Mast cells are critical effectors of allergic inflammation and protection against parasitic infections. We previously demonstrated that transcription factors GATA2 and MITF are the mast cell lineage-determining factors. However, it is unclear whether these lineage-determining factors regulate chromatin accessibility at mast cell enhancer regions. In this study, we demonstrate that GATA2 promotes chromatin accessibility at the super-enhancers of mast cell identity genes and primes both typical and super-enhancers at genes that respond to antigenic stimulation. We find that the number and densities of GATA2- but not MITF-bound sites at the super-enhancers are several folds higher than that at the typical enhancers. Our studies reveal that GATA2 promotes robust gene transcription to maintain mast cell identity and respond to antigenic stimulation by binding to super-enhancer regions with dense GATA2 binding sites available at key mast cell genes. Mast cells are critical effectors of allergic inflammation and protection against parasitic infections. Here the authors demonstrate that GATA2 promotes chromatin accessibility at the super-enhancers of mast cell identity genes and primes both typical and super-enhancers at genes that respond to antigenic stimulation. MCs reside in the skin, mucosal linings of the lung and gut, and connective tissues surrounding blood vessels. In these tissue contexts MCs are among the first cells to encounter antigens, which can be captured by IgE antibodies. MCs express the high-affinity receptor for IgE (FcεRI). Aggregation of FcεRI bound with IgE and their cognate antigens leads to MC activation and rapid degranulation to release pre-existing inflammatory mediators including histamine, heparin, and proteases. MC activation also directs de novo gene transcription of additional inflammatory mediators, including cytokine and chemokine genes.Mast cells (MCs) are critical effectors in immunity that originally evolved to protect against parasitic infections. In modern living conditions, MCs are much more frequently associated with allergic inflammation4. GATA2 is critical for survival and proliferation of hematopoietic stem cells5, granulocyte-monocyte progenitor differentiation6, and basophil and MC differentiation7. Spontaneous mutations in the Mitf gene lead to the failure of MC progenitor cells to differentiate into mature MCs9. GATA2 is also essential in maintaining the MC identity once MCs are fully committed to the MC lineage. We and others demonstrated that MC-specific deletion of the Gata2 gene results in the failure of MCs to maintain the MC identity11. We have demonstrated that the Mitf gene is highly expressed in MCs but not in basophils12 and that overexpression of the Mitf gene is sufficient to drive the differentiation of pre-BMPs into MCs12. Together, this evidence supports a model in which GATA2 and MITF are lineage-determining TFs (LDTFs) in MCs and GATA2 is required for MC identity maintenance.Transcription factors (TFs) GATA binding protein 2 (GATA2) and Microphthalmia-associated transcription factor (MITF) play critical roles in the differentiation of MC progenitor cells into the MC lineage13. Enhancers activate gene transcription by mediating the assembly of higher-order functional domains with promoters14. Enhancers also provide binding hubs for signal-dependent TFs (SDTFs) that respond to the stimulation of receptors by external ligands. Together, signals triggered by ligand-bound receptors modulate activities of enhancers, which in turn drive gene transcription necessary for cell development and function15.Little is known concerning how GATA2 and MITF regulate target gene transcription in MCs. Notably, enhancers that drive MC-specific transcription have not been localized. Generally, enhancers are regulatory modules of a few hundred base pairs in length located within genes or in intergenic regions. They typically comprise clusters of TF-binding sites that bind sequence-specific DNA-binding TFs and associated factors17. Super-enhancers comprise larger tracts of genomic DNA that include multiple smaller constituent enhancers. Super-enhancers are often associated with genes that confer cell identities18 and are enriched in genetic variants that may contribute to disease progression and severity. Originally, the concept of super-enhancers was developed in embryonic stem cells17. Since then, super-enhancers have been described in T cells19, B cells21 and macrophages22 as well as in many other cell systems23. However, it has not been determined whether super-enhancers play similar pivotal roles in regulating transcription and cell identity in MCs.Super-enhancers differ from typical enhancers in multiple ways27. In this study, we used histone modifications to identify typical and super-enhancers in MCs. We found that super-enhancers are enriched at key MC genes. The number and density of GATA2 bound sites were higher at these super-enhancers relative to typical enhancers. We found that the super-enhancers had higher scores predicting their regulatory potential versus typical enhancers. The key MCs genes with higher regulatory potential scores express much higher levels of mRNA than genes with typical enhancers. We demonstrate that GATA2 promotes chromatin accessibility, which correlates with H3K4me1 and H3K27ac modifications at the super-enhancers of key MC genes. Our studies reveal that the increased frequency of GATA2 binding sites within super-enhancers at key MC genes promotes robust gene transcription that maintains MC identity and the response to antigenic stimulation.Remarkable progress has been made in identifying putative enhancers genome-wide. Epigenomic studies have demonstrated that monomethylation of lysine residue 4 on histone 3 (H3K4me1) marks genes that are poised to be transcribed, while acetylation of lysine residue 27 on histone 3 (H3K27ac) identifies genes that are actively being transcribed17. Using the software Ranking Of Super Enhancer (ROSE)17, we localized super-enhancers as regions of chromatin that were at least 12.5 kb long and associated with significant H3K27ac modification. We identified an average of total 9,517 typical enhancers and 667 super-enhancers found in two replicates . We defined potential enhancers as regions of chromatin marked by H3K4me1 together with H3K27ac modifications. We identified an average total of 20801 potential enhancers , transcription factors (Gata2 and Mitf), proteases , enzymes for sulfur metabolism  and signaling molecules (Prkcb and Lyn). These results suggest that super-enhancers direct the programming of key ID genes necessary for MC differentiation and function.MC identity (ID) genes have been defined by the Immunological Genome Project Consortium as those that are expressed at higher or the same levels in MCs relative to basophils, but at low levels or insignificantly expressed in other blood cell types including T cells, B cells, eosinophils, macrophages, dendritic cells and neutrophils31. For example, the Mcpt1 and Mcpt2 genes were expressed at higher levels in mucosal MCs relative to connective tissue MCs, whereas the Mcpt5 gene was expressed at higher levels in connective tissue MCs compared to mucosal MCs. In the Immunological Genome Project, Dwyer et al. performed microarray analysis of primary mouse connective tissue MCs isolated from trachea, tongue, esophagus, skin and peritoneum and defined ID genes in these connective tissue MCs. Despite the differences that exist between the directly ex vivo MCs and in vitro cultured BMMCs and the difference that exist between the mucosal and connective tissue MCs, our key ID gene list shares 50% of identities with the Dwyer ID gene list . The majority (63%) of ATAC-seq peaks co-localized within the typical enhancers and super-enhancers. Representative tracks from one biological replicate of the non-ID genes and key ID genes are shown in Fig. Super-enhancers comprise multiple constituent enhancers that contain clusters of TF binding motifs37. We found that a set of TF binding motifs including those of EGR2, PU.1, GATA2, EGR1 and RUNX1 were enriched at the accessible regions within both the typical enhancers of the non-ID genes and the super-enhancers of the key ID genes  with our ChIP-seq and ATAC-seq data . TF-bound sites that are close to TSS were given higher scores than those that were further away from TSS42. Regulatory potential scores calculated using GATA2-bound sites was 2.1-fold higher for the super-enhancers than for the typical enhancers  BMMCs with 4-hydroxytamoxifen (4-OHT) for five days. The Gata2 inducible knockout mouse was on the C57BL/6 genetic background, which is different from the genetic background of BALB/c mice that were used to generate the H3K4me1 and H3K27ac ChIP-seq data presented in Figs. r = 0.86 under the resting conditions and r = 0.9 under the stimulated conditions  in chromatin accessibility at the super-enhancers compared with less (23%) reduction at the typical enhancers , respectively, while H3K27ac modification was reduced by 43% and 51% at the typical enhancers and the super-enhancers , respectively  showed the greatest reductions in chromatin accessibility (80–99%), 60–79% reduction in H3K4me1 modification and 80-99% reductions in H3K27ac modification. Consistent with the marked reduction in chromatin accessibility, H3K4me1 and H3K27ac modifications, transcripts of these genes were reduced by 80–99% in the absence of GATA2 . The super-enhancers at these genes exhibited 60–79% reduction in chromatin accessibility, 27–74% reduction in H3K4me1, 40–79% reduction in H3K27ac and 60–90% reduction in RNA transcripts . In contrast, only six percent of the typical enhancers of the non-ID genes had reduced chromatin accessibility and histone modifications in the absence of GATA2  that were not stimulated or stimulated with IgE receptor crosslinking for two hours. We identified an average of 1089 genes (n = 3) that were expressed >2-fold in activated MCs relative to resting MCs , we next investigated whether GATA2 regulates genes that are induced to perform specialized functions in response to external stimuli. Although MCs respond to a host of stimuli including IL-18, IL-33 and