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Add to wish list. The Arrangement Details Tab gives you detailed information about this particular arrangement of In Dreams - not necessarily the song. Not the arrangement you were looking for? Even though these experiments were performed in a similar manner, the overlap between any two datasets was small. For example, While there were minor differences in the protocols to isolate mRNA, different platforms and software were used to quantify gene expression. We reasoned that differential gene expression that was not reproducible across platforms was more likely to represent noise.
We asked whether integration of these four datasets would result in a more robust joint dataset comprising genes that were repeatedly identified to be regulated. We plotted the subset of genes that were considered significantly up- or downregulated in one dataset against the sum of the remaining three datasets Figure 1B. We observed that when several datasets agreed on a gene being significantly regulated the more likely that it was also identified by the remaining dataset. Thus, the number of datasets that agree on a gene's regulation reflects a confidence score.
Meta-analysis of TPdependent gene expression. C The number of genes identified in datasets from other cell types treated with Nutlin-3a compared to the sum of the four Nutlin-3a MCF-7 datasets see Supplementary Figure S1E—I for more D Boxplot displaying the sum of the five doxorubicin datasets compared to the sum of the nine Nutlin-3a datasets. Correlation coefficient and two-tailed P -value was calculated using GraphPad Prism version 6. E Integration of 20 datasets on TPdependent gene regulation from multiple cell types and treatments. The number of genes identified in each dataset as either upregulated or downregulated is compared to the sum of the remaining 19 datasets see Supplementary Figures S3 and 4.
B, C and E A two-sided Fisher's exact test was employed to test for significant over- and under-representation of gene sets and P -values were adjusted for multiple testing using Bonferroni correction. Colored and black data points are significantly over- and under-represented, respectively adj.
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White data points are not significantly different. F Hierarchical clustering of the 10 most variant genes across the 20 TP53 datasets. G The number of genes is displayed that is found in each of the 41 p53 Expression Score groups. Next, we asked whether datasets using other cell types treated with Nutlin-3a could be integrated with the MCF-7 datasets. We found that when more Nutlin-3a MCF-7 datasets agreed a gene was regulated by TP53, the more likely it was regulated in other cell lines.
In a similar manner, we integrated five datasets assessing TPdependent genes responsive to doxorubicin treatment 6 , 37 , 42—43 Supplementary Figure S2. Comparing the sum of the five doxorubicin and nine Nutlin-3a datasets revealed a strong correlation Figure 1D with a common set of genes up- or downregulated by TP53 across multiple cell types and treatments. Each of these datasets detected genes that were also identified by many of the remaining 19 datasets Figure 1E ; Supplementary Figures S3 and 4. We based our meta-analysis on these 20 datasets that displayed the least amount of data heterogeneity.
In contrast, a dataset from RITA treated HCT cells 47 and a meta-analysis of IR treated cells 48 failed to find a substantial number of genes that was identified by most of the other 20 datasets Supplementary Figure S5 and were excluded from this analysis see below. We performed unsupervised hierarchical clustering of the regulation profile for the 10 most variant genes to test whether cell types and treatments could be distinguished. Studies using cells overexpressing TP53 clustered separately, while studies using doxorubicin treated cells shared the least similarity with other studies.
Although data heterogeneity could account for substantial differences between any two datasets derived from the same cell type and treatment, the clustering analysis indicates that gene expression profiles of similar TP53 activation mechanism are similar; this also holds for similar cell type, however to a smaller extent. In general, the number of genes declines substantially with the number of datasets that agree on a gene's regulation Figure 1G. Together, integration of the 20 datasets revealed that many genes were commonly regulated across cell types and treatments.
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TP53 itself is the central transcription factor mediating TPdependent gene regulation 1 , 2. To assess the role of TP53 in regulating gene expression, we used the step-wise meta-analysis to integrate 15 genome-wide ChIP datasets of TP53 binding derived from several cell lines with the 20 expression profiling datasets 4—6 , 41—43 , 49— Using the stepwise meta-analysis approach, we ranked genes by the number of datasets that detected a TP53 binding peak near their TSS.
Proximal TP53 binding correlates with transcriptional activation. A two-sided Fisher's exact test was employed to test for significant over- and under-representation of gene sets and P -values were adjusted for multiple testing using Bonferroni correction. A genome-wide analysis of gene regulation by TP53 in mouse embryonic stem cells suggested that distal binding of TP53 to the TSS was more likely to control TPdependent downregulation compared to upregulation by more proximal TP53 binding We observed that distal binding of TP53 was weakly correlated with gene upregulation, but not with downregulation Figure 2C.
