Broadest neuronal peaks showed distinct motif signatures and were centrally positioned in prefrontal gene-regulatory Bayesian networks and sensitive to defective neurodevelopment. Approximately 120 of the broadest H3K4me3 peaks in human PFC neurons, including many genes related to glutamatergic and dopaminergic signaling, were fully conserved in chimpanzee, macaque and mouse cortical neurons. Exploration of spread and breadth of lysine methylation markings could provide novel insights into epigenetic mechanism involved in neuropsychiatric disease and neuronal genome evolution.Regulators of the histone H3-trimethyl lysine-4 (H3K4me3) mark are significantly associated with the genetic risk architecture of common neurodevelopmental disease, including schizophrenia and autism. Typical H3K4me3 is primarily localized in the form of sharp peaks, extending in neuronal chromatin on average only across 500–1500 base pairs mostly in close proximity to annotated transcription start sites. Here, through integrative computational analysis of epigenomic and transcriptomic data based on next-generation sequencing, we investigated H3K4me3 landscapes of sorted neuronal and non-neuronal nuclei in human postmortem, non-human primate and mouse prefrontal cortex (PFC), and blood. To explore whether H3K4me3 peak signals could also extend across much broader domains, we examined broadest domain cell-type-specific H3K4me3 peaks in an unbiased manner with an innovative approach on 41+12 ChIP-seq and RNA-seq data sets. In PFC neurons, broadest H3K4me3 distribution ranged from 3.9 to 12 kb, with extremely broad peaks (~10 kb or broader) related to synaptic function and GABAergic signaling  For example, many active promoters show high levels of histone H3 lysine-4 methylation.4 In particular, the trimethylated form, H3-trimethyl lysine 4 (H3K4me3), with the lysine residue's side chain carrying three methyl groups, is primarily distributed in the form of sharp peaks, extending in neuronal chromatin on average only across 1000–1500 base pairs or less, with the large majority of peaks, or at least 70–80%, positioned within 2 kb of annotated transcription start sites (TSSs).5 However, in some tissues, a subset of sequences epigenetically decorated with H3K4me3 tend to stretch across several kilobases, with the broadest domains measuring up to 60 kb in length.7 Recently, it was proposed that these broader H3K4me3 peaks show strong association with genes expressed in a cell-type-specific pattern, and could have an important role for transcriptional regulation by controlling RNA polymerase-II pausing as a critical variable for general elongation efficiency, and by reducing transcriptional noise.7 Furthermore, the finding that aspects of transcription including H3K4me3 breadth at TSS are linked to cell identity is extremely interesting. This is because until now, epigenetic signatures that critically distinguish between different cell types and tissues otherwise sharing the same genome were mostly confined to distant-acting cis-regulatory enhancer elements ,9 including exceptionally large enhancer domains called super-enhancers, which distinguish from the traditional enhancer concept by the very high occupancy of transcription apparatus such as the mediator complex and cell-type-specific transcription factors to drive expression of associated genes.14More than 100 amino-acid residue-specific histone post-translational modifications (PTMs) exist in the vertebrate cell.15 and to epigenomic alterations in autism and schizophrenia brain.17 H3K4me3 landscapes in human cerebral cortex are subject to highly dynamic regulation throughout a broad window of development, extending deep into or even beyond childhood.18 The goal of the present study was to characterize the broadest H3K4me3 peaks from human prefrontal cortex (PFC), with comparative analyses in non-human primate and rodent, in the context of cell-type-specific regulation, association with neuronal and non-neuronal gene expression and potential implications for neurodevelopmental disease. Our study employs a broad range of bioinformatics approaches on next-generation sequencing-based transcriptomes and epigenomes from sorted neuronal and non-neuronal nuclei from PFC gray and white matter and, for comparison, peripheral blood mononuclear cells.However, currently very little is known about the regulation of the broadest H3K4me3 peaks in the human brain. This is surprising, given that regulators of H3K4 methylation significantly contribute to the genetic risk architecture of autism19 with minor modifications. Whole blood was diluted 1:1 with Hanks balanced salt solution , then peripheral blood mononuclear cells were isolated using Ficoll-Paque Plus . Peripheral blood mononuclear cells, of which a large majority are lymphocytes, were then washed with Hanks balanced salt solution, and once more with phosphate-buffered saline. Samples were then flash frozen and stored at −80 °C until shipping on dry ice.All human subject work was approved by the Institutional Review Board of the University of Illinois at Chicago. Forty milliliter blood samples were collected from three male non-psychiatric control subjects , using aHuman postmortem PFC brain tissue from 25 controls without known neurological or psychiatric disease  were obtained from different brain banks including the University of Maryland Brain and Tissue Bank for Developmental Disorders, the Harvard Brain Tissue Resource Center, the Department of Psychiatry at the University of California at Irvine, and the Maryland Psychiatric Research Center in Baltimore  data sets from seven non-human primates (four chimpanzees and three macaques)21 Nuclei from freshly frozen (never fixed) tissues (250 mg per sample) of postmortem PFC from 25 healthy control subjects were extracted in hypotonic lysis buffer that causes the cells to swell to liberate nuclei. Nuclei were purified by ultracentrifugation and resuspended in 1x phosphate-buffered saline. Neuronal nuclei were immunotagged with anti-neuronal nucleus antibody  and sorted into NeuN(+) and NeuN(−) populations using a FACS Vantage SE flow cytometer. Chromatin was prepared by micrococcal nuclease (MNase) digestion of isolated neuronal nuclei (from minimum 1 × 106 sorted nuclei), because MNase-treated chromatin is more efficient for subsequent immunoprecipitation with specific anti-methyl histone antibodies than crosslinked and sonicated chromatin in brain tissue. Purified mononucleosomal DNA were pulled downed using anti-H3K4me3 antibody (Upstate/Millipore 07473) with chromatin immunoprecipitation assay and then purified. ChIP-seq libraries were prepared from the immunoprecipitated DNA by blunt-ending, A-tailing and ligation to adaptors and PCR amplification for single-end sequencing (36-bp reads). All libraries were sequenced by an Illumina Genome Analyzer II  platform.Nuclei extraction was carried out as previously described.http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/). A low-quality read filter was then applied in which no reads with more than six bases with a minimum phred quality score of 20 were retained. n=39 H3K4me3 ChIP-seq plus two input libraries . Unique alignment percentages were between 63 and 96% of all total reads across all samples , cohort 2 NeuN+ (n=14), NeuN− (n=2), blood (n=3), for each cell type separately. Peaks were annotated to genes and TSSs using HOMER annotatePeaks.pl script with UCSC refGene.23 All aligned read files were corrected for sequencing depth using the signal extraction method proposed by Diaz et al.24 and normalized to the cell-type-specific input to visualize in Integrative Genome Viewer browser.25 For Gene Ontology (GO) term analyses, we used two approaches: gene- and peak-based coordinates by using the web interface of DAVID, Stanford's Genomic Regions Enrichment of Annotations Tool26 and the R package ChIPEnrich (http://sartorlab.ccmb.med.umich.edu/chip-enrich). Additional annotation and analysis including general assessments of overlaps between bed-files and to extract signal intensity scores for defined regions was performed using BEDTools v2.17,27 Pybedtools,28 SAMTools,29 UCSC tools,30 BSGenome, GenomicFeatures, rtracklayer and ChIPpeakAnno packages in R (http://www.bioconductor.org). The snapshots of the H3K4me3 epigenomic profiles were obtained with Integrative Genome Viewer browser.25 Heatmaps displaying normalized read densities of ChIP-seq samples were generated with the deepTools package.31 This ChIP-seq pipeline was applied to the other species, by aligning reads for the chimpanzee to the panTro4, the macaque monkey to rheMac2 and the mouse to the mm9 genome, respectively.The ChIP-seq data analysis was performed using several bioinformatic tools and in-house python and perl scripts. Sequencing read quality was evaluated using FastQC . RNA-sequencing (RNA-seq) raw reads that passed the quality control metric, which is referred to as the ‘chastity filter' by Illumina, were aligned to the UCSC Homo sapiens reference genome build 19 using the STAR aligner32 and were visualized using the Integrative Genome Viewer.25 A raw read-count table was generated using HTSeq python framework.33 Alignment percentages were between 74 and 86% of all total reads across all samples (34 by using UCSC gene annotation file (hg19 in GTF format) as a guiding gene model set. After all short read sequences were assembled into transcripts, their relative expression levels were measured in fragments per kilobase of exon per million fragments mapped (FPKM) unit, where read counts are normalized by the transcript length (exon only) as well as the total number of mappable reads in the sequencing library. Box plot of FPKM distributions for each gene was plotted with the R package ggplots2. To validate neuronal (NeuN+)-specific and non-neuronal (NeuN−)-specific broadest H3K4me3 peaks within the 4-kb window of TSSs of the associated genes, we further investigated differentially expressed genes quantified by DESeq2, limma-voom and edgeR, in subcortical white (n=6) and cortical gray matter (n=6).RNA was extracted from ∼75 mg of gray and white matter dissected from seven adult control PFC specimens  using th samples . Express35 to examine whether the broadest neuron-specific H3K4me3 genomic intervals are associated with functional elements more than expected by chance via simulation within a genomic context including corrections for gene density, chromosomal segments and isochore structure to prevent confounding effects due to different G+C content and provide unbiased measures of the null expectation.30 Before the association analysis, a custom perl script was used to query the public instance of the UCSC MySQL database of the human genome version hg19 at the host genome-mysql.cse.ucsc.edu and created a set of non-overlapping intervals of RefSeq transcripts covering the full genome including promoter, intergenic, intron, CpGislands Centromeres, microRNA, noncoding RNA and exonic regions. Using the bioinformatic analysis tool ROSE Rank Order of Super-Enhancers software (http://younglab.wi.mit.edu/super_enhancer_code.html), 357 super-enhancers and 5120 typical enhancer loci were ranked from H3K27Ac ChIP-seq data of the brain middle frontal lobe. Other brain-related genomic intervals were downloaded from Fantom5  tool from the MEME suite was used to identify nearest matches of the discovered de novo motifs identified from EPIGRAM against a total of 3764 motifs.For motif analyses, including enrichment and comparative matching of Sij] for all possible pairwise genes were converted into an adjacency matrix A=[aij] by power function, that is, aijpower≡|Sij|β. The value of the power adjacency function's exponent (β) was chosen using the scale-free topology criterion proposed in the study by Zhang and Horvath44 to ensure the resulting weighted network exhibited an approximate scale-free topology and a high mean number of connections. To explore the modules of the co-expression