Cis-acting regulatory sequences promote high-frequency gene conversion between repeated sequences in mammalian cells
Bibliographic record
Abstract
In mammalian cells, little is known about the nature of recombination-prone regions of the genome. Previously, we reported that the immunoglobulin heavy chain (IgH) mu locus behaved as a hotspot for mitotic, intrachromosomal gene conversion (GC) between repeated mu constant (Cmu) regions in mouse hybridoma cells. To investigate whether elements within the mu gene regulatory region were required for hotspot activity, gene targeting was used to delete a 9.1 kb segment encompassing the mu gene promoter (Pmu), enhancer (Emu) and switch region (Smu) from the locus. In these cell lines, GC between the Cmu repeats was significantly reduced, indicating that this 'recombination-enhancing sequence' (RES) is necessary for GC hotspot activity at the IgH locus. Importantly, the RES fragment stimulated GC when appended to the same Cmu repeats integrated at ectopic genomic sites. We also show that deletion of Emu and flanking matrix attachment regions (MARs) from the RES abolishes GC hotspot activity at the IgH locus. However, no stimulation of ectopic GC was observed with the Emu/MARs fragment alone. Finally, we provide evidence that no correlation exists between the level of transcription and GC promoted by the RES. We suggest a model whereby Emu/MARS enhances mitotic GC at the endogenous IgH mu locus by effecting chromatin modifications in adjacent DNA.
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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".