The cohesin complex is crucial for chromosome segregation during mitosis and has recently also been implicated in transcriptional regulation and chromatin architecture. The NIPBL protein is required for the loading of cohesin onto chromatin, but how and where cohesin is loaded in vertebrate cells is unclear. Heterozygous mutations of NIPBL were found in 50% of the cases of Cornelia de Lange Syndrome (CdLS), a human developmental syndrome with a complex phenotype. However, no defects in the mitotic function of cohesin have been observed so far and the links between NIPBL mutations and the observed developmental defects are unclear. We show that NIPBL binds to chromatin in somatic cells with a different timing than cohesin. Further, we observe that high-affinity NIPBL binding sites localize to different regions than cohesin and almost exclusively to the promoters of active genes. NIPBL or cohesin knockdown reduce transcription of these genes differently, suggesting a cohesin-independent role of NIPBL for transcription. Motif analysis and comparison to published data show that NIPBL co-localizes with a specific set of other transcription factors. In cells derived from CdLS patients NIPBL binding levels are reduced and several of the NIPBL-bound genes have previously been observed to be mis-expressed in CdLS. In summary, our observations indicate that NIPBL mutations might cause developmental defects in different ways. First, defects of NIPBL might lead to cohesin-loading defects and thereby alter gene expression and second, NIPBL deficiency might affect genes directly via its role at the respective promoters. The cohesin complex is crucial for chromosome segregation during cell divisions but was recently also implicated in transcriptional regulation and chromatin architecture. Cohesin's binding to chromatin depends on NIPBL, a factor that was found to be mutated in 50% of the cases of the human developmental disorder Cornelia de Lange Syndrome (CdLS). To understand the role of NIPBL for cohesin, we need to know when and where the cohesin is loaded onto DNA. Our experiments have identified high-affinity NIPBL binding sites in different cells lines which do not overlap with cohesin-binding, but colocalize with specific transcription factors at active promoters. The activity of the respective genes depends on NIPBL but not cohesin. This is in contrast with other published data showing colocalization of NIPBL and cohesin, and we reveal the existence of different types of NIPBL binding sites that are detected differently by the antibodies used in the different studies. Our observations reveal a dual role for NIPBL in cohesin loading and as potential transcription co-factor, which yields novel insights into how NIPBL defects could cause Cornelia de Lange Syndrome since NIPBL mutations might directly influence developmentally important genes. Genomes need to be stably inherited over numerous cell generations. For each cell division the genetic information has to be replicated, the copies identified and then equally distributed between daughter cells. This process crucially depends on the cohesin complex, consisting of the core subunits SMC3, SMC1A, RAD21, SA1/STAG1 or SA2/STAG2 and several transiently associated regulatory proteins  and MAU2  are crucial for the re-loading of cohesin in G1-phase after its complete dissociation from chromatin during mitosis . This dominant, genetically heterogeneous developmental disorder has a high degree of variability in its clinical presentation with multiple organ systems affected. It is estimated to occur in 1∶60000 to 1∶45000 live births. Characteristic features include craniofacial anomalies, growth retardation, intellectual disability, upper limb defects, hirsutism, and involvement of the gastrointestinal and other visceral organ systems cut and Ultrabithorax genes by remote enhancers. In the case of the cut gene, NIPBL facilitates its long-range activation while cohesin has an inhibitory effect on cut expression NIPBL has already been linked to gene regulation. In Drosophila, NIPBL was found to facilitate the activation of the These observations implied a “dual role” for NIPBL, in loading cohesin and in gene regulation. It is not known whether these two functions are independent of each other, or if NIPBL mediates gene regulation via loading of cohesin onto DNA.In this study we have aimed to determine when and where NIPBL binds to chromatin to determine where cohesin is initially loaded. Furthermore we wanted to elucidate whether the position of NIPBL binding in the genome accounts for the altered gene expression patterns observed in CdLS patients carrying NIPBL mutations To gain insight into the cohesin loading mechanism it is crucial to understand when cohesin interacts with these factors during the loading process. We have therefore compared the timing of the chromatin-localization of cohesin with that of NIPBL and CTCF. Mitotic HeLa cells were fixed with paraformaldehyde (PFA) and immunostained with antibodies specific for CTCF, NIPBL and the cohesin subunits RAD21 and SA2/STAG2 . SpecifiTo analyze the genomic localization of NIPBL binding sites relative to cohesin and CTCF, we selected the NIPBL antibody (referred to as NIPBL#1) that performs best in human cells Suppl.  and perfFurthermore, we have determined the transcriptional activity by RNA-sequencing, and identified active transcription start sites in HB2 cells by ChIP-sequencing of RNA Polymerase II (RNA Pol II). ChIP for NIPBL, SMC3, CTCF and RNA Pol II was performed as described To prove the specificity of the identified peaks for NIPBL we have depleted NIPBL by RNAi and observed greatly reduced ChIP-qPCR signals for the analysed sites Suppl. .Using the criteria described in the Surprisingly, in HB2 cells the NIPBL binding sites do not overlap with cohesin or CTCF binding sites . HeatmapCohesin binding was previously observed on centromeric repeats and Alu elements Nanog, Lefty, Oct4), identified by Kagey et al. in mESC Nipbl+/− mouse embryo  o Suppl.  , the ChIP is strikingly better enriched for NIPBL#1 than NIPBL#6 in both cell types  which overlap with cohesin binding sites.NIPBL “major binding sites” are distributed over the entire genome (repetitive sequences were omitted during the mapping of the reads to the genome) but localize very specifically to the promoter area (+/−1000 bp from transcription start sites) . We obseTo analyse the properties of NIPBL binding sites further, we used the NIPBL binding sites observed in the control LCL's (N5), since a large number of data for histone modifications and transcription factors is available for lymphoblastoid cells like GM12878 from earlier publications Comparing the pattern of different histone modifications around NIPBL sites, we observed that the sites are flanked by histone marks linked to active promoters and enhancers  . HoweverThe missing enhancer-specific histone mark is in contrast with observations in mouse ES cells showing a colocalization of NIPBL with enhancers and cohesin Motif analysis of NIPBL binding sites in HB2 cells and LCL's using MEME To investigate whether other transcription factors colocalize specifically with NIPBL we compared the NIPBL sites in LCL's with available ChIP-sequencing data for transcription factors for GM12878 cells collected by ENCODE www.ingenuity.com) and found to be linked to diverse cellular functions, such as cell cycle control, gene expression, cell death, RNA post-translational modification and control of cellular growth and proliferation  decrease of gene expression levels of the candidate genes  derived from blood samples of severely affected CdLS patients with NIPBL truncation mutations and normal controls (Mutations in the controls .Using the NIPBL#1 antibody we detected 1612 major NIPBL sites in the control (N5) and 2061/2009 sites in the patient-derived lines (PT1/PT9), with 1295 sites overlapping between N5/PT1 and 1273 sites between N5/PT9. In summary 80% of the sites in the control N5 are also found in PT1 and PT9 . The majOSBP, GPR108) seem to be more reduced than weaker binding sites.Although expected from patient-derived cell lines with NIPBL haploinsufficiency, we did not observe significant differences in peak number or peak intensity between controls and patient-derived LCL's. This is explained by the rather small differences of NIPBL protein levels between CdLS patients and controls The position of NIPBL at promoters could be important for the emergence of the developmental defects seen in CdLS cases. Therefore we compared NIPBL binding sites with a list of genes found to be differentially expressed between LCL's from CdLS patients and controls In its best-studied function NIPBL promotes the initial deposition of the cohesin complex onto chromatin, but is dispensable for maintaining the subsequent association of cohesin and chromatin. Rules that regulate the place and time of cohesin loading and targeting to its various functions  are only partly understood. Factors interacting with chromatin-bound cohesin such as the chromatin insulator CTCF First, we have addressed when cohesin, CTCF and NIPBL associate with chromatin. So far, only very weak and probably transient interactions have been reported between cohesin and NIPBL in the non-chromatin-bound pool of nuclear proteins S. cerevisiae) and Scc1 (RAD21 ortholog in S. cerevisiae) by immunofluorescence microscopy on spread chromatin D. melanogaster the NIPBL ortholog, Nipped-b, was found to colocalize with cohesin and often overlap with RNA polymerase II, decorating entire active transcriptional units Second, we determined