Myc is a master transcription factor that has been demonstrated to be required for embryonic stem cell (ESC) pluripotency, self-renewal, and inhibition of differentiation. Although recent works have identified several Myc-targets in ESCs, the list of Myc binding sites is largely incomplete due to the low sensitivity and specificity of the antibodies available. To systematically identify Myc binding sites in mouse ESCs, we used a stringent streptavidin-based genome-wide chromatin immunoprecipitation (ChIP-Seq) approach with biotin-tagged Myc (Bio-Myc) as well as a ChIP-Seq of the Myc binding partner Max. This analysis identified 4325 Myc binding sites, of which 2885 were newly identified. The identified sites overlap with more than 85% of the Max binding sites and are enriched for H3K4me3-positive promoters and active enhancers. Remarkably, this analysis unveils that Myc/Max regulates chromatin modifiers and transcriptional regulators involved in stem cell self-renewal linking the Myc-centered network with the Polycomb and the Core networks. These results provide insights into the contribution of Myc and Max in maintaining stem cell self-renewal and keeping these cells in an undifferentiated state. The nuclear factor c-Myc (Myc) is a basic helix-loop-helix leucine zipper (bHLHZ) transcription factor that binds the consensus DNA sequence known as the E-Box (CACGTG) when dimerized with Max To generate a more comprehensive map of Myc binding within the genome in mouse embryonic stem cells (ESCs), we compared the chromatin immunoprecipitation (ChIP) efficiency of four affinity tags. We generated ESC clones expressing Myc tagged at its N-terminus with either a Biotag, a FLAG-HA, or a V5 epitope and compared the efficiency and selectivity of each in ChIP experiments under different conditions. A genome-wide analysis was performed to compare the results of Bio-Myc ChIP-Seq with previously published ChIP-Seq data obtained with an antibody recognizing endogenous Myc Mouse embryonic stem cells (ESCs) cells were cultured in DMEM high glucose medium (Invitrogen) supplemented with 15% FBS (Millipore), 0.1 mM nonessential amino acids (Invitrogen), 1 mM sodium pyruvate (Invitrogen), 0.1 mM 2-mercaptoethanol, 1500 U of LIF/ml (Millipore), 25 U of penicillin/ml, and 25 µg of streptomycin/ml.KpnI and SpeI sites of pEF6 vector different tag peptides: Biotag, V5 tag or FLAG-HA tandem epitopes. Short hairpin RNA (shRNA) constructs were purchased by Open Biosystems: Myc shRNA 1 (TRCN0000086913), and NMyc shRNA 1 (TRCN0000020694).The cDNA of Myc was cloned into the pEF6/V5-His vector. Myc was N-terminally tagged by introducing into the To obtain tagged BirA-Myc stable clones, BirA-ESCs were transfected with linearized Myc constructs using Lipofectamine 2000 Transfection Reagent (Invitrogen) according to the manufacturer's protocol. Transfected cells were cultured for ten days in growth medium with Blasticidin and drug-resistant clones were selected for Myc expression.2, 5 µM ZnCl2). Then, the cells were resuspended in an isotonic buffer supplemented with 1% NP40 to isolate the nuclei. The isolated nuclei were resuspended in digestion buffer  and treated with micrococcal nuclease at 30°C for 10 min. The nuclear proteins were incubated with 3 µg of the specific antibody overnight at 4°C. The immunocomplexes were incubated with protein G-conjugated magnetic beads  for 2 h at 4°C. To immunoprecipitate tagged Myc, the specific anti-tag affinity beads were used. The samples were washed four times with digestion buffer supplemented with 0.1% NP-40 at RT. The proteins were eluted by incubation with 0.4 M NaCl TE buffer for 30 min and analyzed by western blotting.Cells were harvested in 1× PBS and resuspended in an isotonic buffer  supplemented with different concentration of detergents: 0% SDS, 1% Triton X-100; 0.15% SDS, 1% Triton X-100; 0.5% SDS, 0.5% Triton X-100; and 1% SDS, 0% Triton X-100.Approximately 2×10Extracts were sonicated using the Bioruptor® Twin (Diagenode) for 2 runs of 10 cycles  at high power setting. Cell lysate was centrifuged at 12,000 g for 10 min at 4°C. The supernatant performed in the conditions 3 and 4 was diluted with ChIP Dilution Buffer  before immunoprecipitation step.Bio-ChIP. Streptavidin beads (Dynabeads® MyOneTM Streptavidin T1) were saturated with PBS/1% BSA at RT for 1 hour, and then incubated with sample at 4°C for 4 hours on a rotator. Immunoprecipitated complexes were successively washed with Washing Buffer I (2% SDS), Washing Buffer II , Washing Buffer III  and TE buffer . All washes were performed at RT for 8 min on a rotator. SDS Elution Buffer  was added and incubated at 65°C overnight to reverse crosslink protein-DNA complexes. After decrosslinking, DNA was purified using QIAQuick PCR Purification Kit (Qiagen) according to the manufacturer's instructions.V5-ChIP. Anti-V5 tag-magnetic beads  were saturated with PBS/1% BSA at RT for 1 hour, and then incubated with sample at 4°C for 16 hour on a rotator. Immunoprecipitate complexes were washed as described previously FLAG ChIP was performed as V5-ChIP using anti-FLAG M2 magnetic beads (Sigma) for immunoprecipitation and 3×FLAG peptide (Sigma) or SDS Elution Buffer for elution.For tandem FLAG-HA ChIP, we first immunoprecipitated the sample by incubating with anti-FLAG M2 magnetic beads (Sigma) at 4°C for 16 hours on a rotator, then washed and eluted the immunoprecipitated complexes with 3×FLAG peptide (Sigma) and reincubated the eluate with EZview Red Anti-HA Affinity Gel (Sigma) at 4°C for 4 hr. After five washes, SDS Elution Buffer or HA peptide (Sigma) was added, and DNA was decrosslinked and purified.For reference sample, BirA-ES cells without tagged protein were used.ChIP experiments against endogenous Max, Myc, NMyc and V5-tag (using antibody) were performed as previously described For the ChIP experiments, immunoprecipitated DNA was analyzed by quantitative real-time PCR using the SYBR GreenER kit (Invitrogen). The oligonucleotide sequences are indicated in RNA was extracted and quantified as previously described A library was constructed from the eluted DNA using the ChIP-Seq Sample Prep Kit (Illumina) and sequenced on the Illumina HiScanSQ Platform. The reads from sequencing were mapped to the mouse genome (mm9 assembly) using Bowtie version 0.12.7, reporting only unique hits with up to two mismatches. The redundant reads were collapsed, and peak calling was performed using MACS version 1.4.1. For comparative analysis, we downloaded GEO data for ESC histone modifications (GSE12241 and GSE11172) and transcription factors . RNA-Seq data from ESCs was downloaded from the ENCODE project database Myc has been estimated to bind and regulate approximately 15% of all mammalian genes. However, due to the poor specificity and high background detected with Myc-specific antibodies, only a fraction of these sites have been previously identified. To obtain a comprehensive list of Myc binding sites in ESCs, we employed a tagging approach.We first compared the efficiency of four affinity tags. We cloned Myc under the control of a ubiquitous EF1α promoter (pEF6 vector) that remains unmethylated in ES cells. We tagged Myc at its N-terminus with Biotag, FLAG/HA, or a V5 epitope. The FLAG and HA epitopes were cloned in tandem to perform a double consecutive ChIP for better specificity. We generated stable clones expressing each of the tagged Myc construct in ESCs. The clones maintained the typical ESC morphology and expressed the tagged Myc at a level comparable to the endogenous protein . To funcTo compare the binding to the chromatin of the different tagged Myc proteins, we performed chromatin immunoprecipitation (ChIP) on the Suz12 and Ezh2 promoters, which were previously shown to be Myc targets The biotin-streptavidin affinity allows the use of high SDS concentration in ChIP experiments. We performed ChIP analysis of the Bio-Myc clone using three different concentrations of SDS  in lysis buffer . We obseChIP analysis of the FLAG could only be performed in the absence of SDS. Under these conditions, we were able to observe a 2- to 3-fold enrichment at the positive regions with respect to the negative control regions, while the ChIP with higher stringency lysis buffer (0.15% SDS) did not detect specific binding to the Suz12 or Ezh2 promoters . NotablyTo immunoprecipitate V5-Myc we used anti-V5 magnetic beads and performed ChIP analyses under three different concentrations of SDS . Lysis buffer containing 0.15% SDS gave better results. In addition, elution with the V5 peptide led to a higher enrichment in the positive control regions –C, whileIn conclusion, ChIP performed by immunopurification using the V5 tag or double immunopurification using the FLAG-HA achieved levels of enrichment similar to those obtained with the anti-Myc antibody. However, the ChIP using the Biotag