Streptomyces research. Although the function of this polyketide is still unclear, we now know that its “cryptic” nature is attributed to a very complex and precise mechanism of cpk gene cluster regulation in the model actinomycete S. coelicolor A3(2). It responds to the stringent culture density and timing of the transition phase by the quorum-sensing butanolide system and to the specific nutrient availability/uptake signals mediated by the global (pleiotropic) regulators; many of which are two-component signal transduction systems. The final effectors of this regulation cascade are predicted to be two cluster-situated Streptomyces antibiotic regulatory proteins (SARPs) putatively activating the expression of type I polyketide synthase (PKS I) genes. After its synthesis, unstable, colorless antibiotic coelimycin A reacts with specific compounds in the medium losing its antibacterial properties and giving rise to yellow coelimycins P1 and P2. Here we review the current knowledge on coelimycin synthesis regulation in Streptomyces coelicolor A3(2). We focus on the regulatory feedback loop which interconnects the butanolide system with other cpk cluster-situated regulators. We also present the effects exerted on cpk genes expression by the global, pleiotropic regulators, and the regulatory connections between cpk and other biosynthetic gene clusters.Despite being a yellow pigment visible to the human eye, coelimycin (CPK) remained to be an undiscovered secondary metabolite for over 50 years of  Streptomyces are Gram-positive, filamentous bacteria that are potent producers of secondary metabolites—specialized compounds with adaptive functions (Traxler and Kolter www.secondarymetabolites.org/mining/) and revealed that the model organism Streptomyces coelicolor A3(2) could synthesize more than 20 secondary metabolites, many of them being still unidentified products of so-called cryptic or silent biosynthetic gene clusters (BGCs)  chromosomally encoded bioactive molecules, there are 4 antimicrobial compounds: coelimycin A , calcium-dependent antibiotic (CDA), red-pigmented undecylprodigiosin (RED), and blue-colored actinorhodin (ACT) : CpkO (formerly KasO) and CpkN (cpk cluster), CdaR (cda cluster), RedZ and RedD (red cluster), and ActII-orf4 (act cluster) , most of them acting on multiple biosynthetic pathways  genome sequence publication in 2002 allowed to annotate cpk cluster  was first identified in 1997 by DNA probe hybridization to acyltransferase domain specific for malonyl-CoA  secondary metabolism regulatory networks. The aim of this work is to extract, summarize, and comment on this information from the perspective of coelimycin synthesis regulation.Up to now, “big data” from numerous transcriptomic, proteomic, and chromatin immunoprecipitation (ChIP) experiments have provided fragmentary and scattered information about cpk cluster annotation was limited to SCO6269–SCO6288 genes , precursor supply , post-polyketide tailoring , export (cpkF), and regulatory  genes  have not been assigned a function. Eight transcriptional units    synthase  are suitable for CPK production, the yellow pigment is observed about 24 h earlier than the typical time when other colored metabolites appear  preceded those of other cpk cluster genes. Next was orfB (peak at 23 h time point) followed by the rest of the cpk cluster genes including regulators scbR2 and cpkN (24 h). Transcription of scoT needed to maintain the enzymatic activity of the modular PKS subunits started to increase 1 h later than that of core biosynthetic genes. Interestingly, this sharp peak of cpk cluster regulatory gene transcription preceded the moment traditionally seen as the onset of secondary metabolism correlated with phosphate depletion and upregulation of the pho regulon (35 h in the same study) followed by the expression of red (38 h) and act (43 h) gene clusters  in ΔscbA or scbR was shown to abolish SCB1 production  resulting in derepression of both genes. This leads to upregulation of scbR2 transcript level by CpkO and repression of scbA by ScbR2. The reason for abolished transcription of scbA in ΔscbA mutant may be simply repression of its promoter by ScbR devoid of SCB1.In scbA transcription but rather attributing its reduction to the upregulated ScbR2 level as an indirect consequence of scbR deletion/inactivation allows to provide a mechanism accounting for seemingly contradictory observations  but the turning point was publication of 16 and 58 genome-scale, confirmed binding sites of ScbR and ScbR2, respectively, along with their respective null mutants’ transcriptomic data. It revealed that 30.1% and 42.3% of all genes are at least 20% differentially expressed in ∆scbR and ∆scbR2, respectively, in comparison with the parent strain M145 , all of which were shown to be direct targets for binding by ScbR2  but studies conducted in S. lividans TK24 suggest the existence of such interaction  is outlined in Fig. The network of interactions between the icolor A3 is outliS. coelicolor A3(2) genome encodes an impressive number of 67 typical TCSs, of which only a few have been shown to regulate secondary metabolite synthesis.Two-component systems (TCSs) are key signal transduction mechanisms in bacteria, acting as sensors of environmental condition changes and response modulators of transcription. These functions are accomplished through their membrane-bound histidine kinases (HKs) and response regulators (RRs), respectively , and cdaR  along with secondary metabolism in a medium-dependent manner   genome due to intensive binding of the regulator within coding sequences of cpkB (2 sites) and cpkC (1 site) but not in promoter regions. PhoP was also shown to bind to scbA promoter and the observed enhancement of scbA transcription in ∆phoP mutant led the authors to the conclusion that it is scbA repressor ; however, it reduced ACT production. Further proteomic studies revealed decreased abundance of CpkI protein in the ∆rapA1/A2 mutant. Reverse-transcriptase PCR showed downregulation in both pathway-specific regulatory genes actII-orf4 and cpkO transcripts providing a reason for reduced ACT production and indicating downregulation of coelimycin synthesis in ∆rapA1/A2 mutant, although the product of cpk cluster was not characterized at that time  genome encodes at least 150 TetR-like regulators  TetR family SCO3201 protein was identified in an attempt to find a major repressor of ACT synthesis in S. lividans that accounts for its very weak actinorhodin production capacity. SCO3201 gene disruption in S. coelicolor A3(2) did not produce any distinct phenotype. However, it was demonstrated to inhibit ACT, RED, and CDA synthesis along with morphological differentiation and sporulation upon protein overexpression. SCO3201 is a negative regulator and was shown to bind to the promoter region of scbA but not that of scbR or cpkO. SCO3201 overexpression led to downregulation of scbR and cpkO transcription in exponential but not in the stationary growth phase while the transcription of scbA was upregulated in the stationary growth phase. Authors suggested that SCO3201 may lose its repressory activity upon binding an unknown ligand in the stationary phase  liquid culture but not the exogenous SCB2 γ-butyrolactone caused dissociation of CprB from an artificially synthesized consensus sequence  family of proteins that contains transcriptional factors and kinases associated mainly with primary metabolism. However, both Glk and Rok7B7 were shown to affect not only carbon catabolite repression (CCR) but also growth and secondary metabolism  Glk, glkA null mutant complemented with heterologous Glk from Zymomonas mobilis, which complements only enzymatic activity, was constructed (ScoZm strain)  parent strain on media with both glucose and agar (repressive conditions) or agar as the sole carbon source (non-repressive conditions) were compared. Moreover, transcriptomic profiles of ScoZm and the parent strain grown in repressive conditions were analyzed. The putative kinase orfB and cpkN transcripts were downregulated, and that of scbA was upregulated in ScoZm when compared with the M145 wild-type strain. These effects were not observed in M145 grown in repressive relative to non-repressive conditions, suggesting the dependence of these genes on the regulatory activity of GlkA. On the other hand, the presence of glucose upregulated transcription of genes scF–cpkG (SCO6272–SCO6279) and cpkI–cpkL (SCO6282–SCO6285) in the wild-type strain. Interestingly, the effect of glucose on cpk cluster expression depends on other constituents of the medium—in minimal medium NMMP  plays a major role in carbon catabolite repression (CCR), a mechanism ensuring sequential utilization of carbon sources, from the most to the least preferred, when a mixture of them is available in the cell environment  genes, involved among others in N-acetylglucosamine (GlcNAc) internalization. The protein was shown to be a direct repressor of RED production and an ACT synthesis activator    is a transcription regulator controlling colony development, precursor synthesis/flux, and production of at least 8 secondary metabolites in scbR/A promoter region. In proteomic studies CpkE and CpkJ were found to be more abundant in ∆ndgR mutant than in the parent strain  is a direct transcriptional activator of leucine and methionine biosynthesis . What is more, in ∆gntR mutant, gluconate induced the production of coelimycin, suggesting a connection between gluconate metabolism and the production of this compound. The authors speculated the existence of an unknown protein acting as a switch from ACT and RED synthesis to minor or cryptic secondary metabolite production in the absence of alternative carbon sources and GntR protein  is a FadR subfamily gluconate-binding repressor of the gluconate operon S. coelicolor A3(2) rnc deletion mutant was unable to produce ACT, RED, and CDA in a set of different solid media and SMM liquid medium, optimized for antibiotic production (Sello and Buttner S. coelicolor A3(2) rnc mutant transcriptome revealed strong downregulation of cpkD, cpkE, cpkF, cpkG, cpkO, cpkI, cpkJ, and cpkK along with cdaR and other cda gene transcription in comparison with the wild-type strain. In the same study, RNase III was demonstrated to immunoprecipitate with mRNA of cpkE and cpkI transcripts suggesting RNase III can regulate these gene transcripts’ levels through mRNA binding without cleavage mechanism  along with potential small non-coding RNAs localized in the cpk intergenic regions   is not known. It is unlikely to be a “chemical weapon” against other microorganisms, as the weak antibiotic compound coelimycin A is unstable. We speculate that due to the presence of two reactive epoxide rings, coelimycin A may act as a detector of certain compounds in the environment and take part in an unknown signaling pathway. Whatever its biological function, it apparently requires stringent control and is released only transiently at the beginning of the metabolic switch from exponential growth to antibiotic production.The physiological role of coelimycin in cpk genes remains elevated after the sharp activation peak  allows to explain some previously unclear experimental results. Moreover, ScbR2 appears to be a key player in cross-regulation of other biosynthetic clusters. As opposed to being the transcription repressor of cpk cluster, it seems to activate ACT, RED, and CDA clusters , producing at least 8 different GBLs  genome  may sense the availability of GlcNAc (a preferred carbon and nitrogen source) and hold or induce secondary metabolism according to other signals indicating if the environment is rich in nutrients or not  the butanolide system genes, (ii) the cpk pathway-specific activator cpkO and (iii) the core biosynthetic genes. The reported observations of binding of global regulators within the coding sequences of core biosynthetic genes and emerging evidence of non-coding RNAs’ involvement in the control of secondary metabolism set new challenges in deciphering the multi-level regulation of secondary metabolite synthesis in Streptomyces coelicolor A3(2).Concerted action of global regulators is responsible for the ability of streptomycetes to utilize diverse and often fluctuating food sources. Nutrient limitation leads to vegetative mycelium autolysis and metabolic switch towards secondary metabolite production and morphological differentiation. Through the action of DasR,