the genomic localization of NIPBL by ChIP using a NIPBL-specific antibody (NIPBL#1) form HB2 cells enriched in G1 phase. We observed about 1100 highly enriched NIPBL sites, mostly at active CpG-island promoters but also at several LSU and SSU rRNA repeat regions. However, we do not observe colocalization with cohesin or CTCF. Missing overlap between NIPBL and cohesin was observed before. In yeast, non-overlapping foci were observed for Scc2  used a different NIPBL antibody (NIPBL#6) and reported that NIPBL occupies enhancers and core promoter regions of transcriptionally active genes which are also bound by cohesin and Mediator, a huge transcriptional co-activator complex cut gene, but stromalin/Scc3, the fly orthologs of the SA1/SA2 cohesin subunit, inhibits its activation. A recent study in zebrafish using morpholino knockdown observed a reduced transcription of several genes, including the transcription factors sox17, foxa2 and sox32, after NIPBL knockdown but not in smc3 and rad21 morphants Although we observe a similar localization of NIPBL, we did not detect cohesin binding at NIPBL sites, even with relaxed parameters for peak calling and using different ChIP protocols. We have considered that the apparent discrepancies in the binding patterns might arise due to the different ChIP protocols or differences between pluripotent and differentiated cells, but have disproved these hypotheses by ChIP-qPCR experiments using both antibodies Suppl. . ImportaSTAT3 and YBX1  with candidate CdLS target genes identified by Liu et al. Comparison of NIPBL sites in LCL's with published binding profiles of transcription factors in the lymphoblastoid cell line GM12878 revealed that NIPBL colocalizes with several transcription factors . Pbx3 belongs to the Pbx family of TALE (three amino acid loop extension) class of homeodomain transcription factors, which are implicated in developmental and transcriptional gene regulation in numerous cell types. Pbx3-deficient mice die after birth due to neuronal malfunctions In summary, in this study we have addressed when and where NIPBL binds to the human genome. We have discovered that a subset of very strong “major” NIPBL binding sites preferentially localizes to active promoters, together with a specific set of other transcription factors. NIPBL is important for the activity of the bound genes, suggesting that NIPBL influences transcription in two ways; directly due to its binding to the promoters and indirectly by loading of cohesin complexes which then regulate genes by chromatin insulation and chromosomal long-range interactions. The possibility that NIPBL directly affects expression suggests that NIPBL-deficiency also directly contributes to the complex CdLS phenotype by altering the transcriptional program of developmentally important genes.If different antibodies for the same protein were used the antibodies were numbered to clearly identify them in the different experiments.X. laevis Scc2-1B, purified using the epitope used for immunization (133M).NIPBL#1 - polyclonal rabbit anti-NIPBL antibody raised against residues 2598–2825 of the NIPBL#2 – polyclonal rabbit anti-NIPBL antibody raised against residues 787–1164 of X. laevis Scc-1B, purified using the epitope used for immunization (114M). Generation and characterisation of the NIPBL #1 and NIPBL #2 antibodies have been published already NIPPBL#3 – monoclonal rat anti-NIPBL, isoform A (long isoform) NP_597677 NIPPBL#4 – monoclonal rat anti-NIPBL, isoform B (short isoform) NP_056199 NIPPBL#5 - polyclonal rabbit anti-NIPBL antibody raised against a region between amino acid residues 550 and 600 of human NIPBL (Bethyl Laboratories A301-778A)NIPPBL#6 - polyclonal rabbit anti-NIPBL antibody raised against a region between amino acid residues 1025 and 1075 of human NIPBL (Bethyl Laboratories A301-779A)CTCF#1 –monoclonal mouse anti-CTCF (BD 612149)CTCF#2 – polyclonal rabbit anti-CTCF antiserum (Millipore 07-729)SA2 – monoclonal rat anti-SA2(STAG2) antibody SMC1A#1 - polyclonal rabbit anti-SMC3 antibodies (Bethyl Laboratories A300-055A)SMC3 – polyclonal rabbit anti-SMC3 antibodies obtained from Jan-Michael Peters, described for immunoprecipitation and ChIP in MAU2 – polyclonal rabbit anti-MAU2(Scc4), described in RNA Pol II – polyclonal rabbit antibody (N-20) (Santa Cruz sc-899)Tubulin – mouse anti-tubulin (Sigma)Control IgG – rabbit whole serumRad21 – polyclonal rabbit anti-RAD21 (Jan-Michael Peters), described in HeLa cells were cultured in DMEM supplemented with 0.2 mM L-glutamine, 100 units/ml penicillin, 100 mg/ml streptomycin and 10% FCS.HB2 cells  and were used to deplete NIPBL demonstrate the specificity of the NIPBL antibodies:Control (clone SHC002) non-targeting sequenceGCAGAGACAGAAGATGATGAANIPBL (clone TRCN0000129033) targeting sequence The transfection of the siRNA oligos was performed with Lipofectamine RNAiMAX (Invitrogen) according to the manufacturer's instructions. The transfection of the hairpin siRNA constructs was performed with Lipofectamine 2000 (Invitrogen) according to the manufacturer's instructions. Cells were harvested 48 hours after transfection.HeLa cells were grown on 18-mm cover slips and fixed with 4% PFA. After permeabelization with TX100 and blocking with 3% BSA the slides were stained with the respective antibodies.Images were taken on a Leica DMRBE microscope equipped with a Hamatsu CCD (C4880) camera with a 100× objective. Images were processed with Image J, the colouring; overlay of the images was done with Adobe Photoshop.Cells were fixed with methanol and after RNAse treatment the DNA was stained with propidium iodine. The cells were analyzed with a BD FACS Aria Cell sorter and FlowJo software.See To prepare nuclear extracts from HeLa cells the cells were first lysed by gentle resuspension in hypotonic buffer . Nuclei were collected by centrifugation and extracted for 30 min on ice with extraction buffer  per 50 ml buffer). Debris were removed by centrifugation .The nuclear extract was diluted to 50% with IP buffer  and incubated for 1 h at 4°C with the respective antibodies. Affi-Prep Protein A support beads (BioRad) were added and incubated 1 h at 4°C. The beads were washed 3 times with IP buffer and eluted by boiling with SDS-page loading buffer. Western blots were analyzed with ECL+ reagent and Alliance imaging system.Chromatin immunoprecipitation was performed as described before The respective antibodies were incubated with the lysate for 14 h at 4°C, followed by 2 h incubation at 4°C with blocked protein A Affiprep beads (Bio-Rad) (blocking solution: 0.1 mg/ml BSA or 0.1 mg/ml fish skin gelatine). The beads were washed with washing buffer I , washing buffer II , washing buffer III  and TE-buffer . The beads were eluted twice  for 20 min at 65°C. The eluates were treated with proteinase K and RNase for 1 h at 37°C and decrosslinked 65°C over night. The samples were further purified by phenol-chloroform extraction and ethanol-precipitated. The pellet was dissolved in 50 µl TE buffer.This protocol was used to perform ChIP-qPCR or ChIP-sequencing for CTCF, SMC3, NIPBL and RNA polymerase II. For SMC1A a milder ChIP protocol from Duncan Odom's group was used For NIPBL ChIP sequencing HB2 cells were synchronized in G1 phase by double thymidine block as described For NIPBL ChIP after depletion of NIPBL or control by RNAi the cells were synchronized in G1 phase by double thymidine block, starting 6 hours after transfection of the siRNA oligos. Details of the thymidine block to obtain HeLa cells in G1 phase are described Samples were either submitted for genomic sequencing or analyzed by qPCR using Platinium taq (Invitrogen) according to the manufacturer's instructions as described www.illumina.com). Briefly, 10 ng of ChIPped DNA was end-repaired, ligated to adapters, size selected on gel (200±25 bp range) and PCR amplified using Phusion polymerase as follow: 30 sec at 98°C, 18 cycles of , 5 min at 72°C final extension. Cluster generation was performed using the Illumina Cluster Reagents preparation. The libraries for NIPBL, CTCF, SMC3, RNA PolII and the respective controls generated from HB2 cells were sequenced on the Illumina Genome Analyzer II, the SMC1A ChIP samples from HB2 cells, the NIPBL ChIP samples from LCLs and the respective controls were sequenced with the Illumina HiSeq2000 system. Read lengths of 36 bases were obtained. Images were recorded and analyzed by the Illumina Genome Analyzer Pipeline (GAP 1.6.0. and 1.7.0.). The resulting sequences were mapped against Human_UCSChg18 using the Bowtie The ChIP DNA library was prepared according to the Illumina protocol (http://www.ebi.ac.uk/~swilder/SWEMBL/) as described Peak calling for the ChIP sequencing data was performed with SWEMBL , upstream (−5000 from the TSS), downstream (+5000 from the gene end) and gene body. A region of +/−150 bp was extended around each peak and overlapped with the genomic annotation.Peaks were designated into one category based on the following order of preference: promoter→gene body→upstream→downstream.To investigate the repeat enrichment pattern, we used both uniquely- and multiply-aligned reads. Multiply-aligned reads were divided equally amongst all locations (N-times matched reads were weighted as 1/N reads). The reads which were aligned to reference genome more than 10 times were discarded. We applied RPKM measure (reads per kilobase per million reads) which was utilized for RNA-seq analyses www.illumina.com). The sample was sequenced for 36 bp and raw data was processed using Narwhal HB2 cells were enriched in G1 phase by double thymidine block as described RNA Seq reads