In silico analysis of secondary structures identified distinct NRIR structural domains, comprising putative DNA- and protein-binding regions. In parallel, the presence of a putative DNA-binding domain in NRIR and the five putative NRIR-binding sites in the promoter of NRIR-target genes support the function of NRIR as a transcriptional regulator of its target genes. By use of integrated experimental/bioinformatics approaches, comprising database and literature mining together with in silico analysis of putative NRIR-binding proteins, we identified a list of eight transcription factors (TFs) shared by the majority of NRIR-target genes and simultaneously able to bind TF binding sites enriched in the NRIR-target gene promoters. Among these TFs, the predicted NRIR:STAT interactions were experimentally validated by RIP assay.In this study, we demonstrate the benefit of applying combined strategies to analyze lncRNA action based on bioinformatics and experimental information. This strategy was developed to identify the molecular function of negative regulator of interferon response (NRIR), a type I interferon-stimulated gene (ISG), that we have previously demonstrated to be involved in the upregulation of a subset of ISGs in LPS-stimulated human monocytes. In this study, we provide experimental evidence that NRIR is localized in cellular nuclei, enriched on the chromatin fraction, and upregulates ISGs acting at the transcriptional level.  This strategy was developed for gaining insights about how NRIR regulates the expression of selected IFN-stimulated genes (ISGs) in LPS-stimulated monocytes. In fact, we recently identified NRIR as an lncRNA upregulated in LPS-stimulated monocytes in a type I IFN-dependent manner and demonstrated that NRIR acts as a positive regulator of the expression of a subset of IFN-stimulated genes (ISGs) (Long noncoding RNAs (lncRNAs), defined as transcripts >200 nucleotides in length and lacking protein-coding potential, represent the largest group of non-coding RNAs. In the most recent GENCODE V40 release, 18,805 annotated lncRNAs have been found in the human genome . Howevers (ISGs) . Remarkas (ISGs) , primarys (ISGs) , and syss (ISGs) . In conts (ISGs) . Such bes (ISGs) , the mec+ monocytes were purified from buffy coats of healthy donors using the anti-CD14 microbeads (Miltenyi Biotec), on the autoMACs Pro Separator (Miltenyi Biotec) as described in Supplementary Materials. In selected experiments 8 × 106 cells were transfected with 200 pmol NRIR-specific Silencer Select siRNA  supplemented with 1 U/μl RNase Out (Invitrogen). After centrifugation at 300 g for 2 min, the supernatant was collected as the cytoplasmic fraction. The pellet was resuspended for 10 min on ice in RLN2 buffer  supplemented with 1 U/μl RNase Out. Chromatin was pelleted at 16,100 g for 3 min. The supernatant represented the nuclear fraction. All fractions were resuspended in RLT buffer, and RNA was purified as described below.LPS-stimulated monocytes were resuspended for 10 min on ice in RLN1 buffer . Gene expression was analyzed as reported in Supplementary Material. The primers used in this study are listed in Homo sapiens library, RNA- and DNA-binding protein, full-length proteins (https://www.uniprot.org/). Putative NRIR-binding proteins predicted by both algorithms were scored according to the lncPRO score, and the average calculated from the interaction score and the discriminative power obtained from catRAPID. Proteins common to lncPRO and catRAPID with a score higher than the average values were considered most probable NRIR-binding proteins. PANTHER protein families associated with the most probable NRIR-binding proteins were identified using the Gene List Analysis tool available at http://pantherdb.org/. The interaction between NRIR and each promoter was analyzed with LongTarget using default parameters. Identification of enriched motifs either in Triplex Forming Oligonucleotide (TFO) or Triplex Target Site (TTS) was performed using the Multiple Em for Motif Enrichment (MEME) tool  tool . Only moME) tool . The preME) tool . EnrichmME) tool . PromoteME) tool  were useNRIR structure was predicted using the RNAfold tool of the ViennaRNA suite  with the• No information about the TF for the specific promoter: score 0• Evidence from the literature of a predicted putative transcription factor binding site (TFBS) in the specific promoter: score 2• Experimental evidence from the literature of the TF recruitment to the specific promoter: score 6• Evidence from ENCODE of the TF recruitment to the specific promoter: score 8• TF recruitment to the specific promoter is reported both in literature and ENCODE: score 10Transcription factors (TFs) involved in the transcription of NRIR-target genes were obtained from the ENCODE project database , as wellhclust function in R, with column_split = 4.The matrix obtained by applying these scoring rules was subjected to hierarchical clustering using the p-value < 0.05) enriched in the NRIR target were considered.Identification of TFBSs enriched in NRIR-target genes was performed using PSCAN . The TF 7 CD14+ monocytes were treated with 100 ng/ml LPS for 4 h and harvested in ice-cold PBS prior to cell lysis. The protocol for cell lysis and nRIP analysis is detailed in Supplementary Material. Data are expressed as a percentage of the non-immunoprecipitated RNA (% of input) (Nuclear RNA ImmunoPrecipitation (nRIP) was performed according to f input) .p-value<0.05 was considered significant. GraphPad 8.0 was used.Data are expressed as mean ± SEM, unless otherwise indicated. Statistical evaluation was determined using two-way analysis of variance (ANOVA), followed by Bonferroni’s multiple comparisons test. Additional details are described in Supplementary Materials and Methods.Recently, we have demonstrated that NRIR plays a role in the induction of the expression of 15 out of 56 interferon-stimulated genes (ISGs) in human monocytes activated by LPS . In ordeTo support the role of NRIR in the upregulation of LPS-induced ISG transcription, localization of NRIR was investigated, by RT-qPCR, in cytoplasmic, nuclear, and chromatin-associated fractions obtained from LPS-stimulated human monocytes . Data shin silico analysis of putative NRIR-binding proteins was performed by catRAPID and lncPRO. This analysis returned a list of 2,596 proteins predicted as putative NRIR-interacting proteins that were plotted as a function of their prediction scores  and length (31 nts)  homology with DBM-1 as evaluated by TomTom  . Noticeay TomTom , suggesty TomTom  at posity TomTom , thus stin silico analysis using MAST. All the promoters contain at least 11 NBS. The number of sites for each NBS is shown in The putative motifs recognized by DBM1 in the promoters of CXCL10, CXCL11, DDX58, ESPTI1, IFI44, IFIT2, and MX1 were identified by analyzing all the TTS complementary to TFO1. Motif enrichment analysis of TTS led to the identification of 18 significantly enriched motifs  significantly over-represented in the promoters of the 15 NRIR-target genes. Fifty-nine TFBSs were found significantly enriched in NRIR-target gene promoters as compared to non-targeted gene promoters by PSCAN . In paraThe interaction profile of NRIR with each of the eight TFs identified was performed by catRAPID fragment. This analysis identified a principal protein binding element (PBE-1) spanning 300–480 nt , shared in vivo setting, NRIR has also been demonstrated to be constitutively upregulated, and to account for the type I IFN signature that is a hallmark of this pathology (Among the eight TFs selected as described above, STAT1 and STAT2 have been previously demonstrated to be constitutively upregulated in monocytes from Systemic Sclerosis patients and to correlate with the expression of STAT-target genes . In the athology . Based oathology , was assathology , thus prIn this work, a combined strategy, based on the integration of structure prediction, experimental data, and database mining, was implemented to investigate the NRIR mechanism of action . Each ofvia recruitment of transcription factors to the promoters of target genes is not an uncommon mechanism and has been already described in the case, for instance, of PACER (Mandatory partners of a nuclear lncRNA acting as a “guide” are lncRNA-interacting proteins that get tethered to the promoter/enhancer regions of the gene to be transcribed . Analysiof PACER , RMST (Nof PACER , lncSOX4of PACER  and HOTAof PACER . The intin silico and in vitro data converged to demonstrate that NRIR upregulates transcription of a subset of ISGs by guiding STAT1 and STAT2 to the promoters of its target genes. This model is consistent with the emerging general mechanism, whereby at the transcriptional level, lncRNAs bridge DNA and proteins by binding to chromatin and working as a scaffold for transcription factors (As proof of principle, a direct interaction between NRIR and STAT1/STAT2 was experimentally validated by nuclear-RIP assay. Collectively,  factors . STAT1 a factors . Althoug factors .In this complex scenario, STAT1 and STAT2 are certainly able to modulate the transcription of more ISGs than the subset herein studied. Whether and how the transcriptional activity of different STAT1 and STAT2 complexes are modulated by NRIR needs to be determined. Moreover, since a growing number of lncRNAs have been demonstrated to interact with more than one protein partner, additional protein partners of NRIR cannot be excluded. Thus, the predicted interaction of NRIR with the other five TFs, namely SP1, MYC, MAX, TBP, YY1, and IRF1, should be experimentally tested. Finally, the identification of NRIR-interacting proteins in hepatocytes or epithelial cells, where NRIR has been demonstrated to act as a negative regulator of ISGs expression, might provide further insight into the mechanism, whereby NRIR acts either as a positive or a negative regulator of ISG gene transcription, according to the cellular context.