Oxtr) mRNA in the hippocampus of male, but not female, GD 17.5 offspring from mHFD-induced obese dams (p < 0.05). To determine whether altered histone binding at the Oxtr gene promoter may underpin these transcriptional changes, we then performed chromatin immunoprecipitation (ChIP). Consistent with the Oxtr transcriptional changes, we observed increased binding of active histone mark H3K9Ac at the Oxtr transcriptional start site (TSS) in the hippocampus of mHFD male (p < 0.05), but not female, offspring. Together, these data indicate an increased vulnerability of male offspring to maternal obesity-induced changes in chromatin remodeling processes that regulate gene expression in the developing hippocampus, and contributes to our understanding of how early life nutrition affects the offspring brain epigenome.Maternal obesity during pregnancy increases risk for neurodevelopmental disorders in offspring, although the underlying mechanisms remain unclear. Epigenetic deregulation associates with many neurodevelopmental disorders, and recent evidence indicates that maternal nutritional status can alter chromatin marks in the offspring brain. Thus, maternal obesity may disrupt epigenetic regulation of gene expression during offspring neurodevelopment. Using a C57BL/6 mouse model, we investigated whether maternal high fat diet (mHFD)-induced obesity alters the expression of genes previously implicated in the etiology of neurodevelopmental disorders within the Gestational Day 17.5 (GD 17.5) offspring hippocampus. We found significant two-fold upregulation of oxytocin receptor ( The correct establishment of the epigenome is critical for driving the complex gene expression patterns that underpin normal brain development. Epigenetic regulation via histone modifications, DNA methylation, or non-coding RNA can remodel chromatin structure by changing interactions between DNA and histone proteins, or histone–histone dynamics, to enhance or reduce the accessibility of DNA for the transcriptional machinery. Increasing evidence suggests that these epigenetic mechanisms have a high degree of plasticity during development, and can be modulated by external factors in the in utero environment, including maternal nutrition ,2. In thRecently, studies have identified obesity during pregnancy also incurs a greater risk for neurodevelopmental disorders in offspring ,8. In hureceptors, arginine vasopressin 1A (Avpr1a) and oxytocin receptor (Oxtr), to modulate diverse aspects of human social behavior, and dysfunction of these systems are well established in neurodevelopmental disorders , and stored at −80 °C until required. ChIP validated rabbit polyclonal antibodies  were conjugated to protein G Dynabeads in 0.5% BSA for 3 h at 4 °C. Chromatin was enzymatically sheared using Micrococcal Nuclease, extracted, and lysate was precleared for 1 h at 4 °C. Chromatin was immunoprecipitated with 2 µg of antibody overnight at 4 °C. Immunoprecipitated DNA was washed from Dynabeads, eluted in TE, reverse crosslinked for 4 h at 65 °C, and column purified. All ChIP-qPCR primer sequences are listed in CT−ΔΔ relative quantification method with efficiency correction [CT method, and analyzed by Student’s unpaired t-test. Results were considered statistically significant where p ≤ 0.05. Statistical analyses were performed using Prism (v7.0d) software. For gene expression qPCR, average fold change relative to control males was determined using the 2rrection , using a


We develop PIRCh-seq, a method which enables a comprehensive survey of chromatin-associated RNAs in a histone modification-specific manner. We identify hundreds of chromatin-associated RNAs in several cell types with substantially less contamination by nascent transcripts. Non-coding RNAs are found enriched on chromatin and are classified into functional groups based on the patterns of their association with specific histone modifications. We find single-stranded RNA bases are more chromatin-associated, and we discover hundreds of allele-specific RNA-chromatin interactions. These results provide a unique resource to globally study the functions of chromatin-associated lncRNAs and elucidate the basic mechanisms of chromatin-RNA interactions. RNAs are both the product of transcription and major regulators of the transcriptional process. In particular, long non-coding RNAs (lncRNAs) are numerous in eukaryotes and function in many cases as transcription regulators –3. With A considerable amount of literature has been published concerning protein-RNA interactions. The advent of technologies such as RIP , CLIP 1, fRIP 1, and CARTo address these questions, we developed a new method named Profiling Interacting RNAs on Chromatin followed by deep sequencing (PIRCh-seq), which enriches chromatin-associated RNAs in a histone modification-specific manner and classifies functional lncRNAs based on the patterns of their attachment to nucleosomes with specific chemical modifications. Compared to current techniques for detecting chromatin-RNA association, PIRCh-seq efficiently reduces the influence of nascent transcripts with a significantly lower number of intronic reads. Through performing PIRCh-seq with histone H3 and a number of different histone modification antibodies on different cell types, we identified cell type-specific relationships between lncRNAs and epigenetics. We found that chromatin-associated lncRNAs can be classified into six functional groups based on their association with chromatin modifications, which undergo dynamic changes with cell differentiation. In addition, we found that bases on lncRNAs attached to chromatin tend to be more single stranded in an allele-specific manner. Overall, our PIRCh-seq data provides novel insights into global functional and mechanistic studies of chromatin-associated lncRNAs.R = 0.900–0.988, Additional file We conceived of PIRCh-seq as the inverse of ChIRP, a previously developed and robust method to crosslink endogenous RNA-chromatin interactions in living cells . As testXIST, which coats the inactive X chromosome in female cells, and is known to be associated with heterochromatin with repressive histone modifications [XIST is highly enriched on histone H3 over input in human female fibroblast cells  and intact adult brain , but not enhancer-associated nor repressive modifications (H3K27ac and H3K27me3), indicating that this lncRNA may preferentially associate with H3K4me3 regions . Overlaying lnc-Nr2f1 ChIRP-seq with ChIP-seq data of the histone modifications confirmed that lnc-Nr2f1 does bind to genomic locations with H3K4me3 , consistent with previous findings regarding the function of this lncRNA. These results not only demonstrate the reliability of PIRCh-seq in identifying chromatin-associated ncRNAs, but also suggest potential application of the histone modification-specific PIRCh-seq approach in predicting their functions.PIRCh-seq can also be utilized to identify novel histone modification-specific chromatin-enriched lncRNAs. In our NPC PIRCh-seq, a lncRNA upstream of the isorders . In ordeng GREAT  suggestsP < 0.01, T test) and were almost comparable with input RNAseq from bulk cultured cells  , and askP < 0.05, T test). We then checked the distributions of the expressed and chromatin-associated RNAs on histone H3 and chromatin with other modifications in mESCs, and found that ncRNAs were significantly more frequent on chromatin compared with mRNAs . This suggests that non-coding transcripts are more differentially enriched at distinct chromatin states, consistent with the potential regulatory function divergence of lncRNAs, and naturally prioritizes downstream studies of lncRNAs by activity.Because PIRCh-seq enables transcriptome-wide annotation of chromatin-RNA association, we next determined whether various types of RNA  are differentially affiliated with chromatin. We first filtered the expressed RNAs in edgeR  and normP value < 0.05  of the 23 lncRNAs from the aforementioned experiments, and found the ratio of this overlap from published data . These results further confirm that PIRCh-seq reliably identifies chromatin-associated lncRNAs.We then sought to characterize the ncRNAs significantly on chromatin in mESC from their PIRCh-seq profiles. We considered PIRCh-seq biological replicates versus the inputs in limma  and defi.05 the “” sectionr = 0.89). We observed that H3K4me1 ChIP-seq signal from the same cells as above covers three to four times the genomic regions than other chromatin modifications, which may reflect the differential sensitivities of the different antibodies for ChIP  were clustered together, then the repressive histone modifications , while the promoter (H3K4me3) was grouped in a distinct cluster . In addition, for each cluster, we evaluated the relative contributions of each histone modification based on the enrichment pattern of the chromatin-associated RNAs, and defined the clustered states by active promoters, heterochromatin, weak promoter, strong enhancer, bivalent, and weak enhancer  coding gene expression of the PIRCh-clustered ncRNAs in Fig. P < 0.05, T test). However, when the chromatin-associated ncRNAs were grouped based on their enrichment with each histone modification, no significant expressional differences were observed from nearby coding genes . The chromatin-enriched ncRNAs in MEF also form a combinational pattern with multiple histone modifications , but fewer on chromatin with H4K16ac. This result may reflect the joint presence of activating and repressive histone marks on genome regions, termed bivalent [uc008bcq.1 is broadly enriched in ES cells with high PIRCh-seq signals associated H3K4me1, H3K4me3, H3K27me3, and H3K9me3 modifications, but enriched only on active chromatin of H3K4me1 in MEF and repressive chromatin of H3K27me3 in NPC, implying lineage-specific resolution of chromatin associations . As well known, ES cells are enriched for bivalent chromatin, comprised of both H3K4me3 and H3K4me27 on the same nucleosomes [Embryonic stem cells are characterized by their pluripotency—the ability to give rise to multiple cell types. The chromatin state in ES cells is reported to be more flexible than those of differentiated cells . Interesbivalent  and trivbivalent  in ES celeosomes , 59 thatXist is dramatically more enriched on chromatin carrying H3K27me3 modifications  on each RNA which interacted with chromatin from PIRCh-seq, compared with our input control . Similarly, we took the top 100 most single-stranded RNAs and top 100 most double-stranded RNAs based on their average icSHAPE scores and confirmed that the average chromatin enrichment scores of the most single-stranded RNAs were significantly higher than those of the double-stranded RNAs . These results suggest that RNAs containing more single-stranded regions are more likely to associate with chromatin.As a key player in the central dogma of biological regulation, RNA and its ability to adopt specific structures is intimately involved in every step of gene expression. Previously, multiple approaches have been described in order to probe RNA secondary structure transcriptome-wide in vitro  and in vhromatin . Insteadipt Fig. a. Intereipt Fig. b. This lipt Fig. c. We firsee the “” sectionXist is expressed in this cell line. Consistent with allelic expression, we found that enrichment of Xist in H3K27me3 modification is much higher in 129S1 versus the CAST version of the lncRNA , suggesting that 129S1 allele of Gas5 preferentially associates with chromatin. To further investigate the mechanism under Gas5 allele-specific enrichments, we predicted the secondary structure of the 