were mapped to the Human UCSChg18 genome with Bowtie using the same parameters as for the ChIP seq analysis. The coverage vector was calculated from unique reads and the expression value was determined for each gene by taking the RPKM Motif analysis was performed with the tools MEME and MEME-ChIP To analyse whether the presence of the NFYA motif at NIPBL sites is due to the presence of CpG islands or is a genuine property of NIPBL binding we selected NIPBL binding sites close to only one CpG island promoter (692 sites) and selected the same number of CpG island promoters at random. The presence of the NFYA motif was detected and the counts statistically analyzed using a Fischer-test.We obtained from ENCODE ATF2, ATF3, BATF, BCL1, BCL3, BCLAF, BHLH, BRCA1, CFOS, CHD2, CTCF, EBF1, EGR1, ELF1, ETS1, FOXM1, GABP, GCN5, IRF3, IRF4, JUND, MAX, MEF2, MTA3, MXI1, NFATC1, NFE2, NFIC, NFYA, NFYB, NRF1, NRSF, p300, PAX5, PBX3, PML, Pol2, Pol3, POU2, PU.1, RAD21, RFX, RUNX3, RXLCH, RXRA, SIX5, SMC3, SP1, SPT, SRF, STAT1, STAT3, STAT5, TBP, TCF1, TCF3, TR4, USF1, USF2, WHIP, YY1, ZBTB3, ZEB1, ZNF143, ZNF274, ZZZ3BHLH, BRCA1, CFOS, CHD2, CTCF, EBF1, GCN5, IRF3, JUND, MAX, MXI1, NFE2, NFYA, NFYB, NRF1, p300, RAD21, RFX, SMC3, SPT, STAT1, STAT3, TBP, TR4, USF2, WHIP, YY1, ZNF143, ZNF274, ZZZ3HB2 cells were transfected with the respective siRNA oligos using Lipofectamine 2000 and were harvested after 48 hours. The RNA was prepared using TRI reagent (Sigma). Remaining DNA was removed by DNAse treatment and cDNA synthesis was performed with Superscript reverse Transcriptase (Invitrogen) using oligo-dT primers. The qPCR analysis was performed as described This study was conducted according to the principles expressed in the Declaration of Helsinki. The study was approved by the Institutional Review Board of The Children's Hospital of Philadelphia. All patients provided written informed consent for the collection of samples and subsequent analysis.Figure S1Cohesin loading occurs after nuclear envelop reformation. (A) To test and visualize the specificity of the antibodies used for the immunostaining experiments HeLa cells were treated with the respective siRNA for NIPBL, RAD21 and CTCF and then seeded on cover slips in a mix with control siRNA-treated cells to visualize the effect of the RNAi depletion next to the control cells. The slides were stained with anti-NIPBL #4, anti-RAD21 and anti-CTCF and for each secondary antibody a control slide without primary antibody was included. The undepleted cells are marked with white arrows in the antibody-stained slides.  LaminA-LAP expressing HeLa cells  were stained with antibodies against (panel B) SA2/STAG2 (red) and (panel C) NIPBL (red). Images were taken from interphase cells (I) and different stages during the exit from mitosis (anaphase (A), late anaphase (LA), telophase (T) and early G1 phase (EG1). In panel B the cohesin signal can only be observed overlapping with chromatin when a nuclear envelop is visible (white arrows in telophase and early G1 phase cells). In contrast the NIPBL signal in panel C appears on chromatin already before a nuclear envelop is visible (white arrows in late anaphase cells).(PDF)Click here for additional data file.Figure S2Characterization of NIPBL antibodies. We first characterized different antibodies raised against NIPBL, a 320 kDa protein that is difficult to detect by immunoblotting and immunofluorescense staining. For detection by western blotting we used two rat monoclonal antibodies against the two major isoforms of NIPBL, Isoform A  and Isoform B . The isoforms are splice variants of the last exon, residues 1–2683 are identical but isoform A contains 121 and isoform B 14 unique C-terminal residues. (A) Western blot showing that the band recognized by NIPBL#4 can be depleted by NIPBL-specific siRNA in unsynchronized HeLa cells while it remains well visible in two control siRNA transfections. (B) Immunoprecipitations with the rabbit anti-NIPBL antibodies NIPBL#1 and NIPBL#6 antibodies and anti-SMC3 antibodies were performed from nuclear extract of G1-phase enriched HeLa cells. Two identical western blots were generated which were probed with rat monoclonal antibodies against the two isoforms of NIPBL (NIPBL#3 for isoform A and NIPBL#4 for isoform B) and one re-probed with anti-SMC1 (rabbit) after quenching of the rat antibody signal. Both isoform-specific antibodies detected one major (>250 kDa) and minor NIPBL bands in the G1-phase nuclear extracts (input lane). Multiple bands for NIPBL could occur due to posttranslational modifications of NIPBL. Significant difference between NIPBL#1 and #6 are visible in the immunoprecipitates. NIPBL#1, used by us for ChIP-seq, immunoprecipitates all bands, while NIPBL#6, used by Kagey et al. (PDF)Click here for additional data file.Figure S3Determination of cell cycle stages by FACS analysis. (A) HB2 cells growing logarithmically or enriched in G1 phase for NIPBL ChIP were fixed with methanol, stained for the DNA content with propidium iodine and analyzed by FACS. (B) HB2 cells treated with different siRNA's were enriched in G2 phase. Cells were fixed with methanol, stained for the DNA content with propidium iodine and analyzed by FACS.(PDF)Click here for additional data file.Figure S4Specificity of the NIPBL antibody used for ChIP-sequencing. (A) Genomic binding of NIPBL in a selected region on chromosome 19 in comparison between HB2 cells and HeLa cells. Both cell lines were enriched in G1 phase for the ChIP-sequencing experiment. The position of the peaks is similar between HB2 and HeLa cells, although the enrichment in HeLa was much weaker. As controls the sequencing data from the respective input materials are shown. (B) Western blot showing the depletion of NIPBL in HeLa cells. Since MAU2 is also destabilized when NIPBL is depleted it can be used as marker for NIPBL depletion (PDF)Click here for additional data file.Figure S5AMY) were analysed.  To compare both anti-NIPBL antibodies we performed ChIP from mouse ES cells using the protocol by Kagey et al. (upper panel) (B) and our protocol (C). We tested several NIPBL sites at promoters that were identified by Kagey et al.  and by our study . The left panel in (C) shows the full plot and the right panel a zoom-in on the %IP of input values up to 0.05%IP of input to visualize the ChIP performance at the “minor” low affinity binding sites identified by Kagey et al.. (D) Immunostaining of mouse ES cells derived from Nipbl +/− embryos for ES cell markers. (E) ChIP with NIPBL#1 (left panel) and #6 antibodies (right panel) from control (IB10) and NIPBL +/− mouse ES cells  shows reduced Nipbl binding levels in Nipbl NIPBL +/− cells detected by both antibodies. . (F) To compare the ChIP efficiency of the NIPBL#6 antibodies with NIPBL#1 in human cells we performed ChIP with NIPBL #6 from HB2 cells (right panel) and compared it with the ChIP example also showed in Localization of NIPBL to repeats and comparison of the NIPBL#1 and NIPBL#6 antibodies in mouse ES cells. (A) ChIP/q-PCR validation of NIPBL-binding sites on repetitive regions in HB2 cells. The experiment was performed in duplicate and both samples are shown. Five primers for LSU repeats (LSU) and three for SSU repeats (SSU) and one negative control region ((PDF)Click here for additional data file.Figure S6AMY). The sites analysed are indicated above the graph. The enrichment compared to the control IgG ChIP was calculated. The experiment was performed more than three times and a representative example is shown.NIPBL-binding is reduced in LCL cells derived from CdLS patients. NIPBL (NIPBL#1) and negative control ChIP (IgG) was performed from lymphoblastoid cells derived from CdLS patients and age-matched controls and analyzed by qPCR with primers specific for seven NIPBL binding sites, one cohesin binding site and a negative control site ((PDF)Click here for additional data file.Table S1Parameters used for peak calling with SWEMBL.(PDF)Click here for additional data file.Table S2Peaks identified with SWEMBL in the different datasets.(PDF)Click here for additional data file.Table S3Position of the binding sites identified for the different proteins relative to genes. Around each gene four regions were considered to cluster the binding sites; upstream – −5000 to −1000 bp from the transcription start site (TSS); promoter – −1000 to +1000 bp from TSS; gene body – +1000 bp from TSS until the end of the coding sequence; downstream – from the end of the coding sequence to +5000 bp. Gene activity was scored based on RPKM and genes with RPKM>0.6 were considered as expressed.(PDF)Click here for additional data file.Table S4Classification of promoters and expression status of genes bound by NIPBL, cohesin (SMC1A and SMC3), RNA Pol II and CTCF. The region −1000 to + 1000 bp from TSS was considered as promoter region.(PDF)Click here for additional data file.Table S5Functional annotation of genes with NIPBL binding sites in HB2 cells by IPA analysis.(PDF)Click here for additional data file.Table S6Primers used for ChIP/qPCR and RT-PCR/qPCR.(PDF)Click here for additional data file.Table S7Genes with NIPBL binding sites in a patient-derived lymphoblastoid cells (PT1) found to be differentially expressed in CdLS patients with FDR<0.05 by Liu et al., 2009 (18).(PDF)Click here for additional data file.Table S8NIPBL ChIP signals in HB2 cells on different repeat classes. RPKM measure (reads per kilobase per 10 million reads) was calculated similar to the RNA-seq analyses (56) and an enrichment compared to the input material (control) was calculated.(PDF)Click here for additional data file.


