Regression based predictor for p53 transactivation

p53 positively regulates the expression of cancer stem cell marker CD133 in HCT116 colon cancer cells

Colon cancer stem cells (CSCs), which are highly capable of self-renewal and proliferation, are involved in colon tumorigenesis and response to therapy. CD133 is considered the most robust surface marker for colorectal cancer stem cells. Although the TP53 gene is frequently mutated in colon cancer, it remains not fully understood whether and how tumor protein p53 (p53) is associated with CD133 expression in colon cancer cells. In the present study, the expression of the CSC biomarker CD133 was investigated in terms of p53 status in colorectal carcinoma HCT116 cells. p53 wild-type HCT116 (HCT116 p53^+/+) and depleted HCT116 (HCT116 p53^-/-) cells were used throughout this study. Cells carrying the CSC biomarkers CD133 and CD44 were examined by flow cytometry. A dual-luciferase reporter assay was employed to further confirm the transcriptional regulation of the CD133 promoter by p53. The results demonstrated that there was a significant difference in the % of CD133-positive cells between the HCT116 p53^+/+ cell line (84.84±0.05%) and the HCT116 p53^-/- cell line (4.13±0.02%). The mRNA expression levels of CD133 in HCT116 p53^+/+ cells were also significantly higher compared with HCT116 p53^-/- cells. Knockdown of p53 by specific small interfering RNA greatly reduced the expression of CD133 in HCT116 p53^+/+ cells. Transcription factor binding site analysis indicated that there are several p53 binding elements in the CD133 promoter region. A dual-luciferase reporter assay further demonstrated the transcriptional activation of CD133 promoter by p53. In conclusion, these results suggest that p53 positively regulates the expression of CSC marker CD133 in the HCT116 human colon colorectal cancer cell line. p53 may be involved in the initiation and maintenance of colorectal cancer stem cells through regulating the expression of CD133.

A p53/miR-30a/ZEB2 axis controls triple negative breast cancer aggressiveness

Inactivation of p53 contributes significantly to the dismal prognosis of breast tumors, most notably triple-negative breast cancers (TNBCs). How the relief from p53 tumor suppressive functions results in tumor cell aggressive behavior is only partially elucidated. In an attempt to shed light on the implication of microRNAs in this context, we discovered a new signaling axis involving p53, miR-30a and ZEB2. By an in silico approach we identified miR-30a as a putative p53 target and observed that in breast tumors reduced miR-30a expression correlated with p53 inactivation, lymph node positivity and poor prognosis. We demonstrate that p53 binds the MIR30A promoter and induces the transcription of both miRNA strands 5p and 3p. Both miR-30a-5p and -3p showed the capacity of targeting ZEB2, a transcription factor involved in epithelial–mesenchymal transition (EMT), tumor cell migration and drug resistance. Intriguingly, we found that p53 does restrain ZEB2 expression via miR-30a. Finally, we provide evidence that the new p53/miR-30a/ZEB2 axis controls tumor cell invasion and distal spreading and impinges upon miR-200c expression. Overall, this study highlights the existence of a novel axis linking p53 to EMT via miR-30a, and adds support to the notion that miRNAs represent key elements of the complex network whereby p53 inactivation affects TNBC clinical behavior.

Whole-genome cartography of p53 response elements ranked on transactivation potential

Background

Many recent studies using ChIP-seq approaches cross-referenced to trascriptome data and also to potentially unbiased in vitro DNA binding selection experiments are detailing with increasing precision the p53-directed gene regulatory network that, nevertheless, is still expanding. However, most experiments have been conducted in established cell lines subjected to specific p53-inducing stimuli, both factors potentially biasing the results.

Results

We developed p53retriever, a pattern search algorithm that maps p53 response elements (REs) and ranks them according to predicted transactivation potentials in five classes. Besides canonical, full site REs, we developed specific pattern searches for non-canonical half sites and 3/4 sites and show that they can mediate p53-dependent responsiveness of associated coding sequences. Using ENCODE data, we also mapped p53 REs in about 44,000 distant enhancers and identified a 16-fold enrichment for high activity REs within those sites in the comparison with genomic regions near transcriptional start sites (TSS). Predictions from our pattern search were cross-referenced to ChIP-seq, ChIP-exo, expression, and various literature data sources. Based on the mapping of predicted functional REs near TSS, we examined expression changes of thirteen genes as a function of different p53-inducing conditions, providing further evidence for PDE2A, GAS6, E2F7, APOBEC3H, KCTD1, TRIM32, DICER, HRAS, KITLG and TGFA p53-dependent regulation, while MAP2K3, DNAJA1 and potentially YAP1 were identified as new direct p53 target genes.

Conclusions

We provide a comprehensive annotation of canonical and non-canonical p53 REs in the human genome, ranked on predicted transactivation potential. We also establish or corroborate direct p53 transcriptional control of thirteen genes. The entire list of identified and functionally classified p53 REs near all UCSC-annotated genes and within ENCODE mapped enhancer elements is provided. Our approach is distinct from, and complementary to, existing methods designed to identify p53 response elements. p53retriever is available as an R package at: http://tomateba.github.io/p53retriever.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1643-9) contains supplementary material, which is available to authorized users.

