There are at least four CTCF-enriched sites and two GR-binding sites within the ANGPTL4 locus. Among them, the major CTCF-enriched site is positioned near the ANGPTL4 enhancer that binds GR. We showed that CTCF is required for induction and subsequent silencing of ANGPTL4 expression in response to dexamethasone (Dex) and that transcription is diminished after long-term treatment with Dex. Although the ANGPTL4 locus maintains a stable higher-order chromatin conformation in the presence and absence of Dex, the Dex-bound GR activated transcription of ANGPTL4 but not that of the neighboring three genes through interactions among the ANGPTL4 enhancer, promoter, and CTCF sites. These results reveal that liganded GR spatiotemporally controls ANGPTL4 transcription in a chromosomal context.Glucocorticoid signaling through the glucocorticoid receptor (GR) plays essential roles in the response to stress and in energy metabolism. This hormonal action is integrated to the transcriptional control of GR-target genes in a cell type-specific and condition-dependent manner. In the present study, we found that the GR regulates the  The glucocorticoid receptor (GR) is a member of a family of transcription factors that regulate biological processes, such as basal and stress-associated homeostasis, energy metabolism, and the immune response in a cell-type and condition-dependent manner , 2. In tLcn2 encoding the acute-phase protein lipocalin-2 is co-regulated through long-range interactions with Ciz1 located approximately 30-kb upstream from the Lcn2 GRE [Lcn2 GRE before GR binding [FKBP51, which encodes the GR chaperon FKBP51, is regulated by GR-responsive enhancers far from the transcription start site [FKBP51 and these enhancers are bordered by CTCF-binding sites, which likely contribute to long-range interactions and loop formation. However, whether CTCF contributes to the regulation of GR target genes remains to be determined.Although evidence indicates that the GR regulates gene expression through binding to promoter regions –5, recenLcn2 GRE . Further binding . Moreoveart site . FKBP51 angiopoietin-like 4 gene (ANGPTL4) is a primary target of GR and is dominantly expressed in the liver and adipose tissue [ANGPTL4-null mice and are increased in mice that overexpress ANGPTL4 in the liver [ANGPTL4-null mice exhibit reductions in glucocorticoid excess-induced changes associated with hypertriglyceridemia, hepatic steatosis, and visceral obesity [ANGPTL4 by CTCF as well as GR has not been investigated.The e tissue –16. ANGPe tissue –19. Plashe liver . ANGPTL4 obesity . These fHigher-order chromosome conformations, such as chromatin looping, mediate long-range physical interactions between distal regulatory elements and their target genes . The chrANGPTL4 in a CTCF-mediated chromatin context in the human hepatic carcinoma cell line HepG2. We identified one GR-binding enhancer site adjacent to a liver-specific CTCF-enriched site. Moreover, in HepG2 cells treated with dexamethasone (Dex), CTCF was required for induction and subsequent silencing of ANGPTL4, likely via an enhancer–promoter interaction. Therefore, liganded GR can target ANGPTL4 but not three neighboring genes. Further, induction of ANGPTL4 transcription was decreased by long-term glucocorticoid treatment. These results indicate that liganded GR spatiotemporally controls the transcription of ANGPTL4 depending on the chromosomal context.Here, we report that GR regulates Human hepatocellular carcinoma HepG2 cells were obtained from the Japanese Collection of Research Bioresources Cell Bank . HepG2 cells were cultured in DMEM (Sigma-Aldrich) supplemented 10% (v/v) fetal bovine serum (FBS). The cells were treated with 100 nM Dex (Sigma-Aldrich) after the cells were cultured for 24 to 48 h in DMEM (Sigma-Aldrich) containing 10% (v/v) dextran-coated charcoal (DCC)-treated FBS.36B4 mRNA encoding ribosomal protein, large, P0. Primer sequences are listed in S1 Table.Total RNA was isolated from cultured cells with TRIzol (Invitrogen). For cDNA synthesis, 500 ng of total RNA was reverse-transcribed using a High Capacity cDNA Reverse Transcription Kit (Applied Biosystems), according to the manufacturer’s instructions. We used an ABI Prism 7300 (Applied Biosystems) and SYBR Green to perform qRT-PCR. Each experiment was performed at least three times. Expression levels were normalized to those of RAB11B-AS-siRNA sequence is as follows: 5´-GACCAAAUAACUAAUGAGA(dT)(dT)-3´and 5´-UCUCAUUAGUUAUUUGGUC(dU)(dG)-3´. Cells were transfected for 48 h with siRNAs in the presence of Lipofectamine RNAiMAX (Invitrogen).GL3 (control) and CTCF-specific siRNAs were used as previously reported . The RAB7) were treated with Dex (100 nM) for 3 h and then cross-linked with 1% formaldehyde at room temperature for 10 min. The cells were lysed with the RIPA buffer supplemented with protease inhibitors. The lysates were sonicated using a Branson Bioruptor to yield 200–500 bp DNA fragments. ChIP was performed using anti-glucocorticoid receptor (GR) antibodies . DNA fragments were collected using Dynabeads Protein A/G (Life Technologies). Purified DNA (20 ng) was used to construct libraries for sequencing using an Ion Fragment Library Kit (Life Technologies). High-throughput sequencing was performed using Proton semiconductor sequencers (Life Technologies) according to the manufacturer's instructions. Proton sequence data were aligned to the human reference genome hg19. Peaks were detected using the MACS algorithm included with Avadis NGS software (Agilent Technologies). GR binding sites were detected according to a cut-off value of P = 10−5 and their enrichment by a factor of 10 vs input. ChIP-seq data for CTCF and histone modifications in HepG2 cells were obtained from the ENCODE/Broad Institute via the UCSC Genome Browser website (http://genome.ucsc.edu/). Gene Expression Omnibus (GEO) accession numbers are as follows: GSM733645, CTCF; GSM733743, H3K27Ac; GSM798321, H3K4me1; and GSM733737, H3K4me3.HepG2 cells , anti-GR , anti-H3K27ac , anti-RNA Pol II serine-5 phosphorylation (provided by Dr. Hiroshi Kimura). Rabbit IgG  served as a control. Purified DNA was used in quantitative PCR analyses performed using an ABI Prism 7300 System (Applied Biosystems) and SYBR Green. The threshold was set to intersect the linear segment of the PCR amplification curve, and the number of cycles (Ct) required to reach the threshold was counted. We generated a standard curve using the input sample and calculated the enrichment by applying the Ct value of each sample. Primer sequences are listed in ANGPTL4 locus, RPCI11.C-978J4, was digested with Sau3AI, which is insensitive to Dam methylation, followed by random ligation. After reversing the cross-links, genomic DNA was purified using phenol extraction and ethanol precipitation. We used an ABI Prism 7300 (Applied Biosystems) and a Thunderbird SYBR qPCR Mix (Toyobo) to assess the ligated products. The 3C-qPCR data were normalized to the value of the loading control, using internal primers located within ANGPTL4. The relative frequencies of interactions between the reference and its physically adjacent site were normalized to 1. We used the Student t test to evaluate the results of more than three independent experiments. Primer sequences are listed in Formaldehyde cross-linked chromatin from HepG2 cells was digested with DpnII overnight, followed by ligation catalyzed by T4 DNA ligase at 16°C for 1 h. To prepare standard curves, a bacterial artificial chromosome spanning the t test. To compare three groups, we performed ANOVA followed by Tukey-Kramer post hoc test. P < 0.05 indicates a statistically significant difference.Differences between two groups were analyzed using the Student ChIP-seq datasets of GR are deposited in the Gene Expression Omnibus (GEO) database under the accession number GSE85343.ANGPTL4 locus is located on human chromosome 19p13 where KANK3, ANGPTL4, RAB11B, and RAB11B AS are clustered within an approximately 80-kb region. Among them, ANGPTL is a direct target of GR signaling [ANGPTL4 locus in HepG2 cells , which contain a CTCF-binding consensus motif . We designated AC1 to AC4. The AC3 site is unique to HepG2 cells , suggesting that AC3 plays a cell-type-specific role in regulating ANGPTL4 transcription.The ignaling . To examFig 1A), and identified the GR-enriched sites (AG1 and AG2) in the ANGPTL4 locus, which possess GREs . ChIP-Seq data indicated that the AG1 and AG2 sites were marked with acetylated lysine residue 27 and mono-methylated lysine residue 4 of histone H3 , suggesting that they are potential enhancers. These findings are consistent with the evolutionary conservation of the AG2 sequence among rats, mice, and humans, which exhibits GR-responsive enhancer activity in rat H4IIE cells [S1B Fig), suggesting that these sites cooperate in the GR-dependent expression of ANGPTL4.We performed ChIP-Seq analysis of HepG2 cells treated with 100 nM Dex for 3 h to identify genome-wide GR binding sites . In contrast, Dex treatment did not affect the levels of the three neighboring genes , indicating that ANGPTL4 is a specific GR target. Western blot analysis showed that ANGPTL4 protein was maximally induced at 3 h after Dex addition and decreased from 3 to 24 h .We next tested the expression levels of ANGPTL4 was directly regulated by liganded GR, we analyzed HepG2 cells treated with the GR antagonist mifepristone (100 μM for 1 h) before Dex treatment. Mifepristone completely inhibited ANGPTL4 expression 3 h after Dex addition , indicating that ANGPTL is the only GR target in this chromosomal region.To determine whether ANGPTL4 locus . We first examined the levels of enrichment of GR at AG1 and AG2. GR was not detected at either AG site under basal conditions (Dex 0 h), while GR bound these AG sites 3 h after Dex addition (Dex 3 h) . GR was similarly retained 24 h after Dex treatment (Dex 24 h), although the levels of ANGPTL4 mRNA decreased at this time . In contrast, CTCF-enrichment did not significantly change at AC1 to AC4 after Dex treatment , suggesting that GR does not compete with CTCF for DNA binding at the AC3 and AG2 sites, despite their proximity.We performed ChIP-qPCR analyses to assess the effect of Dex on the chromatin status of the ANGPTL4 promoter and enhancer elements AG1 and AG2 . After Dex treatment for 3 h (Dex 3 h), the H3K27Ac mark increased by a factor of approximately 2 at these sites compared with the basal condition and was detected 24 h after Dex addition. Further, we determined the enrichment of RNA polymerase II (Pol II) at the ANGPTL4 promoter and enhancers . Active Pol II was enriched at the ANGPTL4 promoter by approximately a factor of 4 at 3 h after Dex treatment and then decreased to the basal level at 24 h. In contrast, enrichment of Pol II enrichment at AG1 and AG2 was moderately detected 3 h after Dex addition, indicating that liganded GR bound to distal enhancers and activated transcription mediated by the ANGPTL4 promoter.We next determined the enrichment of the active chromatin mark H3K27Ac in the Long-Term Dex Treatment  altered ANGPTL4 expression . Control HepG2 cells were cultured in DMEM medium containing 10% DCC-treated FBS. In control cells, ANGPTL4 expression increased by approximately 30-fold 3 h after Dex addition and then gradually decreased at 24 h . ANGPTL4 induction in response to Dex treatment was significantly diminished in the LTDT cells . The basal level of ANGPTL4 expression in LTDT cells was higher by a