Plasmodium falciparum and Toxoplasma gondii. A major fraction of the bulk parasite genome is packaged as transcriptionally permissive euchromatin with few loci embedded in silenced heterochromatin. Primary chromatin shapers include histone modifications at the nucleosome lateral surface close to the DNA but their mode of action remains unclear. We now identify versatile modifications at Lys31 within the globular domain of histone H4 that crucially determine genome organization and expression in Apicomplexa parasites. H4K31 acetylation at the promoter correlates with, and perhaps directly regulates, gene expression in both parasites. By contrast, monomethylated H4K31 is enriched in the core body of T. gondii active genes but inversely correlates with transcription, whereas it is unexpectedly enriched at transcriptionally inactive pericentromeric heterochromatin in P. falciparum, a region devoid of the characteristic H3K9me3 histone mark and its downstream effector HP1.An unusual genome architecture characterizes the two related human parasitic pathogens  Apicomplexa phylum clusters thousands of single-celled eukaryotes identified as parasites of metazoans including humans in whom they cause or increase the risk of major public health problems. Preeminent human pathogens include Plasmodium species that are responsible for dreadful malarial disease, as well as Toxoplasma gondii and Cryptosporidium spp., which are leading causes of food-borne and water-borne diseases. A shared characteristic of apicomplexan life cycles is the multiplicity of developmental stages, with progress from one stage to the next occurring alongside precise genetic reprogramming to ensure the survival and transmission of parasite populations. The emerging concept of remarkably dynamic gene expression in Apicomplexa has risen from the observation that large numbers of mRNAs are exclusively expressed in a given developmental stage  of histones, such as lysine methylation (me) and acetylation (ac) among many others. Indeed, PTMs on histone tails have been shown, alone or in combination, to alter the accessibility of effector proteins to nucleosomal DNA and hence to impact chromatin structure, according to the ‘histone code’ hypothesis . In addiApicomplexa have evolved unique ways to modify histones that rival the strategies adopted by the cells they infect and provide zoites with remarkable capacities to multiply or undergo stage differentiation . Some hiIn eukaryotes, PTMs have been detected primarily in the histone tails sticking out from the nucleosome, but an ever-growing list of PTMs characterized as critical regulators of chromatin structure and function have now been identified in the lateral surface of the histone octamer, which directly contacts DNA . Those ‘T. gondii and P. falciparum, unbiased mass spectrometry hasuncovered the repertoire of the most prevalent histone PTMs, including singular marks. However, only few of them were mapped at the outer surface of the octamer  that had previously been largely underestimated , immunofluorescence assays were conducted over 48 hr of culture to probe the ring, trophozoite and schizont stages  for DNA and various chromatin marks. H4K31ac was observed scattered through the nucleoplasm of MEFs but excluded from nucleoli and segregated away from heterochromatic foci, as were the transcription-associated PTMs H3K4ac, H3K9ac and H3K27ac . This paT. gondii nuclei when compared to that of other acetylated residues of the histone tails . We first investigated the relative performance of our homemade antibodies in terms of specificity, sensitivity and the number and distribution of peaks. We observed low variability and a high degree of similarity in read coverage between technical replicates, regardless of the antibodies used for immunoprecipitation . We have re-examined the extent and genome-wide scattering of H3K4me1 using a type II (Pru) genetic background. We observed a high degree of similarity in read coverage between H3K4me1 and H4K31me1  and this discrepancy may be explained by the higher level of SRS or GRA1 gene expression . Cluster analysis revealed varying levels of gene expression with cluster Q1 displaying the highest level, clusters Q2 and Q3 defining intermediate levels, and cluster Q4 displaying the lowest level  was associated with a high level of H4K31ac upstream of the ATG together with an enrichment along the gene body, which coincided with the expected lack of H4K31me1 (MAG1), H4K31ac spread but did not extend over the entire gene body as observed for GRA1 (TGME49_233490 or TGME49_270230) related to a relatively high level of H4K31me1 in the gene body and a restricted mapping of H4K31ac at the promoter, thereby arguing for an inverse correlation between the yield of expression and the level of H4K31 methylation , the lapression . We therst level . High mRH4K31me1 . In highfor GRA1 , indicathylation .T. gondii life cycle. As such, the ENO1 and BAG1 genes, which are well known to be expressed distinctively in the bradyzoite stage during the chronic phase of infection  lack the dual PTM  a cte stage  which in  and are enriched in H3K9me3, which binds HP1  promotes the recruitment of the yFACT subunit Spt16 across transcribed genes, as assessed by the typical change in Spt16 distribution in which occupancy shifts toward the 3’ ends of transcribed genes in the H4K31E yeast mutant  histone chaperone has been shown to assist first in the removal of nucleosomes ahead of the transcribing RNA Pol II and next in their reassembly after polymerase passage. Although we provide evidence that H4K31 modifications may contribute to gene regulation at least in t mutant .The versatility of H4K31 goes beyond even these modifications as ubiquitylation of H4K31 has been reported as an additional regulatory PTM for transcription elongation in human cells . Indeed,T. gondii genome with alanine or glutamine to mimic acetyl lysine or with arginine to mimic nonacetylated lysine but were unsuccessful in achieving these substitutions (data not shown). Engineered budding yeast with those substitutions did not significantly affect cell viability but led to an unexpected increase of telomeric and ribosomal DNA silencing  and rifin genes  and type II strains (Pru ∆ku80) of T. gondii were maintained in vitro by serial passage on monolayers of HFFs. P. falciparum 3D7 strain was grown in RPMI 1640 media supplemented with 0.5% Albumax II, 0.1 mM hypoxanthine and 10 mcg/ml gentamicin. The culture was maintained at 2% hematocrit and 5% parasitemia. The parasites were grown at 37°C and in 1% O2, 5% CO2% and 94% N2 gas mixture concentration. The cultures were free of mycoplasma, as determined by qualitative PCR.HFF primary cells  were culT. gondii-infected HFF cells grown on coverslips were fixed in 3% formaldehyde for 20 min at room temperature, permeabilized with 0.1% (v/v) Triton X-100 for 15 min and blocked in phosphate-buffered saline (PBS) containing 3% (w/v) bovine serum albumin (BSA). The cells were then incubated for 1 hr with primary antibodies followed by the addition of secondary antibodies conjugated to Alexa Fluor 488 or 594 (Molecular Probes). Nuclei were stained for 10 min at room temperature with Hoechst 33258. Coverslips were mounted on a glass slide with Mowiol mounting medium, images were acquired with a fluorescence ZEISS ApoTome.2 microscope and images were processed by ZEN software (Zeiss). P. falciparum asexual blood life stages were washed with PBS and fixed in solution with 4% paraformaldehyde and 0.0075% glutaraldehyde in PBS for 30 min. After one wash with PBS, cells were permeabilized with 0.1% Triton X-100 in PBS for 10 min. Cells were washed twice with PBS, and blocked with 3% BSA in PBS for 1 hr. The cells were then incubated for 1 hr with primary antibodies followed by the addition of secondary antibodies conjugated to Alexa Fluor 488 or 594 (Molecular Probes). Nuclei were stained for 30 min at room temperature with Hoechst 33258. The parasites were finally washed 2–3 times before loading onto glass slides mixed with fluoro-gel (Electron Microscopy Sciences). Images were acquired with a fluorescence ZEISS ApoTome.2 microscope and processed by ZEN software (Zeiss).The final concentration of histone deacetylase inhibitors dissolved in DMSO was, as described , FR-2352To construct the vector pLIC-ENO1-HAFlag, the coding sequence of ENO1(TGME49_268860) was amplified using primers LIC-268860_Fwd (TACTTCCAATCCAATTTAGCgaacatgcaggcaatggcttggctcttc) and LIC-268860_Rev (TCCTCCACTTCCAATTTTAGCttttgggtgtcgaaagctctctcccgcg) using Pruku80 genomic DNA as template. The resulting PCR product was cloned into the pLIC-HF-dhfr vector using the ligation independent cloning (LIC) method as reported previously .TGGT1_254555), GCN5B (TGGT1_243440), MYST-A (TGGT1_318330), MYST-B (TGGT1_207080), HAT1 (TGGT1_293380), HDAC1 (TGGT1_281420), HDAC2 (TGGT1_249620), HDAC3 (TGGT1_227290), HDAC4 (TGGT1_257790) and HDAC5 (TGGT1_202230). Twenty mers-oligonucleotides corresponding to specific GOI were cloned using the Golden Gate strategy. Briefly, primers TgGOI-CRISP_FWD and TgGOI-CRISP_REV containing the sgRNA targeting TgGOI genomic sequence were phosphorylated, annealed and ligated into the linearized pTOXO_Cas9-CRISPR plasmid with BsaI, leading to pTOXO_Cas9-CRISPR::sgTgGOI. T. gondii tachyzoites were then transfected with the plasmid and grown on HFF cells for 18–36 hr. Cloning oligonucleotides used in this study were:The plasmid pTOXO_Cas9-CRISPR was described by TgHDAC1-CRISP-FWD: 5’- AAGTTGCGTCGCCGTTCTCTCACGCG −3’TgHDAC1-CRISP-REV: 5’- AAAACGCGTGAGAGAACGGCGACGCA −3’TgHDAC2-CRISP-FWD: 5’- AAGTTGCGCCCGTCGCCTCCCCCGCG −3’TgHDAC2-CRISP-REV: 5’- AAAACGCGGGGGAGGCGACGGGCGCA −3’TgHDAC3-CRISP-FWD: 5’- AAGTTGATATCGGAAGTTACTACTAG −3’TgHDAC3-CRISP-REV: 5’- AAAACTAGTAGTAACTTCCGATATCA −3’TgHDAC4-CRISP-FWD: 5’- AAGTTGCTGTTGCTGAAGCCCAGGCG −3’TgHDAC4-CRISP-REV: 5’- AAAACGCCTGGGCTTCAGCAACAGCA −3’TgHDAC5-CRISP-FWD: 5’- AAGTTGGCGAGACCGGGGCAGCCGCG −3’TgHDAC5-CRISP-REV: 5’- AAAACGCGGCTGCCCCGGTCTCGCCA −3’TgGCN5A-CRISP-FWD: 5’- AAGTTGCGTGACGAACGACAGGCAAG −3’TgGCN5A-CRISP-REV: 5’- AAAACTTGCCTGTCGTTCGTCACGCA −3’TgGCN5B-CRISP-FWD: 5’- AAGTTGGGTTTCCTGTGTCGAGACCG −3’TgGCN5B-CRISP-REV: 5’- AAAACGGTCTCGACACAGGAAACCCA −3’TgMYSTA-CRISP-FWD: 5’- AAGTTGGCTGCTCCGCGACTCAGCGG −3’TgMYSTA-CRISP-REV: 5’- AAAACCGCTGAGTCGCGGAGCAGCCA −3’TgMYSTB-CRISP-FWD: 5’- AAGTTGCGCGAAGAAGGGAGAGAGCG −3’TgMYSTB-CRISP-REV: 5’- AAAACGCTCTCTCCCTTCTTCGCGCA −3’TgHAT1-CRISP-FWD: 5’- AAGTTGCCGACGGGTCACGGAGACTG −3’TgHAT1-CRISP-REV: 5’- AAAACAGTCTCCGTGACCCGTCGGCA −3’T. gondii RH, RH ∆ku80 and Pru ∆ku80 were electroporated with vectors in cytomix buffer  using a BTX ECM 630 machine (Harvard Apparatus). Electroporation was performed in a 2 mm cuvette at 1.100V, 25 Ω