network, the adjacency matrix was further transformed into a topological overlap matrix to filter very weak connections and to provide more cohesive and biologically meaningful modules. Topological overlap matrix-based dissimilarity measure between all possible pairwise genes was used as input in average linkage hierarchical clustering, followed by a Dynamic Tree cut algorithm to define modules as branches of the resulting cluster tree.45 Each module was assigned a unique color identifier and gray color representing poorly connected genes. Highly co-expressed genes have a small dissimilarity. With the biologically motivated data reduction scheme, we wanted to explore and identify modules of highly co-regulated genes from the 475 broadest H3k4me3 domains within the 4-kb window of refseq genes.The absolute Pearson correlation matrix S=[48 The resulting PFC control network was visualized by Cytoscape 3.1.1 . For Shulha et al.,20 sequences are accessible through http://www.umassmed.edu/zlab/publications/).ChIP-seq and RNA-seq data sets newly generated for this manuscript are deposited in the National Center for Biotechnology Information, accession no. GSE71238. For previously published data sets, see accession no. GSE21172 and additional links provided in the study by Shulha 5 Altogether, our study was comprised of H3K4me3 ChIP-seq data sets from 30 subjects, including 25 NeuN+ and 2 NeuN− samples from the PFC of 25 postmortem brains, and three additional samples of peripheral blood mononuclear cells obtained by venipuncture from living subjects. Two of the NeuN+ samples and all blood-derived samples were newly generated for this study, whereas the remaining samples from brain nuclei had been included in previous publications on cell-type-specific5 and developmental regulation18 of H3K4me3 peaks in the PFC.We isolated nuclei from the rostral PFC for separation and fluorescence-activated sorting (FACS) based on immunolabeling with the NeuN antibody , followed by ChIP-seq for genome-scale mapping of the H3K4me3 mark in neuronal and non-neuronal chromatin.We first examined cell-type-specific regulation of the broadest H3K4me3 peaks (top 5% broadest H3K4me3 peaks (length in base pairs) that were longer than the 95th percent of all H3K4me3) in a cohort of 11 PFC NeuN+ samples  were consistently higher, compared with the correlations observed between neuronal and non-neuronal PFC cells (ranging from 0.82 to 0.87) or blood (0.74–0.76). In fact, across these different cell types, the intra-cell-type correlations were systematically higher than inter-cell-type correlations, each cell type clustering together in an unsupervised pattern . Althoug7 Therefore, we wanted to explore whether the broadest H3K4me3 peaks in our data sets show numerical evidence for cell-type-specific regulation. To this end, we first calculated the average H3K4me3 peak length in our NeuN+ sample, which was ~1.5 kb both in cohort 1 and cohort 2 across all 28573 H3K4me3 peaks and 25212 H3K4me3 peaks identified, respectively (n=743 peaks) and top 5% (broadest) peaks of cohort 2 (n=606 peaks), indicating that a large majority of broad, neuron-specific H3K4me3 peaks are highly reproducible (P<0.001) . Similarly, nucleated blood cells and non-neuronal PFC cells harbored 297 (blood) and 867 (NeuN− in PFC) cell-type-specific peaks among the top 5% broadest H3K4me3 peaks   versus 1.9-fold (top 5%) and cohort 2, 2.9-fold (top 1%) versus 2.2-fold (top 5%) (H3K4me3 breadth had been implicated in cell identity in various cell lines in a recent study.ectively . We thenectively . There woducible . In ordee3 peaks . The broe3 peaks . These e/25 212) . Previou(top 5%) . We concP<10−7) for SMAD3, a member of the SMAD family of transcription factors, and additional transcription factors with weaker enrichments , implicated in epilepsy and attention-deficit hyperactivity disorder and essential for GABAergic signaling in subsets of cortical and hippocampal interneurons55 and LINC00966, a poorly characterized noncoding transcript that encodes within its sequence microRNA 124-2, targeting homeobox transcription factor Dlx5 (ref. 57 (DLX1 and GAD1 (refs IGSF9B60 (n=151 reproducible in cohorts 1 and 2), enrichments (incl. cell type and function) were overall very weak and modest, with no evidence for biological enrichment  will show much more robust expression for many of the NeuN+ (neuron specific) H3K4me3 peaks, as compared with RNA-seq from the neuron-depleted (white matter) compartment. To this end, we first identified 475  peaks that overlapped (±4 kb) with the TSS of an annotated REFseq gene, then quantified for six postmortem PFC specimens by RNA-seq the corresponding transcripts separately for the PFC gray and for the underlying white matter dissected from same tissue blocks. Indeed, the majority of the 475 transcripts associated with broad H3K4me3 peaks in neurons expressed at much higher levels in PFC gray matter with its neuron-rich six cortical layers, compared with the (neuron depleted) subcortical white matter . These include multiple risk genes associated with neuropsychiatric disease such as multiple genes encoding various ion channels associated with synaptic signaling  H3K4me3 peaks reportedly are linked to cell-type-specific expression and ‘transcriptional consistency' contributing to steady-state RNA production.othelium , a findiP-value<0.0001, Wilcoxon matched-pairs signed rank test). These included OPALIN, SEPT4, SOX10 and other key regulators for myelination and oligodendrocyte differentiation and function , we discovered for the 523 broadest top 5% neuronal peaks a strong footprint for neuronal function. GO terms enriched by binomial test for peak regions and hypergeometric test for mapped genes with an false discovery rate <0.05 were considered significant  were lifted over to the human reference genome HG19 (www.qiagen.com/ingenuity) tools to detect molecular pathways that are enriched among these loci. Our analyses revealed dopamine-cAMP and glutamate receptor signaling were the most affected canonical pathways  called for that species' reference genome  as key regulators for neuronal connectivity, and NMDA receptor subunits and other genes associated with glutamatergic signaling . In addition, we compared the average shortest path length and the largest shortest path length  between the two groups of genes , suggesting that perturbations on these broadest peak-related genes could have stronger effects on the integrity of the network.70 Moreover, we found that neuron-specific genes have a significantly larger degree than other genes in the network . We focus on the largest connected component, which includes 7040 genes. In all,158 out of the 475 broadest neuron-specific H3K4me3 peak-associated genes (defined as annotated TSS within 4 kb from a top 5% broadest peak) are found to be within this component of the network . These 1ignaling . As thisignaling . For exaignaling . These rof genes . The res network . Finallyactivity .35 We wanted to examine the potential overlap of the broadest neuron-specific H3K4me3 peaks with functional elements including promoters and enhancers, and with previously published data sets on developmentally regulated H3K4me3-enriched loci in PFC neurons from controls18 and subjects on the autism spectrum.16 These data sets were particularly interesting from the viewpoint of the present study, given that deleterious mutations in regulators of H3K4 methylation rank prominently in exome-sequencing studies on neurodevelopmental disorders, including autism15 and schizophrenia.73 Indeed, there was a robust, 70- to 160-fold enrichment for sequences matching dynamically regulated H3K4me3 peaks, defined by extended age-related changes in PFC neurons, with inclining (or declining) levels of H3K4 methylation from the perinatal period to late childhood/early adulthood16 (DVL1 (Dishevelled Segment Polarity Protein 1), the neurotrophic peptide VGF and genes encoding ion channels and subunits such as CACNA1C and GRIN2D. To examine whether the striking developmental enrichment of the top 5% broadest H3K4me3 peaks was specific, we used to Genomic Association Test tool to conduct similar types of analyses for the top 5% tallest peaks. Strikingly, there was very little enrichment for developmentally regulated H3K4me3 peaks for the extremely tall peaks  subjects. On the basis of the results of the present study, a more detailed analyses of specific histone modification profiles, including spread and breadth of histone H3K4 and other lysine methylation markings in specific cell types, bears promising potential to deliver valuable insights into epigenetic mechanism of normal and diseased brain development and aging. Such type of approaches, in the ‘Big data era' of functional genomics with NIH-sponsored consortia such as PsychENCODE consortium (http://www.psychencode.org) charting brain epigenomes and transcriptomes in hundreds of specimens across the lifespan, are likely to provide critical insights into the neurobiology of psychiatric disorders such as autism and schizophrenia.81In the present study, we characterized the broadest H3K4me3 peaks from human PFC in the context of cell-type-specific regulation, association with neuronal and non-neuronal gene expression and potential implications for normal and diseased development. We first addressed the occurrence and the biological significance of the broadest H3K4me3 peaks in three different cell types, including NeuN+ PFC neurons, NeuN− PFC cells and nucleated blood cells. We identified novel regulators of these three different cell types by focusing on top 5% broadest H3K4me3 peaks (length in base pairs). Of note, the broadest H3K4me3 peaks, which in PFC neurons included >500 peaks in two different cohorts, showed a significantly stronger cell-type-specific signature compared with the complete pool of H3K4me3 peaks. Thus, broadest NeuN+ H3K4me3 peaks in the present study were enriched for genes regulating neuronal connectivity and signaling, including many ion channels, and synaptic plasticity and learning and memory. Broadest H3K4me3 peaks in non-neuronal PFCs showed enrichment for oligodendrocyte and other glial-related genes, in contrast to nucleated blood cells in which broadest peaks were associated with immune functions. The molecular regulators of the broadest H3K4me3 peaks remain to be determined for each cell type. Interestingly, we found that in PFC neurons, the DNA sequences of the top broadest 5% H3K4me3 peaks showed a significant enrichment for a set of motifs with binding affinity for several transcription factors and transcription factor families . These i