complement 5aMCs Fig. .Fig. 4IgSgk1 and Cdkn1a) and MAPK signaling pathways   enrichment analysis of the IgE receptor crosslinking-upregulated (referred to hereafter as activation-induced) transcripts revealed that the upregulated genes were significantly enriched in gene sets that encode signaling molecules, transcription factors, proteases and other enzymes, cell surface molecules, and cytokines and chemokines Fig. . The mosk3) Fig. . The nexMCs Fig. . TranscrMCs Fig. . ConsistWe also observed that 280 genes were significantly suppressed following IgE receptor cross-linking  and used the downloaded MITF ChIP-seq data generated from resting BMMCs38. Because recent studies have shown that chromatin accessibility at super-enhancers can change in just a few hours after stimulation, we also performed GATA2 ChIP-seq on BMMCs that were stimulated with IgE receptor crosslinking for two hours (n = 2). We found that the total frequencies and densities of GATA2 binding sites were higher at the inducible super-enhancers than at the inducible typical enhancers , Fig. To distinguish the activation-induced enhancers in resting MCs from those in activated MCs, we refer to the former as the activation-inducible enhancers. To investigate a potential requirement for GATA2 in regulating the activation-inducible enhancers, we counted the number of GATA2 and MITF binding motifs within these enhancers. The number of GATA2 binding motifs was significantly higher than that of MITF binding motifs at both types of activation-inducible enhancers Fig. . To veriGata2 gene in the inducible Gata2 knockout BMMCs (n = 2). Figure Gata2−/− MCs expressed the IgE receptor FcεR1 at 56% lower levels than WT MCs as measured by MFI , Ms4a2 (encodes IgE receptor β chain), and Fcer1g (encodes IgE receptor γ chain) genes were reduced by 38%, 52% and 45%, respectively, in resting Gata2−/− MCs . These data indicate that signals triggered by the stimulation were delivered to the activation-inducible super-enhancers by a mechanism involving GATA2. Moreover, Gata2−/− MCs completely failed to upregulate gene transcription in response to IgE receptor crosslinking  and ionomycin stimulation, which bypass the need for FcεR1α to activate MCs, also failed to upregulate the genes that normally would respond to antigenic stimulation10, suggesting that without GATA2 priming, the activation-inducible enhancers fail to be triggered and, as a result, they fail to recruit SDTFs. The failure of triggering the activation-inducible enhancers might lead to the inability of MCs to upregulate gene transcription in response to antigenic stimulation.We identified a class of enhancers that mediate responses to antigenic stimulation. These activation-induced typical and super-enhancers have increased chromatin accessibility and H3K27ac modification but do not have increased H3K4me1 modification in response to antigenic stimulation. We propose that binding of GATA2 to the activation-inducible enhancers in resting MCs primes these regions by recruiting the SDTFs that are activated by signals triggered by antigen. Our results provide evidence that the assembly of these activation-induced enhancers is GATA2-dependent. In the absence of the 2+-dependent chromatin domains in a genome-wide fashion55. AP1 binding motifs are enriched at the Ca2+-dependent chromatin domains. Wu et al. showed that EGR2, which is rapidly increased after IgE receptor crosslinking, directly binds to the Ccl1 chemokine gene promoter and is required for CCL1 production but not for CCL3 and CCL9 production40. Our data confirmed that Egr2 mRNA rapidly increased after IgE receptor crosslinking on BMMCs, supporting a role for EGR2 in regulating genes that respond to antigenic stimulation. Because there are a large number of zinc finger family TFs in MCs that potentially bind consensus EGR2 binding motifs, the identity (ies) of the exact SDTFs that bind to the induced regions after IgE receptor crosslinking need to be assessed on an individual basis. Heinz and colleagues reported that strain-specific PU.1, CEBPα and AP1 binding sites co-localize with regions marked with H3K4me2 and H3K27ac modifications. They showed that PU.1, CEBPα and AP1 prime enhancers to respond to macrophage activation signals in a strain-specific manner. The primed enhancers require additional binding of SDTF P65, a component of NFκB, to be fully functional56. Their work illustrates a mechanism by which LDTFs prime enhancers to respond to external stimulations. However, the work was conducted prior to the advancement of next generation sequencing and only a few genes in one cell type were observed. Our proposed model is consistent with these studies of macrophages, but also illuminate how MCs prepare their enhancers to respond to antigenic stimulation. Together, our work adds evidence supporting the priming of enhancers by LDTFs in multiple cell types.TF motif enrichment analysis of the activation-induced enhancers supports roles of the SDTFs AP1 and EGR2, which bind the activation-inducible enhancers in response to antigenic stimulation to recruit the co-activator P300. In turn, P300 adds additional H3K27ac modifications. This proposed model is consistent with recent findings from a study on human MCs. Cildir and colleagues reported that IgE receptor crosslinking induces CaGata2 gene was deleted allows us to estimate that activation-induced H3K27ac potentially persists for 10 days after the SDTFs AP1 and EGR2 leave the activation-induced enhancers. However, activation-induced epigenetic marks could persist much longer under chronic disease conditions. Thus, the delayed removal of H3K27ac at the activation-induced enhancers may serve as a useful epigenetic marker of previous cytokine and chemokine gene transcription following an episode of acute inflammation and during chronic inflammation.Our studies raise the question of whether the activation-induced H3K27ac modification is used as an epigenetic mark to record the history of activation-induced gene transcription. Cytokine and chemokine gene transcription returned to background levels 24 h after antigenic stimulation. The rapid disappearance of cytokine and chemokine mRNA posts a challenge for accurate measurements of cytokine and chemokine gene expression in patients experiencing acute inflammation. A 40% reduction in the activation-induced H3K27ac modification 5 days after the Il1r1 gene encoding IL-33 receptor promotes IL-33 signaling, leading to further upregulation of cytokine genes and chemokine genes, including Il1357. Increased expression of the signaling proteins LYN, JAK1, PIK3CD, and MAPKAPK3 strengthen potential signaling through major signaling pathways, including MAKP, PI3K, and JAK-STAT. Increased expression in the Bcl2 gene suggests that the activated MCs enhance MC survival. Increased expression in the Nlrp3 (Cryopyrin) gene suggests that the MCs can have a stronger ability to release pro-inflammatory cytokines such as IL-1β and IL-18 in response to bacterial infection and other insults. We also observed that a number of genes enriched in gene sets involved in cell proliferation, signal transduction, apoptosis or enriched in genes encoding receptors were significantly suppressed following IgE receptor cross-linking. Downregulation of these genes allow activated MCs to return to resting state. Notably, downregulation of the Kit (encode the receptor for SCF), Gata2, Stat5b by IgE crosslinking suggest that activated MCs become less sensitive to SCF stimulation. Downregulation of the Ma4a2 (encode IgE receptor β chain), Pip5k1c, Pik3cg, Pi3kcd genes could lead to termination of signals triggered by IgE receptor crosslinking. Downregulation of the apoptotic genes promote MC survival after activation. The mechanisms by which IgE crosslinking downregulate these genes are not clear. Around 8.1% of the activation-induced accessible regions in response to IgE receptor cross-linking was located outside the enhancer regions with H3K27ac modification. We found that these activation-induced accessible regions are enriched in transcription repressor binding motifs. A significant number of these repressive TF binding motifs target genes were repressed by IgE receptor crosslinking. Further studies using our combined strategy of TF binding motif enrichment should identify transcription repressors induced by antigenic stimulations.IgE receptor crosslinking induces profound changes in gene expression in MCs. While a small percentage of upregulated genes have been reported before, the majority of upregulated genes have not been reported previously. The activation-induced gene expression enables MCs to perform more robust functions and recruit other types of cells to sites of inflammation. Notably, the increased expression of the 59, posing great challenges for effectively treating MC-mediated diseases. The detailed molecular analysis presented here provides information that may aid in the development of effective preventions and therapeutic strategies.In summary, MCs are the cause of MC disorders. MCs remodel their chromatin landscapes in response to antigen exposure and do not die after antigenic stimulation and degranulationGata2 conditional knockout mice (Gata2f/fRosaYfp/YfpTgCreErt2hemi) or wild-type control mice (Gata2+/+RosaYfp/YfpTgCreErt2hemi) on 129 genetic background backcrossed to C57BL/6 genetic background for 5 generations. These mice were generated by crossing Gata2f/f mice to Cre activity reporter RosaYfp/Yfp mice and inducible Cre mice10. Six to twelve-week old male or female mice were used for experiments. Mice were kept under the standard housing conditions: 72 to 74 °F ambient temperature, 50-60% humidity and 12 h dark/light cycle. All animal experiments were conducted according to protocols approved by the National Jewish Health Institutional Animal Care and Use Committee (Protocol number: AS2703-02-23).Balb/cJ mice (000651) were purchased from the Jackson Laboratory . The inducible TM cellgroTM, Manassas, VA) plus 10% FBS,100 units/mL penicillin, 100 μg/mL streptomycin, and 2 mM beta-mercaptoethanol in the presence of 20 ng/mL IL-3 for four weeks. Over 99% of BMMCs were mast cells as determined by FACS analysis (FcεRIα+ c-Kit+).Bone