PTEN pseudogene, PTENP1. Our analysis of the ENCODE project data also found many transcriptionally active pseudogenes in the GM12878 and K562 cell lines; moreover, it showed that many human pseudogenes produced small RNAs (sRNAs) and some pseudogene-derived sRNAs, especially those from antisense strands, exhibited evidence of interfering with gene expression. Further integrated analysis of transcriptomics and epigenomics data, however, demonstrated that trimethylation of histone 3 at lysine 9 (H3K9me3), a posttranslational modification typically associated with gene repression and heterochromatin, was enriched at many transcribed pseudogenes in a transcription-level dependent manner in the two cell lines. The H3K9me3 enrichment was more prominent in pseudogenes that produced sRNAs at pseudogene loci and their adjacent regions, an observation further supported by the co-enrichment of SETDB1 (a H3K9 methyltransferase), suggesting that pseudogene sRNAs may have a role in regional chromatin repression. Taken together, our comprehensive and systematic characterization of pseudogene transcription uncovers a complex picture of how pseudogene ncRNAs could influence gene and pseudogene expression, at both epigenetic and post-transcriptional levels.Thousands of pseudogenes exist in the human genome and many are transcribed, but their functional potential remains elusive and understudied. To explore these issues systematically, we first developed a computational pipeline to identify transcribed pseudogenes from RNA-Seq data. Applying the pipeline to datasets from 16 distinct normal human tissues identified ∼3,000 pseudogenes that could produce non-coding RNAs in a manner of low abundance but high tissue specificity under normal physiological conditions. Cross-tissue comparison revealed that the transcriptional profiles of pseudogenes and their parent genes showed mostly positive correlations, suggesting that pseudogene transcription could have a positive effect on the expression of their parent genes, perhaps by functioning as competing endogenous RNAs (ceRNAs), as previously suggested and demonstrated with the  Pseudogenes are genomic sequences with high sequence similarity to functional genes but have been presumed to be “non-functional” via their non-coding RNA (ncRNA) products, may play regulatory roles in modulating the expression of their parental genes, as well as non-parental genes cis-effect) has not yet been investigated, although trans-effects have been suggested. For example, the Oct4 pseudogene ncRNA was shown to direct epigenetic remodeling complexes to the Oct4 parent gene Thousands of pseudogenes are found in the human genome; some of them have been suggested to have critical regulatory functions PTENP1, a pseudogene derived from the tumor suppressor gene PTEN, was first shown to act as a competitive decoy for several miRNAs that target PTEN mRNA, thus stabilize expression of its parental PTEN gene PTENP1 and their role in regulating PTENPseudogene transcripts functioning by other mechanisms have also been reported In this study, we have surveyed the landscape of pseudogene transcription across a large number of human tissues and cell lines and begun to explore potential functional and cellular activities of pseudogene ncRNAs from several perspectives. We found that a few thousand human pseudogenes were transcribed and their transcription was overall correlated with increased expression levels and expression diversity of their parental genes. Some pseudogenes, on the other hand, displayed evidence of siRNA production and function, potentially by either interfering with parental gene expression or mediating local epigenetic silencing. Taken together, our results suggest that pseudogene transcription is likely an important process that has provided novel ncRNA elements for modulating the transcriptional fluctuation of protein-coding genes.A major challenge in detecting transcribed pseudogenes is how to map RNA-Seq reads back to their genuine origins when both pseudogenes and their parents are candidates because of their high sequence similarity. The lack of introns may even make a processed pseudogene the preferred candidate for reads originating from exon-exon junctions of the parent. To address these issues directly, we have designed a new computational method to filter out RNA-Seq signals that are likely to have originated from coding genes but can be mapped to pseudogenes due to ambiguity , bottom,To determine pseudogene transcription systematically, we first applied our method to analyze RNA-Seq data from 16 normal human tissues in the Illumina Human Body Map 2 Project, and then to the data from GM12878 and K562 cell lines (see below) in the ENCODE project p<2.2e-16, Wilcoxon test) . A hierarchical clustering analysis showed that a subset of pseudogenes was nearly exclusively transcribed in testis . White bon test) . LincRNAon test) . The tison test) .parents that had a JS score >0.1 for all studies described below unless mentioned otherwise, which effectively excluded nearly all (n = 745) pseudogenes derived from ribosomal protein genes.To determine to what extent the high JS scores for pseudogenes could be explained by their low transcription, we computed JS scores for randomly selected coding genes with maximal FPKMs matched to those of pseudogenes. We found that the JS scores of processed pseudogenes remained lower than their expression-matching coding genes , but for duplicated pseudogenes the difference was more significant at high expression level , suggestIn summary, the above results for the extent of pseudogene transcription and their tissue expression pattern are consistent with previous reports p<0.02 and <2.2e-16 for pseudogenes and genes, respectively; data not shown). The difference remained statistically significant  when the JS score cutoff was set to 0.25 or 0.35. In addition, changing the promoter definition slightly from ±2 kb to ±5 kb or ±10 kb produced similar statistics . Since the data capture a mixture of TF events in 91 cell types, this result suggests that pseudogenes transcribed more broadly contain more potential regulatory sites, but the functional importance of this observation needs further investigation.To explore transcriptional regulation potentially contributing to tissue specificity, we examined the number of transcription factor (TF) binding events in the promoters (−2 kb to transcription start sites (TSS)) of pseudogenes and protein coding genes, using the integrated ChIP-Seq data from the ENCODE project pg:g). The resulting correlation coefficients for both processed and duplicated pseudogenes showed a distribution that was deviated from the theoretical normal distributions  test) and biased towards positive numbers . Since some pseudogenes are close to their parents on chromosomes  and adjacent genes tend to be co-regulated pg:g values for the t2 group remained skewed to positive for both processed and duplicated pseudogenes . In summary, our results indicate that pseudogene transcription is positively correlated with the expression of their parents.The evidence of pervasive pseudogene transcription is compelling, but more important questions are what kinds of biological functions pseudogene ncRNAs can have. Note that the term “biological function” in this report is used in a loose sense, whereas “biochemical activity” may arguably be more appropriate, in accordance with the source of our experimental data and the computational nature of our work. The first obvious question is how pseudogene and parent gene transcription are related, as this information may shed light on how pseudogenes could regulate their most conceivable targets. To this end, we computed the Spearman rank correlation of the 16 tissue transcription levels for each of the 1,270 pseudogene-parent pairs .Although our observation is based on correlation, it is consistent with the ceRNA hypothesis pg:g values than those producing antisense ncRNAs , while duplicated pseudogenes followed the same trends  to each of the miRNAs that it can putatively bind. Likewise, we calculated ρmiRNA:pg. This produced three-way pairwise correlations. To plot the data, we binned genes to groups based on ρmiRNA:g numbers  and ρmiRNA:g  . This pattern implies that the miRNA sponge effect of a pseudogene ncRNA is potentially more significant, manifested as a large and positive ρpg:g, if the shared miRNA shows a larger inhibition to the parent, indicated by a small and negative ρmiRNA:g, further supporting the idea that a miRNA sponge effect could partially contribute to the observed transcriptional correlation between pseudogenes and their parents, especially in the cases when miRNA regulatory effects were large  from a previous study  numbers , x-axis in vivo miRNA target sites in HEK293 cell line as determined by AGO and TNRC6 occupancy using the PAR-CLIP technology PTENP1, and other candidates in To seek additional experimental support, we examined a total of 19,184 high confident pg:g, both the means and variances of parental gene expression were greater in the tissues where pseudogene ncRNA levels were higher , but pseudogene transcripts can also be used to produce small RNAs, which can potentially execute a variety of functions via dsRNA intermediates formed either between pseudogene ncRNAs and parental mRNAs, or in the hairpin loops of pseudogene ncRNAs, whereas “group II” pseudogenes probably can generate sRNAs independent of their parents in a manner similar to repeats and transposons located at heterochromatin regions (p<2.2e-16). We hypothesized that the parental genes of group I pseudogenes would be expressed at lower levels than the parents of both group II pseudogenes and the pseudogenes that did not produce sRNAs . This trend persisted if the threshold was changed to >10 sRNA per kb . Two subsets of such pseudogenes were selected for comparison, based on whether sRNAs were also detected in the flanking regions , with th regions . InteresA per kb , althougA per kb . In TablFor the small RNAs generated from pseudogenes independently of their parents . Furthermore, the extent of H3K9me3 within 15 kb of the transcription start sites showed a clear enrichment that was dependent on pseudogene transcription levels . We hypothesize that these results suggest pseudogene-derived sRNAs can play an active role in the establishment of broad but local silencing chromatin environment for repressing pseudogene transcription, a phenomenon that has been documented in yeast and plants In order to address whether H3K9me3 enrichment was related to pseudogene-derived sRNAs, we compared the H3K9me3 levels between the two groups of sRNA-producing pseudogenes . As showOur findings suggest that many human pseudogenes are transcribed and the transcripts exhibit evidence for various biological activities. It is possible that the human transcriptome and its regulation are sufficiently robust to tolerate the small perturbation introduced by pseudogene transcription. If so, transcribed pseudogenes would not show significantly different evolutionary constraints compared with non-transcribed ones. Therefore, we analyzed nucleotide diversity (based on two population datasets from the HapMap project) and cross-species conservation (based on 46 way phastcon scores) within pseudogenes. The data show that transcribed pseudogenes exhibit significantly