In summary, this study has identified the regulatory regions of genes in three tissues that play key roles in defining health and economically important traits and has set the precedent for the characterization of regulatory elements in ovine tissues using the Rambouillet reference genome.The Ovine Functional Annotation of Animal Genomes (FAANG) project, part of the broader livestock species FAANG initiative, aims to identify and characterize gene regulatory elements in domestic sheep. Regulatory element annotation is essential for identifying genetic variants that affect health and production traits in this important agricultural species, as greater than 90% of variants underlying genetic effects are estimated to lie outside of transcribed regions. Histone modifications that distinguish active or repressed chromatin states, CTCF binding, and DNA methylation were used to characterize regulatory elements in liver, spleen, and cerebellum tissues from four yearling sheep. Chromatin immunoprecipitation with sequencing (ChIP-seq) was performed for H3K4me3, H3K27ac, H3K4me1, H3K27me3, and CTCF. Nine chromatin states including active promoters, active enhancers, poised enhancers, repressed enhancers, and insulators were characterized in each tissue using ChromHMM. Whole-genome bisulfite sequencing (WGBS) was performed to determine the complement of whole-genome DNA methylation with the ChIP-seq data. Hypermethylated and hypomethylated regions were identified across tissues, and these locations were compared with chromatin states to better distinguish and validate regulatory elements in these tissues. Interestingly, chromatin states with the poised enhancer mark H3K4me1 in the spleen and cerebellum and CTCF in the liver displayed the greatest number of hypermethylated sites. Not surprisingly, active enhancers in the liver and spleen, and promoters in the cerebellum, displayed the greatest number of hypomethylated sites. Overall, chromatin states defined by histone marks and CTCF occupied approximately 22% of the genome in all three tissues. Furthermore, the liver and spleen displayed in common the greatest percent of active promoter (65%) and active enhancer (81%) states, and the liver and cerebellum displayed in common the greatest percent of poised enhancer (53%), repressed enhancer (68%), hypermethylated sites (75%), and hypomethylated sites (73%). In addition, both known and  Regulatory element characterization and chromatin state determination in relevant tissues was identified as a critical need for implementing precision breeding within the livestock industry by the Agricultural Animal Genomics Community . To thisIn vivo analysis of regulatory elements will allow researchers to test hypotheses of biological function of putative causal mutations in relevant production tissues. Understanding the phenotypic influences of genetic variance that lie in promoter and enhancer regions is important for trait prediction and the improvement of sheep production.Sheep production for meat, milk, and wool is an important agricultural industry across the globe with more than one billion sheep suited to a diverse range of climates . This diFunctional variants that are causally implicated in phenotypic variation are increasingly found to lie outside of transcribed regions within DNA regulatory elements . These rN = 2 for each tissue by design , the data collected here also provide a resource for evaluating the larger effort by permitting estimation of interindividual variation in the appearance and tissue distribution of regulatory elements. Three tissues were selected for this study based on their prominence in defining production traits and to span tissues of endodermal, mesodermal, and ectodermal origin and because each presents unique procedural challenges for performing chromatin immunoprecipitation with sequencing (ChIP-seq) assays. The liver is an endodermal-derived tissue that is a key metabolic component of the alimentary system  which denotes promoters and acetylation of histone 3 lysine 27 (H3K27ac) which denotes active enhancers . The monDNA methylation data derived from whole-genome bisulfite sequencing (WGBS) were incorporated to validate regulatory regions and chromatin states. In mammals, several groups have identified CpG islands that lack methylation are located at gene promoters . RepressOur objective for this study was to identify the locations of gene regulatory elements in sheep by characterizing histone modifications, CTCF binding, and DNA methylation for the cerebellum, liver, and spleen. Defining regulatory elements in the sheep genome will provide the basis for a greater understanding of the mechanisms that underpin phenotypic variation in important health and production traits in sheep.Tissue was collected postmortem from two pairs of healthy half siblings (one ewe and one wether per pair) totaling four yearling crossbred sheep  as approved by the Washington State University Institutional Animal Care and Use Committee. Small pieces of liver, spleen, and cerebellum tissues were collected within 30 min postmortem, briefly rinsed with ice cold 1 × PBS, and promptly snap frozen in liquid nitrogen. Samples were transferred from liquid nitrogen directly into a −80°C freezer for storage.g for 1 min at 4°C. Chromatin was incubated with 1 μg of antibody overnight at 4°C in a Hula mixer for 16 h. The following morning, protein G-coated magnetic beads were added and incubated 2 h at 4°C in the Hula mixer. The sample was washed twice with a low salt and once with a high salt buffer. Cross-links were reversed by incubating the sample at 65°C for 30 min at 400 rpm in a thermomixer. Purification was performed with the DNA Purification Buffers and Spin Columns Kit following the manufacturer’s instructions .Chromatin immunoprecipitation (ChIP) was performed using commercial antibodies for the histone modifications H3K4me3 , H3K4me1 , H3K27ac , H3K27me3 , and CTCF  with SimpleChIP Plus Enzymatic Chromatin IP Kit according to the manufacturer’s instructions  . BrieflyPurified DNA samples were quantified using the Qubit dsDNA HS Assay Kit . The DNA size and integrity were verified using a Fragment Analyzer . Libraries were prepared with the TruSeq ChIP Library Preparation Kit  for 75 base pair paired-end reads following the manufacturer’s instructions and sequenced to at least 20 million mapped reads for “narrow” histone marks H3K4me3, H3K27ac, and CTCF libraries and at least 40 million mappable reads each for “broad” histone marks H3K4me1 and H3K27me3 libraries.Whole-genome bisulfite sequencing was performed as a service by Novogene  on the liver, spleen, and cerebellum in all four animals. Briefly, DNA extracted from these tissues was subjected to agarose gel electrophoresis to test for DNA degradation and potential RNA contamination. The DNA was then quantified using a Nanodrop spectrophotometer  and a Qubit2.0 fluorometer . Lambda phage DNA was spiked in as a negative control for DNA methylation. Since lambda phage DNA lacks DNA methylation, all the cytosines in its DNA should be converted to uracil during bisulfite conversion. Any unchanged cytosine in the lambda phage DNA can thus be used to determine the efficiency of bisulfite conversion. For library construction, DNA samples were fragmented into 200–400 bp using sonication . Next, end repair, A-ligation, and methylation sequencing adapter ligation was performed. The adapter sequences were 5′ adapter (5′-AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTA CACGACGCTCTTCCGATCT-3′) and 3′ adapter (5′-GATC GGAAGAGCACACGTCTGAACTCCAGTCACATCACGATC TCGTATGCCGTCTTCTGCTTG-3′). Following this, the DNA library was subjected to bisulfite treatment . Library concentration was first quantified by Qubit2.0, diluted to 1 ng/μl before checking insert size on Agilent 2100 , and quantified with more accuracy by quantitative PCR (effective concentration of library > 2 nM). Libraries were then pooled per sample and sequenced paired-end.Oar_rambouillet_v1.0 with Bowtie2  (+2 to −2 kb), exonic, intronic, near a transcription termination site (TTS) (+1 to −1 kb), and intergenic using the Homer annotatePeaks.pl function  were trimmed using Trimgalore v0.4.5 with default parameters. Cleaned data for each sample was aligned to the sheep reference genome rameters . The X-crameters . The mets v0.0.6 . Regionss v0.0.6 .LearnModel function in ChromHMM was implemented with given chromatin states of two through 20 for each animal, and the model with the optimal number of chromatin states was examined using the CompareModels function in ChromHMM  at an average depth of 21 × (range 18× to 26×). Bisulfite conversion rate was ∼99.9% for all the samples. Mapping statistics for each tissue sample per sheep are displayed in Whole-genome bisulfite sequencing of cerebellum, liver, and spleen samples from the four sheep generated a total of 986, 1,070, and 904 million paired end reads, respectively, with a read length of 2 × 150 bp. The number of reads uniquely mapped to the reference genome was 84.24, 78.86, and 82.48% for the cerebellum, liver and spleen, respectively. The uniquely mapped bases covered the reference genome , which is consistent with several tissues in cattle . ChromosOar_rambouillet_v1.0 genome annotation file and these classifications are displayed in The proximity of H3K4me3 peaks to TSS was investigated by comparing consensus H3K4me3 peaks and CAGE data generated by ALB), a gene that encodes a plasma protein synthesized in hepatocytes and expected to be active in the liver, has one promoter and two enhancers annotated in humans that are within 2 kb upstream from the start of the gene , a gene expected to be active in the spleen and encodes a membrane protein involved with macrophage development, displayed sequence pileup for active marks H3K4me3 and H3K27ac and low levels of DNA methylation directly upstream (PAX6) is known to be involved in the development of neural tissues and maturation of granule neurons in the cerebellum and is known to have a promoter and multiple enhancers both upstream and downstream of the gene  is a gene that encodes a meiosis-specific protein involved in the synapsis of sister chromatids that is not expected to be active in the liver, spleen, or cerebellum  and cerebellum , but the correlation in the spleen was markedly lower , and overall, the correlations between the spleen samples were lower than the liver and