marrow-derived mast cells (BMMCs) were cultured from bone marrow cells of Balb/c mice in Iscove’s DMEM  mice or wild-type control (Gata2+/+RosaYfp/YfpTgCreErt2hemi) mice on 129 genetic background backcrossed to C57BL/6 genetic background for 5 generations. BMMCs were treated with 25 nM 4-hydroxytamoxifen  for five days43. Expression of FcεR1α, and c-Kit on mast cells were analyzed by Flow cytometry using 1:200 diluted fluorochrome labeled antibodies . The YFP+ cells were FACS-sorted and deletion of the Gata2 gene was determined by PCR to be greater than 99%.For experiments that examined the role of GATA2, BMMCs were cultured from bone marrow cells of inducible BMMCs were sensitized with 1 μg/mL IgE anti-2, 4, 6-Trinitrophenyl (TNP) antibody . Twenty-four hours later, the cells were challenged with 100 ng/mL TNP-BSA  for 2 additional hours before the cells were collected for further analysis.7 BMMCs that were not treated or treated with IgE receptor crosslinking were fixed with 1% formaldehyde , sonicated by using the Covaris S220 Focused-ultrasonicator in the SDS lysis buffer  and pre-cleared with Protein A Beads at 4 °C for 1 h according to established protocols43. The samples were incubated with 10 μg of following antibodies (1:100 dilution): anti- H3K4me1 antibody , anti-H3K27ac antibody  or anti-GATA2 antibody  at 4 °C overnight and then with protein A agarose/salmon sperm DNA slurry  at 4 °C for 1 h. The beads were washed and eluted as described43. The crosslinking of eluted immunocomplexes was reversed and the recovered DNA was recovered using a QIAGEN QIAquick PCR purification kit . ChIP-seq library was prepared using TruSeq ChIP Library Preparation Kit  according to the manufacturer’s instructions. Briefly, 10 ng of ChIPed DNA was converted into blunt-ended fragments. A single adenosine nucleotide was added to the 3’ ends of the blunt-ended fragments before ligation of indexing adapters to the adenylated 3’ ends. The ligated products were purified, size-selected and PCR amplified according to the manufacturer’s instructions. The quality and quantity of the DNA library were assessed on an Agilent Technologies 2100 Bioanalyzer. Paired-ended sequencing was performed on an Illumina NovaSEQ6000 platform.1 × 106 BMMCs that were untreated or treated with IgE receptor crosslinking using the RNeasy mini kit (Qiagen). RNA was treated with DNase I according to manufacturer’s instructions. The quality of RNA was analyzed using the Agilent Technologies 2100 Bioanalyzer. RNA-seq libraries were prepared using the NEBNext Ultra II RNA Library Prep Kit for Illumina . Briefly, polyA mRNA was purified using the NEBNext Poly(A) mRNA Magnetic Isolation Module . The isolated mRNA was fragmented, reverse transcribed into cDNA, end-repaired, and adapter-ligated. The ligated samples were USER enzyme digested and PCR amplified and purified using AMPure-XP beads . The quality and quantity of RNA libraries were assessed on an Agilent Technologies 2100 Bioanalyzer. Paired-ended sequencing of the RNA libraries was performed on an Illumina NovaSEQ6000 platform. The RNA-seq related to Gata2 deletion experiments was performed by Novogene Corporation Inc., Sacramento, CA.To prepare RNA samples for RNA-seq, total RNA was isolated from 1×1032. Briefly, 50,000 BMMCs that were either untreated or treated with IgE receptor crosslinking were spun down and washed once with cold PBS. The cells were resuspended in 50 μl cold ATAC-RSB-lysis buffer  and incubated for 3 minutes. The lysis buffer was immediately washed out with 1 mL ATAC-RSB buffer . The cell pellet was resuspended in 50 μl transposition mix , 16.5 μl PBS, 0.5 μl 1% digitonin, 0.5 μl 10% Tween-20, 5 μl H2O) and incubated at 37 °C for 30 minutes. The reaction was stopped by adding 2.5 μl of 0.5 M EDTA pH 8 and transposed DNA was purified using Qiagen MiniElute PCR purification kit (Qiagen). Purified DNA was amplified using the following condition: 72°C for 5 min, 98 °C for 30 s, and 7 cycles: 98 °C for 10 s, 63 °C for 30 s, 72 °C for 1 min. The amplified libraries were purified, size-selected, and the quality and quantity of libraries were assessed on an Agilent Technologies 2100 Bioanalyzer. The pair-ended sequencing of DNA libraries was performed on an Illumina NovaSEQ6000 platform.Omni-ATAC-seq was performed according to the established method60 multiBamSummary and plotCorrelation functions. Briefly, the aligned read coverages were computed for consecutive bins of 10 kb size across the mm10 genome from BAM files generated from two biological samples by using multiBamSummary. The similarity in the mapped reads in the bins between two biological samples were analyzed by plotCorrelation. R values were calculated by using Pearson correlation coefficient. The reproducibility of the RNA-seq data was calculated by using the ggscatter function in R package ggpubr (version 0.2). The similarity of reads mapped to genes from two biological samples  was estimated using Pearson correlation coefficient analysis. qPCR and ELISA data represent the results pooled from three independent experiments.Reproducibility of the ChIP-seq and Omni-ATAC-seq data was determined by using the deepTools 3.3.061 with the following setting: Species Assembly, Mouse: GRCm38; Basal plus extension, proximal 5.0 kb upstream and 1.0 kb downstream, plus distal up to 100 kb. The expression of putative target genes was analyzed further by comparison with our RNA-seq expression dat. Only the genes with RPKM higher than 30 were considered as enhancer associated target genes.Genomic Regions Enrichment of Annotations Tool  was used to assign the regulatory regions identified by H3K27ac ChIP-seq and Omni-ATAC-seq to their putative target genes62 and the quality of resulting sequence data was analyzed by FastQC (version 0.11.9). The trimmed reads were aligned to the mm10 reference genome (GRCm38) using Bowtie2 (version 2.3.5.1)63. Peak calling was performed using MACS2 (version 2.1.2) with default parameters with the q-value cut-off of 0.0564. Reads were presented as the read counts per million uniquely mapped reads. The analyzed sequence data was visualized using Integrative Genomics Viewer (IGV). H3K27ac ChIP-seq data were used for identifying typical and super-enhancers using ROSE software17. The H3K27ac ChIP-seq peaks in resting and activated mast cells were stitched within 12.5 kb of each other and excluding 2.5 kb upstream and downstream of the known transcription start sites (TSS). The combined H3K27ac reads within stitched regions were plotted in a ranked enhancer order. Super-enhancers were defined as the enhancers above the inflection point and the rest were defined as typical enhancers. Genomic distributions of the typical enhancers and super-enhancers were analyzed by the R package ChIPseeker (version 1.18.0)65. Average total numbers of typical enhancers and super-enhancers were calculated by dividing total numbers of typical enhancers and super-enhancers found in two replicates with two. Representative ChIP-seq IGV tracks shows in the figures were generated from biological sample 1 and are similar to those in biological sample 2. The GATA2 ChIP-seq peaks generated from resting BMMCs  were trimmed using Trimmomatic (version 0.33)66. Reads at enhancers per gene were calculated as the sum of reads at all enhancers assigned to a gene in two biological replicates and were represented as average reads per million nucleotides mapped (RPM). Normalized reads per enhancer were calculated by dividing the reads at enhancers per gene with a normalized size of enhancers, which is equal to the average sizes of enhancers assigned to a gene divided by 1000 bp. The data were represented as fragments per kilobase per million mapped reads (FPKM). Because pair-ended sequencing was used, reads at enhancers per gene and normalized reads per enhancer were divided by two to adjust to one read per gene or enhancer. TF-bound sites at enhancers per gene were calculated as the sum of bound sites at all enhancers assigned to a gene. Normalized TF-bound sites per enhancer were calculated by dividing the TF-bound sites at enhancers per gene with the sizes of enhancers. Both the reads and bounds sites at enhancers were calculated as the average reads and bound sites from two biological samples.Enhancers were identified using ROSE (version 1). The H3K27ac ChIP-seq peaks were assigned to their putative target genes using GREAT. The enhancer reads were counted by using Bedtools multicov (version 2.29.2)Gata2 deletion analysis) were analyzed and quality checked by FastQC. The reads were aligned to the mm10 reference genome using the Spliced Transcripts Alignment to a Reference  software67. Reads (FPKM) were assembled into reference transcripts and counted using Cufflinks (version 2.2.1). The average reads from two or three biological samples were assessed using Cuffmerge (version 1.0.0). Reads with FPKM < 30 in resting or stimulated BMMCs were filtered out from further analysis. The differential gene expression between the resting and stimulated samples (pooled) was analyzed by using Cuffdiff (version 2.2.1)68. Gene ontology enrichment analysis was performed on genes that responded to IgE receptor crosslinking by using the Database for Annotation, Visualization and Integrated Discovery 70. Genes that were upregulated with log2 ratio of stimulated/untreated >2 in each enriched categories were selected for heatmap representations, which were generated by using Expander software71 (version 7.2). The dendrogram tree for different expression patterns was calculated using the agglomerative algorithm72. The representative RNA-seq IGV tracks showed in the figures were generated from one biological sample, representing two or three biological samples with similar pattens.The raw reads  were adapter trimmed by Trimmomatic 0.33 and the quality of sequenced data was analyzed by FastQC. The trimmed reads were aligned to the mm10 reference genome using Bowtie2 with -very-sensitive and -x 2000 parameters. The read alignments were filtered using SAMtools (version 1.7) to remove mitochondrial genome and PCR duplicates. Peaks were identified by MACS2 with the q-value cut-off of 0.05 and the sequencing data was