of FXR could also regulate the expression of Sqstm1 gene and has important cellular functions. In addition to the well-known role of p62 in facilitating selective autophagy, p62 also activates NF- κB p62 is the protein encoded by the Sqstm1 is a bona fide FXR target gene by showing a novel FXR binding site located within the Sqstm1 gene in mouse liver and ileum using ChIP-seq analysis (Sqstm1 gene regulatory region led to increased transcriptional activation as confirmed by luciferase reporter assay, and this transcriptional activation was abolished when the IR1 FXR response element was mutated (Sqstm1 is tissue-specific because mice treated with GW4064 to activate FXR only had an induced expression of Sqstm1 in the ileum but not in the liver (FXR is a nuclear receptor suspected to have a role in the regulation of homeostasis in both liver and intestine. In the current study, we revealed that analysis . In addianalysis . Further mutated . Howeverhe liver . The resSqstm1/p62 mRNA, despite the fact that FXR binds to a gene regulatory region of Sqstm1 in both liver and ileum, is an intriguing observation. This suggests that p62 is regulated by multiple transcription factors. For example, another known transcriptional regulator of p62 is Nrf2, which is important for regulating the oxidative stress response Sqstm1 gene. In addition, the fact that Sqstm1/p62 expression is not induced in the liver with FXR activation could be due to higher basal expression of Sqstm1/p62 in the liver than in the ileum.The tissue-specific induction of Sqstm1 mRNA expression and p62 protein expression was only seen in mouse ileum and not in mouse liver after FXR activation (Sqstm1 mRNA expression in both the liver and ileum of the VP-FXR transgenic mice (Sqstm1 mRNA expression in the liver of VP-FXR transgenic mice could be due to the presence of constitutively active FXR, which is not present in WT mice treated with GW4064 to activate FXR. Assuming the inability of FXR activation to induce Sqstm1 expression in the liver is due to the presence of a co-repressor or inhibitory transcription factor, then constitutively active FXR in the transgenic mice might be strong enough to remove or override a competing transcription factor or co-repressor bound to the Sqstm1 promoter, and therefore, promotes transcription of the gene.Even though induction of tivation  and 5, tnic mice . This inSqstm1 gene expression in human by binding to one of these IR1 response elements. Furthermore, the ileum-specific regulation of Sqstm1/p62 by FXR may have an implication in intestinal diseases. FXR deficiency has been shown to cause intestinal injury and disease such as inflammation Sqstm1 gene in the ileum in order to mediate selective autophagy or signal transduction to maintain cellular homeostasis, regulate the inflammatory response, and/or conduct tissue repair. If FXR is indeed regulating Sqstm1/p62 expression for these processes, then tissue-specific drug development of a synthetic activator of FXR in the intestine could be beneficial for treating or preventing intestinal diseases. This tissue-specific role of FXR in Sqstm1 gene regulation is a novel finding and subsequent studies will further investigate the role of FXR in the regulation of Sqstm1/p62.The specific IR1 sequence found in mouse is not conserved in human. However, there are several IR1 sites located within this downstream region on Chromosome 5 in human according to analysis by NUBIScan In conclusion, it is known that both p62 and FXR have beneficial effects in maintaining cellular homeostasis and preventing disease. We have shown that FXR transcriptionally regulates p62 expression in the intestine. Understanding the role of FXR’s regulation of p62 may further advance our understanding of p62 function, as well as the underlying molecular mechanism of FXR targeted pathways.


Beyond their role in post-transcriptional gene silencing, Dicer and Argonaute, two components of the RNA interference (RNAi) machinery, were shown to be involved in epigenetic regulation of centromeric heterochromatin and transcriptional gene silencing. In particular, RNAi mechanisms appear to play a role in repeat induced silencing and some aspects of Polycomb-mediated gene silencing. However, the functional interplay of RNAi mechanisms and Polycomb group (PcG) pathways at endogenous loci remains to be elucidated.BX-C). Although Dicer-2 depletion triggers mild transcriptional activation at Polycomb Response Elements (PREs), this does not induce transcriptional changes at PcG-repressed genes. Moreover, Dicer-2 is not needed to maintain global levels of methylation of lysine 27 of histone H3 and does not affect PRE-mediated higher order chromatin structures within the BX-C. Finally bioinformatic analysis, comparing published data sets of PcG targets with Argonaute-2-bound small RNAs reveals no enrichment of these small RNAs at promoter regions associated with PcG proteins.Here we show that the endogenous Dicer-2/Argonaute-2 RNAi pathway is dispensable for the PcG mediated silencing of the homeotic Bithorax Complex (Drosophila.We conclude that the Dicer-2/Argonaute-2 RNAi pathway, despite its role in pairing sensitive gene silencing of transgenes, does not have a role in PcG dependent silencing of major homeotic gene cluster loci in  Drosophila small RNA-mediated gene silencing depends on two groups of genes encoding the key proteins of the Dicer and Argonaute families Drosophila Argonaute proteins can be divided into two groups: the ubiquitous AGO (AGO1 and AGO2) and the germline-specific Piwi subfamilies RNA-based silencing mechanisms are a widespread phenomenon in eukaryotes and act at multiple levels to regulate gene expression Drosophila RNAi components act in the nucleus, preferentially associate with transcriptionally active loci and control RNA polymerase II processivity Drosophila remains unclear Accumulating evidence indicates that RNAi components and small RNAs act in the nucleus to control heterochromatin formation, repeat-induced gene silencing and transposable element mobilization Polycomb Repressive Complex 1 and 2, respectively) subgroups, preventing changes in early-determined transcriptional repressive states of developmentally-regulated genes. The Drosophila PRC2 complex contains the intrinsic Histone Methyl Trasnsferase (HMTase) Enhancer of zeste (E(z)), that methylates preferentially lysine 27 of histone H3 (H3K27), which in turn recruits the Polycomb protein (PC) that is a stoichiometric component of PRC1 The close relationship with heterochromatin inspired also research regarding a potential link between Polycomb group (PcG) proteins and RNAi. PcG proteins convey epigenetic inheritance of repressed transcriptional states through several rounds of cell division by regulating multiple levels of chromatin structure Drosophila Bithorax Complex (BX-C). This locus contains three coding genes Ultrabithorax (Ubx), abdominal A (abd-A) and Abdominal B (Abd-B). In Drosophila, PcG function is mediated by specialised modular DNA elements called Polycomb Response Elements (PREs) that, together with core promoters, assemble in characteristic multilooped structures necessary for the maintenance of PcG mediated silencing Most studies on PcG focused on the well known Tetrahymena EZL1, homolog of E(z), is responsible for H3K27 methylation in a RNAi-dependent manner Drosophila PcG-dependent transcriptional silencing of multiple copies of transgenes (co-suppression) involves the RNAi-machinery HOX loci HOX clusters A possible role for RNAi components in PcG mediated gene silencing has been reported Further, a recent report including high resolution mapping of AGO2 by chromatin immunoprecipitaion followed by high throughput sequencing (ChIP-seq) and systematic genetic analyses could not detect a direct link between RNAi and PcG pathways BX-C homeotic genes and PRE-specific noncoding RNAs. In addition Chromosomal Conformation Capture (3C) analysis showed that higher order structures of the BX-C do not depend on Dcr2. By comparing published data sets of PcG targets with AGO2-interacting small RNAs (sRNAs) we observe no enrichment of AGO2-sRNAs at promoter regions associated with PcG proteins. We conclude that RNAi-related mechanisms are largely not involved in gene silencing at endogenous PcG target loci in Drosophila.Here we show that the Dcr2/AGO2 pathway is not required to maintain global levels of H3K27 methylation, transcriptional silencing of the Drosophila melanogaster we analyzed the presence of putative dsRNAs at BX-C PREs. It is known that the transcription of non-coding RNAs through PREs correlates with the maintenance of the active state of homeotic PcG-targets in the BX-CFab-7, Mcp and bxd PREs by using the primer pairs indicated in Fab-7, Mcp and bxd core PREs produce transcripts from both strands  should then be loaded in the AGO2 RNAi effector complex. To test this hypothesis we analyzed our recently published data set of AGO2-bound small RNAs BX-C region. Briefly, the sequenced sRNA libraries were generated from RNA fractions obtained from S2 cells by immunoprecipitation with antibodies against AGO2 or control IgG. The entire BX-C genomic region was scanned for overlaps with sRNA sequences and no enrichment was detected  and summed the sRNA counts for these regions  function results in a loss of monomethylation, dimethylation and trimethylation of H3K27 and a consequent increase of unmodified H3K27, indicating that E(z) is, so far, the only H3K27-specific methyltransferase active in flies MALDI-TOF mass spectrometry. As shown in In Fab-7 elements. In addition, Dcr2 and other RNAi related proteins influence the nuclear organization of repressed HOX loci in situ hybridization (FISH), that in the repressed state, all major PcG bound BX-C elements, including PREs and core promoters  at transgenic romoters , interacromoters  and PRE/romoters  by 3C inimpaired . Notablyepletion , althougPcG proteins are enriched in promoter regions Drosophila pericentric heterochromatin, recruitment of heterochromatin protein 1 (HP1) and silencing of transgenes that are inserted into pericentromeric heterochromatin Seminal discoveries made in yeast and in plants Other studies proposed a role for RNAi in PcG mediated gene silencing BX-C genes  are unaffected by Dcr2 knock-down. Nevertheless, further experiments are needed to precisely describe the functional correlation between insulator-mediated structures, depending on AGO2, and PcG-mediated multi-looped structures.An important recent publication showed that AGO2 associates with PREs and that the protein, but not its slicing activity, is required for CTCF-mediated chromatin insulator function, indicating that AGO2 has a RNAi-independent role in chromatin looping BX-C. We investigated potential sources of dsRNA at PREs in both active and repressed loci. We found that transcripts from both strands could be detected at PREs  supplemented with 12,5% fetal