cerebellum for all four histone marks. This is evident in H3K27me3 in the spleen  than in the liver  and cerebellum . The correlations of DNA methylation signal between samples ranged from 0.70 to 0.76, with the liver and cerebellum displaying the greatest correlation between the two males (0.76). However, sex differences in correlations were not observed, as each female has a moderate to high correlation with both the other female (0.54–0.84) and both males (0.44–0.92) for each mark within all three tissues.A principal component analysis was performed with the DNA methylation data to investigate similarity and differences between samples and tissues. Eigenvalues were calculated based on the position of CG methylation signal in all animals for all three tissues, and the first two eigenvalues (PC1 and PC2) were plotted . SamplesAverage methylation levels were calculated and compared in each of the three tissues in both the CG and non-CG sites . Non-CG The relative positions of the combination of specific histone marks provide a more complete definition of the overall regulatory chromatin state than individual peak calling. Regulatory elements were defined for two animals (M1 and M2) using a hidden Markov model employed by ChromHMM which assigns 200 bp bins across the genome to a given number of chromatin states based on the combined histone modification signal profiles , 2017. TThe signal of all the histone marks and the nine chromatin states for each tissue is displayed as heatmaps in The correlation of DNA methylation status with predicted chromatin state was examined by estimating the number of hyper- and hypomethylated regions per Mb within the boundaries of the regulatory elements in the nine defined chromatin states. The greatest number of hypomethylated regions was observed in active enhancer regions in the liver and spleen and in active promoter regions in the cerebellum, as expected if our process was correctly identifying regulatory elements and classifying them as actively transcribed genes. The greatest number of hypermethylated regions was observed in poised enhancers and CTCF in the liver, weak poised and poised enhancer regions in the spleen, and poised enhancer regions in the cerebellum, also consistent with the process correctly classifying regulatory elements.The chromosomal segments spanned by regulatory elements, as defined by the histone mark peaks, were combined and summarized to estimate the overall extent and percent of the genome representing regulatory elements and their chromatin state among the three tissues examined. Chromatin states from the ChromHMM analyses were categorized and combined into promoter, active enhancer, poised enhancer including weak poised enhancers, repressed enhancer including weak repressed enhancers, and quiescent or low signal categories and averaged for each tissue . PromoteThe locations of assigned promoter chromatin states were compared with TSS generated from CAGE data for the liver, spleen, and cerebellum. Both the signal distribution and heatmap plots display a strong signal before and after the TSS in all three tissues . This siSimilarities and differences of promoters, enhancers, and methylated regions within and between tissues were examined and percentages of overlap are displayed in P < 0.00001) CTCF-binding motifs both known from previous research and de novo was identified across the genome in the liver, spleen, and cerebellum , compartments of chromatin interactions, across the genome . The locrebellum . Of thesrebellum . Three mThe goal of this study was to characterize regulatory elements in ovine liver, spleen, and cerebellum using ChIP-seq and WGBS. The three selected tissues, the liver, spleen, and cerebellum, each represent a different developmental origin and are important to metabolism, immune response, and motor control, respectively. We have demonstrated the successful application of the micrococcal nuclease ChIP protocol across these tissues and the bioinformatic pipeline for the analysis of ChIP-seq in sheep. Furthermore, this study has incorporated the value of coupled histone modification and DNA methylation data toward a better understanding of regulatory regions in the sheep genome.Micrococcal nuclease was used to shear the chromatin because it provided a consistent and reproducible shearing across samples and tissue types. A limitation of the micrococcal nuclease may be increased likelihood of the appearance of duplicated reads due to similarity of cut sites in the chromatin; however, several studies have not found substantial bias when duplicates were removed . FurtherSequence read pileups were examined in IGV near genes known to be active and inactive in humans and expected to be conserved across species. This provided a means of examining genes with known promoters and expression patterns as positive and negative controls for both ChIP-seq experiments and WGBS and provided insight into the potential similarity of regulatory elements across species. Several genes known to be active across different mammalian species in the liver, spleen, and cerebellum showed a sequence pileup of active histone marks which likely indicated the presence of active regulatory elements. Inversely, genes known to be active during meiotic processes and quiescent during adult stages in several mammalian species showed no sequence pileup of histone marks and presence of DNA methylation, which likely indicates inactivity of regulatory elements.Consistency of regulatory element identification by ChIP-seq and DNA methylation for each tissue between the four individual animals was evaluated by calculating Spearman and Pearson correlations, respectively. Correlations between samples for both ChIP-seq and DNA methylation were within the ranges previously reported with sequence data . FurtherThe CG methylation signal for all four samples clustered distinctly by tissue in a principal component analysis, indicating clear differences in DNA methylation between tissues . This fiThe enrichment of individual histone marks was examined by identifying peaks in each sample. The number of peaks identified in these sheep liver, spleen, and cerebellum samples was consistent with other studies in sheep adipose, cattle liver, cattle muscle, cattle rumen epithelium, human liver, and mouse liver  Villar . Many chOar_rambouillet_v1.0 genome. In the liver, spleen, and cerebellum, the most TSS were identified near (within 2 kb of distance on either side) to H3K4me3 peaks, which is not surprising. Many H3K27ac and H3K4me1 peaks, which indicate the presence of active or poised enhancers, were located in intronic regions. Repressed enhancers marked by H3K27me3 were located mostly in intergenic regions, along with CTCF, which may be indicative of insulated TAD boundaries not in close proximity of genes. Further work with additional animals in combination with RNA expression and TSS analyses is needed to examine regulatory element activity outside of previously annotated regions of the sheep genome.Trimethylation of histone 3 lysine 4 peaks were enriched annotated TSS in all three tissues. The peaks and heatmap signature signals are similar to several other ChIP-seq experiments in human PBMCs and CD14+ cells, as well as mouse liver . Peaks fThe genomic segments identified by histone mark peaks were evaluated for overlap between marks and CTCF binding. This broader view of the regulatory landscape lends a better understanding of gene regulation at each location than individual marks . Active Hypermethylated and hypomethylated regions of the sheep genome were defined across liver, spleen, and cerebellum tissues. The number of hypermethylated and hypomethylated regions per Mb in each of the nine chromatin states was quantified. The data presented in this study demonstrate an enrichment of hypermethylated regions in chromatin states with prominent H3K4me1 (primarily poised enhancers) and hypomethylated regions in active enhancers and promoters enriched with H3K27ac and H3K4me3. These results agree with previous research in humans and mice which indicate that active enhancer activity is inversely correlated with DNA methylation . InteresApproximately 20% of the sheep genome was assigned to a chromatin state category including promoters; active, poised, and repressed enhancers; and CTCF in the liver, spleen, and cerebellum. In cattle, a previous study similarly assigned approximately 30% of the genome to either a chromatin state or areas with open chromatin in rumen epithelium . The locThe CCCTC-binding factor (CTCF) along with cohesins was shown to be present at the boundaries of TADs in humans and mice . Dependide novo motifs identified in more than one tissue. A large number of CTCF-binding motifs are similar in sequence across mammalian species including cattle (Drosophila melanogaster), and yeast (Saccharomyces cerevisiae) within the HOMER motif database Kimberly Davenport (University of Idaho)Stephen White Michelle Mousel Alisha Massa (Washington State University)Kim Worley (Baylor College of Medicine)Alan Archibald Emily Clark Brian Dalrymple James Kijas (CSIRO)Shannon Clarke (AgResearch)Rudiger Brauning (AgResearch)Timothy Smith Tracey Hadfield (Utah State University)Noelle Cockett (Utah State University)PRJEB41457.