Nucleosome organization plays a key role in the regulation of gene expression. However, despite the striking advances in the accuracy of nucleosome maps, there are still severe discrepancies on individual nucleosome positioning and how this influences gene regulation. The variability among nucleosome maps, which precludes the fine analysis of nucleosome positioning, might emerge from diverse sources. We have carefully inspected the extrinsic factors that may induce diversity by the comparison of microccocal nuclease (MNase)-Seq derived nucleosome maps generated under distinct conditions. Furthermore, we have also explored the variation originated from intrinsic nucleosome dynamics by generating additional maps derived from cell cycle synchronized and asynchronous yeast cultures. Taken together, our study has enabled us to measure the effect of noise in nucleosome occupancy and positioning and provides insights into the underlying determinants. Furthermore, we present a systematic approach that may guide the standardization of MNase-Seq experiments in order to generate reproducible genome-wide nucleosome patterns. Eukaryotic chromatin is organized in a compact, precisely regulated, but yet not fully understood manner. Nucleosome is the fundamental structural unit of this compaction ,2, formeGenome-wide studies of micrococcal nuclease (MNase) susceptibility have revealed that nucleosomes are not randomly placed across genome but they are rather enriched in certain areas while depleted in others . The mosin vivo nucleosome positioning still remain elusive. Three main models have been postulated as determinants of nucleosome positioning: (i) statistical positioning, suggesting that a barrier favors deposition of a well-positioned nucleosome, typically after a NFRs, which in turn forces the periodic positioning of the neighboring nucleosomes leosomes ,12,22–28leosomes ,29–31, wleosomes .in vivo nucleosome profile datasets, indicating that in average <10% of nucleosome positioning is actually reproduced by different studies  the experimental conditions (such as MNase digestion levels or sequencing protocol); (ii) data processing for nucleosome calling; (iii) heterogeneity of the samples, derived from diversity of cellular states in the culture, such as cycle phase; (iv) variations among the samples derived from differences in the growth media  and (v) nucleosome dynamics across the genome, that will be detected as positional ‘fuzziness’ in the experimental nucleosomal ,49.Saccharomyces cerevisae. We have been able to reduce cell-to-cell variability and cell cycle-induced nucleosome dynamics by using carefully synchronized cells and compare them against unsynchronized control cells grown under the same basal conditions. We have then explored the potential sources of variability in nucleosome maps related to different MNase digestion levels (from very mild to very strong) and sequencing procedure (single- versus paired-end). Furthermore, to reduce potential uncertainties related to nucleosome calling algorithms, we have applied a single algorithm with standard defaults, in conjunction with analyses of nucleosome architecture, which are independent of the nucleosome calling algorithm.We present here a systematic analysis of the effect of noise and nucleosome dynamics in defining nucleosome profiles in Taken together, our findings have allowed us a comprehensive evaluation of nucleosome positioning and stability that may contribute to partially uncover the underlying principles of nucleosome architecture dynamics. The picture derived from our analyses presents navigating nucleosomes along one-dimensional string (i.e. the DNA fiber) that are mainly positioned at specific places in response to strong nucleosome depletion signals generated by either intrinsic properties of DNA, the presence of competing DNA-binding proteins or chromatin-remodeling systems. The proposed model allows a very simple integration of different nucleosome positioning models, and provides clues on the impact of nucleosome fuzziness in the modulation of gene activity.600 of 0.2. Then, alpha-factor mating pheromone (GenScript) was added to the culture to a final concentration of 10 μM and the culture was incubated for 2 h to induce cell-cycle arrest in late G1. As a control, an asynchronous culture was grown in parallel to an OD600 of 0.8. Both G1-arrest synchronized and asynchronous samples were collected and washed twice with phosphate buffered saline (PBS).Yeast strain BY4741 was grown using fresh YPD media at 30°C until an ODCell synchrony was monitored by three approaches: flow cytometry (FACS), fluorescence microscopy and budding index calculation. For FACS analysis, cells were fixed with 100% EtOH, spun down and washed once with 1× SSC buffer . Removal of RNA and proteins were carried out by incubation with RNase A  and proteinase K , correspondingly. Samples were briefly sonicated by using the Bioruptor system and mixed with 500 µl SSC buffer containing 0.1 mg/ml propidium iodide . Fluorescence emitted from DNA-intercalated PI was measured by Beckman Coulter EPICS® XL flow cytometer.Cell-cycle phase was also monitored by fluorescence microscopy and budding index calculation. For these purposes, cells were briefly sonicated and fixed with EtOH in a similar manner as for FACS. Fixed cells were then resuspended in 200 µl PBS containing Hoechst stain at 30 µg/ml. Finally, cells were placed on a glass slide and visualized by fluorescence microscopy (Nikon E600 microscope). For budding index calculation, a sample from EtOH-fixed cells was placed on a hemocytometer and visualized under a phase contrast microscope to count the number of budded and unbudded cells.Nucleosomal DNA from G1-arrest and asynchronous samples was prepared as previously described . The oveThe percentage of mononucleosomal DNA fragments was examined by 2% agarose gels. Furthermore, the integrity and size distribution of digested fragments were determined using the microfluidics-based platform Bioanalyzer (Agilent) prior to DNA sequencing. Typically, samples containing >80% mononucleosomal fragments were sent for sequencing. In addition, over- and under-digested samples were selected based on the proportion of mononucleosomal fragments. Over-digested samples were obtained by 0.12 U MNase digestions, which yielded to only mononucleosomes. Under-digested samples were obtained by 0.04 U of MNase yielding to mono-, di- and tri-nucleosomes.http://www.ebi.ac.uk/ena) with accession number ERP004019.Libraries of nucleosome fragments were prepared and adapted for deep sequencing using the standard Illumina protocol and sequenced them as single-end paired-end on Genome Analyzer IIx and HiSeq 2000 devices. Data were processed with a standard GA base calling pipeline to convert initial raw images into sequences, as described previously . Raw reaCells were collected at the same interval as nucleosomal DNA samples in icy-water and harvested by spinning for 3–4 min at 6000 rpm, frozen in liquid nitrogen and stored at −80°C. Total cellular RNA was extracted using the RNeasy kit (Qiagen), following the manufacturer’s instructions with the spheroplasting protocol (0.5 mg/ml zymolase). The total RNA was hybridized to Affymetrix GeneChip Yeast Genome 2.0 arrays for gene-expression analysis. Raw and processed files available in ArrayExpress under accession number E-MTAB-2195.6 to obtain the units of reads per million (r.p.m.). Peak calling was performed after noise filtering using nucleR parameters: peak width = 125 bp, peak detection threshold = 35%, maximum overlap = 50 bp.Reads from all samples were mapped to yeast genome  with Bowtie  aligner Using the nucleosome calls obtained previously, we classified every gene according to their nucleosome architecture around the TSS. The closest nucleosome at or immediately downstream TSS was annotated as the +1 nucleosome. The nucleosome immediately upstream of the +1 nucleosome was annotated as the −1 nucleosome. After a visual analysis of the classifications, nucleosome calls were considered as well-positioned (W) when nucleR peak width score  and height score  were higher than 0.4 and 0.6, respectively. Otherwise, the nucleosome call was considered fuzzy (F). Accordingly with previous observations , the NFRIn order to obtain an accurate estimate of gene architecture similarity/dissimilarity between samples, we defined the following metrics. Profile: we considered a gene promoter stable between two samples if Pearson’s correlation in the window of −300:300 bp around TSS was >0.7; conversely, we consider variable if the correlation was <0.5. Cluster: we considered a gene promoter stable if cluster dimensions (–1/NFR/+1) matched; otherwise we annotated as a relevant variation of this architecture when two of the clustering dimensions varied between samples. The +1/–1 nucleosome: we considered a stable nucleosome classification if nucleR provided the same classification (with thresholds for score_w and score_h noted above) for the two samples; we considered a relevant change if the nucleosome call was changing in classification and the absolute difference of the aggregated score (nucleR’s default score = 0.5*score_h + 0.5*score_w) was >0.25 (which implies a change of at least one quartile of the classification). NFR: we considered it stable if the NFR distance was annotated equally between samples (open/close/overlap/missing); or variable when a change in class was happening and the distance between −1/+1 nucleosomes differed >100 bp. Those genes that do not satisfy any of the criteria are considered out of the stability/variability threshold.Elastic energy was calculated using a mesoscopic model of DNA flexibility  with parThe very simple statistical-positioning model featured in this article considers that, after the energetic barrier in the NFR, nucleosomes are arranged statistically with a lineal increasing fuzziness. We decided to simulate a population of nucleosome reads centered in the +1 nucleosome with a dyad deviation of 25 bp. Dyads of downstream nucleosomes  were spaced 147 + 14 bp (accounting for average linker DNA length) with an increasing deviation of the dyad of +5 bp in every step and a decreasing number of reads equal to the 4% of the previous peak. The dyad of the −1 nucleosome was placed 147+100 bp  upstream the +1 for the closed NFR and 147+200 bp  for the open NFR. The following upstream nucleosomes  were defined as in the case of the downstream model but adjusting the deviation in 35 bp in the −1 nucleosome plus 5 bp in every following step, with a linker length of 18 bp. Different values of the different parameters in the model were selected after a grid search maximizing the correlation of the model with the average experimental distribution.Transcription factor binding sites (TFBSs) were derived from the position weight matrices (PWMs) available in JASPAR database for yeast . For eveSupplementary Figure S1) were considered as biological replicas. We labeled them as replicas 1 and 2 and subsequently isolated their nucleosomal DNA under similar MNase-digestion conditions. The digestion level was determined visually by agarose gel electrophoresis and the microfluidics-based platform Bioanalyzer (Agilent), as shown in Supplementary Figure S2. Accordingly, both samples yielded a major peak ∼147 bp corresponding to mononucleosomes, a secondary defined peak ∼295 bp corresponding to di-nucleosomes and residual peaks at ∼60 bp that might be assigned to either tetrasomes or other DNA–protein complexes.In an attempt to eliminate the noise resulting from cell-population heterogeneity, we have synchronized yeast cultures at the late G1 cell-cycle phase. Two synchronized cultures . The remaining 10% could not be classified due to either low coverage, undefined +1 or overlapping nucleosomes and were not considered in the remaining analysis. We