Chromatin accessibility profiling assays such as ATAC-seq and DNase1-seq offer the opportunity to rapidly characterize the regulatory state of the genome at a single nucleotide resolution. Optimization of molecular protocols has enabled the molecular biologist to produce next-generation sequencing libraries in several hours, leaving the analysis of sequencing data as the primary obstacle to wide-scale deployment of accessibility profiling assays. To address this obstacle we have developed an optimized and efficient pipeline for the analysis of ATAC-seq and DNase1-seq data.We executed a multi-dimensional grid-search on the NIH Biowulf supercomputing cluster to assess the impact of parameter selection on biological reproducibility and ChIP-seq recovery by analyzing 4560 pipeline configurations. Our analysis improved ChIP-seq recovery by 15% for ATAC-seq and 3% for DNase1-seq and determined that PCR duplicate removal improves biological reproducibility by 36% without significant costs in footprinting transcription factors. Our analyses of down sampled reads identified a point of diminishing returns for increased library sequencing depth, with 95% of the ChIP-seq data of a 200 million read footprinting library recovered by 160 million reads.We present optimized ATAC-seq and DNase-seq pipelines in both Snakemake and bash formats as well as optimal sequencing depths for ATAC-seq and DNase-seq projects. The optimized ATAC-seq and DNase1-seq analysis pipelines, parameters, and ground-truth ChIP-seq datasets have been made available for deployment and future algorithmic profiling.The online version of this article (10.1186/s12864-018-4943-z) contains supplementary material, which is available to authorized users. Regulatory control of RNA expression is a key component of the central dogma of molecular biology. Under the central dogma, protein and RNA products regulate RNA expression. Modules of expression compose expression states associated with specialized biological activities. The information contained within a genome is expressed in a temporally and spatially coordinated program of ever increasing differentiation. This program starts with the embryonic stem cell and grows progressively more specialized. Control over RNA expression is predominantly achieved through the cooperative behavior of multiple proteins that bind DNA called transcription factors (TF) which modify chromatin and recruit RNA polymerase II (RNAP) prior to transcriptional elongation and mRNA expression .Higher order control over RNA expression has been postulated to exist in terms of an ‘epigenetic landscape’  in whichChromatin accessibility profiling was developed to interrogate the genomic circumstances that drives a given expression state . These thttp://hpc.nih.gov) for each run. The results from each of the 4560 argument combinations was then assessed against ‘ground truth’ ENCODE chromatin immunoprecipitation (ChIP)-seq results describing TF binding within the GM12878 lymphoblastoid cell line for both DNase1 hypersensitivity and ATAC-seq chromatin accessibility profiling [The process of cellular gene regulatory network (GRN) reconstruction via TF footprinting is computationally intensive and requires multiple data transformations, each of which have multiple possible parameters that may or may not significantly impact the quality and accuracy of the network generated. To assess the impact of parameter selection on network generation, we executed a computational grid search across eight parameter arguments and five different pipeline steps, with a total of 4560 different pipelines evaluated. The process of evaluating 4560 different pipelines collectively utilized 72,960 CPUs with 3.75 GB RAM each. The mean total time that each pipeline took was 2 h, and running all pipelines on the Biowulf supercomputing system consumed more than 100,000 CPU hours  to investigate the relationship between read depth, number of footprints detected, and ChIP-seq recovery. Reads were sampled from 20 to 200 million single-end reads; the number of open chromatin regions, the number of footprints, and the ChIP-seq AUC was calculated at each sampling Fig. . InspectAs part of the procedure for ATAC-seq and DNase-seq, the enzymatically-digested genomic fragments are PCR amplified. This process of PCR amplification can introduce bias into the composition of the library via stochastic ‘PCR jackpotting’ in which randomly selected genomic intervals are amplified by the DNA polymerase . We evalWhen PCR duplication removal was introduced, the reproducibility of footprinting data alignments measured as Pearson correlation between two replicates’ genomic bins increased from 0.6 to 0.82 Fig. . Introdui) peak size, controlling the size of the bins; and ii) minDist, controlling the minimum distance allowed between two peaks. Analysis of grid search results indicates that peak size altered the biological reproducibility of detected open chromatin regions .Optimized Snakemake pipelines for ATAC and DNase1 footprinting are available for download at the Chiorini Lab github page  and technical (ATAC-seq) replicates. Similarly, smaller peak sizes of open chromatin regions (OCRs) lead to reproducibility improvements. Because of these small peak sizes, there is no reproducibility-optimized Wellington pipeline, as Wellington requires the sizes of shoulder and footprint sequences to sum to a value greater than 50 base pairs. Picard PCR duplicate removal is used in some AUC-optimized pipelines, whereas small peak sizes are not used in any AUC-optimized pipelines.PCR duplicate removal takes out identically-ended sequencing reads (reads that look like the result of PCR bias). For example, in a chromatin accessibility assay performed on a population of 50,000 cells, it is expected that the enzyme cuts the same base pair position in multiple genomes at some unknown rate. Pipelines that used PCR duplicate removal exhibited as much as a 50% reduction in the number of footprints found relative to their untreated counterpart pipelines. In our data, TF footprinting detection by Wellington was negatively impacted by PCR duplicate removal, with a mean reduction of 84% of the AUC above random. Similarly, larger peak sizes increase the genomic interval in which footprinting is detected while decreasing the degree of biological reproducibility. Lost opportunities to call footprints, in the case of peak size, or lost information to call footprints, in the case of PCR duplicate removal, may explain the apparent contradictions we observed between increased reproducibility and diminished AUC scores for certain pipelines.We noted that pipelines optimized for reproducibility in alignment and peak calling do not score well on AUC, and vice versa. This tradeoff is explicable by the two argument differences between these pipelines: HOMER peak size and bias correction. High reproducibility was found in samples with small peak sizes, and visual inspection determined those peaks overlapped with only the highest points of alignment, leading to safe and reliable peak assertions. However, as peaks are the only regions in which footprints are searched for, fracturing of open chromatin regions limits footprinting in regulatory regions and can reduce AUC. HINT’s known DNase bias correction increases reproducibility in DNase-seq footprints but reduces mean AUC. This phenomenon is currently inexplicable. Interestingly, none of the evaluated bias correction methods improved ATAC-seq footprint reproducibility or mean AUC.It is worth noting that overlap of footprints between replicates was higher in DNase1-seq pipelines optimized for reproducibility relative to AUC-optimized or default pipelines, and lower in ATAC-seq pipelines optimized for reproducibility relative to AUC-optimized or default pipelines. It is not clear why this is the case. Using an AUC-optimized pipeline with HINT results in the highest biological information possible gleaned from footprinting.We also found that AUCs representing ChIP recovery for ATAC-seq data in GM12878 were universally lower than ChIP recovery AUCs from DNase-seq data. Our optimized pipelines for ATAC-seq data used very sensitive alignment but were otherwise identical to optimized pipelines for DNase-seq for each algorithm. It may be the case that algorithms have been optimized on DNase-seq data and modes for using ATAC-seq data are not mature. Neither optimized pipeline using HINT utilized either known DNase1 bias or bias estimated from the data. As footprinting algorithms continue to include ATAC-seq training data, it is very likely that performance on this kind of data will improve.Chromatin accessibility profiling assays such as ATAC-seq and DNase1-seq offer the opportunity to rapidly characterize the regulatory state of the genome at a single nucleotide resolution. Significant optimization of molecular protocols has enabled the generation of sequencing libraries in several hours. The remaining computational analysis of next generation sequencing data is the primary obstacle to wide-scale deployment of accessibility profiling assays . To address this obstacle, we have developed an optimized and efficient pipeline for the analysis of ATAC-seq and DNase1-seq data.Our pipeline was designed to maximize ChIP recovery, the current ‘gold standard’ in transcription factor occupancy assessment. In addition, we performed a computationally intensive grid search across 4560 possible argument combinations to identify the impact of parameter selection on pipeline performance. Our analyses identified the optimal argument combinations for ATAC-seq and DNase1 analysis and these pre-optimized pipelines were translated into Snakemake pipelines for routine and reproducible deployment to supercomputing clusters. Each iteration of these analyses required approximately 11.5 CPU years to reach completion and are being made freely available at the Chiorini Lab Github to avoid duplication of effort within the scientific community. Additionally, the raw datasets and code for pipeline validation have been made publically available allowing investigators to easily profile, and rapidly improve, future pipelines and algorithms for transcription factor footprinting.Genome wide chromatin accessibility mapping offers a unique window into the regulatory state of the cell by allowing an investigator to rapidly map cellular genomic states. We anticipate continued development in