AVD) is a common condition with a progressive natural history, and presently, there are no pharmacologic treatment strategies. Elastic fiber fragmentation (EFF) is a hallmark of AVD, and increasing evidence implicates developmental elastic fiber assembly defects. Emilin1 is a glycoprotein necessary for elastic fiber assembly that is present in both developing and mature human and mouse aortic valves. The Emilin1‐deficient mouse (Emilin1−/−) is a model of latent AVD, characterized by activated TGFβ/MEK/p‐Erk signaling and upregulated elastase activity. Emilin1−/− aortic valves demonstrate early EFF and aberrant angiogenesis followed by late neovascularization and fibrosis. The objective of this study was to test the effectiveness of three different targeted therapies. Aged (12–14 months) Emilin1−/− mice were treated with refametinib , doxycycline (elastase inhibitor), or G6‐31 (anti‐VEGF‐A mouse antibody) for 4 weeks. Refametinib‐ and doxycycline‐treated Emilin1−/− mice markedly reduced MEK/p‐Erk activation in valve tissue. Furthermore, both refametinib and doxycycline attenuated elastolytic cathepsin K, L, MMP‐2, and MMP‐9 activation, and abrogated macrophage and neutrophil infiltration in Emilin1−/− aortic valves. RNAseq analysis was performed in aortic valve tissue from adult (4 months) and aged (14 months) Emilin1−/− and age‐matched wild‐type control mice, and demonstrated upregulation of genes associated with MAPK/MEK/p‐Erk signaling and elastases at the adult stage and inflammatory pathways at the aged stage controlling for age. These results suggest that Erk1/2 signaling is an important modulator of early elastase activation, and pharmacological inhibition using refametinib may be a promising treatment to halt AVD progressionAortic valve disease ( Aortic valve disease (AVD) is a common cause of cardiovascular morbidity and mortality , a hallmark of AVD ; (2) vehicle‐treated Emilin1−/− mice (positive control); (3) Emilin1−/− mice treated with refametinib (RDEA‐119), a selective MEK1/2 inhibitor (p‐Erk1/2 inhibition); (4) Emilin1−/− mice treated with doxycycline (nonspecific elastase inhibition); and (5) Emilin1−/− mice treated with G6‐31 (Vegf‐A inhibition) , a MEK1/2 specific allosteric inhibitor , was administered twice daily using oral gavage at 25 mg/kg body weight per dose. RDEA‐119 was dissolved in DMSO at 10 mmol/L , as previously described , as described previously  and stored according to manufacturer's recommendations. Total RNA was extracted using the micro RNEasy Kit , and RNA quality was assessed using the Agilent Bioanalyzer 2100. RNA was then prepared for sequencing using the Illumina mRNA‐seq Sample Prep Kit. The generated RNA‐seq libraries were subjected to high‐throughput 50 bp single‐end RNA‐sequencing at a read depth of >50 million reads utilizing the Illumina Hi‐Seq 2500 machine. All sequenced reads were aligned to the mus musculus (mm9 sequence database) subset of RefSeq using TopHat, and then processed with Cufflink to generate the transcriptome  and filtered again at a threshold of 10 RPKM. Differential expression analysis was performed on the filtered data set (>10 RPKM) to identify genes with a >2‐fold change.P‐value cutoff of 0.05 using an one‐way ANOVA. Differentially expressed genes were clustered using Topp Cluster web server application for comprehensive analysis of multiple genes list or biological pathways in early and the late stage. The log2‐transformed data were preprocessed by median centering, and then hierarchically clustering method (https://toppcluster.cchmc.org/). Subsequently, enrichment maps and Venn diagrams were generated using cytoscape software  and late (12–14 month) stages, corresponding with time points before and after disease onset, using enrichment analysis and compared with wild‐type control mice. Genes corresponding to differentially expressed transcript clusters were selected for display in hierarchical clustering, with a threshold t values were obtained using Bio‐Rad software. The ΔΔCt method was used to represent mRNA fold change. The experiments were performed in triplicate.RNA from aortic valves  was isolated from vehicle‐ and drug‐treated mice using Trizol. As previously reported, cDNA was generated using ~500 ng isolated RNA and amplified by PCR using gene‐specific primers Table Hinton e. Ct valuAortic valve specimens were obtained from nonsyndromic patients with isolated AVD and undergoing aortic valve replacement (affected), and from age‐matched individuals who died of noncardiac causes at the time of autopsy (control). AVD patients were stratified by age into early‐onset (0–40 years) and late‐onset (41–85 years) groups. Patients with a history of rheumatic heart disease or infective endocarditis were excluded. Control aortic valves from patients of similar ages were obtained at autopsy from individuals who died of noncardiac causes with a maximum ischemic time of 24 h. Aortic valve tissues were fixed in 10% formalin, dehydrated through a graded ethanol series, washed in xylenes, and embedded in paraffin wax and sectioned. Tissue slides were subsequently processed and stained as described previously . Emilin1−/− aortic valves are characterized by a progressive increase in the proliferative index and increased myofibroblast like phenotype in VIC's  and aged (14–18‐month‐old) stages in Emilin1+/+ and Emilin1−/− aortic valves. RNAseq analyses revealed a total of 470 and 456 genes that were differentially expressed  at adult and aged stages, respectively . We studied whether the differentially expressed genes were enriched for specific signaling pathways. Genes involved in biological functions such as extracellular matrix disassembly, cartilage development, angiogenesis, and mechanical stimuli response elements were significantly enriched in Emilin1−/− adult aortic valves . In addition, significant enrichment was found in genes involving TGFβ signaling in Emilin1−/− adults when compared with age‐matched controls. Aged Emilin1−/− aortic valves demonstrated significant enrichment in genes associated with inflammation. At least 17 genes were differentially expressed in both adult and aged Emilin1−/− mice, 4 are shown in the enrichment map (maroon nodes), suggesting different signaling pathways are involved in early and late disease processes .In order to identify molecular signatures involved in AVD progression in . IHC revealed increased expression of the profibrotic gene Periostin, a marker for osteogenic procalcific activity gene Spp‐1 (osteopontin) and the proinflammation gene Ptx‐3 (Pentaxtrin) in Emilin1‐/‐ aortic valves. Our RNAseq findings demonstrated elevated Ptx‐3 expression at both adult and aged stages in Emilin1‐deficient valves. Findings were corroborated using QRT‐PCR . Furthermore, QRT‐PCR showed that Mmp‐2 expression was progressively increased in aged null valves when compared to wild‐type controls . In addition, Emilin1‐/‐ vascular smooth muscle cells (VSMCs) isolated from aortic root demonstrated a robust increase in p‐Erk1/2 expression when stimulated with the osteogenic media (OM). Neither aorta nor aortic valve demonstrated spontaneous gross calcification at any age in the Emilin1−/− mice . VSMC's were isolated and cultured from the aortic root of Emilin1+/+Emilin1−/− mice. Emilin1−/− VSMCs calcify as seen by alizarin red staining when compared with Emilin1+/+ cells. Osteocalcin and Runx‐2 (ossification markers) were upregulated in VSMC's cultured in OM . Osteogenic media induces p‐Erk1/2 activation and downregulation of VEGF‐A, suggesting VSMC calcification is mediated by p‐Erk1/2 activation. Taken together, this suggests AVD pathogenesis in Emilin1−/− valves is dynamic, characterized by early perturbation in TGFβ signaling and ECM, followed by late inflammation and the manifestation of overt disease.Interestingly, control wild‐type valves also demonstrate 281 differentially expressed genes due to aging. To validate the targets of altered differential gene expression in Emilin1 deficiency, additional IHC or QRT‐PCR was performed for selected genes To extend these observations to human tissue and determine the timing of potential p‐ERK1/2 activation in the context of human AVD, p‐ERK1/2 was examined using IHC. All human valves were processed within 6 h if not immediately. These studies demonstrated increased p‐ERK1/2 expression in early‐onset AVD in the absence of inflammation when compared with healthy control aortic valves, and p‐ERK1/2 expression was increased further in the presence of inflammation in late‐onset AVD Fig. A–D. CathTo evaluate EFF in early and late‐onset AVD, histologic staining was performed, demonstrating marked elastic fiber defects in early‐onset AVD. In late‐onset AVD, there were similar elastic fiber abnormalities, including dispersion of fragments away from the ventricularis layer. Desmosine, a breakdown product of elastin as well as an index of elastic fiber turnover, was quantified in healthy and AVD specimens and showed significant reduction in AVD when compared to control valves Fig. .Emilin1‐deficient mouse model of latent AVD , 4 are shown in the enrichment map (maroon nodes), suggesting different signaling pathways are involved in early and late disease processes .Previous studies have established aging as the major risk factor for developing aortic valve disease  valve tissue, recapitulating human AVD pathogenesis that initiates with perturbations in ECM remodeling and progresses to advanced disease with inflammation.Interestingly, control wild‐type valve tissue also depicted altered gene expression related to inflammation, albeit only a lesser degree, due to aging, suggesting the mutant mouse may show advanced aging, consistent with clinical studies identifying age‐related inflammation as an independent risk factor for AVD. The Emilin1‐mutant valve does not only grossly calcify at any age, including 24 months (unpublished), but also there are multiple pieces of data that suggest elevated osteopontin is present in Emilin1−/− mouse valves that showed increased expression of elastolytic enzymes , this suggests that early elastase activation may be related to elastic fiber abnormalities while late elastase activation may be secondary to inflammatory processes and these patterns may be distinct. Importantly, refametinib or doxycycline treatment significantly decreased the expression of proteolytic enzymes Mmp‐9, and cathepsin K and L. Elastic fiber degradation products have shown promise as biomarkers of emerging disease states (Marshall et al. The findings of the current study are relevant to human disease. Human AVD is characterized by a vast array of pathologic findings, and discerning primary and secondary findings is critical to understanding natural history and identifying new therapeutic targets. In the current study, our human data suggest that P‐ERK1/2 was localized more in the nuclei in the early‐onset disease valves in contrast with late‐onset disease that showed more pronounced expression in the matrix. We do not have any evidence that these treatments rescued valve fibrosis. Grossly, pentachrome staining did not demonstrate a difference in collagen content with treatment and quantitatively, QRT‐PCR did not normalize fibrillar collagens. The expression of proteolytic enzymes declined in Emilin1‐deficient valves when treated with refametinib, suggesting a therapeutic role of MEK inhibition in mitigating pathological ECM remodeling and this might be extrapolated to suggest that the progressive nature of fibrosis would discontinue but the existing fibrosis would be unchanged. We also showed a marked increase in Cathepsin S expression in late‐onset human AVD, that is AVD associated with inflammation. Combined with observations in the Emilin1−/− aortic valves, was not reduced with either treatment, suggesting a VIC myofibroblast‐like switch occurred prior to treatment, consistent with the idea that ECM accumulation and disorganization is an early disease process. It did not alter myofibroblast activation, angiogenesis or fibrosis. However, the current study has identified early disease processes involved in progression that will provide a basis to consider new therapies for early‐onset AVD. In our previous study (Munjal et al. This study has significant limitations. One limitation is the lack of data examining the effects of combined therapy using refametinib and doxycycline, which hypothetically may have an additive or synergistic effect, and in the event p‐Erk1/2 upregulation were completely rescued, evidence that both Erk and elastases potentiate reciprocally. Interestingly, myofibroblast cell activation and proliferation, which is markedly increased in C.M., G.M.B, and R.B.H. conceived and designed the study; C.M., A.G.J, A.M.O., R.A.N., C.J.T., J.M.S., and R.P.M. acquired the data for the study; C.M., A.G.J., A.M.O., R.P.M., G.M.B., and R.B.H. analyzed and interpreted the data; C.M and R.B.H. drafted the manuscript; C.M., A.G.J., A.M.O., R.A.N., C.J.T., J.M.S., R.P.M., G.M.B., and R.B.H. carried out critical revision and final approval of the manuscript.None declared.Table S1. Staining quantification of mouse histology.Table S2. Staining quantification of human histology.Table S3. Specification of the primary antibodies.Table S4. Specification of the primers.Click here for additional data file.


