Escherichia coli nucleoid during rapid growth in rich medium and following an induced amino acid starvation that promotes the stringent response. Our analyses identify the presence of origin and terminus domains in exponentially growing cells. Moreover, we observe an increased number of interactions within the origin domain and significant clustering of SeqA-binding sequences, suggesting a role for SeqA in clustering of newly replicated chromosomes. By contrast, ‘histone-like’ protein  -binding sites did not cluster, and their role in global nucleoid organization does not manifest through the mediation of chromosomal contacts. Finally, genes that were downregulated after induction of the stringent response were spatially clustered, indicating that transcription in E. coli occurs at transcription foci.To fit within the confines of the cell, bacterial chromosomes are highly condensed into a structure called the nucleoid. Despite the high degree of compaction in the nucleoid, the genome remains accessible to essential biological processes, such as replication and transcription. Here, we present the first high-resolution chromosome conformation capture-based molecular analysis of the spatial organization of the  Escherichia coli nucleoid . Non. Nonin val [e.g. ] and mayE. coli nucleoid, the overall effect of the SHX-induced amino acid starvation is an expansion of the nucleoid and a change in transcription patterns , we observe a clear relationship between DNA:DNA interactions, copy number and DNA replication. This suggests that nucleoids remain associated after replication, consistent with the cohesion model. Furthermore, SeqA-binding sites exhibit replication-dependent clustering, whereas binding sites for the major histone-like proteins  did not. Finally, we observe a correlation between gene regulation and spatial clustering.Advances in chromosome conformation capture (3C)-related methodologies  enable ter cells . Here, wE. coli strains (Supplementary Table S1) were recovered from −80°C on Luria Bertani (LB) agar (2%) plates . LB medium  starter cultures were inoculated and grown . The optical density (OD600) of cultures was measured and used to inoculate LB test cultures to an OD600 of ∼0.02. The test cultures were grown  until the OD600 reached ∼0.25, and the cells were harvested. For the SHX-treated samples, the cultures were treated with SHX  before harvesting.For GCC analyses , E. coliE. coli chromatin was prepared according to Rodley et al.  cells were lysed  in the presence of protease inhibitor (Roche), and the chromatin was collected . Chromatin was washed and suspended in chromatin digestion buffer . Chromatin samples were digested with HhaI , diluted (∼20-fold) and ligated with T4 DNA ligase . A ligation control was added to the digested chromatin  before ligation. After ligation, cross-links, protein and RNA were removed. pUC19 plasmid was added as a sequencing control before three extractions with 1:1 phenol:chloroform. DNA was column purified  according to the manufacturer’s instructions and eluted in milliQ H2O. Three micrograms of purified DNA was sent for paired-end sequencing (100 bp) at the ATC sequencing facility  on an Illumina Hi-Seq.y et al.  with minSupplementary Materials and Methods). Except where indicated, bioinformatics and statistical analyses were performed on interactions identified by sequence reads that were uniquely mapped onto the reference genome and were above the cut-off value derived from the ligation control interactions . A breakdown of the interactions present in the E. coli samples is provided in Supplementary Table S3. The effect of bar-coding, sequencing lane and biological replicates on the correlation between samples was quantified using the Cohen’s Kappa statistic, showing that these factors did not strongly affect sample correlations . All bioinformatics analysis was performed using in house Perl and Python scripts . Except where indicated, statistical analyses were performed in R   . The E. E. coli was grown in LB  to an OD600 ∼0.2 and harvested directly, or first treated with SHX before RNA isolation. RNA was isolated using hot phenol and finally suspended in DEPC-treated water (Invitrogen). The cDNA libraries were constructed using a SuperScript Double-Stranded cDNA Synthesis Kit (Invitrogen) and sent to Roche-Nimblegen for microarray hybridization. Each experiment  is a pool of three biological replicates. A total of two technical replicates were performed per condition . Genes that were significantly up- or downregulated in SHX-treated compared with exponential samples were identified by calculating the log2 of the SHX/exponential ratio .Briefly, similar to GCC, et al.  was determined from our GCC interaction network. We then generated 1000 random data sets of the same number and length (bp) as the actual region data set using two methods: (i) randomly selecting a start position for each region and then making it the same length as the region for which the random coordinate was being generated [i.e. random spacing (RS)]; or (ii) randomly select the start position for the first region and then sequentially determining the start and end position of all the other regions in the set such that the linear distances between regions were maintained [i.e. conserved linear spacing (CLS)]. This ensured that the particular interaction frequencies we observed were not because of the linear arrangement of the regions around the circular genome. One thousand random data sets were generated for the RS and CLS methods, and the total interaction and clustering frequencies were calculated from our GCC interaction network. The frequency with which the total interaction and clustering frequency of the actual data was higher or lower than the random data sets was used to estimate significance.NAP-binding sites were obtained from Grainger et al. . MatP-bi . The following analyses were only performed on interactions that were above this significance threshold.GCC relies on the intra-molecular ligation of cross-linked loci. However, inter-molecular ligation events resulting from random associations during the procedure can also occur, leading to false positives. To reduce the chances of isolating false positives, we (i) induce expansion of the nucleoid by isolation in a high-salt environment [a ‘high-salt nucleoid’ ], followChromosome interaction networks were determined for rapidly growing cells in rich medium harvested at early exponential phase and exponential cells treated with SHX B and C. Supplementary Table S3). However, SHX treatment results in fewer long distance interactions , shorter loop lengths  and reduced numbers of partners per fragment  when compared with the exponential network. These observations are consistent with SHX, decreasing the overall compaction of the nucleoid . By contrast, treatment with SHX reduces this bias . These results are consistent with the inhibition of replication initiation after SHX treatment leading to a reduction in the Ori:Ter copy number ratio . The distribution of interaction strength and copy number relative to the origin was determined  high clustering (i.e. inter-matS loci interactions) within the exponentially growing cells (Supplementary Table S6). In contrast, clustering of matS sites was not detected in the SHX-treated cells. The clustering in the exponentially growing condition was attributed to a single specific interaction between matS10 and matS5  (Supplementary Table S7). Moreover, these sites are significantly more prone to interact with other loci than random sites . Visualizing the positions of the SeqA–SeqA interactions that formed within the E. coli genome showed that they tend to occur toward, and involve, the Ori domain in exponential cells . However, SlmA sites did exhibit a significantly increased propensity to interact with other genomic loci (P < 0.05) compared with randomly spaced elements for both exponential and SHX-treated cells (Supplementary Table S8). The significant increase in interaction frequency was lost when comparisons were made with random sets that have conserved linear spacing (Supplementary Table S8). Note that the differences observed in significance when the test data set was compared with randomly generated data sets (i.e. RS or CLS) confirm that the linear spacing of E. coli loci is important. Whether this is an effect or cause of spatial organization remains to be determined.SlmA binds at 24 defined sites within the genome  and actsE. coli cells  and are non-randomly distributed across the linear genome . There is no correlation between transcript level and interaction frequency at the level of specific restriction fragments . However, the SHX downregulated genes have high average transcript , clustering and interaction  remained highly clustered  reduced after treatment with SHX can be interpreted as indicating that the nucleoid expands under this condition, consistent with microscopic observations . This interpretation agrees with electron microscope observations of isolated nucleoids [reviewed in  DNA-binding sites is not significant within the gross spatial organization of the iewed in ], gene riewed in ]. These al. 2011 , who ideigure S4 ]. Theref mutants  was like mutants  and episE. coli cells (Pseudomonas aeruginosa (data not shown) and among highly transcribed genes in Schizosaccharomyces pombe  governing the organization of global structure.The bacterial cell is a complex structured entity in which each part exists ‘for and by means of the whole’ . As suchE. coli nucleoid is consistent with empirical measures and modeling, which indicated that intra-nucleoid interactions play a dominant role in shaping the E. coli nucleoid . Expression data has been deposited GSE40304.Supplementary Data are available at NAR Online: Supplementary Tables 1–10, Supplementary Figures 1–6 and Supplementary Materials and Methods.Intramural Research Program of the National Institutes of Health, National Cancer Institute, Center for Cancer Research (to C.C. and J.D.); the Marsden Fund (to J.M.O.S.); Massey University research fund (to J.M.O.S and B.H.A.R.); Massey University scholarship (to R.S.G.). Funding for open access charge: The Liggins Institute Auckland University.Conflict of interest statement. None declared.