displayed using IGV. Normalized reads per enhancer were calculated by dividing the reads at each enhancer (defined by H3K27ac modification) with the size of enhancers in 1000 bp per unit and were represented as fragments per kilobase per million mapped reads (FPKM). The averages of reads per enhancer were calculated from two biological replicates. The representative ATAC-seq IGV tracks showed in the figures were generated from one biological sample, representing two or three biological samples with similar pattens.42. Enhancers located within ±100 kb of TSS of a target gene were assigned to the target gene.GATA2 ChIP-seq, H3K27ac ChIP-seq or Omni-ATAC-seq BAM files were used to generate peak files using MACS2. The peak files were used for regulatory potential score calculations using Binding and Expression Target Analysis (BETA) minus (Version 1.0.7)73. To identify the TF binding motifs enriched in the accessible regions located within the regions marked with the H3K27ac modification, BED files of Omni-ATAC-seq data sets were used to intersect with the typical enhancer and super-enhancer regions identified by H3K27ac ChIP-seq using Bedtools intersect (version 2.29.2)66. The overlapped sequences were subjected to motif enrichment analysis using MEME-ChIP (version 5.2.0) and the JASPAR database. The number of TF motifs was counted by using the Find Individual Motif Occurrences (FIMO) tool of MEME (FIMO). For analysis of TF binding motifs enriched at the activation-induced accessible regions located within the activation-induced enhancers, Omni-ATAC-seq or H3K27ac ChIP-seq reads of BMMC samples that were untreated with IgE receptor crosslinking were compared with those of BMMC samples that were treated with IgE receptor crosslinking using the R package DiffBind (version 2.10.0). The regions that had increased H3K27ac ChIP-seq or Omni-ATAC-seq reads after IgE receptor crosslinking were compared using Bedtools intersect. The sequences of the regions that showed both activation-induced H3K27ac ChIP-seq reads and Omni-ATAC-seq reads were used for TF binding motif enrichment analysis using MEME-ChIP.Multiple Em for Motif Elicitation (MEME)-ChIP was used to identify the TF binding motifs enriched in accessible regions, typical and super-enhancers60. The BigWig files of Omni-ATAC-seq generated from resting BMMCs were used for chromatin accessibility enrichment distribution analysis. The TSS files for the cytokine and chemokine genes were compiled from the TSS files generated by UCSC table browser using the mm10 assembly.Enrichment scores were calculated by using deepTools 3.3.0Gata2 knockout mice or control mice were treated with 4-OHT for five days and the YFP+ cells were FACS-sorted. The sorted cells (1 × 105 cells/ml) were sensitized with 1 μg/mL IgE anti-TNP antibody for 24 h and challenged with 100 ng/mL TNP-BSA for 30 minutes. Both cells and supernatants were collected. β-hexosaminidase in supernatants and cell pellets was incubated with an equal volume of 7.5 mM p-nitrophenyl N-acetyl-α-D-glucosaminide , which was dissolved in citric acid  for 2 h at 37 °C. After incubation, the reaction was developed by adding 0.2 M glycine (pH 10.7) and the absorbances at 405 and 490 nm were measured. The percentage of β-hexosaminidase release was calculated as the percentage of β-hexosaminidase in supernatants relative to total β-hexosaminidase in supernatants and cell pellets.Mast cell degranulation was measured by the release of β-hexosaminidase. BMMCs cultured from bone marrow cells of inducible Gata2-/-or control BMMCs were left untreated or sensitized with 1 μg/mL IgE anti-TNP antibody for 24 h and challenged with 100 ng/mL TNP-BSA overnight. The amounts of IL-6  and TNFα  protein in the supernatants were measured by ELISA.ELISA was used to measure the amounts of chemokines and cytokines released from BMMCs. BMMCs were sensitized with 1 μg/mL IgE anti-TNP antibody for 24 h and challenged with 100 ng/mL TNP-BSA for 2, 4, 6, 8 and 24 h. Untreated BMMCs were included as a control. The amounts of CCL1 , CCL2 , CCL3 , CCL4  and CCL7  protein in the supernatants were measured by ELISA. For cytokine measurement, the sorted t test was used to determine significant differences between two samples.Error bars above the medians represent variants ranging from third quartile to maximum and error bars below the medians represent variants ranging from first quartile to minimum. Two-tailed student’s Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Reporting Summary


Cisplatin-based neoadjuvant chemotherapy has been shown to improve survival in patients with squamous cell carcinoma (SCC), but clinical biomarkers to predict chemosensitivity remain elusive. Here, we show the long noncoding RNA (lncRNA) LINC01011, which we termed cisplatin-sensitivity-associated lncRNA (CISAL), controls mitochondrial fission and cisplatin sensitivity by inhibiting BRCA1 transcription in tongue SCC (TSCC) models. Mechanistically, we found CISAL directly binds the BRCA1 promoter and forms an RNA-DNA triplex structure, sequestering BRCA1 transcription factor-GABPA away from the downstream regulatory binding region. Importantly, the clinical relevance of these findings is suggested by the significant association of CISAL and BRCA1 expression levels in TSCC tumors with neoadjuvant chemosensitivity and overall survival. We propose a new model where lncRNAs are tethered at gene promoter by RNA-DNA triplex formation, spatially sequestering transcription factors away from DNA-binding sites. Our study uncovers the potential of CISAL-BRCA1 signaling as a potential target to predict or improve chemosensitivity.  •CISAL enhances mitochondrial fission and cisplatin sensitivity in TSCC cells through BRCA1•CISAL directly binds the BRCA1 promoter and forms an RNA-DNA triplex structure•CISAL sequesters GABPA away from regulatory binding at BRCA1 promoter•High CISAL predicts favorable neoadjuvant chemosensitivity and prognosis of TSCC patients Biological Sciences; Molecular Biology; Cell Biology; Cancer Around 70% of the human genome is transcribed to RNA, whereas only 2% consists of protein-coding genes . The mancis and in trans by interacting with chromatin and recruiting chromatin modifiers , was markedly increased in patients with neoadjuvant chemosensitive tumors and was mainly localized to the nucleus  reduced BRCA1 expression H, GABPA/BoxB-tethering-based reporter assay (BoxB RNA element to CISAL (BoxB-CISAL) allowed the fusion transcript to be recruited by the RNA-binding domain of λN protein fused with the GAL4 DNA-binding domain (λN–GAL4) when CISAL-binding sequence was deleted and substituted with 5xUAS sites  was unable to inhibit BRCA1 expression A and train vivo. CISAL knockdown led to a significant increase in CAL-27 tumor growth in the presence of cisplatin  and immunohistochemical staining demonstrated that CISAL expression was higher, whereas BRCA1 expression was lower in chemosensitive TSCCs, as compared with resistant tumors  from 113 patients treated with platinum-based neoadjuvant chemotherapies. t tumors A. A signt tumors B. Conseqt tumors A. Additit tumors C. Notablt tumors .Figure 7Next, we analyzed the association of CISAL and BRCA1 expression with the clinicopathological status of TSCC patients . No signIn this study, we show that the lncRNA CISAL regulates mitochondrial fission and cisplatin sensitivity through BRCA1 signaling. In exploring the mechanism by which BRCA1 transcription is inhibited, we found that CISAL can be specifically tethered at BRCA1 promoter by formation of an RNA–DNA triplex structure, which subsequently sequesters BRCA1 TF-GABPA away from its binding to regulatory DNA target. Importantly, we show that CISAL-BRCA1 expression is associated with patients' survival in multiple types of human cancer and TSCC patients' neoadjuvant chemosensitivity. These results provide mechanistic and translational insights into neoadjuvant chemosensitivity and suggest that targeting CISAL-BRCA1 signaling pathway could be used for predicting or improving neoadjuvant chemosensitivity.Numerous studies strongly suggest that lncRNAs in “gene desert” regions play significant roles in tumor occurrence and development and could be used as a biomarker . LncRNAsIn an effort to understand how lncRNAs target the genome, computational approaches are being used to predict the interaction of lncRNAs and chromatin or DNA by (1) suggesting candidate lncRNA-associated TFs according to the enrichments of their binding motifs; (2) proposing the involvement of lncRNA in transcriptional enhancement or repression from enrichments of relevant chromatin markers; and (3) identifying near-complementary DNA sequences within lncRNA-associated regions that might indicate direct RNA–DNA triplex formation . Indeed,Understandably, nuclear lncRNAs mainly regulate transcription activity through histone modification or direct/indirect interaction with TFs. We observed no association between CISAL and histone modification; however, we found a direct interaction between CISAL and GABPA. Broadly, lncRNAs exercise many roles including recruiters, decoys, stimuli, scaffolds, or some combinations thereof . For theEmerging data suggest that abnormal mitochondrial morphology may be relevant to various aspects of disease and apoptosis . We haveIn summary, we are beginning to achieve a full understanding of the molecular mechanism responsible for lncRNA-mediated regulation of transcription. Our study proposes a model where the lncRNA CISAL regulates TSCC mitochondrial fission and cisplatin-based neoadjuvant chemosensitivity, by tethering at BRCA1 promoter and sequestering downstream BRCA1 TF-GABPA away from regulatory binding region, thereby inhibiting BRCA1 transcription and its downstream signaling pathway. Moreover, TCGA analysis revealed that CISAL-BRCA1 axis is associated with OS in multiple types of cancers, suggesting that CISAL-BRCA1 axis could be used as a target to predict or improve neoadjuvant chemosensitivity and patients' overall survival.In this study, we demonstrated that CISAL directly binds the BRCA1 promoter and forms an RNA-DNA triplex structure, sequestering BRCA1 transcription factor, GABPA, away from the downstream regulatory binding region, rather than current functionality of lncRNAs in transcriptional regulatory programs, such as competing the binding sites or playing as the decoy/sponge. It is plausible that CISAL plays important roles in 3D chromatin structure formation. Although the short-range chromatin interactions around 2kb cannot be detected by current technology, future studies evaluating CISAL and long-range interactions between the BRCA1 promoter and its enhancers would likely yield deeper mechanistic insight into the regulation of CISAL-BRCA1 signaling pathway. In addition, taking into consideration the CISAL distribution throughout the nucleoplasm and cytoplasm, further studies are needed to identify additional binding partners and functional properties of CISAL.All methods can be found in the accompanying