algorithms and molecular protocols that will improve the accuracy and efficiency of gene regulatory network inference from ATAC/DNase sequencing. As chromatin profiling is a valuable tool to characterize the networks and TF binding events that define a cell’s operation, we expect that chromatin profiling assays will continue to see use in cancer genomics, cell engineering, and monogenic diseases cases evading exomic diagnosis. The optimized analysis pipelines and quantitative profiling metrics for construction of ATAC/DNase-seq networks provided here contribute to the scientific community by providing an easily accessed platform for analysis and pipeline engineering.http://www.bioinformatics.babraham.ac.uk/projects/fastqc). For ATAC-seq, FastQC adds additional information by identifying the tags remaining in the read from Tn5 tagmentation. Removal of tagmentation tags was accomplished via truncation of 20 bp of signal from the 5′ end of the transcript using Trimmomatic [The structure of our footprinting analysis pipeline is presented in Fig. mmomatic . For theFollowing quality control, the second step is to align reads to the genome using Bowtie2 . A seriehttp://broadinstitute.github.io/picard). These tools are then used to sort and index the aligned reads. If Picard is used to convert the reads to binary format, the aligned reads are also queued for PCR duplicate removal via the Picard MarkDuplicates function. PCR duplicates are identical copies of reads which can introduce PCR bias to the TF footprinting analysis by over-representing a small number of highly duplicated genomic regions. In genomic footprinting, PCR duplicate removal may lower bias or, alternatively, discard biological information.In the third step, aligned reads are converted to a binary format using (as the third argument) either SAMtools  or the BIn the fourth step, the aligned reads are analyzed by the HOMER  findpeakp-value cutoff and a desired false discovery rate. HINT is a hidden Markov model based footprinting algorithm that incorporates bias correction for known DNase1 digestion biases. Usefully, HINT also incorporates functions that enable de novo estimation of enzymatic bias from input data. The only parameter that can be passed to HINT is the mode of bias correction. Both Wellington and HINT return genomic intervals of predicted protein binding. These proposed protein binding intervals are then intersected on the genome with transcription factor position weight matrices to generate predicted transcription factor binding intervals and a final gene regulatory network.The final step of the analysis pipeline is to pass the aligned reads and OCRs to a TF footprinting identification algorithm. We evaluated two footprinting algorithms, Wellington and HINT. Wellington utilizes a beta-binomial distribution to estimate footprints and leverages the observation that enzymatically accessible genomic DNA in the nucleus results in strand-specific increases in enzyme activity around transcription factor footprints. Wellington requires two arguments: a https://www.encodeproject.org/experiments/ENCSR000EJD/ and https://www.encodeproject.org/experiments/ENCSR000EMT/, respectively. ATAC-seq data used to validate the pipeline was acquired from Buenrostro et al. [The data used to validate this pipeline came from GM12878, a lymphoblastoid cell line. DNase1-seq data used to validate the pipeline is ENCODE data  from theo et al.  via the Three metrics of data reproducibility were assayed across the pipeline. The first metric was the correlation of sequence read alignments. Under this metric, the genome is divided into 10 kilobase bins and the number of reads in each bin is compared between replicates for each bin. A summary Pearson correlation coefficient is then calculated, with higher Pearson correlation coefficients between genome bin sets suggestive of similar patterns of aligned reads across the genome.The second metric of reproducibility quantitatively assesses OCR reproducibility between replicates. Peaks are averaged across 10 kilobase bins and compared between replicates to produce a Pearson correlation coefficient that summarizes the reproducibility of the assay to detect OCRs across multiple replicates.The final reproducibility metric assayed was the percentage of TF footprints detected as present and overlapping in two biological replicates. This metric assesses the ability of the assay to detect biologically reproducible TF binding events.In addition to the three metrics of reproducibility, we assessed the ability of chromatin accessibility based data to recover transcription factor binding by comparing identified footprints to the ‘gold standard’ of ChIP-seq for each footprint. Transcription factor ChIP data for BHLHE40, BRCA1, CTCF, EBF1, IRF3, MXI1, NFE2, NFYA, NFYB, NRF1, STAT1, STAT3, USF2, ZNF143 and ZNF274 was downloaded from ENCODE. This data was compared to footprint coverage of the genome at all positions matching that transcription factor’s binding motif to assess the ability of transcription factor footprinting to recover known ChIP-seq binding information.http://www.bioinformatics.babraham.ac.uk/projects/fastqc), seqtk (https://github.com/lh3/seqtk), Bowtie2 v.2.2.9 [http://hpc.nih.gov).Software used in the pipeline includes FastQC  to 20, 40, 60, 80, 100, 120, 140, 160 and 200 million reads. Random seeds for seqtk were set to the same value as the number of reads being downsampled to and three permutations of the data were iteratively sampled. Downsampled samples were used to assess the impact of read number on the total number of transcription factor footprints detected and AUC recovery.ATAC-seq and DNase-seq data was downsampled using seqtk  curves were produced from these predictions and the area under curve (AUC) for each transcription factor was calculated. Across all of the transcription factors tested, there were on average 10 negative TF binding sites for each positive TF binding site.Additional file 1:Table S1. Parameters passed to each pipeline. Default, AUC-optimized and reproducibility-optimized pipelines for ATAC-seq and DNase1-seq are shown using each footprinting algorithm. Parameters for each pipeline are listed. These parameters correspond to Fig. github.com/ChioriniLab. (PDF 182 kb)Additional file 2:Figure S1. FastQC Reveals Tn5 Tags and PCR Chimerism. Non-random distributions of the first ~ 10 bases after tagmentation correspond to the Tn5 tagmentation after library primers are removed. This non-random region at the beginning of the read is characteristics of ATAC-seq data. In the ATAC-seq sample from Buenrostro, a second non-random region can be seen, suggesting PCR chimerism. (PDF 57 kb)Additional file 3:Figure S2. Trimming reads improves alignment of the GM12878 ATAC-seq reads. Tn5 transposase attaches mosaic end (ME) tags that need to be trimmed from the 5′ end of the read. Additionally, however, trimming low-quality base pairs from the 3′ end of the ATAC-seq reads so that all reads had the same length improved alignment to the genome (shown in green). With a 3 billion base pair genome, the chance that a sequence of a certain length will align randomly is high for sequences shorter than 17 base pairs. To minimize random alignment while removing low-quality base pairs for this ATAC-seq data, we trimmed the reads to a final length of 20 base pairs. (PDF 13 kb)Additional file 4:Figure S3. Visual inspection reveals consistent overlap between HOMER peaks and OCRs at peak size of 200. (a) Read coverage for DNase1-seq (top) and ATAC-seq (bottom) shown underneath open chromatin regions called by HOMER at peak sizes ranging from 10 base pairs to 2000 base pairs. Peak reproducibility between replicates was shown to be higher with lower peak sizes. Visualized using the Broad Institute’s Integrative Genomics Viewer (software.broadinstitute.org/software/igv/).  Metrics of reproducibility and biological information plotted against the HOMER argument minDist for all pipelines. Pipelines could have a minDist of 0, 50 or 500, and these values had no effect on correlation between replicates or recapitulation of known ChIP-seq. (PDF 1272 kb)


GATA2 representing one of the key transcriptional regulators commonly mutated in inherited MDS/AML  located within the promoter and 5′UTR of GATA2 overlapping a CpG island offered a marker to distinguish between mutant and WT alleles where this SNP creates/abolishes a CpG dinucleotide within the GATA2 promoter region   Fig. ). Apart GATA2 methylation and expression are accompanied by changes in chromatin structure at the promoter. H3K4me3 and H3K27me3 define poised or closed chromatin, respectively, rendering them more or less accessible for transcription factors, thereby regulating gene expression [GATA2 promoter region encompassing the SNP rs1806462 [C/A]. While there were no apparent allele-specific differences in H3K27me3 deposition across the different time-points of IV.10, an enrichment in the deposition of H3K4me3 on the promoter of the mutant allele (A) relative to the WT allele (C) was noted in IV.10 monoallelic samples (yr. 1 and 3)  Fig. .GATA2-mutated MDS/AML pedigrees, which may be governed by the acquisition of additional co-operating mutations  combined with dynamic epigenetic reprogramming and subsequent allele-specific expression of GATA2 mutant allele, adding another level of complexity to the (epi)genetic basis of familial MDS/AML.Collectively, our findings provide a step forward in understanding the molecular mechanisms underlying reduced penetrance in Supplementary InformationTable S2