129S1 and CAST version of Gas5 using RNAfold  comprise the most prevalent source of variation, and SNPs that alter RNA secondary structures, termed “riboSnitches” (a fusion of SNP and riboswitch), are a recently appreciated source of non-coding variants associated with human diseases . We therRNA Fig. g. An addpression . We founN6-methyladenosine (m6A) modification. Previous studies have shown that m6A can alter base-pairing thermodynamics and destabilize RNA duplexes [. We also evaluated whether RNA modifications affect RNA-chromatin association. We integrated PIRCh-seq data with the transcriptome-wide profiles of RNA m6A modifications in mESCs from our previous study [6A modified regions  and were maintained in Essential 8 media  as described previously [V6.5 mouse ES cells were cultured on 0.2% gelatin-coated plates at 37 °C with mES media: 500 ml Knockout DMEM (Gibco), 90 ml FBS, 6 ml non-essential amino acid , 6 ml glutamine or glutamax (200 mM stock solution), 6 ml Pen/Strep, 1 ml BME, and 60 μlLIF . Mouse embryonic fibroblast (MEF) cells were cultured at 37 °C and 5% CO2 in 450 ml DMEM, 50 ml FBS, 5 ml Pen/Strep, 5 ml NEAA, 5 ml pyruvate, and 4 μl beta-mercaptoethanol. Mouse Neural Precursor cells (NPCs) were cultured in N2B27 medium , Neurobasal , NDiff Neuro-2 Medium Supplement , B27 Supplement ) supplemented with EGF and FGF  . Cells were passaged using Accutase  and cultured on 0.2% gelatin-coated plates. H9 human embryonic stem cells were seeded in a feeder-free system using Matrigel hESC-Qualified Matrix . After inverting several times, the tube was gently shaken for 10 min, and then quenched with 1/10 volume of 1.25 M glycine. The tube was inverted several times, shaken gently for 5 min, and spun down 2000g for 4 min. The pellet was then washed once with 40 ml cold PBS. The pellet was responded in 1 ml/20million cells of cold PBS. Cells were aliquoted at 1 ml each to a fresh Eppendorf tube and spun down 2000g for 4 min. After supernatant was carefully aspirated, cell pellets were flash frozen and stored at − 80 °C if necessary.To harvest the cells for PIRCh-seq, approximately 4 × 10g for 4 min and any remaining PBS was removed. Lysis per 20 million cells was performed with 1 ml of lysis buffer , 1× Proteinase inhibitor (Roche)). Lysate was then sonicated till the chromatin size was ~ 300–2000 bp and the lysate was clear. We used the Covaris E220 equipment with the following settings: fill level 10, duty cycle 15, PIP 140, and cycles/burst 200. In terms of time, we sonicated 20-min pulses to test the optimal time to generate chromatin size ~ 300–2000 bp. The lysates were spun down at 16,000g for 10 min. Supernatants were flash frozen and stored at − 80 °C if necessary.For sonication, prepared cell pellets were spun down at 2000For PIRCh-seq library construction, chromatin was thawed and 10 μl was taken as input. Two hundred microliters was aliquoted per reaction, and 400 μl dilution buffer was added to each reaction. H3 or a specific histone modification antibody was then added , 1× Proteinase inhibitor (Roche)). The reaction was shaken end to end at 4 °C overnight. Fifty microliters of Protein A dynabeads was used per 5 μg antibody IP. Beads were washed with 5 times the original volume of dilution buffer 4 times. Notice that it is important not to exceed 200 μl original volume of beads per tube. During the last wash, beads were aliquoted to 1 tube per reaction. The buffer was aspirated, and 200 μl of the IP sample was used to resuspend and transfer beads to the IP sample. The reaction was shaken end to end at room temperature for 2 h. The beads were then washed with 1 ml wash buffer 4 times and resuspended in 50 μl IP elution buffer . The reaction was then vortexed at setting 1 for 15 min. The supernatant was then transferred to a fresh tube, and the bead elution was repeated. The supernatant was combined for a total of 100 μl. Five microliters of 3 M NaOAc was immediately added to neutralize pH. Ten microliter TurboDnase buffer and 1 μl TurboDnase (Ambion) were added, and the reaction was incubated at 37 °C for 30 min. Three microliters of 500 mM EDTA was added to eliminate divalent ions. Five microliters of Proteinase K (Ambion) was added, and the reaction was incubated at 50 °C for 45 min.To make our sequencing libraries, we extracted RNA using Trizol/chloroform and precipitated the RNA with an equal volume of isopropanol. RNA pellet was washed in 1 ml 70% EtOH, and pellets were resuspended in 10 μl H2O. One microliter of TurboDnase buffer was added, followed by 1 μl TurboDnase, and the reaction at 37 °C for 30 min. 1.2 μl of TurboDnase inactivating reagents were added. The reaction was vortexed for 3 min and spun down. The 10-μl supernatant was heated at 75 °C for 10 min to kill DNase. The reaction was purified using a Nugen Ovation v2 kit and eluted in 5 μl for library preparation.lnc-Nr2f11, we followed protocols previously described [To determine the genome-wide localization of escribed . ChIRP wescribed . “Even” To ensure that chemical crosslinking with glutaraldehyde did not affect antibody specificity, we followed previous study to test antibody specificity using SNAP-ChIP . During Five hundred microliters of fixed chromatin was then added to each tube and pipetted up and down several times to mix well. Ten microliters of nucleosomes mixed with chromatin was taken out of each tube to be used as input during the qPCR. One tablet of Roche complete protease inhibitor was dissolved  in 50 ml of DI water to obtain a working solution of 50× protease inhibitor cocktail. Sixty microliters of 50× protease inhibitor was added to 3 ml of blank dilution buffer . One milliliter of dilution buffer with protease inhibitor was then added to each reaction. Five micrograms of appropriate detection antibody for IP pulldown was added to 300 μl of chromatin mixed with crosslinked nucleosomes for each condition. Samples were then incubated at 4 °C overnight with end-to-end shaking.IP product was eluted as specified during PIRCH library construction. DNA of interest was purified using a Zymo DNA Clean and Concentrator-5 kit . The qPCR reaction was performed using Roche’s LightCycler and Brilliant II SYBR® Green QRT-PCR Master Mix (Agilent). We analyzed enrichment for target histone modifications by amplifying unique DNA barcodes at the 3′ end, using primer sequences provided by EpiCypher.For qRT-PCR analysis, we used Roche’s LightCycler and Brilliant II SYBR® Green QRT-PCR Master Mix (Agilent).Raw reads were uniquely mapped to mm9/hg19 using Tophat with default parameters . SamtoolTo compare the exon/intron ratios between the PIRCh-seq profiles and other chromatin-associated RNA detection technologies, we aligned raw reads to the same hg19 genome index with Tophat and calculated the reads mapped to intron/exon with ensemble annotation gtf file as described above . For thehttp://software.broadinstitute.org/gsea/index.jsp) at the Broad Institute website and was utilized to perform the significant differential chromatin enrichment from PIRCh-seq against ncRNA versus coding genes [GSEA software was downloaded from  of 23 lncRNAs from the aforementioned experiments, and overlapped them with the histone ChIP-seq peaks [In order to validate the PIRCh-enriched candidates by similar methods, we examined 96 published chromatin-association datasets from ChIRP/CHART/RAP/GRID-seq experiments collected by the LnChrom database . We founeq peaks  and got eq peaks  with FDRK-means clustering on the PIRCh enrichment score matrix with the chromatin-associated ncRNAs. The proper K number (K = 6) was determined by silhouette score [To cluster chromatin-enriched ncRNAs in distinct groups for functional prediction, we performed t-SNE and te score .T test.To further evaluate the functional prediction for chromatin-enriched ncRNAs, we first grouped chromatin-enriched ncRNAs by functional classification and then obtained lists of the nearby (± 100 Kb) coding genes. We then calculated the gene expressions of these coding genes and represented them in box plots. Similarly, we obtained a different list of nearby coding genes if the chromatin-enriched ncRNAs were classified based on their chromatin enrichment scores on each histone modification. The significance between each group was estimated by two-tailed Welch’s lnc-Nr2f1. Experimental methods were mentioned above, where independent “even” and “odd” probe sets were applied. LncRNA lnc-Nr2f1 ChIRP-seq data were then analyzed by applying a previously published pipeline [lnc-Nr2f1-associated genomic regions were indeed enriched with H3K4me3 but no other modifications in NPCs, where the NPC ChIP-seq data was obtained from GSE117289, indicating the high specificity of our PIRCh-seq approach.To further validate the PIRCh-seq candidates, we performed ChIRP-seq on one of the H3K4me3-modified PIRCh-seq-enriched lncRNAs named pipeline , where tP value < 0.05 was defined as significant.We first built the CAST/EiJ and 129S1/SvImJ reference genome. The vcf files containing the SNPs in the CAST and 129S1 strains were downloaded from the dbSNP database with the mm9 assembly . Their cP value of each peak as the percentage of cases that were more enriched than observed. Finally, we calculated the relative fold change of each peak with respect to the input control. Significant peaks were filtered based on fold change and P value. Finally, RNA structural and modification information was integrated with PIRCh-seq peaks for downstream analysis.To further investigate the underlying mechanism of RNA-chromatin association, we performed peak calling on PIRCh-seq profiles to identify the bases on each enriched RNA that were mostly affiliated with histone proteins. We first merged data from two replicates of each gene to minimize the experimental deviation bias, and smoothed the normalized read counts on each base through a 5-bp sliding window, along with a 2-bp step size. Peak calling was performed on the smoothed signal with a homemade script. We defined a peak in the local maximum that is fivefold or more amplified relative to the median read counts of the transcript. Next, we applied a bootstrap method by randomly sampling 1000 times with reads from the transcripts, and then estimated the T test. In terms of Gas5 in NPC, the structure information of 129S1 allele was represented by V6.5 icSHAPE data, since they have the same sequence. Structure prediction of 129S1 allele and CAST allele was performed by RNAfold web server with default parameter [Gas5 in NPC shows that riboSnitches around PIRCh peak might be the cause of the allele-specific enrichment of Gas5s in NPC.To estimate the structure information around PIRCh peak, we integrate mouse V6.5 icSHAPE data from previous paper , 65. Eacarameter . For 129P values were calculated via the Mann-Whitney-Wilcoxon test in Python. For data presented in Fig. P values, and FDR were calculated in GSEA. For data presented in Additional file P values were calculated by GREAT. For all T test presented in this paper, including Fig. P values were calculated via two-tailed Welch’s T test in Python. For data presented in Fig. P values were calculated via the chi-square test.For data presented in Fig. Additional file 1: Figure S1. Quality control of histone modification specific PIRCh-seq experiments on distinct cell types. Figure S2. PIRCh-seq enriches ncRNAs associated with chromatin. Figure S3. PIRCh-seq captures low nascent transcription. Figure S4. ncRNAs are more enriched on chromatin than protein coding genes. Figure S5. The chromatin-RNA association of ncRNAs give a hint of cis regulation. Figure S6. Pattern of ncRNA chromatin association is generally conserved in distinct cell types. Figure S7. Allele specific RNA secondary structure and chromatin enrichment of lncRNA Gas5. Figure S8. RNA m6A methylation affects chromatin-RNA association.Additional file 2 Table S1. Chromatin enriched ncRNAs in all mouse samples.Additional file 3 Table S2. Allele specific chromatin enrichment in NPC.Additional file 4. Review history.