demonstrated much better performance than immunopurification with the specific antibody. Importantly, the Biotag showed the best signal-to-noise ratio, which is an important parameter to perform genome-wide ChIP assays.On the basis of the above results, we performed a genome-wide analysis using the Bio-Myc ChIP to identify Myc binding sites in ESCs. To bind DNA at E-Box elements, Myc must form a dimer with Max. We therefore also performed a ChIP-Seq analysis of Max and compared the genome-wide results for co-occupancy of Max with Myc based on the ChIP-Seq of Bio-Myc with the results obtained with Myc.We generated heatmaps of the called peaks around the transcriptional start sites (TSS) ±5 kb of all genes rank-ordered by their mRNA expression levels obtained from the RNA-Seq analysis bona fide Myc binding sites and were not detected due to overexpression of the tagged protein.It has been previously shown that overexpressed Myc could associate with low-affinity targets at an increased frequency and, at even higher levels, with other sequences We next divided all of the genes in two categories on the basis of their promoter binding by Max and calculated the sensitivity and specificity using Bio-Myc or the antibody anti-Myc at different p-values overlapping between the two categories. The ROC curve highlights that Bio-Myc ChIP-Seq is more sensible without loss of specificity, and it demonstrates the better performance of Bio-Myc ChIP-Seq in comparison with the ChIP-Seq performed with anti-Myc antibodies, as shown by the area under curve (AUC) . We obtabona fide binding sites. In fact, while we were able to observe the Myc and NMyc binding to all these promoters by ChIP at each locus by RT-qPCR amplification of the fragment, only the use of Bio-Myc allowed us to identify these binding sites by genome-wide analysis.As shown above, the high level of co-occupancy of Bio-Myc and Max strongly suggests that the biotin/streptavidin protocol identifies true Myc binding sites within the genome. Previous ChIP experiments showed that when coexpressed in the same cells, Myc and NMyc bind to the same promoters The peaks identified by Bio-Myc and NMyc also show a higher overlap with open or active promoters marked by trimethylation of lysine 4 on histone H3 (H3K4me3) and active enhancers marked by H3K4me1 and H3K27Ac as well as a similar degree of overlap with Max binding sites , suggestTo further investigate the genes identified by Bio-Myc ChIP-Seq analysis that were not identified using the anti-Myc antibody, we analyzed the effective role of Myc in the regulation of these genes. We first compared the binding profiles of Bio-Myc, Myc, Max, and NMyc on two groups of genes. The first group is composed of the genes that are also bound by NMyc , while tWe next verified whether these newly identified genes bound by Myc are actually regulated by Myc. To this end, we silenced the Myc protein in ES cells . In agreUsing biotin-tagged Myc, we identified a more comprehensive genome-wide analysis of the Myc binding sites in ESCs than those obtained with Myc specific-antibodies. This approach could be employed in general for the identification of the binding profile of any chromatin-associated protein. The use of the Biotag also demonstrated the advantage of a better comparison between the different factors and the possibility to analyze the binding activities of mutants or alternative splice variants, which could not be distinguished by specific antibodies.To bind to the E-Box element and regulate its target genes, Myc must form a dimer with its partner Max. Importantly, to maintain ESC pluripotency, Myc requires the expression of Max Previous genome-wide analysis of the Myc binding sites in ESCs suggested that the Myc-centered regulatory network is largely independent from the networks controlled by the core pluripotent factors and from Polycomb because each of these networks (or modules) regulates largely independent sets of target genes with distinct roles in maintaining ES cell self-renewal, undifferentiation, and proliferation Figure S1Characterization and Functional Validation of Tagged c-Myc Stable Clones. (A) Western blot analysis of the exogenous tagged c-myc expression in the indicated stable clones. The level of expression was determined by using the antibody 9e10. Beta-actin was used as a loading control. (B–D) Western blot analysis of affinity tags expression in the indicated stable clones. Beta-actin was used as a loading control. (E) Immunoprecipitation analysis of endogenous Max protein in nuclear extracts obtained from the indicated clones demonstrated that Max is able to coimmunoprecipitate with exogenous Myc in each clone analysed. Purified rabbit IgG were used as negative control. (F) Immunoprecipitation analysis of exogenous Myc using the corresponding affinity beads showed its interaction with endogenous Max in each clone analysed.(TIF)Click here for additional data file.Figure S2Analysis of Biotag and Flag-HA ChIP Assays. (A) RT-PCR analysis of Bio-Myc_5B ChIP performed under three different concentrations of SDS  in lysis buffer. The Suz12 and Ezh2 genes were used as positive controls, the H19 and Kdr genes were used as negative controls. The results are shown as a fold difference. (B) RT-PCR analysis of Flag ChIP performed in two different Flag-HA-Myc stable clones. Two different concentrations of SDS  in lysis buffer, and two different types of elution for each condition were used as indicated. The Suz12 and Ezh2 genes were used as positive controls, the H19 and Kdr genes were used as negative controls. The results are shown as a fold difference. (C) RT-PCR analysis of TAP ChIP performed in two different Flag-HA-Myc stable clones. Flag-HA-Myc was first immunoprecipitated with anti-Flag M2 beads and then it was reimmunoprecipitated using anti-HA affinity gel. The immunoprecipitation was performed in lysis buffer containing 0% of SDS. Two different types of elution were used as indicated. The Suz12 and Ezh2 genes were used as positive controls, the H19 and Kdr genes were used as negative controls. The results are shown as a fold difference.(TIF)Click here for additional data file.Figure S3Analysis of V5 ChIP Assay. (A) RT-PCR analysis of V5-Myc_3A ChIP performed under three different concentrations of SDS  in lysis buffer, and two different types of elution for each condition. The Suz12 and Ezh2 genes were used as positive controls, the H19 and Kdr genes were used as negative controls. The results are shown as a fold difference. (B) RT-PCR analysis of V5 ChIP performed in two different V5-Myc stable clones under the indicated concentration of SDS in lysis buffer. V5-Myc was immunoprecipitated either with V5 magnetic beads or with anti-V5 antibody and two different types of elution for each condition were performed. The Suz12 and Ezh2 genes were used as positive controls, the H19 and Kdr genes were used as negative controls. The results are shown as a fold difference. (C) RT-PCR analysis of V5 ChIP performed in two different V5-Myc stable clones under the indicated concentration of SDS in lysis buffer. V5-Myc was immunoprecipitated with V5 magnetic beads and two different types of elution for each condition were performed. The Suz12 and Ezh2 genes were used as positive controls, the H19 and Kdr genes were used as negative controls. The results are shown as a fold difference.(TIF)Click here for additional data file.Figure S4Comparison of Different Types of Affinity Tags for Use in Chromatin Immunoprecipitation. (A) RT-qPCR analysis of ChIP assays of the indicated endogenous and tagged proteins under the conditions indicated. The Suz12 and Ezh2 genes were used as positive controls, the H19 and Kdr genes were used as negative controls. The results are shown as percentage (1/100) of input. (B) Backround average level of the indicated ChIP assays shown as percentage (1/100) of input. Background was calculated as the average of the values obtained from mock or IgG ChIP on two positive  and two negative  regions plus the values of the indicated ChIP on the two negative regions . (C) Overlap between Myc binding regions and promoters and enhancers with the indicated features.(TIF)Click here for additional data file.Figure S5Analysis of the Myc, Core and Polycomb modules target genes in ESC. (A) Venn diagram showing the exact number of promoters bound in the indicated ChIP-Seq analyses. Since Oct3/4 and Polycomb could bind not exactly on TSS, the analysis was performed keeping the genes bound in a larger region near TSS (between -3kb and +2kb). (B) Clustering analysis of the global Pearson correlation of the indicated ChIP-Seq showing the independence of the three modules in ESC.(TIF)Click here for additional data file.Table S1ChiP primers.(DOCX)Click here for additional data file.Table S2RT-PCR primers.(DOCX)Click here for additional data file.