and 25 µF. Stable transgenic parasites were selected with 1 µM pyrimethamine, single-cloned in 96 well plates by limiting dilution and verified by immunofluorescence assay.AB_2713903), mouse anti-HA , rabbit anti-H4K8ac , rabbit anti-H4K12ac , rabbit anti-H3K4ac , rabbit anti-H3K9ac (Diagenode RRID:AB_2713905), rabbit anti-H3K14ac , rabbit anti-H3K18ac , rabbit anti-H3K14ac  and mouse anti-H3K27ac , anti-H4K20me3 , anti-H3K9me3 , anti-H3K4me1  and anti-H3K4me3 . Western blot secondary antibodies were conjugated to alkaline phosphatase (Promega), whereas immunofluorescence secondary antibodies were coupled with Alexa Fluor 488 or Alexa Fluor 594 (Thermo Fisher Scientific). We also raised homemade H4K31acetylation- and H4K31monomethylation-specific antibodies in rabbit against linear peptides corresponding to amino acid residues 23–35 of histone H4 and carrying modified residue K31: C-DNIQGITKme1PAIR; C-DNIQGITKacPAIR and C-RDNIQGITKacPAIR. They were produced by Eurogentec and used for immunofluorescence, immunoblotting and chromatin immunoprecipitation.Primary antibodies: rabbit homemade anti-TgHDAC3 described in ∆ku80 parasites. Intracellular tachyzoites were treated with histone deacetylase HDAC3 inhibitor, 90 nM FR235222 for 18 hr. As an appropriate control, we treated tachyzoites with 0.1% DMSO. Histones were extracted and purified using histone purification kit (Active motif) according to manufacturer’s protocol. For western blotting, histone proteins were run on a NuPAGE 4–12% Bis-Tris polyacrylamide gels in MES-SDS running buffer (Invitrogen) and transferred to a polyvinylidene fluoride PVDF membrane  using NuPAGE transfer buffer (Invitrogen). The blots were probed using primary antibodies: pan acetyl H4, H4K31ac and H4K31me1, followed by phosphatase-conjugated goat secondary antibodies (Promega). The expected band of histones were detected using NBT-BCIP (Amresco). Nucleosomes from T. gondii-infected cells were purified and proteins separated by SDS-PAGE. The band corresponding to H4 was excised and its protein content digested using trypsin. The resulting peptides were submitted to mass spectrometry-based proteomic analysis . Peptides and proteins were identified using Mascot software (Matrix Science).For histone purification, HFF cells were grown to confluence and infected with PruThe MODifiedTM Histone Peptide Array (Activemotif) contains 59 different post-translational modifications for histone acetylation, methylation, phosphorylation and citrullination on the N-terminal tails of histones H2A, H2B, H3 and H4. Each 19mer peptide may contain up to four modifications each. Five control spots are included on each array: biotin peptide, c-Myc tag, no histone peptide and two background spots containing a mixture of modifications that are present on the array. Arrays were blocked with tris-buffered saline (TTBS)  plus 5% nonfat dry milk. Antibodies were diluted in TTBS. Primary antibodies were detected using horseradish peroxidase (HRP)-conjugated anti-IgG antibodies (R and D systems). The blots were developed with the SuperSignal West Pico Chemiluminescent Substrate kit (Thermo Fisher Scientic). The Array Analyze software program designed by Activemotif was used to analyze the spot intensity of the interactions and to generate graphical analysis of the histone peptide–antibody interactions. The Spot Statistics tab gives a comparison of the intensities of each spot in the left and right array. The data points ideally should be on a straight line connecting 0,0 and 1,1 which indicates perfect duplicates. Outliers (or poor replicates) will not fall along the linear line. The Distribution Errors tab displays the errors of the intensities between the right and left spot, normalized to the maximum intensity. Ideally, most of the peptides will contain an error range of 0–5% (which means very little variation between the left and right sides). The Reactivity tab displays the background-subtracted intensity values for all single modified peptides, as well as for control spots. Selecting a modification from the pull-down menu scales the intensity of the selected modification to 1.0 (y-axis). The impact of neighboring modifications on the single modified peptides was further investigated using the Modification Analysis tab and selecting for the modification of interest. The results were graphed as a Specificity factor, which is the ratio of the average intensity of all spots containing the mark divided by the intensity of all spots not containing the mark. All modifications have been accounted for in determining the specificity factor, but only the ten modifications with the highest specificity factor values are graphed. The larger the difference in specificity-factor values for the mark of interest versus other marks, the more specific the interaction. Non-specific signals will decrease the specificity-factor values.∆ku80) strain. Harvested intracellular parasites were crosslinked with formaldehyde  for 8 min at room temperature and the crosslinking was stopped by the addition of glycine  for 5 min at room temperature. Crosslinked chromatin was lysed in ice-cold lysis buffer  and sheared in shearing buffer  by sonication using a Diagenode Biorupter. Samples were sonicated for 16 cycles (30 s ON and 30 s OFF), to 200–500 base-pair average size. Immunoprecipitation was carried out using sheared chromatin, 5% BSA, protease inhibitor cocktail, 10% Triton X-100, 10% deoxycholate, DiaMag Protein A-coated magnetic beads (Diagenode) and antibodies . A rabbit IgG antiserum was used as a control mock. After overnight incubation at 4°C on rotating wheel, chromatin-antibody complexes were washed and eluted from beads by using iDeal ChIP-seq kit for Histones (Diagenode) according to the manufacturer’s protocol. Samples were decrosslinked by heating for 4 hr at 65°C. DNA was purified by using the IPure kit (Diagenode) and quantified using Qubit Assays (Thermo Fisher Scientific) according to the manufacturer's protocol. For ChIP-seq, purified DNA was used to prepare libraries and then sequenced by Arraystar .HFF cells were grown to confluence and infected with the type II (PruT. gondii reference genome (Tgo) using BOWTIE (V2.1.0). Aligned reads were used for peak calling of the ChIP regions using MACS V1.4.0. Statistically significant ChIP-enriched regions (peaks) were identified by comparison of two samples, using a p-value threshold of 10−5. The peaks in each sample were then annotated by the overlapped gene using the newest T. gondii database. The EXCEL/BED format file containing the ChIP-enriched regions was generated for each sample. For data visualization, the mapped 100 bp reads represent enriched DNA fragments identified by the ChIP experiment. Any region of interest in the raw ChIP-seq signal profile can be directly visualized in the genome browser. We used 10-bp resolution intervals (10-bp bins) to partition the stacked reads region, and counted the number of reads in each bin. All of the 10-bp resolution ChIP-seq profiles of each sample are saved as UCSC wig format files, which can be visualized in the T. gondii Genome Browser (http://protists.ensembl.org/Toxoplasma_gondii/Info/ Index). All of these raw and processed files can be found at Series GSE98806.ChIP-Sequencing library preparation was performed according to Illumina’s protocol Preparing Samples for ChIP Sequencing of DNA. For library preparation: 10 ng DNA of each sample was converted to phosphorylated blunt-ended with T4 DNA polymerase, Klenow polymerase and T4 polymerase (NEB). An ‘A’ base was added to the 3' end of the blunt phosphorylated DNA fragments using the polymerase activity of Klenow (exo minus) polymerase (NEB). Illumina's genomic adapters were ligated to the A tailed DNA fragments, and PCR amplification was performed to enrich ligated fragments using Phusion High Fidelity PCR Master Mix with HF Buffer (Finnzymes Oy). The enriched product of ~200–700 bp was cut out from the gel and purified. For sequencing, the library was denatured with 0.1M NaOH to generate single-stranded DNA molecules, and loaded onto channels of the flow cell at 8pM concentration, amplified in situ using the TruSeq Rapid SR cluster kit . Sequencing was carried out by running 100 cycles on Illumina HiSeq 4000 according to the manufacturer’s instructions. For data analysis, after the sequencing platform generated the sequencing images, the stages of image analysis and base calling were performed using Off-Line Basecaller software (OLB V1.8). After passing the Solexa CHASTITY quality filter, the clean reads were aligned to the 8 cells per ChIP used for the previously described protocol  with the Burrows-Wheeler Alignment tool (BWA) using default settings, and then sequences were quality filtered by the Q20 Phred quality score. ChIP-seq peak calling was performed using the MACS2 algorithm. For genome-wide representation of each histone mark’s distribution, the coverage was calculated as average reads per million over bins of 1000 nucleotides using bamCoverage from the package deepTools. Correlations of the different biological replicates were calculated by performing Pearson’s and Spearman’s correlation analysis of pairwise comparisons of BAM alignment files, and ChIP-seq peak enrichment scores (log2) using the MACS2 and deepTools. Circular and linear coverage plots were generated using Circos and Integrated Genomics Viewer, respectively. All of the raw and processed files can be found at NCBI Bioproject ID PRJNA386433.Sequencing reads were mapped to the  In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Wendy Garrett as the Senior Editor.Thank you for submitting your article "Modifications at K31 on the lateral surface of histone H4 contribute to genome structure and expression in apicomplexan parasites" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has assembled a list of concerns that we would like you to address in a response letter.Summary:P. falciparum and T. gondii. Most characterized histone PTMs in these organisms have been within the N terminal tails and globular domain PTMs are a recent focus of study in the chromatin literature. The authors propose a PTM switch on H4K31 that governs chromatin state in T. gondii. The authors also identify the enzyme responsible for addition and removal of H4K31Ac. The modifications described here have not been examined closely in any organism, and the data presented suggest potential for important functions in gene regulation and chromosome function. Data are also presented suggesting a function for H4K31me1 in metazoan mitosis. Overall the findings appear interesting and potentially important, but there are a number of methodological points that must be clarified and analyses that must be supplied. Aspects