C. elegans, dosage compensation is achieved by the activity of the dosage compensation complex (DCC). The DCC binds along both X chromosomes in hermaphrodites to down-regulate gene expression by half, limiting X-linked gene products to levels produced in XO males. Sequence motifs enriched on the X chromosome play an important role in targeting the DCC to the X. However, these motifs are not strictly X-specific and therefore other factors, such as the chromatin environment of the X chromosome, are likely to aid in DCC targeting. Previously, we found that loss of HTZ-1 results in partial disruption of dosage compensation localization to the X chromosomes. We wanted to know whether other chromatin components coordinated with HTZ-1 to regulate DCC localization. One candidate is DPY-30, a protein known to play a role in DCC localization. DPY-30 homologs in yeast, flies, and mammals are highly conserved members of histone H3 lysine 4 (H3K4) methyltransferase Set1/MLL complexes. Therefore, we investigated the hypothesis that the dosage compensation function of DPY-30 involves H3K4 methylation. We found that in dpy-30 animals the DCC fails to stably bind chromatin. Interestingly, of all the C. elegans homologs of Set1/MLL complex subunits, only DPY-30 is required for stable DCC binding to chromatin. Additionally, loss of H3K4 methylation does not enhance DCC mislocalization in htz-1 animals. We conclude that DPY-30 and HTZ-1 have unique functions in DCC localization, both of which are largely independent of H3K4 methylation.Dosage compensation is a specialized form of gene regulation that balances sex-chromosome linked gene expression between the sexes. In  The intricate regulation of gene expression that occurs in all organisms is the result of the coordinated activity of multiple machineries . In order for this to be achieved, these machineries need to be targeted to very specific sites of action within the vast expanse of the genome. How these various proteins and complexes find their proper targets is an important question to answer to further our understanding of how gene regulation is ultimately achieved.C. elegans called the dosage compensation complex (DCC). In organisms such as C. elegans, the difference in sex chromosome number between the sexes, if left uncorrected, would result in an imbalance of X-linked gene expression in the affected sex. To correct this imbalance, the DCC is recruited to the two X chromosomes in C. elegans hermaphrodites and dosage compensated genes are down-regulated an average of two-fold to match expression levels observed in the XO male (reviewed in DC), similar to mitotic and meiotic condensin complexes Here we discuss the targeting of a specialized gene regulatory complex in D. melanogaster, dosage compensation is achieved by up-regulation of the single male X chromosome by the MSL  complex Although the gene regulation function of dosage compensation in flies is opposite of worms, there are similarities in the X-chromosome targeting mechanisms employed in the two species. In C. elegans also involves a similar combination of factors. Analysis of DCC binding by chromatin immunoprecipitation, as well as analysis of DCC binding to transgene arrays, revealed the presence of multiple classes of DCC binding sites rex sites (for recruitment element on X) and all other sites of DCC binding are called dox sites (for dependent on X) rex sites on multicopy transgenic arrays, but fail to attract robust complex binding in single copy chromosomal duplications Motif Enriched on Xin vivo, indicating that the sequence has recruitment function. However, neither the presence of MEX nor high levels of DCC binding strictly correlate with the ability to recruit the DCC DCC binding in Evidence suggests that transcriptional activity may also contribute to DCC targeting. The DCC binds to promoter regions of about half of all transcribed genes on X C. elegans dosage compensation. Indeed, previously we have shown that the H2A.Z variant in C. elegans (HTZ-1) plays a role in regulating DCC targeting to the X chromosomes Arabidopsis and human cells, H2A.Z specifically antagonizes DNA methylation, a signature of long-term gene silencing Chromatin organization also likely plays an important and as yet incompletely understood role in C. elegans, HTZ-1 is found at significantly lower levels on the X chromosomes than autosomes Arabidopsis, and humans, in antagonizing the spread of gene repression activities.In Silent Information Regulator) complex C. elegans, and mammals C.elegansIn search of other chromatin factors regulating DCC binding, we decided to investigate the role of H3K4 methylation. In budding yeast, Htz-1 cooperates with H3K4 methylation to restrict genome-wide spreading of the SIR  analysis in adult hermaphrodite intestinal nuclei. We utilized antibodies specific for the mono-, di-, and trimethylated forms of H3K4 along with antibodies specific for subunits of the dosage compensation complex (DCC) to label the X chromosomes. To compare intensity of the methylated H3K4 species on the X chromosomes to the rest of the nucleus, we measured the ratio of the average H3K4me signal intensity in the DCC positive region compared to the average intensity of the entire nucleus see . We compInterestingly, we found the average H3K4me3 intensity signal to be reduced on the X by nearly 20% compared to the average signal in the nucleus. This is almost identical to the reduction observed for HTZ-1 signal intensity on X. H3K4me2 signal intensity is the same, while the H3K4me1 signal is slightly higher on the X chromosome compared to the rest of the nucleus . These rsdc-1(y415) and dpy-21(e428). We chose these specific mutants because although dosage compensation function is disrupted by these mutations, the DCC still localizes to the X chromosome in situ hybridization (FISH) was not possible as the H3K4 methylation antibodies fail when combined with FISH.We next wanted to investigate whether the differences in methylation levels on the X chromosomes are regulated by dosage compensation. To do this we performed the same analysis as described above in the dosage compensation mutants sdc-1 where the slight enrichment of H3K4 monomethylation was lost. These results indicate that differential H3K4me3 and HTZ-1 levels on the X chromosome are not regulated by dosage compensation. The loss of H3K4me1 enrichment in sdc-1 indicates that dosage compensation may be responsible for monomethylation enrichment on the X chromosomes. However, the fact that enrichment is still observed in dpy-21 mutants indicates that there is no strict correlation between loss of enrichment and loss of dosage compensation function.For the most part, the differential methylation and differential HTZ-1 levels on the X were unchanged in dosage compensation mutants . The onldpy-30(y228) mutant animals decended from heterozygous mothers (m+z). DPY-30 is maternally provided to these progeny, but the zygote does not produce fully functional protein.Next, we wanted to understand the effect of the Set1/MLL component DPY-30 on DCC localization at the resolution of a single cell. Previously, DPY-30, a subunit of Set1/MLL, was described to cause DCC mislocalization dpy-30 nuclei, DPY-27 has a completely diffuse, nuclear appearance with inconsistent overlap with the X chromosomes (htz-1(tm2469). As reported previously, htz-1 animals exhibit ectopic DCC localization to autosomes and this ectopic signal is not sensitive to detergent extraction  and set-16  as well as ash-2 (homologous to Bre2 in yeast and Ash2L in humans) and hcf-1 (homologous to human Hcf1).We next wanted to test whether the loss of Set1/MLL subunits other than DPY-30 has a similar effect on DCC localization. We hypothesized that the role of DPY-30 in dosage compensation was linked with its function in the histone methyltransferase complexes and predicted that loss of other Set1/MLL subunits would also disrupt DCC binding to chromatin. To test this, we depleted individual members of the Set1 and MLL complexes by RNAi and observed DCC localization in the adult animals. Although we tested all homologs by RNAi, we focused on the two SET-domain containing genes set-2 and set-16 as these are the only genes in C. elegans homologous to H3K4 methyltransferases in other organisms and have been shown previously to function in H3K4 methylation ash-2 because the homologs in other organisms are known binding partners of DPY-30 in Set1/MLL complexes and this interaction is essential for full methyltranferase activity hcf-1 because the mammalian homologs, in addition to being a physical component of Set1/MLL, serve as molecular tethers between MLL and other chromatin modifying complexes We focused on in vivo analysis in budding yeast and mammals in vitroset-2(ok952) and hcf-1(ok559), both hypomorph deletion mutants set-2(bn129) and wdr-5.1(ok1417), both null deletion mutants set-2(ok952) and hcf-1(ok559) mutants was less severe than after RNAi depletion (set-2(bn129) as well as a wdr-5.1(ok1417) null strains revealed stronger effects on H3K4me3 and H3K4me2 in particular  and wdr-5.1(ok1417), showed no significant change in DCC localization compared to wildtype hermaphrodites, further demonstrating that loss of these components does not significantly affect DCC localization  deletion mutant background. We observed further reductions of H3K4me3 m+z- background. We then used DCC IF combined with X-Paint FISH to visualize DCC localization to the X chromosomes at the single cell resolution. To measure the degree of DCC mislocalization we measured colocalization of the DCC and X-Paint signals using Slidebook software. The result of this analysis is a Pearson's correlation value (R) where 1 represents complete colocalization of the signals and 0 represents no measureable colocalization Although neither Set1 nor MLL complexes