Biology subjects

Bishop, A. J.

Publications and source records attributed to Bishop, A. J..

3 recordsLinked to original sources

bioRxiv2023

RNA Helicase DDX3 Regulates RAD51 Localization and DNA Damage Repair in Ewing Sarcoma

We previously demonstrated that RNA helicase DDX3X (DDX3) can be a therapeutic target in Ewing sarcoma (EWS), but its role in EWS biology remains unclear. The present work demonstrates that DDX3 plays a unique role in DNA damage repair (DDR). We show that DDX3 interacts with several proteins involved in homologous recombination, including RAD51, RECQL1, RPA32, and XRCC2. In particular, DDX3 colocalizes with RAD51 and RNA:DNA hybrid structures in the cytoplasm of EWS cells. Inhibition of DDX3 RNA helicase activity increases cytoplasmic RNA:DNA hybrids, sequestering RAD51 in the cytoplasm, which impairs nuclear translocation of RAD51 to sites of double-stranded DNA breaks thus increasing sensitivity of EWS to radiation treatment, both in vitro and in vivo. This discovery lays the foundation for exploring new therapeutic approaches directed at manipulating DDR protein localization in solid tumors.

cancer biology↗

bioRxiv2021

Quality-controlled R-loop meta-analysis reveals the characteristics of R-Loop consensus regions

R-loops are three-stranded nucleic acid structures formed from the hybridization of RNA and DNA during transcription. While the pathological consequences of R-loops have been well-studied to date, the locations, classes, and dynamics of physiological R-loops remain poorly understood. R-loop mapping studies provide insight into R-loop dynamics, but their findings are challenging to generalize. This is due to the narrow biological scope of individual studies, the limitations of each mapping modality, and, in some cases, poor data quality. In this study, we reprocessed 693 R-loop mapping datasets from a wide array of biological conditions and mapping modalities. From this data resource, we developed an accurate method for R-loop data quality control, and we reveal the extent of poor-quality data within previously published studies. We then identified a set of high-confidence R-loop mapping samples and used them to define consensus R-loop sites called "R-loop regions" (RL regions). In the process, we revealed the stark divergence between S9.6 and dRNH-based R-loop mapping methods and identified biologically meaningful subtypes of both constitutive and variable R-loops. Taken together, this work provides a much-needed method to assess R-loop data quality and reveals intriguing aspects of R-loop biology.

bioinformatics↗

bioRxiv2021

Exploration and analysis of R-loop mapping data with RLBase

R-loops are three-stranded nucleic acid structures formed from the hybridization of RNA and DNA during nascent transcription. In 2012, Ginno et al. introduced the first R-loop mapping method, DNA:RNA immunoprecipitation (DRIP) sequencing. Since that time, dozens of studies have implemented R-loop mapping and new high-resolution techniques have been developed. The resulting datasets have tremendous potential to reveal the causes and consequences of R-loops genome-wide. However, poor quality and variability between mapping approaches pose serious barriers to the meta-analysis of these data. In our recent work, we reprocessed 693 R-loop mapping samples, devising new quality methods, defining a set of high-confidence mapping samples, and then deriving R-loop regions, consensus sites of R-loop formation. This analysis yielded the largest R-loop data resource to date along with novel computational approaches for R-loop mapping analysis. Now, we introduce RLBase, an innovative web server which builds upon those data and software by providing users with the capability to (1) explore hundreds of public R-loop mapping datasets, (2) explore consensus R-loop regions, (3) analyze user-supplied datasets to generate an HTML quality report, and (4) download all the processed data for the 693 samples we previously reprocessed and standardized. In addition to RLBase, we also describe the other software which, along with RLBase, provides a computational framework for R-loop bioinformatics. RLBase, and the rest of these software (termed "RLSuite"), are provided freely under an MIT license and made publicly available: https://gccri.bishop-lab.uthscsa.edu/rlsuite/. RLBase is directly accessible via the following URL: https://gccri.bishop-lab.uthscsa.edu/rlbase/.

bioinformatics↗