Chromatin accessibility is a major regulator of gene expression. Histone writers/erasers have a critical role in chromatin compaction, as they “flag” chromatin regions by catalyzing/removing covalent post-translational modifications on histone proteins. Anomalous chromatin decondensation is a common phenomenon in cells experiencing aging and viral infection. Moreover, about 50% of cancers have mutations in enzymes regulating chromatin state. Numerous genomics methods have evolved to characterize chromatin state, but the analysis of (in)accessible chromatin from the protein perspective is not yet in the spotlight. We present an overview of the most used approaches to generate data on chromatin accessibility and then focus on emerging methods that utilize mass spectrometry to quantify the accessibility of histones and the rest of the chromatin bound proteome. Mass spectrometry is currently the method of choice to quantify entire proteomes in an unbiased large-scale manner; accessibility on chromatin of proteins and protein modifications adds an extra quantitative layer to proteomics dataset that assist more informed data-driven hypotheses in chromatin biology. We speculate that this emerging new set of methods will enhance predictive strength on which proteins and histone modifications are critical in gene regulation, and which proteins occupy different chromatin states in health and disease. Chromatin accessibility has a fundamental role in a wide range of biological processes including gene regulation and DNA repair. While the dogma open chromatin = transcribed genes still stands, there is still much unexplored in understanding which molecular mechanisms regulate chromatin accessibility, which are inherited, and which are failing in disease pathogenesis . NumerouProteins are actually critical players in chromatin state and dynamics. In eukaryotic cells, DNA exists in close association with histone proteins, which form nucleosomes every ≈147 bp of DNA. Canonical histones are H2A, H2B, H3, and H4, and they are bound especially in heterochromatic silenced domains by the linker histone H1 . HistoneEvents like UV exposure, smoking, viral infection, cancer, and aging correlate with chromatin decondensation, i.e., large and small heterochromatic domains become euchromatic (accessible and prone to transcription) . In factOne of the main features of cancer is the (epi)genome instability. The transition from normal tissue to cancer is sometimes characterized by changes in the distribution of H3K9me2/3, in HP1 expression levels  and by rReduced global heterochromatin, altered histone marks, and global hypomethylation of DNA have also been associated with aging. Significant changes in global nuclear architecture during physiological aging, as well as altered gene expression, might be triggered by the loss of heterochromatin domains . This glExternal events also affect chromatin state. DNA damage accumulates with age, but the process can be accelerated by reactive oxygen species (ROS), exposure to UV irradiation, and alcohol intake. Oxidative stress occurs because of ROS accumulation, affecting chromatin and chromatin modifying-enzymes. In general, it can stimulate global heterochromatin loss and modify histones folding and stability, as well as their PTMs, influencing the expression of genes that are normally in a silenced state ,26,27. BIn summary, maintenance of chromatin structure is essential for proper cellular function over a lifetime. While we have a large set of data on DNA sequence, conformation, and selected proteins defining accessible vs. inaccessible chromatin, a proteomics-centric view of chromatin is still less exploited. What are all the histone modifications defining euchromatin and heterochromatin? What part of the proteome occupies different chromatin domains in health and disease? In this review, we introduce the most widely adopted methods to investigate chromatin accessibility and then we discuss the methods based on mass spectrometry to characterize the chromatin state where the proteome resides. We conclude with examples of available databases reporting properties of the chromatin proteome.Numerous genomic, biophysical, and microscopy-based methods are now established and used routinely for chromatin state analysis . What foGenomic methods to study chromatin include but are not limited to the following. A common method for identifying regions of DNA methylation, or condensed DNA, is Methyl-DNA immunoprecipitation sequencing (MeDIP-seq), which utilizes specific antibodies to pulldown methylated DNA followed by sequencing . The samContrary to genomic methods, biophysical methods can give a wealth of information about the fine molecular mechanisms that occur within chromatin. Biophysical techniques that have been utilized include optical tweezers , which mAlthough genomic and biophysical methods make up a large portion of the chromatin literature, microscopy techniques can provide information that the others cannot. One such piece of information that microscopy can obtain is through visualizing chromatin domains. One method, stochastic optical reconstruction microscopy (STORM), achieves a resolution of 20–30 nm and has been demonstrated to observe chromatin segregated nanoclusters, dispersed nanodomains, and compact large aggregates, all of which form with different histone modifications .While the techniques explained above generate a great amount of chromatin information, newer and lesser-performed techniques are available to pioneer forward and build upon lacking areas of information. A newer technique, demonstrated with cysteine-lacking histones, includes probing histone surface accessibility , in whicThe techniques explained above can all be informative of chromatin state; however, each have limitations. For example, many sequencing techniques, such as ChIP-seq, can be costly per sample and provide broad information in which combination with more sensitive techniques will be needed to investigate in-depth mechanisms. Hi-C data can be noisy and can lack long-range contacts . Other tMass spectrometry is an unbiased and multidimensional tool to analyze spatially resolved proteomes, including histone modifications (gatekeepers of chromatin readout) . A numbeDNA methylation is a known marker of DNA silencing which regulates gene expression and epigenetics inheritance ,57. Chro18 chromatography coupled to UV detection to quantify 1–2% 5mdC from DNA of calf thymus and salmon sperm [18 chromatography, and (4) hybridization analysis using a series of probes. Together, this was used to map the methylated regions of DNA containing an actively and differentially expressed somatic H1 histone gene from sperm, embryo, and adult tissues of Chaetopterus worm [Paper, thin layer, ion exchange and gas chromatography coupled to either UV or mass spectrometry are historical methods to separate and quantify the four major DNA bases  and the methylated DNA bases (5mdC and 5hmdC). In 1980, Kuo and colleagues successfully used Con sperm . DNA wason sperm . The metrus worm .18 columns. Further optimization included varying methanol solvent to decrease surface tension, addition of acetic acid for protonation and found that ammonium acetate/methanol (88:12 v/v) is the best for both chromatographic separation and detection in mass spectrometry using electrospray ionization [DNA methylation was quantified in disease states like leukemia using urine samples . Chromatnization . The addnization . Song annization  reportednization .6 cytosine for 5fC and 3 × 106 cytosine for 5caC. In fact, they required modifications to the chromatographic setup to achieve sufficient sensitivity [18-based chromatography for nucleosides. However, a robust platform for nucleoside quantification using mass spectrometry is still less common in research labs than one could expect. In summary, quantification of DNA modifications has been the pioneer of chromatin state analysis using mass spectrometry since its establishment about 40 years ago. However, for a comprehensive overview of molecular components of accessible and inaccessible chromatin, a protein perspective is required.Ito and colleagues discovered that 5mC is not only converted to 5hmC but also into 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC) by TET proteins . Those msitivity . Chemicasitivity . This ensitivity ,68. Moresitivity ,69 and psitivity  have emeMass spectrometry is currently the only technique that can identify and quantify in a large-scale manner the relative abundance of histone PTMs . Other t6-acetic anhydride [The traditional peptide centric analysis of histone proteins (bottom-up) is similar to the typical proteomics pipeline. Specifically, proteins are usually digested with the protease trypsin (cleaves after lysine and arginine residues) into short (4–20 aa) peptides for analysis, as both chromatographic separation and detection by mass spectrometry are more robust and sensitive than intact protein analysis (top-down). However, histones are very basic proteins, i.e., rich in arginine and lysine residues, and thus trypsin digestion would result in excessively short peptides for reconstructing their position on the protein. For this reason, derivatization of lysine residues is frequently applied to modify the side chain of lysine residues so that trypsin can only cleave arginine residues and generate proper size peptides . Since 2nhydride , to propnhydride  to NHS-pnhydride  to phenynhydride . Indepennhydride ,81,82,83While histone PTM quantification per se does not provide direct information about chromatin state, the relative abundance of selected histone marks is used to define how accessible chromatin is overall. For instance, hyperacetylation of histone H4 on the residues K5/K8/K12/K16 (in particular K16 ) revealsThe differential turnover of nucleosomes has been described in multiple publications ,90,91, fProteomics