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: NCBI BioProject, accession no: Sheep were housed and cared for under Animal Subject Approval Form (ASAF) 04618 titled “Maintenance of MCF and OPPV free sheep flocks” approved by the Washington State University Institutional Animal Care and Use Committee (IACUC) and euthanized for tissue collection under ASAF 6003.MM, MH, SW, SM, TS, and BM designed the study. KD, AM, MM, MH, and BM collected the samples. BM, KD, MM, SW, AM, SM, and SB facilitated the ChIP-Seq and methylation experiments, data analyses, and drafted the manuscript. TS facilitated ChIP-seq library preparation and sequencing. KD, AM, SB, SM, MM, MH, SW, TS, and BM discussed and interpreted results. SM, MM, SW, NC, TS, and BM acquired funding. All authors contributed to the article and approved the final version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Inside the nucleus, chromatin is functionally organized and maintained as a complex three-dimensional network of structures with different accessibility such as compartments, lamina associated domains, and membraneless bodies. Chromatin is epigenetically and transcriptionally regulated by an intricate and dynamic interplay of molecular processes to ensure genome stability. Phase separation, a process that involves the spontaneous organization of a solution into separate phases, has been proposed as a mechanism for the timely coordination of several cellular processes, including replication, transcription and DNA repair. Telomeres, the repetitive structures at the end of chromosomes, are epigenetically maintained in a repressed heterochromatic state that prevents their recognition as double-strand breaks (DSB), avoiding DNA damage repair and ensuring cell proliferation. In pluripotent embryonic stem cells, telomeres adopt a non-canonical, relaxed epigenetic state, which is characterized by a low density of histone methylation and expression of telomere non-coding transcripts (TERRA). Intriguingly, this telomere non-canonical conformation is usually associated with chromosome instability and aneuploidy in somatic cells, raising the question of how genome stability is maintained in a pluripotent background. In this review, we will explore how emerging technological and conceptual developments in 3D genome architecture can provide novel mechanistic perspectives for the pluripotent epigenetic paradox at telomeres. In particular, as RNA drives the formation of LLPS, we will consider how pluripotency-associated high levels of TERRA could drive and coordinate phase separation of several nuclear processes to ensure genome stability. These conceptual advances will provide a better understanding of telomere regulation and genome stability within the highly dynamic pluripotent background. Eukaryotic genomes are dynamic, non-randomly organized structures within the nucleus. A complex and highly hierarchical three-dimensional network of structures organizes chromatin into active/inactive compartments, membraneless bodies, lamina associated domains, protein- or RNA-mediated loops, enhancer–promoter contacts, and chromatin regions with differential accessibility. This complex chromatin architecture is established by epigenetic and transcriptional mechanisms and is spatially and temporally tightly regulated, to ensure the maintenance and viability of cellular functions. Chromatin architecture also segregates the large repetitive and gene-poor domains of the genome, like centromeres and telomeres, into constitutive heterochromatin domains characterized by condensed chromatin fibers, high levels of DNA and histone methylation, and transcriptional repression of the underlying DNA sequences . ConstitTelomeres are nucleoprotein structures formed at the end of chromosomes by the assembly of the shelterin complex  at the TTAGGG telomeric repeats . The hetmESCs derived from the inner-cell mass (ICM) of early blastocysts retain self-renewal and pluripotent capacity, being able to differentiate into any type of cell. However, the self-renewal and high proliferative capacities expose mESCs to high levels of DNA replication stress . CriticaThe chromatin of mESCs has an unusual configuration with open 10 nm chromatin fibers widely dispersed throughout the nucleoplasm, including at constitutive heterochromatin domains . ChromatChd1, esBAF complex, Padi4 or H3K9me3 methyltransferases) affects both self-renewal and differentiation potential of mESCs , but pluripotent cells adopted strategies to minimize accumulation of DNA mutations and preserve genome stability . DDR mecIn mESCs, cyclins A and CDK1/2 are highly expressed and there is an increased transcription of S-phase genes by hyperphosphorylation of retinoblastoma (Rb), forcing a rapid entry in S-phase . This reHeterochromatin is permeable to DNA repair mechanisms , and so DNA replication is essential to the self-renewal and pluripotency capacities of mESCs, whilst conferring an opportunity to alter chromatin with incorporation of new histones or by spatially reorganizing pre-existent histone modifications . ConversIn sum, pluripotent cells acquired mechanisms to balance for high proliferative rates without compromising genome integrity, including at heterochromatin domains.Long telomeres are essential for self-renewal and high proliferative capacities in embryogenesis. Two waves of telomere elongation occur during early embryonic development. Through early cleavage stages, parental telomere length is reset and telomeres are elongated by a recombination-based mechanism known as the Alternative Lengthening of Telomeres (ALT) pathway . TelomerNanog and Oct4  are chanditions . Interes  of pluripotent telomeres may enable access for recombination or telomerase to modulate telomere length . As mESCtability . Similartability . TogetheThe ATRX/DAXX chaperone complex deposits histone H3.3, typically associated with active/open chromatin, at telomeres and pericentromeres in mESCs and embryonic germ cells, but not in differentiated cells . ATRX anIn human cells, the loss of Tousled-like kinases 1 and 2 , lead to chromatin decompaction and increased genome accessibility, particularly at heterochromatin domains. Importantly, chromatin decompaction induces heterochromatin expression and ALT features at telomeres , suggestTelomeric RNA is transcribed by RNA polymerase II moving toward the telomere, from promoters located at subtelomeres, and is composed of G-rich repeats with heterogeneous size  . In humaInterestingly, TRF2-TERRA interactions were proposed to mediate telomere heterochromatin in human cells . As TRF2Phase-separation is based on the spontaneous organization of a solution into two-phases with different densities . The bioin vitro and in vivo  bodies, known as ALT-associated PML Bodies (APBs) . APBs coPluripotent telomeres may also be able to cluster at PML bodies  but whetTelomere maintenance relies on the coordinated crosstalk involving the telomeric structure, TERRA and nuclear processes . Telomere end protection is critical for genome stability and cell proliferation and the mechanisms involved are fairly conserved across cellular backgrounds and species. Thus, it is striking that a core shelterin component, TRF2, is redundant for telomere protection in pluripotent mESCs. Additionally, as pluripotent cells acquired special features to compensate for the unique nuclear environment, it is paramount to further explore the mechanisms governing telomere maintenance in pluripotent cells.One of the hallmarks of pluripotent telomeres is high TERRA levels, which may have pluripotent-specific roles, like shown by the regulation of pluripotent gene expression in iPSCs. Furthermore, TERRA molecules can originate from different genomic locations and have different sizes. Thus, regulation of TERRA properties  may affect its function by modulating (i) its ability to recruit heterochromatinization factors; (ii) competition with yet unknown cell-specific proteins at telomeres and/or iii) the molecular substrate available for weak multivalent interactions that can affect the material state of telomeres. Strong evidence supports a role for RNA molecules to act as a regulatory elements of LLPS condensate formation, size and constitution  what are the signaling triggers that promote phase-separation; (ii) how the nuclear environment modulates distinct condensates at the same loci and at the same phase of the cell-cycle ? New tools that can regulate phase separation in live cells are starting to emerge and will undoubtedly probe cellular functions and the functional possibilities enabled by phase-separation.CN wrote the manuscript and prepared the figure.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Drosophila oogenesis, the enzymatic functions are not equally required, which may indicate that different genes require different enzymatic functions. An analogy for this phenomenon is the handyman principle: while a handyman has many tools, which tool he uses depends on what requires maintenance. Here we analyzed the role of the non-enzymatic core module during Drosophila oogenesis, which interacts with TBP. We show that depletion of SAGA-specific core subunits blocked egg chamber development at earlier stages than depletion of enzymatic subunits. These results, as well as additional genetic analyses, point to an interaction with TBP and suggest a differential role of SAGA modules at different promoter types. However, SAGA subunits co-occupied all promoter types of active genes in ChIP-seq and ChIP-nexus experiments, and the complex was not specifically associated with distinct promoter types in the ovary. The high-resolution genomic binding profiles were congruent with SAGA recruitment by activators upstream of the start site, and retention on chromatin by interactions with modified histones downstream of the start site. Our data illustrate that a distinct genetic requirement for specific components may conceal the fact that the entire complex is physically present and suggests that the biological context defines which module functions are critical.The Spt/Ada-Gcn5 Acetyltransferase (SAGA) coactivator complex has multiple modules with different enzymatic and non-enzymatic functions. How each module contributes to gene expression is not well understood. During  Drosophila oogenesis, while the enzymatic functions are largely dispensable. Despite this differential requirement, SAGA subunits appear to be broadly recruited to all promoter types, consistent with the biochemical integrity of the complex. These results suggest that genetic requirements for different modules depend on the developmental demands.Embryonic development critically relies on the differential expression of genes in different tissues. This involves the dynamic interplay between DNA, sequence-specific transcription factors, coactivators and chromatin remodelers, which guide the transcription machinery to the appropriate promoters for productive transcription. To understand how this happens at the molecular level, we need to understand when and how coactivator complexes such as SAGA function. SAGA consists of multiple modules with well characterized enzymatic functions. This study shows that the non-enzymatic core module of SAGA is required for  The Spt/Ada-Gcn5 Acetyltransferase (SAGA) complex is required for the transcription of most RNA polymerase II (Pol II) genes . It contDrosophila, yeast and mammals indicates that the disruption of different modules has different effects on gene regulation. Our previous work demonstrated that oogenesis requires the SAGA