higher evolutionary constraints than non-transcribed ones, as suggested by the lower degree of polymorphism and greater phastcon scores  of human pseudogenes found to be transcriptionally active in the current study is consistent with previous estimates  parents . Neverthbona fide pseudogene-originating reads could be removed. Although further detailed assessment is needed when new computational algorithms or experimental technologies for better quantifying pseudogene expression become available, we believe these factors have not introduced significant bias to our results. For instance, we examined the expression levels of 161 pairs of pseudogenes, in which each pair were derived from the same parental gene but they had distinct identities to the parent. These pseudogene pairs exhibited no expression difference in all the 16 tissues except two , with larger FPKMs for the pseudogenes that were more similar to their parents. We also found that pseudogenes with higher sequence similarity to their parents had lower JS scores and greater ρpg:g values, indicating that young pseudogenes are more widely transcribed than the old ones, perhaps due to less decay of their promoters.We should point out that pseudogenes with sequences identical to their parents would not have any mapped RNA-Seq reads and thus would be treated as non-transcribed by our current approach, even though they may well be transcribed in the tissue samples. Another caveat of our approach is potential underestimation of expression levels for certain pseudogenes, because some Xist modulating X chromosome inactivation Our study has systematically explored the potential functional activities of transcribed pseudogenes from several perspectives. Our finding of transcription-dependent H3K9me3 enrichment in some pseudogenes suggests that pseudogene-derived sRNAs may play a role in modulating epigenetic repression of pseudogene transcription, probably by the same molecular mechanism(s) underlying sRNA-mediated heterochromatin formation vs 86% .We should point out that high levels of H3K9me3 have also been observed in transcriptionally active genes, particularly at the 3′ exons of zinc finger genes ψPPM1KIn addition to silencing pseudogenes, pseudogene-derived ncRNAs could be a good source of endogenous siRNAs that interfere with the expression of protein-coding genes. We uncovered evidence for this , but thiWe were unable to address whether human pseudogene-derived sRNAs could function as siRNAs to interfere with parental gene expressions or to mediate epigenetic silencing under normal physiological conditions, since the necessary sRNA-Seq and histone modification ChIP-Seq data had not been available for all the 16 tissues in our current study. It will be interesting to revisit this critical issue when the relevant data become available for all or a subset of these tissues, so that data from the same tissues can be studied in order to reduce biological variations.PTENP1, whose transcripts have been shown by an extensive array of genetic and biochemical experiments to compete with its parental gene PTEN for several miRNAs Our study indicates that the predominant effect of pseudogene transcription, however, appears to be related to the increase in the expression levels and diversity of the parental coding genes. Approximately 64% of the ∼4,000 transcribed pseudogenes (FPKM >1) exhibited a transcriptional profile that was positively correlated with that of their parental genes. Furthermore, pseudogenes with higher correlation were found to have more predicted miRNA-targeting sites. More importantly, the expression of parental genes was significantly higher and more variable in tissue where pseudogene ncRNAs are more abundant. All of these observations are consistent with previous reports suggesting that cellular RNAs could serve as miRNA sponges (or “target mimicry”) and regulate the stability of other transcripts In summary, consistent with previous work, our study demonstrates that pseudogene transcription is genuine and prevalent; its impact on other cellular RNAs appears complicated and diverse and in some cases one pseudogene may play multiple molecular roles. Whether these result passively from transcriptional leakage, incomplete chromatin silencing, or represent an active process of nurturing novel ncRNAs certainly will require a more systematic investigation and experimental verification. Our results, on the other hand, suggest that pseudogenes with major regulatory roles are unlikely ubiquitous, since under normal physiological conditions most of the pseudogene ncRNAs are present at much (∼20x) lower levels than their coding counterparts and the majority of them did not display evidence of strong interaction with their parents. Nevertheless, perturbation of pseudogene transcription can affect the homeostasis of gene expression and lead to human diseases and cancers as previously reported.http://www.ebi.ac.uk/arrayexpress), and transcriptomic and epigenomic data from the ENCODE project (http://genome.ucsc.edu/ENCODE/) Our primary datasets are RNA-Seq data from the Illumina Body Map 2.0 Project  and then collected reads mapped to pseudogene loci. These reads were then compared to cDNA sequences of human protein coding genes, extracted from the Ensembl database , using the program Bowtie (version 0.12.7) vs sequence identities using a 200-bp window sliding across the alignment. Noted that all pseudogenes with an annotated parent  were included in this analysis. According to pseudogene identification strategies, the aligned regions are essentially the “exons” of pseudogenes. A moderate correlation between read numbers and identities  was detected before the application of our filtering process, but none existed after filtering   The read filtering step for computing FPKMs from RNA-seq data was not applied to either the ChIP-seq or sRNA-seq alignment data since the alignments of those reads were not be affected by exon-exon junction and a single and best-matched location was kept for each of the ChIP-seq reads.JS (Jensen-Shannon) divergence has been found to be a good metric for quantifying the tissue specificity of a transcript pg:g). As a control, we computed ρ by pairing up each pseudogene with a randomly chosen coding gene. Exclusion from this analysis were the pseudogenes without parent information from GENCODE or whose parents had 0 FPKMs in all the 16 tissues. The same method was applied to compute the correlation between miRNAs and their putative targets.We computed Spearman correlation coefficient (ρ) of the 16 tissue FPKMs to determine the relationship between the transcription profiles of a pseudogene and its parent (ρStrand-specific RNA-Seq data were obtained from a previous study http://www.targetscan.org) We downloaded data of miRNA sequence families and target prediction tools (TargetScan V5.0) from the TargetScan web site  were considered as candidates that could produce small RNAs. A subset of these candidates were defined as group I or II sRNA-generating pseudogenes if the sRNA read densities at their flanking 1 kb regions were  = 0 or >5, respectively ; the resvs non-transcribed pseudogenes, we evaluate the difference by considering the number of mapped ChIP-Seq reads at ±2.5kb of pseudogene TSS (In the comparison of transcribed gene TSS . The sighttp://www.hapmap.org) i-th and j-th individual sequences from YRI population, i-th and j-th individual sequences in each pseudogene locus Sequence conservation was determined by the Phastcon scores downloaded from the UCSC genome browser; the scores were derived from sequence comparison of 46 species including primates, mammals and vertebrates Figure S1Most pseudogenes share <90% sequence similarity with their parents and our method for filtering RNA-Seq reads does not affect the quantification of parental gene expression. Left, histogram of human pseudogene distribution shows the number of pseudogenes (y-axis) at different levels of sequence identity to the parental genes (x-axis). Right, the FPKM values for the parental genes is not affected by our method of filtering and remapping of RNA-Seq reads. Data shown is for brain sample, but results from other tissues yielded the same pattern.(PDF)Click here for additional data file.Figure S2Tissue specificity of pseudogene transcription. A) Three examples of tissue-restrictively transcribed pseudogenes. B) Distribution of the JS scores computed with all RNA-Seq reads Click here for additional data file.Figure S3(A–B) QQ-plot analysis of transcriptional correlation coefficients. The ρpg:g values (y-axis) for transcribed duplicated pseudogenes (A) and processed pseudogenes (B) were significantly deviated from the ρpg:g values (x-axis) calculated for pairs of each transcribed pseudogenes with a randomly chosen coding genes. C) Distinct effect on parental gene expression between sense and antisense pseudogene ncRNAs. The parents of the antisense transcribed pseudogenes  exhibited significantly lower expression than those of sense transcribed pseudogenes  in all the 16 tissues .(PDF)Click here for additional data file.Figure S4Increased levels and variations of parental gene expression in relation to pseudogene transcription. QQ-plot analysis shows that both differences in mean (A) and variance (B) of the parental gene expression between tissues of high  and low  pseudogene transcription were significantly deviated from the normal distribution; the Kolmogorov-Smirnov (KS) statistics are shown at top. The means (C) and variances (D) of the expression of the parent genes (x-axis) across all 16 human tissues also increased as the transcription levels of pseudogenes increased. Color lines plot the distributions of parental genes with pseudogenes transcribed at different levels, defined by the maximal FPKMs among the 16 tissues.(PDF)Click here for additional data file.Figure S5Size distribution of small RNAs from the group I and II pseudogenes. The data were derived from the sRNA reads that were perfectly matched to pseudogene sequences without any gap. Data from GM12878 and K562 are plotted in  and , respectively.(PDF)Click here for additional data file.Figure S6Enrichment of active histone modifications and depletion of repressive histone modifications at transcribed pseudogene loci. Comparison of eight histone modifications between transcribed (‘y’) and non-transcribed (‘n’) pseudogenes was shown by boxplot analysis. The y-axis shows numbers of ChIP-Seq reads mapped to +/− 2.5 kb to TSS in GM12878 (A) and K562 (B) cell lines. C). The average densities of three types of repeats at pseudogenes transcribed in GM12878 (FPKM>1) in 500-bp bin windows, with no enrichment observed at pseudogene loci when compared to adjacent genomic regions.(PDF)Click here for additional data file.Figure S7Correlation of the nucleotide diversities computed from two distinct human populations. For every pseudogene, we determined its nucleotide diversities in the YRI or CEU populations and the results show a high correlation between the data derived from these two populations, indicating the reduction in diversity is not due to a few genes that recently become pseudogenes in human. A), duplicated pseudogenes; B), processed pseudogenes.(PDF)Click here for additional data file.Table S1List of transcribed pseudogenes and their associated features and groups.(XLSX)Click here for additional data file.