of data presentation and literature citation also require improvement.This paper describes the characterization of histone post-translational modifications H4K31Ac and H4K31me1 in the apicomplexan parasites Essential revisions:1) The specificity of the H4K31Ac and H4K31me1 antibodies is crucial, but these data aren't shown. Modification antibodies that are truly specific are difficult to generate, and there have been many reports of commercial H4ac and histone lysine methyl antibodies cross-reacting with other modified residues or reactivity influenced by other nearby PTMs (Rothbart 2015 Mol Cell). Much of the data on H4K31Ac is consistent with the antibody having cross-reactivity to H3K14Ac. Please show antibody specificity data, which should demonstrate that the antibodies do not cross-react with common histone PTMs, perhaps by probing available PTM arrays. This is the most essential revision and the authors should insure that antibody specificity is unambiguously demonstrated.2) 3) More conclusive evidence for the H4K31me1 PTM in Tg and/or Pf must be provided, as the authors did not cite a reference nor could we find published data that such a PTM has been reported using MS. Did the authors detect H4K31me1 in their 3 MS replicates? As cited by the authors, Nardelli (mBIO 2013) detected H4K31succ but the authors do not mention 2 additional modifications, H4K31me2 and H4K31form, that were also reported in that study. Based on the genome localization the abundance of the PTM appears greater than H4K31ac. Further, given the access to specific antibodies, an affinity approach should be straightforward to confirm the PTM even if the bulk histone approach was unsuccessful.4) Conclusions derived from cytological data are usually supported by just one image, or at best several images. The claims regarding the mitotic role of H4K31me1 in metazoans (paragraph four in subsection "H4K31me1 associates in vivo with distinct chromatin patterns") are very weakly supported. The conclusion stated in paragraph three is not supported at all. A quantitative analysis of the cytology should be presented (percentages of images matching those shown) with statistics where appropriate. All conclusions should be supported by data, as in:a) b) c) d) e) The authors state that cyclopeptide inhibitors impact H4K31Ac levels and that other types of HDAC inhibitors do not. However, from 5) There are several references to low levels of H4K31ac, but these are not well-supported, and appear to be derived from subjective interpretation of cytology . Perhaps mass spectroscopy provides a more solid basis for this assertion, but mass spec data are presented very briefly in 6) The presentation of genomics data relies too heavily on genome browser snapshots. These are helpful and appropriate as supporting evidence, but conclusions based on genomic data should be supported by genomic analyses. Some conclusions, like the limitation of H4K31me1 by translational initiation and termination sites  are not supported at all – transcriptional initiation and termination sites could just as easily be involved. The claim that H3K9me3 and H3K14ac co-localize in poorly transcribed (Q4) genes  is incon7) P. falciparum genome reveals 403 H4K31me1 as a novel pericentromeric PTM"), but this information appears to be based upon the genome-wide data for CenH3 published in Brooks et al., 2013 . The Brooks paper, which showed the localization of H3K9me2/3 to Tg centromeres should be discussed and cited since the authors examine similarities and differences between Tg and Pf. The Tg ortholog of HP1, which binds H3K9me3, was reported by Gissot et al., 2012 , and its localization was reported to centromeres and telomeres.8) 9) The data reported for H3K9me3 in this manuscript agree in centromeric localization but conflict in reporting localization to "poised" genes. The definition of "poised" genes is unclear. A table is provided (without a legend or description of samples), but statistical support for enrichment of this mark on poised genes is required, as is further discussion. What proportion of genes in Q4 were poised and were they developmental stage-specific genes?10) There are several species in which ncRNA contribute to the organization of centromeres . The Scherf laboratory has characterized ncRNA in Plasmodium, including several that are transcribed at var loci. Are the H4K31me1 marks that flank centromeres in Pf sites of ncRNA transcription? Does the presence of ncRNA at var loci correlate with H4K31me1 localization?11) The discussion of H4K31Ac and H4K31me1 functionality is overbroad. There are multiple functional claims: "a key role in gene regulation", "pivotal role...in pericentromeric heterochromatin", "regulating chromosomal condensation and segregation during mitosis", "H4K31me [...] substantially slows the progress of the RNA polymerase on active genes". This paper presents no direct functional data and hence cannot claim to demonstrate any role for H4K31Ac or H4K31me1. Reasonable inferences can be made based on genomic localization and correlations with other PTMs and gene expression, but it's important to be very clear that functional tests are yet to be done. This includes the title, which makes unsupported functional claims.12) The discussion of H4K31Ac and H4K31me1 functionality is vague and the descriptions of the genomic localization of H4K31Ac and H4K31me1 are very difficult to follow. H4K31me1 may function by preventing acetylation (but not have a direct influence on nucleosomes), inhibiting histone exchange (or otherwise repressing transcription directly or through a reader), or both. The authors seems to favor the last possibility, but don't explain why.T. gondii and P. falciparum need to be discussed more clearly. More generally, references to evolutionary relationships are confusingly vague. "Higher eukaryote" is an anachronistic term with little meaning; in most cases the authors appear to mean "animals". Chromatin organization in apicomplexa is described as unique, but many fungi have similar distributions of active and silent chromatin. P. falciparum and T. gondii are described as "closely linked" despite a divergence time of about 800 MY.13) The similarities and differences between eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Wendy Garrett as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Modifications at K31 on the lateral surface of histone H4 contribute to genome structure and expression in apicomplexan parasites" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:The reviewers and editors feel that the manuscript has been markedly improved and appreciate the extensive revisions carried out by the authors. However, we do have remaining substantial concerns in two areas.Essential revisions:1) Antibody specificity. The figure supplements for 2) Unwarranted mechanistic claims. The manuscript still contains mechanistic claims about the function of modifications on H4 K31 that are speculative and not supported by data. The primary example of this is in the Abstract where the authors state "H4K31 acetylation promotes a relaxed chromatin state at the promoter of active genes through nucleosome disassembly in both parasites." The manuscript shows that H4 K31 Ac localization correlates with active chromatin but there is no data that shows that H4 K31 acetylation has a direct effect on chromatin structure through nucleosome disassembly. These types of claims should be removed. Essential revisions:1) The specificity of the H4K31Ac and H4K31me1 antibodies is crucial, but these data aren't shown. Modification antibodies that are truly specific are difficult to generate, and there have been many reports of commercial H4ac and histone lysine methyl antibodies cross-reacting with other modified residues or reactivity influenced by other nearby PTMs (Rothbart 2015 Mol Cell). Much of the data on H4K31Ac is consistent with the antibody having cross-reactivity to H3K14Ac. Please show antibody specificity data, which should demonstrate that the antibodies do not cross-react with common histone PTMs, perhaps by probing available PTM arrays. This is the most essential revision and the authors should insure that antibody specificity is unambiguously demonstrated.We performed an exhaustive characterization of both H4K31ac- and H4K31me1-directed antibodies using modified histone PTM arrays and added those data in 2) P. falciparum proteome and acetylome: Saraf et al., 2016 and Cobbold et al., 2016, respectively that we are citing properly in the revised manuscript . We performed several ms assays in T. gondii but modifications at this residue were quite difficult to identify with great confidence by mass spectrometry in our conditions. H4K31ac peptide was at least identified one time with high confidence  as well as in metazoans . Taken together, these findings emphasize the existence H4K31ac on nucleosomes isolated from animal cells or apicomplexa parasites.We did not perform ms-ms identification for Pf H4. However, H4K31ac was identified in Pf by two comprehensive studies of 3) More conclusive evidence for the H4K31me1 PTM in Tg and/or Pf must be provided, as the authors did not cite a reference nor could we find published data that such a PTM has been reported using MS. Did the authors detect H4K31me1 in their 3 MS replicates? As cited by the authors, Nardelli (mBIO 2013) detected H4K31succ but the authors do not mention 2 additional modifications, H4K31me2 and H4K31form, that were also reported in that study. Based on the genome localization the abundance of the PTM appears greater than H4K31ac. Further, given the access to specific antibodies, an affinity approach should be straightforward to confirm the PTM even if the bulk histone approach was unsuccessful.T. gondii  and P. falciparum  while this PTM was identified with high confidence and several times in mammalian cells . We then used as an alternative approach antibodies directed specifically against methylated H4K31. As stated in subsection "H4K31me1 associates in vivo with distinct chromatin patterns", we raised an antibody directed against H4K31me2, a modification reported by Nardelli et al., 2013, however although reacting avidly with the peptide antigen, those did not detect histone H4 in human or parasite protein extracts. We also generated antibodies recognizing H4K31me1 and those were controlled for their specificy as above-mentioned.Identification of H4K31me1 using a bulk histone approach was unsuccessful in our conditions. Two other studies were also not able to identify the mark in the proteome of We edited the manuscript accordingly specifically by mentioning all the other PTM occurring at H4K31; corresponding references were also added.4) Conclusions