Notably, SOX2 bound the same consensus DNA motif in both cell types, suggesting that additional factors contribute to target specificity. We found that, similar to its association with OCT4 (Pou5f1) in ESCs, the related POU family member BRN2 (Pou3f2) co-occupied a large set of putative distal enhancers with SOX2 in NPCs. Forced expression of BRN2 in ESCs led to functional recruitment of SOX2 to a subset of NPC-specific targets and to precocious differentiation toward a neural-like state. Further analysis of the bound sequences revealed differences in the distances of SOX and POU peaks in the two cell types and identified motifs for additional transcription factors. Together, these data suggest that SOX2 controls a larger network of genes than previously anticipated through binding of distal enhancers and that transitions in POU partner factors may control tissue-specific transcriptional programs. Our findings have important implications for understanding lineage specification and somatic cell reprogramming, where SOX2, OCT4, and BRN2 have been shown to be key factors.SOX2 is a master regulator of both pluripotent embryonic stem cells (ESCs) and multipotent neural progenitor cells (NPCs); however, we currently lack a detailed understanding of how SOX2 controls these distinct stem cell populations. Here we show by genome-wide analysis that, while SOX2 bound to a distinct set of gene promoters in ESCs and NPCs, the majority of regions coincided with unique distal enhancer elements, important  In mammals, a few thousand transcription factors regulate the differential expression of more than 20,000 genes to specify ∼200 functionally distinct cell types during development. How this is accomplished has been a major focus of biology. Transcription factors bind non-coding DNA regulatory elements, including proximal promoters and distal enhancers, to control gene expression. Emerging evidence indicates that transcription factor binding at distal enhancers plays an important role in the establishment of tissue-specific gene expression programs during development. Further, combinatorial binding among groups of transcription factors can further increase the diversity and specificity of regulatory modules. Here, we report the genome-wide binding profile of the HMG-box containing transcription factor SOX2 in mouse embryonic stem cells (ESCs) and neural progenitor cells (NPCs), and we show that SOX2 occupied a distinct set of binding sites with POU homeodomain family members, OCT4 in ESCs and BRN2 in NPCs. Thus, transitions in SOX2-POU partners may control tissue-specific gene networks. Ultimately, a global analysis detailing the combinatorial binding of transcription factors across all tissues is critical to understand cell fate specification in the context of the complex mammalian genome. Thus, transcription factors often direct a hierarchy of events controlling cellular identity Pou5f1) and NANOG, SOX2 binds to the proximal promoters of large cohort of genes with known roles in pluripotency  as well as those that function later in development In ESCs, SOX2 is a component of the core transcriptional regulatory circuitry that controls pluripotency. Together with OCT4 , Oct4 promoter in ESCs whereas binding at an intronic enhancer (Nes30) in the Nestin gene was observed in NPCs Analysis of genome-wide binding profiles indicates that SOX2 occupies the promoters of thousands of genes SOX family members weakly bind DNA and cannot robustly activate transcription alone, suggesting roles for additional partner factors in target selection Pou5f1) in ESCs, we identified the Class III POU transcription factor BRN2 (Pou3f2) as a candidate SOX2 partner factor that co-bound a large fraction of distal enhancers with SOX2 in NPCs. Consistent with a functional role, forced expression of BRN2 in differentiating ESCs led to recruitment of SOX2 to a subset of NPC distal enhancers. This recruitment was associated with changes in chromatin structure, activation of neighboring genes, and ultimately precocious differentiation toward a neural-like state. Further analysis of bound sequences showed differences in the arrangement of a SOX-POU binding in ESCs and NPCs and revealed enrichment for additional transcription factor motifs. Together, these data reveal new insights into how SOX2 can function in a context-dependent manner to specify distinct stem cell states. Our work also has important implications for understanding development as well as the process of somatic cell reprogramming.Here, we defined the genome-wide binding patterns of SOX2 in ESCs and NPCs and show that SOX2 occupied a largely distinct set of genomic regions within promoters and distal enhancer elements in the two cell types. Similar to its cooperation with OCT4  promoter was bound only in NPCs, and a site within the Hdac9 promoter was occupied in both cell types  mapped greater than 1 kb from annotated TSSs . Distal The pattern of H3K4me1 and H3K27Ac occupancy can distinguish a given enhancer as active  or poised , states which correlate with high expression of a neighboring gene or the potential of that gene to be expressed later during development, respectively Jag1, Neurog3, and Nkx2-2, whereas those associated with poised enhancers in NPCs included genes with roles in terminal differentiation into neurons and glia such as Atoh1, Lhx8, Id2 and Id4  analysis to determine the function of genes associated with SOX2-bound enhancers. SOX2-bound poised enhancers in ESCs were nearest genes that function in commitment to the neural lineage and morphogenesis and included  and Id4  and S6.  and Id4 . For exaDespite the low overlap of SOX2-bound enhancer regions in ESCs and NPCs, genes linked to SOX2-bound poised enhancers in ESCs had functions in neural development, similar to genes linked to SOX2-bound enhancers in NPCs. Thus, we hypothesized that SOX2 might be regulating a subset of targets in both cell types by occupying distinct enhancer elements. Indeed, direct comparison of these genes revealed that ∼50% of genes  associated with SOX2-bound poised enhancers in ESCs also had a bound enhancer associated with that gene in NPCs , despite5′-CTTTGTT-3′SOX2 binds DNA weakly and it is insufficient to strongly activate transcription without cooperation with additional factors Pou3f2)  . BRN2 wa in ESCs . Moreove5′-ATGCATAT -3′) Although neurogenesis and maintenance of cell identity in the brain require BRN2, its target genes in NPCs were not known. To address this, we performed ChIP-Seq and identified 6,574 BRN2 occupied regions in NPCs . SimilarFbxo15 and to two putative enhancers of Pax6 that have been previously identified based on evolutionary sequence conservation and histone modification patterns Sox2 and NestinOlig1Ascl1 (Mash1) locus We next examined the overlap between SOX2 and the two POU factors  and assayed the potential of these cells to differentiate toward the neural lineage  and transcription factors such as Id2 and Id4 Lrrn1, Abpa2, Kirrel3, Cops2, Id4, and Lemd1, revealed that some were significantly induced in TetO-Brn2 cells compared to controls . This included 125 regions (∼8%) that were unmarked (i.e. lacked H3K4me1 or H3K27Ac) in control cells  motif, consisting of a 5′-SOX motif followed by a 3′ OCT site  in NPCs . Thus, SNF-Ia, NF-Ib, and NF-Ix were expressed at significantly higher level in NPCs than ESCs Ntrk3 (TrkC)Cdk5r1 (p35)We identified 439 SOX2-BRN2-NF-I-motif and 251 SOX2-BRN2-RFX-motif regions in NPCs see . Furtherin genes . In contThe HMG-box transcription factor SOX2 has critical roles in the function of multiple stem cell types including pluripotent embryonic stem cells (ESCs) and multipotent neural progenitor cells (NPCs). How this master regulator can control diverse transcriptional programs has remained an important and unresolved question in the field. While SOX2 occupied many promoters in both cell types, the major class of genomic elements occupied by SOX2 in ESCs and NPCs were distal enhancers . While oWe derived NPCs directly from genetically identical ESCs allowing us to directly analyze SOX2 binding as these cells transition between states. Several criteria support the high quality of our data. First, we identified many known SOX2 binding sites including promoters and enhancers in both ESCs and NPCs. Second, while many binding sites were distinct, we identified a canonical SOX2 motif as highly enriched in both cell types. Third, SOX2 overlapped significantly with POU partner factors in ESCs and NPCs consistent with the expectation that these transcription factor families function together to regulate developmental progression. In addition, we identified a SOX-OCT composite motif as enriched in these co-bound sites.Sox2 and Brn2 in neurogenic regions of the brain, and the neurodevelopmental defects associated with Brn2 loss-of-function suggested that SOX2 and BRN2 together regulate a subset of genes important for neural fate. Consistent with this, we defined a large group of enhancer elements co-bound by SOX2 and BRN2 in NPCs. We identified known functional enhancers bound by SOX2 and BRN2 in NPCs, such as the Nes30 enhancer of the Nestin locus Sox2 locus, SRR2SOX2 occupied largely exclusive sites in ESCs and NPCs, despite using the same DNA motif to recognize these genomic targets. Moreover, SOX2 occupied distinct regions in the same promoter and distinct enhancers associated with the same gene. These data indicated that additional factors dictated SOX2 binding site specificity. While SOX2 co-occupied many binding sites with OCT4 in ESCs, partner factors in NPCs have not been well defined. We found the recognition motif for the Class III POU factor BRN2 was enriched in SOX2 bound regions in NPCs. The evolutionary conservation of the SOX-POU interaction, the co-expression of Brn2 led to up-regulation of neural markers and to differentiation toward the neural lineage. Our work is in agreement with several studies that have implicated Brn2 as an early marker of neural commitment Sox2, Brn2, and the forkhead factor Foxg1 can transdifferentiate fibroblasts to NPC-like cells Consistent with a positive role in regulating neural cell state, forced expression of While the motifs occupied by these factors were highly similar, the arrangement of SOX and OCT motifs in SOX2-POU target sites displayed differences in ESCs and