bovine serum.Schneider 2 (S2) cells were grown in serum free insect medium . Schneider 3 (S3) cells were grown in Schneider's Production of dsRNAs (400–500 bp long) against Dcr2 was produced by in vitro transcription using T7 polymerase . Transcripts were denatured by heating and re-annealed in water by cooling to room temperature. RNAi was performed as described previously GFP-F 5′ACGTAAACGGCCACAAGTTC3′GFP-R 5′TGCTCAGGTAGTGGTTGTCG3′DCR2-F 5′GTTCCGCTTTGGTCAACAAT3′DCR2-R 5′TGATCGTCTTTTCCATGCAG3′The antibody against PC was kindly provided by R. Paro BX-C sequence published previously Primer pairs are listed in Intergenic control region and the Abd-B promoter were described previously Primers for the Total RNA was extracted using the TRIzol reagent (Invitrogen). Strand specific RT-PCR was performed using the SuperScript One step RT-PCR kit (Invitrogen), Samples, DNase I treated, were incubated with primers complementary to upper or lower strand transcripts in first strand cDNA synthesis reactions. A second primer was added after heat inactivation of the reverse transcriptase at 94°C for 2 min so that both primers were present in the subsequent PCR amplification. RT-PCR scheme: 50°C for 30 min once (cDNA synthesis); 94°C for 2 min once (inactivation of the reverse transcriptase); 94°C for 30 sec, 60°C for 1 min, 68°C for 1 min, 35 times; 68°C for 7 min once (PCR amplification).GAPDH1, and relative expression levels were calculated using the following equation: A = 2[Ct(ref)-Ct(ref-control)]-[Ct(sample)-Ct(sample-control)].For non-strand-specific RT-PCR for PRE transcripts about 1 µg of total RNA from each source was used to reverse transcribe target sequences using the QuantiTect Reverse Transcription kit (Qiagen) according to the manufacturer’s directions. The resulting cDNA was analyzed by PCR using the QuantiTect SYBR Green PCR kit (Qiagen) according to the manufacturer’s instructions. The amplified products were analyzed by electrophoresis. No amplification in RT reactions without addition of the reverse transcriptase confirmed that the starting template was DNA-free RNA. Quantitative real time PCR was performed with the DNA Engine Opticon 2 (MJ). Quantification was normalized to the housekeeping gene −ΔCt method Chromatin was prepared and immunoprecipitated as described previously http://manuelito.sourceforge.net/), and differentially modified peptides were quantified About 5–10 pmole of acid extracted histones were separated by SDS-PAGE. Coomassie blue stained bands corresponding to histones H3 and H4 were excised and subjected to chemical modification to derive the free amino groups of lysine residues The 3C assay was performed essentially as described previously 32P-UTP-labeled RNA molecule was hybridized with 20 µg of RNA. The RNA preparations are enriched for molecules smaller than 200 nucleotides . Overnight hybridization was carried out at 44°C and digestion with RNAseA/RNAseT1 (1/50 dilution) was performed at 37°C for 1 hour. Protected fragments were loaded on 16% acrylamide/8 M urea gels and visualized using a Phosphorimager (Typhoon Amersham-Bioscience). Single-stranded RNA molecules were used as molecular weight markers (Decade marker Ambion). The radio-labeled ssRNA probe was prepared by in vitro transcription  of the DNA amplicons containing T7 promoters. To increase the specific-activity of the RNA probes the unlabeled UTP was not included in the transcription reaction. The full-length RNA probe was gel-purified using an 8% acrylamide/8 M urea gel and eluted overnight at 37°C. Primer sequences for the production of the DNA amplicons containing T7 promoters are available on request. The miRNA-bantam target DNA template was amplified with T7 and T3 primers from pBS-miRNA-bantam-target The RNase protection assay was performed using the mirVana miRNA detection kit (Ambion) according to the manufacturer’s instructions with minor modifications. Briefly, a single stranded α-BX-C-sized genomic regions in Drosophila, randomized genome sequences of equal length to BX-C were extracted from the drosophila dm3 genome assembly BX-C region from the randomized selections. The promoter regions of genes  linked to the previously-determined PcG ChIP-seq peaks Basic processing of sRNA libraries is as described previously Figure S1The BX-C is not enriched in AGO2-bound small RNAs. Histogram with each bin containing the number of trials (y-axis) with the labeled number of sRNA counts (x-axis) out of a total of 10,000 trials. Each trial sums the total number of sRNA sequences found along randomly- selected genomic regions of equivalent length to the BX-C region (∼340 kb). The placement of the actual sRNA count for the BX-C region in the histogram is denoted with an arrow on the x-axis.(TIF)Click here for additional data file.Figure S2Homologous small-RNAs are not found at the PRE regions of the BX-C. (A–D) RNAse protection analysis. 32P-UTP radiolabeled RNA probes from the indicated regions have been incubated with an equivalent amount of yeast RNA (control) or Drosophila small-RNAs (shorter than 200 nt) from S2 cells, S3 cells or embryos . Only the portion of the probe paired with complementary RNA molecules will be protected from RNAse cleavage (arrow head). The protected fragments produced in presence of yeast RNA are considered background. (D) As comparison we detected the presence of the miRNA bantam. E: wild type embryos; E ΔFab7: embryos carrying a deletion of the Fab-7 PRE.(TIF)Click here for additional data file.Figure S3Dcr2-depletion does not cause transcriptional activation of homeotic genes. (A–B) Quantitative RT-PCR analysis of core PRE transcripts. (A) The samples analyzed were mock-treated S2 cells (control without dsRNA) or S2 cells treated with Dcr2 dsRNA, (B) S3 cells and embryos (0–18 h after egg laying). (C) Quantitative RT-PCR of the BX-C homeotic genes. The samples analyzed were mock-treated S2 cells (control without dsRNA) or S2 cells treated with Dcr2 dsRNA. The expression levels are shown as fraction of GAPDH transcripts. The results shown are from three independent experiments; error bars show the standard deviation.(TIF)Click here for additional data file.Figure S4Dcr2-depletion does not alter PC and Pol II occupancy at the Abd-B promoter. Crosslinked chromatin from mock treated S2 cells (control transfection without the dsRNA) or S2 cells treated with Dcr2 dsRNA was immunoprecipitated with the antibodies indicated below the bars . The immunoprecipitated DNA was analyzed by quantitative PCR with primers specific for the Abd-B promoter(TIF)Click here for additional data file.Table S1Primer sequences used in RT-PCR and ChIP analyses. The position of PRE-specific primer pairs is given according to the coordinates of the BX-C sequence published previously Intergenic control region and the Abd-B promoter were described previously (PDF)Click here for additional data file.


Aicda gene encodes activation-induced cytidine deaminase (AID). Aicda is strongly transcribed in activated B cells to diversify immunoglobulin genes, but expressed at low levels in various other cells in response to physiological or pathological stimuli. AID’s mutagenic nature has been shown to be involved in tumor development. Here, we used a transgenic strategy with bacterial artificial chromosomes (BACs) to examine the in vivo functions of Aicda regulatory elements, which cluster in two regions: in the first intron (region 2), and approximately 8-kb upstream of the transcription start site (region 4). Deleting either of these regions completely abolished the expression of Aicda-BAC reporters, demonstrating these elements’ critical roles. Furthermore, we found that selectively deleting two C/EBP-binding sites in region 4 inactivated the enhancer activity of the region despite the presence of intact NF-κB-, STAT6- and Smad-binding sites. On the other hand, selectively deleting E2F- and c-Myb-binding sites in region 2 increased the frequency of germinal-center B cells in which the Aicda promoter was active, indicating that E2F and c-Myb act as silencers in vivo. Interestingly, the silencer deletion did not cause ectopic activation of the Aicda promoter, indicating that Aicda activation requires enhancer-specific stimulation. In summary, precise regulation of the Aicda promoter appears to depend on a coordinated balance of activities between enhancer and silencer elements.The  Aicda, is central to antigen-induced immunoglobulin (Ig) diversification, that is, somatic hypermutation (SHM) and class-switch recombination (CSR) in activated B cells Ig loci Ig genes, in which SHM and CSR take place, AID can target many other genes, including proto-oncogenes, although with much lower frequencies Aicda knockout drastically delays tumor development in animal models of plasmacytoma, which is associated with suppressed Ig-Myc translocation Activation-induced cytidine deaminase (AID), which is encoded by Aicda to be regulated very strictly to avoid any leaky expression. Indeed, several reports have shown that strong AID expression is virtually restricted to germinal-center B cells in vivoHelicobacter pylori infection, providing insight into the mechanisms of pathogen-associated tumorigenesis–namely, that AID may act as a mutagen in inflammation-associated tumor development H. pylori-infected gastric epithelial cells, suggesting that the inflammatory signals associated with infection by this pathogen may be involved in inducing AID Given this self-mutating activity of AID, one would expect Aicda is transiently expressed in a fraction of activated T cells, including a subset of T cells that produces IL-10, suggesting that Aicda-inductive stimuli correlate with the development of this T-cell subset Under physiological conditions, AID is also expressed in immature B cells and in some activated T cells at a level one to two orders of magnitude lower than that in germinal-center B cells; the role of such low amounts of AID is unclear Aicda that are well-conserved between humans and mice  Aicda bacterial-artificial-chromosome (BAC) constructs, although in vitro reporter assays have not clarified its regulatory function Aicda-regulation studies have shown that the Aicda promoter (region 1) is not particularly specific to B cells, and promotes transcription in various types of cells Aicda transcription by increased HoxC4 levels Aicda also contains major regulatory elements in regions 2 and 4 in vitro reporter assay of mutations in these elements led us to propose a balanced regulation model of the Aicda promoter, in which B-cell-specific and stimulation-responsive enhancers coordinate to counteract the silencers, de-repressing the Aicda promoter Genome sequencing revealed four regions in and around Aicda, here we used BAC DNA to generate several lines of transgenic mice, and used these mice to verify the in vivo activity of Aicda regulatory elements identified by the previous in vitro reporter assay Aicda is regulated