Arise therefore rar

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After further analysis, we define a set of sites that are bound exclusively in the presence of retinoid signaling, as they are not significantly enriched in the absence of RA exposure compared with control , and their levels of ChIP-seq enrichment are significantly different in the presence and absence of RA see Materials and methods.

Conversely, at least sites are bound only in the absence of retinoid exposure. Intriguingly, some of the shift in RAR binding sites may be explained by a ligand-dependent shift in RAR's binding preference. Sites bound only in the absence of RA contain more direct repeat motifs with 0-bp or 1-bp spacers than sites bound only in the presence of RA Additional files 3 and 4.

Prior studies have shown that such motif configurations can be bound by RAR [ 28 , 29 ]. On the other hand, sites bound exclusively in the presence of RA contain more DR5 motifs. These direct repeat motifs are amongst the set of sequence features that have the most significant difference in occurrence frequency between RAR sites bound exclusively in the presence or absence of retinoid signaling Additional file 5. Therefore, a potential shift in RAR's direct binding preference offers only a partial explanation for the observed condition-exclusive binding patterns.

By comparing the relative occurrence of all known TF binding motifs in each condition-exclusive set, we also find that exclusively post-RA sites contain significantly more E-box and ETS-family motifs than exclusively pre-RA sites Additional file 5.

It is not known which proteins may interact with this motif, although the motif is over-represented in mammalian promoter regions [ 30 ], and has recently been characterized as a regulatory sequence [ 31 ]. The observation of these over-represented secondary motifs suggests that some of the exclusively post-RA binding sites may occur due to ligand-dependent interactions between RAR and cofactors, or some may potentially represent indirect binding events caused by enhancer-promoter looping.

Most of the motifs with significantly higher relative frequency in the exclusively pre-RA sites are related to DR0 or DR1 patterns. In order to determine which RAR binding sites are associated with transcriptional regulation, we characterized the early transcriptional response to retinoid signaling. Of these, 81 genes are up-regulated.

The most prevalent theme in the expression response is the acquisition of rostro-caudal identity; 12 anterior Hox genes are significantly up-regulated, along with the Hox co-factors Meis1 , Meis2 , Pbx2 , and other positioning genes such as Tshz1 and Cdx1. Thirty-five significantly up-regulated genes are within 20 kbp of a post-RA RAR binding event, including many of the most differentially expressed genes Figure 2 ; Additional file 6.

Exclusively post-RA RAR targets are no less associated with differential expression than the constitutively bound targets; while 20 significantly up-regulated genes are nearby constitutively bound RAR sites, 15 up-regulated genes are only bound after RA.

Genes with more than five-fold differential expression after 8 hours of RA exposure are listed. RAR binds to many of the up-regulated genes, with binding more likely for greater degrees of up-regulation. Three functional groups of genes are indicated by coloring the gene names. Information for all more than two-fold differentially expressed genes is tabulated in Additional file 2.

It is likely that many other RAR binding sites play regulatory roles during the retinoid response that are not apparent from microarray-based differential expression analysis. Therefore, the correlation between RAR binding and Pol2 initiation and elongation suggests that RAR may play a wider role in driving and maintaining transcription beyond that observed from microarray-based differential expression analysis.

We again find no evidence that exclusively post-RA RAR binding sites are less associated with Pol2 initiation than constitutively bound sites; both sets of sites are coincident with Pol2-S5P events at similar rates. Within 8 hours of retinoid exposure, the initiating and elongating forms of Pol2 are recruited to these genes. A proposed model of RAR functionality suggests that it acts as a transcriptional repressor in the absence of RA signaling, and becomes an activator after ligand binding [ 7 ].

The pre-RA pattern of RAR binding does not seem to affect the behavior of Pol2 at these sites; both constitutive and exclusively post-RA RAR binding sites are coincident with constitutive Pol2 initiation events at similar rates. Genome-wide, we find a set of only 27 significant Pol2-S5P initiation events that are bound by Pol2 after RA exposure but show no evidence of enrichment in pluripotent cells. Only 11 of these events are near RAR binding events.

In summary, our examination of potential interactions between RAR and Pol2 before and after retinoid exposure adds complexity to the proposed model of RAR functionality. Only a small set of important retinoid targets fit the simple model of RAR recruiting Pol2 to the transcription start site only after RA exposure.

A further set of bound genes is already being actively transcribed before RA exposure. One possibility is that RAR bound sites are distinguished by their chromatin structure profiles and the occupancy of other regulatory proteins in the surrounding genomic region. To assess the regulatory state of RAR binding sites, we compare constitutively bound sites by definition occupied both post-RA and in the preceding pluripotent state to published ChIP-seq data in mouse ES cells, including data for multiple TFs, co-factors, histone modifications, and chromatin modifying proteins [ 24 , 37 — 41 ].