derived from cytological data are usually supported by just one image, or at best several images. The claims regarding the mitotic role of H4K31me1 in metazoans (paragraph four in subsection "H4K31me1 associates in vivo with distinct chromatin patterns") are very weakly supported. The conclusion stated in paragraph three is not supported at all. A quantitative analysis of the cytology should be presented (percentages of images matching those shown) with statistics where appropriate. All conclusions should be supported by data, as in:Quantitation and statistics were added in the new a) Quantitation and statistics were added in the new b) Quantitation and statistics were added in the new c) Quantitation and statistics were added in the new d) Quantitation and statistics were added in the new e) The authors state that cyclopeptide inhibitors impact H4K31Ac levels and that other types of HDAC inhibitors do not. However, from While we agree with the reviewer that both Butyrate and APHA trigger more acetylation of H4K31 in the host cell nuclei they fail to increase H4K31ac signal in parasite nuclei as seen in 5) There are several references to low levels of H4K31ac, but these are not well-supported, and appear to be derived from subjective interpretation of cytology . Perhaps mass spectroscopy provides a more solid basis for this assertion, but mass spec data are presented very briefly in We agree with the reviewer comment that comparing signal intensity across antibodies may lead to subjective interpretations. Therefore and in absence of any quantitative ms-ms data, we have removed any mention to "low level" of H4K31ac .6) The presentation of genomics data relies too heavily on genome browser snapshots. These are helpful and appropriate as supporting evidence, but conclusions based on genomic data should be supported by genomic analyses. Some conclusions, like the limitation of H4K31me1 by translational initiation and termination sites  are not supported at all – transcriptional initiation and termination sites could just as easily be involved. The claim that H3K9me3 and H3K14ac co-localize in poorly transcribed (Q4) genes  is inconT. gondii life cycle. Indeed, we have uncovered that bradyzoite-restricted genes  displayed marks of both active (H3K14ac) and silent (H3K9me3) chromatin. This dual histone PTM was also detected on nucleosomes surrounding genes that have been recognized as exclusively expressed during sexual stages in the definitive feline host, like the merozoite gene GRA11b. While we have shown few examples in the previous version of the manuscript, we are now providing a tremendous amount of data supporting the conclusion that H3K14ac and H3K9me3 form a bivalent chromatin domain capable of silencing developmental genes while keeping them poised for rapid activation upon cell differentiation. For instance, over the 111 members of the SRS superfamily of proteins annotated in ToxoDB, we identified 66 SRS genes displaying H3K9me3/H3K14ac enrichment, including the aforementioned 52 merozoite  along with 8 bradyzoite SRS genes  whereas the 14 SRS genes exclusively expressed in tachyzoite (e.g. SAG1 cluster) lack the dual PTM. All these data are discussed (second paragraph in subsection "Interplay between H4K31ac and H4K31me1 predicts distinctive patterns of gene expression in T. gondii") and displayed in the main new After re-reading the manuscript with the reviewers' comments in mind. While you did not require us to do so, we have re-run our analyses to include new data that overall reinforce our initial hypothesis. Indeed, we assessed a variety of RNA-seq expression data  to re-evaluate the enrichment of H3K9me3 and H3K14ac at genes clustered in Q4. First we uncovered that Q4 genes were indeed transcriptionally heterogeneous. As such, we discovered that the repressive signature H3K9me3 was primarily present at the vicinity of genes typified by low RNA levels in tachyzoites and referenced as stage-specific within the 7) This reviewer is right. H3K4me1 displayed a similar distribution over the body of genes that H4K31me1 as reported in ToxoDB but still unpublished by Kami Kim lab. We have reexamined the extent and genome-wide scattering of H3K4me1 and observed a high degree of similarity in read coverage between H3K4me1 and H4K31me1 and a similar pattern of enrichment over the gene body. However, those PTMs are unevenly distributed across the genome, H3K4me1 being occasionally weakly enriched relative to H4K31me1 at few expressed genes. These new data have been now included in www.Toxodb.org). The Brooks paper, which showed the localization of H3K9me2/3 to Tg centromeres should be discussed and cited since the authors examine similarities and differences between Tg and Pf. The Tg ortholog of HP1, which binds H3K9me3, was reported by Gissot et al., 2012 , and its localization was reported to centromeres and telomeres.8) The Brooks et al., 2013 and Gissot et al., 2012 papers are now properly commented and referenced in the new revised version.9) The data reported for H3K9me3 in this manuscript agree in centromeric localization but conflict in reporting localization to "poised" genes. The definition of "poised" genes is unclear. A table is provided (without a legend or description of samples), but statistical support for enrichment of this mark on poised genes is required, as is further discussion. What proportion of genes in Q4 were poised and were they developmental stage-specific genes?T. gondii  but this study conflicts with our data in reporting enrichment of the mark to "poised" stagespecific genes. This discrepancy could be explained by the genetic background of the parasite strain since Brooks and colleagues infected human cells with a type I (RH) strain that lost the ability to develop into mature cysts while we used infections with a type II strain (Pru) which is more relevant as it does readily develop tissue cysts and latent infections in animals."In the revision, we attempted to describe differences between our data and Brooks et al., 2013, in a balanced manner as following: "It was previously reported that H3K9me3 typifies centrometric heterochromatin in Concerning the proportion of genes clustered in Q4 harboring a poised signature, please see our response to comment # 6 above.10) There are several species in which ncRNA contribute to the organization of centromeres . The Scherf laboratory has characterized ncRNA in Plasmodium, including several that are transcribed at var loci. Are the H4K31me1 marks that flank centromeres in Pf sites of ncRNA transcription? Does the presence of ncRNA at var loci correlate with H4K31me1 localization?We have no clear correlation. Whereas H3K9me3/HP1 is clearly associated with var and also GC elements according to Macs2. However, H4K31me1 enrichment was detected, it was unevenly and at low rates in the vicinity of few var (5 over 64).11) The discussion of H4K31Ac and H4K31me1 functionality is overbroad. There are multiple functional claims: "a key role in gene regulation", "pivotal role...in pericentromeric heterochromatin", "regulating chromosomal condensation and segregation during mitosis", "H4K31me [...] substantially slows the progress of the RNA polymerase on active genes". This paper presents no direct functional data and hence cannot claim to demonstrate any role for H4K31Ac or H4K31me1. Reasonable inferences can be made based on genomic localization and correlations with other PTMs and gene expression, but it's important to be very clear that functional tests are yet to be done. This includes the title, which makes unsupported functional claims.12) The discussion of H4K31Ac and H4K31me1 functionality is vague and the descriptions of the genomic localization of H4K31Ac and H4K31me1 are very difficult to follow. H4K31me1 may function by preventing acetylation (but not have a direct influence on nucleosomes), inhibiting histone exchange (or otherwise repressing transcription directly or through a reader), or both. The authors seems to favor the last possibility, but don't explain why.13) The similarities and differences between T. gondii and P. falciparum need to be discussed more clearly. More generally, references to evolutionary relationships are confusingly vague. "Higher eukaryote" is an anachronistic term with little meaning; in most cases the authors appear to mean "animals". Chromatin organization in apicomplexa is described as unique, but many fungi have similar distributions of active and silent chromatin. P. falciparum and T. gondii are described as "closely linked" despite a divergence time of about 800 MY.Answer to comments 11, 12 and 13:We agree with those assessments. As such, we have endeavored to extend the paper\x92s contributions by enhancing our Discussion section. We now discuss the specific issues the reviewers highlight. We also worked to improve the tone of the paper. Thus, as we revised the paper, we worked to provide a more balanced presentation of our arguments and results. However, we are hesitant to change the present title which is to our opinion illustrates quite well our data. We hope that our revision, based on the input of the reviewers, accomplishes this objective.Essential revisions:1) Antibody specificity. The figure supplements for We have indeed forgotten to include the material and method. This is now done in the new revised version. The methods are now clearly explained including the significance of the specificity factor.Based on the data shown, it appears that the H4 K31 Ac antibody can bind to the unmodified H3 tail. If this is the case, the immunofluorescence data in the manuscript is impossible to interpret. Based on the tight cropping of the gels in We observed a nonsignificant specificity value (below 10) of H4K31ac antibody toward H3 modified peptides  Part C t2) Unwarranted mechanistic claims. The manuscript still contains mechanistic claims about the function of modifications on H4 K31 that are speculative and not supported by data. The primary example of this is in the Abstract where the authors state "H4K31 acetylation promotes a relaxed chromatin state at the promoter of active genes through nucleosome disassembly in both parasites." The manuscript shows that H4 K31 Ac localization correlates with active chromatin but there is no data that shows that H4 K31 acetylation has a direct effect on chromatin structure through nucleosome disassembly. These types of claims should be removed.As suggested, we replaced in the Abstract the aforementioned sentence by the following: "H4K31 acetylation at the promoter correlates with, and perhaps directly regulates, gene expression in both parasites". We have also removed when needed unwarranted mechanistic claims throughout the new revised version.