factor of approximately 5, compared with that of the control cells.An excess of endogenous or exogenous glucocorticoid causes hypertriglyceridemia, insulin resistance, fatty liver, hepatic steatosis, and obesity as well as Cushing’s syndrome . These cFig 3C). GR enrichment at AG2 differed significantly between these conditions. GR did not bind AG2 under basal conditions (Dex 0 h) in control cells and was significantly enriched by Dex treatment (Dex 3 h). In contrast, moderate GR binding at AG2 in LTDT cells was detected initially (Dex 0 h), and the levels were approximately 50% of those of the Dex-treated control cells and did not increase after Dex addition (Dex 3 h). Thus, newly added Dex reduced ANGPTL4 induction in LTDT cells and did not increase GR binding to AG2.We next examined GR enrichment at AG sites in the control and LTDT cells . ChIP-qPCR analysis using anti-CTCF antibodies revealed that the amounts of CTCF at the AC sites were comparable in the control and LTDT cells. Moreover, we did not detect significant changes in GRα (encoding GR) and CTCF mRNAs in control and LTDT cells .We next checked whether CTCF was involved in the reduction of GR enrichment at AG2 in LTDT cells . Further, we detected a decrease in the level of GR in nuclear extracts prepared from LTDT cells , suggesting that a negative feedback mechanism regulates GR levels in LTDT cells. Together, these results suggest that GR targeted ANGPTL4 and the interaction was affected by long-term exposure of cells to excess concentrations of Dex.To clarify the mechanism of reduced GR binding to AG2 in LTDT cells, we performed western blot analysis of whole-cell lysates and found that the amount of GR significantly decreased in LTDT cells . In addition, the depletion of CTCF did not affect GR expression at the protein level . When the CTCF knockdown cells were treated with Dex, ANGPTL4 mRNA levels significantly increased 3 h after Dex addition, and induction was maintained for 24 h . In contrast, ANGPTL4 expression increased 3 h after treatment and then decreased in control cells. Similarly, the increased level of ANGPTL4 mRNA in the CTCF-knockdown cells was detected in LTDT cells . Moreover, we confirmed that the expression of another GR-target gene FKBP5 was hardly affected by CTCF knockdown .To determine the role of CTCF in the regulation of ANGPTL4 locus, the direction of RAB11B-AS transcription is opposite of that of ANGPTL4 and RAB11B  and extends to the AC3/AG2 sites in the 3´-region of ANGPTL4 gene . To test whether the RAB11B-AS transcripts influenced ANGPTL4 expression, we inhibited the expression of RAB11B-AS in HepG2 cells  and found that Dex-induced ANGPTL4 expression was unchanged .Several lines of evidence suggest that antisense transcripts influence sense-strand transcription . FurtherFig 4E). We detected a significant reduction of CTCF enrichment at AC3, and the loss of CTCF caused an increase in GR binding to AG1 and AG2 by a factor of approximately 2 at 3 h after Dex treatment. These data suggest that CTCF negatively regulated the induction of ANGPTL4 transcription by reducing the amount of liganded GR bound to AG sites. Further, at 24 h after Dex addition, GR enrichment at the AG sites was equivalent in the control and CTCF-knockdown cells, although ANGPTL4 was expressed at a high level when CTCF was depleted . Moreover, the decrease in the level of ANGPTL4 mRNA coexisted with a high level of GR binding to the AG sites 24 h after Dex addition . Together, these results suggest that Dex-induced ANGPTL4 expression was regulated by the CTCF-mediated chromatin context as well as by GR binding to the enhancers. We also observed that the expression level of nearby genes of ANGPTL4 was partly increased by CTCF knockdown , suggesting that the activity of the ANGPTL4 enhancer may spread to nearby genes in the CTCF-knockdown cells. Thus, CTCF was required for proper induction and subsequent silencing of ANGPTL4 in cells treated with Dex.We next performed ChIP-qPCR analysis of CTCF-knockdown HepG2 cells to determine whether depletion of CTCF affected GR binding to the AG1 and AG2 sites.  assay of Dex-treated HepG2 cells. The experiments were performed under the conditions as follows: basal condition (Dex 0 h), Dex treatment for 3 h (Dex 3 h) and 24 h (Dex 24 h). There are at least four CTCF-enriched sites (AC1–AC4) and two GR-binding enhancer sites (AG1 and AG2) in the ANGPTL4 locus . DpnII was used to convert these sites and the ANGPTL4 promoter into distinct fragments, except for the composite AC3/AG2 site. Using quantitative PCR analyses of the intramolecular ligation products, we first tested the interaction frequency of AC3/AG2 as a reference, which is the counterpart of a previously demonstrated enhancer element [ANGPTL4 promoter was consistently high under basal and Dex conditions . The interaction between AC3/AG2 and AC2 increased by a factor of 2.5 at 3 h after Dex addition and then decreased to the basal level at 24 h. Dex treatment induced a significant dynamic change at AC2, because the interaction frequencies of AC3/AG2 with the other sites were largely constant.To investigate higher-order chromatin regulation in the  element , with thANGPTL4 promoter as a reference . The interaction frequency of the ANGPTL4 promoter with AC2 significantly increased 3 h after Dex addition and then decreased to the basal level at 24 h, similar to case of the reference AC3/AG2 . The interaction of the ANGPTL4 promoter with other fragments was unchanged during Dex treatment, and the ANGPTL4 promoter possibly interacted with the AC3/AG2 sites. We repeated the 3C assay of CTCF-knockdown and LTDT cells, but the data were not reproducible because of the higher-order chromatin instability of the ANGPTL4 locus under these conditions (data not shown). Together, our results suggest that 1) the ANGPTL4 locus maintains the stable higher-order chromatin conformation under basal conditions and Dex treatment and 2) ANGPTL4 is selectively activated by liganded GR via the associations among the enhancer, promoter, and CTCF sites.To further evaluate the higher-order chromatin conformation in this locus, we performed the 3C assay using the ANGPTL4 in a CTCF-mediated chromatin context in the human hepatic carcinoma cell line HepG2. Liganded GR selectively targeted ANGPTL4 but not the three neighboring genes via interactions among the enhancer, promoter, and CTCF sites. In CTCF-depleted cells, ANGPTL4 transcription was more inducible by Dex and was persistently up-regulated for 24 h, indicating that CTCF was required for proper induction and subsequent silencing of transcription. Further, long-term Dex treatment subsequently diminished the induction of ANGPTL4 transcription. Our results therefore identified the mechanism of the regulation of ANGPTL4 transcription by CTCF as well as GR.In the present study, we show that GR regulated Fig 6 shows a model of the regulation of ANGPTL4 transcription and the conformation of higher-order chromatin in cells treated with Dex. In its basal state, ANGPTL4 is not activated in the absence of glucocorticoid. However, the higher-order chromatin structure of the locus is prearranged. In the transcriptionally active state (Dex 3 h), liganded GR binds to AG sites and increases the affinities of the interactions among enhancer, promoter, and CTCF sites in ANGPTL4, which specifically activate ANGPTL4 but not the neighboring genes KANK3, RAB11B-AS, and RAB11B. Since AC2 and AC3 show convergent orientations to the CTCF-binding sites, these may facilitate the formation of a chromatin loop [ANGPTL4 transcription is down-regulated. During these processes, CTCF is required for both induction and subsequent silencing of the ANGPTL4 gene.tin loop . In the ANGPTL4 was induced after 3 h and was silenced after 24 h. During this reaction, ANGPTL4 locus maintained a higher-order chromatin conformation during basal and induced states, together with specific interactions between the ANGPTL4 enhancer and promoter that were required for selective transcriptional activity. In contrast, under long-term Dex treatment, ANGPTL4 induction was significantly inhibited, at least in part, by the decrease in the level of GR. When the levels of glucocorticoids are up-regulated in vivo [ANGPTL4 transcription to promote the catabolism. In contrast, long-term elevation of endogenous or exogenous glucocorticoids causes hypertriglyceridemia, insulin resistance, fatty liver, and obesity [Glucocorticoids are essential regulators of lipid homeostasis and energy metabolism, mainly via GR signaling. Dex treatment of HepG2 cells showed that  in vivo  under fa obesity . Such a CTCF expression in HepG2 cells shows that ANGPTL4 transcription was highly induced by 3 h and was subsequently up-regulated for 24 h after Dex addition. Since there was no difference in liganded GR binding 24 h after Dex addition to control and CTCF-depleted cells, enhancer-promoter interactions in ANGPTL4 may contribute to persistent activation. Moreover, CTCF-binding is known to be affected by DNA methylation in the genome [de novo CpG methylation[IGF2 imprinting is altered in aged and senescent human epithelial cells [ANGPTL4 transcription, possibly caused by changes in DNA methylation and the down-regulation of CTCF levels. In conclusion, our study of the human ANGPTL4 locus sheds light on the molecular basis of the regulation of GR target genes and its biological significance.The siRNA-mediated inhibition of e genome , 43, andthylation, 45. Indal cells . Our preal cells . Thus, tS1 Fig(B) The close localization of AC3 with AG2 in the 3’-region of ANGPTL4.(A) The consensus motifs of four CTCF-binding sequences (AC1–AC4) and two GR-binding sequences (AG1 and AG2). (TIF)Click here for additional data file.S2 FigANGPTL4 locus. The CTCF-enriched AC3 site is uniquely present in the genome of HepG2 cells.ChIP-Seq data reveal four major CTCF-enriched sites in the (TIF)Click here for additional data file.S3 FigKANK3, ANGPTL4, RAB11B, and RAB11B AS genes in cluster. In cells treated with Dex, ANGPTL4 mRNA was only induced by Dex. **P < 0.01. (B) Western blot analysis of ANGPTL4 expression in HepG2 cells under the presence or absence of Dex.(A) The expression patterns the (TIF)Click here for additional data file.S4 FigGRα) and CTCF mRNAs in LTDT cells. In the presence of Dex, there were no significant differences in the levels GRα and CTCF transcripts in control and LTDT cells. *P < 0.05. (B) Western blot analysis of GR expression in LTDT cells. Uncropped image of western blot analysis is shown in S6 Fig.(A) Analysis of the expression of glucocorticoid receptor ((TIF)Click here for additional data file.S5 Fig(A) qRT-PCR analysis of GRα (left) and FKBP5 (right) in siRNA-transfected HepG2 cells treated with Dex. (B) The transcribed sequence of RAB11B and RAB11B AS in the human ANGPTL4 locus. The arrow indicates the transcriptional start site. (C) Close localization of AC3/AG2 with the 3´-region of RAB11B AS. (D) qRT-PCR analysis of four genes within human ANGPTL4 locus in siRNA-transfected HepG2 cells treated with Dex. The relative expression level is indicated as a value normalized to the level of 36B4 mRNA. Asterisks indicate statistically significance between control and CTCF-knockdown cells at each time point. *P < 0.05, **P < 0.01, ***P < 0.005.(TIF)Click here for additional data file.S6 Fig(A) Uncropped images of Fig 3E. (B) Uncropped image of S4B Fig.(TIF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.