P-value = 0.0006). Of variants associated with complex traits, 1531 resulted in ASE, and 1662 showed ASB. For example, we characterized that the Crohn's disease risk variant for rs3810936 increases NFKB1 binding and results in altered gene expression.Many variants associated with complex traits are in noncoding regions and contribute to phenotypes by disrupting regulatory sequences. To characterize these variants, we developed a streamlined protocol for a high-throughput reporter assay, Biallelic Targeted STARR-seq (BiT-STARR-seq), that identifies allele-specific expression (ASE) while accounting for PCR duplicates through unique molecular identifiers. We tested 75,501 oligos  and identified 2720 SNPs with significant ASE (FDR < 10%). To validate disruption of binding as one of the mechanisms underlying ASE, we developed a new high-throughput allele-specific binding assay for NFKB1. We identified 2684 SNPs with allele-specific binding (ASB) (FDR < 10%); 256 of these SNPs also had ASE (OR = 1.97,  Genome-wide association studies (GWAS) have identified thousands of common genetic variants associated with complex traits, including normal traits and common diseases. Many GWAS hits are in noncoding regions, so the underlying mechanism leading to specific phenotypes is likely through disruption of gene regulatory sequence. Quantitative trait loci (QTLs) for molecular and cellular phenotypes , such asMassively parallel reporter assays (MPRA) have been developed for the simultaneous measurement of the regulatory function of thousands of constructs at once. For MPRA, a pool of synthesized DNA oligos containing a barcode at the 3′ UTR of a reporter plasmid is transfected into cells, and transcripts are isolated for RNA-seq. Expression driven by the synthesized enhancer region is estimated from the number of RNA reads normalized by the number of corresponding DNA reads . An alteIn addition to using reporter assays to measure enhancer function on gene expression, there are several methods to directly measure binding affinity of DNA sequences for specific transcription factors. These methods include Spec-seq , electroSupplemental Fig. S1). We selected different categories of regulatory variants for this study, including eQTLs . We also included the use of unique molecular identifiers (UMIs), added during cDNA synthesis. With these random UMIs we are in effect tagging identifiable replicates of the self-transcribing construct, which improves the analysis of the data by accounting for PCR duplicates. Our protocol also has the advantage of being highly streamlined. Unlike STARR-seq, our method does not require preparation of DNA regions for use in the assay, such as whole-genome fragmentation  to test for allele-specific effects in regulatory regions  . The DNA library was then transfected in LCLs , and we were able to recover a total of 43,500 testable SNPs . Read counts for the nine biological replicates were highly correlated  . To identify SNPs with allele-specific effects, we applied QuASAR-MPRA . To investigate the importance of UMIs in this experimental approach, we reanalyzed our data without removing duplicates. For the combined replicates, inflation (genomic inflation test) (Methods) is greatly increased (from 1.10 to 1.73). If only a single RNA library replicate is considered, the number of detected ASE is about fourfold reduced   . Additionally, by combining the ASE results with the direction of the motif, we can characterize whether the motif is active in both directions or only in one direction. This would suggest that some TF binding motifs tend to function specifically in one direction. We found that when both alleles are covered in both directions, the allelic effects on gene expression tend to agree in direction and magnitude. If we categorize these directional allelic effects per motif, we do not observe major differences with the notable exception of CTCF (P = 0.02) for ASE when the direction of transcription of the reporter gene is opposite to that of the motif strand . Although position within the window does not affect the ASE signal, the main effect seems to depend on the presence/absence of the tested site within the DNase window  and, in a lesser degree, it depends on the peak size . SNPs were originally selected based on the CentiSNP annotation, but when we considered chromatin states (Broad ChromHMM state), we detect enrichment for promoter state among SNPs with ASE , which is an important regulator of the immune response in LCLs and other immune cells . Previoulmonella .Supplemental Tables S7–S10). As expected, these regions were enriched  for NF-κB complex footprints , with a higher portion of these regions in the middle concentration of NFKB1 compared with the low or high concentrations , for a total of 2684 SNPs when aggregating all experiments . These spanned our designed regulatory categories, with the greatest number covering CentiSNPs . When we considered these ASB SNPs in combination with the ASE results from the BiT-STARR-seq , we found that SNPs with ASE are enriched for NFKB1 ASB  , which are factors known to antagonize NF-κB complex binding  overrepresented regions in the bound library (trations B. We hyptrations  to identtrations C. We suc× 10−16) D. For AS binding , 2011.ThP-value <0.05) and 1769 CentiSNPs with ASE  associated to a complex trait in the GWAS catalog  or from fgwas  in the BiT-BUNDLE-seq assay and ASE  in both directions in the BiT-STARR-seq. This SNP is a synonymous variant in gene TNFSF15 , which encodes for a cytokine that belongs to the tumor necrosis factor (TNF) ligand family.We used ASB and ASE in combination with transcription factor binding motifs to assign mechanistic function to putatively causal SNPs linked to complex traits. We found 2054 CentiSNPs with ASB .Increased TL1A expression has been reported in inflamed Crohn's disease tissue, compared with noninflamed areas, and in ulcerative colitis patient serum . TL1A gielopment . In addielopment , a cortiThe recent adaptation of MPRA to investigate ASE allows for validation of regulatory variants in transcription factor binding sites, which have been shown to be functionally relevant to fine-map eQTLs  and GWASOur results show that using existing annotations to prioritize regulatory variants for high-throughput reporter assays is an effective strategy. The CentiSNP annotation, in particular, contains information that can be used to analyze ASB/ASE for individual transcription factor motifs and investigate potential molecular mechanisms of action. We found that direction is an important factor in the case of CTCF, most likely due to how CTCF functions as an insulator between the enhancer and the promoter when they are in anti-parallel directions. Previous studies have shown that CTCF, a well-characterized insulator, has binding sites at the anchors of chromatin loops. These are arranged in forward–reverse orientations , where ttrans-acting effects  should affect both alleles similarly and therefore should not induce false positives in the allele-specific signal.Generally, caution should be used in interpreting reporter assay gene expression differences across cell types, because transfection may perturb the cell state. However, it is important to highlight that any We used our library of oligos also in a BiT-BUNDLE-seq assay for identification of ASB for NFKB1. This is a novel approach to combine ASB and ASE identification in high-throughput assays using the same sequences. Our results show that this integration is a useful approach to validate the molecular mechanism for specific transcription factors. Allelic effects on transcription factor binding and gene expression are not always concordant. Some of this discordance is due to lack of power to detect ASB/ASE overlap, as well as other technical considerations. For example, in BiT-BUNDLE-seq, only one single TF (NFKB1) is available for binding, whereas in BiT-STARR-seq, other cofactors are present in the cell to affect binding. Additionally, there can be discordance in direction of effect, where, for example, an allele can lead to increased binding of a factor with repressing activity on gene expression . These regulatory events are likely to be captured in the BiT-STARR-seq assay, which is performed in LCLs where CREB1, AML1, and NFKB1 are active. These results highlight that multiple types of assays are necessary to capture the detailed molecular mechanism of gene regulation. Additionally, integration with GWAS can identify and further characterize the molecular mechanisms linking causal genetic variants with complex traits.Supplemental Tables S2–S4 report the annotations we have considered with their sources, and Supplemental Tables S16 and S12 include the library composition. Each regulatory region was designed to have two oligos: one for each of the alleles. DNA inserts 230 bp long, corresponding to 200 bp of regulatory sequence, were synthesized by Agilent to contain the regulatory region and the SNP of interest within the first 150 bp . We performed a first round of PCR to generate double-stranded oligos and complete the sequencing primers, followed by a subsequent round of PCR to amplify the material. Plasmid pGL4.23 (Promega) was linearized using CloneAmp HiFi PCR Premix (Clontech) primers [STARR_F_SH and STARR_R_SH]. Inserts were cloned into the linear plasmid using standard In-Fusion (Clontech) cloning protocol. Clones  were transformed into XL10-Gold Ultracompetent Cells (Agilent) in a total of seven reactions. DNA was extracted using EndoFree maxiprep kit (Qiagen).2 in RPMI1640 with 15% FBS and 1% Gentamycin for 24 h. Cell pellets were then lysed using RLT lysis buffer (Qiagen) and cryopreserved at −80°C. For RNA libraries, total RNA was isolated from the thawed lysates using RNeasy Plus Mini Kit (Qiagen). RNA-seq libraries from the polyadenylated RNA were prepared using a custom protocol described in the Supplemental Methods (library preparation). We prepared seven replicates of the DNA library using a modified version of the PCR protocol as previously described .The DNA library was transfected into LCLs (GM18507) using standard nucleofection protocol, program DS150. We performed nine biological replicates of the transfection from seven independent cell cultures. After transfection, cells were incubated at 37°C and 5% COSupplemental Methods (BiT-STARR-seq protocol).We developed BiT-BUNDLE-seq, by modifying the design of the BUNDLE-seq protocol . Specififtp://ftp.ccb.jhu.edu/pub/infphilo/hisat2/data/grch37_snp.tar.gz) so as to avoid reference bias. Realigning the reads to GRCh38 should not affect the conclusions as any problematic region of the genome is excluded from any analysis . We then ran UMItools . These were then compared using ΔAST  to identhttps://www.ncbi.nlm.nih.gov/sra) under accession number SRP154945.The sequencing data from this study have been submitted to the NCBI Sequence Read Archive (SRA;