NPCs. Regulation of SOX-POU target genes appears to depend not only on the presence of a SOX and an OCT motif in close proximity to each other, but also on other DNA sequence determinants, including the spacing and orientation of these motifs with respect to each other in vivo and their loss in development leads to defects in central nervous system formation and specifically NPC dysfunction Sox2 in the brain were also shown to be partially mediated by aberrant SHH signaling Combinatorial interactions among transcription factors allow cells to respond to environmental and developmental cues in a tissue-specific manner. A classical example involves the regulation of interferon-β expression through cooperative binding of transcription factors and chromatin proteins to an enhancer, collectively known as the interferon-β enhancesome Brn5, like Sox11, is expressed in differentiated cell types in the CNS and thought to play a role in regulating cell state Recent data showed that SOX2 functions as a pioneer factor in ESCs by marking a subset of genes for activation by other SOX family members, namely SOX4 in the B-cell lineage, SOX3 in NPCs and SOX11 in immature neurons ChIP-Seq and Affymetrix microarray data are deposited on GEO database under the accession numbers GSE38850 and GSE35496.in vitro differentiation from V6.5 ESCs as described C57/BL6-129JAE (V6.5) mouse embryonic stem cells were cultured in as described 8 cells were cross-linked and chromatin fractions were sheared by sonication. ChIP-enriched and input DNA were purified and genomic libraries were prepared using the ChIP-Seq Sample Prep Kit (Illumina 1003473) according to the manufacturers protocol (Illumina 11257047) for selecting library fragments between 200 and 350 bp. Samples were run using the GA2X genome sequencer .ChIP in NPCs was performed as described previously 7 formaldhyde-crosslinked cells were lysed and as above on an IP-Star (Diagenode). Chromatin was sonicated on the Bioruptor (Diagenode) to an average size of 0.2–1 kb. ChIP was performed on chromatin from approximately 5 million cells with 3 µg of antibody (above) using the IP-Star Automated System (Diagenode) and 2.5% of chromatin was used for each whole cell extract (WCE). Following reversal of crosslinks, sample and WCE DNA was purified. ChIP and WCE DNA was dissolved in water and barcoded genomic libraries were prepared using the TruSeq DNA Sample Prep Kit (Illumina) and multiplexed on the HiSeq 2000 (Illumina).For ChIP in ESCs, and in TetO-Brn2 cells and control cells were cross-linked and harvested as above. Approximately 5×10Antibodies used in ChIP experiments are as follows: SOX2 ; BRN2 ; H3  H3K4me1 ; H3K27Ac .http://bowtie-bio.sourceforge.net/index.shtml) using murine genome NCBI Build 36 (UCSC mm8) as the reference genome with default settings for mismatch tolerance and non-unique mapping events. Mapped reads were analyzed as described −8 to minimize false positives. We then used an empirical background model (whole cell extracts (WCE) for transcription factors or pan-histone histone H3 ChIP-Seq (H3) for chromatin marks) that requires bins to be enriched relative to background to eliminate non-random enrichment. Replicate datasets were combined and analyzed in one batch. Previously published datasets for enhancer associated histone marks were analyzed as described Images acquired from the Illumina/Solexa sequencer were processed using the bundled Solexa image extraction pipeline. Sequences were aligned using Bowtie (http://genome.ucsc.edu/cgi-bin/hgGateway).. wig files were generated from ChIP-Seq reads and density was normalized to reads-per-million. Published datasets were used to correlate SOX2 bound regions to histone modification patterns for enhancer analysis ChIP–seq plots for individual genes were generated using the UCSC Genome Browser  according to the manufacturer's protocol and DNAse treated using the DNA-Free RNA kit (Zymo Research R1028). Samples were then prepared for Affymetrix GeneChip Expression Array analysis. 5 µg total RNA was used to prepare biotinylated cRNA according to the manufacturer's protocol (Affymetrix One Cycle cDNA Synthesis Kit). Samples were prepared for hybridization, hybridized to arrays, and washed according the Affymetrix hybridization manual using the Affymetrix GeneChip Hybridization, Wash and Stain Kit. GeneChip arrays (Mouse 430) were hybridized in a GeneChip Hybridization Oven at 45°C for 16 hours at 60 RPM. Arrays were scanned on a GeneChip Scanner 3000 and images were extracted and analyzed using GeneChip Operating Software v1.4.th and 75th percentiles, respectively. Whiskers depict+1.5*IQR (interquartile range) for top, −1.5*IQR for the bottom. To define differentially expressed genes, array data was RMA normalized using updated annotation from the BrainCDF the site and remapped from Ensembl Gene ID to Gene Name using Biomart table. For finding differentially expressed (DE) genes, the biological replicates were subjected to moderated welch test (MWT). Genes were called differentially expressed if the MWT FDR<0.05 and the fold change of the mean of the replicates was more than 1.5 fold up or down.To define expression levels of genes linked to bound promoters and enhancers, and to define fold change of expression levels of transcription factors linked to enriched TRANSFAC motifs between ESCs and NPCs, biological replicates were analyzed using the Affymetrix GCOS program and the mean intensity for each probe across three arrays was calculated. Maximum probe mean values for each gene were taken as gene expression levels. Box and Violin plots were constructed depicting median values as the center line, and bottom and top of the box representing the 25http://gostat.wehi.edu.au/cgi-bin/goStat.pl) for genes linked to SOX2 bound promoters or the GREAT algorithm http://great.stanford.edu/) for regions associated with SOX2 bound enhancers. GOSTAT was performed using the mgi (mouse) GO annotation database for promoter-associated regions. Since GREAT analysis requires inputs in the mm9 genome build, lift-over of mm8 called regions was performed using the Galaxy web tool prior to input into GREAT (main.g2.bx.psu.edu/). In general, terminal “GO Biological Process” terms were presented in figures to maximize the specificity of the information presented. In some cases terminal terms contained few genes and were thus misleading, so more informative parent terms encompassing less specific but more relevant descriptions of biological processes are presented.Gene ontology analysis was performed using GOSTAT (MEME (meme.sdsc.edu/) Brn2 inducible ESCs were generated using the “flp-in” system described previously Brn2 cDNA were flipped into the Col1a1 locus of KH2 ESCs harboring an M2-rtTA gene in the Rosa26 locus.Brn2 and control (KH2) ESCs were passaged off feeders and cultured in ESC medium with 2 µg/ml Dox. Twenty-four hours after passage, cells were culture in N2B27 (without Vitamin A) media without LIF or serum for the duration of the experiment Inducible Trizol-isolated RNA from three biologically independent samples was purified, DNAse treated  and reverse transcribed using a First Strand Synthesis Kit (Invtirogen). cDNA was analyzed in triplicate for each biological sample by quantitative PCR using an ABI Prism 7000 (Applied Biosystems) with Platinum SYBR green qPCR SuperMix-UDG with ROX (Invitrogen). All primers used in this study are listed in Regions of SOX2-BRN2 co-occupancy in TetO-Brn2 cells were defined as above. To define regions of differential chromatin state between TetO-Brn2 and control cells, we first compared H3K4me1 enrichment in these cells to define regions common to both cell types or unique to one or the other. Common regions were merged and treated as one enhancer if detected in both cell types. A similar analysis was performed for H3K27Ac enrichment. SOX2-BRN2 regions were then compared to regions of H3K4me1 and H3K27Ac in order to define SOX2-BRN2 binding events that resulted in changes in chromatin state between TetO-Brn2 cells and controls.100 base pair windows around the max peak of SOX2-bound regions (in ESCs and NPCs) regions were analyzed for the presence of overrepresented DNA binding motifs. Similarly, 150 base pair windows around the midpoints between the max peaks of SOX2 and OCT4 or BRN2  in co-bound regions were analyzed for the presence of overrepresented DNA binding motifs. We used a hypothesis-based approach to identify known protein-DNA recognition elements enriched in each dataset. The set of hypotheses are derived from all vertebrate position-specific scoring matrices (PSSMs) from TRANSFAC k-mers in the sequence for a motif of width k). This ranked list was used to compute the U statistic for the foreground set from which we computed a p-value and applied a Benjamini-Hochberg multiple hypothesis correction. Because many motifs in the databases are very similar to each other, we present the motif within each cluster with the most significant p-value.In the first approach, we determined whether motifs were overrepresented in a foreground set of all bound regions  compared to a background set of randomly generated sequences which matched the GC content of the foreground using the Mann-Whitney-Wilcoxon (MWW) ranked sum test. For each independent motif test, sequences were ranked by the maximum motif score in each sequence , and SOX2-OCT4 site pairs (ESCs). Site pairs were defined by matching each SOX2 bound site to the closest cofactor bound site within 200 bases. Distance was calculated as the cofactor chromosomal coordinate subtracted from the SOX2 chromosomal coordinate.i and motif j, a motif score ijs: Spacing between SOX and OCT sites was determined using a motif-based approach to determine specific spatial arrangement of the motifs in SOX2-OCT4 (ESCs) and SOX2-BRN2 (NPCs) co-bound regions. Max motif scores were calculated as described above and normalized as in The Mann-Whitney Z-score test result was used to rank all vertebrate TRANSFAC motifs in order of enrichment for SOX2 and OCT4 bound regions in ESCs, and SOX2 and BRN2 bound regions in NPCs. The change in rank (ΔRank) from ESC SOX2-OCT4 bound regions to SOX2-BRN2 bound regions was determined for each motif. Motifs were filtered to include only motifs with a rank less than or equal to 200 in the two ranked lists. Gene expression fold