Form and function in biology are intimately related aspects that are often difficult to untangle. While the structural aspects of chromatin organization were apparent from early cytological observations long before the molecular details of chromatin functions were deciphered, the extent to which genome architecture may impact its output remains unclear. A major roadblock to resolve this issue is the divergent scales, both temporal and spatial, of the experimental approaches for examining these facets of chromatin biology. Recent advances in high-throughput sequencing and informatics to model and monitor genome-wide chromatin contact sites provide the much-needed platform to close this gap. This mini-review will focus on discussing recent efforts applying new technologies to elucidate the roles of genome architecture in coordinating global gene expression output. Our discussion will emphasize the potential roles of differential genome 3-D structure as a driver for cell fate specification of multicellular organisms. An integrated approach that combines multiple new methodologies may finally have the necessary temporal and spatial resolution to provide clarity on the roles of chromatin architecture during development. Numerous studies in the past decades have suggested that the transcription activity across the genome can be correlated with a number of microscopic features, such as chromatin compactness . The probabilities of intra-chromosomal interaction, even for loci that are 200 Mb apart, are much greater than that of inter-chromosomal interactions. This characteristic of Hi-C data has been utilized to assist genome-wide haplotype reconstruction and genome assembly scaffolding  The chromatin beacon line residing at the domain boundary (CCT432) appeared much closer to the NOR in 3-D compared to the other three beacons in the proposed domain. The result suggests that association with the nucleolus helps to create and/or maintain the topological domain. This model is akin to observed cases in which topological domain boundaries overlap with lamina-associated domains. (3) The reporter gene in line CCT432, as well as the flanking genes surrounding the transgene in this line, is actively expressed. This is consistent with the observation that housekeeping genes are enriched in topological domain boundaries.Using time-lapse fluorescent imaging of four chromatin beacon lines, each containing a single copy of an identical transgene that has been mapped to a 100 kb region adjacent to the nucleolus organizing region (NOR) on l domain . This prIn animal systems, enhancers are distal regulatory elements that can locate from several kb to over one Mb away from the promoter that is being modulated. Enhancers are typically marked by H3K4me1 and H3K27Ac and are depleted of H3K4me3 . SubsetsIn contrast to the case in mammalian systems, evidence supporting gene regulation by distal elements as a prominent mechanism in plants is scarce. Enhancer traps have been successfully applied in plant systems for the establishment of cell-type-specific marker lines . Howeverbooster (b1) locus provided the most convincing example of a plant distal regulatory element to date. Among many alleles of the b1 locus, B–I and B′ were found to contain identical DNA sequences but are associated with distinct chromatin and expression characteristics and were therefore considered epi-alleles , respectively. Extrinsic cues  and signals from stem cell niches have been shown to modulate stem cell activities and to shape plant development . Chromatin architectural proteins , in a combinatorial manner, may facilitate the pluripotency factors to bridge cell type-specific chromatin looping , was shown to directly bind to the WUS promoter for activation of WUS expression in the SAM  and MUTE, which encode bHLH transcription factors  method was demonstrated to enable production of cell-type-specific ChIP-seq in Arabidopsis root cells (Caenorhabditis elegans and mesoderm cells of Drosophila embryos (Figure 2, this method can be readily adapted to specifically tag distinct populations of epidermis-derived leaf cell types such as trichomes and GCs in Arabidopsis. Using this approach, we can now deploy development time- and cell type-specific promoters to enable the rapid isolation of nuclei from distinct cell types at defined steps of development. The integration of this method with other genome-wide, sequence-based techniques as well as the chromatin beacon resource should narrow the temporal gap in our methods to study the architecture of chromatin at a defined developmental time and space. We are hopeful that this fusion of new methodologies should finally bring us closer to mapping the early changes in chromatin organization that may drive predictable genome outputs and cell fates.While the 3C-derived chromatin proximity assay and chromatin beacon platform in ot cells , as well embryos . As showThe 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.


SND1) encodes a coactivator involved in inflammatory responses and tumorigenesis. While SND1 is known to interact with certain transcription factors and activate client gene expression, no comprehensive mapping of SND1 target genes has been reported. Here, we have approached this question by performing ChIP-chip assays on human hepatoma HepG2 cells and analyzing SND1 binding modulation by proinflammatory TNFα. We show that SND1 binds 645 gene promoters in control cells and 281 additional genes in TNFα-treated cells. Transcription factor binding site analysis of bound probes identified motifs for established partners and for novel transcription factors including HSF, ATF, STAT3, MEIS1/AHOXA9, E2F and p300/CREB. Major target genes were involved in gene expression and RNA metabolism regulation, as well as development and cellular metabolism. We confirmed SND1 binding to 21 previously unrecognized genes, including a set of glycerolipid genes. Knocking-down experiments revealed that SND1 deficiency compromises the glycerolipid gene reprogramming and lipid phenotypic responses to TNFα. Overall, our findings uncover an unexpected large set of potential SND1 target genes and partners and reveal SND1 to be a determinant downstream effector of TNFα that contributes to support glycerophospholipid homeostasis in human hepatocellular carcinoma during inflammation.The NF-κB-inducible Staphylococcal nuclease and tudor domain-containing 1 gene ( SND1) is an evolutionarily conserved gene, usually present as a single copy in all organisms from fission yeast to humans  , SND1 waSND1 gene promoter activation are far from being elucidated. Therefore, SND1 is an interesting protein and how it can be involved in multiple and seemingly unrelated processes is an important question yet to be answered.New information emerging over the last decade has demonstrated that SND1 receives regulatory input through multiple stimuli, serving what are considered post-transcriptional regulatory functions to control important physiological events. SND1 is involved in RNA maturation, including but not limited to spliceosome assembly and pre-mRNA splicing ,23,24, RSND1 is an inducible gene that responds to TNFα through a transcriptional network involving Sp1, NFY and NF-κB binding on the promoter -chip assays on human hepatoma HepG2 cells in normal and TNFα-induced inflammatory conditions. This study represents the first large-scale attempt to address the nature of gene targets of SND1 at cellular level. We have identified a broad collection of potential SND1 target genes with functions in transcription regulation, development and regulation of cellular metabolism and validated a subset. Subsequent knocking-down experiments have revealed that SND1 is a determinant downstream effector of TNFα, helping to sustain glycerophospholipid homeostasis in human HCC cells.6) were seeded in 10 cm diameter plates and grown in Eagle's minimal essential medium (EMEM) (ATCC) supplemented with 2 mM L-glutamine, 100 IU/ml penicillin, 100 μg/ml streptomycin  and 10% (v/v) fetal bovine serum (ATCC) at 37°C and 5% CO2. Cultures were either left untreated or treated with TNFα (50 ng/ml) for 24 or 8 h before harvesting for ChIP-chip or gene expression assays, respectively.HepG2 cells (ATCC) (3.5 × 105 HepG2 cells in 2.5 ml EMEM (6-well plates) or 1.2 × 104 cells in 0.1 ml EMEM (96-well plates) were added to the siRNA-containing well and cultured for 48 h. A negative siRNA (Life Technologies) was used as a control. For mRNA and protein determination, 8 h before harvesting, cells were treated with TNFα (50 ng/ml). For luciferase activity assays, medium was replaced by 0.1 ml fresh EMEM medium with or without TNFα (50 ng/ml), and cells were transfected using 0.6 μl X-tremeGENE HP transfection reagent (Roche Applied Science) and received 0.1 μg of the appropriate CHPT1 , LPGAT1 , LPIN1  or PTDSS1  promoter cloned into the Firefly luciferase reporter vector pRP  and 0.1 μg of Renilla luciferase pRL-TK (Promega), as internal control for transfection efficiency. Mock transfections with the empty vector pcDNA3 were carried out in all cases. After 24 h, cells were lysed and luciferase activity measured using the Dual-Luciferase Reporter Assay System (Promega) in a Synergy HT Multi-Detection Microplate Reader (BioTek Instrument Inc)  at 10 nM final concentration and 5 or 0.25 μl lipofectamine (Life Technologies) were dissolved in Opti-MEM I Reduced Serum (Life Technologies). Then, 2–2.5 × 10ent Inc) . Firefly6 HepG2 cells, control and TNFα-treated, were washed and protein–DNA complexes were cross-linked in 1% formaldehyde in phosphate buffered saline (PBS) for 10 min at 37°C. Cells were washed twice with PBS, harvested in protease inhibitor cocktail and pelleted by centrifugation at 800 x g for 10 min at 4°C. The cell pellet was resuspended in lysis buffer containing a protease inhibitor cocktail and incubated for 15 min. Cross-linked material was fragmented by sonication to shear chromatin to 800–1000 bp using a Soniprep 150 sonifier (MSE) at high power. The sonicated chromatin solution was precleared by incubating with Protein A beads for 50 min at 4°C, aliquoted and incubated overnight at 4°C with anti-SND1 antibody (ChIP was performed using the EpiTectChIP One-Day-Kit (SABiosciences) following the manufacturer's instructions. Briefly, exponentially growing 4–6 × 10antibody  or non-iPurified ChIP DNA was amplified with the GenomePlex Complete Whole Genome Amplification kit (Sigma-Aldrich) following the manufacturer's instructions. Using the Agilent Genomic DNA Enzymatic Labeling Kit (Agilent Technologies), the input samples were labeled with Cyanine-3 (Cy3) and the immunoprecipitated sample with Cyanine-5 (Cy5) according to Agilent instructions. Labeled nucleotides were hybridized to Agilent SurePrint G3 Human Promoter ChIP-chip Microarray 1 × 1M, Agilent Microarray Desing ID 027811 p/n G4873A. The microarray contains over 960 000 oligonucleotides covering the region −9/+2 kb from the transcription start site (TSS) of 21 000 well-defined genes along the human genome. The hybridation was performed in SureHyb hybridation chambers (Agilent Technologies), incubating 