Insights into p53 transcriptional function via genome-wide chromatin occupancy and gene expression analysis

Nikulenkov, Fedor; Spinnler, Clemens; Li, H.; Tonelli, C.; Shi, Yiting; Turunen, Mikko; Kivioja, Teemu; Ignatiev, I.; Kel, Alexander; Taipale, Jussi; Selivanova, Galina

doi:10.1038/cdd.2012.89

Cited by 175 publications

(254 citation statements)

References 36 publications

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“…If so, then these artifacts should not be reproducible across different experimental conditions. To test this, we turned to other publicly available data sets and collected 15 different TP53 ChIP-seq experiments performed in seven different cell lines under different TP53-stimulating conditions (Supplemental Table S7; Smeenk et al 2011;Nikulenkov et al 2012;Zeron-Medina et al 2013;Botcheva and McCorkle 2014;McDade et al 2014;Sánchez et al 2014;Desantis et al 2015;Hünten et al 2015;Sammons et al 2015). After calling peaks for each experiment, we categorized all peaks in each data set as direct or indirect using the random forest model.…”

Section: Indirect Chip-seq Peaks Have No Regulatory Functionmentioning

confidence: 99%

Multiplex enhancer-reporter assays uncover unsophisticated TP53 enhancer logic

et al. 2016

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Transcription factors regulate their target genes by binding to regulatory regions in the genome. Although the binding preferences of TP53 are known, it remains unclear what distinguishes functional enhancers from nonfunctional binding. In addition, the genome is scattered with recognition sequences that remain unoccupied. Using two complementary techniques of multiplex enhancer-reporter assays, we discovered that functional enhancers could be discriminated from nonfunctional binding events by the occurrence of a single TP53 canonical motif. By combining machine learning with a meta-analysis of TP53 ChIP-seq data sets, we identified a core set of more than 1000 responsive enhancers in the human genome. This TP53 cistrome is invariably used between cell types and experimental conditions, whereas differences among experiments can be attributed to indirect nonfunctional binding events. Our data suggest that TP53 enhancers represent a class of unsophisticated cell-autonomous enhancers containing a single TP53 binding site, distinct from complex developmental enhancers that integrate signals from multiple transcription factors.

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Section: Indirect Chip-seq Peaks Have No Regulatory Functionmentioning

confidence: 99%

Multiplex enhancer-reporter assays uncover unsophisticated TP53 enhancer logic

et al. 2016

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“…On the basis of these 10 genes, an inclusion criteria to be used Data sets were obtained from three independent studies on p53 targets. Individual genes were highlighted with background colors when successfully validated by the current study (red), Wei et al 17 (purple) or Nikulenkov et al 18 (orange). (e) Overlap of well-characterized reported p53 targets validated by the current study (red), Wei et al 17 (purple) and Nikulenkov et al 18 (orange) as a cutoff point was calculated to be median FCZ0.248 (Po0.05).…”

Section: Resultsmentioning

confidence: 84%

“…Individual genes were highlighted with background colors when successfully validated by the current study (red), Wei et al 17 (purple) or Nikulenkov et al 18 (orange). (e) Overlap of well-characterized reported p53 targets validated by the current study (red), Wei et al 17 (purple) and Nikulenkov et al 18 (orange) as a cutoff point was calculated to be median FCZ0.248 (Po0.05). To enhance the identification of direct p53 targets, we also performed genome-wide chromatin immunoprecipitation coupled sequencing (ChIP-seq) on two different cell types HCT116 (p53 þ / þ ) and normal human peripheral blood mononuclear cells (PBMCs) and their combined data set were used to provide more robust coverage of chromosome segments.…”

Section: Resultsmentioning

confidence: 84%

“…Among the 664 genes, 72 were found to be well-characterized reported targets and they showed a high degree of concordance in their differential expression patterns with the remaining 592 novel gene targets (Supplementary Figure 8; Supplementary Tables 4-6). In contrast, only 8 genes from Wei et al 17 and 24 genes from Nikulenkov et al 18 were similarly found to be present (Figure 4a). The mRNA expression of these novel p53 targets in 11 p53WT-like SNP-transfected cell showed a strong correlation with p53WT, whereas the 5 p53null-like SNPs showed no correlation (Figures 4b-d; Supplementary Figure 8).…”

Section: Resultsmentioning

confidence: 87%

“…When analyzing these genes as a whole, the p53WT-like variants correlated strongly with the p53WT control ( Figure 3a Two other genome-scale studies that utilized comparable microarray profiling and chromatin-IP coupled sequencing techniques had listed a number of p53-regulated genes. A search within these 2 studies for genes that corresponded to the reference list of 135 reported p53 target genes showed that only 24/135 genes (17.8%, Wei et al 17 ) and 55/135 genes (40.7%, Nikulenkov et al 18 ) were correctly identified among the reference list. In comparison, our current study was able to validate 118/135 (87.4%) reported p53 target genes ( Figure 3d).…”