Non-random spatial organization of the chromosomal material inside the nuclei of brain cells emerges as an important regulatory layer of genome organization and function in health and disease. Here, we discuss how integrative approaches assessing chromatin in context of the 3D genome is providing new insights into normal and diseased neurodevelopment. Studies in primate (incl. human) and rodent brain have confirmed that chromosomal organization in neurons and glia undergoes highly dynamic changes during pre- and early postnatal development, with potential for plasticity across a much wider age window. For example, neuronal 3D genomes from juvenile and adult cerebral cortex and hippocampus undergo chromosomal conformation changes at hundreds of loci in the context of learning and environmental enrichment, viral infection, and neuroinflammation. Furthermore, locus-specific structural DNA variations, such as micro-deletions, duplications, repeat expansions, and retroelement insertions carry the potential to disrupt the broader epigenomic and transcriptional landscape far beyond the boundaries of the site-specific variation, highlighting the critical importance of long-range intra- and inter-chromosomal contacts for neuronal and glial function.  To date, many types of studies assess each gene or regulatory unit including promoters and enhancers as distinct entities, which when surveyed on a genome-wide level, are then further analyzed by some type of functional pathway or gene ontology-based analyses. Such type of approach has been extremely valuable for constructing cell- and circuit-specific ‘registries’ from a small number of reference brains [2–103 bp wide nucleosomal arrays marked by specific histone modifications associated with regulatory elements, such as promoters and enhancers. Larger scale epigenomic modifications include 106 bp scaling chromosomal domains typically encompassing multiple transcriptional units, and ultimately the 6.4 × 109 bp 3D nucleome, also referred to as ‘4D nucleome’ when assessed in context of dynamic changes during development and differentiation [The human brain is comprised of an estimated 170 billion neurons and glia, each of which contains a vast amount of genomic information stored in >6 gigabases of diploid genome. According to recent estimates, up to 80% of the human genome sequence undergoes low level transcription , mostly e brains ,5,6, andThe structural organization of chromosomes, beyond the ‘beads-on-string’ elementary composition of chromatin fibers as arrays of nucleosomes interconnected by linker DNA, is broadly guided by two independent folding principles, namely phase separation (PS)-driven molecular forces and actively regulated loop extrusion mechanisms. An illustrative example for PS is provided by the intra- and inter-chromosomal polymer-polymer ‘lattice’ of heterochromatin-associated protein 1 (HP1) bound to nucleosomes tagged with a specific type of repressive histone mark . PS has 5 base pair-scaling topologically associating domains (TADs) with their characteristic ‘folded-upon-self’ appearance in Hi-C chromosomal contact matrices [In contrast, ATP-dependent loop extrusion mechanisms, mediated by cohesion, condensin, and various other chromatin-and DNA-binding proteins, such as CTCF, furnish kilo- to megabase-scaling chromosomal loop formations and the ~10matrices ,18. Enhamatrices  and showmatrices . Loops amatrices ,18. The aforementioned principles are likely to apply to all somatic tissues including brain. Thus, developmentally regulated A/B compartment switching in the differentiating brain closely tracks the corresponding gene expression changes. This association has been broadly confirmed by recent studies mapping the spatial genome and the transcriptome in proliferative as compared to the differentiated layers of fetal human cerebral cortex , in mousIn addition to compartment switching at the site of developmentally regulated genes, the differentiation of neural precursors into neurons is accompanied by widespread remodeling of chromosomal loop formations, specifically a shift away from shorter range loopings to more long-range (>100 kb) loops , and reaFurthermore, 3D genome organization in the young brain shows a surprising degree of environmental sensitivity. This is best illustrated in studies comparing chromosomal conformations in cerebral cortex and hippocampus of adolescent and young adult mice reared in enriched cages compared to standard cage housing. Housing in an enriched cage, just like challenging animals with a cognitive task or drug-induced synaptic activation, triggered genome-scale changes in transcription-associated chromatin accessibility and histone acetylation together with widespread remodeling of chromosomal conformations affecting promoter-enhancer loopings and compartment organization across hundreds of loci ,29,30. There is also increasing evidence that, beyond this initial critical period in the young developing brain, chromosomal conformations remain highly plastic during a wide window of brain development and aging. One intriguing example for spatial genome plasticity recently emerged from a chromosomal conformation study on ventral hippocampal neurons of the adult female brain in which they compared 3D genome plasticity across the estrous cycle, and also to the male brain. The study discovered evidence for a hormone-cycle driven decondensation of many X chromosomal domains, together with highly dynamic changes in promoter-enhancer loopings at hundreds of autosomal loci occupied by estrogen-associated transcription factors and nuclear hormone receptors . SpecifiMany studies have also compared 3D genome structure across the evolutionary spectrum. Notably, chromosomal conformation mappings across multiple mammalian lineages have confirmed that TAD organization is robustly conserved across syntenic blocks of genome, an effect ascribed to the preservation of CTCF binding sites at TAD boundaries, even in genomes evolutionarily separated by tens of millions of years . This geMus musculus-derived inbred lines as compared to the wild-derived Mus Spretus inbred strain that diverged from Mus musculus 1.5 million years ago [Many of these human-specific chromosomal conformation changes in the brain are linked to phylogenetically young retroelements which propagate throughout the genome via cut-and-paste mechanisms involving RNA intermediates. These retroelements include various Alu, LINE and HERV subtypes ,35,36 thears ago , are assears ago . This efears ago . Disease-associated changes in the brain’s 3D genomes can be divided into three broad categories. The first category refers to monogenic neurodevelopmental disorders with structural mutations in chromatin architectural proteins, that typically result in loss of function for an essential component of the chromosomal scaffolding machinery. A classical, and frequently cited example is Cornelia de Lange Syndrome (CdLS), a neurodevelopmental disorder defined by intellectual disability and autism, often in the absence of gross morphological changes in the brain. The large majority of CdLS patients are affected by structural variation in cohesin complex genes or the cohesion loading factor NIBPL . Importatrans, or inter-chromosomal, connectivity [The second category of 3D genomic disruption is comprised of localized structural DNA mutations that cause the disruption of domain boundaries and the reconfiguration of intra- and inter-chromosomal contacts of the affected domain. This category includes copy number variant (CNV) syndromes associated with neuropsychiatric disease, including the 22q11 and 1q21.1 microdeletion syndromes which, remarkably, include severe alterations and disruptions of chromosomal connectomes that extend beyond the deletion site and its immediate flanking regions. Indeed, alterations in the chromosomal contact map emerge across the entire length of the affected chromosome as well as alterations to ectivity . LYPD6, which is involved in WNT/β catenin signaling [FMR1 gene, Friedreich’s ataxia at the FXN gene, Huntington’s disease at the HTT gene, a type of motor neuron disease at the C9ORF72 locus, and spinocerebellar ataxia type 1 at the site of the ATXN1 gene [SLTIRK2 and SLTIKR4 [trans with the Fragile X locus, altogether affecting more than 10 Mb of genome sequence [This second category of 3D disease in the brain also include numerous examples of disrupted TAD domain boundaries. Notably in the primate lineage, genomic rearrangements at TAD level disproportionally occur at TAD boundaries, and include neurodevelopmental risk genes, such as ignaling . This apignaling ,47. In aignaling . This liXN1 gene . ImportaXN1 gene . These pXN1 gene . Even mo SLTIKR4 , in addisequence . Therefosequence . Guided The third category of genome-scale chromosomal disorganization is exemplified by a group of cognitive and psychiatric disorders with typical onset in early adulthood, including schizophrenia and depression, which are often viewed as neurodevelopmental in origin. These disorders show complex genetic etiologies, and according to monozygotic twin studies from large national registries, carry a heritable risk ranging as high as 80% with twin concordance rates around 33% . The hyp5 base pair range, firmly embedded within the Hi-C based TAD chromosomal domain landscape [However, these studies mapped schizophrenia heritability loci onto the 3D genome of a few reference brains. Therefore, it is still unclear whether chromosomal organization is altered in the diseased brains. To this end, a recent study, generating 739 histone acetylation and methylation profiles from prefrontal cortex of schizophrenia, bipolar and control subjects, identified thousands of cis-regulatory domains (CRDs), with each CRD defined by the coordinated regulation of sequentially positioned acetyl- and methyl-histone peaks distributed across a ~10andscape . Notablyandscape . These fandscape , up to 2andscape . A subseandscape . Interesandscape . Thus, tFrom this perspective, chromosomal domains could be viewed as information storage units that maintain, in the adult brain, epigenetic scars of neurodevelopmental defects that occurred many years prior to the onset of disease. There is evidence that some of the other regulatory layers of the epigenome also harbor clues for a neurodevelopmental etiology of disease in schizophrenia. For example, DNA methylation profiling for 456,000 autosomal CpG dinucleotides in more than 520 prefrontal cortex (PFC) specimens collected from 14th week of gestation to 80 years of age, including 191 adult specimens from subjects diagnosed with schizophrenia, revealed that while the DNA methylation alteration in the affected brains only affected a minute (<0.5%) fraction of the overall CpG pool, an overwhelming majority of these disease-sensitive CpGs showed evidence for dynamic methylation changes during the transition from the pre- to the postnatal period of normal development . InteresIntriguingly, a number of viral infections have recently emerged as examples of 3D genome dysregulation in the nervous system, including the SARS-CoV-2 infected sensory olfactory epithelium . This viSARS-CoV-2 is likely not the only virus to disrupt 3D genome organization in susceptible brain cells. For example, Human Immunodeficiency Virus (HIV), an exogenous Long-terminal-repeat (LTR) type of retrovirus that stably inserts itself into the host’s nuclear genome, infects cells in brain, primarily microglia and perivascular macrophages. Ultimately, HIV infection can cause a wide range of neurological conditions that exist along the spectrum of HIV-associated neurocognitive disorders (HAND), which affect 20–50% of the 37 million people living with HIV (PLWH) ,71,72. SHere, we review a number of recent discoveries in the field of 3D genomics and nucleomics, which highlight the delicate regulation of spatial genome organization including chromosomal folding and intranuclear topographies, during normal brain development and disease. The 3D genome organization in neurons and glial cells emerges as a highly cell type-specific layer of epigenomic organization, broadly relevant for the cell’s transcriptional programs via the furnishing of promoter-enhancer loopings or silencing of retrotransposons and cryptic transcription from intergenic sequences. Deeper exploration of the brains 3D genomes will also be highly relevant for the molecular pathology of a number of neurodevelopmental and neurological conditions. This is because it is increasingly recognized that alterations in genome organization and function due to localized structural variations of the genomic DNA, including micro-deletions and -duplications or abnormal repeat expansions, are not limited to the site of mutation and its immediate surroundings but could impact, via intra- and inter-chromosomal conformations, gene expression programs at additional loci. Similarly, as exemplified by SARS-CoV-2 exposed olfactory neuroepithelium and HIV exposed microglia, infection and inflammation is capable of triggering, in susceptible brain cells, a significant reorganization of chromosomal conformations.