appear to play a role in DCC localization or binding to chromatin on their own, it is possible that these complexes may function with other chromatin components to affect DCC localization. Previous work in budding yeast has demonstrated that Set1C cooperates with the histone variant H2A.Z/Htz1 to limit genome-wide mislocalization of the SIR complexes htz-1 mutant background revealed that the degree of DCC mislocalization is unchanged upon Set1/MLL depletion . The average R (0.43–0.48) and profiles for Set1/MLL depletion are very similar to empty vector control RNAi in the htz-1 homozygous background. We used one-way ANOVA analysis to determine whether there was any statistical difference among the colocalization data collected. When comparing the control and Set1/MLL RNAi samples in the htz-1 background, no statistical difference was observed. However, addition of the control RNAi in wildtype data indicated a significant difference . By Tukey's test, the significant differences in colocalization are found between the wildtype control and each of the htz-1 background samples and not between the htz-1 control and the Set1/MLL RNAi samples in the htz-1 background   . This suC. elegans, ectopic expression of the pharynx-specific transcription factor, PHA-4 in intestinal cells does not result in binding of PHA-4 to pharynx-specific genes, suggesting that these sites are unavailable for binding in the intestinal nuclei Drosophila has recently revealed that chromatin accessibility is a better predictor of target site occupancy than in vitro affinity C. elegans also involves both sequence and chromatin organization It has long been hypothesized that chromatin organization plays a substantial role in driving protein complexes to their proper targets within the genome. The predicted mechanism is that chromatin organization blocks access to all but a subset of potential binding sites at any given time. In support of this, functional transcription factor binding sites are commonly depleted of nucleosomes in human T-cells C. elegans the X and autosomal chromatin makeup is significantly different both at the population level by high resolution ChIP-chip and single-cell resolution microscopy studies sdc-1 animals abolishes enrichment of H3K4me1 suggests that the transcriptional regulation function of dosage compensation may involve H3K4me1. However, the fact that we do not observe any changes in DCC localization in the Set1/MLL depletion animals with lowest H3K4me1 signal (set-2 or set-16(RNAi) in set-2 mutant animals, see set-2 or set-16 in dosage compensation, suggesting that the overall contribution of H3K4me1 to chromosome-wide gene regulation by the DCC may be minor. Alternatively, the function of an as-yet unidentified methyltranferase may be responsible for the enrichment of H3K4me1 on the X chromosomes and this methyltransferase may function in dosage compensation. This hypothesis could be tested in the future by looking for expression changes of dosage compensated genes in animals with reduced H3K4me1 and by testing other methyltransferase genes for function in dosage compensation.Recently, it has become increasingly clear that in htz-1 upon depletion of H3K4me3 by Set1/MLL depletion or mutation suggests that this is unlikely . The proposed interaction between SDC-2 and DPY-30, if direct, is likely through a domain entirely different from the ASH-2 interacting domain, or the interaction may be indirect, requiring an unidentified intermediate. The DPY-30 homolog in yeast, Sdc1, functions in the dimerization of the whole complex through interactions with Bre2 (ASH-2) and in homodimer formation How does DPY-30 contribute to dosage compensation if not through the Set1/MLL complexes? Current evidence indicates a physical interaction between DPY-30 and the DCC recruitment protein SDC-2. DPY-30 immunoprecipitation pulls down SDC-2 , and DPY-30 is found at DCC binding sites in an SDC-2 dependent manner dpy-30 animals suggests that DPY-30 plays a distinct role in dosage compensation. In sdc-2 mutant adult nuclei, IF experiments revealed loss of the Condensin IDC signal  IV; xol-1(y9) sex-1(y263) X; TY4161sdc-1(e415) and TY3936 dpy-21(e428); TY1936 dpy-30(y228)/nT1[unc-?(n754dm)let-?];EKM11 htz-1(tm2469)IV/nT1(qIs51) IV,V; EKM32 hcf-1(ok559)IV; and EKM31 set-2(ok952)III. Null mutants set-2(bn129)III and wdr-5.1(ok1417)III  were also used. hcf-1(ok559) and set-2(ok952) were back-crossed four times prior to analysis. Analysis of dpy-30(y228) and htz-1(tm2469) was performed in m+z- animals.All strains used were maintained as described E. coli HT115 bacteria expressing double stranded RNA for capg-1, set-2,dpy-30, hcf-1, F52B11.1 (CFP1 homolog), F21H12.1 (RbBP5 homolog), wdr-5.1, swd-3.2, swd-3.3, swd-2.1, and swd-2.2 or vector control (polylinker), were used for feeding RNAi using the Ahringer feeding RNAi clones set-16 and the 3′ ash-2 the regions were PCR amplified, digested with NotI and XhoI (set-16) or XhoI and SpeI (ash-2), and cloned into the DT7 vector as described set-16:cgcggccgccgagcaacaacagggag and cctcgaggctgagaaggctgtttgcash-2:gactcgaggctacacgttcacctgcaaa and tgactagtgccattcctttttctgcttghim-8(e1489) IV; xol-1(y9) sex-1(y263) X were conducted as described RNAi experiments for IF/FISH and to score male rescue in Adult animals were dissected and stained as described FISH probe templates were generated by degenerate oligonucleotide primed PCR to amplify purified yeast artificial chromosome DNA. The labeled X-paint probe was prepared and used as described 3-D z-stack images of nuclei were collected as described above. 3i Slidebook imaging software was used to set masks of histone modification/variant localization  and DCC localization on the 3-D image  and X-Paint (Cy3) signals on images obtained as described above. The FITC:Cy3 correlation coefficient was recorded and used as an indication of colocalization between DPY-27 and X-Paint as described in situ hybridization followed by immunofluorescence.Detergent extraction of nucleoplasmic protein from dissected nuclei was performed by dissecting animals in 1X sperm salts plus 1% Triton detergent 100 (2X lanes) and 50 (1X lanes) young adult Set1RNAi worms (24 hours post-L4) were picked into 1XM9, washed, and incubated for ten minutes at 95°C in 19 µl SDS-PAGE loading dye  plus 1 µl β-mercaptoethanol. 10 ul of the worm extract in loading buffer was loaded per lane into a 15% polyacrylamide gel. SDS-PAGE was performed and protein was transferred onto nitrocellulose. The following antibodies and dilutions were used: Millipore Anti-Monomethyl Histone H3 (Lys4) rabbit polyclonal antibody at 1∶500, Abcam H3 dimethyl K4 goat polyclonal antibody at 1∶1000, Anti-Trimethyl Histone H3 (Lys4) clone CMA304 mouse monoclonal antibody at 1∶1000, and mouse monoclonal Anti-α-tubulin at 1∶1000. Secondary antibodies used were as follows: anti-mouse-HRP, anti-goat-HRP, and anti-rabbit-HRP (Jackson ImmunoResearch) all at 1∶5000.Figure S1Depletion of Set1/MLL components reduces H3K4me3 but does not affect DCC binding. H3K4me3 (green) and DPY-27 (red) IF in control RNAi and Set1/MLL depletion animals shows a reduced H3K4me3 signal upon Set1/MLL RNAi in adult, hermaphrodite intestinal nuclei. The names of the mammalian homologs are included for reference. In all panels DAPI is shown in grayscale and scale bar equals 5 µm.(TIF)Click here for additional data file.Figure S2H3K4me1 and me2 in Set1/MLL depletion nuclei. A. H3K4me1 (green) and CAPG-1 (red) co-staining in control and Set1/MLL depletion nuclei (adult intestine). Reduction in H3K4me1 is only observed in SET-2 and SET-16 depletion animals. B. H3K4me2 (green) and DPY-27 (red) co-staining slight reductions in signal are observed in all Set1/MLL depletion nuclei except in dpy-30. In all panels DAPI is shown in grayscale and scale bar equals 5 µm.(TIF)Click here for additional data file.Figure S3H3K27me3 signal unchanged in Set1/MLL depletion and mutant adults. H3K27me3 (green) and DPY-27 (red) co-staining in control and Set1/MLL depletion (A) and mutant (B) nuclei reveal no change in H3K27Me3 staining. In all panels DAPI is shown in grayscale and scale bar equals 5 µm.(TIF)Click here for additional data file.Figure S4H3K4 methylation levels upon Set1/MLL depletion in wild-type, hermaphrodite adults. Western blot analysis of H3K4 methylated species in young adult hermaphrodites (100 and 50 per lane). H3K4me1 is reduced in set-2 and set-16. H3K4me2 levels are reduced in all except dpy-30. H3K4me3 levels are reduced in set-2, set-16, ash-2, and dpy-30. α-Tubulin is used as a loading control.(TIF)Click here for additional data file.Figure S5H3K4 methylation in set-2, hcf-1, and wdr-5.1 deletion mutants. H3K4 methylation staining is in green, DPY-27 (with H3K4me2 and me3 co-stain) and CAPG-1 (with H3K4me1 co-stain) is shown in red. hcf-1(ok559) adult intestinal nuclei show reduced H3K4me3 staining, while set-2(ok952), a hypomorphic allele, set-2(bn129) and wdr-5.1(ok1417) both null alleles (TIF)Click here for additional data file.Figure S6H3K4me1 and me2 after Set1/MLL depletion in set-2(ok952) adults. A. H3K4me1 (green) and CAPG-1 (red) IF and B. H3K4me2 (green) and DPY-27 IF in adult intestinal nuclei. Although effects on methylation can be detected, there is no effect on DCC localization in the set-2 mutant and Set1/MLL depletion animals.(TIF)Click here for additional data file.Figure S7Statistical analysis of X-Paint/DCC colocalization study in htz-1(tm2469) background. One-way ANOVA of all colocalization data reveals statistically significant differences between samples. When the wildtype control data is omitted, the null is accepted. Tukey's test reveals that the significant differences between samples are specifically between the wildtype control and each of the htz-1 (control and Set1/MLL depletion) samples.(TIF)Click here for additional data file.