explored the variability of the results based on different assignment criteria, and notably, nucleosome calls appeared quite robust regardless the threshold parameters applied in nucleR (Supplementary Table S1), proving the accuracy of the algorithm and further validating that detected variability does not respond to bioinformatics artifacts.Eventually, replicas 1 and 2 were sequenced both as single-end (1x) and paired-end (2x), assuming a minimal sequencing bias. We obtained high sequencing read depths in all our datasets , which were then processed using nucleR package as described in methods . NucleosSupplementary Table S2, which shows the distribution of genes according to classifying parameters such as −1/+1 positioning or NFR width. Conversely, the percentage of −1 or +1 fuzzy nucleosomes is considerably reduced in paired-end sequenced samples, confirming that single-end sequencing yields an artificial enrichment of fuzzy nucleosomes. Interestingly, while the general coverage around TSSs is reasonably preserved (Supplementary Table S3 first column), only ∼38–36% of the nucleosome architectures are conserved  and ∼23%of the genes show clearly distinct nucleosome classifications depending on the sequencing (1x versus 2x) method . Intriguingly, when we analyzed the individual parameters describing a nucleosomal architecture, we observed that 63–64% of +1 nucleosome positions have the same annotation in both single- and paired-end sequencing, and only 5–7% display very different annotations. In the case of −1 nucleosome position, the preservation is less pronounced, with 61% conservation (52% for replica 2) and 7% discrepancy (12% for replica 2). Moreover 69% (56% in replica 2) of the NFRs maintain the same annotation, while 6%–9% can show width changes of up to >100 bp.In principle, since every sample was both single- and paired-end sequenced, nucleosome maps should strongly resemble one another. However, when we compared the nucleosome profiles around TSSs, they displayed different architectures depending on the sequencing method . However, when we analyzed individual genes (Supplementary Table S3), significant differences arise between replicas. Nearly 90% of genes show reasonably similar coverage profiles, but only 48% of them maintain their nucleosome architecture in the two replicas. Even though the majority of changes are subtle, usually only affecting one nucleosome position (e.g. W→F or F→M), ∼15% of genes show significant changes in class annotation. Interestingly, only 3% of genes show dramatic changes in NFR in contrast to 6–7% that show different −1 and +1 nucleosome localizations (Supplementary Table S3), indicating that NFR is more conserved and less prone to variations than flanking nucleosome positions.In order to minimize the noise coming from experimental protocols (out of the specific sequencing procedure), we compared the nucleosome maps of the two synchronized replicas generated by paired-end sequencing, which both present a similar coverage of 50X. As shown in Supplementary Figure S4), which might contribute to phasing. Similarly, atomistic molecular dynamics simulations detect spontaneous sliding of one base step at the multi-nanosecond time scale . Fuzziness is then likely to be the default state for nucleosomes in a random DNA in the absence of additional factors, such as NFR.Taken together, our findings show that inter-replica variations are not negligible and point out that nucleosome positioning is intrinsically highly plastic and dynamic. In accordance with this observation, elastic energy models propose that a 10-bp sliding of a nucleosome would face a general energy barrier (i.e. the difference between the best and worse wrapping configuration) of ∼13 kcal/mol in average , while in a larger scale, we would find mesoscopic barriers of ∼47 kcal/mol . This sample also produced well-defined nucleosomal maps, where 94% of yeast genes had well-located +1 nucleosomes and around 87% of promoters could be assigned to a particular nucleosomal architecture around TSS.In order to determine the variability caused by cell heterogeneity, we included an asynchronous yeast culture in our analysis, labeled as ‘asynchronous’ sample , suggesting that cell-cycle progression may induce chromatin rearrangements around TSSs that may be reflected as diffuse nucleosome signals in MNase-Seq experiments derived from asynchronous samples.The asynchronous sample shows clear differences when compared to the synchronized replicas 1 and 2, as shown in Supplementary Table S2, columns 6 and 7). As anticipated from Supplementary Tables S1 and S2). In average 475 genes pass from WoW or WcW in rep1/2 to some fuzzy structure in the asynchronous replica. As a result the ratio well-positioned/non-well-positioned −1 nucleosomes decreases from 1.8–2.3 (replicas 1 and 2) to 1.4 (in asynchronous) , and similarly in case of +1 nucleosomes, going from 5.0–7.0 (replicas 1 and 2) to 3.7 . Conversely, NFR width between synchronized and asynchronous samples shows similar changes as between biological replicas, suggesting that cell cycle-dependent chromatin rearrangements do not lead to massive nucleosome eviction around TSSs, which would dramatically alter NFR dimensions .Interestingly, analysis of individual genes provides a clearer impact of cell heterogeneity-dependent variability. Differences in nucleosome coverage profiles and nucleosome architectures are higher when the asynchronous sample is compared against biological replicas than when biological replicas are directly compared . Indeed, we observed that 105 genes displayed very similar coverage profiles around TSSs between biological replicas (Pearson’s correlation > 0.7), but clearly differed with the asynchronous sample (Pearson’s correlation < 0.5). Of note, although GO and pathway annotation analyses were not able to find any particular enrichment in this set of genes, 15 of them are annotated as cell cycle periodic genes in Cyclebase . Interestingly, cell cycle-related genes belong to the G1/S regulon rather than to the alpha-factor pathway, indicating that the source of variation does not derive from alpha-factor stimuli. A more detailed analysis would require data for every individual cell stages, but nonetheless, the present results support the notion that chromatin reorganization might be coupled to cell-cycle regulation mechanisms.Overall, our detailed comparison of cell cycle synchronized and asynchronous samples reveals that asynchronous experiments contain an additional source of noise due to the cell cycle-dependent nucleosome dynamics, and that caution is necessary with maps derived from asynchronous samples , since average maps can mask the existence of two populations with completely different nucleosomal architectures. Fuzzy nucleosome signals can be in reality the result of a mixed population where some cells contain well-localized nucleosomes while others are nucleosome-depleted at the same region, or rather that a well-localized nucleosome has moved to a neighboring position in different cells , we have used two additional MNase-Seq experiments derived from a G1-synchronized culture but treated under either more aggressive (over-digested sample) or milder (under-digested sample) MNase-digestion conditions. The samples were sequenced using paired-end technology and a similar data processing for a direct comparison with other replicas.Supplementary Figure S2), over-digestion of chromatin leads to the disappearance of the di-nucleosome signal and to a broader mono-nucleosome peak that is shifted towards shorter fragments, probably caused by certain intra-nucleosomal cleavage. Nucleosome architectures of the over-digested sample are well-defined, with a clear +1 nucleosome signal in 95% of the gene promoters and unambiguously assigned nucleosome families, similar to replicas 1 and 2. Yet, the analysis of nucleosome pattern distributions reveals clear differences between the over-digested sample and replicas 1 and 2 (Supplementary Table S2). In over-digested chromatin, the prevalence of canonical nucleosome classes (i.e. WoW and WcW) decreases and fuzzy −1 nucleosomes are enriched. Thus, ∼800 genes move from WoW/WcW patterns in replicas 1 and 2 to a fuzzier configuration in the over-digested sample . The number of missing −1 nucleosomes increases from 711–510 (replicas 1 and 2) to 1154 . Overall, these findings suggest that excessive MNase digestion can lead to partial degradation of some well-positioned nucleosomes, resulting in fuzzier nucleosome peaks, or even to the complete disassociation of unstable nucleosomes, leading to loss of certain nucleosome signals. This behavior is further confirmed with a higher mean deviation of the nucleosome dyad position in the over-digested sample  .As shown in the Bioanalyzer histograms (Supplementary Table S3). While only 15% of the genes show clearly different nucleosome arrangements between replicas, up to 25% (18% in replica 2) change with respect to the over-digested sample. Similarly, the variability in −1 and +1 nucleosome annotations also increases from 6–7% to 10–13%. In contrast, NFR width seems to be very resistant to digestion conditions, pointing out that a more aggressive digestion mostly result in partial degradation of nucleosomes but rarely in their complete eviction around TSSs . Intriguingly, paired-end sequencing only yielded 78% of gene coverage and ∼69% of TSSs could be classified into nucleosome families, whereas the classification was 86.5 ± 3.7% in previous experiments. The significantly  enrichment of depleted areas might account for the under-representation of longer fragments in the sequencing reactions, since it is well established that deep sequencing favors the amplification of short over long fragments , which might correspond in reality to DNA in complex with other proteins. Interestingly, the uncovered regions specific to under-digested sample are distributed over the entire genome without any significant enrichment according to GO analysis. Yet, they show a clear preference for AT-rich segments (3.38 higher fold) and intergenic regions .On the other hand, the under-digested sample exhibits well-defined mono-, di-, tri- and even tetra-nucleosomal peaks  and asyncronized (2x) datasets, with a coverage of 146X and 177X, respectively), the nucleosomal maps do not show better agreement than when comparing datasets with lower read depth . Certainly, low read-depth can increase the noise in nucleosome maps but it does not seem to be a dramatic contributor in the present article.P-value < 0.0002). Notably, this differential organization is increased to 13% when gene body is considered .In order to assess the possible effect of noise due to different transcription rates, we measured the absolute levels of gene expression in our G1 synchronized samples (replicas 1 and 2) using expression arrays . Based on the hybridization results, we then selected the top 500 and the bottom 500 genes according to their normalized expression level and analyzed in detail the nucleosome organization around TSSs. Lowly expressed genes display better defined nucleosome organization than highly expressed genes . This set (comprising 3096 genes) represents the well-conserved nucleosome architectures in late G1 cell-cycle phase and reveals that WcW (1306 genes) and WoW (1164 genes) are the most pervasive classes, followed by M–W (263 genes) and FcF (155 genes) classes. We can use then these maps to evaluate the goodness of different predictive models, without being exposed to noise and uncertainties in the experimental data.We compared the experimental WcW and WoW nucleosome maps with an extremely simple statistical model  that places nucleosomes every 161–165 bp (147 + 14 – 18 bp due to linker variation), starting from NFR with decreasing nucleosome phasing A. Once tTaken together, our analysis of robust