has become a discipline with many applications, most of them contingent with appropriate sample preparation. The routine procedure of sample preparation for proteomics is arguably one of the simplest among the -omics; most extracted or purified proteins are soluble in water, and they can be prepared for mass spectrometry with a rapid three steps procedure, i.e., reduction, alkylation, and digestion into peptides. For this reason, a myriad of alternative procedures have been engineered to enhance sensitivity, specificity, and quantitative dimensions . In other words, we can analyze the proteome of a specific chromatin state if we establish a dedicated chromatin fractionation procedure that allows to physically isolate chromatin fractions and analyze them separately by mass spectrometry or selectively label those domains . In 2003Another option is salt fractionation of chromatin, which can isolate chromatin fractions with completely different genome-wide profiles. According to Henikoff and colleagues, low-salt soluble fraction corresponds to a highly accessible chromatin and high-salt soluble fraction represents the condensed chromatin. The method was validated by showing that the insoluble fraction is enriched in transcriptionally active chromatin and is closely linked to engaged RNA polymerase ,102. ExtSeveral chromatin readers have already been identified, and they have been categorized depending on protein domains that have been characterized to bind different types of histone PTMs. Bromodomains are known to recognize acetylated lysine residues, while PhD fingers can bind to acetylations and also methylations related to active gene transcription such as H3K4me3. Chromodomains are typical of proteins binding histone methylations associated with chromatin silencing. WD40 repeats recognize mainly mono- and dimethylations. Tudor domains are typical of methylated residues such as H3K4me3, H3R17me2, H4R3me2, and H4K20me3. 14-3-3 domains bind serine phosphorylations. MBT and YEATS domains are currently not as characterized, but they are known to bind methylated lysine residues and acyl-modifications such as lysine crotonylation, respectively. Yun and colleagues describe all these domains in a comprehensive review .Schizosaccharomyces pombe. Similarly, Zukowski and colleagues assembled a repressive heterochromatin domain from purified components, conjugated to magnetic beads, to recruit factors that play a role in regulating heterochromatin function [A strategy combining chromatin immunoprecipitation (ChIP) of histone modifications and mass spectrometry, occasionally named ChroP (Chromatin Proteomics), has been used to analyze protein components characterizing distinct chromatin regions ,108. Chrfunction .The requirement of specific antibodies directed against each regulatory protein in ChIP methodology is one of the bottlenecks to studying gene-regulatory factors. To address this issue, catalytically dead RNA-guided nuclease Cas9 (dCas9) has been used to isolate specific genomic regions and their associated proteins. A dCas9-targeted chromatin-based purification strategy was recently used by Tsui and colleagues to identify chromatin-associated proteins and its potential role on the modulation of histone expression . As an aA potential new direction of proteomics for chromatin state analysis could be the selective labeling of proteins accessible to solution by using existing methods for single protein characterization. In mass spectrometry, protein folding can be characterized using hydrogen-deuterium exchange (H/D-X)  or Fast Bioinformatics is a vibrant sub-discipline in proteomics. Hundreds of software are available to identify spectra , accuraHomo sapiens, is also freely available [The Chromatin Database) is another database containing information about chromatin-related proteins that are conserved across eukaryotic species [Homo sapiens and Saccharomyces cerevisiae. Different from ChromDB and HIstome databases, DbHiMo platform offers a broad archive and analysis tool for histone-modifying enzymes encoding genes, with emphasis in the fungal species (other than yeast) [As described above, mass spectrometry has been used as the method of choice to study chromatin-state as well as for histone PTMs analysis. In order to accurately quantify peptides detected by the LC-MS/MS approach, different software tools were developed. For instance, EpiProfile 2.0 is software purposely designed to extract (un)modified histone peptides in LC-MS/MS runs . The sofvailable . WERAM p species . This ton yeast) . This daDifferent from the databases described above, HistoneDB2.0 and MS_HistoneDB are databases based on histones sequences. HistoneDB2.0 with variants is a manually curated database that comprehends canonical histones and histone variants, their sequence, structural, and functional features . It alloAnalysis of proteomics data can be performed with a chromatin binding perspective by loading the regulated subproteome identified into software tools that predict or match specific chromatin-binding domains. In general, specific protein domains can provide insights into protein specific functions. There is a lot known about proteins with domains that recognize either chromatin modifications or DNA motifs and there are software tools that computationally predict these domains. SMART (Simple Modular Architecture Research Tool), Pfam, and Prosite databases are resources for identification and annotation of complete protein domains and the analysis of domain structure ,131,132.The focus of databases is to display sets of highly curated data and make this information available to the research community. However, one of the challenges relies on the fact that databases require constant updates based on new experimental data and it is not always feasible to keep them curated. Although they contain a large amount of useful data, ChromDB was last updated in February 2015, HIstome in October 2017, and DbHiMo in 2015.Chromatin biology has been immensely enriched by the development of new technologies to analyze chromatin state. Those methods, spanning from ATAC-seq to super resolution microscopy, have allowed to observe new quantitative dimensions of chromatin in health and disease. Mass spectrometry has yet to emerge as routine method to quantify chromatin state and define the composition of (in)accessible chromatin domains. However, it is of paramount importance to understand which proteins occupy specific chromatin domains in health and disease. Each protein has specific functions in the cell depending on its localization and its interactions, and this is intuitively true also for chromatin occupancy. Therefore, protein analysis needs to become a regular analysis when investigating chromatin regulation. In this review, we highlighted the approaches that are currently emerging that combine proteomics with state-specific chromatin analysis, aiming to increase awareness about the importance of analyzing DNA from the protein perspective.


The outer layer of the skin, the epidermis, is the principal barrier to the external environment: post-mitotic cells terminally differentiate to form a tough outer cornified layer of enucleate and flattened cells that confer the majority of skin barrier function. Nuclear degradation is required for correct cornified envelope formation. This process requires mRNA translation during the process of nuclear destruction. In this review and perspective, we address the biology of transcriptional bursting and the formation of ribonuclear particles in model organisms including mammals, and then examine the evidence that these phenomena occur as part of epidermal terminal differentiation. The principal function of the outer layer of the skin, the epidermis, is to provide a barrier to the external environment. The majority of this barrier is formed from dead but functional cells called corneocytes. In order for keratinocytes in the basal layer of the epidermis to differentiate into corneocytes, they undergo wide-ranging morphological and biochemical changes. They become rigid, water sealed and resilient by undergoing a developmental program which involves expressing large amounts of structural proteins as well as an array of enzymes which are used to covalently cross-link components of the cornified envelope (CE) ,2. The CChanges in gene expression observed in keratinocytes during terminal differentiation are orchestrated by a massive upsurge in transcriptional and translational activity. This is demonstrated by gene expression studies using immunofluorescence and in situ hybridisation for single genes, such as with the change in keratin pairs being expressed in differentiating epithelium . We haveKeratohyalin granules are electron-dense bodies observed in the upper layers of the epidermis. For structures recognised by pathologists for over 100 years, the function and formation of these bodies are remarkably unclear. Loss of these bodies is frequently associated with impaired epidermal terminal differentiation. Could they be RNP granules as well as the main region of filaggrin localisation in the epidermis ,7 and poDictyostelium discoideum [E. coli [One of the big mysteries of biology is why cells within a seemingly homogenous population have very heterogenous gene expression. Studies in the late-1990s and mid-2000s ,9 showedscoideum , E. coli[E. coli , and in [E. coli , demonstTranscriptional bursting behaviour is mainly determined by the probabilistic nature of the random events required for gene expression to occur, and the low numbers of molecules that are involved ,14. For In single-celled organisms, stochastic cell fate decisions allow for variability in the population, providing greater heterogeneity for cell survival despite the cells being genetically identical. This has been observed in bacteria, where a small minority of the population can enter a transient