In order to analyse whether SUMOylation affects binding of L3MBTL2 to chromatin, we performed ChIP-Seq analysis with chromatin of wild-type HEK293 cells and with chromatin of HEK293 cells stably expressing either FLAG-tagged SUMOylation-competent or SUMOylation-defective L3MBTL2. Wild-type FLAG-L3MBTL2 and the SUMOylation-defective FLAG-L3MBTL2 K675/700R mutant essentially occupied the same sites as endogenous L3MBTL2 suggesting that SUMOylation of L3MBTL2 does not affect chromatin binding. However, a subset of L3MBTL2-target genes, particularly those with low L3MBTL2 occupancy including pro-inflammatory genes, was de-repressed in cells expressing the FLAG-L3MBTL2 K675/700R mutant. Finally, we provide evidence that SUMOylation of L3MBTL2 facilitates repression of these PRC1.6-target genes by balancing the local H2Aub1 levels established by the ubiquitinating enzyme RING2 and the de-ubiquitinating PR–DUB complex.Lethal(3) malignant brain tumour like 2 (L3MBTL2) is an integral component of the polycomb repressive complex 1.6 (PRC1.6) and has been implicated in transcriptional repression and chromatin compaction. Here, we show that L3MBTL2 is modified by SUMO2/3 at lysine residues 675 and 700 close to the C-terminus. SUMOylation of L3MBTL2 neither affected its repressive activity in reporter gene assays nor it’s binding to histone tails  Drosophila MBT-domain proteins L domains, together designated as the ‘Royal family’ of chromatin binding domains . MBT-domroteins Lmbt, Scm roteins L.Drosophila polycomb group protein Sfmbt. It possesses a zinc finger domain at the N-terminal part and four centrally located MBT domains, of which the most C-terminal one mediates binding to methyl groups in vitro . Forty-eight hours post transfection, cells were lysed in 1 ml of 6 M guanidinium HCl, 0.1 M sodium phosphate buffer, pH 8.0, 0.05% Tween 20, 20 mM imidazole. His-SUMO modified proteins of transfected HEK293 cells or of HeLa cells stably expressing 6xHis-SUMO1, 6xHis-SUMO2 or 6xHis-SUMO3  and two individual OnTarget plus siRNAs (J-010678-17/-18) from Thermo Scientific Dharmacon were used for RNAi-mediated knockdown of human L3MBTL2. For depletion of RING2 (J-006556), ASXL1 (J-012856), BAP1 (J-005791) and PIAS1 (J-008167) pools of four individual OnTarget plus siRNAs were used. The siGenome non-targeting siRNA #1 (D-001210-01) and an siRNA targeting firefly luciferase (D-002050-01) were used as unspecific siRNA controls. HEK293 cells on six-well plate were transfected with 20 nM siRNA using OligofectamineTM (Invitrogen). Three days post-transfection 3 × 10For depletion of endogenous SUMO isoforms, HEK293 cells were transiently transfected with shRNA expression constructs targeting SUMO1 or SUMO2/3. Following selection with 1 µg/ml of puromycin or 300 µg/ml of zeocin for 96 h, whole-cell extracts were prepared and analysed by western blotting.Expression and purification of recombinant SUMO1, SUMO2, E1 (6xHis-AOS1/UBA2) and E2 enzyme (UBC9) were described ,23. SUMOFive microgram of histone peptides  bearing C-terminal cysteine residues were immobilized on 40 µl of SulfoLink Coupling Resin (Thermo Scientific) in 100 µl of coupling buffer  for 90 min at room temperature. Free iodoacetyl groups were blocked with 50 mM L-cysteine for 90 min at room temperature. Upon extensive washing with 1 M NaCl, beads were blocked with 50 µl bovine serum albumin (10 mg/ml) for 1 h at 4°C. Immobilized peptides were incubated on a rotating wheel with recombinant proteins in 1 ml of binding buffer  for 1 h at 4°C. Beads were washed six times with binding buffer, and bound proteins were analysed by western blotting.Supplementary Material.ChIP-qPCR and ChIP-Seq experiments were performed as described previously ,25 usingn 2 –e 70). Reads mapping to multiple locations on the genome were discarded (–m 1 –k 1), and Bowtie output was converted to BAM format. Reads were filtered to a maximum of two reads per start position and start positions with more than seven reads were censored. The following amounts of usable, uniquely matching reads were obtained: HEK293 cells, anti L3MBTL2: 19 660 936; HEK293 cells, anti FLAG: 26 605 142; HEK293 cells, anti IgG: 22 346 403; HEK293 expressing 3xFLAG L3MBTL2 WT, anti FLAG: 22 064 463; HEK293 expressing 3xFLAG L3MBTL2 K675/700, anti FLAG: 20 599 800.Raw ChIP-Seq data were aligned to the human genome assembly GrCh37 (hg19) with Bowtie version 0.12.7 allowing for two mismatches in the seed and a total mismatch quality sum score of 70 (–http://code.google.com/p/pysam/), and peaks with >100 tags in the IgG control or the anti FLAG control were excluded. This yielded 41 570 , 37 846  and 44 781  putative L3MBTL2-binding sites, of which 8009, 14 986 and 9664, respectively, had a MACS defined false discovery rate (FDR) of ≤0.001.Peak calling was performed with MACS version 1.4.0rc2 20110214 modified for reading BAM files via pysam . The resulting gene sets were paired via a one-to-one bi-directional relationship in Ensembl Compara, and a Venn diagram depicting whether the pair was L3MBTL2-bound in HEK293 cells, mES cells or both was drawn. Genes that were L3MBTL2-bound but did not have a one-to-one correspondence with the other species (species-specific genes) were included as separate circles.RNA polymerase II and H3K4 ChIP-Seq were retrieved from the UCSC genome browser . Both datasets were used in pre-processed form.Motif search was performed with MEME-ChIP version 4.9.0 and Centrimo ,27 usingFunctional enrichment analysis was performed with ChIP-Seq-derived gene lists versus predefined gene sets using Fisher’s exact test. The Benjamini–Hochberg  procedurhttp://dec2011.archive.ensembl.org). Known transcription factor motifs were retrieved from Jaspar, UniProbe and Matbase (Genomatix). Gene sets from the PANTHERDB version 7.0 were used for pathway analysis  and assigned the identifier E-MTAB-1731 and ERP003468, respectively.Genome sequences, annotation and homology data were retrieved from Ensembl revision 65  at K215, K433 and K541 within the first, third and fourth MBT domains, respectively, and at K675 and K700 close to the C-terminus A. We anain vitro SUMOylation assay using purified recombinant E1 (AOS1 and UBA2 dimer) and E2 (UBC9) enzymes along with recombinant SUMO2 and His-L3MBTL2 or the His-L3MBTL2 K675/700R mutant as substrates. Wild-type L3MBTL2 but not the L3MBTL2 K675/700R mutant was efficiently SUMO-modified in vitro . Whether some residual PIAS1 activity was sufficient to SUMOylate L3MBTL2 or whether other SUMO E3 ligases catalyze SUMOylation of L3MBTL2 in vivo remains to be established.Finally, we asked whether PIAS1 could act as an E3 ligase for SUMOylation of L3MBTL2. In brief, the addition of purified recombinant PIAS1 drastically enhanced the rate of SUMOylation . Snapshots of representative L3MBTL2-binding sites are shown in We sought to analyse whether SUMOylation of L3MBTL2 affects chromatin occupancy or target gene expression. As a prerequisite we carried out genome-wide chromatin immunoprecipitation followed by sequencing (ChIP-Seq) for L3MBTL2 in HEK293 cells. In brief, using an FDR of ≤0.001, MACS peak calling  identifiSupplementary Figure S2).Comparison of L3MBTL2-binding sites with promoter regions containing H3K4me3 marks E, and wiTo assess whether SUMOylation affects chromatin occupancy of L3MBTL2, we generated HEK293 cells stably expressing either 3xFLAG-tagged wild-type L3MBTL2 or the 3xFLAG-tagged L3MBTL2 K675/700R mutant. Consistent with the assignment of K675 and K700 as the SUMO-target sites, FLAG-tagged wild-type L3MBTL2 but not the L3MBTL2 K675/700R mutant was SUMOylated A. SingleSupplementary Dataset S1). Merging these two datasets with the dataset of endogenous L3MBTL2 revealed a strong overlap  that were present in the dataset of endogenous L3MBTL2 and FLAG-L3MBTL2 WT but not in the dataset of the L3MBTL2 K675/700R mutant. The absence of a listed peak in one of the three datasets could also be due to the high stringency threshold of FDR ≤ 0.001 that we used for initial peak selection. Therefore, we applied less stringent conditions for dataset comparison. Merging all peaks with an FDR ≤ 0.001 in any of the three L3MBTL2 datasets yielded 16 569 peaks. Requiring a 2-, 3- or 4-fold difference in tag counts of individual L3MBTL2 peaks revealed a progressive greater overlap of the three ChIP-Seq datasets , the H2AK119ub1 mark was specifically and reproducibly reduced up to 2-fold in cells expressing the FLAG-L3MBTL2 K675/700R mutant , the ASXL1 level was slightly but reproducibly increased in cells expressing the L3MBTL2 K675/700R mutant. The correlation between reduced H2AK119ub1 levels and increased ASXL1 occupancy at these promoters may suggest that SUMOylation of L3MBTL2 negatively affects recruitment of PR-DUB.Since RING2 occupancy was unchanged at promoters that showed reduced H2AK119 ubiquitination  in cellsL3MBTL2 siRNAs. Effective knockdown of L3MBTL2 was confirmed at the transcript  increased by >2-fold  complex. Knockdown of nchanged D. This rf PR-DUB .In this study, we have unambiguously established the polycomb group protein L3MBTL2 as a target for SUMOylation. It is worthy of note that L3MBTL2 was also identified as a putative SUMO substrate in two previous proteomic studies ,37. L3MBin vitro. However, RNAi-mediated depletion of PIAS1 did not reduce SUMOylation of endogenous L3MBTL2. It is conceivable that other PIAS family members could also catalyze SUMOylation of L3MBTL2 in vivo. The polycomb protein PC2 (CBX4) is another well-established SUMO E3 ligase (in vivo.We have found that PIAS1 can act as a SUMO E3 ligase 3 ligase ,39 that 3 ligase , DNMT3a 3 ligase  and CTCF3 ligase . Since Lin vitro also allowed us to analyse whether SUMOylation of L3MBTL2 affects binding to histone tails. Unmodified as well as SUMOylated full-length L3MBTL2 bound equally effective to H3 and H4 peptides regardless of mono-, di- or tri-methylation at H3K9 or H4K20 (The most conspicuous structural feature of L3MBTL2 is the 4xMBT repeat domain, which was shown to bind specifically to H4K20me1 and H4K20me2 peptides in their isolated form  as well Our ChIP-Seq analyses identified >8000 high confident L3MBTL2-binding sites in HEK293 cells, preferentially around TSSs. Comparison with ChIP-Seq datasets for H3K4me3 and RNA polymerase II  revealedL3MBTL2 is a subunit of the E2F6.com1  and the The chromatin localization as well as the amount of chromatin-bound wild-type FLAG-L3MBTL2 or the FLAG-L3MBTL2 K675/700R mutant was similar to endogenous L3MBTL2 despite of an ∼25-fold over-expression of the tagged proteins B indicatDrosophila (Our ChIP experiments revealed that high and low levels of L3MBTL2 correlate with high and low levels of E2F6 and RING2, which is consistent with the presence of all three proteins in the PRC1L4/PRC1.6 complex ,15. Surposophila ,46. AppaSupplementary Data are available at NAR Online.