Escherichia coli K‐12 nrf operon encodes a periplasmic nitrite reductase, the expression of which is driven from a single promoter, pnrf. Expression from pnrf is activated by the FNR transcription factor in response to anaerobiosis and further increased in response to nitrite by the response regulator proteins, NarL and NarP. FNR‐dependent transcription is suppressed by the binding of two nucleoid associated proteins, IHF and Fis. As Fis levels increase in cells grown in rich medium, the positioning of its binding site, overlapping the promoter −10 element, ensures that pnrf is sharply repressed. Here, we investigate the expression of the nrf operon promoter from various pathogenic enteric bacteria. We show that pnrf from enterohaemorrhagic E. coli is more active than its K‐12 counterpart, exhibits substantial FNR‐independent activity and is insensitive to nutrient quality, due to an improved −10 element. We also demonstrate that the Salmonella enterica serovar Typhimurium core promoter is more active than previously thought, due to differences around the transcription start site, and that its expression is repressed by downstream sequences. We identify the CsrA RNA binding protein as being responsible for this, and show that CsrA differentially regulates the E. coli K‐12 and Salmonella nrf operons.The  Escherichia coli K‐12 nrf operon encodes the NrfA periplasmic formate‐dependent nitrite reductase, which is responsible for reducing nitrite to ammonium ions to support bacterial growth under anaerobic conditions  and expression is induced by the global transcription activator protein, FNR, in the absence of oxygen  and three DNA sites for Fis (Fis I to III)  and Salmonella enteric serovar Typhimurium promoters. Using this approach, we have uncovered differences in the regulatory strategies used at these promoters in these organisms and identify the global regulator, CsrA, as an additional regulator of this complex operon in enteric bacteria.During their evolution, bacterial pathogens are exposed to the particular environmental conditions within their host organism. Over time, their genomes accumulate mutations, many of which will have no effect, whilst others can change protein function or gene regulation. To understand how this process has shaped the expression of the ens Fig. , particuE. coli K‐12 pnrf53 promoter fragment, which carries the nrf promoter sequences from −209 upstream of the transcription start site (+1) to +131 downstream , enteroaggregative E. coli (EAEC), enteropathogenic E. coli (EPEC), Shigella flexneri and S. enterica serovar Typhimurium  was less active, as previously demonstrated  . Thus, the improvement of the pnrf EHEC −10 element allows some NrfA expression to occur in the absence of FNR and in the presence of oxygen. Note that amino acid sequences of the EHEC FNR, Fis and various RNA polymerase subunits are either identical or extremely similar to those of E. coli K‐12 .Previously, we generated the CT) Fig.  and is aE. coli K‐12 nrf promoter is repressed in rich medium, being down‐regulated by Fis binding to Fis I . However, expression from the pnrf53 EHEC promoter fragment was relatively insensitive to medium composition, showing only a 2.2‐fold decrease. Thus, we conclude that improvement of the −10 element makes the EHEC promoter less sensitive to nutrient quality.The et al., E. coli K‐12 and EHEC pnrf97 promoter fragments, which both carry pnrf DNA sequences from −87 to +10 . However, the diffuse nature of these complexes suggests that they are unstable during electrophoresis and that Fis interferes with RNA polymerase binding. Conversely, for the pnrf97 EHEC fragment, stable RNA polymerase‐containing complexes were detected when both FNR and Fis were present in the reaction mix , indicating that Fis has less effect on the ability of RNA polymerase to bind to the EHEC promoter. Thus, we conclude that the EHEC pnrf promoter can bypass this repression as its improved −10 element allows RNA polymerase to out compete Fis when binding at the EHEC nrf promoter.As it is likely that the improvement of the EHEC E. coli K‐12 pnrf97 promoter fragment, RNA polymerase shifted species were observed in the absence of FNR , thus, corroborating our assignment of the RNA polymerase complexes observed in Fig. We note that for the FNR Fig. C: lane 7S. enterica serovar Typhimurium nrf promoter is the least active of the promoters tested and previously we demonstrated that sequences upstream of position −87 do not influence expression as the Salmonella promoter lacks the stimulatory upstream IHF III site  from E. coli K‐12 and EHEC into the low copy number lac expression vector pRW224, to generate lacZ transcriptional fusions . Constructs were transformed into strains JCB387 and JCB3884 (narL narP) and promoter activity was determined in cells grown in minimal medium. Surprisingly, results in Fig. pnrf97 STM transcriptional fusion was much higher than the E. coli K‐12 derivative, resembling that of pnrf97 EHEC. This suggests that the Salmonella promoter is stronger than previously thought and that expression from the longer pnrf53 STM fragment might be repressed by an additional factor, which binds downstream of position +10. Note that the introduction of the p14C mutation into the pnrf97 STM promoter fragment, which disrupts the extended −10 element of the promoter, completely abolished promoter activity, indicating that new promoter elements had not been generated during construction of this fragment . Results in Fig. pnrf53 STM fragment. Note that amino acid sequence of the S. enterica serovar Typhimurium CsrA is identical to that of E. coli K‐12 .CsrA is a sequence‐specific RNA binding protein, which directly represses the translation of many nce Fig. A. To invite Fig. A. DNA prpnrf53 STM fragment is regulated by CsrA, β‐galactosidase expression, from the pnrf53 STM and the pnrf53 STMp + 104A fragments (positions −246 to +133), cloned into pRW50, was examined in the Δlac strain CF7789 and in TRCF7789 in which csrA is disrupted . Note that both the p + 104A and p + 102A substitutions maintain the arginine codon at this position in the nrfA mRNA when compared to the wild‐type sequence . Results in Fig. pnrf53 STM p + 104 was elevated ∼twofold in CF7789, whilst no increase was observed in TRCF7789. This suggests that CsrA inhibits expression from the pnrf53 STM construct. Conversely, the expression from the E. coli pnrf53 p + 102A fragment was indistinguishable from that of the wild‐type pnrf53 fragment in the both strains, indicating that CsrA does not regulate expression from the E. coli K‐12 pnrf53 fragment. As CsrA predominantly influences translation . The disruption of the GGA motif in both the pnrf53 STM and the E. coli K‐12 pnrf53 fragments resulted in an increase of anaerobic expression in CF7789, which was absent in TRCF7789   cells carrying pQE60/csrA . Therefore, JM109 cells, carrying either pQE60 NdeI or pQE60/csrA, were transformed with pRW244 carrying various pnrf53 and pnrf53 STM fragments, cloned as translational fusions. Cells were grown anaerobically in minimal salts medium, containing 0.4% glucose, and the effect of leaky uninduced CsrA‐6His expression was determined by measuring β‐galactosidase expression. Results in Table pnrf53 and pnrf53 STM wild‐type fragments, when compared to cells carrying the empty pQE60 NdeI vector. However, for pnrf53 and pnrf53 STM fragments, carrying substitutions in the CsrA binding site  the effect of CsrA expression was considerably less. This confirms that CsrA represses expression of both E. coli K‐12 and Salmonella nrfA using the CsrA binding sites identified by this study.To confirm that CsrA regulates both the Salmonella pnrf97 STM fragment is elevated in comparison to the E. coli K‐12 pnrf97 fragment. To identify which parts of the Salmonella promoter that were responsible for this, we generated chimeric pnrf97 fragments, in which the upstream and downstream sequences from pnrf97 STM were introduced into the E. coli K‐12 pnrf97 fragment  cells, during aerobic and anaerobic growth in minimal medium. Results in Fig. E. coli K‐12 pnrf97 fragment  only those at positions +1 and +4 led to small increases in expression, in comparison to pnrf97   indicated that many serovars possessed the same differences around +1 as S. enterica serovar Typhimurium, indicating that these improvements are largely conserved. However, two S. enterica serovars (Dublin and Newport) possess a G at position +4, which is the same as the E. coli K‐12 promoter at this position . Thus, we would expect in these Salmonella serovars expression from pnrf to be lower.The core promoter of the ter Fig. B. Chimerter Fig. B. At bacS. enterica serovar Typhimurium core promoters have similar anaerobic expression levels  CsrB and CsrC (CsrB/C), which both contain multiple CsrA binding motifs that sequester CsrA away from its target transcripts  coordinating expression in response to environmental and metabolic conditions    with genomic DNA as template. The S. flexneri pnrf DNA was amplified using nrfSFX Up and nrfA Down primers. PCR products were restricted with EcoRI and BamHI and cloned into pRW50 to generate lacZ transcriptional fusions.The DNA sequences of the k/) Fig.   was excised from plasmid pCSB12  to a final concentration of 1 mM for 3 hrs. Total cellular protein samples were prepared and Western blotting was carried out using anti‐6His  HRP linked antibody (Invitrogen)  and FNR DA154 and Fis proteins were purified as described previously , 100 mM potassium glutamate, 1 mM EDTA, 50 μM DTT, 5% glycerol and 25 μg ml−1 herring sperm DNA. The final reaction volume was 10μl. FNR and Fis