nucleosome maps suggest that simple statistical positioning is a key responsible of the basal nucleosome positioning, with NFR being the main signal organizing nucleosome string. Physical properties and binding to effector proteins, such as TFs, act in a synergistic manner to define NFR location and boundaries and to define then the phasing of the nucleosome string. Clearly, the chromatin remodelers will act on this basal activity to facilitate positioning of nucleosomes in a regularly spaced array.Supplementary Tables S4–S5). Interestingly, our findings indicate that the conclusions obtained for TSS regions are also valid for gene body regions. Clearly, the heterogeneity and plasticity in nucleosome architecture is not a differential property of the TSS vicinities, since also coding regions show a significant degree of flexibility that surely reflects the intrinsic mobility of nucleosomes, especially for highly expressed genes.Lastly, we analyzed the heterogeneity in nucleosome architecture along coding regions, taking into account that they do not show the typical TSS nucleosome pattern  or related to experimental procedures (such as MNase different activity rate).In an attempt to remove, as much as possible, experimental and sequencing biases, we have addressed several sources of noise that might lead to artifactual nucleosome fuzziness in MNase-Seq derived nucleosome maps . Single-Computational processing of raw data is another source of ‘spurious noise’. Converting read-alignment information into nucleosome positions implies the assumption of some predictive models . Furthermore, the classification of nucleosome signals also requires the use of arbitrary thresholds. We have minimized these potential factors by applying various classification thresholds in the nucleR nucleosome calling algorithm and by the use of orthogonal metrics such as direct read information. Our findings suggest that the detected variability in our analyses do not respond to computational artifacts. Additionally, the observed variability is not a consequence of poor sequencing coverage either, since no reduction of variability is found when samples with relatively high and low coverage are compared.Up to date, most of the available MNase-Seq derived nucleosome maps originate from asynchronous populations. This asynchrony can produce considerable amount of noise due to cell cycle-dependent nucleosome changes which may be related to gene-expression alterations or chromatin compaction and relaxation. Therefore these maps actually reproduce average nucleosome phasing in different populations, some of which might be stable and well-positioned in one cell while more diffuse in others. Such variations can generate misleading nucleosome maps that might not accurately reproduce the actual chromatin organization, and in fact only reflect the statistical averaging of different cell populations. Consistently, our comparative analysis between synchronized and asynchronous samples shows that asynchrony contributes to an increase of ∼30% in nucleosome fuzziness. Regions with striking chromatin alterations along cell cycle are usually detected as regions with fuzzy and not well-defined nucleosomes in the usual asynchronous maps. Interestingly, highly active genes in a certain cell-cycle stage show in general less-conserved nucleosome distributions, connecting gene activity and nucleosome mobility.We have tried to further minimize the biological noise level by generating nucleosome maps from cell cycle synchronized biological replicas, in order to have a reliable detection and accurate analysis of nucleosome positioning. Although average nucleosome maps are identical, differences at the gene level are not negligible. Thus, even if general nucleosome profiles are reasonably conserved, only 30% of the well-positioned nucleosomes are located at the same genomic place (within ±5-bp deviation) in two biologically equivalent replicas. Our results suggest that in principle nucleosomes are mobile along the DNA fiber, since intrinsic sliding barriers are small. This can lead to a kind of ‘spread’ of signal along average position, or alternatively, to the population of different nucleosomal arrangements in different cells, giving in all cases to a fuzzy signal in nucleosomal maps.in vitro and in vivo) and by regions which are highly prevalent in TFBSs (in vivo). Under normal physiological conditions, sequence-encoded physical properties and protein binding (including TFBSs) act synergistically to define the NFR and accordingly, the nucleosome array. Such synergy can be enhanced or reduced by chromatin remodelers, which are shown to facilitate nucleosome positioning by packing them against a barrier, like NFRs  have found obeys at great extent to a simple statistical positioning, where nucleosome arrays are placed starting from NFRs , with a lineal decrease in positioning as separated from the NFR. NFR borders are marked by unusual DNA physical properties that hampers wrapping of DNA around histone core  with accession number ERP004019. Raw and processed expression data for G1 stage is available in ArrayExpress platform under accession number E-MTAB-2195.Raw reads are available at the ENA-SRA website ; European Research Council (SimDNA project). Funding for open access charge: Spanish Ministry of Science and Innovation [BIO2009-10964 and Consolider E-Science], Instituto de Salud Carlos III (INB-Genoma España and COMBIOMED RETICS) and Fundación Marcelino Botín.Conflict of interest statement. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


Previous studies have shown that GAGA Factor (GAF) is enriched on promoters with paused RNA Polymerase II (Pol II), but its genome-wide function and mechanism of action remain largely uncharacterized. We assayed the levels of transcriptionally-engaged polymerase using global run-on sequencing (GRO-seq) in control and GAF-RNAi Drosophila S2 cells and found promoter-proximal polymerase was significantly reduced on a large subset of paused promoters where GAF occupancy was reduced by knock down. These promoters show a dramatic increase in nucleosome occupancy upon GAF depletion. These results, in conjunction with previous studies showing that GAF directly interacts with nucleosome remodelers, strongly support a model where GAF directs nucleosome displacement at the promoter and thereby allows the entry Pol II to the promoter and pause sites. This action of GAF on nucleosomes is at least partially independent of paused Pol II because intergenic GAF binding sites with little or no Pol II also show GAF-dependent nucleosome displacement. In addition, the insulator factor BEAF, the BEAF-interacting protein Chriz, and the transcription factor M1BP are strikingly enriched on those GAF-associated genes where pausing is unaffected by knock down, suggesting insulators or the alternative promoter-associated factor M1BP protect a subset of GAF-bound paused genes from GAF knock-down effects. Thus, GAF binding at promoters can lead to the local displacement of nucleosomes, but this activity can be restricted or compensated for when insulator protein or M1BP complexes also reside at GAF bound promoters. Transcriptional regulation is critical for proper gene expression in response to environmental changes and developmental programs. Eukaryotes have evolved multiple mechanisms by which transcription factors regulate transcription. One mechanism is the reorganization of chromatin to allow Pol II recruitment. Another is the release of promoter-proximal paused Pol II, where Pol II transcription that is halted 20–60 bases downstream of the transcription start site (TSS) is allowed to enter into productive elongation through the gene body. The Drosophila transcription factor GAF binds to genes that undergo pausing and interacts with nucleosome remodelers and the pausing factor NELF. Thus, GAF can regulate multiple points necessary for transcription, but its mechanistic role is not fully understood genome-wide. We depleted GAF from cells and examined the genome-wide changes in Pol II and nucleosome distributions across genes. We found that GAF depletion reduces polymerase density at genes where GAF binds just upstream of the TSS, and results in nucleosomes moving into the promoter region. Our results show that GAF is important for maintaining the promoter accessibility, allowing Pol II to be recruited to promoters and enter the pause sites downstream of the TSS. Thus, GAF is critical for providing the chromatin environment necessary for the proper control of gene expression. Transcription is controlled by transcription factors (TFs) that modulate various steps in the transcription process. Two major points of transcription regulation are recruitment of Pol II to a preinitiation complex (PIC) and promoter-proximal pausing. PICs form when general transcription factors bind to accessible nucleosome-free promoters and recruit Pol II. TFs can change the rate of PIC formation by altering either nucleosome placement on promoters or Pol II recruitment . In addiTrithorax-like (Trl), is a Drosophila sequence-specific TF that is associated with the promoters of many genes [GAF, encoded by the gene ny genes . GAF wasny genes –9. The Gny genes ,6,10. Hony genes ,12. In any genes –17. GAF ny genes ,19 and any genes ,21.Genome-wide studies have identified many genes bound by GAF ,11,22–24Several reports support a role of GAF as an anti-repressor for genes . The GAFHere, we examine the role of GAF in transcriptional regulation and nucleosome positioning genome-wide, using global run-on sequencing (GRO-seq) to map transcriptionally-engaged polymerases and MNase-seq to map nucleosome positions in control and GAF-RNAi depleted Drosophila S2 cells. Also, we define GAF binding sites at high resolution and assess their sensitivity to GAF knock-down using ChIP-seq. This allows GAF binding in promoters to be correlated with its effects on transcription and pausing and other factors that function redundantly to GAF or protect genes from the effects of GAF knock-down. Finally, MNase-seq mapping of nucleosomes genome-wide in control and GAF-RNAi cells supports a mechanism by which bound GAF maintains a nearby nucleosome free region at both the promoters of many genes and non-promoter sites.Hsp70. Under basal  conditions, GAF is bound to the Hsp70 promoters and Pol II transcribes 20–40 bases downstream from the transcription start site (TSS) and stably pauses. GAF binding was previously implicated in Hsp70 pausing, as a Hsp70 transgene with a mutant GAGA element showed reduced pausing [Hsp70 genes, we first treated cells with dsRNA targeting all isoforms of GAF, and reduced GAF levels to less than 10% of those in untreated or control cells treated with LacZ dsRNA (Hsp70 promoters (-154bp from the TSS) decreased about 4-fold in NHS GAF-RNAi cells  and decreased in the gene body to near the levels on a non-transcribed (bkgd.) region  region . GAF kno) region . These rPrevious ChIP-chip studies have shown that about 1,500 genes are bound by GAF in S2 cells and these genes are enriched for paused Pol II ,25,26. T-9, binomial test). These results support a role for GAF, beyond Hsp70, in maintaining levels of Pol II on the 5’ end of genes.The GRO-seq biological replicates were used to identify genes that significantly change between control and GAF-RNAi treatments. The biological replicates gave reproducible results: the promoter and gene body GRO-seq read counts for all biological replicates were highly correlated, with Pearson’s correlation coefficients (r) between 0.907–0.968 . Consist-16, binomial test), demonstrating that, as a group, gene body Pol II decreased along with promoter proximal Pol II. These changes suggest that GAF plays a role early in the transcription cycle, allowing Pol II to initiate transcription and establish pausing at certain genes, which in turn influence