The Kruppel-like factor (KLF) family of transcription factors regulates diverse cell biological processes including proliferation, differentiation, survival and growth. Previous studies have shown that KLF15 inhibits cardiac hypertrophy by repressing the activity of pivotal cardiac transcription factors such as GATA4, MEF2 and myocardin. We set out this study to characterize the interaction of KLF15 with putative other transcription factors. We first show that KLF15 interacts with myocardin-related transcription factors (MRTFs) and strongly represses the transcriptional activity of MRTF-A and MRTF-B. Second, we identified a region within the C-terminal zinc fingers of KLF15 that contains the nuclear localization signal. Third, we investigated whether overexpression of KLF15 in the heart would have therapeutic potential. Using recombinant adeno-associated viruses (rAAV) we have overexpressed KLF15 specifically in the mouse heart and provide the first evidence that elevation of cardiac KLF15 levels prevents the development of cardiac hypertrophy in a model of Angiotensin II induced hypertrophy. Cardiac hypertrophy is an early hallmark and important risk factor for the development of heart failure. Hypertrophy of cardiomyocytes occurs in response to pathological stimuli such as hypertension, aortic valve stenosis, myocardial infarction or genetic mutations in sarcomeric proteins and is regarded as a maladaptive process, since left ventricular hypertrophy (LVH) often progresses to heart failure Mechanistically, KLF15 acts as a repressor of the cardiac transcription factors MEF2 and GATA4 and the transcriptional coactivator myocardin Despite growing evidence for a role of KLF15 as a regulator of many biological and pathological processes, only few studies have addressed the function of the different domains within the KLF15 protein We set out this study to characterize the interaction of KLF15 with MRTFs and found that KLF15 strongly represses the transcriptional activity of MRTF-A and MRTF-B. Second, we identified a region within KLF15 that contains the nuclear localization signal. Third, we investigated if overexpression of KLF15 in the heart has therapeutic potential for the repression of cardiac hypertrophy. Using recombinant adeno-associated virusses (rAAV) we have overexpressed KLF15 specifically in the mouse heart and provide the first evidence that elevation of cardiac KLF15 levels prevents the development of cardiac hypertrophy in a model of Angiotensin II induced hypertrophy.The ANF(638) and the SM22(505) luciferase reporter plasmids were previously described Luciferase assays were performed as previously described 35S labelled proteins were translated using a cell free TNT quick coupled translation system  and labelled with 35S methionine (Perkin Elmer). Proteins labelled with 35S were assayed for binding to GST-KLF15.GST-KLF15 and GST-empty plasmids were transformed into Bl21-Gold (DE3) cells (Stratagene) and protein translation was induced by adding 0.5 mM Isopropyl β-D-1-thiogalactopyranoside (IPTG). Proteins were isolated and bound to Glutathione Sepharose 4B Beads . Quality of the GST proteins was checked by running 5 ul of protein on a 10% SDS gel followed by staining with PageBlue™ protein staining solution (Fermentas). All To determine cellular distribution of the KLF15 mutants, COS-7 cells were seeded in 24-well plates and transfected with 500 ng expression plasmid using GeneJammer (Stratagene). Twenty-four hours after transfection, cells were washed with PBS and fixed with 4% paraformaldehyde for 15 minutes at room temperature. Cells were washed with PBS and permeabilized for 10 minutes with 0.1% triton-X in PBS. After two PBS washes, cells were incubated at 37°C for 45 minutes with a primary antibody against the FLAG epitope (1∶250) using the anti-FLAG-M2 antibody (Stratagene). Cells were washed with PBS and exposed at 37°C for 45 min with an Alexa532-conjugated goat-anti mouse antibody (1∶200) . Nuclei were stained with DAPI.The described studies are comprised within gene transfer/therapy research of the University of Leuven, which was reviewed and approved by the university Ethical Commission for Animal Experimentation (file N° P03102). Precautions were taken to minimize animal suffering by implementing anesthesia (e.g. isofluorane) for implantation of osmotic minipumps and to perform to euthanasia by cervical dislocation when the experiment was terminated.10 vector genomic copies (vg) of AAV9-KLF15 and AAV9-GFP were injected into the tail vein of eight week old male C57BL/6 mice (n = 4 in the control groups and n = 5 in the AngII groups). After one week, mini-pumps  filled with either AngII  or saline and a pumping rate of 1.5 ug/g/day were placed subcutaneously as described earlier pAAV9-cTnT-GFP and pAAV9-cTnT-FLAG-KLF15 were derived by subcloning the open reading frame of GFP and mouse KLF15 into pAAV-MCS (Stratagene). Subsequently, the CMV promoter was removed by restriction and replaced by the −374+38 chicken troponin T promoter. Virus was produced in HEK293 cells and purified as previously described Mouse hearts were fixed overnight in 4% paraformaldehyde, dehydrated, and embedded in paraffin using standard techniques. Consecutive 7-µm serial sections were cut and stained with hematoxylin and azophloxine. To determine the collagen content, deparaffinized sections were processed for Picrosirius red stain.Cell size was determined in hematoxylin and azophloxine- stained sections using 20× magnification. To determine cell size, we measured cross-sectional areas of ∼150 individual transversely cut cardiomyocytes using the image processing software Scion Image. Cross-sectional areas are expressed as arbitrary units (AU).Sirius red staining was quantified by using an in house made quantification macro. Per heart/section, 160 fields were recorded and of those 160 fields, 20 fields were randomly chosen for quantification. Perivascular fibrosis was manually omitted from the sections. The percentage of Sirius red positive areas in each section was automatically calculated as a percentage of the total tissue area.Data are presented as mean ± standard errors. Unpaired t test was used to calculate p-values. P values of ≤0.05 were considered statistically significant.One requirement for a transcription factor to function is that it has to be translocated to the nucleus. Since KLF15 lacks the typical nuclear localization signal (NLS) PKKKRKV, KLF15 must have an alternative sequence to drive nuclear import. To identify the region that contains the NLS of KLF15 we subcloned several flag-tagged deletion mutants of KLF15 and overexpressed these in COS7 cells. We performed immunostainings with an antibody against the flag epitope to determine its cellular localization. In To study whether the zinc fingers are not only necessary for nuclear localization but also sufficient to act as an NLS, we created an expression vector containing the open reading frame of enhanced green fluorescent protein (eGFP) fused to one or more zinc fingers of KLF15  and several KLFs  have been shown to bind this sequence Since it is currently unknown whether consensus KLF binding sites in the promoters/enhancers of cardiac genes regulate gene expression, we searched for KLF binding sites in promoters of genes known to be repressed by KLF15. We noted a typical KLF binding site (CACCC) in the promoter of SM22, 122 bp upstream of the CArG box  via subcutaneously placed osmotic minipumps  and myocyte enhancer factor 2 (MEF2), two transcription factors with an established role in cardiac biology Although it is known that KLF15 is a repressor of several transcriptional regulators, the exact mechanism by which KLF15 represses transcriptional activity remains unknown. This is largely because of a lack of knowledge on the functional domains within the KLF15 protein. The zinc finger region at the c-terminus of KLF15 is the only functional domain described so far. The zinc fingers are required for DNA binding in vitro studies have previously revealed an inhibitory effect of KLF15 overexpression on cell growth, in vivo studies were lacking During cardiac hypertrophy and heart failure, KLF15 is consistently downregulated in patients, mice and rat models Efficient and long-term delivery of potential therapeutic genes to the heart remains a major challenge for clinical implementation. One of the most promising tools in this context are the AAVs, which are able to take up large DNA fragments and enable stable gene transfer to the heart Swinnen et al. showed that rAAV9-mediated gene transfer is not only very efficient, but it also has therapeutic potential for heart disease. When they overexpressed thrombospondin-2 (TSP-2) using AAV9-CMV-TSP vectors in the hearts of TSP-2 null mice they were able to rescue accelerated age-induced cardiomyopathy completely Specificity of gene transfer to cardiomyocytes can be increased by incorporating a cardiac promoter, instead of the CMV promoter into the AAV9 vector Previous work of Wang et al. showed that KLF15 also has a function in the cardiac fibroblast, where it represses the expression of the pro-fibrotic cytokine CTGF In conclusion, the fact that cardiomyocyte-specific KLF15 overexpression may reduce cardiomyocyte hypertrophy seems beneficial and suggests that KLF15 may be used as a therapeutic target for hypertrophy and heart failure. Future studies are warranted to establish whether overexpression of KLF15 improves cardiac function and whether it can also prevent hypertrophy in more severe models of hypertrophy and heart failure, such as transverse aortic constriction.Figure S1Sequence of the −505 sm22 and −423+98 hBNP reporters. The Sm22 and BNP reporters contain several conserved transcription factor binding sites: CArG-box (yellow), GATA (blue), KLF (red).(TIF)Click here for additional data file.Figure S2KLF15 competes with SRF for MRTF interaction. Cos cells were transfected with Sm22 luciferase and expression vectors encoding myoardin (panel A), MRTF-A (panel B) or MRTF-B (panel C) and increasing concentrations of KLF15 and SRF as indicated. Luciferase activity is expressed as fold change over the empty expression vector pcDNA3.1. 50 indicates that 50 ng was transfected to a well of a 24-well plate. Increasing concentrations indicated by the triangle range from 1 to 50 ng.(TIF)Click here for additional data file.