The eponymous member of the interferon regulatory factor (IRF) family, IRF1, was originally identified as a nuclear factor that binds and activates the promoters of type I interferon genes. However, subsequent studies using genetic knockouts or RNAi-mediated depletion of IRF1 provide a much broader view, linking IRF1 to a wide range of functions in protection against invading pathogens. Conserved throughout vertebrate evolution, IRF1 has been shown in recent years to mediate constitutive as well as inducible host defenses against a variety of viruses. Fine-tuning of these ancient IRF1-mediated host defenses, and countering strategies by pathogens to disarm IRF1, play crucial roles in pathogenesis and determining the outcome of infection. IFNB1), a prototypical type I IFN, were not unveiled until the 1980s  GAA) . The IRFsequence . Type IIll types .Not surprisingly, the contribution of IRF1 to positive feedback regulation of ISG responses induced by distinct IFNs is complementary, rather than redundant to the role of IRF9 and other IFN-inducible IRFs such as IRF7. This was shown many years ago by northern blot analysis of ISG transcripts in IFNα- or IFNγ-stimulated IRF1 and IRF9 knockout mouse embryonic fibroblasts . More reTNF is a pleiotropic cytokine that is closely associated with inflammation, as well as both innate and adaptive immune responses to pathogens. Engagement of its receptors (TNFR1 and TNFR2) triggers distinct signal transduction cascades leading to transcriptional upregulation of selected genes. TNFR1 and TNFR2 differ in their intracellular domains, with TNFR1 having a cytoplasmic ‘death domain’ that is not present in TNFR2. They thus recruit distinct adaptor molecules (TNFR-associated death domain (TRADD) versus TNFR-associated factor (TRAF) 1/2, respectively), although both receptors commonly signal to activate NF-κB ,57.IRF1, are similarly induced in human lung epithelial cells stimulated with TNF [Irf1 transcription in both macrophages and endothelial cells. Whereas macrophages did not require de novo synthesis of IRF1 protein for this, endothelial cells required de novo protein synthesis. Notably, this latter observation is reminiscent of IRF1-mediated IFN responses triggered by RLRs [TNF may contribute to the induction of IFNβ responses. TNFR1 engagement mediates increased expression of IRF1 and IFNβ, leading to prolonged expression of proinflammatory chemokines via STAT1 ,58. Sevewith TNF . These fwith TNF ,60. TNF  by RLRs . However by RLRs .Francisella novicida, is sensed by the DNA sensor cyclic GMP-AMP synthase (cGAS)—stimulator of IFN genes (STING) pathway that activates type I IFN-dependent expression of IRF1. IRF1 then drives the expression of guanylate-binding proteins (GBPs), leading to intracellular killing of the bacteria and the release of DNA that induces AIM2 activation. Similarly, IRF1 plays an essential role in formation of the NLRP3 inflammasome during influenza A virus infection by inducing transcription of Z-DNA binding protein 1 (Zbp1) [In addition to canonical IFN responses, inflammasome assemblies involving nucleotide-binding domain and leucine rich repeat containing family, pyrin domain containing 3 (NLRP3) and absent in melanoma 2 (AIM2) proteins provide another layer of innate host defense against pathogens. Moreover, type I IFN signaling contributes to both NLRP3 and AIM2 inflammatory responses despite the distinct signals that activate the assembly of these inflammasomes –63. IRF11 (Zbp1) . By cont1 (Zbp1) .de novo transcription in actinomycin D-treated cells [Fig 4A). A similar phenomenon was noted in human hepatoma cells lacking functional RLR and TLR signaling [Most studies examining the role of IRF1 in pathogen defense suggest it is an inducible factor, triggered by infection and contributing to the activation of innate immune responses induced by pathogen sensors such as RLRs, TLRs, or cGAS, as described above. However, a considerable number of studies provide evidence that IRF1 also functions at a basal level, providing constitutive defense against a variety of viruses. Obtaining a quantitative measure of basal antiviral activity is generally challenging, as virus infections are typically associated with unavoidable activation of cell-intrinsic responses via PRRs. However, hepatitis A virus (HAV), an hepatotropic picornavirus, is capable of limited replication in immortalized human hepatocytes without detectable activation of IRF1, or other RLR, TLR, or STING-dependent host antiviral responses . This ised cells . These rignaling .IRF1 knockout hepatocytes has provided insight into the role played by basal levels of IRF1 in maintaining constitutive expression of functionally diverse classes of antiviral genes  [https://genome.ucsc.edu) confirms the presence of IRF1-binding motifs in the promoter regions of these genes, validating these genes as bona fide targets of IRF1 [RARRES3, APOL3, APOL1, OSBPL6, APOL6), have not previously been recognized to possess antiviral activity. A specific combination of these IRF1-regulated genes acts to restrict replication of HAV, whereas other combinations restrict HCV, DENV and Zika virus, all Flaviviridae [Fig 4B). Another recent study identified 260 genes expressed constitutively in human respiratory epithelial cells under IRF1 control [High-throughput sequencing of the RNA transcriptome in  . The chr of IRF1 . Importaiviridae . An exce control . These i control . While I control , and witThe specific features that render a promoter responsive to basal levels of IRF1 are uncertain, but the environment surrounding the promoter is likely to be important. Basal IRF1 transcriptional activity does not require STAT1, which is often involved in IRF1 transcription triggered by IFNγ or TLR signaling. Recent studies have revealed the existence of IRF1-targeted promoter sites that lack concomitant STAT1 recruitment and are subject to histone acetylation favoring transcription at the basal level . Thus, IIt is uncertain whether IRF1 orthologs (or IRF11) expressed in fish provide a similar basal level of protection against pathogens. Nevertheless, the existence of IFN homologs exclusively in vertebrates suggests that their evolution has lagged behind that of the IRFs, and that the manner in which the IRFs function in host immunity is likely to be qualitatively different in invertebrates. This does not necessarily imply that the basal protection against virus infections afforded by IRF1 in vertebrates is a vestige of its primordial role in invertebrates. However, it is tempting to speculate that constitutive IRF1 expression may also protect against pathogen invasion in invertebrates, which lack inducible IFN-mediated innate immunity, by regulating genes under control of the IRF-E promoter element.cis-regulatory promoter and enhancer elements. At least two copies of this minimal IRF1 binding site exist within interferon-responsive ISRE elements  analyses suggest that the switching of IRF1 activity from a basal to an agonist-induced state may be regulated in part epigenetically at the level of histone acetylation . In the Fig 6).Phylogenetic analyses of the IRF proteins suggest that IRF1 represents a crucial component of host defense against pathogen invasion. Indeed, the ability to govern events at the IRF1 line of defense contributes substantially to the outcome of pathogenic infections. Thus, both host and pathogen have evolved strategies to control IRF1 signaling in an effort to gain the upper hand in this conflict. In this section we focus on the diverse mechanisms that regulate IRF1 activity at the mRNA transcript and protein levels  signaling, although mechanistic details are lacking [IRF1 expression and thus inhibit IRF1-mediated innate immune responses. For instance, human cytomegalovirus (HCMV) has recently been shown to induce the expression of roquin, an RNA-binding protein that interacts with a stem-loop in the 5’ untranslated region (5’UTR) of IRF1 transcripts to inhibit their translation [Airway infections with rhinovirus or respiratory syncytial virus (RSV), RNA viruses with distinctly different genome structures and replication strategies, can result in oxidative tissue damage leading to an epithelial-mesenchymal transition (EMT), thereby silencing cription . Mechaniing RelA . Respira lacking . Alternanslation .IRF1 transcripts to downregulate IRF1 expression. miR-301a is transcriptionally induced by Japanese encephalitis virus (a neurotropic virus of the Flaviviridae family) through NF-κB, leading to reduced expression of IRF1 [IRF1 transcript at late time points following IFNγ stimulation [IRF1 expression, other miRNAs can potentiate intrinsic innate immune responses via IRF1. One such example is the liver-specific miR-122, which targets receptor tyrosine kinases leading to reduced phosphorylation of STAT3 at Tyr705, thereby counteracting the negative impact of STAT3 on IFN signaling by facilitating IRF1 expression [Viruses also modulate the expression of microRNAs (miRNAs) that target the 3´UTRs of  of IRF1 . An intemulation . Whereaspression . Long nopression .IRF1 mRNA [de novo synthesis of IRF1 protein [IRF1 mRNA from PKR-mediated global shutdown of cellular protein synthesis, and is crucial for RLR-initiated cytokine responses in immortalized primary adult human hepatocytes [Despite the predominant transcriptional regulation of IRF1, IRF1 responses are also