RNA-cDNA hybrids mediate transposition via different mechanisms
Bibliographic record
Abstract
Retrotransposons can represent half of eukaryotic genomes. Retrotransposon dysregulation destabilizes genomes and has been linked to various human diseases. Emerging regulators of retromobility include RNA-DNA hybrid-containing structures known as R-loops. Accumulation of these structures at the transposons of yeast 1 (Ty1) elements has been shown to increase Ty1 retromobility through an unknown mechanism. Here, via a targeted genetic screen, we identified the rnh1Δ rad27Δ yeast mutant, which lacked both the Ty1 inhibitor Rad27 and the RNA-DNA hybrid suppressor Rnh1. The mutant exhibited elevated levels of Ty1 cDNA-associated RNA-DNA hybrids that promoted Ty1 mobility. Moreover, in this rnh1Δ rad27Δ mutant, but not in the double RNase H mutant rnh1Δ rnh201Δ, RNA-DNA hybrids preferentially existed as duplex nucleic acid structures and increased Ty1 mobility in a Rad52-dependent manner. The data indicate that in cells lacking RNA-DNA hybrid and Ty1 repressors, elevated levels of RNA-cDNA hybrids, which are associated with duplex nucleic acid structures, boost Ty1 mobility via a Rad52-dependent mechanism. In contrast, in cells lacking RNA-DNA hybrid repressors alone, elevated levels of RNA-cDNA hybrids, which are associated with triplex nucleic acid structures, boost Ty1 mobility via a Rad52-independent process. We propose that duplex and triplex RNA-DNA hybrids promote transposon mobility via Rad52-dependent or -independent mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".