Kaposi’s sarcoma-associated herpesvirus is an oncogenic γ-herpesvirus that causes latent infection in humans. In cells, the viral genome adopts a highly organized chromatin structure, which is controlled by a wide variety of cellular and viral chromatin regulatory factors. In the past few years, interrogation of the chromatinized KSHV genome by whole genome-analyzing tools revealed that the complex chromatin landscape spanning the viral genome in infected cells has important regulatory roles during the viral life cycle. This review summarizes the most recent findings regarding the role of histone modifications, histone modifying enzymes, DNA methylation, microRNAs, non-coding RNAs and the nuclear organization of the KSHV epigenome in the regulation of latent and lytic viral gene expression programs as well as their connection to KSHV-associated pathogenesis. On the other hand, immune suppression along with other environmental and physiological factors, including oxidative stress, inflammatory cytokines, hypoxia or infection by other pathogens, each can favorably contribute to the physiological conditions required for KSHV to transition from latency to viral reactivation and virus production . I. Ide novlication . Thus, ide novo infection and their expression persists during latency, it is plausible that latent proteins are involved in the repression of lytic genes. One of the latent proteins is the latency-associated nuclear antigen or LANA, which is a nuclear protein that binds to several sites on the KSHV genome, most notably the TR region  to shed light on the nature of the chromatin that is associated with the KSHV episomes in infected cells ,51. SpecStrikingly, while H3K9me3 is restricted to two loci that encode mainly late genes, H3K27me3 is widespread across the entire KSHV genome, with the exception of the latency locus A 50,51],5150,51]de novo infection and its maintenance on the latent genome are still not understood.Polycomb group (PcG) proteins are cellular transcription repressors, which form two enzymatically distinct complexes called PRC2 and PRC1, both of which have been shown to inhibit transcription . A numbeIt has been shown that most polycomb target genes are repressed by both PRC2 and PRC1 complexes. Recently, a comprehensive proteomic and genomic analysis revealed that there are six major PRC1 complexes comprising a distinct set of proteins . Howeveri.e., in the absence of exogenous PAN RNA expression. Thus, the means by which repressive histone marks are initially removed from the RTA promoter is still not understood.Another interesting aspect of KSHV epigenetic regulation is the process by which PcG protein-mediated repression of lytic gene expression is reversed during reactivation. Importantly, the lysine 27 residue on histone H3 (H3K27) can either be acetylated (H3K27ac) or mono-, di-, or trimethylated. H3K27ac is associated with the activation of genes and is catalyzed by the histone acetyltransferases CBP/p300 in mammals . The indThe treatment of latently infected cells with histone deacetylase (HDAC) inhibitors can also trigger the induction of RTA and leads to the dissociation of EZH2 and the decline of H3K27me3 on the RTA promoter . SeveralH3K9me3 occupies two specific regions of the KSHV genome, both of which encode primarily late genes A 50,51,,51,77. AIn vitro demethylase assays revealed that K-bZIP can inhibit the demethylase activity of JMJD2A. Moreover, it was shown that K-bZIP can recognize the H3K9me3 moiety and thereby interfere with the binding of JMJD2A with H3K9me3. Furthermore, K-bZIP could also block the demethylation of H3K9me3 caused by overexpressed JMJD2A in 293T cells. Despite of the robust expression of K-bZIP H3K9me3 level does not change significantly on the KSHV chromatin during reactivation. Thus, the effect of K-bZIP on H3K9me3 must be limited to a subset of K-bZIP target genes in infected cells. In fact, K-bZIP expression in KSHV-negative cells causes a global repression of host genes and increased H3K9me3-marked heterochromatin formation on cellular genes, including host immune-related genes ,8077,80]ed genes .In vitro histone H3 peptide pull-down assays using purified LANA showed that LANA can interact with H3, H3K9me1 or H3K9me3 but not H3K9me2. Finally, the shRNA knockdown of JMJD1A resulted in decreased lytic reactivation, suggesting that JMJD1A is involved in the maintenance of H3K9 methylation-free chromatin on latent and a specific subset of lytic genes .,51.50,51There are a number of examples known of crosstalk between different histone modifications. For instance, H2B monoubiquitination at lysine 120 by the RAD6/BRE1 ubiquitin ligase complex is required for the methylation of H3K4 in human cells . FurtherAcetylation and deacetylation of lysine residues of histone tails have antagonistic effect on gene transcription. While acetylation of histones by histone acetyltransferases (HATs) unravels chromatin and activates transcription, histone deacetylases (HDACs) induce chromatin condensation and gene silencing ,91. ShorThe interplay between HATs and HDACs in the regulation of RTA transcription was examined in detail. Lieberman’s group analyzed the chromatin structure of the core promoter of RTA in the presence or absence of the HDAC inhibitors (HDACi) such as sodium butyrate (NaB) and trichostatin A (TSA) . They foHistone acetylation and nucleosome positioning also play a critical role in the regulation of the chromatin structure of the origin of latent replication (Ori-P), which is embedded in the GC-rich TR region of the KSHV genome. Ori-P contains two LANA-binding sites, where LANA binds and recruits the cellular DNA replication proteins, ORC2 and MCM, to facilitate the replication of the viral genome concomitantly with the replication of the host genome during mitosis ,40. It wde novo DNA methyltransferases [In mammalian cells, DNA methylation occurs mainly on cytidine residues in the context of CpG dinucleotides. Methylated CpGs are often found in clusters called CpG islands . Hypermesferases .de novo infection, DNA methylation patterns were established comparatively slower, indicating that DNA methylation does not play a role in establishment of latency. On the other hand, DNA methylation may reinforce the inhibition of lytic genes at late timepoints of infection [It has been shown that the treatment of PEL cells with the DNA methyltransferase inhibitor 5-AzaC can induce lytic reactivation of KSHV, suggesting that DNA methylation of lytic promoters is involved in the suppression of lytic genes during latency . Recentlnfection . It is known that the KSHV episome is tethered to the cellular chromosome by LANA, which interacts with histones and several components of the cellular chromatin. One question that remains is whether tethering of the KSHV genome is random or prone to localize on specific regions of the host chromosome. Infected cells harbor approximately 30–80 copies of the KSHV genome and ChIP-seq analysis has revealed at least 256 LANA-binding sites on the cellular genome, suggesting that not all LANA proteins may be involved in tethering the viral genome . An inteThe structural organization of chromatin domains in the cellular genome plays an important role in the regulation cellular gene expression. Similarly, the chromatinized KSHV episome is also compartmentalized into different chromatin domains, which can interact with each other by a looping mechanism mediated by cellular factors that bind to the KSHV genome . The maiEvery KSHV infected cell carries multiple copies of KSHV genomes, which raises the question whether they all have the same nucleosome structure during latency, which change simultaneously during reactivation or they show diversity. To answer this question a recent study used single-molecule footprinting assays called MAPit , which allows the detection of multiple chromatin states at selected loci within a cell population . The chrIn vitro experiments have shown that KSHV infection of endothelial cells results in transcriptional reprogramming such that infected lymphatic endothelial cells are driven toward a blood vessel endothelial cell phenotype and vice versa [ce versa ,106. In ce versa . These oce versa . These vce versa ,109. Becce versa .de novo DNA methyltransefarses DNMT3a and DNMT3b genes [KSHV encodes 12 miRNA genes that produce 25 mature miRNAs. These are small non-coding gene regulatory RNAs that can bind to mRNAs resulting in translational block or their degradation to repress genes . A recen3b genes . Express3b genes . PAN RNA3b genes ,113. Viral proteins often targets cellular histone modifying enzymes to relocate them on the cellular genome, modulate their enzymatic activity or their expression, which can affect the expression of a large number of cellular genes, all of which serve the needs of the virus for efficient infection of the host. Several KSHV proteins such as LANA, K8 and vIRF1 can bind to the histone acetyltransferase CBP/p300 and inhibit CBP/p300-mediated cellular transcription ,114,115.in vitro can induce the expression of EZH2, and this is mainly mediated by the KSHV latent proteins LANA and vFLIP via the upregulation of the NF-κB pathway [EZH2, the H3K27me3 histone methyltransferase of the PRC2 complex has been shown to be overexpressed in KSHV-infected cells in Kaposi’s sarcoma (KS), suggesting that KSHV could be involved in EZH2 upregulation . Indeed, pathway . Importa pathway .KSHV infection of B cells in human can lead to the development of the KSHV-associated primary effusion lymphoma (PEL), which is characterized by the disruption of the B-cell specific transcriptional program. Protein expression of several transcription factors that are essential for B-cell development is significantly altered in PEL cells compared to uninfected B-cells . While PChIP-on-chip analysis of the KSHV episome revealed distinct chromatin domains on the viral genome, which are characterized by different histone modification and DNA methylation patterns indicative of targeted recruitment of specific cellular chromatin modifying enzyme complexes . The benCurrently, at least 130 different posttranslational modification sites have been identified on histones and there are at least 150 histone-modifying enzymes known . Because