HAT module function, but not the DUB module function, indicating that not all genes require all enzymatic functions simultaneously /Dpy[+]; noc[Sco]/SM6a. The F1 larvae were screened for DsRed signal and all adult males with DsRed positive eyes mated in single-male crosses w[1118]/Dpy[+]; noc[Sco]/SM6a. Male F2 progeny with DsRed balanced by Sm6A were again mated to this balancer create stable stocks of the genotype w[118];saf6CDSdelDsRed/SM6a, abbreviated as saf6[CDSdel].The wda[EMS] stock was generated by a ethyl methanesulfonate (EMS) screen performed by Xuanying Li and Susan Abmayr. Adult males (P{neoFRT}82B ry[506]) were starved at 3–5 days post-eclosion for three hours and transferred to vials saturated with 30mM EMS, 100mM Tris-HCl and 10% sucrose. After 20 hours, the males were transferred to fresh food for three changes to clear the EMS and mated to virgin females  bw[D], S[To create germline clones, one needs a recombinant stock that contains an FRT site that is located at the same arm as where the mutated allele is positioned. . The saf6 mutant allele was created in BL55821 and FRTThe actual germ line clones were created by crossing virgins from each “mutation, FRT stocks” to the corresponding ovoD males to introduce recombination. Larvae were heat-shocked for 2hr at 37°C for two days starting from the second day after hatching. Homozygous virgins were collected and used for further analyses.For each protein of interest, we tested where possible the efficacy of multiple RNAi sequences expressed from either the second or third chromosomes using pValium20 and pValium22 lines where possible. The knockdown was tested at 25 and 29°C. UAS RNAi lines to target WDA, SAF6 or TAF10b were generated by Rainbow Genetics Inc . To induMale and female wild type flies were conditioned for three days on apple juice plates supplemented with yeast paste to fatten the ovaries. The dissections were performed in fresh and cold Shields and Sang M3 insect medium within 30 minutes. Total RNA was extracted using the QiagenEasy RNA extraction kit according to the manufacturer’s instructions, with biological triplicates for each condition. For RNA-sequencing, reads generated were 51-base-pair (bp) single-end, poly-A-selected and directional using the Illumina protocol.Drosophila genome version dm6, using bowtie with parameters -k 1 -m 3. Gene definitions utilize Ensembl 87. ChIP-seq samples were scaled to the input (sonicated whole cell extracts from ovaries) to account for differences in sequencing depth using the signal extraction scaling (SES) method and the occupancy was displayed as the log2 ratio of the IP over the input.ChIP was performed on ovaries as described before . To mini7 Kc167 cells were fixed with 1% formaldehyde in SFX culture media at room temperature for 10 minutes with rotation. Fixed cells were washed with cold PBS, incubated with Orlando and Paro’s Buffer  for 10 minutes at room temperature with rotation, and then centrifuged and re-suspended in ChIP Buffer . Sonication was performed with a Bioruptor Pico for five rounds of 30 seconds on and 30 seconds off. Chromatin extracts were then centrifuged at 16000 g for 5 minutes at 4°C, and supernatants were used for ChIP.For each ChIP-nexus experiment, 10To couple Dynabeads with antibodies, 50 μl Protein A and 50 μl Protein G Dynabeads were used for each ChIP-nexus experiment and washed twice with ChIP Buffer. After removing all the liquid, Dynabeads were resuspended in 400 μl ChIP Buffer. 10 μg antibodies were added, and tubes were incubated at 4°C for 2 hours with rotation. After the incubation, antibody-bound beads were washed twice with ChIP Buffer.f exonuclease for generating Pol II footprints at high resolution. After each enzymatic reaction, the chromatin was washed with the Nexus washing buffers A to D and Tris buffer .For chromatin immunoprecipitation, chromatin extracts were added to the antibody-bound beads and incubated at 4°C overnight with rotation and then washed with Nexus washing buffer A to D . End repair and dA-tailing were performed using the NEBNext End Repair Module and the NEBNext dA-Tailing Module. ChIP-nexus adaptors with mixed fixed barcodes  were ligated with Quick T4 DNA ligase and converted to blunt ends with Klenow fragment and T4 DNA polymerase. The samples were treated with lambda exonuclease and RecJf exonuclease digestion, the chromatin was eluted and subjected to reverse crosslinking and ethanol precipitation. Purified single-stranded DNA was then circularized with CircLigase, annealed with oligonucleotides complementary to the BamHI restriction site and linearized by BamHI digestion. The linearized single-stranded DNA was purified by ethanol precipitation and subjected to PCR amplification with NEBNext High-Fidelity 2X PCR Master Mix and ChIP-nexus primers. The ChIP-nexus libraries were then gel-purified before sequencing with Illumina NextSeq 500.After RecJ56.4741) were re-annotated using CAGE-seq data from Drosophila melanogaster kc167 cells (modENCODE_5333) and virgin fly ovaries (modENCODE_5368). Before alignment, raw fastq files were trimmed using Cutadapt to remove the 9-nt adapter sequence (GATCAGCAG for kc167 and ACGCAGCAG for ovaries) attached to the 5’ end of reads. Adapter-removed reads were then aligned to dm6 using STAR and further processing and analysis was done with the CAGEr package from R. For TSS tag clustering (TCs) low-quality reads were removed and replicates were merged (only for Kc167 cells). Clusters with only one TSS (singletons) were discarded unless the normalized signal (TPM) was equal or above 5. TSSs within 40-bp of each other were clustered together and only clusters with at least 12 TPM from all TSS positions were considered expressed and kept for further analysis.TSSs from non-overlapping Flybase protein coding genes (fb-rPromoter types were defined by the presence or absence of the motifs listed in (boxplots) were generated by calculating the total number of RPM at each promoter in a 101-bp window centered at the TSS for ChIP nexus data, whereas for ChIP-seq boxplots were generated using the maximum value in 501-bp window centered at the TSS.DNA sequences at each promoter category  were obtained from the dm6 genome and represented as heatmap. The motifs logos were generated using the R package ggseqlogo. ChIP-nexus average binding profiles (metaprofiles) were plotted in 201-bp window using the average footprints for each factor in reads per million (RPM) on the positive strand (above line) and negative strand (below line) across the different promoter groups. Signal Most DIC images were obtained using a Nikon Ti2 motorized widefield microscope equipped with Nikon Perfect Focus technology and a sCMOS camera. However, due to COVID-19 research restrictions selected images had to be captured using an Axioplan2 wide field microscope using a 20x air objective. Multiple images were captured to cover each ovariole and merged with Photoshop using the default settings (Files➔ Automate ➔photo merge). Grey boxes have been placed behind the image to obtain equal panel sizes when required.The gel filtration samples were resolved on 10% SDS-PAGE gels. The HAT assay samples were resolved on 15% SDS-PAGE gels. Gels were transferred to a PVDF membrane and blocked for one hour at 4°C in 5% milk in Tris-buffered saline (TBS) and 0.1% Tween-20. Primary antibodies were diluted in 5% milk in TBS and 0.1%Tween-20 and incubated overnight at 4°C. The following antibodies were used: Gcn5 ; Ada2b ; anti-Flag- horseradish peroxidase ; H3 ; H3K9/K14ac ; Donkey anti-rabbit IgG-horseradish peroxidase . The rabbit TBP antibody was kindly shared by the Zeitlinger laboratory.S1 TableDrosophila stocks that were used in this study.This table includes an overview of the (DOCX)Click here for additional data file.S2 TableThis table includes the sequences of the PCR primers used in this study.(DOCX)Click here for additional data file.S3 TablePromoter groups were defined based on presence or absence of the different motifs. Each motif was scanned within the start and end windows using the TSS as a reference. A positive match for either DPE_O  or DPE_K(DOCX)Click here for additional data file.S1 Fig(A) Spt20 RNAi ovariole. (B) Spt7 RNAi ovariole. Scale bar: 50 μM.(TIF)Click here for additional data file.S2 FigThe 4,000 active genes were divided into ten quantiles based on Oregon R wild type ovary RNA-seq expression levels (decreasing from left to right) and each box plot panel displays the SAGA subunit occupancy per quantile (log2 ratio over input). This shows that SAGA occupancy levels correlate with transcriptional activity.(EPS)Click here for additional data file.S3 Fig(A) The DNA sequence of each promoter type  is shown as colored letters for a 101 bp window centered around the transcription start site. (B) The position weight matrix (PWM) logo for each promoter type .(EPS)Click here for additional data file.S4 Fig(A) Average binding of SAGA subunits at gene promoters within each promoter group  SAGA occupancy does not drastically change between promoter types. For each promoter group, binding occupancy at each gene is calculated as the maximum value in a 500 bp window centered at the TSS.(EPS)Click here for additional data file.S5 Fig(A) The DNA sequence of each promoter type  is shown as colored letters for a 101 bp window centered around the transcription start site. (B) The position weight matrix (PWM) logo for each promoter type .(EPS)Click here for additional data file.