regulated at the level of protein synthesis. IFNγ stimulation rapidly activates MAPK-interacting protein kinase 1 (Mnk1) and its downstream target, eIF4E, in a Mek/Erk-dependent manner, thereby ensuring translation of IFN-induced RF1 mRNA . SurprisRF1 mRNA , is esse protein . Mechaniatocytes .Post-translational modifications also add a variety of functionalities to IRF1. In particular, the turnover and transcriptional activity of IRF1 is tightly regulated by K48-linked ubiquitination of the 39-residue C-terminal region of IRF1, which leads to its degradation via the proteasome . VirusesSUMOylation also modifies IRF1 activity to suppress its transcriptional activity. This occurs at sites that are identical to the major ubiquitination sites within the C terminus of IRF1, enhancing its resistance to degradation via the ubiquitin-proteasome pathway . K48-linIn addition to protection from infection by pathogens, IRF1 is recognized to be a key regulator of tumor progression and to have tumor suppressor properties. Genetic deletions of either or both alleles of IRF1 located on chromosome 5q are frequently detected in human leukemia and myelodysplasia . LikewisSimilar to the antagonism of host innate immunity by pathogens, tumors evolve to escape from anti-tumor immune surveillance. A notable example is programmed cell death 1 ligand 1 (PD-L1), a ligand of the programmed cell-death protein 1 (PD-1) immune inhibitory checkpoint that is induced in tumors upon IFN exposure and leads to immune evasion . The expAlthough an ortholog of protein kinase R (PKR), an IRF1-regulated, IFN-inducible protein with tumor-suppressor activity, exists in zebrafish , the tumResearch over the past 30 years has revealed in fine molecular detail how IRF1 is regulated and the roles it plays in maintaining host homeostasis. Among other housekeeping activities exemplified by its role in tumor suppression, IRF1 protects the host from invading pathogens at multiple levels, ranging from an intrinsic ‘basal’ level to a robust ‘inducible’ function. IRF1 thus acts as a master-regulator of innate immunity by regulating antiviral status at a basal, steady state, and also by orchestrating multiple cellular signaling cascades that effect a wide spectrum of antiviral responses that range from alterations in lipid metabolism to protein degradation machineries. The fundamental role of IRF1 has been conserved throughout evolution. Only recently, however, have we begun to appreciate how competition between host and pathogen, coupled with the emergence of the interferon system in vertebrates, has shaped the role of IRF1.RARRES3) might provide narrower, yet equally effective and safer host-directed therapeutic approaches. Given the diverse array of effectors that exist in the IRF1 regulome, understanding the mechanisms underlying the protective role of IRF1 can be expected to provide key insights into microbial infections and host defense, as well as cancer biology where IRF1 also plays a pivotal role in disease outcome.Several important gaps exist in our mechanistic understanding of IRF1 function. For instance, it is entirely unclear how the basal activity and nuclear localization of IRF1 are maintained. Are there upstream components that post-translationally modify the IRF1 molecule via ubiquitination or phosphorylation in a fashion analogous to an agonist-activated state? Do the profiles of IRF1 target genes similar in different cell types? Is there therapeutic potential in manipulating IRF1-responsive pathways, a possibility that has yet to be explored? Therapies that augment IRF1 activity could be beneficial in defeating a broad range of pathogens, but they could also pose serious threats to the host if uncontrolled immune activation led to autoimmune-like disease. Drugs that specifically boost individual IRF1 effector functions (such as the phospolipase


The pathological hallmarks of Alzheimer’s disease (AD) involve alterations in the expression of numerous genes associated with transcriptional levels, which are determined by chromatin accessibility. Here, the landscape of chromatin accessibility was studied to understand the outline of the transcription and expression of AD-associated metabolism genes in an AD mouse model.The assay for transposase-accessible chromatin by sequencing (ATAC-seq) was used to investigate the AD-associated chromatin reshaping in the APPswe/PS1dE9 (APP/PS1) mouse model. ATAC-seq data in the hippocampus of 8-month-old APP/PS1 mice were generated, and the relationship between chromatin accessibility and gene expression was analyzed in combination with RNA sequencing. Gene ontology (GO) analysis was applied to elucidate biological processes and signaling pathways altered in APP/PS1 mice. Critical transcription factors were identified; alterations in chromatin accessibility were further confirmed using chromatin immunoprecipitation assays.Sele, Clec7a, Cst7, and Ccr6. The signatures of numerous transcription factors, including Olig2, NeuroD1, TCF4, and NeuroG2, were found enriched in the AD-associated accessible chromatin regions. The transcription-activating marks of H3K4me3 and H3K27ac were also found increased in the promoters of these genes. These results indicate that the mechanism for the upregulation of genes could be attributed to the enrichment of open chromatin regions with transcription factors motifs and the histone marks H3K4me3 and H3K27ac.We identified 1690 increased AD-associated chromatin-accessible regions in the hippocampal tissues of APP/PS1 mice. These regions were enriched in genes related to diverse signaling pathways, including the PI3K-Akt, Hippo, TGF-β, and Jak-Stat signaling pathways, which play essential roles in regulating cell proliferation, apoptosis, and inflammatory responses. A total of 1003 decreased chromatin-accessible regions were considered to be related with declined AD-associated biological processes including cellular response to hyperoxia and insulin stimulus, synaptic transmission, and positive regulation of autophagy. In the APP/PS1 hippocampus, 1090 genes were found to be upregulated and 1081 downregulated. Interestingly, enhanced ATAC-seq signal was found in approximately 740 genes, with 43 exhibiting upregulated mRNA levels. Several genes involved in AD development were found to have a significantly increased expression in APP/PS1 mice compared to controls, including Our study reveals that alterations in chromatin accessibility may be an initial mechanism in AD pathogenesis.Supplementary information accompanies this paper at 10.1186/s13195-020-00598-2. Alzheimer’s disease (AD), an age-associated chronic progressive neurodegenerative disorder, is characterized by progressive loss of memory, cognitive impairment, and behavioral changes. It is the most common form of dementia. A global systematic analysis of AD from 1990 to 2016 that included 195 countries showed that the global number of individuals who lived with dementia was 43.8 million in 2016, showing a 117% increase compared to 1990 [APOE) gene is a well-known genetic risk factor identified for AD. The association of the APOE ε4 allele with AD risk has been repeatedly demonstrated by different studies, whereas the ε2 allele has been found to be related to a protective effect [APP, PSEN1, and PSEN2 are discovered to be associated with autosomal dominant familial AD, which is found in about 70% of familial AD patients [TREM2 are considered a major genetic risk factor for AD [SORL1 and ABCA7 are demonstrated to carry numerous loss-of-function variants leading to strong increases in AD risk [IGHG3 and ZNF655 [The pathological hallmarks of AD are characterized by β-amyloid (Aβ) deposition, neurofibrillary tangles induced by phosphorylation of tau protein, upregulation of inflammation, and neuronal apoptosis. All these processes involve alterations in the expression and regulation of numerous genes. The Apolipoprotein E , TFs must induce chromatin remodeling of nucleosomal structures, which results in different levels of chromatin accessibility [cis-regulatory elements and activate gene transcription, while closed chromatin regions impair the accessibility of promoters and enhancers to transcription factors and other regulators of transcription inducing gene silencing.In recent years, it has been recognized that chromatin is a dynamic central regulator of transcription. The chromatin structure defines the scenario where interactions between TFs and their cognate regulatory regions take place. To successfully interact with sibility , 17. TheRecently, the results of several studies have demonstrated substantial changes in chromatin accessibility in human brains –20. McClIn this work, we used the Assay for Transposase-Accessible Chromatin by sequencing (ATAC-seq), a sensitive tool for integrative epigenomic analysis, combined with RNA sequencing (RNA-seq) to investigate the pattern of genome-wide chromatin accessibility in AD in an APP/PS1 mouse model, which resembles the familial AD in humans, and characterize the chromatin accessibility landscape in APP/PS1 mice. We analyzed the differences in chromatin-accessible regions, which allowed us to identify the landscape of binding events, regulatory DNA sequences, and putative TFs that are likely responsible for these alterations.n = 3), and male, age-matched C57BL/6 mice (n = 3) were purchased from Nanjing Biomedical Research Institute of Nanjing University . All animals were housed in a specific pathogen-free room under a 12-h light-dark cycle with ad libitum access to food and water. Mice were acclimatized for a week, after which they were euthanized by cervical dislocation, and hippocampal tissues were collected. All animal procedures were performed according to the criteria outlined in the Guide for the Care and Use of Laboratory Animals  and with approval of the Animal Care and Use Committee of Xuanwu Hospital of Capital Medical University, China.Male APPswe/PS1dE9 mice, 8 months of age  [Nuclei were isolated from frozen hippocampal tissues. In brief, frozen hippocampal tissue (50–60 mg) was homogenized with a mortar and pestle on ice. The homogenized samples were placed in 1.0 mL of chilled lysis solution, mixed gently, and incubated