state where they can take up DNA under stress ,24. ThesNanog reporter line in mice showed that mESCs may have a very permissive chromatin environment for transcriptional bursting, and this behaviour may help them maintain their pluripotent state, or help decide cell fate stochastically [The control of developmental transition in metazoans also appears to be controlled by pulsatile gene expression ,26. Noisstically .A major issue in the field is that the analysis of in vivo transcription dynamics has mainly been investigated with reporter genes and only a small number of endogenous genes. A popular system used to obtain real-time transcriptional event information is the MS2 stem–loop reporter system. This combinatory genetic/fluorescent system uses the natural interaction of stem–loop RNA structures, contained in the MS2 phage genome, with fluorescently labelled phage coat protein. Inserting phage-loop RNA at the end of a reporter gene, and co-incident expression of the tagged phage coat protein allows visualisation of single gene transcription events .Single-cell RNA sequencing (scRNA-seq) can be used to probe transcriptional bursting in systems to complement the validation of these events using fluorescent reporter gene systems. In a mammalian lymphoblastoid cell line one group used a ‘pool and split’ technique of sequencing both single cells and sequencing lysed cells in pools (30 and 100 cells) of varying sizes. From this it appeared that scRNA-seq can capture some genes that are stochastically expressed due to biological reasons, not just technical noise . MonoallIn order to understand the experimental data produced by these experiments, gene bursting in eukaryotes has been modelled in various different ways. The use of two or more states to reflect the different conditions any gene can be in, such as varying degrees of activity, is central to this modelling. In two-state models, a gene can be either in an ‘on’ or ‘off’ state, one of the most notable being Peccoud and Ycart’s two-state telegraph model, which posits that a promoters state is controlled by intrinsic variability in transcription initiation . The twoTranscriptional bursting has additionally been linked to the resource limitations cells experience, and the way they share resources for processes . TerminaAs mentioned above, bursts can be defined by several parameters including their duration, size and frequency. Another metric that is also being used is ‘burst fraction’, the proportion of time each transcription site transcribes the gene within a cell population ,45. AtteDictyostelium discoideum MS2 reporter genes revealed that the promoter is the dominant factor for determining bursting behaviour [Drosophila shows two gene promoters can synchronise bursting when activated by the same enhancer [In ehaviour . Howeverehaviour . Work inenhancer , suggestenhancer  showed tenhancer .In mouse fibroblast cells, longer gene loci have smaller-sized bursts and certain promoter elements, such as a TATA box, have a dominant effect on burst size . The samReporter genes inserted into the genome will show bursting, whilst the same reporter on a plasmid does not . RandomlAnother study has shown that raising the number of chromatin contacts on the β-globin gene promoter increases the burst fraction, but not the burst size during erythroid maturation if you force those contacts . FurtherNeurospora involved the development of simple models studying the kinetics of bursting in relation to TF binding [Transcription factors (TFs) are essential to the assembly of the basal transcription machinery on gene promoters. Therefore, TFs may be one of the earlier points by which bursting behaviour is regulated dynamically. Imaging has provided evidence that local transacting factors at the gene promoter can affect the frequency but not the size of bursts . However binding . It foun binding .External factors, such as intercellular signals can also have an effect on the measurable parameters of transcriptional bursting. In vitro, culture conditions such as inhibiting signalling pathways can result in changes in transcriptional burst size and frequency. The stochastic expression of Nanog in mouse embryonic stem cells was affected when changing between serum and inhibited conditions  and consDictyostelium [A variety of signalling pathways have been shown to influence bursting behaviour. Natural cAMP concentration fluctuations oscillate with the same period as transcriptional bursting activity in ostelium . Endocriostelium , the gluostelium , and traostelium  in mammaostelium ,71. Keraostelium ,73 as weNeurospora, and has been shown to allow fast responses to stimuli [Mammalian transcription appears to be dominated by bursting. Experiments with lentiviral vectors in mammalian cells analysing 8000 different genomic loci found that episodic bursts of transcription occurred at almost all of them . However stimuli .Studies in mouse fibroblast cell lines found burst frequency and size are uncoupled, indicating they have differing molecular controls . A transTranscriptional noise in the mouse embryo is highest before cells become committed to a differentiation pathway, and subsides after commitment . TherefoIn a similar fashion to keratinocytes, lens epithelial cells have a program of differentiation that requires the removal of all organelles, coincident with high expression of crystallin genes required for lens transparency. Possible sites of crystallin genes are ‘protected’ from degradation until the last possible moment . In thesA model used to control for factors such as cell size and other cell state features showed, however, that the contribution of transcriptional bursting may be over exaggerated in many studies . The modThe overall noise profiles of eukaryotic genes are likely due to a interplay of transcriptional and translational regulatory events . Steady-Recent data have shown that membraneless bodies which behave like phase-separating bodies can be formed from filaggrin repeats in granular layer keratinocytes . AblatioThe term biomolecular condensates (BMCs) is used to describe subcellular compartments within eukaryotic cells which are non-membrane bound . They arBMCs are formed by cooperative interaction between polynucleotides and proteins. Mixtures of RNA and RNA-binding proteins form these condensates in vivo ,102,103.The internal structures of BMCs are not homogenous. For example, co-existing subcompartments within the nucleolus have been identified. Multilayered liquid droplets may facilitate sequential reactions with differing conditions, such as rRNA processing in the nucleolus. These subcompartments form droplets in vitro when components are isolated, and will even form a multiphase droplet when recombined, recapitulating an approximation of their in vivo conditions . AdditioBMCs do not use lipid-based membranes to separate themselves from their surrounding media, and instead a model based on liquid–liquid phase separation (LLPS) has been developed to understand their behaviour and dynamics . HoweverTwo cytosolic examples of these BMCs are P bodies (PBs) and stress granules (SGs) which are RNP granules that typically form in the cytosol as a consequence of altered RNA homeostasis ,112,113.SGs and PBs are considered distinct organelles. However, a percentage of them within cells are found docked against each other ,119. GivSaccharomyces cerevisiae with an Edc3p deletion show a strong reduction in the number of P bodies observed [Mutating or deleting BMC component molecules will reduce granule formation in vivo ,121. Forobserved , and mamobserved . These pPBs and SGs are known to form due to altered RNA homeostasis ,112,113.Housekeeping genes are generally sequestered in RNPgs. This could be a way of freeing up polysomes for massive translational upregulation of tissue-specific genes. This could allow the setting up of expression gradients in development. In keratinocytes, this could be a way of prioritising the genes required during epidermal terminal differentiation. Tissue- or cell-specific RNPgs have been identified, with Balbiani bodies and germ granules being exclusively found in germ cells, and RNA-transport granules exclusive to neuronal cells. The BMCs observed by Quiroz et al.  may be aIt should be considered that the phospho-Lamin A-positive cytoplasmic RNP granules are likely to be symptomatic of higher expression but may not regulate it. This may primarily have an effect on translation, Therefore the presence of these granules could affect the proteome, rather than the transcriptome, by controlling protein concentration noise .The similarities between how lens epithelial cells and the keratinocytes differentiate would suggest that there is a clear role for RNPgs and pulsatile expression of genes. How do these phenomena intersect with processes occurring during epidermal differentiation, in particular the destruction of the nucleus?While the field of transcriptional bursting has been rapidly adding to the evidence for the phenomena there are some good examples that indicate that while transcription may occur in bursts, protein abundance resulting from this mRNA bursting is negligibly affected ,84. As nRNP granules appear as spherical objects, often forming a ‘corona’ where multiple key components are formed, with distinct layers forming the outer shell and inner core . ImagingAdditionally, there are other lines of evidence phase-separating condensates may play a role in keratinocyte terminal differentiation. Actin is actively remodelled during the epidermal terminal differentiation process. AKT1-phosphorlyated HSP27 stops stabilising cortical actin leading to collapse of the network concomitant with nuclear destruction . The ARPKeratinocytes are under a number of different stresses in the final stages of term differentiation: modified apoptosis, autophagic destruction of the nucleus and organelles , oxidatiStress