Oligodendrocyte development and myelination rely on an unusual membrane-associated transcription factor that shares functional domains with bacteriophage proteins. Myrf), as being vital for CNS myelination. Myrf is required for the generation of CNS myelination during development and also for its maintenance in the adult. It has been controversial, however, whether Myrf directly regulates transcription, with reports of a transmembrane domain and lack of nuclear localization. Here we show that Myrf is a membrane-associated transcription factor that undergoes an activating proteolytic cleavage to separate its transmembrane domain-containing C-terminal region from a nuclear-targeted N-terminal region. Unexpectedly, this cleavage event occurs via a protein domain related to the autoproteolytic intramolecular chaperone domain of the bacteriophage tail spike proteins, the first time this domain has been found to play a role in eukaryotic proteins. Using ChIP-Seq we show that the N-terminal cleavage product directly binds the enhancer regions of oligodendrocyte-specific and myelin genes. This binding occurs via a defined DNA-binding consensus sequence and strongly promotes the expression of target genes. These findings identify Myrf as a novel example of a membrane-associated transcription factor and provide a direct molecular mechanism for its regulation of oligodendrocyte differentiation and CNS myelination.The myelination of axons is a crucial step during vertebrate central nervous system (CNS) development, allowing for rapid and energy efficient saltatory conduction of nerve impulses. Accordingly, the differentiation of oligodendrocytes, the myelinating cells of the CNS, and their expression of myelin genes are under tight transcriptional control. We previously identified a putative transcription factor, Myelin Regulatory Factor ( Oligodendrocytes are a highly specialized cell type that surround axons of the vertebrate central nervous system with myelin, electrically insulating them and allowing rapid and energy-efficient propagation of nerve signals. We previously identified a protein, MYRF, that is required for the final stages of oligodendrocyte differentiation and myelination. Although we proposed that MYRF might act as a transcription factor, it remains uncertain whether this is true, given that MYRF and related proteins contain a transmembrane domain that might preclude localization to the nucleus. Here, we show that the MYRF protein undergoes an activating cleavage event to release the functional transcription factor from the transmembrane domain that otherwise anchors it to the endoplasmic reticulum. Unexpectedly, this cleavage event is mediated by a portion of MYRF that is related to a self-cleaving domain found in bacteriophage proteins. This distinguishes it from other membrane-associated transcription factors that are cleaved via regulated proteolysis within the membrane bilayer. We find that the N-terminal product of MYRF cleavage directly binds to a wide range of genes involved in myelination, stimulating their expression. Many of these MYRF binding sites identify previously uncharacterized enhancers for these myelin genes. Olig2 is required for specification of OPCs from subventricular zone precursor cells, at least within ventral regions of the CNS Olig2 is continually expressed in the lineage and has later roles in directing the chromatin-remodeling enzyme Brg1 to regulatory elements of target genes during differentiation Olig1Nkx2.2Ascl1/Mash1Zfp191Sox10Oligodendrocytes are the myelinating cells of the vertebrate CNS; their development and the ensheathment of receptive neuronal axons are vital for the rapid propagation of nerve impulses. Accordingly, the differentiation of oligodendrocyte progenitor cells (OPCs) into oligodendrocytes and their subsequent myelination of axons are highly regulated processes. At the transcriptional level, the factors involved in the development of the oligodendrocyte lineage have been relatively well characterized. The transcription factor Myrf; previously known as Gene Model 98 and MRF) as a transcript that is highly induced during oligodendrocyte differentiation and absent in other CNS cell types Ndt80Myrf might act as a direct transcriptional regulator of CNS myelination. Consistent with this hypothesis, conditional ablation of Myrf causes severe CNS dysmyelination, with oligodendrocytes stalling at the pre-myelinating stage and showing severe deficits in myelin gene expression Myrf in mature oligodendrocytes of the adult CNS also causes a rapid down-regulation of myelin gene expression followed by a gradual degeneration of CNS myelin Olig1, Olig2, Sox10, Nkx2.2, and Ascl1, Myrf is expressed only at the postmitotic stage of the oligodendrocyte lineage, suggesting that its induction is a key step in the regulation of myelination.We recently identified Myelin Regulatory Factor  oligodendrocytes . Co-staining with the two antibodies resulted in labeling of the same cells, with the anti-N-terminal antibody staining the nucleus and the anti-C-terminal monoclonal resulting in extranuclear staining of the same cells and in the majority of Sox10+ cells  revealed a region of significant homology to RCBS structural entries 3GUD and 3GW6   . Unlike  protein . Immunosanuclear , indicatThese results demonstrate that the MYRF protein is cleaved via a domain related to the ICD chaperone domain of bacteriophage tail spike proteins to yield a nuclear-targeted N-terminal fragment consisting of the proline-rich region and DBD. The C-terminal cleavage product containing the transmembrane region is excluded from the nucleus.To examine whether the N-terminal cleavage product of MYRF binds directly to the genome in oligodendrocytes, we performed a ChIP-seq experiment immunoprecipitating the Myc-tagged MYRF construct and associated chromatin from cultured primary rat oligodendrocytes using anti-Myc. Previously published ChIP-Seq comparisons between expressed tagged and endogenous transcription factors have shown a high degree of agreement between the peaks identified with each method Mbp and Plp1) are reliant on MYRF for their expression, however whether MYRF directly targets these genes is unknown. To determine whether MYRF binding sites are preferentially located proximal to these genes, we generated lists of the 200 most enriched genes in neurons, astrocytes, or postmitotic oligodendrocytes based on our published transcriptome database (We have previously shown that many of the oligodendrocyte-specific genes induced during differentiation (including abundant myelin genes such as p<0.0001 by Chi-squared test).Based on the 2,085 observed MYRF peaks and an estimated size of the rat genome of 2.75 Gb Plp1, Mbp, Mag, Trf, and Cntn2), showing identified peaks for each, is shown in For a more detailed analysis of the positioning of the MYRF peaks relative to TSSs, we plotted the incidence of MYRF peaks relative to the TSS of an unbiased gene list  as well as the neuron-, astrocyte-, and oligodendrocyte-specific gene lists . PlottinPlp1, Mbp, Cnp, and the oligodendrocyte-enriched miRNA miR219-2. This background was present in both the Myc-MYRF and the untagged MYRF transfected samples  was low throughout most of the genome, high background was observed within a number of genes highly expressed by oligodendrocytes including p = 3.29e-6 and 5.50e-6, respectively; These findings indicate that the MYRF binding sites are not distributed randomly throughout the genome, but instead are overrepresented around genes usually induced during oligodendrocyte differentiation. Consistent with this, when we submitted the peak coordinates to GREAT  or Myrf conditional knockout  mice. These OPCs were placed in differentiating conditions for 48 h before being assayed for luciferase activity to induce the expression of endogenous MYRF. The enhancers increased luciferase expression ∼60-fold in control oligodendrocytes relative to the promoter-only constructs, indicating that these ChIP-Seq-identified regions represent powerful oligodendrocyte enhancers. In contrast, only a modest increase in luciferase activity was seen in the MYRF conditional knockout cells (p<0.001 between genotypes for each construct), mimicking the loss of endogenous MBP expression in these cells  as the strongest candidate . Parallel analysis submitting all 2,085 Myc-MYRF peak sequences to DREME ACAA[A/T]G , a close match to the consensus sequence for the oligodendrocyte transcription factor Sox10 , consistent with it being the primary binding motif for MYRF  PCR mutagenesis of the CTGGYAC motif completely abolished the effect of MYRF (Cntn2 and Trf), mutation of the motif had no effect on the ability of MYRF to enhance transcription from these DNA regions, however other close matches for the CTGGYAC motif present in these enhancers may suggest redundancy in binding and explain the retained function.To assess the functional significance of the  of MYRF . In the Rffl intronic enhancer, to magnetic beads  were initially identified as putative transcription factors due to apparent conservation of several basic amino acids required for the DNA binding activity of yeast Ndt80 sequence  in DNA pMobp, Mbp, Cntn2, and Josd2 genes). We extended this analysis by including a recently published dataset of 25,787 binding sites for the transcription factor Olig2 in differentiating rat oligodendrocytes Given the overrepresentation of the Sox10 consensus motif in the regions bound by MYRF, as well as the similar CNS dysmyelination phenotype displayed in the absence of Sox10 and MYRF Myrf is required for oligodendrocyte differentiation and the generation and maintenance of CNS myelin Ndt80 and including MrfA in Dictyostelium and MYRF in mice We have previously demonstrated that There are varied examples of membrane-associated transcription factors in nature, including Notch, the SREBPs, and ATF6, which are synthesized as inactive membrane-bound precursors linked to the plasma membrane or endoplasmic reticulum. Upon a biological signal , these proteins are cleaved by Regulated Intramembrane Proteolysis (RIP) to release nuclear targeted products that effect transcription In contrast to Notch, the SREBPs, and ATF6, however, the activating cleavage of MYRF occurs several hundred residues away from the transmembrane domain in a region homologous to the ICD of bacteriophage tailspike proteins. Mutation of key residues required for autoproteolytic cleavage of the tailspike proteins also blocks cleavage of MYRF, confirming that MYRF cleaves via this ICD rather than the RIP mechanism common to these previously described membrane-associated transcription factors. It is extremely tempting to speculate that the cleavage of MYRF is regulated in response to a biological signal, as is the case for other known membrane-associated transcription factors. To a large extent, however, MYRF's cleavage appears to be consitutive, as we observed cleavage when MYRF was expressed in cultured primary oligodendrocytes, the CG-4 oligodendrocyte cell line, 293T cells, or even when a GST-fusion construct including MYRF amino acids 330–765 was expressed in bacteria . SimilarMYRF, the closely related paralog Myelin Regulatory Factor-like/Gene Model 239, and their orthologs. This cleavage mechanism appears to extend well back in evolution, however, with the DNA-binding portion of the MrfA protein in Dictyostelium having recently been reported to run as a ∼75 kda protein Dictyostelium protein and other orthologs , receptors , oligodendrocyte/neuron junctional proteins , transmembrane proteins , lipid metabolism proteins , as well as other transcription factors . In addition, we were able to identify MYRF binding sites proximal to many oligodendrocyte-enriched genes whose roles in myelination have not yet been investigated, such as Josd2, Rffl, Nipal4, and Rnf220. These targets give a broader perspective of the cellular mechanisms by which MYRF coordinates the differentiation of oligodendrocytes and their myelination.In spite of this challenge, the use of ChIP-Seq methods in conjunction with genome-wide expression analysis offers an opportunity for broad target identification. MYRF binding sites were present proximal to genes encoding important protein components of myelin including logy see . These iCTGGYAC). Mutation of this sequence in luciferase constructs was sufficient to prevent the activity of MYRF and prevent interaction with the DBD of MYRF in DNA pull-down assays. This motif is distinct from the binding motif for Ndt80 (CACAAA[A/G]) and also from recently described binding sequences for the Dictyostelium ortholog MrfACTGGYAC motif could