proteins were incubated at 37°C for 15 minutes, after which RNA polymerase was added and samples were incubated at 37°C for a further 15 minutes. Samples were loaded directly onto a running 6% polyacrylamide gel (12 V cm−1), containing 2% glycerol and 0.25 × TBE and analysed using a Bio‐Rad Molecular Imager FX and Quantity One software (Bio‐Rad).Gel retardation assays using purified FNR, Fis and RNA polymerase were carried out as detailed by Browning et al. . Purifiepnrf97 promoter fragment was cloned into plasmid pLSR, such that the divergent pnrf and pascP1 promoters are both cloned upstream of a lambda oop transcription terminator   at 37°C for 20 min in a reaction mixture containing 40 mM Tris‐Cl (pH 7.9), 10 mM MgCl2, 50 mM KCl, 0.1 mM dithiothreitol (DTT), 0.2 μg of bovine serum albumin (BSA) μl−1, 0.5 mM ATP, 0.5 mM CTP, 0.5 mM GTP, 0.05 mM UTP and 5 μCi of [α‐32P]UTP, with a final reaction volume of 20 μl. Purified E. coli RNAP was then added to a final concentration of 50 nM, and the mixture was incubated at 37°C for a further 20 min, after which it was stopped by the addition of 25 μl of formamide buffer . Samples were then loaded onto a 5.5% denaturing polyacrylamide gel, containing 1 × TBE, and were analysed using a Bio‐Rad Molecular Imager FX and Quantity One software (Bio‐Rad).The The authors declare no conflict of interest.DFB and SJWB conceived and designed the research programme. REG, DJL and DFB performed the experiments. DFB and SJWB wrote the manuscript with input from all authors.Supporting InformationClick here for additional data file.


Studies reported here demonstrate that σ8 is an important regulator of both avermectin production and stress responses in S. avermitilis. σ8 inhibited avermectin production by indirectly repressing expression of cluster-situated activator gene aveR, and by directly initiating transcription of its downstream gene sav_742, which encodes a direct repressor of ave structural genes. σ8 had no effect on cell growth or morphological differentiation under normal growth conditions. Growth of a sig8-deletion mutant was less than that of wild-type strain on YMS plates following treatment with heat, H2O2, diamide, NaCl, or KCl. sig8 transcription was strongly induced by these environmental stresses, indicating response by σ8 itself. A series of σ8-dependent genes responsive to heat, oxidative and osmotic stress were identified by EMSAs, qRT-PCR and in vitro transcription experiments. These findings indicate that σ8 plays an important role in mediating protective responses to various stress conditions by activating transcription of its target genes. Six σ8-binding promoter sequences were determined and consensus binding sequence BGVNVH-N15-GSNNHH  was identified, leading to prediction of the σ8 regulon. The list consists of 940 putative σ8 target genes, assignable to 17 functional groups, suggesting the wide range of cellular functions controlled by σ8 in S. avermitilis.Alternative σ factors in bacteria redirect RNA polymerase to recognize alternative promoters, thereby facilitating coordinated gene expression necessary for adaptive responses. The gene  Streptomyces are characterized by the ability to produce valuable secondary metabolites having antimicrobial, anticancer, anthelmintic, immunosuppressive, or other biological activities  and σ25 (SAV3351), inhibit avermectin production by indirectly affecting aveR transcription  based on differential possession of four conserved domains , phylogenic relationships and physical functions , reduced production of undecylprodigosin (RED) and lack of aerial mycelium formation on R2YE or NA plates  inhibitor, suggesting involvement of σB in response to changes in temperature or ROS level , encodes an alternative σ factor, σ8, homologous to σB in S. coelicolor. We describe here characterization of σ8 as an important regulator that controls avermectin biosynthesis and multiple stress responses in S. avermitilis. σ8 represses avermectin biosynthesis in part through direct initiation of sav_742 transcription. Unlike its S. coelicolor homolog σB, σ8 responds to heat, osmotic and oxidative stress by directly regulating expression of its own gene and certain other stress protection genes, but is not involved in morphological differentiation or cell growth. Moreover, we predicted the σ8 regulon based on the consensus σ8-binding promoter sequence.We demonstrated recently that SAV742, a novel AraC-family transcriptional regulator in ntiation . The genTable 1, and the primers in Supplementary Table Escherichia coli and S. avermitilis strains were as described previously  genomic DNA. A 529-bp 5′-flanking region (positions -337 to +192 relative to the sig8 start codon) was amplified with primers SD2A/SD2B, and a 541-bp 3′-flanking region (positions +835 to +1375) was amplified with primers SD3A/SD3B. The two fragments were cut with HindIII/BamHI and BamHI/EcoRI, respectively, and then simultaneously cloned into HindIII/EcoRI-digested pKC1139 to generate sig8 deletion vector pKCDsig8. Non-methylated pKCDsig8 was transformed into WT protoplasts, and double-crossover recombinant strains were isolated as reported previously (sig8 deletion mutants were verified by colony PCR using primers SD23A/SD23B (flanking the exchange regions) and SD39A/SD39B (located within the deletion region of sig8)  ; Thus, the remaining fragment was unlikely to be functional.A eviously . Resultisig8sav_742 double deletion mutant, the pKCDsig8 vector was transformed into D742 protoplasts (To construct a toplasts . The expsig8 promoter and open reading frame (ORF) was amplified with primers SD1B/SD1D, and inserted into the integrative vector pSET152 to give sig8-complemented vector pSET152-sig8, which was then transformed into Dsig8 to obtain complemented strain Csig8.For complementation of Dsig8, a 1618-bp PCR fragment containing the sig8, a 987-bp fragment carrying the sig8 ORF was amplified using primers SD1B/SD1C, and inserted into pJL117 to generate pJL117-sig8, in which sig8 was controlled by ∗permE (Streptomyces strong constitutive promoter). The 1.3-kb EcoRI/HindIII fragment containing ∗permE and sig8 ORF from pJL117-sig8 was ligated into pKC1139 to generate sig8 overexpression vector pKC1139-erm-sig8, which was transformed into WT protoplast to construct sig8 overexpression strain Osig8.For overexpression of S. avermitilis strains and HPLC (high-pressure liquid chromatography) analysis of avermectin yield in fermentation broth were performed as described previously  for RNA isolation. Genomic DNA contamination was removed by treatment of RNA samples with RNase-free DNase I . 4 μg total RNA was used for cDNA synthesis. qRT-PCR was performed using primers listed in Supplementary Table rRNA as internal control. Each experiment was repeated three times.sig8 coding region (279 amino acids) was amplified from WT genomic DNA using primers SD1A/SD1B. The obtained PCR product was inserted into pET-28a (+) to generate pET-sig8. After confirmation by DNA sequencing, pET-sig8 was transformed into E. coli BL21 (DE3) for His6-σ8  overexpression. Following IPTG induction, cells containing His6-σ8 were harvested, washed, resuspended in a lysis buffer .The 953-bp s buffer , and diseviously . Purifie6-σ8 and DNA probes, a ∼300-fold excess of each specific or non-specific (hrdB) unlabeled probe was added to the reaction mixture before incubation.EMSA probes carrying respective tested promoter regions were obtained by PCR using primers listed in Supplementary Table 6-σ8. Transcription was initiated by addition of 3.7 pmol E. coli core RNA polymerase (core-RNAP) .DNA templates containing respective promoter and partial coding region were amplified by PCR using primers listed in Supplementary Table S. avermitilis strains were cultured in FM-II at 28°C for 2 days, and then subjected to various stress conditions. Mycelia were harvested after various durations of stress treatment and used for RNA isolation. 4 μg total RNA was reverse transcribed with 40 pmol gene-specific primer SP1. Purified cDNAs were added to oligo-dA tails at the 3′ end by TdT  treatment at 37°C for 30 min. The tailed cDNA was used as template for first-round PCR using second inner gene-specific primer SP2 and oligo-dT anchor primer. The resulting PCR product was diluted to appropriate concentration, and used as template for second-round PCR with nested primer SP3 and an anchor primer. The purified final PCR product was sequenced . TSS was determined as the first nucleotide following the oligo-dT sequence.The transcriptional start site (TSS) of selected genes was identified using a 5′/3′ RACE kit . sig8 (sav_741) gene in the S. avermitilis chromosome contains 840 nucleotides (nt) and encodes a 279-amino-acid σ70-family alternative σ factor, σ8. The downstream convergently transcribed gene sav_742, located 377 nt from sig8 , encodes an AraC-family transcriptional regulator that was recently characterized as a global regulator of avermectin biosynthesis, cell growth, and morphological development  and S. hygroscopicus σB (78.29%).The elopment . The ups8 in avermectin biosynthesis, we constructed sig8 deletion mutant Dsig8, complemented strain Csig8 and overexpression strain Osig8, and compared their avermectin yields with that of WT ATCC31267 cultured in FM-I for 10 days. Relative to WT yield, that of Dsig8 was ∼96% higher, that of Osig8 was ∼50% lower, and that of Csig8 was similar . Yields of two vector control strains, WT/pSET152 and WT/pKC1139, were almost the same as that of WT . These findings indicate that σ8 has a negative effect on avermectin production.To investigate the role of σ8 on S. avermitilis growth, we measured biomasses of WT, Dsig8 and Csig8 cultured in soluble FM-II. Biomass of Dsig8 and Csig8 was similar to that of WT , indicating that σ8 has no effect on cell growth, and that avermectin overproduction in Dsig8 does not result from alteration of growth. Dsig8 and Osig8 grew normally on YMS, R2YE, and MM plates .To investigate the relationship between σ8 on expression of ave genes was assessed by qRT-PCR analysis of transcription levels of aveR (CSR gene) and aveA1  in WT and Dsig8 grown in FM-I for 2 days  or 6 days (stationary phase). Transcription levels of aveR and aveA1 in Dsig8 did not differ from those in WT on day 2, but were notably higher on day 6 , consistent with the observed avermectin overproduction in Dsig8. These findings indicate that σ8 controls avermectin production at the transcription level by repressing ave genes, primarily in the late fermentation stage.The effect of σ8 on aveR and aveA1 are therefore likely to be indirect. To test this idea, we performed EMSAs using refolded soluble recombinant His6-σ8 and probes aveRp and aveA1p prepared by labeling promoter regions of aveR and aveA1. His6-σ8 did not bind to the probes even at high concentration (1 μM) , confirming that σ8 indirectly represses ave genes.