DDX5 prevents RNA accumulation at DNA double strand breaks to facilitate end resection and homologous recombination repair
Bibliographic record
Abstract
An R-loop is a DNA:RNA hybrid formed when a nascent RNA molecule displaces the non-template DNA strand, forming a single stranded DNA "loop".R-loops serve multiple cellular functions such as the restriction of DNA methylation at promoter regions and the facilitation of RNA polymerase II pausing to ensure efficient transcriptional termination.Yet, unresolved Rloops may cause transcription-replication conflicts that can lead to double strand breaks (DSBs), the most toxic form of DNA damage.Moreover, R-loops may form at the sites of DSBs themselves, predominantly in transcriptionally active loci or through DSB ends acting as de novo promoters.Our lab has previously identified the dead box helicase 5 (DDX5) as an unwinder of DNA:RNA hybrids both during transcription and at DSB sites.Previous studies have shown that DDX5 interacts with breast cancer associated 2 (BRCA2) at DSBs with elevated DNA:RNA hybrid levels, and its knockdown leads to defects in both the non-homologous end joining (NHEJ) and homologous recombination (HR) DNA repair pathways.Herein, through DNA-RNA immunoprecipitation sequencing (DRIP-seq), we show that DDX5-deficient U2OS cells display a global increase of DNA:RNA hybrids at sites of DSBs compared to control cells following DNA damage.This increase extends several kilobases on either side of the break site.At DSB sites with elevated R-loop levels, DDX5-deficient cells exhibit decreased amounts of ssDNA generated via end resection, suggesting that DDX5 removes R-loops to facilitate this process.Additionally, knockdown of DDX5 was shown to lead to a sites three-fold decrease in DNA ligase 4 (LigIV) recruitment to DSBs as shown by chromatin immunoprecipitation (ChIP)-qPCR, although this Preface This thesis is written in a traditional, non-manuscript-based format.All experiments, figure conceptualization, and text written were accomplished by the author.Bioinformatic analysis and figure generation used for Figures 3.1B, 3.2, 3.3B-E, and 3.4 were performed courtesy of Oscar D. Villarreal and are described in-depth throughout Section 2.7.Please note that while the research objectives, results, and discussion sections may refer to experiments performed by "we", this refers to those performed by the author only.An exception is made for Figure 3.2, which is a reanalysis of data previously published by Villarreal et al. (1).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".