change was determined for each transcription factor associated with at least one TRANSFAC motif. After assigning a pseudocount of 1 to the normalized Affymetrix gene expression values for each transcription factor at the ESC and NPC stages, the log (base 2) of the fold change was calculated. Motifs were mapped to associated transcription factors according to the vertebrate all profile accessible on ExPlain 3.0 containing 656 motifs. The TF with the fold change that best agreed with the ΔRank of the associated motif was chosen as the ‘representative’ factor for the motif  Scaled motif ΔRank values and the associated log-transformed gene expression fold change values were sorted in order of log-transformed gene expression fold change, and viewed in a heatmap . Only motifs that had associated transcription factor expression values were considered.A spearman correlation coefficient was calculated between the ΔRank values of the motifs and the expression-fold change of their associated transcription factors. This required each motif to be associated with a single transcription factor. In the case where multiple transcription factors are known to bind a single motif, the TF was selected as described in the previous section. The significance of the spearman correlation coefficient was assessed by a Monte-Carlo algorithm. The input to the Monte-Carlo algorithm was a table in which each row of the first column was a motif and each row of the second column was the set of transcription factors known to bind the motif. The column containing the motifs was randomly permuted 100,000 times (thereby randomizing the associations of transcription factors to motifs), and the process of selecting a single transcription factor to be associated with each motif was repeated. After associating each motif with a single random transcription factor, the spearman correlation between the ΔRank of the motifs and the log-fold-change in expression of the transcription factors was computed. The fraction of randomized tables that produced a higher spearman correlation than the original table was reported as the p-value. Only motifs for which the rank in either the SOX2-OCT4 list or the SOX2-BRN2 list was in the top 200 were used. The motifs also had to have at least one associated transcription factor for which gene expression data was available.Genomic intervals corresponding to enriched transcription factor binding motifs in SOX2-BRN2 bound regions were determined. The single nearest gene to a given region was determined using the GREAT algorithm. Genes associated with a motif having a motif similarity score of equal to or greater than 0.85 (439 NF-I motif regions and 251 RFX motif regions) were used to generate a non-redundant target gene list. This gene list was then used as the input for Ingenuity Pathway Analysis. Ingenuity recognized 431 NF-I associated genes and 249 RFX associated genes. Overlap of genomic regions containing motif sequences was performed using Galaxy.Figure S1Comparison of SOX2 ChIP-Seq datasets in ESCs and NPCs. (A) Correlation between published and replicate ChIP-Seq datasets. (B–D) Overlap of SOX2-bound regions in ESCs (blue) and NPCs (red) (B) All regions; (C) TSS-associated regions within 1 kb of an annotated TSS; (D) Genes associated with TSS-associated regions. If multiple genes were associated with a single SOX2-bound region, all genes were included in this list. Increase in overlap as compared to (C) reflects SOX2 binding at distinct sites within the same promoter in ESCs compared to NPCs.(EPS)Click here for additional data file.Figure S2Overlap of SOX2-bound enhancers reveals SOX2 occupies distinct enhancers of the same gene in ESCs and NPCs. (A) Overlap of enhancer-associated SOX2-bound regions in ESCs and NPCs. (B) Overlap of nearest gene to SOX2-bound enhancers in ESCs and NPCs. Increase in overlap results from distinct enhancer regions mapping to the same gene being occupied by SOX2 in ESCs as compared to NPCs.  Overlap of genes linked to SOX2-bound promoters and enhancers in ESCs (C) and NPCs (D). By including the genes lined to SOX2-bound enhancers, knowledge of the network controlled by SOX2 increased 3.3 fold  and 1.4 fold  in ESCs and NPCs, respectively. (E) Pie chart depicting fractions of genes associated with SOX2-bound poised enhancers in ESCs which become associated with SOX2-bound poised enhancers in NPCs (purple), with SOX2-bound active enhancers in NPCs (green), or neither (blue). If a gene gained both a poised and active enhancer, it was grouped with active genes. p-values reflect the probability, based on a cumulative hypergeometric distribution, of obtaining by chance an overlap as high as or higher than that which was observed. (F) Expression of genes linked to SOX2-bound poised enhancers in ESCs  and the subsets that become associated with SOX2-bound poised enhancers  or with SOX2-bound active enhancers  in NPCs. If a gene gained both a poised and active enhancer, it was grouped with active genes. * reflects p-value<0.0001, unpaired T-test, two tailed. y-axis reflects relative gene expression level within the indicated cell type.(EPS)Click here for additional data file.Figure S3Oct4 (Pou5f1) locus, a known SOX2-OCT4 target. y-axis corresponds to reads per million. Genomic positions reflect NCBI Mouse Genome Build 36 (mm8). Gray boxes indicate regions of SOX2-OCT4 co-occupancy. * denotes known enhancer region. (H–I) Venn diagrams depicting overlap of SOX2 and BRN2 in NPCs (H) All bound regions; (I) Genes associated with TSS associated regions; (J) H3K4me1 and/or H3K27Ac marked enhancers. (K) Gene plots of the Sox2 and Nestin loci, which contain known SOX2-BRN2 target enhancers. y-axis corresponds to reads per million. Genomic positions reflect NCBI Mouse Genome Build 36 (mm8). Gray boxes indicate regions of SOX2-BRN2 co-occupancy. * denotes known enhancer region.Comparison of SOX2-POU bound sites in ESCs and NPCs. (A) Fraction of total start sites or total marked enhancers associated with BRN2 in NPCs. (B) MEME motif analysis of highest enriched ChIP regions for OCT4 in ESCs and BRN2 in NPCs, demonstrating the similarity in their motif preference. (C) Venn diagram reflecting overlap of bound regions of OCT4 in ESCs compared to BRN2 in NPCs (D–F) Venn diagrams depicting overlap of SOX2 and OCT4 (D) All bound regions; (E) Genes associated with TSS associated regions; (F) H3K4me1 and/or H3K27Ac marked enhancers. (G) Gene plot of the (EPS)Click here for additional data file.Figure S4Brn2 from the Col1A1 locus (adapted from Brn2 induced (black) differentiating ESCs. y-axis represents relative expression normalized to Gapdh in 3 biological replicates, measured in triplicate. * denotes p-value<0.05, * denotes p-value<0.01 based on ANOVA with Bonferroni correction. (C) ESC lines were exposed to doxycycline and after 24 hrs. the medium was replaced with N2B27 medium. Cells were harvested after 24 hrs. in N2B27 and represented the day 1 time point. (D) Phase contrast images of control and Brn2 inducible ESCs at day 2 of differentiation used for ChIP-Seq analyses. (E) Quantification of BRN2 and SOX2 bound regions that map within 1 kb of annotated TSSs and those which are distal to TSSs in TetO-Brn2 cells.  Metagene analysis within 701 regions depicted in Characterization of TetO-Brn2 inducible system. (A) Schematic of transgenic system for inducible expression of (EPS)Click here for additional data file.Figure S5n between the SOX and OCT motifs reflects the variability of the spacer between these motifs at target loci, where n can be any number greater than or equal to zero. Blue boxed motif represents canonical SOX-OCT motif preferred by SOX2-OCT4 in ESCs when n = 1. Red boxed motif represents SOX-OCT configuration utilized by SOX2-BRN2 more frequently then SOX2-OCT4 when n = 5.Examples of SOX-OCT composite motifs in the four possible configurations. N(EPS)Click here for additional data file.Table S1ChIP-Seq bound regions for all experiments. ChIP-Seq bound regions called for SOX2 and OCT4 in ESCs, SOX2 and BRN2 in NPCs, and SOX2, BRN2 H3K4me1, and H3K27Ac in TetO-Brn2 and control cells.(XLSX)Click here for additional data file.Table S2Bound Promoters and Enhancers for ChIP-Seq Experiments. Bound promoters, active enhancers, and poised enhancers for SOX2 and OCT4 in ESCs, and SOX2 and BRN2 in NPCs.(XLSX)Click here for additional data file.Table S3GOstat gene ontology analysis of SOX2-bound promoters. Full lists of GOstat gene ontology IDs enriched in SOX2-bound promoters in ESCs and NPCs.(XLSX)Click here for additional data file.Table S4Microarray gene expression data in ESCs and NPCs.(XLSX)Click here for additional data file.Table S5GREAT analysis of SOX2-bound enhancers in ESCs. Full lists of GREAT terms enriched in regions associated with active and poised enhancers occupied by SOX2 in ESCs.(XLSX)Click here for additional data file.Table S6GREAT analysis of SOX2-bound enhancers in NPCs. Full lists of GREAT terms enriched in regions associated with active and poised enhancers occupied by SOX2 in NPCs.(XLSX)Click here for additional data file.Table S7Hypothesis-driven motif search in SOX2-bound regions in NPCs. Mann-Whitney U test and THEME results for TRANSFAC motifs enriched in SOX2 regions in NPCs.(XLSX)Click here for additional data file.Table S8GREAT analysis of SOX2-BRN2-co-bound enhancers in NPCs. Full lists of GREAT terms enriched in regions associated with active and poised enhancers occupied by BRN2 in NPCs and with active and poised enhancers occupied by SOX2-BRN2 in NPCs.(XLSX)Click here for additional data file.Table S9GREAT analysis of BRN2-bound regions common to TetO-Brn2 cells and NPCs, and SOX2-BRN2-co-bound regions in TetO-Brn2 cells. Full lists of GREAT terms enriched in regions bound by BRN2 in both day 2 TetO-Brn2 cells and in NPCs, and regions co-bound by SOX2-BRN2 in day 2 differentiating TetO-Brn2 cells which are not bound by OCT4 or SOX2 in ESCs or SOX2 in control cells.(XLSX)Click here for additional data file.Table S10Hypothesis-driven motif search in SOX2-BRN2-co-bound regions in NPCs. Mann-Whitney U test and THEME results for TRANSFAC motifs enriched in SOX2-BRN2 regions in NPCs.(XLSX)Click here for additional data file.Table S11qRT-PCR primers used to assay for gene expression in TetO-Brn2 and control cells.(XLSX)Click here for additional data file.


