Processing of double-R-loops in (CAG)·(CTG) and C9orf72 (GGGGCC)·(GGCCCC) repeats causes instability
Bibliographic record
Abstract
R-loops, transcriptionally-induced RNA:DNA hybrids, occurring at repeat tracts (CTG)n, (CAG)n, (CGG)n, (CCG)n and (GAA)n, are associated with diseases including myotonic dystrophy, Huntington's disease, fragile X and Friedreich's ataxia. Many of these repeats are bidirectionally transcribed, allowing for single- and double-R-loop configurations, where either or both DNA strands may be RNA-bound. R-loops can trigger repeat instability at (CTG)·(CAG) repeats, but the mechanism of this is unclear. We demonstrate R-loop-mediated instability through processing of R-loops by HeLa and human neuron-like cell extracts. Double-R-loops induced greater instability than single-R-loops. Pre-treatment with RNase H only partially suppressed instability, supporting a model in which R-loops directly generate instability by aberrant processing, or via slipped-DNA formation upon RNA removal and its subsequent aberrant processing. Slipped-DNAs were observed to form following removal of the RNA from R-loops. Since transcriptionally-induced R-loops can occur in the absence of DNA replication, R-loop processing may be a source of repeat instability in the brain. Double-R-loop formation and processing to instability was extended to the expanded C9orf72 (GGGGCC)·(GGCCCC) repeats, known to cause amyotrophic lateral sclerosis and frontotemporal dementia, providing the first suggestion through which these repeats may become unstable. These findings provide a mechanistic basis for R-loop-mediated instability at disease-associated repeats.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".