Rational engineering of biological systems is often complicated by the complex but unwanted interactions between cellular components at multiple levels. Here we address this issue at the level of prokaryotic transcription by insulating minimal promoters and operators to prevent their interaction and enable the biophysical modeling of synthetic transcription without free parameters. This approach allows genetic circuit design with extraordinary precision and diversity, and consequently simplifies the design-build-test-learn cycle of circuit engineering to a mix-and-match workflow. As a demonstration, combinatorial promoters encoding NOT-gate functions were designed from scratch with mean errors of <1.5-fold and a success rate of >96% using our insulated transcription elements. Furthermore, four-node transcriptional networks with incoherent feed-forward loops that execute stripe-forming functions were obtained without any trial-and-error work. This insulation-based engineering strategy improves the resolution of genetic circuit technology and provides a simple approach for designing genetic circuits for systems and synthetic biology. Unwanted interactions between cellular components can complicate rational engineering of biological systems. Here the authors design insulated minimal promoters and operators that enable biophysical modeling of bacterial transcription without free parameters for precise circuit design. Correspondingly, the engineering of biological systems—a process used to adapt biological components from nature and apply them in novel design combinations—often suffers from unwanted complexity at various biological levels2. Therefore, in order to enhance the reliability and predictability of biological systems engineering, this complexity must be managed or reduced3. Synthetic biology seeks to address this issue by developing foundational theories and technologies at the systems level6 using various approaches, such as developing design strategies for synthetic genetic circuits based on principles of mature engineering disciplines8, reverse engineering of natural genetic circuits to create their synthetic counterparts11, and exploration of design constraints for dealing with the complex interactions between genetic circuits and their biochemical, host, and environmental factors14. Developments have also occurred at the biological parts and modules level and include composable parts that regulate gene expression with high orthogonality and have minimal interferences with the host cells18, biological insulators that eliminate or buffer against unexpected interferences at the functional19 and physical23 interfaces of parts/modules, and a corresponding programming environment that supports automated, high-throughput composing of parts/modules for non-experts24.Biological systems such as living cells are characterized by highly complex interactions between numerous genetic and cellular components25. Canonical views assume that this architecture is modular and that operators simply acts as docking sites for transcription factors that either enhance or repress transcription initiation at the promoter core27. However, studies have revealed that the interplay between sub-promoter elements could be far more complex than hypothesized by this “modular assumption”, as the operator sequences alone can significantly perturb the intrinsic activity of promoter cores and vice versa30. Consequently, despite the intensive efforts focused on interpreting and dissecting these modes of interplays in order to develop a rational basis for promoter engineering, either computationally or experimentally33, the bottom-up design of prokaryotic promoters using sub-promoter elements is still largely an ad hoc exercise, with prokaryotic promoters are generally regarded as functionally “indivisible” parts in genetic circuit engineering.Despite these abovementioned developments that enable composing biological parts to build circuits by managing or reducing the systems complexity, methods that enable the rational design of individual basic parts, such as promoters, a crucial need for understanding and manipulating basic processes in gene expression, remain elusive. This is because the interactions between sub-part components  are also complex. For instance, prokaryotic promoters constitute a category of biological parts that encode transcriptional control of gene expression and function as key factors in information processing. In general, prokaryotic promoters have a multi-element architecture consisting of a minimal promoter  for transcription initiation and one or several operators for transcription factor bindingWe reasoned that, if the highly interconnected sub-promoter elements could be decoupled to satisfy the “modular assumption”, we would be able to precisely design promoter parts from scratch by simply combining sub-promoter elements. This easy mix-and-match approach would significantly boost our overall capacity for rationally designing genetic circuits. As a demonstration, we randomly combined 53 different promoter cores and 36 different synthetic operators to design 83 combinatorial promoters that encode NOT-gate functions. Furthermore, these insulated transcriptional elements were utilized to build four-node transcriptional networks with incoherent feed-forward loop (IFFL) topology encoding a stripe-forming function. In silico screening of all 30,528 possible designs directly captured the one with the best stripe-forming performance without any trial-and-error work, as verified experimentally . This insulation-based, sub-promoter-resolution circuit engineering methodology provides a mix-and-match approach for the bottom-up design of genetic circuits and will greatly facilitate rational gene expression engineering in synthetic and systems biology.R2 = 0.95 across all 1397 predictions), corresponding a success rate of >96%.We first developed a design strategy to functionally insulate the promoter core and surrounding operators and prevent their interaction Fig. . Naturallac promoter was selected as the archetype of promoter cores recognized by the Escherichia coli housekeeping σ factor σ70  that are responsible for the specific binding of transcription factors. To evaluate how intrinsic transcriptional activity of promoter cores is perturbed by operator identity, we collected 11 single operators recognized by different transcriptional repressors  recognized by bacterial extracytoplasmic function (ECF) σ factor σECF1134, and the T7 promoter (PT7) recognized by T7 RNA polymerase ; similarly, 1.9-fold variation in transcriptional activity was observed for PT7 . Segments R8 and R9, which are located between the −10 box and the transcription-start site, were found to be less crucial but still important . All of the segments outside of the promoter core, however, were found to be dispensable . The new results were basically the same as those shown in Fig. ECF11 and PT7 are indeed functionally modular, with a minimal length of 19 and 21 bp, respectively.In order to systematically evaluate the functional modularity of the promoter cores of Pent Fig. . For any34, the cognate promoters of two additional ECF σ factors (σECF16 and σECF20) and three T7-family RNAPs  were experimentally scanned in the same manner using saturation mutagenesis. The results revealed indispensable promoter cores with dispensable surrounding sequences similar to those of PECF11 and PT7 . An experimental framework reported in our previous study37 was used. In this framework, the expression level of a transcriptional activator is indicated by an independently characterized circuit in which superfolder green fluorescent protein (sfGFP) is driven by the same “input promoter” , the best-fit parameters sufficiently interpreted the experimental data for σECF11  are sufficient to describe the interactions of ECF11 and T7 RNAP with their respective promoter cores. Therefore, the biological roles of promoter cores in transcription initiation can be parameterized as a table of “transcriptional activation-dependent parameters” . This suggests that, in addition to the conventional equilibrium assumption, possible non-equilibrium competition between transcriptional activators and repressors should also be taken into consideration. For instance, during the DNA replication process, most DNA-bound proteins temporally detach from the DNA, which creates a time window  for both transcriptional activators and repressors to re-bind to the promoter40. Therefore, we introduced a correction term into Eq. . When σECF11was used as the activator and PECF11 as the promoter core, the response function fitting for cI434 operators was insensitive to the addition of the non-equilibrium term: fitting using Eq. , N sets of activation-dependent parameters from promoter cores and M sets of repression-dependent parameters from operators could be combined to design N × M novel combinatorial promoters with parameter-free prediction power ; for those using T7 RNAP-dependent promoter cores, however, the resulting predictions were far from precise , indicating an unambiguous requirement for the non-equilibrium term in T7 RNAP-dependent transcriptional activation.The necessity of the non-equilibrium correction term containing “erm (Eq. ), predicKA term, was computationally abstracted from the characterization data of this library  used to obtain the repression-dependent parameter database was not included.Putting the precision of promoter design at sub-promoter resolution into the context of genetic circuit engineering, we realized that we might be able to transform the process of circuit engineering from the conventional design-build-test-learn cycle to a much simpler, mix-and-match workflow. As a proof of concept, we sought to manufacture combinatorial promoters encoding genetic NOT-gate functions using a large quantity of insulated transcriptional elements Fig. . The tabary Fig. ; the proT7M43 + O2-cI434, PT7M11 + O3-HKcI, and PT7M48 + O3-TP901cI) had 1.50-, 1.76-, and 2.20-fold mean errors of model prediction, respectively, which means that they were also firmly within the same order of magnitude, albeit beyond the cutoff. To illustrate the design precision more intuitively, the predictions and measurements for all 127 T7 RNAP-dependent NOT gates, including those shown in Fig. R2 value was 0.95 across the entire set of 1397 data points  level of input can activate the output Fig. . Such a 3-cI434 and O4-cI434, were selected in order to achieve the strong repression needed for the stripe-forming function. Accordingly, a no-free-parameter model describing such a network designs was derived from Eq. , which obviously did not match the measurements. If, on the other hand, this term was included, the secondary increase of GFP expression in the response curves could be precisely predicted , once again highlighting the necessity of this non-equilibrium term. The explanation for the secondary increase of GFP expression is straightforward: at very high IPTG concentrations, the contributions of the equilibrium terms to the overall output tends to be negligible due to the high expression level and high Hill coefficient of cI434. In such a situation, the overall output depends primarily on the contribution of δR . The experimental results showed that this network executes the stripe-forming function in response to IPTG concentration gradients with 66-fold pulsing . To verify the reliability of this computational search, four more network designs were selected throughout the ranking and subjected to the same experimental evaluation. As expected, both the network fitness and response functions of these network designs were precisely predicted .Subsequently, we used our modeling framework and insulated transcriptional elements to design transcriptional IFFL networks with minimal a priori consideration, in order to determine whether the mix-and-match design process is indeed feasible. Links L1 and L2 in the IFFL networks were assigned to four Pped Fig. . The neting Fig. . MoreoveT7wt promoter . For ECF σs, the promoter cores function comparably when transferred from E. coli to Klebsiella oxytoca34, probably due to