Bombyx mori asparagine synthetase (BmASNS), a gene involved in the biosynthesis of asparagine. We show that the cis-regulatory elements of YY1-binding motifs and the CpG island present on the BmASNS promoter are required for the recruitment of PcG proteins and the subsequent deposition of the epigenetic repression mark H3K27me3. RNAi-mediated knockdown of PcG genes leads to derepression of the BmASNS gene via the recruitment of activators, including BmC/ebp, to the promoter. Intriguingly, we find that PcG proteins and BmC/ebp can dynamically modulate the transcriptional output of the BmASNS target in a cell cycle-dependent manner. It will be essential to suppress BmASNS expression by PcG proteins at the G2/M phase of the cell cycle in the presence of BmC/ebp activator. Thus, our results provide a novel insight into the molecular mechanism underlying the recruitment and regulation of the PcG system at a discrete gene locus in Bombyx mori.Epigenetic modifiers and transcription factors contribute to developmentally programmed gene expression. Here, we establish a functional link between epigenetic regulation by Polycomb group (PcG) proteins and transcriptional regulation by C/ebp that orchestrates the correct expression of  Polycomb group (PcG) proteins are conserved transcriptional repressors involved in regulating body formation during embryonic development Drosophila or their counterparts in mammals, is involved in recognizing the chromatin marked with tri-methylated histone H3 on lysine 27 (H3K27me3) Drosophila has revealed a conserved target site for Pho with a 17 bp binding sequence containing a core “CCATTTT” motif HOXD11 and HOXD12 locus has been identified in human embryonic stem cells, and the YY1-binding sites have been reported to contribute to the repression of this locus, but are not required for it PcG proteins function via three key multiprotein complexes: Polycomb repressive complex 1 (PRC1), Polycomb repressive complex 2 (PRC2), and Pleiohomeotic repressive complex (PhoRC) Bombyx moriasparagine synthetase (ASNS) gene is similarly up-regulated after RNA interference (RNAi)-meditated knockdown of some PcG genes, such as BmSCE, BmESC, BmPHO, and BmSCM , in silkworm cells ASNS gene encodes an enzyme product that catalyzes the biosynthesis of asparagine using glutamine and aspartate as substrates, and is extensively expressed in mammalian cells ASNS gene transcription is activated by the amino acid response (AAR) or endoplasmic reticulum stress response (ERSR) pathway, respectively, through two response elements, nutrient-sensing response elements (NSRE)-1 and −2 within the ASNS promoter region ASNS expression can be activated by a basic leucine zipper transcription factor, CCAAT/enhancer-binding protein (C/ebp) ASNS promoter and activate ASNS expression in response to nutrient stress ASNS gene, but relatively little is known about how this gene is negatively regulated in normal cells in which the basal transcription is maintained. Recently, however, a microarray screening confirmed that ASNS transcription was induced in mouse cells by a deletion of either BMI1 or SU(Z)12, which are members of the PcG protein family in mammals ASNS gene may be a conserved target gene for PcG-mediated epigenetic repression. However, the molecular mechanism underlying this potential regulation remains largely unknown.We previously identified the conserved 13 PcG genes in the silkworm, ASNS expression, suggesting that the ASNS protein has an important function in cancer progression ASNS expression is not yet well defined, it would be reasonable to suppose that ASNS expression is precisely regulated so as to prevent cancer development in normal mammalian cells, especially by a potential suppression of PcG regulation.In addition, the emerging evidence has also shown that most cancer cells display high levels of ASNS gene expression, we here analyzed the structure and function of the ASNS promoter in the context of the ASNS regulation in Bombyx. Our results demonstrated that the Bombyx ASNS (BmASNS) gene is regulated by PcG-mediated repression and BmC/ebp-mediated activation in a cell cycle-dependent manner. BmPHO and BmSCM RNA interference (RNAi) had little effect on BmASNS expression at the late G1 phase, suggesting that the dissociation of PcG proteins from chromatin would occur before DNA replication. A CpG island on the BmASNS promoter, and, to a lesser degree, several YY1-binding motifs are considered to play a crucial role in the transcriptional repression by recruiting PcG complexes. These findings, for the first time, reveal an epigenetic and cell cycle-specific regulation of ASNS gene expression by a PcG system.To define the role of PcG proteins in the regulation of BmASNS gene in Bombyx, we first cloned the full-length cDNA of the BmASNS gene according to the reported nucleotide sequence (NP_001037414.1). Amino acid sequence-based multiple alignment of Asns proteins from Bombyx, Drosophila, human, and mouse revealed high identities between insects and mammals , indicating an important role of BmAsns in cell proliferation. Taken together, these results led us to identify Asns as an evolutionarily conserved protein in Bombyx that can contribute to amino acid metabolism.In human, ASNS exhibits a functional response to nutrient stress BmSCE, BmESC, BmPHO, or BmSCM, all increased BmASNS expression BmASNS expression in PcG gene-silenced BmN4-SID1 cells by using a semi-quantitative PCR assay   , and fouosophila  [9]. GenBmASNS promoter and then introduced it into BmN4 cells to confirm this transcriptional regulation by measuring luciferase activity at 72 h after transfection. This promoter exhibited a stronger promoter activity compared with that of the promoter-less luciferase reporter plasmid , BmESC, or BmSU(Z)12, greatly reduced the H3K27me3 marks , did not induce a drastic reduction of H3K27me3 levels in comparison with the RNAi for PRC2 components. Therefore, it was shown that PcG complexes bind predominantly to the high-affinity binding site at the nearby P2 region of the chromosome, probably the YY1-3 site.To determine whether the tri-methylation of H3K27 at the P2 region was mediated by the PcG system, a ChIP-qPCR assay in PcG gene-silenced BmN4-SID1 cells was performed. We found that the depletion of PRC2 components, such as e3 marks , which rBmASNS promoter , while knockdowns of BmSCM and BmC/EBP (Lane 4) resulted in a slight reduction compared with BmPHO and BmC/EBP knockdowns, but still higher than the control treatment, and together these findings indicated the significant repressive effects of the PcG proteins on BmASNS promoter activity. It should also be noted that there was a reduced expression of BmC/EBP in response to the BmPHO and BmSCM depletions (Lane 2), although the mechanism of these effects was unknown. However, this weak decrease of BmC/EBP did not affect the up-regulation of BmASNS expression (Lane 2) because the higher expression of BmASNS was observed in the other two lanes (Lanes 3 and 4) when suffered from a significant loss of BmC/EBP expression. In contrast to the transcription from the chromosomal BmASNS locus, the effect of BmPHO knockdown on the deletion constructs was different from that of BmSCM RNAi in the absence of BmC/ebp , and after cell division, the expression was up-regulated, particularly at the late-G1 phase (BmCDT1 knockdown), consistent with the profile of the ASNS gene in human cells BmASNS expression level differs during different phases of the cell cycle, and indicated that the PcG system allows timely control of BmASNS expression to accommodate the needs of cells.Following the clue that human cells can be induced to express the etaphase  [28]. BaBmPHO, BmSCM, or BmC/EBP in the knockdown cells from the foregoing experiment, respectively. As shown in BmMYC, BmCDT1, or BmRNRS RNAi), the concomitant silencing of BmPHO or BmSCM did not greatly change the expression of the BmASNS gene. By contrast, a significant increase of BmASNS levels were shown in the cells arrested at the G2/M phase (BmCDK1 or BmCDC27 RNAi) when there was an additional depletion of BmPHO or BmSCM. On the other hand, the loss of BmC/EBP expression only caused significant decrease of BmASNS transcription in the cells that underwent BmMYC, BmCDT1, or BmRNRS RNAi treatment, but not in those that underwent BmCDK1 or BmCDC27 RNAi treatment. Collectively, these results revealed that PcG proteins cooperate with BmC/ebp to regulate the BmASNS promoter activity in a cell cycle-dependent manner.To test this possibility, we further performed separate knockdowns of Bombyx and elucidate a new mechanism under the regulation of the PcG complexes on its target gene BmASNS, namely, PcG complexes can counteract with the C/ebp transactivator in the specific cell phase of the cell cycle through the cis-regulatory elements of YY1-binding motifs and the CpG island present on the BmASNS promoter.In this study, we report the first evidence that the PcG system directs gene regulation in a given locus in Drosophila PREs containing YY1-binding motifs for Pho binding serve as a platform for the binding of the Pho protein and other PcG proteins that participate in the modification of histone and compaction of chromatin in the local region BmASNS promoter have also been shown to be involved in recruiting the PcG complexes. However, what is different from the model in Drosophila is that the CpG island in the BmASNS promoter could facilitate this repressive effect from PcG proteins through a potential mechanism of increasing the quantity of PcG proteins recruited or elevating the repressive H3K27me3 marks on the neighboring histone. This is in agreement with the well-known observation on the involvement of CpG islands in the regulation of PcG complexes in mammals Drosophila and mammals, the PcG system generally controls the expression of target genes by influencing the status of H3K27 methylation deposited by the PRC2 complex in their promoter regions. Our ChIP assays revealed a high level of H3K27me3 on the BmASNS promoter, especially near the CpG island. Moreover, the H3K27me3 was maximally reduced after down-regulating the expression of PRC2 components, revealing that it is the PRC2 complex rather than other complexes that contributes to the tri-methylation of H3K27 in Bombyx. Thus, it is reasonable to consider that the formation of a condensed chromatin structure through H3K27me3 mediated by the PRC2 complex will result in a high repressive effect of the PcG system on the prompter activity, just as in our previous observation of the depletion of BmESCBmASNS may be ascribed to the binding of PcG proteins per se or the established H3K27me3. We have previously suggested the presence of at least two types of regulatory complexes, namely, the whole PcG system and Pho/Scm complex Bombyx PcG system is not enough to suppress the BmASNS promoter activity without the continuous expression of the BmPho or BmScm protein, suggesting that the complex of BmPho/BmScm will be required to facilitate this repression from the intact PcG complexes. Indeed, we observed a similarly repressive effect of BmPho or BmScm overexpression on BmASNS promoter activity using the episomal constructs. Therefore, all these findings suggest that the removal of PcG proteins from this region is more crucial than the loss of histone modification to achieve the activation of BmASNS expression.In BmASNS expression from PcG complexes could also be regulated by DNMTs, though it should be noted that the silkworm genome was shown to be poorly methylated in a previous study Bombyx DNMT1 and DNMT2 (only two DNMT genes are found in the silkworm genome) could not induce the expression of BmASNS  on the promoter of target gene to repress its expression f BmASNS , indicatASNS promoter contained the di-methylated histone H3 lysine 4 (H3K4me2) and tri-methylated histone H3 lysine 9 (H3K9me3) ASNS promoter may be involved in the regulation of its own expression. To determine whether the H3K9me3 also contributes to the regulation of BmASNS expression, we performed RNAi against Bombyx H3K9me3-related genes, BmHP1a, BmHP1b, and BmSU(VAR)3–9BmASNS expression revealed no significant changes compared with the control RNAi treatment  For the amino acid deprivation assay, the modified IPL-41 medium was prepared according to the manufacturer's protocol (Sigma) but without addition of all amino acid components.Bombyx mori (NP_001037414.1), Drosophila melanogaster (NP_996132.1), Homo sapiens (AAA52756.1), and Mus musculus (AAA85125.1) were downloaded from NCBI (http://www.ncbi.nlm.nih.gov/). Sequence alignments were performed by using ClustalX software.The Asns protein sequences of BmASNS gene was evaluated by semi-quantitative polymerase chain reaction (semi-quantitative PCR) using gene-specific primers  gene was used as an endogenous control. The relative expression levels of the BmASNS gene were quantitated by ImageJ software and normalized to the BmGAPDH levels.Total cellular RNA was isolated and reversed according to the procedure described previously  primers , and theBmASNS gene sequence (NP_001037414.1), we designed primers to amplify and obtain the cDNA clone (XhoI (underlined in TM11 (Invitrogen) vector, and verified by DNA sequencing.Based on the reported NA clone . The cDNBmASNS