the level of Pol II that progresses into the gene body.A reduction in recruitment and entry of Pol II into the pause site can lead to a decrease in elongating (gene body) polymerase. Indeed, recent studies have shown that disrupting initiation reduces both pausing and elongating polymerase ,41. FollTo assess if the effects on promoter-proximal polymerase levels are likely to be a direct effect of GAF knock down, we used ChIP-seq to analyze GAF binding sites and the sensitivity of GAF binding at each site to the reduced GAF protein levels in GAF-RNAi cells . ChIP wa-16) and for paused genes  or all other transcribed genes , which is consistent with previous reports [To evaluate if GAF is preferentially associated with promoter-proximal pausing, we first determined all the genes that have GAF ChIP-seq peaks within the promoter (within 500bp upstream of the TSS) and gene body. In our set of 9,452 non-overlapping genes, GAF was bound to 1,939 . The maj reports  Table 2-16 and fThe majority of genes with significantly reduced promoter GRO-seq reads in GAF RNAi-treated cells show GAF binding in untreated cells. Of the 140 genes with significant reduction in promoter GRO-seq reads between the GAF-RNAi and LacZ-RNAi libraries (reduced promoter), all of them were paused and 134 (95.7%) were bound by GAF . This suPromoter-bound GAF cannot be the sole determinant for pausing because less than 14% of paused genes with GAF-bound promoters had significant reductions in promoter GRO-seq reads upon GAF knock-down. To investigate this further, we divided genes into two sets: paused genes with GAF-bound promoters that had significant reductions in promoter GRO-seq reads (hereafter referred to as Pause Reduced) and the other paused genes with GAF-bound promoters (hereafter referred to as Pause Unchanged). Then we looked for molecular signatures at GAF binding sites that correlated with the magnitude of pausing change after knock-down. The level of GAF binding on promoters was significantly higher in Pause Reduced genes than Pause Unchanged , 39% of Pause Unchanged promoters had hcGAF peaks, indicating many Pause Unchanged genes are likely truly bound by GAF.The lower levels of GAF binding on Pause Unchanged genes raised the concern that these peaks could be an artifact and not bona fide GAF binding sites. To identify a high confidence subset of GAF peaks, we selected peaks with 2 additional criteria: they must overlap a peak region called in a dataset from an independent GAF antibody and they must contain a GAGA element. We used the modENCODE GAF ChIP-chip as the independent antibody dataset , and fouTo identify the basis of the differential effects of GAF knock-down, we assessed whether other characteristics of paused genes with GAF-bound promoters correlate with the reduction in pausing. Individual labs and the modENCODE consortium have determined the genome-wide binding profiles for many chromatin-bound factors and histone modifications. We used this information to investigate if any of the factors with genome-wide data in S2 cells correlate with the GAF-RNAi effects on pausing . SeveralMotif-1 Binding Protein (M1BP) is a transcription factor recently shown to be enriched on a set of paused genes, largely distinct from GAF-bound paused genes, and is believed to function analogously to GAF in Pol II pausing . The strGAF has been shown to affect promoter accessibility through interactions with nucleosome remodelers ,32,33,47The nucleosome-sized MNase-seq reads used may not necessarily be produced by a nucleosome. Therefore, to further validate these results, we immunoprecipitated the promoter-enriched histone variant H2AvD from the MNase-seq material. As expected, H2AvD levels were highest at the -1 and +1 nucleosomes bordering promoters of actively transcribed genes and these nucleosomes were not changed genome-wide by GAF knock-down . SimilarRecently, it was shown that the paused Pol II itself was important for preventing nucleosome encroachment into the promoter . TherefoHsp70 promoter [In this study, we examine the role of GAF in transcription and pausing genome-wide using GRO-seq to map transcriptionally-engaged polymerases in Drosophila S2 cells depleted for GAF. Almost all of the 140 paused genes with significant reductions in promoter-proximal polymerase levels upon GAF depletion had GAF bound in their promoters. This result indicates that these reductions were direct effects of GAF knock-down and GAF functioned locally at these genes to maintain paused Pol II levels. Moreover, we demonstrate that GAF has a prominent role in creating a chromatin accessible promoter for the recruitment and initiation of Pol II transcription. This opening of chromatin can be seen at GAF binding sites in promoters, but also at intergenic sites that are far from promoters. These results provide strong in vivo and genome-wide support for the hypothesis that GAF can mediate nucleosome displacement proximal to its binding site, as was proposed from in vitro studies that examined the interplay of GAF binding and an ATP-utilizing remodeler (NURF) on the promoter .Several points in the transcription cycle can be targeted to regulate the level of promoter-paused Pol II ,49. A TFPrevious studies have shown that GAF can interact with several nucleosome remodelers and maintain promoters in a transcription-competent conformation, but these results have been limited to a few specific genes –33. We fCollectively, our analyses demonstrate how TFs can work together to regulate the expression of target genes. We find that only a subset of the paused genes with GAF-bound promoters had reductions in promoter polymerase levels upon GAF knockdown. GAF levels were higher on these genes, and this may reflect that stable binding of GAF is necessary to maintain the chromatin in an open conformation. Interestingly, the set of GAF bound genes whose promoter polymerase levels are insensitive to GAF knockdown are enriched for the transcription factor M1BP, the insulator protein BEAF, and the BEAF-interacting protein Chriz. M1BP was recently found to be enriched on paused genes that are mostly distinct from the group bound by GAF , suggestHsp70 more proximal to the TSS [GAF may function to specify pausing on bound genes by altering multiple steps in the transcription cycle. As we have shown, GAF can indirectly help to establish pausing by binding the promoter and maintaining nucleosome-free promoter regions that allow recruitment and initiation by Pol II. GAF may also have a direct role in initiation, as others have shown that GAF can itself act as an activator through its poly-glutamine domain ,19 or ma the TSS , althougWhile GAF may act by more than one mechanism to generate and maintain paused Pol II, our results provide strong support for the hypothesis that GAF functions genome-wide to keep adjacent regions of chromatin nucleosome free. Our hypothesis is also consistent with previous reports that support a role of GAF as an anti-repressor for genes . GAF mig6cells/ml. After splitting to 1x106cells/ml in serum-free M3 media (at least a 1:3 split), the desired volume of cells were mixed with 10μg/ml double-stranded RNA (dsRNA), incubated at 25°C for 45 minutes, and then, an equal volume of M3+BPYE+20% serum was added. After 5 days, the cells were harvested for the experiments. The dsRNAs were generated from a PCR template with T7 promoters on each end, targeting either a region conserved in all GAF isoforms or a region of B-galactosidase (LacZ) gene serving as a control.Drosophila S2 cells were grown in M3+BPYE+10% serum to a density between 3–5x10GAF Forward: GAATTAATACGACTCACTATAGGGATGGTTATGTTGGCTGGCGTCAAGAF-Reverse: GAATTAATACGACTCACTATAGGGATCTTTACGCGTGGTTTGCGTLacZ-Forward: GAATTAATACGACTCACTATAGGGATCGTCAGTAGAAGAGCACCGAGTLacZ-Reverse: GAATTAATACGACTCACTATAGGGAGAGATATCCTGCTGATGAAGC8cells/ml in sonication buffer . The cells were sonicated 12 times for 20 seconds each time with a 1 minute rest in between at 4°C using a Bioruptor sonicator (Diagenode) on the highest setting.ChIP was performed as it was previously . BrieflyThe sonicated material was centrifuged at 20,000xg for 10 min at 4°C, and the supernatant was saved for the immunoprecipitation (IP). For each IP, 25μl of cleared sonication material was mixed with 1ml IP buffer  with the antisera  were carried out for each condition, as previously described , and seq7 nuclei in run-on buffer , 1mCi/ml 32P-CTP (100μCi/ run-on), 500μM Br-UTP, 0.4 units Superase-In, 1mM DTT, 40 units Superase-In (Ambion), 0.6% N-lauroyl-sarcosine), and stopped with 1.5ml Trizol and 200μl chloroform. After extraction with acid phenol:chloroform and chloroform, the precipitated RNAs were resuspended in 20μl DEPC-treated ddH2O, and hydrolyzed in 200mM NaOH on ice for 18 minutes. The hydrolyzed RNAs purified by three bead bindings to Anti-Br-dUTP beads (blocked with 0.1% polyvinylpyrrolidone and 1μg/ml BSA). The beads were washed once with 500μl binding buffer, once with 500μl Low salt buffer , once with 500μl High salt wash , and twice with 500μl TET wash . After elution with elution buffer , the precipitated RNAs were resuspended in 20μl DEPC-treated ddH20. After the first bead binding, RNAs are treated with T4 polynucleotide kinase (PNK) without ATP to create a 3’ hydroxyl group. Illumina linkers were added using polyadenylation with E. coli polyA polymerase and reverse transcription from a poly(dT)-3’linker covalently attached to the 5’ linker with a 18 carbon spacer, as previously used [INOO3: 5’-pTAGAGATCGTCGGACTGTAGAACTCT-iSp18-CAAGCAGAAGACGGCATACGATTTTTTTTTTTTTTTTTTTTVN, INOO4: 5’-pTGATGATCGTCGGACTGTAGAACTCT-iSp18-CAAGCAGAAGACGGCATACGATTTTTTTTTTTTTTTTTTTTVN). The cDNA was circularized using Circligase  to connect the 5’ linker to the 5’ end of the cDNA. After PCR amplification, the libraries were gel purified away from the primers, each replicate library was combined in equal amounts, and sequenced for 50 bases on one lane of an Illumina GIIAx sequencer.GRO-seq libraries were constructed using previous methods . Brieflysly used . Each liMNase-seq material was created similar to previous studies . BrieflyMACS1.4 was used to initially call peaks using the combined replicate data compared against pre-immune IP data . Closely clustered subpeaks, within broad regions of MACS-identified peaks, were deconvoluted by using the Subpeaks tool contributed to MACS .