A statistical framework for predicting critical regions of p53-dependent enhancers

Abstract

P53 is the ‘guardian of the genome’ and is responsible for regulating cell cycle and apoptosis. The genomic p53 binding regions, where activating transcriptional factors and cofactors like p300 simultaneously bind, are called ‘p53-dependent enhancers’, which play an important role in tumorigenesis. Current experimental assays generally provide a broad peak of each enhancer element, leaving our knowledge about critical enhancer regions (CERs) limited. Under the inspiration of enhancer dissection by CRISPR-Cas9 screen library on genome-wide p53 binding sites, here we introduce a statistical framework called ‘Computational CRISPR Strategy’ (CCS), to predict whether a given DNA fragment will be a p53-dependent CER by employing 7-mer as feature extractions along with random forest as the regressor. When training on a p53 CRISPR enhancer dataset, CCS not only accurately fitted the top-ranked enriched single guide RNAs (sgRNAs) but also successfully reproduced two known CERs that were validated by experiments. When applying it to an independent testing dataset on a tilling of a 2K-b genomic region of CRISPR-deCDKN1A-Lib, the trained model shows great generalizability by identifying a CER containing five top-ranked sgRNAs. A feature importance analysis further indicates that top-ranked 7-mers are mapped onto informative TF motifs including POU5F1 and SOX5, which are differentially enriched in p53-dependent CERs and are potential factors to make a general p53 binding site to form a p53-dependent CER, providing the interpretability of the trained model. Our results demonstrate that CCS is an alternative way of the CRISPR experiment to screen the genome for mapping p53-dependent CERs.

Tumor suppressor p53: from engaging DNA to target gene regulation

“…A novel CRISPR-based screen identified CEBPB as a key regulator of the p53-responsive enhancer controlling CDKN1A expression and the establishment of a senescent cell state ( 151 ). Analysis of this CRISPR screen using multiple machine learning approaches identified additional TF motifs predicted to contribute to the activity of enhancers bound by p53 ( 152 ). p53 binding to nucleosome rich loci correlates with a stronger adherence to a consensus p53RE, whereas p53 binding to regions with high accessibility are linked to lower scoring or non-canonical p53RE motifs ( 25 , 26 ).…”

Bioinspired polydopamine nanoparticles as efficient antioxidative and anti-inflammatory enhancers against UV-induced skin damage

“…4 A, B). P53 is known as the “guardian of the genome” since p53 protects the integrity of DNA [ 26 ], therefore we validated p53 expression in vivo and in vitro . Immunofluorescence staining revealed that PDA NPs significantly reduced p53 expression in UVR-exposure lesions of mice (Fig.…”

Computational methods for identifying enhancer‐promoter interactions

“…In machine learning‐based methods, the enhancer/promoter identification problem can be reformulated into a binary classification problem (yes or no). Since 2010, support vector machine (SVM) [20,44,46,48,49,51,53,55,59,64,66,86,91,92,140,141], regression [45,60], random forest [47,58,63,65,66,101], boost‐based [50,66], and other traditional machine learning methods [52,56,61,62,83,84,87,93–96,103] have all been applied to predict enhancers and promoters. The SVM‐based method combined with feature selection has been the most used, even within the last three years.…”

“…), we recommend that users who do not want to run code using the web server iEnhancer‐2L [53] should identify enhancers and their strengths using pseudo k‐tuple nucleotide composition. For users who want to run code by themselves, we recommend that they choose gkm‐svm [48], REPTILE [58], and CCS [65]. These tools provide detailed information and example data for users to get up to speed and run them quickly.…”