on ice for 20 min. The mixture was then filtered through a cell-strainer , mixed with 1.8 mL 1.8 M sucrose buffer, and loaded on the surface of a 1.0 mL 1.8 M sucrose cushion buffer. The nuclei were centrifuged at 27,550×g at 4 °C for 45 min and the pellets were resuspended in 0.5 mL chilled nuclei storage buffer and centrifuged at 500ego, CA) . After thttp://www.bioinformatics.bbsrc.ac.uk/projects/fastqc) and reads with Phred quality score > 30 were used for further analysis. Removing and trimming the adaptor sequences were performed to obtain clean data. The clean reads were then aligned to mouse genome mm10 with Burrows-Wheeler Aligner (0.7.10) [http://broadinstitute.github.io/picard) was used for duplicate removal. Samtools (v.1.3.1) was performed to filter multiply mapped reads, and BED tools were used for filtering out mitochondrial reads [n = 3) or normal hippocampal samples from aged-matched control mice (n = 3) were merged and peak calling was conducted using MACS2 (v.2.1) [Each ATAC-seq library was sequenced on the Illumina Hiseq PE150 sequencer to obtain 80–100 million of 2 × 150 bp paired-end reads per sample. The quality of sequencing data was evaluated with FastQC  . Picard al reads , 25. Ali (v.2.1) . Peaks t (v.2.1) . For the (v.2.1) . This an (v.2.1) . Tracks  (v.2.1) . Heatmaphttp://www.bioinformatics.bbsrc.ac.uk/projects/fastqc). Reads were aligned to mm10 using the STAR splice-aware aligner (v.2.5.2b). QoRTs (v.1.0.7) were used to gather read counts per sample per gene for differential gene expression analysis [Total RNA was extracted using an RNeasy mini extraction kit (Qiagen), and RNA integrity was quantified by the Agilent RNA 6000 Nano Kit (Agilent) using the Agilent Bioanalyzer. Purified RNAs were reverse transcribed, and the resulting cDNA was further tagmented and PCR-amplified using the Nextera XT DNA sample kit (Illumina) to add the sequencing adaptors. The libraries were pooled and sequenced on the Illumina Hiseq PE150 sequencer to an average depth of 70 million reads per sample. The quality control of sequencing was performed via FastQC  in three quantile groups according to their means using the R statistical package. Violin plots were produced using ‘ggplot2’ from the R package. For comparison and visualization, ATAC-seq and RNA-seq enrichment data at various genomic features are shown on a log2 scale. In addition, the 25 most highly expressed genes and the 25 least highly expressed genes in APP/PS1 mice and controls were identified, and their transcriptional start sites (Ensembl) were extracted from the UCSC Table Browser. Regions expanded to 100 kb surrounding these TSSs were intersected and overlapped with the ATAC-seq libraries, then quantified and plotted, and the ATAC-seq signal over the most and least highly expressed genes was also quantified and plotted. In addition, the top 50 altered (highest and lowest) ATAC-seq peaks were extracted by P values < 0.05 were considered significant.Gene ontology (GO) analysis was performed to facilitate elucidating the biological implications of unique genes in the significant profiles of the gene. We annotated the genes associated with the accessible chromatin regions using the Genomic Regions Enrichment of Annotations Tool  . The annP values < 0.05 were considered significant.Pathway analysis was performed to explore the annotated genes or differential genes enriched signaling pathways. The annotated genes or differential genes were analyzed based on KEGG database to obtain all the involved pathway terms. The significant level of each pathway term was calculated by the Fisher test. Chromatin immunoprecipitation (ChIP) assay was performed using a Pierce™ Agarose ChIP Kit  according to the manufacturer’s protocol. The hippocampal tissue lysates were incubated with anti-histone 3 trimethylated lysine 4 , anti-histone 3 acetylated lysine 27 , or anti-rabbit IgG . Purified DNA and input DNA were analyzed by qPCR. The primers used for ChIP-qPCR are listed in Supplementary Table St test. Differences between groups were considered to be statistically significant at P < 0.05. Statistical analyses were performed using the GraphPad Prism 6 software .Data are expressed as mean ± SD. Comparison between two groups was conducted with Student’s As APP/PS1 mouse is a widely used animal model to study Alzheimer’s disease , we emplAD-associated metabolism involves numerous genes that participate in multiple signaling pathways. To investigate the functional role of the chromatin-accessible regions in the regulation of gene expression involved in signaling pathways and gene function, we analyzed different pathways and biological processes in AD model mice and WT mice. Our results showed that 204 annotated genes were associated with chromatin-accessible regions in AD mice and were involved in active signaling pathways. These regions were enriched in different signaling pathways including the PI3K-Akt, Hippo, TGF-β, and Jak-Stat signaling pathway that play essential roles in regulating of cell proliferation, cell apoptosis, and inflammatory responses Fig. . In addiPrnp, Olfr31, and Ifi44, had gained open chromatin architecture, whereas regions around low expression genes, for example Cox8b, Selenov, and Pax7, showed lost chromatin signals (Fig. To explore the relationship between chromatin accessibility and transcription genome-wide, we performed RNA-seq and integrated with ATAC-seq data that computed the ATAC-seq signal level at known TSSs in groups of genes categorized with low, medium, and high mRNA level. Results revealed that the level of enrichment in ATAC-seq signal at the TSS region positively correlated with mRNA abundance of the annotated gene Fig. a, indicaAPP gene, which encodes the amyloid protein precursor, was also found upregulated in AD mice (Supplementary Table Scis-regulatory elements in the APP gene. The results showed that the locus containing the upstream APP enhancer exhibited higher chromatin accessibility (Fig. The heatmap of RNA-seq is shown in Fig. ity Fig. b. These ity Fig. a. Meanwhity Fig. b. Moreovity Fig. c, and doity Fig. d.Fig. 4APP, GSAP, and SORL1 (Fig. To elucidate potential mechanisms of the relationship between over-expression genes and open chromatin regions, we investigated DNA transcription factor binding at AD-associated chromatin-accessible sites. The 50 most differential peaks of ATAC-seq in AD model mice and WT mice are shown in Fig. RL1 Fig. c. In addSele, Ccr6, CD300lf, Clec7a, and Cst7. The results showed that four of the five most upregulated genes, except for CD300lf, displayed a higher level for both H3K4me3 and H3K27ac marks in chromatin-accessible gene regions in the hippocampus of AD model mice than in WT mice. Notably, the level of H3K27ac in the Cst7 gene and the levels of H3K4me3 and H3K27ac in the Ccr6 gene exhibited more than fivefold increases (Fig. It is well established that active histones are important marks for gene regulation, as H3K4me3 and H3K27ac are characteristic of TSS/promoter region and are associated with TF binding –41. ThusThe genome-wide landscape of AD has been studied widely, and the gene expression patterns involved in in the process of AD have been described in detail , 43. HowOur results showed that the most accessible regions localize around the TSS and that open chromatin at these sites is predictive of active transcription, in agreement with the pattern reported in previous studies . Given tWe also found the Jak-Stat and PI3K/Akt signaling pathways to strongly associate with AD. Several studies have demonstrated the Jak-Stat signaling as a possible underlying pathogenetic mechanism of AD, showing the importance of inflammatory pathways involved in AD , 57. BesSele, Clec7a, Cst7, Ccr6, and CD300lf, which may have profound significance in the AD process. The Sele gene encodes a cell-surface glycoprotein E-selectin that has a role in immunoadhesion [To observe the differential chromatin accessibility in APP/PS1 mice associated with gene expression, we examined 740 chromatin-accessible regions in AD with 43 upregulated associated genes and identified several genes with a notable increasing expression, including adhesion . It is tadhesion . Clec7a adhesion , 62. Addadhesion . On the Sele, Clec7a, Cst7, and Ccr6 genes. A recent study has reported the patterns of H3K27ac in entorhinal cortex samples from AD cases and identified several H3K27ac-associated transcriptional variation genes, suggesting that histone marks may indeed be critical events causing activation of genes favoring the progression in AD [Accessibility at regulatory regions modulated by chromatin remodeling processes involves not only nucleosome occupancy, but also histone modifications, especially H3K4me3 and H3K27ac, which are necessary for transcription factor binding. Therefore, we investigated these histone modifications in the upregulated genes in AD. We found that both H3K4me3 and H3K27ac marks are highly expressed in the promoter regions of on in AD .In summary, our work provides a novel strategy to study transcriptional regulation in AD through the description of open chromatin profiling by ATAC-seq. The utilization of this technique in AD allows us to identify the regulatory elements that play critical roles in the AD process. Furthermore, our data present an association between TF binding, chromatin-accessible regions, and gene regulation, providing new insights into the molecular mechanisms of AD. However, the future challenge is to elucidate how the alteration of TF influences the malfunctioning of critical genes involved in AD pathogenesis.Additional file 1:Supplementary Table S1. List of primers used in ChIP-qPCR. Additional file 2.Additional file 3.Additional file 4.Additional file 5.Additional file 6.Additional file 7.Additional file 8.Additional file 9.Additional file 10.Additional file 11.Additional file 12:Figure S1. Principal Component Analysis of ATAC-seq libraries in APP/PS1 mice and WT mice. The plot shows the percentage of variance explained by PC1 and PC2 axes and the distribution of the samples along the two first Principal Components.Additional file 13:Figure S2. Motif enrichment for regions of decreased chromatin accessibility in AD.