-4) using the JASPAR Trl motif [We defined high-confidence GAF peaks based on overlap with peaks in an independent GAF dataset and the presence of a GAGA element within our peak. We selected our untreated ChIP-seq peaks that overlapped with the “Regions_of_sig_enrichment” in the modENCODE GAF ChIP-chip GFF3 file . We idenrl motif ,63.All mapping, quantification, and transcriptional status determinations were performed as in previous studies . The reaWe identified paused genes based on higher levels of engaged polymerase in the promoter region than the gene body compared to the number of reads in each region when the reads are uniformly distributed across the gene . Transcriptional activity was defined exactly as previously . We calcRegions with significant changes in GRO-seq reads between Untreated or LacZ-RNAi and GAF-RNAi were called using the edgeR package (v.1.4.1) setting a false discovery rate threshold of q = 0.01 . The MNaThe Fisher’s exact test showing GAF-bound genes are enriched for transcriptional activity compared the number of transcriptionally active genes for GAF-bound genes (1580 out of 1939) to all genes (4102 out of 7513). Because GAF-bound genes are dramatically enriched for actively transcribed genes, the Fisher’s exact test showing GAF-bound genes are enriched for pausing compared the number of paused genes for GAF-bound genes (1484 out of 1939) to all other genes (3074 out of 7519). The Fisher’s exact test showing Pause Reduced promoters are more likely to have hcGAF peaks than Pause Unchanged promoters compared the number of hcGAF-bound Pause Reduced promoters (87 out of 134) to hcGAF-bound Pause Unchanged promoters (320 out of 1078).A binomial test was used to show that genes with statistically significant gene body changes are more likely to be down-regulated than up-regulated, assuming that gene body changes are equally likely to be up- or down-regulated. A binomial test was also used to test whether there is a correlation between changes at the promoter and in the gene body, at genes with significant changes in promoter read counts. To address this, we asked whether genes with significantly reduced promoter GRO-seq reads are more likely to have a positive or negative gene-body log fold-change than expected by chance, under an equal probability for reduced and increased reads.Quantifications used in the graphs were obtained from the genome-wide datasets for factors/modifications created from S2 cells. For the Pearson correlation in The genomic data in this work is deposited in the Gene Expression Omnibus under the accession numbers: GSE58957 and GSE40646.S1 Fig(A) Promoter-proximal GRO-seq reads of each gene for LacZ-RNAi and Untreated libraries plotted in an MA plot. The log2 ratio of Untreated to LacZ-RNAi reads is plotted on the y-axis and log2 of the average of LacZ-RNAi and Untreated reads on the x-axis. There were no significantly changed regions. (B) Gene body GRO-seq reads of each gene for LacZ-RNAi and Untreated libraries are plotted as in B. Genes with significantly different reads between the two libraries are colored orange. One gene with significantly different reads did not have any reads in the Untreated library and could not be plotted. (C) Promoter-proximal GRO-seq reads of each gene for GAF-RNAi and Untreated libraries plotted as in A. Genes with significantly different reads between the two libraries are colored red. (D) Gene body GRO-seq reads of each gene for GAF-RNAi and Untreated libraries are plotted as in C. (E) The average LacZ-RNAi (black or maroon) and GAF-RNAi (gray or red) GRO-seq reads (per million mapped reads) binned by 10bp between 200bp upstream to 1000bp downstream of the TSS of the paused genes with GAF-bound promoters, separated into Pause unchanged (black or gray) and Pause reduced (maroon or red). (F) Boxplot showing log2 of ratio of GAF-RNAi to LacZ-RNAi promoter and gene body GRO-seq reads for Pause reduced and Pause unchanged genes.(TIF)Click here for additional data file.S2 Fig(A) Western blot of whole cell extracts from Untreated and GAF-RNAi (GAF) cells for GAF and a loading control, TFIIS . (B) A plot showing log10 of the peak intensity for untreated ChIP-seq data on the x-axis and log2 of the ratio of peak intensity between the GAF-RNAi and untreated ChIP-seq data on the y-axis for all peaks (12582). The colors indicate the number of data points within the area, created using the hexbin R package. (C) A plot comparing the GAF ChIP-chip intensities from the modENCODE project and the GAF ChIP-seq intensity in untreated cells. (D) A plot comparing the signal for GAF ChIP-qPCR and the ChIP-seq intensity in untreated cells at select GAF peaks. The Pearson’s correlation coefficient is indicated in the top-left of the panels.(TIF)Click here for additional data file.S3 Fig(A) Heatmap showing the Pearson’s correlation coefficients comparing the change in promoter GRO-seq reads and the level of various factors, histones, and histone modifications at GAF-bound promoters. (B-G) The median intensity (ChIP-chip or ChIP-seq reads) 500bp upstream and downstream of the TSS for paused genes with GAF-bound promoters separated into genes with significantly reduced promoter GRO-seq reads  and all other paused genes with GAF-bound promoters  for the BEAF_HB ChIP-chip [(H-M) Fraction of Pause reduced and Pause unchanged promoters overlapping with region of enrichment in the same datasets as in B-G within 500bp of their TSS, plotted as in hIP-chip , BEAF32 hIP-chip , and ChrhIP-chip , CP190_HhIP-chip ,CP190_VChIP-chip , and CTChIP-chip  plotted (TIF)Click here for additional data file.S4 Fig(A-D) The average profile of LacZ-RNAi (gray line) and GAF-RNAi (red line) MNase-seq reads 1Kb upstream and downstream of the TSS for all genes (A), paused genes (B), LacZ-RNAi pausing p-value < 0.01), non-paused genes (C), LacZ-RNAi pausing p-value > 0.01), silent genes . (E-H) The average profile of LacZ-RNAi and GAF-RNAi H2AvD reads 1Kb upstream and downstream of the TSS for the same gene groups.(TIF)Click here for additional data file.S5 Fig(A) Heatmaps showing the p-value for the GRO-seq promoter read reduction upon GAF-RNAi (left panel), LacZ-RNAi H2AvD read level (second panel), GAF-RNAi H2AvD read level (third panel), the change in H2AvD reads , and p-values for an increase in H2AvD reads (100bp upstream to 50bp downstream of each TSS) upon GAF-RNAi (right panel), as in (B) Heatmaps showing the same data as in A for intergenic hcGAF peaks arranged based on the reduction in GAF binding upon GAF-RNAi, as indicated by the left heatmap. The p-values for increased MNase-seq reads within 100bp upstream and downstream of each peak summit are indicated in the right heatmap.(TIF)Click here for additional data file.S6 Fig(A) Boxplot showing log10 of the total LacZ-RNAi GRO-seq read within 200bp of promoters, gene body, and intergenic GAF peaks. (B) Boxplot showing log10 of the total LacZ-RNAi GRO-seq read within 200bp of intergenic hcGAF peaks, separated into quintiles based on reduction in GAF binding upon GAF-RNAi.(TIF)Click here for additional data file.S1 TableA file with the total GRO-seq reads mapped to genes transcribed by RNA polymerase II, RNA polymerase I, the 5S rRNA, tRNAs, RNA polymerase III, and total reads mapped. The Pearson’s correlation coefficients GRO-seq read counts in promoter and gene body regions for 9452 genes.(XLS)Click here for additional data file.S2 TableThe table contains 9452 genes unambiguous promoters and gene body regions (as used previously for classifying Paused genes . Column (XLS)Click here for additional data file.S3 TableThe columns show the chromosome, start, end, and summit, peak name, GAF level in untreated and GAF-RNAi ChIP-seq, whether the peaks are also called in the individual replicates, overlap with “region of significant enrichment” in untreated modENCODE ChIP-chip GFF3 file, number of GAGA elements in peak region, strand location for the majority of GAGA elements within the peak region, high-confidence call, location relative to genes, and total GRO-seq reads in LacZ-RNAi libraries within 100bp of the peak summit.(XLS)Click here for additional data file.S4 TableThe total sequenced paired-end reads, reads that passed filters, mapped reads and 120–180bp mapped reads for LacZ-RNAi and GAF-RNAi MNase and H2AvD triplicate libraries. The Pearson’s correlations are for read counts in 500bp bins across the genome for MNase-seq and H2AvD replicates.(XLS)Click here for additional data file.


Accordingly, Rfx5-knockout mice phenocopy Nlrc5 deficiency with respect to defective MHCI expression. Analysis of B cell lines lacking RFX5, RFXAP, or RFXANK further corroborated the importance of the enhanceosome for MHCI expression. Although recruited by common DNA-binding factors, CIITA and NLRC5 exhibit non-redundant functions, shown here using double-deficient −/−CIIta−/−Nlrc5 mice. These paradoxical findings were resolved by using a “de novo” motif-discovery approach showing that the SXY consensus sequence occupied by NLRC5 in vivo diverges significantly from that occupied by CIITA. These sequence differences were sufficient to determine preferential occupation and transactivation by NLRC5 or CIITA, respectively, and the S box was found to be the essential feature conferring NLRC5 specificity. These results broaden our knowledge on the transcriptional activities of NLRC5 and CIITA, revealing their dependence on shared enhanceosome factors but their recruitment to distinct enhancer motifs in vivo. Furthermore, we demonstrated selectivity of NLRC5 for genes encoding MHCI or related proteins, rendering it an attractive target for therapeutic intervention. NLRC5 and CIITA thus emerge as paradigms for a novel class of transcriptional regulators dedicated for transactivating extremely few, phylogenetically related genes.MHC class II (MHCII) genes are transactivated by the NOD-like receptor (NLR) family member CIITA, which is recruited to SXY enhancers of MHCII promoters via a DNA-binding “enhanceosome” complex. NLRC5, another NLR protein, was recently found to control transcription of MHC class I (MHCI) genes. However, detailed understanding of NLRC5’s target gene specificity and mechanism of action remained lacking. We performed ChIP-sequencing experiments to gain comprehensive information on NLRC5-regulated genes. In addition to classical MHCI genes, we exclusively identified novel targets encoding non-classical MHCI molecules having important functions in immunity and tolerance. ChIP-sequencing performed with  Major histocompatibility complex class I (MHCI) molecules are central to immunity and immunological disorders, and constitute a major obstacle in organ transplantation. It is therefore vital to gain insight into the regulation of their expression. NLRC5 was recently found to regulate MHCI gene transcription. However, we lack a thorough understanding of its target gene specificity and mechanism of action. Our work addresses these questions, delineating the unique consensus sequence required for NLRC5 recruitment and pinpointing conserved features conferring its specificity. Furthermore, through genome-wide analyses, we confirm that NLRC5 regulates classical MHCI genes and identify novel target genes, all encoding non-classical MHCI molecules exerting an array of functions in immunity and tolerance. We thereby demonstrate that NLRC5 exclusively transactivates genes of the MHCI pathway, rendering it an attractive target for future therapeutic intervention. The most striking feature of NLRC5 is its restricted and highly focused transcriptional activity, which has been described so far only for one related factor, CIITA. NLRC5 and CIITA therefore emerge as prototypes for a novel kind of extremely specific transcriptional regulator. CIITA gene lead to severe immunodeficiency .,11.in viNlrc5−/−CIIta−/− mice, and studied MHC expression in different immune cell subsets by flow cytometry. Concomitant ablation of Nlrc5 and CIIta did not substantially reduce H2-K and H2-D levels compared to single Nlrc5-deficiency, neither in any hematopoietic cell type analyzed nor in medullary TECs  and thymic epithelial cells (TECs) , which eCs Figs. , althoug−/− mice . MHCII ent cells . These dNlrc5F/F), Nlrc5−/−, and Rfx5−/− mice. NLRC5-bound chromatin was enriched by ChIP and submitted to deep sequencing.To gain a comprehensive view of genes regulated transcriptionally by NLRC5, we performed ChIP-seq experiments in T cells, which express NLRC5 abundantly and exhibit a dramatic defect in MHCI levels upon its ablation. Chromatin was extracted from T cells derived from control (WT and Nlrc5F/F) cells but absent in Nlrc5−/− cells , validating the quality of the ChIP-seq analysis . Two are predicted genes of unknown function  situated in the reverse orientation immediately upstream of H2-T10 and H2-T22   . The latNlrc5−/−, and Rfx5−/− CD8+ T cells  by flow cytometry and observed a virtually complete loss in all tested cell types in the absence of Nlrc5 or Rfx5  background, were bred at the animal facility of the University of Lausanne. Nlrc5−/− and CIIta−/− were intercrossed to generate double-deficient animals. Rfx5−/− [Rfx5+/- littermate controls on a mixed Sv129/C57BL/6 (H2b) background were bred at the animal facility of the University of Geneva Medical School. Sex and age-matched 6 to 12 week-old mice were used.Mice were treated in