Glioblastoma  is an aggressive, primary brain tumor. Despite extensive tumor resection followed by radio- and chemotherapy, life expectancy of GBM patients did not improve over decades. Several studies reported transcription deregulation in GBMs, but regulatory mechanisms driving overexpression of GBM-specific genes remain largely unknown. Transcription in open chromatin regions is directed by transcription factors (TFs) that bind to specific motifs, recruit co-activators/repressors and the transcriptional machinery. Identification of GBM-related TFs-gene regulatory networks may reveal new and targetable mechanisms of gliomagenesis.VIM, FOSL2 or UPP1. Binding of c-Jun to the VIM gene promoter was stronger in GBM-derived cells than in cells derived from benign glioma as evidenced by gel shift and supershift assays. Regulatory regions of the majority of c-Jun targets have distinct DNA methylation patterns in GBMs as compared to benign gliomas, suggesting the contribution of DNA methylation to the c-Jun-dependent gene expression.We predicted TFs-regulated networks in GBMs in silico and intersected them with putative TF binding sites identified in the accessible chromatin in human glioma cells and GBM patient samples. The Cancer Genome Atlas and Glioma Atlas datasets  were explored to elucidate TFs-gene regulatory networks and effects of the epigenetic background. In contrast to the majority of tumors, c-Jun expression was higher in GBMs than in normal brain and c-Jun binding sites were found in multiple genes overexpressed in GBMs, including GBM-specific TFs-gene networks identified in GBMs differ from regulatory pathways attributed to benign brain tumors and imply a decisive role of c-Jun in controlling genes that drive glioma growth and invasion as well as a modulatory role of DNA methylation.The online version contains supplementary material available at 10.1186/s13148-023-01446-4. Malignant gliomas account for 80% of malignant central nervous system (CNS) tumors and are the most common primary CNS tumors in adults. An integrated classification introduced by the World Health Organization (WHO) categorized gliomas into malignancy grades (I–IV) based on tumor morphology and molecular information , 2. CompA crucial step in the regulation of gene expression is an initiation of transcription, which is strictly regulated by DNA-binding proteins known as transcription factors (TFs) . AlteredCYCLIN D1 [. JunB and JunD act frequently as negative regulators [Initial ENCODE studies revealed three main TFs localizing almost exclusively within accessible chromatin: c-Jun, GATA1 and NRF1 . c-Jun iYCLIN D1 –16. JunBgulators , 18.VIM, UPP1) in glioma cells and found a stronger binding in GBM-derived cells in comparison with cells derived from the low grade glioma. We demonstrated that GBM-specific DNA methylation patterns exist in c-Jun target genes and verified that methylation in the c-Jun binding site may affect its binding to the target sequence. Our findings associate specific TF-gene regulatory networks with malignant glioma pathogenesis.To identify TF-gene regulatory networks driving transcriptional deregulation in gliomas, we exploited public TCGA datasets as well as the Glioma Atlas created in our laboratory, which encompasses genome-wide profiles of chromatin accessibility, histone modifications, DNA methylation and gene expression from the same tumor sample  a summa: Fig. S1First, we predicted transcription factor binding sites (TFBS) from chromatin accessibility peaks generated by ATAC-seq and identified in LN18 and LN229 human glioma cells. Only peaks consistently detected in both cell lines were considered for further analysis. Secondly, we compared the obtained results with our in-house generated ATAC-seq data from two GBM samples (having sufficiently deep sequencing coverage to reliably detect TFBS) . Data weTFBS identified within open chromatin regions in cultured glioma cells contained numerous motifs for the AP2D, PAX5, and ZFX binding proteins, among many others Fig. C. Next, We annotated these grade-specific transcription factors into TF families using the HOCOMOCO v11 database , and fouNext, we examined transcriptomic differences between GIV and GII gliomas from the TCGA dataset. Patient samples clustered depending on a glioma grade  and GBM, JUN expression was increased as compared to normal adjacent tissues. Furthermore, the TCGA dataset showed that JUN expression increased with glioma grade and was highest in GIV gliomas , cell proliferation, migration, invasion  and radio-resistance (TRIB1). All of these genes were significantly up-regulated in GIV when compared with GII  was associated with a worse prognosis. Patient survival analysis of the LGG patients showed that high expression of c-Jun targets is associated with a worse prognosis.We studied if patient survival is associated with the expression of HOXD11, HOXD9, HOXC10, HOXC11, HOXC6, HOXB3, HOXA2, HOXA1) were associated with a glioma grade and significantly overexpressed in GIV (Wilcoxon rank-sum and BH padj < 0.05). HOX genes are involved in developmental processes [JUN was significantly up-regulated in GIV when compared to GII . Then, to better understand biological functions of these genes, we performed a pathway enrichment analysis  in all of the cases, with the highest positive correlation for genes encoding interferon related developmental regulator 1 (IFRD1), Vimentin (VIM), and FOS Like 2 (FOSL2). Using publicly available reverse protein phase assay (RPPA) data [FOSL2 and VIM were the most positively correlated targets  data , we compets Fig. E.We had previously identified enhancers enriched in active histone H3K27ac marks in topologically associating domains (TADs) in GI , and HGGs  . In the We evaluated cumulative hypergeometric probabilities to quantify the enrichment of particular TFBS within glioma enhancers and discovered that several basic leucine zipper (bZIP) transcription factors, including c-Jun, are found significantly at the top of our TFBS ranking Table B. This fThe ATAC-seq dataset from LN18 and LN229 glioma cells encompasses 94 c-Jun motifs (Table IDH1/2 (isocitrate dehydrogenase 1/2) mutations globally change DNA methylation patterns in gliomas [IDH1 wild type (IDHwt) or mutant (IDHmut) status from the Glioma Atlas [ma Atlas . Out of VIM, that are overexpressed in GBM similarly as JUN and contain a specific TFBS in their gene regulatory regions localized within open chromatin. Vimentin is an intermediate filament essential for cell migration, adhesion and cell division, and is frequently upregulated in cancer or metastatic cells [VIM gene promoter, we performed an electrophoretic mobility shift assay (EMSA) using nuclear extracts from normal human astrocytes (NHA) as a control, GII glioma-derived cell cultures (WG12) and two human established cell lines derived from GIV . In all tested cells, c-Jun was expressed and there were no significant differences in mRNA or protein levels  in GII/GIII-IDHwt and GIV glioma patients from the Glioma Atlas [RIN1, RAB36, UPP1, SLFN12 and VIM), and the second one encompassing genes with low DNA methylation  regardless of the tumor grade   were hypomethylated regardless of tumor grade , among other regulators, are differentially methylated between patients with short- and long-term survival [DNA methylation can repress transcription by preventing specific TFs binding to their recognition motifs, while removing site-specific DNA methylation might reverse the process , 33. In survival .UPP1 gene promoter contained numerous differentially methylated cytosines around the c-Jun binding site.We explored the previously collected data on DNA methylation in gliomas of different grades . The anaUPP1 promoter region using EMSA. Nuclear extracts from LN18 and LN229 produced a shift of the UPP1 probes and the binding of nuclear extracts from WG12 cells to both probes was lower  promotes gliomagenesis and cooperates with factors such as MYC, FOS, or JUN, which are highly expressed in gliomas [c-Myc expression [In this study, we analyzed a broad range of publicly available datasets and experimental data from gliomas and human glioma cultured cells to identify novel TF-gene regulatory networks contributing to transcriptional deregulation in malignant brain tumors. Using chromatin accessibility data from glioma samples and cultured glioma cells, we identified GBM-specific TFs binding sites that were also present in human LN18 and LN229 glioma cells. ATAC-seq was used to identify accessible chromatin regions, the majority of which were localized at gene promoters. Open chromatin areas were enriched in TFBS encompassing binding sites for AP2D, PAX5, and ZFX transcription factors. The activator protein 2 (AP-2) is involved in a variety of pathological processes, including cancer . Transcr gliomas . The zin gliomas , and maipression . While tJUN expression was higher in normal tissues than in tumors. Only in thymomas and GBMs, JUN was significantly overexpressed in tumors when compared to adjacent normal tissue. Moreover, GIII-GIV gliomas showed increased JUN expression.A close inspection of genes overexpressed in GBMs versus LGG revealed the enrichment in specific TFBS in the promoters of these genes, which suggests the contribution of the TFs to glioma development or progression. While, it is clear that grade II astrocytomas and oligodendrogliomas are very different from GBMs in terms of their molecular/genetic profiles, grade II gliomas are benign brain tumors and were selected as a reference to analyze GBM-specific TF networks. As a result of multilayered in silico analyses we identified c-Jun as a prominent candidate for novel TF-gene regulatory network driving glioma growth and invasion. It is a well-known proto-oncogene, involved in proliferation, angiogenesis, migration and apoptosis in several cancers , 42. IntVIM, FOSL2, PTN, GPR3, SIAH2, UPP1, S100A2 are associated with cell migration and epithelial-mesenchymal transition (EMT) in several cancers [We detected c-Jun binding motifs in the promoters of 16 genes overexpressed in GBMs. Many of these genes Fig. C–E, such cancers –50. The HOX genes, JUN and TWIST, in an agreement with previous reports [Our observation that several TFBS for Jun-  and Fos-related factors  overlap with c-Jun binding sites emphasizes the complexity of c-Jun-regulated networks. c-Jun, a component of the AP-1 complex, may interact with different Jun and Fos proteins to form homo- or heterodimers  at the s reports , 54., measured by immunohistochemistry, is a poor prognostic factor in GBMs [VIM expression in GBMs. The prediction was verified in cultured human GBM cells, in which c-Jun is present and binds to the VIM gene promoter, as demonstrated by EMSA and supershift assays. DNA–protein complexes with VIM gene promoter probe were more efficiently formed in nuclear extracts from GBM-derived than in benign GII glioma cells. The concentrations of nuclear proteins used for reactions were the same across all cell lines with equivalent levels of c-Jun, and the reduced binding suggests that transcription of c-Jun target genes is lower in GII-derived cells. Regulation of Vimentin by c-Jun in glioma cells was further confirmed with the use of a JNK inhibitor, which decreases phosphorylated c-Jun levels and subsequently Vimentin levels. This is consistent with previous findings in epithelial cells [The abundance of Vimentin in GBMs . We predal cells , indicatS100A10, S100A2, IFRD1, RIN1, RAB36, UPP1, SLFN12 and VIM. DNA methylation patterns varied primarily in the flanking regions of the