Innovative Tools for DNA Topology Probing in Human Cells Reveal a Build-Up of Positive Supercoils Following Replication Stress at Telomeres and at the FRA3B Fragile Site
Bibliographic record
Abstract
Linear unconstrained DNA cannot harbor supercoils since these supercoils can diffuse and be eliminated by free rotation of the DNA strands at the end of the molecule. Mammalian telomeres, despite constituting the ends of linear chromosomes, can hold supercoils and be subjected to topological stress. While negative supercoiling was previously observed, thus proving the existence of telomeric topological constraints, positive supercoils were never probed due to the lack of an appropriate tool. Indeed, the few tools available currently could only investigate unwound (Trioxsalen) or overwound (GapR) DNA topology (variations in twist) but not the variations in writhe (supercoils and plectonemes). To address this question, we have designed innovative tools aimed at analyzing both positive and negative DNA writhe in cells. Using them, we could observe the build-up of positive supercoils following replication stress and inhibition of Topoisomerase 2 on telomeres. TRF2 depletion caused both telomere relaxation and an increase in positive supercoils while the inhibition of Histone Deacetylase I and II by TSA only caused telomere relaxation. Moving outside telomeres, we also observed a build-up of positive supercoils on the FRA3B fragile site following replication stress, suggesting a topological model of DNA fragility for this site.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".