Single-Strand Annealing Plays a Major Role in Double-Strand DNA Break Repair following CRISPR-Cas9 Cleavage in <i>Leishmania</i>
Bibliographic record
Abstract
Due to differences in double-strand DNA break (DSB) repair mechanisms, CRISPR-Cas9 gene editing efficiency can vary greatly in different organisms. In contrast to mammalian cells, the protozoan parasite Leishmania uses microhomology-mediated end joining (MMEJ) and, occasionally, homology-directed repair (HDR) to repair DSBs but lacks the nonhomologous end-joining pathway. Here, we show that Leishmania predominantly uses single-strand annealing (SSA) instead of MMEJ for DSB repairs (DSBR), resulting in large deletions that can include multiple genes. This strongly indicates that the overall DSBR in Leishmania is inefficient and therefore can influence the outcome of CRISPR-Cas9 gene editing, highlighting the importance of using a donor DNA to improve gene editing fidelity and efficiency in Leishmania .
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".