accordance with the Swiss Federal Veterinary Office guidelines. lrc5−/− , CIIta−IIta−/− , and C57Rfx5−/−  and Rfx5in vitro generated B cell mutants have been described and are established human cell lines [Human BLS cell lines and ll lines ,30. T cell lines  were wasNlrc5F/F, Nlrc5−/−, and Rfx5−/− T cells as described [Chromatin was purified from MACS-sorted WT (C57BL/6), escribed . Five miescribed .Nlrc5F/F mice were used as biological repeats. Five pseudo-replicates of 30 million reads each were used for the WT data set, as proposed by the ENCODE consortium [Immunoprecipitated DNA was sequenced using the Illumina HiSeq 2000 platform. >300 million reads were obtained for WT samples. >20 million reads were obtained for all other samples. ChIP samples from WT and nsortium . Reads wnsortium . Only rehttps://www.encodeproject.org/search/?type=software&used_by=ENCODE&software_type=quality%20metric) [Fragment length was estimated using cross-correlation . The Pha0metric)  was used0metric) . The RSC0metric) .Nlrc5F/F data sets was first done with MACS2 using the default settings . This led to the identification of a surprisingly low number of reproducible peaks. The numbers of peaks were 6 and 11, respectively for the WT and Nlrc5F/F datasets. The low number of peaks called using the initial strategy prompted us to use second strategy based on using a lower peak calling stringency followed by Irreproducible Discovery Rate (IDR) analysis. This was done to ascertain that that the low number of peaks identified by our initial procedure was not in fact an artifact resulting from overly-stringent peak selection. Peaks were called using MACS2 2.0.10.20130520 [Nlrc5−/− samples were used as negative control for peak calling. Reproducible peaks were obtained by assessing the IDR for all pairs of pseudo-replicates using a threshold of 0.01 (Nlrc5F/F) but found to be absent in the Rfx5−/− and Nlrc5−/− samples. These 11 peaks were the same as those identified in the Nlrc5F/F dataset with the first peak identification strategy.Peak calling for WT and 20130520  with no- of 0.01 . Only 11The Fraction of Reads in Peaks (FRiP)  was alsode novo motif discovery using the package cosmo [For each gene, all annotated exons (release GRCm38.69) from all isoforms were used to create a unique gene model in which all exons were merged into a single mRNA. The TSS of this unique gene model was defined as the TSS for the corresponding gene. The promoter region was defined as the region spanning −500bp to +50bp of the TSS. Peaks overlapping with promoter regions were used for ge cosmo  availablge cosmo . An initge cosmo . Peaks wge cosmo . Genome Nlrc5F/F, Nlrc5−/−, Rfx5−/− and Rfx5+/-MACS-sorted T cells . Retrotranscription to cDNA, quantification, and data analysis have been described [Total RNA was extracted using the TriFastescribed . Expresshttp://www.informatics.jax.org/): H2-Ke2 (MGI:95908), H2-K1 (MGI:95904), H2-Ke6 (MGI:95911), H2-Oa (MGI:95924), H2-DMa (MGI:95921), H2-DMb2 (MGI:95923), H2-DMb1 (MGI:95922), H2-Ob (MGI:95925), H2-Ab1 (MGI:103070), H2-Aa (MGI:95895), H2-Eb1 (MGI:95901), H2-Eb2 (MGI:95902), H2-D1 (MGI:95896), H2-Q1 (MGI:95928), H2-Q2 (MGI:95931), H2-Q4 (MGI:95933), H2-Q6 (MGI:95935), H2-Q7 (MGI:95936), H2-Q10 (MGI:95929), H2-T24 (MGI:95958), H2-T23 (MGI:95957), H2-T22 (MGI:95956), H2-T17 (MGI:95949), H2-M10.1 (MGI:1276522), H2-T10 (MGI:95942), H2-T3 (MGI:95959), H2-M10.2 (MGI:1276525), H2-M10.4 (MGI:1276527), H2-M1 (MGI:95913), H2-M9 (MGI:1276570), H2-M10.3 (MGI:1276524), H2-M11 (MGI:2676637), H2-M10.5 (MGI:1276526), H2-M5 (MGI:95917), H2-M3 (MGI:95915), H2-M2 (MGI:95914), Mill1 (MGI:2179988), Cd1d1 (MGI:107674), B2m (MGI:88127), Mr1 (MGI:1195463), Azgp1 (MGI:103163), Mill2 (MGI:2179989), Fcgrt (MGI:103017), Cd1d2 (MGI:107675), H2-Q8 (MGI:95937), H2-Q9 (MGI:95938) and H2-T9 (MGI:95965). Alignment was performed using the Muscle tool [The following amino-acid sequences were downloaded from MGI  in the same reporter plasmid was used as negative control. DNA fragments corresponding to the SXY regions were generated using partially complementary primers that were annealed and amplified by PCR using GoTaq polymerase (Promega). Primer sequences used are listed below. Extensions containing the MluI and BglII restriction sites (underlined) used for cloning are indicated in smaller font.Luciferase reporter plasmids were created by replacing the MluI—BglII fragment spanning the HLA-DRA SXY region in the pDRAprox plasmid  with theACGCGTCCACAGTTTCACTTCTGCACCTAACCTGGGTCAGGTCCTTCTGTCCGGACACTGTTG 3’ (forward primer)5’ATGCAGATCTCGCCACCCAATGGGGGTAAGAGCTGACTGCGCGTCAACAGTGTC 3’ (reverse primer)5’TGGTACGCGTAACTGCAAGTTTCAGAAGGGGACCTGCAAACTGAATCTCTAACTAGCAACTGATGA 3’ (forward primer)5’ATGCAGATCTTGGGAGCCAATCAGCATCAAAGGAGTCCAGCATCATCAGTTG 3’ (reverse primer)5’TGGTACGCGTAACTGCAAGTTTCAGAAGGGGACCTGGGTCAGGTCCTTCTGTCCGG ACACTGTTG 3’ (forward primer)5’ATGCAGATCTCGCCACCCAATGGGGGTAAGAGCTGACTGCGCGTCAACAGTGTC 3’ (reverse primer)5’TGGTACGCGTCCACAGTTTCACTTCTGCACCTAACCTGGGTCAGGTCCTTCTGACTAGCAACTGATGA 3’ (forward primer)5’ATGCAGATCTCGCCACCCAATGGGGGTAAGAGCTGACTGCGCATCATCAGTTG 3’ (reverse primer)5’TGGTACGCGTCCACAGTTTCACTTCTGCACCTAACCTGGGTCAGGTCCTTCTGTCCGGACACTGTTG 3’ (forward primer)5’ATGCAGATCTTGGGAGCCAATGGGGTAAGAGCTGACTGCGCGTCAACAGTGTC3’ (reverse primer)5’TGGTACGCGTCCACAGTTTCACTTCTGCACCTAACCTGCAAACTGAATCTCTAACTAGCAACTGATGA 3’ (forward primer)5’ATGCAGATCTTGGGAGCCAATCAGCATCAAAGGAGTCCAGCATCATCAGTTG 3’ (reverse primer)5’TGGTACGCGTAACTGCAAGTTTCAGAAGGGGACCTGCAAACTGAATCTCTATCCGGACACTGTTG3’ (forward primer)5’ATGCAGATCTTGGGAGCCAATCAGCATCAAAGGAGTCCAGCGTCAACAGTGTC 3’ (reverse primer)5’TGGTACGCGTAACTGCAAGTTTCAGAAGGGGACCTGCAAACTGAATCTCTAACTAGCAACTGATGA 3’ (forward primer)5’ATGCAGATCTCGCCACCCAATGCAGCATCAAAGGAGTCCAGCATCATCAGTTG 3’ (reverse primer)5’TGGTHEK293T cells were subconfluently seeded into a 96-well plate and co-transfected with Polyfect reagent (Qiagen) following the manufacturer’s instructions with 25 ng of empty, human NLRC5 or human CIITA (pIII) expression vectors and 25 ng of the indicated luciferase reporter constructs [Statistical differences were calculated as described in the Figure legends. Differences were considered significant when p≤0.05 (*), very significant when p≤0.01 (**) and extremely significant when p≤0.001 (***).Mice were treated in accordance with the Swiss Federal Veterinary Office guidelines. Human cell lines are established cell lines.S1 Fig+ T cells (gated as CD3+CD8+), CD4+ T cells (gated as CD3+CD4+), NK cells (gated as NK1.1+CD3-), NKT cells (gated as NK1.1+CD3+), B cells (gated as CD19+), and DCs (gated as CD11chiCD11bint-hi) from control (n = 6), Nlrc5−/− (n = 6), CIIta−/− (n = 6) and double-deficient (n = 5) mice. Results depict the mean ± SEM from two pooled experiments, and are representative of three independent experiments. Differences among multiple groups were analyzed by 2-way ANOVA adjusted by Bonferroni correction over 6 samples and are shown only for the effect double as compared to single-deficiency. (B) MFIs for H2-K and H2-D expression by Nlrc5−/− (n = 4 mice), CIIta−/− (n = 4 mice), and Nlrc5−/−CIIta−/− (n = 4 mice) mTECs (gated as CD45− UEA1+) are expressed as percentage of Nlrc5+/−CIIta+/− (n = 4 mice) control mice mTECs. Results represent the mean ± SEM derived from two pooled experiments, and are representative of three independent experiments. The histogram overlay for MHCII expression shows a representative mouse for each group. Differences among multiple groups were analyzed by 2-way ANOVA and are shown only for the effect double as compared to single-deficiency. (C) Percentages and numbers of splenic CD8+ (gated as CD3+CD8+) and CD4+ T cells (gated as CD3+CD4+) from control (n = 6), Nlrc5−/− (n = 6), CIIta−/− (n = 6) and Nlrc5−/−Ciita−/− (n = 5) mice are shown. Results depict the mean ± SEM from two pooled experiments, and are representative of three independent experiments.(A) Graphs depict the MFIs of H2-D for CD8(TIF)Click here for additional data file.S2 FigB2m and Psmb9/Tap1. The tracks depict reads mapping to regions spanning between 5kb upstream and 5kb downstream of the TSS. After normalization as rpm (reads per million), read coverage was expressed relative to the maximal value observed in the region. TSSs are positioned as annotated in Refseq (Psmb9) or ENSEMBL (others). (B) Antibodies specific for NLRC5 were used to immunoprecipitate cross-linked chromatin fragments derived from WT, Nlrc5−/− and Rfx5−/− B cells. Immunoprecipitates were analyzed by quantitative PCR for the abundance of promoter sequences from the indicated genes. Relative promoter binding is shown. Results represent the average ± SD of technical triplicates.(A) NLRC5-ChIPseq tracks are shown for the NLRC5 targets (TIF)Click here for additional data file.S3 FigPositions of the SXY modules and distal regulatory elements (interferon stimulation response element (ISRE) and enhancer A) defined in classical MHCI promoters are indicted by colored boxes. Predicted motifs were identified by homology searches on the basis the sequence of the upper (boxes above line) and/or or lower (boxes below line) strands.(TIF)Click here for additional data file.S4 FigS-X and X-Y spacing distributions are shown for SXY modules occupied by NLRC5 (top graphs) and all genomic matches to the SXY consensus module (bottom graphs) defined by two different motif discovery approaches giving more  or less  weight to S-X and X-Y spacing. The percent of modules (Y axis) are plotted as a function of distance in base pairs (X axis) between the S and X motifs (red bars) or the X and Y motifs (blue bars). The S, X and Y sequence logos are shown below. The most prominent spacing characteristic of NLRC5-occupied modules are indicated at the top and highlighted by red and blue underlays.(TIF)Click here for additional data file.S5 FigAlignment of sequence motifs situated within NLRC5-occupied peaks found in promoter regions of the indicated genes. S-X and X-Y distance constraints used for identifying the S and Y motifs were set at a maximum of 100 base pairs for each. Distances (bp) between motifs are indicated. The sequence logo for the consensus NLRC5-module is shown below the alignment and is compared with that previously defined for human CIITA.(TIF)Click here for additional data file.S6 FigNlrc5F/F dataset. Peak heights corresponding to the average fragment size (dashed blue lines) are markedly higher than peak heights corresponding to read length (first dashed red lines to the right of the dashed blue lines). NSC, RSC, and Qtag values are indicated below each graph. Low NSC values are expected due to the low number of peaks. RSC and Qtag values attest to the quality of the ChIP-seq peaks. (B) Irreproducible Discovery Rate (IDR) analysis on pseudo-replicates. Coloured lines represent all pairwise comparisons between the 5 WT pseudo-repeats. Peaks were called with the MACS2 peak caller using p<0.001 and the “–to-large” setting. Numbers of called peaks before IDR analyses were 459, 529, 1436, 1043 and 1207 respectively. The IDR threshold was set at 0.01. (C) Fraction of Reads in Peaks (FRiP) values. Left panel: FRiP values are represented for each WT pseudo-replicate, before and after IDR analysis. Numbers of peaks called in each pseudo-replicate before IDR analyses were as in B. The number of final peaks  was 11. Right panel: FRiP values are shown for each pseudo-replicate and the original WT data set when the peak calling procedure was performed using the default parameters of MACS2. Numbers of called peaks were 11, 11, 11, 11, 10 and 6 for the pseudo-replicates and the complete WT dataset, respectively. Low FRiP values are expected due to the low number of peaks.(A) Strand cross-correlation analysis was performed for 5 WT pseudo-replicates , the complete WT dataset (All_WT), and the (TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 TableClick here for additional data file.


