BRCA1 heterozygosity promotes DNA damage-induced senescence in a sex-specific manner following repeated mild traumatic brain injury
Bibliographic record
Abstract
Emerging evidence suggests cellular senescence, as a consequence of excess DNA damage and deficient repair, to be a driver of brain dysfunction following repeated mild traumatic brain injury (rmTBI). This study aimed to further investigate the role of deficient DNA repair, specifically BRCA1-related repair, on DNA damage-induced senescence. BRCA1, a repair protein involved in maintaining genomic integrity with multiple roles in the central nervous system, was previously reported to be significantly downregulated in post-mortem brains with a history of rmTBI. Here we examined the effects of impaired BRCA1-related repair on DNA damage-induced senescence and outcomes 1-week post-rmTBI using mice with a heterozygous knockout for BRCA1 in a sex-segregated manner. Altered BRCA1 repair with rmTBI resulted in altered anxiety-related behaviours in males and females using elevated zero maze and contextual fear conditioning. Evaluating molecular markers associated with DNA damage signalling and senescence-related pathways revealed sex-specific differences attributed to BRCA1, where females exhibited elevated DNA damage, impaired DNA damage signalling, and dampened senescence onset compared to males. Overall, the results from this study highlight sex-specific consequences of aberrant DNA repair on outcomes post-injury, and further support a need to develop sex-specific treatments following rmTBI.
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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 | 0.001 | 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) | 0.003 | 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".