TRIM25 promotes glioblastoma cell growth and invasion via regulation of the PRMT1/c-MYC pathway by targeting the splicing factor NONO

BACKGROUND

Ubiquitination plays an important role in proliferating and invasive characteristic of glioblastoma (GBM), similar to many other cancers. Tripartite motif 25 (TRIM25) is a member of the TRIM family of proteins, which are involved in tumorigenesis through substrate ubiquitination.

METHODS

Difference in TRIM25 expression levels between nonneoplastic brain tissue samples and primary glioma samples was demonstrated using publicly available glioblastoma database, immunohistochemistry, and western blotting. TRIM25 knockdown GBM cell lines (LN229 and U251) and patient derived GBM stem-like cells (GSCs) GBM#021 were used to investigate the function of TRIM25 in vivo and in vitro. Co-immunoprecipitation (Co-IP) and mass spectrometry analysis were performed to identify NONO as a protein that interacts with TRIM25. The molecular mechanisms underlying the promotion of GBM development by TRIM25 through NONO were investigated by RNA-seq and validated by qRT-PCR and western blotting.

RESULTS

We observed upregulation of TRIM25 in GBM, correlating with enhanced glioblastoma cell growth and invasion, both in vitro and in vivo. Subsequently, we screened a panel of proteins interacting with TRIM25; mass spectrometry and co-immunoprecipitation revealed that NONO was a potential substrate of TRIM25. TRIM25 knockdown reduced the K63-linked ubiquitination of NONO, thereby suppressing the splicing function of NONO. Dysfunctional NONO resulted in the retention of the second intron in the pre-mRNA of PRMT1, inhibiting the activation of the PRMT1/c-MYC pathway.

CONCLUSIONS

Our study demonstrates that TRIM25 promotes glioblastoma cell growth and invasion by regulating the PRMT1/c-MYC pathway through mediation of the splicing factor NONO. Targeting the E3 ligase activity of TRIM25 or the complex interactions between TRIM25 and NONO may prove beneficial in the treatment of GBM.

IRFinder-S: a comprehensive suite to discover and explore intron retention

Accurate quantification and detection of intron retention levels require specialized software. Building on our previous software, we create a suite of tools called IRFinder-S, to analyze and explore intron retention events in multiple samples. Specifically, IRFinder-S allows a better identification of true intron retention events using a convolutional neural network, allows the sharing of intron retention results between labs, integrates a dynamic database to explore and contrast available samples, and provides a tested method to detect differential levels of intron retention.

SPLICE-q: a Python tool for genome-wide quantification of splicing efficiency

BACKGROUND

Introns are generally removed from primary transcripts to form mature RNA molecules in a post-transcriptional process called splicing. An efficient splicing of primary transcripts is an essential step in gene expression and its misregulation is related to numerous human diseases. Thus, to better understand the dynamics of this process and the perturbations that might be caused by aberrant transcript processing it is important to quantify splicing efficiency.

RESULTS

Here, we introduce SPLICE-q, a fast and user-friendly Python tool for genome-wide SPLICing Efficiency quantification. It supports studies focusing on the implications of splicing efficiency in transcript processing dynamics. SPLICE-q uses aligned reads from strand-specific RNA-seq to quantify splicing efficiency for each intron individually and allows the user to select different levels of restrictiveness concerning the introns' overlap with other genomic elements such as exons of other genes. We applied SPLICE-q to globally assess the dynamics of intron excision in yeast and human nascent RNA-seq. We also show its application using total RNA-seq from a patient-matched prostate cancer sample.

CONCLUSIONS

Our analyses illustrate that SPLICE-q is suitable to detect a progressive increase of splicing efficiency throughout a time course of nascent RNA-seq and it might be useful when it comes to understanding cancer progression beyond mere gene expression levels. SPLICE-q is available at: https://github.com/vrmelo/SPLICE-q.

RNA-Seq approach for accurate characterization of splicing efficiency of yeast introns

Introns in different genes, or even different introns within the same gene, often have different splice sites and differ in splicing efficiency (SE). One expects mass-transcribed genes to have introns with higher SE than weakly transcribed genes. However, such a simple expectation cannot be tested directly because variable SE for these genes is often not measured. Mechanistically, SE should depend on signal strength at key splice sites (SS) such as 5'SS, 3'SS and branchpoint site (BPS), i.e., SE = F(5'SS, 3'SS, BPS). However, without SE, we again cannot model how these splice sites contribute to SE. Here I present an RNA-Seq approach to quantify SE for each of the 304 introns in yeast (Saccharomyces cerevisiae) genes, including 24 in the 5'UTR, by measuring 1) number of reads mapped to exon-exon junctions (N_EE) as a proxy for the abundance of spliced form, and 2) number of reads mapped to exon-intron junction (N_EI5 and N_EI3 at 5' and 3' ends of intron) as a proxy for the abundance of unspliced form. The total mRNA is N_Total = N_EE + p * N_EI5 + (1-p) * N_EI3, with the simplest p = 0.5 but statistical methods were presented to estimate p from data. An estimated p is needed because N_EI5 is expected to be smaller than N_EI3 due to 1) step 1 splicing occurs before step 2 so EI5 is broken before EI3, 2) enrichment of poly(A) mRNA by oligo-dT, and 3) 5' degradation. SE is defined as the proportion (N_EE/N_Total). Application of the method shows that ribosomal protein messages are efficiently and mostly cotranscriptionally spliced. Yeast genes with long introns are also spliced efficiently. HAC1/YFL031W is poorly spliced partly because its splicing involves a nonspliceosome mechanism and partly because Ire1p, which participate in splicing HAC1, is hardly expressed. Many putative yeast genes have low SE, and some splice sites are incorrectly annotated.