Data from: PITAR, a DNA damage-inducible Cancer/Testis long noncoding RNA, inactivates p53 by binding and stabilizing TRIM28 mRNA
Data files
Aug 09, 2024 version files 187.45 KB
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ChIRP_RNA-Seq_Data.xlsx
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README.md
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siFAM95B1_RNA-Seq_Data.xlsx
Abstract
In tumors with WT p53, alternate mechanisms of p53 inactivation are reported. Here, we have identified a long noncoding RNA, PITAR ( p 53 I nactivating T RIM28 a ssociated R NA), as an inhibitor of p53. PITAR is an oncogenic Cancer/testis lncRNA and is highly expressed in glioblastoma (GBM) and glioma stem-like cells (GSC). We establish that TRIM28 mRNA, which encodes a p53-specific E3 ubiquitin ligase, is a direct target of PITAR. PITAR interaction with TRIM28 RNA stabilized TRIM28 mRNA, which resulted in increased TRIM28 protein levels and reduced p53 steady-state levels due to enhanced p53 ubiquitination. DNA damage activated PITAR, in addition to p53, in a p53-independent manner, thus creating an incoherent feedforward loop to inhibit the DNA damage response by p53. While PITAR silencing inhibited the growth of WT p53 containing GSCs in vitro and reduced glioma tumor growth in vivo, its overexpression enhanced the tumor growth in a TRIM28-dependent manner and promoted resistance to Temozolomide. Thus, we establish an alternate way of p53 inactivation by PITAR, which maintains low p53 levels in normal cells and attenuates the DNA damage response by p53. Finally, we propose PITAR as a potential GBM therapeutic target.
README: Data from: PITAR, a DNA damage-inducible Cancer/Testis long noncoding RNA, inactivates p53 by binding and stabilizing TRIM28 mRNA
https://doi.org/10.5061/dryad.3j9kd51t7
We have submitted our normalized gene abundance data (ChIRP_RNA-Seq_Data.xlsx and siFAM95B1_RNA-Seq_Data.xlsx). The raw reads were quality-analyzed, and upon satisfactory assessment, Kallisto was used with default parameters to quantitate and obtain TPM-normalized gene abundance. The fold change was brought using Deseq2.
Descriptions:
ChIRP_RNA-Seq_Data
Chromatin Isolation by RNA Purification (ChIRP), performed using biotinylated FAM95B1 (PITAR) specific antisense probes labeled with Biotin-TEG at the 3′ ends. We incubated biotin probes with U87 cell lysates and then used Streptavidin C1 magnetic beads for RNA purification. The beads were then treated with RNA elution buffer to release the RNA, which was subsequently subjected to total RNA sequencing. Here we used three probes for RNA pulldown. The probes are given below:
1. Odd antisense probes labelled with Biotin-TEG at the 3′ ends (n=7)
2. **Even **antisense probes labelled with Biotin-TEG at the 3′ ends (n=7)
3. **LacZ **antisense probes labelled with Biotin-TEG at the 3′ ends (n=12)
The antisense probes sequence details provided in the materials and method section of the main article.
The ChIRP data demonstrated that 827 mRNAs were enriched in the pulldowns using even and odd antisense probes compared to LacZ probes. To choose the physiologically relevant target(s) for further studies, we intersected the ChIRP RNAs interactome with i) GBM-associated differentially expressed transcripts that show a significant positive correlation (p <0.05 and r > 0.25) with FAM95B1 (PITAR) transcript ii) GBM (Glioblastoma) upregulated transcripts and iii) GSC (Glioma stem cells) upregulated transcripts. This analysis identified 15 transcripts as potential targets of FAM95B1 (PITAR) among them TRIM28 is the selected target for our study.
siFAM95B1_RNA-Seq_Data
Total RNA was extracted from U87 cells with siNT and siFAM95B1 (PITAR), and Poly(A) RNA sequencing (RNA-seq) was performed in duplicates. RNA-Seq data of FAM95B1 (PITAR) silenced U87 cells identified 946 differentially regulated genes (526 upregulated and 420 downregulated). Gene ontology analysis of DEGs showed significant enrichment of biological processes such as "cell cycle," "apoptosis," and "p53". Furthermore, the Gene Set Enrichment Analysis (GSEA) showed significant enrichment of p53-regulated gene networks indicating that FAM95B1 (PITAR) executes its functions by interfering with p53 functions. All data are provided in main article.
Code/Software
Kallisto was used with default parameters to quantitate and obtain TPM-normalized gene abundance. The fold change was brought using Deseq2. IGV was used to visualize the raw reads. David and GSEA were performed both at log2fold 0.58 and p<0.05
Methods
The targets of LncRNA were identified using ChIRP-RNA sequencing, which was described earlier (Chu, Quinn and Chang, 2012). Antisense DNA probes for PITAR were designed using the Stellaris Probe Designer tool. Probes were labeled with Biotin-TEG at the 3′ ends. U87 cells were crosslinked with 1% glutaraldehyde for 10 min at 37 °C and then quenched with 0.125 M glycine buffer for 5 min. U87 cells were lysed in lysis buffer (50 mM Tris, pH 7.0, 10 mM EDTA, 1% SDS, DTT, PMSF, protease inhibitor, and RNase inhibitor) on ice for 30 min, and genomes were sonicated three times into fragments 300–500 bp in length. Chromatins were diluted twice the volume of hybridization buffer (750 mM NaCl, 1% SDS, 50 mM Tris, pH 7.0, 1 mM EDTA, 15% formamide, DTT, PMSF, protease inhibitor and RNase inhibitor). Biotin-TEG labeled probes (odd, even, and LacZ) were added, and mixtures were rotated at 37 °C for four hours. Streptavidin-magnetic C1 beads were blocked with 500 ng/µl yeast total RNA and 1 mg/ml BSA for one hour at 25 °C and washed three times before use. We incubated biotin probes with U87 cell lysates and then used Streptavidin C1 magnetic beads for capture. Finally, beads were resolved for RNA by the RNA elution buffer. The eluted RNA was subjected to RNA sequencing. The raw reads were quality-analyzed to quantify the abundance of mRNA in the ChIRP assay. Upon satisfactory assessment, Kallisto was used with default parameters to quantitate and obtain TPM-normalized gene abundance.
Total RNA was extracted from siNT and siFAM95B1 cells using the Trizol method (Qiagen). RNA quality was assessed using an Agilent TapeStation system, and a cDNA library was made. According to published protocols, each sample was sequenced using the Illumina HiSeq 2000 (with a 100-nt read length). To quantitate the abundance under this condition. The raw reads were quality-analyzed, and upon satisfactory assessment, Kallisto was used with default parameters to quantitate and obtain TPM-normalized gene abundance. The fold change was brought using Deseq2. David and GSEA were performed both at log2fold 0.58 and p<0.05. The gene expression matrix between siPITAR and Control was used to construct a volcano plot to visualize differentially expressed genes.