Escherichia coli, in which negative auto-regulation is part of a complex regulatory network.Gene regulation networks are made of recurring regulatory patterns, called network motifs. One of the most common network motifs is negative auto-regulation, in which a transcription factor represses its own production. Negative auto-regulation has several potential functions: it can shorten the response time , stabilize expression against noise, and linearize the gene's input-output response curve. This latter function of negative auto-regulation, which increases the range of input signals over which downstream genes respond, has been studied by theory and synthetic gene circuits. Here we ask whether negative auto-regulation preserves this function also in the context of a natural system, where it is embedded within many additional interactions. To address this, we studied the negative auto-regulation motif in the arabinose utilization system of araC under constitutive expression, the input dynamic range of the arabinose system is reduced by 10-fold. The apparent Hill coefficient of the induction curve changes from about n = 1 with negative auto-regulation, to about n = 2 when it is disrupted. We present a mathematical model that describes how negative auto-regulation can increase input dynamic-range, by coupling the transcription factor protein level to the input signal.We find that when negative auto-regulation is disrupted by placing the regulator Escherichia coli increases the range of arabinose signals over which the system can respond. In this way, negative auto-regulation may help to increase the input dynamic-range while maintaining the specificity of cooperative regulatory systems. This function may contribute to explaining the common occurrence of negative auto-regulation in biological systems.Here we demonstrate that the negative auto-regulation motif in the native arabinose system of  Escherichia coli show negative auto-regulation [Transcription regulation networks are largely made up of recurring regulatory patterns called network motifs -4. Thesegulation , as do mgulation ,8-11.NAR has been suggested experimentally and theoretically to have several functions. The first is increased homeostasis or buffering of the auto-regulated gene product concentration against stochastic noise -14. BecaE. coli [A second feature of NAR is its ability to speed the response time of gene circuits ,19,20. Ret al. that NAR can also linearize dose responses [S. cerevisiae as a function of inducer  levels. NAR was found to transform a sigmoid induction curve into a more linear curve . This fsee also ,22-25. TSuch theoretical and synthetic-circuit studies are a powerful approach because one can study the function of circuits such as NAR without of interfering effects. In natural systems, however, this motif is embedded inside a large regulatory network with many other interactions. These additional interactions might in principle modify its function. Therefore, to fully test the function of a motif requires, in addition to theory and synthetic circuits, experiments on the motif in its natural context, wired into the full interaction networks of the cell.E. coli. This system has been characterized over the past decades by Schleif and colleagues  in which the TF is not negatively auto-regulated [The arabinose system is composed of 9 genes arranged in 5 operons: sporters -32; and n Figure . The sysn Figure , which aechanism ,34. AraCC Figure . In a stegulated .ara system .In order to determine the input dynamic range of ed assay ,39, with library . Each stReporter strains were grown on glucose minimal medium containing saturating amount of cAMP  and increasing amounts of L-Arabinose ,41. PromaraBAD in the parental strain  as a function of arabinose concentration is shown in Figure n = 1 ± 0.3 (s.e.), and halfway induction point of K = 1.1 ± 0.4 mM (s.e.). The input dynamic range is R = 100 ± 40 (s.e.). These results are similar to measurements of the input function of the araBAD reporter strain in wild-type MG1655 (U429) [n = 1, in which R = 81.The promoter activity of the 5 (U429) , and arearaC promoter activity on arabinose. Since AraC negatively regulates its own promoter, arabinose is expected to affect araC expression. Indeed, using an araC reporter strain (U428), we find that arabinose increases the activity of the araC promoter in a dose-dependent manner  which provides constitutive expression . The plasmid has a tetR controlled promoter, repressed by a chromosomal tetR gene. Without induction, this plasmid produces levels of AraC that are comparable to the induced wild-type AraC level, as assessed from the maximal promoter activity of the araBAD reporter. It should be noted that the parental strain in this study (U424) also contains chromosomal tetR as well as an emptly pZE11 vector, in order to preserve genotypic identity between the two strains.To study the role of the negative auto-regulation of  Figure. . For thiaraBAD is significantly steeper than the parental input function , and halfway induction point of K = 42 ± 0.6 mM (s.e.). The measured input dynamic range spans between 14 mM - 135 mM, and thus has R = 10 ± 3 (s.e.), in comparison to R = 100 ± 40 (s.e.) in the parental strain. Thus, decoupling araC from its negative auto-regulation reduces the input dynamic-range of its downstream genes by about an order of magnitude.We find that in the absence of NAR, the arabinose-dependent input function of n Figure , with anWhat is the main effect at play that allows negative auto-regulation to increase input dynamic range? To understand this, we analyzed a mathematical model of the NAR motif. We sought to make the model as simple as possible, in order to be able to understand it intuitively, and at the same time not too simple so as not to lose the essence of the problem. A more comprehensive model, based on mass-action kinetics, which includes a dual transcription factor that acts as both a repressor and an activator, is given in Additional File X, that binds its inducer s with a dissociation constant Ks. The amount of X bound to s, which is the active form of the transcription factor X*, is described by Michaelis-Menten binding:Consider a transcription factor whose concentration is X* binds the promoter of a downstream gene Z with Michaelis-Menten-like kinetics, so that the steady-state level of the Z gene product is:The active transcription factor Kz is the dissociation constant of X* from the promoter of Z, βZ is the maximal production rate of Z, and α is its degradation/dilution rate [Where ion rate .X is independent of the inducer levels. We denote this constant level X0. Using Eq. (1) in Eq. (2) with X = X0 results in a sigmoidal regulation function with an input dynamic range of R = 9.Without negative auto-regulation, the concentration of X, negative auto-regulation allows the signal s to modify the concentration of X, an effect termed direct coupling [ara system, the promoter that encodes X is repressed by free X, (denoted Xf) a repression which is relieved when X is bound to the signal.It is at this point that negative auto-regulation has an important effect: instead of a constitutive level of coupling . With neX that is repressed by Xf [α, so that:To analyze this, consider the rate of production of ed by Xf , balanceKx is the dissociation constant of X from its own promoter, and the free X (Xf) is given by the unbound fraction, Xf = X-X*:Where Kx<<Xf, one finds that at steady-state the level of X increases as the square root of the input signal s:Substituting Eq. (5) into Eq. (4) and assuming strong binding of the regulator to its own promoter A2 = Kxβx/α. In other words, the transcription factor (X) levels increases with the signal (s) levels , results in an input-function that is less steep, because of the square-root dependence of X on s:Using this expression for R = 81, which is 9 fold wider than that of Eq. (3). Thus, NAR increases the input dynamic range.Where Kx<<Xf is not crucial for the increased input dynamic range, and was used only for the sake of simplicity. In Additional File Note that the assumption araBAD input function without NAR has an apparent Hill coefficient of about 2, suggesting that the AraC regulator is cooperative with n = 2. In Figure n = 1 and n = 5. It is seen that NAR increases the input dynamics range in all cases. For example, at n = 2, the input dynamic range without NAR is R = 9, but can reach up to R ~ 1000 with NAR .We further investigated the effect of NAR on input functions with different cooperativity in the binding of the TF to the promoter, as described by Hill equations. In the present system, the K of downstream genes (relative to no NAR). The direction and size of the shift depends on the mode of regulation. For repressors, K generally increases with regulator levels, whereas activators show the converse dependence. Since AraC both activates and represses araBAD, the detailed model in Additional File K shown in Figure Furthermore, the model explains how the change in regulator levels caused by NAR can cause a shift in the halfway induction point To summarize the conclusions of this analysis, negative auto-regulation causes regulator levels to increase with inducer level. This enhances the input dynamic range by extending the range of inputs that can affect the downstream genes.E. coli, in which NAR is embedded within multiple feedback loops and regulatory interactions. Disruption of the NAR in the arabinose system reduced the input dynamic range by an order of magnitude.This study supports a role for negative auto-regulation in increasing the input dynamic range of downstream genes. Previous studies suggested this role theoretically ,22-24 anWhat is the intuitive explanation for the enhancement of the input dynamic range provided by negative auto-regulation? Negative auto-regulation in the arabinose system allows the transcription factor concentration to be modulated by its own input signal. As the concentration of input signal increases, the concentration of transcription factor also increases. This extends the response range of downstream promoters, which would otherwise reach maximal activity when the transcription factor becomes saturated with input signal.A related but distinct feature was studied by M. Savageau ,42, in wUse of NAR to increase input dynamic range might be especially useful for regulators that bind the promoter cooperatively. Such cooperative binding is thought to increase specificity . HoweverThe present study adds to our understanding of the functions of negative auto-regulation network motif, showing that it can increase the input dynamic range of the response, even when embedded in a relatively complicated native gene circuit. Integration of negative auto-regulation within a system with high cooperativity (high specificity and steep activation curve), enables the system to respond to a wide range of input signal (making the activation curve wide) while maintaining the system's specificity to the signal. This function can be experimentally tested in the numerous additional gene systems which bear this network motif across organisms. Because the negative auto-regulation motif is not limited to transcription networks this feature might also apply to other biological systems including protein-level interactions.ori, kanR, with gfpmut2 [araC (coordinates 69973-> 70469) and araBAD (coordinates 70469-> 69973) promoters are from the fluorescent reporter library given in detail in [araBAD and araC  and once in the minus strand orientation (araBAD promoter).GFP reporter plasmids  for theetail in . In shorC Figure , with moaraC was decoupled from its native regulation by cloning it into the pZE11 plasmid  was PCR amplified from MG1655 genomic DNA with the following start and end coordinates: 70387-71299 (positive strand), by using the following primers: 5' ggcggtaccatggctgaagcgcaaaatgatcc for the 5' end and 5' ggcaagcttccgtcaagccgtcaattgtctg for the 3' end. The PCR product and the pZE11 plasmid were digested with KpnI and HindIII, and then were ligated, yielding pZE11-araC. A self-ligated pZE11 was generated as well to serve as a control plasmid.PLtetO-1 ) using ttetR gene  from DH5αZ1 was P1 transduced into the wild-type MG1655 chromosome (K12 strain MG1655: F- lambda- ilvG- rfb-50 rph-1), yielding strain U423 (MG1655z1). The pZE11 plasmid and the araBAD reporter plasmid were transformed into U423, yielding strain U424.In order to achieve maximal genotypic identity between the wild-type (with NAR) and the mutant (without NAR) strains, a modified wild-type strain was constructed. araC