in vivo by the balance between the positive and negative transcription factors that were identified in our in vitro experiments.To understand the physiological mechanisms regulating Animal housing and experiments were performed according to the Animal Experiment Guidelines Riken Center for Developmental Biology and the Regulation on Animal Experimentation at Kyoto University. All protocols involving mice were approved by the Kobe Animal Experiments Committee at RIKEN and the Animal Research Committee of the Graduate School of Medicine, Kyoto University (Permit Number: AH13-03-59 and 10055). Mice were euthanized according to the Animal Experiment Guidelines Riken Center for Developmental Biology and the Regulation on Animal Experimentation at Kyoto University before removing lymphoid organs.Aicda gene, from BACPAC CHORI (http://bacpac.chori.org/) Aicda on the BAC by homologous recombination in bacteria, as described previously To generate BAC transgenic mice, we obtained the BAC clone RP24-68I7, which is 190 kb in length and harbors the entire The following primers were used to amplify the homology arms:For the dR2-ac-cre transgene,5′-ATATGCAGCTCATGATGAGCCA-3′,5′arm-dR2-Fwd: 5′-AGGGGTGAGGGAGAGTGGGAGG-3′,5′arm-dR2-Rv: 5′- GTGCTGGATGGAGGTGCCCAGT-3′,3′arm-dR2-Fwd: 5′- GTTATAATAATAAGCACAGGTA-3′.and 3′arm-dR2-Rv: For the dCEBP-ac-cre transgene,5′- GAGCGTAGAGGTCAGTGGACAA -3′,5′arm-dCEBP-Fwd: 5′- CAACTTCTGCATTCCCCGATTTT-3′,5′arm-dCEBP-Rv: 5′- AGCTGGTGTCCAAAAATAGTGA-3′,3′arm-dCEBP-Fwd: 5′- CTCTTACAGATGCCGTACACAT-3′.and 3′arm-dCEBP-Rv: For the dR4-ac-cre transgene,5′-TCACCACACTCAGTTTGAAGCCTTTTAATTTCATAGTATTCCATTTGAAAATTCTTTTTCTCACTATAGGGCTCGAGGAA-3′, and5′ homology arm (80 bp): 3′ homology arm (80 bp):5′-CCACACCCTCTCTTACAGATGCCGTACACATGCCATAGGAACAAATACAAAACCCTTGAGAATTCGATATACGAAGTTAT-3′.5′-CTTTTCCTTCTCCTCCTTCTCC-3′ and 5′arm-ME-Rv: CCTCTCCTCGGGATGTCCCTCC. We then cloned the amplified fragment into the pGEM-T easy vector (Promega). The whole plasmid was PCR-amplified with the primers myb-mut-F 5′-NNGGATCCACATCCTGAGCCCTCAAAAAGCA-3′ and myb-mut-R 5′-NNGGATCCTTGTCTAGCATGTGTGAGGTCTTC-3′. The PCR product was digested by BamHI and self-ligated. The other homology arm, the adjacent region of the E2F site, was amplified with the primers 3′arm-ME-Fwd: 5′-GCTCCTAGCTAGAGTTGAGGGG-3′ and 3′arm-ME-Rv: 5′-TCTAATCCAGCCAGTTTTAAAC-3′. Two homology arms were ligated with both ends of the PGK-Neo cassette, and were used for homologous recombination in E. coli harboring Aid-cre-cd2. The nucleotide sequences of the modified area in the final constructs were checked by sub-cloning and sequencing.The dME-ac-cre transgene was generated in two steps. First, we amplified the fragment containing the c-Myb-binding motifs using the primers 5′arm-ME-Fwd: http://www.cdb.riken.jp/arg/TG%20mutant%20mice%20list.html) and the Reproductive Engineering Team at the Institute of Virus Research at Kyoto University. We used founder mice that had been backcrossed to C57BL/6 mice for more than five generations. All mice were bred and maintained in specific pathogen-free conditions at Kyoto University and at the RIKEN CDB.Transgenic mice were generated by injecting supercoiled BAC DNA into CD-1 or C57BL/6 fertilized eggs; this was performed by the Laboratory for Animal Resources and Genetic Engineering, RIKEN Center for Developmental Biology (CDB)  were washed by PBS for three times and were suspended in PBS at a concentration of 5×10HindIII, separated on a 0.8% agarose gel, and transferred to a nylon membrane (Roche). The blots were air-dried, and the DNA crosslinked using UV light. The DIG-labeled DNA probes indicated in 5′- CGGCATGGTGCAAGTTGAATAAC-3′ and CreRP34 5′- GCTAACCAGCGTTTTCGTTCTGC-3′, which amplify 458 bp DNA of the cre-coding region.DNA for PCR and Southern blot analysis was obtained by tail biopsy. For Southern blotting, the DNA was digested with The following PCR primers were used to confirm the generated BAC constructs:5′-ATATGCAGCTCATGATGAGCCA-3′, dR2-Rv 5′- GTTATAATAATAAGCACAGGTA-3′; dR4-Fwd 5′-TGAATTGGTTCACTCCCCCTA-3′, and dR4-Rv 5′-CAGCTTACAGGAAACTTCCCA-3′.dR2-Fwd Single-cell suspensions from tissues, in FACS buffer , were stained with the following monoclonal antibodies conjugated with FITC, PE, PE-Cy7, APC, APC-Cy7, Biotin, or Pacific-Blue: antibodies specific for B220 (RA3-6B2), CD93 (AA4.1), IgM (R6-60.2), IgD (11–26), CD38 (90), CD3 (500A2), CD4 (GK1.5), CD8 (53-6.7), CD44 (IM7), CD62L (MEL-14), and hCD2 (RPA-2.10) (BD Biosciences or eBioscience). Biotinylated reagents were detected with either streptavidin-APC-Cy7 or streptavidin-APC (BD Biosciences). Stained cells were analyzed by FACSCantoII (BD Biosciences) or sorted by FACSAria. The acquired data were analyzed using FlowJo software . Live lymphocyte populations were gated according to forward and side scatters, and 7-amino-actinomycin D staining.Gapdh expression. The following primers were used: hCD2-forward 5′-GACCACCAGCCTGAGTGCAA-3′, hCD2-reverse 5′-GCTCCTCATCATTTCTCCGAC-3′; Gapdh-forward 5′-TGTGTCCGTCGTGGATCTGA-3′, Gapdh-reverse 5′-CCTGCTTCACCACCTTCTTGAT-3′.Total RNA was extracted by TRIzol reagent (Invitrogen Life Technologies), and cDNA was synthesized with TaqMan® Reverse Transcription Reagents (Applied Biosystems). Gene expression was assessed by real-time PCR using iQ Sybr green supermix . The data were normalized to the in vivo function of elements that regulate Aicda, we used a BAC transgenic strategy. We used the BAC clone RP24-68I7, which covers the 190-kb region of Aicda and its flanking regions, to establish a reliable system for monitoring Aicda expression Aicda BAC contains three regulatory regions  in addition to the promoter (region 1), as assessed by an in vitro culture system To examine the in vivo function of the Aicda regulatory elements identified in vitro, we added deletion mutations to the basic construct to obtain four additional constructs: dR2-ac-cre, in which region 2 was entirely deleted; dME-ac-cre, in which the region 2 c-Myb- and E2F-binding sites–the silencing elements–were deleted; dR2-ac-cre, in which region 4 was entirely deleted; and dCEBP-ac-cre, which contains a restrictive deletion of the two C/EBP sites   . We concin vitro reporter assay also demonstrated the importance of two C/EBP sites in this region Aicda reporters, indicating that these C/EBP sites are in fact indispensable for region 4’s enhancer activity in activated germinal-center B cells  as compared to the Aicda-cre-cd2 mice  drastically increased the percentage of hCD2-positive B cells in germinal centers, suggesting that these elements reduce the frequency of Aicda promoter activation within the gated population. On the other hand, when comparing the Aid-cre-cd2 and dME-ac-cre hCD2-positive germinal-center B cells, the mean fluorescent intensity of hCD2 staining was not significantly different. Since the hCD2 fluorescent intensity correlates with the reporter gene’s transcription efficiency in individual cells, this observation indicates that the E2F and c-Myb sites do not affect the active reporter gene’s transcription efficiency. These results may suggest that E2F and c-Myb negatively regulate the Aicda promoter’s activation, but do not affect its transcription efficiency once activated.In the present study, we demonstrated that regions 2 and 4 are important for inducing Aicda is usually expressed strongly only in activated B cells. If Aicda silencing were not stably maintained in other cells, even minimal exposure to various environmental stimuli could induce this dangerous internal mutagen. In this context, E2F proteins are known to associate with the pRB protein to function as a constitutive and tentative gene repressor Aicda suppression. We expected to observe ectopically expressed Aicda reporters in the absence of the silencers (dME-ac-cre mice). However, we did not observe ectopic or enhanced reporter expression in either immature B cells or developing T cells, in which the Aicda promoter is activated modestly under normal conditions Aicda expression. It is likely that the activity of the B-cell-specific enhancer proteins (Pax5 and E proteins) is not sufficient to activate the Aicda promoter in most cells, including immature B cells and activated T cells; this indicates that a coordinated balance between enhancers and silencers is critical for lineage- and activation-specific AID expression. Alternatively, there might be other unidentified silencers that are required for maintaining lineage specificity. However, this is less likely, since previous extensive deletion analysis did not find other obvious silencer elements, either in the AID gene or in its flanking regions Aicda expression Aicda expression records in immature B and activated T cells using this system in the present study. Since Aicda expression in these cells has been confirmed by other methodologies by various groups, it is unlikely that our previous observation was an artifact A previous study clearly showed that a BAC transgenic system using cre recombinase coupled with floxed RFP can detect low-level Aicda regulation was first demonstrated in vitro by Tran et al. in vivo the critical role of region 4’s enhancer elements, which include binding sites for NF-κB, STAT6, Smad3/4, and C/EBP. NF-κB, STAT6, and Smad are major transcription factors in the CD40, IL-4, and TGF-β signaling pathways, respectively. Although dCEBP-ac-cre selectively deleted only the tandem C/EBP-binding elements, leaving the other elements intact, it completely abolished the expression of Aicda reporters. This demonstrated C/EBP’s indispensable role in regulating Aicda, as was previously shown in vitro.Region 4’s role in While some factors are well-known to contribute to CSR induction, the involvement of C/EBP proteins has not been previously appreciated. Chromatin immunoprecipitation assays have suggested that C/EBPβ binds to region 4 Aicda expression in vivo is reasonable. It will be interesting to elucidate the molecular mechanisms by which C/EBP and NF-κB interact and cooperatively regulate Aicda expression. In summary, the activation of AID expression in vivo requires not only B-cell-specific enhancers in region 2, but also stimulation-dependent enhancers in region 4.For example, in renal medullary interstitial cells, a mutation of the C/EBP-binding site abolishes NF-κB-dependent activation of the COX2 promoter, which contains a tandem array of NF-κB- and C/EBP-binding sites Figure S1Confirmation of deletion/replacement of regulatory elements on BAC constructs. PCR analysis confirmed the deleted of region 2 (0.5-kb) in dR2-ac-cre and that of region 4 (0.8-kb) in dR4-ac-cre (A) (B). Sequencing analyses to confirm the modifications introduced to dME-ac-cre (C) and dCEBP -ac-cre (D) were shown. The wild type sequences around the targeted elements (boxed) are presented. Sequencing chromatograms were shown below; corresponding region were indicated by dotted lines.(PDF)Click here for additional data file.