Escherichia coli (E. coli) F18 is the main pathogen responsible for post-weaning diarrhea (PWD) in piglets. Resistance to E. coli F18 depends on the expression of the cognate receptors in the intestinal epithelial cells. However, the molecular mechanism of E. coli F18 resistance in weaned piglets remains unclear. Here, we performed a comparative transcriptome study of the duodenal tissue from Sutai E. coli F18 sensitive and resistant pigs by RNA-seq, and pig α fucosyltransferase 2 (FUT2) was identified as a host differentially expressed gene controlling the E. coli F18 infection. Function analysis showed that the FUT2 expression was high in the duodenum and jejunum, with higher levels detected in sensitive individuals than in resistant individuals (p < 0.01). Expression levels of FUT2 were upregulated in IPEC-J2 cells after lipopolysaccharide (LPS)-induction or E. coli stimulation. FUT2 knockdown decreased the adhesion of E. coli F18 to IPEC-J2 cells (p < 0.05). FUT2 overexpression markedly increased the adhesion of E. coli F18 to IPEC-J2 cells (p < 0.05 or p < 0.01). Furthermore, the FUT2 mRNA levels correlated with methylation levels of the mC-22 site in the specificity protein 1 (Sp1) transcription factor (p < 0.05). Electrophoretic mobility shift assays (EMSA) showed that Sp1 interacts with the wild-type FUT2 promoter DNA, but not with methylated DNA. Our data suggested that FUT2 methylation at the mC-22 site inhibits Sp1 binding to the FUT2 promoter, thereby reducing FUT2 expression and enhancing E. coli F18 resistance in weaned piglets. These observations highlight FUT2 as a promising new target for combating E. coli F18 susceptibility in weaned piglets. Escherichia coli F18 (ETEC18), which is one of the major pathogens responsible for PWD, adheres to the small intestinal epithelial cells of piglets through its pili. The subsequent binding to the brush border F18 receptors of these cells leads to the enterotoxin production that causes diarrhea in piglets. Hence, the pathogenicity of E. coli F18 depends on the expression of the corresponding receptors by the brush border of the piglet small intestinal epithelial cells [E. coli F18-resistant and -sensitive individuals [E. coli F18-sensitive and -resistant pigs using RNA-seq. Furthermore, we identified the differential expression of the FUT2, FUT3, TLR5, TAP2, IL1β genes in the duodenum, indicating that these genes probably play a crucial role in the resistance to E. coli F18. Our previous studies concentrated on the relationship of immune gene expression and E. coli F18 resistance in pigs [FUT1) gene M307 G/A mutation on the adhesion of E. coli F18 to pig intestinal epithelial cells [E. coli F18 receptor is the type 1 H-antigen of the ABO blood group antigens [FUT2 gene (but not the FUT1 gene) directly catalyzes the formation of the E. coli F18 receptor in pigs. To explore the relationship between the FUT2 gene expression and E. coli F18 resistance in weaned piglets, we used the qPCR and western blot analyses to investigate whether the FUT2 expression correlated with E. coli F18 resistance in an LPS-induced or bacteria-stimulated small intestinal epithelial cell line (IPEC-J2), as well as in intestinal tissues of Sutai sensitive and resistant pigs. We also performed a functional analysis of the E. coli F18 adhesion in vitro using RNA interference and overexpression.Porcine post-weaning diarrhea (PWD) causes serious economic losses to large-scale pig farms. Enterotoxigenic al cells . To obtaividuals . In this in pigs ,5,6,7,8,al cells , but itsal cells ,11,12. Tantigens . ABO Bloantigens ,15. HereFUT2 promoter region in intestinal tissues of weaned piglets. Furthermore, we analyzed the effects of the important methylation site on mRNA expression and the key transcription factors in the promoter region, and further verified key transcription factors using electrophoretic mobility shift assay (EMSA) and in vitro methylation. This study not only revealed the regulatory mechanism of FUT2 expression controlling E. coli F18 resistance in weaned piglets, but also provided the basis of strategies for the bioengineering regulation of E. coli F18 resistance in human and pigs.DNA methylation is a basic element of epigenetic modification that does not change the base sequence or composition. Nevertheless, methylation of promoter region cytosines tends to inhibit gene transcription and is associated with some diseases ,17,18. FE. coli F18-resistant  and sensitive piglets  . We obtained 132.46, 140.51, 132.18, 146.49, 137.19, and 126.64 million raw reads for SR1, SR2, SR3, SS1, SS2, and SS3, respectively. We performed sequence alignment against the reference pig genome  using MapStat. After quality control, approximately 82.0% (81.2–82.7%) of reads were mapped to the reference genome. Of these, 68.5–71.8% mapped uniquely to the pig reference genome, and 9.4–13.5% showed multiple matches. To further clarify the molecular mechanism by why resistance to E. coli F18 infection is regulated, we identified 238 DEGs between E. coli F18-resistant and sensitive piglets according to the principle that p < 0.05 and |log2(fold change)|< 1. Of these, 112 DEGs were upregulated in the E. coli F18-resistant group compared with the sensitive group  and “Antigen processing and presentation” (TAP2)) and a glycolipid-synthesis pathway ). The previous study showed that the FUT1 gene M307 G/A mutation can affect the adhesion of E. coli F18 to pig intestinal epithelial cells [TLR5, IL1β, FUT1, FUT2, FUT3) for qRT-PCR validation. The results showed (FUT2 and FUT3 were extremely significantly higher in the E. coli F18-sensitive group than that in the E. coli F18-resistant group (p < 0.01); the expression levels of TLR5, IL1β, and TAP2 were significantly higher in the E. coli F18-sensitive group than that in the E. coli F18-resistant group (p < 0.05). However, FUT1 had no significant difference between the E. coli F18-sensitive group and the -resistant group (p > 0.05). Meijerink et al., 1997 demonstrated that the synthesis of the E. coli F18 FedF adhesion protein was in association with FUT1 and FUT2 [FUT2 gene for further in-depth analysis.We performed a comparative transcriptome sequencing analysis of duodenal tissues from ve group . Furthers showed  that theand FUT2 . TherefoFUT2 gene expression profiles in different tissues of Sutai piglets, with a relatively high expression detected in the duodenum and jejunum  . IPEC-J2 cells were exposed to LPS (0.1 μg/mL) for 4, 8, and 12 h, as well as to the E. coli F18ab, F18ac, and K88ac strains (109 CFU/mL). qRT-PCR analysis showed that FUT2 expression was increased at 4 and 8 h (p < 0.05) (E. coli strain-stimulated IPEC-J2 cells (p < 0.01)  B and wesqRT-PCR) C, differ < 0.05) D or in E < 0.01) E. Westermulation F.FUT2 in the pig epithelial cell line IPEC-J2. As shown in FUT2 in Lenti-RFUT2-n  treated IPEC-J2 cells compared with non-treated cells was 85.1%, 86.5%, 71.6%, and 38.3%, respectively, and Lenti-RFUT2-2 treated IPEC-J2 cells were used for the subsequent investigations (E. coli F18 to IPEC-J2 cells treated with FUT2-RNAi via F18ab/ac-expressing fimbriae compared with that of non-treated and Lenti-RFUT2-NC treated cells (p < 0.01). As shown in FUT2 in IPEC-J2 cells with pcDNA3.1-FUT2 increased to 9.6 times. Bacteria enumeration (E. coli F18 to IPEC-J2 cells treated with pcDNA3.1-FUT2 via F18ab/ac-expressing fimbriae compared with that of pcDNA3.1 treated cells (p < 0.05 or p < 0.01). Together, all these results suggested that the decreased expression of FUT2 contributes to enhancing E. coli F18 resistance in piglets.Besides, we used lentivirus-mediated RNAi to mediate the specific suppression of igations B. Relatiigations C and bacigations D showed meration B and relmeration C showed FUT2 gene. MethPrimer analysis of the 2 Kb upstream region of the FUT2 gene revealed a single CpG island (Based on an integrative genomics viewer (IGV) analysis of transcriptome sequencing A, we obtG island B. PCR amG island C.FUT2 promoter in intestinal tissues between E. coli F18-sensitive and -resistant individuals , with significant correlation coefficients being obtained only for the mC-6 and mC-22 sites (R = −0.42). Furthermore, mC-6 was located in F1bD and mC-22 was located in the Sp1 (specificity protein 1) transcription factor binding site (TFBS). Studies showed the methylation of some CpG sites could decrease the efficiency of binding between the transcription factors and promoters, thereby lowering the gene transcription rate. Sp1 belongs to the methylation-dependent transcription factors and Sp1 binding to DNA has a transcription-activating effect [FUT2 methylation at the mC-22 site suppresses the FUT2 expression.Using bisulfite amplicon sequencing (BSAS), we performed the methylation analysis of the ividuals . As show tissues F. MoreovXR-alpha E. Pearsog effect ,26,27. TFUT2 methylation on Sp1 binding to the FUT2 promoter was investigated using EMSA (FUT2 wild-type probes (Lanes 2–4), while none were detected with methylated wild-type (Lane 6) and mutant-type (Lane 8) probes. These observations confirmed that FUT2 wild-type probes bind with nuclear extracts from duodenal tissues. Furthermore, in the presence of the Sp1 antibody, a strong supershifted band was observed with the unmethylated wild-type probe only (Lane 3), providing further evidence that Sp1 from nuclear extracts binds specifically to the unmethylated FUT2 wild-type probe. The above results indicated that FUT2 methylation at the mC-22 site inhibits Sp1 binding to the FUT2 promoter.The effect of ing EMSA . SeveralE. coli F18 adherence to the small intestinal epithelial cells of piglets through its fimbriae and binding to the F18 receptors in the brush border of these cells. Thus, the pathogenicity of E. coli F18 depends on the expression of these cognate receptors [E. coli F18 receptor expression was elevated with increasing age, with gradually increased levels detected during the first three weeks and stably maintained levels detected from 3–23 weeks [E. coli not only because the receptors were yet to be formed in the intestinal tract, but also because the antibodies in breast milk protected the piglets from enteropathogen infections [E. coli F18 infections. In this study, we undertook a strict selection of E. coli F18-resistant and -sensitive post-weaned piglets for transcriptome analysis. We identified 238 DEGs probably related to E. coli F18 resistance, including FUT2. E. coli F18 resistance mainly depends on the expression of receptors on intestinal epithelial cells. Studies have shown that the smallest antigenic determinant of the E. coli F18 receptor is the type 1 H-antigen of the ABO blood group antigens [FUT2) [FUT2 gene expression in the intestinal tract and other ectodermal tissues also illustrates this point. In this study, we found a lower FUT2 expression in lower intestinal tissues of resistant individuals compared with that in sensitive individuals. Meijerink et al., 2000 also reported a very high FUT2 expression in sensitive individuals, while almost no expression was detected in resistant individuals [FUT2 knockdown reduced the ability of E. coli F18 to adhere to IPEC-J2 cells. Taken together, these data suggest that the downregulation of FUT2 expression reduces the FUT2 enzymatic activity while enhancing resistance to E. coli F18.Post-weaning diarrhea (PWD) is caused by enterotoxin production resulting from eceptors . In a st23 weeks . Unweanefections . Accordiantigens . Type 1 s [FUT2) ,15. Thess [FUT2) . The higividuals . In addiFUT2 gene promoter region was high; therefore, C loci in those genes with an average cytosine ratio exceeding 1% were selected for the analysis of methylation levels in each sample. Although the methylation levels of the FUT2 gene amplified fragments were relatively conserved, the negative correlation with mRNA levels suggested that CpG island methylation might inhibit FUT2 gene transcription. Barrera et al., 2012, found that different CpG islands possessed different genomic elements with distinct gene regulation functions [FUT2 gene CpG islands (p < 0.05), suggesting that these sites might be critical for the regulation of gene transcription, while other methylated sites might be auxiliary.In this study, the cytosine ratio in the partial unctions . There munctions . This suunctions . InsteadFUT2 gene amplified fragments were