σ factors function as initiators of transcription. The negative regulatory effects of σB homologes typically initiate their own transcription (8 indirectly represses ave genes suggested that σ8 initiates transcription of direct repressor(s) of aveR or aveA1. We recently characterized PhoP  and sav_742 (probe sav_742p), but not with those of avaR2 (probe avaR2p) or phoR-phoP operon (probe phoRp) . No shifted band was observed for negative probe control hrdB or protein control BSA. Binding specificity was confirmed by competitive assays using excess unlabeled specific probe (lanes S) and non-specific probe hrdB (lanes N). qRT-PCR analysis showed that transcription levels of sig8 and sav_742 on days 2 and 6 were lower in Dsig8 than in WT , indicating that σ8 positively regulates expression of its own gene and adjacent gene sav_742. The lower sav_742 transcription level and higher avermectin yield in Dsig8 are consistent with SAV742’s function as a repressor of avermectin production . Core-RNAP alone did not initiate transcription at the sig8 or sav_742 promoter, whereas the Eσ8 holoenzyme did . When core-RNAP concentration was kept constant and σ8 concentration was increased, transcript levels of sig8 and sav_742 increased, but no aveR transcript was detected. These findings indicate that σ8 initiates transcription of its own gene and sav_742, but not that of aveR. The presence of sig8 and sav_742 transcripts in Dsig8 suggests that additional σ factors may recognize sig8 and sav_742 promoters and initiate their transcription, but with lower transcription efficiency.sig8sav_742 double deletion mutant Dsig8–742  stresses, but similarly sensitive to tert-butyl hydroperoxide (TBHP) and sucrose stresses . These findings indicate that σ8 is required for WT levels of resistance to multiple stresses, e.g., heat stress, salt stress, and oxidative stress (from H2O2 or diamide).Alternative σ factors are usually involved in modulation of stress responses . To detesig8 mutant Dsig8 was more sensitive to various environmental stresses, and that σ8 was autoregulated, suggested that sig8 itself may be induced by these environmental stresses in a σ8-dependent manner. We tested this possibility by qRT-PCR comparison of sig8 transcription levels in WT and Dsig8 under stress conditions. Cells were cultured in soluble FM-II for 2 days, and then treated with heat (42°C), H2O2, diamide, or NaCl. RNA samples were isolated from cells before (0 min) and after treatment . For WT, sig8 transcription level increased to a maximal value within 10–60 min for each stress type . For Dsig8, maximal induction was sharply decreased under each stress type  and delayed for heat (from 10 to 30 min) and salt (from 60 to 80 min) . These findings indicate that σ8 itself responds to various environmental stresses at the transcription level in either a σ8-dependent or σ8-independent manner. The rapid and robust induction of sig8 in WT may be due to σ8-dependent control, whereas the decreased and/or delayed induction in Dsig8 may be due to σ8-independent control.The observations that 8 was strongly induced in response to a variety of stresses, suggesting that it mediates defensive systems against stresses. To identify σ8 target genes that respond to heat stress, we initially performed a series of EMSAs using His6-σ8 and potential promoter probes of heat shock genes, including dnaK1p for dnaK1-grpE1-dnaJ1-hspR operon, dnaK2p for dnaK2-grpE2-dnaJ2 operon, groES1p for groES1-groEL1 operon, groEL2p, htpGp, hsp18_1p and hsp18_2p. His6-σ8 bound specifically to dnaK1p, but not to other probes tested . In vitro transcription analysis confirmed that dnaK1 transcription was initiated by σ8 . dnaK1 transcription level in Dsig8 was downregulated at two time points . These findings demonstrate that σ8 is a direct activator of dnaK1.σgrpE1, dnaJ1, and hspR are cotranscribed with dnaK1, and therefore are also σ8 targets. dnaK1, grpE1, and dnaJ1 encode molecular chaperones for interaction with denatured proteins and facilitate refolding to the native state following heat stress, and hspR encodes a putative transcriptional repressor of its operon. Transcription levels of dnaK1 in WT and Dsig8 under heat stress were recorded to determine whether dnaK1p for its operon was induced in a σ8-dependent manner. In WT, dnaK1 transcription level increased rapidly to ∼45-fold after 10 min exposure to 42°C, and then gradually declined. In Dsig8, maximal induction was reduced to ∼35-fold and delayed after 30 min . These findings indicate that σ8 facilitates rapid adaption to heat stress mainly by direct regulation of the dnaK1-grpE1-dnaJ1-hspR operon. The slower, lower-degree induction of dnaK1p in Dsig8 presumably depends on other factors not addressed here.8 responds to H2O2 and diamide, which respectively cause peroxidative and thiol-oxidative stress. Bacteria often respond to H2O2 stress by producing catalases and peroxidases that degrade H2O2. We showed that responses to H2O2 stress in S. avermitilis involve three catalase genes , the ahpCD operon , and two peroxide-sensing transcriptional factor genes   and msh genes  (S. avermitilis contains one sigR homologous gene (sig22), six thioredoxin genes , two thioredoxin reductase genes , and four msh genes . Possible interactions of σ8 with these oxidative stress-related genes were investigated by EMSAs. His6-σ8 bound specifically to the bidirectional promoter probes catR_katA1_int and oxyR_ahpCD_int, and to promoter probes trxA3p, trxB2p, mshAp, mshCp, mshDp and sig22p, but not to probes katA2p, katA3p, trxA1p, trxA2p, trxA4p, sav_2810p for sav_2810-2809-trxA5-2807-cyp12 operon, trxA6p, trxB1p, or mshBp . In vitro transcription analysis showed that σ8 initiated transcription of catR, oxyR, trxA3, trxB2, mshA, mshC, mshD and sig22, but not that of katA1 or ahpC . Transcription levels of catR, oxyR, trxA3, trxB2, mshA, mshC, mshD, and sig22 were all reduced in Dsig8 relative to WT on day 6, or days 2 and 6 . These findings indicate that catR, oxyR, trxA3, trxB2, mshA, mshC, mshD, and sig22 are directly activated by σ8.σR, catR) . In S. c buffer) . S. aver2O2 or diamide for various durations. In WT, catR and oxyR were induced to maximal transcription level  by H2O2 within 40 min . In Dsig8, H2O2 treatment caused only a slight increase of transcription level (∼4-fold induction) of these two genes. These findings indicate that σ8 mediates H2O2 induction of the peroxide-sensing regulators CatR and OxyR. Diamide treatment of WT caused notable induction of trxA3 (∼43-fold), mshA (∼17-fold) and sig22 (∼17-fold) within 20 min, and slight induction of mshC (∼2.2-fold), mshD (∼3-fold) and trxB2 (∼1.8-fold) within 40 min, whereas diamide treatment of Dsig8 had much lower  or no effect (for trxB2) on expression of these genes . These findings indicate that σ8 helps S. avermitilis to cope with thiol-oxidative stress by activating transcription of its target genes trxA3, trxB2, mshA, mshC, mshD, and sig22.For analysis of oxidative stress responses, WT and Dsig8 were treated with 1 mM H8 homolog, S. coelicolor σB, responds to osmotic stress caused by NaCl and KCl. To investigate σ8 target genes involved in osmoprotective functions, a series of candidates were selected for EMSA analysis: ect genes (encoding enzymes for biosynthesis of ectoine) (opu genes (encoding ABC transporters for uptake of osmoprotectants)  (osaAB (encoding a two-component system) and katB . osaAB and catB (katB homolog) were reported to be essential for osmoadaptation in S. coelicolor . In vitro transcription assay confirmed initiation of transcription of ectA, opuBA1, opuBC1, sav_5148, osaA, osaB, and katB by σ8 . Transcription levels of seven newly identified σ8 target genes were all lower in Dsig8 than in WT , demonstrating the role of σ8 as an activator of these genes. ectB, ectC, and ectD are cotranscribed with ectA, and opuBB2, opuBA2, opuBB1, and opuBC2 are cotranscribed with sav_5148; therefore, these are also target genes of σ8.The σectoine) , opu genectants) , osmC (eprotein) , osaAB , suggesting that ectoine plays as a key osmoprotective role. Maximal induction by NaCl treatment in WT was ∼3.5-fold to 8.7-fold for opuBA1, opuBC1, opuBB2 , osaA, osaB, and katB . In Dsig8, NaCl treatment had no effect on transcription of opuBC1, osaA, osaB, or katB, indicating that salt stress induces these genes in a σ8-dependent manner. Maximal induction by NaCl treatment on ectA, opuBA1, and opuBB2 transcription was lower in Dsig8 than in WT, suggesting that these three genes are induced by salt stress in both σ8-dependent and σ8-independent manners.