c-Jun binding sites. We found stronger c-Jun binding to the UPP1 gene promoter in GBM-derived cells than in GII-derived cells using EMSA, although its binding did not depend on the methylation status of the promoter probes. These results indicate that hypermethylated flanking regions do not always have an impact on c-Jun binding to a specific TFBS.The physical access of TFs to DNA can be modulated by nucleosome positioning, histone modifications or DNA methylation , 56. WheJUN in GBMs (which is uncommon in other pan-cancer samples) and the enrichment of c-Jun binding motifs in the accessible chromatin regions of genes upregulated in GBM samples (GBM-specific genes). Several of these GBM-specific genes are known to be implicated in tumor growth and invasion, and the high expression of putative c-Jun targets is associated with poor survival of glioma patients. We demonstrated that binding of c-Jun to VIM and UPP1 promoters is stronger in human glioma cells derived from GIV versus GII tumors. The inhibition of phosphorylation of c-Jun with the upstream kinase inhibitor resulted in the reduction of Vimentin levels, indicating the role of c-Jun in the regulation of this protein.Identification of TF-gene regulatory networks responsible for the dysregulated expression of certain genes in malignant gliomas holds a key to potential pharmacological manipulations. We demonstrate the increased expression of Human established glioma LN18 and LN229 cells were obtained from the American Type Culture Collection . The cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum , 100 units/mL penicillin and 100 μg/mL streptomycin. In some experiments LN18/LN229 were treated with 10 µM SP600125 inhibitor  for 3 or 24 h, control cells were treated with 0.05% DMSO. Freshly resected glioma specimens were acquired from two neurosurgical departments of the Medical University of Warsaw and the Mazovian Brodno Hospital. The tissue collection protocol was approved as described . Tumor s7 LN18 and LN229 cells using Tri-Reagent . DNA purity was estimated using NanoDrop 2000  and quantity with Agilent Bioanalyzer as described [Genomic DNA was extracted from 50 to 100 mg of glioma sample  and from 2 × 10escribed .Tumor sample aliquots corresponding to 50–100 mg of tissue were drawn through a syringe needle approximately 50 times. Mechanical homogenization was followed by 5 min of centrifugation at 2400 g at 4 °C. Each pellet was resuspended in 10 ml of cold lysis buffer L1  and shaken for 20 min at 4 °C. The tissue was then mechanically disrupted, residual debris pre-cleared by filtration through nylon mesh filters, and the lysis buffer was replaced with PBS. Cells were automatically counted using the NucleoCounter NC-100, and 50,000 cells were lysed as previously described [broad --nomodel --shift -100 --extsize 200. Resulting peaks were then intersected with human (hg38) ENCODE blacklisted genomic regions (https://github.com/Boyle-Lab/Blacklist/tree/master/lists) to eliminate anomalous and unstructured signals from the sequencing.Only two GBM samples had sequence data that were deep enough to support confident motif analysis with the BMO tool. The FastQC tool was used to evaluate the quality of raw fastq data . After tn = 200 times) to maximize statistical power and robustness of the gene selection. The sample function from the base R library (version 3.6.2) was used as a sampling technique, with each sample having an equal chance of being chosen.We used TCGA data to find overexpressed genes in glioblastomas (GIV) compared to benign gliomas  using normalized RNA-seq expression values . RNA-seq data from 408 glioma patients (248 GII gliomas and 160 GIV gliomas) were analyzed. The biotype of genes from RNA-seq data was restricted to protein-coding genes . We randt test p values for all the genes for each of 200 random comparisons of 20 GII vs 20 GIV, the p values were then corrected using multiple testing (FDR), and the means of these adjusted-p values from all of these comparisons were calculated. We used DESeq2 methods [p value means < 0.01).We then calculated Student's  methods  on the s methods  R librarp value less than 1e−4 on both DNA strands were considered. Motifs found on the mitochondrial genome were discarded for the subsequent analysis. Overall, motif occurrences were computed independently for each of the 735 motifs. The BMO algorithm [p values below 0.05 (Benjamini–Yekutieli correction procedure) were used. Only motif instances at the same chromosomal localization in LN18 and LN229 cells were considered. We intersected the resulting TFBS with the promoters of protein coding genes after selecting motif instances that were common in both glioma cell lines. Transcription start sites and their flanking DNA regions upstream (1.5 kb) and downstream (1.5 kb) were used to identify gene promoters [The human HOCOMOCO v11 motif database  in the Mlgorithm  was usedromoters . If a pa6 cells were lysed with 350 µL RLT buffer supplemented with 1% β-mercaptoethanol. The extraction procedure was carried out in accordance with the manufacturer's instructions. The total RNA was eluted with 25 µl of sterile H2O and its concentration was estimated with Nanodrop 2000 . The KAPA Stranded mRNA Sample Preparation Kit was used to prepare polyA-enriched RNA libraries . Trimmomatic [Total RNA from glioma cells was isolated using a Qiagen RNeasy kit. In brief, 1 × 10mmomatic   values were extracted from different TCGA and Genotype-Tissue Expression (GTEx) datasets, and median gene expression in each cancer and paired normal tissues was calculated and used as an input to R. The following cancers and corresponding healthy tissues were examined: Adrenocortical carcinoma (ACC) and Adrenal Gland; Bladder Urothelial Carcinoma (BCLA) and bladder; Breast invasive carcinoma (BRCA) and breast; Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) and cervix uteri; Colon adenocarcinoma (COAD) and colon; Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (DLBC) and blood, Esophageal carcinoma (ESCA) and esophagus; Glioblastoma multiforme (GBM) and brain; Kidney Chromophobe (KICH) and kidney; Kidney renal clear cell carcinoma (KIRC) and kidney; Kidney renal papillary cell carcinoma (KIRP) and kidney; Acute Myeloid Leukemia (LAML) and bone marrow; Brain Lower Grade Glioma (LGG) and brain; Liver hepatocellular carcinoma (LIHC) and liver; Lung adenocarcinoma (LUAD) and lung; Lung squamous cell carcinoma (LUSC) and lung; Ovarian serous cystadenocarcinoma (OV) and ovary; Pancreatic adenocarcinoma (PAAD) and pancreas; Prostate adenocarcinoma (PRAD) Prostate, Rectum adenocarcinoma (READ) and colon; Skin Cutaneous Melanoma (SKCM) and skin; Stomach adenocarcinoma (STAD) and stomach; Testicular Germ Cell Tumors (TGCT) and testis; Thyroid carcinoma (THCA) and thyroid; Thymoma (THYM) and blood; Uterine Corpus Endometrial Carcinoma (UCEC) and uterus; Uterine Carcinosarcoma (UCS) and uterus. The JUN mRNA expression profile was compared between tumor samples (TCGA) and paired normal tissues , and statistical significance was determined using one-way ANOVA and disease state .We used the Gene Expression Profiling Interactive Analysis (GEPIA2)  to deterThe QIAseq Ultra Low Input Library Kit was used to create DNA libraries for chromatin immunoprecipitation with the appropriate antibodies , as described . End-repWe had acquired histone ChIP-seq data from gliomas of different grades from the Glioma Atlas  focusingTo select active enhancers in glioma, we considered the presence of H3K27ac peaks in non-promoter regions and used a set of active enhancers identified previously . First, EZ DNA Methylation-Lightning Kit was used to bisulfite-convert DNA samples . SeqCap Epi CpGiant Enrichment Kit  probes were used to enrich each Bisulfite-Converted Sample Library in the predetermined distinct genomic regions of 80.5 Mb capture size, which included 5.6 million CpG sites on both DNA strand. The libraries were created using the “NimbleGen SeqCap Epi Library Workshop Protocol, v1.0” and “SeqCap Epi Enrichment System User's Guide, v1.2” from Hoffmann-La Roche. In brief, genomic DNA concentration was determined using a Quantus Fluorometer with QuantiFluor dsDNA System , and 1 g/mL streptomycin input DNA, as well as 165 pg Bisulfite-Conversion Control  were fragmented to an average size of 200 bp using the Covaris M220 . DNA fragments were tested on a 2100 Bioanalyzer using the High Sensitivity DNA Kit . Using the KAPA LTP Library Preparation Kit , SeqCap Adapter Kit A and B (Hoffmann-La Roche), and DNA purification beads, the DNA fragments were “End-Repaired,” “A-tailed,” and the index adapters were ligated . Following that, adapter-enhanced DNA fragments were size-selected using Agencourt AMPure XP beads (SeqCap EZ Pure Capture Bead Kit) and Solid Phase Reversible Immobilization technology to exclude DNA fragments larger than 450 and smaller than 250 bp. Libraries were bisulfite transformed as described [n = 4), GIV (n = 10) and GII/GIII-IDHmut gliomas (n = 4). In the end, each sample yielded ~ 3.5 × 106 of well-covered cytosines. However, due to DNA degeneration, the total number of cytosines shared by all samples was only ~ 350,000. The further analysis focused on differentially methylated regions rather than individual cytosines.The methylation analysis workflow was carried out using the CytoMeth tool , which tp values were corrected with FDR (significant if < 0.05). DiffMeth [The DiffMeth  module wDiffMeth  was set CpG sites in regulatory regions of c-Jun putative target genes were intersected with promoter regions defined as 2 kb upstream/500 bp downstream relative to TSS with the annotation for the hg19 human genome . GBM andThe GlioVis web application  was usedP values were calculated using GraphPad software and considered significant when *p < 0.05 (column statistics t test). Gene expression analysis was performed as described in [Whole-cell protein extracts were prepared, resolved by electrophoresis and transferred to a nitrocellulose membrane  as described . After bribed in . Antibod2, and 1.5 µg Poly (dI-dC) (in 30 μL) and all components were incubated for 30 min at room temperature and subjected to electrophoresis  in 6% polyacrylamide gels with 10% glycerol and Tris–borate–EDTA buffer. Then, electrophoretically separated material was transferred onto a 0.45-µm Biodyne nylon membrane (Thermo Scientific cat. # 77016) in Tris–borate–EDTA buffer and detected by chemiluminescence using a Chemidoc camera (Bio-Rad). For a competition assay, the mixture was pre-incubated with a 100-fold unlabeled probe. For a supershift assay, the protein extracts in the reaction mixture were pre-incubated for 30 min with 2 µg of anti-pS63 c-Jun antibody before adding to the DNA. Antibodies and probes used for EMSA are listed in Additional file Nuclear extracts were prepared from cultured cells using a nuclear extraction kit: NE-PER Nuclear and Cytoplasmic Extraction Reagents (Thermo Scientific cat# 78833) according to the manufacturer instructions. Protein concentration was measured using THERMO Labsystems Multiscan EX at wavelength 570 nm with a Bradford Reagent (Sigma Life Science cat no. B6916) and a bovine serum albumin standard (Thermo Scientific cat no. 23209) was used for calibration. For EMSA, biotin-labeled and unlabeled oligonucleotides were provided by Metabion  or 0.5% (nuclear staining) Triton-X100 and blocked-in mix of 2% donkey serum and 1.5% FBS, followed by 2-h incubation with primary antibodies. Cells were then washed in PBS, incubated with corresponding Alexa Fluor A555 secondary antibodies, counterstained with DAPI  and mounted. For reagent specifications, catalog numbers and concentrations, see Additional file DNA was extracted using standard phenol/chloroform methods. The purity and concentration of DNA were estimated after collecting absorbance readings at 260/280 nm. DNA (2 μg) was treated with bisulfite . The modified DNA was amplified using primers specific for methylated or unmethylated MGMT gene promoters, as listed in Additional file Additional file 1. Comprises supplemental tables S1–S5 listing search results, the characterization of a grade II glioma cell line (WG12) and the antibodies and primers used in western blot and immunostaining.Additional file 2. Comprises supplemental figures S1–S7 illustrating the study’s experimental and computational design, transcriptomic analyses from the TCGA between low- and grade- glioma, patient survival analyses concerning c-Jun gene targets, the DNA methylation external validation (TCGA) in c-Jun regulated genes in human gliomas, DNA methylation levels validation in Glioma Atlas concerning c-Jun motifs in putative glioma enhancers, EMSA for nuclear extracts employing probes with different methylation patterns within the UPP1 promoter, as well as the characterization of a patient-derived primary WG12 cell line.