the evolutionary conservation of bacterial RNAP subunits47. Collectively, these results provide evidence for the transferability of insulated transcriptional elements.In this work, synthetic transcriptional regulation was defined by specific interactions between defined components. For instance, transcriptional activation by T7 RNAP at the T7 promoter is fully independent of the host transcription machinery. Regarding this biological simplicity, the insulated transcriptional elements hold the promise of being transferred from 46 and further combined with “part mining” technology48 to source more candidates for refinement, in accordance with the scale-up of circuit construction. The toolset of synthetic operators is even more extensive due to the higher abundance of available repressor–operator pairs35. Intriguingly, regarding the simple role of operators in our promoter architectures, it is possible to incorporate dCas9-sgRNA complexes into our promoter design as the repressors to further enhance the diversity of our toolset.The insulated promoter cores used in this study were derived from T7 and ECF11 promoters. We have shown that an insulated promoter core is a common feature of T7-family RNAPs and bacterial ECF σ factors , even in the absence of T7 RNAP; this activity could not be explained by either the promoter core or the operator alone  for the activator to re-bind. Although our explanation might not be consistent with the biological scenario, the non-equilibrium term is still necessary for precise model predictions and does capture the salient features of underlying biological processes. Interestingly, the non-equilibrium effect was found to be negligible for σECF11, indicating that our modeling is not contradictory to observations in previous studies where σ70-dependent promoters were usually used, regarding the highly similar biochemical nature of σ70 and ECF σ factors58. Differences in the non-equilibrium effect between the three types of transcriptional machinery could be important criteria for part selection in circuit design.In modeling transcriptional repression, we introduced a correction term to describe the non-equilibrium effect. Accordingly, post-DNA replication competitive binding of activators and repressors was proposed as a possible explanation that proved to be consistent with our many experimental observations. For instance, operators with low repressor-binding affinity generally tend to have high ues Fig. . This fi59. For example, if the repressor/activator of an IFFL network is shared by newly added output promoters, the repressor/activator would be titrated away from its original circuit; this effect is also called retroactivity61. In such cases, the assumption that the activator/repressor copy number greatly exceeds that of the promoters, breaks down59. In order to retain the accuracy of predictions, the mathematical model describing the transcriptional regulation of a given promoter should be adjusted from the total concentration of the repressor/activator  to the concentration of free repressor/activator ([Repressor/Activator]free).Regarding the future use of insulated transcriptional elements, one issue we might encounter is activator/repressor titration arising from the connection of more than one downstream promoters to a circuitE. coli K-12 DH10B was used for plasmid construction, parts characterization, and circuit measurements throughout this study. This strain was also used for the construction of strains that chromosomally express phage RNAPs or ECF σ factors in a constitutive or inducible manner. The vast majority of the plasmids used in this study were constructed using two basic vectors, pPT and pRG, which were constructed and preserved in our laboratory. pPT was derived from the standard BioBrick backbone pSB4C5 and was used to construct the cassettes for the “output promoter”-driving reporters. pRG with p15A-AmpR backbone was used as the carrier for the “input promoter” to express transcriptional repressors in response to IPTG. Golden Gate Assembly was used to incorporate the output promoter into pPT and RBS-CDS into pRG  or cloned into the plasmid pRG . Saturation mutagenesis of a given promoter segment was conducted using degenerate primers via PCR annealing. Specifically, a random DNA sequence corresponding to the segment was designed near the 5′ end of one primer, and the sequence of the other primer was defined; these two primers were annealed at their 3′ ends and further extended using Phusion DNA polymerase  to create the entire promoter region. The resulting promoter mutation library as a mixture was then cloned into the plasmid pPT via Golden Gate Assembly for the subsequent colony picking and in vivo analysis.For identification of insulated promoter cores and the refinement of synthetic operators, the constitutively expressed RNAP was integrated into the chromosome of TAC-RNAP cassette was integrated into the chromosome of E. coli DH10B using pOSIP, and the plasmid carrying the pPT-promoter core cassette was introduced into the resulting strain, along with the plasmid pRGc (pRG plasmid with the PTAC-lacZα fragment deleted).For characterization of transcriptional activation, the PRNAP genes were all integrated into the chromosome of E. coli DH10B using pOSIP for constitutive expression, and the pPT-derived plasmids carrying the combinational promoters were introduced into the resulting strain, along with the pRG-derived plasmid carrying the corresponding repressor.For characterization of transcriptional repression, the TAC promoters was exchanged for sfgfp. A blank plasmid (pPTc), containing the same replication origin and resistance gene as pPT, was co-transformed with the “input promoter” plasmid into the corresponding strains to standardize the antibiotics usage.For construction of dose–response curves for the “input promoters”, the RNAP/repressor gene downstream of the PThe plasmid specifications of the transcriptional IFFL networks were similar to those used for the characterization of transcriptional repression, except that the cassette integrated into the chromosome was the same as that used for the characterization of transcriptional activation. The plasmids used in each experiment discussed above are summarized in Supplementary Table 37 that measures gene expression at steady state was used. Briefly, bacteria harboring the parts or circuits of interest were first inoculated from single colonies into a flat-bottom 96-well plate for overnight growth, after which the cell cultures were diluted 196-fold with M9 medium. After 3 h of growth, the cultures were further diluted 700-fold with M9 medium, and incubated for another 6 h. Finally, 20-μl samples of each culture were transferred to a new plate containing 180 μl per well of PBS supplemented with 2 mg ml−1 kanamycin to terminate protein expression. The fluorescence distribution of each sample was assayed using an LSRII flow cytometer (BD Biosciences) with appropriate voltage settings; each distribution contained >20,000 events. Each sample was experimentally assayed at least three times. The arithmetical mean of each sample was determined using FlowJo software (v7.6). For identification of insulated promoter cores and operator refinement, the quantitative steady-state method was adjusted to allow for high-throughput measurement. The differences were: (i) the culture duration after the second dilution was decreased to 4.5 h; and (ii) cell fluorescence was assayed using a Synergy Mx microplate reader (BioTek) and normalized to the OD600  for each sample.All incubations were carried out using a Digital Thermostatic Shaker (AOSHENG) maintained at 37 °C and 1000 r.p.m., using Corning flat-bottom 96-well plates sealed with sealing film . For characterization of parts and circuit response functions, a previously developed quantitative method−1 tryptone, 5 g l−1 yeast extract, and 10 g l−1 NaCl. For agar plates, 15 g l−1 agar was added. M9 medium: 6.8 g l−1 Na2HPO4, 3 g l−1 KH2PO4, 0.5 g l−1 NaCl, 1 g l−1 NH4Cl), 0.34 g l−1 thiamine, 0.2% casamino acids (BD Biosciences), 0.4% glucose, 2 mM MgSO4, and 100 μM CaCl2. Antibiotic concentrations: where appropriate, the media contained ampicillin at a final concentration of 100 μg ml−1 from a 100 mg ml−1 aqueous stock solution and chloramphenicol at a final concentration of 25 μg ml−1 from a 34 mg ml−1 stock solution in absolute ethanol; for pOSIP-KO-mediated chromosomal integration, 50 μg mL−1 kanamycin-sulfate was used. The indicated concentrations of antibiotics were used for both LB and M9 media. Inducer concentration: an isopropyl-d-1-thiogalactopyranoside gradient was prepared at 0, 1, 5, 10, 20, 30, 50, 70, 100, 200, and 500 μM final concentrations, diluted from a stock in DMSO. Phosphate-buffered saline (PBS): 8 g l−1 NaCl, 0.2 g l−1 KCl, 1.44 g l−1 Na2HPO4, and 0.24 g l−1 KH2PO4. Kanamycin (2 mg ml−1) was added to the PBS before sampling in order to terminate protein expression.All chemicals used in the study were purchased from Sigma-Aldrich unless stated otherwise. LB medium: 10 g lTAC promoter variants was adapted from the Supplementary Eq. . The fitness function was calculated by directly multiplying the widths and heights of the peaks in the response functions, wherein the width was defined as the magnitude of the input change when the output began to increase from 0.1-fold of the maximum value, and subsequently reverted to this value, and the height was defined as the magnitude of the output change when the output increased from the minimum to the maximum value (left height) or decreased from the maximum to the minimum value (right height). Therefore, the mathematical expression of the fitness function is width×(left height)×(right height).The parameters in Eqs –3, descrtary Eq.  of a preWe declare that all relevant data supporting the findings of this study are available within the article and its Supplementary InformationPeer Review File


Sequence motifs are short, recurring patterns in DNA that can mediate sequence-specific binding for proteins such as transcription factors or DNA modifying enzymes. The androgen response element (ARE) is a palindromic, dihexameric motif present in promoters or enhancers of genes targeted by the androgen receptor (AR). Using chromatin immunoprecipitation sequencing (ChIP-Seq) we refined AR-binding and AREs at a genome-scale in androgen-insensitive and androgen-responsive prostate cancer cell lines. Model-based searches identified more than 120,000 ChIP-Seq motifs allowing for expansion and refinement of the ARE. We classified AREs according to their degeneracy and their transcriptional involvement. Additionally, we quantified ARE utilization in response to somatic copy number amplifications, AR splice-variants, and steroid treatment. Although imperfect AREs make up 99.9% of the motifs, the degree of degeneracy correlates negatively with validated transcriptional outcome. Weaker AREs, particularly ARE half sites, benefit from neighboring motifs or cooperating transcription factors in regulating gene expression. Taken together, ARE full sites generate a reliable transcriptional outcome in AR positive cells, despite their low genome-wide abundance. In contrast, the transcriptional influence of ARE half sites can be modulated by cooperating factors. Prostate cancer is the most common malignancy in American men1Reactivation of the androgen receptor  under low androgen condition is believed to drive the development of CRPC3de novo by biosynthesis from cholesterol. Such intra-tumoral androgen synthesis permits maintenance of certain intracellular androgen levels and results in reconquered mitogenic AR activity1314Reactivation of the AR in CRPC can be due to changes of the AR or its steroid ligands67891011Studies of AR chromatin binding by chromatin immunoprecipitation (ChIP) approaches like ChIP-on-chip or ChIP in combination with next generation sequencing (ChIP-Seq) have shed light onto the mechanisms of global regulation of AR activities in prostate cancer cell lines or tissues. Most AR chromatin binding studies were performed in the LNCaP cell line or its sublines34679101141718481119AREs are well studied but poorly defined