strain was obtained by deleting araC from the MG1655z1 chromosome (coordinates 70391-> 71244) using the phage λ Red recombination system [araC). Primers 5' ggacaattggtttcttctctgaatggtgggagtatgaaaagtatggtgtaggctggagctgcttc 3' (for the 5 prime end) and 5' gccgtcaattgtctgattcgttaccaattatgacaacttgacggctaccatatgaatatcctccttag 3' (for the 3 prime end) were used to amplify the kanamycin resistance gene from the pKD4 plasmid with extensions homologous to the 5' and 3' ends of the araC gene, to allow recombination. Kanamycin resistance was removed from the deleted strain using FLP recombinase, as described [araC deletion and the integrity of the araC\araBAD divergent chromosomal promoter were verified using PCR and sequencing of the scar region. araBAD reporter and pZE11-araC plasmids were transformed into U425, yielding strain U426. Transformation of pZE11-araC into the U425 strain restored its ability to grow on L-Arabinose as a sole carbon source. This strain produced AraC levels, similar to that of the wild-type strain .An isogenic Δn system ,47, yielMG1655z1 with empty-pZE11 and pUA66 promoterless reporter plasmid (strain U427) was used for fluorescence background subtraction for U424 & U426.Strain U66 ,40 was uaraC, since its basal expression level was found to be close to the wild type AraC level. Using a robotic liquid handler , flat bottom 96-well plates (Nunc) were prepared with 150 μl of M9 minimal medium containing 0.2% glucose 0.05% casamino acids, 30 mM cAMP, 50 μg/ml ampicillin and 25 μg/ml. L-Arabinose, in increasing concentrations was added. The wells were inoculated with the reporter strain at a 1:600 dilution from the overnight culture. Wells were then covered with 100 μl of mineral oil (Sigma) to prevent evaporation  at 37°C. No aTc was used to induce pZE11-r growth ,49, and Promoter activity for each well was calculated from the OD and GFP measurements after subtracting the OD and GFP backgrounds. GFP background was obtained for each well from the promoterless control strains U427 (for strains U424 and U426) and U66 (for strain U428) (Additional File R: ampicillin resistance; cAMP: cyclic adenosine mono phosphate; CRP: cAMP receptor protein; aTc: anhydrotetracycline; GFP: green fluorescent protein; kanR: kanamycin resistance; NAR: negative auto-regulation; OD: optical density; PA: promoter activity; s.e.: standard error; specR: spectinomycin resistance; TF: transcription factor.ampThe authors declare that they have no competing interests.DM designed the research, preformed the molecular genetic manipulations and the experiments, analyzed data and wrote the paper. ED designed the research, created the mathematical model, analyzed data and wrote the paper. AB designed the research, preformed the molecular genetic manipulations, analyzed data and wrote the paper. UA designed the research, analyzed data and wrote the paper. All authors read and approved the final manuscript.Supplementary on-line material. Detailed model of the arabinose system and examples for raw data figures.Click here for file


Humans are symbiotic organisms; our genome is populated with a substantial number of endogenous retroviruses (ERVs), some remarkably intact, while others are remnants of their former selves. Current research indicates that not all ERVs remain silent passengers within our genomes; re-activation of ERVs is often associated with inflammatory diseases. ERVK is the most recently endogenized and transcriptionally active ERV in humans, and as such may potentially contribute to the pathology of inflammatory disease. Here, we showcase the transcriptional regulation of ERVK. Expression of ERVs is regulated in part by epigenetic mechanisms, but also depends on transcriptional regulatory elements present within retroviral long terminal repeats (LTRs). These LTRs are responsive to both viral and cellular transcription factors; and we are just beginning to appreciate the full complexity of transcription factor interaction with the viral promoter. In this review, an exploration into the inflammatory transcription factor sites within the ERVK LTR will highlight the possible mechanisms by which ERVK is induced in inflammatory diseases. These regnancy ,5. At thregnancy ,7. Thus,regnancy -10. It iregnancy , Systemiregnancy , Schizopregnancy , Amyotroregnancy , and mulregnancy . Severalregnancy ,17. Thesregnancy . In cont′ LTR promotes sense transcription of the viral genome . The 5e Figure . It remad HTLV-1 -21. Eachproposed , perhapsproposed . We are ERVs have been transcriptionally silenced over evolutionary time from accumulation of point mutations and deletions. Additionally, epigenetic mechanisms, particularly CpG methylation and cytosine deamination of the LTR, control the basal expression of ERVK in various cell types and tissues; these have been extensively reviewed elsewhere . The metThe activity of ERVK LTRs has been further repressed by deamination of methylated CpG dinucleotides. Spontaneous deamination is a major source of abundant G to A and C to T mutations observed in many ERVK LTRs , which rAPOBEC3G-mediated cytidine deamination is another common source of G to A, and less frequently C to T, mutations in the ERVK LTR . This prDespite the layers of epigenetic control, basal and inducible ERVK expression is evident in normal physiology and inflammatory disease ,33, indiOther than epigenetic factors, cellular transcription factors are also crucial for regulating the activity of the ERVK LTR. Different types, expression levels and activity of key transcription factors may be required to achieve distinct tissue-specific ERVK expression. However, relatively little is known about the transcriptional regulation of ERVK when pertaining to tissue specificity, conditions of inflammation and disease states. Interestingly, many ERVK LTRs have intact and conserved binding sites for human transcription factors Table , yet fewSp1 and Sp3 are ubiquitously expressed transcription factors, which have been pathologically implicated in cancers, neuroinflammation, and rheumatic diseases -93. InduSeveral mechanisms have been depicted by which Sp1 and Sp3 may promote transcription of ERVK. Sp1 interacts with TFIID, a complex consisting of TATA binding protein (TBP) and other associated general transcription factors, and thus tethers the transcription pre-initiation complex to the TATA-independent promoter of the ERVK LTR . FormatiInterestingly, Sp3 may also repress ERVK expression. Since Sp3 and Sp1 are closely related and have similar affinity for the GC boxes , Sp3 canFurthermore, other members of the Sp family are also closely related to Sp3 and Sp1. These include Sp4, BTEB1, TIEG1, and TIEG2 . The criYY1 is a ubiquitous transcription factor, which is frequently overexpressed in cancers, degenerating neurons, and rheumatic diseases ,73; hencenv and rec proteins [Melanoma, a type of skin cancer, frequently exhibits enhanced expression of ERVK proteins . Recent proteins . Melanomproteins , and hasproteins  , located at nt379 and nt563. These conserved motifs may accommodate IRF binding, in conjunction with NF-κB, fulfilling the transcriptional priming of traditional interferon-stimulated genes . Ofiewed in . IFNα siiewed in , and seviewed in -107. Thicis-elements, another potential mechanism to activate the ERVK LTR.In contrast, pro-inflammatory cytokines, such as TNFα and IL-6, have been shown to modulate ERVK transcriptional activity . TNFα caenv expression as compared to normal breast tissues [env transcripts has been shown to be up-regulated 5 to 10-fold in breast cancer cell lines upon estradiol treatment followed by progesterone [env expression, enzymatically active ERVK reverse transcriptase protein has also been detected in breast tumor biopsies and the breast cancer cell line T47D [The effect of estrogen and progesterone on ERVK expression has been exclusively studied in breast cancer tissues. Most breast cancer cell lines and many breast tumor tissues exhibit significantly higher levels of ERVK  tissues ,111. Theesterone ,112, sugesterone ,86,112. ine T47D . This suThe gene expression of ERVK may not only be influenced directly by cellular and viral transcription factors, but also indirectly by various pharmaceutical agents ,100 whicBesides exploiting cellular transcription factors, ERVK can also utilize virally-encoded proteins for its induction. This versatility in part explains the up-regulation of ERVK by exogenous viruses such as Human Immunodeficiency Virus-1 (HIV-1), Human T-Lymphotrophic Virus-1 (HTLV-1), Herpes Simplex Virus-1 (HSV-1) and Epstein Barr Virus (EBV) -117. Thede novo ERVK infectivity, as demonstrated experimentally in vitro using virions derived from reconstituted elements [The expression of ERVK is often abnormally elevated in HIV-1 infected individuals, reflecting vastly increased viral RNA titres in their plasma ,118,119.elements ,31. Howeelements .gag RNA by 21- and 15-fold, respectively [rec and np-9 transcripts and the expression of the ERVK capsid protein also increased in the presence of Tat in several cell lines, as well as in primary lymphocytes that are major targets of HIV-1 infection [Moreover, the direct interaction of Tat with nascent ERVK RNA, and thus the induction of viral transcript expression, had always been suspected. It has only recently been demonstrated that HIV-1 proteins Vif and Tat independently activate ERVK expression ,122. Traectively . The levnfection . Similarnfection .Several mechanisms have been proposed by which HIV-1 Tat may trans-activate ERVK. Tat activates transcription from the HIV-1 promoter by interacting with Cyclin T1 and recruiting the host positive transcription elongation factor b (P-TEFb) to its LTR . The Tatgag RNA levels. ChIP assays further confirmed that NF-κB and NFAT-1 were activated and interacted with multiple binding sites on ERVK LTR .The Tax protein produced by HTLV-1 may modestly trans-activate ERVK, as demonstrated by Tax-driven activation of a luciferase reporter under the control of a TD47 ERVK LTR in Jurkat T cells . The Taxin vitro[in vitro by facilitating the binding of Oct-1 to its binding site on the LTR [in vivo induction of ERVK by HSV-1 immediate early proteins is yet to be confirmed.The immediate early protein, ICP0, produced by HSV-1 induces the LTR directed transcription of ERVK in vitro. This efin vitro. Destrucin vitro. HSV-1 min vitro. IEP1 ha the LTR . In addi the LTR . Since Eenv-derived superantigen (SAg) specific to T cells that express T cell receptor β chain variable-13 (TcRβCV-13) [cd48 gene. Trans-activation of ERVK-18 can be driven by EBV-encoded latent membrane protein LMP-2A [env expression [env gene abolished production of the SAg [Similarly, EBV infection has been reported to stimulate the production of an ERVK-18 RβCV-13) , a mechan LMP-2A ,129. A sAlthough several studies indicate the effects of epigenetic mechanisms in controlling ERVK expression, the literature clearly awaits more studies on tissue-specific, pro-inflammatory and hormone-regulated transcription factors that promote or repress ERVK transcription in health and inflammatory diseases. Surprisingly, very few transcription factors – Sp1, Sp3, YY1, MITF-M, and estrogen/progesterone – have been experimentally shown to induce ERVK; however, these transcription factors are not necessarily tissue-specific as they are ubiquitously expressed. Bioinformatic examination of several ERVK LTRs clearly unveils the multitude of possible binding sites for unique and ubiquitous transcription factors, but it is yet to be determined whether these sites are at all functional . One cleA large part of the debate surrounding a causal relationship of ERVK in disease pathology is the issue of bystander activation – advances in our understanding of the transcriptional regulation of the ERVK LTR will clarify whether ERVK expression is a consequence, cause or conjoined mechanism of inflammatory disease. Thus, we are indeed strongly in need of an ERVK Transcriptome Project ,130,131,ALS: Amyotrophic Lateral Sclerosis; EBV: Epstein Barr Virus; ERV: Endogenous retrovirus; ERVK: Endogenous retrovirus-K; ERVW: Endogenous retrovirus-W; HIV: Human Immunodeficiency Virus; HSV-1: Herpes Simplex Virus-1; HTLV-1: Human T-Lymphotrophic Virus-1; ISRE: Interferon-stimulated response element; LTR: Long terminal repeat; NF-κB: Nuclear factor-κB; RA: Rheumatoid Arthritis; SLE: Systemic Lupus Erythematosus.The authors declare that they have no competing interests.MM performed the sequence alignments and bioinformatic annotations. Both MM and RND drafted the manuscript. Both authors read and approved the final manuscript.


