Charting the regulatory landscape of TP53 on transposable elements in cancer [ChIP-seq]
收藏NIAID Data Ecosystem2026-05-02 收录
下载链接:
https://www.ncbi.nlm.nih.gov/sra/SRP496771
下载链接
链接失效反馈官方服务:
资源简介:
The relationship between p53 and transposable elements (TEs) has been obscure. Thus, comprehensive profiling of TE-derived transcripts dynamics under the regulation of p53 provides valuable resources for more clarity in p53's roles in cancer. In this study, we created three cancer cell lines with p53 genetic status as the only variable and combined long-read RNA-seq and short-read RNA-seq to define TE and TE-derived transcripts' regulatory dynamics. We identified in total 2,503 transcripts that use TE as potential promoters, among which, 141 to 210 are activated by p53. We found ERVs to be a main driver of potential promoters, followed by LINEs. Epigenomic profiling including chromatin accessibility and DNA methylation provided additional support for active promoter potential for p53 upregulated TE-derived transcripts. Short-term restoration of p53 partially recovered chronic p53-regulated TE-derived transcript profile but gain of function TP53 mutations, R175H and R273H, did not show evidence to act via TE network. Overall, we provide a controlled isogenic cancer cell line system with TP53 mutation status as the only genetic variable, deliver a high confidence TE and TE-derived transcript atlas and comprehensively identify active TE promoters that are direct and indirect targets of p53. Overall design: To investigate p53 regulation on TE, we used three pairs of cancer cell lines, A549, HCT116 and RKO. Each pair has one sample with endogenous WT TP53 and one sample with CRISPR knock down of TP53. With lentiviral transduction, we introduced WT TP53, mutant TP53 R175H, mutant TP53 R273H and empty vector to cells with TP53 KO background. All cells are treated with either doxorubicin for p53 activation or dmso as control. We then did gene expression analysis using RNA-seq. Using both short-read RNA-seq and long-read RNA-seq, we created an atlas detailing TE-derived transcripts. Transcript-level differential expression analysis was then conducted for high-confidence TE-derived transcripts.
创建时间:
2025-05-21