It has been reported that PDSS2 is downregulated in several types of tumors and acts as a potential tumor suppressor gene to inhibit the proliferation and migration of cancer cells. However, the regulatory mechanism of PDSS2 expression remains elusive. In the present study, we first identified and characterized the PDSS2 promoter region. We established four different luciferase reporter constructs which mainly cover the 2 kb region upstream of the PDSS2 gene transcription initiation site. Series luciferase reporter assay demonstrated that all four constructs have prominent promoter activity, and the core promoter of PDSS2 is mainly located within the 202 bp region near its transcription initiation site. Transcription factor binding site analysis revealed that the PDSS2 promoter contains binding sites for canonical transcription factors such as Sp1 and GATA-1. Overexpression of Sp1 significantly inhibited PDSS2 promoter activity, as well as its endogenous expression, at both mRNA and protein levels in lung cancer cells. Site-directed mutagenesis assay further confirmed that the Sp1 binding sites are essential for proximal prompter activity of PDSS2. Consistently, a selective Sp1 inhibitor, mithramycin A, treatment repressed the PDSS2 promoter activity, as well as its endogenous expression. Chromatin immunoprecipitation (ChIP) assay revealed that Sp1 binds to the PDSS2 promoter in vivo. Of note, the expression of Sp1 and PDSS2 are negatively correlated, and higher Sp1 expression with low PDSS2 expression is significantly associated with poor prognosis in lung cancer. Taken together, our results strongly suggest the essential role of Sp1 in maintaining the basic constitutive expression of PDSS2, and the pathogenic implication of Sp1-mediated PDSS2 transcriptional repression in lung cancer cells. Prenyl diphosphate synthase subunit 2 (PDSS2) is the first key enzyme in the CoQ Previous researches showed that the concentration of CoQ10 in serum from patients with malignant tumors, such as lung cancer, breast cancer, and cervical cancer, is significantly lower than that of patients without malignant tumors. A low level of CoQ10 in patients was positively correlated with poor prognosis [10 can inhibit malignant tumor cell invasion with its antioxidant capacity [Coenzyme Q 10  is located in chromosome 6q21 and composed of eight exons and seven introns, approximately 30 kb in length. PDSS2 is the first key enzyme in the CoQ10 biosynthesis pathway [PDSS2 mutations cause CoQ10 deficiency and contribute toward metabolic and nephritic diseases [PDSS2 as a tumor suppressor gene has been investigated, and it has been reported that PDSS2 is downregulated in gastric cancer, liver cancer, and melanoma, and its downregulation is closely related to tumor stage and grade, suggesting that PDSS2 may be a new potential tumor suppressor gene [PDSS2 also has a decreased expression and tumor-suppressing activity in human lung cancer cells [The human gene of Prenyl diphosphate synthase subunit 2  was purchased from Sangon Biotech  and dissolved in DMSO . For transient transfection, cells were seeded at a density of 2 × 105 cells/well in 12-well culture plate and then incubated overnight, followed by transient transfection with the indicated plasmids using Neofect DNA® transfection reagent  according to the manufacture protocol. Sp1 expression plasmid, pcDNA-HA-Sp1, was a kind gift from Dr. Jingde Zhu of the Shanghai Cancer Research Institute. The indicated luciferase reporter constructs were transiently co-transfected, the cells were subjected to luciferase assay 48 h after transfection. Twenty-four hours after transfection, MitA (100 nM) was added to the cells. Twenty-four hours after MitA treatment, cells were collected and subjected to further analysis.Human Lung cancer cell lines, A549 and H1299, were cultured in DME-F12 for A549 or RPMI 1640 for H1299 medium containing 50 units/mL penicillin, 50 mg/mL streptomycin, and 10% (v/v) FBS ). The cells were routinely maintained in humidified atmosphere containing 5% COPDSS2 gene, the luciferase reporters P2031 (−1768/+263), P764 (−501/+263), P464 (−201/+263), and P202 (−201/+1) were established based on the firefly luciferase promoter-less vector pGL3-Basic using the seamless cloning kit . The primer sequences and restriction enzymes are listed in To assay the promoter activity of the CCCACAAC at −173 bp was changed into CACTCGCCGTAGACAAC (underlined means changed Nucleotides). For M2 mutant, the putative Sp1 binding site of CAGAGCGGCGGGGTGGG at −126 bp was changed into CAGAGCGAATTTTTGGG. For M3 mutant, the putative Sp1 binding site of AAAGCACCGCCCCTGCGG at −64 bp was changed into AAAGCATAGAATCTGCGG. For M4 mutant, the putative Sp1 binding site of TGCGGGGGCGTTCTCGG at −77 bp was changed into TGCGGGTTAGTTCTCGG. All of the mutations were verified by direct DNA sequencing. The primer sequences are listed in The luciferase reporters P202M1, P202M2, P202M3, and P202M4 were generated by a site-directed mutagenesis kit  on the basis of the indicated parental construct P202 (−201/+1) according to the manufacturer’s instruction. For M1 mutant, the putative Sp1 binding site of CACTCGCCGPDSS2 gene. The assay was conducted as described previously [PDSS2 promoter fragments (100 ng) using Neofect DNA® transfection reagent. Forty-eight hours later, cells were lysed, and luciferase activity was determined using the Dual-Luciferase assay system .Luciferase reporter assay was used to assess the promoter activity of the eviously ,16. In bPDSS2 were downloaded from GeneBank and the UCSC database. Transcription factor binding sites were predicted using online software JASPAR, PROMO, and MatInspector. Alignment for the PDSS2 promoter sequences from multiple species was conducted using DNAMAN and Clustal Omega.The mRNA and genomic sequences of ChIP was performed with the EZ ChIP™ Chromatin Immunoprecipitation kit  as described previously ,16. The ® Premix Ex TaqTM . The sequences of the primers are offered in Real time quantitative PCR (RT-PCR) was conducted as described previously . BrieflySp1 overexpression experiment, Sp1 expression plasmids and corresponding empty vectors were transiently transfected in cells. Forty-eight hours after transfection, whole cell lysates were prepared and subjected to Western blotting analyses. GAPDH was used as an internal control.Western blotting was conducted as described previously ,16. BriePDSS2 expression was analyzed using published lung cancer microarray data as described previously [The prognostic value of eviously . Microareviously .t-test. p < 0.05 was considered statistically significant.All data were statistically analyzed using SPSS 16.0 software . The luciferase reporter gene activity was compared between two groups using the unpaired PDSS2 gene, as well as its chromatin state, we deeply retrieved and mined the UCSC genome database (http://genome.ucsc.edu/). As shown in PDSS2 gene is located at human chromosome 6p21 and contains eight exons interspaced by seven introns. According to the ENCODE histone modification data, the transcription elongation hallmark of H3K36me3 was enriched within the whole PDSS2 gene body. In line with this evidence, ChromHMM chromatin state data mining also suggested that the PDSS2 gene was actively transcribed in cells. Of note, the first exon and the 5′-region of first intron of the PDSS2 gene contained a classic CpG island, and was marked with DNase I hypersensitivity and H3K4me3 , highly suggesting that the PDSS2 gene promoter is located near its first exon region.To analyze the genomic organization of the human PDSS2 gene, a variety of the PDSS2 gene promoter fragments that differed in length were inserted into the firefly luciferase reporter vector, pGL3-Basic. In total, we generated four luciferase reporter constructs, including P2031 (−1768/+263), P764 (−501/+263), P464 (−201/+263), and P202 (−201/+1)  . As showPDSS2 gene, we employed multiple software to analyze transcription factor binding sites for the PDSS2 promoter. As indicated in PDSS2 promoter contains several consensus binding sites for canonical transcription factors such as Sp1. In addition, homology analysis revealed that the PDSS2 promoter sequence is evolutionarily conserved across species of human, mouse, and rat  led to significant repressed promoter activity in H1299 but not A549 cells, suggesting a cell type-dependent mechanism. Of note, disruption of each of the four Sp1 binding sites completely abolished the inhibitory effect of Sp1 overexpression on PDSS2 promoter activity. Therefore, these results strongly suggest that all the four Sp1 binding sites in the PDSS2 core promoter region are not only critical for its constitutive core promoter activity, but also essential for Sp1-mediated repression of PDSS2 transcription. In addition, Chromatin immunoprecipitation (ChIP) assay demonstrated that Sp1 binds to the PDSS2 core promoter region in vivo in cells  is known to bind to GC-rich DNA sequences, displacing Sp1 transcription factor binding to its target promoters, which inhibits their expression [PDSS2 promoter reporters in A549 and H1299 cells. Furthermore, MitA treatment also led to remarkable repression of endogenous PDSS2 expression at both mRNA and protein levels in both A549 and H1299 cells  and GATA-1 binding sites. It has been reported that OCT-1 promotes cancer cell proliferation, migration, and invasion, such as in lung cancer, prostate cancer, and cervical cancer, and its high expression is closely related to the poor prognosis of patients [PDSS2 is regulated by OCT-1 or GATA-1.In addition, we also found that the patients ,26,27,28patients ,30. The patients ,32,33. Gpatients . As impoPDSS2 gene for the first time and demonstrated that Sp1 transcription factor could directly regulate the transcription of PDSS2, which not only lays a solid foundation for further elucidation of the PDSS2 regulation mechanism and molecular behavior, but also contributes to further analysis of the biological function of PDSS2 in the development of cancer.In conclusion, our preset study identified the promoter region of the