Monocyte-derived macrophages and dendritic cells (DCs) are important in inflammatory processes and are often used for immunotherapeutic approaches. Blood monocytes can be differentiated into macrophages and DCs, which is accompanied with transcriptional changes in many genes, including chemokines and cell surface markers.To study the chromatin modifications associated with this differentiation, we performed a genome wide analysis of histone H3 trimethylation on lysine 4 (H3K4me3) and 27 (H3K27me3) as well as acetylation of H3 lysines (AcH3) in promoter regions. We report that both H3K4me3 and AcH3 marks significantly correlate with transcriptionally active genes whereas H3K27me3 mark is associated with inactive gene promoters. During differentiation, the H3K4me3 levels decreased on monocyte-specific CD14, CCR2 and CX3CR1 but increased on DC-specific TM7SF4/DC-STAMP, TREM2 and CD209/DC-SIGN genes. Genes associated with phagocytosis and antigen presentation were marked by H3K4me3 modifications. We also report that H3K4me3 levels on clustered chemokine and surface marker genes often correlate with transcriptional activity.Our results provide a basis for further functional correlations between gene expression and histone modifications in monocyte-derived macrophages and DCs. Human peripheral blood monocytes are derived from myeloid progenitors in bone marrow. After their maturation, they migrate to the periphery and form approximately 5-10% of human leukocytes. Monocytes circulate in the blood stream for several days and then enter to peripheral tissues where they undergo a phenotypic change and differentiate further into macrophages or dendritic cells (DCs) . In tissin vivo, it still is the most efficient way to obtain human antigen presenting cells. Furthermore, monocyte-derived DCs are by far the most common type of cells used in immunotherapeutic clinical approaches [The peripheral blood monocytes exhibit a heterogeneous phenotype with respect to their size and nuclear morphology. They can be relatively easily identified by high expression of the cell surface marker CD14 and can be divided into at least two major subpopulations according to their CD16 expression . DCs derproaches .Histone modifications within promoter regions have an important function in regulation of gene expression . The majHere we studied the histone H3K4me3, AcH3 and H3K27me3 modifications in human monocytes, monocyte-derived macrophages and DCs. To gain insight into the role of these modifications during the differentiation process, we compared genome-wide gene expression profiles with histone changes in promoter regions over the genome. We show that H3K4me3 and AcH3 marks generally correlate well with gene expression and that H3K27me3 is associated with inactive genes. We also show that H3K4me3 levels are increased on the promoters of several marker genes and gene groups during differentiation. In addition, we provide evidence that the chromatin statuses of gene families within genomic clusters, including chemokine and surface receptor genes, are coordinately modified.P value less than 0.05, we found that compared to monocytes, a similar number of genes were upregulated in macrophages and DCs . This represents 8% of the total number (ca 21 000) of human genes . Cells were cross-linked and chromatin was immunoprecipitated with antibodies to H3K4me3, H3K27me3 and AcH3, as well as to general H3. The immunoprecipitated material was then hybridized to the NimbleGen promoter array covering 19222 gene promoters in the human genome that overlap with 17289 genes on the Illumina Human-6 v2 BeadChip expression array. The ChIP-chip data with each modification-specific antibody was further normalized to random genomic background and to general H3.We first studied the occupancy and position of H3K4me3, AcH3 and H3K27me3 marks in relation to transcription start sites within the promoter regions. H3K4me3 and AcH3 signals coincided up to 1000 bp upstream and 500 bp downstream showing almost complete overlap on both sides of the transcriptional start sites  box transcription factor binding sites. It is likely that the presence of these transcription factors binding sites reflects their activity in recruitment of histone modifier proteins to the promoters ,22.We noticed that while many genes had lower peak scores, they still gained or lost their histone modifications during differentiation. This was particularly evident with many cell surface marker and chemokine genes. To identify these changes on all promoters, we subtracted the monocyte-specific peak scores from those identified in macrophages or DCs. This additional normalization identified further changes that were not apparent by standard peak identification. The profiling by Gene Ontology indicated that the gain of H3K4me3 mark often occurs on genes associated with signal transduction, inflammation and chemokine activity both in macrophages and DCs after the differentiation whereas the AcH3 was more associated with cell cycle, general mRNA processing and gene expression regulation . All participants were older than 18-years and gave written informed consent. PBMC were prepared by density gradient centrifugation on Ficoll-Paque™PLUS . Monocytes were purified by positive sorting using anti-CD14-conjugated magnetic microbeads and by two runs through LS columns (both from Miltenyi Biotech) to obtain purities over 99%. The cells were differentiated into macrophages using 50 ng/ml GM-CSF and into DCs using 50 ng/ml GM-CSF and 25 ng/ml IL-4 (both from PeproTech) for 6 d at 1 million cells/ml in RPMI 1640 supplemented with 2 mM L-glutamine, 100 U/ml penicillin, 100 μg/ml streptomycin and 10% FCS . The expression of surface proteins on monocytes, macrophages and DCs were analyzed using fluorescence conjugated antibodies CD14, DC-SIGN, CD80 and CD83 (Miltenyi) and FACSCalibur (BD Biosciences) to confirm the characteristic phenotype  and RNeasy Mini Kit (Qiagen). The concentration of RNA was assessed with NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies) and the quality with Agilent RNA 6000 Nano Kit and Agilent 2100 Bioanalyzer (Agilent Technologies). RNA integrity numbers (RIN) indicated good quality of RNA, ranging from 6.5 to 9.3. Illumina TotalPrep RNA amplification Kit (Ambion Inc.) was used to amplify 330 ng RNA for hybridization on Illumina Human-6 v2 BeadChips (Illumina). The data were analyzed with BeadStudio Gene Expression Module v3.3.7 (Illumina) using Illumina's custom rank invariant method. The complete lists of upregulated and downregulated genes are available at ArrayExpress databank with accession number E-TABM-976  and 4 μg of H3 , H3K4me3 , H3K27me3  or AcH3  antibodies were used in each immunoprecipitation. DNA probes from ChIP were amplified using GenomePlex Complete Whole Genome Amplification (WGA) Kit and reamplified using GenomePlex WGA Reamplification Kit (both from Sigma-Aldrich). NimbleGen Systems using 385K RefSeq Promoters array set performed sample labelling, hybridization and data extraction. The NimbleGen RefSeq Promoter array data is available at ArrayExpress databank with accession number E-TABM-979 . Briefly, formaldehyde crosslinking was carried out in PBS containing 1% formaldehyde and 0.5 mM EGTA with the density of 7x10http://bioinfo.ut.ee/mprimer3/[For conventional ChIP, purified DNA samples were analyzed in triplicate by quantitative PCR (qPCR) as described in . In addimprimer3/ and are http://bioinf.uta.fi/Immunome/[http://www.ingenuity.com/[The intensities of the histone H3, H3K4me3, H3K27me3 and AcH3 probes were first normalized to the random genomic background. We additionally normalized the modification intensities by subtracting H3 intensities from the corresponding modification intensities to get specific values for each modification. The normalized values correspond to log2 ratio between corresponding modification and H3 signals. We used the same normalization to find the changes induced by differentiation but then we subtracted the values of probe intensities in monocyte from corresponding probe intensities in macrophage and DCs. Both sets of data were further used to find peaks in the promoters. The peaks were mapped to the genes, where they fell into the range 2000 bp upstream to 500 bp downstream from the transcription start site. The peak scan and assembly with normal RefSeq genes for human genome  was performed using Nimblescan 2.5 software with default settings. The number of transcript probes in Illumina gene expression matrix was 42620 and the Nimblegen promoter database contained 19222 genes. In the analyses combining gene expression and promoter modification data, the overlap between these two sets (17289 genes) was used. The genes for functional classes were selected from Immunome Database uity.com/.The transcription factor analysis was performed using web resource Opossum , which ihttp://biit.cs.ut.ee/gprofiler/[The functional profiling for the groups of genes with changes in the studied modifications was carried out using the g:GOSt tool at g:Profiler website profiler/ that retprofiler/.AcH3: acetylation of histone H3 lysines; ChIP: chromatin immunoprecipitation; DC: dendritic cell; FCS: fetal calf serum; GM-CSF: granulocyte macrophage colony-stimulating factor; HLA: human leukocyte antigen; H3K27me3: histone H3 trimethylation of lysine 27; H3K4me3: histone H3 trimethylation of lysine 4; IL-4: interleukin-4; MF: macrophage; MO: monocyte; PBMC: peripheral blood mononuclear cell; RIN: RNA integrity numberLT and RK performed experiments and analyzed data; AR, KK and TO designed research and analyzed data; HP and JV contributed analytical tools for bioinformatics analyses; PP designed the research and wrote the manuscript. All authors have read and approved the final manuscript.Tserel et al BMC Genomics. Contains Supplementary Table S1 and Supplementary Figures S1-S8. Size 1.2 MBClick here for fileTserel et al BMC Genomics. Contains Supplementary Tables S2-S6. Size 7.2 MBClick here for file


This review summarizes results obtained with the mouse, rat, human, and ovine promoters either by transient transfection assays or by means of transgenic mice.The GnRH receptor (GnRHR) plays a central role in the development and maintenance of reproductive function in mammals. Following stimulation by GnRH originating from the hypothalamus, GnRHR triggers multiple signaling events that ultimately stimulate the synthesis and the periodic release of the gonadotropins, luteinizing-stimulating hormone (LH) and follicle-stimulating hormones (FSH) which, in turn, regulate gonadal functions including steroidogenesis and gametogenesis. The concentration of GnRHR at the cell surface is essential for the amplitude and the specificity of gonadotrope responsiveness. The number of GnRHR is submitted to strong regulatory control during pituitary development, estrous cycle, pregnancy, lactation, or after gonadectomy. These modulations take place, at least in part, at the transcriptional level. To analyze this facet of the reproductive function, the 5′ regulatory sequences of the gene encoding the GnRHR have been isolated and characterized through  The amplitude and frequency of GnRH pulses relayed by the GnRHR appear critical in the development of the reproductive function, in the onset of puberty and throughout the menstrual or estrous cycle.Two GnRH systems (GnRH decapeptides and specific receptors) originally existed in the common mammalian ancestor but a large number of extant species have lost either the second type GnRH or its receptor . Humans via Gs or Gi  type 1 receptor (see review in Gnrhr).The activation of GnRHR may trigger several intracellular signaling pathways in gonadotrope cells depending on the cellular context. Two mouse gonadotrope tumor-derived cell lines expressing the GnRHR, αT3−1 and LβT2 cell lines, have been used as homogenous cell models to study gonadotrope function . In bothGs or Gi . GnRHR aGnrhr is composed of three exons and two introns and is approximately 15–31 kb in size depending on the species . It is localized to chromosome 4q21.2 in human, chromosome 4, 5, 6, or 8 in bovine, murine, ovine, and porcine species, respectively  and confirmed that this receptor belongs to the serpentine family of transmembrane G protein-coupled receptors , progesterone, or testosterone also regulate the levels of Gnrhr mRNA  detected at embryonic day 11.5 (E11.5), Gnrhr detected at E13.5 and steroidogenic factor 1  detected at E14.5. The LβT2 cell line further expresses later marker genes, namely those encoding the β-subunits of LH (Lhb), from E16.5 onward, and FSH (Fshb), from E17.5. These data together with the way by which these cell lines were generated – by targeted oncogenesis using the promoter of Cga and Lhb, respectively – strongly suggest that the αT3−1 cell line may be representative of the gonadotrope lineage at early developmental stages, between E14 and E16.5. In contrast, the LβT2 cell line would be derived from cells at later stage, beyond E17.5 . Mutation of all three elements fully abrogates promoter activity. The identity of SF1 was further corroborated by gel-shift and antibody super-shift assays. Using similar approaches, the AP1 complex has been found to involve factors likely belonging to the FOS and JUN families. The nature of the factors that bind the GRAS element were also deciphered. They consist of SMAD3 (mothers against decapentaplegic homolog 3) which interacts with