Arabidopsis 2-oxoglutarate–dependent dioxygenase, an activity associated with histone demethylation in other organisms, physically associates with the Polycomb Repressive Complex 2. We propose that physical association of histone methylation/demethylation activities will be generally important to coordinate switching between chromatin states.Epigenetic regulation of gene expression is associated with switching between chromatin states characterized by distinct histone modifications. Polycomb/Trithorax regulation involves the mutually exclusive H3K27me3/H3K36me3 modifications, but how these states are faithfully inherited through DNA replication, yet can switch from one to another, is still poorly understood. One mechanism that would aid switching is the association of histone methyltransferases with factors demethylating the opposing histone modification. Here, we show that an  Arabidopsis INCURVATA11 (ICU11) as a Polycomb Repressive Complex 2 accessory protein. ICU11 robustly immunoprecipitated in vivo with PRC2 core components and the accessory proteins, EMBRYONIC FLOWER 1 (EMF1), LIKE HETEROCHROMATIN PROTEIN1 (LHP1), and TELOMERE_REPEAT_BINDING FACTORS (TRBs). ICU11 encodes a 2-oxoglutarate–dependent dioxygenase, an activity associated with histone demethylation in other organisms, and mutant plants show defects in multiple aspects of the Arabidopsis epigenome. To investigate its primary molecular function we identified the Arabidopsis FLOWERING LOCUS C (FLC) as a direct target and found icu11 disrupted the cold-induced, Polycomb-mediated silencing underlying vernalization. icu11 prevented reduction in H3K36me3 levels normally seen during the early cold phase, supporting a role for ICU11 in H3K36me3 demethylation. This was coincident with an attenuation of H3K27me3 at the internal nucleation site in FLC, and reduction in H3K27me3 levels across the body of the gene after plants were returned to the warm. Thus, ICU11 is required for the cold-induced epigenetic switching between the mutually exclusive chromatin states at FLC, from the active H3K36me3 state to the silenced H3K27me3 state. These data support the importance of physical coupling of histone modification activities to promote epigenetic switching between opposing chromatin states.Molecular mechanisms enabling the switching and maintenance of epigenetic states are not fully understood. Distinct histone modifications are often associated with ON/OFF epigenetic states, but how these states are stably maintained through DNA replication, yet in certain situations switch from one to another remains unclear. Here, we address this problem through identification of  Arabidopsis is the floral repressor locus, FLOWERING LOCUS C (FLC)  and associated with the accessory proteins VERNALIZATION5 (VRN5) and VERNALIZATION INSENSITIVE3 (VIN3) nucleates H3K27me3 silencing at a small intragenic site and this confers a metastable silencing  (Epigenetic silencing is mediated by conserved histone-based mechanisms in many organisms. Polycomb Repressive Complex 2 (PRC2) delivers a H3K27me3-based silencing that plays major roles in developmental and environmental epigenetic regulation –3. The P4 C (FLC) . The pro C (FLC) –18. FLC ilencing . After rilencing . Resetti6 (ELF6) .FLC has provided a generic view of how opposing chromatin states, marked by a transcriptionally active H3K36me3 state and a silenced H3K27me3 state, can provide epigenetic stability, yet switch from one to another   . WhetherDs transposon-tagged Arabidopsis mutation showing pleiotropic developmental phenotypes we identify an activity associated with H3K36me3 demethylation. The mutation was initially called wavy leaves and cotyledons furled back (wlc-1) and was identified as an early flowering, deformed leaf mutant in the Landsberg erecta genotype  domain with homology to Arabidopsis prolyl-4-hydroxylases (P4Hs) and alpha ketoglutarate-dependent dioxygenase Bs (AlkBs) . In addition, the icu11-3 mutant expresses approximately fourfold less ICU11 at the mRNA level . Transformation of the icu11-3 mutant with genomic ICU11 alone, or genomic ICU11 with C-terminal enhanced green fluorescent protein (eGFP) or 3xHA fusion constructs complemented icu11-3, rescuing the observed morphological defects  and the misexpression of target genes in young seedlings . In addition, revertants were generated through Ac-induced remobilization of Ds to generate wild-type (WT) plants and fully stable mutant plants; in the latter the excision had led to a frame shift in the coding sequence. ICU11 is widely expressed, appearing elevated during later plant development , while pICU11::ICU11-eGFP constructs showed broad expression of the protein in roots, nuclear localization at the subcellular level and association with metaphase chromosomes .The s screen  through s screen A and B.  (AlkBs) . In icu1icu11-3 in short days, we compared the expression of the floral activator FT, a known PRC2 target, during early seedling development under short-day growth conditions. FT was up-regulated in icu11-3 compared with the progenitor line (C12b), with the overexpression phenotype becoming more pronounced with seedling age . Interestingly, we did not find ICU11 interacting with VIN3 or its homologs VRN5 and VEL1, reinforcing the view of distinct Polycomb complexes operating over different spatial and temporal timescales.To investigate ICU11 function further, we performed coimmunoprecipitation/mass spectrometry (coIP-MS) experiments using 3xHA- and GFP-tagged ICU11 proteins as bait. Our IP-MS revealed core PRC2 complex components CLF, SWN, FERTILIZATION INDEPENDENT ENDOSPERM (FIE), MULTICOPY SUPPRESSOR OF IRA1 (MSI), and EMBRYONIC FLOWER (EMF)2 and accessory proteins EMF1, LHP1, and TELOMERE-REPEAT-BINDING (TRB) 1–3, as ICU11 interactors. EMF1 and LHP1 have been found in several studies as direct interactors of PRC2 , 29, andicu11-3 14-d-old seedlings. We observed a small increase in H3K4me2/me3, and H3K36me3 in icu11-3 relative to WT . These data, together with the analysis of FLC derepression in the icu11-3 mutant, raised the possibility that a primary activity of ICU11 was demethylation of histone modifications associated with active chromatin states, for example, H3K4me2/3 and H3K36me3. Loss of one protein may destabilize the whole-protein complex that contains putative demethylases specific for different histone modifications. A combination of activities, e.g., ICU11 and JMJ14 in the whole complex would then link demethylation of active histone modifications at multiple sites on the histone tail. This linking of activities in vivo means in vitro analyses are the best way to define the specific activity of different demethylases. The 2OGD domain of ICU11 falls within the same enzymatic superfamily as Jumonji C-domain histone demethylases. Despite considerable effort using in vitro assays using recombinant protein and commercial histones, or by colocalization assays of demethylation and expressed ICU11 protein in Nicotiana benthamiana transient assays, we could not generate reproducible data to clearly support a direct H3K4 or K36me3 demethylase role for ICU11. This is not uncommon for this family of proteins and may indicate that ICU11 requires accessory proteins or specific posttranslational modification for histone demethylase activity. It is also possible that ICU11 modifies another component of the complex, which then allosterically influences histone demethylation.To further investigate the role of ICU11 in epigenetic regulation we used Western blots to determine levels of histone modifications associated with active chromatin  and those associated with silenced chromatin (H3K27me3) in WT and icu11 mutants prompted us to look for changes in other epigenetic marks. In plants, DNA methylation occurs in CG, CHG, and CHH contexts . CG methylation is broadly distributed, present in both gene bodies and repeat-rich regions; while CHG and CHH methylation are primarily found in transposable elements . To further investigate the consequence of the observed changes in DNA methylation, we compared expression of a subset of transposons in early-stage seedlings . We observed some slight up-regulation in icu11-3 compared to WT and clf-2, though to a lesser extent than in DNA methylation mutants ddm1-1 and cmt3-7.The wide range of phenotypes in elements , 32. Sou. Consistent with the previous report  and no substantial changes of CG methylation levels over transposons . The repetitive nature of the pericentromeric regions complicates their analysis, but we observed a slight reduction in CG methylation at cen180 repeats in icu11-3 . From the combination of bisulfite sequencing and Southern analysis we can conclude that a small proportion of the centromeric repeats lose their CG DNA methylation in icu11-3. CHG and CHH  methylation over transposon sequences were slightly increased in the icu11-3 mutant , but these slight changes in the non-CG contexts are consistent with the variations seen among plant tissues and plants grown in different environmental conditions due to the dynamics of non-CG methylation . Cold treatment of different durations revealed a clear vernalization defect in icu11-3 fca-1 plants; flowering was significantly delayed after 2 wk cold treatment  and was only marginally accelerated with longer cold. No difference in flowering was observed in the icu11-3 single mutant following vernalization .To clarify the functional implications of the physical and genetic interaction of ICU11 with PRC2, we asked how the FT in fca-1 and icu11-3 fca-1 mirrored the differences observed in flowering time. Without cold, FT was expressed at ∼10-fold higher levels in icu11 fca-1, even though expression of FLC (its repressor) was also high. This presumably reflects misregulation of all Polycomb target genes in an icu11 background , on the final cold day after 2, 4, or 6 wk cold (T0), and at 10-d intervals  upon return of plants to warm, long-day conditions, allowing us to examine the expression of key regulatory genes in the vernalization-induced flowering pathway. Expression of ckground C. Followlization D, and althe cold E, but exaffected F.FLC is dependent on PRC2-mediated switching between bistable opposing epigenetic states: an active state, marked by high levels of H3K36me3 at the nucleation region of FLC; and a silent state, marked by a peak of H3K27me3 in the nucleation region that accumulates during cold exposure and spreads to cover the entire FLC locus upon return to the warm. We had previously shown that H3K4me3 at FLC follows transcription rather than functioning as the opposing chromatin state to H3K27me3 . Thus, ICU11 is required for the cold-induced epigenetic switch between the active H3K36me3 state to the silenced H3K27me3 state at FLC.Comparison of nditions . In conting cold . Compariicu11-3, a mutation in the Landsberg erecta (Ler) background. In Ler, a Mutator-like transposable element (MULE) in the first intron of FLC leads to low expression before vernalization compared with other alleles. We wanted to confirm the effect of ICU11 at FLC was on Polycomb switching rather than an influence FLC through an effect of the MULE insertion. First, we performed chromatin immunoprecipitation to define sites of association of ICU11 at FLC, using a GFP-tagged ICU11 construct transformed into the icu11-3 fca-1 background. ICU11 was localized at the FLC 5′ end coincident with the H3K36me3 enrichment, both before vernalization and at the end of a 4-wk cold treatment  experiment did not indicate enrichment of ICU11 signal over the MULE.These analyses had been undertaken using reatment E. We alsFLC via an effect of the MULE, we utilized a pFLC::FLC-GUS reporter construct generated using the Col-0 FLC allele (pFLC::FLCCol-0-GUS), which lacks the MULE. This construct was crossed into the icu11-3 (Ler) background, and WT and icu11-3 siblings from an F2 population examined in nonvernalizing conditions. In icu11-3, FLC expression was strongly up-regulated before cold even in the absence of the MULE . All of the other raw data and materials that support the findings of this study are available from the corresponding authors upon reasonable request.The full list of mass spectrometry results is provided as Supplementary FileSupplementary File