and have been shown to contain two hexamers with a three base-pair spacer with an inverted repeat in the second hexamer2324CWR22Rv1 is a human prostate carcinoma epithelial cell line derived from a xenograft that was serially propagated in mice after castration-induced regression and relapse of the parental, androgen-dependent CWR22 xenograft26Lentiviral vectors encoding AR shRNA were purchased from Open Biosystems, and packaged in 293T cells by the calcium phosphate transfection. The supernatant containing lentiviral particles were collected 48 hours after transfection. CWR22Rv1 cells were transduced with the supernatant of lentiviral particles in the presence of polybrene (8 μg/ml) for 24 hours before replacement with the fresh growth media. Cells were analyzed at 72 hours post-transduction. The knockdown efficiency was confirmed by quantitative real time polymerase chain reaction (qRT-PCR) and Western-blot analysis .Total RNA from prostate cancer cells was extracted using a mammalian RNA mini preparation kit  and then digested with deoxyribonuclease I . Complementary DNA (cDNA) was synthesized using random hexamers. Triple replicate samples were subjected to SYBR green  qRT-PCR analysis in an Eco system . Gene expression profiles were analyzed using the ΔΔCT method. RT QPCR threshold cycle (CT) values were normalize the housekeeping gene cyclophilin A . The following primers served for qRT-PCR analysis of human gene transcripts: PPIA: 5′-GACCCAACACAAATGGTTC-3′; 5′-AGTCAGCAATGGTGATCTTC-3′; AR: 5′-CTCCGCTGACCTTAAAGACATC-3′; 5′-TGCCCCCTAAGTAATTGTCCTT-3′.Whole cell lysates were harvested using radio-immunoprecipitation assay (RIPA) buffer composed of 50 mM trisaminomethane hydrochloride (Tris-HCl) pH 7.5, 150 mM sodium chloride (NaCl), 1% Triton X-100, 0.1% sodium dodecyl sulfate (SDS), 0.1% sodium deoxycholate, 1.0 mM EDTA, 1.0 mM sodium orthovanadate, and 1x protease inhibitor cocktail. Lysates were subjected to sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and proteins transferred to a nitrocellulose membrane . The membrane was probed with an AR antibody  or actin antibody  followed by a secondary antibody conjugated to fluorescent dye, and blots were imaged using the odyssey detecting system (LI-COR Biotechnology).Cells were crosslinked using 1% formaldehyde for 10 min at 298 K. Formaldehyde was diluted to a final concentration of 125 mM by adding 5 M glycine. Nuclear extracts were collected and sonicated to obtain 300 bp chromatin fragments using the Covaris S2 ultrasonicator . 100 μg of chromatin was incubated with 5.0 μg of AR antibodies  overnight at 277 K followed by incubation with 30 μl of protein A/G beads for 4 hours. After four washes, crosslinking was reversed, and chromatin was digested with ribonuclease A (RNaseA) followed by proteinase K. The DNA was purified using spin columns. The size of the DNA was confirmed by a bioanalyzer .The purified DNA library was sequenced using an Illumina HighSeq2000 at the Sanford-Burnham Medical Research Institute, National Genome Library Core Facility . This study included next generation sequencing reads of ChIP-Seq experiments of the androgen-independent CWR22Rv1 cell line as well as of the androgen-dependent LNCaP cell line. For the CWR22Rv1 cell line32De novo motif analysis programs MEME and DREME identifies similar reoccurring DNA sequences, and allows easy submission genomic sequence databases to find similarity to previously studied DNA binding protein motifs43Computational response element searching algorithms are able to estimate a sequence’s likelihood in belonging to the response element of the query transcription factor using position site specific matrices where each position in the query transcription factor model gives each of the four letters in the DNA alphabet a score based on the probability of that nucleotide being found at that position 40. ChIP-CWR22v1 cells were transduced with lentiviral pLKO.1 control vector or AR shRNA for 72 h. Total RNA was isolated from cells, and 500 ng was used for synthesis of biotin-labeled cRNA using an RNA amplification kit . Biotinylated cRNA was labeled by incubation with streptavidin-Cy3 to generate a probe for hybridization with the GeneChip Human Transcriptome Array 2.0 . Four samples from two experimental groups (n = 2 per group) were hybridized to the chip to obtain raw gene expression data, which was processed to obtain raw data in the form of expression intensities. Raw data was then exported for further processing and analysis using R statistical software version 2.15 in combination with the BioConductor packageth wiggle signal. Without putting any knowledge into the search for ARE motifs, we first attempted to conduct a pattern search independent of existing databases. A de novo motif discovery search showed an ungapped 30mer logo on the ChIP-Seq data using the MEME tool225253ChIP-Seq data represents an enrichment of loci related to the binding of the protein of interest selected by the immunoprecipitation. Our ChIP-Seq dataset contained 35073 broad peaks including 4731261 wiggle signals detected by MACS and significantly enriched above the genomic control. 3017 (8.6%) of the detected peaks contained AREs, with many peaks showing multiple motif incidences. Furthermore, we detected DNA motifs resembling ARE sequences in about every 40Nucleotide preference and deviation within a response element is critical in determining both the selectivity of transcription factors that bind to that sequence and the necessity for cooperating factors to assist with AR binding. The sequences detected by the ideal, lenient, half site, and extended PSSM model searches were quantified and sorted into tiers reflecting how much the sequence motifs deviated from an ideal ARE sequence . The ideNext, we sought to compare the functional content of different ARE tiers, in particular ARE half vs full sites. In agreement with the ChIP-Seq data, the majority of AREs falls into intergenic and intronic regions . While pGenotypic variation can modulate transcription factor binding, chromatin structure, and gene expression. In cancer progression, somatic copy number alterations (SCNAs) play critical roles by activating oncogenes and inactivating tumor suppressorsAndrogen-insensitive cell lines, such as CWR22Rv1, express AR(FL) and AR splice variantsWe next sought to identify potential transcription factors that would cooperate with the AR to regulate gene expression. Using the Jaspar motif database, we grouped significant transcription factor logos within a window of ±160 bp from the ARE with a p-value of less than 0.05. Top hits included forkhead box (FOX), Krüppel-like factors (KLF), basic helix-loop-helix (BHLH), sterol regulatory element binding factor (SREBF), and v-myc avian myelocytomatosis viral oncogene homolog (MYC) families of transcription factors. Interestingly, several members of cooperating transcription factor families showed amplifications at the copy number level in the CWR22RV1 cell line as well as in TCGA PRAD patients. Detected somatic amplifications of transcription factors were maintained at the transcriptional level. Somatic copy number amplifications on chromosome 3 included Krüppel-like family member Krüppel-like factor 15 . Similarly, amplified regions on chromosome 7 included transcription factors v-myc avian myelocytomatosis viral oncogene homolog  and forkhead box K1 . We then organized recorded distances of detected, enriched, and/or amplified transcription factor families to the ARE into histograms with 5bp bins and analyzed spacing of the transcription factor motifs. We noticed an increase of detected transcription factor motifs at 15 bp for KLF, and 45 bp for SREBF-related transcription factors between full and half site ARE . This suNext, we characterized transcription factor cooperation of the AR with the Krüppel-like family. We quantified the number of KLF sites with respect to the detected ARE tiers to evaluate effect of transcriptional cooperation with respect to motif degeneracy . We founGene set enrichment analysis of identified putative AR target genes revealed several functional clusters. In addition to the identified 759 and 743 genes confirmed by ChIP-Seq binding as well as transcriptional activity, we tested all ARE full site and half site tiers for pathway enrichment . Gene seWe refined AR-binding and AREs in AR-positive but androgen-insensitive CWR22Rv1 prostate cancer cells using ChIP-Seq and motif-guided genome-wide analysis. The independence of androgen ligand for AR activity makes the CWR22Rv1 cell line a favorable model for genome-scale characterization of AR binding events in prostate cancer. Therefore, one can accomplish a comprehensive, unprecedented picture of the ARE by using ChIP-Seq analysis of AR-specific immunoprecipitation. We classified AREs according to their degeneracy and their transcriptional involvement. We quantified ARE utilization in response to somatic copy number amplifications, AR splice-variants, and steroid treatment. Our AR ChIP-Seq mapping shows that a majority of AREs are imperfect AREs with several base pairs deviating from the canonical palindromic 15 mer ARE full site. Our results fit in with previous assessments that AR DNA binding is possible despite base pair deviation from the canonical ARE full site sequenceSomatic copy number alterations of TCGA prostate cancer patients correlated with amplifications of chromosome arms 1q, 3p, 3q, 7p, 7q, 8p, 8q, 12p, and 12q observed in the cellular CWR22Rv1 prostate cancer model. Aneuploidy of the CWR22Rv1 model was recognized early on during the genetic characterization of the metastatic cell line5758We applied genome-wide model-based motif searches to different prostate cancer models and investigated ARE occurrence dependent on AR-splice variants and steroid regulation. Derived from an androgen-dependent CWR22 mouse xenograft that relapsed during androgen deprivation, the CWR22Rv1 prostate cancer cell line is androgen-insensitive, expresses different AR isoforms4892730820AREs can be modulated by cooperation with other transcription factors, which can compensate for missing canonical contacts of imperfect AREs and nevertheless result in successful gene expression events23645566676974A predominant signature of imperfect AREs is the GC content of the 3 bp spacer as well as the flanking regions of extended motifs. The GC content is richest in higher, less perfect tiers of AREs. CpG dinucleotides within the genome enable the cell to control ARE availability by DNA 5′-cytosine methylation7779808385Our data refined the recognition of ARE sequences within CWR22Rv1 and LNCaP prostate cancer cell lines. We expanded the nucleotide specificity of the ARE, identified potential modes of regulation these response elements are subject to, and outlined a protocol for identifying coordinating transcription factors to assist with weaker site recognition. While a major disadvantage remains in possible false-negatives being identified in computationally predicted sites, future experimental verification will have to determine if these response elements play a role in gene regulation. Future ChIP-Seq studies could look into prominent histone modifications accompanying detected response element sites within prostate cancer and facilitate insight how chromatin alteration affects AR gene targeting. Importantly, we identified significant differences in the genomic landscape of ARE full and half sites. Despite the fact that ARE half sites outnumber ARE full sites by 2-fold, stronger ARE were more frequently confirmed at the transcriptional level than weaker AREs. Nevertheless, weaker AREs are affected by AR expression or regulation, and may have strong functional impact by multiplicity and/or genomic proximity. ARE impact depends on somatic alterations, motif-receptor-binding specificity, tissue-specific lineage, cooperating factors , distance to neighboring motifs, AR-splice variants, and steroid regulation.How to cite this article: Wilson, S. et al. Refinement of the androgen response element based on ChIP-Seq in androgen-insensitive and androgen-responsive prostate cancer cell lines. Sci. Rep.6, 32611; doi: 10.1038/srep32611 (2016).