EPAS1, to enable a better understanding of EPAS1 activity and regulation and support future research. Endothelial PAS domain-containing protein 1 (EPAS1), also HIF2α, is an alpha subunit of hypoxia-inducible transcription factor (HIF), which mediates cellular and systemic response to hypoxia. EPAS1 has an important role in the transcription of many hypoxia-responsive genes, however, it has been less researched than HIF1α. The aim of this study was to integrate an increasing number of data on EPAS1 into a map of diverse OMICs elements. Publications, databases, and bioinformatics tools were examined, including Ensembl, MethPrimer, STRING, miRTarBase, COSMIC, and LOVD. The EPAS1 expression, stability, and activity are tightly regulated on several OMICs levels to maintain complex oxygen homeostasis. In the integrative EPAS1 map we included: 31 promoter-binding proteins, 13 interacting miRNAs and one lncRNA, and 16 post-translational modifications regulating EPAS1 protein abundance. EPAS1 has been associated with various cancer types and other diseases. The development of neuroendocrine tumors and erythrocytosis was shown to be associated with 11 somatic and 20 germline variants. The integrative map also includes 12 EPAS1 target genes and 27 interacting proteins. The study introduced the first integrative map of diverse genomics, transcriptomics, proteomics, regulomics, and interactomics data associated with  EPO), which regulates red blood cell production and gene for vascular endothelial growth factor (VEGF), which promotes angiogenesis and increases vascular permeability and local tissue oxygenation. In addition, HIFs regulate genes involved in iron metabolism and bone marrow microenvironment adjustments, which facilitate erythroid progenitor proliferation and maturation. EPAS1 regulation of glycolytic enzymes and glucose transporters decreases oxygen consumption with metabolic switch from oxidative to glycolytic [Endothelial PAS domain-containing protein 1 (EPAS1), also known as Hypoxia-inducible factor 2 alpha (HIF2α), is an alpha subunit of heterodimeric transcription complex HIF-2. Transcription complexes HIFs function as master regulators of oxygen homeostasis [ycolytic . Due to ycolytic .2), as the subunit HIFα is degraded by hydroxylation with the egl-9 family hypoxia inducible factors , referred also as prolyl hydroxylases PHD1, 2, and 3, and subsequent ubiquitination with the von Hippel-Lindau tumor suppressor protein (VHL). Hydroxylation is an oxygen-dependent post-translational modification, therefore under hypoxic conditions (~1% O2), hydroxylase activity is inhibited. A stable HIFα subunit accumulates and forms HIF dimer complex, which activates the transcription of target genes in the nucleus [HIF factors are comprised of an alpha subunit, HIFα, and a beta subunit, HIFβ. The first discovered isoform of an alpha subunit was HIF1α , while E nucleus ,9.HIFα expression pattern. HIF1α is expressed ubiquitously in all cells, whereas EPAS1 is expressed only in certain tissues. There is also growing evidence of cooperation with other transcription activators and repressors for modulation of specific HIF activity [Stable HIF dimer complex binds to the hypoxia-response element (HRE) with core-binding motif RCGTG in the regulatory regions of HIF-target genes ,11. At factivity . HIF1α was recently published by Kunej (2021) [EPAS1 gene from publications and databases. Integrative map for j (2021) . In comphttps://www.ensembl.org/index.html, accessed on 22 October 2021) [https://www.ncbi.nlm.nih.gov/, accessed on 22 October 2021) [https://www.proteinatlas.org/, accessed on 22 October 2021) [https://www.uniprot.org/, accessed on 9 November 2021) [EPAS1 transcription start site (TSS) and of 3′UTR region for further analysis of CpG islands and miRNAs were retrieved from Ensembl, release 104. CpG islands analysis was performed using MethPrimer  [https://genome.ucsc.edu/, accessed on 22 October 2021). Protein interactions were retrieved from the Search Tool for the Retrieval of Interacting Genes/Protein (STRING)  [https://reactome.org/, accessed on 22 October 2021) [https://mirtarbase.cuhk.edu.cn/, accessed on 25 October 2021) [http://geneontology.org/, accessed on 22 October 2021), using PANTHER classification system [https://omim.org/, accessed on 22 October 2021) [http://simple-clinvar.broadinstitute.org/, accessed on 22 October 2021) [EPAS1 variants associated with disorders, we reviewed the literature, the Catalogue of Somatic Mutations in Cancer (COSMIC)  [https://www.lovd.nl/, accessed on 22 October 2021) [Information regarding DNA and RNA sequences was collected from genome browsers Ensembl, release 104 er 2021) , and Nater 2021) . Data aber 2021) . Informaer 2021) . Nucleot21 , and er 2021) . Scientin system . Informaer 2021) . For EPAer 2021)  and the er 2021) . The arter 2021) . All repEPAS1 gene has genome size 89291 base pairs (bps) and it is mapped on chromosome 2, cytogenetic band 2p21  [Based on the mRNA expression profile obtained from the Human Protein Atlas database, the he lungs . In the nophils) .EPAS1 transcript NM_001430.5 is coding for the protein sequence marked in NCBI with an ID NP_001421.2. It has 870 amino acids (aa) and a protein mass 96.5 kDa , which are validated to bind to the EPAS1 mRNA or protein; and post-translational modifications (PTMs) that regulate EPAS1 protein levels.Regulomics encompasses all elements that regulate gene expression and EPAS1 protein levels. In this section, we gathered data of Based on the data from the UCSC genome browser generated by the Eukaryotic Promoter Database, two experimentally validated promoter sequences of 60 bps were identified. The first promoter sequence is located at the genomic loci chr2:46297358-46297417 and starts 49 bps upstream of the TSS. The second identified promoter at the genomic loci chr2:46297717-46297776 is located within the 5′UTR region, starting 310 bps downstream of the TSS.EPAS1 and neuroblastoma tumor progression and showed that the EPAS1 gene is in neuroblastoma cells also regulated at the level of DNA transcription. With the novel engineered method, they identified 27 proteins with specific binding to the promoter of EPAS1 in neuroblastoma cells  transcription factor, with a putative role in the negative regulation of EPAS1 expression [EPAS1 was associated with the progression of other types of cancer. Cui et al. (2016) showed that the protein methyl-CpG binding protein 3 (MBD3) binds to the EPAS1 promoter in breast cancer cells and contributes to enhanced EPAS1 transcription. MBD3 binds to the CpG-rich promoters and induces demethylation of promoters and active EPAS1 gene expression [EPAS1 expression in cancer cells, tumor necrosis factor superfamily member 14 (TNFSF14), also known as LIGHT. LIGHT-induced expression of EPAS1 requires binding of p52 protein to the EPAS1 promoter [EPAS1 transcription [Regulation of HIFα subunits is mostly considered on the protein level with post-translational modifications. Recently, Hamidian et al. (2018) studied the correlation between high expression of ma cells . The majpression . High expression . D’Ignazpromoter . Transcrcription  , and chronic obstructive pulmonary disease [A sequence of 4000 bps (±2000 of the TSS) in the TSS site . The isl disease ,36,37,38EPAS1 mRNA has been experimentally confirmed to be targeted by 13 microRNAs (miRNA) in humans [EPAS1 mRNA, which is supported by reporter assays, western blot (WB), or quantitative PCR (qPCR) methods . Zhu et al. (2021) showed that the lncRNA zing finger antisense 1 (ZFAS1) interacted with the EPAS1 protein and up-regulated the protein levels of EPAS1. The exact mechanism for upregulation of EPAS1 is still unknown .Under normal oxygen conditions, EPAS1 is continually transcribed and translated; however, normal oxygen tension leads to proteasomal degradation of EPAS1. Proteasomal degradation and regulation of EPAS1 stability and activity involves PTMs . While mAccording to the STRING database, the EPAS1 protein is directly interacting with 21 proteins, which were experimentally determined . Among tVEGFA, SLC2A1, and EPO, their interaction with transcription factor EPAS1 is confirmed in more than one study  are reported [Somatic variants in the reported . Eleven reported ,74,75,76EPAS1 is broadly associated with Familial erythrocytosis type 4 (ECYT4), a congenital disease with an excessive erythrocytes mass, elevated hemoglobin, and/or hematocrit. The review of the literature revealed 20 variants in the EPAS1 gene associated with erythrocytosis; 19 missense substitutions, and one deletion in exons 2, 9, 12, and 16 [Based on the OMIM and Simple ClinVar databases, , and 16  , which bind to the EPAS1 and potentially regulate EPAS1 mRNA or protein levels [EPAS1 expression, many more present and future studies need to be reviewed.Since EPAS1 is involved in many molecular mechanisms for adaptation to hypoxia, the regulation of EPAS1 plays a critical role and is very complex. The precise mechanism of EPAS1 regulation reflects also in non-ubiquitous expression and distinct expression patterns over different tissues. The mechanisms of EPAS1 degradation at the protein level have been very well studied. Through literature, we gathered information of 16 different PTMs , includin levels ,39. HoweEPAS1 throughout the body is more restricted than of its paralog HIF1α, it has an important role in the response to low body oxygenation and the development of various diseases. Over the years, it became evident that EPAS1 may play a broader role in tumorigenesis than previously thought. From the literature, we found that EPAS1 expression is increased in nine different types of solid tumors, often associated with poor prognosis. However, this association is not absolute, as low EPAS1 protein expression was in some cases associated with advanced tumor stage [EPAS1 mRNA was reported in 15 different cancer types, with the highest expression and as a prognostic marker in renal carcinoma (Even though the expression of or stage . In the arcinoma . This isEPAS1 induction in cancer is intratumoral hypoxia, but also genetic alterations. The highest number of studies associated somatic variants with paraganglioma and pheochromocytoma; however, variants were also found in other types of cancer, for instance in colon cancer [EPAS1 variants in separate and functionally distinct tumors from the same patient suggest that variants could occur in multiple cells during embryogenesis and predispose them to tumorigenesis [The obvious molecular mechanism for n cancer  and NSCLn cancer , but aren cancer ,74. In sn cancer ,75. Findigenesis ,83,84,85With the literature review, we gathered 12 downstream targets , but as EPAS1 gene from different OMICs levels and visualize data as an integrative map. The EPAS1 gene, located at chromosome 2, is transcribed into two protein-coding transcripts, which have enhanced expression in the lungs. Protein is located in the nucleus and cytosol and has eight domains important for EPAS1 activity and stability. At the regulomics level, we found: 31 upstream regulators, which bind to one of the two EPAS1 promoters, overlapping one CpG island; 13 miRNAs that target the EPAS1 mRNA and 1 lncRNA that targets the EPAS1 protein; and 16 PTMs that regulate proteasomal degradation of EPAS1 and its activity. According to the two databases of protein–protein interactions, the EPAS1 protein is directly interacting with 27 proteins. Also, we found interactions with 12 hypoxia-responsive target genes. Expression of EPAS1 was detected in more than ten types of cancer and was also associated with other diseases, including erythrocytosis. Germline and somatic variants around important PTMs for EPAS1 stability were extensively associated with the development of the erythrocytosis type 4 and neuroendocrine tumors paraganglioma and pheochromocytoma.The purpose of the study was to combine the data of the With this study we presented a comprehensive view of the EPAS1 to facilitate a better understanding of the gene regulation, role, and activity and encourage supplementation of the data and further studies on EPAS1. We showed that the regulation of EPAS1 is a complex process on diverse OMICs levels. The number of publications on this topic is still rising and more studies need to be reviewed to gain profound knowledge into the EPAS1 regulation. There is a substantial research interest in the HIF family of genes, as they have been shown to be involved in cancerogenesis and are potential targets for cancer treatment. Therefore, we could expect more studies on EPAS1 in the future. To make the research more accessible, we propose fusion of diverse new data on EPAS1 in the form of integration maps, as presented in this study.


