The RNA processing factors THRAP3 and BCLAF1 promote the DNA damage response through selective mRNA splicing and nuclear export

mRNA splicing and export plays a key role in the regulation of gene expression, with recent evidence suggesting an additional layer of regulation of gene expression and cellular function through the selective splicing and export of genes within specific pathways. Here we describe a role for the RNA processing factors THRAP3 and BCLAF1 in the regulation of the cellular DNA damage response (DDR) pathway, a key pathway involved in the maintenance of genomic stability and the prevention of oncogenic transformation. We show that loss of THRAP3 and/or BCLAF1 leads to sensitivity to DNA damaging agents, defective DNA repair and genomic instability. Additionally, we demonstrate that this phenotype can be at least partially explained by the role of THRAP3 and BCLAF1 in the selective mRNA splicing and export of transcripts encoding key DDR proteins, including the ATM kinase. Moreover, we show that cancer associated mutations within THRAP3 result in deregulated processing of THRAP3/BCLAF1-regulated transcripts and consequently defective DNA repair. Taken together, these results suggest that THRAP3 and BCLAF1 mutant tumors may be promising targets for DNA damaging chemotherapy

A Workbench using Evolutionary Genetic Algorithms for analyzing association in TCGA Data

Modern methods of acquiring molecular data have improved rapidly in recent years, making it easier for researchers to collect large volumes of information. However, this has increased the challenge of recognizing interesting patterns within the data. Atlas Correlation Explorer (ACE) is a user-friendly workbench for seeking associations between attributes in The Cancer Genome Atlas (TCGA) database. It allows any combination of clinical and genomic data streams to be searched using an evolutionary algorithm approach. To showcase ACE, we assessed which RNA sequencing transcripts were associated with estrogen receptor (ESR1) in the TCGA breast cancer cohort. The analysis revealed already well-established associations with XBP1 and FOXA1, but also identified a strong association with CT62, a potential immunotherapeutic target with few previous associations with breast cancer. In conclusion, ACE can produce results for very large searches in a short time and will serve as an increasingly useful tool for biomarker discovery in the big data era. Significance: ACE uses an evolutionary algorithm approach to perform large searches for associations between any combinations of data in the TCGA database.</p

BCLAF1 Chip-chip control (siGFP) and BRCA1 depleted (siBRCA1) 293T cells in the absense or presense of Etoposide

BCLAF1 is a serine-arginine (SR) protein implicated in transcriptional regulation and mRNA splicing. We have recently identified BCLAF1 as part of a novel mRNA splicing complex that is recruited to different genetic promoters by the breast cancer susceptiblity protein, BRCA1 in response to DNA damage. This ChIP-chip study was designed to identify genes/promoters regulated by the BRCA1/BCLAG1 mRNA splicing complex by identifying promoters bound by BCLAF1 in the absense and presense of BRCA1 in control cells and cells treated with etoposide to induce DNA damage. This study includes tripicate BCLAF1 ChIP-chip experiments in untreated and etoposide treated (1uM 16 hours) control cells (siGFP) and cells depleted of BRCA1 (siBRCA1)

Net number of γ-H2AX foci induced by the second radiation exposure.

<p>Estimated number of γ-H2AX foci induced by the second irradiation only as calculated by subtracting the predicted residual number of foci from the first exposure (using the single irradiation kinetic data) from the total number of foci measured 30 minutes after the second irradiation. </p

Clonogenic data survival following single and split dose irradiations.

<p>Comparison of clonogenic survival following acute (2 Gy) and split dose (1 Gy + 1 Gy) exposures with 1 hr time gap. Error bars represent the standard error of the mean of 3 independent experiments.</p

γ-H2AX foci kinetics following split multiple radiation exposures.