We observe that the locations of constitutively bound RAR binding sites are highly coincident with the binding sites of many regulatory proteins in ES cells Figures 4a and 5. Coincidence rates between 10, random genomic locations and ES cell binding events are shown for reference. In cases where the same protein was profiled by multiple labs, we denote the source using the following abbreviations: B, Bernstein lab [ 38 — 40 ]; N, Ng lab [ 24 ]; Y, Young lab [ 37 ].

HSC, hematopoietic stem cell. Color shading denotes scaled ChIP-seq read depth see Materials and methods. According to the hypothesis that all developmental enhancers are epigenetically marked at the earliest stages of development [ 20 , 21 ], RAR will bind post-RA to sites that are already bound by other regulators in ES cells. Alternatively, RAR may recognize unbound developmental enhancers that are specific to neuronal fate. However, the associations between ES cell binding sites and exclusively post-RA RAR sites are less than those with constitutively bound RAR sites, and thus our observations are not fully consistent with the hypothesis that all developmental enhancers are marked in ES cells.

While all of these stage-specific TFs bind to the same regions as ES cell TFs at a higher rate than expected by chance Figure 4a , none of them approaches the rate of overlap observed for RAR during early differentiation. The observed relationships between RAR binding and earlier binding events suggest that TF binding information from ES cells can be used to predict where signaling TFs will bind in a proximal developmental state.

Predicting if a motif sequence will be bound based on motif similarity alone leads to high rates of additional predictions Figure 6 [ 44 ]; for a motif similarity threshold with which we can correctly predict post-RA bound RAREs, we also predict that approximately 65, additional sites should be bound.

Recent reports demonstrate the use of co-temporal histone modification ChIP-seq data for predicting TF binding to motif sequences [ 14 , 16 , 45 ].

We can similarly combine the motif-similarity score with a score based on the sum of normalized read counts from ES cell TF ChIP-seq experiments in bp windows around the sites see Materials and methods.

As shown in Figure 6 , this combined score significantly decreases the rate of additional predictions for a given true-positive rate. ChIP-seq data improves motif specificity. The true positive and additional prediction rates are shown when predicting post-RA RAR binding sites by ranking sites according to motif similarity or when combining motif information with various other data sources see Materials and methods.

We can compare the predictive performance of ES cell TF data sources to that of histone modification information by training a supervised classification technique to classify sites as bound or unbound. Specifically, we trained support vector machines SVMs to discriminate between sites that are bound by RAR and a negative set of 10, unbound sites. Interestingly, our SVM results suggest that the ES cell TF binding landscape is more informative than ES cell histone modification data when predicting the genomic locations that are bound by signal-responsive TFs.

This observation holds true when predicting sites that are only bound by RAR before or after RA exposure.

By profiling the dynamics of RAR occupancy at the initiation of neurogenesis, we have characterized a ligand-dependent shift in binding targets. This shift in binding targets is relevant to RAR's role in gene regulation, as both constitutively and exclusively post-RA bound sites are associated to a similar degree with gene expression and polymerase recruitment.

Recent analyses of RAR binding profiled genome-scale occupancy only in the presence of retinoids, and thus did not observe a ligand-dependent shift in binding [ 9 — 11 ]. Some of RAR's shift in binding may be explained by ligand-dependent binding preference or ligand-dependent interactions between RAR and co-activators or co-repressors. In addition, a mixture of RAR isoforms is active at the initiation of neurogenesis, and changes in the composition of this mixture may lead to changes in binding occupancy.

We have also found that the binding sites of RAR after RA signaling are extensively associated with the binding of other regulatory proteins in the temporally preceding pluripotent environment.

Furthermore, we have demonstrated that we can accurately predict where RAR will bind in the genome given knowledge of the preceding regulatory state.

The apparent dependence of RAR binding on prior cellular state suggests that the response of differentiating cells to external signals may be context and developmental-stage dependent, with some future binding events being potentiated by current genomic occupancy patterns. The causal relationships underlying the association between RAR binding and the ES cell regulatory network remain unclear, so we can only summarize possible explanations for the observed data.

ChIP-seq data from ES cells may provide a read-out of accessible regions of the genome, thereby indicating which regions are amenable to TF binding in that environment. Since the predictive capacity of ES cell regulatory data decreases with temporal distance from ES cell state Table 1 , we do not believe that ES cell ChIP-seq data merely serves as an indicator of all enhancers that may be bound under any condition or cell type. Rather, the regions bound by regulatory proteins in a given developmental stage may be more likely to remain accessible for TF binding in a related future stage.