be found near the centre of the majority of the strong MYRF peak sequences, it is unlikely to be the only determinant of MYRF binding to DNA. Intriguingly, the second most enriched motif identified in our ChIP-seq sequences was a good match for the consensus sequence of the oligodendrocyte transcription factor Sox10, and consistent with this, a strong degree of overlap was seen for ChIP-Seq peaks obtained for both MYRF and Sox10 . This finding is consistent with MYRF binding DNA as a multimer, though the majority of ChIP-Seq-identified binding sites had a single CTGGYAC motif, suggesting that a single copy is adequate for biologically relevant levels of binding. The exact stoichiometry of optimal binding sites and how this relates to MYRF's regulation of gene expression will be an important point to clarify in future work.Alignment of the MYRF protein with the ICD of the phage neck appendage protein  revealedOlig2 and Brg1This study greatly clarifies the molecular mechanisms by which MYRF promotes myelination, demonstrating that it directly binds to and promotes the expression of genes underpinning the myelination process. A number of important questions remain for future studies. Firstly, what is the role of the C-terminal portion of MYRF? Is its role simply to ensure the correct folding, self-association, and cleavage of the protein, or does it have additional binding partners and regulatory roles that influence the function of MYRF in myelination? Secondly, the consensus sequence for Sox10 was overrepresented in regions targeted by MYRF, indicating these two factors likely interact in their regulation of target genes. Consistent with this, mutations for each gene result in a block in oligodendrocyte differentiation at the premyelinating stage FloxedMyrf and CreOlig2 mouse lines to generate Myrf CKO oligodendrocytes have previously been described Generation and use of the Wt/FlMyrf; Wt/CreOlig2 or Fl/Fl; Olig2Wt/CreMyrf cortices using anti-PRGFRα  after removal of contaminant microglia by panning with BSL1 . Rat OPCs were positively immunopanned with anti-O4 after removal of astrocytes and postmitotic oligodendrocytes with anti-Ran-2 and anti-GalC, respectively. Cells were grown in SATO serum-free media as previously described Primary OPCs were isolated from enzymatically dissociated brains as previously described 6 per condition) were transfected with either pCMV-Sport6-Myc-MYRF or pCMV-Sport6-MYRF. Cells were then cultured for 48 h in differentiative conditions  to allow time for construct expression and for the cells to differentiate to the point where MYRF would usually be endogenously expressed and active. Chromatin-immunoprecipitation against the Myc-tag (Abcam ab9132) was performed on formaldehyde cross-linked chromatin (∼50 µg/sample) by GENpathway , as were subsequent analyses. Briefly, ChIP samples were used for Illumina library construction and libraries were submitted to the Stanford Functional Genomics Facility for single-read 36-base Illumina sequencing (>20 million reads/sample). The 35-nt sequence reads were mapped to the rat 2004 genome (rn4) assembly using the ELAND algorithm, using only tags that mapped uniquely with no more than two mismatches. Sequence tags were 3′ extended in silico to 110 bp. BAR files were generated based on mapping fragments to the genome in 32 nucleotide bins and were viewed in the Affymetrix Integrated Genome Browser (IGB). Peaks/intervals were identified using the MACS peak finding algorithm p value = 10exp-6) and a threshold of 16 tags. Based on the number of negative peaks, FDR was estimated to be 12.3%. Browser Extensible Data (BED) files of the interval and peak coordinates were compiled for viewing the data on the UCSC browser Cultured primary rat OPCs , which incorporates the MEME (Multiple Em for Motif Elicitation), DREME, and CentriMo programs For analysis of peak proximity to cell-type specific genes, the top ranked 200 mouse genes for each cell type The UniProt mouse MYRF sequence (accession number Q3UR85) was submitted to several subcellular targeting motif and feature predictors including ELM Luciferase assays were performed using the Promega dual luciferase reporter assay kit as per the manufacturer's instructions. All cells were co-transfected with both the test pGL3 luciferase construct and a constitutive renilla construct, and luciferase readings normalized to the renilla levels. All luciferase results are shown as the mean and SEM of at least three independent experiments, with two-way ANOVA with Bonferroni posttests used to calculate statistical significance. Site-directed mutagenesis was performed using the Thermo Scientific Phusion High-Fidelity PCR Kit and primers as indicated in g for 20 min at 4°C and adjusted to a total protein concentration of 1 µg/µl. To prepare oligonucleotide-conjugated beads, 30 µl Dynabeads MyOne Steptavidin T1 (Invitrogen) were washed three times in 500 µl buffer A  and then incubated in 100 µl buffer A containing 1 µg biotinylated, annealed oligonucleotides for 30 min at room temperature with rotation. The oligonucleotide-conjugated beads were washed twice with 500 µl buffer A, three times with 500 µl buffer C  before being resuspended in 100 µl buffer C and added to 300 µl cell lysate and sheared salmon sperm . Beads were incubated with cell lysates for 30 min at room temperature with rotation and then washed three times in 500 µl buffer C. Bound protein was eluted boiling the beads in 2× Laemmli buffer and subject to gel electophoresis and detection by Western blot with anti-Myc . Forty µl of pre-bead cell lysate and 40 µl of nonbound protein from the beads served as 10% input and eluate controls, respectively.For DNA pulldown assays CG-4 cells were transfected with the MRF Myc-DNA-C constructs (Myc-tagged residues 328–1139 or respective mutants for that construct) for 24 h before being lysed in NP40 buffer . Lysates were clarified by centrifugation at 15,000× R stain (Sigma). Expression was induced by addition of 0.2 mM IPTG for 2 h at 28°C before bacteria were lysed and the fusion proteins column purified using glutathione Sepharose 4B . The eluted GST-MYRF393–766 protein was used to generate monoclonal antibodies , also confirming the presence of the cleavage in bacterial expression systems  in frame with GST and transformed into the BL21(DE3)pLysS-T1 systems .393–766 and mapped to the C-terminal fragment), anti-Myc tag , rabbit anti-Flag , mouse anti-Flag , mouse anti-gm130 , rabbit anti-calnexin , CC1 monoclonal , rabbit anti-Sox10 , and chick anti-GFP .Mice for immunohistochemistry were anesthetized with sodium pentobarbital  and perfused with PBS and 4% PFA. Tissue was harvested and postfixed for 2 h in 4% PFA before being sunk in 30% sucrose and processed for cryosections. Cells for immunostaining were fixed for 10 min in 4% PFA and washed in PBS. For immunostaining, cells on coverslips or cryosections were blocked for 1 h in 10% normal goat serum and 0.3% Triton X-100 in PBS and incubated overnight in primary antibodies diluted in blocking solution. Coverslips or cryosections were then washed three times in PBS and stained with appropriate fluorescent conjugated antibodies  for 1 h, washed three times in PBS, and coverslipped. Primary antibodies were anti-N-terminal MYRF , anti-C-terminal MYRF mAb  Western blot analysis of N-terminal Myc-tagged truncated MYRF constructs expressed in 293T cells. All constructs that include the proline-rich region, DBD, and up to or including the transmembrane region  give the same 75 kDa N-terminal cleavage product. The Myc-Proline-DBD construct (including resides 2–540 of MYRF) gives a product several kDa smaller. Similarly, the truncated Myc-DBD-C construct (residues 328–1139) product runs several kDa larger than the Myc-DBD construct (residues 328–540). Anti-Actin and anti-GFP are used to confirm protein loading and transfection levels, respectively. (B) Expression of GST-fusion constructs including residues 393–540 of MYRF  or residues 393–766 . As in mammalian cells, the bacterially expressed MYRF fusion construct is subject to cleavage shortly after the DBD. Both constructs are expressed in the BL21 (DE3) pLysS-T1RE. coli strain at 28°C, purified 2 h after IPTG induction, and visualized with coomassie blue on a denaturing gel.(TIF)Click here for additional data file.Figure S2ChIP-Seq “noise” within transcribed regions of highly expressed oligodendrocyte genes. ChIP-Seq signal from the Myc-MYRF sample and the untagged control sample (MYRF) for the genomic regions surrounding the Mbp (chr18), Plp1 (chrX), mir219-2 (chr3), and Cldn11 (chr10) genes. Note the presence of MACS-identified peaks corresponding to specific signal within the Myc-MYRF sample (*) as well as the increased background signal present in both the Myc-MYRF and the untagged control samples within the transcribed regions of the genes. This high background was only observed within a small number of genes highly expressed by oligodendrocytes.(TIF)Click here for additional data file.Figure S3Comparison between peaks for MYRF, Sox10, and Olig2. (A–D) ChIP-Seq signal for MYRF in cultured oligodendrocytes and Sox10 in the rat spinal cord for the genomic regions surrounding the oligodendrocyte-enriched Cntn2 (chr13), Mobp (chr8), Mbp (chr18), and Josd2 (chr1) genes. Peaks were identified that were specific to MYRF  or specific to Sox10  as well as peaks shared by both factors . Note consensus sequences for both MYRF and Sox10 in this shared Josd2 peak. (E) Analysis of the degree of direct overlap between peaks obtained for MYRF and Olig2 (TIF)Click here for additional data file.Figure S4Expression of the N-terminal region of MYRF is sufficient to promote myelin gene expression. (A) Luciferase assay co-expressing Myc-tagged truncated MYRF constructs with the pGL3-Plp1 (2) construct  Primary rat oligodendrocytes were co-transfected with GFP and the Myc-tagged truncated MYRF constructs, seeded in proliferative conditions (+PDGF), and assayed for MBP expression 72 h posttransfection (four coverslips per condition). Expression of either the full-length construct or constructs including up to or including the transmembrane region promoted MBP expression. (C) Schematic of the protein domains included in each construct used in (A) and (B). One construct consisting of the proline-rich and DBDs only (residues 2–540) was sufficient to drive luciferase expression in luciferase assays (A) but not MBP expression in primary cells (B). Statistical significance calculated via one-way ANOVA with Bonferroni posttest, comparing all conditions to the empty vector control. *p<0.05, **p<0.01, **p<0.001, ****p<0.0001.ruct see  in the C(TIF)Click here for additional data file.Figure S5Conservation of the ICD region. Alignment of the ICD for human MYRF/C11Orf9 protein and its orthologs, as well as bacteriophage proteins GA-1 neck appendage protein and Endo-N-acetylneuraminidase, showing conservation of the serine lysine dyad required for cleavage .(TIF)Click here for additional data file.Supporting Information S1Peak coordinates. Genomic coordinates of MyRF (-ve control) and Myc-Myrf ChIP-Seq peaks. Coordinates are based on the Rattus norvegicus genome Rn4.(XLSX)Click here for additional data file.Table S1Programs used to predict MYRF protein features. Online programs used to identify the predicted features of the MYRF protein and the associated E-values or scores for each feature are listed. *Values represent E-values unless otherwise stated as being a p value or a score. †Using MYRF residues 546–763 as input.(DOCX)Click here for additional data file.Table S2Gene lists used to assess MYRF binding proximal to neuron-, astrocyte-, or oligodendrocyte-specific genes. Cell-type-specific gene lists were generated from (XLSX)Click here for additional data file.Table S3Position of MYRF peaks relative to the TSSs of oligodendrocyte-specific genes. Table giving the transcript IDs and genomic coordinates of the TSS for each of the 200 oligodendrocyte-specific genes, as well as the positions of any MYRF peaks detected within 100 kb of these TSSs. Links are provided to the expression data for each gene.(XLSX)Click here for additional data file.Table S4Genomic coordinates of genomic regions used for luciferase assays. Genomic coordinates of the regions of the genome  corresponding to MYRF peaks that were cloned into pGL3-Promoter vector for luciferase assays. The left columns show the genes that these MYRF peaks are associated with and the genes' expression levels in CNS cell types ((DOCX)Click here for additional data file.Table S5Sequences of mutagenesis primers.(DOCX)Click here for additional data file.