5 μg Cy3 (input sample) and 5 μg Cy5 (IP sample) in 490 μl during 40 h at 65°C and 20 rpm. Arrays were washed using the Stabilization and Drying Solution and the ozone-barrier slide covers in order to minimize the ozone-mediated Cy5 degradation, and finally scanned, all according to the manufacturer instructions. Labeling and hybridization was performed by the genomic and proteomic core facility (SGIKer) of the University of the Basque Country.The information was extracted with the Feature Extraction Software (version 10.7.3.1), and the SND1-DNA binding events were recognized by the Genomic Workbench Lite Edition program (version 6.5). This program also calculates false discovery rate (FDR) for each peak.http://ceas.cbi.pku.edu.cn/ (http://meme.nbcr.net/meme/intro.html/)  server at .edu.cn/ . The ideo.html/) , and comcrf.gov/ , PinkThi.ru.nl/l  and CEASThe quantification of immunoprecipitate-enriched DNA sequences to validate ChIP-chip assays was performed by quantitative real-time PCR (qPCR) analyses on positive regions of representative genes. The double stranded DNA dye SYBR green (Life Technologies) methodology was used for the amplification reaction, using 5 μl of the immunoprecipitate material and 0.1 μM of the specific primer set in an ABI 7000 Sequence Detection System (Applied Biosystems). Sequences of PCR primers for ChIP-chip validation are listed in Supplementary Table S1. The PCR reactions were as follows: 94°C for 3 min; 40 cycles at 94°C for 20 s, 59°C for 30 s and 72°C for 30 s; and final extension at 72°C for 2 min. PCR reactions were performed in triplicate on all ChIP samples used for ChIP-chip. The results are given as the enrichment of the immunoprecipitation relative to the negative control.6 HepG2 cells) was performed with TRIzol reagent (Life Technologies) and quantified . First strand cDNA was synthesized from 1 μg RNA of each cell sample using the Transcriptor First Strand cDNA Synthesis Kit (Roche Applied Science). The cDNA was then used as the template for individual PCR reactions, programmed as stated above. Normalization was performed using normalization factors computed by GeNorm for 18 s, β-actin, GAPDH and TATA box binding protein mRNAs, as detailed previously  in control and TNFα-treated HepG2 cells, both expressing endogenous levels of SND1 or residual SND1 levels after SND1 knocking-down by siRNA treatment. Sequences of PCR primers for expression analysis are listed in Supplementary Table S2. RNA extraction , according to the manufacturer's indications. The levels of SND1, CHPT1 and LPGAT1 protein were also measured in whole cell lysates. Protein concentrations were determined using a commercially available kit (Bio-Rad). Ten to twenty micrograms of protein were loaded in each lane, fractionated on 9% sodium dodecyl sulphate-polyacrylamide gel electrophoresis at 170 V for 1 h and transferred to PVDF membranes (Bio-Rad) by semi-dry transference (1 h at 20 V). SND1, CHPT1 and LPGAT1 were detected by using anti-SND1  (0.3 μg/3/MeOH (The major glycerophospholipid classes (phosphatidylcholine (PC), phosphatidylserine (PS), phosphatidylethanolamine (PE) and phosphatidylinositol (PI)), free cholesterol (FC), triacylglycerol (TAG) and cholesteryl esters (CE) were quantified in control and TNFα-treated HepG2 cells expressing endogenous or residual levels of SND1 after SND1 knocking-down by siRNA treatment. Cells were lysed and lipids were extracted from cell lysates with a preparation of CHCl3/MeOH  and drie3/MeOH  using stt-test. Statistical significance is defined as P ≤ 0.05 unless otherwise stated.Except for the hybridization binding in microarrays, results are reported as the mean ± SD and were analyzed by the two-tailed Student's The ChIP-chip data have been submitted to GEO data set NCBI (GSE61539) and will be released upon publication.SND1 transcriptional activity is induced in HepG2 cells by TNFα treatment through NF-κB binding to its response element on the SND1 promoter  and 822 in TNFα-treated cells were found to be bound to SND1 in the microarrays  and the others were located downstream mainly in the first exon and intronic regions of the target genes Figure . Most ofP-values below 10−5  were the regulation of gene expression, the regulation of transcription and DNA and RNA metabolism, cellular biosynthetic processes, organ morphogenesis and central nervous system development, with Looking at the enriched terms for molecular functions, almost all were related to transcription and transcription factor activity (data not shown), which reinforces the view that SND1 plays a regulatory role of gene expression at the transcriptional level.KEGG database allows the identification of pathways in which a set of target genes can have an impact. Application of KEGG to our lists of SND1 target genes extracted pathways involved in cancer , viral infection , inflammation (chemokine and cytokine-cytokine receptor interaction), as well as metabolic pathways  in a collection of 41 representative genes of the enriched GO categories using primers complementary to positive binding regions. We used chromatin that has been immunoprecipitated with the anti-SND1 antibody or with the unspecific anti-IgG antibody from untreated and TNFα-treated cells and considered significant enrichment the specific/unspecific amplification ratios >2 . The CHPT1 gene product catalyzes the final step of the denovo synthesis of phosphatidylcholine, LPIN1 encodes a protein that acts both as a phosphatidate phosphohydrolase involved in diacylglycerol formation from phosphatidic acid and as a coactivator of PPARGC1A, PTDSS1 is involved in the base-exchange mediated conversion of phosphatidylcholine to phosphatidylserine and LPGAT1 catalyzes the reacylation of lysophosphatidylglycerol. SND1 binds the promoters of the four genes in treated and control cells, with the exception of LPIN1, which is bound solely during inflammation . We found that cells with endogenous expression of SND1 displayed the expected TNFα-induced stimulation in the LPGAT1 and LPIN1 promoter activity, whereas the activity of PTDSS1 promoter showed no significant modification . Analogous reactions are responsible for PE synthesis. In addition, PC and PE are converted in PS via PTDSS1 and PTDSS2 catalyzed base-exchange reactions . An impoglycerol ,72. Phosglycerol . Lipins glycerol . They exglycerol . Notablyglycerol . Becauseglycerol , by compCHPT1, LPGAT1, LPIN1 and PTDSS1, is provided by silencing the SND1 gene by interference RNA and determining the expression change of the SND1 targets caused by TNFα. Binding of SND1 to CHPT1, LPGAT1 and PTDSS1 occurs independently of TNFα stimulation. Our findings suggest that under non-inflammatory conditions, SND1 appears to coactivate CHPT1 and LPGAT1 gene expression, as transcript levels significantly diminished (50%) when SND1 was silenced (Figure PTDSS1, whose basal expression was unaffected by SND1 silencing. It is quite usual that regions identified by ChIP methods do not have any effect on the expression of the bound gene, as happens in the PTDSS1 gene. The relevance of the SND1 coactivator is manifested in cells that handle an inflammatory stress. We noted that the glycerolipid gene expression adaptation to TNFα is impaired upon SND1 silencing, given that the changes in the CHPT1, LPGAT1, LPIN1 and PTDSS1 transcripts are abolished upon SND1 depletion (Figure The impact of SND1 on the transcription of d Figure  and B. Tminished 0% when Sn Figure . This waSND1 promoter activity via transcription factor NF-κB binding at −116 and −174 sites (Elucidation of whether or not the gene expression and PC homeostasis are directly related with SND1 binding on promoter targets and the identity of the partner transcription factors requires further study. Nevertheless, the hypothesis (illustrated in Figure 74 sites . It is s74 sites ,77. Our 74 sites , though 74 sites  and the In conclusion, we have identified a broad collection of potential SND1 target genes with functions in transcription regulation, development and regulation of cellular metabolism and validated a subset. Though the exact function of SND1 in mammals remains to be revealed, our findings place SND1 within the TNFα signaling pathway (see Figure


Multiple positions within the SWI/SNF chromatin remodeling complex can be filled by mutually exclusive subunits. Inclusion or exclusion of these proteins defines many unique forms of SWI/SNF and has profound functional consequences. Often this complex is studied as a single entity within a particular cell type and we understand little about the functional relationship between these biochemically distinct forms of the remodeling complex. Here we examine the functional relationships among three complex-specific ARID (AT-Rich Interacting Domain) subunits using genome-wide chromatin immunoprecipitation, transcriptome analysis, and transcription factor binding maps. We find widespread overlap in transcriptional regulation and the genomic binding of distinct SWI/SNF complexes. ARID1B and ARID2 participate in wide-spread cooperation to repress hundreds of genes. Additionally, we find numerous examples of competition between ARID1A and another ARID, and validate that gene expression changes following loss of one ARID are dependent on the function of an alternative ARID. These distinct regulatory modalities are correlated with differential occupancy by transcription factors. Together, these data suggest that distinct SWI/SNF complexes dictate gene-specific transcription through functional interactions between the different forms of the SWI/SNF complex and associated co-factors. Most genes regulated by SWI/SNF are controlled by multiple biochemically distinct forms of the complex, and the overall expression of a gene is the product of the interaction between these different SWI/SNF complexes. The three mutually exclusive ARID family members are among the most frequently mutated chromatin regulators in cancer, and understanding the functional interactions and their role in transcriptional regulation provides an important foundation to understand their role in cancer. The SWI/SNF chromatin remodeling complex is made of multiple subunits, and the inclusion of different mutually exclusive subunits allows potentially many biochemically distinct forms of SWI/SNF. Members of this complex are among the most frequently mutated genes in cancer. Here, we present the first genome-wide localization maps of three mutually exclusive ARID subunits, and identify their role in regulating transcription. The ARID subunits have largely overlapping localization, and affect transcription of many of the same genes. Within these overlapping classes we identified extensive cooperation between ARID1B and ARID2 in the repression of hundreds of genes, as well as competitive interactions between ARID1A and the other ARIDs. These distinct regulatory modalities were characterized by co-localization with specific transcription factors. Together these data provide insight into the functional relationships between multiple distinct SWI/SNF complexes. SWI/SNF controls gene expression at thousands of genes and in most of these