XylR, the master TF for expression of the m-xylene biodegradation operons encoded in the TOL plasmid pWW0 of Pseudomonas putida, responds not only to the aromatic compound but also, albeit to a lesser extent, to many other aromatic compounds, such as 3-methylbenzylalcohol (3MBA). We have examined whether such a relaxed regulatory scenario can be reshaped into a high-capacity/high-specificity regime by changing the connectivity of this effector-sensing TF within the rest of the circuit rather than modifying XylR structure itself. To this end, the natural negative feedback loop that operates on xylR transcription was modified with a translational attenuator that brings down the response to 3MBA while maintaining the transcriptional output induced by m-xylene (as measured with a luxCDABE reporter system). XylR expression was then subject to a positive feedback loop in which the TF was transcribed from its own target promoters, each known to hold different input/output transfer functions. In the first case , the reporter system displayed an increased transcriptional capacity in the resulting network for both the optimal and the suboptimal XylR effectors. In contrast, when xylR was expressed under the weaker Ps promoter, the resulting circuit unmistakably discriminated m-xylene from 3MBA. The non-natural connectivity engineered in the network resulted both in a higher promoter activity and also in a much-increased signal-to-background ratio. These results indicate that the working regimes of given genetic circuits can be dramatically altered through simple changes in the way upstream transcription factors are self-regulated by positive or negative feedback loops.Prokaryotic transcription factors (TFs) that bind small xenobiotic molecules  often display a promiscuous effector profile for analogs of the  Pu, which drives expression of an operon for the biodegradation of m-xylene by the soil bacterium P. putida mt-2. While XylR, the chief TF of this system, binds this substrate and activates Pu, the same regulator responds, to a lesser extent, to 3-methylbenzylalcohol and thus also activates the promoter. This work provides evidence that such natural effector promiscuity of the system can be altogether suppressed by replacing the naturally occurring negative autoregulation loop that governs XylR expression with an equivalent positive feedback loop. Based on this result, we argue that signal specificity of a given regulatory device depends not only on the TF involved but also on TF connectivity to upstream signals and downstream targets.It is generally taken for granted that promoters regulated by transcriptional factors (TFs) that respond to small molecules control their specificity to given effectors by tightening or relaxing the intrinsic dual interaction between the TF and the particular inducer. One such promoter is  Pseudomonas putida mt-2 is a good example. This plasmid encodes two catabolic operons for biodegradation of m-xylene 54-dependent regulator XylR, which, in the presence of its natural inducers, acts on the Pu promoter of the upper TOL operon. In addition, XylR triggers the expression of the gene that encodes a second regulator, XylS, via the Ps promoter Ps and the Pr promoters  Pu and Ps is in turn limited by intracellular concentrations of the TF 54Pu promoter to low capacity i.e. poor maximum output. The existing characteristics of the XylR-based regulatory network have likely evolved for adjusting the tradeoff between transcriptional efficiency and physiological burden in the natural context, constraining the output of the system. This natural control of the xylR expression loop in the context of the TOL plasmid limits the value of the system as the primary component of whole-cell biosensors Pu-based biosensing devices have included in vitro evolution of the TF The mechanisms that bacteria use to transduce external stimuli into specific responses rely on connected transcriptional factors that shape circuit-like input/output devices Pu regulatory node of the plasmid. To this end, we adopted a Pu-luxCDABE reporter integrated into the chromosome of P. putida for accurately measuring system performance. In this genetic background, we then designed and tested various combinations of translation signals, promoter strengths and regulatory loops aimed at [i] suppressing the effect of effector promiscuity on XylR/Pu output and [ii] enhancing the response to optimal inducers . The results herein demonstrate that the working regimes of regulatory nodes, including their signal specificity, can be dramatically altered by changing the upstream connectivity of the TFs involved in the network instead of mutating the structure of the corresponding proteins.In this work, we report one strategy to overcome the constraints imposed by the natural architecture of the TOL network on the function of the XylR/Pu promoter for expression of the upper operon Pr promoter that transcribes xylR, [iii] the XylR protein itself and [iv] the Pu promoter that is targeted by XylR. Although many other host factors influence the activity of the system in vivoPr/XylR/Pu forms the master regulatory device that determines signal specificity, i.e., the responsiveness of Pu to different aromatic effectors Pu and represses Pr, i.e., downregulates its own transcription. To have a reliable test system for comparing the inputs and outputs associated with this node, we engineered these components in a strain of P. putida bearing a transcriptional Pu-luxCDABE fusion inserted into its chromosome via a mini-transposon vector  or to 1.0 mM of a suboptimal effector (3MBA) and the resulting bioluminescence was recorded 5–6 h post-addition. This timing does not significantly affect luminescence  of the xylR gene  was 4–7-fold lower than the strain carrying xylR controlled by its natural upstream region (P. putida BX). As a control, we also measured the response of the two strains with the different xylR 5′-UTRs to the optimal effector, m-xylene. As shown in Pu output in the xylR 5′-UTR-modified strain P. putida Pr·RBX displayed a similar trend  than the reference counterpart P. putida BX. However, we observed that the response of the cells to each inducer was more divergent in strain P. putida Pr·RBX than in strain P. putida BX. These results suggested that decreasing concentrations of XylR had the effect of widening the relative gap between the induction caused by 3MBA and m-xylene. Yet, the change in the xylR 5′-UTR was insufficient to entirely suppress the response of Pu to the suboptimal inducer. Moreover, lower XylR levels also caused a low-capacity regime with the optimal effector. Therefore, the next question was how to keep and even enhance Pu readout in response to m-xylene while removing the effect of 3MBA.We next verified that the changes in the (UTR) of nduction . The patpression . The evover time . The resxylR 5′-UTR sequence caused a reduction of Pu response to m-xylene by approximately 50%. It is thus plausible that the intracellular levels of the regulator determine the capacity  of the promoter. As the intracellular level of XylR under its native transcriptional control xylR transcription subject to a PFL  by Pu, the promoter that is triggered by effector-bound XylR  and in P. putida Pu·RBX in the presence and absence of m-xylene. As shown in P. putida BX expressed XylR at low levels with a tendency to accumulate at later growth stages m-xylene under the same conditions resulted in lower XylR levels that appeared to decrease over time. The situation with the strain engineered with a forward loop (P. putida Pu·RBX) was similar under non-induced conditions but entirely different when cells were exposed to m-xylene. As shown in xylR worked as predicted but also that the effect of m-xylene on Pu was enough to switch the state of the loop to from low to high activity  with a transcriptional strength comparable to the wild-type (P. putida Pu·RBX to 3MBA over time (P. putida BX). The results of our experiments  vs. those with 0.25 mM (Pu high to very high) and above. This phenomenon likely reflects the switch-on typically caused by the passing of a threshold in auto-inducing regulatory loops The result of the creation of the construct discussed above was a circuit  that resild-type  because ver time  as well ver time . As a coeriments  indicatePu, see above), they demonstrated that the response of the XylR/Pu node to inducer could be made more digital in a fashion dependent on the concentration of the TF involved. These results suggested that one could keep the node in an entirely off state when XylR levels are below a certain threshold, while triggering a high activity regime once the threshold has been surpassed. On this basis, we recreated the same PFL but pursued a higher limit for XylR auto-induction in a way such that optimal and suboptimal effectors could trigger or not, respectively, a high-activity of the downstream Pu promoter.While the results noted in Pu response to both optimal (m-xylene) and suboptimal effectors (3MBA), which means that both effectors cause XylR (controlled by the attenuated Pu-based PFL) to reach the TF threshold imposed by this auto-inducing architecture , using the same methods employed for construction of other strains , P. putida Ps·RBX (wild-type xylR expressed through the Ps-based PFL) and P. putida Ps·RBX17 (same but xylRv17) strains with and without 3MBA. The results indicate that P. putida Ps·RBX17 was nearly as responsive to this effector as the strain bearing the native configuration of the regulatory system. Consistent with the results reported above  medium and handled with standard procedures. E. coli CC118λpir was used as the host for propagating plasmids based on an R6K origin of replication m-xylene. Antibiotics were used at the following concentrations: piperacillin (Pip) 40 µg/ml, chloramphenicol (Cm) 30 µg/ml, gentamycin (Gm) 10 µg/ml, tetracycline (Tc) 10 µg/ml and potassium tellurite (Tel) at 80 µg/ml. PCR reactions were performed as follows: 50–100 ng of the template indicated in each case was mixed in a 100-µl reaction mixture with 50 pmol of each of the primers specified and 2.5 units of Pfu DNA polymerase (Stratagene). The samples were then subjected to 30 cycles of 1 min at 95°C, 1 min at 58°C and 3 min at 72°C. Clones were first verified by colony PCR xylR gene under the control of different promoter architectures were cloned in vector pTn7-Gm FRT attTn7 site of the P. putida chromosome 7 delivery vectors, we first engineered a series of pUC18Not derivatives KpnI-SacI fragment of pBBXylR Pr → xylR  was inserted in a pUC18Not variant that lacked the EcoRI site, thereby generating pBXe. This plasmid was used as the frame for replacing the native promoter region of xylR with the 3 alternative 5′-upstream sequences employed in this work. In the first case, P. putida mt-2 genomic DNA was amplified with the primersPR 1F (5′-CGctcgagGTTAACATAATCGGAGACTGC-3′) and PRrbs 2R (CATGCTTAATTTCTCCTCTTTTTGTTTTCCTCTTGTTTTTAT-3′5′-CCGgaattc). The resulting 545-bp product contained the native Pr promoter and the adjacent sequence down to the