The biological actions of the vitamin D receptor (VDR) have been investigated intensively for over 100 years and has led to the identification of significant insights into the repertoire of its biological actions. These were initially established to be centered on the regulation of calcium transport in the colon and deposition in bone. Beyond these well-known calcemic roles, other roles have emerged in the regulation of cell differentiation processes and have an impact on metabolism. The purpose of the current review is to consider where applying systems biology (SB) approaches may begin to generate a more precise understanding of where the VDR is, and is not, biologically impactful. Two SB approaches have been developed and begun to reveal insight into VDR biological functions. In a top-down SB approach genome-wide scale data are statistically analyzed, and from which a role for the VDR emerges in terms of being a hub in a biological network. Such approaches have confirmed significant roles, for example, in myeloid differentiation and the control of inflammation and innate immunity. In a bottom-up SB approach, current biological understanding is built into a kinetic model which is then applied to existing biological data to explain the function and identify unknown behavior. To date, this has not been applied to the VDR, but has to the related ERα and identified previously unknown mechanisms of control. One arena where applying top-down and bottom-up SB approaches may be informative is in the setting of prostate cancer health disparities. Much of the discovery in biomedicine has been centered on the classic paradigms of reductionist biology, in which phenotypes are interpreted as the interaction of either single or small groups of molecules. Across biomedical sciences, there are many remarkable examples of how this approach has led to the discovery of drivers of human disease, and equally remarkable examples of new therapies designed to target these drivers. For example, high-profile examples of this reductionist approach include the discovery of the oncogenic role of the BCR-ABL fusion gene in the etiology of chronic myeloid leukemia  and the This is a striking example of the so-called bench-to-bedside research and represents one of the earliest examples of precision medicine. At times, however, this reductionist approach can appear limited both theoretically and clinically as the etiology of Imatinib-resistant phenotypes only too well demonstrates . Across Systems biology (SB) aims to apply mathematical approaches to build a predicative and quantitative model of biological systems, with the goal to use model predictions to define specific physiological or pathophysiological states and outcomes. The models derived from such SB approaches applied to experimentally derived biological data aim to identify the dynamic behavior of networks that are the center of cellular behaviors ,5. TheseFurthermore, the models built by SB approaches aim to define the functioning of living organisms not solely by looking at the constituent molecules such as DNA, RNA, proteins, and metabolites but rather by the process-level biological systems they constitute, such as mitosis and metabolic control ,8. A keyA consequence of the integrated nature of biological signaling is the emergent complexity, which underpins human health. For example, the dexterous control of transcriptional networks is derived from a high ratio of transcription factors to regulated mRNA or miRNA targets. This ratio, combined with large regulatory regions, results in unparalleled plasticity over the choice, amplitude, and period of transcription ,13. MoreThe concept of SB builds on so-called “holistic biology” developed in the 1960s and coupled with informatics theory and modeling approaches developed through the 20th century. However, the application of SB methodologies and the expansion of SB concepts in biomedicine has been boosted since the early 2000s by the technological advances in the development of high dimensional data approaches frequently derived from next-generation sequencing technologies coupled with bioinformatic approaches. This progression from the 1950s to the current state has been profoundly catalyzed by the human genome project, with its draft sequence published in 2001, and a final reference genome published in 2022  alongsidThere are several well-justified advantages of applying SB approaches, which have the genuine potential to complement and extend the reductionist paradigm. Expressing the complete interactions within biological systems in mathematical terms reduces the impact of biases introduced by focusing on single proteins, and instead can reveal under-explored control points in the system. Furthermore, expressing biological systems in the context of processes, rather than well-understood individual components, has the potential to assimilate more readily new high dimensional data generated in biological experiments. Consequently, it is reasonably anticipated that significant, novel, and unpredicted strides will be made in understanding the control and responsiveness of biological events, which in turn can generate new insights into disease susceptibility and therapeutic opportunities ,18.At its core, SB approaches have an asymptotic recursion between research activities in the wet lab and dry lab. Models are built in the dry lab that reflects the biological observations in the wet lab and are used to generate predictions for how the system can behave. Such predictions are then formulated into testable interventions in the wet lab to generate data for model refinement in the dry lab. It is this oscillation between wet and dry labs that is so potentially powerful, but at the same time so daunting. Broadly, two strategies have emerged to develop such models, by either top-down or bottom-up approaches .In the top-down approach, large datasets are interrogated with statistical methods to find patterns in the data with which to derive predictions on the system organization . The modBy contrast, the bottom-up approach begins with the hypothesis of biological mechanism and formulating equations on how the components of the system interact and then running simulations to generate predictions. This approach relies on experimental studies to determine the kinetic and chemical properties of the components, and starts with formulating system behaviors in rate equations, for example, expressed as differential equations, of the constituting parts of each system. These formulations are then integrated to predict the system behavior, with the goal to combine pathway models into a model for a larger system . The datThe power of these approaches arises from being able to identify and direct experiments to test fundamental questions of a biological system. From the top-down perspective, these questions include identifying in a genome-wide manner the interactions of all components in a system to define the metabolic control that ultimately brings about cell, tissue, or organism behavior. Arising from this approach questions can be asked of a system. For example, what is the interconnectedness of the system and how does that change between health and disease states, or in different development or differentiation states? Which hubs in such networks are central and which are peripheral? Again, how does hub distribution shift in disease and development transitions? How do changes in gene and protein expression combine to control metabolism? How do germline or somatic structural variants change network topology and metabolic flux?Similarly, from the bottom-up perspective, a model is curated from known biological interactions and expressed in mathematical terms to test behavior and make new predictions. For example, with any signaling system how is activation controlled and silenced? Given the ubiquitous role of enzymes in biology, a common question is how does changing the kinetics of activating/de-activating enzymes lead to altered signal amplitude? To what extent do germline or somatic structural variants in enzymes, or co-factors modulate enzymatic capacity and what effect does that exert on signal strength? More broadly, how are different signaling systems integrated and what is the quantitative effect of convergence? Does crosstalk generate synergistic events in regulatory complexes or are they merely convergent downstream at endpoints? What is the magnitude of control at each step and is there an order to control ranging from find-tuning to signal-independent activation? Finally, questions can be asked of the system in disease or development states and tested for pharmacological relevance including identifying which targets may be impactful but have deleterious side effects.A systems-level appreciation for vitamin D signaling has existed for several centuries, given that a description of rickets was first described in the 17th century ; rickets2D3. Several features of how the VDR functions are important from an SB perspective. A feature of Type II receptors such as the VDR is that independent of ligand the receptor is significantly associated with the genome bound to cis-regulatory elements (CRE) and may exert repressive effects, which is reversed by ligand activation leading to genomic redistribution and transactivation . Secondtivation ,30 and stivation  to also tivation ,33, to itivation . Indeed tivation ).2D3, the levels of which are dynamically controlled in terms of synthesis of the precursor 25(OH)D3 which is held in a relatively tight range in the serum, and signs of deficiency in the VDR system can be seen when this serum level is diminished. The various metabolic steps that lead to the generation of 25(OH)D3, the active ligand 1α,25(OH)2D3, and a range of metabolites that ultimately lead to its catabolism are all tightly controlled by enzymatic reactions both in an endocrine and tissue-specific local intracrine manner.Finally, of central importance to the complete VDR signaling system is the generation of the ligand, 1α,25(OH)From a top-down SB perspective, it is interesting to ask in what context the VDR itself, or coregulators, or ligand generation are identified as a central hub in gene networks associated with different cell phenotypes and disease states, and from a