granules have been proposed to modulate some pathways that are key to keratinocyte terminal differentiation such as the mTOR, as well as RACK1 and TRAF2 signalling pathways. They may modulate these pathways by sequestering components of the pathways into a condensate ,134,135.PSORS1 locus, a risk locus for psoriasis, could modulate the localisation and function of P bodies in patient keratinocytes that were positive for the SNPs associated with PSORS1 [Another approach to understanding a possible role of ribonuclear granules and transcriptional bursting in epidermal differentiation and nuclear destruction is looking at diseases that have impaired terminal differentiation and nuclear degradation, such as psoriasis and atopic dermatitis that are common skin diseases that display hyperkeratosis (impaired stratification) and parakeratosis (nuclei retained in the granular layer of the epidermis). Coiled-Coil alpha-helical Rod protein 1 (CCHCR1) is a component of P bodies , and in h PSORS1 .Additionally, lens epithelium-derived growth factor/dense fine speckles 70 kDa protein LEDGF/DFS70, is a major autoantigen in atopic dermatitis, with 30% of patients having antibodies to this protein. LEDGF activates expression of crystallins in lens epithelial cells ,145. LEDEukaryotic transcription appears to be dominated by stochastic bursts, suggesting that genes involved in keratinocyte terminal differentiation could be transcribed stochastically. Transcriptional events could occur stochastically within the terminally differentiating population of granular keratinocytes, much like other documented cell types, which explore differentiation states before commitment ,77,78. GTranscription in terminally differentiating keratinocytes could potentially continue beyond nuclear permeation, meaning these bursts would be unbuffered by the nuclear envelope and export mechanisms involved in normal RNA transit. This would lead to rapid concentration spikes of mRNA in the cytosol, unbuffered by NPC export, potentially allowing local concentrations of RNA and protein to phase separate, which could form granules. We show RNA localising to bodies in the cytosol of granular keratinocytes concomitant with lamina degradation . These bThese putative RNP granules could form a sink for mRNAs being transcribed , before


We found diversity in the genomic binding profiles of different Hox TFs, even among the posterior group paralogs that share similar DNA-binding domains. These differences in genomic binding were explained by differing abilities to bind to previously inaccessible sites. For example, the posterior group HOXC9 had a greater ability to bind occluded sites than the posterior HOXC10, producing different binding patterns and driving differential gene expression programs. From these results, we propose that the differential abilities of posterior Hox TFs to bind to previously inaccessible chromatin drive patterning diversification.Although Hox genes encode for conserved transcription factors (TFs), they are further divided into anterior, central and posterior groups based on their DNA-binding domain similarity. The posterior Hox group expanded in the deuterostome clade and patterns caudal and distal structures. We aimed to address how similar Hox TFs diverge to induce different positional identities. We studied Hox TF DNA-binding and regulatory activity during an The people behind the papers’ interview.This article has an associated ‘ Highlighted Article: Analyses of Hox transcription factor (TF) DNA-binding and regulatory activity demonstrates that the ability of mammalian posterior Hox TFs to bind to previously inaccessible chromatin is the predominant force driving their patterning diversification. HoxA, HoxB, HoxC and HoxD). Each cluster contains a subset of 13 similar paralogous Hox genes, genomically arranged in the same linear order as their spatial and temporal expression patterns in the developing embryo, a phenomenon known as collinearity (Hox genes encode a highly conserved transcription factor (TF) family that endows cells with positional identity during embryonic development . In mamminearity . ChangesDrosophila there is only one Abd-B, whereas several Abd-B-related Hox genes assign different posterior positional identities during vertebrate development (Hox genes encode for similar homeodomain (HD)-containing TFs . HDs areelopment   and central Hox paralog groups (HOX6-8) prefer to bind the canonical TAAT core sequence, whereas the posterior paralog groups (HOX9-13) preferentially bind TTAT core sequences . To mitigate this technical limitation, we opted for an embryonic stem cell (ESC) differentiation system that recapitulates ventral spinal cord early development . In respHoxa5 . MoreoveHoxa5 . Thus, EDrosophila gene. From these results, we propose that the differential abilities of posterior Hox TFs to bind to previously inaccessible chromatin is the predominant force driving their patterning diversification.To understand how Hox TFs control different fates, we induced individual Hox TFs in progenitor MNs. We analyzed the differential binding of the Hox TFs in the context of their underlying sequence motifs and interactions with the pre-existing chromatin accessibility environment. We focused on a subset of HOXC TFs  due to their importance in inducing spinal cord identities. We complemented these studies by examining additional HOX9 paralogs and the posterior HOXC13. Our results suggest that limb-innervating fate is not the product of identical central and posterior Hox binding patterns, as HOXC10 does not mimic HOXC6 and HOXC8 binding profiles. Although posterior group Hox TFs have similar DNA motif preferences, they do not bind to the genome with identical patterns. This difference is mainly due to their differing abilities to bind motifs occluded in inaccessible chromatin. For example, HOXC9 and HOXC10 bind similar DNA sequence motifs, whereas HOXC9 has a greater ability to bind previously inaccessible chromatin. In summary, our work describes divergence in the abilities to engage inaccessible chromatin among vertebrate posterior group Hox factors derived from a single Drosophila ortholog, Abd-B, yet they pattern different spinal cord fates. We focused our attention on a subset of HoxC genes due to their cardinal spinal cord patterning activities , Hoxc8 , Hoxc9 (thoracic) or Hoxc10 (lumbar) upon doxycycline (Dox) treatment   .Fig. 1.As time progresses, cells become neurons, peaking at 48 h after Dox addition when the culture mostly consists of postmitotic MNs as well as interneurons. We refer to these as iHoxc6, iHoxc8, iHoxc9 and iHoxc10 neurons. Importantly, all Hox proteins are N-terminally Flag-tagged, which allows for immunoprecipitation with the same antibody, eliminating any bias that could occur from different antibody affinities.Hoxc6, Hoxc8 and Hoxc9 inductions during in vitro MN differentiation have been individually characterized with expression changes in a few downstream genes, and they produce the expected phenotypes . In a principal component analysis (PCA), the first two principal components explained 80% of the variance in RNA-seq tag counts, reflecting a combination of the paralog group and the subtype identities specified by the Hox proteins  and iHoxc9. Multidimensional scaling (MDS), a non-linear dimensionality reduction technique, produced a similar lower-dimensional embedding separating each inducible Hox line . Overall, these data show that Hox TFs induce distinct gene expression profiles during in vitro spinal cord differentiation, with an increasing transcriptional diversity by posterior Hox TFs .HOXC6, HOXC9 and HOXC10 divide the spinal cord into three important levels: brachial, thoracic and lumbar, respectively. enotypes . To haveproteins C. Contro control C. The slHoxc6, Hoxc8 and Hoxc10 overexpression induced canonical lateral motor column (LMC) markers Raldh2 and FoxP1   . Importantly, Hox induction during in vitro spinal cord differentiation did not derail the ability of cells to acquire an MN identity . Thus, Hox TF activity during ESC differentiation induces distinct spinal cord fates, recapitulating aspects of embryonic differentiation.Although there is no global gene expression characterization of Hox expression manipulation during embryonic development, iHox lines induced the expression of marker genes in agreement with previous studies. For example, . S2A,B) . Hoxc9 o. S2A,B) . The varFig. S1A). These time points are crucial for Hox positional identity patterning because Hox TFs control MN types at early postmitotic states , we found that the posterior HOXC9 and HOXC10 displayed divergent genomic binding patterns . HOXC10 primarily bound a subset of HOXC9 binding sites; while 90% of the top 10,000 binding sites for HOXC10 showed similar enrichment levels for HOXC9 and HOXC10, an additional 5230 sites were bound preferentially by HOXC9 . Similarly, HOXC6 and HOXC9 bound to some sites at similar levels but there were also sites differentially bound by HOXC6 or HOXC9 . Thus, the different patterning abilities of central and posterior Hox proteins might be explained in part by differential genome-wide binding profiles.Next, we compared the binding patterns of the central HOXC6 TF versus each of the posterior Hox TFs, HOXC9 and HOXC10. Although both induce limb-innervating spinal cord fate, HOXC6 and HOXC10 do not have identical binding patterns. Although there are sites that HOXC6 and HOXC10 bind at similar levels, there are also unique HOXC6 and HOXC10 sites in differentiating cells . This analysis revealed that 4892 sites were bound similarly by HOXC6, HOXC9 and HOXC10 (‘c6=c9=c10’) , and Hox TF binding patterns were mostly the same in newborn and young postmitotic MNs .To better characterize the diversity of sites bound by the various Hox TFs, we performed a joint differential binding analysis for HOXC6, HOXC9 and HOXC10 (c9=c10’) E. HOXC6 Altogether, we found a set of sites that are bound by all assayed Hox TFs, regardless of paralog group or fate inducing activity. The brachial HOXC6 and thoracic HOXC9 bind additional sets of unique sites. Finally, although the posterior HOXC10 and HOXC9 are predicted to share sequence specificity, HOXC10 cannot bind to a large fraction of HOXC9 sites.in vitro binding preference studies reported that anterior and central Hox paralogs prefer the TAAT core motif, whereas the posterior paralogs prefer the TTAT motif . The distribution of progenitor ATAC-seq read density at HOXC6, HOXC8, HOXC9 and HOXC10 sites revealed that HOXC9 binding sites had the lowest median accessibility before each factor is induced .The difference in the abilities of Hox TFs to bind to inaccessible chromatin prompted us to investigate whether Hox TFs change the accessibility landscape after binding. Would the ability of HOXC9 to bind inaccessible sites be coupled with increasing accessibility after binding? To characterize the accessibility changes after Hox binding, we compared the accessibility status of progenitors and postmitotic neurons at Hox binding events  gained accessibility in both iHoxc9 and iHoxc10 but did not significantly change in iHoxc6 neurons B,C. Stripression B,C. Conspression B,C. AccoThe dynamic accessibility changes after Hox binding revealed that not all Hox TFs have the same ability to modify chromatin accessibility. Among those we analyzed, HOXC9 stands out in its ability to bind to a large set of sites in relatively inaccessible chromatin and increase the accessibility status after binding.We next wondered whether the ability of HOXC9 to bind to a large set of previously inaccessible sites is unique among posterior Hox TFs. Thus, we compared the binding of HOX9 paralog group TFs. A comparison of induced HOXA9 and HOXD9 ChIP-seq using MultiGPS revealed that they share a majority of their binding sites A. HoweveHox13 paralogs are also posterior group genes but have the unique ability to terminate axial elongation. To assess whether HOXC13 would bind like the other posterior Hox TFs, we compared HOXC9, HOXC10 and HOXC13 genomic binding overlaps . Motif analysis at these sites revealed that HOXC13 binds distinct motifs containing the TTTAC sequence , in agreement with previous in vitro binding characterizations . PC1, which explains 43% of the variance between the TFs, separated HOXC13 from the central Hox TFs, HOX9 paralogs and HOXC10. On the other hand, PC2 and PC3, which cumulatively explain 32% of the variance, separated the TFs into three clusters. Although distinguishable, HOXC6 and HOXC8 clustered close to each other. HOXC9 clustered by itself, whereas HOXC10, HOXA9 and HOXD9 clustered together.To visualize the overall variation in Hox TF genome-wide binding profiles, we performed PCA on Hox TF ChIP-seq read counts associated with the top 10,000 binding sites for at least one Hox TF , or differing in both aspects (HOXC9 versus HOXC13).We wondered if variation in the HD is largely responsible for the differential abilities of Hox TFs to engage inaccessible chromatin. Thus, we generated chimeric Hox proteins by following the previously published logic of swapping the N-terminus (N) and DNA-binding domain+C-terminus (HD+C) . We chosFig. S6A). HOXC8 and HOXC10 predictions benefit from included previous chromatin data, and HOXC6 and the other HOX9 paralogs (HOXA9 and HOXD9) display substantial gains in predictive performance when training includes chromatin tracks. These results support the hypothesis that even Hox TFs from the same group rely on previous chromatin states to different degrees for their genomic binding.Finally, to gain an overview of the relative dependence on the pre-existing chromatin environment of each Hox TF, we applied Bichrom to analyze the data  preprintP-value<0.01) between RNA-seq derived gene sets and Hox TF binding categories. Genes that are equally upregulated in iHoxc6, iHoxc9 and iHoxc10 neurons associated strongly with c6=c9=c10 sites, with some association with c6>c9,c10 and c9,c10>c6 sites as well . Overall, these results suggest that similar cell fates are not always the product of identical Hox TF binding patterns. HOXC10 sequence preference is in line with the posterior group. However, the ability of HOXC10 to bind to inaccessible chromatin is more similar to central limb-innervating HOXC6 and HOXC8. Thus, HOXC10 diverges from HOXC9 by having a lower preference for inaccessible chromatin. The high preference of HOXC9 for inaccessible chromatin is not shared by other HOX9 paralogs. Thus, the results from this study may point to how members of the same paralog group diverge their patterning abilities. HOXC9 is a strong repressor of anterior Hox genes and limb-innervating fates ,2014. Hopoptosis . Our datin vitro differentiation strategy and forced expression of a single Hox gene does not recapitulate limb-innervating LMC neuron patterning into specific pools, nor does it induce thoracic PGC fate after Hoxc9 overexpression . MEIS binding appears to follow or reflect Hox binding, as opposed to being exclusively associated with particular subsets of differentially bound sites.In agreement with  neurons . Central neurons A. An int neurons . Besides neurons . DiffereFig. S2A,B). We attempted similar experiments with PBX factors in two of the inducible Hox lines, but the PBX antibody produces a weak ChIP-seq signal . We also failed to detect a CTCF motif at Hox binding sites, which is reported to co-bind with some HOXA/D proteins ; and HOXC13:HOXC10.2 has amino acids 1-230 of HOXC13 and amino acids 232-342 of HOXC10 . HOXC10:HOXC13 chimeras have the following protein sequences: HOXC10:HOXC13.1 has amino acids 1-267 of HOXC10 and amino acids 260-330 of HOXC13 ; and HOXC10:HOXC13.2 has amino acids 1-231 of HOXC10 and amino acids 231-330 of HOXC13 . cDNAs were amplified using Phusion polymerase and Flag tags were introduced during the amplification step at the N. p2lox plasmids were generated by In-Fusion cloning (Takara) the respective cDNAs into the p2lox plasmid backbone. The ICE system was used to generate cell lines .2. In vitro differentiation of mESCs to MNs has been described previously (2 (day −2). On day 0, EBs were split 1:2 and AK medium was replenished and supplemented with 1 μM all-trans RA and 0.5 μM smoothened agonist (SAG) . TF induction was performed by adding 3 μg/ml of Dox  on day 2. For RNA-seq and ATAC-seq experiments, 3.5×105 mESCs were plated in 100 mm suspension dishes (Corning). For ChIP-seq experiments, 3-3.5×106 mESCs were plated in 245 mm×245 mm square dishes (Corning).mESC lines were cultured in 2-inhibitors-based medium  on 0.1% gelatin-coated (Millipore) plates at 37°C and 8% COeviously . Briefly6 aliquots, pelleted by centrifugation at 275 g and frozen at −80°C. After thawing cells on ice, lysis was performed in 5 ml of 50 mM HEPES-KOH (pH 7.5), 140 mM NaCl, 1 mM EDTA (pH 8.0), 10% glycerol (v/v), 0.5% Igepal (v/v), 0.25% Triton X-100 (v/v) with 1× protease inhibitors  for 10 min at 4°C. Cells were centrifuged at 1200 g for 5 min, resuspended in 5 ml of 10 mM Tris-HCl (pH 8.0), 200 mM NaCl, 1 mM EDTA (pH 8.0), 0.5 mM EGTA (pH 8.0) with 1× protease inhibitors, and incubated for 10 min at 4°C on a rotating platform. Cells were centrifuged at 1200 g for 5 min and resuspended in 2 ml of sonication buffer . Sonication was performed by splitting each sample in two Bioruptor tubes with sonication beads and using the Bioruptor Pico (Diagenode) for 18 cycles of 30 s on and 30 s off to sheer crosslinked DNA into an average size of ∼200 bp. Immunoprecipitation was performed for 16 h at 4°C on a rotating platform by incubating in 0.5% bovine serum albumin solution with Dynabeads protein G (Thermo Fisher Scientific) conjugated with 5 µg of one of the following antibodies : mouse monoclonal to Flag ; rabbit polyclonal to HA ; rabbit polyclonal to V5 ; mouse monoclonal to Pbx1/2/3/4 ; or goat polyclonal to Meis1/2 . Following the immunoprecipitation, washes were performed with the following buffers (ice cold): sonication buffer; sonication buffer with 500 mM NaCl; LiCl wash buffer ; and TE buffer [10 mM Tris-HCl (pH 8.0) and 1 mM EDTA (pH 8.0)]. Elution was performed by incubation in elution buffer  for 45 min at 65°C. Reversal of crosslinks was performed by incubation for 16 h at 65°C. RNA digestion was performed by the addition of 200 μl of TE and RNase A (Sigma-Aldrich) at a final concentration of 0.2 mg/ml, and incubation for 2 h at 37°C. Proteinase K (Invitrogen) was added at a final concentration of 0.2 mg/ml, supplemented with CaCl2, to digest protein at 55°C for 30 min. DNA was purified with phenol:chloroform:isoamyl alcohol  (Invitrogen) and by performing ethanol precipitation. DNA pellets were resuspended in water. lllumina DNA sequencing libraries were prepared with one third of the ChIP sample or a 1:100 dilution of the input sample in water. Library preparation was performed by end repair, A-tailing and ligating Illumina-compatible Bioo Scientific multiplexed adapters. Unligated adapters were removed using Agencourt AmpureXP beads (Beckman Coulter). Amplification was performed by PCR with Phusion polymerase (New England Biolabs) and TruSeq primers (Sigma-Aldrich). Libraries were gel purified (Qiagen) between 250 and 550 bp in size. Final quantification of the library was performed using a KAPA Library Amplification kit on a Roche LightCycler 480 before pooling. The libraries were sequenced on an Illumina NextSeq 500 using V2 and V2.5 chemistry  or on an Illumina NovaSeq 6000 using the SP Reagent Kit  at the Genomics Core Facility at New York University. The H3K27me3 and H3K27ac ChIP-seq datasets in Cells were collected 24 h and 48 h after Dox treatment (day 3 and 4 of RA/SAG differentiation). Crosslinking was performed at room temperature in 1 mM DSG (ProteoChem) for 15 min, followed by the addition