Nucleosomes in all eukaryotes examined to date adopt a characteristic architecture within genes and play fundamental roles in regulating transcription, yet the identity and precise roles of many of the trans-acting factors responsible for the establishment and maintenance of this organization remain to be identified. We profiled a compendium of 50 yeast strains carrying conditional alleles or complete deletions of genes involved in transcriptional regulation, histone biology, and chromatin remodeling, as well as compounds that target transcription and histone deacetylases, to assess their respective roles in nucleosome positioning and transcription. We find that nucleosome patterning in genes is affected by many factors, including the CAF-1 complex, Spt10, and Spt21, in addition to previously reported remodeler ATPases and histone chaperones. Disruption of these factors or reductions in histone levels led genic nucleosomes to assume positions more consistent with their intrinsic sequence preferences, with pronounced and specific shifts of the +1 nucleosome relative to the transcription start site. These shifts of +1 nucleosomes appear to have functional consequences, as several affected genes in Ino80 mutants exhibited altered expression responses. Our parallel expression profiling compendium revealed extensive transcription changes in intergenic and antisense regions, most of which occur in regions with altered nucleosome occupancy and positioning. We show that the nucleosome-excluding transcription factors Reb1, Abf1, Tbf1, and Rsc3 suppress cryptic transcripts at their target promoters, while a combined analysis of nucleosome and expression profiles identified 36 novel transcripts that are normally repressed by Tup1/Cyc8. Our data confirm and extend the roles of chromatin remodelers and chaperones as major determinants of genic nucleosome positioning, and these data provide a valuable resource for future studies. The genome in eukaryotic cells is packaged into nucleosomes, which play critical roles in regulating where and when different genes are expressed. For example, nucleosomes can physically block access of transcription factor to sites on DNA or direct regulatory proteins to DNA. Consistent with these roles, nucleosomes assume a stereotypical pattern around genes: they are depleted at the promoter region that marks the start of genes and assume a regularly spaced array within genes. To identify factors involved in this organization, we generated high-resolution nucleosome and transcriptome maps for 50 loss-of-function mutants with known or suspected roles in nucleosome biology in budding yeast. We show that nucleosome organization is determined by the combined effects of many factors that often exert opposing forces on nucleosomes. We further demonstrate that specific nucleosomes can be positioned independently within genes and that repositioning of nucleosomes at the start of genes may affect expression of these genes in response to environmental stimuli. Data mining of this extensive resource allowed us to show that general transcription factors act as insulators at diverging promoters to prevent the formation of cryptic transcripts, and also revealed 36 novel transcripts regulated by the Tup1/Cyc8 complex. Chromatin is comprised of repeating units of nucleosome particles S. cerevisiae is 165 bp, with a linker region of ∼18 bp separating adjacent nucleosomes Genome-wide nucleosome profiling studies have revealed a high degree of organization around genes In vitro nucleosome reconstitution studies with purified histones and DNA indicate that as much as half of all nucleosome positions may be determined by intrinsic nucleosome-DNA sequence preferences in vivoin vivo shows several features consistent with statistical positioning in vitro profiles obtained using varying histone to DNA ratios do not in vivo. Indeed, a recent in vitro reconstitution study demonstrated that an ATP-dependent mechanism, presumably via the action of chromatin remodeling enzymes, is required to produce periodic nucleosome patterns. Based on these observations, an alternative model was proposed in which nucleosomes are actively stacked against the NDR barrier at the 5′ ends of genes In contrast to promoter NDRs, the determinants of genic nucleosomal organization are less well understood. in vivo chromatin architecture. Additional factors contribute to the regulation of genic nucleosomes, e.g. histone chaperones such as Spt16 and Spt6 are thought to affect genic nucleosome distribution by virtue of their role in histone turnover and nucleosome reassembly during transcription Several studies have highlighted the importance of remodeler ATPases in nucleosome organization around genes S. cerevisiae for their effects on nucleosome occupancy, positioning and transcription. Loss of the remodeler ATPases Chd1, Ino80 and Isw1, the CAF-1 complex  or Spt16 leads to significant displacement of 5,886 nucleosomes on 3,616 genes such that they assume positions that are more consistent with their intrinsic DNA-binding preferences. Most rearrangements of individual genic nucleosomes are within the linker regions, with little effect on neighboring nucleosomes, indicating that there is considerable positional flexibility within genic nucleosome arrays. Changes were most apparent at distal genic nucleosomes relative to the TSS; however, we also frequently observed repositioning of the +1 nucleosome, for example, upon histone depletion and in strains lacking Ino80 and Isw1. In the case of Ino80, selected genes with +1 nucleosome shifts involved in iron and glucose homeostasis showed altered gene expression responses in response to environmental stimuli. Changes in nucleosome occupancy and positioning were coupled to genome-wide transcription changes, giving rise to antisense transcripts in CAF-1 complex mutants, while loss of Reb1, Abf1, Tbf1, and Rsc3 function resulted in cryptic transcripts in the promoter regions of their target genes. Our findings demonstrate the utility of our compendium as a valuable resource for future studies.Here, we examined a compendium of 55 mutations and conditions in ts) or tetracycline promoter-shutoff (tet) alleles  directly upstream the TSS and a regularly spaced nucleosome array across the gene when considered in aggregate . The lacIn addition to the impact of gain or loss of nucleosomes, DNA-associated processes are also affected by repositioning of assembled nucleosomes, for example through modulation of regulatory site accessibility Our nucleosome position analyses revealed striking patterns of genic nucleosome shifts relative to the TSS in 20 tested conditions, with median deviations ≥4 bp for at least one genic nucleosome position that were not seen in the WT reference profiles and other compendium conditions see . ConditiS. cerevisiaein vivo movements of genic nucleosomes in yeast are sterically limited by their neighbors, the linker regions do allow for positional flexibility. Notably, several conditions also exhibited hundreds of nucleosome shifts that exceeded the average size of the linker region, discussed below. We did not observe nucleosome shifts in a strain deleted for the non-essential the linker histone Hho1 (data not shown), indicating that this histone does not play a major role in setting inter-nucleosomal linker distance. This is consistent with the fact that Hho1 is present at much lower levels compared to core histones The patterns of nucleosome shifts differed markedly between conditions, with the predominant effects being on either proximal or distal nucleosomes relative to the TSS, as well as a median shift towards or away from the TSS  and FRE7 (−15±4.2 bp shift), that are known to be induced in iron-limiting conditions MIG1 (+26±8.5 bp shift) and RGT1 (+19±2.8 bp shift) FRE1 and FRE7 in an ino80-Δ background compared to WT strains, following treatment with the iron chelator and transcription inhibitor 1,10-Phenantroline (RGT1 repression (vs. WT) upon a shift from YPD to YPG media  affected by remodelers, and those locations on the DNA that are intrinsically preferred by nucleosomes, by comparing the Using a promoter shutoff strategy spt10-Δ strain in our compendium showed reduced (>3.5-fold) expression at the HTA2-HTB2 locus, encoding histones H2A/H2B, as well for as the redundant HHF1-HHT1 and HHF2-HHT2 loci (>2-fold) encoding histones H3/H4. In the spt21-Δ strain, the HTA2-HTB2 (>2-fold) and HHF2-HHT2 loci (>1.4-fold) were affected. Surprisingly, loss of the histone gene regulators Spt10 and Spt21 led to some of largest rearrangements of genic nucleosomes in the compendium  and Spt21 (19%) , in linempendium  despite mpendium . In the nhp6 mutants bearing deletions of NHP6A and NHP6B, despite a 20–30% reduction in histone levels in vitro nucleosome reconstitution study in which whole cell extracts with additional ATP were used to reproduce in vivo patterning also did not find global changes in the spacing of +1–4 nucleosomes upon reduction of the histone∶DNA ratio by 50% in vitro reconstitution vs. in vivo depletion). In addition, these studies focused primarily on large-scale rearrangements and may have missed smaller scale effects. Indeed, our reanalysis of the data for both studies shows similar shifts of the +1–4 nucleosomes upon reduction of histone levels  and cac2-Δ (19%) strains  We identified 9,471 distinct regions with significant transcription changes in at least one compendium condition that did not overlap annotated strands of known features, totaling 7.0 Mb (29%) of the two strands of the genome sequence (24 Mb). The bulk of these regions were antisense to known genes ; howeverPATs share characteristics with cryptic unstable transcripts (CUTs) detected after disruption of the exosome complex We identified the Tup1 and Cyc8 single deletion mutants as having a particularly strong relationship between changes in 5′ NDR occupancy and the expression of their target genes; almost all genes that showed an increased NDR depletion had increased expression, and vice versa . This prTCR1, TCR21, TCR5, TCR4, TCR20, TCR15 and TCR29) TCR1, TCR4-6 and TCR20-21 bear striking similarity to flocculation genes, however, the presence of