Oryza sativa utilize three distinct classes of transcription factors: those that bind preferentially to the  region (188 “promoter-specific” transcription factors), those that bind preferentially to the  region (282 “5′ UTR-specific” TFs), and 207 of the “promiscuous” transcription factors with little or no location preference with respect to TSS. For the most informative motifs, their positional preferences are conserved between dicots and monocots.Computational analysis of promoters is hindered by the complexity of their architecture. In less studied genomes with complex organization, false positive promoter predictions are common. Accurate identification of transcription start sites and core promoter regions remains an unsolved problem. In this paper, we present a comprehensive analysis of genomic features associated with promoters and show that probabilistic integrative algorithms-driven models allow accurate classification of DNA sequence into “promoters” and “non-promoters” even in absence of the full-length cDNA sequences. These models may be built upon the maps of the distributions of sequence polymorphisms, RNA sequencing reads on genomic DNA, methylated nucleotides, transcription factor binding sites, as well as relative frequencies of nucleotides and their combinations. Positional clustering of binding sites shows that the cells of  Core promoters are the 5' regions adjacent to the transcriptional start site (TSS) and containing binding sites for transcription factors (TFBS). Computational analysis of the eukaryotic promoters is hindered by their complex architecture –3. Each Arabidopsis thaliana, modern algorithms identify TATA-containing promoters with sensitivities up to 95% and specificities up to 97%  nucleotides upstream from the respective TSS. At the TSS, nucleotide consensus scores (46% of T and 49% of C followed by 65% of A) were stronger for NPEST then for TAIR (43% of T and 35% of C followed by 53% of A). When NPEST predictions were compared to experimentally confirmed promoters from other databases, similar patterns of nucleotide consensus were observed.In the last decade, several improvements in the promoter prediction process were made. Troukhan  combinedhromatin . In 2013hromatin , that al as TAIR , 25, Pla as TAIR  and Plan as TAIR . For exahttp://snp-seek.irri.org), the largest and the most dense SNP collection for higher plants  region ; Class 2, which binds preferentially to the  region ; and Class 3, which includes predominantly “promiscuous” transcription factors with weak or no location preference for respective TSS (N = 207), see Positional clustering of TFBS demonstrates that the cells of i and Ei correspond to observed and expected numbers of genes in ith category.To conduct the comparative gene ontology analysis of Class 1, 2 and 3 transcription factors , the chiClass 1 TFs of the rice are enriched in the following GO terms: “sequence-specific DNA binding”, “protein dimerization activity”, “systemic acquired resistance, salicylic acid mediated signaling pathway”, “regulation of transcription from RNA polymerase II promoter”, “response to bacterium”, “jasmonic acid mediated signaling pathway”, “carpel development”, “protein binding”, “negative regulation of defense response”, “protein targeting to membrane”, “regulation of plant-type hypersensitive response”, “plant ovule development”, “response to ozone”. Class 2 TFs are enriched in GO terms “DNA binding”, “ethylene-activated signaling pathway”, “response to water deprivation”. Class 3 TFs are enriched in “cellular response to nitrogen levels”.To compare expression specificity of Class 1 and Class 2 transcription factors, we used the difference of proportions test : Z=p1−p2nt roots , 40: posGGGCCC consensus sequence. Presence of GGGCCC in the region upstream of TSS is associated with the elevated level of gene expression TAGGT[A/G]  . MYB111 According to the Kolmogorov-Smirnov test, three classes of TFs differ in the significance of over-representation of their TFBS in promoters and in the randomly shuffled sequences: thirty-seven percent of the Class 1 TFs with motifs located predominantly upstream of TSS were significantly overrepresented , In the Class 2 TFs with TFBS located in 5′ UTRs, overrepresentation was confirmed for 20% of the PWMs. The TFBS for Class 3 TFs were distributed evenly. For the latter group, significant over-representation was detected for 15% of class members .In summary, three classes of TFBS differ in their position specificity, percentages of PWMS significantly over-represented in real promoters, functional classification of their downstream genes, and the patterns of their gene expression.Oryza sativa and Zea mays, and in the dicot Arabidopsis thaliana  in monocots thaliana . FollowiCorrelation of the TFBS position information content in two monocots (rice and corn) were higher than that for the rice and a dicot plant Arabidopsis. By extracting TFBS with more than 10,000 matches in each of three plant genomes, a list of 46 “common” informative TFBS was compiled. Each of these TFBS was classified into either “promoter-specific” or “5′ UTR-specific” category in each species. Between rice and corn, 42 of 46 “common” TFBS are consistent in their position preference see . BetweenSince TRANSFAC database tends to accumulate all published motifs, some of collected motifs appear to be redundant. For example, several PWMs may be independently built and reported for the same transcription factor . Also, tCore promoters and 5′ UTR regions located within 200 bp around the TSS are both protected against accumulation of nucleotide variants . This prq − q*| for the TFBS scores before (q) and after (q*) nucleotide change, and compared its values to empirically determined thresholds. Calculations of the scores q and q* were done according to the MATCH scoring formula . If Δ ≥ Δ0, the site was considered as “lost” or “gained” depending which score value was larger, q or q*.To achieve that, we considered distribution of SNPs and their effects on loss and gain of TFBS. For each nucleotide change, we have calculated Δ = |CACGTGGC) and CBF4 transcription factors were the most protected from the site loss. In abscisic acid signaling, ABF factors govern osmotic stress response through modulation of the gene expression downstream of SnRK2 kinases, while CBF4 regulates adaptation to drought. For several important transcription factors, such as MADS8 (involved in the control of flowering time), GT-1 and GATA-1 (response to light), we observed that variation was avoided in positions where nucleotide change can lead to the site gain. Additional data and the results of the analysis of SNPs in TFBS could be seen in the Frequencies of site losses and site gains for the promoters and for the random subset of 18,389 intergenic sequences, each 2,000 nt in length, were compared. We hypothesized that functionally important promoter motifs will have less variation causing the loss of sites. For each TF, we calculated the ratio of the site losses in intergenic sequences to the site losses in promoters. All entries were than ranked according to these ratios, which reflected relative “suppression” of the site losses by SNPs . RelativThe binding sites for AT2G20350 and ARF1 transcription factors were the most “protected” from the site loss. AT2G20350 factors regulate activity of ethylene-activated signaling pathway. The plant hormone ethylene is involved in many aspects of the plant life cycle, including seed germination, root hair development, root nodulation, flower senescence, abscission, and fruit ripening . ARF1 is a member of the auxin response factor family, involved in hyperosmotic salinity response. For several important transcription factors, such as WRKY23 , FUS3 (plays a role in embryonic development ending in seed dormancy and response to auxin stimulus), we observed that variation was avoided in positions where nucleotide change can lead to the site gain.Predictably, an analysis of mapped RNA-Seq reads near TSS  showed that, on average, coverage peaks are observed immediately downstream of TSS . HoweverThree-stranded nucleic acid R-loop structure is formed between nascent RNA transcript and DNA template . Length In the intergenic regions and within functional classes of genes and their promoters, the patterns of DNA methylation predictably differ , 55. TheWe used 18,389 “promoter” (positives) and 18,389 “non-promoter” (negatives) sequences. To train the model, we used 14,711 positives and negatives; and the remaining 3,678 positives and negatives were used for testing. The binary classifier interrogates the candidate sequence and reports whether the sequence is “promoter” or “non-promoter”. The best combination of features was: composition of DNA sequence, GC-skew value and presence/absence of the CA-motif in every position. It achieved the best accuracy (0.9995) and has the Matthews correlation coefficient of 0.9989 see . Other fIn this work, we have investigated several features of promoter area, identified characteristic patterns of their distribution and assessed utility of these features for identification of TSS location. Accuracy of TSS identification affects the overall quality of regulatory region analysis. To date, large amounts of the “mapped” TSS are, in fact, defined only approximately. A significant fraction of promoters has multiple alternative TSS, many of which are not yet annotated. These features make prediction of exact positions of TSS a very complex problem. Further work toward exact mapping of all TSS positions using various promoter characteristics in multiple species is warranted. It is essential to find and annotate tissue- and condition-specific transcription start sites and associate them with alternative splice form, gene regulatory network, and protein function.Intelligent integration of multiple types of genomic information  may improve annotation of tissue- and developmental stage-specific genes that are often misidentified due to their atypical sequence composition in grasses –56. We sIn our work, we focused on the 2000 nucleotide long region around rice TSS, supported by experimental evidence. In rice, the median length of 5′ UTR is 120 nt; with less than 1.2% of 5′ UTRs being larger than 1000 nt . TherefoAnalysis of SNPs in the context of TFBS in promoter and non-promoter region indicated that TFBS differ by their tolerance to nucleotide variation. It is of note that the binding sites for AT2G20350 and ARF1 transcription factors were the most “protected” from the site loss. Both of these factors are involved in plant hormone signaling . We concab initio gene prediction program , containing 200 nucleotides from promoter and 50 nucleotides from 5’ UTR, was designated as the “promoter” region, and sequence , from the coding part of the gene, as “non-promoter”.Quality of prediction was assessed using the following measures: True Positives (TP), True Negatives (TN), False Positive (FP), False Negative (FN), Accuracy, Sensitivity, Specificity, Matthews correlation coefficient (CC):S1 Data(ZIP)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.