was obtained by PCR screening of a genomic DNA template extracted from the silkworm strain p50T using a set of primers . The recombinant plasmid was termed pASNS-DEST38 and its nucleotide sequences were determined by DNA sequencing.The upstream promoter region of  primers  to yieldTM11-Luc according to the gateway reaction system. pENTRTM11-Luc vector was generated using a pENTRTM11 (Invitrogen) vector into which the Firefly luciferase reporter gene was cloned For the reporter assay, the reporter plasmid (pASNS-Luc) was made by recombinant reaction between pASNS-DEST38 and pENTRBmASNS upstream sequence, we further designed the specific primers  gene was used as a control in this study. The knockdown efficency for each RNAi gene was quantitated by ImageJ software and normalized to the BmGAPDH levels.Knockdown of genes was performed with the gene-specific double-stranded RNAs (dsRNAs) in BmN4-SID1 cells according to our previous procedure 5 were transfected with the luciferase reporter vectors mentioned above or the control promoter-less vector pENTRTM11-Luc according to our previous method For normal transfection, the BmN4 cells seeded in 24-well plates with a density of 0.5×105 and additional dsRNAs according to the requirements of experiment. After 72 h of incubation, the cells were transfected and analyzed with the same method described above.For RNAi transfection, the BmN4-SID1 cells were pre-cultured in 24-well plates with a density of 0.2×10The transfection efficiency between dishes was measured by cotransfection with the pEXP38-βgalΔIE-1 vector expressing a β-galactosidase, and the β-galactosidase activity was used to normalize luciferase activity data Co-immunoprecipitation (Co-IP) was performed according to our previous procedure Chromatin immunoprecipitation (ChIP) analysis was performed according to a modified protocol of NimbleGen Systems, Inc. (Roche). Briefly, BmN4 cells or BmN4-SID1 cells (treated with dsRNAs) were harvested by gentle centrifugation and resuspended in growth medium on ice for 10 min. Cells were cross-linked in a 1/10 volume of crosslinking solution and mixed gently by inversion. The reaction was stopped by the addition of a 1/20 volume of 2.5 M glycine solution. Cross-linked chromatin was sonicated for 30 min with cycles of 30 s on/30 s off at an M2 setting using a Biorupter sonicator (Diagenode) to produce chromatin fragments of 0.2∼0.5 kb on average. After centrifugation and quantification, 100 μg of the supernatants was used as an input and 1 mg of the supernatants was incubated with 2 μg of H3K27me3 antibody . A rabbit IgG  was used as the nonspecific antibody control. The antibody-bound complex was precipitated by Dynabeads® Protein G . The DNA fragments in the immunoprecipitated complex were captured using an MPC-S magnet and released by reversing the crosslinking at 65°C for 12 h, and then purified using a QIAquick PCR purification kit (Qiagen).BmASNS promoter sequence are shown in Purified DNA was amplified by quantitative real-time PCR (qPCR) using SYBR Green Master Mix (Applied Biosystems) on a Thermal Cycler Dice Real Time System (Takara). The primers used for the qPCR to amplify the Cell cycle distributions were analyzed by measuring the cellular DNA content using a Flow cytometer (Millipore) according to the previously described procedure The statistical significance of difference between the treated and the corresponding control was evaluated by the Student's t-test, and a P-value <0.05 was considered statistically significant.Figure S1Activity of the constructed BmASNS promoter. BmN4 cells were transfected with pASNS-Luc (Luciferase gene under the control of the BmASNS promoter) or pNone-Luc (Luciferase gene without the promoter) vector. The luciferase activities were measured 72 h after transfection and normalized to the levels of transfected β-galactosidase expression. BmASNS promoter activity was calculated as the fold of the luciferase activity of pASNS-Luc to that of pNone-Luc and pNone-Luc was set as 1.(TIF)Click here for additional data file.Figure S2Knockdown of BmSCE or BmESC can also induce the luciferase activities of different constructs of the BmASNS promoter. The RNAi treatment and luciferase measurements were performed as shown (TIF)Click here for additional data file.Figure S3Knockdown efficiency for cell cycle-regulated factors. Knockdown of BmMYC, BmCDT1, BmRNRS, BmCDK1, or BmCDC27 in the BmN4-SID1 cells specifically reduced the expression of the corresponding genes.(TIF)Click here for additional data file.Figure S4Bombyx DNA methyltransferase genes are not involved in the regulation of BmASNS expression. Knockdown of the BmDNMT1 and BmDNMT2 genes did not affect the expression level of BmASNS gene by semi-quantitative PCR analysis.(TIF)Click here for additional data file.Figure S5H3K9me3 may play a minor role in the regulation of BmASNS expression in Bombyx. Knockdown of H3K9me3-related genes, including BmHP1a, BmHP1b, and BmSU(VAR)3–9 did not alter the expression level of the BmASNS gene by semi-quantitative PCR analysis.(TIF)Click here for additional data file.Table S1List of primers used in this study.(TIF)Click here for additional data file.


In the Author Contributions Statement, the second author (JHW) should be noted as one of the authors who conceived and designed the experiments, and the list of authors who performed the experiments should read in the following order: JHW MK YRL TM.In addition, there were multiple errors in the Results. The eighth sentence of the third paragraph of the "Gene Expression Profiles Induced by Vitamin D3 Metabolites" section of the Results should read: "Tounderstand the specific role of each vitamin D3 metabolite, we grouped the regulated genes into commonly and individually regulated gene groups ." The first sentence of the second paragraph of the "Functional Annotation of Genes Regulated by Vitamin D3 Metabolites" section of the Results should read: In hP29SN stromal cells, the highly enriched GO categories related to 1a, 25(OH)2D3-treatment include those involved in nucleotide biosynthesis, ubiquitin conjugation, ion binding, and macromolecule biosynthesis (Table S2). 25(OH)D3 plays numerous roles in intracellular organelles, DNA-binding transcription factors, protein-protein interaction, transcription coregulator activity, RNA-binding motif, and organic acid biosynthesis (Table S2).


Vibrio parahaemolyticus is a leading cause of infectious diarrhea and enterogastritis via the fecal-oral route. V. harveyi is a pathogen of fishes and invertebrates, and has been used as a model for quorum sensing (QS) studies. LuxR is the master QS regulator (MQSR) of V. harveyi, and LuxR-dependent expression of its own gene, qrr2–4 and aphA have been established in V. harveyi. Molecular regulation of target genes by the V. parahaemolyticus MQSR OpaR is still poorly understood.parahaemolyticus OpaR, V. harveyi LuxR, V. vulnificu SmcR, and V. alginolyticus ValR were extremely conserved, and that these four MQSRs appeared to recognize the same conserved cis-acting signals, which was represented by the consensus constructs manifesting as a position frequency matrix and as a 20 bp box, within their target promoters. The MQSR box-like sequences were found within the upstream DNA regions of opaR, qrr2–4 and aphA in V. parahaemolyticus, and the direct transcriptional regulation of these target genes by OpaR were further confirmed by multiple biochemical experiments including primer extension assay, gel mobility shift assay, and DNase I footprinting analysis. Translation and transcription starts, core promoter elements for sigma factor recognition, Shine-Dalgarno sequences for ribosome recognition, and OpaR-binding sites were determined for the five target genes of OpaR, which gave a structural map of the OpaR-dependent promoters. Further computational promoter analysis indicated the above regulatory circuits were shared by several other closely related Vibrios but with slight exceptions.The bioinformatics analysis indicated that V. opaR, qrr2–4 and ahpA, by OpaR in V. parahaemolyticus. These characterized regulatory circuits were conserved in V. harveyi and V. parahaemolyticus.This study gave a comprehensive computational and characterization of the direct transcriptional regulation of five target genes,  Vibrio species that are natural inhabitants in seawater, boundary regions between sea and river, and aquatic products. V. harveyi (a pathogen of fishes and invertebrates) and V. cholerae (the causative agent of the disease cholera) have been used as models for QS studies  is a process of population density-dependent cell-to-cell communication through synthesizing, releasing, and detecting signal molecules called autoinducers. The QS systems are widely distributed in V. harveyi uses three distinct autoinducers, harveyi autoinducer 1 (HAI-1), autoinducer 2 (AI-2), and cholerae autoinducer 1 (CAI-1), which are synthesized by the autoinducer synthases LuxM, LuxS, and CqsA, respectively , Qrr1–4. The Qrr sRNAs accompanying with the RNA-binding protein Hfq in turns inhibit the translation of the mRNA of the master QS regulator (MQSR), e.g., LuxR in V. harveyi, and HapR in V. cholerae, which leads to the ceased production of MQSR. The over-production of Qrr sRNAs and LuxO-P triggers three feedback regulatory loops: i) LuxO-P represses the transcription of its own gene, ii) Qrr sRNAs inhibits the translation of luxO, and iii) Qrr sRNAs repress the translation of luxMN encoding LuxM and its cognate receptor LuxM; these feedbacks will contribute to control the Qrr levels within physiological states, since the qrr expression requires the signal transduction among LuxO, LuxM, and LuxN.At low cell density (LCD) , the autqrr transcription. Existing Qrr sRNAs are rapidly turned over, as Hfq-dependent sRNAs are degraded stoichiometrically with their target mRNAs. In the absence of the Qrr sRNAs, the MQSR mRNA is translated, and MQSR is produced abundantly to act as either a transcriptional activator or a repressor for its target genes. Overproduced MQSR will feed back to repress its own transcription.At high cell densities (HCD) , the accqrr genes, and this MQSR-qrr feedback loop (qrr genes requires the simultaneous presence of LuxO-P (abundant only at LCD) and MQSR (abundant only at HCD). LuxO-P and MQSR are thought to be simultaneously present immediately following the switch from HCD to LCD. Therefore, the above negative feedback loop dramatically accelerates the transition HCD to LCD, but it has no effect on the QS behaviors at steady-state LCD or HCD.MQSR is able to activate the transcription of ack loop  in turnsThe above five negative feedback loops control the integration of multiple signals, and maintain the signal transmission fidelity of QS through affecting the input-output dynamic range of signal transmission and modulating the noise in the output V. parahaemolyticus is a leading cause of infectious diarrhea and enterogastritis via the fecal-oral route V. harveyi, V. parahaemolyticus, and six additional closely related species  constitute the Harveyi clade that is a subset of the Vibrio core group V. harveyi QS system can be annotated to be intact in the genome V. parahaemolyticusV. harveyi and V. parahaemolyticus (olyticus .V. parahaemolyticus OpaR V. harveyi LuxR. The opaR gene (VP2516) V. parahaemolyticus is still poorly understood. In the present work, the consensus constructs were built to represent the conserved cis-acting signals recognized by the four extremely conserved MQSR proteins, V. parahaemolyticus OpaR, V. harveyi LuxR, V. vulnificus SmcR, and V. alginolyticus ValR, which was followed by a comprehensive molecular characterization of the transcriptional regulation of five target genes, opaR, qrr2–4 and ahpA, by OpaR in V. parahaemolyticus.The V. parahaemolyticus strain RIMD 2210633 (WT) is a pandemic O3:K6 strain isolated from a patient with traveler's diarrhea in Japan in 1996 opaR mutant derived from WT and the corresponding complemented mutant are described below. For the common bacterial growth and maintenance, bacteria were cultured in Luria-Bertani (LB) broth or LB agar with addition of 2% NaCl at 37°C, and chloramphenicol was added at 5 µg/ml when needed. For the longtime storage, bacteria were stored in Difco™ Marine (MR) broth 2216 (BD Bioscience) with addition of 30% glycerol at −85°C.The wild-type opaR was deleted from RIMD 2210633 to generate the opaR null mutant strain ΔopaR, using the suicide plasmid pDS132 opaR, respectively, were amplified by PCR, purified, and used as the templates to create a 871 bp deletion construct, through PCR, which was subsequently inserted between the PstI and SphI sites of pDS132. This generated a recombinant vector that contained the deletion construct and the sacB gene conferring sensitivity to sucrose. All the primers used in the present work were listed in Escherichia coli S17-1(pir), and then transferred into RIMD 2210633 by conjugation. The mutant strain was selected using resistance to 10% sucrose and sensitivity to chloramphenicol, and further verified by PCR.The entire coding region of ΔopaR mutant, a PCR-generated DNA fragment containing the opaR coding region together with its promoter region (539 bp DNA region upstream of the coding sequence) and transcriptional