The ESC and HSC TRM predictions were highly precise, yielding 77 and 96 proteins, of which ∼75% have been independently shown to be involved in the regulation of these cell types. Furthermore, rTRM successfully identified a large number of bridging proteins with known roles in ESCs and HSCs, which could not have been identified using genomic approaches alone, as they lack the ability to bind specific DNA sequences. This highlights the advantage of rTRM over other methods that ignore PPI information, as proteins need to interact with other proteins to form complexes and perform specific functions. The prediction and experimental validation of the co-factors that endow master regulatory TFs with the capacity to select specific genomic sites, modulate the local epigenetic profile and integrate multiple signals will provide important mechanistic insights not only into how such TFs operate, but also into abnormal transcriptional states leading to disease.Transcription factors (TFs) combine with co-factors to form transcriptional regulatory modules (TRMs) that regulate gene expression programs with spatiotemporal specificity. Here we present a novel and generic method (rTRM) for the reconstruction of TRMs that integrates genomic information from TF binding, cell type-specific gene expression and protein–protein interactions. rTRM was applied to reconstruct the TRMs specific for embryonic stem cells (ESC) and hematopoietic stem cells (HSC), neural progenitor cells, trophoblast stem cells and distinct types of terminally differentiated CD4 In an early example of ChIP-seq done for the TF Tal1 (Scl), an essential regulator of hematopoietic stem cells (HSCs), we found that Tal1 regulates other TFs indispensable for HSC identity and function , epigenetics and global chromatin structure inside the nucleus at specific genomic loci . Of thesfunction . The comfunction . Interesfunction ]. This sfunction  plus a nfunction . TRMs gefunction ,5,7. It function . This isfunction .a priori the co-factors whose co-localization should be assessed by ChIP-seq in a specific cell type and context. To fill this gap, here we present a computational framework (rTRM) for the reconstruction of TRMs that exploits information on known PPIs. Given a set of experimentally determined genomic coordinates (e.g. from the ChIP-seq peaks of a target TF), we combine TF motif analysis on these genomic coordinates and cell type-specific gene expression to identify a list of candidate co-factors, and then use rTRM to predict TRMs from PPI networks. We applied rTRM to reconstruct the TRMs characteristic of several embryonic and differentiated cell types, including ESCs, HSCs, neural progenitor cells (NPCs), trophoblast stem cells (TSCs) and several types of differentiated T cells. The prediction of TRMs in ESCs and HSCs in particular yielded dozens of co-factors of which ∼75% have been independently reported to regulate the transcriptional programs of these two cell types. The ability of rTRM to identify key regulatory proteins in ESCs and HSCs  with a high degree of precision suggests that it can be applied to reconstruct TRMs in other cell types and transcriptional contexts.The systematic identification of TRMs by ChIP-seq is not presently viable even in those cases where the genome-wide binding sites of a key regulatory TF are known. This is because in the absence of additional information , we cannot shortlist Supplementary Figure S1 provides a detailed explanation of the computational workflow).The goal of the rTRM method is to reconstruct TRMs from a list of candidate TFs using PPI information. To determine the list of candidate TFs, we integrate experimental evidence of TF binding with TF binding site predictions and cell type-specific gene expression data. Briefly, a set of experimentally determined genomic regions are scanned for enriched TF binding motifs, which are subsequently mapped to their corresponding genes. Next, this list of TF genes is filtered to remove non-expressed candidates. Finally, rTRM maps the list of candidate TFs onto the organism’s PPI network, and our own module-finding algorithm is applied to reconstruct a TRM (Xenopus) (Supplementary Table S1). All 1298 PWMs were uniformly mapped to Entrez Gene identifiers using the original annotations provided in the respective databases . Finally, all Entrez Gene identifiers were mapped to orthologous genes across species with the Biomart tool as implemented in the biomaRt Bioconductor package  representing the binding specificities of a large number of TFs have been made available in recent years using a diversity of experimental approaches. We compiled a library of 1298 PWMs by integrating the vertebrate entries from the JASPAR database   and the  package . The libTF genes ].Supplementary Figure S2. Finally, all datasets including PWMs, orthology mapping and TFClass classification were organized into tables and stored in an SQLite database. The rTRM package provides an API for easy data query.The TFs mapped onto the PWM library were annotated using the structural classification scheme defined in the TFClass database , where het al.  the ChIP-seq libraries from Chen et al.  included  in at least 80% of the replicates. The final list of enriched motifs was matched against our PWM library using Tomtom (q < 0.05) (th HOMER  for each < 0.05) .Supplementary Figure S3). For instance, Pou5f1 (Oct4) and Sox2 are expressed in ESCs but not HSCs, and these were used to determine the expression cutoff in HSCs . On the other hand, Sfpi1 (Pu.1) and Gata2 are expressed in HSCs but not in ESCs, and so these were used to determine the expression cutoff in ESCs . For TSCs and the CD4+ T cell subsets, paired RNA-seq datasets were obtained from the original publications . Raw counts were converted to counts per million (CPM) and a CPM > 1 cutoff was used to determine the list of expressed genes.Microarray expression data were obtained from the NCBI Gene Expression Omnibus for ESCs [GSE27708  and GSE3Mouse PPI data were obtained from the BioGRID database  . We remoSupplementary Figure S4. The target protein is defined as the TF used in the immunoprecipitation step of the ChIP-seq experiment. Briefly, in the first step, the proteins directly connected to the target TF (closest neighbors), as well as the enriched TFs, are retrieved. This is followed by the extraction of the subnetwork of shared nodes, including all neighbors. Finally, those nodes in the subnetwork that are separated from the target TF by more than a maximum set distance are removed. To account for all the equivalent paths connecting any two nodes in the network, all the shortest paths were computed. The shortest path function returns all the nodes connecting any two nodes in the network, including the target nodes themselves. This means that for directly connected nodes, the shortest path results in two nodes, which corresponds to a distance of 1 in the network (as measured by the number of edges connecting two nodes). By default, a maximum of three nodes in the shortest path (distance of 2) was used in this study to allow for the presence of one bridge protein. In rTRM, this parameter can be adjusted by specifying the maximum number of bridge proteins (by default maximum bridge = 1).A local search algorithm was developed to reconstruct TRMs. Our algorithm works by finding proteins in PPI networks separated by a maximum network distance to a target protein, as detailed in Supplementary Figure S5. Venn diagrams were used throughout to report the degree of similarity of any two networks. However, to find groups of TRMs with similar structures when comparing multiple networks, a matrix was computed to record the pairwise network Jaccard index of the common edges (see Supplementary Methods), followed by hierarchical clustering using Pearson distance and complete linkage .The similarity between any two TRMs was determined by calculating the number of common edges. The advantage of using common edges over common nodes to assess the similarity between two networks is illustrated in et al. that considers the dependency structure of GO terms was performed to avoid the enrichment of duplicated or dependent terms  and future updates to the package will be made available through the Bioconductor Web site (http://www.bioconductor.org). Specific details on rTRM installation and software dependencies are available in the Supplementary Material.The rTRM method is licensed under GPL-3 terms and freely available as a package in the language . The rTRWe have developed a novel method (rTRM) for the reconstruction of TRMs from PPI networks that takes advantage of experimentally determined genomic binding sites for a specific TF (the ‘target TF’). We integrate motif enrichment analysis on the experimentally characterized binding regions and gene expression data to determine a set of candidate TFs. The mapping of TF gene identifiers to a common organism (ortholog mapping) allows TFs to be filtered by expression, and to be mapped onto PPI networks. Then, we use a specific module-finding algorithm to identify TRMs from the list of candidate TFs and the PPI network .As proof of principle, we tested