Esrrb is an orphan nuclear receptor that binds to hormone response element motifs. However, direct interactions between Esrrb and RAR are not required for cooperativity to arise. Delacroix et al.

Both Hua et al. Hua et al. A number of previous studies have demonstrated that certain regulatory information may be used to predict co-temporal TF occupancy.

For example, enrichment of p [ 18 ], H3K4me1 [ 17 , 45 ], H3K4me3 [ 15 , 45 ], and regions of open chromatin as assayed by DNaseI hypersensitivity [ 12 , 46 ] have each been correlated with the binding of TFs in ES cells and other tissues.

Ours is the first demonstration that regulatory information in a given cell type may be used to predict future TF binding events. Furthermore, the markers examined in the previous studies are typically associated with active enhancers. In our study, we use all available information to predict any RAR binding event, regardless of its association with transcription. Our rationale is that binding events that do not produce co-temporal transcription are not necessarily neutral, especially in the context of differentiation.

For example, binding events that do not produce transcription under one set of conditions may disrupt chromatin structure enough to allow different proteins to bind to proximal sites during a future developmental stage. We have described a compact transcriptional response to RA at the initiation of neurogenesis, which may be potentiated by associations between RAR and earlier regulatory events. As more regulatory data are collected from a greater diversity of cell types and developmental stages, it will be of interest to further elucidate temporal dependencies between the genomic occupancy of regulatory proteins.

Indeed, exploring such temporal networks of binding events may lead to greater understanding of the influences on cell fate during differentiation. ES cells were differentiated as previously described [ 22 ]. Medium was exchanged on days 1, 2 and 5 of differentiation.

Expression microarray experiments were performed in biological triplicate for each analyzed time point. Arrays were scanned using the GeneChip Scanner ChIP protocols were adapted from [ 51 ]. Descriptions of these protocol modifications have been previously published [ 52 ]. Samples were spun down for 3 minutes at 3, rpm, resuspended in 5 ml lysis buffer 2 10 mM Tris-HCl, pH 8. After approximately 16 hours of bead-lysate incubation, beads were collected with a Dynal magnet. Samples were digested with RNase A and Proteinase K to remove proteins and contaminating nucleic acids, and the DNA fragments precipitated with cold ethanol.

In addition, ChIP-seq experiments for other nuclear receptors were used in the construction of Additional file 1 : mouse ES cell Nr5a2 as published in Heng et al. Sequence reads were aligned to the mouse genome version mm8 using Bowtie [ 55 ] version 0. Only uniquely mapping reads were analyzed further. Multiple hits aligning to the same nucleotide position were discarded above the level expected at a 10 -7 probability from a per-base Poisson model of the uniquely mappable portion of the mouse genome.

Binding event detection for RAR, Pol2-S5P, and various published TF ChIP-seq experiments was carried out using a customized methodology that uses statistical significance testing to find regions producing an over-abundance of sequenced reads in the signal experiments compared with the control. The algorithm is run twice across the data. The first pass estimates a scaling factor for control sequencing read depth and a model of the distribution of sequencing read alignment hits around binding events.

The second pass applies these parameters to predict a final set of significant events. Before the first pass, the scaling factor is initialized to be the ratio of total hit counts between the signal and control channels.

The binding distribution model is initialized to be an empirical distribution estimated around predicted binding events in Oct4 ChIP-seq data [ 37 ] Additional file 7. All alignment hits are extended in both 3' and 5' directions, mirroring the observed distribution of hits around binding events.

The extension magnitudes are set equal to the positions where the binding model distribution intersects a uniform distribution over the same area Additional file 7.

Control channel hit counts are scaled using the signal-control scaling factor. A sliding window of bin width 50 bp and offset 25 bp is run over the genome. Overlapping extended hit counts are calculated for both the signal and scaled control channels. The background distribution of ChIP-seq hits is modeled as a non-homogenous Poisson process with parameters estimated from the scaled control hit counts. The use of this dynamic background model is motivated by the desire to correct local ChIP-seq enrichment biases that appear in the signal and control channels, and is similar to the model employed by MACS [ 56 ].

A given bin is denoted as potentially enriched if the overlapping hit count exceeds that expected from the background model at a P -value of 10 P -values for each potentially enriched bin's over-representation in the signal channel over the control are calculated using the binomial distribution CDF [ 57 ].