SMAD4 and bind to the 5′ end of the GRAS motif, AP1 that binds within the middle of the motif, and FOXL2, a member of the forkhead family, that interacts with the 3′ end of the GRAS motif. GRAS is thus a composite regulatory element whose functional activity is dependent on a multi-protein complex  belongs to the expanding family of bicoid-related vertebrate homeobox genes. It appears very early, being involved in the specification of the adenohypophyseal placode and its expression in the anterior pituitary persists afterward until adulthood  assay, and short interfering RNA strategy  transcription factors might be involved in tissue-specific promoter activity. b/HLH transcription factors (124 in numbers in mouse) have been classified into six groups based on their evolutionary relationship, protein structure characteristics, and sequence binding affinities , PER2, cryptochrome 1 (CRY1), and CRY2 were detected, signifying that dynamic inhibition by CRY/PER of CLOCK/BMAL1-directed gene transcription may take place in this gonadotrope cell line. Although the actors of the molecular loop essential for rhythmic timekeeping are all present, circadian rhythms of Gnrhr expression were not observed in cultured gonadotrope αT3−1 cells. Subsequent study considering E-box transcription factors has demonstrated the involvement of two other b/HLH transcription factors, neurogenic differentiation 1 (NEUROD1) and mammalian achaete-scute homolog 1 (MASH1), that both interact with E-box 3, suggesting that the CLOCK/BMAL1 heterodimer bind to either E-box 1 or 2 but not to E-box 3 . The analysis further reveals full lack of homology beyond 2.1 kb, suggesting that master regulatory domains in rodents are constrained within 2 kb upstream of the ATG codon. Furthermore, the transcriptional start sites appear similarly clustered within the 110 bp proximal region. A first set of major sites is located between −110 and −103, depending on the authors and a second set of both major and minor sites is located around position −30. The rat promoter was proved to be strongly active in αT3−1 and LβT2 gonadotrope-derived cells as well as in GT1−7 hypothalamic-derived cell line. Within the proximal domain, two elements that are identical to the AP1 (−352/−346) and SF1 (−245/−237) response elements of the mouse promoter are required to mediate gonadotrope-specific activity  lies on a more distal part of the rat promoter (−1135/−753). Two major response elements located at positions −994/−960 and −871/−862 are responsible for GnSE action  region is strictly conserved among bovine, porcine, ovine, canis, human, rat, and mouse sequences . However, the inhibitory effects of these sequences are modest and cannot account for the whole lack of Gnrhr expression in non-gonadotrope pituitary cells. Furthermore, they partially overlap regulatory regions characterized as positive in a gonadotrope cell context.As underlined above, the GH3 lactosomatotrope-derived cell line expresses the mouse promoter at a low but detectable level. The stably transfected GGH(3) cell, first designed in order to evaluate GnRH action, was thereafter used to identify the elements that allow constitutive expression in this non-gonadotrope, SF1-lacking cell line . A CRE eGnrhr since, as we shall see in more detail below, SF1 is involved in PACAP- and PKA-regulated activity, AP1 in the homologous up-regulation by GnRH, and GRAS in the up-regulation by activin.It is often noticed that tissue-specific transcription factors participate either directly or as co-regulators in the modulation of promoter activity by extra-cellular signals. This is also true for the rodent Gnrhr promoter under GnRH treatment . The SURG−2 element corresponds to the AP1 element and is absolutely required since mutations within this sequence totally abolished GnRH-induced stimulation of promoter activity whereas mutations within the SURG−1 element (5′-GCTAATTG−3′) only attenuated the response. Based on targeted mutagenesis of each of the GRAS, AP1, and SF1 elements that compose the tripartite basal enhancer, the study by Gnrhr promoter activity may thus require functional activation of a PKC-dependent pathway that involves the ERK- or JNK-signaling cascade or both and ultimately converges to AP1. However, accurate analysis of the intracellular events induced by GnRH treatment of αT3−1 cells strongly suggested that the JNK-signaling cascade is predominantly involved  that is involved in tissue-specific activity seems to be also involved in the GnRH and PKA-dependent regulation. The stimulatory effect of GnRH was indeed partially abolished and that of cAMP analog fully abrogated by targeted mutations of the CRE element. It is then rather probable that GnRH action also occurs through the SURG2 element in GGH3 cells.The GnRHR is known to be expressed in several extra-pituitary tissues, notably in some areas of the brain, as well as in gonads and placenta. In this regard, it is important to consider the modalities of expression and regulation of the reatment . Subsequreatment , 2001. Areatment , 1999. Ireatment . HoweverGnrhr promoter activity as demonstrated by nuclear run-off assays or after transient transfection of the 1.2 kb mouse promoter linked to the luciferase reporter gene  termed PACAP response element (PARE) I that includes the SF1-binding site and a second region (−136/−101) referred to as PARE II that contains the CRE (TGACGTTT). Gel-shift experiments revealed that SF1 and SAP together bind to PARE I while a protein immunologically related to the CREB interacted with PARE II. Altogether, these findings indicated that PACAP regulates the rat Gnrhr at the transcriptional level in αT3−1 cells. Interestingly, the mouse promoter was similarly proved to be regulated by PACAP (Fst) gene expression which restrains activin signaling and thus represses Fshb and Gnrhr expression as well as other activin-responsive genes. Therefore, in mouse gonadotrope cells, the PACAP direct stimulatory effect on Gnrhr promoter may be weakened by the indirect inhibitory action of PACAP on activin-stimulated promoter activity. The rat promoter is not submitted to this down-regulation because both the GRAS element and the DARE region are inactive. Whether PACAP regulation also occurs in vivo and is a common trait in mammals remains to be elucidated however.Specific type 1 receptors for PACAP are present in gonadotrope cells of the anterior pituitary gland as well as in mouse gonadotrope-derived αT3−1 cells. By transient transfection in αT3−1 cells, by PACAP . Howeverby PACAP , the SF1Gnrhr promoter has been independently isolated by two groups . Secondly, in contrast to the rodent promoters, the transcription start sites are located far upstream of the ATG codon and disperse within two domains localized between −578 and −826 and between −1347 and −1751, indicating the existence of two core promoter regions .Following transfection in αT3−1 cells, it appeared that crucial elements are located in the close vicinity of the translation start codon because the −173/+1 deletion abolished promoter activity . TargeteThe existence of a distal promoter active in non-pituitary cell lines has been strongly suggested in the early study by In gonadotrope-derived cells, the active elements of the upstream promoter are located within a −1705/−1674 region and are not functional in the reverse orientation. They consist of a pyrimidine-rich initiator (−1685/−1676) and additional upstream motifs (−1705/−1687) that include a CCAAT box. Multiple proteins ranging from 40 to 54 K are capable of binding this upstream promoter. Interestingly, a downstream silencer element (−1674/−1577) prevents upstream promoter activity, at least in gonadotrope cells. This silencer is efficient only when positioned downstream of the promoter in either reverse or forward orientation. Two sequence-specific repressor motifs of 28 and 8 bp (−1670/−1643 and −1630/−1623) are essential for silencer activity as evidenced by linker-scanning mutagenesis and gel mobility shift assays .In JEG−3 and IEVT placental cell lines, a similar organization seems to occur, combining positive regulatory sequence and downstream silencer, however with different localization and distinct response elements . The plaA third distal promoter was characterized in human ovarian granulosa-luteal cells. It extends from −1300 to −1018 and exhibited the highest promoter activity in SVOG−4o and SVOG−4m immortalized granulosa-luteal cells . Howevercis-acting regulatory elements were identified at −1300/−1018 and −2197/−1900, as deletion of either region induced a dramatic decrease in promoter activity -mediated mechanism (−8 M E2 after a 48 h-long treatment (80% reduction of promoter activity). Repression occurred via an AP1-like element located at position −130/−124. This element was able to bind c-FOS and c-JUN but not ERα as demonstrated by EMSA. DNA binding by ERα was not needed for the E2 repression of Gnrhr promoter activity. Further experiments strongly suggested that recruitment of AP1 and ERα to CREB binding protein (CBP) was mutually exclusive leading to inhibition of AP1-stimulated promoter activity by E2.In OVCAR3 ovarian carcinoma and MCF7 breast carcinoma cell lines, Eechanism . This ocin vitro studies of promoter functions often suffer from limitation of the cellular models used in transient transfection analyses, the next and complementary approach consists in analyzing promoter activity in an in vivo context through the generation of transgenic mice.Since Gnrhr promoter activity by GnRH. A 1900-bp fragment corresponding the mouse Gnrhr promoter fused to the luciferase reporter gene has been microinjected in mouse embryos and mice harboring the transgene were selected. Analysis of luciferase activity in various tissues showed that the fusion gene was expressed essentially in the pituitary gland, to a 10-fold lower level in the brain and to a limited extend in the testis. These data are in agreement with those deduced from autoradiographic studies using 125I-labeled GnRH or in situ hybridization  large T antigen and, secondly, the mouse promoter was the shortest used since it was 1.2-kb long only . With thelopment .ALPP) and inserted into the mouse genome. The resulting transgenic mice expressed the ALPP exclusively in gonadotropes within the pituitary gland -like element located at −2.5 kb may be involved in the inhibition of Gnrhr gene expression before birth. Finally, GnRH treatment of rat hippocampus primary cell culture led to stimulation of several markers of neurogenesis such as EGR1, synaptophysin, and spinophilin. Using this transgenic mouse model, several additional sites of transgene expression were detected, notably in the oculomotor pathway  factor(s), several parameters must be considered. From an ontogenetic point of view, the date of appearance of the Gnrhr transcripts must be correlated with the presence of the factor in the fetal pituitary or in fetal tissues that will express the GnRHR in the adult. For instance, binding sites for GnRH have been detected as early as 12–13 days of gestation in the rat developing pituitary and GnRHR mRNA at 14.5 and 15.5 days post-conception in testis and ovary from rat fetuses . In gel-shift experiment, both the SF1 element and the CRE display an apparent weaker affinity for their cognate transcription factors than the SF1 binding site from the rat aromatase gene or the canonical CRE sequence . This is well correlated with Gnrhr mRNA levels that are not detected in lactosomatotrope and corticotrope cell lines. Constitutive Gnrhr transcription is thus likely controlled by mechanisms involving trxG proteins in gonadotrope cells whereas in other pituitary cells, PcG proteins stably repress Gnrhr expression. In mammals, composition of trxG and PcG protein complexes displays broad diversity depending on the considered family of genes. A critical point would be to determine what are the main actors involved in histone methyltransferase and demethylase activity regarding the Gnrhr. Furthermore, PcG complexes are known to be clustered into subnuclear structures that look like nuclear foci termed polycomb bodies (Gnrhr into these subnuclear structures (Gnrhr expression, on gonadotrope cell lineage specification and differentiation, and more generally on organ development.An increasing number of studies on gene regulations and promoter activities aim to demonstrate the existence of specific regulatory units that bring together several response elements. From one tissue to another, the same elements may bind different factors resulting in tissue- or cell-specific modulations of gene expression. This ability to bind different factors is likely facilitated by sequence degeneracy of the response elements that lie within these regulatory units. Consequently, the degenerated response elements may be less efficient than their canonical counterparts when they are isolated from their promoter context. Efficiency is recovered in the original promoter context through synergistic interactions with other transcription factors belonging to the regulatory unit. Interestingly, this synergy may be attenuated if the wild-type degenerated element is artificially replaced by its canonical counterpart . In the sequence ,b. PACAPsequence . The SUR context . In a go context . Whether context . They ar context . PcG pro context . This reb bodies . Conversb bodies . In the ructures . The eluThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