found to be methylated. In terms of the inhibition of expression by DNA methylation, it is generally believed that cytosine C methylation blocks transcription by directly acting on methylation-sensitive transcription factors to destroy their ability to bind DNA binding; it can also act on non-methylation-sensitive transcription factors by attaching themselves to repressor proteins [FUT2 gene was identified on the transcription factor Sp1 binding sites. Studies have shown that CpG sites are present in the specific binding sites of some transcription factors. The methylation of these sites decreases the efficiency of binding between transcription factors and promoters, thereby lowering the gene transcription rate. Such methylation-dependent transcription factors include specificity protein 1 (Sp1) [Combined with the feature analysis of amplified sequences, all CpG sites in the proteins . In this 1 (Sp1) ,26,27, c 1 (Sp1) , myelocy 1 (Sp1) ,38, upst 1 (Sp1) , CCCTC-b 1 (Sp1) , GATA bi 1 (Sp1) , and APE 1 (Sp1) . Sp1 bin 1 (Sp1) ,44, and E. coli F18 resistant and sensitive resource population pre-established by our team . Based on the adhesion assays for E. coli F18 binding to pig epithelial cells (IPEC-J2), the previous study has obtained three pairs of F18-resistant and -sensitive individuals with an identical birth weight, weaning weight, body shape, coat color with and feeding in the same environment [E. coli F18 infection and prone to manifestation of diarrheic symptoms, piglets were humanely sacrificed using an intravenous injection of pentobarbital sodium as necessary to ameliorate suffering and collected heart, liver, spleen, lung, kidney, stomach, muscle, thymus, lymph node, jejunum, and duodenum tissues. All tissues were stored in liquid nitrogen in situ and then stored in a −70 °C refrigerator for later use.Experimental Sutai pigs were obtained from the ironment . During The animal study proposal was approved by the Institutional Animal Care and Use Committee (IACUC) of the Yangzhou University Animal Experiments Ethics Committee (permit number: SYXK (Su) IACUC 2012-0029; Approval Date: 09-12-2012). All experimental procedures were performed in accordance with the Regulations for the Administration of Affairs Concerning Experimental Animals approved by the State Council of the People’s Republic of China.n = 3, SR) and sensitive individuals  were used for RNA sample preparation. The total RNA was isolated using TRIzol, after which the purity, concentration, and integrity were determined using a NanoDrop spectrophotometer  and a Qubit RNA Assay Kit with the Qubit® 2.0 Fluorometer . RNA libraries were sequenced using an Illumina HiSeq 2000 Nano 6000 Assay Kit and analyzed with the Bioanalyzer 2100 system . The libraries were sequenced on an Illumina HiSeq 2000 platform  and 100 bp paired-end reads were generated.A total amount of 3 μg RNA per sample of duodenum tissues from F18-resistant individuals  < 1| were set as the identification principle of differential expression genes.After the measurement of reads per kilobase per million mapped reads (RPKM) value, we performed a differential expression analysis of the transcript between Sutai F18-resistant and -sensitive piglets by Cuffdiff method . A correhttp://geneontology.org/) was used for excavating the biological function of differential expression genes, including molecular function, cellular component, and biological process. GO terms were analyzed by the software GOSeq [p-values < 0.05. KEGG  is a database that could explain the network of molecular interactions in a particular species. Using KOBAS software [GO . At 80–90% confluence, the cells were exposed to 0.1 μg/mL LPS . The cells incubated with culture medium without LPS-induction were regarded as the negative control (NC). Each treated group had three replicates. Cellular RNA and protein were extracted in LPS-induced cells at 4 h, 8 h, and 12 h and control cells, which was used for qRT-PCR and western blot analysis.Pig intestinal epithelial cells (IPEC-J2) offered by the University of Pennsylvania  were cultured at 37 °C and 5% COE. coli F18ab, E. coli F18ac, and E. coli K88ac  fimbriae standard strains were inoculated into a Luria–Bertani (LB) culture medium and incubated for 12 h on a rotating shaker table (200 rpm). After centrifugation for 5 min at 4000 rpm, the three types of E. coli culture supernatants were filtered  and used to resuspend the bacteria, which were then washed three times.FUT2 mRNA  and one off-target control sequence (NC: 5′-TTCTCCGAACGTGTCACGT-3′) were cloned separately into the LV3-H1/GFP&Puro vector  and co-transfected with packaging plasmids into 293T cells (GenePharma). The virus was collected and used to infect the target cells (IPEC-J2). Positive cells were selected by the addition of 10 μg/mL puromycin total RNA, and proteins were extracted from representative cells at 24 h intervals for the analysis of FUT2 gene expression at the transcriptional and translational levels. The cells infected with R1, R2, R3, or R4 lentivirus FUT2 shRNA vector were designated Lenti-RFUT2-n  cells, respectively. Full-length DNA encoding FUT2 genes (Gene ID: NM_214069) were amplified using the RT-PCR and cloned to pcDNA3.1 vector . The primer sequences for cloning the full length of FUT2 were as follows: F: 5’-AGTTTAAACGGATCTCTAGCGAATTCGCCACCATGCTCAGCATGCAGGCATCTTTCTTC-3’; R: 5’-TCCGGCCTTGCCGGCCTCGAGCGGCCGCTCACTTATCGTCGTCATCCTTGTAATCGTGC-3’. For the IPEC-J2 control, Lenti-RFUT2-n, and pcDNA3.1-FUT2 cells, the in vitro evaluation of F18 fimbriae adhesion was performed as previously described [E. coli adhesion to small intestinal epithelial cells in pigs.Four RNAi sequences targeting escribed . A relatescribed , in addi2O to make the volume 10 μL. The reaction condition was set as 25 °C for 10 min, 50 °C for 30 min, 85 °C for 5 min, and 4 °C forever. A real-time quantitative PCR instrument ABI7500  was used to conduct qPCR analysis and the amplification program was set as: 95 °C for 30 s; 95 °C for 5 s and 60 °C for 34 s, followed by 40 cycles. The primers of FUT1, FUT2, FUT3, TLR5, IL1β, TAP2 amplified fragments and housekeeping gene  were shown in Ct−ΔΔ method [Tissues and cells RNA from Sutai pigs were extracted in accordance with the Trizol Reagent instructions (Invitrogen). The RNA integrity was checked by 1% formaldehyde denaturing agarose gel electrophoresis, and the RNA concentration was determined by a ND-1000 nucleic acid/protein concentration tester. cDNA was synthesized by PrimeScript RT-PCR kit . Reverse transcription reaction system: 5 × qRT SuperMix II 2 μL, total RNA 500 ng, add RNase free ddHThe total proteins from tissues and cells were extracted in accordance with the instructions of NE-PER kit . A BCA kit  was used to normalize protein levels. Proteins were transferred to PVDF membranes, which were then immunoblotted with relevant primary detection antibodies (FUT2 (1:600) and β-actin (1:4000)) followed by a secondary detection antibody (horseradish peroxidase (HRP) conjugated goat anti-rabbit IgG ).http://www.broadinstitute.org/igv/) was used to confirm the transcriptional start site (TSS) and the promoter region of FUT2 genes based on our transcriptome sequencing of duodenal tissues of 18-resistant and -sensitive piglets. Moreover, the MethPrimer (http://www.urogene.org/cgi-bin/methprimer/methprimer.cgi) and Alibaba  software were used to analyze the CpG islands and transcription factor binding sites based on the sequence of the FUT2 promoter region.The Integrative Genomics Viewer (IGV) software . The PyroMark Assay Design software was used to design PCR primer sequence: forward, 5’-TTGAAGTAGGTATTTTTAGTTGGTGTAG-3’, reverse, 5’-CCATTCTACTAACTTACATCTCTCTTTTT-3’. DNA bisulfite conversion and bisulfite specific PCR, NGS library preparation and sequencing using the MiSeq platform , NGS data analysis, and digital methylation quantitation were performed according to the previous methods .FUT2 wild-type (wt)  and FUT2 mutant-type (mut)  probes were generated based on the FUT2 promoter region for Electrophoretic Mobility Shift Assays (EMSA). Antibody supershift assay Sp1 antibodies (CSB-PA298794) were obtained from Cusabio Biotech . The EMSA test was performed by the previous methods [The  methods p < 0.05 or p < 0.01. A correlation analysis was performed pairwise for gene expression and methylation level by Pearson correlation.Comparison between groups was made using Student’s t-test in the SPSS version 18.0 software . Each treatment had three repeats, and the results were presented as mean ± standard deviation (x ± SD). Differences were considered statistically significant with FUT2 correlates with E. coli F18 resistance. Furthermore, we found that the methylation of the mC-22 site residue in the FUT2 promoter inhibited FUT2 expression and that the Sp1 transcription factor might be regulated by methylation, suggesting that the methylation-induced inhibition of Sp1 binding to DNA reduces the transcriptional activity of the FUT2 gene promoter, thereby improving the E. coli F18 resistance in piglets.Our study showed that the downregulation of


The number of reported examples of chromatin architecture alterations involved in the regulation of gene transcription and in disease is increasing. However, no genome-wide testing has been performed to assess the abundance of these events and their importance relative to other factors affecting genome regulation. This is particularly interesting given that a vast majority of genetic variations identified in association studies are located outside coding sequences. This study attempts to address this lack by analyzing the impact on chromatin spatial organization of genetic variants identified in individuals from 26 human populations and in genome-wide association studies.We assess the tendency of structural variants to accumulate in spatially interacting genomic segments and design an algorithm to model chromatin conformational changes caused by structural variations. We show that differential gene transcription is closely linked to the variation in chromatin interaction networks mediated by RNA polymerase II. We also demonstrate that CTCF-mediated interactions are well conserved across populations, but enriched with disease-associated SNPs. Moreover, we find boundaries of topological domains as relatively frequent targets of duplications, which suggest that these duplications can be an important evolutionary mechanism of genome spatial organization.This study assesses the critical impact of genetic variants on the higher-order organization of chromatin folding and provides insight into the mechanisms regulating gene transcription at the population scale, of which local arrangement of chromatin loops seems to be the most significant. It provides the first insight into the variability of the human 3D genome at the population scale.The online version of this article (10.1186/s13059-019-1728-x) contains supplementary material, which is available to authorized users. Around 20 million base pairs of a normal human genome (0.6%) are under structural variations, including deletions, duplications, insertions, and inversions. This makes structural variants (SVs) the most prominent source of genetic variation among human individual genomes.The potential malicious effect of SVs has been recognized but almost solely associated with altering gene copy number and gene structure—a number of studies relate copy number variants (CNVs) affecting gene regions to cancer , intellecis-regulatory elements [A part of the SVs emerging in non-coding regions alters genomic loci recognized by proteins which organize the human genome in the cell nuclear space. Recent studies provided some insights into the impact SVs can have on a spatial organization of the human genome. Examples of SVs altering the borders of TADs in EPHA4 locus and causing pathogenic phenotypes by enabling spatial contacts between formerly isolated genomic functional elements were reported . Positioelements . Accumulelements . Hi-C maelements . Disruptelements . An atteelements . EfficacHowever, to our knowledge, there was no genome-wide systematic study on the impact of SVs on genome spatial organization analyzing the level of individual chromatin loops. One of the most recent reviews on the topic  highlighThe recent advancements in chromosome conformation capture techniques, namely