terminator region (327 bp DNA region downstream) were cloned between the SalI and SphI sites of the vector pBRMob (kindly proved by Prof. Hin-chung Wong from Taiwan Soochow University) which is the ligation product of a 3219 bp fragment (containing the RP4 mob DNA region for plasmid mobilization) from pDS132 digested with HindIII, and the HindIII-digested plasmid pBR328 (harboring a chloramphenicol resistance gene) ΔopaR, yielding the complemented mutant strain C-opaR.For complementation of the The glyceric stock of bacterial cells was inoculated into 5 ml of the MR broth for growing at 30°C with shaking at 200 rpm for 24 h. The cell culture was 40-fold diluted with the PBS buffer (pH 7.2), and then 150 µl of the diluted cells were spread onto a HI plate  with a diameter of 5 cm. After 8 h of growth at 30°C, cells were harvested from the plate by adding the mixture of 1.5 ml of RNAprotect (Qiagen) and 0.5 ml of PBS. Bacterial cultivations were done at least in triplicate  for each strain.Total bacterial RNAs were extracted using the TRIzol Reagent (Invitrogen) 32P] end-labeled reverse primer using a Primer Extension System (Promega) according to the manufacturer's instructions. The same labeled primer was also used for sequencing with the fmol® DNA Cycle Sequencing System (Promega). The primer extension products and sequencing materials were concentrated and analyzed in a 6% polyacrylamide/8 M urea gel. The result was detected by autoradiography (Kodak film).For the primer extension assay opaR gene of strain RIMD 2210633 was directionally cloned between the BamHI and HindIII sites of plasmid pET28a (Novagen). The recombinant plasmid encoding the 6× His-tagged OpaR protein (His-OpaR) was transformed into Escherichia coli BL21λDE3 cells. Expression of His-OpaR was induced by the addition of 1 mM IPTG . The overproduced protein was purified under native conditions using an Ni-NTA Agarose Column (Qiagen). The purified protein was concentrated with the Amicon Ultra-15 centrifugal filter device (Millipore), and the protein purity was verified by SDS-PAGE.Preparation of the purified OpaR protein was performed as previously described 32P] ATP and T4 polynucleotide kinase. DNA binding was performed in a 10 µl reaction volume containing binding buffer , labeled DNA (1000 to 2000 c.p.m/µl), and increasing amounts of the His-OpaR protein. Three controls were included in each EMSA experiment: 1) cold probe as specific DNA competitor , 2) negative probe as nonspecific DNA competitor (the unlabeled coding region of the 16S rRNA gene), and 3) nonspecific protein competitor [rabbit anti-F1-protein polyclonal antibodies]. The F1 protein is the protective antigen from Yersinia pestisThe 300 to 600 bp promoter-proximal region of each indicated gene was amplified by PCR. For EMSA 32P-labeled end were PCR amplified with either the sense or antisense primer being end-labeled. The PCR products were purified using the QiaQuick columns (Qiagen). Increasing amounts of His-OpaR were incubated with the purified, labeled DNA fragment (2 to 5 pmol) for 30 min at room temperature, in a final 10 µl reaction volume containing the binding buffer used in EMSA. Before DNA digestion, 10 µl of Ca2+/Mg2+ solution (5 mM CaCl2 and 10 mM MgCl2) was added, followed by incubation for 1 min at room temperature. The optimized RQ1 RNase-Free DNase I (Promega) was then added to the reaction mixture, and the mixture was incubated at room temperature for 40 to 90 s. The reaction was quenched by adding 9 µl of stop solution , followed by incubation for 1 min at room temperature. The partially digested DNA samples were extracted with phenol/chloroform, precipitated with ethanol, and analyzed in 6% polyacrylamide/8 M urea gel. Protected regions were identified by comparison with the sequence ladders. For sequencing, we used the fmol® DNA Cycle Sequencing System (Promega). The templates for sequencing were the same as the DNA fragments of DNase I footprinting assays. Radioactive species were detected as previously described.For DNase I footprinting retrieve-seq’ program matrices-consensus’ tool matrices-paster’ tool The 300 bp promoter regions upstream of the start codon of each indicated gene were retrieved with the ‘Vibrio species tested . A phylogenetic tree (V. fischeriThe OpaR regulator shares high identity (≥70%) in a.a. sequences with the orthologous MQSRs in other six tic tree  was consV. harveyi, V. parahaemolyticus, V. vulnificus, and V. alginolyticus. Known or predicted binding sites of LuxR, OpaR, SmcR, and ValR , indicating the successful mutation of opaR and the complementation of the opaR mRNA level.Real-time RT-PCR experiments were performed to assess the relative mRNA levels of ahpA gene was negatively regulated by OpaR. To test whether the opaR mutation had the polar effect, the primer extension assays were conducted to detect the yield of the primer extension product of ahpA that represented the ahpA mRNA levels in WT, ΔopaR, and C-opaR. Herein, the ahpA mRNA level was significantly enhanced in ΔopaR relative to WT, while no obvious change in the ahpA transcription was observed between WT and C-opaR , the d C-opaR . This anΔopaR grown at 30°C in the MR broth or in the HI broth were determined  were observed on the agar]. It was thought that, unlike the liquid cultivation for which the autoinducers would disperse into the liquid media, the cultivation on solid medium would enable the enrichment of autoinducer molecules within the bacterial lawns with little dispersal into the agar.opaR in WT and ΔopaR. The primer extension assay detected a single transcription start site located at 74 bp upstream of opaR; therefore, a single promoter was transcribed for opaR under the growth condition tested. In addition, the opaR promoter activity was under the negative control of OpaR. A 334 bp promoter-proximal region of opaR was amplified, radioactively labeled, and subjected to EMSA with a purified His-OpaR protein  and from 159 to 109 bp (site 2) upstream of opaR, respectively, were considered as OpaR-binding sites. Taken together, OpaR is able to recognize the promoter of its own gene to directly repress its activity in V. parahaemolyticus.The primer extension experiments  were con protein . The resprinting , the purqrr2–4, and this assay also indicated that the promoter activity of all the thee qrr genes was under the positive control of OpaR. Each of the promoter-proximal regions of qrr2–4 was subjected to EMSA with the purified His-OpaR protein  luxCDABEGH operon luxCDABEGH, and thus stimulates this operon in a direct manner exsA, a transcriptional activator of T3SS1, within the T3SS1 gene loci The V. cholerae HapR is a repressor of virulence: i) HapR directly represses the transcription of aphAhlyA; for the later one, HapR directly stimulates the transcription of hapA encoding a metalloprotease that in turns degrades the HlyA hemolysin). HapR is also a repressor of biofim formation in V. choleraevpsT encoding a transcriptional activator of biofilm formation; and ii) HapR represses the cellular c-di-GMP levels  and cytolysin/hemolysin (encoded by vvhA) rtxA1 and vvhA is repressed by SmcR through directly repressing the transcription of hlyUrtxA1 and vvhAvvpE is also found in V. vulnificusvvpE in a juxtapositioned manner, and thus they function synergistically to coactivate the transcription of vvpE by the RpoS-dependent promoter at the stationary growth phase V. vulnificus.The V. parahaemolyticus OpaR is a repressor of cytotoxicity to host cells V. parahaemolyticus (data unpublished). The molecular mechanisms employed by OpaR to regulate biofilm formation and virulence need to be elucidated.The V. choleraeV. choleraeBiofilm formation can be concisely linked to the bacterial survive in adverse conditions outside of the host, thus aiding in bacterial persistence during inter-epidemic seasons cis-acting signals recognized by the four extremely conserved MQSR proteins, V. harveyi LuxR, V. parahaemolyticus OpaR, V. vulnificus SmcR, and V. alginolyticus ValR. These consensus constructs could be also applied to all the other members of the Harveyi clade in addition to the above four bacteria.Two consensus constructs, a box and a PFM,  were buicis-acting regulatory DNA as a dimer. The box is a contiguous oligonucleotide, and thus it presents limited information that are originally presented in the MQSR-binding sites. Representation of the cis-acting regulatory motif with a PFM is able to give a much more comprehensive description of the uneven composition in each position, i.e., some nucleotides occurred much more frequently than others. Thus, the PFM presentedd here will over-represent the MQSR-binding sites more accurately than the 20 bp box sequence.The 20 bp MQSR box was further annotated as an inverted repeat sequence. This dyad symmetry structure indicated that LuxR, OpaR, SmcR, and ValR, like other TetR-type proteins, bind to V. parahaemolyticus for further biochemical validation.The PFM can be used to statistically predict the presence of MQSR consensus-like elements within the promoter-proximal sequences tested, which will generate a weight score for each gene, and a higher score denoted the higher probability of regulator-promoter association . This asV. harveyiV. vulnificuscis-acting DNA sequences from four closed related Vibrios, rather than from the artificial sequences The PFM construct herein is essentially in agreement with those previously characterized for LuxR in valR, opaR, luxR, smcR, vtpR, vanT, and hapR were aligned in 70 recognition, Shine-Dalgarno (SD) sequences for ribosome recognition, and MQSR box-like sequences representing the conserved signals for recognition by OpaR or its orthologs. This analysis gave a structural map of these auto-repressed promoters.Direct transcriptional repression of their own genes have been established for LuxR valR, opaR, luxR, or smcR, indicating that two sites were recognized by the relevant regulatory protein for each target gene. Indeed, corresponding two binding sites have been experimentally determined for opaR (this study) or luxRvtpR, vanT, or hapR, indicating a single site within each of these promoter-proximal regions were recognized by the relevant regulatory protein; without no exception, a single corresponding HapR-binding site has been detected for hapRTwo MQSR box-like sequences, upstream and downstream of the transcription start site, respectively, were annotated for V. harveyi, V. parahaemolyticus, and V. vulnificus contain qrr1–5, and whereas, the more distantly related V. cholerae harbors only qrr1–4. Any one of Qrr1–4 in V. cholerae is perfectly sufficient to repress hapR, and thus, the four Qrr sRNAs are functionally redundant V. harveyi, and Qrr5 may be an evolutionary vestige V. cholerae, Qrr1–4 in V. harveyi function additively to control QS behaviors; these sRNAs function to translate increasing autoinducer concentrations (following the transition from LCD to HCD) into a precise gradient of LuxR protein, and that LuxR in turns induces a gradient of expression its target genes V. parahaemolyticus and V. vulnificus need to be elucidated.All of the three closely related organisms qrr transcription constitutes a negative feedback loop most likely maintaining the QS behaviors during the transition HCD to LCD V. harveyi LuxR directly binds to the upstream DNA regions of qrr2–4 and stimulates the transcription of qrr2–4, but it has no regulatory effect on qrr1 and qrr5V. parahaemolyticus OpaR also bound to the promoter regions of qrr2–4 to activate their transcription. To the best of our knowledge, this is the first report of experimentally determining MQSR-binding sites and transcription starts of qrr genes. It should be noted the experimental transcription starts of qrr genes (this study) are 1 bp downstream of the predicted ones As mentioned above, the LuxR- or OpaR-mediated stimulation of qrr2–4 in V. harveyi and V. parahaemolyticus, and of qrr3 in V. vulnificus (but not qrr2 and qrr4 of V. vulnificus). In addition, the box elements could not be predicted from qrr1 and qrr5 of all these three closely related Vibrios. These indicated that the MQSR-qrr2–4 feedback loop was conversed between V. harveyi and V. parahaemolyticus. We aligned the promoter-proximal regions of qrr2–4  strain RIMD 2210633, and then the complemented mutant strain C-opaR was constructed. As determined by several distinct methods (see text), the transcription of ahpA was under the negative control of OpaR. Herein, an oligonucleotide primer, which was complementary to the RNA transcript of ahpA, was employed to detect the primer extension product that represented the relative mRNA level of ahpA in WT, ΔopaR, and C-opaR. The primer extension products were analyzed with 8 M urea–6% acrylamide sequencing gel. Lanes C, T, A, and G represented the Sanger sequencing reactions. The transcription start site (nucleotide C), which was located at 200 bp upstream of ahpA, was underlined in the DNA sequence. The ahpA mRNA level was significantly enhanced in ΔopaR relative to WT, while no obvious change in the ahpA transcription was observed between WT and C-opaR, which confirmed that the detecting enhanced transcription of ahpA in ΔopaR was due to the opaR mutation rather than a polar mutation.(TIF)Click here for additional data file.