cases multiple distinct forms of the complex interact to specify the gene’s transcription status in a particular cell. Understanding the role of each distinct form of SWI/SNF within a cell is a critical first step towards untangling the effects of SWI/SNF subunit loss in cancer. The SWI/SNF (SWItch/Sucrose Non-Fermentable) family of chromatin remodeling proteins plays a critical role in developmental processes . In miceThe ARID (AT-Rich Interacting Domain) domain containing proteins of the SWI/SNF complex are examples of three mutually exclusive subunits, and their inclusion distinguishes three biochemically distinct forms of SWI/SNF. ARID1A and ARID1B are found in the BAF-A and BAF-B (Brahma Associated Factor) complex respectively, while ARID2 is found in the PBAF (Polybromo BAF) complex. The functional interactions between distinct forms of this heterogeneous remodeling complex have not been studied genome-wide. All three ARID genes are mutated in a wide variety of tumors –15. Of pIn this paper we examine two questions related to the functional overlap of distinct forms of the SWI/SNF remodeling complex. First, do different SWI/SNF complexes extensively cooperate or compete to regulate transcription? Second, do distinct forms of the SWI/SNF complex interact with unique or common cis-regulatory modules to mediate their role in transcription? To address these questions we performed ChIP-seq and RNA-seq in HepG2 cells, a human liver cancer cell line for which extensive transcription factor binding data are available from the ENCODE project .We present evidence that ARID1A, ARID1B, and ARID2 have a highly overlapping role in transcriptional regulation in HepG2 cells. Additionally, we provide the first genome-wide mapping of biochemically distinct SWI/SNF complexes and show a high degree of overlap between binding sites for the different ARID proteins. We identify putative regulatory modules specific for the different types of ARID mediated regulation. Using RNAi against combinations of ARIDs we show ARID1A and ARID2 have frequent competitive interactions, while ARID1B and ARID2 are both required for repression and are not redundant. Our study provides insight into how distinct forms of the SWI/SNF complex regulate transcription in an interdependent manner and provides mechanistic insight into recent observations of synthetic lethal interactions between SWI/SNF subunits , 17, 19.All three ARID members of the SWI/SNF complex are expressed in the human liver cancer cell line HepG2, and in hepatocellular carcinoma all three ARIDs are recurrently mutated in a mutually exclusive fashion –12. ARID−12, ARID1B <2.2 × 10−16, ARID2 <2.2 × 10−16; hypergeometric test). In the remainder of the manuscript we refer to genes transcriptionally regulated by a single ARID as ‘alone’ and those regulated by more than one ARIDs as ‘jointly’ . Of these 3453 genes 747 were affected by multiple ARIDs (21.6%). Together, these data suggest an extensive role for functional interaction among biochemically discrete forms of SWI/SNF.−37). While ARID1B primarily represses genes, it also is significantly more likely to repress transcription when functioning with another Arid (−7).We next compared the direction of gene expression changes of genes regulated alone with genes regulated jointly . While A2 = 0.185, ARID1A-ARID2 r2 0.03, If ARID subunits are mainly functioning in opposition in HepG2 cells we would expect them to have discordant effects on gene expression. We compared changes in expression for pairs of ARID proteins at jointly regulated genes. Those genes where both ARIDs caused an expression change in the same direction are said to be concordant, while those regulating expression in opposite directions are called discordant. From this analysis we cannot conclude the complexes directly compete or cooperate, but instead use these terms in relation to the functional consequence of the dual regulation by a pair of ARID proteins. When ARID1B or ARID2 were regulating genes in conjunction with ARID1A there we observed both concordant and discordant changes . The majority of these interactions consisted of a requirement for both ARID1B and ARID2 to repress gene expression. Together these results suggest that ARID1B and ARID2 regulate a highly similar transcriptional network.However, when ARID1B and ARID2 regulated the same gene there was an extreme degree of concordance .We then compared ARID binding at all transcription units in HepG2 cells . All thr−16)).Using ENCODE data in HepG2 cells, we performed a similar analysis using all enhancers identified in HepG2 cells . We defiFinally, we analyzed the distribution of peak locations to determine if biochemically distinct forms of SWI/SNF localized to common types of genomic features. ARID1A and ARID2 were found at similar annotations in the genome. These peaks were more skewed towards promoters and gene bodies (exons), while ARID1B bound to more distal and intronic features. Over 70% of ARID1A peaks were in promoters of genes (TSS +/- 2kb), while ARID2 peaks were slightly less localized to the promoter (60%), and ARID1B peaks were found in promoters only 25% of the time . This diGiven the high level of signal for each ARID at promoters and enhancers it is apparent that many of the ARID binding sites in the genome are shared by more than one ARID. We found approximately half of the sites for a particular ARID were bound by multiple ARIDs , while an upstream regulatory element is more variably occupied  enrichment of these transcription factors was relatively consistent. We clustered peaks based on enrichment scores for several defining members of the above clusters and most peaks fell within one large cluster for each group . A table of all expressed genes with transcription changes and ARID binding status can be found in BibliographyS16.Next we sought to determine what fraction of genes were directly regulated by ARID binding. To address this we first assigned each ChIP peak to the nearest gene and for each set of peaks we determined what fraction was associated with a gene that was altered following knockdown. This showed that 30–50% of genes were putative direct targets of the associated ARID . ARID1A We next repeated the analysis of concordant and discordant gene expression changes  to deterARID1B and ARID2 alone and multiple peaks were associated with distinct genomic regions. We next asked if direct targets of ARID1B and ARID2 regulation were associated with ARID peaks at distinct genomic regions, and whether this distribution was different for activated compared to repressed genes. ARID1B peaks associated with repressed genes were more likely to be bound in an intron than were genes associated with activation following ARID1B loss, consistent with a model where ARID1B is involved in enhancer repression . In contThe co-factors associated with each of the distinct ARIDs may be unique to each type of transcriptional regulation. To address these relationships we considered two sets of directly regulated genes, those regulated by ARID1B and ARID2 and those regulated by ARID1A and ARID2. We split each set based on the relationship between the ARIDs in the RNA-seq data. For ARID1B and ARID2, we compared peaks associated with activation to those associated with repression, and for ARID1A and ARID2 we compared peaks associated with cooperation to those associated with competition. Identification of enriched transcription factors specifically associated with one of these groups relative to the others would provide evidence of heterogeneity within SWI/SNF complexes defined by inclusion of a particular ARID subunit.Most factors bound at sites associated with ARID1A and ARID2 regulated genes were found equally at competitive and cooperative genes . This isWe identified several factors that were specific for either active or repressive associated peaks . For exaChromatin regulating proteins bind throughout the genome and play a critical role in transcriptional regulation. Studies have shown genomic co-localization for many chromatin regulators  as well Using RNA-seq and ChIP-seq we classified transcriptional regulation based on the whether a gene was regulated by a single ARID containing complex, or by multiple ARID complexes. Additionally, we classified shared regulatory function based on whether two ARID proteins act concordantly or discordantly to regulate transcription. Using our ChIP-seq data combined with the rich dataset provided by the ENCODE project  we were ARID1B and ARID2 coordinately regulated transcription of a large set of genes. The majority of this overlap was seen in genes cooperatively repressed by ARID1B and ARID2. While SWI/SNF has been shown to function in repression –34, no sUnderstanding how ARID1B and ARID2 mediate these cooperative activities is an important ongoing question. Our study suggests one possible mechanism involves the co-factors recruited to each class of sites. When ARID1B and ARID2 are activating gene expression, these sites appear more commonly located near the transcription start site. Additionally, these sites are occupied by transcriptional activators such as BRCA1 and MAX, both of which have previously been linked to SWI/SNF function , 35, butWe identified many examples of competitive interactions amongst the ARID subunits, and validated the mechanistic importance of this competition . This suEvidence of a central role for SWI/SNF in oncogenesis comes from the increasing variety of tumors in which the complex is recurrently mutated , 38–40. The idea of therapeutically targeting SWI/SNF, either in the context of other SWI/SNF mutations, or in tumors with specific genetic lesions, is beginning to gain traction , 19. WheHepG2 cells (UNC Tissue Culture Facility) were grown in DMEM  supplemented with 10% Fetal Calf Serum and penicillin/streptomycin .Arid1a: Bethyl A301–041A (western), Santa Cruz sc-32761x (ChIP). Arid1b: Abcam ab57461 (western), Santa Cruz sc-32762x (ChIP). Arid2 Bethyl A302–230A (western/ChIP). Nucleolin Bethyl (A300–711A). H3K27ac (ChIP) Abcam ab4729.siRNA 2 × 106 HepG2 cells were grown as above and reverse transfected with RNAimax reagent (Life Technologies) using 30nM siRNA and 10uL RNAimax in 6cm dishes at 6.6 × 105 /mL. After 72 hours of incubation RNA was harvested using Trizol (Life Technologies) and further purified using a RNAeasy clean up kit (Qiagen). siRNAs used were—Arid1a (siGenome L-017263-00-0005 (Dharmacon), Silencer Select s15786 (Life Technologies), Arid1b (siGenome M-013970-00-0005 (Dharmacon), 1027416 (Qiagen)), Arid2 (Silencer Select s47042(Life Technologies), siGenome M-026945-01-0005 (Dharmacon), Non-targeting Control .shRNA shRNA constructs targeting ARID1A  and a non-silencing construct were used to generate lentivirus by transfecting HEK293T cells with the pLKO.1 lentiviral constructs, pMD2.G and psPAX2 (psPAX2 and pMD2.6 were a gift from Didier Trono—Addgene plasmid # 12260 and plasmid # 12259). After 72 hours viral supernatent was harvested concentrated using Lenti-X (Clontech). Knockdowns for ChIP experiments were generated by transducing HepG2 cells on 6 cm plates and selecting for puromycin