high-throughput conformation capture (Hi-C) , 15 and Taking advantage of the high-quality ChIA-PET and population-scale SVs data, we discuss a mechanistic model of the impact of SVs on the chromatin looping structure, provide the first genome-wide analysis of this impact for the human genome, and model SV-induced changes in 3D genomic structures observed in human population.In our analyses of the impact of SVs on the 3D chromatin organization of the human genome, we pay a specific attention to chromatin interactions associated with enhancer regions and gene promoters. These interactions are likely to play a distinguished role in the regulation of gene transcription in a mechanistic fashion, bringing the genes and the regulatory elements close together or separating them in the nuclear space of the cell. We observe an interesting interplay between such genomic interactions and SVs.In this study, we use ChIA-PET interactions as a representation of the higher-order spatial organization of the human genome. ChIA-PET targeting on CTCF and RNAPII performed on the GM12878 cell line , 18 was ChIA-PET generates high-resolution (~ 100 bp) genome-wide chromatin contact maps. It identifies two types of chromatin interactions mediated by specific protein factors. The first type is highly reliable enriched interactions which appear in the data as closely mapped on the genome clustered inter-ligation paired-end-tag products (PET clusters). The second type is singletons, which reflect higher-order topological proximity .We inspected the anchoring sites of PET clusters identified by the CTCF ChIA-PET experiment for the co-occupancy by CTCF and cohesin (SMC3 and RAD21 subunits), to select the set of high-quality chromatin interactions mediated by CTCF in GM12878 cell see the “” sectionThe interactions mediated by CTCF are not uniformly distributed over the genome but rather form highly interacting, predominantly hundreds of kilobases-long chromatin blocks (which we will further refer to as chromatin contact domains (CCDs)) separated by segments of weak and rare contacts (gaps). Based on the CTCF ChIA-PET data, the genome of GM12878 cell was segmented into 2267 CCDs , and we Even though CTCF ChIA-PET captures only the interactions mediated by the CTCF protein, it detects structural features exhibited by the non-specific Hi-C data . It was Following the authors of the dataset, we investigated the directionality of CTCF motifs in the anchors of the CTCF chromatin loops. Thirty-seven thousand two hundred eighty-nine out of the 44,380 PET clusters had motifs of unique orientation in both anchors. Among the 37,289, we found 24,181 (65%) interactions with motifs in the anchors having convergent orientation (convergent loops), 6118 (16%) interactions in tandem right orientation (tandem right loops), 6089 (16%) tandem left loops, and 901 (2%) divergent loops see the “” sectionThe described ChIA-PET dataset is further used as the reference 3D genome of a human lymphoblastoid cell.It was demonstrated, by phasing CTCF PET clusters identified in GM12878, that allele-specific single-nucleotide variation in genome sequence can result in haplotype-specific chromatin topology .We further show that relative values of haplotype-specific CTCF binding signals see the “” sectionIn this study, we concentrate on predicting how SVs impact genome looping organization. SVs are the major source of sequence variation among human genomes and given their larger size have a higher potential than SNPs to induce changes in chromatin folding. They were also shown to contribute more than SNPs to variation in gene expression among human samples . ChromatOur analysis shows that CTCF binding profiles at long-range interaction sites are highly similar across lymphoblastoid cells. Over 99% of interacting anchors occupied by CTCF peaks in GM12878 cell are supported by CTCF peaks in each of the 13 other samples  , includiMost of the SVs reside inside CCDs, not intersecting borders of domains nor CTCF-mediated interaction anchors Fig. e.While the SVs that miss the interacting binding sites in most cases have limited impact on the final structure , the SVs that overlap the interacting sites may partially modify the interaction pattern and in turn cause serious changes of the 3D structure  and hematological parameters  , the Americas (AMR), East Asia (EAS), Europe (EUR), and South Asia (SAS). These populations are defined by the 1000 Genomes Project  . According to the data, genomes from East Asia, not South Asia, carry the largest number of the homozygous SVs Fig. h. HoweveThe fruitful study of natural human knockouts performed on a cohort of 10,503 Pakistanis by the Human Knockout Project  made us For each of the continental groups, we prepared a list of patterns (similar to those described in the previous section) of anchor-intersecting SVs, which alter CCDs in the individuals belonging to this group. Even though most of the patterns are population-specific, we found 312 (6%) patterns common for all 5 continental groups  on the expression data, which pinpointed 14,853 genes having the biggest variation in expression rates among individuals from all 23,722 genes present in the gEUVADIS dataset. We then related the expression levels of each of the 14,853 genes to genotypes see the “” sectionp values were adjusted for multiple testing to control the false discovery rate (FDR). We set a threshold of acceptance for FDR ≤ 10%  are deletions Fig. a.Fig. 6REarlier studies on eQTLs were limited in exploring the causal relation between genetic variation and gene expression to analyzing gene-variant and exon-variant intersections , 45, or In agreement with , 46, 47,Active promoters and TFBSs are enriched with proximal eQTLs demonstrating their importance as gene-adjacent regulatory sites. Enhancers apart from being enriched with proximal eQTLs are enriched with the distal ones and represent regulatory elements interacting with genes by the nuclear space. However, the genomic elements most enriched with both proximal and distal eQTLs are anchors of RNAPII PET clusters Fig. d. The abAs an example of eQTLs altering chromatin looping mediated by RNAPII, we investigate 5 deletions located in a HLA region Fig. i. All ofWe hypothesize that proximal eQTLs modify TFBSs, TSSs, and TESs of genes as well as gene sequences but mostly they alter genes’ spatial contacts with regulatory elements and possibly with interaction centers, which has an immediate and straightforward influence on genes’ expression levels. Distal eQTLs have in turn higher potential to, apart from altering long-range RNAPII interactions, disrupt CTCF interactions that are longer than RNAPII-mediated chromatin loops Fig. b and shaDeletion chr1:248849861-248850138 is one of the eQTLs intersecting CTCF-mediated interaction anchors  data, we mapped the identified eQTLs on genomic interactions reported in Mifsud et al. , 48. AncTo state more firmly the relationship between proximal and distal eQTLs and chromatin activity, we collected see the “” sectionWe repeated the eQTL analysis described above for housekeeping genes only (selected based on Eisenberg and Levanon ) to see On the other hand, we separately analyzed immune-related genes as genes specific to the lymphoblastoid cell lines see the “” sectionTwo of the immune-related eQTLs (deletion chr22:39357694-39388574 and CNV chr22:39359355-39379392) cover the same CTCF anchor  is an interesting question. However, more extensive testing has to be done to explore this hypothesis.It is already well established that part of the transcriptional variation between genomes or pathogenic phenotypes can be caused by chromatin topological alterations, but we still lack extensive genome-wide testing to assess the abundance and importance of these events. Our understanding of the importance of chromatin architecture in genome regulation is based mainly on particular cases of SVs disrupting local 3D chromatin structure and subsequently leading to the deregulation of transcription of particular genes (in most of the studied cases associated with disease) , 9, 11. We mapped genetic variants identified in individuals from 26 human populations in the 1000 Genomes Project and disease-associated SNPs from GWAS onto chromatin three-dimensional structure of human lymphoblastoid cell line represented by CTCF and RNAPII ChIA-PET data. Our strategy for analyzing high-resolution CTCF and RNAPII genomic interaction data gives a comprehensive insight into the impact of SVs on chromatin organization. In agreement with previous studies , 50, 51,We identified African and South Asian genomes as exhibiting the highest rates of structural variation in genomic interaction anchors and topological domain boundaries Fig. a, b. SouThe analysis of spatially interacting genomic segments was possible using data from ChIA-PET experiments which identify such segments with high accuracy genome-wide. We applied additional filtering on CTCF ChIA-PET interacting segments, checking them for the co-occupancy by CTCF and cohesin ChIP-seq peaks, to obtain a highly credible set of CTCF-mediated chromatin interactions supported by cohesin. In case of CTCF ChIA-PET, it is even possible to identify individual CTCF binding motifs involved in the formation of the interactions, which brings high precision to the analysis. Such analysis would not be possible using Hi-C data in case of which genomic interaction segments are demarcated artificially, by segmenting the genome into adjacent bins of equal size and which resolution is rarely under the order of tens of kilobases, as a high resolution is obtained at the cost of very deep genome sequencing. The limitation of ChIA-PET experiment is that it captures chromatin interactions mediated by a particular protein, in contrast to Hi-C which identifies interactions of all kinds. Thus, Hi-C is a more complete representation of chromatin contacts present in the cell nucleus. However, CTCF ChIA-PET detects structural features exhibited by the non-specific Hi-C data. Data from these sources are highly correlated at the global whole-chromosome scale (Spearman’s correlation coefficient in the range of 0.7–0.9)  and idenWe used ChIA-PET data for GM12878 cell and treated it as a reference 3D genome for mapping SVs from other lymphoblastoid cell lines. CTCF and RNAPII ChIA-PET datasets for GM12878 are of the highest quality, and to our knowledge, no such datasets are currently available for any other lymphoblastoid cell line. Based on these highly credible genomic interactions and information on SVs identified in a population of 2504 human lymphoblastoid cells, we computationally predict chromatin interaction patterns for those cells. We claim that our computational tool is very useful for obtaining individualized chromatin interaction patterns and in silico models of chromatin structures in the absence of experimental data, which generation requires expertise and certain money investments. On the other hand, because of the data unavailability, we could not confront our predictions with experimentally generated genomic interaction maps. No new biological experiments were conducted to support the correctness of our predictions, as this is purely computational analysis. However, we presented examples supported by available ChIP-seq datasets  using BEDTools (version 2.26.0) getfasta utility  and provThe CTCF motifs were overlapped with CTCF PET clusters. Forty-four thousand three hundred eighty out of the 80,157 CTCF PET clusters had both anchors overlapped by CTCF/cohesin consensus peaks  identified by Tang et al. .In this study, we tested various genomic elements for the enrichment or depletion with structural variants (SVs). These tests were conducted according to a common scenario. Genomic elements of a given type represented by their positions in the hg19 reference genome were intersected with the positions of SVs. The ones having at least 1 bp overlap with at least 1 SV were counted. The genomic elements were then intersected with simulated SVs from 1000 sets generated by randomly shuffling positions of the original SVs, and the null distribution of counts of genomic elements overlapped with SVs was calculated. Each shuffled set contained the same number of elements in total and the same number of elements in subsets  as the real SV set. Elements of these sets were equally distributed