Epigenetic modifications, transcription factor (TF) availability and differences in chromatin folding influence how the genome is interpreted by the transcriptional machinery responsible for gene expression. Enhancers buried in non-coding regions are found to be associated with significant differences in histone marks between different cell types. In contrast, gene promoters show more uniform modifications across cell types. Here we used histone modification and chromatin-associated protein ChIP-Seq data sets in mouse embryonic stem (ES) cells as well as genomic features to identify functional enhancer regions. Using co-bound sites of OCT4, SOX2 and NANOG  and co-bound sites of MYC and MYCN (limited enhancer activity) as enhancer positive and negative training sets, we performed multinomial logistic regression with LASSO regularization to identify key features.Cross validations reveal that a combination of p300, H3K4me1, MED12 and NIPBL features to be top signatures of co-OSN regions. Using a model from 10 signatures, 83% of top 1277 putative 1 kb enhancer regions  overlapped with at least one TF peak from 7 mouse ES cell ChIP-Seq data sets. These putative enhancers are associated with increased gene expression of neighbouring genes and significantly enriched in multiple TF bound loci in agreement with combinatorial models of TF binding. Furthermore, we identified several motifs of known TFs significantly enriched in putative enhancer regions compared to random promoter regions and background. Comparison with an active H3K27ac mark in various cell types confirmed cell type-specificity of these enhancers.The top enhancer signatures we identified  will allow for the identification of cell type-specific enhancer regions in diverse cell types. Chromatin immunoprecipitation followed by massively parallel sequencing (ChIP-Seq) has enabled genome-wide investigation of chromatin features and epigenetic modifications within the non-coding regions of mammalian genomes in high resolution . ChIP-SeDistal regulatory elements have been identified which regulate gene transcription from several kilobases (kb) away and have even been found to regulate genes located on separate chromosomes -4. FunctFugu or ultra-conserved regions between human-mouse-rat (>200 bp of 100% identity) are predictive of regulatory regions involved in conserved processes such as embryonic development [Prior to the availability of ChIP-Seq and ChIP-chip data, computational approaches based solely on genomic sequences were used to identify enhancer regions. Initially these approaches compared the genomic sequence with TF binding motifs represented by position specific scoring matrices (PSSM) from TRANSFAC  and JASPelopment ,18. As melopment , bindingelopment , intergeelopment , and a celopment  have beeelopment .in vivo activity. For example, 47% (246/528) of human genomic regions predicted by sequence conservation were confirmed as enhancers in a transgenic mouse assay [These different approaches show variable success at predicting enhancers with se assay ,18. The se assay . Heintzmse assay . They ob 47% 246/28 of hum 47% 246/28 of humAlthough previous approaches showed promising performance, a potential issue with non-integrated approaches is that each marker may be an incomplete representation of the relevant enhancers in a particular cell type. For example, although sequence conservation is frequently used to identify regulatory elements, ultra-conservation has been reported to identify only a small subset of developmentally related enhancers, specifically, those involved in development of the nervous system . FurtherIn this study we used 31 public high throughput mouse ES cell RNA-Seq and ChIP-Seq datasets, as well as 5 genomic features to identify the characteristics most predictive of enhancers. We applied multinomial logistic regression with LASSO regularization ,26 to idTo identify and validate enhancer regions active in mouse ES cells we used ChIP-Seq data sets from mouse ES cells including: 12 transcription factors (TFs) ,28, 8 hiIn our model we classified 1 kb genomic bins into 3 categories: enhancer positive (Enh), promoter-like (PrL) and unknown. As multiple combinations of features have been used to predict enhancers, we performed Naive Bayes classifications using various combinations of features. Assessment of the model was carried out using four indices, area under curve (AUC), precision, modified precision, and recall (detailed in Methods), using 10 fold cross validation on the training set data Figure . The claThe Naive Bayes classification approach revealed the importance of feature selection and allowed us to determine the most appropriate combination of features for optimal enhancer identification, however, each feature is assumed to contribute independently. As some features might be partially or fully redundant (e.g. histone modifications) and some may add no or little predictive value, we modified our approach and used a LASSO regularized multinomial logistic regression model ,26 to asp = 0.0002) to an independent luciferase assay data set in which 67 regions bound by CHD7 were tested for enhancer activity [A total of 19200 1 kb regions were predicted to be Enh, 67672 were predicted to be PrL, and 2567872 regions were predicted as unknown using the LASSO regularized model  than top PrL candidates, which more frequently overlap TSSs. To examine the distribution of Enh and PrL candidates more specifically with respect to TSSs we calculated the distance to the closest TSS for each candidate. Enh candidates tend to be further away from TSSs compared to PrL candidates  Enh and PrL candidates are distributed relative to annotated genes similarly to the training data Figure . As expep = 4.1 × 10−46), Enh&PrL genes showed significantly higher expression than that of PrL-only genes (p = 2.3 × 10−18). These findings suggest that Enh and PrL candidates coordinately regulate transcription of a subset of target genes in mouse ES cells, while other genes are regulated solely by PrL signatures. It is important to note that Enh candidates are expected to be more difficult to assign to the correct gene(s) as they are often located in intergenic regions and may in fact not regulate the closest gene in the linear genome. Regardless, we observe the presence of a candidate enhancer is associated with increased levels of expression in addition to the presence of a promoter candidate.To assess the regulatory potential of the Enh and PrL candidates in ES cells, we assigned candidates to the closest gene TSSs and compared the ES cell gene expression distribution among subsets of genes: associated with both Enh and PrL, either PrL or Enh only, and genes without an associated Enh or PrL candidate  Figure . Distribp < 2.2 × 10−16). The enhancer associated transcripts identified by Kim et al. 2010 [Short intergenic transcripts have been found associated with enhancers in neurons , while tal. 2010  are gene−06, 0.0018, and 0.03 respectively), and the genes include several TFs involved in ES cell transcriptional regulation: Oct4, Myc, Mycn, Sox2, Esrrb, Phc1 and Zic3  indicating these genes are associated with more basal cellular functions. This indicates that, in addition to promoters, Enh candidates play a significant role in regulating the ES cell transcriptional program to maintain pluripotency.In order to probe the functional significance of the genes regulated by Enh or PrL, we investigated functional enrichment in these categories using DAVID ,43 follop < 10−30; Figure As we initially trained our classifier based on the binding of either co-OSN (Enh signature) or co-MYC (PrL signature), we wanted to determine whether we also identified regions bound by additional TFs. Single TF binding sites are found at many locations in the genome while MTL are more limited and have been shown to be associated with increased regulatory activity ,24. We eOct4 (Pou5f1)NanogPhc1 and Lefty1 are all predicted Enh with probabilities greater than 0.8 . In addition to the Lefty1 promoter-interacting enhancer, we identified novel contiguous enhancer regions over 3–5 kb upstream of Lefty2 (prob = 0.9107 and 0.8999).We next looked at how well the model predicts previously identified mouse ES cell enhancer regions associated with mediator and cohesin proteins and shown to form chromatin loops with the nearby gene TSSs . These tOct4 Pouf1NanogPhSox2 gene and around 100 kb downstream  [Sox2, SRR2, overlaps partially with a lower confidence Enh . In addition to these previously validated enhancers, we identified a cluster of eight high confidence enhancers downstream of Sox2 overlapping multiple TF peaks as well as p300 and MED12 peaks. Notably, the furthest downstream enhancer region is ranked in the top 4 with an Enh probability of 1. Located between the Sox2 gene and the enhancer cluster is a RefSeq transcript Gm3143m which is not expressed in ES cells and therefore unlikely to be regulated by the cluster of eight high confidence enhancers. We also noted an expressed lincRNA immediately downstream of the distal enhancer cluster  ,48,50, wmiR-290 microRNA cluster. The miR-290 cluster, though not sufficient to maintain ES cell pluripotency alone, inhibits ES cell differentiation when over-expressed [miR-290 overlap with MTL  confirming the motif enrichment results for these transcription factors.To verify binding of transcription factors at identified motifs we used KLF4, ESRRB, and STAT3 ChIP-Seq data from Chen et al. 2008 . We compp < 2.2 × 10−16, logOdds = 3.86; Figure p < 2.2 × 10−16, logOdds = 0.77) . This significant association with the H3K27ac suggests our modeling preferentially identifies active rather than poised enhancer regions. As H3K27ac overlaps with our Enh candidates and was not included in our initial model we next investigated whether or not this mark is predictive of Enh regions. Introducing H3K27ac into our model showed positive weighting of H3K27ac for PrL rather than Enh indicating that H3K27ac is a predictor of PrL rather than Enh  Figure . We alsop < 3 × 10−5). We observed an increase in the overlap to H3K27ac and H3K4me1 in ES cells, and a decrease in the overlap in the differentiated cell types for the high probability Enh set compared to all Enh  and avoiding the use of cell-type specific TF signatures in the modeling we have retained the potential to apply this model to other cell types. The features used  are genomic sequence features, ubiquitously expressed proteins, and histone modifications which we expect mark enhancers and promoters in all cell types. In fact, p300 has been used to identify enhancers in several different cells types. In contrast to methods identifying enhancers using p300, alone or in combination with H3K4me1, in ES cells we found that our enhancer candidates overlapped the active H3K27ac mark but not the repressive H3K27me3 mark ,69,71. TDespite the reported association of H3K27ac with active enhancers, introducing H3K27ac into our