resistance starting 48 hours post-transduction. Cells for ChIP were handled as described below.6 cells were fixed in 0.3% formaldehyde for 30 minutes at 4°C and then quenched with 0.125M glycine for 5 minutes at room temperature. Pellets were washed three times in cold PBS and frozen in liquid nitrogen and stored at -80°C. For each ChIP 2 × 107 cells were thawed on ice and resuspended in 1mL swelling buffer  with 1mM PMSF and incubated 10 minutes on ice. Pellets were dounced 20 strokes with B pestle and spun at 2000RPM for 7 minutes at 4°C in a swinging bucket centrifuge. Cell pellets then resuspended in 5mL sucrose buffer A  and layered gently over 5mL sucrose buffer B  and centrifuged 10 minutes at 3000 RPM in a swinging buffer centrifuge at 4°C. Nuclei were washed once in 10mL digestion buffer  and pelleted for 10 minutes at 2000 RPM. They were then resuspended in 1mL per 4 × 107 cells with 3.3uL CaCl2 per mL with 1X protease inhibitor (Roche) and 1mM PMSF and incubated for 5 minutes at 37°C. 0.5uL MNAse  was added to tubes and incubated 15 minutes at 37°C. MNase digestion was stopped by adding 0.1 volumes 0.5M EDTA and reactions were incubated for 5 minutes on ice. Nuclei were then passed 5 times through a 20G needle, 1 volume of double concentration LB3  was added and lyaste was passed 5X through 25G needle. Triton X-100 was added to final concentration of 1%, and cells were spun at 13000G in microfuge at 4° for 15 minutes. Supernatant was saved, and the pellet was resuspended in 1X LB3 buffer with 1% Triton X-100 and incubated 1–2 hours rotating at 4°. Following another 13000G spin, the supernatants were combined and 10% was reserved as input DNA. 1mL  was added to the prepared beads and incubated overnight. Beads were washed 8 times in RIPA  and 1 time in TE  supplemented with 50mM NaCl before elution in 100uL 1%SDS, 100mM NaHCO3 for 17 minutes at 65 degrees with agitation. Supernatant was removed and supplemented with 5uL of 5M NaCl and incubated overnight at 65°C. 3uL RNAseA(30mg/mL) was added to reactions and incubated for 30 minutes at 37°C, followed by 5ul Proteinase K (20mg/mL) at 56° for 1 hour. Reactions were purified using a Zymo Clean and Concentrator Chip Kit (D5205) and concentration was measured using qubit (Life Technologies) before library preparation.Prior to starting ChIP, 5–10ug antibody were incubated with 50uL Dynal beads (Life Technologies), for rabbit antibodies we used a 1:1 mix of protein A and protein G beads, for mouse antibodies protein G only. Antibody/bead mixture was incubated overnight at 4°C in 0.5% Bovine Serum Albumin (BSA) in PBS. 50–100 × 10Small-scale nuclear extracts were prepared as previously described . ImmunopRNA-seq library preparation was done using the dUTP incorporation method modified from . Briefly− and Illumina Adapters were ligated to molecules using the Quick Ligation Module (E6056S). Libraries were then purified twice with Ampure XP beads . Then molecules were A-Tailed using Klenow exohttp://python.org) and packages indicated below. Code used in analysis is available at http://github.com/jraab/raab_swisnf_2015.Data were processed using R 3.0  and Pythhttp://bowtie-bio.sourceforge.net/bowtie2/index.shtml [https://github.com/taoliu/MACS/ [Single end 50bp reads from two independent experiments were aligned to hg19 using bowtie2 (2.2.1) (ex.shtml  filteredex.shtml  prior toiu/MACS/ ) using –iu/MACS/ , 50 to mSingle-end 50bp reads were aligned to hg19 using Tophat2 (2.0.13)  and htsehttp://genome.ucsc.edu/ENCODE/downloads.html) and were downloaded as aligned reads (BAM format). Replicates were merged and signal centered on each ARID peak was calculated using python scripts (available in github repository). Median signal over each peak was used to calculate a pairwise correlation (Spearman) for each pair of ChIP-seq peaks. These were clustered (1-cor) and visualized using the gplots::heatmap.2 function in R. Clusters were assigned using k-means (k = 9) and the network diagram of similarities between clusters was derived from the number of shared transcription factors (edges) between any two clusters (nodes). We removed any clusters with a median enrichment of <0.1.Data sets for transcription factors and histone modifications were obtained from the ENCODE portal (RT-PCR was performed by reverse transcribing RNA using Multiscribe (Life Technologies—4311235), followed by quantitative PCR using Ssofast EvaGreen Supermix (Bio-Rad—172-5203) reagents per manufacturers instructions on a Bio-Rad CFX-96 machine. Primer sequences were as follows: Arid1a-F AGATGGGACACCCAAGACAG, Arid1a-R CTTCCTCTCAGGCTCACCAC; Arid1b-F TCTCCTGTTGGCTCTCCTGT, Arid1b-R GTGTGCCTGCCATAAAACCT; Arid2-F GGAGCAGATCCAAGCACTGT Arid2-R GACTGCCACAGACACATGAAG; Epha2-F AGGCTACGAGAAGGTGGAGG, Epha2-R CAAGCAGGGGCTCTCAGAT; S100A14-F TGGCTCCTCCTGTCTTGTCT S100A14-R CCCACTGTGTCTGGTCCTTT; Spock2-F TGCCAGAAGGTGAAGTGCAG Spock2-R TTTCCATGGAGTTTCACGGT; TNS4-F AGCTCTTCATACCGAGGCTC, TNS4-R CTTTGGCAGACGACTCGATG. AVIL-F AGGCAGAGCACTACCTGTGG; AVIL-R AGTGTTCTCGCTGCCATCA; AXL-F CCGTAACCTCCACCTGGTC; AXL-R ATCCCATCGTTTGACAGCAC; CD109-F TTTCCTCCTAATACAGTGACTGGC; CD109-R CCATTGATGGAAGGACCAAG; EPHA1-F GGAGAGTGACCAGGATGTGG; EPHA1-R ACACAAGGTCTCGAATGGTGA; S100A3-F AAATACAAGCTCTGCCAGGC; S100A3-R GTAGAGACAGAGGCAGGCAA; FOXM1-F AGGAGAATTGTCACCTGGAGC; FOXM1-R GTAAGAGTAGGGTGGCCGCT; KISS1R-F AACTCGCTGGTCATCTACGT; KISS1R-R AACTTGCACATGAAGTCGCC; MCM7-F GCGATGGCACTGAAGGACTA; MCM7-R TTCCCGAGTTCATCATCCTG; TIE1-F GACTGACCCAGCTTTTGCTC; TIE1-R CTGCAATCTTGGAGGCTAGG; All experiments were normalized to expression of Gusb , and presented as the log2 ratio of siArid/siNTG.Gene lists were compared to the MSigDB database (downloaded—2015-05-05). P-values were generated using the hypergeometric test, using all possible genes in the MSigDB as the background and were adjusted using the Benjanmi-Hochberg correction for multiple testing.All animal work was conducted according to relevant national and international guidelines and was approved by UNC Chapel Hill’s IACUChttp://github.com/jraab/raab_swisnf_2015 All sequencing Data are also available as a UCSC Genome Browser Trackhub using http://genome.ucsc.edu/cgi-bin/hgTracks?db=hg19&hubUrl=http://trackhubs.its.unc.edu/magnuslb/jraab/swi_snf/hub.txtRaw and processed data can be found at GEO GSE69568—Analysis scripts can be found at S1 FigARID1A, ARID1B, and ARID2 TPM values from ENCODE experiments . The red(EPS)Click here for additional data file.S2 FigA. Western blot of protein levels of each ARID following knockdown. B. Expression values (log2 Fold Change) measured by RNA-seq for each ARID following loss of each ARID.(EPS)Click here for additional data file.S3 FigA-C. Validation of RNA-seq data using the primary set of siRNA used in RNA-seq (siRNA 1) and a second siRNA set (siRNA 2) at selected genes following knockdown of ARID1A (A), ARID1B (B), and ARID2 (C). Error bars represent standard error of the mean for 2 independent experiments.(EPS)Click here for additional data file.S4 FigA-C. Overlap of ARID1A, ARID1B, or ARID2 with SNF5/BAF47, demonstrating fraction of ARID peaks associated with core SWI/SNF factor by peak calls. D. Union of all ARID peaks overlapping SNF5/BAF47 peaks. E. Comparison of SNF5/BAF47 peak calls in our manuscript with those in Euskirchen et. al. .(EPS)Click here for additional data file.S5 FigH3K4me1, H3K4me3, and H3K27ac signal was measured centered on the ARID bound regions using information from the ENCODE project , 55. Dat(EPS)Click here for additional data file.S6 FigThe signal at each set of ARID peaks , for each of the ARIDs and SNF5/BAF47  +/- 2kb from the midpoint of the peak to demonstrate the accuracy of the ‘Alone’ vs ‘Multiple’ peak definitions.(EPS)Click here for additional data file.S7 FigThe average enrichment for each of 77 ENCODE mapped factors  at the f(EPS)Click here for additional data file.S8 FigWe calculated the signal enrichment for each of the members of the ARID1B-specific cluster  at three types of genomic features . The top row consists of ARID1B-multiple ARID sites, and the bottom row is ARID1B-single ARID sites. The colors distinguish the 3 genomic locations of these peaks. Solid line represents average signal intensity with the shaded region denoting the 95% confidence interval centered at the midpoint of the ARID1B peak +/- 2.5kb.(EPS)Click here for additional data file.S9 FigA. Western blot of combinatorial knockdown using siRNA set #1 from (EPS)Click here for additional data file.S10 FigA. Pairwise gene expression changes of ARID1A and ARID1B directly bound co-regulated genes. B. ARID1A ChIP-seq enrichment at all genes significantly regulated by ARID1A and ARID1B C. ARID1B ChIP-seq enrichment at all genes significantly regulated by ARID1A and ARID1B D. Pairwise gene expression changes of ARID1A and ARID2 directly bound co-regulated genes. E. ARID1A ChIP-seq enrichment at all genes significantly regulated by ARID1A and ARID2. F. ARID2 ChIP-seq enrichment at all genes significantly regulated by ARID1A and ARID2 G. Pairwise gene expression changes of ARID1B and ARID2 directly bound co-regulated genes. H. ARID1B ChIP-seq signal at all genes significantly regulated by ARID1B and ARID2. I. ARID2 ChIP-seq signal at all genes significantly regulated by ARID1B and ARID2.(EPS)Click here for additional data file.S11 FigPercent of ARID1B or ARID2 peaks that are involved in activation or repression that localize to different classes of genomic features. The shift towards distal/intronic peaks in the repressed category is statistically significant for ARID1B peaks, but not for ARID2 peaks . N = 72, 233, 80, 314 for ARID1B activated, ARID1B repressed, ARID2 activated, ARID2 repressed, respectively.(EPS)Click here for additional data file.S12 FigThese data underly the selection of genes in (EPS)Click here for additional data file.S13 FigThese data underly the selection of genes in (EPS)Click here for additional data file.S1 TableBed file of ARID1A bound regions (hg19).(BED)Click here for additional data file.S2 TableBed file of ARID1B bound regions (hg19).(BED)Click here for additional data file.S3 TableBed file of ARID2 bound regions (hg19).(BED)Click here for additional data file.S4 TableBed file of SNF5 bound regions (hg19).(BED)Click here for additional data file.S5 Table(CSV)Click here for additional data file.S6 TableCSV file of all expressed genes and the transcriptional changes associated with each ARID at that gene and whether an ARID peak is assigned to that gene.(CSV)Click here for additional data file.S7 TableExcel file with pathway analysis of alone and jointly regulated ARID1A genes.(XLSX)Click here for additional data file.S8 TableExcel file with pathway analysis of alone and jointly regulated ARID1B genes(XLSX)Click here for additional data file.S9 TableExcel file with pathway analysis of alone and jointly regulated ARID2 genes.(XLSX)Click here for additional data file.S10 TableCSV file containing all significantly changed genes following ARID knockdown. Contains ‘Alone’ or ‘Jointly’ regulated label.(CSV)Click here for additional data file.S1 TextSingle PDF of additional figures and text.(PDF)Click here for additional data file.