Transcription factors (TFs) often interact with one another to form TF complexes that bind DNA and regulate gene expression. Many databases are created to describe known TF complexes identified by either mammalian two-hybrid experiments or data mining. Lately, a wealth of ChIP-seq data on human TFs under different experiment conditions are available, making it possible to investigate condition-specific (cell type and/or physiologic state) TF complexes and their target genes.Here, we developed a systematic pipeline to infer Condition-Specific Targets of human TF-TF complexes  by integrating ChIP-seq data and TF motifs. In total, we predicted 2,392 TF complexes and 13,504 high-confidence or 127,994 low-confidence regulatory interactions amongst TF complexes and their target genes. We validated our predictions by (i) comparing predicted TF complexes to external TF complex databases, (ii) validating selected target genes of TF complexes using ChIP-qPCR and RT-PCR experiments, and (iii) analysing target genes of select TF complexes using gene ontology enrichment to demonstrate the accuracy of our work. Finally, the predicted results above were integrated and employed to construct a CST database.We built up a methodology to construct the CST database, which contributes to the analysis of transcriptional regulation and the identification of novel TF-TF complex formation in a certain condition. This database also allows users to visualize condition-specific TF regulatory networks through a user-friendly web interface.The online version of this article (doi:10.1186/s12864-016-3450-3) contains supplementary material, which is available to authorized users. Transcription factors (TFs) interact with one another and with their co-factors to form TF complexes, with constituents that vary in different cell types or under different cellular conditions. These TF complexes regulate different sets of target genes to determine cellular state . Given tTF-TF interactions can be experimentally identified using electrophoretic mobility shift assays (EMSAs), X-ray crystallography, immunoprecipitation, yeast two-hybrid systems, mammalian two-hybrid systems and luciferase assays. Because of technical limitations, most human TF-TF interactions represent potentials of physical binding rather than physiological interactions under specific conditions. For example, Ravasi et al. developed a database of physical TF-TF interactions using a mammalian two-hybrid system in hamster cells , in whicChromatin immunoprecipitation followed by DNA microarray or high-throughput sequencing (ChIP-chip/ChIP-seq) techniques are powerful for identifying TF binding sites. These approaches discover binding “peaks”, i.e. regions of chromatin and the corresponding sequences enriched for TFs. Consequently, condition-specific TF peaks can be identified by altering cellular conditions, which further reveal motifs recognized by DNA-binding TFs or their co-regulatory counterparts. For example, the CENTDIST web server identifies co-regulatory TFs in complexes by investigating TF motifs enriched in the ChIP-seq peaks for a TF . In addiThanks to a comprehensive TF motif database, CST is the first pipeline that uses data from a single ChIP-seq experiment to predict both TF partners and their target genes. Chen et al. predicted TF complexes and their target genes using yeast TF ChIP-chip data , but thehttp://syslab3.nchu.edu.tw/DBCST.CST uses ChIP-seq data after immunoprecipitation of the primary TF along with a database containing known TF binding sequence motifs to identify partner TFs. Finally, we integrated the predicted results and constructed a database called DBCST. DBCST allows users to upload their own ChIP-seq data and analyse them for TF complexes and their regulatory targets. DBCST is freely available at P = 2.2× 10−16, see Additional file Using high-confidence criteria , whereas that for SpaMo was approximately 18 (at the 60% decile). These results indicated that Target Identification from Profiles (TIP) method [For the first validation, we compared the degree of enrichment for the CST-predicted TF complexes present in an empirically determined TF complex database (see Methods) against that of TF complexes created randomly among potential TF pairs in the CST pipeline (i.e. a background). We also included TF complexes predicted by SpaMo  for a fa) method  togetherP = 3*10−4; Fisher’s exact test) after further comparisons for the TRM-predicted TF complexes  in haematopoietic stem cells (HSCs) proposed by Diez et al. (see Methods) . Brieflyxes Fig.  and for xes Fig. . In ordeUsing USF2 ChIP-seq data in K562 cells, our pipeline predicted that USF2 and NFYA form a TF complex that possesses a significant motif spacing of 9 bps and binds to five genomic locations, regulating eight target genes with high confidence Table . USF2 isin vitro validation. For qPCR amplification targets, we selected the promoters of high-confidence target genes (EIF4E and GLYR1), a low-confidence target gene (HoxB7) and HoxB4 . To ensure PCR accuracy, we designed two primer sets for HoxB7  of a given TF often result in alterations in target genes to elicit different biological functions. To examine these phenomena, we performed gene ontology (GO) enrichment analysis of target genes for given TF complexes to determine how they varied with TF complex composition. Using USF2 ChIP-seq data in K562 cells, CST predicted that USF2 and IRF1 form a TF complex. Among the top 10 GO enrichment results for the USF2-IRF1 complex targets  potential TF complexes for scrutiny in MCF-7 cell lines. By contrast, there are 966 distinct vertebrate TF motifs in the TRANSFAC database (version 2013.2), resulting in 966 × 40 potential combinations for CST. Hence, CST is powerful and insightful, particularly for cell lines having few ChIP-seq datasets.The attraction of CST for predicting condition-specific TF complexes arises from its rich database containing vast vertebrate TF motifs. Thus, the results of CST are not hindered by cell lines that have little ChIP-seq data. There are thousands published ChIP-seq datasets, among which the MCF-7 cell line has a maximum of 40 distinct TF ChIP-seq datasets . When ChWe are confident that CST will be helpful for detecting condition-specific TF complexes and their target genes because of its top-performing methods for target gene prediction (TIP ) and forTo the best of our knowledge, CST is the first pipeline that infers both condition-specific TF complexes and their target genes using human ChIP-seq datasets. Integrating the results of CST pipeline from 359 ChIP-seq ENCODE datasets, we constructed DBCST database. DBCST provides a searchable platform for TF complex and regulatory function discovery. DBCST is not only a database but also a web server and can perform CST pipeline from user’s own ChIP-seq experiment. User also can download CST package, which reports the list of primary TF targets and its binding sites when inputting wig file and narrow peak data, from DBCST download function. Using the result of CST package and then running MEME, FIMO and SpaMo, user can perform CST pipeline in their computer. We hope that DBCST will be a useful resource and provide insightful assistance for biologists studying transcriptional regulation going forward.de novo motifs extracted from ENCODE ChIP-seq data using MEME [ftp://ftp.ncbi.nlm.nih.gov/gene) [Wiggle files and narrow peak data of 359 SYDH  ENCODE ChIP-seq experiments were downloaded from the UCSC Genome Browser . One huning MEME  to our ming MEME . GO annoov/gene) .The main steps of the CST pipeline are described below Fig. .Conventionally, TF target genes are identified by first selecting the binding peaks of the TF using a peak-calling algorithm  and theTo examine TF binding motifs and their relative spacing, for each primary TF, the locations of binding peaks at the promoters of TIP-predicted target genes must first be identified. To accomplish this goal, we used the ENCODE narrow peak data to search for these peaks Fig. . Putativp-value) and used MEME (version 4.9.0_4 in the MEME suite) [To discover primary TF binding motifs, we retrieved 120bps DNA sequences centred at the summits of the top 500 binding peaks (ranked by E suite)  with thep-value < 1e-4) [A given binding peak may contain different motifs other than the primary binding motif for a primary TF. We employed FIMO (version 4.9.0_4 in the MEME suite with a  < 1e-4)  to selecWe selected 300 bps DNA sequences centred on the primary binding motif after referencing a thermodynamic model of TF-TF interactions  in whichde novo motifs identified in step 3  and 146 additional motifs from the JASPAR CORE motif database [p-value < 0.05 and E-value < 10).SpaMo identified TF-TF pairs with enriched spacing between the primary and secondary motif. Given intervals centred on the primary TF binding sites from the ChIP-seq data to identify the significant spacing between motif pairs, SpaMo assumes that the number of observed spacings between the primary TF and the secondary TF motifs follows a binomial distribution. Our pipeline for the secondary binding motifs included the database . The prip-value < 0.05 and E-value < 10). By contrast, a low-confidence target gene is called if standard (iii) is not met. The latter is used to describe TFs with variable spacing because of their binding on wrapped DNA strings or nucleosomes.GO enrichment analysis was performed for predicted target genes Fig.  using FiTo validate our results, we employed a three-step approach: (1) comparison of predicted TF complexes against an external and empirically derived TF complex database; (2) spot validation of the target genes using ChIP-qPCR and RT-PCR; and (3) GO enrichment analysis of the target genes.p-values and then examined the TF complexes in groups . Similar steps were conducted on TF complexes predicted using SpaMo as a reference. Next, enrichment ratios for CST and SpaMo were determined relative to a randomly generated list of TF complexes in CST or SpaMo based on the following formula: the observed number (CST or SpaMo TF complexes in the Ravasi database) divided by the expected number (randomly generated TF complexes in the Ravasi database).Two procedures were used in this step. First, we compared our list of TF complexes to a TF-TF interaction database experimentally derived and collected by Ravasi . To calcSecond, we used another external TF repository of transcriptional regulatory modules (TRMs)  for furtChromatin immunoprecipitation quantitative PCR (ChIP-qPCR). Four ChIP-qPCR experiments for the NFYA and USF2 TFs in the K562 and HeLa cell lines were performed using a selection of the NFYA-USF2-predicted TF complex target genes. HoxB4, a literature-derived external positive control from Zhu et al., was one of the qPCR amplification targets . ChIP waTarget - CTTBP), whereas the relative expression of the target gene was calculated using the 2–ΔCT method. The detailed procedures of immunoblotting were performed as described previously [Real-time reverse transcription-polymerase chain reaction (RT-PCR) and Western blotting. USF2-specific siRNA (SI02780785) and scrambled control were purchased from Qiagen  and employed to silence the USF2 expression in HeLa cells. Subsequently, total RNAs were purified by Trizol reagent (Invitrogen) and then subjected to SYBR Green RT-PCR using an ABI Prism 7300 sequence detection system , as described previously . The prieviously . The antet al [To validate that CST captured the phenomenon mentioned in Mullen, A. C. et al , we perf