Identification of new p53 target microRNAs by bioinformatics and functional analysis

BACKGROUND

The tumor suppressor p53 is a sequence-specific transcription factor that regulates an extensive network of coding genes, long non-coding RNAs and microRNAs, that establish intricate gene regulatory circuits influencing many cellular responses beyond the prototypical control of cell cycle, apoptosis and DNA repair.

METHODS

Using bioinformatic approaches, we identified an additional group of candidate microRNAs (miRs) under direct p53 transcriptional control. To validate p53 family-mediated responsiveness of the newly predicted target miRs we first evaluated the potential for wild type p53, p63β and p73β to transactivate from p53 response elements (REs) mapped in the miR promoters, using an established yeast-based assay.

RESULTS

The REs found in miR-10b, -23b, -106a, -151a, -191, -198, -202, -221, -320, -1204, -1206 promoters were responsive to p53 and 8 of them were also responsive to p63β or p73β. The potential for germline p53 mutations to drive transactivation at selected miR-associated REs was also examined. Chromatin Immuno-Precipitation (ChIP) assays conducted in doxorubicin-treated MCF7 cells and HCT116 p53+/+ revealed moderate induction of p53 occupancy at the miR-202, -1204, -1206, -10b RE-containing sites, while weak occupancy was observed for the miR-23b-associated RE only in MCF7 cells. RT-qPCR analyses cells showed modest doxorubicin- and/or Nutlin-dependent induction of the levels of mature miR-10b, -23b, -151a in HCT116 p53+/+ and MCF7 cells. The long noncoding RNA PVT1 comprising miR-1204 and -1206 was weakly induced only in HCT116 p53+/+ cells, but the mature miRs were not detected. miR-202 expression was not influenced by p53-activating stimuli in our cell systems.

CONCLUSIONS

Our study reveals additional miRs, particularly miR-10b and miR-151a, that could be directly regulated by the p53-family of transcription factors and contribute to the tuning of p53-induced responses.

PhysBinder: Improving the prediction of transcription factor binding sites by flexible inclusion of biophysical properties

The most important mechanism in the regulation of transcription is the binding of a transcription factor (TF) to a DNA sequence called the TF binding site (TFBS). Most binding sites are short and degenerate, which makes predictions based on their primary sequence alone somewhat unreliable. We present a new web tool that implements a flexible and extensible algorithm for predicting TFBS. The algorithm makes use of both direct (the sequence) and several indirect readout features of protein-DNA complexes (biophysical properties such as bendability or the solvent-excluded surface of the DNA). This algorithm significantly outperforms state-of-the-art approaches for in silico identification of TFBS. Users can submit FASTA sequences for analysis in the PhysBinder integrative algorithm and choose from >60 different TF-binding models. The results of this analysis can be used to plan and steer wet-lab experiments. The PhysBinder web tool is freely available at http://bioit.dmbr.ugent.be/physbinder/index.php.

CFMDS: CUDA-based fast multidimensional scaling for genome-scale data

Background

Multidimensional scaling (MDS) is a widely used approach to dimensionality reduction. It has been applied to feature selection and visualization in various areas. Among diverse MDS methods, the classical MDS is a simple and theoretically sound solution for projecting data objects onto a low dimensional space while preserving the original distances among them as much as possible. However, it is not trivial to apply it to genome-scale data (e.g., microarray gene expression profiles) on regular desktop computers, because of its high computational complexity.

Results

We implemented a highly-efficient software application, called CFMDS (CUDA-based Fast MultiDimensional Scaling), which produces an approximate solution of the classical MDS based on CUDA (compute unified device architecture) and the divide-and-conquer principle. CUDA is a parallel computing architecture exploiting the power of the GPU (graphics processing unit). The principle of divide-and-conquer was adopted for circumventing the small memory problem of usual graphics cards. Our application software has been tested on various benchmark datasets including microarrays and compared with the classical MDS algorithms implemented using C# and MATLAB. In our experiments, CFMDS was more than a hundred times faster for large data than such general solutions. Regarding the quality of dimensionality reduction, our approximate solutions were as good as those from the general solutions, as the Pearson's correlation coefficients between them were larger than 0.9.

Conclusions

CFMDS is an expeditious solution for the data dimensionality reduction problem. It is especially useful for efficient processing of genome-scale data consisting of several thousands of objects in several minutes.

A flexible integrative approach based on random forest improves prediction of transcription factor binding sites

Transcription factor binding sites (TFBSs) are DNA sequences of 6–15 base pairs. Interaction of these TFBSs with transcription factors (TFs) is largely responsible for most spatiotemporal gene expression patterns. Here, we evaluate to what extent sequence-based prediction of TFBSs can be improved by taking into account the positional dependencies of nucleotides (NPDs) and the nucleotide sequence-dependent structure of DNA. We make use of the random forest algorithm to flexibly exploit both types of information. Results in this study show that both the structural method and the NPD method can be valuable for the prediction of TFBSs. Moreover, their predictive values seem to be complementary, even to the widely used position weight matrix (PWM) method. This led us to combine all three methods. Results obtained for five eukaryotic TFs with different DNA-binding domains show that our method improves classification accuracy for all five eukaryotic TFs compared with other approaches. Additionally, we contrast the results of seven smaller prokaryotic sets with high-quality data and show that with the use of high-quality data we can significantly improve prediction performance. Models developed in this study can be of great use for gaining insight into the mechanisms of TF binding.