GATA1, demonstrate increased overall survival relative to non-DS pediatric patients, suggesting a potential role for GATA1 in chemotherapy response. In this study, we confirmed that amongst non-DS patients, GATA1 transcripts were significantly higher in AMKL blasts compared to blasts from other AML subgroups. Further, GATA1 transcript levels significantly correlated with transcript levels for the anti-apoptotic protein Bcl-xL in our patient cohort. ShRNA knockdown of GATA1 in the megakaryocytic cell line Meg-01 resulted in significantly increased cytarabine (ara-C) and daunorubicin anti-proliferative sensitivities and decreased Bcl-xL transcript and protein levels. Chromatin immunoprecipitation (ChIP) and reporter gene assays demonstrated that the Bcl-x gene (which transcribes the Bcl-xL transcripts) is a bona fide GATA1 target gene in AMKL cells. Treatment of the Meg-01 cells with the histone deacetylase inhibitor valproic acid resulted in down-regulation of both GATA1 and Bcl-xL and significantly enhanced ara-C sensitivity. Furthermore, additional GATA1 target genes were identified by oligonucleotide microarray and ChIP-on-Chip analyses. Our findings demonstrate a role for GATA1 in chemotherapy resistance in non-DS AMKL cells, and identified additional GATA1 target genes for future studies.It has been previously shown that acute myeloid leukemia (AML) patients with higher levels of GATA1 expression have poorer outcomes. Furthermore, pediatric Down syndrome (DS) patients with acute megakaryocytic leukemia (AMKL), whose blast cells almost universally harbor somatic mutations in exon 2 of the transcription factor gene  GATA1 gene , resulting in the introduction of premature stop codons and the synthesis of a shorter GATA1 protein  initiated from a downstream initiation site and distinguishable from the wild-type GATA1 (50-kDa) , while at the protein level, GATA1 was reduced to approximately one-third and one-half that of GATA1 Neg in the GATA 4-14 and GATA 5-13 clones, respectively  but not an unrelated region. Activation of the Bcl-x promoter activity by GATA1 was further confirmed using a luciferase reporter assay after cotransfection of D. Mel-2 cells with increasing amounts of a GATA1 expression vector along with the Bcl-x promoter construct. As shown in Bcl-x promoter. Taken together, these studies provide direct evidence that GATA1 binds to the GATA1 sites in the Bcl-x upstream region and transactivates its activity in Meg-01 cells.To provide evidence that GATA1 directly activates expression of Bcl-xL by binding to the putative GATA1 sites in the With currently available treatments, it is not feasible to directly target GATA1 in AMKL cells. Based on a previous report , we hypo50s) by 1.6- and 2.7-fold, respectively  were identified, with a false discovery rate of 6.4%. Of these, 1521 were down regulated in the knockdown clones, while 1689 were upregulated. Validation of the microarray results was performed using real-time RT-PCR and separate RNA samples from the Meg-01 stable clones. As shown in In order to identify additional GATA1 target genes that may contribute to chemotherapy resistance, oligonucleotide microarray analyses on RNAs from the GATA1 5-13/4-14 clones and GATA1 Neg control were performed. Average log ratios, representing the differences in expression between the bona fide GATA1 target genes. Interestingly, there were many genes in this overlapping group that were found by DAVID ontology analysis to be associated with either regulation of cell death, cell cycle, or proliferation  and has shown single agent efficacy in leukemia patients [This study established that for patients whose leukemic blasts express high levels of GATA1, one potential mechanism of resistance involves overexpression of the anti-apoptotic protein Bcl-xL. A direct role for GATA1 in the expression of Bcl-xL was implicated by knocking down GATA1 in the megakaryocytic cell line, Meg-01. Upon knocking down GATA1, Bcl-xL expression was partially abrogated, accompanied by increased basal apoptosis and sensitivity to ara-C and DNR. Although this finding was not entirely unexpected, as the Bcl-2 family proteins are known to inhibit apoptosis and lead to chemotherapy resistance, and Bcl-xL has a well-documented role in the maintenance of the megakaryocyte lineage , it nonepatients ,33.Another potential approach to enhance the treatment of AML with high expression of GATA1 involves the use of HDAC inhibitors combined with standard chemotherapy regimens. While HDAC inhibitors are well known to exhibit modest anti-leukemic activity as single agents, preclinical work from our group has demonstrated their strong enhancement of standard agents ,34. In tbona fide GATA1 targets. In addition to those, other potentially important genes were identified. One such gene is DYRK1A, which encodes the dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 1A. This gene was recently identified as a driver of megakaryocytic leukemia in a mouse DS AMKL model [While Bcl-xL appears to be an important GATA1 target, it was of interest to identify additional genes that were regulated either directly or indirectly by GATA1. Using gene expression microarray analysis, we were able to identify over 3000 genes that were differentially expressed upon GATA1 knockdown including genes involved in regulating chromatin structure and cell death. By cross-referencing the differentially expressed genes with regions identified by ChIP-on-Chip, we were able to generate a list of 317 genes corresponding to KL model . Neural KL model  and earlKL model . VasculaKL model  and is aKL model . Both EvKL model . CD34 isKL model , is of pKL model ,43. ThouKL model .Bcl-x promoter coupled with the high correlation between GATA1 expression and Bcl-xL transcript levels in primary patient samples offers a potential explanation for why AML patients with high GATA1 expression have been found to have poorer outcomes. Furthermore, treatment with the HDAC inhibitor, VPA, was shown to decrease both GATA1 and Bcl-xL expression in Meg-01 cells and sensitize them to treatment with ara-C. Finally, by combining gene expression microarray and ChIP-on-Chip analyses, we were able to identify additional GATA1 target genes to serve as a basis for future studies of both potential chemotherapeutic interventions and AMKL biologyIn summary, in this study we were able to further clarify the role of GATA1 in AMKL. Blasts from patients with AMKL overexpressed GATA1 relative to those with other AML subtypes. The finding that GATA1 is able to bind and activate the Figure S1A Western blots demonstrating the impact of GATA-1 knockdown on Bcl-2 and Mcl-1 expression. 100µg of protein were loaded in each lane, with an excess of THP-1 lysate as a positive control (far right). B Western blots demonstrating overexpression of Bcl-xL in both the parental Meg-01 and Meg-01 4.14 cell lines. B–C Indicated cells were treated for 24 hours at the indicated drug dose and viability was determined using trypan blue exclusion. * indicates p < 0.05 compared to RFP or between indicated columns.(PDF)Click here for additional data file.Table S1(XLSX)Click here for additional data file.Table S2(XLSX)Click here for additional data file.Table S3(XLSX)Click here for additional data file.Table S4(XLSX)Click here for additional data file.Table S5(XLSX)Click here for additional data file.Table S6(XLSX)Click here for additional data file.Methods S1(DOCX)Click here for additional data file.