During the last 30 years, systematic biochemical and functional studies have significantly expanded our knowledge of the transcriptional molecular components and the pre-mRNA processing machinery of the cell. However, our current understanding of how these functions take place spatiotemporally within the highly compartmentalized eukaryotic nucleus remains limited. Moreover, it is increasingly clear that “the whole is more than the sum of its parts” and that an understanding of the dynamic coregulation of genes is essential for fully characterizing complex biological phenomena and underlying diseases. Recent technological advances in light microscopy in addition to novel cell and molecular biology approaches have led to the development of new tools, which are being used to address these questions and may contribute to achieving an integrated and global understanding of how the genome works at a cellular level. Here, we review major hallmarks and novel insights in RNA polymerase II activity and pre-mRNA processing in the context of nuclear organization, as well as new concepts and challenges arising from our ability to gather extensive dynamic information at the single-cell resolution. In eukaryotic cells, the regulation, expression, and subsequent processing steps of genomic sequences tend to be localized to defined spaces within the nucleus . In the Chromosome territories delimitate a region of the nucleus  that is relatively empty of dense chromatin and is hypothesized to be highly interconnected across the nucleus with a higher-order organization –15. The in vitro run on assays [In the early 1990s, two groups reported the use of novel techniques that allowed the visualization of transcriptionally active sites within the nucleus through the incorporation of bromo-UTP in nascent transcripts . In thesn assays , 23, a mn assays . Accordin assays )).This model is consistent with the looped conformation model that several other independent approaches have suggested exists for an active eukaryotic gene –31. In tκB/TNFα activation axis [α module system [The existence of factories provides us with an elegant conceptual framework to explain the coregulation of functionally related groups of genes in specific contexts . It has ion axis , 35 and e system . Moreovee system . Finallye system , 38.β-d-ribofuranosylbenzimidazole  does not abolish the association of previously engaged genes with the RNAPII foci, at least for erythroid lineage-specific genes [Recent studies have reported on the stability of RNAPII foci upon disrupting transcription . Interesic genes . Observaic genes .in situ spectroscopy have recently been carried out to determine the composition of the transcription factories [Initial studies using actories . In thesactories , 42. Another important feature of transcription factories is the enhancement of the physical and functional coupling of transcription and downstream RNA processing steps. This is facilitated by the regulated recruitment of neighboring machinery for cotranscriptional mRNA maturation in an appropriate fashion and timing. This notion would expand the category of these structures to integrated “mRNA factories,” providing an intuitive physical framework for the numerous observed interactions among transcriptional and mRNA processing factors –46. OtheAlthough this model of transcription factories  explainsMany independent studies in the last two decades have led to a model in which the maturation of nascent transcripts take place simultaneously to their synthesis, that is, cotranscriptionally , 49. ThiIf pre-mRNA processing is performed largely in a regulated cotranscriptional fashion, the dynamic distribution of pre-mRNA processing factors should be correlated with the organization of transcriptionally active sites in the nucleus. The distribution of pre-mRNA processing factors in the eukaryotic nucleus, as observed using immunofluorescence staining and light microscopy, is not homogeneous and shows a dynamic pattern of localized accumulation in 10–30 irregular domains termed speckles, “SC35 domains” or “splicing factor compartments” (SFCs). At the level of electron microscopy, they correspond to two distinct structures: (i) interchromatin granules clusters (IGCs), composed of particles measuring 20–25 nm in diameter and (ii) perichromatin fibrils, 3–5 nm fibrillar structures localized both at the periphery of IGCs and in other nucleoplasmic regions, which are the sites of nascent pre-mRNAs  TheSeveral models, which are not mutually exclusive, have been proposed to explain the role of these nuclear bodies in the regulation of gene expression: (1) they function as storage/assembly/modification compartments that can supply processing factors to the surrounding active transcription sites; (2) they function as sequestering sites for the dynamic control of transcription and processing factors; (3) they serve as functional “hubs” for coregulated genes and their products; (4) they play an active role in posttranscriptional pre-mRNA processing and surveillance and/or in the coupling of early steps of mRNA biogenesis . The concept that nuclear speckles are transcriptionally inactive compartments that serve as storage or recycling sites of pre-mRNA metabolism complexes from which these complexes are recruited to nearby sites of active transcription according to demand is a widely held view supported by many experimental results –77. ImpoMALAT1 large noncoding (nc) RNA has been proposed to regulate the phosphorylation-dependent dynamics of splicing factors and their equilibrium between nucleoplasm availability and nuclear speckle sequestration [This proposal is compatible with the view that speckles act as inhibitory sites where specific factors are actively sequestered when their functional repression is required. The essential splicing factor SRSF1 is sequestered into these regions upon the induction of stress through a mechanism dependent on the dynamic interaction of SRPK with stress chaperone complexes, including Hsp70 and Hsp90 . Similarstration . Linkingstration . Howeverstration . Rigoroude novo, (ii) gathering by “coalescence” of smaller speckles, or (iii) recruiting the active gene to their surface. Interestingly, this inducible spatial correlation was dependent on the integrity of the inducible promoter driving the construct, Hsp70, which is known to be regulated by the activity of this model gene at the stage of elongation. Again, these observations might indirectly support a functional coordination between transcriptional elongation and the recruitment of pre-mRNA processing machinery. It remains to be fully resolved whether the nuclear speckles have an active role in the higher-order organization and functional coordination of the expression of specific genes or whether they rather arise as a consequence of the spatial concentration of required factors in areas proximal to active, coregulated genes. Importantly, it also remains to be elucidated if there is any posttranscriptional advantage for coregulated genes to converge at the same speckle. It would be interesting to unravel, for example, if subsets of genes that preferentially localize to the nuclear speckle periphery are enriched in genes that are mostly regulated at the level of elongation and whether both their synthesis and processing are enhanced upon appropriate recruitment to these compartments. Nuclear speckles are most often adjacent to a relatively high density of transcriptionally active regions , 83–85,  What is the behavior of the synthesized pre-mRNAs and mRNAs as related to nuclear speckles? A majority of introns are spliced, presumably in a cotranscriptional fashion, outside of the nuclear speckles –92. Thisde novo synthesis of transcripts at nuclear speckles, as determined by UTP analogue incorporation, suggest that these hyperphosphorylated forms of RNAPII are not engaged in active transcription. An intriguing interpretation might be that these RNAPII molecules serve as a platform for posttranscriptional splicing of transcripts that are trafficking through the speckles. Intriguingly, although transcription does not take place within nuclear speckles, a large subset of transcription factors accumulates in these regions, and transcription elongation factors are specifically enriched in these regions. RNAPII is also found to be associated with these compartments , 68, 105The study of spatial and dynamic properties is essential for understanding gene expression regulation. Techniques, such as FRAP and fluorescence loss in photobleaching (FLIP), are used to obtain very detailed information about diffusion rates, residency times, or proportion of immobile or stably tethered subpopulations of a given molecule in a delimited volume in the cell at a very high temporal resolution. Fluorescence Resonance Energy Transfer (FRET) and fluorescence lifetime imaging microscopy (FLIM), BiFluorescent complementation (BiFC), and specific applications based on fluorescence correlation spectroscopy (FCS) allow the monitoring and semiquantitation of close (mostly direct) interactions between molecules and the mapping of such interactions in relation to different structures of the cell. The use of these techniques in the study of transcription and pre-mRNA processing has led to interesting new concepts regarding their regulation and nuclear organization –112. Not all nuclear factors freely diffuse through the nuclear space. The movement of some of these are compatible with a model in which factors “scan” unspecific genomic sequences or/and bind components of the RNA machinery through a weak and transient binding until they engage in a favorable, specific assembly on their target sites . NuclearSpecific interactions among different spliceosomal components have also been studied in the context of nuclear organization and live-cell behavior using FRET and FLIM techniques. The dynamic model mentioned above is also compatible with the presence of preassembled subcomplexes, such as the spliceosomal components, which can be dynamically recruited to form even higher-order functional complexes . Of note∼4.3 kb/min, although these figures can also vary widely depending on the experimental setting used [∼1.7 kb/min [Recent adaptations of these techniques have been used to study fluorescent proteins that interact with high affinity and specificity to DNA and RNA sequences, such as the LacZ bacterial repressor and the MS2 and PP7 nucleocapsid coating proteins, respectively. The use of engineered constructs containing several tandem repeats of these target elements allows for the efficient recruitment of fluorescent molecules to the desired chromatin template or nascent transcript, amplification of the signal, and reliable detection by conventional light microscopy in living cells. FRAP measurements can then be used to estimate the rates of transcript elongation and release because these stages of mRNA biogenesis correlate with distinct kinetic steps, which can be mathematically dissected with appropriate modeling using the gathered data. Additionally, by combining this transcript-tagging system with the tagging of RNAPII with a distinct fluorophore, the dynamics of promoter binding and transcription initiation can also be inferred using the aforementioned data of transcript synthesis kinetics . Interesing used , 121. Fo7 kb/min , 119. Anin vivo functional coupling between transcription elongation and pre-mRNA processing, allowing us to obtain novel insights into the basis of pre-mRNA processing in the living cell. For example, it has been determined that early-spliceosomal components are actively recruited to transcribing genes lacking intronic sequences [These tools have been used not only for characterizing transcription dynamics, but also for facilitating the study of the equences . These oequences . In factequences . ImportaFinally, innovative microscopy and spectroscopy tools, coupled with powerful statistical analysis and modeling, have led to the first studies in estimating the dynamics of transcription at the single-molecule resolution , 125. ThThe quantitative study of the spatial and dynamic aspects of transcription and pre-mRNA processing is revealing itself as an essential complement to well-established, classical biochemistry-based approaches to fully understand how the regulation of gene expression is exerted in the cell. As stated in this review, many recent insights that help to explain long-standing questions regarding mRNA biogenesis could not have been achieved otherwise. However, these studies also give rise to important new questions. What is the functional relevance of spatial organization and regulation of dynamics in the different stages of mRNA biogenesis for the cell in a given context? Can we obtain a genome-wide picture of these parameters for all protein-coding genes in a systematic manner? How is cell-to-cell variability regulated within a cellular population to be advantageous for the cellular population as a whole? Is the dynamic regulation of the spatial distribution of the involved factors an essential component for the fine tuning of functional coupling of transcription elongation and pre-mRNA processing? It is expected that there will be a remarkable increase in the usage and optimization of these approaches, combined with more conventional biochemical and functional approaches, in the study of all aspects of mRNA formation and function.