The epigenetic regulation of gene expression is implicated in complex diseases in humans and various phenotypes in other species. There has been little exploration of regulatory elements in the pig. Here, we performed chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-Seq) to profile histone H3 lysine 4 trimethylation (H3K4me3) and histone H3 lysine 27 acetylation (H3K27ac) in the pituitary gland of adult Bama Xiang and Large White pigs, which have divergent evolutionary histories and large phenotypic differences. We identified a total of 65,044 non-redundant regulatory regions, including 23,680 H3K4me3 peaks and 61,791 H3K27ac peaks , augmenting the catalog of pituitary regulatory elements in pigs. We found 793 H3K4me3 and 3,602 H3K27ac peaks that show differential activity between the two breeds, overlapping with genes involved in the Notch signaling pathway, response to growth hormone (GH), thyroid hormone signaling pathway, and immune system, and enriched for binding motifs of transcription factors (TFs), including JunB, ATF3, FRA1, and BATF. We further identified 2,025 non-redundant super enhancers from H3K27ac ChIP-seq data, among which 302 were shared in all samples of cover genes enriched for biological processes related to pituitary function. This study generated a valuable dataset of H3K4me3 and H3K27ac regions in porcine pituitary glands and revealed H3K4me3 and H3K27ac peaks with differential activity between Bama Xiang and Large White pigs. Drosophila melanogaster  and H3 lysine 4 trimethylation (H3K4me3) , have ennogaster . This hanogaster . Meanwhinogaster , cows, snogaster .The pig is an important livestock species, serving as both a major food source and an excellent animal model for biomedical research in humans . EuropeaThis study profiles and characterizes the distribution of H3K4me3 and H3K27ac in the pituitary glands of two Bama Xiang and two Large White adult pigs and identifies the differential activity of H3K4me3 and H3K27ac peaks between the two breeds.1  of 150-day-old LW  and 132-day-old female BMX pigs, respectively, following the standardized sample collection protocols of the FAANG Project1 . The ChI® Plus Enzymatic Chromatin IP Kit  with 500 μg chromatin and 3 μg antibody H3K4me3  and 5 μg antibody H3K27ac . The detailed protocols were obtained from https://www.encodeproject.org/about/experiment-guidelines/ and https://www.animalgenome.org/community/FAANG. The dissected tissues were treated with 37% formaldehyde to cross-link the proteins covalently with DNA. This was followed by cell disruption and sonication to shear the chromatin to a target size of 100–300 bp  and remapped the fastq files to Sus scrofa 11.1 using STAR-2.5.3a (We downloaded RNA sequencing data (only BAM files are available) of the pituitary glands of three Bama Xiang pigs and three Large White pigs from gsa.big.ac.cn under the GSA number CRA0008764 . We extrR-2.5.3a . The traR-2.5.3a . After rR-2.5.3a  was used5 was used to investigate H3K4me3 and H3K27ac enrichment across genomic features with the following parameters: gat-run.py –segment-file = segments.bed.gz –workspace-file = workspace.bed.gz –annotation-file = annotations.bed.gz. The bedtools output was visualized with Integrative Genomics Viewer version 2.4, a genomic dataset viewer that allows for the visualization of genomic features. We downloaded whole genome GERP score from Ensembl Database and computed the GERP score of a peak by averaging the GERP scores of bases within that peak. GC-content of a peak was calculated using EMBOSS geecee software  in the corresponding set of peaks.Gene functions were annotated using DAVIDd ClueGO , and theThe ROSE algorithm  was usedWe profiled H3K4me3 and H3K27ac in the pituitary glands of two Bama Xiang (132 days old) and two Large White pigs (150 days old) using chromatin immune-precipitation, followed by high-throughput sequencing (ChIP-Seq) of single end 50 bp reads . The aveP < 2.2 × 10–16)  , suggest× 10–16) .BCL6, CDKN1B, ESRP1, PITX2, and SIX6 genes had functions related to the pituitary based on the annotation information from the DAVID bioinformatics resources8. For example, BCL6 is involved in the reverse coordination of endogenous impulses released by pituitary growth hormone (GH) by interacting with STAT5 . The 100 peaks overlapped with 114 genes involved in the intracellular estrogen receptor signaling pathway, positive regulation of blood pressure, and neuroepithelial cell differentiation. Among these genes, th STAT5 , and CDKeoplasms .Next, we examined the overlap of genomic features with the H3K4me3 and H3K27ac peaks. The H3K4me3 and H3K27ac proximal peaks were mostly located in the intron (61.36 and 66.96%) and intergenic (24.61 and 20.57%) regions, while the H3K27ac distal peaks tended to be located in intron (71.88%) and intergenic regions (23.81%) . The H3KNext, we quantified the peak activity in each sample based on read depths in the corresponding peak region normalized by peak length, using the R package DESeq2 . We evaluated the correlation among samples in terms of genome-wide H3K4me3 and H3K27ac peak activity. The mean correlations based on H3K4me3 peak activity (0.81) were higher than those based on the H3K27ac activity (0.42), suggesting that the activity of the promoters was more conservative than those of enhancers.P ≤ 0.05, we identified 793 H3K4me3 peaks   , and 3,6ctively) . We dispctively) .P = 0.004, AP3B1, HPS4, and ZEB2), regulation of T cell differentiation , and activated T cell proliferation  . These genes were enriched in melanosome organization (d SATB1) . We dispunctions  , response to GH , the thyroid hormone signaling pathway , and the Notch signaling pathway   . These r TSPAN5) . The Not TSPAN5) . We provHB1 gene  that plaHB1 gene , and locHB1 gene . AnotherJX1 gene , which iJX1 gene  and as aJX1 gene . Among tmaturity   in LW-specific peak regions , central nervous system neuron development (P = 0.004), and the thyroid hormone signaling pathway (P = 0.01)  . These p = 0.01) . Chr15:1 = 0.01)  are reprisorders .To the best of our knowledge, we present the first comprehensive exploration of regulatory elements in the pituitary glands of pigs using ChIP-Seq target H3K4me3 and H3K27ac, two histone modifications with important roles in regulating gene expression. We identified 65,044 non-redundant cis-regulatory sequences, including 23,680 in the H3K4me3 region (putative promoters) and 61,791 in the histone H3K27ac region, which include 12,318 proximal peaks (putative promoters) and 49,473 distal peaks (putative enhancers). It is worth noting that there are 86 H3K4me3 breed-specific regions that overlap with breed-specific regions of H3K27ac. This study enlarges the database of regulatory elements in the pituitary glands of pigs. The identified peaks are valuable for annotating the putative functional mutations identified in genome wide association studies of complex traits in pigs, as well as cross-species comparative studies.GSE178380 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE178380). The RNA sequencing data of pituitary glands downloaded from three Bama Xiang pigs and three Large White pigs from gsa.big.ac.cn under the GSA number of CRA000876 (https://ngdc.cncb.ac.cn/gsa/browse/CRA000876)  with accession code: A000876) .The animal study was reviewed and approved by Committee on Animal Biosafety of Jiangxi Agricultural University. Written informed consent was obtained from the owners for the participation of their animals in this study.WL and BY conceived and designed the experiments. ZiZ, ZeZ, and TJ performed the experiments. ZiZ, BY, YZ, TJ, and ZL analyzed the data. YZ, BY, and ZiZ wrote the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Anopheles coluzzii population. The model accounts for circadian rhythm and ageing effects allowing high confidence identification of transcription factors with key roles in pesticide response. The associations generated by this model show high concordance with lab-based validation and identifies 44 transcription factors putatively regulating insecticide-responsive transcripts. We identify six key regulators, with each displaying differing enrichment terms, demonstrating the complexity of pesticide response. The considerable overlap of resistance mechanisms in agricultural pests and disease vectors strongly suggests that these findings are relevant in a wide variety of pest species.The increasing levels of pesticide resistance in agricultural pests and disease vectors represents a threat to both food security and global health. As insecticide resistance intensity strengthens and spreads, the likelihood of a pest encountering a sub-lethal dose of pesticide dramatically increases. Here, we apply dynamic Bayesian networks to a transcriptome time-course generated using sub-lethal pyrethroid exposure on a highly resistant  Anopheles vectors sc is linked with sensory tissue development sc elopment ; kayak itivation ; Hnf4 istabolism ; Cyc conn ryhthm ; REL1 anresponse ; Kr-h1 ielopment  and Pan