Escherichia coli, Klebsiella pneumoniae, Salmonella Typhimurium, Pseudomonas auroginosa, Geobacter sulfurreducens Helicobacter pylori, Chlamydophila pneumoniae, Synechocystis sp., Synechoccocus elongates, Bacillus anthracis, and the archaea Sulfolobus solfataricus. The main anchor point for these promoter regions were transcription start sites identified through high-throughput experiments or collected within large curated databases. Prokaryotic promoter regions were found to be less stable and less flexible than the genomic mean across all studied species. However, direct comparison between species revealed differences in their structural profiles that can not solely be explained by the difference in genomic GC content. In addition, comparison with functional data revealed that there are patterns in the promoter structural profiles that can be linked to specific functional loci, such as sigma factor regulation or transcription factor binding. Interestingly, a novel structural element clearly visible near the transcription start site was found in genes associated with essential cellular functions and growth in several species. Our analyses reveals the great diversity in promoter structural profiles both between and within prokaryotic species. We observed relationships between structural diversity and functional features that are interesting prospects for further research to yet uncharacterized functional loci defined by DNA structural properties.The structural properties of the DNA molecule are known to play a critical role in transcription. In this paper, the structural profiles of promoter regions were studied within the context of their diversity and their function for eleven prokaryotic species;  The DNA molecule is not a uniform linear macromolecule as it is often represented but displays local structural variations that depend on its base composition and sequence Several previous studies have analyzed structural profiles of prokaryotic promoter regions bona fide TSSs. Primary transcripts have a 5′ tri-phosphate group, but they usually get quickly processed. This results in a new 5′ that might not represent the actual TSS containing a mono-phosphate instead of a triphosphate. Enrichment for 5′ tri-phosphate transcripts, e.g. by selective digestion of RNA molecules capped with a 5′ mono-phosphate group or by ligating a biotinilated oligonuclotide, helps to enrich for primary mRNA and sRNA transcripts, whereas the processed transcripts or most of the cellular rRNA (which due to its polycistronic nature is mostly mature 5′ monophosphate) are removed Recent technical advances have inspired us to re-evaluate the structural properties of the prokaryotic promoter region. Next generation sequencing allows the characterization of TSSs on a genome-wide scale with single-nucleotide precision with much greater ease than before. Additionally, new techniques now allow isolation of primary transcripts from the RNA pool to detect In this paper the structural properties of prokaryotic promoter regions were characterized with three goals in mind. The first goal was to describe the structural profiles of TSSs in model prokaryotic organisms, and to compare them to past observations made with more limited data sets in order to verify whether the common assumptions regarding their characteristics remain valid. The second goal was to gain novel biological insights regarding the role that the structural properties of the promoter DNA play in transcription. For example, we investigated whether certain patterns in the structural profiles can be linked to gene expression characteristics associated with these promoters. The third goal was to expand the analyses of promoter region to an evolutionary context by comparing the structural profiles of different related and more distant species.bona fide TSSs is crucial to all subsequent analyses, since TSSs limit the downstream region of the promoter. The wealth of experimental data, particularly for E. coli, generated with different methods also allowed us to investigate whether the experimental approaches used to determine the TSS have any influence on the structural properties of the promotor region.As mentioned in the introduction, there are many experimental methods to determine the location of TSSs in a genome. An exact determination of the E. coli TSS data into three broad categories based on their experimental origin: low-throughput experiments (LT) from classic single-object experiments, collected in curated databases; high-throughput experiments (HT), e.g. RNAseq without preprocessing; and high-throughput experiments enriched for primary transcripts (HTE), e.g. RNAseq preceded by terminator exonuclease treatment or adapter ligation. The overlap between the TSSs of these data sources, allowing only for a deviation of three nucleotides (plus minus three), is presented in E. coli genome. The regions from 200 nt upstream to 50 nt downstream from the TSSs were considered as the ‘promoter region’. The downstream 50 nt are cautiously included, as some regulatory elements, such as repressor binding sites, are known to occur after the TSS We can divide the −9) and between HT and HTE . Given the relation between DNA sequence and structure, it seems likely that there is also a difference in the nucleotide sequence at this position. Indeed comparison of the nucleotide sequences reveals that the HTE-derived TSSs have a much higher frequency of either guanine or cytosine at the TSS position or downstream, compared to either the HT- or LT-generated TSS  is lower than that of the genomic mean (50.79%) in E. coli. To discover if the structural properties for promoter regions differ from randomly selected genomic sequences, the distribution of the mean value for the different structural features was compared between them, as shown in The E. coli is shown on the left side of It has been reported in prior studies that there are local differences in the structural property profiles across the promoter region et al.However, the average profiles are not representative for all promoters, since only 50% do not show any curvature see . This diE. coli has 7), which are recruited as part of the RNA polymerase complex and are responsible for directing the binding of the complex to the promoter. Different sigma factors have a preference to bind distinct sets of promoters. The constitution of the cellular sigma factor pool thus regulates the affinity of the RNA polymerase to different promoters on a genome-wide scale It has been established that not all promoter regions follow the same structural profile but that they can be divided or grouped into several categories. In eukaryotic species, different promoter categories have been linked to distinct RNA polymerases −5.Following the analyses of the structural profiles, we can evaluate whether certain sigma factors have a tendency to co-occur with specific structural patterns in the promoter. Such patterns may thus be defining characteristics involved in the specific recognition by different sigma factors. For the analysis of the effect of the sigma factors on the promoter region, we opted to narrow down our search window to the region where sigma factors will most likely interact: from 50 bp upstream to 10 bp downstream of the TSS. Indeed, as can be seen in −164). The curvature of the DNA molecule thus co-occurs with the presence of TF binding sites, implying that DNA curvature may play a role in TFBS function. Given this relationship and the diversity in TFs, we evaluated whether there might be a certain subset of TFs that are functionally related to the curvature. If we compare the curvature at the position of all the binding sites of a TF to those of the other TFs, we do indeed identify a set of TF binding sites that occur at significantly high or low curvature, as shown in The curvature profiles of the promoters assigned to different sigma factors showed similar results. Again we found differences between the profiles of the various sigma factors but they were not significant. However, as can be seen in i.e. the conditions under which a gene is up- or downregulated. In a recent study we described three large expression classes in the Escherichia coli transcriptome, where each class is a set of genes that share global expression behavior across a large amount of experimental conditions Given the role that the promoter plays in transcription regulation, there might be a direct or indirect link between the groupings of promoters based on their structural profiles and the expression behavior of their downstream genes. To investigate if such a relationship exists, the promoter categories can be compared against the gene expression domains, −11). Given this large difference in stability and the known correlation between stability and GC content, it is not surprising that the G/C frequency of the promoters of genes in the growth class is higher than that of either the stress or general expression class. Detailed analysis of the base sequence shows that the stability difference at the TSS is caused by a higher preference for guanine and cytosine from minus 5 bp to minus 1 bp relative to the TSS, without any clear pattern for a specific sequence in the growth class promoters AAT consensus sequence at the −10 position, it seems mostly absent in P. aeruginosa and G. sulfurreducens. Finally, most species also displayed significantly higher curvature values in their promoter regions than in the remainder of their genome. The only exception in this regard is H. pylori, which also features extreme curvature regions in the remainder of its genome and thus the high curvature of the promoter region was not found to be significant.The conservation of the promoter structural profile among related species can be studied by expanding the analysis to other prokaryotes. The Proteobacteria clade, of which nces see . While tE. coli. The clear relation that was found between the stability of the TSS region and the functional class of the downstream gene in E. coli suggests that gene ontology information could be used to test if such a functional relation also exists in these species. For this test, the genes were extracted with a low base stacking energy value in the base stacking profile at the −4 position of their promoter (the site of maximum difference between the classes in E. coli) and their gene ontology enrichment was calculated. This analysis revealed that the promoters of the two Enterobacteria that featured high stability at this position could be linked to several ontology terms. The K. pneumoniae genes with a stable region upstream from the TSS were found to be enriched only in processes that were also enriched in the E. coli growth functional expression class (as can be found in −5), tRNA processing  and ncRNA processing . Similar results were found for S. enterica, where genes with the stable region were enriched for nucleoside metabolic process , organophosphate metabolic process , glycosyl compound metabolic process  and tRNA modification . Again each of these four terms was also enriched in the E. coli growth functional expression class, as shown above.The lack of large expression compendia or sigma factor annotation for these species prevents us from performing equivalent analyses as those performed on Bacillus anthracis of the Firmicutes, Chlamydophila pneumoniae of the Chlamydiae , the cyanobacteria Synechocystis sp. and Synechococcus elongates, and the archeae Sulfolobus solfataricus. All except S. solfataricus are bacteria and of these bacteria all except B. anthracis are Gram-negative.The previous analyses were expanded to other non-Protebacteria prokaryotic species for which large TSS data sets are available: C. pneumoniae. The absolute base stacking values are again correlated to the genomic GC-content of the species, with the highest being 55% for S. elongates and the lowest 35% for B. anthracis. The base stacking profiles all show extreme values at the −10 position, with