Tumor models are critical for our understanding of cancer and the development of cancer therapeutics. Here, we present an integrated map of the genome, transcriptome and immunome of an epithelial mouse tumor, the CT26 colon carcinoma cell line.We found that Kras is homozygously mutated at p.G12D, Apc and Tp53 are not mutated, and Cdkn2a is homozygously deleted. Proliferation and stem-cell markers, including Top2a, Birc5 (Survivin), Cldn6 and Mki67, are highly expressed while differentiation and top-crypt markers Muc2, Ms4a8a (MS4A8B) and Epcam are not. Myc, Trp53 (tp53), Mdm2, Hif1a, and Nras are highly expressed while Egfr and Flt1 are not. MHC class I but not MHC class II is expressed. Several known cancer-testis antigens are expressed, including Atad2, Cep55, and Pbk. The highest expressed gene is a mutated form of the mouse tumor antigen gp70. Of the 1,688 non-synonymous point variations, 154 are both in expressed genes and in peptides predicted to bind MHC and thus potential targets for immunotherapy development. Based on its molecular signature, we predicted that CT26 is refractory to anti-EGFR mAbs and sensitive to MEK and MET inhibitors, as have been previously reported.CT26 cells share molecular features with aggressive, undifferentiated, refractory human colorectal carcinoma cells. As CT26 is one of the most extensively used syngeneic mouse tumor models, our data provide a map for the rationale design of mode-of-action studies for pre-clinical evaluation of targeted- and immunotherapies.The online version of this article (doi:10.1186/1471-2164-15-190) contains supplementary material, which is available to authorized users. Murine CT26 (Colon Tumor #26) cells were developed in 1975 by exposing BALB/c mice to N-nitroso-N-methylurethane (NMU), resulting in a rapid-growing grade IV carcinoma that is easily implanted and readily metastasizes . MoreoveIn sharp contrast to its frequent use in drug development, there have been no comprehensive studies of the genome and transcriptome of CT26. Kras is mutated in CT26  but otheFurther, the lack of comprehensive data on the murine CT26 colon cancer data sharply contrasts to the extensive molecular characterization of human colorectal cancer (CRC). As a group, human CRC is highly heterogeneous with multiple evolutionary paths, with molecular signatures classifying subtypes and steps from adenoma to carcinoma. Many human CRC genomes are now known and multiple molecular signatures, classifications and biomarker concepts are published . As compTo answer these questions, we utilized next-generation sequencing, bioinformatics and immuno-informatics to create an integrated mouse solid tumor mutanome, transcriptome and immunome, providing an overdue analysis of the CT26 cancer cell line.The CT26 tumor genome: using the NGS reads, we assessed copy number and nucleotide variations by comparing CT26 to BALB/cJ DNA. We determined absolute DNA copy number using the ratio of exome-seq reads mapping to each gene from CT26 versus those from BALB/cJ, and integrating variant allele fraction  exomes. Of these, only 1.6% show a discrepancy between the CT26 and 2011 BALB/cJ genomes. Thus, while this does not eliminate genetic drift or conclusively identify the substrain that gave rise to CT26 cells, it demonstrates that the genome of the mouse that originally created the CT26 cells is similar to that of the current BALB/cJ mouse.Spontaneous human CRC tumors contain primarily C > T/G > A SNVs . Of the Of the 3,023 CT26 SNVs, 296 10%) are homozygous or heterozygous .In summary, the data imply that the CT26 has a complex genome of high ploidy which underwent several amplification events. Relative to a 2011 BALB/c genome, the number of mutations is higher than average, with many non-synonymous mutations. The mutation pattern reflects the treatment with the NMU alkylating agent, a similar but distinct pattern than found in spontaneous primary CRC.CT26 SNVs in onco-relevant genes: we investigated whether mutations associated with CRC ).The mutation-containing epitope and associated MHC allele were selected using the IEDB algorithm v2.5 [Additional file 3: The 363 insertion and deletion mutations. Columns include chromosomal position of the mutation, reference and observed nt(s), location of indel, frameshift mutation, classification non/synonymous, in UTR, gene symbol, UCSC transcript ID (if the mutation can occur in more than one transcript of this gene they are separated by a space), transcript ID and affected exon, mean expression (combined of gene expression and exon expression), possible repeat region, dbSNP ID and dbSNP validation source, allele frequency for CT26 and Balb/c replicates, number of reads per sample supporting this indel. (XLSX 97 KB)Additional file 4: Gene expression values for CT26 and ENCODE normal mouse colon samples in RPKM values and raw read counts.(XLSX 13 MB)Additional file 5:Results of the GSEA gene set Reactome pathway enrichment, including Reactome pathway name, gene membership and FDR q-values.(XLSX 38 KB)Additional file 6:Results of the GenePattern enrichment using ranked ordered expression. Gene sets included those curated from literature and overexpression was determined using GenePattern [Additional file 7: ePattern . Gene meContains the Gene Pattern gene set membership and enrichment values in an html format. The file index.html is the entry point. (ZIP 13 MB)Additional file 8:


Drosophila. The recently sequenced Tsetse fly genome provides a unique opportunity to analyze transcription initiation regulation machinery in blood-feeding insects.Transcription initiation regulation is mediated by sequence-specific interactions between DNA-binding proteins (transcription factors) and cis-elements, where BRE, TATA, INR, DPE and MTE motifs constitute canonical core motifs for basal transcription initiation of genes. Accurate identification of transcription start site (TSS) and their corresponding promoter regions is critical for delineation of these motifs. To this end, the genome scale analysis of core promoter architecture in insects has been confined to D. melanogaster, and G. m. morsitans had propensity for CA and AA dinucleotides respectively. The 1393 tag clusters comprised narrow promoters (5 %), broad with peak promoters (23 %) and broad without peak promoters (72 %). Two-way motif co-occurrence analysis showed that the MTE-DPE pair is over-represented in broad core promoters. The frequently occurring triplet motifs in all promoter classes are the INR-MTE-DPE, TATA-MTE-DPE and TATA-INR-DPE. Promoters without the TATA motif had higher frequency of the MTE and INR motifs than those observed in Drosophila, where the DPE motif occur more frequently in promoters without TATA motif. Gene ontology terms associated with developmental processes were overrepresented in the narrow and broad with peak promoters.A computational method for identification of TSS in newly sequenced Tsetse fly genome was evaluated, using TSS seq tags sampled from two developmental stages namely; larvae and pupae. There were 3134 tag clusters among which 45.4 % (1424) of the tag clusters mapped to first coding exons or their proximal predicted 5′UTR regions and 1.0 % (31) tag clusters mapping to transposons, within a threshold of 100 tags per cluster. These 1393 non transposon-derived core promoters had propensity for AT nucleotides. The −1/+1 and 1/+1 positions in G.m. morsitans uses the MTE to compensate for the lack of a TATA motif. The increasing availability of TSS seq data allows for revision of existing gene annotation datasets with the potential of identifying new transcriptional units.The study has identified different motif combinations associated with broad promoters in a blood-feeding insect. In the case of TATA-less core promoters, The online version of this article (doi:10.1186/s12864-015-1921-6) contains supplementary material, which is available to authorized users. Glossina spp) are the biological vectors for Trypanosomes, the causative agents of Human African Trypanosomiasis (HAT). HAT is a debilitating disease that continues to present a major public health problem and a key factor limiting rural development in vast regions of tropical Africa. To augment the current vector control efforts, the tsetse fly (Glossina morsitans morsitans (G. m. morsitans)) genome was recently sequenced [G. m. morsitans core promoters and their corresponding transcription factor binding sites were analyzed in this study.Tsetse flies  extending either upstream or downstream for ~50 bases –4. The TRecent developments in high-throughput next generation sequencing (NGS) technologies have been employed to facilitate the elucidation of transcriptional control mechanisms through promoter identification and expression profiling. For example, TSS seq , a recenDrosophila melanogaster, narrow promoters mainly harbor the TATA and INR motifs while broad promoters tend to harbor variably located core promoter motifs [High-throughput methods for TSS detection on a genome-wide scale have revealed that metazoan transcription initiates across genomic windows of varying lengths . This var motifs , 15.Drosophila [Computational prediction coupled with experimental validation has helped identify an ever-increasing number of core promoter elements such as motif ten element (MTE) . This asosophila , suggestG. m. morsitans promoters present a valuable tool both for studying vector parasite interactions and development of novel Trypanosomiasis control strategies such as Trypanosome-refractory Tsetse flies through expression of anti-Trypanosome genes. With the completion of the G. m. morsitans genome [G. m. morsitans were used to identify the TSS of corresponding genes. Regions flanking the TSSs were used to assess the core promoter architecture in G. m. morsitans.Genome wide analysis of core promoter architecture is poorly understood, despite advances in deciphered motif profiling of specific genes in genomes of blood feeding insects such as mosquito –23. G. ms genome , this stG. m. morsitans genome consists of 13,807 scaffolds of which 3058 contain at least one gene [G. m. morsitans genome. Overall, 3134 tag clusters were obtained with a cluster defined by at least 100 reads per cluster (Table The current assembly of er Table . About 6Core promoters were defined as 100 nucleotides (−50/+50) TSS surrounding the TSS. By setting the threshold at 100 tags per cluster, 1424 tag clusters were located on the first coding exons and their proximal 5′UTR regions. 197 genes had more than one candidate tag cluster. In such a case, the cluster with more tags was selected for further analysis. Their corresponding core promoters were extracted. Approximately 31 core promoters were entangled with transposons and were therefore excluded from further analysis, effectively reducing core promoter set used for motif assignment to 1393.Sg value of 1 while those with the highest number of TSS (220) had an Sg value of 0.0023. Essentially higher Sg values represent peaked distributions and vice versa. At least 70 % of tag clusters had no peak, as defined by a cut off of at least 50 % of the total reads in the mode position for a tag cluster  of a tag cluster decreased with increasing tag cluster size. Clusters with single TSS had G. m. morsitans and D melanogaster core promoters  TC(C/T) to a single dinucleotide (pyrimidine (C/T)–purine (A/G)) . Notablyers Fig. . The nucEvery motif finding algorithm is ordinarily exposed to spurious matches that may appear as significant as the ones in question  and the G. m. morsitans core promoters. Overall, the INR, MTE and TATA motifs were present in 34 %, 33 % and 30 % of these core promoters respectively. The BREd motif was present in 29 % while the BREu motif was present in 21 % of the core promoters  is significantly over-represented within the broad without peak core promoter category. The BREu–MTE and BREd-DPE pairs are under-represented within the narrow promoter category, and BREu-DPE and BREd-DPE are under-represented within the broad with peak core promoter category indicating possible non-cooperativity in binding. All other motif pairs fell outside the 95 % confidence interval. Nevertheless the higher frequency of co-occurrence for the INR-MTE and MTE-DPE (Broad with peak promoters), and TATA-INR and TATA-DPE pairs (narrow promoters) points to possible cooperativity during transcription activation. The TATA-INR and MTE-DPE cooperation has been observed in mammalian and romoters , 27  to characterize the promoters and delineate the primary TSS location.The species-specific differences associated with nucleotide sequence composition informs the parameters used to develop ab initio gene prediction algorithms. For example, Lenhard and colleagues  demonstrIn this study, approximately 1300 core promoters were extracted from the recently assembled Tsetse genome. Most of tag clusters (70 %) were identified in the 5′UTR and the first coding exon. Carninci and colleagues  used theD. melanogaster [D. melanogaster, AT enrichment peaks at approximately −200 bp from the TSS and microarray analysis showed these regions as nucleosome free mostly for active genes in D. melanogaster [Saccharomyces cerevisae [G. m. morsitans and D. melanogaster. These mechanisms have yet to be elucidated. While profiling ascidian promoters, Okamura and colleagues [The nucleotide frequency and distribution in Tsetse fly core promoters are similar to that of nogaster  where thnogaster  and Saccerevisae . The poserevisae , 18. Thelleagues  postulatlleagues , 32, 33.Drosophila core promoters as well as mammals have suggested that