of 1% FA (v/v) for an additional 15 min. After quenching with glycine, cells were washed with 1×PBS, divided into ∼25-30×10Cells were collected before TF induction (day 2 of RA/SAG differentiation) and 48 h after Dox treatment (day 4 of RA/SAG differentiation). RNA was extracted by using TRIzol LS Reagent (Life Technologies) and purified using the RNAeasy Mini Kit (Qiagen). Agilent High Sensitivity RNA Screentape  was used to check RNA integrity. A 500 ng quantity of RNA was used to prepare RNA-seq libraries and spiked-in with ERCC ExFold Spike-In mixes . RNA-seq libraries were prepared using a TruSeq Stranded mRNA Library Prep kit . Library size was verified using High Sensitivity DNA ScreenTape . The KAPA Library Amplification kit was used on a Roche LightCycler 480 for library quantification before pooling. The libraries were sequenced on an Illumina NextSeq 500 using V2.5 chemistry  at the Genomics Core Facility at New York University.2 and freshly added 0.1% NP-40 (v/v), and centrifuged at 500 g for 10 min at 4°C. Pellets were resuspended in 25 µl of 2× tagmentation DNA buffer, 2.5 µl TDE1  and 22.5 µl of water, and incubated at 37°C for 30 min. The sample was purified using the MinElute PCR Purification Kit . A qPCR reaction with 1× SYBR Green (Invitrogen), custom-designed primers and 2× NEB Master Mix  was performed to determine the optimal number of PCR cycles (one third of the maximum measured fluorescence) (Cells were collected before TF induction (day 2 of RA/SAG differentiation) and 48 h after Dox treatment (day 4 of RA/SAG differentiation). Cells  were aliquoted and washed twice in ice-cold 1× PBS. Cell pellets were resuspended in 10 mM Tris (pH 7.4), 10 mM NaCl, 3 mM MgClescence) . PCR enrChIP-seq reads were aligned to the mm10 genome using Bowtie (v1.0.1) , using o2 fold change (LFC) in gene expression levels between iHox neurons versus day 2 progenitors or control neurons (no Dox treatment) was estimated using DESeq2. A q-value<0.01 and LFC>2 was used to define differentially expressed genes between day 2 progenitors, control neurons, iHoxc6, iHoxc8, iHoxc9 and iHoxc10 neurons. We filtered out genes that were not expressed in any iHox neuron, retaining 19,019 genes.Fastq files obtained from RNA-seq were aligned to the genome using the splice-aware STAR (Spliced Transcripts Alignment to a Reference) aligner (v2.7.0c) . Mapped www.github.com/seqcode/seqcode-core/blob/master/src/org/seqcode/projects/seed/DomainFinder.java).Paired-end ATAC-seq reads were mapped to the mouse mm10 genome using Bowtie2 (v2.2.2) . Genome-www.github.com/seqcode/seqcode-core/blob/master/src/org/seqcode/viz/metaprofile/MetaMaker.java). Raw reads from the ChIP-seq data were extended to 100 bp and read counts were binned into 100 bp bins. Binned reads were plotted over 1000 bp windows centered on MultiGPS binding events. Color thresholds used to produce heatmaps were determined from binding data using the MetaPlotLineMaxEstimator program in SeqCode. Specifically, all 100 bp bins were ordered by the number of reads overlapping each bin. The maximum value for the heatmap color scale was set to the number of read counts at the 85th percentile bin. The minimum value for all heatmaps was set to five reads. Finally, all binding events represented were ordered based on peak strength .Heatmaps were used to plot the ChIP-seq and ATAC-seq reads at multiple categories of genomic sites in iHox neurons. Heatmaps were made using the MetaMaker program in SeqCode  was plotted using Seaborn in Python.Fig. S2A). Volcano-style plots were used to simultaneously plot the LFC and P-values of significantly differentially expressed marker genes in the iHox versus control neuron comparisons . Volcano plots were used to represent the overall differential expression landscape between iHox and control neurons .The LFC of gene expression levels in iHox neurons versus day 2 progenitors or control neurons (no Dox treatment) was estimated using DESeq2. The LFC values were plotted for previously established marker genes in iHoxc6 neurons versus day 2 progenitors, iHoxc8 neurons versus day 2 progenitors, iHoxc9 neurons versus day 2 progenitors, iHoxc10 neurons versus day 2 progenitors and control neurons versus day 2 progenitors D. The LFFig. S4A. GREAT first calculates the probability that a randomly selected cis-regulatory element will be associated with a given GO term. It then uses a binomial test parameterized by this probability to determine whether a predefined subset of ChIP-seq peaks is significantly associated with that GO term. The process is repeated for all GO terms and a list of significant associations is returned   was usedreturned . We usedP<0.01), as well as not differentially expressed between the iHox neurons (|LFC|>2), were assigned to the gene set ‘shared-upregulated’. A similar logic was used to identify the ‘shared-downregulated’ genes. Pairwise comparisons between iHox neurons were used to identify genes significantly upregulated or downregulated (|LFC|>2 and adjusted P<0.01) in individual iHox neurons. Genes that were significantly upregulated or downregulated in two out of three iHox neurons were assigned to gene sets using the same thresholds.In order to construct gene sets, pairwise comparisons of gene expression levels between iHoxc6, iHoxc9 and iHoxc10 neurons versus day 2 progenitors were performed using DESeq2. Additionally, pairwise comparisons were also performed between the iHox neurons: iHoxc6 versus iHoxc9, iHoxc6 versus iHoxc10 and iHoxc9 versus iHoxc10. Genes that were upregulated in all iHox versus day 2 progenitor comparisons . Specifically, each peak was assigned to the gene with the nearest transcriptional start site. ChIP-Enrich uses a logistic regression model to test whether gene set membership (controlled by mappable gene length) predicts whether a gene is associated with a ChIP-seq peak . The sigrrection . For eacDe novo motif discovery was performed using MEME-ChIP (v. 5.1.0)  (. 5.1.0)  with the. 5.1.0)  via the (v. 1.0)  with set(v. 1.0) B. A moti(v. 1.0)  was usedwww.github.com/seqcode/iTF).For each Hox TF, we first trained a hybrid convolutional and long short-term memory neural network to predict induced Hox binding using only DNA sequence information (seqnet). We then applied Bichrom to the Hox TFs, which integrates DNA sequence and pre-existing chromatin to predict induced TF binding. In particular, Bichrom takes the trained sequence-only network and incorporates additional chromatin data using a secondary chromatin sub-network  preprint


However, coordinated cis and trans regulation of differentially expressed genes in ear and tassel controlling inflorescence architecture. We further systematically map chromatin interactions at high-resolution in corresponding tissues using in situ digestion-ligation-only Hi-C (DLO Hi-C). The extensive chromatin loops connecting OCRs and genes provide a 3D view on cis- and trans-regulatory modules responsible for ear- and tassel-specific gene expression. We find that intergenic SNPs tend to locate in distal OCRs, and our chromatin interaction maps provide a potential mechanism for trait-associated intergenic SNPs that may contribute to phenotypic variation by influencing target gene expression through chromatin loops.We identify 56,055 and 52,633 open chromatin regions (OCRs) in developing maize ear and tassel primordia using ATAC-seq and characterize combinatorial epigenome features around these OCRs using ChIP-seq, Bisulfite-seq, and RNA-seq datasets. Our integrative analysis of coordinated epigenetic modification and transcription factor binding to OCRs highlights the Our comprehensive epigenome annotations and 3D genome maps serve as valuable resource and provide a deep understanding of the complex regulatory mechanisms of genes underlying developmental and morphological diversities between maize ear and tassel. Usingpression , 6, 8–12pression , 14. Recpression –18. To e animals , 17, 19.chnology –25; howechnology , 26. Genchnology , 26.Zea mays) is a major crop worldwide with two types of inflorescence, the tassel and ear, which initiate by similar developmental processes but develop distinct architectures. Tassel and ear primordia originate from shoot apical meristems and axillary meristems, respectively [TASSEL BRANCHED 1 (TB1) is a well-known domestication gene that suppresses vegetative branching (tillers) [tb1 mutants develop more elongated tillers tipped with a male inflorescence, due to a lack of suppression of axillary meristems and of stamens. TB1 is more strongly expressed in stamen primordia of ear florets than in tassel florets and may suppress outgrowth of the ear. TASSELSEED1 and 2 (TS1 and TS2) encode hydroxysteroid dehydrogenases that participate in JA biosynthesis, and produce a cell death signal for pistil abortion in tassel florets [SILKLESS1 (SK1), is expressed specifically in upper florets of the ear, where it protects pistils from TS1- and TS2-mediated elimination signals [Maize (ectively . They arectively –31. Seveectively –38. TASStillers) –41; tb1  florets , 35. An  signals . Therefo signals . In addi signals –48. Howetrans- and cis-regulatory modules regulating ear- and tassel-enriched gene expression were associated with loops connecting OCRs and genes. We also found that many intergenic SNPs identified in GWAS for ear and tassel agronomic traits are in chromatin loops. Our results highlight how chromatin loops and epigenetic states of OCRs regulate gene expression to influence the different architectures and identities of the maize ear and tassel.In this study, we identified extensive local and distal OCRs in ear and tassel, respectively, and characterized their epigenetic features by investigating genome-wide chromatin accessibility, DNA methylation, and three types of histone modification. The dynamic chromatin states of OCRs affected TF access and were significantly associated with