multiple stop codons and indels suggests that they no longer encode functional proteins. Translated blast analysis (blastx) identified TCR27 as potentially encoding a salt tolerance protein  and distant hits for TCR7, TCR8, TCR22, and TCR30, indicating that these five TCRs may be protein-coding (pseudo)genes. Finally, several of the intergenic transcripts we identified are divergently oriented relative to promoters of neighboring protein coding genes targeted by Tup1/Cyc8  or tup1-Δ background, subjected them to a panel of 14 different stress conditions, and assessed changes in growth by serial dilution assays. The results of these assays are summarized in ura8-Δ background showed a growth phenotype in any of the stress conditions. In contrast, several deletions exhibited growth defects in the tup1-Δ background  or increased resistance   , indicatin vivo positioning, and often exert opposing net forces on positioning. We report new roles in genic nucleosome positioning for Spt10, Spt21 and the CAF-1 complex, increasing the number of chromatin modifiers involved in nucleosome organization around genes. While the factors examined in this study predominantly affect different subsets of nucleosomes and genes, their loss generally leads to displacement of nucleosomes towards more intrinsically preferred sequences. Together these observations underscore the multiple ways in which cells actively counteract intrinsic nucleosome binding preferences in genic regions to achieve chromatin “homeostasis”. This holds true even for the +1 nucleosome, whose placement at the 5′ end of genes is considered extremely stable. Many of these perturbations of nucleosomes led to widespread transcription defects, which we captured by performing parallel genome-wide transcript analysis.In this study we find, by virtue of mutational inactivation and biochemical perturbation, multiple factors that act in concert to disrupt intrinsic nucleosome binding preferences and maintain in vitro reconstitution of chromatin with reduced histone levels mainly affect the position of distal genic nucleosomes, with little effect on spacing and positioning of proximal nucleosomes in vivo, including a pronounced shift of +1 nucleosome away from the TSS. Although this observation is not necessarily incompatible with a packing model (e.g. it could reflect a reduction in packing efficiency due to nucleosome loss), it does indicate that there are additional forces acting on proximal nucleosomes which oppose TSS stacking. Such opposing forces are also apparent in the variability in nucleosome shifts among many of the compendium mutants, with some increasing and others decreasing packing against the TSS. The overall movement of distal nucleosomes in Chd1, Ino80 or Isw1 loss-of-function mutants makes it unlikely that these remodelers are responsible for packing distal nucleosomes against the TSS, although roles for other remodeler ATPases such as Isw2 Of all the models that aim to capture the principles of nucleosome organization, the barrier-packing model The TSS packing model further predicts that shifts of proximal nucleosome should propagate throughout the array of genic nucleosomes. Although we find that local shifts can affect the positions of adjacent neighboring nucleosomes, these effects rarely spread to more distal nucleosomes. Indeed, many of the genes with a strong displacement of the +1 nucleosome away from the TSS in Ino80 and Isw1 still show an overall movement towards the TSS for more distal nucleosomes. A likely explanation for this effect is that nucleosome shifts can be buffered by changes in linker region length, which allows for a degree of local flexibility in genic arrays and decoupling of proximal and distal genic nucleosome positioning.Within arrays, individual nucleosome shifts appear to be mainly driven by underlying sequence, with nucleosomes moving to positions that are more consistent with their intrinsic DNA-sequence preferences upon loss of Ino80, Isw1, Chd1, the CAF-1 complex, histone H4, Spt6 and Spt16 (data not shown). This extends observations previously made for Isw2 and reductions in histone H3 levels PMA1 in a transcription-dependent manner, with patterns of association with the gene body resembling those of Spt6 and Spt16 In the perturbations interrogated here, the loss and/or repositioning of nucleosomes appears to drive the appearance of the cryptic transcripts in the parallel transcriptome maps. For example, the increased genic nucleosome spacing in strains bearing deletions of Spt10, Spt21 and components of the CAF-1 complex is accompanied by an increase in antisense transcripts. The patterns of antisense transcripts are similar to those observed in spt6 and spt16 mutants and upon histone H4 depletion, albeit to a lesser degree. The CAF-1 effects on genes are likely direct – the Msi1, Rlf2 and Cac2 subunits have been shown to be recruited to S. cerevisiae S288c reference strain . Combined signatures of chromatin modifications and changes in transcript levels have been successful in identifying new functional RNAs and our results indicate that the same principles can be used to identify new functional transcripts in yeast.The discovery of novel transcripts regulated by Tup1/Cyc8 shows that new genes and/or pseudogenes can still be found, even in the well-studied budding yeast genome and transcriptome. Several of these transcripts in subtelomeric regions bear strong similarity to flocculation genes, but the presence of multiple stop codons suggests that they no longer encode for functional proteins. A blast analysis of related yeast strains shows that, in contrast to the Our study has focused on a core set of chromatin modifiers, transcription factors and histones, yet even within this small set we identified several new factors that impact genic nucleosome architecture and transcription. By extrapolation, it is therefore likely that many other factors that play a role in nucleosome positioning remain to be identified. Crucially, there is still a lack of understanding of the interplay between nucleosome modifiers and chaperones and how they coordinately regulate and control nucleosome architecture at genes. For example, reported differences in nucleosome positioning and spacing between species, with their attendant effects on evolution Saccharomyces cerevisiae S288c genome. The design allows for 8- or 4-bp resolution hybridizations of single- or double-stranded samples, respectively.Microarrays were designed in collaboration with Lars Steinmetz and Ron Davis at the Stanford Genome Technology Center The strains used in this study are listed in For the histone depletion time course, the UKY403 strain harboring a deletion of both Histone H4 genes , a plasmid with the GAL1 promoter driving expression of Histone H4 (HHF2) and a plasmid with the GAL1 promoter driving expression of Histone H4 (HHF2), was grown in YP with 2% galactose until mid log phase The haploid yeast strain BY4741  was used for drug treatments. Cells were grown overnight and then diluted to an OD600 of 0.4. The drugs Nicotinamide , Sodium Butyrate , 1,10-Phenanthroline  or 6-Azauracil , all obtained from Sigma-Aldrich, were added to the media for 2 hours. Drug concentrations were predetermined to inhibit growth by 50% and/or 90%.2, 1 mM CaCl2, 0.075% NP-40] with freshly added 1 mM β-mercaptoethanol and 500 µM spermidine. Spheroplasts were divided into 6 aliquots of 300 µL each and transferred into 1.5 mL Eppendorf tubes. Micrococcal nuclease  dissolved in water at 0.1 U/µL stock was added to the tubes at concentrations of 0, 25, 50, 75, 100, and 150 U per sample. The digestion reactions were incubated at 37°C for 45 min and reactions were stopped by adding 75 µL of . Remaining proteins were digested by adding 3 uL of 20 mg/mL proteinase K solution (Qiagen) to each tube for an overnight incubation at 65°C.Nucleosomal DNA was prepared according to Lee et al. DNA from each MNase aliquot was isolated by phenol/chloroform extraction, concentrated via ethanol precipitation and treated with RNaseA (Fermentas) at a final concentration of 1 mg/ml for 1 hour. Samples were then separated on a 2% agarose gel and bands corresponding to ∼147 bp  were gel-extracted using the QIAquick kit (Qiagen) for each of the digestions. Samples were then analyzed on the 2100 Agilent Bioanalyzer to determine the precise fragment size and quantity, and the samples with the greatest proportion of ∼147 bp fragments were used for hybridization.Isolation of total RNA and hybridization onto the tiling arrays followed , except that Actinomycin D was added during cDNA synthesis to prevent antisense artifacts http://www.affymetrix.com/support/technical/fluidics_scripts.affx).Nucleosomal DNA and cDNA samples were prepared in batches, where treatment conditions were paired with a wild-type sample that was harvested and prepared in parallel. Samples were fragmented by nuclease digestion in a solution containing 1× One-Phor-All buffer  and 1 µL of 1∶16 DNase I mix  at 37°C for 2 minutes, separated on a 2% agarose gel, and stained with SYBR green (Molecular Probes) to confirm that digestion produced a distribution of fragments less than 100 bp in size and a mean of 50 bp. These fragments were then labeled with terminal deoxynucleotidyl transferase  and biotinylated ddATP  at 37°C for 2 hours, hybridized on Affymetrix arrays at 45°C for 24 hours, washed and stained according to protocol EukGE-WS2v4_450 in an Affymetrix Fluidics Station 450 and scanned in an Affymetrix 7G scanner  v1.1 using perfect-match probes only and a bandwidth of 30 nt. Raw data from cDNA hybridizations from mutant strains were normalized against cDNA from a wild-type control sample grown in parallel (mutant/WT). Data was visualized using the Integrated Genome Browser (IGB) S. cerevisiae genome  using Bowtie v0.12.7 For the nucleosome-sequencing experiments, paired-end sequencing reads were aligned to the A Gaussian filter (Matlab Statistics Toolbox) with a bandwidth of 50 nt, a mean of zero and a standard deviation of 25 was applied to the genomic-DNA normalized microarray intensity data, and nucleosome center positions were sequentially assigned to highest local maxima in the Gaussian