These new insights into EpiSC gene regulatory networks gained from this study are highly relevant to early stage embryogenesis.To obtain insight into the transcription factor (TF)-dependent regulation of epiblast stem cells (EpiSCs), we performed ChIP-seq analysis of the genomic binding regions of five major TFs. Analysis of  Summary: Surveying the binding profiles of the key transcription factors ZIC2, OTX2, SOX2, POU5F1 and POU3F1 in mouse epiblast stem cells reveals distinct ZIC2-OTX2-centered gene regulation from that in embryonic stem cells. Mammalian somatic cells originate from the epiblast in postimplantation embryos. The success in establishing epiblast stem cells (EpiSCs) from the epiblasts of egg-cylinder-stage mouse embryos  created A promising approach to investigate gene regulatory networks is to determine the genomic binding sites of various transcription factors (TFs) using ChIP-seq. In a previous study, we identified ZIC2, OTX2, SOX2, POU5F1 and POU3F1 as major regulators in EpiSCs and during the transition to ANP. These TFs change their anteroposterior localization in the mouse epiblast after embryonic day (E) 6.5, and their overexpression and/or shRNA-mediated knockdown strongly impacts the downstream TFs involved in the derivation of various somatic lineages . OTX2 hain vivo biotinylation of a TF were transfected into EpiSCs at high efficiency, albeit under conditions that avoid overdosing cells with exogenous TFs and that maintain the epiblast state. Cell chromatin was then fixed with formaldehyde and whole-cell lysates, rather than isolated nuclei, were processed for the isolation of biotinylated TF-DNA complexes.To perform parallel ChIP-seq analysis on a number of TFs with comparable efficiency, we developed a simplified version of the ChIP-seq technique that uses biotinylated TFs . This ciThe parallel ChIP-seq analysis for five TFs revealed the following new features about the TF binding events. (1) Throughout the entire genome, genomic domains rich in ZIC2 binding of megabase (Mb) scale and those rich in POU3F1 binding alternate, with strong correlation with gene distribution. This presumably reflects the loose clustering of two types of regulatory sequences that act at a short distance and at a long distance from the gene, reflecting the characters of bound TFs. (2) Mouse ESCs and EpiSCs share their SOX2 and POU5F1 binding sites in limited proportions, reflecting the shift in the TFs of major regulatory functions from SOX2/POU5F1 in ESCs to ZIC2/OTX2 in EpiSCs. This shift in the major acting TFs appears to be primed by binding of ZIC2 at relevant genomic positions in ESCs. Thus, the ChIP-seq data on the five TFs in EpiSCs provide new genomic resources that can be utilized in studies on the regulation of stem cells of early stage embryogenesis.in vivo by co-expressed E. coli-derived biotin ligase (BirA)  (ChIP-seq using a biotinylated TF requires the expression of an exogenous TF fused to a biotin ligase recognition peptide (BLRP) that is biotinylated e (BirA) . Previoue (BirA) . We chosig. S1A)  of the tNes) enhancer, the BLRP-biotin-tagged SOX2 and POU5F1 showed full activity . As shown in our previous study, the expression of all five TFs in EpiSCs 24 h after the transfection of shRNA vectors was strongly downregulated . In OTX2-transfected and SOX2-transfected cells, exogenous TF expression increased the nuclear TF levels only to 137% and 130%, respectively, relative to untransfected cell populations. By contrast, POU3F1 appeared to have a relatively low endogenous expression level. Even in this case, the average POU3F1 levels in transfected cell nuclei were estimated to be 252% of normal, and the majority (68%) of nuclei remained with the POU3F1 level within the range of 200% of untransfected cell average.Analogous data for other TFs generated in parallel experiments are shown in Fig. S1Da). These populations did not change significantly after exogenous expression of the aforementioned TFs . Analogous analysis of POU5F1 expression in the TF-transfected populations (other than POU5F1 transfection) did not show transfection-dependent changes (data not shown). These analyses indicated that transfection with any of the TFs used for the ChIP-seq analysis did not interfere with the EpiSC state.To confirm that exogenous TF expression does not interfere with the EpiSC state, we analyzed NANOG expression levels in the transfected populations. Even in untransfected EpiSCs, NANOG expression showed variation in its levels, which were divided into brighter and dimmer nuclear populations using a threshold (Table S1). This step was followed by standard ChIP-seq procedures. Peak calling using MACS1.4 identified 60,000-120,000 ChIP-seq peaks (Table S2).One day after transfection with a biotinylated TF-BLRP expression vector for 8 h, the culture was processed for chromatin fragmentation using whole lysate of formalin-fixed cells, yielding chromatin fragments with an average DNA length of 150 bp A. The TFAs explained in Sox2 upstream N2 enhancer, which is activated via co-binding by ZIC2, OTX2 and a POU factor   with a sTo substantiate these observations, we divided the genomic sequence of all chromosomes into 2582 segments  and scored the sum of the peak widths for each TF per Mb (peak densities) or the number of genes in a segment. The distribution of the ChIP-seq peak densities for individual TFs over the genome is summarized as a heat map in Fig. S2B). The peak density per Mb genomic segment for ZIC2 and POU3F1 showed a very broad distribution from very high (>5) to very low (<0.1) values, corresponding to peak-rich and peak-sparse genomic domains. By contrast, the ChIP-seq peak densities for OTX2, SOX2 and POU5F1 had a much narrower distribution.To further confirm our observations, we performed the following statistical analysis. After normalizing the average peak density to a value of 1 for all TFs, the ranges of variation in peak density in individual Mb segments were plotted for the five TFs  indicated that ZIC2 and POU3F1 binding sites reiterate frequently, resulting in >2000 bp peak ranges occupying 20% and 29% of peaks, respectively. By contrast, 60% and 70% of OTX2 and SOX2 binding sites, respectively, were in ChIP-seq peaks <1000 bp, indicating that they very frequently occur as singlet binding sites. The distribution of POU5F1 binding sites appears to be intermediate between these two cases.An additional effect of dense clustering of TF binding sites is that individual ChIP-seq peaks corresponding to the TF binding sites fail to separate and instead form a peak range that covers a long genomic span. The analysis of ChIP-seq peak widths for the TFs . The majority (55%) of ZIC2 and OTX2 peaks were within 50 kb of a TSS. By contrast, more than 80% of the SOX2, POU5F1 and POU3F1 peaks were at least 50 kb away from a TSS. These observations indicated that ZIC2 and OTX2 most often function by binding to gene-proximal regulatory sequences, whereas SOX2 and POU factors primarily function via gene-remote regulatory sequences.The distance of ChIP-seq peaks from neighboring genes classified the TFs into two classes . The local TSS density per ChIP-seq peak at 5 kb points averaged over the genome varied depending on the TF. Approaching the ChIP-seq peak position, strong positive TSS peaks (maximum ∼0.01) occurred with ZIC2. By contrast, POU3F1 and POU5F1 peaks, starting from already low TSS densities, further decreased at the peak position, indicating that the overlap of a POU3F1 or POU5F1 peak with a TSS is very rare. The aggregation plots using OTX2 and SOX2 had bumps at ∼1000 bp from the ChIP-seq peaks, but depressions at these ChIP-seq peak midpoints, indicating that OTX2 or SOX2 ChIP-seq peaks are more enriched at ∼1000 bp from TSSs.The ChIP-seq peaks within 5 kb of a TSS were further characterized using aggregation plots , and their tendency for clustering  appear to form the basis of the alternating patterns of genomic domains rich in ZIC2 and those rich in POU3F1 peaks  ChFig. S1C), we investigated the possibility that EpiSCs comprise alternative subpopulations of high ZIC2 expressers and high POU3F1 expressers, or of POU5F1 expressers and POU3F1 expressers, which might account for the poorly overlapping binding events between ZIC2 and POU3F1, as well as the occurrence of individually unique binding sites for POU5F1 and POU3F1 , indicating that the ChIP-seq peak overlap patterns shown in As the expression levels of TFs among the EpiSC population are not perfectly homogeneous .To evaluate the correlations between TF binding and the combined histone H3 modification signatures, we analyzed the frequency of ChIP-seq peak overlaps between the TF peaks and modified H3 peaks  Fig. 4CCFig. 4C.Fgf5 and Pax6 loci shown in Fgf5, a characteristic EpiSC-expressed gene, is activated by ZIC2, OTX2 and POU3F1  , mouse eIt has been shown that the SOX2-POU5F1 TF pair plays major regulatory roles in ESCs . AlthougA comparison of the SOX2 and POU5F1 ChIP-seq peaks between ESCs and EpiSCs indicated that only 5-10% of the peaks overlapped B. This aTable S4) were transformed into mouse genome coordinates (mm9), and the overlap frequencies of the human ESC ChIP-seq peaks with those of mouse ESCs and EpiSCs were scored  and ZIC2/OTX2-co-bound regions in EpiSCs  was limited to less than 10% (1626) C. This wWe used published ChIP-seq data to examine ZIC2 and OTX2 binding genomic regions in ESCs and EpiLCs. There were fewer ChIP-seq peaks reported for these TFs in ESCs than in EpiSCs, presumably reflecting increase in their binding sites after exit from ESCs, as previously shown for OTX2 . CompariMycn, Nanog, Nodal and Sall4   confirmed proper culture conditions. Images were taken using an Axioplan 2 microscope (Zeiss) and DP71 camera (Olympus) and measured for nuclear fluorescence intensities using ImageJ (E. coli-derived biotin ligase (BirA) from the same plasmid, the BLRP-IRES-BirA module of pREP4-BLRP-TEV-His6  , and dupg ImageJ . To exprTEV-His6  was tranTEV-His6  to produTEV-His6 A, and moTable S1. Typically, several nanograms of precipitated DNA were obtained from five 10-cm diameter dish cultures. The DNA fragments were ligated with adapters and amplified by ten cycles of PCR using the TruSeq ChIP Sample Prep Kit (Illumina) to prepare libraries for DNA sequence determination using an Illumina HiSeq2500 and single-end reads of 100 bp.A suspension of Accutase-dissociated EpiSCs was plated on a 10-cm diameter dish and immediately transfected, using Lipofectamine 2000 (ThermoFisher), with 5 µg pCAGGS-BLRP-BirA plasmid to express a BLRP-tagged TF. After 8 h, the culture medium was refreshed to remove the transfection reagents and 50 µg/ml biotin was added. After 24 h, the cultures were fixed with 1% formalin for 10 min at room temperature, and processed according to the procedure shown in Table S4) were analyzed using the default settings. The Bowtie-aligned peaks and MACS-determined peak positions were visualized using IGV (http://chip-atlas.org). The above data processing was performed using the software package SraTailor (Table S4).FASTQ sequences were aligned to the mouse mm9 genome sequence using Bowtie2  and convsing IGV  and ChIPraTailor . Peak ovraTailor , and theraTailor  in orderraTailor . To compraTailor , and theraTailor . Microra