gene expression, which help explain the differences in ear and tassel architectures. We also generated high-resolution chromatin interaction maps using in situ digestion-ligation-only Hi-C (DLO Hi-C) method . A largetrans factors bind CREs in OCRs and regulate gene expression [Pearson’s correlation coefficients from 0.92 to 0.97) epigenome datasets showed that both H3K4me3 and H3K9ac modifications at dOCRs were weaker than those at LoOCRs in both ear and tassel . We next asked about the potential information content of OCRs and found that many putative TF binding motifs were enriched in LoOCRs and dOCRs, such as predicted TCP, LBD, SBP, HB, and EREB binding motifs  , and ANTHER EAR1 (AN1) [SK1, TS1, and TS2 [p < 0.01, Wilcoxon test)   , TB1, AN1, TS1, and TS2 [The discrepancy in architectures between ear and tassel is likely due to transcriptional differences. We identified 434 ear-enriched and 818 tassel-enriched genes from our generated reproducible RNA-seq datasets  , 36, 55, and TS2 , 35, 38. and TS2  , a domestication gene responsible for the reduced, soft glumes found in modern maize compared to teosinte, previously reported as a putative TB1 target [The OCRs with appropriate chromatin structure and environment serve as docks that allow TFs to bind specific DNA sequences and regulate gene transcription , 63. A l1 target , 68, andhttp://www.epigenome.cuhk.edu.hk/C3C4.html) ), JA response genes (such as jasmonate ZIM-domain protein ZmJAZ6 and ZmJAZ7) [TS2, ZmLOX5, and ZmAOS1c, the other target genes were also found to have more open chromatin states in tassel than in ear  from the pre-release cistrome database for 112 TFs’ ChIP-seq in maize leaf protoplast ( ZmJAZ7) , 69, andPearson’s correlation coefficients, 0.90 for ear and 0.93 for tassel) and total 3.2 and 2.2 billion raw read pairs for young ear and tassel, respectively  at 5 kb resolution in ear and tassel, respectively    , UNBRANCHED2 (UB2), and TUNICATE1 (TU1) that control inflorescence architectures [The extensive dOCRs in maize ear and tassel inspired us to investigate if the dOCRs could interact with their target genes by intergenic-gene loops. Interestingly, we found that more than 60%  of the intergenic anchors of all intergenic-gene loops contained at least one dOCR  and tassel-active dOCRs . We found that 34% (250/738) and 23% (318/1367) of ear- and tassel-active dOCRs were involved in dOCR-gene loops in ear and tassel, respectively  is known to regulate TB1 transcription to control branching [TB1 was more highly expressed in ear than in tassel, which helps elicit its suppression on outgrowth of stamen primordia in ear florets [TB1, respectively) overlapping with the genetically mapped distal region of TB1  has identified many genic and intergenic SNPs associated with agronomic traits in maize , 76. TheTERMINAL EAR 1 (TE1) and a GTPase activating protein (GAP)  Fig. d. te1 muing time . We alsoime Fig. e. In anoime Fig. f. Taken cis-CREs and trans-protein factors are likely to be a driving force during ear and tassel development. Genome-wide identifications of CREs based on open chromatin using DNase I hypersensitive sites (DHSs) and MNase-hypersensitive (MNase HS) regions have been carried out in Arabidopsis, rice, cotton, and maize, which greatly promoted the study of plant functional genome [Proper development of ear and tassel is critical for reproduction and grain yield in maize. At an early developmental stage, the ear and tassel have very similar morphologies, but eventually establish different architectures and identities . Exploril genome , 9, 24. l genome . The degl genome . A subsel genome , 78–80.TB1, GT1, AN1, SK1, and TS1 [In this study, we identified extensively potential CREs based on open chromatin regions using ATAC-seq and characterized their epigenome features in developing ear and tassel of maize. By integrating hundreds of DEGs between ear and tassel, we provide a valuable resource for understanding the regulation of inflorescence development and sex determination genes, such as  and TS1 –40, 55.  and TS1 , 59, 69.http://plants.ensembl.org/index.html). Interestingly, we found that 6 TF binding sites are overlapping with the LoOCRs of half of the tassel-enriched genes, indicating their key roles in tassel-specific regulation. A regulatory network of JA biosynthesis and responses derived from these 6 tassel-enriched TFs provides a critical view on the mechanism of JA signaling in controlling pistil abortion during tassel development. Taken together, our results demonstrate that TFs tend to bind the LoOCRs of target genes, and suggest these differentially expressed TFs are critical to determine inflorescence development and architecture.We identified several TFs with ear or tassel preferential expression, which inspired us to explore genome-wide TF binding sites. Using published ChIP-seq datasets generated from different tissues , we found an extensive overlap between TF binding sites and OCRs, suggesting that OCRs could act as a proxy for TF binding. Furthermore, the signal strength of open chromatin correlated with the strength of TF binding, indicating that OCRs can be used to predict TF access. We also found that a considerable number of binding sites overlap between different TFs, suggesting that they can function together to regulate transcription. To be noted, using the ChIP-seq datasets generated from different tissues may underestimate the differences of TF binding patterns in ear and tassel, although we only considered the high-confident TF binding sites overlapping with ear/tassel OCRs for further analysis. In addition, the DNA binding profile of TB1, which is preferentially expressed in ear, gives us valuable information to investigate organ-specific regulatory pathways. To understand tassel-specific regulation, we examined six tassel-enriched TFs from pre-release maize cistrome data , and ZmCCT9, which are related to important agronomic traits, have been genetically mapped. These regions likely contain CREs and control their target gene expression [cis-regulatory elements in the seedling of maize [cis-regulatory modules [3D genome topology already provides a lot of information to investigate the genome structure and interaction between CREs and genes in animal , 19, 81.chnology , 25, 82.chnology , 26. In pression , 83, 84.pression  revealedof maize . Interes modules . In our TB1, an important domestication gene [TB1 and its dOCRs (~ 64 kb upstream) were more open and contained substantially fewer H3K27me3 modifications in ear than in tassel, indicating that these dOCRs and the TB1 gene may be co-modified due to their loop-driven spatial proximity. Our findings highlight a new mechanism for the enriched TB1 expression in ear. Furthermore, the binding of several TFs, including KN1, FEA4, and potentially bHLHs, EREBs, WRKYs, ARFs, and TB1 itself, to the dOCRs of TB1 may be involved in the dynamics of epigenetic features. Meanwhile, TB1 was enriched with H3K4me3 marks in only the ear. Thus, we speculate that the more active epigenetic features at the TB1 might be the reason for its more open state in ear, which would facilitate TF access and higher TB1 transcription in ear. In this way, the differential expression of TB1 can influence development and morphological differences between ear and tassel.The distal OCR to gene chromatin interactions identified in this study provides a mechanism for cooperative epigenetic modifications and transcription factor binding. In the case of ion gene , both thcis regulation. Furthermore, some of these intergenic SNPs located in dOCRs, suggesting they may influence the functions of CREs and their target genes. Chromatin loops identified in this study provide valuable information on connections between these trait-associated intergenic SNPs and their potential target genes, important for understanding the regulatory mechanism of these SNPs. As an interesting case, a SNP for flowering time immediately adjacent to one dOCR was found to form a loop with the gene, TE1, whose mutation interrupts leaf initiation and leads to form more leaves, a process tightly related to flowering time [TE1 was also found to interact with another dOCR and the gene GAP through chromatin loops which provided a spatial regulatory module for a comprehensive understanding of TE1 and its components on the control of flowering time and the relevant traits. These results demonstrate that trait-associated intergenic SNPs can be linked to target genes to understand their potential roles in phenotypic variation.In recent years, several GWASs have identified a large number of ear and tassel trait relevant SNPs , 76, witing time . Particucis and trans regulation of target genes. Integrated analysis of coordinated epigenetic modifications and TF binding on distal and local OCRs showed that the 3D genome topology can coordinate trans and cis regulation of differentially expressed genes in ear and tassel. In addition, agronomic trait-associated intergenic SNPs can be connected to potential target genes via loops that may control phenotypic variations. These findings provide insights into ear- and tassel-specific regulatory pathways and can be utilized in crop improvement.In summary, our comprehensive epigenome characterization on distal and local OCRs, and high-resolution 3D genome maps, provides a deep understanding of the control of differential transcription underlying developmental and morphological diversity of the maize ear and tassel. Our genome-wide identification of OCRs aids an underlying CRE annotation to illustrate the dynamic cis and trans regulation on DEGs that may control ear and tassel inflorescence architecture and identity. We also identified extensive chromatin loops connecting OCRs and genes that provide a 3D view on cis- and trans-regulatory modules for ear- and tassel-specific gene expression. We finally propose a potential mechanism for ear and tassel