score until no more peaks could be identified >100 nt away from already called positions S. cerevisiae genes such that the +1 nucleosome is the first nucleosome with a center position downstream of the TSS. Positive and negative numbers correspond to shifts away or towards the TSS, respectively.Nucleosome position shifts were assessed by comparing the wild-type midpoint position to that of the closest identified nucleosome position in the treatment condition within a 100 bp window around the WT condition. This window was conservatively defined to accommodate cases where an equivalent nucleosome could not be identified, e.g. because it was lost, which could otherwise lead to erroneous calculation of shifts relative to the positions of flanking nucleosomes. Our classification of nucleosome shifts is distinct from the concept of ‘positioning’ as defined by S. cerevisiae transcripts in the context of whole-genome transcription and nucleosome occupancy data. Transcript boundaries were determined based on whole-genome tiling array hybridizations of Poly(A) and total RNA from wild-type strains in this study, which were quantile normalized using Affymetrix Tiling Array Software (v1.1), together with genomic DNA hybridizations as a control group to correct for cross-hybridization and probe GC content. Additional data sets included Poly(A) RNA-Seq data A manually curated set of transcript starts and ends  was prepTwo-sided p-values were determined for each perfect-match probe using Affymetrix Tiling Analysis Software (TAS) v1.1 and a bandwidth of 30 bp, setting the mutant strains as the “treatment” and the WT strain as the “control” channel. Differentially transcribed regions were detected by setting combined thresholds for a p-value≤0.05 and a fold-change ≥2 in mutant compared to wild-type strains. These thresholds had to be met for a minimum of 80 nt (10 consecutive probes), with a maximum gap of 48 nt between probes exceeding this threshold. At these stringent settings some larger transcribed regions were detected as fragments and an additional step was therefore included to merge neighboring transcribed fragments (transfrags) if they met the following criteria: i) maximum gap of 250 bp; ii) mapped to the same strand; iii) same direction of change (both up or both down); iv) all intervening probes showed a consistent expression change in the same direction. To exclude known transcripts, transfrags that overlapped annotated coding and non-coding genes on the same strand for more than 20% of their length were removed from subsequent analyses. Remaining transfrags were classified as “tandem-sense”, “tandem-antisense”, “diverging”, “converging” or “intragenic antisense”, relative to overlapping and neighboring genes in a 3 kb region flanking the transfrag start.Each strain was grown in the exact same condition as described for the genome-wide assays of nucleosome occupancy and transcriptome profiling. 2.0 OD600 units of cells were pelleted, the supernatant removed and the cell pellets frozen. SDS-PAGE and immunoblotting was performed as described ino80-Δ mutant cells derived from this strain were cultured at 30°C in Yeast-Peptone-Dextrose (YPD) medium, YPGalactose medium or the presence of 1,10-phenanthroline. Cells were grown to mid-log phase (OD600∼0.8), collected by centrifuging at 3,000 rpm for 5 minutes and further grown in medium lacking the presence of glucose. Cells were collected by centrifuging at 3,000 rpm for 5 min and immediately frozen in liquid nitrogen, and stored at −80°C.YJM789 wild-type cells and 5′-CCTGAACAGAAACAACTACAA-3′ and 5′-CCATTGTTGTTTGGATTTGCATTG-3′ for MIG1, 5′ GCCAGGTGGCAAGTTTTTCG-3′, 5′-CTGTACTTCATTATGTGAATCACG-3′ for RGT1, 5′ GTA TAG TGG CAA CGA TAT TAA TGT C 3′ and 5′ GTA CTA CCA TTG TCA CAC CC 3′ for FRE1, and 5′-CCTGGTAGAAGTTTCATGGC-3′ and 5′-GGTCAACACATATTCGTGAGAAC-3′ for FRE7. Fold change in the MIG1, RGT1, FRE1 and FRE7 transcript level normalized to ACT1 (5′-TGTGATGTCGATGTCCGTAAG-3′ and 5′-CGGTGATTTCCTTTTGCATT-3′) was calculated using the 2−ΔΔCt method. At least three independent replicates of each reaction were performed. Student's t-test was applied for statistical analysis .Total RNA was extracted from the cells. Reverse transcription was performed with the SuperScript™ II Reverse Transcriptase from Invitrogen. In brief, 25 ng of total RNA was used as the starting material for single strand synthesis in the presence of Actinomycin D. Quantitative PCR was performed with the SYBR Green PCR Master Mix (Applied Biosystems) in an Applied Biosystems 7900HT Fast Real-Time PCR System using Sequence Detection System software version 2.3. The following temperature profile was applied: 50°C for 2 min; 95°C for 15 min; 40 cycles of the sequence 95°C for 15 s; 58°C for 1 min; 95°C for 15 s; 60°C for 15 s; 95°C for 15 s. Primer pairs used were TCR genes, TCR01, TCR03, TCR05, TCR06, TCR08, TCR11, TCR15, TCR20, TCR21, TCR22, TCR27, TCR29, TCR30 and TCR31 in the strains ura8-Δ and tup1-Δ were created using a PCR-based gene deletion strategy to generate a start- to stop- codon deletion of each of the genes in the yeast genome  to determine the intensity profile of the pixels that present the yeast colonies.Deletion of a selection of t genome . As part primers . TCR delAffymetrix tiling array data and next-generation sequencing data are available at GEO (record GSE44879).Dataset S1Yeast transcript annotation R script and data files. Please note this file is 81.9 MB and may be difficult for some readers to download due to its size.(GZ)Click here for additional data file.Figure S1S. cerevisiae genes, expressed as the log2 ratio of probe intensities of MNase-treated nucleosomal DNA samples over MNase-treated genomic DNA.Nucleosome occupancy profiles for all compendium conditions. Intensity plots of nucleosome occupancy relative to the curated transcription start sites of 5,043 (PDF)Click here for additional data file.Figure S2Correlation between NDR occupancy changes and gene expression changes in transcription factor mutants. Hierarchical clustering of NDR nucleosome occupancy changes ≥1.5 fold in at least one of the transcription factor loss-of-function mutants included in the compendium (left). Expression changes for the genes associated with each NDR are shown in comparison (right). NDRs were defined as the 200 bp region directly upstream of curated transcription start sites.(PDF)Click here for additional data file.Figure S3Selection of nucleosome shift profiles. Nucleosome shift profiles for representative essential (A) and non-essential (B) loss-of-function mutants, drug treatments (C) and wild-type (WT) reference strains (D). The position changes in the 5 WT strains are plotted relative to the median nucleosome position across all 35 WT reference profiles used in this study.(PDF)Click here for additional data file.Figure S4Remodeler ATPase binding is not increased at positions with nucleosome shifts in deletion mutants. WT (top row) and Ino80 and Isw1 deletion mutant (middle row) occupancy profiles at genic nucleosome positions are shown in comparison to MNase-ChIP binding profiles for these factors at the same locations (bottom row). MNase-ChIP data were obtained from Yen et al. (PDF)Click here for additional data file.Figure S5Changes in global histone H3 levels for selected compendium mutants. Change in histone H3 levels in 11 compendium conditions compared to wild-type controls. Each strain was grown in the exact same condition as described for the genome-wide assays of nucleosome occupancy and transcriptome profiling. Equal amount of OD units were loaded for each mutant and histone H3 bands were quantified from western blots using Image J. Error bars correspond to the standard deviation of the changes in normalized histone levels in three western blot replicates, using PGK1 as a loading control for normalization.(PDF)Click here for additional data file.Figure S6Nucleosomes disrupted after 6 hours of histone depletion and after loss of Spt16 function assume more intrinsically preferred positions. A) Occupancy profiles at genic nucleosome positions in WT conditions (top row) and after 3–6 hours of histone depletion or loss of Spt16 (middle row) are shown in comparison to predicted occupancy profiles (Lasso model score) based on intrinsic sequence preferences obtained from Kaplan et al. (PDF)Click here for additional data file.Figure S7in vitro reconstituted nucleosomes with 0.5∶1 and 1∶1 histone∶DNA ratios (red) Reanalysis of previously published histone depletion and reconstitution data reveals shifts of proximal genic nucleosomes. Nucleosome shifts in nhp6 mutants compared to wild-type strains (PDF)Click here for additional data file.Figure S8Nucleosomes disrupted in CAF-1 complex mutants assume more intrinsically preferred positions. A) Cac2, Msi1 and Rlf2 WT (top row) and deletion mutant (middle row) occupancy profiles at genic nucleosome positions are shown in comparison to predicted occupancy profiles (Lasso model score) based on intrinsic sequence preferences obtained from Kaplan et al. (PDF)Click here for additional data file.Figure S9Large changes in NDR nucleosome occupancy 6 hours after histone H4 transcription shutoff do not result in cryptic promoter transcripts. Correlation between changes in promoter nucleosome occupancy  and transcription changes across the gene body and 1 kb intergenic flanking regions . In each panel, genes are ranked according to the average change in NDR nucleosome occupancy.(PDF)Click here for additional data file.Table S1Strain information.(XLS)Click here for additional data file.Table S2Listing of antisense and intergenic regions with significant expression changes.(XLS)Click here for additional data file.Table S3PATs identified in Abf1, Rap1, Rsc3 and Tbf1 loss-of-function mutants.(XLS)Click here for additional data file.Table S4Genomic locations of Tup1/Cyc8 repressed (TCR) transcripts.(XLS)Click here for additional data file.Table S5Nucleosome-Seq read counts.(XLS)Click here for additional data file.Table S6List of manually curated gene starts and ends.(XLS)Click here for additional data file.Table S7TCR gene deletions.Deletion and confirmation primers for (XLS)Click here for additional data file.