CLL), a diverse set of genetic mutations is embedded in a deregulated epigenetic landscape that drives cancerogenesis. To elucidate the role of aberrant chromatin features, we mapped DNA methylation, seven histone modifications, nucleosome positions, chromatin accessibility, binding of EBF1 and CTCF, as well as the transcriptome of B cells from CLL patients and healthy donors. A globally increased histone deacetylase activity was detected and half of the genome comprised transcriptionally downregulated partially DNA methylated domains demarcated by CTCF. CLL samples displayed a H3K4me3 redistribution and nucleosome gain at promoters as well as changes of enhancer activity and enhancer linkage to target genes. A DNA binding motif analysis identified transcription factors that gained or lost binding in CLL at sites with aberrant chromatin features. These findings were integrated into a gene regulatory enhancer containing network enriched for B‐cell receptor signaling pathway components. Our study predicts novel molecular links to targets of CLL therapies and provides a valuable resource for further studies on the epigenetic contribution to the disease.In chronic lymphocytic leukemia ( Our comprehensive data set represents a rich resource for studying gene regulation and epigenomics in CLL. We exploited it to integrate chromatin features and TF binding with gene expression programs in CLL B cells and suggest molecular mechanisms for the aberrant survival of malignant CLL cells.+ B cells from peripheral blood from 23 CLL patients and from 17 pools of non‐malignant B cells (NBCs) of healthy donors  locus as an example for a gene upregulated in CLL , regions of transcription, and active or repressed regions . As an example, TCF4 and selected deregulated target genes are shown in Fig The changes of the chromatin landscape were linked to the deregulated activity of TFs and chromatin modifiers in CLL according to the workflow depicted in et al, et al, et al, P = 0.005, Wilcoxon rank‐sum test, P = 1.6E‐4, Wilcoxon rank‐sum test, P = 2E‐48, Fisher's exact test, Fig et al, When comparing DNA methylation in CLL with NBC controls, we found a global hypomethylation in CLL as previously reported . Differences in apparent promoter bivalency could also result from a heterogeneity of the individual marks. However, a truly bivalent state with H3K4me3 and H3K27me3 has only very low levels of transcription  lost H3K4me3 in CLL Fig E. Only ~The active ChromHMM states 1, 8, 9, and 11 comprised 238,820 regions (368 Mb) and represent loci that potentially contain enhancers. This set overlapped with the corresponding B‐ and T‐cell chromatin states of the Roadmap consortium to 89.8 and 72.2%, respectively Fig . The majet al, TCF4, CREB3L2, and FMOD , the immune checkpoint receptor CTLA4, FMOD as a CLL tumor‐associated antigen allowing for expansion of specific CD8+ autologous T lymphocytes  was reduced in CLL, which has been reported to be a p53‐independent tumor suppressor  on the RNA level was apparent in CLL  for different potential enhancer loci and excluded a ± 1 kb region around promoters. The annotations included differential ATAC signals (Dataset EV10) at ChromHMM states 1, 8, 9, and 11 Fig A and C, The differential CTCF occupancy as inferred from ATAC‐seq was corroborated by a CTCF ChIP‐seq analysis and revealed clear differences between CLL cells and NBCs Fig A. Using For the further analysis, we compiled lists of “active enhancers in CLL” and “active enhancers in NBCs”. They were derived from the consensus of individual ATAC‐seq peak regions flanked by active chromatin states 1 and 9 . The twoNext, we identified accessible chromatin loci in single cells via single‐cell ATAC‐seq (scATAC‐seq), which can resolve the heterogeneity between individual cells . We analet al, NFKB2 locus that is downregulated in CLL is shown  as evident from the RNA‐seq tracks .All CLL patients analyzed in this study gave written informed consent  and were characterized in the diagnostics department of the University Hospital Ulm . The medet al, DNA was extracted with the AllPrep DNA/RNA Mini Kit (Qiagen), spiked with unmethylated lambda DNA (Promega) and sonicated to achieve an average size of 250–300 bp. After bisulfite conversion using the EZ DNA Methylation Kit (Zymo), strand‐specific WGBS‐seq libraries were prepared as described previously . ChIP‐seq of both CTCF and EBF1 was done with the SimpleChIP‐seq kit from Cell Signaling Technology according to the manufacturer's instructions. After purification of the DNA, libraries were cloned with the NEBNext Ultra II library kit (NEB). The antibodies used for ChIP‐seq were H3K4me1 (Abcam ab8895), H3K4me3 (Abcam ab8580), H3K9ac (Active Motif 39137), H3K9me3 (Abcam ab8898), H3K27ac (Abcam ab4729), H3K27me3 (Abcam ab6002), H3K36me3 (Abcam ab9050), H3 (Abcam ab1791), CTCF (Active Motif 61311), and EBF1 (Sigma SAB2501166) and are listed in Patient‐derived cells were cross‐linked with 1% methanol‐free formaldehyde for 10 min. After quenching with glycine, cells were washed three times with PBS and the cell pellet was frozen in liquid nitrogen. For analysis, the cell pellet was thawed and treated with four units MNase per 1 × 102, and 0.1% IGEPAL CA‐630) and incubated with Tn5 transposase (Illumina) for 30 min at 37°C or viable frozen cells (three out of seven NBC samples) were directly incubated with Tn5 for 30 min at 37°C in the presence of 0.1% Igepal CA‐630 (Sigma‐Aldrich). After incubation, Tn5 was stopped with EDTA, for previously fixed samples, cross‐links were reversed, and the DNA was purified with AMPure beads (Beckman Coulter). Barcodes were added by PCR, and the libraries were purified again with AMPure beads. The scATAC‐seq sequencing libraries were generated with the Fluidigm C1 system as described previously  cells were isolated with Nuclei EZ lysis buffer (Sigma‐Aldrich), washed once in ATAC‐seq lysis buffer , RNA was digested by DNase I (Promega) for 30 min at 37°C and depleted from rRNA with RiboZero Gold, and finally, directional cDNA synthesis and sequencing library preparation were conducted according to the NEBNext Ultra Directional RNA Library Prep Kit (NEB). All libraries were 50‐bp single‐end sequenced on the HiSeq 2000 machine.ex vivo. Cells were sampled before as well as 2 and 24 h after initiation of treatment. Western blots were conducted to evaluate changes in the abundance of H3K9ac and H3K27ac. The samples were subject to ChIP‐seq experiments for seven histone modifications and the H3 control and also to RNA‐seq as described below. Malignant and NBCs of each of four individual CLL patients or healthy donors, respectively, were enriched for CD19‐positive. A total of 10,000 cells were seeded in serum‐free medium and incubated for 30 min with DMSO as mock control or different concentrations of panobinostat (25 pM–250 nM). HDAC‐Glo I/II assay (Promega) was performed in technical triplicates according to manufacturer's protocol and measured with the Promega GloMax device.Primary CLL cells from three treatment‐naïve patients and NBCs in a pool of 28 healthy donors were treated with 5 nM panobinostat (Biozol) et al, et al, et al, et al, et al, et al, P < 0.05, the minimum length of 200 base pairs and the differential methylation threshold of 0.3. The DMRs and PMDs overlapping with the UCSC “gap” table, comprising centromeres, telomeres or regions with no sequence information, were discarded from further analysis. Normal B‐cell programming might account for some of the methylation changes from the comparison of CLL to NBCs , peaks were called with MACS for H3K4me1, H3K4me3, H3K9ac, and H3K27ac using H3 as a background control. For H3K9me3, H3K27me3, and H3K36me3, SICER was used to determine enriched regions with histone H3 serving again as a control. In order to computationally validate the quality of the ChIP‐seq experiments, we clustered the different IP samples using their Pearson correlation. Additionally, quality scores suggested by the ENCODE project were used to assess the overall quality of each ChIP‐seq data set . Peaks wet al  with theDifferential histone modification and TF binding analysis from ChIP‐seq data were performed using the peaks called for each sample and the corresponding reads counts within the alignment files using DiffBind . Read coThe chromatin was segmented and annotated using a multivariate Hidden Markov model with the ChromHMM software , duplicate reads were discarded using rmdup of SAMtools version 1.3  derived from the spurious correlations obtained after randomly permuting insertions  or very frequent (more than three standard deviations above the average) integrations were removed from the analysis. Variations in accessibility for different TF binding motifs were determined with chromVAR : (i) In order to identify TF motifs gained or lost in CLL within the ATAC peak regions, the differential ATAC signal was intersected with either all four potential enhancer containing ChromHMM states 1, 8, 9, and 11 Fig A and C oet al, et al, et al, To identify TF motifs in regions of interest with gained or lost occupancy, we used the HOMER package with appropriate background controls for each individual data set  using STAR version 2.3.0 . Regulatory proteins were taken from a previously compiled list of 5,927 proteins  based on gene ontology annotations  and all differential expressed non‐regulators (P < 0.01) with a log fold change of −1.7 < LFC > 1.7 were selected for further analysis. To identify aberrant chromatin modifier in CLL vs. NBCs, the list of deregulated regulators/genes was intersected with the EpiFactors database (Medvedeva et al, A human B‐cell regulatory network was constructed from 264 publicly available samples (Basso All enhancer–promoter pairs with a correlation of 0.22 or higher in the scATAC‐seq analysis  were useAcquisition of patient samples: SJK, SSt, and DM; Acquisition of data: J‐PM, SSc, and SJK; Analysis of data: J‐PM, MI, NI, LCK, JMM, VBT, AMP, SG, FE, DT, SDK, MZ, and KR; Development of integrative model: J‐PM, MI, NI, DM, and KR; Drafting of manuscript: J‐PM, MI, NI, DM, and KR; Supervision and manuscript reviewing: BB, RK, DR, SSt, PL, MV, MZ, DM, and KR; Study design and coordination: DM and KR.The authors declare that they have no conflict of interest.AppendixClick here for additional data file.Expanded View Figures PDFClick here for additional data file.Dataet EV1Click here for additional data file.Dataet EV2Click here for additional data file.Dataet EV3Click here for additional data file.Dataet EV4Click here for additional data file.Dataet EV5Click here for additional data file.Dataet EV6Click here for additional data file.Dataet EV7Click here for additional data file.Dataet EV8Click here for additional data file.Dataet EV9Click here for additional data file.Dataet EV10Click here for additional data file.Dataet EV11Click here for additional data file.Dataet EV12Click here for additional data file.Dataet EV13Click here for additional data file.Dataet EV14Click here for additional data file.Review Process FileClick here for additional data file.