σ8 in S. avermitilis evidently plays a pleiotropic role in control of avermectin production and in protection against a variety of stresses. To clarify broader roles of σ8 in this species, more σ8 target genes need to be identified. The recognition and binding sites of σ70-family factors are the -35 and -10 hexamers of its target promoters. We conducted 5′-RACE assays to determine promoter structures of several σ8 targets and identified a consensus σ8-binding sequence. TSSs of sig8, dnaK1, oxyR, trxA3, sig22, and opuBC1 were determined by 5′-RACE analysis of WT gene transcripts under stress conditions , which resembles that of σB-specific promoters of S. coelicolor (GNNTN-N14-16-GGGTAY) (Y: C or T) (σ C or T) .B-binding sequence is GNNTN-N14-16-GGGTAY. We therefore employed the sequence pattern BGVNVH-N14-16-GSNNHH to scan the S. avermitilis genome using the PREDetector program to predict candidate members of the σ8 regulon, and selected putative promoters that were located within 300 nt upstream of the translational start codons. Using a cut-off score of ≥6.5, we identified 940 putative σ8 target genes, of which 453 have unknown function or are unclassified  directly initiating transcription of sav_742; (ii) indirectly repressing expression of aveR. The observation that σ8 has an indirect effect on aveR expression suggests that it regulates aveR through a “cascade” mechanism. However, our search for aveR upstream regulator(s) that mediate the repression of σ8 on aveR showed that the two known aveR direct repressor genes, phoP (avaR2 (8. Future studies will identify such direct regulator(s) of aveR and clarify the mechanisms underlying σ8 function in avermectin production.AveR is an essential activator for transcription of the  cluster . We shownd aveA4 , but notes, phoP  and avaRP  is upstream of sigB (sco0600)  is not located near sig8 (sav_741), and sig8-adjacent genes have no similarity to rsbA, suggesting that the regulatory mechanism of σ8 activity is different from that of σB. Further studies to identify regulators associated with σ8, based on co-immunoprecipitation assays within S. avermitilis cells, are in progress.In many Gram-positive bacteria, activity of σ factors , which isco0600) . In S. a8 responds rapidly to heat stress by directly activating transcription of the dnaK1-grpE1-dnaJ1-hspR operon. Heat shock proteins include not only chaperones for refolding denatured proteins, but also proteases for degrading more denatured proteins. S. avermitilis has three putative heat shock protease genes lonA, htpX1, and htpX2, and whether they are under direct control of σ8 needs to be investigated. Under H2O2 stress, σ8 directly activates transcription of H2O2-sensing regulator genes catR and oxyR, but not of kat or ahpCD, suggesting that CatR and OxyR mediate H2O2 induction of peroxide-scavenging enzymes. We showed that OxyR in S. avermitilis directly activates expression of antioxidant enzyme genes ahpCD, katA1, katA2, and katA3 in response to H2O2 stress  in S. avermitilis are directly controlled by σ8 in response to diamide, indicating that σ8 facilitates a rapid response to thiol-oxidative stress through both direct and cascade regulatory mechanisms. σ8 targets involved in osmoprotection, identified in this study, are ect genes, opuB genes, osaAB and katB. The specific substrate of OpuB transporter in S. avermitilis remains unknown. Our findings are consistent with the observations that ectoine is osmoprotectant, and osaAB and katB are required for σB-dependent osmoprotection in S. coelicolor  are in progress to experimentally verify additional σ8 targets in S. avermitilis and thereby elucidate the wide range of cellular functions controlled by this important σ factor.Using the identified σYW and DS: designed the research, DS: performed experiments, QW, ZC, and JL: contributed study materials, YW and DS: wrote the 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.


Mc2r promoter activity is activated by JDP2 in a dose-dependent manner. Next, by mapping the Mc2r promoter, we show that cAMP response elements (between −1320 and −720-bp) are mainly required for Mc2r activation by JDP2 and demonstrate that −830-bp is the major JDP2 binding site by real-time chromatin immunoprecipitation (ChIP) analysis. Mutations of cAMP response elements on Mc2r promoter disrupts JDP2 effect. Furthermore, we demonstrate that removal of phosphorylation of JDP2 results in attenuated transcriptional activity of Mc2r. Finally, we show that JDP2 is a candidate for SUMOylation and SUMOylation affects JDP2-mediated Mc2r transcriptional activity. Taken together, JDP2 acts as a novel transcriptional activator of the mouse Mc2r gene, suggesting that JDP2 may have physiological functions as a novel player in MC2R-mediated steroidogenesis as well as cell signaling in adrenal glands.Jun dimerization protein 2 (JDP2), a basic leucine zipper transcription factor, is involved in numerous biological and cellular processes such as cancer development and regulation, cell-cycle regulation, skeletal muscle and osteoclast differentiation, progesterone receptor signaling, and antibacterial immunity. Though JDP2 is widely expressed in mammalian tissues, its function in gonads and adrenals  is largely unknown. Herein, we find that JDP2 mRNA and proteins are expressed in mouse adrenal gland tissues. Moreover, overexpression of JDP2 in Y1 mouse adrenocortical cancer cells increases the level of melanocortin 2 receptor (MC2R) protein. Notably,  Therefore, AP-1 plays an important role in controlling cellular processes including apoptosis, differentiation, and proliferation [r region ,3. In adet genes . Recent et genes . JDP2 alet genes . Overallet genes , controlet genes , promotiet genes , mediatiet genes , mediatiet genes , interacet genes ,13, mainet genes , controlet genes , and conet genes .JDP2 gene activity is elevated in head and neck squamous cell carcinoma cell lines of tongue and larynx [JDP2 also controls the expression of cell-cycle regulators such as cyclin A2, cyclin E2, and p16 to induce cell-cycle arrest via RB-p16 and p53-p21 pathways ,17. Seved larynx . JDP2 haMc2r transcriptional activity in the present study. We investigated the expression levels of JDP2 in mouse adrenal glands as well as whether JDP2 is a novel activator of Mc2r transcription in multiple human and mouse cell lines. The majority of the transcription factors are functionally regulated by post-translational modifications (PTMs) such as phosphorylation, methylation, ubiquitination, and SUMOylation. JDP2 has been shown to be phosphorylated at T148 and phosphorylation of JDP2 by the c-Jun N-terminal kinase targets it for proteosomal degradation [Melanocortin 2 receptor (MC2R), a G-protein couple receptor, is an important membrane receptor selectively involved in the adrenocorticotropic hormone (ACTH)-mediated signaling, which has been shown to be the major endocrine signaling pathway in adrenal cortex. The binding of ACTH to MC2R results in the activation of MAPK- and PKA-dependent signaling cascades . Mutatioradation . Anotherradation ,29,30,31Jdp2 transcripts in the adrenal glands. As expected, adrenal glands expressed transcripts of Nr5a1 and Gapdh (housekeeping gene) as positive control. As shown in Jdp2 or empty vectors were transfected into Y1 cells. As shown in Jdp2 was transfected, the level of MC2R protein was increased (approximately three-fold). These results indicate that adrenal glands express JDP2 protein and JDP2 enhances MC2R expression in Y1 adrenocortical cancer cells.The mRNA and protein levels of JDP2 in the adrenal glands of the mouse were determined using RT-PCR and western blot, respectively. Total RNA was extracted from the adrenal glands of adult mice. As shown in Mc2r promoter is regulated by JDP2. The −2000-bp Mc2r promoter-LUC reporter plasmid was cotransfected with wild-type Jdp2 into several different cell lines and Mc2r promoter activity was determined by measuring LUC activity in cell lysates 48 h after transfection. As shown in Mc2r gene transcription in a LUC assay using HEPG2, JEG3, Ishikawa, Y1, and MCF7 cells. In order to prove the DNA binding domain is required for JDP2-mediated Mc2r promoter activity, we truncated out the DNA binding domain from the wild-type JDP2. As shown in Mc2r promoter. A similar result was observed in JEG3 cells (data not shown). These findings indicate that JDP2 is an activator of the Mc2r transcription.Since JDP2 is expressed in adrenal glands, we next determined whether the Mc2r promoter contains numerous cAMP response elements, the Mc2r promoter was truncated to determine the minimal region that is important for transcriptional activation by JDP2. As shown in Mc2r promoter.Because the −2000-bp mouse Mc2r promoter-LUC plasmids. Consistent with the previous results, mutations of 15 cAMP response elements dramatically reduced (approximately 90% loss) JDP-mediated Mc2r promoter activity in HepG2 cells  or JDP2 chromatin immunoprecipitation (ChIP) assay with qPCR analysis. As shown in To further determine whether the cAMP response elements are required for JDP2-mediated activation, we next generated 15 cAMP response element mutations (TGA or TCA were mutated to ACC) in −1320-bp G2 cells A. SimilaMc2r promoter. We cotransfected Mc2r-LUC reporter gene with either wild-type, T148A (mimicking de-phosphorylated), or T148D (mimicking phosphorylated) Jdp2 expression plasmid and determined LUC activity 48 h post-transfection. As shown in Mc2r promoter