Zfp423 mutations produce a range of developmental abnormalities in mice and humans related to the ciliopathies. Surprisingly, computational analysis of clustered Zfp423 and partner motifs in conserved genomic sequences predicts enrichment in Zfp423 and Ebf genes. In cell culture models selected for Zfp423 and EBF expression, we identify strong and reproducible occupancy of two Zfp423 intronic sites using chromatin immunoprecipitation with multiple independent antibodies. Both sites are significantly enriched in either quantitative PCR or massively parallel sequencing assays. A site in intron 5 acts as a classical enhancer in transient assays, but does not require the consensus motif for activity, suggesting a redundant or modulatory role for Zfp423 binding in this context. We speculate that Zfp423 may repress this enhancer as part of a developmental ratchet.Zfp423 is a 30 zinc finger transcription factor that forms regulatory complexes with EBF family members and factors targeted by canonical signaling pathways.  Zfp423 in mice produce severe midline defects in developing brain, most notably loss of the cerebellar vermis ZNF423 result in a range of nephronophthisis-related ciliopathy (NPHP-RC) phenotypes, including characteristic defects in cerebellar vermis and kidney, with cellular deficits in DNA damage response Mammalian zinc finger protein 423  is a transcriptional regulator important to development and disease. Mutations of Zfp423 is composed of 30 C2H2 zinc fingers, clustered into multi-finger domains reported to bind DNA or other transcription factors. Zfp423  was first identified as a binding partner that inhibits Early B-cell factor  subfamily of basic helix-loop-helix transcription factors through its last three zinc fingers Zfp423 gene itself showed reproducible occupancy by Zfp423 in ChIP-PCR and ChIP-seq assays. The stronger of these sites, in Zfp423 intron 5, also showed enhancer activity in heterologous classical promoter-reporter assays in P19 cells. Surprisingly, Zfp423 appears to act as a negative regulator at the stronger of the two sites, suggesting a negative feedback cycle that may be conditional on signaling and cell state.In order to identify direct targets of Zfp423, we initiated an in silico strategy based on cross-species conservation of clustered consensus motifs Zfp423 and Ebf3 genes  and medulloblastoma (D238) derived cell lines . We further examined ZNF423 expression among four neuroblastoma cell lines by quantitative RT-PCR (qRT-PCR). IMR32 and SK-N-SH each expressed high relative level of ZNF423 RNA compared to SHEP or GIMEN in a single experiment  experiments . Semi-qunditions .Zfp423 and Ebf genes in P19 cells. Quantitative PCR detected modest but significant ChIP enrichment at the more 5′ (and more deeply conserved) site in intron 3 . Targeting Ebf1 with shRNA resulted in a similar loss of reporter expression (p<10−7), suggesting that Ebf1 contributes to activation of this enhancer. Targeting Ebf2, which appeared to be expressed at lower levels based on qRT-PCR data  below the level achieved with Ebf1 shRNA alone. Expression of the GFP-marked shRNA construct dramatically reduced Zfp423 immunofluorescence in P19 nuclei using either the E20  does not appear to alter enhancer activity. It is possible that the repression caused by exogenous expression is indirect and a consequence of ZNF423 activity on other genes. However, as the enhancer is directly bound by Zfp423 in a conserved fashion and mutation of the presumed binding sites eliminates the effect of overexpression /Ct(reference). Quantitative PCR from ChIP samples used either a pre-immune IgG mock ChIP or input fraction as indicated for relative quantification among samples.PCR primers , 10 mM DTT, 10 mM sodium orthovanadate, 8 M urea and treated with 100 U Benzonase nuclease (EMD) until minimize viscosity. Extracts were incubated in a sample buffer . Membranes were incubated with the indicated primary antibodies (1∶500) in Odyssey blocking buffer (Li-Cor) with 0.2% Tween 20. Immunoreactivity was measured with infrared-conjugated secondary antibodies (Rockland) detected on an Odyssey imaging station (Li-Cor).Zfp423 sites and sequence verified. For luciferase reporter assays, P19 cells were co-transfected in triplicate for each experiment and the ratio of firefly luciferase to Renilla luciferase activity was taken as the experimental measure. Experiments were replicated 3 times with independent DNA preparations.Plasmid pGL4 including the pTAL minimal promoter was modified to incorporate the indicated fragments from mouse http://www.graphpad.com/quickcalcs/) or in R; all other statistical tests were performed in the R version 2.8.1 (2008-12-22) base package environment.Standard one and two-sample t-tests were performed in the GraphPad online calculator Click here for additional data file.


This pipeline represents a significant step towards the dissection of embryonic neuronal development in vertebrates.The developing vertebrate nervous system contains a remarkable array of neural cells organized into complex, evolutionarily conserved structures. The labeling of living cells in these structures is key for the understanding of brain development and function, yet the generation of stable lines expressing reporter genes in specific spatio-temporal patterns remains a limiting step. In this study we present a fast and reliable pipeline to efficiently generate a set of stable lines expressing a reporter gene in multiple neuronal structures in the developing nervous system in medaka. The pipeline combines both the accurate computational genome-wide prediction of neuronal specific  Recent years are witnessing a flood of new discoveries in neuroscience largely resulting from the ability to monitor living cells in the context of the developing nervous system using reporter gene expression In vertebrates, the most widely used strategy to express reporters in anatomical structures relies on the use of regulatory elements, often promoters of genes known to be expressed in the desired structures (promoter bashing). This trial and error process is slow and tedious. Thus, to maximize the chances of getting the right regulatory sequences, entire loci around selected genes employing BAC technology have been used Despite advantages of one approach over the other, all these methodologies have the significant drawback of lacking specificity. Testing semi-random elements in vertebrates either by promoter bashing or enhancer traps results in high screening efforts, while BAC technology, which addresses the specificity issue by using the entire locus instead, is experimentally tedious and cannot be scaled up easily.In parallel, progress has been made towards the computational identification of regulatory regions in sequenced genomes. Previous work has shown that, without experimental priors, functional constraints acting on non-coding sequences are one of the most predictive information to locate regulatory elements Oryzias latipes (medaka) embryos. This pipeline represents two major breakthroughs compared to previous methodologies: A selective step to predict neuronal specific regulatory regions, combined with a new reliable enhancer assay in medaka to efficiently obtain stable lines expressing the reporter gene in neuronal structures , Takifugu rubripes (pufferfish) and Gasterosteus aculeatus (stickleback). Finally, clusters of conserved homotypic or oligotypic binding sites were identified and predicted as CRMs  and preferentially within 100 kilobases (kb) distance to the nearest transcription start site (TSS) .The algorithm resulted in the identification of 23,011 predicted CRMs  which contain on average 62 putative TFBSs. These regions, despite being broadly distributed over the genome, are found significantly more often in intergenic regions  and 97.98% are located less than 100 kb away from the nearest TSS . These trends are accentuated compared to the ones observed for the entire set of predicted CRMs.It has previously been shown that vertebrate conserved non-coding elements are functional enhancers http://premod.mcb.mcgill.ca) and listed in Supplementary in-situ expression of medaka genes or CRM activity information is available, PreMod links to the corresponding experimental data stored in the 4DXpress database http://4dx.embl.de/4DXembl/reg/all/searchbyspecies/line.do?speciesID=4).Both sets of predicted CRMs  are stored in the PreMod database Danio rerio (zebrafish) in-situ annotations from ZFIN in-situ annotation of the zebrafish genes onto their orthologs in medaka ( and S4). More specifically, 74% of vertebrate conserved CRMs are associated with genes annotated as being expressed in the central nervous system . On the other hand, enrichment is not observed in non-neuronal tissues .Next, we took advantage of the large compendium of This finding, empirically observed in mouse enhancer analysis We developed a new enhancer assay to rapidly test genomic regions for enhancer activity and to derive stable transgenic lines. Aiming to set up a pipeline for large-scale analysis, we particularly focused on generating a quick and reliable readout, which required live monitoring of the expression pattern directly in injected embryos. The ability to record GFP expression in a live embryo throughout its development is a clear advantage of the fish system compared to the mouse embryo. Thus, we expect an increased sensitivity in the detection of expression patterns and better characterization of these expression patterns over time.We use meganuclease mediated transgenesis We also developed a robust and efficient experimental setup to distinguish between the absence of enhancer activity and the failure of the injection experiment. For this, we use the hsp70 core promoter that conveniently triggers a strong and specific lens expression from stage 28 onward Table S5). The percentage of successfully injected embryos showing reproducible expression outside the lens is calculated and should be above 50% in order to call a genomic region positive for enhancer activity. To be significant, a consistent pattern should be seen for at least 10 individual fish. This typically requires injecting less than a hundred embryos, which is easily achievable in a single injection experiment. About 1 in every 50 successfully injected embryos shows non-consistent expression most likely resulting from the activity of local enhancers (enhancer trap). Following our defined criteria, the enhancer trap expression pattern does not pass the quality control and is therefore discarded. This quality control measurement is a significant improvement over previously described enhancer assays from which the distinction between injection failure and lack of enhancer activity cannot be made.We therefore monitor the number of lens-positive embryos (injection success rate) and the number of embryos showing reproducible GFP expression in other domains (Table S5). These highly reproducible patterns are a good indication that the expression patterns we observe are solely the result of the tested enhancer activity.In a typical experiment we obtain an injection success rate around 46%, and, in the case of a functional enhancer, on average 66% of successfully injected embryos show a consistent expression pattern (Table S6a). To evaluate the global success rate of the pipeline, an additional 10 predicted CRMs evenly distributed among the 200 top scoring candidates were tested for enhancer activity (Table S6b).The top 10 computationally predicted vertebrate CRMs located in eight genomic loci were experimentally tested for enhancer activity and the injected fish were raised to generate stable transgenic lines  to less than a week (time for embryogenesis in medaka).Further confirming the computational predictions, all the positive elements drive reporter gene expression in various neuronal structures. Some patterns are limited to very specific areas of the brain or the peripheral nervous system, sometimes, with just a few cells being labeled. This specific expression remains in stable lines suggesting that the reporter gene expression is activated in only one or a few cell types. For example, MEDMOD021885 highlights a cluster of a few dozen neurons located bilaterally in the diencephalon .For a general analysis of the neuronal system, a complete coverage of brain structures would be desired. We found that all major subdivisions of the vertebrate CNS include labeled cells in our assay. Reporter gene expression is found in telencephalic domains (line MEDMOD021953), the diencephalon , the mesencephalon , the rhombencephalon , and the spinal cord (line MEDMOD070042). Other neuron-containing structures, such as the nasal epithelium were also labeled (lines MEDMOD21953 and MEDMOD074008) .The algorithm defines a list of transcription factors predicted to bind to the predicted CRMs. To evaluate how pertinent this information is, we selected three experimentally confirmed CRMs whose activity is restricted to a very defined neuronal structure . Using