Evidence that ARK2N is not a core factor in transcription-coupled DNA repair
Bibliographic record
Abstract
DNA lesions can obstruct RNA polymerase II (RNAPII) during transcription elongation.Stalling of RNAPII at transcriptionblocking lesions triggers transcription-coupled DNA repair (TCR), starting with the recognition of stalled RNAPII by the CSB repair factor ( 1 ).However, the mechanisms underlying CSB's specific recognition of DNA-damage-stalled RNAPII and the stabilization of this interaction remain unclear.A recent study by Luo et al. suggests that ARK2N (C18orf25/ARKL1), in partnership with the Casein Kinase 2 (CK2) complex, may initiate TCR by enhancing the interaction between CSB and DNA-damage-stalled RNAPII ( 2 ).To investigate the role of ARK2N in TCR, we generated four clonal ARK2N-knockout (KO) cell lines in RPE1-hTERT cells using two different crRNAs: crRNA-21 from the Toronto KnockOut (TKOv3) library ( 3 ) and crRNA-22 as described by Luo et al. ( 2 ).The generated ARK2N-KOs were validated through Sanger sequencing, which confirmed out-of-frame indel formation ( Fig. 1A ), and western blotting ( Fig. 1B ).To try and confirm the described interaction between ARK2N and CSB ( 2 ), we performed western blotting following coimmunoprecipitation (co-IP) using an ARK2N antibody in cells UVirradiated 1 h before harvesting.Consistent with earlier reports ( 2 , 4 ), we observed an interaction between ARK2N
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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) | 0.001 | 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 | 0.001 | 0.000 |
| 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".