the ability of rTRM to reconstruct the TRM of mouse ESCs. The TFs essential for ESC identity have been extensively characterized ,30–32, ade novo enriched motifs were subsequently matched against our library of PWMs, and then mapped onto mouse Entrez Gene identifiers. TF genes were filtered out by expression using the ESC microarray expression data published by Ho et al. . Supplementary Tables S2 and S3 include specific details on the enriched motifs and genes constituting the final TF-specific TRMs. The predicted TRMs  displayed a diversity of TF structural classes, including homeodomain, nuclear receptor, basic helix-loop-helix (bHLH), Rel (RHR), Stat and basic leucine zipper. In general, the TRMs harbor a large number of highly connected TFs . Importantly, all the 11 reconstructed TRMs recovered a high percentage of the target TFs. For instance, the Klf4, Pou5f1, Smad1, Stat3 and Tfcp2l1 TRMs included 6 of the 11 target TFs (54%), whereas the E2f1, Myc, Nanog and Sox2 TRMs included 5 of 11 target TFs (45%). The TRMs recovering the smallest number of target TFs were those of Esrrb (4/11 or 36%) and Myc (3/11 or 27%). This result shows that rTRM is capable of identifying TFs critical for ESC biology starting from a single ChIP-seq library of a target TF.The peaks reported for all target TFs were analyzed with HOMER to identify enriched DNA-binding motifs. The o et al. , and theet al.  and edges (interactions) in the Chen TRM are found in the Lodato TRM . The larger number of proteins and interactions found in the Lodato TRM can be explained by the greater sequencing depth of the Lodato ChIP-seq experiment, which resulted in nearly three times more peaks than the Chen dataset . A consequence of this is the identification of a larger number of enriched TFs . For Pou5f1 (Oct4), we found almost identical results , suggesting that rTRM is capable of predicting comparable TRMs for the same TF profiled by different laboratories.To assess the reproducibility of the TRMs predicted in ESCs, we predicted TRMs for Sox2 and Pou5f1 (Oct4) also done in ESCs but in a different laboratory. Besides the ChIP-seq studies for Sox2 and Oct4, Lodato et al.  also proJaccard index of shared interactions (edges)  and identified two distinct clusters: one containing Mycn, Myc and E2f1, and a second cluster including the other eight TRMs. Within the larger cluster, the Pou5f1 and Klf4 TRMs share the highest degree of similarity (85%), followed by Smad1, Nanog and Tcfcp2l1 (68%), and Esrrb and Stat3 (58%). The TRMs constituting the first cluster  displayed a smaller degree of similarity (42%). These results support the existence of two independent TRMs in ESCs: a larger one composed by Esrrb, Klf4, Nanog, Pou5f1, Sox2, Stat3, Smad1 and Tfcp2l1, and a smaller one composed by E2f1, Myc and Mycn. This observation is in agreement with the cluster membership previously identified in the analysis of the genome-wide binding site profiles where ESC TFs were found to be wired into two separate clusters: one containing Nanog, Oct4, Sox2, Smad1 and Stat3, and a second cluster containing E2f1, Myc, Mycn and Zfx . Next, we compared the predicted TRMs with the ESC PPI network published by Wang et al. (Supplementary Table S4). The ESC-combined TRM was plotted using the same concentric layout as before. To highlight the importance of each of the identified proteins/interactions in the ESC transcriptional regulatory network, the sizes of the nodes and the widths of the edges were scaled to represent the abundance of the individual proteins and interactions across the 11 distinct TF-specific TRMs. For instance, Esrrb, Klf4, Nanog, Pou5f1, Smarca4, Sox2 and Sp1 constitute the larger nodes in the ESC-combined TRM because they are present in the majority of the 11 TRMs. However, the Myc and Mycn nodes are relatively small because they could only be found in the E2f1, Myc and Mycn TRMs. The PPI network described by Wang et al. contains 37 proteins and 69 interactions, and the analysis of the overlap with the ESC-combined TRM resulted in 13 proteins and 27 interactions in common . The genes Esrrb, Klf4, Msx1, Nanog, Pou5f1, Rif1, Sall4, Smarca4, Sox2 and Stat3 were all associated with ‘stem cell differentiation’ (P = 2.1e-13 ∼ 5.5e-3), whereas Esrrb, Klf4, Rif1, Sall4, Smarca4 and Stat3 were associated with ‘stem cell maintenance’ (P = 2.7e-8 ∼ 2.9e-2). Enrichment analysis using the genetic deletion phenotypes from the Mouse Genome Informatics database showed that ∼17% (13/77) of the nodes in the ESC-combined TRM were associated with ‘complete embryonic lethality between implantation and somite formation’ (P ∼ 0) and/or ‘partial embryonic lethality between implantation and somite formation’ (P = 1.5e-5)  Biological Process category on both the 11 TRMs and the ESC-combined TRM identified specific terms common to all TRMs and related to ESC biology ( 1.5e-5) , thus prSupplementary Table S4).Finally, we investigated the published literature for independent evidence of the 77 proteins of the ESC-combined TRM as having a role in ESC biology. We found that ∼72% of the genes in the ESC-combined TRM had been independently reported to play fundamental roles in the biology of ESCs . Lyl1 was not present in the PPI network and hence a corresponding TRM could not be reconstructed. Visual inspection of the 9 TRMs highlighted a preference for homeodomain (13 proteins), bHLH (9 proteins), C2H2 zinc finger  . Moreover, examination of the genes in the HSC-combined TRM identified 22 of 96 (23%) as leading to impaired hematopoiesis on genetic deletion, and the following terms as being significantly enriched: ‘impaired hematopoiesis’ (P ∼ 0), ‘abnormal hematopoiesis’ (P = 2.9e-11), ‘abnormal blood cell morphology/development’ (P ∼ 0) and ‘abnormal lymphopoiesis’ (P = 8.9e-6) (Supplementary Table S4). For instance, Ldb1 is essential for HSC maintenance in the mouse, where it forms a complex with Lmo2, Tal1 (Scl) and Gata1 (or Gata2)  . Collectr Gata2) . TherefoSupplementary Figures S6 and S10), followed by hierarchical clustering, identified two clearly distinct clusters: one containing all the TRMs of ESCs, and a second cluster harboring all the TRMs of HSCs  and found that both networks share 27 distinct proteins. Comparing the number of edges yielded 29 (16%) interactions in common between the HSC and ESC-combined TRMs, with 141 (82%) edges being unique to ESCs and 155 (84%) edges being specific to HSCs B. An all of HSCs C. This i+ T cells. TRMs were identified for Cdx2 in TSCs, for Sox2 in NPCs and for Gata3 in Th1, Th2, Th17 and iTreg cells, with all resulting TRMs shown in Supplementary Figure S12. The Cdx2-based TRM for TSCs included some proteins essential for the determination and function of trophoblast cells. For instance, Runx1 is known to regulate the expression of Ada, a gene expressed in placental trophoblast cells that plays a fundamental role in the developing embryo , as well as differences. To provide a comprehensive snapshot of all these T cell TRMs, all networks were combined and the nodes colored according to the cell type where they had been predicted . Most nodes are common to all cell types (red nodes), whereas others are specific to iTreg cells (blue nodes), suggesting that iTreg cells are the most functionally distinct cell type. Even though all these T cell types have clearly distinct functions, the interpretation of these graphs is problematic, given the cells’ developmental proximity, and probably additional factors such as epigenetic and temporal regulation, should be taken into account in these cases. For instance, Runx1 is a factor common to all T cells, but under specific circumstances, it is known to inhibit the differentiation of naïve CD4+ T cells into Th2 cells by repressing Gata3 expression, another common factor  displaying similarity to both HSCs and CD4+ T cells. This suggests a critical role for Sfpi1 (Pu.1) in different aspects of hematopoietic cell development, which is supported by the high interdependence between Gata3 and Sfpi1 (Pu.1)  ,61.Figuet al. (We have implemented a framework (rTRM) for the systematic identification of TRMs from PPI networks. Using rTRM, we built TRMs specific for the well-studied ESCs and HSCs, which were highly enriched in known regulatory factors in both cell types as ∼75% of the proteins in the corresponding TRMs had been reported to be involved in the function of either ESCs or HSCs. Moreover, the comparison between the ESC-combined TRM and the experimentally determined PPI network by Wang et al.  demonstret al. . BridginThe reconstruction of TRMs can yield extremely valuable information when experimental datasets are scarce, or when no regulatory proteins have yet been identified. This is especially true for poorly characterized systems such as the IL10/JAK1/STAT3 pathway that drives the anti-inflammatory response in various immune cells and where few co-factors of STAT3 have been identified ,64. We ret al.  through the WPI-IFReC Research Program and a Kakenhi grant; Kishimoto Foundation; ETHZ-JST Japanese-Swiss Cooperative Program (to D.M.S.); Funding for open access charge: WPI research program.Conflict of interest statement. None declared.