bottom-up SB perspective, it is attractive to develop models that accurately capture the cycling interactions of the VDR with different co-factor and predict how this relates to diverse transcriptional outputs, again across cell phenotypes and disease states.2D3, or deleting the VDR. From a top-down SB perspective, the question of VDR function is alternatively phrased to identify biological circumstances where the VDR is identified as a hub in the transcriptional network independent of any knowledge of the system. In this manner, analyses of across myeloid cells [2D3 and revealed its capacity to initiate differentiation of leukemia cells [High dimensional data approaches have been applied to ask questions about what the transcriptional effects are of adding 1α,25(OH)id cells , granuloid cells , and megid cells  have ideia cells ,39. Thesia cells , suggestTogether, these findings suggest that whilst the VDR is biologically impactful in the normal differentiation, for example, the process to monocytes, it is not itself so frequently disrupted in leukemia such that when it is mutated it acts in an oncogenic manner to disrupt normal progenitor differentiation, and it cannot be targeted to induce leukemia cell differentiation in patients. At first glance, these findings may appear contradictory, but most likely reflect the nature of redundancy in the system, and the role of VDR to undergo diverse protein interactions. Specifically, the VDR is known to interact with CEBPs  to inducMycobacterium tuberculosis demonstrated a role for Toll-like receptors to initiate a signal transduction cascade in macrophages that upregulated the VDR and induced an antimicrobial response. Intriguingly, this response appeared to be dampened in people with African genomic ancestry perhaps associated with low serum levels of 25(OH)D3 [Mycobacterium tuberculosis infection of lung cells identified VDR as a key regulated transcription factor [These identified roles for the VDR in monocyte differentiation reflect a broader function for the receptor to signal in differentiation processes  and immu25(OH)D3 . Similarn factor . Anothern factor . More ren factor . An unbin factor ,53,54,55Viewed from the perspective of unbiased top-down statistical analyses has revealed critical roles for the VDR in innate immunity, control of inflammation, regulation of differentiation, and metabolic control ,57,58, b2D3 in normal colon cells [GWAS studies have continued to evolve in terms of the complexity of study design  and cohoon cells .3 (either measured or predicted) and their causal relationship with different clinical phenotypes. In this manner 25(OH)D3 levels and components of the VDR axis are significantly casual in multiple sclerosis, hand grip strength, bacterial infection, and type 2 diabetes [These methodological developments and the ever-increasing repertoire of high dimensional data are likely to increase the understanding of VDR and vitamin D signaling phenotypes and will most likely become ever more important to develop new hypotheses over how signaling occurs and where it is most biologically relevant. Perhaps this is readily illustrated by the application of Mendelian randomization methodology to test the factors such as levels of serum 25(OH)Ddiabetes ,64,65,66diabetes ,67,68.2D3 in a target tissue, VDR genomic interactions, such as initial concentrations in the nucleus of the proteins involved, their half-life, and their associations, to explain how these actions predict the downstream transcriptional impact. Model refinement and validation can then be undertaken by empirical measurements of the cycling of VDR genomic interactions and transcriptional outputs.Applying a bottom-up SB approach to VDR-dependent gene regulation requires constructing a kinetic model that captures the function of different VDR-containing complexes and the quantitative regulation patterns of VDR target genes. Such a model would combine spatial-temporal measurements for the generation of 1α,25(OH)The working assumption is that the transcription factor complex contains enzymes that remodel chromatin to allow signaling to the RNA polymerase complex and the initiation of transcription. In the case of a transcription factor such as the VDR, which has nuclear residence independent of ligand exposure, the complex exists in two states of genomic interaction, namely with and without ligand, and in each with qualitatively and quantitatively different cofactors, and also in terms of distribution . The output of the model will then be to predict the kinetics of mRNA accumulation in a manner that reflects the different ligand-dependent and independent states, and spatial association to target genes. Of course, these components are most likely too simple to predict mRNA accumulation accurately. However, even such a simple model for different genes and the VDR could begin to define how much of the difference between mRNA accumulation could be explained by merely considering spatial and temporal factors for transcription factor residence on a gene. Model components could then be added to consider other well-understood biological components such as disease states, and chromatin states, as well as the impact of DNA helicases required for transcription, or even the role of non-coding RNA to impact mRNA accumulation.To date, such an approach has been applied to several transcription factors in human cells. Within the nuclear receptor superfamily, kinetic modeling of ERα predicted a role of receptor phosphorylation to act as an underexplored feedback mechanism to predict RNA accumulation and was validated by empirical measurements . OutsideThese approaches are powerful, and it is perhaps surprising why they have not been more widely applied to other nuclear receptors beyond the ERα, for example to the clinically relevant VDR. Several impediments no doubt include the time, resources, and interdisciplinary expertise required for model development and refinement. One impediment is establishing collaborations that bring insight from the wet and dry labs. To be successful these collaborations require sufficient data density of dynamic cellular events, such as precise measurements of the concentration of key proteins and RNA molecules in a cell, the frequency of transcription factor genomic interactions, and levels of mRNA accumulation to justify modelling in mathematical terms. Furthermore, these challenges are sometimes impeded by an uneasy relationship between the biological and mathematical communities . The inc3 levels and incidence and progression risks of PCa [3 supplementation in the VITAL cohort [p = 0.07) reduction in cancer risk, indicating that larger cohorts may be more informative [3 supplementation in AA and EA PCa patients only significantly modulated prostate gene expression in the AA patients [Men of African genomic ancestry in American ) experience higher risks of developing more aggressive prostate cancer (PCa) than European American (EA) counterparts ,79,80,81s of PCa ,114,115.L cohort ,117 had ormative ,119,120.patients , associapatients  in AA an3 levels and immune-modulatory factors such as interleukin (IL)-6 [3. One biologically impactful consequence of this is the loss of control of inflammatory signals. These studies also highlight that African ancestry is itself divergent, and is also further modified by admixture, for example in the AA population (refs).These data support the concept that one cell function that appears to be most highly responsive in a manner that reflects genomic ancestry is the role of VDR to modulate innate immunity ,125,126,n (IL)-6 ,128. IL6n (IL)-6 ,130,131.2D3, and the extent of relationships between serum 25(OH)D3 levels and PCa in a manner that reflects genomic ancestry, and if, and to what extent, this is impacted by admixture. In experimental models, it is emerging that the VDR genomic interactions differ between EA and AA prostate cell models (ref), and recently AR genomic interactions also appear different in EA and AA PCa samples. However, it is unclear if either the underlying sequence, for example at enhancer binding sites, or other mechanisms are impacting why these different genomic interactions arise. Finally, given the multi-parameter process of cis-regulatory interactions that drive transcription through to processed RNA and translation to protein, it is also unclear how genomic ancestry impacts these processes potentially in an emergent manner.There are significant knowledge gaps in identifying how genomic ancestry, the environment, and lifestyle choices may combine to impact prostate health and disease. The application of SB modeling could be used to generate biologically plausible hypotheses to test specific relationships in these complex interactions. These knowledge gaps include whether germline structural variants impact the generation of 1α,25(OH)Together, these knowledge gaps can be combined in a hypothesis; namely that PCa risks in AA men reflect the interplay of genomic ancestry, including admixture, coupled with altered environmental signals that combine to drive qualitatively and quantitatively distinct functioning of the VDR and potentially other transcription factors and underscore the health disparities in PCa. Potentially, this is also an attractive setting to develop a series of SB models with which to test these interactions in silico and generate predictions to validate in vitro and in vivo .3 levels [3 levels depending on genomic ancestry.This provides an opportunity for both top-down and bottom-up SB modeling. From the top-down perspective, high dimensional data are available to test develop models that test how genomic ancestry significantly determines the associations between serum 25(OH)D3 levels ,119,120 3 levels ,133, epi3 levels ,89,90,913 levels ,91,134 aTo complement these approaches, other top-down strategies applied to high dimensional data sets could then determine epigenomes such as CpG methylation, histone modification, and chromatin accessibility ,136 and To meet these top-down approaches, bottom-up methods can be applied to build kinetic models that take into consideration the amount of the VDR and its key interacting factors in AA and EA prostate cells to simulate its ligand-dependent and independent genomic states and distribution across epigenetic states. The goal of this modeling would be to provide biological plausibility for why the VDR appears to be significantly transcriptionally divergent between AA and EA PCa. Such predictions would form the justification for validation in the wet lab using AA prostate cell line models and patient-derived xenografts. Finally, such modeling may well justify tailoring recommendations for healthy vitamin D serum levels that reflect genomic ancestry.The VDR signaling system is physiologically impactful and when its functions are altered it is associated with several disease states. Systems biology approaches to the analyses of VDR functions have been applied in several top-down studies that have identified significant VDR functions. To date, however, no bottom-up approaches to kinetic modeling have been applied. The incentive in biomedicine to continue to apply SB approaches would be to develop insight into what are the most prominent contexts where the VDR system exerts biological impact and in turn how this can be exploited in diagnostic, prognostic, or therapeutic contexts. For example, the application of SB approaches, both from a top-down and bottom-up perspective, has the potential to be impactful in the context of prostate cancer health disparities.