the ZFIN database, we compared the expression pattern of the factors predicted to bind these CRMs to the observed enhancer activity and searched the literature for those transcription factors being expressed in overlapping domains (For the CRM active in the rhombomeres (MEDMOD086628) we found, among others, following transcription factors: MafB , known to be required for hindbrain segmentation and rhombomere formation For the CRM active in the diencephalon (MEDMOD045693), four transcription factors are predicted to bind this CRM  and show overlapping and specific expression patterns. Pou3f2, for example, is required for oxytocin neuronal development in the hypothalamus Taken together, these results show that the factors predicted to bind these CRMs can be used as starting points to prioritize further experiments.al. We describe a new hybrid methodology aimed at identifying neuronal regulatory elements in fish. With 95% success rate after experimental validation and a 100% success rate in transgenesis, this pipeline is, to date, the most efficient procedure to obtain stable transgenic lines expressing reporter genes in various neuronal structures. Furthermore, the orthologs of three of the 20 CRMs analyzed in our study have previously been tested in mouse in-situ data, it will therefore be possible to further target the pipeline to select regions most likely active in specific neuronal structures. This task is facilitated by the fact that the computational predictions stored in PreMod are linked to expression data stored in 4DXpress. Furthermore PreMod provides CRMs in their genomic context as well as a score for each predicted regulatory region. As a result, prior to in-vivo testing, CRMs can be targeted based on their genomic context and score.We have also shown that the patterns of reporter gene expression in our lines are reminiscent of the expression of genes neighboring the tested CRMs. Using gene expression information such as Finally, we have shown that the predicted CRMs conserved across vertebrates are enriched around genes known to be expressed in neuronal tissues. Such enrichment cannot be detected for non-neuronal tissues  suggesting that this trend is essentially neuronal specific. This analysis,  indicates that pan-vertebrate conserved CRMs have preferred activity in neuronal structures. Our results are in accordance with a recent finding reporting that a large population of heart enhancers is poorly conserved Our pipeline, designed to create neuronal tissue specific markers, is of great interest for analyzing enhancer activity, identifying genetic markers and finally as a cost effective enhancer screening tool.We collected a comprehensive set of 402 non-redundant PWMs based on Transfac (version 9.2) All non-vertebrate transfac matrices were removed, except for 8 drosophila matrices for factors known to be involved in vertebrate development;Matrices linked to more than two different TFs (from the same species) were discarded;Among different matrices for the same TF, only that with the highest quality value was kept or, if not available, the predicted sites that are the most conserved through vertebrate evolution were used .Oryzias latipes, assembly HdrR, Oct 2005 Tetraodon nigroviridis, assembly, Tetraodon 7, Apr 2003 Gasterosteus aculeatus, assembly Broad S1, Feb 2006, Broad Institute) and takifugu  sites that are slightly misaligned, up to 20 bp. In addition, conservation was weighted as follows: hitScorealn =  hitScoremedaka+max. hitScore will then depend on both the score of the binding site in medaka and its conservation in at least one other teleost. Note that a binding site can have a high score without being conserved if the medaka scoring hit is strong enough. CRMs are predicted genome-wide and are not targeted to specific regions .A subset of 491 CRM predictions was selected using criterion combining high CRM score and conservation with human (vertebrate conserved CRMs). Specifically, predicted CRMs with a BLASTZ in-situ annotations available from the ZFIN in-situ database in-situ annotation to the medaka orthologs using the BioMart utility Table S3). Only tissues associated with at least 20 vertebrate conserved CRMs are retained for further analysis. We then calculated the significance of the overrepresentation of CRMs showing annotation for specific tissues comparing the vertebrate conserved dataset to a background set . The significance of this overrepresentation was calculated with a one-sided fisher test. All tissue and stage annotations follow the OBO ontology.Each predicted CRM is associated with the closest gene independently of the genomic distance between them. We took advantage of the large collection of genes with zebrafish For each CRM, the distance to the nearest annotated TSS (as defined in Ensembl version 61) is retrieved and categorized into distances of less than 1 kb, 1 to 10 kb, 10 to 100 kb or more than 100 kb. We also assessed if the CRMs are localized in annotated genes or in intergenic regions (<100 or >100 kb away from the nearest gene as defined in Ensembl version 61). One hundred randomizations consisting of the same number of random locations (with the same size distribution) in the medaka genome as the number of CRMs in the real dataset has been produced. The same location analysis was then performed on these random datasets and the significance was calculated from these randomizations.Table S8). After restriction digest the fragments were cloned into a pBlueScript-based transgenesis vector containing two recognition sites for the meganuclease ISce-I The identified CRMs were PCR amplified  from genomic medaka DNA and flanking HindIII restriction sites introduced (for primer sequences see Injections were done as described A Leica fluorescent microscope (Leica MZFLIII) was used to examine GFP expression in live embryos. Injected embryos were analyzed at different stages to determine the spatio-temporal pattern of GFP expression. As the hsp70 core promoter is activated by temperature changes, the embryos were kept and examined at constant room temperature. Developmental stages were determined by morphological features as described by Iwamatsu scamp1, fign(1 of 2), atg4c, gon3_oryla and kcnh7 expression patterns, fragments were PCR amplified from medaka cDNA  and subcloned using the TOPO TA Cloning Kit (Invitrogen). After verification by sequencing, Digoxigenin incorporated antisense-RNA probes were generated by in-vitro transcription with Sp6 or T7 RNA Polymerase (NEB).For analysis of in-situ hybridization were performed as described previously in-vitro transcription with Sp6 or T7 RNA Polymerase directly from these clones.Probe preparation and whole mount The medaka nervous system ontology is derived from the medaka fish anatomy and development OBO ontology (medaka_ontology.obo). The descendent terms of nervous system at various stages were extracted. A total of 32 different terms were found and used for the controlled vocabulary annotation. Reporter gene expression was found in 20 (62%) of these anatomical terms.Figure S1A. Location of the predicted CRMs relative to genes in the medaka genome. The percentage of intragenic, intergenic (<100 kb) and intergenic (>100 kb) locations for all the 23,011 predicted CRMs (left), the 491 vertebrate conserved CRMs (center) and 23,011 random positions in the genome (right) is calculated. The distribution of the CRMs in each category is significantly different compared to random locations, with more CRMs being intergenic (>100 kb). B. Locations of the predicted CRMs relative to the nearest annotated TSS in the medaka genome. For all the 23,011 predicted CRMs (left), the 491 vertebrate conserved CRMs (center) and 23,011 random positions in the genome (right) the distance to the nearest TSS is calculated and binned into less than 1, 1–10, 10–100 and more than 100 kb windows. The percentage of regions for each bin is then calculated. The distribution of the CRMs in each bin is significantly different compared to random locations, with more CRMs being closer to the nearest TSS than expected.(TXT)Click here for additional data file.Figure S2(a) MEDMOD021445 (b) MEDMOD092210 (c) MEDMOD062490 (d) MEDMOD057815 (e) MEDMOD021442 (f) MEDMOD093196 (g) MEDMOD062408 (h) MEDMOD047799 (i) MEDMOD083481 (j) MEDMOD062206.Enhancer activity of the additional 10 predicted CRMs evenly distributed among the 200 top scoring candidates. Example of injected fish showing a reproducible expression pattern. (TXT)Click here for additional data file.Figure S3Stable lines at various developmental stages and views for MEDMOD021953. Stage 26–30: Telencephalon, retina ganglion cells (RGCs), tectum central zone, cerebellum, hypothalamus, rombomeres, alar plate. Stage 33–35: Telencephalon, tectum central zone, hypothalamus, cerebellum, hindbrain, RGCs, tegmentum.(TXT)Click here for additional data file.Figure S4Stable lines at various developmental stages and views for MEDMOD062451. Stage 27: One group of cells in the hypothalamus  or tegmentum. Stage 32: Optic tectum differentiated cells , torsus semicircularis, few cells in cerebellum, lateral part of the myelencephalon, hindbrain: two lateral rows cells and two medial rows of cells (motor neurons). Stage 35: Telencephalon posterior (area ventro-posterior), optic tectum: differentiated cells , torsus semicircularis, midbrain dorsal, a few cells in the cerebellum anlage, hindbrain: Two lateral rows cells and two medial rows of cells (motoneurons).(TXT)Click here for additional data file.Figure S5Stable lines at various developmental stages and views for MEDMOD074008 and MEDMOD021885. MEDMOD074008 stages 28–37: Tectum central zone and olfactory pits. MEDMOD021885 stages 34–35: Diencephalon. Two groups of neurons with contralateral projections.(TXT)Click here for additional data file.Figure S6Stable lines at various developmental stages and views for MEDMOD070042. Stage 32–34: Diencephalum pretectal nuclei, four rows of cells, two median two lateral row of cells. Medials are motoneurons, the lateral ones are sensory or interneurons.(TXT)Click here for additional data file.Figure S7Stable lines at various developmental stages and views for MEDMOD046007 and MEDMOD045693. MEDMOD046007 stage 32–34: Optic tectum , diencephalon and hypothalamus. MEDMOD045693 stage 24–32: Hypothalamus and maybe pectoral fins.(TXT)Click here for additional data file.Figure S8Stable lines at various developmental stages and views for MEDMOD086628. Stage 28–34: Rhombomeres.(TXT)Click here for additional data file.Figure S9Stable lines at various developmental stages and views for MEDMOD062537. Stage 22: Dorsal part of the retina and hypothalamus. Stage 25–32: Retina, forebrain and hypothalamus.(TXT)Click here for additional data file.Table S1List of all predicted CRMs. Medaka coordinates (genome version MEDAKA1) of all the predicted CRMs.(PDF)Click here for additional data file.Table S2List of predicted vertebrate conserved CRMs. Medaka coordinates (genome version MEDAKA1) of all the predicted vertebrate conserved CRMs.(PDF)Click here for additional data file.Table S3List of conserved and non-conserved CRMs for each tissue. Tab delimited list of tissues from ZFIN (column 1) with the corresponding medaka genes (column 2) and the corresponding CRM ids (column 3). The conservation of the CRMs is reported in column 4. For details, see the material and (PDF)Click here for additional data file.Table S4Enrichment of vertebrate conserved CRMs around genes expressed in neuronal tissues. For each selected developmental tissue (first column) and stage, the percentage of genes expressed in the given tissue that are linked to at least one vertebrate conserved CRMs (third column) or to at least one predicted CRMs (fourth column) is calculated. The statistical significance is calculated with a one-sided fisher test (second column). For details see (PDF)Click here for additional data file.Table S5Injection success rate. “Alive” column corresponds to the number of injected embryos which passed gastrulation. “Expression” corresponds to the number of embryos with expression pattern in the lens (successful injection) and “Specific Expression” corresponds to the number of embryos with reproducible expression pattern excluding the lens specific pattern.(PDF)Click here for additional data file.Table S6a) For the 10 top scoring candidates. (b) For the 10 candidates evenly distributed amongst the 200 top scoring candidates.Genomic location, length (in bp), scores and enhancer activity of the tested CRMs. ((PDF)Click here for additional data file.Table S7Candidates transcription factor predicted to bind CRMs. MEDMOD062537, MEDMOD045693 and MEDMOD086628. For each transcription factor, the name of the factor (from transfac or Jaspar), the name of the zebrafish homolog, the PWM and the partial expression pattern (from ZFIN) is recorded.(PDF)Click here for additional data file.Table S8in-situ probe generation).Primer list. Description of all the primers used in this study Click here for additional data file.