trait-associated intergenic SNPs that may contribute to phenotypic variation by influencing target gene expression through loops. In a general perspective, our comprehensive characterizations on OCRs at epigenetic level, cis and trans levels, as well as at 3D view would provide a deep understanding of gene differential regulation underlying developmental and morphological diversity in maize.We generate high-resolution Hi-C maps and characterize epigenome features of OCRs in maize ear and tassel, which are the two types of inflorescences critical for corn yield. Our findings highlight the epigenetic features of OCRs and TFs binding to OCRs responsible for the Ear and tassel at 2–4 mm in length were harvested from maize inbred line B73 grown in the field. For Hi-C and ChIP-seq, the samples were cross-linked in PBS with 1% formaldehyde for 15 min under vacuum, and the fixation was terminated by adding glycine to 0.2 M for another 5 min under vacuum. Then, cross-linked samples were rinsed thrice with deionized water and immediately frozen in liquid nitrogen and stored at − 80 °C before use. The RNA-seq datasets had 3 replicates, and ATAC-seq, ChIP-seq, and Hi-C datasets had 2 replicates for each tissue. BS-seq had one replicate with sufficient sequencing depth (30×).The Hi-C libraries were constructed according to the published in situ DLO Hi-C protocol with some modifications . For eacB73 AGPv4 reference genome was used to analyze all the data. For Hi-C, we filtered linkers and mapped the sequences against the maize genome using ChIA-PET2 . UnmappeThe ATAC-seq libraries were constructed according to the previous study with some modifications . Intact Total RNA of ears and tassels were extracted using Direct-zol RNA Miniprep (Zymo Research), and libraries were constructed using NEBNext Ultra Directional RNA Library Prep Kit according to the instructions; BS-seq libraries were constructed as previously described . About 5The quality of raw sequencing data was controlled using Trimmomatic with parameters “LEADING:20 TRAILING:20 SLIDINGWINDOW:4:15 MINLEN:20” . The clehttp://systemsbiology.cau.edu.cn/agriGO v2/index.php).GO analysis was performed using Agrigo v2.0  , and the TF browser tracks were obtained from JBbrowse at the website above.ChIP-seq data for TB1, FEA4, and KN1 were downloaded from NCBI SRA or GEO . Reads were mapped to the maize genome version 4 using bowtie2 . After rFor LoOCRs and dOCRs, they were adjusted to the same size (200 bp) centered peak summits and de novo motif discoveries of these loci sequences were performed using DREME in the MEME suite with default settings. We selected the fully conserved TAD boundaries between ear and tassel for accuracy and focused on the motifs associated with OCRs in conserved TAD boundaries, given that the interactions between potential structural proteins and DNA are more likely located in easily accessible chromatin regions . These OTo investigate the correlation for gene pairs in chromatin loops, the Pearson correlation coefficient (PCC) of gene pairs was calculated using RNA-seq datasets of 65 different tissues from B73 . As a coAdditional file 1: Figure S1. Reproducibility analysis between replicates. Figure S2. Mapping of OCRs and epigenome marks. Figure S3. GO analysis of DEGs and dynamic LoOCRs and chromatin modifications associated with differential gene expression between ear and tassel. Figure S4. OCRs can be a platform for TF binding. Figure S5. Quality control of Hi-C data. Figure S6. Characterization of TADs. Figure S7. De novo motif analysis of TAD boundaries. Figure S8. Characterization of chromatin loops. Figure S9. Interaction between genes and their genetically mapped regions. Figure S10. Characterization of dOCR-gene loops in which some known genes are involved. Figure S11. Dynamic activities of dOCRs contribute to tissue-specific gene expression. Figure S12. Epigenetic features around distal trait-associated SNPs.Additional file 2: Table S1. Summary of data generated in this study. Table S2. Open chromatin regions in ear. Table S3. Open chromatin regions in tassel. Table S4. Differentially expressed gene with their changed epigenetic feature and bound TF in ear and tassel. Table S5. Differential OCRs. Table S6. Differential H3K4me3. Table S7. Differential H3K27me3. Table S8. Genes co-bound by TFs in ear. Table S9. Chromatin loops in ear. Table S10. Chromatin loops in tassel. Table S11. dOCR-gene loops in ear. Table S12. dOCR-gene loops in tassel. Table S13. Ear-active dOCR-gene loops. Table S14. Tassel-active dOCR-gene loops. Table S15. The number of genes looped by dOCRs. Table S16. The number of tissue-enriched genes looped by tissue-active dOCRs. Table S17. Significant ear and tassel trait-associated SNPs used for analysis.Additional file 3. Review history.


Mycobacterium tuberculosis and Crl from Escherichia coli suggest that activator-driven changes in σ conformation can be a widespread regulatory mechanism in bacteria.Transcription initiation is a key checkpoint and highly regulated step of gene expression. The sigma (σ) subunit of RNA polymerase (RNAP) controls all transcription initiation steps, from recognition of the −10/−35 promoter elements, upon formation of the closed promoter complex (RPc), to stabilization of the open promoter complex (RPo) and stimulation of the primary steps in RNA synthesis. The canonical mechanism to regulate σ activity upon transcription initiation relies on activators that recognize specific DNA motifs and recruit RNAP to promoters. This mini-review describes an emerging group of transcriptional regulators that form a complex with σ or/and RNAP prior to promoter binding, remodel the σ subunit conformation, and thus modify RNAP activity. Such strategy is widely used by bacteriophages to appropriate the host RNAP. Recent findings on RNAP-binding protein A (RbpA) from  Escherichia coli consensus motif T–12A–11T–10A–9A–8T–7) and −35 elements (E. coli consensus motif T–35T–34G–33A–32C–31A–30) of the promoter (70 in E. coli and σA in other species (Mycobacterium tuberculosis (Mtb)] (E. coli σS and Mtb σB) lack the N-terminal variable domain σ1.1 and have a shorter non-conserved region (NCR) located in domain σ2 (–7 base of the −10 element (70 NCR interacts with promoter DNA at positions –16/–17 (R157Eco) and is implicated in DNA unwinding (70 NCR/β′ interaction facilitates promoter escape (E. coli) contains the extended −10 motif  that is located one base upstream of the −10 element  holoenzyme from the catalytic core  and the promoter–specificity subunit sigma (σ). The RNAP holoenzyme binds to promoter DNA, and forms a closed promoter complex (RPc) that isomerizes into the open promoter complex (RPo) through several intermediates . Upon ispromoter  and prompromoter . All bac species ] that ens (Mtb)] . Alternas (Mtb)] . Compareomain σ2 . RNAPs homain σ2 . The σ2 omain σ2 . About 7 element  and cont element . The σ70nwinding . The σ70r escape . Domain r escape . Most ba element  and inte element  , but not to group 3 and group 4 σ subunits  even after assembly of the σB-RNAP holoenzyme (B (∼83 Å distance between σ2 and σ4), required for its tight binding to core MtbRNAP and to promoter DNA . Based on structure and hydrogen–deuterium exchange mass spectrometry analysis of σS conformation, S-RNAP holoenzyme assembly mainly by modifying σ2 conformation, but not through its contacts with the β′-clamp  promoters, and activates the expression of ∼200 genes that play an important role in cell cycle regulation during swarmer-to-stalked cell transition  is a transcription factor from ansition . Analysiansition . GcrA isansition . GcrA-DBpromoter . StructuChlamydia trachomatis. GrgA activates σA-  and σ28-dependent transcription by interacting with σ-NCR and binding to DNA in a non-sequence-specific manner  is a transcription factor from the human pathogen c manner . The Grg 269–316   measurements demonstrated that in the P7-σ70-RNAP complex, the σ70 subunit adopts a closed or partially closed conformation . Scc4 inhibits transcription initiation from −10/−35 class promoters, but not from extended −10 type promoters. Although structural studies are needed to determine the mechanism of inhibition, on the basis of similarities with the mechanism of action of the phage proteins we hypothesize that Scc4 disrupts the σ4/Flap interaction and prevents RPo formation at −10/−35 promoters.Scc4 (ORF CT663) is a ∼15-kDa protein from the human pathogen nfection . Scc4 waP β-FLAP . Scc4 biWe can draw two basic principles of transcription regulation by RPB-TFs: positive regulation through strengthening of σ2/β′10 element interactions, and negative regulation through weakening of σ4/β-flap/−35 element interactions. All contacts made by the three σ-activators RbpA, Crl and GcrA overlap and are clustered in four σ regions  that are responsible for core RNAP binding and −10 element recognition/melting . ConsequAt least for RbpA, the stimulation of the “closed-to-open” transition is part of the σ activation mechanism required for efficient transcription initiation at the −10/−35 class promoters, but not at the extended −10 class promoters . It remaThe σ-activators and σ-repressors illustrate how σ conformational dynamics, controlled by contacts with core RNAP, can be used for fine-tuning transcription in a lineage-specific manner. Considering the huge diversity in lifestyles of bacterial species, the number of the currently known σ-regulators of bacterial origin is strikingly low. The reason might be that most of these proteins are of small size and are not easy to detect. Yet, their discovery in pathogenic bacteria may offer new targets for developing pathogen-specific drugs. We expect that the number of the described σ-regulators and the diversity of regulatory mechanisms will continue to grow.KB and RV wrote the manuscript. Both authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.