Neighboring regions in the set of potentially enriched regions are merged, and the maximal P -value observed for the constituent bins is attached to the resulting merged region. The P -values are corrected for multiple hypothesis testing using Benjamini and Hochberg's method, and all regions with corrected P -values above 0. False-discovery rates are estimated by repeating the event discovery procedures after swapping the scaled control channel and the signal channel.

After the first pass, the scaling factor is estimated by carrying out linear regression on the hit counts observed in 10,bp windows that are devoid of potentially significant events in both the signal and control channels. These regions are aligned around the 'peak' location, defined as the position of maximum probability when scanning the current binding model over the region's hit landscape.

The above technique was also used when estimating enriched 'domains' for histone modifications, certain chromatin-associated proteins, and Pol2-S2P.

However, to capture broader domains of enrichment, the bin width was set to bp and the bin offset to bp. The background model was also set to a homogeneous Poisson threshold estimated from the entire genome. De novo motif finding was performed in bp windows centered on the top-ranked peaks for each examined ChIP-seq experiment. A third-order Markov model of the mouse genome version mm8 was employed as background, and a prior based on known mammalian TF binding motifs was also used [ 59 ].

STAMP [ 63 ] was used to cluster the discovered motifs and remove degeneracy in the results. Log-likelihood scoring thresholds for the discovered DR5 and DR2 motifs were calculated by simulating 1,, bp sequences using a third-order Markov model of the mouse genome mm8 version. An arbitrary log-likelihood threshold of 5.

When scoring dimers, the same scoring threshold was used for both halves of the site, and the spacer sequence was unpenalized. A bp window was used to define coincident locations between post-ES cell binding sites and ES cell binding sites or domains.

The clustergrams in Figures 1 and 5 were generated by plotting the overlapping read counts where reads have been artificially extended to bp in 1-kbp windows centered on RAR peaks.

The ordering of peaks was determined by clustering bp-binned data using Matlab's clustergram function and the optimal leaf-ordering algorithm [ 65 ]. Since the various examined ChIP-seq experiments have different dynamic ranges and degrees of typical enrichment, a single color scale is not appropriate for all tracks. The saturation colors in Figure 5 are therefore chosen such that only 0.

As outlined in the main text, an RAR binding site is defined as being exclusively bound post-RA if it fulfills the criteria of being: i significantly enriched post-RA in relation to the WCE control; ii not significantly enriched pre-RA in relation to the WCE control; and iii significantly enriched post-RA in relation to the pre-RA signal. The aim is to ensure that a post-RA binding site does not display any ChIP-seq enrichment before RA exposure; for example, we wish to exclude events that display ChIP-seq enrichment just below the threshold of statistical significance in the pre-RA experiment.

The motif specificity analysis presented in Figure 6 is based on genome-wide matches to the DR2 or DR5 motifs. Thus, the set of 'additional predictions' contains , positions. Some of these additional predictions may serve as binding sites for other TFs, or indeed for RAR under different cellular conditions. However, the vast majority are expected to be false positive predictions.

Next, for various ES ChIP-seq experiments, the number of reads contained in a bp window surrounding each motif match was counted, and these counts were normalized according to the total uniquely mapped read count for the experiment of interest. The motif matches were then ranked separately for each collection according to the summed normalized read counts.

Average phastCons [ 66 ] conservation scores were also calculated for a bp window around each motif match, and this scoring was again used for ranking bp, bp, and bp windows were also tested for generating phastCons scores, without any increase in performance.

The product of both scores is taken for each motif match, and the matches are again ranked based on this score. Again, the additional prediction rate for each true positive rate is plotted in Figure 6 for each combined score. We note that the described method for combining motif and ES cell data scores is simplistic, and more sophisticated schemas for incorporating knowledge of ES cell ChIP-seq data may attain much greater improvements in motif specificity than presented in Figure 6.

Positive training sets were generated by randomly selecting bound sites from each set of predicted peaks. A negative set of 10, randomly chosen sites was also defined. ChIP-seq read counts were extracted from bp windows surrounding the positive and negative positions. During each SVM training run, 50 positive sites and 50 negative sites were randomly extracted as test data.

The SVMs were trained on the remaining data, and predictive performance was tested on the held out data. Article Google Scholar. Sockanathan S, Jessell TM: Motor neuron-derived retinoid signaling specifies the subtype identity of spinal motor neurons. Novitch B, Wichterle H, Jessell T, Sockanathan S: A requirement for retinoic acid-mediated transcriptional activation in ventral neural patterning and motor neuron specification.

Niederreither K, Dolle P: Retinoic acid in development: towards an integrated view.