Section: Resultsmentioning

confidence: 99%

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Mapping the p53 transcriptome universe using p53 natural polymorphs

et al. 2013

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The tumor suppressor p53 has defined roles in varied cellular processes including apoptosis and DNA repair. While conventional genomic approaches have suggested a large number of p53 targets, there is a need for a systematic approach to validate these putative genes. We developed a method to identify and validate p53's transcriptional behavior by utilizing 16 non-synonymous p53 single-nucleotide polymorphism (SNP) variants. Five SNPs located within the DNA-binding domain of p53 were found to be functionally null, whereas the other 11 SNPs were p53WT-like in behavior. By integrating p53 ChIP-seq analysis with transcriptome data from the p53 SNP variants, 592 genes were identified as novel p53 targets. Many of these genes mapped to previously less well-characterized aspects of p53 function, such as cell signalling, metabolism, central nervous system, and immune system. These data provide pivotal insights into the involvement of p53 in diverse pathways of normal physiological processes and open new avenues for investigation of p53 function. Cell Death and Differentiation (2014) 21, 521-532; doi:10.1038/cdd.2013.132; published online 27 September 2013The tumor suppressor p53 acts as a master regulator of cellular stress responses through transcriptionally regulating specific target genes involved in diverse cellular functions including cell cycle arrest, apoptosis, and DNA repair. 1 With the aid of large-scale screening techniques, it has become clear that the scope of p53 functions is much broader than previously thought, 2,3 expanding to cellular and physiological processes such as metabolism 4 and development. 5 To have a better understanding of this important tumor suppressor, we must be able to study the full complexity of its downstream effects and this requires the efficient identification and validation of genes directly regulated by p53. To date, there are fewer than 150 validated p53 target genes 6-8 and at the same time validation studies have been slow, which is a limitation on the progress in this field.In order to be regulated by p53 a gene must possess the appropriate cognate response element (RE) within its regulatory region. However, our previous study has shown that simple sequence motif identification is not sufficient, as the p53RE exhibits variable binding kinetics that are dependent upon the dinucleotide core 'CWWG' combinations. 9 These variables often result in spurious outcomes and hence we are in need of more robust approaches to accurately identify p53 transcriptional networks.Here, we describe an unbiased method to review 135 reported p53 target genes and at the same time identify new p53 targets on a genome-wide scale. We utilized wild-type (WT) p53 together with 16 allelic replicates comprising all reported non-synonymous single-nucleotide polymorphism (SNP) variants of p53. As the transcriptional functions of these 16 SNPs have not been well studied, we first developed a customized luciferase assay with promoter reporter vectors encompassing all combinatorial forms of the dinucleotide ...

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Drastic Effect of GermlineTP53Missense Mutations in Li-Fraumeni Patients

et al. 2013

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In contrast to other tumor suppressor genes, the majority of TP53 alterations are missense mutations. We have previously reported that in the Li-Fraumeni syndrome (LFS), germline TP53 missense mutations are associated with an earlier age of tumor onset. In a larger series, we observed that mean age of tumor onset in patients harboring dominant negative missense mutations and clearly null mutations was 22.6 and 37.5 years, respectively. To assess the impact of heterozygous germline TP53 mutations in the genetic context of the patients, we developed a new functional assay of the p53 pathway on the basis of induction of DNA damage in Epstein-Barr-virus-immortalized lymphocytes, followed by comparative gene-expression profiling. In wild-type lymphocytes, we identified a core of 173 genes whose expression was induced more than twofold, of which 46 were known p53 target genes. In LFS lymphocytes with canonical missense mutations, the number of induced genes and the level of known p53 target genes induction were strongly reduced as compared with controls and LFS lymphocytes with null mutations. These results show that certain germline missense TP53 mutations, such as those with dominant negative effect, dramatically alter the response to DNA damage. This probably explains why TP53 alterations are predominantly missense mutations.

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Insights into p53 transcriptional function via genome-wide chromatin occupancy and gene expression analysis

Cited by 175 publications

References 36 publications

Multiplex enhancer-reporter assays uncover unsophisticated TP53 enhancer logic

Multiplex enhancer-reporter assays uncover unsophisticated TP53 enhancer logic

Mapping the p53 transcriptome universe using p53 natural polymorphs

Drastic Effect of GermlineTP53Missense Mutations in Li-Fraumeni Patients

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