natural RBS (bold) but with an added RBSII (underlined in the sequence EcoRI and XhoI restriction sites introduced in the primers (lower case in the sequences above). In the second case, primers PU 1F (5′-ACGCctcgagCCCGGGAAAGCGCGATGA-3′) and PUrbs 2R (CATGCTTAATTTCTCCTCT TTTGAAGGGTCACCACTATTTTT-3′5′-CGgaattc) amplified a 464-bp segment containing Pu all the way down to the RBS of the xylU gene (bold), which was then followed by the native RBSII (underlined) and ATG  of the xylR gene, flanked by EcoRI and XhoI sites (lower case). Finally, the primers PS 1F (5′-CGctcgagTTGTTTTCCTCTTGTTTTTATCG-3′) and PSrbs 2R (CATGCTTAATTT CTCCTCTTTAGTTCACGGTTCTCTTATT-3′5′-CGgaattc) resulted in a EcoRI-XhoI 256-bp fragment containing the second XylR-responsive promoter Ps of the TOL plasmid (xylS gene (bold) and followed by the RBSII (underlined) and ATG  of the xylR gene as before. Each of these 3 EcoRI-XhoI restriction products were cloned into the corresponding sites of the pBXe plasmid, thereby replacing the original xylR upstream region with refactored counterparts and originating pPrRBX, pPuRBX and pPsRBX. These plasmids were separately digested with NotI, which excised DNA segments carrying Pr RBSII) → xylR, Pu-(RBSPu RBSII) → xylR and Ps-(RBSPs RBSII) → xylRPr → xylR, Pr- → xylRv17Ps- resistance cassette 5 Tel-Pu·LUX. This plasmid was conjugally transferred to P. putida KT2440 (see below) and TelR exconjugants tested for insertion of the hybrid mini-Tn5 Tel element carrying the Pu-luxCDABE fusion. One of these clones was called P. putida Pu·LUX and retained for further use as receptor of the different variants of the mini-Tn7 transposons borne by the plasmids pTn7-BX, pTn7-Pr·RBX, pTn7-Pu·RBX, pTn7-Ps·RBX (see above). Insertion of the corresponding segments in the naturally occurring attTn7 site of P. putida Pu·LUX was confirmed through colony PCR using a primer that anneals within the sequence of the B domain of xylR (NDoCAvrII: 5′-GCGAATGGCCTAGG CCGTAATACTG-3′) and one at the attTn7 insertion site within the glmS gene (PpuglmS 2R: 5′-GTGCGTGCCCGTGGTGG-3′). The ensuing collection of GmR strains were deleted of this antibiotic marker by transient expression of yeast flippase encoded by the plasmid pBBFLP, which brings about site-specific recombination of the FRT sequences that flank the resistance gene R marker was not removed in these cases. The final outcome of all these manipulations was the isogenic strain collection P. putida BX, P. putida Pr·RBX, P. putida Pu·RBX, P. putida Ps·RBX, P. putida BX17 and P. putida Ps·RBX17.Standardization of the various regulatory devices for 5Tel-Pu·LUX and mini-Tn7 derivatives  and pBBFLP were conjugally passed from the donor E. coli strain indicated in each case into the different P. putida recipients with a filter mating technique E. coli HB101 (pRK600) was deposited on 0.45-µm filters in a 1∶1∶3 ratio and incubated for 8 h at 30°C on the surface of LB-agar plates. Mini-Tn7 derivatives were co-mobilized along with the transposase-encoding genes tnsABCD into the recipient strains by including E. coli CC118λpir (pTNS1) in the mating mixture 4, and the appropriate dilutions plated on M9/succinate amended with suitable antibiotics for counter-selection of the donor and helper strains and growth of the P. putida clones that had acquired the desired insertions. Bona fide transposition was verified in every case by checking the sensitivity of individual exconjugants to the delivery vector marker, piperacillin.pTnP. putida cells carrying luxCDABE fusions, 2-ml cultures of the strains under study were first pre-grown in 10-ml test tubes overnight in LB medium at 30°C. The cultures were then diluted to an OD600 of 0.05 and grown up to an OD600 = 1.0 in 100-ml flasks. At this point, the cultures were exposed to m-xylene or 3MBA, as indicated for each case. When required, 200-µl aliquots of these cultures were placed in 96-well plates (NUNC), and light emission and OD600 were measured in a Victor II 1420 Multilabel Counter (Perkin Elmer). The specific bioluminescence values were calculated by dividing the obtained values of total light emission (in arbitrary units) by the optical density of the culture (OD600). The specific bioluminescence values shown represent the average of at least three biological replicates.To measure bioluminescence production of P. putida cells was denatured in a sample buffer containing 2% SDS and 5% ß-mercaptoethanol and run on 10% polyacrylamide gels. These were subsequently blotted onto a polyvinylidene difluoride (PVDF) membrane  using a semi-dry electrophoresis transfer apparatus (BioRad). After protein transfer, the membranes were blocked for 2 h at room temperature with MBT buffer . For detection of XylR, the membranes were incubated with MBT buffer containing a dilution 1/2000 of anti-XylR Phab Protein analyses were performed according to published protocols


AtJMT in Arabidopsis thaliana and BcNTR1 in Brassica campestris encode jasmonic acid carboxyl methyltransferases, which catalyze methyl jasmonate (MeJA) biosynthesis and are involved in JA signaling. Their expression is induced by MeJA application. To understand its regulatory mechanism, here we define a novel JA-responsive cis-element (JARE), G(C)TCCTGA, in the AtJMT and BcNTR1 promoters, by promoter deletion analysis and Yeast 1-Hybrid (Y1H) assays; the JARE is distinct from other JA-responsive cis-elements previously reported. We also used Y1H screening to identify a trans-acting factor, AtBBD1, which binds to the JARE and interacts with AtJAZ1 and AtJAZ4. Knockout and overexpression analyses showed that AtBBD1 and its close homologue AtBBD2 are functionally redundant and act as negative regulators of AtJMT expression. However, AtBBD1 positively regulated the JA-responsive expression of JR2. Chromatin immunoprecipitation from knockout and overexpression plants revealed that repression of AtJMT is associated with reduced histone acetylation in the promoter region containing the JARE. These results show that AtBBD1 interacts with JAZ proteins, binds to the JARE and represses AtJMT expression.Jasmonates (JAs) are important regulators of plant biotic and abiotic stress responses and development.  Jasmonic acid and its derivatives, collectively referred to as jasmonates (JAs), act as important regulators in plant biotic and abiotic stress responses COI1 complex-mediated 26S proteasome degrades JAZs ArabidopsisThe molecular mechanisms by which JA regulates gene expression were illuminated by the discovery of jasmonate ZIM-domain proteins (JAZs) and the finding that the SCFRecent reports on JA-responsive TFs have further improved our understanding of JA-responsive regulatory mechanisms. AtMYC2, a bHLH TF, is a primary target in the JA signaling pathway and interacts with some members of the AtJAZ family to regulate various JA-responsive target genes VSP1 in ArabidopsisPIN2 in potato VSPB in soybean ORCA3 in CatharanthusPDF1.2 in ArabidopsisPMT in tobacco STR in Catharanthuscis-elements in the genes’ promoters cis-elements are key to understanding both JA-specific signal transduction and inter-hormonal cross-talk.These TFs bind to specific promoter elements of downstream genes and propagate JA signaling. One well-defined JA-responsive element, which is bound by MYC2, is the G-box (CACGTG) or G-box like motif (core ACGT) Arabidopsis HDAC6 or HDAC19 knockout mutants and overexpression plants Histone acetyltransferases (HATs) and histone deacetylases (HDACs) play key roles in regulating gene expression through histone modification. The addition of acetyl groups to conserved lysine residues neutralizes the positive charge of histone tails and decreases their affinity for DNA Arabidopsis, the expression of AtJMT, which encodes a jasmonic acid carboxyl methyltransferase responsible for MeJA formation, is developmentally regulated and induced upon wounding or JA application BcNTR1 encodes the orthologous JA carboxyl methyltransferase in Brassica campestrisAtJMT expression. In this study, we identified a novel JA-responsive cis-element (JARE) in the AtJMT and BcNTR1 promoters and isolated a trans-acting factor, AtBBD1, which binds to the JARE and interacts with AtJAZ1 and AtJAZ4. We also showed that AtBBD1 regulates transcription of AtJMT and another JA-regulated gene.One key aspect of JA signaling is feedback regulation of JA synthesis. In Arabidopsis thaliana ecotype Columbia (Col-0) was used as the wild type for all experiments. The Brassica campestris variety and source were described in Song et al. (2000) Arabidopsis was transformed with Agrobacterium tumefaciens (strain C58C1) using the ﬂoral dip method GUS expression in response to JA. We identified homozygous lines by testing T3 progeny for resistance to antibiotics. The basal level and MeJA responsive induction of reporter gene were variable among transformants. A line showing medium level of expression was selected from each construct by RT-PCR analysis. Several lines showing extremely high or low level of basal expression were excluded. For chemical treatment, solutions of 100 µM MeJA (Aldrich), 100 µM (±)-JA (Duchefa), 100 µM (±)-ABA (Duchefa), 50 µM SA (Sigma), or 5 mM ethephon were applied to soil-grown 4-week-old plants by spraying., a cDNA library was prepared by RT-PCR from MeJA-treated seedlings of Arabidopsis Col-0 into pGADT7-Rec2. Bait DNA (−3518 to −3390 bp) containing JARE was cloned into the pHIS2 reporter vector. Positive clones were identified by nucleotide sequencing with AD sequencing primers. To identify the AtBBD1 binding sequence, various promoter fragments were cloned into the pHIS2 vector. The full length CDS or specific domains of AtBBD1 were cloned into pGADT7-Rec2.The yeast one-hybrid screening was performed using MATCHMAKER One-Hybrid Library Construction and Screening Kit (Clontech). To isolate JARE-binding proteinsAtJAZ genes were amplified by RT-PCR from 14-day-old seedlings of Arabidopsis Col-0 . Full-length cDNAs for 12 is Col-0 . Each geEscherichia coli. A soluble crude extract of recombinant protein was used for EMSA. DNA fragments labeled with [γ-32P]dCTP were incubated with MBP-AtBBD1 or MBP-AtBBD1DB in the binding buffer  for 1 hour. For competition analysis, unlabeled DNA fragments were included in the binding reactions as competitors in 10-fold molar excess relative to the labeled probes in each step. The reaction mixture was analyzed by 10% polyacrylamide gel electrophoresis and the wet gel was exposed and detected by BAS reader .Full length CDS or DNA binding domain (a.a. residue 257 to 325) of AtBBD1 were fused in frame with the maltose-binding protein (MBP) at the C-terminus and expressed in 15 using Superscript III reverse transcriptase (Invitrogen) according to the manufacturer’s instructions. PCR was performed using sequence-specific primers described in Northern blot analysis and RT-PCR were carried out as described by Seo et al. (2011) In vitro pull-down assays were carried out according to the procedure of Seo et al. (2011) AtJAZ1 was fused in frame with the maltose-binding protein (MBP) at the C-terminus in pMAL-c2E vector, expressed in Escherichia