Escherichia coli. We show that CRP binds between two convergent genes. When bound, CRP regulates transcription of a small open reading frame, which we term aatS, embedded within one of the adjacent genes. Our work demonstrates that non-canonical sites of transcription factor binding can have hidden functionality.A commonly accepted paradigm of molecular biology is that transcription factors control gene expression by binding sites at the 5' end of a gene. However, there is growing evidence that transcription factor targets can occur within genes or between convergent genes. In this work, we have investigated one such target for the cyclic AMP receptor protein (CRP) of enterotoxigenic  A long standing view is that promoters, and transcription factor binding sites, should locate to the 5' end of an annotated gene . UnexpecIn this work we have focused on the the cAMP receptor protein (CRP). Widely conserved in bacteria, CRP is global regulator of transcription that responds to cAMP levels . When boStrains and plasmids used are listed in aatS was 2-fold more active in stationary phase cultures compared to log phase cultures the cells from the former phase of growth were used in all experiments. Values shown are the mean of three independent experiments. Error bars represent the standard deviation of three independent experiments. To calculate P we used a paired, two-tailed, student’s t test.β-galactosidase assays were done according to the method of Miller . Cells wet al. [Δcrp derivative) harbouring pRW50 containing the aatS1 promoter fragment. Extractions were done using a Qiagen RNeasy mini kit according to the manufacturer’s instructions and residual DNA was removed using a Turbo DNA-free kit (Ambion). RNA integrity was then checked by visualisation following agarose gel electrophoresis. The ratio of absorbance at 260 nm and 280 nm was used to assess the purity of the RNA. Primer extension products were analysed on a 6% denaturing polyacrylamide gel alongside size standards generated by manual sequencing of M13 phage DNA. Full gel images are provided in the supporting information  UTP, 500 mM DTT, 5 mM MgCl2, 100 μg ml-1 BSA and 0.2 mM cAMP. Template DNA  was incubated with CRP at 37°C for 5 minutes in reaction buffer, prior to the addition of RNA polymerase holoenzyme to start the reaction. Full gel images are provided in the supporting information  [aatC (encoding a subunit of a transport system) and tnpA (encoding a transposase). The genomic context of the CRP site is shown in aatC coding region  were also identified  . This wog region . A 189 bentified . Note thaatS (PaatS) was functional. Thus, a 134 bp DNA fragment containing the sequence shown in EcoRI and HindIII restriction sites, was generated. The DNA fragment, named aatS1, was cloned upstream of lacZ in plasmid pRW50 to create a PaatS::lacZ fusion. Next, E. coli M182 and the Δcrp derivative were transformed with the resulting plasmid. Transformants were cultured in liquid media and RNA was isolated as a template for primer extension. A 166 nucleotide primer extension product was observed using RNA from M182. Conversely, little extension product was observed using RNA from the Δcrp derivative  in length. Note that, pSR also encodes RNAI; a 108 nt factor independent transcript derived from the plasmid replication origin. Thus, we observed two bands on denaturing PAGE gels  and protected the flanking DNA regions (underlined). The footprint aligns precisely with the predicted DNA target for CRP. The CRP site is 39.5 bases upstream of the PaatS transcription start site  [aatS RBS is located 6 bp upstream of the aatS start codon, a position suitable for translation initiation [aatS1::lacZ fusion and explored the effects of mutating the RBS on lacZ expression. Thus, we generated a derivative of the aatS1 fragment, called aatS2, where the sequence of the RBS was altered to 5'-UUUUCUUCA-3' . Residual LacZ expression, driven by the aatS2::lacZ fusion in M182Δcrp cells, was not significantly different from background LacZ activity (P = 0.096). Background LacZ activity was obtained using M182Δcrp transformed with pRW225 having no promoter insert.The predicted AGGU-3') , 21. In itiation . To deteCUUCA-3' . Both aao pRW225 . Resulti culture . LacZ exaatS drives transcription of a small or antisense RNA. However, the presence of a functional RBS, appropriately positioned upstream of the aatS open reading frame, is consistent with the transcript being an mRNA. If this is the case, translation would result in the production of a 62 amino acid AatS peptide. To better understand AatS we used BLAST to identify 38 microbial proteins with a significantly similar sequence  in the NCBI blastp database. The search also reveals that AatS shares 67% identity with a conserved domain of unknown function . Genes encoding DUF1602 are found in diverse species in all kingdoms of life  found in many pathogenic bacteria [aatS, suggests that the gene encodes a small protein rather than a regulatory RNA. Interestingly, many small proteins in bacteria localise to the membrane and function as accessory factors in transport systems [aatS is genetically associated with genes encoding transport systems in many bacteria. Furthermore, a potential transmembrane helix is predicted between residues 4 and 21 of AatS [In this study we have characterised the function of a predicted CRP binding site located between two convergent genes. We show that CRP activates transcription of bacteria , 25. The systems . Consist of AatS .rpmH is encoded within rnpA in Thermus thermophilus and setAB resides inside the pic gene of E. coli 042 and Shigella flexneri [Mycobacterium tuberculosis transcription factors, 75% of binding targets were located inside genes [E. coli, identified many intragenic binding events [aat locus reveals that CRP acts via a well-established mechanism and it is the position of aatS, embedded within aatC, which confounds the situation. In summary, whilst some bacterial transcription factors bind primarily in expected locations [Documented instances of overlapping, protein-encoding, genes in bacteria are rare . We are flexneri , 30. Howde genes . Similarg events . Presumaocations –35 many ocations ,11,36,37ocations . This woS1 FigPanels A-C show raw gel images from Figs (PDF)Click here for additional data file.


Specificity proteins Sp1, Sp2 and Sp3 are paradigmatic of closely related transcription factors. They share amino-terminal glutamine-rich regions and a conserved carboxy-terminal zinc finger domain that can bind to GC rich motifs in vitro. All three Sp proteins are ubiquitously expressed; yet they carry out unique functions in vivo raising the question of how specificity is achieved. Crucially, it is unknown whether they bind to distinct genomic sites and, if so, how binding site selection is accomplished. In this study, we have examined the genomic binding patterns of Sp1, Sp2 and Sp3 in mouse embryonic fibroblasts by ChIP-seq. Sp1 and Sp3 essentially occupy the same promoters and localize to GC boxes. The genomic binding pattern of Sp2 is different; Sp2 primarily localizes at CCAAT motifs. Consistently, re-expression of Sp2 and Sp3 mutants in corresponding knockout MEFs revealed strikingly different modes of genomic binding site selection. Most significantly, while the zinc fingers dictate genomic binding of Sp3, they are completely dispensable for binding of Sp2. Instead, the glutamine-rich amino-terminal region is sufficient for recruitment of Sp2 to its target promoters in vivo. We have identified the trimeric histone-fold CCAAT box binding transcription factor Nf-y as the major partner for Sp2-chromatin interaction. Nf-y is critical for recruitment of Sp2 to co-occupied regulatory elements. Equally, Sp2 potentiates binding of Nf-y to shared sites indicating the existence of an extensive Sp2-Nf-y interaction network. Our results unveil strikingly different recruitment mechanisms of Sp1/Sp2/Sp3 transcription factor members uncovering an unexpected layer of complexity in their binding to chromatin in vivo.Transcription factors are grouped into families based on sequence similarity within functional domains, particularly DNA-binding domains. The  bona fide zinc finger DNA binding domain. Yet, each of the three proteins carries out unique functions in vivo, and each is absolutely essential for mouse development. By genome-wide binding analysis, we found that Sp1 and Sp3 on the one hand, and Sp2 on the other hand engage completely different protein domains for their genomic binding site selection. Most strikingly, the zinc finger domain of Sp2 is dispensable for recruitment to its target sites in vivo. Moreover, we provide strong evidence that the histone-fold protein Nf-y is necessary for recruitment of Sp2. Conversely, Sp2 potentiates Nf-y binding showing that binding of Sp2 and Nf-y to shared sites is mutually dependent. Our findings uncover an unexpected mechanistic diversity in promoter recognition by seemingly similar transcription factors. This work has broader implications for our understanding of how members of other multi-protein transcription factor families could achieve specificity.A major question in eukaryotic gene regulation is how transcription factors with similar structural features elicit specific biological responses. We used the three transcription factors Sp1, Sp2 and Sp3 as a paradigm for investigating this question. All three proteins are ubiquitously expressed, and they share glutamine-rich domains as well as a conserved  Eukaryotic transcription factors are grouped into families based on their common structural features. Prototypical zinc finger-containing transcription factors are the evolutionary conserved Specificity proteins/Krüppel-like factors (Sps/Klfs)  tnull as well as Sp2null embryos are severely growth-retarded and die before embryonic day 10 [Sp2 as a gene essential for proper mitosis in HeLa cells [null mice develop until birth but are not viable due to manifold defects including impaired lung, cardiac, bone and red blood cell development [Despite their structural similarities and broad co-expression there seems to be little functional overlap between Sp1, Sp2 and Sp3 ,8,9. Bric day 10 ,9. Condic day 10 . MoreoveLa cells . Finallyelopment ,14,15.in vitro. In contrast, Sp2 has largely escaped attention since its initial discovery [At the molecular level, the functional properties of Sp1 and Sp3 are well characterized. Particularly, numerous publications reported binding of Sp1 and Sp3 to the GC box (GGGGCGGGG) and related motifs iscovery  likely biscovery ,18, and iscovery .in vivo. However, in the global chromatin context, it remains a largely unanswered question whether the very similar transcription factors Sp1, Sp2 and Sp3 are bound to the same promoters in vivo and whether regions outside the bona fide DNA-binding domain contribute to their binding site selection.We have recently determined the genome-wide occupancy of Sp2 in mouse embryonic fibroblasts (MEFs) and in HEK293 cells, and have found that Sp2 occupies numerous proximal promoters of essential genes . BioinfoSp2ko and Sp3ko) MEFs, we found that the zinc finger region mediates chromatin binding of Sp3. Unexpectedly, the bona fide zinc finger DNA-binding domain is completely dispensable for binding of Sp2. Rather, it is exclusively the glutamine-rich N-terminal domain, which mediates recruitment of Sp2 to its genomic sites. We further show that Sp2 colocalizes with the trimeric CCAAT-binding transcription factor Nf-y at a large fraction of Sp2 binding sites. We