Oryctolagus cuniculus) are economically important domestic animals. In this study, a histological analysis shows that the tissue content, thermogenic capacity, and lipid content of FBAT dramatically increases from gestational day 21 (G21) to gestational day 24 (G24) in rabbits. RNA-seq, microRNA-seq (miRNA-seq), and the assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) show that many genes, miRNAs, and chromatin-accessible regions (referred to as peaks) were identified as significantly changed from G21 to G24, respectively. The upregulated genes from G21 to G24 were significantly enriched in the mitochondrial metabolism and thermogenesis-related signal pathways. The integrated analysis of transcriptome and chromatin accessibility reveals that the peaks in the promoters have a more regulatory effect than peaks in other genomic elements on the expression of their nearby genes in FBATs. The upregulated genes that are associated with increased chromatin accessibility in the promoter regions are involved in the energy metabolism-related signaling pathways. The genes that have a greater tendency to be regulated by miRNAs than the chromatin accessibility in gene promoters are involved in the apelin, insulin, and endocytosis signaling pathways. Furthermore, genome-wide transcription factor (TF) footprinting analysis identifies early B-cell factor1 (EBF1) as playing a key role during early FBAT development. The carbon metabolism, citrate cycle, and PPAR signaling pathways are significantly enriched by the predicted EBF1-regulated cascade TF-network. In conclusion, our work provides a framework for understanding epigenetics regulatory mechanisms underlying the early development of FBAT and identifies potential TF involved in the early development of FBAT in rabbits.In domestic mammals, cold stress decreases the survival rate of newborns and increases the cost of management. Brown adipose tissue (BAT) is the main thermogenic organ for cubs, and well-developed fetal BAT (FBAT) is beneficial for newborns to maintain core temperatures during the first several days of life. However, our knowledge of the epigenetic mechanisms during the early development of FBAT remains largely unknown. Rabbits ( The peak time period for the loss of newborn domestic mammals is from the time of birth through the first several days of life ,2, durinBrown adipose tissue (BAT) is the main thermogenic organ for newborns, which dissipates chemical energy as heat to protect animals from hypothermia . The nonIn humans and mice, a network of transcription factors (TFs), such as Early B-cell factor2 (EBF2), peroxisome proliferative activated receptor-gamma (PPARG), and nuclear factor I-A (NFIA) were identified as controlling brown adipocyte commitment or differentiation . During Oryctolagus cuniculus) are economically important domestic animals. Despite the low body fat-rate of adults, the newborn rabbits have considerable BAT mass at birth , which is mainly distributed in the interscapular and cervical regions  their footprints from G21 to G24 across genome-wide peaks, respectively  in peaks. To further identify key TFs involved in FBAT development, we performed a TF footprinting analysis for the obtained ATAC-seq reads, which enabled us to learn the potential genome-wide binding events of TFs. For the 765 individual vertebrate TFs that were subjected to the TF footprinting analysis, 41 and 36 TFs were found to significantly increase and decrease [all TFs with −log10  above the 95% quantile or differential binding scores smaller than the 5% quantile] footprints from G21 to G24  above the 95% quantile or differential binding scores larger than the 95% quantile] footprints from G21 to G24, among which EBF1 had the highest gene expression level and the highest ratio of upregulated bound genes | > 1 and Padj < 0.01) from G21 to G24, respectively . The sig1 to G24 C. For ea1 to G24 C. On thend genes C. Notablnd genes G, showinTHRA to increase THRA expression [log2(fold-change) = 0.44 and Padj = 0.001], then the protein of upregulated THRA was predicted to bind to the promoter of EBF1′s secondary target-TF gene NR4A1 to increase NR4A1 expression ((log2(fold-change) = 1.92 and Padj = 0.0002, Although our data shows that EBF1 is an important TF involved in the early development of FBAT development, there is still a poor understanding of how EBF1 regulates its primary downstream targets, and consequently its secondary targets, during early FBAT development. We therefore performed the cascaded binding prediction of EBF1. For instance, EBF1 was predicted to bind to the promoter of its primary target-TF gene In order to investigate whether miRNAs were involved in the EBF1-regulated gene network, we integrated the results of the miRNA target prediction  binding, and has become a powerful tool for finding key TFs involved in the various biological process ,50,62. EIn conclusion, this study represents the first to describe the histological, transcriptome, miRNAs, and chromatin accessibility of the early development of FBAT in rabbits. Both miRNAs and chromatin accessibility are identified as potential epigenetic factors for FBAT development, and the genes that tend to be regulated by chromatin accessibility and miRNAs are identified, respectively. Moreover, combined analyses predict an EBF1-regulated cascade TF-network, which plays an important role in regulating FBAT development. Our work provides a framework for understanding epigenetics regulatory mechanisms during the early FBAT development and identifies potential TF involved in the early development of FBAT in rabbits. Therefore, these data can be used for future research on the BAT-based thermogenic capacity of domestic animals.