NSPCs). An improved understanding of the underlying mechanisms that drive age‐associated neurogenic deficiency could lead to the development of strategies to alleviate cognitive impairment and facilitate neuroregeneration. An essential step towards this aim is to investigate the molecular changes that occur in NSPC aging on a genomewide scale. In this study, we compare the transcriptional, histone methylation and DNA methylation signatures of NSPCs derived from the subventricular zone (SVZ) of young adult (3 months old) and aged (18 months old) mice. Surprisingly, the transcriptional and epigenomic profiles of SVZ‐derived NSPCs are largely unchanged in aged cells. Despite the global similarities, we detect robust age‐dependent changes at several hundred genes and regulatory elements, thereby identifying putative regulators of neurogenic decline. Within this list, the homeobox gene Dbx2 is upregulated in vitro and in vivo, and its promoter region has altered histone and DNA methylation levels, in aged NSPCs. Using functional in vitro assays, we show that elevated Dbx2 expression in young adult NSPCs promotes age‐related phenotypes, including the reduced proliferation of NSPC cultures and the altered transcript levels of age‐associated regulators of NSPC proliferation and differentiation. Depleting Dbx2 in aged NSPCs caused the reverse gene expression changes. Taken together, these results provide new insights into the molecular programmes that are affected during mouse NSPC aging, and uncover a new functional role for Dbx2 in promoting age‐related neurogenic decline.Adult neurogenesis declines with aging due to the depletion and functional impairment of neural stem/progenitor cells ( In these regions, neural stem/progenitor cells (NSPCs) are maintained throughout adulthood and give rise to daughter cells that undergo differentiation into neurons and glia. Increasing evidence supports a role for adult neurogenesis in normal brain function and suggests that neurological disorders and neurodegeneration may be caused, at least in part, by reduced neuronal output of adult NSPCs  and aged 18 months) mice and expanded the cells for two passages (2–3 weeks) in nonadherent culture conditions. We used neurospheres that formed during the second passage to generate expression profiles by RNA sequencing (RNA‐seq). Analysis of these data revealed that the majority of genes were expressed at similar levels between adult and aged samples, although 254 genes were differentially expressed  analysis of the 125 genes that were upregulated in the aged NSPCs compared with the young adult NSPCs. This analysis revealed an enrichment for processes associated with neuron differentiation, cell signalling and cell morphogenesis . Using this approach, we identified 330 DMRs that overlapped with CGIs  and 5‐hydroxymethylcytosine (5hmC) using whole‐genome bisulphite (BS‐seq) and oxidative bisulphite (oxBS‐seq) sequencing. Globally, the levels of 5mC and 5hmC were very similar between the young adult and aged NSPCs Figure a,b. In ks Figure . There ws Figure e and wers Figure f.Bmi1, Prdm1, Tbx2 and with multicellular organism development  and where the loss of DNA methylation was associated with a transcriptional upregulation . These findings show that DNA methylation levels and distribution are very similar between adult and aged NSPCs, although we have identified several hundred regions that are differentially methylated and are near to genes.GO analysis of the genes with hyper‐DMRs revealed an enrichment for biological processes associated with transcription factors including p < 1E−9) that were associated with 13,030 transcriptional start sites (TSS) in adult NSPCs, and 28,854 peaks associated with 12,955 TSS in aged NSPCs. The majority of H3K4me3 signal was centred on the TSS of genes, and this distribution profile was identical for the adult and aged NSPCs .We also profiled the genomewide localization of histone H3 lysine 4 trimethylation (H3K4me3) and histone H3 lysine 27 trimethylation (H3K27me3) using chromatin immunoprecipitation combined with sequencing (ChIP‐seq). We identified 30,406 H3K4me3 peaks  that were associated with 3,819 TSS in adult NSPCs, and 43,569 peaks associated with 3,836 TSS in aged NSPCs. The distribution of H3K27me3 across gene promoter regions was similar for the adult and aged NSPCs, with a broad peak centred on the TSS of genes , although there were several examples where the gain of H3K27me3 was associated with a transcriptional downregulation  and also for the converse situation .Analysis of the H3K27me3 ChIP‐seq data identified 43,640 peaks . Corresponding epigenetic changes were present near to the Dbx2 promoter, including increased H3K4me3 signal and reduced DNA methylation at an overlapping CGI  and aged (18 months) mice  or a GFP control transgene (GFP‐NSPCs). Immunofluorescence microscopy using an anti‐Nestin antibody confirmed the homogeneous NSPC identity of the cultures when maintained in adherent or nonadherent proliferating culture conditions  and SVZ tissue  NSPCs  following a partial (35%) reduction in Dbx2 mRNA by means of a shRNA plasmid (Figure We confirmed these transcriptional changes in four ways. First, the above described gene expression differences were reproducible in an independent pair of s Figure F–P. Secos Figure , indicats Figure A–D and ts Figure E–N that d Figure .Dbx2 can affect adult NSPC self‐renewal and function by inducing a specific transcriptional programme that includes age‐associated regulators of NSPC proliferation and differentiation.These results lead us to propose that altered levels of 3Mouse SVZ and SGZ are complex niches where neurogenesis persists throughout adult life due to a finely regulated balance among NSPC self‐renewal, expansion and differentiation. During aging, this regulation is progressively altered, and neurogenic output is curtailed. To investigate the intrinsic molecular changes upon NSPC aging, we report here a comprehensive set of transcriptional and epigenetic maps in young adult and aged SVZ NSPCs. We found that the NSPC epigenome was largely unchanged upon aging, which is broadly consistent with prior studies that profiled somatic stem cells from other aged tissues (Beerman et al., Dbx2 as a putative age‐associated regulator of NSPC functional decline. Increased Dbx2 expression in young adult NSPCs promoted age‐related phenotypes, including the reduced proliferation of NSPC cultures and the altered expression levels of age‐associated regulators of NSPC proliferation and differentiation. In particular, elevated Dbx2 expression decreased the size, but not the number, of neurospheres generated from adult SVZ NSPCs, supporting a causal link between Dbx2 upregulation and the defective proliferation of aged NSPCs. Dbx2 overexpression slightly increased the fraction of nonviable cells and decreased the fraction of cycling cells, suggesting that cell death and cell cycle exit contribute to the reduced expansion of the transgenic neurospheres. Nonetheless, as the majority of the cells remained positive for proliferation markers, alternative changes, including lengthening of the cell cycle, could be a main driver of the growth phenotype elicited by Dbx2. This hypothesis is consistent with the previously described alterations in the cell cycle of aged NSPCs (Apostolopoulou et al., Dbx2 is part of a cohort of transcription factor genes that are enriched in quiescent NSPCs of the SGZ and SVZ and are downregulated in NSPCs actively engaged in cell proliferation and neurogenesis (Codega et al., Dbx2 may negatively regulate NSPC proliferation in both adult neurogenic niches. Elevated Dbx2 expression in adult NSPCs also phenocopied several of the molecular effects of aging, including the altered expression of key genes such as Sox2 and p21 that are associated with proliferation and differentiation. Thus, Dbx2 may act as a crucial regulator of age‐dependent transcriptional programmes, as also suggested by the reversed trend of some molecular markers of SVZ aging following moderate Dbx2 knockdown in aged NSPCs. As cell proliferation was not affected by this partial knockdown, an exciting future line of research will be to abrogate Dbx2 function in aged NSPCs by knockout approaches in vitro and in vivo. Furthermore, investigating the direct targets of Dbx2 might help to expand the gene regulatory networks involved in NSPC dysfunction.Despite the overall similarities, we did identify several hundred genes with significant differences in the levels of transcription and/or epigenetic modifications in NSPCs from the aged SVZ. These data will serve as an important resource for normal and aging studies and could be used to identify biomarkers and candidate regulators of NSPC aging. As an initial step, we identified and characterized the homeobox gene Prrx1 and Igf2bp2 are additional candidate regulators of NSPC functional decline during SVZ aging. Both genes were downregulated in NSPCs from the aged SVZ, and for Igf2bp2 this correlated with increased levels of repressive epigenetic marks at the promoter. Prrx1 is expressed in NSPCs of the SVZ and the SGZ and promotes proliferation of SGZ NSPCs at the expense of neuronal differentiation (Shimozaki et al., Igf2bp2 is expressed in embryonic cortical progenitors where it promotes neurogenesis at the expense of astrogenesis (Fujii et al., Igf2bp1 supports proliferation and prevents premature differentiation of embryonic cortical progenitors, but its expression is extinguished postnatally (Nishino et al., Igf2bp1 in maintaining embryonic NSPC proliferation is superseded by Igf2bp2 in the postnatal SVZ, and that the combined decay of Igf2bp1/2 levels underlies the progressive decline of NSPC proliferation during postnatal life. Increased Dbx2 expression in adult NSPCs caused the downregulation of Prrx1 and Igf2bp2, providing a potential explanation, along with the transcriptional effects on Sox2 and p21, for the antiproliferative activity of Dbx2.In conclusion, by comparing the molecular profiles of young adult and aged SVZ, our work suggests that aging does not cause widespread gene dysregulation in NSPCs, but acts through the modulation of specific gene expression programmes. Among them, we have identified a transcriptional pathway involving the upregulation of the homeodomain protein Dbx2 in aged NSPCs. Dbx2, in turn, can influence a cohort of age‐associated regulators of NSPC function, and restrain NSPC proliferation. These results provide a platform to investigate how changes in extrinsic signals acting in the SVZ niche due to aging, injury or disease could impinge on intrinsic transcriptional mechanisms controlling adult neurogenesis.44.1Young adult and aged SVZ NSPCs were derived from 3 months or 18 months male C57BL/6 J/Babr mice, respectively. Methods for their derivation and subsequent in vitro culture were reported previously (Soldati et al., Sequencing data are available at GEO under accession number GSE101610.None declared.G.L. performed conceptualization, resources, funding acquisition, investigation, methodology, supervision, validation, visualization and writing. P.S.N. performed investigation. P.E. and M.A.K. were involved in investigation and visualization. Y.‐L.P. and C.L.N. performed investigation and methodology. B.S. carried out methodology and formal analysis. S.B. performed conceptualization and funding acquisition. H‐K.L. involved in investigation and supervision. P.B. was involved in visualization, resources and funding acquisition. E.C. performed conceptualization, resources, funding acquisition, investigation, methodology, visualization and writing. P.J.R.‐G. performed conceptualization, resources, formal analysis, funding acquisition, supervision, validation, visualization and writing. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