provide evidence that Nf-y is necessary for recruitment of Sp2 and suggest that, in turn, Sp2 potentiates Nf-y binding to shared sites, since binding of Nf-y to sites that are also bound by Sp2 is attenuated in Sp2ko MEFs. Therefore, we have discovered that the seemingly similar transcription factors Sp1/Sp3 and Sp2 utilize completely different modes of genomic binding site selection and shed light on the previously enigmatic properties of Sp2.To address this important question, we have compared the genome-wide chromatin occupancy of Sp1, Sp2 and Sp3 with each other in mouse embryonic fibroblasts. We found that Sp1 and Sp3 essentially occupy the same promoters and localize to GC boxes. In marked contrast, Sp2 predominantly localizes at CCAAT motifs. By re-expression of various Sp2 and Sp3 mutants in corresponding Sp2 and Sp3 knockout . The second enriched motif is “CCAAT”, which is a binding site for the transcription factor Nf-y. Essentially, the same two motifs are found at the Sp2 binding sites [We performed an unsupervised ng sites . Howeverng sites . This anIntrigued by the nicely shaped distribution of the CCAAT motifs at the Sp2 binding sites, we examined the distribution of the CCAAT motif in greater detail. We found that ~70% of the top Sp2 binding sites but only ~23% of the Sp1/Sp3 sites contain two, or more, perfect CCAAT motifs. Moreover, ~65% of the Sp2-specific sites but only ~8% of the Sp1/Sp3-specific sites contain more than one CCAAT motif . Finallyin vivo via binding to the GC box [Finally, we also determined the binding sites of Sp1 in HEK293 cells and compared the motifs at the Sp1 binding sites with those at the Sp2 binding sites . Similare GC box .in vivo. We focused on Sp2 and Sp3 because corresponding knockout MEFs are available [Intrigued by the finding that Sp1 and Sp3 are present at GC boxes, whereas Sp2 is located primarily at CCAAT motifs, we sought to identify the protein domains that are responsible for the differences in binding site selection vailable ,23 that vailable ,24,25.Nxt1, Sp1 and Sp2), and promoters that are preferentially bound by either Sp2 (Gas2l3 and Osbp) or Sp3  revealed specific binding of re-expressed full-length Sp2 and the full-length Sp3 isoforms (Sp3li and Sp3si)  . The Sp3d Sp3si) . The picd Sp3si) . Strikinngth Sp2 . Thus, tSp2ko MEFs re-expressing Flag-tagged versions of full-length Sp2 (Sp2FL), the Sp2 N-terminal part (Sp2NT) or the Sp2 zinc finger domain , a hallmark of the Sp transcription factor family members [Sp2ko MEFs with a C-terminal deletion mutant . Nearly wild type Nlk mRNA levels are restored in Sp2ko MEFs expressing either full-length Sp2 or the Sp2NT mutant, but not in cells expressing the Sp2ZF mutant . Nf-y iSp2ko MEFs  independently of the immunoprecipitation order . The eluate from Sp2 antibody chromatin precipitation was subjected to precipitation with Nf-yb antibodies, and on order , but notSp1, Osbp, Amd1 and Dcnt4), promoters that are co-bound by Nf-y and Sp2 but not by Sp1 and Sp3 (Oxr1 and Plcl1), and promoters that are bound by all three Sp factors but not by Nf-y (Fanci and Taf1c)  . Lower eSp2ko MEFs. Expression of Nf-y is similar in both cell types (Sp2ko MEFs (wt/Sp2ko), we found attenuated Nf-y occupancy in Sp2ko MEFs at many promoters that are co-bound by Sp2 in wild type MEFs   . Bindingng cells . Taken tin vitro DNA-binding studies, Sp1 and Sp3 localize to GC boxes and essentially occupy the same promoters. Unexpectedly, Sp2 primarily localizes at CCAAT motifs, often arranged in tandem with a mean distance of 35 bp  and DNA-binding independent, whereby one transcription factor binds to another that, in turn, binds to DNA (tethering). Tethering to chromatin has been reported for several transcription factors including recruitment of the glucocorticoid receptor by AP1 ,35 or STed sites , and theed sites  particuled sites . Such a De novo motif discovery at these sites revealed enrichment of the Ets transcription factor-binding motif GGAAG. Future work aims to identify the Ets factor that occupies these sites. A promising candidate is the widely expressed heterodimeric Ets transcription factor Gabp [Finally, a small fraction of the Sp2 binding sites does not contain CCAAT boxes and are not co-occupied by Nf-y. Nevertheless, binding of Sp2 to these sites is also mediated by the N-terminal region suggesting that binding of Sp2 to these sites is by a similar mechanism. tor Gabp  that bintor Gabp .In conclusion, the data provided in this report challenge the prevailing view that the transcription factors Sp1/Sp3 and Sp2 regulate transcription by binding to similar promoter elements via their zinc finger domains. Instead, Sp1/Sp3 and Sp2 have distinctive binding landscapes, and their modes of genomic binding site selection are completely different. Collectively, our findings uncover strikingly different recruitment mechanisms of very similar transcription factors, and add another crucial level of detail to the current model of transcription factor binding to chromatin.For Western blotting and ChIP of Sp1, Sp2 and Sp3 in MEFs, we used homemade rabbit antibodies ,42 affinSp2ko and Sp3ko MEFs were as described [Retroviral expression plasmids for 3xFlag-Sp2 and 3xFlag-Sp3 mutants were generated by restriction cloning of PCR fragments into a pBABE3xFlag-puro plasmid. Primer sequences used for PCR can be found in escribed .Sp3ko MEFs, and MEFs with floxed Sp2 alleles (Sp2fl/fl) were isolated from E13.5 embryos using standard methods and subsequently immortalized by serial passages. Sp2ko MEFs were obtained by retroviral transduction of pBABE-Cre-neo as described [Sp2ko MEFs have severely impaired proliferation rates [MEFs were cultured in a 1:1 mixture of Dulbecco’s modified Eagle’s medium—high Glucose (PAA) and HAM’s F-10 (PAA) supplemented with 10% (v/v) fetal calf serum (PAA) and 1% Penicillin-Streptomycin. Wild type and escribed . Sp2ko Mon rates . Cells t4 MEF cells expressing 3xFlag-Sp2 mutants were grown on coverslips in 6-well plates overnight. Cells were fixed in 4% PFA/PBS for 25 min, permeabilized in 0.2% TritonX-100/PBS for 20 min, and blocked with 3% BSA/PBS for 1 hr. Incubation of the anti-Flag M2 antibody  was for 1 hr at RT. Secondary antibody incubation  was performed for 1 hr at RT in the dark. After a final washing step, coverslips were mounted onto glass slides using Vectashield mounting medium with DAPI .Immunofluorescence and microscopy was performed as described . In brie6 cells were replated, and transfected a second time. Additional three days later, cells were collected and cross-linked chromatin was prepared. To monitor knockdown efficiency at the protein level, a chromatin sample was incubated with an equal amount of 2xLaemmli buffer at 100°C for one hour and subsequently analyzed by western blotting. To monitor knockdown efficiency at the transcript level, RNA was isolated using the RNeasy Mini kit (Qiagen) and expression of Nf-ya, Nf-yb and Nf-yc was analyzed by RT-qPCR.For RNAi-mediated depletion of mouse Nf-ya, Nf-yb and Nf-yc, pools of four On-target plus siRNAs (GE Dharmacon) were used . The siGenome non-targeting siRNA #1 (D-001210–01) was used as unspecific siRNA control. Wild type MEFs on 15 cm plates were transfected with 24 nM siRNA using Oligofectamine (Invitrogen). Three days post-transfection 2x10Expression analysis by quantitative RT-qPCR was performed as described in  using ge3, 1% SDS, 10 mM DTT for 30 min at 37°C. Eluates were diluted 1:50 with ChIP buffer and subsequently subjected to a second ChIP in accordance with the One Day ChIP kit manual performing an overnight antibody incubation at 4°C. Enrichment was calculated relative to the input of the first ChIP. Primer sequences for ChIP-qPCRs are listed in ChIP experiments were performed as described  using thFor ChIP-seq experiments, four to six individual ChIPs were pooled, and precipitated DNA was purified on QIAquick columns (Qiagen). Five nanograms of DNA were used for indexed next generation sequencing library preparation using the MicroPlex library preparation kit (Diagenode) in accordance with the manufacturer’s instructions. Library purification was performed with AMPure magnetic beads (Beckman Coulter) as described in the MicroPlex kit manual. Libraries were quantified on a Bioanalyzer (Agilent Technologies) and subsequently sequenced on an Illumina HiSeq1500 platform, rapid-run mode, single-read 50 bp  according to manufacturer´s instructions. An overview of the various ChIP-seq results is shown in Sp2ko or Sp3ko) normalized read ratio of at least 3. Further analysis of peaks such as association with transcripts in the vicinity, classification of genomic position and Venn diagram generation was performed using a custom Python based pipeline. Genome annotation data from Ensembl revision 74 [De novo motif search including central motif analysis [Raw ChIP-seq data were aligned to the mouse genome assembly mm10 or the human genome assembly hg19 using Subread 1.3.3-p3 . Reads wision 74  was usedanalysis  was perfanalysis  using seRaw sequencing data were deposited at ArrayExpress under accession number E-MTAB-2970.S1 FigSp2ko and Sp3ko MEFs were probed with two different affinity-purified Sp1-, Sp2- and Sp3-specific antibodies. (B) Sp1, Sp2 and Sp3 were expressed in insect S2 cells lacking Sp factors. Subsequently, nuclear extracts were subjected to Western blot analysis using the affinity-purified Sp1-, Sp2- and Sp3-specific antibodies. The asterisks indicate non-specific bands.(A) Nuclear extracts of wt, (PDF)Click here for additional data file.S2 FigNxt1, Raf1 and Grb2 gene regions in wt, Sp2ko and Sp3ko MEFs. All three Sp factors are bound at the Nxt1 promoter, Sp1 and Sp3 but not Sp2 are bound at the Raf1 promoter, and Sp2 but not Sp1 and Sp3 is bound at the Grb2 downstream promoter. Due to the lack of Sp1ko MEFs, which are not viable, IgG ChIPs served as controls for the Sp1 ChIPs.Shown are genomic binding patterns of Sp1, Sp2 and Sp3 at the (PDF)Click here for additional data file.S3 FigE-value).Sequence motifs (left) and their distribution (right) at Sp1 and Sp2 peaks in HEK293 cells were obtained by running MEME-ChIP  with 300(PDF)Click here for additional data file.S4 FigFanci and Taf1c promoters was analyzed by ChIP-qPCR. Anti-Flag antibodies were used for ChIP. The percent of input values are mean +/- SD (n = 3).(A) Sites of Sp2 that are not co-bound by Nf-y represent low tag count Sp2 binding sites. Individual native Sp2 peaks in wt MEFs  were plo(PDF)Click here for additional data file.S5 FigShown are genome browser snapshots.(PDF)Click here for additional data file.S6 FigShown are genome browser snapshots.(PDF)Click here for additional data file.S7 FigThe Sp box (yellow), the Btd box (blue) and the zinc finger region (bold) are highlighted. Colored letters within the N-terminal part highlight basic amino acids , acidic amino acids  and glutamine residues (gray).(PDF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 TableFor each ChIP-seq sample, the number of usable reads, the number of filtered peaks, the average number of tags per peak, and the genomic distribution of the peaks  is shown.(XLSX)Click here for additional data file.S3 TableEach sheet contains the following columns: A, chromosome; B and C, chromosomal region spanning the peak; D, genomic position ; E, distance of peak to next TSS; F and G, name and Ensemble ID of the next gene; H, tag counts at individual peaks; I, tag counts normalized to 1 million reads. The sheets are sorted by column I.(XLSX)Click here for additional data file.