Polycomb group (PcG) repressor proteins play a central role in gene regulation through differentiation and development, conferring repressive chromatin configurations at target gene promoters through their inherent histone modification activities. Recruitment of Polycomb repressor proteins to defined targets has been attributed to instructive mechanisms in which sequence-specific binding proteins and/or noncoding RNAs interact biochemically with the major Polycomb repressive complexes and thus define their sites of action. Here we highlight that this viewpoint is increasingly incompatible with experimental observations. We propose an alternative perspective based on the concept that Polycomb recruitment is responsive rather than instructive. Specifically, we suggest that Polycomb complexes sample permissive chromatin sites, and through positive feedback mechanisms, accumulate at those sites lacking antagonistic chromatin modifying activities linked to ongoing transcription.Drosophila, where they play a specific role in maintaining the normal segmental patterns of Hox gene expression through successive cell generations. Conversely, Trithorax group (TrxG) factors were identified based on their capacity to maintain the expression of Hox gene loci. Subsequent studies revealed that both PcG and TrxG proteins are highly conserved in multicellular organisms, where they perform an essential and pervasive role in epigenetic regulation of gene expression in differentiation and development PcG repressor proteins were first discovered in Drosophila proteins Polycomb (CBX2/4/6/7/8) and Polyhomeotic (PH1/2/3), and noncanonical forms in which catalytic subunits interact with one of two closely related proteins RYBP or YAF2  1 and 2 , that function as landing platforms for sequence-specific binding factors that are thought to directly recruit PcG complexes Drosophila occurs in a stepwise process with binding of the sequence-specific TFs, notably Pho, resulting in direct recruitment of PRC2 complexes. In turn, H3K27me3 deposited on histones by PRC2 is thought to result in the hierarchical recruitment of canonical PRC1 through its intrinsic capacity to recognize this modification The recruitment of TrxG complexes has been linked to binding of sequence-specific transcription factors (TFs) and establishment of transcription complexes. Similarly, studies examining the Current models invoking PcG recruitment through direct biochemical interaction with sequence-specific TFs and ncRNAs are summarized in In light of accumulating challenges to the concept of instructive targeting of PcG complexes, we propose an alternative model based not on direct recruitment by sequence-specific TFs and ncRNAs, but rather on the recognition by PcG complexes of common chromatin features at target loci. This simplified and generic targeting regime would permit the concerted establishment of PcG repression at a subset of inactive loci, while counteracting features elsewhere would limit PcG protein activity. Within the confines of this conceptual framework, we envisage PcG activity at target loci will respond to the transcriptional state of the associated gene, as opposed to the prevailing view which posits that PcG protein function instructs transcriptional outcomes via physical interaction with and targeting by sequence-specific binding factors.Key features of our model, focusing on PcG recruitment at target gene promoters, are illustrated in In support of a role for CGIs in PcG recruitment, a direct molecular link between unmethylated CGIs and PcG localization has recently emerged. KDM2B, a ubiquitously expressed ZF-CxxC domain–containing protein, forms a noncanonical PRC1 complex which can bind to nonmethylated CpG sites Could a similar CGI sampling activity also play a role in localization of PRC2 complexes? At present, there is no evidence for PRC2 binding preferentially to unmethylated CpG dinucleotides, but Jarid2, a substoichiometric PRC2 component, has been reported to bind GC-rich DNA sequences C. elegans embryos, mutations in the MES4 protein, the major methyltransferase catalyzing H3K36me2, result in redistribution of PRC2 from the X chromosome where it is normally enriched to sites throughout the autosomes The idea that a dynamic sampling process may underpin the functionality of PcG complexes on chromatin is supported by the observation that PRC2 redistributes in mammalian cell lines If CGI hypomethylation, and linked to this, CGI-specific chromatin configurations, define sites where variant PRC1 and PRC2 can engage productively, what are the mechanisms that restrict H3K27me3 and H2AK119ub1 accumulation to nonexpressed targets? As outlined above, we propose that in a given cell type the subset of CGI sites that ultimately become PcG targets transition from a “sampled” state into an “established” PcG protein occupied and repressed state through the concerted action of both PRC1 and PRC2. This may be mediated through positive feedback loops driven by the function of their chromatin modifying activities. For example, PRC2-mediated H3K27me3 can lead to hierarchical recruitment of canonical PRC1 complexes via CBX proteins which bind H3K27me3. Furthermore, recently it was shown that PRC2 prefers to methylate compact chromatin substrates Conversely, the majority of CGIs that remain free of established PcG silencing must possess a degree of “anti-silencing” that counteracts PcG protein establishment. How might this be achieved? One simple explanation would be that the major activity underpinning anti-silencing at most CGI promoters is the occupancy of positively acting transcription factors, the transcriptional machinery itself, and the function of TrxG proteins at these sites. Based on our evolving molecular understanding of PcG protein activity, there may be some sense in this idea. For example, although there exists some inherent level of H3K4me3 at CpG islands through ZF-CxxC domain–mediated targeting mechanisms If PcG proteins simply function to identify CGI loci that already lack significant transcriptional activity, in a manner that is responsive to transcription, why would their silencing services be required at all? One possibility lies in the fact that PcG systems appear to play important roles in keeping genes off in tissues where they should not normally be expressed. During development, as a cell transitions from one lineage to another, the expression of a subset of genes which are specific to the parental cell type must diminish in expression. A constant regime of CGI sampling would provide an opportunity for PcG proteins to identify these CGI sites in the genome, presumably by their lack of activated transcription and reduced capacity to anti-silence. Full and productive engagement of both PRC1 and PRC2 at these sites could then initiate, amplify, and fully establish a classical polycomb-repressed domain in response to this diminished anti-silencing. This would allow the cell to partition genes that have lost the capacity to be efficiently transcribed, an outcome that would be beneficial as it would help to protect the transcriptional identity of the cell from stochastic gene expression events that could lead to aberrant functionality. Furthermore, it would force the cell to invoke strong and persistent transcriptional signals to activate genes necessary for the transition to new and alternative transcriptional states. Not inconsistent with these ideas, removal of PcG proteins in embryonic stem cells affects transcription levels of only a proportion of target loci and then only by a small increment Drosophila. For example, the type of positively acting chromatin modifying activities  that we suggest are amplified with transcription and mechanistically correspond to anti-silencing at CGIs were originally identified in genetic interaction screens in Drosophila as PcG-counteracting TrxG proteins Drosophila still remain incompletely described. However, in light of our model, it is tempting to speculate that alternative sampling processes which do not rely on nonmethylated DNA may be at play in selecting PcG protein occupied sites in Drosophila. Indeed, there is indirect evidence that supports the possibility that PREs are continually sampled for susceptibility to PcG establishment The model we propose for PcG protein function in vertebrates is mechanistically rooted in an underlying requirement for nonmethylated DNA at CGIs, a feature that is not conserved in many invertebrate species which generally lack pervasive genome DNA methylation. Nevertheless, we believe that conceptually the interplay between “silencing” and “anti-silencing” at CGIs very much parallels the observations and ideas about PcG protein function that have emerged through the genetic and molecular study of the PcG system in The concept of CGIs as sampling platforms for chromatin modifying enzymes has broader implications for how we view chromatin structure as part of transcription. If as we propose PcG and TrxG proteins respond, as opposed to instruct transcription, these factors would likely act as important modulators of transcriptional outcomes as opposed to drivers. This may be achieved by the creation of a bistable chromatin switch at CGIs through the opposing activities of PcG and TrxG complexes. For example, if PcG proteins can sample CGIs and then identify those that are in the nontranscribed state, positive feedback mechanisms linking PRC1 and PRC2 could help to reinforce a monostable chromatin state that is inhibitory to transcription . If CGIsInterestingly, high-resolution mapping of histone modifications in mammalian cells has revealed a class of repressed PcG CGI promoters at which TrxG-mediated H3K4me3, histone acetylation, and RNA polymerase II occupancy are all detectable, albeit at relatively low levels, referred to as bivalent promoters In support of the concept of chromatin bistability at CGIs, recent studies examining PcG chromatin modifications, TrxG chromatin modifications, and gene expression during stem cell differentiation also propose a bistable chromatin state that appears to describe the system with some degree of accuracy C. elegans, higher plants, and Drosophila.In summary, we suggest that our responsive model for PcG protein function in vertebrates overcomes many of the issues that complicate the prevailing instructive models, notably the requirement to invoke that PcG complexes physically interact with a unique set of TFs or ncRNAs in individual cell types. It should however be noted that the two types of model are not necessarily mutually exclusive. Furthermore, many of the features of our model adhere to the general principles describing PcG and TrxG protein function that were developed around observations from early genetic studies which suggest that these systems do not define transcriptional states but instead function to maintain predetermined transcriptional states A review of bivalent gene promoters that reaches similar conclusions to some of those expressed in this viewpoint was published when this article was under revision Formulation of our model required the synthesis of many ideas and observations suggested previously by others and we wish to unequivocally acknowledge the derivative nature of our proposal. Due to limitations in the scope and depth of our viewpoint, we may have failed to reference all relevant papers and we apologize for any instances where this has occurred. We wish to thank Tom Milne, our lab colleagues, and the wider community for stimulating discussions.