FMR1 gene, through expansion and methylation of a CGG triplet repeat (methylated full mutation). An antisense transcript (FMR1-AS1), starting from both promoter and intron 2 of the FMR1 gene, was demonstrated in transcriptionally active alleles, but not in silent FXS alleles. Moreover, a DNA methylation boundary, which is lost in FXS, was recently identified upstream of the FMR1 gene. Several nuclear proteins bind to this region, like the insulator protein CTCF. Here we demonstrate for the first time that rare unmethylated full mutation (UFM) alleles present the same boundary described in wild type (WT) alleles and that CTCF binds to this region, as well as to the FMR1 gene promoter, exon 1 and intron 2 binding sites. Contrariwise, DNA methylation prevents CTCF binding to FXS alleles. Drug-induced CpGs demethylation does not restore this binding. CTCF knock-down experiments clearly established that CTCF does not act as insulator at the active FMR1 locus, despite the presence of a CGG expansion. CTCF depletion induces heterochromatinic histone configuration of the FMR1 locus and results in reduction of FMR1 transcription, which however is not accompanied by spreading of DNA methylation towards the FMR1 promoter. CTCF depletion is also associated with FMR1-AS1 mRNA reduction. Antisense RNA, like sense transcript, is upregulated in UFM and absent in FXS cells and its splicing is correlated to that of the FMR1-mRNA. We conclude that CTCF has a complex role in regulating FMR1 expression, probably through the organization of chromatin loops between sense/antisense transcriptional regulatory regions, as suggested by bioinformatics analysis.Fragile X syndrome (FXS), the leading cause of inherited intellectual disability, is caused by epigenetic silencing of the  FMR1, a gene mapping on the X chromosome and containing a CGG repeat in its promoter region. Expansion of this unstable sequence beyond 200 repeats (full mutation) is followed by DNA methylation and histone changes, leading to the transcriptional inactivation of FMR1 and to the lack of the FMRP protein. Recently, an antisense transcript (FMR1-AS1) spanning the CGG repeats and a region of transition of DNA methylation (boundary) located upstream of the CGG repeats have been identified in transcriptional active FMR1 alleles. Several nuclear proteins bound to the methylation boundary have been described, such as the zinc-finger protein CTCF, the first known insulator in mammals. This protein is an important transcriptional regulator of genes harboring trinucleotide repeats and it is mostly active in chromatin organization. For the first time, we have investigated the role of CTCF protein in the transcriptional regulation of the FMR1 gene. Our results define a complex role for CTCF acting through chromatin organization of the FMR1 locus.Fragile X syndrome is the most common cause of inherited intellectual disability, accounting for about 1∶3000 males and 1∶4000 females. It is caused by a dynamic mutation of  FMR1 gene, an X-linked gene which contains a CpG island in its promoter FMR1 gene and the lack of the FMRP protein FMR1 locus showed histone H3 and H4 hyperacetylation, lysine 4 of histone 3 (H3-K4) methylation, lysine 9 of histone 3 (H3-K9) hypomethylation, lysine 27 of histone 3 (H3-K27) dimethylation and lack of DNA methylation FMR1 locus, allowing transcription. A similar epigenetic status can be induced by treatment of FXS cells with the DNA demethylating agent 5-aza-2-deoxycytidine (5-azadC), which also causes histone changes , the latter actually preceding DNA demethylation FMR1 in human embryonic stem cells seems to begin from histone modifications prior to DNA methylation Fragile X syndrome , the most studied and best known FRAXopathy, is the leading cause of inherited intellectual disability (ID) FMR1 promoter. This methylation boundary (MB) appears to be lost in completely methylated FXS alleles. The boundary is also conserved in the mouse genome, even if human and mouse are only 46.7% identical in the 5′ region upstream the FMR1 gene In FXS cell lines DNA methylation extends further to approximately 1 kb upstream the CGG repeat sequence CCCTC-binding factor), the first insulator protein found in mammals C-MYC oncogene in vitroMethylation boundary regions are characterized by the presence of binding sites for various nuclear proteins including CTCF (DM1 locus of myotonic dystrophy SCAANT1 (SCA7 antisense noncoding transcript 1) expression. Loss of SCAANT1 de-represses ataxin-7 sense transcription in a cis-dependent manner and is accompanied by chromatin remodeling FXN gene, CTCF depletion was observed in the 5′ UTR of the mutant alleles. This depletion is associated with high levels of the transcript antisense of FXN (FAST-1), supporting the hypothesis of an epigenetic silencing of the corresponding “sense” gene An important regulatory role of CTCF was described in expanded triplet diseases. Specific binding sites for this protein were recognized flanking the CTG triplet at the FMR1 locus, suggesting a role of this protein in the regulation of the gene FMR1 gene (FMR1-AS1) spanning the expanded CGG repeat was identified in normal and premutated alleles, but not in FXS alleles. The authors suggested a possible pathogenic role of FMR1-AS1 in FXS and also in the fragile X tremor-ataxia syndrome (FXTAS) associated with premutated alleles. However, they did not study the presence of the antisense transcript in UFM cells.Four CTCF binding sites have been identified within the FMR1 gene and in chromatin organization of the corresponding locus including the methylation boundary region, in different cell lines derived from normal (WT), FXS and UFM individuals, respectively. Through molecular and bioinformatics approaches we demonstrate that CTCF does not preserve the methylation boundary of the FMR1 locus, but is required for its proper transcription. Significant results were obtained from the further characterization of the rare UFM cell lines by mapping the methylation boundary region and by measuring the FMR1 antisense transcript.In this paper we investigate the role of CTCF in transcriptional regulation of the Figure S1A) and 73–74 in fibroblasts , respectively. As expected, no boundary was present in FXS cells. Despite the presence of the CGG expansion, the transcriptionally active UFM cell lines retained the methylation boundary as in WT cells, both in lymphoblasts and in fibroblasts .The extended region upstream the CGG repeats described by Naumann et al. [2009] FMR1-AS1 transcript levels and observed higher levels of transcription in UFM lymphoblasts (around 6-fold higher) and fibroblasts (around 3-fold higher) compared to WT, while no antisense transcript was detected in FXS cells, as expected FMR1-AS1 cDNA in WT and UFM cells confirmed the presence of the splicing corresponding to the intron 1 of the sense transcript .In WT cells, we confirmed CTCF binding to the MB site between CpG pairs 66–69. As expected, no CTCF binding was found in FXS fibroblasts, given the complete methylation of this region. Instead, UFM fibroblasts showed binding levels similar to those of WT cells . However, as indicated in FMR1 gene in exon 1, promoter and boundary region (MB site).We speculated that after DNA demethylation CTCF might rebind to its sites on the FMR1 regulatory region in transcriptionally active cells, we went on to investigate whether CTCF protein had a regulatory function in FMR1 gene transcription.After demonstrating that CTCF binds to the CTCF transcript into WT and UFM fibroblasts to reduce CTCF mRNA and to verify the effect of this reduction on FMR1 transcription. In each knock-down experiment CTCF mRNA depletion was confirmed by quantitative RT-PCR, in comparison with GAPDH mRNA levels, used as control (data not shown). The CTCF reduction was also confirmed on protein levels both in WT and UFM cells  was observed in the promoter and exon 1 of WT fibroblasts  and increased methylation of H3-K9 in the promoter region, compared to the untreated WT cells  shapes regulatory chromatin loops, we performed a statistical and computational analysis of DNA structural properties of known regulatory loops determined by 5C experiments Text S1 and Figure S2).Our data support a mechanism of transcriptional co-regulation between FMR1 gene TSS were tested in silico, pairing the CTCF binding sites illustrated in FMR1 gene. The results of this predictions are reported in FMR1-AS1 transcriptional start site was identified, were predicted with high confidence both in WT and in the expanded allele. The in silico analysis excluded loops formation between exon 1 and all the other CTCF binding sites.All putative CTCF-mediated loops in the proximity of the FMR1 gene promoter, but not in FXS cells DMPK, in the ICR (Imprinted Control Region) of IGF2 and in the neighboring BLU and RASSF1A loci of the 3p21.3 gene cluster region FMR1 gene transcriptional regulation.Emerging evidence underlines the dynamic status of the chromatin, previously thought to be static, showing that a given region may be condensed (heterochromatin) and decondensed (euchromatin), according to the cell needs for transcriptional activity of that region. The discovery of proteins capable of establishing physical, as well as functional connections among distant genomic regions, even among different chromosomes, adds complexity to an already intricate network of gene-gene interactions. CTCF can be considered a leading candidate mediating these complex interactions FMR1 locus were already identified FMR1-AS1. We firstly showed that these three sites are bound to CTCF in UFM cells, both lymphoblasts and fibroblasts, and the binding level is quite similar to WT cells. These latter cell lines showed differences in CTCF binding in the two cell types analyzed (lymphoblasts and fibroblasts) and these variations should be related to differences between primary fibroblasts and Epstein-Barr-transformed and clonal lymphoblasts, as previously observed for other chromatin marks Binding sites for CTCF in the FMR1 methylation boundary was already described A CTCF binding site located in the FMR1 gene in FXS cell lines by inducing DNA demethylation with 5-azadC. DNA demethylation resulted in FMR1 transcription reactivation as expected, while CTCF binding to its specific sites on promoter, exon 1 and boundary region was not restored. This result might be explained by failure of drug-induced DNA demethylation to reverse all modifications that occur during gene silencing. As observed on p16 and MLH1 gene, 5-azadC treatment did not completely restore normal histone code and post-translational modifications of DNA binding proteins to reestablish long-term expression FMR1 promoter resumed its methylated status FMR1 gene.We then tried to restore CTCF binding to the FMR1 gene transcription. To investigate this potential role, we induced both silencing and overexpression of CTCF transcript. In those experiments in which siRNA-mediated CTCF knock-down did not correlate with FMR1 transcript reduction, epigenetic marks  were unmodified in promoter and exon 1 regions. On the other hand, the level of CTCF protein still bound to the gene was found reduced in CTCF knock-down experiments coupled with FMR1 mRNA reduction. Moreover, FMR1 decreased expression correlated with increased levels of heterochromatinic marks, such as H3-K4 demethylation and H3-K9 hypermethylation in the 5′ UTR of the gene. Interestingly, these epigenetic changes, known to favor heterochromatinic configuration, were not followed by the spreading of DNA methylation from the boundary region towards the FMR1 promoter, not only in WT alleles, but also in UFM alleles, suggesting that a CGG expansion is not by itself sufficient to induce methylation, even in absence of CTCF. This latter result implies that CTCF does not work as an insulator at the FMR1 locus. Therefore, other still unknown proteins must act as barrier elements in this specific region, as already hypothesized FMR1 promoter region. Its binding is partially inhibited by DNA methylation and it might be a hypothetical candidate as insulator for the FMR1 gene These data seemed to suggest a functional role of the CTCF protein in regulating FMR1 antisense transcript characterization, particularly in UFM cell lines, both before and after CTCF transcriptional