the TATA occurs in approximately 10–30 % of all genes within a genome [G. m. morsitans core promoters harbored a TATA. Apart from the BREu motif, TATA-less promoters record a higher frequency of all other core promoter motifs. A similar observation was made by Gershenzon and colleagues [G. m. morsitans and D. melanogaster genes indicate conservation of core promoter machinery among these insects.To further validate reliability of our TSSs identification method, the presence of canonical core promoter motifs was examined. Variations in motif frequencies in narrow and broad promoters were investigated. Narrow promoters are characterized by only one or a few consecutive TSSs and are associated with genes that are expressed in tissue-specific manner. These promoters are enriched for the TATA motif. Broad promoters contain several TSSs over a large genomic window . In mammals, they are CpG rich and are usually found in constitutively expressed genes . In thia genome , 34, 35.lleagues  where thlleagues . NucleotG. m. morsitans core promoter’s two-way motif co-occurrence revealed that the TATA-INR pair has the highest frequency among narrow core promoters whilst the MTE-DPE pair has the highest frequency for broad core promoters. Since high frequency of co-occurrence may indicate that the motifs exert their functions cooperatively, we postulate that the corresponding TFs for the TATA and INR as well as MTE and DPE exhibit synergistic interactions during transcription initiation for narrow and broad core promoters respectively. Indeed, the TATA-INR and MTE-DPE co-operation has been reported by other studies such as [Most core promoters have at least one core-promoter motif at a functional position working as anchors for the basal transcription initiation machinery. However, the presence of a synergetic combination of two core promoter elements is often considerably stronger than a single element as it dictates the position of TSS. It is extremely rare for all motifs to be present in any given core promoter. Analysis of  such as , 27, 37. such as . Further such as , 39. Int such as . Structu such as . Notably such as . It is hG. m. morsitans genome is riddled with repeat motifs and 31 out of 1424 core promoters in this dataset were located on transposons. It has been recently discovered that retrotransposons and repeat elements are recruited as promoters and there is growing interest in the role of repeat elements in gene regulation. Indeed laboratory investigations have confirmed many specific examples of mammalian genes regulated by promoters donated by endogenous transposable motifs. For example, while using reporter constructs for Ewing cell lines, Guillon and colleagues [S. cerevisae, as many as 25 % of all gene promoters contain tandem repeat sequences and these genes driven by repeat-containing promoters show significantly higher rates of transcriptional divergence where variations in repeat length result in fluctuations in expression and local nucleosome positioning. This observation could be used in follow-up studies towards understanding of the effect of these tandem repeats on transcription control in upcoming Glossina genomes.Approximately one third of the lleagues , repeat-lleagues  has showlleagues , showed G. m. morsitans. We refer to them as intergenic tag clusters. However, we are aware that the term ‘intergenic’ is loosely defined as the genome is yet to be fully assembled. In addition, manual refinement of the predicted gene models is on-going. In insects, non-coding RNAs appear to occur primarily in intergenic and intronic sequences and at intron-exon junctions. In addition they are significantly associated with genes encoding developmental regulators [Glossina spp evolution.Non-coding RNA genes include highly abundant and functionally important RNAs encompassing several groups involved in diverse cellular processes. Out of 3134 clusters fitting our inclusion criteria, 87 were located on gene-less scaffolds, whilst 1014 were located outside the candidate genic regions. These tag clusters constitute approximately one third of the total count. We postulate that these intergenic tag clusters may represent TSSs for several classes of non-coding RNA genes in gulators , 47. ExpIn general, broad promoters have been associated with developmentally regulated genes whereas narrow promoters are associated with tissue specific expression . HoweverDrosophila embryo [Drosophila larval GTPase Miro has been implicated in dysfunction of the axonal mitochondrial transport, leading to abnormal subcellular distribution of mitochondria in neurons and muscles [Drosophila embryonic development [The small GTPase binding activity constituted the bulk of GO annotations in the broad without peak category. GTPases are required for several developmental events such as organization of the actin cytoskeleton and signaling by c-Jun N-terminal kinase and p38 kinase cascades , 50. Thea embryo . Loss of muscles . The GTPelopment .Aedes aegypti larvae and pupae, this protein family has been shown as an important indicator of stress and may function as crucial proteins to protect and improve survival [D.melanogaster, expression of HSP60A is post-transcriptionally regulated in a highly dynamic order, even under heat-shock conditions suggesting novel roles for HSP60 family proteins throughout Drosophila development [The GO term heat shock binding activity is over-represented in the broad without peak promoters. In survival . During elopment .G. m. morsitans TSS using experimental data, the study has provided insight into the promoter architecture of G. m. morsitans. To our knowledge, this is the first study to locate TSSs and core promoters in the newly sequenced G. m. morsitans genome and by extension the first blood feeding vector. Results presented herein have generated testable hypothesis, such as the impact of transposons on transcription regulation. This study provided useful insights using G. m. morsitans genome data that would be employed as a platform to assess the conservation of transcriptional control mechanisms in upcoming Glossina genomes.By locating G. m. morsitans RNA samples from pupae and larvae were prepared using the TRIzol protocol [fastx_clipper. Trimming was performed using the fastx_trimmer with quality filtering by flagging –t for minimum quality threshold and -l for minimum length of read to be retained after trimming. Several rounds of trimming with variations in –l were performed to facilitate optimization of the mapping process. After trimming, the reads were processed with the fastq_quality_filter with –q and –p flags set at 31 and 50 respectively. For the reads that passed the quality filtering step, mapping was done using NOVOALIGN [TSS seq is a method that enables high throughput analysis of the mRNA sequence immediately downstream of the transcriptional start sites. This method replaces the cap structure of mRNA with the synthetic oligo, which contains the sequence adaptor sites, by enzymatic reactions . G. m. mprotocol  at the Yprotocol  experimeprotocol  was usedOVOALIGN . A summa(i)Bona fide 5′UTR clusters = tag clusters mapping onto 5′UTR regions.(ii)Other 5′UTR clusters = tag clusters mapping to genes without a demarcated 5′UTR region.The methodology for assigning tag clusters is similar to that employed by Carninci et al. . The priTag cluster(s) were retained if they mapped within 300 bases (mean 5′ UTR length) upstream from the start of the first coding exon. In addition, genes with an extremely short (1–10 bases) 5′UTRs but with a tag cluster(s) mapped within 300 bases (mean 5′ UTR length) upstream from the start of the 5′UTR were also included. We did not exclude the tag clusters that mapped in the first coding exons because it has been shown that the locations of TSS fluctuate to some extent in most genes , 61, 62.ftp://ftp.sanbi.ac.za/Glossina_TSS).A TSS position was defined as the position with the highest frequency of tag counts for each delineated cluster. A custom script was used to extract and calculate the frequency of read start positions for each cluster. A comprehensive illustration of the TSS and promoter identification pipeline is attached in Additional file s” of a tag cluster “g” by the formula:Core promoters were defined as one hundred bases in the milieu of the TSS that is the −50/+50 positions relative to the TSS (+1). Some broad core promoters exhibit properties of both narrow and broad initiation patterns where they exhibit propensity for one TSS. These are classified as “broad with peak” while those that do not exhibit propensity for one particular TSS are referred to as “broad without peak” . To delim is the tag count at the dominant peak (the mode) n is the total number of reads in the distribution, w is the width of the distribution, that is, the genomic window covered by the tag cluster. Accordingly, each cluster had a discrete individual peakedness score. Notably the maximum individual peakedness score had value of 1, where a cluster has only one TSS position. The higher the individual peakedness score, the more defined the TSS genomic location. An illustration of an implementation of this formula is shown in Additional file Where The absolute count of TSS positions in a given tag cluster was used to classify broad versus narrow promoters. All clusters with 10 or less TSS positions were classified as narrow whilst the remainders were classified as broad. Broad clusters were further categorized based on their peakedness. A broad cluster was deemed as one with a dominant peak if the TSS with the highest frequency of tag counts constituted at least 50 % of the total tag count. If this condition was not met, the broad cluster was classified as “without a peak”  TSS surrounding the TSS. An in house script was used to extract the core promoters. For candidate regions with more than one tag cluster, the cluster with the highest number of TSS seq tags was selected for further analysis. Here, the TSS position with the highest number of TSS seq tags was selected for core promoter extraction. To avoid confounding results during motif search, core promoters entangled with transposons were eliminated.Drosophila promoter datasets utilized in the study by FitzGerald and colleagues [To compare the nucleotide composition between mammalian and insect genomes, human and lleagues  were obtlleagues . Regionslleagues .Glossina morsitans hitherto, core promoter associated PWMs were downloaded from the JASPAR database [e-02 to distinguish true positives from false positives and the program was set to estimate a background model from the input sequence. Only those core promoter motifs occurring at their corresponding biologically functional genomic windows were captured. Because core promoter motif placement often exhibits elasticity [bona fide motifs from remaining sequence. This phenomenon has been described as the motif “twilight zone” where every motif finding algorithm is ordinarily exposed to spurious matches that may appear as significant as the ones in question [bona fide and randomly generated core promoter datasets. Motif co-occurrence suggests combinatorial regulation of transcription via physical interactions between corresponding TFs. To elucidate patterns of motif co-occurrence in various core promoter classes, two-way and three-way motif co-occurrences were evaluated for each core promoter sequence. Further, core promoter sequences with a TATA in a biologically functional window (TATA-containing) were separated from core promoters without a TATA in a biologically functional window (TATA-less). The frequency of motifs in TATA-containing versus TATA-less categories was also evaluated. A summary of the methodology implemented for annotation of core promoter motifs is shown in Additional file In the absence of experimentally verified core promoter motifs for database . MATRIX-database  of the Rdatabase  was usedquestion . By knowquestion  and Jin question . Though Essentially, positive associations between motifs suggest likelihood of physical interactions between the TFs that bind the co-occurring motifs. On the other hand, negative correlations imply that the TFs that bind them have divergent functions. The two-way and three-way motif co-occurrence was computed for each core promoter class producing fifteen and twenty possible combinations respectively. For each core promoter category, the combination with the highest percent co-occurrence was identified. To evaluate for over-representation of the various combinations, a 95 % confidence interval of each combination with respect to the sample size was calculated.The ability of TSS seq to facilitate digital expression profiling was exploited. Since larvae and pupae samples were used to create TSS-seq libraries, it was hypothesized that gene products characteristic of development would be over-represented. To evaluate for overrepresentation of certain biological/molecular processes in the three promoter classes, Gene Ontology (GO)  annotatiNo ethical approval was needed for this study.ftp://ftp.sanbi.ac.za/Glossina_TSS).The scripts used in these analyses are available on the South African National Bioinformatics Institute permanent data archive   experime