FOXA1 and FOXA3 binding patterns in HepG2 cells, together with their possible molecular interactions with FOXA2 and each other, are revealed by ChIP-seq. The forkhead box/winged helix family members FOXA1, FOXA2, and FOXA3 are of high importance in development and specification of the hepatic linage and the continued expression of liver-specific genes.in vivo, while FOXA1 and FOXA3 do not appear to interact. Additionally, we detected diverse patterns of trimethylation of lysine 4 on histone H3 (H3K4me3) at transcriptional start sites and directionality of this modification at FOXA binding sites. Using the sequence reads at polymorphic positions, we were able to predict allele specific binding for FOXA1, FOXA3, and H3K4me3. Finally, several SNPs associated with diseases and quantitative traits were located in the enriched regions.Here, we present a genome-wide location analysis of FOXA1 and FOXA3 binding sites in HepG2 cells through chromatin immunoprecipitation with detection by sequencing (ChIP-seq) studies and compare these with our previous results on FOXA2. We found that these factors often bind close to each other in different combinations and consecutive immunoprecipitation of chromatin for one and then a second factor (ChIP-reChIP) shows that this occurs in the same cell and on the same DNA molecule, suggestive of molecular interactions. Using co-immunoprecipitation, we further show that FOXA2 interacts with both FOXA1 and FOXA3 We find that ChIP-seq can be used not only to create gene regulatory maps but also to predict molecular interactions and to inform on the mechanisms for common quantitative variation. The forkhead box/winged helix (FOX) family of transcription factors (TFs) is conserved from yeast to mammals, and in humans consists of approximately 40 members -3. A subforkhead domain [forkhead domain, FOXA3 has much less similarity with these factors [et al. [FOXA1, -2, and -3 share great homology in the DNA binding domain. FOXA1 shares 95% and FOXA2 90% sequence identity with FOXA3 within the d domain . While F factors . In the There have been some chromatin immunoprecipitation (ChIP)-chip and ChIP with detection by sequencing (ChIP-seq) studies on members of the FOXA family published, specifically on FOXA1 (in MCF-7 and LNCaP cells) -14 and FModifications of the amino-terminal tails of the histones can change the accessibility of the chromatin for TFs and the transcriptional machinery and thereby regulate the expression of genes. Although combinations of these modifications are indicated as a prerequisite for activation or repression of the transcriptional activity , genome-in vivo. We found that FOXA1 and FOXA3 have dissimilar distributions of binding sites in HepG2. Intriguingly, although there were sites of FOXA1 and FOXA3 co-binding together with FOXA2, FOXA1 and FOXA3 did not seem to interact in vivo. Furthermore, we discovered that trimethylation of lysine 4 at histone H3 reveals different patterns or 'signatures' depending on the promoter structure and transcriptional activity. Importantly, H3K4me3 was often found at a distance of about 200 bases from the sites of FOXA1-2-3 binding, frequently directed towards the nearest TSS. Finally, we demonstrate that ChIP-seq can be used for detecting allele-specific binding and candidate functional single nucleotide polymorphisms (SNPs).The new generation of sequencers, generally known as high throughput sequencers, has made the detection of DNA resulting from ChIP for genome-wide studies easier and more cost-effective. In this study, we aimed to characterize the genome-wide binding sites of FOXA1 and FOXA3, for the first time, in the hepatocellular carcinoma cell line HepG2 through ChIP and sequencing on the SOLiD platform. Furthermore, we intended to examine their possible interactions with each other, with FOXA2, and their correlation with H3K4me3 and other TFs For the genome-wide analysis of FOXA1 and FOXA3 binding sites and regions of H3K4me3 in HepG2 cells, ChIPs and detection by a high throughput sequencer was performed. In order to get a detailed view of the regions of H3K4me3, we decided to treat the chromatin with micrococcal nuclease (MNase). MNase recognizes the naked DNA, which is not tightly wrapped around the nucleosomes, and digests it. This, in combination with the ChIP, will lead to nucleosome-sized DNA (147 bp) that can be sequenced by high throughput sequencers, resulting in a fine mapping of the H3K4me3 pattern in the genome. After alignment of the raw reads and calculation of overlap signals, we compared the results between the different libraries prepared for sequencing and detected a good correlation . Thereafter, the aligned reads were merged, ordered on genomic positions, and extended by the average fragment size . We also sequenced a fraction of the input material, generated in the ChIPs, to use as a negative control for detection of regions where repeats may cause false positive overlap signals. Then we identified peaks with significant ChIP-enrichment by considering both the ChIP- and input signals.de novo motif finding program BCRANK [We detected 8,175 peaks for FOXA1 and 4,598 peaks for FOXA3 in the human genome in the HepG2 cells  analysis, where some categories were recurrent for FOXA1 and FOXA3 . Here, we consider a gene to be regulated by FOXA1 or FOXA3 when it contains a binding site within 1 kb of the TSS. Therefore, we analyzed the data for possible co-binding sites for these two factors. As presented in Table In a genome-wide study of FOXA1 binding sites in the human breast adenocarcinoma cell line (MCF-7), 12,904 regions have been found at a 1% false discovery rate . Of thesWe found 41,780 H3K4me3 regions in the HepG2 genome . Instead, cluster V has significantly lower expression than the average (P < 0.0001). Genes with the highest expression in HepG2  tend to be more enriched for H3K4me3 than any other cluster. Opposed to this is cluster V , which contains the lowest expressed genes with no or very low enrichment for H3K4me3. The common feature of the six clusters with high H3K4me3 levels is that a nucleosome with this modification was centered at approximately 125 bp downstream of the TSS. Furthermore, these six clusters also contained genes from different GO categories than those of cluster V, which had GO categories overrepresented for genes involved in development .Next, we aimed to discover patterns of H3K4me3 that could be indicative of different types of promoters. Therefore, we extracted the H3K4me3 signals around the TSSs of about 24,000 genes for which the expression measurements in HepG2 are available. We then performed k-means clustering of the H3K4me3 signals to partition the genes into seven clusters, each with its individual H3K4me3 signature Figure . Nearly Considering clusters I, II, and III with high enrichments for H3K4me3 upstream of the TSS, we suspected the existence of bidirectional transcription in these regions. Therefore, the clusters were compared with the data for CAGE tags -24 in HePrevious studies have suggested that bidirectional promoters occur in CpG-rich sequences ,23. ThusThus, by unsupervised clustering of enrichment signals around the TSSs, we detected different H3K4me3 signatures depending on the structure of the promoter, sequence elements present in the promoter, and the level of expression of the downstream gene. A similar type of analysis was also performed for H3K4me3 at the 3'-end of genes . Some of the clusters with higher signals at the 3'-ends were associated with high expression of the gene, suggesting a reciprocal H3K4me3 signal at the beginning and the end of some genes. These clusters also have higher frequency of CAGE tags at the 3'-ends . For further comments, see the supplementary results in Additional data file 1.We have previously examined the genome-wide location of FOXA2 binding in HepG2 cells, where we found 7,253 binding sites for this factor [in vivo. Therefore, we employed co-immunoprecipitation (Co-IP) to examine the existence of these complexes in HepG2. For this, we immunoprecipitated the three endogenous factors and immunoblotted with the same antibodies, testing all six possible combinations. We found that FOXA2 interacts with FOXA1 and the data suggest an interaction between FOXA2 and FOXA3 as well  and reimmunoprecipitated the material with another of the three antibodies. The sequence of the pairs was then reversed in independent replicates in order to verify the results from the first round. The resulting DNA was then analyzed by PCR with primers amplifying a region containing enriched peaks for both factors in the complex. As a negative control, we used primers for regions containing a binding site for only one of the factors in the pair and primers for a region with no binding site for any of the factors. Theoretically, if two of the factors co-bind in a region, that sequence should be enriched in the ChIPed DNA, while sequences with a single binding should not be enriched as they are selected against by the serial immunoprecipitation. As demonstrated in Figure With these results, we demonstrate regions of pair-wise binding for FOXA1, FOXA2, and FOXA3, where these factors co-bind in close proximity and, as indicated by the Co-IP data, some of these factors may even interact at the site of binding.FOXA TFs are known to be involved in opening of compacted chromatin. Accordingly, we examined the H3K4me3 footprint pattern in the regions with FOXA1-2-3, FOXA1-2, FOXA2-3, and FOXA1-3 binding. Regions with FOXA1-2 and FOXA1-3 binding seem to have a lower enrichment for H3K4me3 than FOXA2-3 regions Figure . This waWe looked further into this double peak by k-means clustering of the signal for H3K4me3 in four different clusters Figure . Two of In the next step, we correlated these clusters with CAGE tags from HepG2 within the same distance as above. This comparison revealed a higher percentage of TSSs near the combined FOXA binding sites . When considering CAGE-tags within 1 kb, the difference in directionality for clusters I and II became more evident, with more CAGE tags in the plus direction for cluster I and in the minus direction for cluster II. Furthermore, by creating separate footprints of H3K4me3 around the FOXA1-2-3 regions with or without a TSS within 5 kb, we observed that both groups exhibit a double peak, each peak with its centre at a distance of approximately 200 bp from the binding site Figure . The H3KFOXA genes and other TF genes in the liver. Therefore, we examined the binding pattern at the FOXA genes and compared this with our data on upstream stimulatory factor (USF)1 and USF2 [FOXA1 and FOXA2 had binding sites for all three factors, FOXA3 does not seem to be regulated by any of the FOXAs . Moreover, FOXA1 and FOXA3 both seem to co-bind with the other factors at a similar rate  in 1,000,000 positions (data not shown). Among these, 220,000 were heterozygous SNPs , which we screened in the ChIP-seq data for allele-specific binding. After taking multiple testing into account as described in the Materials and methods section, we found three examples for FOXA1, two for FOXA3, and six for H3K4me3 .INSR), revealed