The immediate early gene activity‐regulated cytoskeletal protein (Arc)/Arg3.1 and the neurotrophin brain‐derived neurotrophic factor (BDNF) play important roles in synaptic plasticity and learning and memory in the mammalian brain. However, the mechanisms by which BDNF regulates the expression of Arc/Arg3.1 are unclear. In this study, we show that BDNF acts via the ERK1/2 pathway to activate the nuclear kinase mitogen‐ and stress‐activated protein kinase 1 (MSK1). MSK1 then induces Arc/Arg3.1 expression via the phosphorylation of histone H3 at the Arc/Arg3.1 promoter. MSK1 can also phosphorylate the transcription factor cyclic‐AMP response element‐binding protein (CREB) on Ser133. However, this is not required for BDNF‐induced Arc.Arg3.1 transcription as a Ser133Ala knockin mutation had no effect on Arc/Arg3.1 induction. In parallel, ERK1/2 directly activates Arc/Arg3.1 mRNA transcription via at least one serum response element on the promoter, which bind a complex of the Serum Response Factor (SRF) and a Ternary Complex Factor (TCF). Arcactivity‐regulated cytoskeletal proteinBDNFbrain‐derived neurotrophic factorCREBcyclic‐AMP response element‐binding proteinCREcyclic‐AMP response elementsMSK1mitogen‐ and stress‐activated protein kinase 1NMDAN‐Methyl‐d‐aspartic acidSREserum response elementThe transcriptional regulation of immediate early (IE) genes plays an important role in translating incoming signals into an appropriate cellular response. IE genes have been implicated in many neuronal processes ranging from development and synapse formation to synaptic plasticity and the encoding of memory Serum response elements can be bound by a combination of the serum response factor and an ETS domain transcription factor such as Elk1. ERK1 and 2 are able to directly phosphorylate Elk1, leading to transcriptional activation of SRE containing promoters Mitogen‐ and stress‐activated protein kinase localise to the nucleus and, in addition to CREB, they have been found to phosphorylate other nuclear proteins including the histone H3, HMG14 and Trim28 In most cell types, MSK1 and MSK2 are able to compensate for each other, and knockout of both MSK1 and MSK2 together is required to block the phosphorylation of MSK substrates Mitogen‐ and stress‐activated protein kinase 1 is activated in hippocampal CA1 pyramidal neurons during fear conditioning The mechanism underlying how MSKs affect CNS function is not fully understood. Loss of MSK1 protein or kinase activity does not appear to have any gross effects on CNS development In agreement with this, enrichment enhanced the amplitude of miniature excitatory postsynaptic currents in wild‐type mice, but not mice with a D194A kinase dead KI mutation in the N‐terminal kinase domain of MSK1, which also showed a blunted increase in spine density following EE Here, we report that in primary cortical neuronal cultures, Arc/Arg3.1 transcription is directly regulated by MSK1 in response to BDNF, and that this occurs independently of CREB phosphorylation but correlates with the phosphorylation of histone H3 on the Arc/Arg3.1 promoter.The generation and genotyping MSK1/2 double knockout, MSK1 D194A KI and CREB S133A KI mice has been described previously m Na2SO4, 30 mm K2SO4, 16 mm MgCl2, 0.25 mm CaCl2, 32 mm HEPES). The tissue was rinsed by gravity precipitation twice in dissociation media and twice in Neurobasal A (Invitrogen) and gently triturated into a single‐cell suspension with a 5 mL plastic serological pipette. The supernatant containing single cells was spun down and the cells were resuspended in Neurobasal A (Invitrogen) supplemented with 2% B27, 1 mm l‐glutamine, 50 U·mL−1 penicillin, and 50 μg·mL−1 streptomycin (Invitrogen) and plated on poly‐d‐lysine‐coated 6 or 12 well plates . Cells were used between 7 and 10 days in culture at which point the density was 80–90% confluent.The neocortex was excised from postnatal pups (day 0 or 1), finely minced and digested 37 °C for 10 min with trypsin  in dissociation media  and N‐Methyl‐d‐aspartic acid . Enzyme and transcription inhibitors used in the study are as follows: the PI3 kinase inhibitor PI103 ; the p38 inhibitor SB203580 ; the CaMK inhibitor Kn‐93 ; the MEK1/2 inhibitor PD184352 , the MSK1 inhibitor SB‐747651A , the broad spectrum kinase inhibitor H89 , and the inhibitor of transcription, actinomycin D .Stimulation of cortical neurons involved application of BDNF  using Supersignal ECL reagent .Following stimulation, total RNA was isolated using RNeasy kits  according to the manufacturer's protocols. RNA was reverse‐transcribed using iScript (Qiagen) and qPCR carried out using Sybergreen‐based detection . The primers used are listed in Table −1) for 30 min. Cells were then fixed for 10 minutes with 1% formaldehyde and chromatin was isolated using the chromatin immunoprecipitation (ChIP)‐IT Express magnetic kit  according to the manufacturer's protocols. ChIP was performed using 6 μg phospho‐S10 Histone H3 antibody per IP. Primers sequences are provided in Table Primary cortical neurons were either left unstimulated or stimulated with BDNF 50 ng·mL− for 30 mTo generate Arc/Arg3.1 reporter constructs, −1302 to +98 (sequence starting AGTGGGGGGCAT to CCTCCGGCACCG) of the Arc/Arg3.1 promoter was PCR amplified and cloned into the pGL4 luciferase vector . PCR‐based mutagenesis was used to mutate the SRE (CCTTATATGG to TTTTATATTT) and EGR‐binding site (CGGGCG to TTTTTT). All inserts were fully sequenced to ensure the absence of random PCR‐generated mutations. Cortical neurons were transfected at 3 DIV using Lipofectamine 2000 (Invitrogen) and analysed on day 5. A Renillia luciferase vector  was used as a transfection control. Luciferase activity was detected in cell lysates using the dual luciferase assay system (Promega) according to the manufacturer's instructions.P < 0.05. originpro 2016  was used for statistical analysis.Data are presented as mean ± 1 SD of multiple biological replicates. Statistical analysis of the data involved one‐ or two‐way ANOVAs as appropriate as described in the figure legends. Statistical significance was accepted if 2+ and MAPK signalling pathways In cultures of primary cortical neurons, Arc/Arg3.1 mRNA levels were increased by stimulation with BDNF  were used for stimulation (Fig. Although MSK1 can be activated by either the ERK1/2 or p38α MAPK pathways Changes in mRNA levels can reflect a change in either transcription or in mRNA stability. To address by which mechanism MSK1 might regulate Arc/Arg3.1 mRNA, the primary transcript for Arc/Arg3.1 was analysed and actinomycin D chase experiments carried out. Changes in the level of primary unspliced transcripts can give a better indication of changes in transcription compared to measurement of the spliced mRNA. In wild‐type cells, BDNF was able to increase the levels of the Arc/Arg3.1 primary transcript. This induction was significantly lower in MSK1 D194A KI cells Fig. A. While To identify how MSK might regulate the Arc/Arg3.1 promoter, a 1.4 kb fragment corresponding to −1302 to +98 of the Arc/Arg3.1 genomic sequence was fused to a luciferase reporter and transfected into primary cortical neuronal cultures. This construct was inducible by BDNF, as evidenced by an increased luciferase activity following a 4 h treatment with BDNF (Fig. In silico analysis of the sequence cloned into the Arc/Arg3.1 reporter did not reveal any strong consensus CREB binding sites within this region, suggesting that MSK1 may regulate Arc/Arg3.1 independently of CREB. It is possible however that CREB may bind outside the region used in the Arc/Arg3.1 reporter, and that the phosphorylation of CREB at these sites may contribute to the ability of MSKs to regulate Arc/Arg3.1 mRNA induction. To address this, we analysed the effect of a KI mutation of Ser133 to alanine in the endogenous CREB gene (CREB Ser133Ala KI To date, most of the target genes identified for MSKs are also regulated by the MSK substrate CREB st exon. In primary cortical neurons, BDNF strongly induced H3 phosphorylation in only one of these regions (Fig. The above results indicate MSKs may regulate Arc/Arg3.1 induction independently of their ability to phosphorylate CREB. As MSKs also phosphorylate histone H3 on Ser10 ons Fig. B, C. Theons Fig. C.Both Srf and Egr transcription factors have been linked to Arc/Arg3.1 transcription In neurons, Arc/Arg3.1 mRNA has been shown to be induced by other stimuli in addition to neurotrophins. As MSK1 plays an important role in Arc/Arg3.1 induction downstream of BDNF, we also examined if it played a similar role in response to the glutamate receptor agonist NMDA. As expected, Arc/Arg3.1 mRNA levels were induced by NMDA in wild‐type cells Fig. A. ComparWe show here that MSK1 is required for the maximal induction of Arc/Arg3.1 mRNA in response to BDNF in primary neuronal cultures. This was independent of the ability of MSKs to phosphorylate CREB but correlated with the phosphorylation of histone H3 at the Arc/Arg3.1 promoter. While most MSK‐regulated genes described to date are regulated by CREB phosphorylation Arc/Arg3.1 has been found to be upregulated following intrahippocampal infusion of BDNF in an ERK1/2‐dependent manner. This study also showed that CREB Ser133 phosphorylation was also ERK1/2‐dependent in this model The synaptic activity‐responsive element also contained binding sites for the serum response complex and MEF2, and this MEF2–CREB–SRE motif has subsequently been identified in other activity‐regulated genes in vitro and in vivo stimuli e.g. in vivo in an MSK1‐dependent manner The role of histone H3 phosphorylation in the direct regulation of Arc/Arg3.1 expression has not been extensively studied. However, changes in chromatin structure The phosphorylation of histone H3 by MSK1 would require the targeting of MSK1 to chromatin, but the mechanism by which this occurs is not fully understood. Our data could agree with a model in which the SRE may be recruiting MSK1 to this region of chromatin. A similar mechanism has been proposed to occur in HeLa cells at SREs in the c‐fos and egr1 promoters where knockdown of the ternary complex factor Elk1 reduced MSK1 recruitment to these promoters In conclusion, our observations are consistent with a prominent role of MSK1 in BDNF‐dependent regulation of Arc/Arg3.1, an IE gene critical for a range of synaptic and experience‐dependent synaptic plasticity Carried out experiments and analysed data: CJH, SAC, LP, KMSER, KM, RT; wrote paper and directed study: BGF, JSCA.