model showed positive weighting of H3K27ac for PrL rather than Enh, indicating that it is a predictor of promoters rather than enhancers. This is in agreement with higher enrichment overall of H3K27ac regions in PrL compared to Enh; whereas, the previously reported H3K27ac regions ,69 contaOct4 and Nanog gene expression through binding to their proximal promoters [We have shown that Enh candidates are significantly further from gene TSSs than PrL candidates, and the absolute expression distribution of genes associated with Enh and PrL candidates is significantly higher than that of genes associated with PrL alone, indicating additional activation of gene expression by Enh candidates. These results are consistent with the enhancer/promoter DNA looping model which promotes cell-type specific gene activation ,22,87,88romoters ,90. Bindromoters ,61. InteWe identified a widely applicable set of features to identify regulatory enhancers and promoter regions in a given cell type. Use of these features yields enhancer regions associated with increased gene expression of neighbouring genes and ES cell-specific histone modifications consistent with active enhancers.http://www.r-project.org/) and Bioconductor [The bioinformatics analyses were done in R 2.12.0 (onductor  unless oThirty public domain ChIP-Seq raw data sets in mouse ES cells were obtained from Gene Expression Omnibus (GEO) : 12 TFs http://vancouvershortr.sourceforge.net/). The same procedure was done for all ChIP-Seq data sets to obtain a vector of values for each protein or TF. While ChIP-Seq data files for each TF in the same experimental setting were combined, exact duplicate tags were removed to avoid PCR amplification bias generated in the sequencing library preparation. As the control ChIP-Seq data sets were not uniformly distributed throughout the genome, 180 bins with read counts in top 0.5 percentile for all 5 controls were first excluded from the analysis. The genomic features were subsequently quantified in each bin using counts without normalization, eg. number of CpG islands within each bin. In enhancer candidate plots, significant TF binding peaks (p < 0.001) predicted with SISSRs algorithm (v1.4) [Reads from ChIP-Seq data were aligned to mouse mm9 assembly using Bowtie alignment  by supprm (v1.4)  are labeBinding regions of each TF used as training data were defined to be regions with number of tags in the top 0.5 percentile. As ChIP-Seq co-bound regions of the pluripotency transcription factors OCT4, SOX2 and NANOG were confirmed to have enhancer activity in 25/25 cases and ChIP-Seq co-bound regions of MYC or MYCN were shown to have very weak or no ES-cell-specific enhancer activity in luciferase assays [Naive Bayes classifiers with various feature combinations were used to classify 1 kb genome bins into the three categories. Due to limited amount of training data, 10-fold cross validation was performed to the training data set by randomly leaving 10% of the data as a validation set. Classifier assessment was carried out using the mean of area under curve (AUC), precision, modified precision, and recall values computed from the validation set. AUC is the area under ROC curve. Precision, normally given by true positive over sum of true positive and false positive, was modified to report the percentage of co-OSN regions out of putative enhancers that were originally Enh or PrL. This was done to avoid penalizing potential Enh candidates in the unknown validation set. Recall, given by true positive over sum of true positive and false negative, measures the percentage of all co-OSN regions predicted to be enhancers. These indices provide different aspects on model assessment. Ranking of the classifiers with different feature combinations were sorted by the average ranking of all indices.All the features were first standardized by subtracting the mean and dividing by the standard deviation in order to prevent biased shrinkage of feature weights. Multinomial logistic regression was applied to model sequence features, chromatin features, and associated proteins to predict the genome-wide location of enhancers. Cross entropy was used as the error function for multi-group classification. To assess predictability of features, LASSO regularization was used to introduce extra penalization with a power raised on the weight vector ,26. UsinRPKM, the number of reads per kilobase of exon region per million mapped reads derived from RNA-Seq data has been shown to be approximately proportional to the absolute abundance of mRNAs in cell . We obtaCandidates were first assigned to genes through identifying the closest TSSs. 469 Enh candidates (Enh prob ≥ 0.9) and 2239 PrL candidates (PrL prob ≥ 0.99) were used to carry out the analysis. The discrepancy in probability cut-offs between sets was due to the large number of high confidence PrL candidates. DAVID functional annotation website was used to assess functional enrichment on both Enh and PrL candidate sets compared to all genes in the mouse genome ,43. As gTo determine if the putative enhancer candidates are enriched in MTL, we separate Enh, PrL and unknown candidates, and all genome bins into 3 groups: 0 TF peak enrichment, enrichment of 1 to 3 TFs, and enrichment of over 4 TFs (MTL) within the bin. The MTL enrichment of Enh is assessed using Chi-squared statistics of the counts of the last two bins in comparison to that of PrL, unknown and genomic bin sets.Clover algorithm  was usedSignificantly enriched regions of H3K4me1, H3K27ac and p300 in ES, adult liver, progenitor B and neural progenitor cells were obtained from , and genES cell: Embryonic stem cell; co-OSN: OCT4 SOX2, and NANOG ChIP-Seq co-bound; co-MYC: MYC and MYCN ChIP-Seq co-bound; TF: Transcription factor; PSSM: Position-Specific Scoring Matrix; MTL: Multiple transcription factor-bound loci; TSS: Transcription start site; RNAPII: RNA polymerase II; Enh: Enhancer; PrL: Promoter-like.The authors declare that they have no competing interests.JAM and CYC conceived and designed the study. QM helped supervise the computational analysis. CYC conducted all experiments. JAM and CYC interpreted the results. CYC and JAM wrote the manuscript which all authors approved.Table S1. Predicted enhancer and promoter-like candidates. The Enh and PrL candidates in mouse ES cells are listed. Columns represent the chromosome location , Enh / PrL probability (Enh.prob / PrL.prob), gene symbol of the closest Ensembl transcript (geneName), the location relative to genes (location), the distance of the enhancer to the closest transcript (distance2Transcript), the previously validated enhancers that overlap with Enh from Chen et al. 2008 , and whether the candidate overlaps an exon (OverlapExon). Click here for fileFigure S1. Heatmap of features used in LASSO regression for top 50 enhancer and promoter-like candidates. The dark red and blue side bar on the left denotes putative Enh and PrL 1kb genome bins, whereas the dark red and blue side bar on top denotes indicative Enh and PrL feature sets. Feature values are scaled to exhibit the contrast between the Enh and PrL. Click here for fileFigure S2. Genomic distribution of categories and relative position to TSS (The entire set). (A) Pie charts representing the genomic distributions of the co-OSN and co-MYC training sets as well as all Enh and PrL candidates. Intergenic regions are defined to be regions ≥ 10kb away from the closest TSS or transcription end site; whereas upstream regions are regions within 10kb upstream of TSSs. (B) Violin plots demonstrating the distances to TSSs of the closest transcript for each set. Click here for fileFigure S3. Gene Ontology analysis of the Enh and PrL candidate sets. Enriched functions of Enh (A) and (B) PrL identified from DAVID (FDR<0.1) and plotted using Cytoscape Enrichment map plug-in. Functions are further circled and grouped into general categories labeled aside. Line thickness between nodes is proportional to number of genes shared between nodes. Colors are used for the purpose of visualization contrast between functional groups. Click here for fileFigure S4. Four known mouse ES cells enhancers that interact with nearby promoters through looping mechanisms. Plot showing previously validated enhancers around Pou5f1 (Oct4), Nanog, Phc1, and Lefty1. The Enh and PrL probabilities of 1kb bins are shown in red and blue bars, respectively. Only probabilities greater than 0.8 are shown for higher stringency , and the y-axis scale is from 0.5 to 1. Transcription factors peaks identified using the SISSRs algorithm are illustrated in rectangle boxes to demonstrate overlaps of the enhancers with TFs. Click here for fileFigure S5. LincRNA downstream of Sox2. Plot showing novel putative enhancers downstream of the Sox2 gene. The Enh and PrL probabilities of 1kb bins are shown in red and blue bars, respectively. Only bins with probabilities greater than 0.8 are displayed for higher stringency, and the y-axis scale is from 0.5 to 1. A lincRNA approximately 100kb downstream of Sox2 near a distal enhancer cluster is shown. Transcription factors peaks identified using the SISSRs algorithm are illustrated in rectangle boxes to demonstrate overlaps of the enhancers with TFs. The coverage plot for RNA-Seq data in ES cells is shown at the bottom. Click here for fileFigure S6. Detailed plots for novel putative enhancer regions. Detailed coverage plots of novel enhancer regions identified including (A) multiple putative enhancers upstream of miR-290 cluster, (B) multiple contiguous enhancer regions upstream of Tet1 and around a non-coding small nuclear RNA, U6, (C) two putative enhancers around Zic3, and d) the putative enhancer region located 10kb upstream of C80913. The Enh and PrL probabilities of 1kb bins are shown in red and blue bars, respectively. Only bins with probabilities greater than 0.8 are displayed for higher stringency, and the y-axis scale is from 0.5 to 1. Transcription factors peaks identified using the SISSRs algorithm are illustrated in rectangle boxes to demonstrate overlaps of the enhancers with TFs. Click here for fileFigure S7. Active enhancers and cell specificity of all enhancer candidates. (A) Venn diagrams of all enhancer candidates with distal (TSS +/- 1kb removed) H3K27ac marks (active) and H3K27me3 marks (repressive). (B) The stacked bar plot shows the percent overlaps of all enhancers with distal H3K27ac / H3K4me1 in various cell types. All overlaps presented here allow a 500 bp gap.Click here for fileFigure S8. Feature coefficients determined from Lasso regularization (H3K27ac included). The plot shows feature weights in each class with respect to logged lambda, the penalization parameter, in LASSO regularized multinomial logistic regression. Weights of features less discriminative of the three categories shrink to 0 as the lambda is increased. H3K27ac, a positive predictor of PrL group, is highlighted in a blue box. Click here for fileTable S2. Features found at predicted enhancer and promoter-like candidates. The numbers of predicted Enh and PrL regions that overlapped with the indicated features are shown. Both the full set of predictions and the probability >0.8 set are shown as well as the overlaps with the unknown regions. P300, H3K4me1 and H3K27ac data are from Creyghton et al. 2010, CHD7 data is from Schnetz et al. 2010. * Data not shown as peaks overlapping TSS were removed from Creyghton et al. 2010 data and PrL frequently overlap TSS. Transcription factor peaks includes: KLF4, STAT3, SMAD1, E2F1, TFCP2L1, ZFX, and ESRRB ChIP-Seq data from mouse ES cells. LincRNA (large intergenic non-coding RNA). Click here for file