activity eight-fold, T148A JDP2 significantly reduced this effect (approximately 38% loss). This result suggests that phosphorylation of JDP2 at T148 is required for full JDP2-mediated Mc2r promoter activity.Because JDP2 has been shown to be phosphorylated at T148 by JNK  and p38 kinase , we nextTP53 gene activity and trans-activation of p53 [Previously, we have shown that ATF3 can be SUMOylated and SUMOylation/deSUMOylation of ATF3 regulates n of p53 . Since JMc2r promoter activities. As shown in Mc2r promoter-driven LUC reporters. Notably, expression of the SUMO1-JDP2 (mimicking SUMOylated JDP2) reduced the activation by 30%. As expected, 1-100 JDP2 mutant (loss of DNA binding domain) was not able to activate Mc2r promoter.Therefore, we next tested whether SUMOylation of JDP2 alters ATF3 gene is one of the JDP2 target genes, we next assessed the SUMOylation effect of JDP2 on ATF3 promoter. As shown in ATF3 promoter, 1-100 JDP2 mutant (loss of DNA binding domain) was not able to repress ATF3 promoter. Interestingly, we observed that both expression of T148A mutant protein and SUMO1-JDP2 fusion protein relieved the reduction by 20% and 10%, respectively. These results suggest that both phosphorylation and SUMOylation play a significant role in JDP2-mediated transcriptional activities.Though JDP2 binds to ATF proteins, JDP2 suppresses the transcription of its homologue immediate early gene counterpart, ATF3 . BecauseMc2r gene, including adrenocortical cancer cells.Transcription factors regulate target genes by responding to a wide variety of physiological stimuli and thus function as important mediators of development, metabolism, and reproduction. JDP2 is a transcription factor and widely expressed in tissues of humans and animals. However, its physiologic functions in the endocrine system, especially adrenal glands, is unknown. Herein, we show for the first time that JDP2 acts as a transcriptional activator of the Mc2r promoter activation.Importantly, our data showed that JDP2 (mRNA and proteins) are normally expressed in mouse adrenal glands, indicating a possible role of JDP2 in adrenal gland development and steroid biosynthesis. Indeed, we observed that overexpression of JDP2 enhances the level of MC2R, a critical and essential receptor for ACTH function, in mouse Y1 adrenocortical cancer cells. Our promoter and ChIP analysis further supports the previous findings  that JDPMc2r promoter activity. NR5A1 has long been considered the major transcription factor in regulating MC2R activity [MC2R or mouse Mc2r promoters have been mapped. Previously, we have showed that FOXL2, an important transcription factor in ovarian development, synergistically activates Mc2r promoter with NR5A1 [Mc2r transcriptional activity. One piece of data supporting our hypothesis is that our preliminary data suggests that JDP2 can interact with NR5A1 and regulates NR5A1-mediated transcription (unpublished data). Therefore, more studies are required to dissect how NR5A1, FOXL2, and JDP2 work together in regulating human MC2R and mouse Mc2r transcriptional activities and MC2R-mediated signaling pathways.In the present study, our data revealed that JDP2 is expressed in adrenal glands and up-regulates activity . The NR5th NR5A1 . This inMc2r promoter. This further highlights that T148 is a critical PTM site for JDP2. In the past two decades another PTM, SUMOylation, has emerged as an important event in a variety of biological processes and regulations, including apoptosis, cell cycle regulation, genomic instability, inflammation, metabolism, transcriptional regulation, and tumor initiation and progression [trans-activation and stability as well as prostate cancer proliferation [Reversible post-translational modifications (such as phosphorylation) which regulate protein functions in cells are one of the core principles in biochemistry and cell biology. As a transcription factor, JDP2, has been shown to be phosphorylated at T148 and this phosphorylation event is critical for JDP2’s turnover/degradation . In the gression ,29,30,31feration ,35. SincMc2r promoter activity and that PTMs (phosphorylation and SUMOylation) regulate JDP2 activity. However, more studies are needed to expand our understanding of how JDP2 coordinates with NR5A1 and FOXL2 to influence MC2R activity in adrenal glands and adrenocortical cancers. Collectively, our results not only extend the conclusion that JDP2 is involved in the steroidogenesis in adrenal glands through MC2R but also provide the novel mechanism for how PTMs regulate JDP2 activity.In summary, we demonstrated that JDP2 through cAMP response elements activates All cell culture reagents were purchased from Life Technologies . Antibodies against JDP2, MC2R, and β-Actin , SUMO1 and SUMO3 , NR5A1 , and FLAG  were purchased commercially. Luciferase activity was measured using the Dual Luciferase Assay System . GST-JDP2 recombinant proteins were purchased from Abnova .Jdp2 cDNA was PCR-amplified and ligated into the HindIII and BamHI sites of pcDNA3+ to create pcDNA3-HIS-FLAG-Jdp2 expression plasmid. T148A, T148D, and 1-100 Jdp2 expression plasmids were created by PCR-based mutagenesis . The murine Mc2r promoter (−2000-bp) and deletion constructs were previously generated in our laboratory, as described by Yang et al. [ATF3-pGL2 promoter construct was kindly provided by Ami Aronheim . All constructs were verified by nucleotide sequencing.Mouse HIS-FLAG-g et al. . The cAM2 at 37 °C and cultured for less than six months. After incubation, the cells were transfected using Fugene HD Transfection Reagent  according to manufacturer’s instructions. Forty-eight hours after transfection, the cells were harvested and lysed. Luciferase activity was measured and normalized with Renilla activity. All experiments using transfection method were performed three times in triplicate.MCF7, HepG2, Ishikawa, Y1, and JEG3 cells were obtained from the American Type Culture Collection . Cells were maintained in Dulbecco’s-modified eagle medium (DMEM) supplemented with 10% fetal bovine serum and antibiotics  in humidified air containing 5% COJdp2 fragments. Two primers (5′-CCTGAACAACCACAGCCTCGTAAAGGAC-3′ and 5′-CTGCATGCTCAGGGCCCGCACCTCCACC-3′) were used to amply 147-bp mouse Nr5a1 fragment. Two primers (5′-CATCACCATCTTCCAGGAGCGA-3′ and 5′-GTCTTCTGGGTGGCAGTGATGG-3′) were used to amplify 341-bp mouse glyceraldehydes-3-phosphate dehydrogenase (Gapdh) fragments. For real-time ChIP, the extracted DNA fragments were quantified by real-time PCR using pairs of primers that covered the cAMP response region within the mouse Mc2r promoter (between −1320-bp and −720-bp). The primers used for PCR were −830RE: CATCCTTTTGTTGATAGTTT (forward) and GTTTTGATTGGATCTGGA (reverse), −1080RE: CTTTTGTACAGTGTCACTAT (forward) and GCACAGAGGTTATAAGCAT (reverse), and −1320RE: GCTGCTCACAGCTAGCTGGC (forward) and GATATGCATCCAACGGGAT (reverse).The animal study was approved by the University of Alabama at Birmingham Institutional Animal Care and Use Committee . Adrenal glands were harvested from normal adult mice and frozen in a dry-ice ethanol bath and then stored at −80 °C until use. Total RNA from adrenal glands were extracted using TRIzol reagent and treated with DNase  to remove genomic DNA. The RNA concentration was quantified by ultraviolet spectrometry. One microgram of total RNA was used to synthesize cDNA using the iScript kit (Bio-Rad) according to the manufacturer’s instructions. The final cDNA product was purified and eluted in Tris-EDTA buffer using QIAquick PCR purification kits  according to manufacturer’s instructions. Two primers (5′-CCTGGGCAGATCCCAGAC-3′ and 5′-CTTCACGGGTTGGGGCCTC-3′) were used to amplify 189-bp 6) were plated onto 100-mm cell culture dishes. Forty-eight hours after transient transfection, cells were harvested and lysed. Protein lysates were placed onto a rotator to rotate at 4 °C for 30 min, and protein concentrations of the high-speed supernatant were quantified using the BCA™ Protein Assay kit assay . Immunoblotting was performed as previously described [Y1 or HepG2 cells  according to the manufacturer’s instructions. Wild-type JDP2 full-length recombinant proteins with a GST tag  were incubated with wild-type SUMO1 (or wild-type SUMO3) or SUMO1 mutant (or SUMO3 mutant) along with SUMO E1 activating and SUMO E2 conjugating enzymes at 30 °C for 3 h. The SUMOylation reaction was terminated by adding an equal volume of 2× SDS–PAGE loading buffer. The samples were advanced to immunoblotting using anti-SUMO1 (or anti-SUMO3) or anti-JDP2 antibodies. The immune detection was carried out with the Supersignal West Dura Extended Duration Substrate kit . Protein band intensity was determined by ImageJ program.t test. When more than two groups were compared, a one-way ANOVA was performed. After the ANOVA analysis, the post hoc multiple comparisons were determined by using Tukey’s honest significant difference (HSD) test to determine the statistical difference between subgroups. For each test, p < 0.05 was considered to indicate significant difference.Statistical analyses were done by using the Student’s Mc2r promoter, and that the post-translational modifications (phosphorylation and SUMOylation) play a critical role for JDP2’s transcriptional activity. Our study also adds a new layer of information to the previous understanding of how JDP2 functions in regulating adipocyte and skeletal muscle differentiation, interacting with and regulating progesterone receptor, controlling bone homeostasis and antibacterial immunity, and contributing to metastasis.In summary, this investigation demonstrated that JDP2, one of AP-1’s repressors, is a novel activator of