Hira in embryonic mouse hearts leads to cardiac septal defects. Loss of function mutation in HIRA, together with other chromatin modifiers, was found in patients with congenital heart diseases. However, the effects of HIRA on gene expression at earlier stages of cardiogenic mesoderm differentiation have not yet been studied. Differentiation of mouse embryonic stem cells (mESCs) towards cardiomyocytes mimics some of these early events and is an accepted model of these early stages. We performed RNA-Seq and H3.3-HA ChIP-seq on both WT and Hira-null mESCs and early cardiomyocyte progenitors of both genotypes. Analysis of RNA-seq data showed differential down regulation of cardiovascular development-related genes in Hira-null cardiomyocytes compared to WT cardiomyocytes. We found HIRA-dependent H3.3 deposition at these genes. In particular, we observed that HIRA influenced directly the expression of the transcription factors Gata6, Meis1 and Tbx2, essential for cardiac septation, through H3.3 deposition. We therefore identified new direct targets of HIRA during cardiac differentiation.HIRA is a histone chaperone known to modulate gene expression through the deposition of H3.3. Conditional knockout of  Gata4 or Tbx2, important cardiac transcription factors, are associated with endocardial cushion and cardiac septal defects [Heart development is a complex process involving cardiogenic mesodermal differentiation into endocardium and myocardium. The myocardium is generated sequentially from cardiac precursors, primitive cardiomyocytes and definitive cardiomyocytes . It requ defects , 4. Chro defects , 6. A di defects . The his defects  and line defects –11. HIRAHIRA is part of one of the two complexes that deposits H3.3 in a replication independent manner , 13. TheHira expression in chick cardiac neural crest results in common arterial trunk, suggesting a role for HIRA in outflow tract (OFT) septation [Hira-null mouse embryos showed abnormal heart looping and pericardial oedema at embryonic day (E)10.5 [HIRA was found to be one of 46 chromatin modifiers that had a significant loss of function mutation  in a large congenital heart disease cohort study [Hira in the cardiogenic mesoderm resulted in embryonic lethality with atrial and ventricular septal defects at E15.5. Following genome-wide analysis of the transcriptome of these conditional knockout mouse hearts at E12.5, we identified a dysregulation of certain cardiac genes such as Tnni2, Tnnt3 and Epha3 [Tte that regulates the expression of Tnni2 and Tnnt3 during cardiac development.Absence of HIRA in mice  and Xenoeptation . Hira-nu (E)10.5 . Recentlrt study . We prevnd Epha3 . We showHira-null undifferentiated and mESC-derived cardiomyocytes at day 15 of differentiation. We report here that HIRA is required for the expression of cardiac transcription factors involved in septation during cardiac development. These transcription factors displayed diminished H3.3 enrichments in their gene bodies or near their Transcription Start Site (TSS) in the absence of HIRA. This reflects the requirement for HIRA in the expression of important cardiac transcription factors through H3.3 deposition.However, the role of HIRA at the early stages of cardiogenic mesoderm differentiation has not been studied. Differentiation of mESCs towards cardiomyocytes mimics the early stages of cardiogenic mesodermal differentiation , 21. HerHira-null (Clone 104) mESCs have been cultured and maintained as described previously [2 on 0.1% gelatin coated flasks in Knockout™ D-MEM , supplemented with 15% ES-FCS (Millipore ES-009B), 1× Glutamax (GIBCO 35050-038), 1× Penicillin/Streptomycin (GIBCO 15140), 1× MEM NEAA (GIBCO 11140-035), 0.1 mM 2-β-mercaptoethanol (SIGMA M-7522) and 103 Units/ml LIF .H3.3-HA tagged wild type (W9.5) and eviously . These cDifferentiation was adapted from the previously described hanging drop method, with some modifications , 23. BriHira-null cells at day 0 and day 15 using TRIzol reagent (Life technologies 15596-018) following the manufacturer’s instructions. The quality and quantity of RNA were determined using Nanodrop spectrophotometer ND-1000 (Lab tech) and using High Sensitivity RNA screenTape®, samples with RIN of seven or more were used for subsequent library preparation. Library preparation was done using KAPA stranded mRNA-seq kit (KAPABIOSYSTEMS KK8421) and KAPA mRNA capture kit (KAPABIOSYSTEMS KK8441). RNA sequencing was performed by Illumina NextSeq 500, with the production of paired-end reads. Reads were aligned to mouse genome mm9 using bowtie [p value ≤ 0.05 and an absolute fold change of ± 2. Gene Ontology analysis was done using DAVID bioinformatics functional annotation tool [RNA was extracted from two different biological samples of both WT and g bowtie  and diffion tool . Enrichmion tool . Functioion tool .Gapdh.The High-Capacity RNA-to-cDNA™ Kit (Thermo fisher 4387406) was used to obtain cDNA and was used according to the manufacturer’s instructions. Primers for qRT-PCR were designed using the primer-blast tool of NCBI. The amplicon size was set between 70 and 200 bp (bp). Quantitative real time PCR (qRT-PCR) was performed on the CFX96 Touch™ Real-Time PCR Detection System using SYBR green (BIO-RAD 1708882) on three biological replicates and analysis was done using the standard curve method. Results were normalized to 7 cells as previously described [Native ChIP was performed on 1 × 10escribed . Brieflyhttps://www.ncbi.nlm.nih.gov/tools/primer-blast/).Primers for validation of ChIP-seq by q-ChIP were designed as follow; H3.3 significant peaks were uploaded in Integrative Genome browser (IGV). H3.3 enriched and depleted regions were identified. The corresponding nucleotide sequences were then extracted and used for primer design using NCBI primer design tool  [Hira-null sample [Hira-null peaks from the WT peaks using bedtools.The eluted DNA from both Input and ChIP was processed through library preparation using the NEB DNA Ultra kit and selecting fragment sizes of around 200 bp. Samples were sequenced using Illumina NextSeq 500, with the production of paired-end reads. Reads were aligned to mouse genome mm9 using BOWTIE 1.1.2 allowing no more than three mismatches . Samtool ≤ 10−5) . BEDTOOLl sample . Gene lil sample . Genome-l sample . The HIRHira-null mESCs were differentiated towards a cardiac mesodermal lineage using an adapted hanging drop protocol to increase the cardiomyocyte yield [Hira-null cells. These foci increased gradually in size during differentiation until reaching 60–70% confluence at day 15. These beating areas arose concomitantly with the expression of the cardiac transcription factor Nkx2.5 at day 8 [Gata4, Mef2c, Myh6 and Myh7, as cardiac specific marker at day 15 in both WT and Hira-null cells, and the expression of the pluripotency markers Nanog and Pou5f1 at day 0, by qRT-PCR  stage. We found that 68.6% and 67.4% of the expressed genes in WT and Hira-null dataset respectively overlap with the reported CM stage datasets, indicating that loss of HIRA does not prevent general cardiogenic differentiation.WT and te yield , 23. Beaat day 8 . We confHira-null differentiated cardiomyocytes. Genes related to heart development and morphogenesis were expressed in both cell types, indicating successful differentiation into cardiac lineage in both cells . On the contrary genes related to the nervous system development were upregulated in the absence of HIRA. We found 1680 DEGs of which 1641 encode known genes including 108 cardiac genes and 39 were non-coding RNAs   biological process analysis was performed on the genes expressed in WT and lls Fig. b. Howevep value ≤ 0.01 and FDR ≤ 0.1). Enrichment map was used to visualize and functionally categorize these different gene sets, and showed an under representation of GO-terms related to cardiovascular development, immune system development and DNA repair, and over representation of GO terms related to nervous system development and neurotransmission  was performed on the DEGs and we identified 52 gene sets negatively enriched and 54 gene sets positively enriched in the absence of HIRA . Interestingly, calcium signalling pathway was the most positively enriched  = 1.82, p value = 0.019 and FDR = 0.19). In addition, axon guidance and neuroactive ligand-receptor interaction pathways, two pathways related to the nervous system, were positively enriched. Conversely, the vascular smooth muscle contraction pathway was negatively enriched . Considering the studies that link HIRA to interferon-stimulated genes [We then investigated the pathways affected by the absence of HIRA. KEGG pathway analysis was done on the pre-ranked list of DEGs using GSEA  region with lesser deposition in the distal intergenic region (23.4%), and the promotor region (9.2%). In the absence of HIRA, the proportion of H3.3 deposition increased at the distal intergenic region (42.2%) compared to the intronic region (29%) and the promotor region (3%)  in the absence of HIRA. Notably, there was co-localization of HIRA-dependent H3.3 deposition with NKX2.5 binding sites at a previously identified enhancer site. A previous study showed that the sequential binding of MEIS1 then NKX2.5 to enhancers can influence cardiogenesis [Tte in the absence of HIRA, we hypothesize that the absence of HIRA leads to a diminished expression of Meis1 and then to a diminished binding of NKX2.5 to the MEIS1-NKX2.5 target sites, thereby disrupting cardiac septum morphogenesis.Interestingly, knockout of  in mice , and an  in mice . In our ogenesis . Taken tGata6 expression was down regulated (FC = − 6.1) in the absence of HIRA at day 15 of differentiation, and we identified HIRA-dependent H3.3, H3K4me1 and NKX2.5 enrichments co-localising at 5.5 kb upstream of the TSS of Gata6 in WT cells at day 15 of differentiation. It is interesting to note that the identical locus had been shown previously to be an enhancer bound by NKX2.5 and regulating Gata6 expression in developing murine hearts [Similarly, e hearts .Tbx2 in WT cells differentiated at day 15 and a down regulation of Tbx2 expression (FC = − 4.6) in the absence of HIRA at day 15 of differentiation. This locus coincides with known NKX2.5 binding sites and a peak of the active transcription mark H3K4me3. This result suggests that HIRA may play a role in the atrioventricular canal and the OFT development by influencing the transcription of Tbx2 through H3.3 deposition at its promotor.TBX2 plays an essential role in the atrioventricular canal and OFT development . We founHira-conditional mice. Further investigations on cardiac contractility in Hira-mutant mice would be interesting.In addition, we found that calcium signalling pathway was significantly upregulated in the absence of HIRA at day 15 of differentiation. Calcium signalling controls several physiological processes in the heart, including cardiac contractility . KEGG caOutside of heart development, it is of interest to note that in our study, gene sets involved in DNA repair, the development of the immune and nervous systems were significantly dysregulated in the absence of HIRA. Of relevance, HIRA has been shown to have an essential role in transcriptional recovery after DNA damage , 49, andHira-null differentiated cardiomyocytes with a significant lower expression in Hira-null cells. We show that 31.6% of HIRA-dependent H3.3 peaks co-localize with active enhancers loci. We found that HIRA directly influence the expression of the cardiac transcription factors Meis1, Gata6 and Tbx2. We suggest that HIRA influences their expression through the deposition of H3.3 at their enhancers, supporting our previous published model that HIRA acts at enhancers during cardiovascular development, and potentially in concert with NKX2.5 [In conclusion, we show that cardiovascular development-related genes are expressed in both WT and h NKX2.5 .