GNAS locus loss of NESP RNA can cause autosomal dominant Pseudohypoparathyroidism type 1b (AD-PHP-Ib), while at the SNRPN-UBE3A locus a long ncRNA and processed snoRNAs play a role in Angelman-Syndrome (AS) and Prader–Willi-Syndrome (PWS). The ncRNAs silence overlapping protein-coding transcripts in sense or anti-sense orientation through changes in histone modifications as well as DNA methylation at CpG-rich sequence motifs. Their epigenetic modulatory functions are required in early development in the pre-implantation embryo or already in the parental germ cells. However, it remains unclear whether the sequence homology-carrying ncRNA itself is required, or whether the process of its transcription through other promoters causes the silencing effect.Non-coding RNAs (ncRNAs) have long been recognized at imprinted gene loci and provided early paradigms to investigate their functions and molecular mechanisms of action. The characteristic feature of imprinted genes, their monoallelic, parental-origin-dependent expression, is achieved through complex epigenetic regulation, which is modulated by ncRNAs. This minireview focuses on two imprinted gene clusters, in which changes in ncRNA expression contribute to human disorders. At the  GNAS and SNRPN-UBE3A imprinting clusters, in which disturbances of the ncRNAs are associated with human hereditary disorders. Functions of ncRNAs at other clusters have been covered in recent excellent reviews  have been investigated Barlow, . Imprint Barlow, .Gnas locus  and two regulatory non-coding transcripts termed Nespas and Exon 1A (EXON A/B in human). A fifth transcription unit, Nesp, exerts a dual function within the locus through epigenetically regulating other transcripts and by encoding a protein ” with or without “Albright’s Hereditary Osteodystrophy (AHO).” Typically, these comprise resistance to parathyroid hormone, thyroid stimulating hormone, growth hormone releasing hormone, gonadotrophins, and α-melanocyte stimulating hormone  epigenetic changes at the differentially methylated regions (DMRs) of the GNAS locus , although alternative mechanisms  cannot be excluded at this stage  of the locus  revealed further insights into the mechanisms, by which the ncRNA silences Nesp  in mediating the silencing and stable suppression of Nesp in cis through low levels of H3K4me3 and, consequently, DNA methylation via Dnmt3a/L. The attraction of histone modifying enzymes, e.g., a histone demethylase, through the ncRNA appears to be a likely molecular mechanism. Whether the changes of Nespas and Nesp expression on the paternal allele of these mouse models further affect the downstream Exon 1A and Gnas transcription units, potentially leading to loss of imprinting of Gnas as described in the Nespas ICR deletion mice, remains to be analyzed  became silenced and the somatic Nesp DMR methylated on the maternal chromosome  extends to a third RNA of the SNRPN, is located on human chromosome 15q11-13 and is largely conserved on mouse chromosome 7 , MAGEL2, and parts of the long ncRNA (lncRNA) SNRPN contribute to this complex disorder , and overlap anti-sense with Ube3a  region, which remains unmethylated on the paternal allele and is thought to contain an activator function for all paternally expressed genes, including those located upstream of the lncRNA , termed SNORD115 and SNORD116, which are predominantly expressed in the brain and, in the case of SNORD116, contribute to many of the PWS symptoms  in oocytes brings about the DNA methylation and silencing of Snrpn exon 1 on the maternal allele  caused transcription through the PWS-IC/Snrpn promoter in growing oocytes. This transcriptional activity led to DNA methylation at the PWS-IC in cis and silencing of the Snrpn lncRNA expression, i.e., establishment of the maternal imprint mark. There is also evidence for the presence of a corresponding U-exon in the human AS-IC region, which might allow for a similar mechanism of transcription/RNA mediated epigenetic modification (Farber et al., Gnas locus, it remains unclear whether the RNA is required or whether the process of transcription through the Snrpn promoter suffices, to establish the epigenetic/imprinting mark at the PWS-IC (Chotalia et al., U-exons of Snrpn and Nesp of the Gnas locus are evident, and it has been shown recently that transcription is a common feature associated with the establishment of DNA methylation in oocytes (Smallwood et al., In humans, the expression of the C Figure . The AS-The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


A transcriptional feedback loop is the simplest and most direct means for a transcription factor to provide an increased stability of gene expression. In this work performed in human cells, we reveal a new negative auto-regulatory mechanism involving an alternative transcription start site (TSS) usage. Using the activating transcription factor ZNF143 as a model, we show that the ZNF143 low-affinity binding sites, located downstream of its canonical TSS, play the role of protein sensors to induce the up- or down-regulation of ZNF143 gene expression. We uncovered that the TSS switch that mediates this regulation implies the differential expression of two transcripts with an opposite protein production ability due to their different 5′ untranslated regions. Moreover, our analysis of the ENCODE data suggests that this mechanism could be used by other transcription factors to rapidly respond to their own aberrant expression level. Alternas on DNA . These ts on DNA ,4,5.Auto-regulation is one of the simplest and the most efficient regulatory mechanism used by the cell to maintain the proper gene expression at multiple levels. Good examples are RNA-binding proteins such as TDP-43 ,7 and HuSec gene  switch. Finally, we hypothesize that this mechanism could certainly represent a more general transcriptional auto-regulation strategy.Supplementary Materials and Methods.Antibodies and constructs used in this study are described in Supplementary Materials and Methods) expressing a chimeric ZNF143 protein (ZNF143-EGR2) wherein the seven zinc fingers DBD was substituted by that of EGR2  . The FLP143, FLP143-HA, FLP_THAP11-HA and FLP76 inducible stable cell lines were obtained and maintained as described in . The FLP KROX20)  composedSupplementary Table S1. All reactions were carried out in triplicate. The relative expression ratio was calculated using the CFX Manager™ Software (Bio-Rad). The tissue qPCR arrays gene expression analysis was performed on human disease tissues  and on mouse tissues . The cDNAs from human and mouse qPCR arrays were normalized against ß-actin and GAPDH levels, respectively. The relative expression was calculated using the ΔΔCt method.The ectopic gene expression in the stable cell lines was induced with doxycycline, and the cells were collected at different time points post-induction. Total RNA was extracted using TRI-REAGENT (Euromedex), and Poly(A)+ RNA was enriched from total RNA using µMACS mRNA Isolation Kit (Miltenyi Biotec). The cDNA obtained by reverse transcription with random primers (dN9) was amplified with specific primers on the CFX96 Touch™ Real-Time PCR Detection System (Bio-Rad) using EvaGreen qPCR Mix Plus (Euromedex). Primer sequences are available in Cells were collected at different time points post-induction and lysed. The proteins from the lysate were separated on an 8% SDS–PAGE and subjected to western-blot analysis using antibodies against ZNF143, α-tubulin and TBP. The western-blot signal quantification was performed on Quantity One Software (Bio-Rad).Ct method. Primer sequences are available in Supplementary Table S1.Chromatin immunoprecipitation (ChIP) assays were performed as previously described . The ave+ nylon membrane (Amersham Biosciences), UV crosslinked  and probed with internally 32P labeled RNA or 5′ 32P labeled oligodeoxynucleotides. RNA probes were synthesized by in vitro T7 transcription with α32P labeled ATP using specific PCR templates. The sequences of the primers used to amplify the PCR templates are available in Supplementary materials (Supplementary Table S1). The prehybridization and hybridization were carried out using PerfectHyb™Plus Solution (Sigma) at 42°C and the washes performed according to the supplier’s recommendations. Transcripts were visualized and quantified with a Fujifilm Bio-Imaging Analyzer System.FLP143 or SH1_7 cells were treated with 1 µg/ml of doxycycline 48 h prior the transfection performed using Lipofectamine™ 2000 (Invitrogen) according to the manufacturer’s instructions. The total RNA was isolated 24 h post-transfection and treated with recombinant DNase I (Roche). RNA samples [20–30 µg of total RNA or 5 µg of poly(A)+ enriched RNA] were loaded on 1% formaldehyde agarose gels, transferred onto a Hybond N-Luciferase® Reporter Assay System (Promega) according to the manufacturer recommendations. The luminescence signal was measured on a GloMax® 96 Microplate Luminometer (Promega). Each essay was performed in three biological replicates, and the Renilla luciferase signal was normalized to the firefly luciferase activity.FLP143 and SH1_7 cells were induced or not induced with doxycycline 48 h prior the transfection in 96 wells plates with the different luciferase constructs. The luciferase assay was performed 24 h post-transfection using the DualSupplementary Table S1). The cDNA from transfected pNG107 construct was targeted with two specific inner/outer PCR primer pairs: PN270/PN500 and PN270/PN640 (Supplementary Table S1). The nested-PCR products were analyzed on 2% agarose gel and sequenced to identify the 5′ end of