Furthermore, we review the environmental factors influencing plant regeneration from explant sources, basal culture medium, plant growth regulators, and light/dark treatment. Additionally, we analyse the molecular mechanisms underlying two pathways. This knowledge will promote an understanding of the fundamental principles of plant regeneration from precursor cells and lay a solid foundation for applying plant micropropagation and genetic modification.Plant regeneration occurs when plants repair or replace damaged structures based on the totipotency and pluripotency of their cells. Tissue culture is one of the most widely used regenerative technologies. Recently, a series of breakthroughs were made in the study of plant regeneration. This review summarizes two regenerative pathways in tissue culture: somatic embryogenesis and  Moreover, Japonica varieties show a higher capacity for callus formation than Indica varieties in rice  and WUSCHEL (WUS)2 in maize resulted in rapid formation of abundant somatic embryos on the scutella  ovary tissues , the WPM medium achieved the highest primary somatic embryoids development, whereas enhanced maturation of primary somatic embryoids occurred on MS medium , N6, Woody Plant Medium (WPM), and B5, are used for callus induction and shoot differentiation and significantly influence plant regeneration in tissue culture ; however tissues . HoweverS medium . During S medium . AdditioS medium . Sugar pS medium . MoreoveS medium . HoweverS medium .WUS, BBM, and leafy cotyledon 2  preferable for shoot and root differentiation , LEC2, and abscisic acid insensitive 3 (ABI3), encode plant-specific B3-domain-containing proteins that are members of the AFL subfamily of transcription factors (LEC1-encoded CCAAT-binding transcription factors harbouring a HAP3 subunit form LAFL complexes (LEC2 and agamous-like 15 (AGL15) encode a MADS-box transcription factor that controls each of their respective expression in a regulatory feedback loop that also regulates the expression of the auxin-responsive protein gene IAA30, a primary factor in auxin signalling , as well as those encoding key enzymes involved in auxin biosynthesis, such as tryptophan aminotransferase of Arabidopsis 1 (TAA1) and YUCCA (YUC) genes  genes encode similar sequences to the WUS homeodomain and a specific WUS box downstream of the homeodomain , which encodes another APETALA2/ethylene-responsive element-binding factor transcription factor, induces the acquisition of regeneration competency (Arabidopsis response regulator (ARR) genes] of CK response, whereas WIND1 stimulates the expression of positive regulators (type-B ARR genes) of CK response  belongs to the RLK gene family, and as the first key gene screened in a carrot hypocotyl regeneration study, it regulates the transition from somatic cells to embryonic cells 1 and VAL2 exhibited embryo-like proliferations, suggesting that VAL1 and VAL2 negatively regulate somatic embryogenesis  and the auxin signalling gene IAA30, and WUS and WIND1 negatively and positively regulate type-A ARR and type-B ARR genes corresponding to CK responses. Additionally, polar auxin transport induces concentration gradients maximal necessary for plant development. Pin-formed (PIN) and AUX proteins achieve differential distributions by controlling auxin efflux and influx, respectively (AUX/IAA genes and auxin response factors (ARFs), the core components of the auxin signalling pathway, is related to induction of somatic embryogenesis (PHB/PHV), positive regulators of LEC2 expression, and ARF genes , respectively  results in decreased CG methylation and dysregulated expression of the auxin efflux carrier PIN1 engaged in polar auxin transport during somatic embryogenesis 1 and PRC2 are required to establish and maintain stable epigenetic suppression in response to developmental or environmental signals ; Table 1o traits . PRC1 retination . A recenand PIN1 . Additioand PIN1 . Similarand PIN1 . Furtherand PIN1 . Trichosand ABI3 .WOX5, scarecrow (SCR), short root (SHR), plethora (PLT)1, PLT2, and root clavata-homolog 1 (RCH1), are significantly upregulated , which is activated via ARFs and promotes the expression of WOX5, PLT1, and PLT2  mediating auxin biogenesis inhibits the expression of WOX11 and prevents the fate transition of competent cells  strongly decreased the de novo root organogenesis rate by suppressing the transcription of WOX11 and WOX5  and histone 3 lysine 4 trimethylation (H3K4me3) at WUS sites increases, whereas the abundance of inhibitory markers histone 3 lysine 9 di-methylation (H3K9me2) decreases during shoot regeneration  directly activate WUS expression following binding to its promoter (WUS expression (SPL) mRNA by miR-156 decreases regulation of the activities of type-B ARRs in an age-dependent manner  and the environmental factors and molecular mechanisms affecting these two pathways. This information offers a reference for scientific research and technology development in this field.In this review, we discussed how regeneration happens through two different pathways .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.


Enterobacteriaceae (SPATEs) are a large family of virulence factors commonly found in enteric bacteria. These secreted virulence factors have diverse functions during bacterial infection, including adhesion, aggregation and cell toxicity. One such SPATE, the Pic mucinase (protein involved in colonisation) cleaves mucin, allowing enteric bacterial cells to utilise mucin as a carbon source and to penetrate the gut mucus lining, thereby increasing mucosal colonisation. The pic gene is widely distributed within the Enterobacteriaceae, being found in human pathogens, such as enteroaggregative Escherichia coli (EAEC), uropathogenic E. coli (UPEC) and Shigella flexneri 2a. As the pic promoter regions from EAEC strain 042 and UPEC strain CFT073 differ, we have investigated the regulation of each promoter. Here, using in vivo and in vitro techniques, we show that both promoters are activated by the global transcription factor, CRP (cyclic AMP receptor protein), but the architectures of the EAEC and the UPEC pic promoter differ. Expression from both pic promoters is repressed by the nucleoid-associated factor, Fis, and maximal promoter activity occurs when cells are grown in minimal medium. As CRP activates transcription in conditions of nutrient depletion, whilst Fis levels are maximal in nutrient-rich environments, the regulation of the EAEC and UPEC pic promoters is consistent with Pic’s nutritional role in scavenging mucin as a suitable carbon source during colonisation and infection.The serine protease autotransporters of the  Escherichia coli (EAEC), an important human pathogen that causes diarrhoea in adults and children and is endemic both in industrialized and developing countries -ATP. In each reaction, 0.5 ng of each 32P labelled fragment was incubated with different amounts of Fis protein. The composition of the EMSA reaction buffer was 20 mM HEPES (pH 8.0), 5 mM MgCl2, 50 mM potassium glutamate, 1 mM DTT, 25 μg ml−1 herring-sperm DNA, 0.5 mg ml−1 BSA and 5% (v/v) glycerol . Samples were incubated at 37°C for 20 min, and then immediately separated on a 6% polyacrylamide gel (12 V cm−1), containing 0.25 x TBE and 2% glycerol. Dried gels were analysed with a Bio-Rad Molecular Imager FX and Quantity One software (Bio-Rad).EMSA assays using purified Fis were carried out as detailed in Godfrey et al. . Purifie32P-end-labelled pic promoter fragments, as in Browning et al. . Th. ThpicU ng cells  and usedfragment . The datpic promoter activity, we transformed the lac expression vector, pRW224, carrying either the picp042 or picp073 fragment into M182 Δlac Δcrp cells and then introduced a second plasmid, pD, carrying wild type CRP, or CRP carrying defective AR1, defective AR2 or defective AR1 and AR2. The data, illustrated in picp042 fragment, disruption of AR1 prevents CRP-dependent activation, whilst disruption of AR2 increases activation. In contrast, with the UPEC picp073 fragment, alteration of either AR1 or AR2 leads to a reduction in CRP-dependent activation.The activation of transcript initiation by CRP at target promoters is dependent on specific CRP-RNAP interactions involving AR1 at Class I promoters, and both AR1 and AR2 at Class II promoters ,23. To mpicp042 or picp073 fragments cloned in pRW224, we inactivated either the upstream -10 element, or the downstream −10 element, or both , or due to transcription and translation (using the pRW225 derivatives), and differences in the ratio of the two values will reflect differences in translation, due to altered initiation or changes in transcript stability. With promoter fragments derived from picp042, this ratio, within experimental error, remains constant, irrespective of the mutations in either −10 element  [picU gene and promoter is found in many UPEC strains  but also faecal isolates, including the well-known probiotic E. coli strain Nissle 1917  [E. coli are considered to be the major source of bacteria causing urinary tract infections, this is perhaps not surprising. However, it is of note that Nissle 1917 has many virulence determinants that are similar to UPEC isolates (Table S3). Many studies that have examined the distribution of Pic have not distinguished between the different Pic variants carried by bacteria  and so it is unclear if picU is more likely to be carried by UPEC isolates [The Pic mucinase is distributed widely in enteric bacteria ,47,52. Tgure S1) ,53. The,gure S1) . As faecisolates ,47,52.et al. [pic promoter and concluded that a single strong promoter was responsible for the majority of pic expression. Here we have reported the location of this promoter and the transcription factors involved in Pic regulation. The EAEC and UPEC pic promoters are unusual as they have evolved two precisely placed 10 hexamer elements, and, in the UPEC picp073 fragment, both appear to be functional. Thus, the UPEC strain CFT073 picU promoter is the first example of what we dub as an ambiguous Class I/II CRP-dependent promoter. The advantage of this is still unclear, but data in pic gene translation levels differ according to which 5` transcript end is selected. Similarly, the factors that bias CRP-dependent activation in the picp042 fragment towards the Class I option are unclear, but this study underscores that bacterial promoters can be active, whilst not conforming to simple textbook models. Interestingly, Citrobacter rodentium also carries the PicC mucinase, which has 79.15% and 78.57% identity to those from EAEC and UPEC, respectively  [picC promoter with that from EAEC and UPEC suggests that it is likely CRP-dependent but a hybrid version of these two E. coli promoters, carrying the upstream of the UPEC picU promoter and the downstream region of the EAEC version . We surmise that the C. rodentium pic promoter functions by a Class II mechanism only, as the downstream −10 element is poor. Note that CRP in C. rodentium, and the Enterobacteriaceae in general, is highly conserved . Thus, it seems that evolution has produced three flavours of the pic promoter, CRP-dependent Class I only (EAEC), CRP-dependent Class II only (C. rodentium picC) and CRP-dependent Class I/II ambiguous (UPEC picU).Previously, Behrens et al.  investiggure S2) . CompariE.coli genomes encode over 300 transcription factors that support a complex regulatory network and, amongst the factors that make the most connections, are CRP and Fis [pic promoters is maximal in minimal medium . Presumably, this is part of a larger more general bacterial foraging response to cope with starvation and slow growth, and the ancestral gene was exapted in pathogens, such as EAEC and UPEC, specifically to facilitate mucin digestion, without its transcriptional regulation being altered. It is of note that the DNA surrounding the pic genes from EAEC 042, S. flexneri and UPEC CFT073 contains many genes from insertion sequences , suggesting that these genes are readily mobile and can be swapped between bacteria. As both CRP and Fis are found in all Enterobacteriaceae, should pic gene exchange occur, regulation will be maintained, perhaps highlighting the choice of global regulators over “bespoke” virulence transcription factors. and Fis  . CRP ac and Fis ,21,59,60 and Fis ,26,31,32Enterobacteriaceae carry additional virulence genes, which encode other SPATE proteases, such as the Pet, SigA and Sat cytotoxins from EAEC 042, S. flexneri and UPEC CFT073, respectively [Enterobacteriaceae.In addition to Pic, many pathogenic ectively ,62. In eectively ,19. ThusClick here for additional data file.