the exception of B. anthracis, as shown in E. coli, differing only due to the difference in GC-content. However this conservation around the −10 position is much less clear in the sequence  and the low curvature TF (GntR) both belong to the LacI-GalR protein family where induction of DNA curvature is known to play a critical role in their regulatory mechanism Patterns in the DNA curvature profiles diverged strongly from the other structural scales as it is influenced by long range interactions. The findings for the curvature property were not consistent across the studied species; only the promoter regions of some of the Proteobacteria and The structural patterns in prokaryotic promoter regions were re-examined in this study. Despite a lack of data in the past, the estimated structural patterns present at prokaryotic promoter regions obtained by TSS determination seems to have been mostly correct for all tested Gram-negative bacteria; promoter regions are less stable, more rigid and often more curved than genomic DNA. However, large interspecies differences in the structural profiles themselves could be observed. Additionally specific patterns found within the structural profiles of promoters could be linked to the expression behavior of the downstream genes and regulation by sigma factors. The most significant of these findings was a stable/unstable region downstream from the TSS that was associated to the expression class of the downstream gene. These findings have possible implications on promoter prediction tools that use structural properties, as they may be biased towards a certain type of promoter based on sigma factor recognition or a single gene expression class. Finally this work offers new prospects for future research into yet uncharacterized functional elements that are defined by DNA structural properties.E. coli were grouped together into a single data set. To compile a complete set of all TSS from these different data sources without biases by including the same region more than once, we choose to limit our analysis to one TSS per gene. The TSS chosen was always the furthest upstream TSS that had been found from the gene, the rationale being that these TSSs are the least likely to be a false positive, e.g. 5′ end of a processed transcript.An overview of the TSS data utilized in this study is given in The DNA sequences were derived from the genome information available at NCBI for the specific strain for which the TSS was determined, namely E. coli K12 (NC_000913), K. pneumoniae MGH 78578 (NC_009648), H. pylori 26695 (NC_000915), G. sulfurreducens (NC_002939.5), S. enterica SL1344 (NC_016810), P. aeruginosa PA14 (NC_008463.1), B. anthracis str. Sterne (NC_005945), Synechocystis sp. PCC 6803 (NC_000911.1), S. elongatus PCC 7942 (NC_007604.1), S. solfataricus P2 (NC_002754.1) and C. pneumoniae CWL029 (NC_000922) et al. (2013) was not included in the experimental compilation as it represents a mixture between a high-throughput method with and without preprocessing E. coli TSS data set.For the comparison of the data sources, we compiled a set of those found by low-throughput methods (RegulonDB and PromEC curated lists), HT methods without preprocessing steps  and HT experiments with enrichment for tri-phosphate-capped RNA . The RNAseq results detailed in Salgado Sigma factor and transcription factor binding sites were obtained from the annotations in RegulonDB (version 7.5) supported by strong evidence. Enrichment calculations were performed based on a hypergeometric distribution. The likelihood that two samples are derived from the same distribution was evaluated based on a two-sided Kolmogorov-Smirnov (KS) test. Note we apply the KS-test for two types of analyses in this study. The first is a statistical comparison between distributions of mean values for entire sequences, e.g. in the comparative analysis of average structural values between promoter and non-promoter sequences. The second is a screening of an entire profile to identify positions where the profile differs significantly from a background. In this case the two distributions that are compared are that of the structural values at a certain position within a specific subset of promoter regions, e.g. those associated with a sigma factor or a gene expression class, and the structural values at the same position in all remaining promoter regions of the same species. Both the hypergeometric and KS testing were performed using the tools available within Matlab 2013a. The resulting p-values were evaluated for significance at a threshold of 0.05 divided by the number of tests performed within the experiment as per the Bonferroni correction.The structural properties used in this manuscript were derived from experiments or theoretical approaches and stored in structural scales. The structural scale model is based on the neighbor model of DNA structure, which postulates that the primary structural characteristics of the DNA molecule are the result of neighboring base interactions. As such the structure of a DNA molecule can be derived with reasonable accuracy from the sequence if the contributions of each di- or trinucleotide to the overall structure are known. The structural scales detail this contribution for each di−/trinucleotide. The structural scales can be applied to any DNA sequence to calculate the structural profile, a vector listing the contribution of each di−/tri-nucleotide in the sequence at the respective position. This profile can readily be used for study of the DNA structure, except for the curvature calculation requiring additional steps (see below). In all cases discussed here however, a loess-smoothing with range 10 nt was applied. When dealing with average profiles, the smoothing was done prior to the calculation of the average.et al.Base stacking energy is a dinucleotide scale that was derived from the theoretical calculations of Ornstein Denaturation temperature is a dinucleotide scale derived from the experimental observations of Delcourt and Blake et al.B-DNA twist is derived from the observations made by Olson et al.Z-DNA-philicity is a dinucleotide scale derived from the calculations by Ho et al.Major groove bendability is a trinucleotide scale derived from the DNase-I cutting frequencies as defined by Brukner Curvature is calculated in the manner of the BEND algorithm as detailed by Goodsell and Dickerson Smoothing of the structural properties is performed by applying a loess regression to the numerical vector derived from the structural scales after application on the DNA sequence. Unless indicated otherwise, all structural profiles were smoothed with a span of 10 nt.The GC content profiles are derived from a scale where each dinucleotide is assigned a value identical to the number of strong bases it contains . While it is not a true structural scale, it is well known that the GC content influences many aspects of the DNA molecular structure. However the reported GC content frequencies, e.g. in the cross-species and functional gene expression class analyses, are not derived directly from this scale but are simply the percentage of either G or C in the corresponding genomic sequences.The transcription factor binding sites density was calculated by assigning a value of ‘one’ to each genomic position where a transcription factor is reported to bind according to the data available in RegulonDB and ‘zero’ if not E. coli expression compendia found in COLOMBOS  ij is the correlation of the genei versus genej over all conditions. A hierarchical clustering method was then applied which identified three large categories of genes present in the genome. These three expression classes could then be related to three general biological states: survival in stressful conditions, growth and general metabolism. More information about the nature and construction of these clusters can be found in Meysman et al. The expression classes are derived from the large To analyze the functional relationship between the stability of the TSS region and the functional annotation of the downstream genes, the TSSs with low stability at the −4 position were extracted from the collection of structural profiles for each species. The cut-off for ‘low stability’ was determined based on the distribution of the base stacking values at this position and differed based on the GC content. The cut-off for the Enterobacteriaceae was set at −8.5, while those of the other more AT-rich species was set at −7.5. The extracted TSSs were then mapped unto the first gene in their associated operon. The functional annotation of the resulting gene list was then evaluated based on the gene ontology annotation available in UniProtKB-GOA . Enrichment for all gene ontology terms mapped to these genes and all of the ancestor ontology terms were estimated using a hypergeometric distribution. The background set for comparison consisted of all the genes downstream from a described TSS with gene ontology annotation. A p-value cut-off of 0.1, corrected for multiple testing with a Bonferonni approach by dividing by the number of tested ontology terms, was used for significance.Figure S1Motif logos generated with WebLogoof the promoter sequences from 50 bp upstream to 10 bp downstream from the TSS. Each set is grouped by the experimental method used to determine the TSS, namely curated low-throughput methods, high-throughput methods without enrichment procedures, and high-throughput methods preceded by an enrichment step for primary transcripts.(PDF)Click here for additional data file.Figure S2Average curvature profiles of four groups of clustered E. coli promoters. The clustering was achieved by using the k-means algorithm as available in Matlab 2013a on the curvature profiles of all E. coli promoters in our data set. The number of clusters was fixed at four and thus the promoters were grouped together in four groups based on similarities in their curvature profiles, termed Cluster 1 (387 promoters), Cluster 2 (1174 promoters), Cluster 3 (327 promoters) and Cluster 4 (318 promoters).(PDF)Click here for additional data file.Figure S3Average structural profiles for promoters annotated to be regulated by a given sigma factor smoothed with a span of 5. Top: Average bendability profiles for promoters annotated to be regulated by the given sigma factor with high values corresponding to higher flexibility. Middle: Average base stacking profiles for promoters annotated to be regulated by the given sigma factor with higher values corresponding to lower stability. Bottom: Average curvature profiles for promoters annotated by the given sigma factor where higher values correspond to more curvature.(PDF)Click here for additional data file.Figure S4Motif logos generated with WebLogoof the promoter sequences from 50 bp upstream to 10 bp downstream from the TSS. Each set is grouped by the expression class of the first downstream gene, namely the stress expression class, general metabolism expression class, and growth expression class.(PDF)Click here for additional data file.Figure S5Average base stacking profiles of the three expression classes grouped by experimental method used to determine the TSS.(PDF)Click here for additional data file.Figure S6Average base stacking energy profiles of the three experimental categories used to determine the TSS location grouped by functional expression class.(PDF)Click here for additional data file.Figure S7Motif logos generated with WebLogoof the promoter regions (50 bp upstream to 10 bp downstream the TSS) from the six studied Proteobacteria.(PDF)Click here for additional data file.Figure S8Motif logos generated with WebLogoof the promoter regions (50 bp upstream to 10 bp downstream the TSS) from the six studied species from different phyla.(PDF)Click here for additional data file.Data S1Gene ontology enrichment results for each of the three expression classes compared against the set of all genes for which expression data is available.(PDF)Click here for additional data file.