N-terminal kinase (JNK) inhibitor SP600125. These results suggest that physcion upregulates hST8Sia VI gene expression via ERK and p38 MAPK pathways in SK-N-BE(2)-C cells.In this research, we firstly demonstrated that physcion, an anthraquinone derivative, specifically increased the expression of the human α2,8-sialyltransferase (hST8Sia VI) gene in SK-N-BE(2)-C human neuroblastoma cells. To establish the mechanism responsible for the up-regulation of hST8Sia VI gene expression in physcion-treated SK-N-BE(2)-C cells, the putative promoter region of the hST8Sia VI gene was functionally characterized. Promoter analysis with serially truncated fragments of the 5′-flanking region showed that the region between −320 and −240 is crucial for physcion-induced transcription of hST8Sia VI in SK-N-BE(2)-C cells. Putative binding sites for transcription factors Pax-5 and NF-Y are located at this region. The Pax-5 binding site at −262 to −256 was essential for the expression of the hST8Sia VI gene by physcion in SK-N-BE(2)-C cells. Moreover, the transcription of hST8Sia VI induced by physcion in SK-N-BE(2)-C cells was inhibited by extracellular signal-regulated protein kinase (ERK) inhibitor U0126 and p38 mitogen-activated protein kinase (MAPK) inhibitor SB203580, but not c-Jun  Sialic acid (NeuAc) residues play crucial roles in diverse cellular events, including intercellular adhesion, cell differentiation, microbial attachment, and malignant transformation . The siaPolygonum multiflorum roots on the human sialyltransferase gene expression in SK-N-BE(2)-C human neuroblastoma cells.Recently we studied the transcriptional regulation mechanism of human sialyltransferase genes that are specifically expressed at high levels in cell lines treated with natural compounds ,9,10,11.Microsporum sp. [Physcion is an anthraquinone derivative isolated from rhubarb, a Chinese herb medicine  and the orum sp. . Previouorum sp. , anti-miorum sp. , and antorum sp. . In addiorum sp. , breast orum sp. , and colorum sp. . It was orum sp. . HoweverTherefore, this work was undertaken to assess the effects of physcion on the gene expression of human sialyltransferases and the underlying mechanisms. In the present study, we discovered for the first time the specific increase of the human α2,8-sialyltransferase (hST8Sia VI) gene expression by physcion in human neuroblastoma SK-N-BE(2)-C cells and the molecular basis of physcion-induced hST8Sia VI gene expression was also investigated.P. multiflorum with ethyl acetate and evaporation of the solvent, the remaining residue was chromatographed over silica gel. Among the compounds isolated by an extensive separation process, a compound displaying induction activity for hST8Sia VI gene expression in SK-N-BE(2)-C cells was selected as the active compound. This compound was clarified to be physcion (1H- and 13C-nuclear magnetic resonance (NMR). GC-MS analysis showed that the isolated physcion had a purity of more than 97%.After extraction of the dried roots of physcion  by gas cTo investigate the effect of physcion on the human sialyltransferase gene expression in SK-N-BE(2)-C cells, mRNA levels of nineteen human sialyltransferases were checked by RT-PCR using total RNAs obtained from cells after 24 h treatment with 40 μM physcion. The result showed that with the exception of hST8Sia VI, the expression levels of eighteen human sialyltransferase genes were not affected by physcion treatment when compared to the control without physcion . Next, wUsing the NCBI database, sequence comparison of the cDNA and the genomic DNA of the hST8Sia VI gene showed that the predicted transcription start site was located at 136 bp upstream of the ATG start codon. Based on this result, a 2660 bp region upstream of the transcription start site for the hST8Sia VI gene was obtained by LA-PCR and sequenced. By using a transcription factor binding site-prediction program, putative transcription factor binding sites (TFBS) within this region were screened and 52 TFBS were predicted (similarity margin ≥95%).To assess whether or not the 5′-flanking region of the hST8Sia VI gene includes a physcion-responsive promoter, six kinds of luciferase reporter plasmids  were constructed by inserting truncated fragments of the 5′-flanking region of the hST8Sia VI gene into the pGL3-Basic vector for analysis of luciferase activity. To identify physcion-responsive promoter activity, we transfected pGL3-Basic plasmid as a negative control along with the six reporter plasmids into physcion-untreated SK-N-BE(2)-C cells and checked the hST8Sia VI promoter activity by physcion induction. As shown in Based on this result, to identify the minimal physcion-responsive region controlling the maximal promoter activity of the hST8Sia VI gene in SK-N-BE(2)-C cells, we constructed two different kinds of reporter plasmids (pGL3-1982/-320 to pGL3-1982/-1066 and pGL3-1503/-320 to pGL3-1503/-1066) containing progressive deletions from the 3′ end of the hST8Sia VI gene promoter and transfected them into physcion-untreated SK-N-BE(2)-C cells, then checked for promoter activity by physcion stimulation. As shown in To identify physcion-responsive elements in the nucleotide −320 to −240 region of the hST8Sia VI gene, we searched for TFBS within the nucleotide −320 to −240 region using bioinformatics analysis. Sequence analysis using the PROMO program  showed pTo uncover the mechanism underlying the transcriptional activation of hST8Sia VI gene by physcion induction, we examined the MAPK signal pathway in response to physcion stimulation in SK-N-BE(2)-C cells. As shown in To further assess whether physcion-stimulated transcriptional activity of a pGL3-574 promoter is induced via ERK and p38 MAPK signal pathways, we checked the promoter activity of pGL3-574 by physcion induction in SK-N-BE(2)-C cells pre-treated with MAPK inhibitors. As shown in O-Ser/Thr or Neu5Acα2-6GalNAcα1-O-Ser/Thr in O-glycans was highly expressed in MCF-7 breast cancer cell line, whereas its expression was very low in MDA-MB-231 and T47-D breast cancer cell lines and in HT-29 and Caco-2 colon cancer cell lines [It was previously reported that hST8Sia VI acting on Neu5Acα2-3Galβ1-3GalNAcα1-ll lines . HoweverPhyscion, an anthraquinone derivative, has been reported to have a variety of pharmacological effects such as hepatoprotective , anti-micis-acting elements were found in this region, typical TATA and CCAAT boxes were not existed. Moreover, we demonstrated that a functional promoter in response to physcion was located at the 5′-flanking region (pGL3-2660) of the hST8Sia VI gene in SK-N-BE(2)-C cells. Our present result clarified that the region between −574 and −240 is the core promoter essential for transcriptional activation of hST8Sia VI gene in physcion-induced SK-N-BE(2)-C cells, as evidenced by deletion analysis. This region contains NF-Y (−289 to −282) and Pax-5 (−262 to −256) binding sites. By site-directed mutagenesis experiments and ChIP assay, we also verified that the Pax-5 binding site in this region plays a pivotal role in the transcriptional activation of the hST8Sia VI gene in physcion-stimulated SK-N-BE(2)-C cells.In this study, we isolated the 2.6 kb 5′-flanking region upstream of the transcription start site of the hST8Sia VI gene. While a number of putative regulatory Pax-5 is well known as the B cell-specific transcription factor that plays a crucial role in the development and malignancy of B cells ,24. Pax5It was recently reported that stimulation of mouse primary B cells by lipopolysaccharide (LPS)-induced T-independent (TI) or IL4/IL5/CD40L-induced T-dependent (TD) pathway resulted in remarkable elevation of the mouse ST8Sia VI gene expression in mature and plasma cell populations as compared with ST8Sia II and ST8Sia IV . This fiOur data show that ERK, JNK and p38 MAPK activation were induced by physcion in SK-N-BE(2)-C cells, although physcion-mediated MAPK signaling pathways are not known at present. The present result also indicates that transcriptional activation of hST8Sia VI is mediated through ERK and p38 MAPK pathways induced by physcion in SK-N-BE(2)-C cells, as demonstrated by luciferase assay using cells treated with chemical inhibitors that selectively or specifically block these pathways.Polygonum multiflorum were carried out by the same procedures as those described previously [1H and 13C NMR spectroscopy with a Varian Spectrometer  at 600 MHz with CDCl3 was determined. The gas chromatography-mass spectrometry (GC-MS) profiles of the purified physcion were recorded as described previously [1H NMR and 13C NMR results were compared with the data presented in the references [Extraction and isolation of physcion from the dried root of eviously . The streviously . 1H NMR ferences ,34.2 incubator.Human neuroblastoma SK-N-BE(2)-C cells were obtained from American Type Culture Collection  and cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 100 μg/mL of streptomycin, 100 U/mL of penicillin, and 10% fetal bovine serum  at 37 °C in a 5% COCell viability assay was performed as described previously ,9,10,11.Total RNA isolation and first-strand cDNA synthesis were performed as described previously . PCR ampTaq polymerase . LA-PCR was performed with the sense primer P-2660S containing a KpnI site and the antisense primer P-2660A containing a XhoI site  based on TRANSFAC version 8.3 [Using the sequence information for hST8Sia VI cDNA (GenBank accession number BC137102) and Homo sapiens chromosome 10, GRCh38.p2 Primary Assembly (GenBank accession number NC_000010 GPC_000001302) from the National Center for Biotechnology Information (NCBI), a 2660 bp fragment from the 5′-flanking sequence of the hST8Sia VI gene was amplified by long and accurate PCR (LA-PCR) with LA-hoI site . Human gsion 8.3 ,21 with KpnI and XhoI sites, respectively  according to the manufacturer’s protocol using oligonucleotide primers , were constructed by LA-PCR with sense and antisense primers containing ectively , using t primers . The preRenilla luciferase vector  were co-transfected into cells using 1 μL Lipofectamine 2000 . After 4 h culture, the transfection medium was replaced by normal medium without physcion and incubated for 7 h. Then, the medium was changed to medium containing 40 μM physcion  and incubated for 24 h. Cells were harvested and assayed using the Dual-Luciferase Reporter Assay System  and a GloMax™ 20/20 luminometer (Promega).Transient transfection and luciferase assay were carried out as described previously ,8,9, 0.5Western blot analysis was conducted as described previously . Thirty-ChIP assay was performed using the ChIP kit  according to the manufacture’s protocol. Formaldehyde cross-linking of cells and shearing of chromatin were carried out as described previously ,35. ImmuIn the present study, we have demonstrated for the first time that hST8Sia VI gene expression was upregulated in SK-N-BE(2)-C cells stimulated with physcion and that the region between −320 and −240, which contains putative binding sites for Pax-5 and NF-Y in the hST8Sia VI promoter, acts as the major promoter for transcriptional activation of hST8Sia VI in physcion-induced SK-N-BE(2)-C cells. Furthermore, the Pax-5 binding site at position −262 to −256 plays a crucial role in the transcriptional activation of hST8Sia VI by physcion induction, as demonstrated by mutagenesis and ChIP assay. Our data suggest that physcion-induced transcriptional activation of hST8Sia VI is mediated through ERK and p38 MAPK pathways in SK-N-BE(2)-C cells.