ATR Kinase Is a Crucial Player Mediating the DNA Damage Response in Trypanosoma brucei
Bibliographic record
Abstract
DNA double-strand breaks (DSBs) are among the most deleterious lesions that threaten genome integrity. To address DSBs, eukaryotic cells of model organisms have evolved a complex network of cellular pathways that are able to detect DNA damage, activate a checkpoint response to delay cell cycle progression, recruit the proper repair machinery, and resume the cell cycle once the DNA damage is repaired. Cell cycle checkpoints are primarily regulated by the apical kinases ATR and ATM, which are conserved throughout the eukaryotic kingdom.Trypanosoma bruceiis a divergent pathogenic protozoan parasite that causes human African trypanosomiasis (HAT), a neglected disease that can be fatal when left untreated. The proper signaling and accuracy of DNA repair is fundamental toT. bruceinot only to ensure parasite survival after genotoxic stress but also because DSBs are involved in the process of generating antigenic variations used by this parasite to evade the host immune system. DSBs trigger a strong DNA damage response and efficient repair process inT. brucei, but it is unclear how these processes are coordinated. Here, by knocking down ATR inT. bruceiusing two different approaches (conditional RNAi and an ATR inhibitor), we show that ATR is required to mediate intra-S and partial G1/S checkpoint responses. ATR is also involved in replication fork stalling, is critical for H2A histone phosphorylation in a small group of cells and is necessary for the recruitment and upregulation of the HR-mediated DNA repair protein RAD51 after ionizing radiation (IR) induces DSBs. In summary, this work shows that apical ATR kinase plays a central role in signal transduction and is critical for orchestrating the DNA damage response inT. brucei.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".