The array of specialized neuronal and glial cell types that characterize the adult central nervous system originates from neuroepithelial proliferating precursor cells. The transition from proliferating neuroepithelial precursor cells to neuronal lineages is accompanied by rapid global changes in gene expression in coordination with epigenetic modifications at the level of the chromatin structure. A number of genetic studies have begun to reveal how epigenetic deregulation results in neurodevelopmental disorders such as mental retardation, autism, Rubinstein–Taybi syndrome and Rett syndrome. In this review we focus on the role of the methyl-CpG binding protein 2 (MeCP2) during development of the central nervous system and its involvement in Rett syndrome. First, we present recent findings that indicate a previously unconsidered role of glial cells in the development of Rett syndrome. Next, we discuss evidence of how MeCP2 deficiency or loss of function results in aberrant gene expression leading to Rett syndrome. We also discuss MeCP2's function as a repressor and activator of gene expression and the role of its different target genes, including microRNAs, during neuronal development. Finally, we address different signaling pathways that regulate MeCP2 expression at both the post-transcriptional and post-translational level, and discuss how mutations in MeCP2 may result in lack of responsiveness to environmental signals. Understanding the molecular mechanisms that control the phenotypic identity of distinct neuronal classes at defined regions within the central nervous system constitutes a widely relevant issue in developmental neuroscience. Neurons permanently exit the cell cycle and remain quiescent. Neuronal mitotic quiescence is crucial for the maintenance of the complexly wired brain. Therefore, neuronal differentiation encompasses an elaborate developmental program in which neurogenic and antiproliferative signals work in concert to ensure the differentiated state .During development, mitotically active precursors located within the neuroepithelium give rise to all the neuronal and glial cells of the brain. Recent genetic studies in vertebrate and invertebrate models have shown that neuronal differentiation is under the control of complex regulatory networks of transcription factors, which are activated in response to inductive signals. The coordinated actions of these proteins direct neuronal fate identity in appropriate spatial and temporal contexts during central nervous system development ; Fig. 1.Environmental cues, via specific intracellular signaling pathways, control neuronal differentiation at multiple levels: neuronal precursor cell cycle control, migration, synaptogenesis, neuronal survival and neurotransmitter phenotype. As mentioned above, these cellular responses result from the induction of timely coordinated cascades of gene expression controlled by specific and ubiquitous transcription factors. These transcription factors in turn induce either the expression of structural genes required for specific functions of a given neuronal lineage and/or the expression of non-coding RNAs that play an important regulatory role. Among the small non-coding RNAs identified so far are those with (i) gene-silencing effects [small interfering RNAs (siRNAs) and microRNAs (miRNAs)] at the post-transcriptional level and (ii) small modulatory double-strand RNAs (smRNAs) that regulate gene expression at the transcriptional level through double-stranded RNA/protein interactions . In mammde-novo methylation is carried by two enzyme families: Dnmt3a and Dnmt3b , RE1-silencing transcription factor (REST) and REST corepressor 1 (CoREST), as well as with various chromatin remodelers such as histone deacetylases (HDACs), Dnmt1, Polycomb, Brahma and Alpha thalassemia/mental retardation syndrome X-linked (ATRX) . TherefoMethyl-CpG binding protein 2 was one of the first members of the MBD family to be discovered ; Fig. 3.MeCP2 gene (CDKL5) have also been associated with RTT  and distal-les homeobox 5 (dlx5), which is on an imprinted region in chromosome 7  mice using microarray screening. This study identified 2582 genes that were misregulated in both MeCP2-tg and MeCP2-null mice. The authors identified 2148 genes that were upregulated in the MeCP2-tg mice and downregulated in the null mice, and only 377 that were downregulated in MeCP2-tg mice and upregulated in null mice. Among these genes are different neurotransmitters (e.g. corticotropin-releasing hormone and somatostatin) and neurotransmitter receptors  that could help to explain the hypothalamic dysfunction. Also of note are different genes coding for regulatory proteins like Bdnf, reelin, cAMP responsibe element binding protein 1 (Creb1) and Creb3, as well as chromatin remodelers like different HDACs and transformation/transcription domain-associated protein (Trapp), the latter being previously identified as an enriched transcript in the hypothalamus , which is upregulated in MeCP2-null mice, correlating with the role of MeCP2 as a repressor for miR-29b. However, both miR-146a and miR-146b were downregulated in the MeCP2-null mice and had MeCP2 enrichment at their methylated 5′ ends, supporting a role for MeCP2 as a transcriptional activator. Both of these miRNAs target IRAK1  that correlates with increased methylated CpGs , a H3K27 methyltransferase, has been reported as a putative target for miR-137  and Bone morphogenetic protein 2 (BMP2); FGF-2 promoters  are in continuous replacement . Mbd2 hand MBD2b . MBD2a intiation .In addition to Dnmt–HDAC interaction, recent evidence shows the existence of specific interactions between Dnmts and histone methyltransferases that incorporate repressive histone marks. Dnmt3 together with the H3K9 methyltranferase G9A cooperatively ensures proper neurogenesis in zebrafish  and proopiomelanocortin (Pomc) among others  on Ser421 in response to neuronal depolarization and Bdnf  preventing MeCP2 DNA binding through phosphorylation mediated by CDKL5 and/or CAMKII and (ii) reducing MeCP2 levels by inducing miR-132 via CREB, thus ensuring the expression of Bdnf and other genes involved in neurite outhgrowth . This domain also contains Ser350 and Ser360, which could be phosphorylated by CDKL5 , activates distinct signal transduction pathways  that regulate neural functions . MeCP2 pby CDKL5 . 14.3.3 by CDKL5 . The intby CDKL5 . Taken tby CDKL5 .Several of the mutations observed in MeCP2 associated with RTT are located near putative phosphorylation sites – mutation of the Arg110 residue could alter the phosphorylation of Ser113 by PKCα/β, mutation of Arg294 might affect phosphorylation of Ser292 by PKCε or PKCδ, and mutations of Arg168 could impair Ser164 phosphorylation by CDKL5 . This stEpigenetic regulation of chromatin architecture plays an essential role in brain development and synaptic plasticity, particularly in brain areas related to cognitive and emotional processes. Although the gross anatomy of the brain is largely established at birth, cognitive development during the post-natal period depends upon reorganization in synaptic connectivity driven by sensory experience . SeveralFurther mechanistic understanding of RTT depends on identifying and validating additional genes responsible for this disorder, specially focusing on the targets that MeCP2 might have on different brain cell types and distinct neuronal phenotypes. Accordingly, a recent study demonstrates that MeCP2 regulates L1 retrotransposition specifically in neurons (


Drosophila, one of the earliest developmental decisions made in the embryo is that of determining its sex. This process involves regulating the expression of the X chromosome sensing promoter of Sex-lethal (Sxl) at its establishment promoter, peSxl/TM6C, Sb; E(z)[EY21318]; E(z)32A40/TM6C, Sb, Tb; Su(z)12[4]/TM6C, Sb, Tb; wild-type w[1118] and Ore R. In situ hybridization in 0-4 hour embryos was used to analyze peSxl expression levels using a Digoxygenin labeled RNA probe specific to peSxl transcripts; Chromatin Immunoprecipitations were performed with antibodies specific to H3K4me3, H3K27me3 and HP1a on chromatin prepared from 1-3 and 2-4 hour embryos, also from the above mentioned stocks. Quantitative real-time PCR quantified Sxl sequences in the DNA from each ChIP.sh1, Su(z)12 and E(z) mutant embryos show they affect both the timing and strength of transcription of the sex establishment promoter, pe.Sxl qRT-PCR analyses support the in situs, showing a change in SxlPe mRNA levels. We also observe that these proteins are necessary for proper histone 3 lysine 4 (H3K4) and histone 3 lysine 27 (H3K27) methylation at the promoter. Surprisingly, we find that embryos deficient in E(z), Su(z)12 or ASH1 protein also affect the binding of HP1a at PeSxl sequences.We find that the PcG/Trx-G proteins genetically interact with mutations in the sex determination pathway and influence the ability of females to determine their sex. In situ analysis of aOur data support the idea that PcG proteins function on a larger set of target genes with a broader role in gene regulation and development. Additionally, we find a novel heterochromatin role for PcG/Trx-G proteins which influences HP1a in the regulation of gene expression.