DSB structure impacts DNA recombination leading to class switching and chromosomal translocations in human B cells
Bibliographic record
Abstract
Class switch recombination (CSR) requires activation-induced cytidine deaminase (AID) to trigger DNA double strand breaks (DSBs) at the immunoglobulin heavy chain (IGH) in B cells. Joining of AID-dependent DSBs within IGH facilitate CSR and effective humoral immunity, but ligation to DSBs in non-IGH chromosomes leads to chromosomal translocations. Thus, the mechanism by which AID-dependent DSBs are repaired requires careful examination. The random activity of AID in IGH leads to a spectrum of DSB structures. In this report, we investigated how DSB structure impacts end-joining leading to CSR and chromosomal translocations in human B cells, for which models of CSR are inefficient and not readily available. Using CRISPR/Cas9 to model AID-dependent DSBs in IGH and non-IGH genes, we found that DSBs with 5' and 3' overhangs led to increased processing during end-joining compared to blunt DSBs. We observed that 5' overhangs were removed and 3' overhangs were filled in at recombination junctions, suggesting that different subsets of enzymes are required for repair based on DSB polarity. Surprisingly, while Cas9-mediated switching preferentially utilized NHEJ regardless of DSB structure, A-EJ strongly preferred repairing blunt DSBs leading to translocations in the absence of NHEJ. We found that DSB polarity influenced frequency of Cas9-mediated switching and translocations more than overhang length. Lastly, recombination junctions from staggered DSBs exhibited templated insertions, suggesting iterative resection and filling in during repair. Our results demonstrate that DSB structure biases repair towards NHEJ or A-EJ to complete recombination leading to CSR and translocations, thus helping to elucidate the mechanism of genome rearrangements in human B cells.
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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) | 0.001 | 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 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".