HD-Zip genes using transcriptomic and genomic datasets from a wide range of plant species indicate that the HD-Zip protein class was already present in green algae. Later, HD-Zips experienced multiple duplication events that promoted neo- and sub-functionalizations. HD-Zip proteins are known to control key developmental and environmental responses, and a growing body of evidence indicates a strict link between members of the HD-Zip II and III families and the auxin machineries. Interactions of HD-Zip proteins with other hormones such as brassinolide and cytokinin have also been described. More recent data indicate that members of different HD-Zip families are directly involved in the regulation of abscisic acid (ABA) homeostasis and signaling. Considering the fundamental role of specific HD-Zip proteins in the control of key developmental pathways and in the cross-talk between auxin and cytokinin, a relevant role of these factors in adjusting plant growth and development to changing environment is emerging.HD-Zip proteins are unique to plants, and contain a homeodomain closely linked to a leucine zipper motif, which are involved in dimerization and DNA binding. Based on homology in the HD-Zip domain, gene structure and the presence of additional motifs, HD-Zips are divided into four families, HD-Zip I–IV. Phylogenetic analysis of  The homeodomain-leucine zipper (HD-Zip) class of proteins appears to be present exclusively in the plant kingdom and is characterized by the presence of a homeodomain closely linked to a leucine zipper motif . The AraHD-Zip genes are evolutionary highly conserved and there is evidence that they were already present in green algae [en algae ,9,10,11.Experimental work has demonstrated that the HD-Zip domain, but not the HD by itself, interacts with DNA , and it Beside the homeodomain-leucine zipper motif, HD-Zip I proteins have no other established functional domain; conversely, most of the HD-Zip II transcription factors contain an LxLxL type of ERF-associated amphiphilic repression (EAR) motif ,22 Figu, and theHD-Zip proteins are known to control key developmental and environmental responses ,31,32,33HD-Zip I genes show transcriptional changes in response to treatments with abscisic acid (ABA) [ARABIDOPSIS THALIANA HOMEOBOX (ATHB) 7 and ATHB12 (belonging to the clade I) [PYRABACTIN RESISTANCE1 (PYR1)/PYR1-LIKE (PYL) gene family, encoding the ABA receptors [Arabidopsis [Sequence comparison and phylogenetic analysis indicated that members of the HD-Zip I protein family can be classified into six different clades, I to VI, and that the presence and the position of conserved sequences may be related to specific function(s) ,34. Genoid (ABA) ,39,40,41clade I) ,43,44. Feceptors ,46,47. Tbidopsis . Evidencbidopsis . HELIANTHUS ANNUUS HOMEOBOX4 (HAHB4) gene, encoding a protein homologous to ATHB7 and ATHB12, is also regulated by ABA and drought, as well as by methyl-jasmonic acid (MeJa) or ethylene (ET) or biotic stresses [The stresses ,51. The stresses . In addiATHB13 (clade V) gene, positively regulated by low temperature, drought, and salinity, can confer cold, drought and broad-spectrum disease resistance when overexpressed [It is worth mentioning that in some plants there is evidence that ABA synthesis and signaling is relevant to fully activate defense responses against insect herbivores and, in general, hormonal interactions are important for regulating plant responses to abiotic stresses and growth-defense tradeoffs . Of intexpressed ,53,54. TATHB12 and some ABA signaling components are regulated by KANADI1 (KAN1) [Medicago truncatula HB1, highly related to ATHB7 and ATHB12, interacts with both ABA and auxin signaling in the regulation of organ development. The MtHB1 gene is strongly regulated by salt, osmotic and ABA stresses. There is evidence that MtHB1 controls the emergence of lateral roots likely by repressing the auxin-regulated LOB-Binding Domain 1 (LBD1) gene [Together with the effects on stresses described above, the overexpression of HD-Zip I proteins very often resulted also in alterations of the shape and growth of the plant, including cotyledon, leaf and supporting organs ,56, sugg1 (KAN1) , a facto1 (KAN1) . Further1 (KAN1) . In addiD1) gene , a membeD1) gene . EXPANSIN3 (EXP3), a gene involved in cell wall extension [ATHB1 (Clade III) is a direct target of PHYTOCHROME INTERACTING FACTOR 1 (PIF1), a basic helix-loop-helix (bHLH) transcription factor involved in the regulation of light responses downstream of phytochromes [ATHB1 expression is positively regulated by ethylene in Arabidopsis [1-AMINOCYCLOPROPANE-1-CARBOXYLATE OXIDASE (ACO) gene encoding a key enzyme in ethylene biosynthesis [In the post-embryonic development, ATHB5  behaves as a growth-promoting transcription factor of the hypocotyl. In particular, it promotes the gibberellin acid (GA)-mediated expansion of the epidermal and cortex cells by a positive direct modulation of the expression of xtension . ATHB1  genes, all belonging to clade VI. These HD-Zip I proteins are necessary and sufficient to enhance the expression of 9-CIS-EPOXICAROTENOID DIOXIGENASE 3 (NCED3), a key ABA biosynthesis gene, and for ABA accumulation inside axillary buds in conditions of low Red/Far-Red (R/FR) ratio light or short photoperiod. This, in turn, causes suppression of bud development. Relevantly, the BRC1/ATHB21/40/53 regulatory module appears to be conserved in monocot and dicot species [Very recent work implicated three related HD-Zip I proteins belonging to clade VI  in the c species .(BDL)/IAA12 and a loss-of-function mutant of MP/ARF5 lack a seedling root and display cotyledon defects [BDL/IAA12 expression. Overexpression of ATHB5 during embryogenesis transcriptionally suppresses the expression of BDL/IAA12 and rescues the rootless phenotype of the bdl/iaa12 dominant mutant. Together the data lead to the hypothesis that ATHB5 may contribute to spatially restrict BDL/IAA12 expression during embryogenesis [BDL/IAA12 have also been provided [Besides the multiple links found between HD-Zip I proteins and ABA, evidence of a direct interaction between HD-Zips I and auxin also exists . Auxin h defects ,74,75,76ogenesis . Evidencprovided . ATHB52 gene is positively regulated by ETHYLENE-INSENSITIVE3 (EIN3), a key transcription factor of the ethylene signal transduction pathway. A molecular and genetic analysis has shown that ATHB52 binds the promoters of PIN FORMED2 (PIN2), coding for a polar auxin carrier, and of WAVY ROOT GROWTH1 (WAG1) and WAG2, encoding PIN polarity regulators. The positive modulation by ethylene of the PIN2/WAG1/WAG2 module exerted through ATHB52 could affect the local polar auxin transport in the root tip resulting in the inhibition of primary root elongation [Finally, the cross-talk between ethylene and auxin in the control of root elongation mediated by ATHB52 has been recently uncovered. It is very well established that root elongation is inhibited by ethylene in Arabidopsis and other species through the action of auxin ,78,79,80ongation .HD-Zip II γ  and δ  genes are rapidly induced by changes in the R/FR ratio light that promote shade avoidance in the Angiosperms [The HD-Zip II protein family contains ten members which can be divided into four clades (α-δ) . Remarkaiosperms ,82 and siosperms ,85,86,87Interestingly, evidence demonstrates that, besides their function in shade avoidance, HD-Zip II γ and δ transcription factors play a crucial role in embryo apical development and essential developmental processes in sunlight, including shoot apical meristem (SAM) activity, organ polarity and gynoecium development ,89,90,91hat3 athb4 and hat3 athb4 athb2 mutant embryos [Several links have been established between HD-Zip II γ and δ proteins and auxin. Plants with elevated levels of ATHB2 display a constitutive shade avoidance response, and it was shown that ATHB2-induced elongation of the hypocotyl depends on the auxin transport system, as it is abolished by auxin transport inhibitors . Further embryos ,31. HAT1 is a direct target of BES1 [hat1 hat3 displayed a reduced BR response stronger than that of the hat1 and hat3 single mutants. Expression levels of several BR-repressed genes are increased in hat1 hat3 double mutant and reduced in HAT1 overexpressing lines, thus strongly suggesting that HAT1 functions to repress the expression of a subset of BR target genes. Consistently, it was found that HAT1 binds to DNA elements in BR-repressed gene promoters and functions as a BES1 corepressor [Recently, HAT1 has been linked to brassinosteroid (BR) signaling pathway. BRs signal through a plasma membrane-localized receptor kinase to modulate the BES1/BZR1 (BRI1-EMS SUPPRESSOR 1/BRASSINAZOLE RESISTANT1) family of transcription factors that positively and negatively regulate a large number of genes ,95,96,97 of BES1 . HAT1 fuepressor . Furtherepressor ,100,101 Furthermore, HAT1 