Seed germination is of immense significance for agriculture and has been studied for centuries. Yet, our understanding of the molecular mechanisms underlying regulation of dormancy and germination is still in its infancy. Gibberellins are the key phytohormones that promote germination, and the DELLA protein RGL2 is the main signalling intermediate involved in this response. Germination is completely inhibited if functional RGL2 is overexpressed and/or stabilized; however, the molecular mechanisms of RGL2 function are still largely unknown. We therefore attempted to shed light onto some of the genetic events downstream of RGL2.Gene ontology of the transcriptome differentially regulated by RGL2, as well as extensive cross-comparison with other available microarray data indicates that RGL2-mediated inhibition of germination causes seeds to enter a state of dormancy. RGL2 also appears to differentially regulate a number of transcription factors, many of which are known to be involved in light- or phytohormone-mediated aspects of germination. A promoter analysis of differentially expressed genes identified an enrichment of several motifs that can be bound by specific transcription factors, for example GAMYB, ARF1, or Dof-type zinc fingers. We show that Dof-binding motifs indeed play a role in RGL2-mediated transcription. Using Chromatin Immunoprecipitation (ChIP), we show that RGL2 directly downregulates at least one cell wall modifying enzyme, which is predicted to constrain cell growth thereby leading to inhibition of seed germination.Our results reveal that RGL2 controls various aspects of germination. Through the repression of cell wall modifying enzymes, cell growth is directly constrained to inhibit germination. Furthermore, RGL2 likely interacts with various types of proteins to regulate transcription, and differentially regulates several transcription factors. Collectively, our data indicate that gibberellins, acting via RGL2, control several aspects of seed germination. GIBBERELLIC ACID REQUIRING 1 (GA1) encodes for the ent-kaurene synthetase A of the gibberellin biosynthetic pathway, ga1 plants are therefore unable to synthesise gibberellins. These mutants are extremely dwarfed, male-sterile, and most importantly their seeds fail to germinate without exogenous gibberellin Ltd., Bangalore, India for microarray analysis (Project No. GT-537_E), including RNA quality control using Bioanalyser, reverse transcription and labelling, single colour hybridisation onto Agilent ga1-3 rga-t2 rgl2-1 35S::RGL2-GR plants. Plants were sprayed with either of the following: 0.01% ethanol (MOCK), or 10μM DEX, and flower buds were harvested 3h after treatment. ChIP was performed according to Kaufmann et al.[g). For the detection of RGL2-GR, total extracts, flow through and eluates prior to proteinase K treatment were resolved under reducing conditions on 12% SDS/polyacrylamide gel, and proteins were transferred onto polyvinylidene difluoride (PVDF) membranes (Bio-Rad Laboratories). PVDF membranes were blocked by incubation with 5% milk powder in Phosphate-buffered saline + 0.05% Tween 20 (PBS-T) over night, followed by incubation with monoclonal mouse anti-GR antibody  at room temperature for 2h. After washing with PBS-T, membranes were incubated with secondary antibody, horseradish peroxidase- (HRP-) conjugated rabbit anti-mouse antibody  for 2h at room temperature. Membranes were then washed four times with PBS-T, and immune complexes were detected on x-ray film  using the Enhanced Chemiluminescence (ECL) Detection kit according to the manufacturer’s instructions (Amersham Pharmacia).Chromatin Immunoprecipitation (ChIP) was performed using flower buds of 3-4-week-old nn et al., with minn et al., prior tArabidopsis tissues using ‘MAXIMA® First Strand cDNA Synthesis Kit’ (Fermentas), with ‘KAPA SYBR® FAST qPCR Kit’ (KAPA Biosystems) using the ‘StepOne™ Real-Time PCR Systems’ (Applied Biosystems). All qRT-PCR data were generated from biological duplicates. Relative quantification of expression was determined using ‘StepOne Software’ (v2.1). For a list of primers, see Additional fileComparative analysis of selected genes was performed by qRT-PCR. Reactions were performed on cDNA, prepared from RNA of various The entire sets of up-regulated (253) and down-regulated (354) genes, respectively, were analysed for enrichment of promoter motifs. Sequences of promoter regions  were extracted from our in-house promoter sequence database. Over-represented motifs were detected using the Dragon Motif Builder algorithm with EM2 option, and thim35S::GFP. Synthetic promoter constructs containing three tandem copies of selected promoter motifs were generated by overlapping PCR using synthesised oligonucleotides  was amplified with SmaI and BamHI restriction sites, and cloned into pGreen (HY105 backbone) containing mGFP (cloned with SpeI and XbaI), to generate pGreen-Arabidopsis (Col-0) plants following the protocol described in Yoo et al.[3. Images were acquired four to six hours after transfection using a Carl Zeiss Axiovert 200M confocal laser microscope (http://www.zeiss.de/axiovert200) with excitation at 488nm. For GFP detection, one channel was configured between 505 and 530nm. All images were recorded with the same detection settings. Average and maximum signal intensity of 6 to 10 selected protoplasts was determined using the Carl Zeiss LSM software (ver. 4.0).Leaf mesophyll protoplasts were isolated from 3- to 4-week-old wild-type oo et al.. For eacEXPA8: Expansin A 8; GA: gibberellic acid; GFP: green fluorescence protein; RGL2: RGA-like 2; qRT-PCR: quantitative real-time PCR.ChIP: chromatin immunoprecipitation; DEX: dexamethasone; Dof: DNA binding with one finger; There are no competing interests.ab initio promoter analysis. ELT generated the inducible line. HY helped in designing the project and writing the manuscript. PPK was responsible for overall supervision of experimental design, data analysis, and writing the manuscript. All authors approved the final manuscript.PS designed and performed all experiments, including all statistical and bioinformatics analyses, and wrote the manuscript. PR optimised and performed RNA extractions, ChIP and qRT-PCR experiments. BM performed the Genes differentially regulated in seeds of ga1-3 rga-t2 vs. ga1-3 rga-t2 rgl2-1. List of genes identified as RGL2-UP and RGL2-DOWN, respectively, with a fold-change of at least 2, and a p-value ≤0.01.Click here for fileGene Ontology (GO) analysis of the RGL2-mediated transcriptome in seeds. GO analysis according to AgriGO (http://bioinfo.cau.edu.cn/agriGO/index.php), with respect to biological process  and molecular function  in RGL2-UP  and RGL2-DOWN .Click here for fileCross-comparison of RGL2-mediated transcriptome in seeds with other available microarrays. List of genes differentially regulated by RGL2 in seeds that were identified in other available microarray data sets. Overlapping genes are indicated by ‘+’.Click here for fileRelative expression levels of ATHB2 and ATHB5 in response to abscisic acid. Seeds were stratified in water or 5μM abscisic acid, and RNA was extracted after 12h. Expression levels of ATHB2 and ATHB5 were determined by qRT-PCR in imbibed seeds of ga1-3 rga, relative to Tubulin (TUB), and compared to ga1-3 rga rgl2-1. RQ = relative quantity of transcript.Click here for filePrimers and oligonucleotides used. List of primers and oligonucleotides used for genotyping of T-DNA insertion lines, qRT-PCR, ChIP-qRT-PCR, and for construction of reporter constructs containing different promoter motifs.Click here for file