Initiation and elongation of RNA polymerase II (RNAPII) transcription is regulated by both DNA sequence and chromatin signals. Recent breakthroughs make it possible to measure the chromatin state and activity of core promoters genome-wide, but dedicated computational strategies are needed to progress from descriptive annotation of data to quantitative, predictive models.Here, we describe a computational framework which with high accuracy can predict the locations of core promoters, the amount of recruited RNAPII at the promoter, the amount of elongating RNAPII in the gene body, the mRNA production originating from the promoter and finally also the stalling characteristics of RNAPII by considering both quantitative and spatial features of histone modifications around the transcription start site (TSS).As the model framework can also pinpoint the signals that are the most influential for prediction, it can be used to infer underlying regulatory biology. For example, we show that the H3K4 di- and tri- methylation signals are strongly predictive for promoter location while the acetylation marks H3K9 and H3K27 are highly important in estimating the promoter usage. All of these four marks are found to be necessary for recruitment of RNAPII but not sufficient for the elongation. We also show that the spatial distributions of histone marks are almost as predictive as the signal strength and that a set of histone marks immediately downstream of the TSS is highly predictive of RNAPII stalling.In this study we introduce a general framework to accurately predict the level of RNAPII recruitment, elongation, stalling and mRNA expression from chromatin signals. The versatility of the method also makes it ideally suited to investigate other genomic data. Regulation of transcription initiation is controlled by several distinct processes, including binding of transcription factors to distal and proximal binding sites and the accessibility of DNA -3. The aEarly studies raised the hope of deriving a "histone code" which baThe mRNA level of a gene is essentially a function of its rate of RNAPII elongation and its degradation, but is not necessarily correlated with the rate of recruitment of RNAPII in the core promoter: recent studies -9,22 havTherefore, in this study, we extend previous computational efforts by exploring the predictability of RNAPII recruitment, elongation and the release of stalled RNAPII from chromatin signals in the regions around the TSS, taking both the strength of signals and their spatial distribution into account.We show that RNAPII recruitment, elongation and stalling can be predicted from the chromatin features in the promoter region, and that the positional distribution of marks in the promoter is almost as predictive as the signal intensities of the same marks. Interestingly, when predicting RNAPII stalling/release, binding sites of transcription factors reported to have a key role in this process have less predictive importance than the chromatin signals. We further demonstrate that the significant and joint enrichment of H3K9ac, H3K27ac, H3K4me2 and H3K4me3 is necessary for RNAPII enrichment in the promoter but not sufficient for elongation.To predict promoter usage from chromatin signals, we constructed a computational framework. We wanted this framework to be able to incorporate any type of signal distributed around the TSS, and take both the signal strength as well as the spatial distribution of the signal into account. Therefore, the -975 to +975 region around human TSSs were divided into 13 sub-regions, each 150 nucleotides (nt) wide, designated "bins", where the center bin was centered on an annotated TSS. While the bin size was originally chosen to mimic the span of DNA wrapped around a nucleosome, our results are robust to changing the number of bins and their position, as described below and in Methods.et al. [In each such bin, we counted the contribution from each type of signal by summing the aligned tags from a given ChIP-seq experiment falling into the region; this formed the primary input to the predictive model Figure . All datet al. , countinWe estimated mRNA levels using RNA-Seq tags mapping to the first 1, 000 nt exonic sequence downstream of the TSS, excluding the intronic intervals. This definition was a compromise between the following two factors: i) as RNA-seq reads are randomly sheared and therefore have problems capturing edges of exons and transcripts, we needed a reasonably large RNA space to measure expression and ii) for very long genes, we wanted to avoid counting contributions from possible alternative promoters, or alternative splice forms. The effects of changing the thresholds are described in Methods.Given this data, we used a Random Forest  method fP < 10^-16)  score of) Figure .The mRNA regression results are similar to those of previous studies ,21, whicP < 0.05, see Methods and Additional file We wanted to see if our results were dependent on our specific methods and thresholds. First, we applied linear regression models (see Methods) to compare the results with the Random Forest method. Although the correlation scores obtained using Random Forest were consistently and j indicates the bin:To investigate this further, for each promoter, we normalized the contribution of each input feature This normalization will retain the shape of the distribution of each modification in a single promoter but not the overall magnitude of signals. We then plotted the normalized distribution of H3K4me3, H3K4me2, H3K9ac and H3K27ac for the promoters broken up by their level of mRNA expression Figure . From thSeveral studies have shown that several histone marks have characteristic patterns around promoters - for instance, H3K4me2 and me3 marks tend to have a double peak around active TSSs and flanking the nucleosome-depleted region at active TSSs ,31,32. SUsing the normalized data, we tried to distinguish active and inactive promoters from each other and predict the usage rate of the active ones. Classification using only the distribution shape gives an AUC value of 0.969, compared to 0.973 Figure  when alsRegression using only the positional distribution gave a PCC for RNA-Seq of 0.67 compared to 0.76 when also using the signal strength. For RNAPII in the promoter, the corresponding values are 0.77 and 0.83 and for RNAPII in the gene body RNAPII 0.41 and 0.65.P < 0.05 in all cases, see Methods and Additional file While the regression results using signal strength were always significantly better than using only the normalized shape as input .To do this, we needed a threshold defining whether a certain mark is enriched or not. We used a threshold based on the 95th or 99th quantile, or using a Poisson-based test with P < 0.05 or P < 10-5 significance thresholds as used in Ernst et al.[We next investigated the impact of co-occurrence of H3K9ac, H3K27ac, H3K4me3, and H3K4me2 for RNAPII occupancy, measured by RNAPII density in either the core promoter or in the gene body. We subdivided the set of 9, 693 non-overlapping transcripts (see Methods) depending on whether they have none, some, or all of the above marks present. This analysis showed that most promoters either have none or all the marks enriched, which fits with the finding that H3K4me3 and AcH3 marks are prone to be more stable when they occur together . Moreovest et al. did not Since the K562 cell line is known to have specific chromosomal aberrations ,36, we wThis implies that all of the four marks are necessary in the recruitment of RNAPII at the TSS but not sufficient for elongation. A necessary caveat is that this causality (histone marks causing recruitment) cannot be proved rigorously since i) these results could be explained by a third confounding variable that always co-occurs with the four marks, ii) we are not measuring a single cell and we do not know if the marks physically co-occur on the same nucleosome and iii) we do not know if the histone modifications are needed to recruit RNAPII or vice versa.et al. [We hypothesized that some of these modifications occur simultaneously. This fits with a recent study where Pasini et al.  showed tSeveral recent studies have shown that a substantial set of promoters recruits RNAPII, which is not released for elongation ,39. ThisA possible solution is to correlate the elongation rate of RNAPII with the amount of downstream marks that are found in the transcribed regions, such as di- and tri-methylation of H3K36 and H3K79me2 shown by earlier studies ,10,40-42et al. [The amount of stalling vs. elongation has typically been measured by taking the ratio between the density of RNAPII at the core promoters vs. in the gene body - called either the travelling ratio or the stalling index(S index),26. Hereet al. :d is the number of RNAPII ChIPed tags per nt in the given region. This will give a value between ~-2 and ~4  by using the 13 bin framework as above, and compared predicted and actual S index values. We achieved a mean PCC of 0.83, similar to our previous regression results. Addition of dinucleotide densities as additional features only resulted in a slight improvement (PCC = 0.85).We reasoned that while the epigenetics data clearly has substantial predictive power by itself, including additional features might increase this value even further. Several transcription factors are known to be correlated with polymerase elongation, including the negative elongation factor NELF that pauses the elongation of RNAPII , and thevs chromatin signals could be due to several reasons. Firstly, a recent study has shown that release-associated factors such as cMyc can be found in both stalled and elongating promoters [Surprisingly, the prediction based on only cMYC and NELFe alone gave a lower PCC of 0.7. The lower amount of information in the transcription factor binding sites romoters . SecondlThe most informative epigenetic features for the regression are still H3K27ac, H3K9ac, H3K4me2, and H3K4me3 with a clear preference for the first bin after the TSS Figure , suggestSince the same marks are indicated to be important in the stalling prediction as in the elongation prediction, we wanted to see whether we predict the S index as a by-effect of predicting the elongation. Therefore, we split the testing promoters by expression level into five classes by RNA-Seq and predicted the S index for each subset by using the original model  to predict the vs the signal strength.A few recent studies have demonstrated that epigenetic data is predictive of the production of mRNAs as measured by microarray and RNA-Seq ,21. In tet al. found H3K4me3 and H3K79me1 to be most informative in predicting the expression level in low CpG content promoters whereas the expression in high CpG ones depend more on H3K27ac and H4K20me1 [et al., H3K4me2 and H3K79 sets are reported to be more predictive than RNAPII in predicting the gene expression [Predictive methods are even more usable if they are not "black boxes" and can identify what biological features are the most important for accurate predictions. Previous computational methods identified different sets of histone marks to be the most predictive of mRNA production; Karlic pression . This diIn our study, we have broken up RNAPII recruitment and elongation and found that the most predictive marks for both the processes are H3K4me2, H3K4me3, H3K9ac and H3K27ac, but the location of the marks have different predictive importance for respective processes. In fact, the positional distributions of marks are almost as informative in the prediction as the signal strength (the number of ChIP tags). In agreement with a previous experimental study , the metet al found that 90% of all promoters having a RNA polymerase II pre-initiation complex (PIC) also contain acetylation of H3 and/or H3K4me2 [et al found a modification backbone of 17 modifications that co-localize in ~25% of human promoters and only a very few promoters have a subset of these modifications [However, a follow-up analysis showed that almost all promoters with high amounts of recruited RNAPII have all these marks simultaneously enriched. This fits well with previous studies based on individual correlations - for instance, Kim  H3K4me2 . Consistet al showed that H3K4 methylation is needed for acetylation of H3K9 and that both marks are required for the recruitment of RNAPII, but not sufficient for the elongation [While our study showed that all of the four marks have to be present for RNAPII recruitment in three different cell lines, the causality or order of recruitment is not clear. The study by Wang ongation . Howeverongation ,48.In addition, among these four marks, H3K4me2/3 have been reported to be respectively present in 97% and ~ 75% of all promoters in human cells, but only ~ 50% of these promoters produce detectable transcripts [It is surprising that adding ChIP data for the transcription factors cMyc and NELF, known to be involved in the release of stalled RNAPII, does not give a substantial improvement in the prediction; in fact, if only using the ChIP data from the transcription factors in the proximal region, the prediction results are much lower, suggesting the detected binding sites of these two factors are not very informative. One possible reason for this is that the histone marks or other chromatin signals capture the effect of these factors. Alternatively, the interaction between these transcription factors and RNAPII might not be detected by the ChIP experiments.The field of genomics is now in a situation where large data sets can be produced with small effort and cost compared to previously, meaning that the challenge has shifted towards analyzing and understanding the data produced. For this, we need frameworks that are both flexible, easily used and that can systematically mine the data to produce viable hypotheses to understand the underlying biology. In this study we have shown the feasibility of predicting RNAPII stalling, transcription and mRNA production at the core promoter level using a relatively simple machine learning framework which can also suggest new biological mechanisms, or reinforce previous hypotheses in a more statistically rigorous way.We have found that the spatial distribution of marks is almost as predictive as their signal, and that different parts of the promoter are informative for prediction of the recruitment, elongating and release of RNAPII. Moreover, we find that the four marks, H3K4 di- and tri- methylation, H3K9ac and H3K27ac are nearly always co-occurring in promoters where RNAPII is recruited. Among these marks, H3K4 di- and tri- methylation are more informative for determining the promoter position whereas acetylation marks are more predictive of the amount of promoter usage.http://genome.ucsc.edu/ENCODE/. Only data labeled as unrestricted (9 months after release date) were used.All primary data was downloaded from the ENCODE UCSC browser ,50http:/We made the main analysis using the K562 cell line data and used HUVEC and NHEK data for validation, all from the NCBI36 (HG18) assembly.We used the UCSC known gene track ,50 as geAll TSSs were derived from the gene track mentioned above. Since the alternative transcripts may cause ambiguous cases when measuring the tag expression, we only used transcripts that do not overlap any other transcript from the track. This gave a set of 12, 872 core promoters. Due to that we measure the mRNA expression by RNA-Seq in the downstream 1, 000 exonic regions , all the transcripts that were not long enough were discarded, resulting in a 9, 115 set for final analysis.RNAPII as well as the histone modification data for H3K4me1, H3K4me2, H3K4me3, H3K9ac, H3K9me1, H3K27ac, H3K27me3, H3K36me3, H4k20me1 in all the three cell lines were downloaded from 'Broad Histone' tracks ,37,51 inWe used the nuclear CAGE libraries from the K562 cell lines from the 'ENCODE RIKEN RNA Subcellular Localization by CAGE Tags' track in the UCSC browser. These tracks are produced by the RIKEN Omics Science Center -55. The P < 0.05) lower than when using the definition above which typically gave PCC scores of 0.75 or higher. While the decrease is not large in terms of absolute numbers (~0.4-0.1 difference in mean PCCs), it probably reflects the issue discussed above - the shorter definition might reflect the issues with detecting exon edges while including all known exons increases the risk of not capturing relevant splice form or erroneously the contributions of unannotated downstream alternative promoters.We used K562 Tier 1 polyA+ RNA-Seq data produced by the Snyder laboratory -59 from We downloaded the ChIP-seq c-Myc and NELFe data from the K562 cell line from the ENCODE 'Open Chromatin' ,61 trackWe downloaded the ENCODE methylation data for K562 from the Hudson Alpha lab [et al. [We extracted the promoter sequences for all used genes and divided them into bins. For each bin we counted the number of occurrences of each dinucleotide and divided by the length of the bin-1. These 16 numbers for each bin were used as input features in the prediction. The normalized GC-content was computed as defined by Saxonov et al. .To retain the positional distribution as well as signal strength as inputs we separated the +/-975 nt regions around the TSS into 13 150 nt wide bins. Starting from setting up the center bin +/- 75 around the TSS, flanking ones were gradually extended towards upstream and downstream. Given a bin and a ChIP dataset, we counted the number of TPMs from the ChIP data set mapping to the region. This results in the size of an initial feature set (number of bins)*(number of data sets).These thresholds were selected based on biological and practical reasons. The +-975 region was mean to encompass the core promoters as well as its flanking regions. The reason for not using +-1000 is that the region has to be dividable by the 150 nt bins, whose sizes was chosen not only for the practical reason that it gives a reasonable number of bins, but also as it roughly corresponding to the area occupied by a nucleosome. However, as described in the main text, changing the number of bins and thereby their positions has no substantial impact on the results. Likewise, as shown in Figure Given the above input feature framework, we constructed two types of predictive models: one for classification  and one for regression .All the classifications were made by using the Random Forest method , as implFor performing regression we used Random Forest and four different versions of linear regression. The linear models included ordinary linear regression as implemented in R function lm and regularized versions of it, namely: ridge-, lasso- and elastic net regression. These three methods are designed to prevent over fitting and perform feature selection when the number of predictive variables is large. We fitted these linear models using glmnet  package We classified active, inactive and randomly selected non-promoter regions from each other using chromatin signals as inputs, as described below.The active promoter set  was defined as +/-1, 000 nt regions containing both CAGE and RNA-Seq tags. We considered only genes that were long enough (exonic length of 1000nt or more) for a reliable RNA-Seq density measurement.The inactive (or silent) promoter set  was defined as promoters with no tags from either CAGE within +/-75 nt around the TSS or RNA-Seq in the first 1000 nt exonic region. We selected random genomic regions of the same size for the random position set.For training and evaluating the results for the classification, we used a hold-out strategy wrapped by 10-fold cross-validation. In order to minimize the bias from unbalanced sizes of the binary classes, we randomly selected the same amount of data from the larger class according to the size of smaller class in each run of the cross-validation. Then with two equal-sized classes, we further divided the data for training and testing by the proportion of 70% and 30%. The local AUC and importance for one fold was evaluated from the performance of the trained model in the test set. After finishing 10-fold repeats, the overall AUC and importance were calculated as the mean of the results.For predicting the expression levels we considered only the active promoters used in the classification, described above. We applied log2 transformation to the data in order to make it more suitable for the regression task. To avoid taking the logarithm of 0, we added a pseudo count of 0.001 to both input features and output.2 TPM values. This way we could estimate both the regression accuracy and also how stable the accuracy is, which is important when comparing the results from different methods.We assessed the performance of the predictions using a repeated hold-out scheme. At each step we randomly divided the dataset of 5, 131 promoters defined above into 10 equally sized parts. Then we trained the model using the 9 proportions of them and tested the model predictions on the exclusive part. We train on the data 10 times until all of the subset had been used as a test set. For evaluations, we calculated the Pearson Correlation Coefficients between predicted and actual logTo study the influence of interactions on the expression level prediction, we used linear models with interaction terms. In our original dataset we divided the promoter into 13 bins, to assess the positional influences of the modifications. However, including interactions between all these parameters would make the model too large. Since we achieve almost as good result using 1 bin per modification as 13 in the regression problem, we used only 1 bin per mark for testing interactions. We then tested the model using a cross-validation schema as described above.S were calculated as described in the main text. For the regression of s index, we randomly selected 30% of the data from the 9, 115 set as the testing data and used the rest in the training procedure. We then used the same 13-bin prediction framework and methods as we used in the previous regression problems. In addition, c-MYC and NELFe ChIP-seq signals were also used as optional input features.The stalling index value th quantile of the random distribution meaning that 5% of the random regions would be considered to have the mark present. Thresholds for RNAPII present in the promoter region and gene body were defined in the same way. As mentioned in the text, we also varied the threshold to the 90th or 99th percentile, and also tested the Poisson based methods as the one also used by Ernst et al. [P < 0.05 or P < 10-5.To be able to say with confidence if a promoter has a specific histone mark present we need to assess the random expectation of tags from the given mark in a genomic region of the same size. We sampled 33.000 random genomic regions and counted the number of tags for each mark in each region. We set the threshold to the 95t et al. , with a For measuring the significance of the difference between different classification tasks, a list of p values for one particular pairwise classification was computed in each fold of the cross-validation procedures, by the Hanley and McNeil test  implemenWe predicted TATA-boxes in the -50 to -10 region of each TSS using the TATA-box positon weight matrix from the JASPAR database  and a scWe made all plots in R using the ggplot  and ROCRThe authors declare that they have no competing interests.YC, MJ, RK, EV, XZ and AS performed the analysis and interpreted results. BP and JW assisted with statistical issues. YC, MJ, RK and AS made all figures. All authors wrote the manuscript. All authors read and approved the final manuscript.Supplementary figures with legends. This file contains Supplementary Figure S1-S10 and Supplementary Table S1-S6Click here for file