coli and purified using amylose resin. Pulled-down mixtures were separated by SDS–PAGE, transferred to nitrocellulose membrane (Whatman), and detected using anti-c-myc antibody (Santa Cruz Biotechnology).Agrobacterium tumefaciens strains C58C1 carrying either the 6xMYC-BBD1 or 3xHA-AtJAZ1 construct under the control of the 35S promoter were grown at 28°C in Luria-Bertani medium and resuspended in infiltration media. For cotransfections, Agrobacterium cultures carrying each construct were mixed in equal proportion. Leaves of 4-week-old Nicotiana benthamiana plants were infiltrated with a needleless syringe carrying bacterial cultures through the abaxial air spaces.Agroinfiltration for transient protein expression in tobacco leaves was performed according to the method described by Voinnet et al. AtJMT. PCR products were separated on ethidium bromide-stained agarose gel or real-time PCR was used to quantify the amplification. For real-time PCR, amplification of JMTP (near the JARE) was normalized to that of Actin ChIP-PCR was carried out as described by Saleh et al. (2008) AtJMT and BcNTR1 are orthologues, which are both induced by MeJA treatment BcNTR1 and AtJMT JA-responsive expression, we first defined the promoter regions that mediate induction by JA. Promoter regions 4.4 kb upstream of BcNTR1 and 4.5 kb upstream of AtJMT, respectively, were combined with the GUS coding sequence and transformed into Arabidopsis. Transcription of GUS was induced within 1 hour after MeJA treatment in both sets of transgenic plants (cis-acting elements (JARE). Also, these results showed that the BcNTR1 promoter works in Arabidopsis although it originated from Brassica.c plants , showingAtJMT promoter. A series of 5′-deleted promoters was fused to the GUS coding region and transformed into Arabidopsis. Each construct contains from 4.5 kb to 500 bp of the promoter region  showed GUS induction within 1 hour; however, those containing promoter fragments shorter than 2.0 kb (JP2.0∼JP0.5) did not show GUS induction  present in the r region . GUS expnduction . These rAtJMT promoter. As before, promoter regions were fused to GUS and transformed into Arabidopsis. When GUS mRNA levels were analyzed by RT-PCR after MeJA treatment, constructs containing 2,400 and 2,294 bp of the AtJMT promoter were responsive to MeJA, but constructs containing regions shorter than 2,294 bp were not responsive to MeJA  and the BcNTR1 promoter (39 bp) showed a highly conserved sequence motif, TCCTGA  of the CaMV 35S promoter and a GUS coding sequence. Also a mutant version, in which the core 6 nucleotides, TCCTGA, were mutated to TTTTTT, was constructed in the same manner to determine the role of this core element in response to JA (Arabidopsis (Col-0) and transgenic lines were treated with MeJA. Histochemical staining of transgenic plants showed that JA-responsive GUS activity was present only in 4xJARE:GUS lines, but not in 4xmJARE:GUS lines , which acts in abscisic-acid (ABA)-dependent callose deposition Arabidopsis thaliana genome also contains an AtBBD1 homologue, AtBBD2, with 81% amino acid sequence identity to AtBBD1 (We next carried out yeast one-hybrid (Y1H) screening to isolate protein factors that bind to the JARE. A segment  of the o AtBBD1 . AtBBD1 o AtBBD1 .BcNTR1 promoter fragment, or to another cis-element, the bait region (−3518 to −3390) of the BcNTR1 promoter was divided into three segments; a (−3518 to −3471), b (−3471 to −3430) and c (−3430 to −3390) (To determine whether AtBBD1 binds to the putative JARE core sequence of GTCCTGA in the o −3390) . Each seTo further narrow down the binding sequences within this segment (−3518 to −3471), a series of mutated bait segments (a1 to a8) was designed by changing 6 nucleotides of each subsegment into 6 adenines  and testJMT  or PJMT (−2305 to −2278 of the AtJMT promoter), which contain the JARE, were used as bait in the Y1H assay. For the G-box, PG-box is a mutant version of PNTR1 in which the JARE was replaced with a G-box. The G-box is a typical JA-responsive element and is bound by AtMYC2 JMT and PNTR1 but did not interact with PG-box  that is known to interact with full-length AtBBD1. Constructs BBD1B (116–325) and BBD1E (257–325), which both contain the C-terminal predicted UVR domain, showed DNA binding activity in yeast, but the other constructs showed no DNA binding activity , a domain of unknown function 151 (DUF151), and a UV responsive (UVR) domain at the C-terminus activity . This reE. coli. Crude extracts containing MBP-BBD1E bound to the 70 bp DNA fragment containing the JARE of the BcNTR1 promoter. Competition assays with unlabeled probe showed the specificity of binding (To confirm the DNA binding activity of BBD1E (257–325) by electrophoretic mobility shift assays (EMSA), we made a construct, MBP-BBD1E, which fused amino acids 257–325 with Maltose Binding Protein (MBP) and expressed this fusion protein in  binding . These rArabidopsis JAZ proteins. Full-length AtBBD1 was fused to the GAL4 DNA binding domain (BD) and the full-length protein for each of of 12 AtJAZs was fused to the AD. AtBBD1 showed strong interactions with AtJAZ1 and AtJAZ4 in Y2H assays  assays to test whether AtBBD1 interacts with H assays .in vitro pull-down assays were carried out. Recombinant MBP-AtJAZ1 bound to amylose resin was incubated with plant extracts prepared from a 35S::6xMYC-AtBBD1 transgenic plant and pulled-down proteins were analyzed by Western blotting with anti-MYC antibody. Recombinant 6xMYC-AtBBD1 was pulled down by recombinant MBP-AtJAZ1 , and H (81–116) interacted with AtJAZ1 in yeast, indicating that the HCR domain between amino acid residues 81 and 116 of AtBBD1 interacts with AtJAZ1 .Arabidopsis.Reciprocally, to identify the domain of AtJAZ1 that mediates interaction with AtBBD1, 5 truncated protein constructs containing the ZIM/TIFY or Jas domains of AtJAZ1 were designed for Y2H assays. JAZ1A (1–204) and JAZ1B (100–181) fragments as well as JAZ1F (full length JAZ1) interact with AtBBD1. These results indicate that the amino acid sequence from 100 to 181 of AtJAZ1, which contains the ZIM/TIFY domain, is responsible for interaction with AtBBD1 . Therefoin vivo function of AtBBD1 in regulating AtJMT gene expression, a T-DNA insertion knockout mutant, atbbd1, was examined. When treated with MeJA, the atbbd1 mutant showed no difference from wild type plants (Col-0) in AtJMT gene expression  (JR2 gene expression was enhanced in overexpression plants. These results further support the hypothesis that AtBBD1 functions as a repressor of AtJMT gene expression in vivo by binding to JARE but acts as a positive regulator of JR2.To understand ed lines . Transge (Col-0) . Also, JAtJMT gene expression, we next examined the level of histone acetylation in the promoter region of AtJMT. It has been reported that AtJAZ1 interacts with HDA6 directly and contributes to histone deacetylation AtJMT promoter region neighboring the JARE (JMTP ). Upon MeJA treatment, the levels of histone H3 and H4 acetylation in the promoter region of AtJMT were enhanced in atbbd1 atbbd2 mutant plants but reduced in OX-4 compared to wild type plants TCCTGA. The JARE was identified in two orthologous genes encoding an enzyme involved in JA biosynthesis, −3480 bp upstream of BcNTR1 and –2290 bp upstream of AtJMT, respectively. The same JARE sequence is present in both genes, although at slightly different positions in each promoter. Considering that the two genes are orthologous, encoding the same enzymatic activities in the same plant family, the two loci could have conserved mechanisms of transcriptional regulation in which they share homologous cis-acting elements and trans-acting factors. The JARE is also found in the promoters of other Arabidopsis JA-responsive genes, including LOX2, COI1, JAZs , WRKY70, PDF1.2, VSP1, and MYBs (MYB24 and 44), and in other plants, including the promoter of the rice JA-responsive gene OsbHLH148. Therefore, JA-responsive regulation through the JARE may affect many Arabidopsis genes and may also be conserved beyond the Brassicaceae.Here we have identified a novel BcNTR1 and AtJMT promoters, but tests of promoter deletions and multimerized JARE constructs showed that these G-box elements are not necessary for the JA response of AtJMT and BcNTR1 expression (AtJMT gene expression in response to MeJA treatment was not affected in myc2 knockout plants (jin1–7 and jin1–8)  and GCC motifs (GCCGCC) are known JA-responsive elements in plants pression . Moreove jin1–8) . These rcis-elements. For example, the GCC motif was initially defined as an ethylene (ET)-responsive element in EREBPs PDF1.2 gene nopaline synthase (nos) and barley lipoxygenase 1 (LOX1) genes CGTCAATGAA) and JASE2 (CATACGTCGTCAA) of Arabidopsis OPR1 are also reported to be JA-responsive motifs cis-element controlling JA-responsive gene expression.The JARE is also distinct from other reported JA-responsive AtJMT gene expression regulation by the JARE cis-acting element, we identified a trans-acting factor, AtBBD1, which binds to the JARE. AtBBD1 is an Arabidopsis homologue of OmBBD1, which is involved in ABA dependent callose deposition AtBBD1 expression was induced by various plant hormones such as MeJA, SA, ABA, and ETP TCCTGA, and does not bind to other sequences such as the G-box . Sequencs plants . TherefoAtJMT. This repression is shown by induction of AtJMT expression in mutants lacking both AtBBD1 and its close homologue AtBBD2 -responses through histone deacetylation Unlike other JA-dependent transcription factors, which act as transcriptional activators after release from JAZ interaction, AtBBD1 acts to repress expression of the JARE-regulated target gene e AtBBD2  and S6. e AtBBD2  and Y2H e AtBBD2 . These rAtJR2 expression in the atbbd1 atbbd2 mutant  but regulate target gene expression in the opposite manner. ERF1, 2, and 5 function as activators and ERF3, 4, and 7–12 function as repressors cis-element TIR1 complex in an auxin-dependent manner Regulation by competing positive and negative transcription factors has substantial precedent. In plants, a family of transcription factors, such as the auxin-responsive element binding factors (ARFs) or ET-responsive element binding factors (ERFs), can share the same DNA binding domain and the same AtJMT could be postulated to function as follows Click here for additional data file.Figure S2Analysis of atbbd1 and atbbd1 atbbd2 knockout lines.(TIF)Click here for additional data file.Figure S3Analyses of transgenic Arabidopsis overexpressing AtBBD1.(TIF)Click here for additional data file.Figure S4AtJMT expression pattern in myc2 mutants.(TIF)Click here for additional data file.Figure S5Expression pattern and localization of AtBBD1.(TIF)Click here for additional data file.Figure S6Gene expression pattern in atbbd2 mutant.(TIF)Click here for additional data file.Figure S7Interaction of AtBBD2 with AtJAZ1 and AtJAZ4 in yeast.(TIF)Click here for additional data file.Table S1List of constructs and primer sequences used in this study.(PDF)Click here for additional data file.Procedure S1(PDF)Click here for additional data file.