nfection .Table 1LAnopheles coluzzii was used ; the single time point used for model confirmation . Four transcript interactors were chosen randomly for each transcription factor based on a posterior probability of > 0.1. To determine the change in transcript expression post-exposure and to determine whether predicted interactors were influenced by the knock down of the stated transcription factor 2 comparisons were made: (i) GFP-injected exposed vs GFP-injected unexposed and (ii) Exposed transcription factor knockdown compared to exposed GFP-injected for the two comparisons respectively (Supplementary Table 1). Of the 16 interactors (4 transcription factors x 4 interactors), 11 demonstrated concordance with the model, showing a substantial change in expression due to transcription factor knockdown, indicating 69% model precision . Any association between this transcription factor and the target gene would then be purely due to chance. This process was then repeated 500 times, inferring the edges for all 44 transcription factors each time. The resulting marginal posterior probability values were then analysed for the randomised transcription factor and showed that a threshold of 0.39 was sufficient to only produce one false positive out of 500 randomizations, or a false positive rate of 0.002 (Appendix 1), which resulted in assignment of 5136 transcripts to the 44 transcription factors.Dm, Met and Maf-S all previously linked with the insecticide resistance phenotype . A large number of GO terms were significant across different transcription factor interactions analysed; however, the terms were largely non-overlapping indicating that the transcription factors are playing differing roles in insecticide response . Seven GO terms  were significant across four transcription factors and relate to terms clearly involved in stress response and associated behavioural changes.KEGG enrichments were present for 39/44 transcription factors , again there was minimal overlap in the enriched pathways, in agreement with the divergent enriched GO terms. One KEGG pathway was significant for six transcription factor associations (neuroactive ligand-receptor interaction) and two terms were significant for four transcription factor associations .a priori knowledge of insecticide resistance, enrichment analysis was also carried out for detoxification gene families, the cuticular hydrocarbon synthesis pathway and chemosensory proteins; three well described resistance mechanisms  is the transcription factor with the highest number of interactors at 951. Chm interactors are strongly enriched in transmembrane signalling activity (p = 1.32e-8), sensory perception (p = 3.39e-6) and chemosensory behaviour (p = 8.74e-4). 26 other transcription factors interact directly with Chm including Abd-B (AGAP004664-RA) including a known Drosophila interaction and so (AGAP011695-RA), Fer3 (AGAP003756-RA), disco (AGAP01106-RA), C15 (AGAP003674-RA), zfh1 (AGAP000779-RA), hkb (AGAP004517), all known secondary interactors. 14 interactors have posterior probabilities of >0.90, including fringe (AGAP006439-RA) a gene involved in regulating the Notch signalling pathway (Chm interactors (p = 0.012) and Roquin (AGAP007114-RA) a protein that translocates to stress granules on chemical induced toxicity  is a central network hub with 91 interactors and its interactors are enriched in multiple KEGG pathways such as N-glycan biosynthesis, protein processing in the endoplasmic recticulum and starch and sucrose metabolism (Supplementary Table 3)  . Previouexposure ; this roexposure . Interesnopheles . Dm alsosquitoes .  is the second most well-connected node with 535 interactors. Dmrt93B is enriched in multiple GO-terms related to xenobiotic metabolism, including oxidoreductase activity (p = 7.7e-3), heme-binding (p = 2.6e-4) and monooxygenase activity (p = 0.014) as well as being highly enriched in the a priori detoxification gene families; cytochrome p450s (p = 5.53e-6), COEs (p = 0.023) and GSTs (p = 0.029). Taken together, these enrichments indicate that dmrt93B is playing a central role in the response of metabolic transcripts to insecticide exposure. Although not showing enrichment in a related term, 14 cuticular proteins are present in this interactome, one of which CPLCP11 (AGAP009758-RA) has been shown to be up-regulated in resistant mosquitoes compared to susceptible (CPR133 (AGAP009872-RA), has the highest posterior probability (0.93).ceptible  and anotMaf-S (AGAP010405-RA) and Met (AGAP006022-RA) have previously been shown to have important roles in insecticide response  has 119 interactors and is enriched for GO terms related to the ribosome (p = 0.026) and RNA binding (p = 0.048) and is highly enriched in the KEGG ribosome (p = 4e-4). The role of mbf1 in Drosophila involved translocation to the nucleus upon cellular stress, where it serves as a co-activator of stress response genes  and an oxidative stress sensing protein (AGAP000705-RA) are present in this network. AGAP002667 has the highest posterior probability in the network (0.84) and encodes the homolog of Drosophila Tctp which is necessary for genomic stability under genotoxic stress .Of the 44 transcription factors, 3 had previously been linked with insecticide resistance in  context . All buta priori transcript families with links to resistance. Interestingly, the overlap of enriched terms was low, indicating that each transcription factor may play a differing role in the response to insecticides. 20 transcription factors show enrichments in a priori gene families; this may be an unsurprising feature of this dataset given the obvious change in expression across multiple members of these families within this dataset and their documented importance in insecticide metabolism  (Drosophila (mbf1) (chameau and dmrt93B). These transcription factors are likely to be involved in different facets of insecticide response and represent pathways that should be further explored. The modelling approach taken here, which accounts for both circadian patterns and ageing, two key determinants in pesticide resistance, can be applied widely to other pest or vector species. Using this approach will provide invaluable information on changes to pest biology post-pesticide exposure and will elucidate new pathways to characterise and target to tackle the ongoing threat of pesticide resistance.This study provides not only previously unreported transcription factors that are involved in the transcriptional response to pesticide exposure but demonstrates the utility of applying a model-based approach to lower-resolution time course data in ascertaining these associations. Here, six transcription factors and their interactomes were delineated as hub transcripts within the network, all of which have either been previously linked to resistance or stress response in and Met)  or Drosoa (mbf1)  or are hMicroarrays were taken from  and consAnopheles gambiae species complex were used from Ingham et al. 2018   were useInference of the network structure can be done via a Markov Chain Monte Carlo algorithm, with discrete moves allowing for adding and removing edges during the sampling. Convergence was assessed by running each MCMC chain twice from independent starting points and comparing the marginal posterior edge probability estimates. We ran the MCMC algorithm for 500,000 iterations, discarding the first quarter as burn-in, which ensured good convergence across all target genes. For full details on the model and inference procedure, please see Appendix 1. Note that here we employ a simplified version of the model in  which doPrior to applying the network inference model, we pre-processed the log-fold change data by first averaging the values for genes with multiple probes to obtain one measurement per gene. We then employ LOESS estimation , a localX = We further extend the model to correct for circadian rhythms and ageing effects in the gene expression levels. For the circadian rhythm correction, we assume that all circadian rhythms have a period of 24 hours, and augment the design matrix The resulting harmonic regression model with automatically correct for circadian rhythms, including under phase shift, by adding the periodic signal as a parent in the network, while non-periodic genes will remain unconnected to this signal.Similarly, we add additional columns for the data arising from the ageing controls to correct for the effect of ageing. Note that here we only have data starting from 8 hours, so earlier time points will be uncorrected, and the corresponding values in the design matrix will be set to zero. The final autoregressive model looks as follows:We summarize the results of the DBN analysis using the marginal posterior probability of each transcription factor – target gene association, which can be calculated by obtaining samples from the converged Markov chain and averaging over the presence/absence status of each edge. In order to determine a sensible threshold for the marginal posterior probability that keeps the false discovery rate low, we implement the following permutation test to estimate the posterior probabilities under the null hypothesis of no associations: for each of n=500 iterations, we randomly permute the log-fold changes for one transcription factor. Any associations with the target gene should then be entirely by chance. Taking all n=500 samples of the null distribution obtained in this way, we determine that a threshold of 0.39 was sufficient to only produce one false positive out of 500 randomizations, or a false positive rate of 0.002. Further detail of the model can be found in Appendix 1. The network was displayed using Cytoscape .To estimate how the computational time needed scales with the number of transcription factors, we repeatedly selected 10 target genes and p transcription factors, where p∈. Network inference using EDISON was then performed on a computational cluster with two Intel Xeon E5-2660 v4s, which have 14 physical cores running @ 2.00GHz each, and 256 GB of RAM, and the resulting computational time is recorded. All MCMC chains for the network inference algorithm are run for 500,000 iterations .The NetworkVis app and associated data can be downloaded on Github (https://github.com/VictoriaIngham/NetworkVis_TimeCourse) and installed as described. ShinyR  was usedAnopheles database produced using AnnotationForge (Drosophila pathway membership was