Thousands of such regulatory mutations are already recorded in HGMD, OMIM, ClinVar and other databases containing published disease causing and associated mutations. It is therefore important to properly annotate genetic variants occurring in experimentally verified and predicted transcription factor binding sites (TFBS) that could thus influence the factor binding event. Selection of the promoter sequence used is an important factor in the analysis as it directly influences the composition of the sequence available for transcription factor binding analysis.The regulatory effect of inherited or In this study we first establish genomic regions likely to be involved in regulation of gene expression. TRANSFAC uses a method of virtual transcription start sites (vTSS) calculation to define the best supported promoter for a gene. We have performed a comparison of the virtually calculated promoters between the best supported and secondary promoters in hg19 and hg38 reference genomes to test and validate the approach. Next we create and utilize a workflow for systematic analysis of casual disease associated variants in TFBS using Genome Trax and TRANSFAC databases. A total of 841 and 736 experimentally verified TFBSs within best supported promoters were mapped over HGMD and ClinVar mutation sites respectively. Tens of thousands of predicted ChIP-Seq derived TFBSs were mapped over mutations as well. We have further analyzed some of these mutations for potential gain or loss in transcription factor binding.We have confirmed the validity of TRANSFAC’s approach to define the best supported promoters and established a workflow of their use in annotation of regulatory genetic variants. The paradigm that meaningful alterations in DNA sequence have to be in the coding regions of genes and must lead to significant changes in protein structure and functionality  has beenIn this context, experimentally verified transcription factor binding sites (TFBSs), which overlap with variants in non-coding regions are of particular importance. TRANSFAC , the mosTRANSFAC implements Match algorithm  for predIn this study we first establish genomic regions likely to be involved in regulation of gene expression. TRANSFAC uses a method of virtual TSS calculation to define the best supported promoter for a gene. We perform a comparison of the virtual promoters between the best supported and secondary promoters in hg19, as well as in hg38 reference genomes to test and validate the approach. Next we create and utilize a workflow for systematic analysis of casual disease associated variants in TFBS using Genome Trax  and TRANWhen analyzing promoter properties such as the pattern of distribution of transcription factor binding sites and other features we considered three distinct groups of promoters: single promoters, best supported promoters, and secondary promoters. Single promoters are designated as such because their vTSS was the only one identified for the associated gene. As described in the Working with TRANSFAC version 2014.4, which is based upon EnsEMBL  version The vTSS at position 38468271 was identified as the best supported based on the clustering score of 95. The remaining vTSSs defines a secondary promoter.Looking at the graphical display of the best supported promoter Fig.  we can sIn contrast, looking at the sequence that flanks the vTSS of the secondary promoter located at position 38457480, we see a similar level of conservation but no mapped features surrounding or lying immediately upstream of the vTSS Fig. .While these examples provide a detailed view of two individual promoters, for a broader view of promoter distribution across the genome we looked at the number of promoters identified for each human gene. We found that the number of promoters per protein-encoding gene ranged from 1 to 12, with 46 % having a single promoter and 81 % having three promoters or fewer  are excluded from the statistics due to the EnsEMBL ID having changed, mostly due to deprecated IDs or gene clusters.In addition to looking at how the absolute count of promoters changed across the set of genes we also looked at how the promoters themselves changed by comparing the positions of the vTSSs between the hg19 and hg38 reference genomes for all promoters as well as the best supported promoters. From a total of 71,118 promoters identified in the hg19 reference genome 77–82 % remain unchanged, a range that increased to 83–86 % when a shift of 10 or more fewer nucleotides was allowed  was extracted from TRANSFAC and various combinations of TFBS sequences were created with variations as shown in Fig. Second example is multiple mutations within HNF-4 binding site in the promoter of F7 gene , thus the used ChIP-seq data do not represent all diversity of in vivo gene regulation. For the DNase hypersensitivity sites the data are derived from a larger set of different cell lines and tissues, but may still not cover all of them. The experimental environments for the individual sites are usually more varied, however still may have a bias, as certain cell lines are used more frequently in the laboratory practice than others.We understand that our approach for defining and validating the best supported promoters does not take tissue-specific use of alternative promoters into account. Thus, for individual genes the promoter actually used in a particular tissue may deviate from the “best supported” promoter. However our data indicate that in general the “best supported” promoters are supported by different lines of evidence and that they allow to increase efficiency of NGS data filtering as well as significance of results of comparative promoter studies based on gene-specific microarray experiments . In cases where transcript-specific information is available, as RNA-seq, we propose to use the TSSs of the actual transcripts as reference points for analysis.Prediction algorithms, such as Match analysis, of disease causing and disease associated mutations could be introduced in routine of NGS annotation process, particularly if the detected variants are located in best supported promoters and within a range of vTSS that contains majority of experimentally verified TFBS. Such approach could compensate for limited experimental findings suitable for direct annotation of regulatory effects, and complement the array of prediction tools used for estimation of effect of mutations on protein functionality.Genomic sequence assemblies created by the international sequencing consortia are extracted from the EnsEMBL database. Promoter sequences are extracted through the process of virtual transcription start site calculation for all EnsEMBL genes of type protein- or miRNA-encoding. Genes on mitochondria are excluded, due to their special modes of transcription.The calculation of ‘virtual TSSs’ as reference points for promoter extraction is based on a collection of TSSs for a given gene. TSSs are taken as the first nucleotide of the most 5' exon of an EnsEMBL mRNA model. As multiple mRNA models may exist for a given gene, and those models may have very different start sites, collected TSSs for a given gene are often widespread throughout the sequence instead of located in tight clusters of only a few dozen nucleotides in length.In order to define a reasonable number of vTSSs for a given gene from this data collection, an algorithm was designed which applies a set of rules to the data collection in order to find ‘clusters’ of TSSs. A window of 3000 nucleotides in length is slid along the sequence fragment defined by the set of TSSs for a given gene. A ‘clustering score’ is calculated by summing up weighted contributions from each TSS in the window. Each TSS derived from an EnsEMBL mRNA model starts with a score of 5. The scores are then weighted by multiplying by a distance score: the central position is multiplied by 1, the outer positions are multiplied by 0, and all positions in between by a value taken from a cosine function, according to the distance from the center of the window. The peaks of the resulting clustering score are regarded as potential vTSSs.The set of potential vTSSs for a given gene is analyzed further to determine which will be used for promoter selection and extraction. For genes meeting the minimum cut-off for cumulative vTSS score, all vTSSs with a score that represents 8 % or more of the total are selected to define a promoter. The vTSS with the greatest percentage score is selected and defines what TRANSFAC describes as the best supported promoter. All other vTSSs, if present, are selected and define what TRANSFAC describes as secondary promoters.Promoter sequences are determined using the genomic coordinate of the vTSS as the starting point. The bounding genomic coordinates that lie 10,000 nucleotides upstream of the vTSS and 1,000 nucleotides downstream of the vTSS are calculated and used to extract the intervening sequence. The calculation of vTSSs and the subsequent data extraction are fully automated processes.For some genes only a handful of evidence points are available, thus resulting in multiple virtual TSSs, each consisting of only a few evidence points. For all genes with a sum of vTSS scores less than the minimum cut-off, the most 5' vTSS is selected as the sole vTSS for the gene. For cases where there are two equally prominent peaks, the most 5' of the two vTSSs is selected to define the best supported promoter and all others are selected to define secondary promoters.We have analyzed disease causing and disease associated mutations overlapping with experimentally verified TFBS located within intervals of −500 to +100 bp relative to vTSSs of 19,398 best supported promoters of human protein coding genes using Genome Trax annotation database. A total of 841 HGMD and 736 ClinVar mutations occurring in TFBS have been detected. Using Match analysis we have then evaluated the impact of some of these mutations on gain or loss of transcription factor binding affinity. For the analysis we have used TRANSFAC Professional 2014.3 data and non-redundant set of 148 high quality PMWs from vertebrates (provided with TRANSFAC) with the minSUM matrix cut-off.Not applicable.Not applicable.http://www.biobase-international.com/.The datasets used in the analyses are parts of Transfac Professional and Genome Trax databases available at


Signal Transducer and Activator of Transcription (STAT) proteins are a family of cytoplasmic transcription factors consisting of 7 members, STAT1 to STAT6, including STAT5a and STAT5b. STAT proteins are thought to be ideal targets for anti-cancer therapy since cancer cells are more dependent on the STAT activity than their normal counterparts. Inhibitors targeting STAT3 and STAT5 have been developed. These included peptidomimetics, small molecule inhibitors and oligonucleotides. This review summarized advances in preclinical and clinical development of these compounds. Signal Transducer and Activator of Transcription (STAT) proteins are a family of cytoplasmic transcription factors consisting of 7 members, STAT1 to STAT6, STAT5a and STAT5b ,3. SpeciEach STAT protein is encoded by a separate gene. However they share structural similarities with six conserved domains. These conserved domains from N to C terminus include oligomerization domain, coiled coil, DNA binding domain, linker domain, SH2 domain, and transactivation domain. Alternate mRNA splicing or proteolytic processes can give rise to multiple isoforms lacking part of the c-terminal domain and referred to as STATβ isoforms as opposed to regular full length STATα. These truncated β isoforms put forth dominant negative effect and compete with regular α isoforms. β isoforms are isolated in case of STAT1, 3 and 5 .RNA interference (RNAi) is a natural post-transcriptional gene-silencing mechanism to turn off unwanted genes. The process is initiated by the presence of double stranded RNA, not a constituent of a normal cell cytoplasm. The dsRNAs are cleaved by an endonuclease named dicer into 20–25 nucleotide dsRNA referred to as Short or Small Interfering RNAs (siRNAs). RNA-induced silencing complex separates the two strands, and one of these strands then serves as a guide for sequence-specific degradation of homologous mRNA. This mechanism was initially utilized to study gene function by silencing it. However, it can potentially be used clinically to knockout genes causing disease of interest.Utility of this approach is limited because transfected RNAs have very short lives. This therefore requires frequent administration of siRNA in to the cells. Using DNA directed RNA interference technique, a short hairpin RNA  is expressed in the cell after insertion of a DNA construct in to the nucleus. These shRNAs then enter RNAi pathway. In this strategy gene silencing lasts for as long as the cell continues to produce its own shRNA .This strategy is under evaluation in several clinical trials for the treatment of several diseases including cancers . However, data regarding siRNA targeted silencing of STAT genes for cancer therapy are limited to in vitro studies and in vivo studies of animal models only -81.G-rich (Guanine rich) oligonucleotides can form inter- or intra-molecular four-stranded structures, called as G-quartets. G-quartets arise from the association of four G-bases into a cyclic Hoogsteen H-bonding arrangement. They have been shown to play vital role in several cellular processes. G-quartet–forming oligonucleotides can block STAT3 activity by targeting STAT3 proteins and inhibit their ability to bind DNA . Jing anJAK/STAT pathways are major signaling channels for transmitting extracellular signals into the nuclei of cells. Monoclonal antibodies have been developed to target the signaling molecules along the related pathways -87. In aThe authors have no relevant competing interests.DL and MF designed the study. All authors have contributed to data preparation, drafting and revising the manuscripts. All authors have read and approved the final manuscript.