some interesting results , 1% NP-40, 0.5% Na-deoxycholate, 0.1% SDS, 0.004% Na-azide) for sonication with a Bioruptor  to obtain fragments with a size range between 150 and 300 bp. Sonicated chromatin was then pre-cleared and incubated with the antibody overnight. After a short incubation with Protein-G agarose , the chromatin-antibody-bead complex was washed four times with RIPA-buffer, once with 0.01 M Tris-HCl (pH 8), 0.25 M LiCl, 0.001 M EDTA, 1% NP-40 and 1% Na-deoxycholate, and once with TE-buffer. Then the chromatin was eluted with 1% SDS and 0.1 M NaHCO3 before RNase-treatment and reversal of the cross-linking at 65°C for 6 hours and Proteinase-K treatment overnight. The DNA was eluted with phenol-chloroform and precipitated with ethanol. Enrichment for each antibody was tested by semiquantitative PCR with primers for known binding sites.The human hepatocellular carcinoma cell line HepG2 was cultured in RPMI-1640 with non-inactivated fetal bovine serum and penicillin-streptomycin at 37°C with 5% COChIP-reChIP was performed as described in , except Antibodies used in this study were for FOXA1 , FOXA2 , FOXA3 , and tri-methylation-histone H3 (K4) (05-745 (CL MC315100), Upstate, Temecula, CA, USA).2 concentration to 3 mM, the suspension was aliquoted to have 30 × 106 cells per milliliter. These were incubated at 37°C for 5 minutes before addition of 300 U of micrococcal nuclease to each aliquot, after which the incubation was continued for another 5 minutes. To stop the reaction 90 mM HEPES pH 7.9, 220 mM NaCl, 10 mM EDTA, 2% Triton X-100, 0.2% Na-deoxycholate, 0.2% SDS, 0.5 mM PMSF, and 2 μg/ml aprotinin was added. The solution was centrifuged and the supernatant was used to set up the ChIP-assay as described above.Pelleted HepG2 cells were resuspended in ice-cold buffer A containing 320 mM sucrose, 15 mM HEPES pH 7.9, 60 mM KCl, 2 mM EDTA, 0.5 mM EGTA, 0.5% bovine serum albumin, 0.5 mM spermidine, 0.15 mM spermine, and 0.5 mM DTT. After a short incubation on ice, cells were homogenized with a Dounce homogenizer. The homogenate was then layered on an equal volume of buffer B, which was the same buffer as A except that it did not contain bovine serum albumin and the sucrose concentration was 30%. Collected nuclei were washed once with and resuspended in 0.34 mM sucrose, 15 mM HEPES pH 7.5, 60 mM KCl, 15 mM NaCl, 0.5 mM spermidine, 0.15 mM spermine, and 0.15 mM β-mercaptoethanol. After adjustment of CaClAll preparations of libraries for sequencing and the sequencing itself were according to the SOLiD™ System 2.0 Fragment Library Preparation: Lower Input DNA and User Guide standard protocols, with the following modifications.For the FOXA1 library three ChIPs were performed, resulting in five immunoprecipitations, which were pooled. The majority of the fragments were in the size range of 150 to 300 bp after sonication during the ChIP. Therefore, to obtain a homogeneous fragment size and to avoid biased fragment amplification, we decided to prepare three fragment libraries. After ligation of adapters and purification, the DNA was separated by PAGE and three fragment size ranges selected: 150 to 200 bp, 200 to 250 bp, and 250 to 300 bp including the adapters. These were used for 15 cycles of in-gel PCR. For the FOXA3 library 0.5 μg of immunoprecipitation material and 4 μg of input were used as the starting material. Here, three fragment sizes were also chosen for the immunoprecipitation material, but only the 250- to 300-bp fragment size was chosen from the input. The in-gel PCR was performed for 13 cycles. Amplified fragments were sequenced with 50-bp read lengths for FOXA1 and 35-bp read lengths for FOXA3. For H3K4me3, five immunoprecipitations from independent MNase-ChIP assays were pooled and used as the starting material. For this library preparation only one fragment size was chosen, as the sizes of fragments were uniform due to micrococcal nuclease treatment. The library was sequenced with a 35-bp read length.® LDS sample buffer  containing reducing agent (Invitrogen) at 70°C for 10 minutes. Samples were separated on NuPAGE® 4-12% for western blotting and detection with ECL detection system . In order to verify the equal loading and as a negative control for the Co-IP experiments, one blot was stripped with 1× PBS, 0.1% Tween-20, and 0.2% SDS for 30 minutes at 65°C and incubated with GAPDH antibody (data not shown).Cells were grown as described for ChIP. These were washed twice with cold 1× PBS, resuspended in a buffer containing 100 mM Tris-HCl (pH 8.0), 100 mM NaCl, 0.2% NP-40, and protease inhibitor cocktail for 30 minutes on ice and centrifuged in cold to collect nuclei in the pellet. The pellet containing intact nuclei was washed once with RIPA and resuspended in the same buffer, followed by homogenization in a Dounce homogenizer. Precleared nuclear lysate was incubated with antibody or IgG at a concentration of 1 μg per approximately 500 μg of total protein and 50 μl Protein G-agarose at 4°C overnight. Immune complexes were washed twice with RIPA buffer and eluted in NuPAGEChIP-seq data were aligned using the SOLiD™ matching pipeline . Three mFor FOXA1 and FOXA3, only reads starting at unique points were considered as those fragments were randomly sonicated and therefore expected to start at different positions. H3K4me3 ChIP-DNA had been sheared with MNase and the fragments were thus expected to be of defined lengths. Therefore, we did not merge H3K4me3 reads starting at the same position. Input reads were not merged either. Reads were then extended to represent the average fragment length in each of the samples  and genome-wide signals were created for reads on the forward strand, reads on the reverse strand, and overlapping fragments.lf is placed randomly onto a genome of length lg, the probability that one single base in the genome is covered by that fragment is lf/lg. If n reads are placed independently, the probability of having a certain number of overlaps at one position will follow the binomial distribution:Peaks were then detected by first calculating a cutoff on the overlap signal. This cutoff was estimated in the following way. Assuming that a fragment of length P-values for observing at least k overlapping reads at any given position. These tests are then performed for each base in the genome. Because of the high number of reads in these experiments, and since the fraction lf/lg is very small, the binomial distribution can be effectively approximated by Poisson (λ) in this analysis where λ = lf/lg. So, in practice, this part of our peak finding method will yield the same results as other approaches that are based on the Poisson distribution [P-value correction.In this way, we can calculate ribution . NeverthP-value below 0.01: 15 for FOXA1, 21 for FOXA3, 15 for H3K4me3 and 16 for input. Peaks were defined by consecutive positions with overlap at least as high as the cutoff. In the next step, FOXA1, FOXA3, and H3K4me3 peaks that were also significantly enriched for input were removed. Examples of such regions are demonstrated in Figure S7 in Additional data file 1. High signals both in input and ChIP DNA are mostly found in regions close to centromeres where repeats are abundant. Such signals can arise when the reference genome has not been properly assembled. In the final step, we keep only regions with a significant number of overlapping forward reads upstream of the peak, or a significant number of reverse reads downstream. To take the forward and reverse reads into account is a commonly used strategy in ChIP-seq analysis [lf now being the read length instead of fragment length as before. We allowed the forward or reverse peak to be at most a fragment length distance from the peak center. The reason why we do not require both a forward and a reverse peak is that repetitive elements immediately upstream or downstream of a peak can make it impossible to get uniquely aligned reads there.Cutoffs were then obtained by selecting the lowest overlap value that gives a corrected analysis ,39. We cde novo motif searches in FOXA1 and FOXA3 data. BCRANK takes a ranked list as regions and works best on lists containing many regions, where the sequences at the bottom are not always bound directly by the TF. Therefore, we created ranked lists with FOXA1 and FOXA3 regions using more relaxed cutoffs than the ones described above by not applying the cutoffs for forward and reverse peaks. This resulted in 16,578 relaxed regions for FOXA1 and 22,066 for FOXA3.We used the Bioconductor package BCRANK  for de nWe annotated our regions to two types of transcripts. For well-characterized genes, we downloaded the UCSC Genes table from the UCSC Genome Browser , which cExpression data were downloaded from Gene Expression Omnibus , accessiFor all 23,898 genes with expression levels in HepG2, we extracted the H3K4me3 signals in a 4-kb window surrounding the TSSs. The H3K4me3 signals were stored in numerical vectors with one value for each base in the window. k-means clustering was then applied to the 23,898 vectors. In this way, genes with similar H3K4me3 patterns around TSSs could be identified in an unsupervised manner. The same clustering strategy was performed for H3K4me3 signals at the 3'-end of the genes.P-value < 0.001 were considered significant. To reach the significance threshold, a SNP must be covered by at least ten reads and, in that case, all reads must contain the same allele. For SNPs with even higher coverage, both alleles can be present but one significantly more often than the other. By multiplying the number of tested heterozygous SNPs  with the P-value significance threshold 0.001, we obtain the number of expected false positive allele-specific binding events for each factor. A comparison with the number of observations reveals that the false discovery rate is about 0.1 in our results, indicating that about one of the 11 identified SNPs in Table S8 in Additional data file 1 is expected to be a false positive.Around 220,000 SNPs, heterozygous in HepG2, were identified by genotyping 1,000,000 SNPs on an Illumina array (data not shown). In the next step, we filtered out SNPs that were covered by at least ten reads. This resulted in three heterozygous SNPs for FOXA1, two for FOXA3, and six for H3K4me3. Among these heterozygous SNPs, we aimed to detect instances where ChIP-seq data were significantly biased towards one of the alleles. For this, we used SNP calling results from the SOLiD™ SNP calling pipeline  for eachThe raw ChIP-seq data are freely accessible at the European Read Archive  with theCAGE: cap-analysis of gene expression; ChIP: chromatin immunoprecipitation; ChIP-seq: ChIP with detection by sequencing; Co-IP: co-immunoprecipitation; FOX: Forkhead box/winged helix; GABP: GA binding protein; GO: Gene Ontology; H3K4me3: trimethylation of lysine 4 on histone H3; HNF: hepatocyte nuclear factor; MNase: micrococcal nuclease; PBS: phosphate-buffered saline; SNP: single nucleotide polymorphism; TF: transcription factor; TSS: transcriptional start site; USF: upstream stimulatory factor.CW and MM conceptualized the study; MM, MSRB, KP, MAB, and KM performed the experiments, library preparation, and the sequencing; AA, MM, JM, and JK conducted the analyses; OW provided data on FOXA2; MM, AA, and CW wrote the manuscript.The following additional data are available with the online version of this paper: a PDF file with supplementary results, materials and methods, Figures S1 to S8, and Tables S1 to S14 (Additional data file Supplementary results, materials and methods, Figures S1 to S8, and Tables S1 to S14.Click here for fileThis file can be uploaded in the UCSC genome browser.Click here for fileThis file can be uploaded in the UCSC genome browser.Click here for fileThis file can be uploaded in the UCSC genome browser.Click here for fileThis file can be viewed with MS Excel.Click here for file