was identified among the transcription factors interacting with the GIBBERELLIN INSENSITIVE (GAI) DELLA protein, a master negative regulator in gibberellin (GA) signaling ,103 Fig. FurtherHAT1 and HAT3 is indeed repressed by ABA. Evidence have been provided that HAT1 can bind to specific DNA sequences on the promoters of NCED3 and ABA DEFICIENT (ABA) 3, two key ABA biosynthesis genes, and negatively regulate their expression, thus resulting in a reduction of ABA synthesis. In addition, it was observed that HAT1 overexpressing plants display reduced sensitivity to ABA and less tolerance to drought stress, whereas the double loss-of-function hat1 hat3 mutant show opposite phenotypes. Finally, it was found that Sucrose non-fermenting 1-related protein kinase (SnRK) 2.3, a positive component of ABA signaling, physically interacts with and phosphorylates HAT1, decreasing its protein stability and binding activity [Recent work has demonstrated that HAT1, apart from its role in BR-mediated growth responses , in GA sactivity  , are linked to ABA ,107. ATHnt stage . Recent The HD-Zip III family contains five members: ATHB8, CORONA (CNA), PHABULOSA (PHB), PHAVOLUTA (PHV), and REV. It is well established that HD-Zip III proteins act as master regulators of embryonic apical fate , are reqHD-Zip III genes are regulated at the post-transcriptional level by the microRNAs miR165/166, which negatively affect their expression through mRNA cleavage [HD-Zip II genes HAT3, ATHB4, ATHB2, and HAT2, and evidence exists that PHB and PHV are involved in the control of HAT3 expression [The pattern of HD-Zip III expression largely coincides with that of auxin distribution ,121,122.cleavage ,123. Relpression ,20. It hpression .TRYPTOPHAN AMINOTRANSFERASE OF ARABIDOPSIS 1 (TAA1) and YUCCA5 (YUC5) [LIKE AUXIN RESISTANT 2 (LAX2) and LAX3, and response are also direct targets of REV [NAKED PINS IN YUC MUTANTS 1 (NPY1) and WAG1, encoding an AGC protein kinase highly similar to PINOID (PID) [NPY genes encode proteins with a Broad-Complex, Tramtrack, and Bric-a-brac (BTB)-Poxvirus and Zinc Finger (POZ) domain that together with AGC kinases determine the subcellular polar targeting of the PIN efflux carriers, thus establishing the direction of auxin transport [The interconnection between HD-Zip III transcription factors and auxin began to be clarified especially through the molecular-genetic analysis of the vascular system . REV dir5 (YUC5) ,125. Furs of REV ,125,126.ID (PID) . NPY genransport ,130,131.MP/ARF5, a transcription factor gene playing a major role in vascular development, and IAA20, encoding an IAA protein that is stable in the presence of auxin and able to interact with MP. The double mutant of IAA20 and its closest homolog IAA30 forms ectopic protoxylem, whereas elevated levels of IAA30 result in discontinuous protoxylem, analogous to a weak mp mutant. It has therefore proposed a mechanism in which PHB stabilizes the auxin response within the xylem axis by activating both MP and its repressors IAA20 and IAA30 to ensure correct vascular patterning and differentiation of xylem cells [A recent study reinforces the interconnection between HD-Zip III transcription factors and auxin signaling . HD-Zip em cells .AHP6 expression, either directly or by alteration of auxin signaling [Cross-talk between auxin and cytokinin (CK) is crucial during several developmental processes, including vascular development. Several studies have indicated that ARABIDOPSIS HISTIDINE PHOSPHOTRANSFER PROTEIN 6 (AHP6), an inhibitory pseudophosphotransfer protein, is positively regulated by auxin and counteracts CK signaling, allowing protoxylem formation in the root. Conversely, CK signaling negatively regulates the spatial domain of AHP6 expression ,134. Intignaling ,133.ISOPENTENYLTRANSFERASE 7 (IPT7), a gene coding for a rate-limiting component of the cytokinin biosynthesis pathway. This in turn promotes cell differentiation and regulates root length [SHORT HYPOCOTYL 2 (SHY2), a CK primary target necessary and sufficient to promote cell differentiation at the TZ [phb phv double loss-of-function mutants with respect to wild type but is reestablished after CK treatment. In addition, the root meristem phenotype of the dominant shy2-2 mutant is suppressed in phb phv, further confirming the hypothesis that PHB-dependent CK biosynthesis in the distal part of the root influences cell differentiation at the proximal TZ [PHB and miR165 [The interaction between HD-Zip III proteins and cytokinin network is further strengthened by the finding that PHB directly activates t length . These rt length , suggestt the TZ , is weakximal TZ . Relevand miR165 . The autd miR165  , a gene essential in maintaining SAM activity [A recent study on the mechanism underlying shoot regeneration in Arabidopsis provided an additional link between HD-Zip III transcription factors and CK signaling , somehowoc locus . Four B-ild type ,145,146.ild type . Remarkaactivity ,148.PYL6 is oppositely directly regulated by KAN1, a key determinant of abaxial cell fate in the leaf [REV, PHB and PHV significantly decreases upon ABA application, as a consequence of ectopic induction of miR165 expression [Beside the evidence of molecular interactions between HD-Zip III proteins and key components of the auxin and cytokinin networks, it has been recently reported that REV positively directly regulates the expression of the gene encoding the ABA receptor protein PYL6 ,126. Remthe leaf ,127,149.pression ,126. It pression ,126.MIR165A, thus leading to increased miR165 levels that repress HD-Zip III transcription factors in the stele. Together the data nicely show how a pathway known to control core developmental processes is used as a mean to adjust xylem formation under conditions of abiotic stress [Finally, it is worth mentioning that recent work has revealed that the expression of miR165/166 is regulated by a complex hormonal cross-talk during root development in Arabidopsis . Evidencc stress .HD-Zip IV genes were shown to be specifically or preferentially expressed in the epidermis of developing embryos and/or other plant organs [Many of the t organs . It is wt organs ,155.WEREWOLF (WER) and GL2, master regulators of epidermal patterning, and of CAPRICE (CPC), a direct downstream target of WER [GLABRA2 (GL2), the first identified member of the HD-Zip IV protein family, promotes trichome differentiation , and supt of WER .Recent work has also established a link between HD-Zip IV proteins and GA signaling . It was HOMEODOMAIN GLABRA 11 (HDG11) was identified via activation tagging as a gene involved in drought tolerance. The mutant has higher levels of ABA than the wild type and displays enhanced root growth with more lateral roots and reduced stomatal density. Overexpression of HDG11 also conferred drought tolerance associated with augmented lateral roots and reduced leaf stomatal density in both Arabidopsis and tobacco [EXP5, resulting in a well-developed root system [Apart from their role in development, interactions between HD-Zip IV transcription factors and environmental responses have also been reported . For exa tobacco . Higher  tobacco ,163. It t system .WUSCHEL RELATED HOMEOBOX 8 (WOX8) gene whose product leads to asymmetric division of the zygote [Intriguingly, HDG11 acts as a maternal regulator of zygote asymmetry through a direct activation of the e zygote . A fundamental question in plant biology is how plants integrate environmental signals with intrinsic developmental programs and how coordinate the growth of different organs depending on resource availability. Over recent years remarkable progress has been made, and the molecular mechanisms controlling these processes are being elucidated. The functional analysis of the HD-Zip proteins revealed that they are part of complex networks involved in the integration of external signals through the regulation of hormonal pathways involved in the control of fundamental developmental processes. Although many factors belonging to each HD-Zip family are implicated in specific processes, it is interesting to note that the simple overexpression of ATHB2 (and other members of the HD-Zip II family) and HDG11 is sufficient to generate plants looking for optimal light and water for growth, respectively. This could be explained by the existence of organ- and/or tissue-specific hubs that stimulated by external and/or internal  signals converge to coherently adjust the development and growth to a specific environmental signal. These hubs may have been generated during evolution through multiple duplication events that have promoted neo and sub-functionalization of factors operating on specific pathways. The identification of these hubs it has the potential to lead to a more unified vision of the development and growth of the plant according to environmental stresses that could be applied for the improvement of the cultivated plants.