Steroidogenic factor 1 (SF-1) is a transcription factor expressed in the ventral medial nucleus of the hypothalamus that regulates energy homeostasis. However, the molecular mechanisms of SF-1 in the control of energy balance are largely unknown. Here, we show that nutritional conditions, such as the presence or absence of serum, affect SF-1 action. Serum starvation significantly decreased hypothalamic SF-1 levels by promoting ubiquitin-dependent degradation, and sumoylation was required for this process. SF-1 transcriptional activity was also differentially regulated by nutritional status. Under normal conditions, the transcriptional activity of hypothalamic SF-1 was activated by SUMO, but this was attenuated during starvation. Taken together, these results indicate that sumoylation and ubiquitination play crucial roles in the regulation of SF-1 function and that these effects are dependent on nutritional conditions, further supporting the importance of SF-1 in the control of energy homeostasis. Steroidogenic factor 1 (SF-1), also known as NR5A1, is a nuclear receptor (NR) that plays an important role in the development and regulation of steroidogenesis in adrenal glands and gonads2313568et al. recently showed a role for SF-1 in the regulation of glycolytic genes, supporting the notion that SF-1 may play roles not only for body weight homeostasis but also for glucose homeostasisSF-1 is a transcription factor that controls the expression of numerous downstream target genes, including several genes known to be important in body weight homeostasis, such as cannabinoid receptor 1 (CB1), brain-derived neurotrophic factor (BDNF), and corticotrophin releasing hormone receptor 2 (Crhr2)11121314The transcriptional activity of SF-1 itself is regulated by a number of transcription factors and co-regulators and by post-translational modifications1718201919Many NR transcription factors are transcriptionally regulated by sumoylation2627In the present study, we investigated the role of SF-1 post-translational modifications, including sumoylation and uibiquitination, in the regulation of energy homeostasis. Our results demonstrate that serum starvation leads to a sumoylation-mediated, ubiquitin-dependent degradation of SF-1 and that sumoylation regulates the transcriptional activity of SF-1.in vitro results, SF-1 expression was dramatically decreased in organotypic slices deprived of serum  to bind to the same target lysine residues; such that ubiquitination promotes proteasome-dependent proteolysis, while sumoylation stabilizes target proteins363738404119We next investigated the role of sumoylation and ubiquitination in the regulation of transcriptional activity of SF-1. To address this question, we employed luciferase constructs containing the SF-1 response element of the CB1 or BDNF promoters, which have previously been identified as downstream targets of SF-1 in the VMH1112To examine whether the changes in nutrient-dependent transcriptional activity of SF-1 correlated with the ability of SF-1 to bind to the CB1 promoter, we performed chromatin immunoprecipitation (ChIP) assays. After transfection of Flag-tagged SF-1 with GFP-SUMO and/or HA-ubiquitin into Neuro2A cells, we performed immunoprecipitation using an anti-Flag antibody and the DNA was analyzed by PCR using primers specific for the CB1 promoter region, including SF-1 binding sites. SF-1 binding at the CB1 promoter was severely attenuated by ubiquitin alone or together with SUMO . MoreoveMany peripheral hormone levels are directly regulated in response to nutritional status. For instance, ingestion of food promotes the secretion of insulin, while fasting induces ghrelin expression. These hormones can act directly on the brain to regulate food intake and body weight homeostasis439454647To investigate the effect of insulin-PI3K signaling on the regulation of transcriptional activity of SF-1, we fasted cells with or without insulin and measured the transcriptional activation of CB1. Transcriptional activation of CB1 in serum starvation conditions was repressed in a similar fashion by co-expression of SUMO or ubiquitin . ImportaRecent work has highlighted the importance of hypothalamic SF-1 in energy homeostasis, particularly in the control of diet-induced thermogenesis through leptin and insulin signaling pathways. Mice with a VMH-specific SF-1 knockout show high-fat diet-induced obesity due to impaired thermogenesis and reduced energy expenditure. On the other hand, mice with a VMH-specific deletion of the upstream transcription factor FoxO1 overexpress SF-1 and remain lean when fed a high-fat diet due to increased energy expenditure. FoxO1 directly regulates SF-1 transcription by binding to the SF-1 promoter region5Sumoylation of SF-1 is important for the development of endocrine tissues such as testes and adrenal glands and for its function as a transcription factor192223Sumoylation and ubiquitination are both involved in the regulation of downstream target gene expression515342Our data suggests that activation of insulin signaling plays a protective role for starvation-induced SF-1 degradation . It has 4649Environmental stressors such as heat shock, oxidative and osmotic stress can affect the SUMO modification of some proteins57In conclusion, our findings demonstrate a nutrient-dependent dual role of sumoylation for energy homeostasis and provide new insights of SF-1 regulation by post-translational modifications in the brain. Our data suggest that sumoylation and ubiquitination of SF-1 are crucial for the control of energy balance via the regulation of both protein stability and transcriptional activity of SF-1 under different nutritional conditions, supporting the importance of a diet-induced regulation of SF-1.2. For transient transfection, HEK293 cells were seeded in 12-well plates the day before transfection and transfected with specific DNAs using Lipofectamine 2000 reagents (Invitrogen). One day after transfection, cells were incubated in serum-free medium and/or treated with drugs such as insulin , wortmannin (1 μM) and LY294002  for 6 hours and then cells were lysed and subjected to Western blot or immunoprecipitation.HEK293 and Neuro2A cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and 100 U/ml penicillin-streptomycin (Invitrogen) at 37 °C under 5% COHEK293 cells were plated in 6-well plates and then incubated with Flag-tagged SF-1 and HA-tagged ubiquitin. One day after transfection, cells were treated with MG132 at 25 μM for 6 hours in the presence or absence of serum. Cells were harvested in RIPA buffer (50 mM Tris-HCl (pH 8.0), 150 mM NaCl, 1% Triton X-100, 1 mM EDTA, 1 mM EGTA, 0.5% sodium deoxycholate, 1× protease inhibitor cocktail and 1× phosphatease inhibitor cocktail (Roche)) and centrifuged at maximum speed for 20 min at 4 °C. Equal amounts of total protein were incubated with 2 μl anti-SF-1 specific antiserumFor Western blots, proteins were resolved on 4–12% gradient SDS-PAGE gels (Invitrogen) and transferred to Nitrocellulose membranes (Bio-Rad). Target proteins were detected using anti-SF-1The hypothalamic slices were made as previously describedTo prepare BDNF reporter plasmid, a Bdnf IV fragment containing the SF-1 response element was generated by PCR using the previously described construct as a templateChromatin immunoprecipitation (ChIP) assays were performed according to the manufacturer’s instructions (EDM Millipore). SF-1 (WT or K194R mutant), SUMO1, or ubiquitin were expressed in Neuro2A mouse neuroblastoma cells, and the cells were incubated in the culture medium with or without serum for 6 hours. Cells were cross-linked with 1% formaldehyde for 10 minutes and each cross-linked sample was immunoprecipitated using 2 μg of anti-Flag (Sigma) antibody, and the DNA was purified by phenol-chloroform extraction and ethanol precipitation. PCR primers for the CB1 promoter spanned the proximal region of the SF-1 binding site of the CB1 promoter, 5′-CAGATCCCTTGGCGGAGT-3′ and 5′-CTTCGTTCTCCGGCTCTC-3′.P < 0.05 was regarded as a statistical significance difference.The data are presented as means ± SEM. Comparison between multiple groups were determined for statistical significance by one-way ANOVA and Student’s t-test, and all data were tested for normal distribution. How to cite this article: Lee, J. et al. Nutritional conditions regulate transcriptional activity of SF-1 by controlling sumoylation and ubiquitination. Sci. Rep.6, 19143; doi: 10.1038/srep19143 (2016).


Dysregulation of the common stress responsive transcription factor ATF3 has been causally linked to many important human diseases such as cancer, atherosclerosis, infections, and hypospadias. Although it is believed that the ATF3 transcription activity is central to its cellular functions, how ATF3 regulates gene expression remains largely unknown. Here, we employed ATF3 wild-type and knockout isogenic cell lines to carry out the first comprehensive analysis of global ATF3-binding profiles in the human genome under basal and stressed (DNA damage) conditions.Although expressed at a low basal level, ATF3 was found to bind a large number of genomic sites that are often associated with genes involved in cellular stress responses. Interestingly, ATF3 appears to bind a large portion of genomic sites distal to transcription start sites and enriched with p300 and H3K27ac. Global gene expression profiling analysis indicates that genes proximal to these genomic sites were often regulated by ATF3. While DNA damage elicited by camptothecin dramatically altered the ATF3 binding profile, most of the genes regulated by ATF3 upon DNA damage were pre-bound by ATF3 before the stress. Moreover, we demonstrated that ATF3 was co-localized with the major stress responder p53 at genomic sites, thereby collaborating with p53 to regulate p53 target gene expression upon DNA damage.These results suggest that ATF3 likely bookmarks genomic sites and interacts with other transcription regulators to control gene expression.The online version of this article (doi:10.1186/s12864-016-2664-8) contains supplementary material, which is available to authorized users. These pBAX Fig. . ConsistBAX Fig. , includiFL3 Fig. . Using rBC3 Fig. . Of thesBC3 Fig. . MoreoveBC3 Fig. . Of noteell Fig. , suggestion Fig. . Therefocis-acting element localized in gene promoters and regulate expression of genes associated with human diseases [e.g., p53) that generally occupy a few thousands of genomic sites  [It is often shown that ATF3 binds the ATF/CRE diseases –14. We cdiseases . As 81.7diseases , ATF3 midiseases . While tdiseases . It is wdiseases , but sigtes Fig. . It is ttes Fig. , we fountes Fig. . Thus, ltes Fig. . In addi., JunB) , the dec., JunB) –45, it me.g., microarray and RNA-seq) may not serve as accurate measurements of transcription activity [e.g., JunB) [Like other TFs , 42, bin., JunB)  might coAn important finding from this study is that ATF3 bound to 37 % of genomic sites that were bound by p300 and characterized by high levels of H3K27ac under the basal condition Fig. . These gi.e., co-localization with p53 at genomic sites. Indeed, we found that ATF3 was co-localized with p53 at more than 20 % of p53-binding sites identified by ChIP-seq  were cultured in McCoy’s 5A medium supplemented with 10 % fetal bovine serum. H1299 cells and 293 T cells were cultured in RPMI 1640 and DMEM medium, respectively. We knocked out ATF3 expression in HCT116 cells using a rAAV-based approach . BrieflyAcceptor  via restAcceptor , and use7) treated with or without 1.5 μM of CPT for 4 h were cross-linked with 1 mM of di(N-succinimidyl) glutarate (DSG) for 45 min, followed by 1 % formaldehyde for 10 min at room temperature. After treating with 0.125 M of glycine for 5 min, cells were resuspended in 10 ml of Solution I , and incubated at 4 °C for 10 min. Cells were further incubated with 10 ml of Solution II  at 4 °C for 10 min before lysed in cold FA lysis buffer . Chromatin was sheared by sonication using Bioruptor to an average fragment size of 500 bp, and then incubated with 2 μg of the antibody  or normal IgG  purchased from Santa Cruz, at 4 °C overnight. Immunocomplexes were precipitated with 30 μl of ssDNA-protein A/G agarose (Millipore) at 4 °C for 2 h, and sequentially washed with Buffer I , Buffer II (buffer I with 500 mM NaCl), Buffer III , and TE buffer . Bound chromatin was eluted with 0.3 ml of Elution buffer . After reversal of crosslinking, RNase A and Proteinase K was added, and DNA was purified by phenol extraction and ethanol precipitation. For re-ChIP assays, chromatin immunoprecipitated with the ATF3 antibody was eluted in 0.15 ml of Elution buffer, and then diluted by 20 times with re-ChIP buffer , followed by incubation with the p53 antibody as described above.Chormatin immunoprecipitation was performed essentially as described previously . BrieflyChIP-seq libraries were prepared according to standard protocols using Biosicentific’s DNA Sample Kit (cat#514101) . LibrariTotal RNA was prepared using Agilent Total RNA Isolation Mini Kit (cat# 5185–6000). Microarray expression profiling was performed using HumanHT-12 v 4.0 Expression BeadChip (Illumina). Data were preprocessed and normalized by GenomeStudio. Differentially expressed genes were identified by Bioconductor limma package and GenePattern. Heatmap view of differentially expressed genes was created by Cluster and Java Treeview. GO term enrichment was determined using DAVID.3VO4, and protease inhibitor cocktail (Roche), and then resolved in SDS-polyacrylamide electrophoresis for immunoblotting. Quantitative PCR assays were carried out using SYBR Green as described elsewhere [Western blotting assays were performed as described previously . In brielsewhere .http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token=yfozycagbfojfkz&acc=GSE74363).The data sets supporting the results of this article are available in the GEO with the accession number GSE74363 (