Perinuclear tethers license telomeric DSBs for a broad kinesin- and NPC-dependent DNA repair process
Bibliographic record
Abstract
DNA double-strand breaks (DSBs) are often targeted to nuclear pore complexes (NPCs) for repair. How targeting is achieved and the DNA repair pathways involved in this process remain unclear. Here, we show that the kinesin-14 motor protein complex (Cik1–Kar3) cooperates with chromatin remodellers to mediate interactions between subtelomeric DSBs and the Nup84 nuclear pore complex to ensure cell survival via break-induced replication (BIR), an error-prone DNA repair process. Insertion of a DNA zip code near the subtelomeric DSB site artificially targets it to NPCs hyperactivating this repair mechanism. Kinesin-14 and Nup84 mediate BIR-dependent repair at non-telomeric DSBs whereas perinuclear telomere tethers are only required for telomeric BIR. Furthermore, kinesin-14 plays a critical role in telomerase-independent telomere maintenance. Thus, we uncover roles for kinesin and NPCs in DNA repair by BIR and reveal that perinuclear telomere anchors license subtelomeric DSBs for this error-prone DNA repair mechanism. Damaged DNA is often targeted to nuclear pore complexes for repair. Here, the authors show that kinesin-14 mediates this process ensuring error-prone repair, while perinuclear telomere attachment licenses damaged telomeric loci for this repair and kinesin-14 blocks senescence in the absence of telomerase.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".