Our finding is in agreement with reported distal TP53 binding in enhancer regions that may convey long-distance upregulation The previous finding of gene downregulation through distal TP53 binding might stem from the use of mouse stem cells Thus, we conclude that proximal TP53 binding to a gene promoter contributes to transcriptional activation but not repression, while distal TP53 binding appears to have a relatively minor but positive influence on transcription. These thresholds ensure that TP53 regulation and binding was observed in at least two different cell lines or treatments.
We found that of these genes Our approach complements information that dropped below the thresholds of individual studies and dismisses information that was unique to a comparably small number of studies. Based on our analysis, we identify 83 potential direct TP53 target genes that to our knowledge have not been previously described as TP53 targets.
To identify biological processes for TPregulated genes, we performed an enrichment analysis for gene ontology GO terms 53 among genes up- or downregulated upon TP53 activation in at least half of the datasets Figure 2D. As expected, the genes upregulated by TP53 were enriched for regulation of cell proliferation, induction of apoptosis and DNA damage response.
CC genes have often been found to be downregulated upon TP53 activation To illustrate the utility of the meta-analysis approach, we selected 20 direct TP53 target genes Supplementary Table S3 and 20 previously published DREAM targets 17 , 22—23 , 32 , 54—56 and examined their regulation across the 20 TP53 expression profiling datasets Figure 2F and Supplementary Figure S8. The meta-analysis approach identifies target genes that were missed in some datasets but identified in several others.
We plotted the number of genes that were significantly up- or downregulated by Nutlin-3a or doxorubicin treatment against the p53 Expression Score. These results indicate that p21 is required for downregulation of gene expression upon TP53 activation. Downregulation upon TP53 induction requires p However, we observed that a significant number of genes were downregulated by doxorubicin treatment in the absence of p21 Figure 3D.
This indicates that a subset of genes was significantly downregulated by doxorubicin treatment in a p21 and TP53 independent manner. To examine the effect of TP53 on CC genes in more detail, we analyzed five genome-wide expression profiles of CC-dependent gene regulation 11—15 Supplementary Table S5. Similar to what was observed with the TP53 analysis, the overlap of CC-regulated genes between any two datasets was small ranging from Strikingly, when an increasing number of expression profile datasets agreed on a gene being regulated by the CC, it was more likely to also be bound by DREAM Figure 4C.
After performing this step-wise meta-analysis of CC genes, we plotted the number of CC datasets against the p53 Expression Score. A Venn diagram displaying the overlap between the five CC datasets. B The number of genes identified in each of the five CC datasets is compared to the number of the remaining datasets that identify these genes.
D Boxplot displaying the number of datasets that find a gene to be a CC gene across the 41 p53 Expression Score groups. Given these results, we asked whether any other gene sets were commonly downregulated by TP CC terms were primarily enriched among these genes although they were not previously identified in the five CC studies Figure 4E. Consequently, we asked whether a negative p53 Expression Score could accurately predict CC genes that were not previously detected in the five CC datasets.
The step-wise meta-analysis approach was able to generate a more complete assessment of TP53 target genes by integrating a large number of expression profiling and ChIP-seq datasets. Notably, of We found 26 of 27 The number of common D E2F4 or G p bound genes is compared to the number of datasets that identify a gene as being a CC gene. I Boxplot displaying the number of datasets that find a gene to be targeted by DREAM compared to the number of datasets that identify a gene as CC regulated. We found that both E2F4 and p were increasingly enriched for gene binding and reflected the confidence a gene was found to be either a CC gene or downregulated by TP These criteria were met by genes Supplementary Table S7.
The finding that all 20 DREAM target genes displayed in Figure 2F were identified in this list demonstrates the ability of this approach to identify bona fide candidates.
Together, our screening approach identifies several hundred novel potential targets expanding the number of DREAM target genes to strong candidates. For example, the gene encoding the apoptosis enhancing nuclease, AEN , was identified in the meta-analysis data as a CC gene but was also found to be upregulated by TP53 p53 Expression Score Taken together, the stepwise meta-analysis approach enabled identification of specialized subgroups of DREAM target genes.
Temporal separation of gene transcription likely influences interactions of the encoded proteins. To address whether CC genes diverged into distinct subgroups, we analyzed the network of protein—protein interactions between high confidence CC genes identified in at least three out of the five CC datasets. We used a message-passing approach to detect significant communities in the network of CC genes.