<p>Number of γ-H2AX foci per cell after exposure to 1 Gy at time 0 plus 1 Gy delivered20 min later (panel A), 1 hour later (panel B), 2 hours later (panel C), 5 hours later (panel D) and 12 hours later (panel E) using 225 kV_p X-rays. In Panel F representative pictures of cells in different exposures scenario are presented, i.e. cells fixed after 30 minutes in the cases of 20 minutes split dose exposure (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0079541#pone-0079541-g001" target="_blank">Figure 1</a>), 1 hour split dose exposure (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0079541#pone-0079541-g002" target="_blank">Figure 2</a>), 2 hours split dose exposure (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0079541#pone-0079541-g003" target="_blank">Figure 3</a>) and 5 hours split dose exposure (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0079541#pone-0079541-g004" target="_blank">Figure 4</a>). Error bars represent one standard error of the mean of 3 independent experiments. Lines are guides for the eyes.</p

Split dose irradiations with small initial dose.

<p>Number of γ-H2AX foci after exposure to 0.1 Gy at time 0 plus 1 Gy delivered 1 hr after (left column) and 4 hr later (right column). Top panels report the experimental data;bottom panels, data are fitted using the modelling <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0079541#eqn4" target="_blank">equation (4)</a>. In the bottom panels, the solid line represents the modelling function for the split dose scenario. The dotted lines represent the modelling function for the individual radiation exposures. Error bars represent the standard error of the mean of 3 independent experiments. </p

Cancer-Associated SF3B1 Mutations Confer a BRCA-Like Cellular Phenotype and Synthetic Lethality to PARP Inhibitors

Mutations in SF3B1 have been identified across several cancer types. This key spliceosome component promotes the efficient mRNA splicing of thousands of genes including those with crucial roles in the cellular response to DNA damage. Here, we demonstrate that depletion of SF3B1 specifically compromises homologous recombination (HR) and is epistatic with loss of BRCA1. More importantly, the most prevalent cancer-associated mutation in SF3B1, K700E, also affects HR efficiency and as a consequence, increases the cellular sensitivity to ionising radiation and a variety of chemotherapeutic agents, including PARP inhibitors. Additionally, the SF3B1 K700E mutation induced unscheduled R-loop formation, replication fork stalling, increased fork degradation and defective replication fork restart. Taken together, these data suggest that tumour-associated mutations in SF3B1 induce a BRCA-like cellular phenotype that confers synthetic lethality to DNA damaging agents and PARP inhibitors, which can be exploited therapeutically

Impact of Variable RNA-Sequencing Depth on Gene Expression Signatures and Target Compound Robustness : Case Study Examining Brain Tumor (Glioma) Disease Progression

Purpose: Gene expression profiling can uncover biologic mechanisms underlying disease and is important in drug development. RNA sequencing (RNA-seq) is routinely used to assess gene expression, but costs remain high. Sample multiplexing reduces RNAseq costs; however, multiplexed samples have lower cDNA sequencing depth, which can hinder accurate differential gene expression detection. The impact of sequencing depth alteration on RNA-seq-based downstream analyses such as gene expression connectivity mapping is not known, where this method is used to identify potential therapeutic compounds for repurposing. Methods: In this study, published RNA-seq profiles from patients with brain tumor (glioma) were assembled into two disease progression gene signature contrasts for astrocytoma. Available treatments for glioma have limited effectiveness, rendering this a disease of poor clinical outcome. Gene signatures were subsampled to simulate sequencing alterations and analyzed in connectivity mapping to investigate target compound robustness. Results: Data loss to gene signatures led to the loss, gain, and consistent identification of significant connections. The most accurate gene signature contrast with consistent patient gene expression profiles was more resilient to data loss and identified robust target compounds. Target compounds lost included candidate compounds of potential clinical utility in glioma (eg, suramin, dasatinib). Lost connections may have been linked to low-abundance genes in the gene signature that closely characterized the disease phenotype. Consistently identified connections may have been related to highly expressed abundant genes that were ever-present in gene signatures, despite data reductions. Potential noise surrounding findings included false-positive connections that were gained as a result of gene signature modification with data loss. Conclusion: Findings highlight the necessity for gene signature accuracy for connectivity mapping, which should improve the clinical utility of future target compound discoveries.publishedVersionPeer reviewe