Oxytricha trifallax is a model system for the study of gene regulatory mechanisms. It possesses a heterochromatin-rich germline micronucleus, and a transcriptionally active somatic macronucleus (MAC). The MAC genome is highly fragmented, consisting of >16,000 unique “nanochromosomes”, with a mean size of 3.2kb. Consequently, only a limited number of nucleosomes can be accommodated in short nanochromosomes. Nanochromosome size does not vary in multiples of ~147bp corresponding to mono-nucleosome-sized DNA, suggesting that nucleosome depleted regions may be especially prevalent. This unusual genome architecture presents novel challenges for the regulation of gene expression. The efficacy and prevalence of chromatin-mediated gene regulation thus remain unclear in Oxytricha. In addition, the paucity of non-coding DNA suggests that promoters in Oxytricha are highly compact, and may be organized differently from other eukaryotes. Our study aims to uncover how the chromatin structure of promoters and coding regions is constrained by chromosome length in the vegetative Oxytricha MAC. To address this, we characterize the chromatin landscape of the MAC through genome-wide mapping of nucleosome positions and DNase I hypersensitive sites. By integrating these studies with existing RNA Polymerase II ChIP-seq and mRNA-seq data, we investigate how chromatin organization varies with nanochromosome length, gene number, and transcriptional output. These data will also be coupled with transcription start site annotations to define promoter architecture in the Oxytricha MAC, including the presence and position of regulatory motifs, the distribution of transcription start sites within a promoter, and local chromatin accessibility. Together, this integrated analysis will test the contribution of chromatin structure to gene expression regulation, and illuminate the variation of eukaryotic gene regulatory mechanisms.The pond-dwelling ciliate


Vibrio cholerae is the causative agent of the disease cholera, characterized by profuse watery diarrhoea. Two of the main virulence factors associated with the disease are cholera toxin (CT) and toxin-coregulated pilus (TCP). Expression of CT and TCP is regulated via a complex cascade of factors that respond to environmental signals, but ultimately ToxT is the direct transcriptional activator of the genes encoding CT and TCP. Recent studies have begun to unveil the mechanisms behind ToxT-dependent transcription. We review current knowledge of transcriptional activation by ToxT and the environmental stimuli that allow ToxT to regulate virulence gene expression, resulting in cholera pathogenesis. Vibrio cholerae is the aquatic bacterium responsible for the disease cholera. In addition to being well-equipped to survive various environmental conditions, V. cholerae is also capable of infecting humans, resulting in substantial loss of fluid in the form of diarrhoea and frequently leading to death due to dehydration, if left untreated. The human intestine provides the ideal conditions for the expression of virulence factors that allow the organism to colonize, cause disease, and prepare for re-entry into the environment. The two main virulence factors are cholera toxin (CT) and toxin-coregulated pilus (TCP). CT is an ADP-ribosylating toxin composed of two subunits that causes an increase in cAMP in intestinal cells, leading to diarrhoea due to the osmotic imbalancectxAB, are encoded on a lysogenic filamentous bacteriophage, CTXΦtcp genes are located on the Vibrio pathogenicity island (VPI), and are under the direct control of ToxT. The toxT gene is also encoded within the VPI, as are additional ToxT-dependent factors, such as the accessory colonization factor (ACF) genes  and this ultimately culminates in the transcription of ctxAB, tcpABQCRDSTEF, tcpI, tagA orf23 mop tagD, aldA, acfD and acfA operonstoxTtcpJacfBCtagE operon is located downstream of the tcpA operon, ToxT also autoregulates its own expression as well as that of the other genes in this operon, which are also under the control of the ToxR-TcpP-dependent promoter immediately upstream of the toxT genetcpPH genes, the entire VPI is under the regulatory control of ToxT, emphasizing the importance of this virulence regulator to V. cholerae pathogenesis.ToxT, an AraC-like activator protein, directly activates transcription of the ctx, tcp, and acf genes, indicating that modulation of ToxT may allow for such differential activation in vivo. This is supported by previous findings that tcp gene transcription precedes ctx gene transcription in vivo, despite both being activated by ToxTToxT is composed of two domains: the N-terminus is involved in dimerization and environmental sensing, while the C-terminus contains two helix-turn-helix (HTH) motifs that facilitate binding to the promoter regions of the ctx, tcp, and acf genestcpA, ctxAB and tcpI-2 promoters, and as inverted repeats in the acfD/acfA, tagA and tcpI-1 promoters (two separate binding sites have been identified in the tcpI promoter region). Interestingly, the aldA promoter appears to only contain one toxboxtcpA promoter revealed 10 nucleotides within these sequences that are critical for ToxT-dependent transcription, and notably the “T” triplet found in each toxbox was important mportant . These tant  between the proximal toxbox and the -35 region of RNAP. Since the “toxboxes” are all located upstream of the -35 promoter region, this places all ToxT-dependent promoters under the class I promoter category of AraC family activated promoters. This suggests that ToxT directly interacts with the α subunit C-terminal domain (α-CTD) of RNAP to activate transcription, which was supported by transcription experiments performed with ToxT and a α-CTD deletion mutant of RNAPThe spacing between the ToxT binding sites and the -35 region bound by RNA polymerase (RNAP) is also critical for transcriptional activationV. cholerae, a deletion of hns (vicH) resulted in high level expression of the tcpA and ctx genes, even under non-inducing conditionstcpA expression were also observed in an hns mutant lacking ToxT. These data indicate that H-NS binds these promoters to repress virulence gene expression in the absence of ToxT. Under inducing conditions, H-NS repression at the ctx and tcpA promoters can be overcome by ToxT displacement of H-NSNucleoid-associated proteins (H-NS) bind to DNA and have the capability of altering its structure, which in turn can affect transcription. In tcpA and ctxihfA and ihfB, which causes sharp bends upon binding to DNA. The deletion of these two genes resulted in approximately five-fold reduction in ctx and tcpA expression. It has also been demonstrated that IHF binds at the -162 region of the tcp Apromoter and bends the DNA. This region also overlaps with the binding region of H-NS, allowing IHF to displace H-NS, which in turn permits virulence gene expression. However, IHF has not been shown to bind the ctx promoter. Presumably, positive ToxT-dependent regulation at the tcpA promoter leads to increased levels of ToxT, which in turn enhances ctx expressionIntegration host factor (IHF) has been shown to positively regulate expression of tcpAA expression. Because low levels of c-diGMP favour ToxT transcription as well as ctx transcription, it is not clear whether c-diGMP specifically affects ToxT-dependent transcription at the ctx but not tcpAA promoter or whether this phenomenon only involves ToxT expression but not ToxT activitycAMP-CRP similar to H-NS, also negatively regulates the expression of CT and TCP under certain environmental conditions. However, these are due to effects on the ToxR regulon leading to ToxT expression, rather than effects on ToxT-dependent transcription18ctxA, tcpAA and several other genes21V. cholerae virulence factor transcription revealed that unsaturated fatty acids  within bile repressed ctxAB and tcpAA expressionctxA transcription, but this effect is likely to be mediated by the effect on H-NS binding, rather than the effect on ToxT activityInitial studies showed that environmental signals are able to modulate ToxT activity. Both variations in temperature and/or the presence of bile could reduce or eliminate ToxT-dependent transcription of tcpA, acfD and tagA promoters9A synthetic small molecule, virstatin, was identified for its ability to inhibit ToxT-dependent transcriptiontcpA and ctxA, whereas elevated levels of bicarbonate lead to maximal transcription of ctx and tcp. It is suggested that bicarbonate is found at high concentrations in the intestinal epithelium and, thus, may act as a relevant in vivo signal modulating the activity of ToxTBicarbonate has the opposite effect of bile/virstatin on ToxT activity. Low levels of bicarbonate result in low ToxT dependent transcription of V. cholerae biofilm formation and persistence in the aquatic environment, but it is repressed during in vivo growth by ToxT. Repression of MSHA allows V. cholerae to evade binding of sIgA and establish residence within the hostmshA transcription by binding to three binding sites within the msh operonmshA transcription; ToxT either containing a dimerization point mutation, or lacking the entire N-terminus was able to repress msh transcriptiontcp genes, suggesting that ToxT binding sites can be differentiated between those that require dimerization for binding (ToxT-activated promoters) and those that can bind monomeric ToxT (ToxT-repressed promoters). One notable exception appears to be the ToxT-activated aldA promoter, which appears to only bind a monomer of ToxT1314The type IV mannose-sensitive haemagglutinin (MSHA) contributes to V. cholerae pathogenesis. ToxT is able to differentially regulate virulence gene expression by directly binding to degenerate promoter DNA sequences. Available data imply that ToxT needs to dimerize in order to activate genes , but it can bind as a monomer to repress (msh) and, possibly, even activate  other genes. ToxT binds to “toxboxes” that is oriented differently at the various promoters. This suggests a model whereby ToxT shows flexibility in its DNA binding capabilities and interactions with RNAP. ToxT activity is modulated by environmental signals resulting in optimal temporal and spatial virulence factor expression within the host. Further research is required to fully characterize the complexity of ToxT-dependent virulence gene expression in V. cholerae.ToxT-dependent transcription is a critical component of