We detected two robust communities in the network Figure 6A. The magenta subgroup contained proteins with known functions during the G1 and S phases of the CC, such as E2F transcription factors, members of the minichromosome maintenance complex and histones. In contrast, the orange subgroup contained proteins with well-known functions during mitosis, such as kinesins and centromere proteins.
A Message passing clustering of high confidence CC genes based on their protein—protein interaction network obtained from string-db. To explore the characteristics of the two major CC gene subgroups in more detail, we took advantage of information on peak expression provided in the five CC datasets. While the list of MMB-FOXM1 targets is not exhaustive due to the stringent criteria, loosening these criteria would result in an unfavorable increase in false positives.
We classify this group as RB-E2F target genes. This expanded dataset identifies potential CC genes including 24 Octagons represent the transcriptional regulators. All other nodes represent target genes. Edges represent predicted direct regulation. The size of the nodes reflects the number of CC datasets that identify the gene as CC regulated. The node color reflects the gene's p53 Expression Score.
Genome-wide approaches have increasingly shaped our understanding of TP53 and CC gene regulatory networks. However, due to the nature of these genome-wide datasets, the overlap between any two can be small even when the underlying data were derived from the same cell line undergoing identical treatments. We have developed an approach that captures the information from many of the recently reported datasets to gain a more complete overview of the TP53 and CC regulatory landscape. To avoid including highly heterogenic datasets, we tested each TP53 expression dataset against the sum of the remaining datasets and consequently included 20 out of 22 datasets tested Figure 1E ; Supplementary Figures S3 and 4.
Since most studies included stringent thresholds to identify genes that are significantly differentially expressed, only a few genes were identified in all datasets. Despite the stringent thresholds, many genes were found differentially expressed in only one or two datasets displaying little reproducibility across datasets. These genes may represent either false negatives or, alternatively, genes that are regulated uniquely through certain treatment-cell type combinations.
Considering that the findings of our meta-analysis approach are based on the data provided by the underlying datasets, there is a bias toward genes regulated by Nutlin-3a or doxorubicin treatment as these were applied in most studies.
Treatment-cell type combinations that are present solely once have little influence on the whole meta-analysis. Although we find that many TP53 responsive genes are regulated by TP53 robustly across multiple cell types and treatments, our findings also support that there is cell type and treatment specific gene regulation by TP Thus, when investigating treatment-cell type combinations that are not represented, insights from our meta-analysis may be limited.
Integration of these various gene expression profiling datasets results in a robust joint dataset comprised of genes that were repeatedly identified to be regulated. By integrating publically available datasets, high confidence lists of TP53 and CC regulated genes were generated. This meta-analysis approach complemented incomplete information in individual studies with data from other studies with noise lowered using stringent thresholds.
In contrast, by including five additional binding studies of DREAM components, our meta-analysis approach identified 2, potential DREAM bound genes that display a binding peak in at least four out of the nine binding profiles. Out of these genes also met the criteria of being regulated across multiple conditions and were consequently predicted to be high confidence targets Supplementary Table S5.
Our scoring system is based on the number of datasets that agree on transcription factor binding sites or on a gene's regulation. The scoring system can be used as a measure of confidence and enables visual evaluation of the impact of additional studies in the meta-analysis. Furthermore, our approach visualizes thresholds and provides ranked maps of regulated genes. The stepwise meta-analysis approach refines our understanding of the TP53 and CC gene regulatory networks.
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It enabled us to integrate a variety of genome-wide gene expression datasets with transcription factor binding profiles from multiple treatments and cell types. Combining various types of studies revealed a more comprehensive insight into how gene expression is regulated by TP53 and the CC. Our meta-analysis approach also provided clear insight into the role of TP53 as an activator through proximal promoter binding Figure 2A—C.
In agreement with the latest model on TPdependent transcriptional regulation 10 , a direct repressor function of TP53 appears to be absent. The most striking finding was that the TP53 target gene and CDK inhibitor p21 is critical to TPmediated transcriptional downregulation in general Figure 3.
These results challenge gene regulatory models that do not incorporate p21 in mediating TPdependent transcriptional downregulation, such as E2F7 7 , multiple microRNAs 73 and long non-coding RNAs The meta-analysis approach also enabled identification of several interesting exceptions. Significantly, our target gene resource can help to identify co-expression signatures identified in other cell types and treatments.
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