on chromosomes and equally distributed in length to the real SVs. The operations of shuffling and intersecting genomic segments were performed with BEDTools (version 2.26.0) . The enrFor individual tests, the set of SVs was divided into various subsets. In particular, SVs with variant allele frequency (VAF) lower than 0.001 were considered separately in certain tests. In others, SVs were grouped according to the ancestry of individuals they were identified in, and sets of SVs emerging uniquely in one subpopulation were also created. The special set of SVs correlated with gene expression (eQTLs) was subdivided into 2 sets: a set of eQTLs located closer on the DNA chain than 17,800 bp apart from the genes they modified and a set of eQTLs located further apart from their genes. The distances were calculated between TSSs (as defined in GENCODE version 12)  and centThe set of GWAS SNPs used in this study was derived from the NHGRI-EBI GWAS Catalog, version from January 31, 2018 . SNPs ofAnalyzed in permutation tests, genomic elements associated with genes (annotated protein-coding sequence regions (CDSs), untranslated regions (UTRs) in protein-coding regions, exons, and introns) were adopted from the GENCODE release 27 (mapped to hg19). Permutation tests with eQTLs were an exception—in this case, gene elements from version 12 of GENCODE were used to maintain the consistency with the expression data which was analyzed with the earlier version of GENCODE. The positions of transcription factor-binding sites (TFBSs) were adopted from a file with uniform TFBS peaks downloaded from ENCODE . Second, for each gene, we permutated the expression rates relative to genotypes 1000 times, recalculating at each iteration the linear regression for each gene-SV pair and recording the minimal p value among all pairs. Adjusted p values were calculated for each gene by dividing the ranks of the observed p values in the list of p values obtained in permutations by the number of permutations. Finally, to correct for multiple testing across genes, we applied the Benjamini-Hochberg procedure to the adjusted p values, estimating q values. At FDR 0.1, we found 192 genes with eQTLs. The same procedure was employed to identify eQTLs for housekeeping genes, except that PCA step was omitted. By mapping the names of housekeeping genes reported in Eisenberg and Levanon [The gEUVADIS Consortium provides RNA-seq data for 462 samples, but only a subset of these (445 samples) was genotyped in the 1000 Genomes Project. Thus, our analyses were performed on a population of 445 individuals for which both transcription and genotype data were available. Only the genotypes of abundance higher than 1% were considered. Sex chromosomes were excluded from the analyses. We took the logarithms of the PEER-normalized expression levels for calculations to correct it for far outliers and standardized the data. We started the analysis by performing principal component analysis (PCA) in the 23,722-dimensional space of gene expression rates. Based on the Scree Plot  were obtained from the raw sequencing data . The rear2 value was calculated with PLINK (version 1.07) [Linkage disequilibrium (LD) between selected SVs was calculated in the CEU population. Genotype information for 99 individuals from CEU population was extracted from phase 3 of the 1000 Genomes Project vcf files and passed to vcftools (version 0.1.15)  to conveon 1.07)   representing strong, specific chromatin interactions, and singletons, numerous (in the order of tens of millions), but representing mostly non-specific and spurious contacts.To make the best use of the information carried by these two distinct types of contacts, we employed a multiscale approach: first, we used the singletons to guide the low-resolution, megabase-scaled modeling, and then we used PET clusters to refine the obtained structures, achieving resolutions up to a few kilobases. We note that this approach is consistent with the widely accepted model of genome organization, in which the main roles are played by topological domains and chromatin loops. Here, at the stage of the low-resolution modeling, we attempt to position the topological domains relative to each other, and in the high-resolution, we model the position and shape of individual chromatin loops.fij between a pair of regions i and j as a proxy of 3D distance dij between corresponding beads, assuming an inverse relationship α being a scaling exponent, and use Monte Carlo simulated annealing to position the beads to minimize the energy function rij is the actual distance between beads corresponding to regions i and j.The structure of a chromosome is represented using a “beads on a string” model. First, the chromosome is split into a number of approximately megabase-sized regions. Ideally, each region would correspond to a single topological domain. In practice, the split is made based on the patterns of PET clusters interactions . The energy function is identical in the form to the one used at the chromosome level, and we again use Monte Carlo simulations to find the optimal arrangements of the anchors. Then, in the subloop step, we keep the anchors’ positions fixed, and we try to model the loops so that their shape, as well as relative position to other loops, best fit both the data and the physical constraints. Each loop is represented by k subanchor beads inserted between the neighboring anchors. We define stretching and bending energy terms as Nj, j + 1 is a genomic distance between anchors j and j + 1, j to j + 1, and β is a constant parameter. To model the influence of short-range singleton interactions, we calculate the expected distances between all subanchor beads given only the physical constraints and then modify these distances based on the high-resolution singleton heatmaps for blocks. These updated distances are used in the third energy term dij and rij defined previously, but now for subanchor beads. The total energy function is simply Es + Eb + Eh, and again, the Monte Carlo simulation is used for the optimization.In the second step, we model the shape and position of chromatin loops inside a single domain. We begin by splitting the interaction network given by PET clusters contained within a domain into a number of disjoint connected components that we call The algorithm modifies the reference genomic interactions and topological domains introducing information on SVs. The resulting genomic interaction data is then passed to the 3D-GNOME modeling engine to obtain predicted 3D structures adjusted for SVs.Anchors intersected by deletions are removed from the reference interaction pattern. As a result, all the interactions stemming from these anchors are eliminated yielding a structure with fewer loops, and with the loops directly neighboring the deletion being shorter or longer depending on the interaction pattern and the deletion size. If an outermost anchor in a CCD is intersected by a deletion, the boundary of the CCD is deleted and the CCD is fused with a neighboring CCD. CCDs covered by deletions are removed and the ones partially excised fused with neighboring CCDs. The anchors intersected by duplications are duplicated along with the contacts they have with other genomic segments in a way that interactions with anchors located upstream from the duplication are kept by the affected anchor and downstream interactions in respect to the duplication are acquired by the duplicate. The duplicate is positioned downstream from the affected anchor. If larger genomic fragments are duplicated, interactions between anchor duplicates are established equivalently to those between the duplicated anchors and anchor duplicates are not linked with the anchors of the original fragments. If a duplication expands over a CCD boundary, parts of the CCDs placed at the breakpoint after duplication are fused. Introducing duplications also results in elongation of loops overlapping the duplicated site. Inversions of genomic segments containing anchor sites result in changing positions of the anchors and its directionality relative to other anchors. After an anchor is inversed, we delete all its previous contacts and link it to the closest anchor with which it can form a convergent loop to reflect the preference of CTCFs to have symmetric conformation in dimers , 17. In The algorithm accounts also for SVs that miss the CTCF binding sites, but the introduction of these results only in shortening or extending the corresponding chromatin loops.In order to assess the fraction of CTCF interaction segments conserved among different lymphoblastoid cell lines, we calculated a number of CTCF anchors identified in GM12878, which were intersected with CTCF ChIP-seq peaks called in the remaining lymphoblastoid cell lines.Since the available sets of CTCF ChIP-seq peaks for multiple lymphoblastoid cell lines  differedThe consensus CTCF binding sites were downloaded via BioMart interface in genomic coordinates of hg38 assembly and converted to hg19 coordinates using UCSC liftOver tool .Comparable datasets were obtained by the filtering  and ChIP-seq target protein (CTCF or RNAPII). We describe it using an example of CTCF interacting segments affected by deletions.First, CTCF anchors intersected by deletions exhibited by at least one of the 10 lymphoblastoid cell lines with available CTCF ChIP-seq data  were ideAdditional file 1:Supplemental tables. (XLSX 12146 kb)Additional file 2:Supplemental figures. (PDF 17323 kb)


DNA–histone interactions in eukaryotic organisms and have been well characterized in yeast and Arabidopsis. While there are maize proteins with similar domains as known remodelers, the ability of the maize proteins to alter nucleosome position has not been reported. Mutant alleles of several maize proteins  with similar functional domains to known chromatin remodelers were identified. Altered gene expression of Chr101, Chr106, Chr127, and Chr156 was demonstrated in plants homozygous for the mutant alleles. These mutant genotypes were subjected to nucleosome position analysis to determine whether misregulation of putative maize chromatin proteins would lead to altered DNA–histone interactions. Nucleosome position changes were observed in plants homozygous for chr101, chr106, chr127, and chr156 mutant alleles, suggesting that CHR101, CHR106, CHR127, and CHR156 may affect chromatin structure. The role of RNA polymerases in altering DNA–histone interactions was also tested. Changes in nucleosome position were demonstrated in homozygous mop2‐1 individuals. These changes were demonstrated at the b1 tandem repeats and at newly identified loci. Additionally, differential DNA–histone interactions and altered gene expression of putative chromatin remodelers were demonstrated between different maize haplotypes.Chromatin remodelers alter  The basic unit of chromatin is the nucleosome, which is made up of 150 base pairs of DNA wrapped 1.65 times around a histone octamer that is made up of two each H2A, H2B, H3, and H4 histones. DNA is further packaged with the addition of histone H1 and scaffold proteins to linker DNA between nucleosomes. While these DNA–histone interactions are in part determined by the DNA sequence itself, they can also be determined by chromatin remodeling complexes  ScSNF2  AtDDM1  proteins highly similar to the Arabidopsis AtDDM1  regions of a random selection of 400 classical genes on a microarray. Although sequencing has become a favored approach with the increasing availability of high‐throughput sequencing, this microarray provided a cost‐effective way to identify changes at several loci in uncharacterized genotypes. Herein, several mutant maize alleles of putative chromatin remodelers were identified and tested with nucleosome protection assay to identify nucleosome position changes.22.1http://www.yeastgenome.org/) was used to acquire the amino acid sequences and identifiers (ID) for ScSNF2 (SGD ID S000005816) and ScRAD54 (SGD ID S000003131). The Arabidopsis amino acid sequences were acquired from The Arabidopsis Information Resource , along with the Arabidopsis Genome Initiative (AGI) identifiers for AtDDM1 (AGI ID At5g66750), AtCLSY1 (AGI ID At3g42670), and AtDRD1 (AGI ID At2g16390). These proteins were queried against Zea mays using NCBI's BLASTP . The percent identities at each amino acid were downloaded and averaged to determine domain