silencing. The FMR1 antisense RNA is transcribed starting from the second intron of the gene in WT and premutated alleles FMR1-AS1 RNA in UFM cell lines and also showed that the levels of this antisense transcript were higher in UFM cells, compared to normal controls, similar to what happens with the sense transcript FMR1 intron 1, that uses the complementary splice donor and acceptor to FMR1, representing a non-consensus CT to AC splice site. Moreover we observed in UFM cells the same splicing variant of the FMR1-AS1 previously described as premutation-specific alternative splicing in intron 2 that also uses a non-consensus CT-AC splice site CTCF depletion the reduction of FMR1 mRNA was always coupled with the decrease of FMR1-AS1 transcript. These data indicated a co-regulation of transcription and splicing mechanisms at the FMR1 locus in transcriptional active alleles. On the other hand, CTCF knock-down did not have always the same effect: in only one third of all the experiments we observed a diminished transcription of both sense and antisense FMR1. These results suggested a partial and/or indirect role of CTCF in regulating FMR1 expression and led us to hypothesize that the sites located within the FMR1 locus may form chromatin loops mediated by CTCF homodimers capable of bringing in close proximity molecular machineries for transcription, splicing and epigenetic modifications. The formation of these loops would be partially affected by CTCF knockdown but not by CTCF overexpression, i.e. additional CTCF protein would not affect loop formation FMR1 transcription. On the other hand, it would seem that other factors, yet to be identified, could activate self-preserving mechanisms that maintain FMR1 transcription unchanged despite the absence of the loop, as observed in a fraction of our experiments. Indeed, how chromatin configurations may influence gene expression still remains unclear. The “loop” hypothesis was supported by antisense transcription data, as well as by CTCF depletion/overexpression experiments. The presence of a CTCF binding site in FMR1 intron 2, near one of the transcription starting sites of FMR1-AS1, previously observed by Ladd et al. FMR1 gene both in normal and in the expanded alleles, such as UFM. This loop may not form after 5-azadC-induced demethylation, which cannot reestablish native epigenetic modifications. In fact, as previously observed, 5-azadC effect is only transient FMR1 promoter (approximately 50 kb) was previously studied through 3C technique, which demonstrated reduced interaction frequencies FMR1 gene with CGG expansion, such as in premutation and UFM cells. The 3C technique is only capable of detecting chromatin loop interactions greater than 10 kb and for this reason a chromatin loop formation in our region of interest cannot be excluded. We investigated the possibility of looping between CTCF sites using an in silico analysis of DNA structural characteristics of experimentally validated DNA regulatory loops. For this purpose, we elaborated a new predictor system that showed good performances in discriminating between real loops and control genomic regions. This predictor (SVM) confirmed that putative loops can form involving the CTCF binding site in intron 2, both in WT and in expanded alleles.Interesting results came from the In silico data should be interpreted considering the biological context in which the FMR1 gene is located. Therefore, loop formation in FXS alleles was excluded by the existence of DNA methylation of the entire region upstream the FMR1 promoter, that prevents CTCF from binding its sites. The formation of loops between intron 2 and MR sites could also be excluded because the MR site is located in a region that is extensive methylated in WT and in expanded alleles. Our in silico results affirmed that a chromatin loop mediated by CTCF homodimers can exist between intron 2 and the methylation boundary region or promoter in normal and UFM alleles. These bioinformatics data will deserve further experimental validations.The bioinformatics approach takes into account parameters concerning the nucleotide sequence but not molecular and epigenetic characteristics, such as DNA methylation. FMR1 expression through chromatin organization. CTCF was firstly described as the only known insulator FMR1 gene. A role of CTCF in genome and locus organization acting to secure long-range intra- and inter-chromosomal interactions was abundantly described FMR1 locus chromatin organization and loop formation. Indeed, reduction of FMR1 sense and antisense transcription after CTCF depletion underscores the importance of the CTCF-mediated loop complex. This study will be help in further clarifying the processes by which cell type specific patterns of gene expression can be established and maintained.In conclusion our results delineate a role for CTCF as transcriptional regulator of 2.Lymphoblastoid cell lines were established by Epstein–Barr virus transformation from peripheral blood lymphocytes of FXS, UFM and normal control (WT) males. The FXS cell lines employed in these experiments were E3 and S1, with 250 and 450 CGGs, respectively; the UFM cell line (MA) contains 265–430 CGGs Primary fibroblast cultures were obtained from skin biopsies derived from the UFM individual (MA). We have also employed one FXS line (GM04026) and three WT lines , provided by the Coriell Institute . Fibroblasts were grown in BIO-AMF2 complete medium .5 cells/ml and 5-azadC was added daily at 1 µM  for 7 days. At the end of the treatment, cells were harvested to measure viability with the propidium iodide method  and to perform RNA and DNA extraction.FXS lymphoblasts were treated with the demethylating agent 5-azadC (Sigma-Aldrich), as previously described CTCF transcripts was carried out in UFM and in all three WT fibroblast lines with synthetic siRNAs . Complete sequences of the siRNAs are listed in Table S1. Negative control to check the efficiency of CTCF depletion was performed using scramble siRNA (IDT). In accordance with the protocol of the manufacturer, 40 nM of siRNA were transfected by Lipofectamine RNAiMAX  and cultures were harvested after 72 hours.Knock-down of CTCF was transfected into the cells through the expression plasmid pCMV6-Entry  (Origene). 100 ng of plasmid DNA was transfected in fibroblasts with Lipofectamine 2000  and cells were collected after 48 h, according to manufacturer's instructions, and after 120 h to asses if a longer overexpression could affect FMR1 transcription.The human open reading frame of Proteins extracted from untreated and siRNA-treated WT and UFM fibroblasts were resuspended in Laemli buffer, boiled, separated on 8% polyacrylamide gel electrophoresis, transferred to Hybond-ECL membrane , immunostained and visualized after film exposure using the ECL Western Blotting Kit , according to the manufacturer. Primary antibodies were used at the following concentrations: 1∶1000 anti-CTCF rabbit policlonal antibody (Millipore) and 1∶10000 anti-GAPDH mouse antibody (Sigma-Aldrich).Genomic DNA was isolated from siRNA-treated and untreated fibroblasts both WT and UFM by DNeasy Blood & Tissue kit (Qiagen) The DNA concentration was checked both by absorbance measurements at 260 and 280 nm and on agarose gel. Bisulfite DNA transformation was performed as previously described CTCF (Hs00902008_m1), GAPDH (402869), FMR1 (Hs00233632_m1). For FMR1-AS1, custom-made assay was designed -TGGAATCATCTCCCC-(TAMRA)-3′(Applied Biosystems), according to Ladd et al. Total RNA was extracted by TRIzol . RNA concentration and purity were checked on agarose gel and by UV spectrophotometer. RNA samples were treated with TURBO DNA-free DNase (Ambion) to remove contaminating DNA. Afterwards, 1 µg of total RNA was retro-transcribed into cDNA by MoMLV-RT  using random hexamers. For a relative quantification of each transcript, the following pre-developed TaqMan assays (Applied Biosystems) were used: FMR1-AS1 transcript, cDNA was generated using specific primers, with a linker (LK) sequence: 5′-CGACTGGAGCACGAGGACACTGA-3′attached to the 5′ end. Primers were those employed by Ladd et al. To analyze the FMR1 and HPRT.ChIP assay was performed according to the manufacturer . After 10 minutes at 37°C with 1% formaldehyde, cells were seeded and washed with 1× PBS and Protease Inhibitor Cocktail (Sigma-Aldrich). To obtain 200–1000 bp DNA fragments, cell pellets were sonicated. Histone methylation analysis was performed using two different antibodies against dimethyl lysine 9  and dimethyl lysine 4  on histone 3. Binding of CTCF protein was assayed using the specific antibody . In each ChIP assay antibody against rabbit IgG  was employed and also no template control was included. Immunoprecipitated DNA (IP-DNA) was extracted by standard procedure  and then quantified by real-time PCR  using fluorescent probe and primers specific for both Table S2. Standard curves for the three FMR1 and for the single HPRT amplicon were constructed with five different DNA dilutions of known concentration (X axis = log[X]) and the corresponding Ct values (Y axis). The unknown amount of methylated histone and CTCF-binding IP-DNA of FMR1 and HPRT (X axis = log[X]) was calculated from Ct values, through the standard curve plot. Normalized FMR1 levels were estimated dividing the amount of FMR1 IP-DNA by the amount of HPRT IP-DNA.Primers and probes employed for PCR analysis are listed in All variables were analyzed by means of descriptive statistics . Data were analyzed with non-parametric statistical Kruskal-Wallis test and with K sample test. The level of significance was set at p≤0.05. Data analysis was performed using STATA Intercooled v. 9.2 software .Text S1. Briefly, a machine learning method was trained to recognize known chromatin loops from control genomic regions, and then used to test putative regulative loops in the proximity of FMR1 transcription start site.In order to analyze the structural characteristics of CTCF-mediated DNA loops, a bioinformatics approach was developed and is detailed in the Supplementary Data are available online: Supplementary Figure S1FMR1 gene in WT, UFM and FXS cell lines: lymphoblasts (A) and fibroblasts (B). Every line corresponds to bisulfite sequencing of an individual cell. Black and white squares correspond to methylated and unmethylated CpG sites, respectively. CpG pairs between 45 and 54 are within the promoter region, whereas CpGs between 55 and 82 are located upstream. UFM cells present a transitional region of methylation similar to WT cells; in FXS cells this methylation boundary is completely lost. Black bars indicate CTCF binding sites in the MB and in the promoter region.Methylation boundary region analysis. Bisulfite sequencing of the methylation boundary region of (TIF)Click here for additional data file.Figure S2FMR1 locus chromatin conformation. Parameter distribution between real DNA loops (in the POS dataset) and both the random genomic controls (NEG1) and the CTCF-related controls (NEG2). The POS loops appear more bendable than NEG1 controls, but less than the NEG2 ones. A similar behavior can be observed for the DNA cleavage intensity, while the POS loops seem to be more stable to thermal denaturation than both controls. POS loops appear to have a lower average curvature than random genomic regions, and curvature values for POS loops were strongly inversely correlated to their bendability index (Pearson's correlation coefficient −0.9). This observation is not surprising since curved DNA is often the result of the interaction with chromatin proteins, and the associated entropy reduction is less unfavorable for less flexible DNA.Computational analysis for (TIF)Click here for additional data file.References S1List of references included in the (DOC)Click here for additional data file.Text S1FMR1 locus. We analyzed DNA structural properties of known CTCF-mediated regulatory loops determined by 5C experiments (POS dataset) FMR1 gene TSS, pairing the CTCF binding sites illustrated in Methodological details and performance evaluation for chromatin loops inside the (DOC)Click here for additional data file.Table S1CTCF transcript with the corresponding sequences.List of siRNA against (DOC)Click here for additional data file.Table S2Primers and probes used for qPCR after ChIP assays.(DOC)Click here for additional data file.