DNAR-11. Differential DNA Damage and Repair burden in proliferating neural progenitor cells
Bibliographic record
Abstract
Abstract While radiation therapy offers strong anti-cancer benefits, it can also have serious side effects for children undergoing brain tumor treatment. These include disruptions in white matter development, delays in functional and cognitive growth, and an increased risk of secondary tumors within the area that was treated. Age-dependent susceptibility to these adverse effects is tightly linked to critical windows of neurodevelopment, particularly during the periods of active neural cell differentiation, maturation, and the rapid onset of myelination in infancy and early childhood. We previously demonstrated that oligodendrocyte precursor cells (OPCs) exhibit heightened sensitivity to IR-induced DNA damage compared to other neural stem/progenitor cells (NSPC) populations – a difference that is associated with their decreased ability to form RAD51 filaments (a critical step in RAD51-mediated homologous recombination repair of DNA double-strand breaks). Recently, we performed genome-wide chromatin immunoprecipitation followed by sequencing (ChIP-seq) to map endogenous γH2AX binding sites—a sensitive marker of DNA damage response—in both S-phase OPCs and NSPCs. Our data identified 71,794 γH2AX-enriched regions in OPCs versus 29,154 in NSPCs, highlighting the elevated genotoxic stress burden in OPCs. Interestingly, we observed γH2AX binding is depleted at transcription start sites and transcription termination sites, however OPCs show more γH2AX peaks compare to NSPCs across all protein-coding genes. Furthermore, our data show cell type specific γH2AX binding sites. Together, these findings establish a comprehensive and unbiased genomic framework for elucidating the mechanisms underlying OPC vulnerability to DNA damage. Future studies will examine different DNA damage pattern in response to radiation.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".