downloaded from Reactome.org (https://reactome.org/)   for eachme.org/)  was then FlyBase  was usedWe first performed a simulation study to determine the number of associations that need to be tested experimentally in order to obtain an accurate estimate of the precision of our network inference method. We made the following assumptions: (i) The mean number of gene regulated by each transcription factor is 10, and the actual number of regulated genes follows a Poisson distribution; (ii) The rate of true positives (correctly predicted associations) of our network is 0.75, and the rate of true negatives (correctly predicted non-associations) is 0.997; this results in a precision of ∼0.56 and a recall of ∼0.72 (Appendix 2); (iii) Transcription factors and regulated genes to test are selected randomly and (iv) The qPCR knockdown test is 100% accurate. The results of the simulation study can be found in Appendix 2. We concluded that testing 4 regulatory relationships for 7 transcription factors has a 70% chance of obtaining an estimate of the precision that falls within 10% of the true precision, and a 95% chance of obtaining an estimate that falls within 20% of the true precision.Maf-S, Met, Dm or through a random number generator.In order to choose associations for validation, we then chose interactors by extracting the transcription factor of interest and associated transcripts from the results of the network inference. Transcripts were listed as 1 to n based on posterior probability in descending order. A random number generator was then used to select 4 transcripts for validation from 6 transcription factors chosen based on previous knockdown in the case of An. coluzzii VK7 colony reared and profiled at Liverpool School of Tropical Medicine were used for all experiments  or thro25-30 female mosquitoes were exposed to 0.05% deltamethrin impregnated papers for one hour in a standard tube bioassay kit following WHO guidelines. Post-exposure mosquitoes were transferred into holding tubes and maintained on sucrose solution.RNA was extracted from 7-10 female mosquitoes in biological triplicate for each experimental group. RNA was extracted from homogenised mosquitoes using a PicoPure RNA isolation kit  following manufacturers’ instructions and treated with DNAase (Qiagen) to remove any DNA contamination. Quality of RNA was checked using a nanodrop spectrophotometer (Nanodrop Technologies UK). 1-4µg of RNA from each experimental set was reversed transcribed using OligoDTT (Invitrogen) and Superscript III (Invitrogen) according to manufacturers’ instructions. The following experimental groups were used: (i) knockdown efficacy for each transcription factor and the GFP control using females 48-hours post RNAi injection and (ii) pathway validation using females 48-hours after they were exposed to 0.05% deltamethrin for 48-hours post-injection for transcription factors and GFP controls.Quantitative real-time PCR was performed using SYBR Green Supermix III  using an MX3005 and the associated MxPro software v4.10 . Primer Blast (NCBI) was used to design primer pairs. Where possible, primers were designed to span an exon junction (Supplementary Table 5). Each 20µl reaction contained 10µl SYBR Green Supermix, 0.3µM of each primer and 1µl of 4ng/µL cDNA. Standard curves for each primer set were used to calculate efficiency, using five 1:5 dilutions of cDNA to ensure that all primer sets met the MIQE guidelines (90-120% efficiency) . qPCR waVAI and FD designed and implemented the experiment. SCN performed the SILGGM analysis, FD modified and implemented the dynamic Bayesian network, SE provided rearing, bioassay and molecular biology support, VAI performed the lab-based experiments and analysed all data. VAI and FD drafted the manuscript.The datasets used in this experiment are available at ArrayExpress under E-MTAB-9422 and E-MTAB-9423. The authors declare that all other data supporting the findings of this study, are available within the article and its Supplementary Information files or are available from the authors upon request.Code used for analysis in this study is available on GitHub. Network visualisation is available at https://github.com/VictoriaIngham/NetworkVis_TimeCourse, model code is available on the CRAN repository: https://cran.r-project.org/web/packages/EDISON and full analysis is available at https://github.com/FrankD/AnophelesInsecticideExposure.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Chromatin architecture plays an essential role in gene regulation, cell differentiation, response to external cues, and disease progression, and hence can be used as a biomarker for cell phenotyping. Traditionally, methods based on confocal microscopy and imaging using fluorescent tags have been employed to study the chromatin architecture inside the nucleus by measuring the location and folding of chromosomes. However, existing methods lack specificity and/or are reliant on extrinsic labeling that prevents yielding structural information for chromatin in its native state by potentially interfering with ongoing biological processes. In this Research Topic, we focus on technical advancements in (i) measuring chromatin changes at the single-cell level, (ii) studying chromatin modification through DNA methylation, and (iii) visualizing chromatin by super-resolution microscopy. We also review the literature on (i) protein post-translational modifications and their impact on the scaffold/matrix interactions of the chromatin, (ii) advancements in using epigenomic biosensors during radiotherapy, and (iii) the effect of small molecule inhibitors on chromatin-associated cellular responses.Morrish et al. describe a label-free imaging approach based on two complementary chromatin probing methods—Raman microscopy in microfluidic devices and transcriptomic analysis—that enables direct linking of Raman spectral signatures of the nucleus of lymphocyte cells with >17,000 distinct transcripts upon immune activation of B lymphocytes. This approach, built on a previous method based on Raman spectroscopy, was applied to image >100 activated and non-activated live B cells at high resolution in a specially designed microfluidic chip. This complex set of cellular Raman maps was analyzed via a multivariate common k-means cluster analysis approach. This analysis permitted identifying distinct spectral profiles associated with the nucleus and cytoplasm. Next, principal component analysis (PCA) and linear discriminant analysis (LDA) allowed elucidating the discriminants between activated and non-activated cells. This label-free imaging classification has exciting potential in biology. For instance, it can be used in cell sorting based on chemical phenotyping of the nucleus, or in clinical translation for label-free disease diagnosis and prognosis. Finally, the team quantified linear correlations between nuclear Raman spectra and transcriptomic data, generating a partial least squares (PLS) regression matrix to predict Raman data from transcriptomic profiles. This approach was used to identify the key transcripts and thereby reveal genes or pathways that are essential for the immune activation process, including the immunoglobulin genes Ighm and Igha, regulatory RNAs and proteins.Imm et al.).An alternative approach for tracing chromatin dynamic changes used genome-wide quantification of DNA methylation in induced pluripotent stem cells (iPSCs) during differentiation and maturation into cortical neurons. This method, based on data-driven trajectory inference, allows identification of DNA methylomic trajectories of neuronal differentiation. Up to 6,843 Bonferroni-significant loci  were identified based on the presence of progressive alterations in DNA methylation during differentiation into neurons and subsequent maturation. By applying a gene-gene interaction network analysis, the authors were able to identify 60 densely connected genes that were influential in neuronal differentiation from iPSCs, particularly those encoding transcription factors and epigenetic regulators (Xu and Liu). SMLM-based super-resolution imaging of nanoscale chromatin architecture and its dynamic behaviors was applied to study healthy and pathological processes in developmental biology, immunology, and oncology. The authors propose that in the future the SMLM imaging approach for tracing chromatin architecture could improve cancer diagnosis, enabling the development and evaluation of new early detection strategies.A complementary approach for visualizing chromatin architecture and its epigenomic states with nanometric resolution in cells and tissue used super-resolution imaging based on single molecule localization microscopy (SMLM), as reported in this review (Roychowdhury and Chattopadhyay).Chromatin architecture is based on the interaction between DNA and histones as well as the post-translational modifications (PTMs) of non-histone-associated proteins. Among the main proteins responsible for maintaining the 3D genomic architecture, S/MAR-binding proteins (S/MARBPs) play a key role in binding to the scaffold/matrix attachment regions (S/MARs) and mediating tethering of the chromatin to the protein matrix. A review of the current understanding, scope, disease implications, and future perspectives of the diverse post-translational modifications that regulate the functions of S/MARBPs is featured here  biosensors may lead to improvement in cancer treatment. However, although beneficial, such approaches have been evaluated in several ongoing clinical trials for limited cancer types, partly due to a lack of knowledge on the mechanisms on radiation-induced epigenomic regulation and chromatin remodeling. Mello). VPA indirectly promotes histone acetylation by inhibiting the deacetylation process, and therefore it can induce chromatin decondensation, as revealed through imaging techniques such as Fourier Transform Infrared (FTIR) spectroscopic imaging. This finding suggests that VPA could affect the methylation status of histones, and possibly DNA, and hence could have therapeutic potential due to its antitumour effects.One example of such epigenetic drugs, valproic acid (VPA), was originally prescribed for the treatment of seizure disorders and is now known to affect epigenetic markers and chromatin structure, as reviewed here (FP wrote the manuscript with support from SP, AS, and RC. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.