the targeted mRNA.The 5′ RACE-PCR experiment was performed using the FirstChoice® RLM-RACE Kit (Life Technologies) according to the manufacturer recommendations on total RNA extracted from FLP143 cells treated or not with 1 µg/ml of doxycycline. The endogenous ZNF143 cDNA was targeted with two gene-specific inner/outer PCR primer pairs: PN627/PN628 and PN625/PN626 (32P-labeled (Supplementary Table S1). Binding assays were performed essentially as described (Recombinant ZNF143 DBD was produced using the GST gene fusion system as previously described . The DNAescribed  with 20 Quantitative competitive electromobility shift assay (EMSA) was used to compare the relative affinities of the ZNF143-binding sites as described previously . BrieflyIn this study, we used the ZNF143 ChIP-seq data from our previous study (Geo access: GSE39263)  and EncoSupplementary Figure S1A). Moreover, we observed that this phenomenon is reversible. Indeed, after the inductor withdrawal in FLP143 cells, the endogenous ZNF143 expression level returned to normal both at the protein and mRNA levels .We generated stable cell lines expressing an HA-tagged (FLP143-HA) or a non-tagged ZNF143 protein (FLP143) under the control of an inducible promoter. Interestingly, we observed that the doxycycline-induced ectopic expression of ZNF143-HA resulted in a decrease in the endogenous ZNF143 protein level as seen by western blot A. The doTo test if the aforementioned phenomenon is restricted to ZNF143, we monitored the endogenous expression of two other transcription factors following the over-expression of their analogous transgenes. For this purpose, we used FLP76, FLP_THAP11-HA, FLP143 and FLP-mock (control) inducible stable cell lines over-expressing, respectively, ZNF76, THAP11-HA, ZNF143 and no-protein. ZNF76 was previously characterized as the ZNF143 paralog with the same DNA-binding specificities . THAP11 Supplementary Figure S2A). The presence of this peak suggests the possibility of an auto-regulatory feedback in the control of ZNF143 transcription. As a prerequisite to the investigation of this phenomenon, we defined in a first attempt the ZNF143 minimal promoter. We engineered a Renilla luciferase reporter construct headed by the 593 nt located upstream of the TSS, the first exon (+1/+112) and the shortened ZNF143 first intron (5′-part: +113/+234 associated with the 3′-part: +10170/+ 10344) . The region −75/+234 also contains three potential ZNF143-binding motifs: SBS/A (position −15), SBS/B (position +80) and SBS/C (position +133), with the SBS/B and SBS/C being conserved in vertebrates genomes  B. We tra+ 10344) B. The hi+ 10344) C, locate+ 10344)  revealed+ 10344) C. ChIP-S genomes C. The th genomes D, likelyg assays F. The reg assays G. The FLg assays G. On theg assays G containg assays . This ZNSupplementary Figure S3). We did not observe any enrichment for H3K9me3 repression marks. On the other hand, we obtained similar Pol II enrichments before and after induction, showing that this region stays in a transcriptionally active state. Histone H3K4me3 activation mark showed a slightly lower enrichment 24 h post-induction, suggesting a different chromatin context. In a second step, we mapped the TSS of ZNF143 by 5′ RACE-PCR on total RNA extracted from FLP143 cells before and after induction. Two families of TSS were identified in non- and 24 h-induced conditions . The first family (TSS1) is located in a 90-bp window close to the reference TSS (NM_003442). The second family (TSS2) is located at 148–149 bp from the reference TSS, in the putative Inr element at the beginning of the first intron . These 5′ RACE-PCR data are supported by the presence of two populations of expressed sequence tags . The sequencing of a cDNA clone H04D117B14 (obtained from the RIKEN human cDNA library) initiated at the TSS2, combined with the 5′ RACE-PCR results, clearly evidenced the presence of an alternative first non-coding exon (Exon 1 b) . The two populations of ZNF143 transcripts (TSS1 and TSS2 transcripts)  have exactly the same coding sequence (CDS) but have strikingly different 5′ UTRs. The canonical TSS1 transcript has a 119 nt long 5′ UTR composed of one non-coding exon, whereas the alternative TSS2 transcript has a 1222 nt long 5′ UTR composed of four non-coding exons . Only a 7-bp region located in 3′ of the 5′ UTRs, coming from the coding exon, is common to both transcripts . Taking advantage of the differences in 5′ UTR, we specifically followed both TSS1 and TSS2 transcripts by RT-qPCR in cells over-expressing or not ZNF143. Before induction, we noted the considerable difference in the number of cycles required for the TSS1 and TSS2 transcripts detection . Considering the six cycles difference, the TSS2 transcript is around 20 times less present than the TSS1 transcript before ZNF143 over-expression. After the induction of FLP143 cells, the total level of ZNF143 mRNA in the cell goes up by four times already after 6 h  and histone modification marks (H3K4me3 and H3K9me3) on the ZNF143 promoter region before and 24 h post-induction . I. IRenillin vitro B and upsin vitro B in the in vitro C. No mutin vitro C. We usein vitro A recognipression G. HoweveWe recapitulated the previous observations by following the expression of the reporter luciferase A gene inNext, we used the SH1_7 inducible stable cell line, to follow the luciferase activity of the same constructs in ZNF143 knockdown conditions D, right.Renilla transcript level stays relatively stable after ZNF143 over-expression and increases noticeably after ZNF143 knockdown , the TSS2 transcript (probe 2) and both transcripts (probe 3) A. The raThe northern-blot analysis endorsed the expected results D and valSupplementary Figure S5A). The 5 weeks adult mice have an over whole 80% lower ZNF143 expression level than the 13 days embryo . Instead, in kidney tumors , ZNF143 expression stays constant compared to normal tissues.In our previous work, we showed that ZNF143 could be important for rapid cell proliferation . In thiss embryo A. ZNF143s embryo B. ZNF143y tumors B as wellThese observations show that in some situations ZNF143 levels fluctuate within a high range without being auto-regulated. Endogenous ZNF143 mRNA levels can even reach a 10-fold increased expression like in breast cancer. High ZNF143 expression seems to be associated with fast-proliferative cells like during the embryonic development or in tumors. Instead, differentiated and normal tissues seem to require a lower ZNF143 expression.We previously showed that the transcription factor ZNF143 is involved in the control of genes related to rapid cell proliferation . Such a We observed that following ZNF143 artificial over-expression there is a decreased expression of the endogenous gene. This down-regulation is directly linked to the DNA-binding specificity of the ectopically produced ZNF143 protein. We showed that this specific down-regulation is reversible and correlates with an increased ZNF143 occupancy on its own promoter region. In addition to the canonically initiated ZNF143 transcript (TSS1 transcript), we identified an alternative ZNF143 transcript (TSS2 transcript) initiated downstream of the TSS1. The balance between the canonical TSS1 and the alternative TSS2 usage is responsible for the ZNF143 transcriptional auto-regulation mechanism. Both the TSS1 and TSS2 initiated transcripts have the same coding potential but differ by their 5′ UTR. The most abundant TSS1 transcript has a short 5′ UTR and permits an efficient ZNF143 protein production. On the other hand, the less abundant TSS2 transcript has a long 5′ UTR originating from alternative non-coding exons located in the canonical intronic region and is poorly translated.The ZNF143 transcriptional auto-regulation mechanism is protein-dependent, since by treating the FLP143 cells with cycloheximide, we abolished the down-regulation of ZNF143 (data not shown). We did not detect any anti-sense transcript and did not observe any effect on ZNF143 auto-regulation after knocking down the exosome complex (data not shown). A mechanism involving a TSS switch and that is responsive to ZNF143 protein level was, in our opinion, the only plausible hypothesis that we confirmed using an heterologous reporter system.in vitro ZNF143 binding on SBS/B and C sites, the TSS1 transcription is not anymore responsive to the ZNF143 over-expression in contrast to the TSS2 transcription that is still slightly efficient. This could be explained by a weak ZNF143 binding on mutated SBS/C and SBS/B sites in vivo that would affect only the TSS2 transcription. We cannot exclude that the SBS/A site could be also bound by ZNF143 in vivo and participate in the TSS2 transcription.The TSS switch mechanism is dependent of ZNF143 low-affinity binding sites that are not required for the activity of the ZNF143 promoter. In the human genome, these binding sites are located downstream of the ZNF143 promoter in a highly conserved non coding region of 98 bp exhibiting 100 and 71% identity with the orthologous regions of the mouse and zebrafish genomes respectively. Such conservation of a genomic non-coding region is highly indicative of an important transcriptional mechanism conserved in all vertebrates . If both proteins have redundant functions, we hypothesize that ZNF76 would have been maintained during evolution as a safety measure to keep a basal ZNF143 transcription factor level in the cell.It is noteworthy to point out that ZNF76 over-expression also induced ZNF143 down-regulation. This observation was not surprising since the two proteins have the same DBD  and occuin vivo . MoreoveDespite its auto-regulatory feedback loop, we observed that ZNF143 level is fluctuating in tumors and development. In stage IV breast cancer, for example, ZNF143 is 10 times more expressed than in normal tissue. In these situations we could speculate that ZNF143 escapes somehow from its auto-regulation. This evasion could be mediated by external stimuli that have been already reported to affect ZNF143 expression, such as lactogenic hormones , insulinAround 35% of human coding genes contain introns in the 5′ UTR, in particular genes with regulatory roles . Alternasupplementary material (Supplementary Table S2). Surprisingly, of the 87 sequence-specific transcription factors, we found that 39 of them have at least one binding event in their own regulatory region ; Association pour la Recherche contre le Cancer (to R.P.N.); Ligue Contre le Cancer (CCIR-GE). Funding for open access charge: CNRS and Ligue Contre le Cancer (CCIR-GE).Université de Strasbourg; Conflict of interest statement. None declared.