conservation.The Saccharomyces Genome Database  was used as the outgroup.Phylogenetic tree was generated, and the number of amino acid substitutions was determined in the Helicase_C domains using MEGA7  and have been maintained through self and sibling pollination. Seeds for chr101‐m1 (B73 background), chr101‐m3 (B73 background), chr106‐m1 (B73 background), and chr106‐T11 (W22 background) were generously donated by the Springer Lab (University of Minnesota) and were originally described by Li et al. (chr127‐m1 (mu1048254) and chr156‐m1 (mu1055648)  and were acquired from the Maize Genetics Cooperation Stock Center. The chr127‐m1 and chr156‐m1 genotypes are in the W22 background and have been maintained through self or sibling pollinations. For rmr1‐1 (mixed background), seed stocks from segregating families of homozygous mutants and heterozygotes were used , homozygous mutants and wild types from segregating families were used  according to the manufacturer's instructions. The resulting RNA was DNaseI (Promega) treated at 37°C for 30 min and then cleaned using RNA clean and concentrator‐25 (Zymo Research). Superscript III (Invitrogen) was used to reverse transcribe 1 ug of RNA into cDNA according to the manufacturer's instructions. cDNA was used as a template for qRT‐PCR using gene‐specific primers and 2.5m Tris‐HCl pH 7.8, 250 mm sucrose, 5 mm MgCl2, 5 mm KCl) and 1% formaldehyde by gently stirring for 10 min on ice. Fixation was stopped by stirring with 125 mm glycine for 5 min. The cells were lysed by continuing to stir on ice for 5 min with 0.25% Triton X‐100 (EMD Millipore) and 0.1% BME. This mixture was filtered through two layers of Miracloth  and suspended over 50% Percoll density gradient media  in Apel buffer. The nuclei were floated by centrifugation at 2000 g for 15 min at 4°C in a swinging‐bucket centrifuge. The band of nuclei floating on top of the Percoll were transferred to new ice‐cold tubes and resuspended in a minimum of twice the nuclei‐band volume of MNase digestion buffer . Nuclei were pelleted by centrifugation at 500 g for 15 min, resuspended in 1 ml of MNase digestion buffer, aliquoted and flash frozen in liquid nitrogen, and stored at −80°C until use.Nuclei were isolated from 10 g of leaf tissue as described previously .Digested nucleosomal DNA and reference undigested DNA were labeled with Cy3 and Cy5 (NimbleGen), respectively, according to the manufacturer's instructions. The labeled DNA were then hybridized to a ~400 classical gene transcription start site microarray  for at least 50% of their probes. Only these 364 regions were included in subsequent analysis. To determine the statistical significance of the identified changes in nucleosome position, Student's t test statistical analysis was performed at each probe using the raw GFF files for the nucleosomal and reference data. Regions having a significant difference  between wild‐type and mutant nucleosomal DNA while having no significant difference  between the wild‐type and mutant reference sequences through a minimum of 140‐bp continuous probe coverage were identified.Initially, to eliminate highly polymorphic regions, the raw GFF files for the genomic DNA of B73 and W22 were compared. 364 of the 3000‐bp probe regions were identified with no significant difference  with robust nucleosome position differences between the genotypes identified by microarray analysis were also tested by alternate MNase‐qPCR method  with a consistent nucleosome position region between genotypes was identified from the microarray analysis, and it was also tested by MNase‐qPCR −Ct(gDNA)]) and normalizing each 2−∆Ct to that of Mwp1  and was used to generate the appropriate Markov model from the transcription start site regions of the ~400 classical genes of the microarray used in this study. This Markov model was used as the background when searching for shared sequence motifs at the altered nucleosome position loci. Regions of the DNA sequences with significant nucleosome position change, as determined by the microarray analysis, were searched for the presence of common motifs using MEME  and exons , as well as a nonsense point mutation  analysis, gene expression was significantly reduced in all four homozygous mutants compared to wild type  were identified  and in B73 at the other six loci. As differential expression of some genes has been demonstrated between the two haplotypes ‐digested samples at Fdx3_NP1 and Stc1_NP1 rather than microarray. The Tpi3_NP1 region was not amenable to qPCR analysis. Although statistical significance was not demonstrated, the nucleosome position changes observed in chr106‐T11 at Fdx3_NP1 and Stc1_NP1 were also observed in chr106‐m1  made it possible to include the entire gene in the 3000‐bp region of study, and the nucleosome position change observed at this locus in chr127‐m1 was detected in the 3′UTR region, suggesting that the nucleosome position changes in this genotype were not limited to transcription start sites. Nucleosome position changes were detected for 13 genes in plants homozygous for chr156‐m1. Of these 13 genes, three had multiple nucleosome position changes, which contributed to a total of 16 discrete nucleosome position changes detected in this genotype  pathway‐dependent short sequences rich in asymmetric cytosine methylation (CHH) islands have been identified between some genes and transposable elements (TEs) and have been suggested to function as barriers between euchromatic and heterochromatic regions of the genome  had a higher correlation between mutant and NOL   confirmed that nucleosome position changes observed in both mop2‐1/mop2‐1 and chr127‐m1/chr127‐m1 plants were not detectable in mop1‐1/mop1‐1 plants (data not shown). These results indicate that nucleosome position at Stc1 and Tdy1 is likely associated with a PolV complex that includes MOP2 and CHR127 rather than a PolIV complex that would also include MOP1.While MOP2 is the second largest subunit of PolIV and PolV  and B' (low expressing), are associated with distinct chromatin structure at a regulatory region 100‐kb upstream of the b1 gene  with characterized chromatin structural changes .MOP2 and other genetically interacting proteins are required for appropriate transcriptional regulation of the s Figure c, sugges3.10b1tr in mop2‐1 homozygous plants  were also tested for nucleosome position change in plants homozygous for mop2‐1. Neither of the loci had a significant nucleosome position change in mop2‐1 (data not shown), suggesting that nucleosome position phenotypes are shared between mop2‐1 and chr127‐m1, but not mop2‐1 and chr156‐m1. The loss of positioned nucleosomes in homozygous chr156‐m1 plants  was identified at nine loci Figure  and 3. ATdy1_NP1, Motif‐1 was compared with the regulatory sequence of b1, which is a series of tandem repeats  match. This match was present in six of the seven tandem repeats of b1tr , although very little information is available about the biological function of ID0180. The comparison to Arabidopsis revealed that the most similar previously identified motif is the plant‐specific C2C2‐D of transcription factor recognition motif . These results could suggest that the chromatin proteins of this study and a C2C2‐Dof‐like transcription factor may act at Motif‐1 sequences to modify nucleosome position and facilitate transcriptional regulation. It can also be suggested that the motif corresponding to ID0180 could be a C2C2‐Dof transcription factor recognition sequence.Although SWI/SNF chromatin remodelers interact with and modify chromatin, direct DNA interaction has not been demonstrated, and many chromatin remodelers have been shown to be recruited by transcription factors . All of the chromatin proteins tested in this study are homologous with known members of SWI/SNF complexes in other species. The majority of the loci with nucleosome position changes in homozygous mutants showed evidence of nucleosomes being moved or held away from sequence‐dependent positions by functional chromatin proteins. These results are consistent with the human hSWI/SNF proteins, which move nucleosomes away from their sequence‐favored positions (Sims, Baughman, & Schnitzler, rmr1‐1 have phenotypes related to the transcriptional regulation of gene expression, suggesting that some functionality is disrupted by this mutation, no changes in nucleosome position were observed in plants homozygous for rmr1‐1. This could be because RMR1 does not function to modify or maintain nucleosome positioning, because RMR1 regulates loci other than the ones investigated, or because this particular allele of RMR1 does not disrupt the specific function of RMR1 in nucleosome positioning. RMR1 possesses the domains common to chromatin remodelers, including a Helicase_C domain, but it is possible that RMR1‐dependent regulatory mechanisms do not involve changes in nucleosome position and these domains instead support functions other than chromatin remodeling, like resolving the double helix during transcription. In support of this, two genes known to be transcriptionally regulated by RMR1 (Hale et al., rmr1‐1 homozygous plants. It is also possible that the rmr1‐1 mutation may not be sufficient to impede RMR1‐mediated nucleosome positioning, although the phenotypes associated with rmr1‐1 suggest that this is a deleterious allele. It is also possible that analysis of additional tissues, developmental stages, or regions not on the microarray may reveal RMR1‐dependent nucleosome position phenotypes.Interestingly, although plants homozygous for Chr106 between B73 and W22. This study also identified nucleosome position changes between B73 and W22 in regions with minimal sequence polymorphisms. Shared nucleosome position changes between the two haplotypes and in the homozygous chr106‐T11 mutants, and reduced Chr106 expression in W22 suggests that differential gene expression of chromatin proteins may influence haplotype‐specific phenotypes.Consistent with prior comparisons of gene expression between maize haplotypes (Dong et al., chr101 and chr106 genotypes. Previous research has demonstrated the lethality of double mutants and some phenotypic differences in genomewide DNA methylation between the chr101 and chr106 genotypes (Li et al., CHR101 and CHR106 are highly similar to one another, and this analysis detected both shared and distinct changes in nucleosome position in Fdx3_NP1, Tdy1_NP1, Bx1_NP2, Hm1_NP1, Pac1_NP1, Adh2_NP1, Lhcb1_NP1, Mads1_NP1, and Gpc1_NP1, which seem to have nucleosome position regulated by CHR101, CHR106, CHR127, and CHR156. This may indicate that a C2C2‐Dof type transcription factor recruits these chromatin proteins to regulated loci.Sequence similarity with a known Arabidopsis transcription factor binding motif suggests that a C2C2‐Dof type transcription factor may interact with some loci with changes in nucleosome positioning. Motif‐1 was detected in b1 (Alleman et al., It has been proposed that RdDM activity at CHH islands maintains silencing of transposable elements by forming an epigenetic barrier that blocks transcription from adjacent genes from spreading into the transposable elements (Li et al., chr127‐m1 allele used in this study is associated with increased gene expression of Chr127 compared to wild type, while mop2‐1 homozygous plants are loss‐of‐function mutants (Sidorenko et al., chr127‐m1 plants share the same phenotype as plants homozygous for the loss‐of‐function mop2‐1 alleles suggests that the transposon insertion in chr127‐m1 also results in loss of function.The This work has demonstrated altered nucleosome positions in several maize genotypes, including inbred haplotypes and mutant genotypes. Nucleosome position changes were observed at genes encoding proteins with a range of predicted biological functions. Collectively, these results suggest that nucleosome position may contribute to the phenotypic diversity observed in maize haplotypes and may be useful in exploiting and understanding this important crop plant. MNase‐sequencing approaches using these and other mutant genotypes would likely identify additional loci that are impacted by these mechanisms.K.M.M. and L.K.S. designed the experiments. L.K.S. performed the experiments. K.M.M. and L.K.S. interpreted the data and wrote the article. Click here for additional data file. Click here for additional data file. Click here for additional data file.