SuUR mutation found in Drosophila makes heterochromatin replicate earlier than in wild type and reduces the level of repressive histone modifications. SUUR protein was shown to be associated with moving replication forks, apparently through the interaction with PCNA. The biological process underlying the effects of SUUR on replication and composition of heterochromatin remains unknown.In eukaryotes, heterochromatin replicates late in S phase of the cell cycle and contains specific covalent modifications of histones. SuUR-sensitive chromosomal regions that demonstrated unusual characteristics: They do not contain Polycomb and require SUUR function to sustain H3K27me3 level. We tested the role of SUUR protein in the mechanisms that could affect H3K27me3 histone levels in these regions. We found that SUUR does not affect the initial H3K27me3 pattern formation in embryogenesis or Polycomb distribution in the chromosomes. We also ruled out the possible effect of SUUR on histone genes expression and its involvement in DSB repair.Here we performed a functional dissection of SUUR protein effects on H3K27me3 level. Using hidden Markow model-based algorithm we revealed Obtained results support the idea that SUUR protein contributes to the heterochromatin maintenance during the chromosome replication. A model that explains major SUUR-associated phenotypes is proposed.The online version of this article (10.1186/s13072-017-0163-z) contains supplementary material, which is available to authorized users. In higher eukaryotes, transcriptionally active and silent regions of the genome are known to replicate asynchronously. Heterochromatic regions complete replication late in S phase when the rest of the chromosome has already been copied –3. This As in other metazoans, heterochromatic state in Drosophila is established by two major repressive pathways: HP1-dependent  and PolyIn salivary gland cells of Drosophila larvae, polytene chromosomes are formed when, after several sequential endocycles lacking mitosis and cell division, 500–1000 DNA strands stay tightly bound together by cohesin molecules –14. The Suppressor of Under-replication (SuUR) mutants [SuUR mutation on polytenization is reached without altering the origin distribution [SuUR mutants than in wild type, where SUUR protein is functional. Extra copies of SuUR gene lead to increased under-replication in polytene chromosomes [Phenomenally, under-replicated regions complete replication earlier and become fully polytenized in  mutants , 20. Theribution , suggestomosomes , 15.SuUR mutation affects the levels of H3K27me3 and H3K9me3 marks that are depleted in the pericentric regions of SuUR mutants [SuUR mutants lose H3K27me3 mark [SuUR mutation results in two major effects on polytene chromosomes: Chromosomal regions that are under-replicated in wild type become fully polytenized [Recent study revealed that  mutants . Furtherme3 mark . Neverthme3 mark , 22. Thuytenized ; these rytenized .SuUR mutation on repressive histone modifications remains poorly studied.SUUR protein interacts with replication complex indicating that it is directly involved in replication process , 23. A rSuUR mutation either in the context of polytenization or H3K27me3 levels. We showed that the formation of H3K27me3 domains in early embryos is unaffected by SuUR mutation. Our results support the idea that SUUR is a part of the mechanism that specifically re-establishes repressive chromatin during the chromosome replication [In this work, we explore the effect of SUUR on H3K27me3 level using polytene chromosomes as a model system. We performed a comprehensive analysis of chromosomal regions that are sensitive to lication . As thesSuUR mutants observed in previous studies [SuUR mutants [The first step to deciphering the mechanism involving SUUR protein was to establish causal relationship between the H3K27 methylation and under-replication phenomenon. Indeed, locally elevated polytenization level could result in the decrease in H3K27me3 ChIP/input signal in  studies , 21. For studies , 27, whi mutants , 18. AltSuUR mutants, this effect would be restricted only to the regions that are under-replicated in wild type and would never be found in the regions that are fully polytenized. However, comparing the H3K27me3 profiles to the polytenization levels published in [SuUR mutation is not restricted to the under-replicated sites, but also appears in many regions that are fully polytenized  regardless of their polytenization levels. H3K27me3 ChIP-chip data were quantile normalized prior to the analysis. This approach identified 193 chromosomal areas  to the published ChIP-chip profiles  and deteSuUR mutation on H3K27 methylation level [SuUR mutants and in wild type control and compared them to the published ChIP-chip profiles obtained with Abcam #6002 antibodies. The result of this comparison summarized in the Additional file SuUR mutation [It has to be mentioned that the antibodies  that were used to demonstrate the effect of on level  predominmutation  is indeeSuUR-sensitive regions detected by HMM to the previously published under-replication regions [SuUR mutants − 0.06, paired t test P = 1.1 × 10−40). However, 94 SSRs had no overlap with any under-replicated areas . Figure Then, we compared  regions –14. For  regions –14 . This mutation results in replication stress and homeotic phenotypes reminiscent of Polycomb group mutations. Mxc protein specifically regulates histone genes expression in cell cycle, but is not a part of the PRC complexes. The effects of mxc mutation are apparently mediated by the several fold over-production of H3 histone and are rescued by His3 mRNA depletion [Next, we aimed to explore the role of histones in under-replication using mutation in the epletion .mxc mutants also affects under-replication, we measured DNA polytenization in salivary glands of viable hemizygous mxcG43 male third instar larvae. FM7, Tb [mxcG43 mutants demonstrated substantial increase in polytenization levels in 25A and 75C regions as well as in the pericentric regions, which strongly supports the idea that under-replication is controlled at the histone level.To check if misregulation of histone genes in  FM7, Tb  balancer FM7, Tb  from H3Kmxc mutation leaves a possibility that SuUR mutation could likewise affect under-replication through misregulation of histone genes. This possibility seemed quite plausible as the recent study [mxc mutation. To address this possibility, we measured the expression levels of histone genes in salivary glands of SuUR mutants using qPCR and compared them with the wild type strain. SuUR mutants showed no significant differences in expression levels of H1 and H3 histones (and other histones) compared with wild type  contains Bithorax complex, which is densely covered with Pc, but is still under-replicated in wild type and fully polytenized in SuUR mutants [SuUR mutation effect on H3K27me3 level turns out to be associated with the presence of Pc protein. However, the effect of SuUR mutation on under-replication apparently is largely dependent on the size of the repressed domain, rather than its overall level of H3K27me3. These conclusions are consistent with earlier cytological observations based on immunostaining [Although the presence of Polycomb protein in SNRs apparently compensates the effect of  mutants , 44. Sim mutants , 14. Bot mutants . Hence, staining . DiscoveThe fact that SSRs do not bind Pc suggests two plausible mechanisms of how SUUR could maintain the level of H3K27me3 in these regions. On the one hand, SUUR could mediate the interaction between the replication complex and H3K27-specific methylase PRC2 and possibly other histone-modifying enzymes . On the Little is known about the specific maintenance mechanisms for repressive histone marks, but those few that are known to date appear to be very distinct. Recent reports indicate that during replication in mammalian cells parental H3K9me3 and H3K27me3 histone molecules are diluted twofold with new unmodified histones and then follows a lengthy  process of the re-establishment of histone methylation to original levels , 47. A shsp70 > loxP-Stop-loxP > DamMyc vector (JN993988) containing a stop cassette flanked by lox sites between the hsp70 minimal promoter and the CDS [Dam-Pc (KT921801) construct was made using the ctor JN9988 contaictor JN9988 contaiSuUR mutation was described in [y1w67c23  flies were obtained from the Bloomington Stock Center (# 6599) and used as the wild type control in all experiments. Fly strain for DamID of Pc was generated by inserting the corresponding construct into the attP18 landing site [ribed in  and is aing site . Flies eing site . Flies wg for 5 min. Upper phase was transferred to a new 1.5-ml tube. Interphase carryover was avoided. Next, 3 µl of RNase A (10 mg/ml) were added to the sample and incubated for 20 min at 37 °C to remove RNA. Equal volume of chloroform was added and mixed thoroughly by hand, followed by centrifugation at 16,000g for 5 min to remove RNase A and any traces of phenol. Upper phase was transferred to a new 1.5-ml tube. DNA was precipitated with 1.5 × volume of 100% isopropanol. The pellet was washed thoroughly with 1 ml of 70% ethanol, followed by centrifugation at 16,000g for 5 min. The supernatant was removed, and DNA pellet was air-dried for at most 5 min to prevent over-drying. DNA yield was measured using Nanodrop 2000 device. DNA integrity and RNA contamination level were assessed using agarose gel electrophoresis.Salivary glands (10–20 pairs) were accurately dissected in PBS, fat body was completely removed. Total genomic DNA was isolated using optimized phenol–chloroform extraction method, as detailed below. Salivary glands were quickly homogenized by mixing with 600 µl of lysis buffer  and passing the lysate 4–6 times through a 1-ml syringe fitted with 27-gauge needle. 10 µl of Dispase II solution (100 mg/ml) were added and incubated for at least 3 h at 56 °C. Equal volume of phenol–chloroform was added and mixed thoroughly by hand to obtain a uniform emulsion. The mixture was centrifuged at 16,000http://biolabmix.ru/). Final concentration of primers in a 25-µl reaction was 0.5 µM. Each reaction contained 10 μl of DNA template. Primers to Actin 42A gene were used for the inter-strain normalizations. Each data point was acquired from two biological replicates run in triplicates. The annealing temperature for all primers was 60 °C. The results were analyzed using Bio-Rad CFX software and in MS Excel.DNA polytenization was measured using standard curve method on CFX96 System (Bio-Rad). Genomic DNA from 25 larval ganglia was used to prepare standards, starting from 1 ng/μl concentration, with three successive 5 × dilutions. Genomic DNA from salivary glands was diluted to approximately 0.5 ng/ul. SYBRGreen qPCR reactions were set up using 2 × master mix  according to the manufacturer’s recommendations. 1 µg of total RNA was used in random-primed reverse transcription reaction using VILO kit (Invitrogen). Expression was assessed using standard curve procedure, as described above. hsp70 promoter of the pUAST vector [DamID was performed according to the previously published protocol , 50. In T vector . Dam-Pc T vector .The flies were kept at 23 °C. Fifty salivary glands were carefully dissected and fat body was removed completely. DNA was isolated using phenol–chloroform method as described above. 1ug of genomic DNA was digested by DpnI endonuclease that only cuts Dam-methylated GATC sequences. Digested DNA was ligated with double-stranded DNA adapters and then digested with DpnII endonuclease that cuts non-methylated GATC sequences. At this step, adapters remained ligated only to the fragments between two neighboring methylated GATC sequences in the genome. DpnII digestion step is needed to increase the specificity of the method. Next, methylated fragments were selectively amplified using adapter-specific primer. On an agarose gel, DamID products appeared as a smear ranging between 100 and 800 bp.Library preparation and data analysis were performed as described earlier . Before The reads obtained were mapped to BDGP R5/dm3 Drosophila genome assembly using MOSAIK software  with theTo perform H3K37me3 ChIP-seq, we used the True MicroChIP & MicroPlex Library Preparation™ Package (Diagenode), according to manufacturer’s recommendations. About 20 µl of 0–4-h embryos or 25 pairs of dissected salivary glands were used as starting material. Abcam anti-H3K27me3 antibodies (#6002) were used for chromatin immunoprecipitation throughout the study, except for the specificity tests, where Cell Signaling Technology #9733 anti-H3K27me3 and Millipore #07-452 anti-H3K27me2 antibodies were used  was used. Prior to the analysis, the data were quantile normalized. The resulting list of the regions is provided in the Additional files.To identify the regions enriched with H3K27me3 in wild type strain and depleted for H3K27me3 in Additional file 1: Table S1. Coordinates of 193 HMM-defined SSRs. First column—chromosome name, second column—start coordinate, third column—end coordinate. Each line corresponds to a single SSR.Additional file 2: Figure S1. Comparison of ChIP results in SuUR mutants and in wild type obtained with H3K27me3 antibodies from different vendors and with the antibodies against H3K27me2. A—scatter plot of ChIP-chip signals obtained with the Abcam #6002 antibodies in SuUR mutants (abscissa) and in wild type (ordinate) [B—scatter plot showing H3K27me3 ChIP-seq signals obtained with Cell Signaling Technology #9733 (CST #9733) antibodies in the same genotypes. C—the same analysis performed with Millipore #07-452 antibodies against H3K27me2. Datapoints inside 193 SSRs are shown in red. In both cases (A and B) H3K27me3 antibodies produce the characteristic skew (arrows): SSRs systematically show stronger signal in wild type strain as compared to SuUR mutants. This tendency is absent in case of H3K27me2 (C).rdinate) . B—scattAdditional file 3: Table S2. Coordinates of all reported under-replicated regions [ regions –14. The Additional file 4: Figure S2. Examples of SSRs that are not under-replicated in salivary gland of wild type strain. The color code and legend are the same as in Fig. 2(IP/inp) values.Additional file 5: Table S3. Coordinates of 158 HMM-defined SNRs. First column—chromosome name, second column—start coordinate, third column—end coordinate. Each line corresponds to a single SNR.Additional file 6: Figure S3. Comparison of SUUR DamID profiles in Kc167 cells and in salivary gland. Data for Kc167 cells were taken from [ken from , profileAdditional file 7: Figure S4.SuUR mutation has no effect of the expression levels of histone genes. Expression of the histone genes was measured using qPCR in SuUR mutant salivary glands and in wild type control. No significant difference was detected using t test.