Alkyladenine DNA glycosylase deficiency uncouples alkylation-induced strand break generation from PARP-1 activation and glycolysis inhibition
Bibliographic record
Abstract
Abstract DNA alkylation damage is repaired by base excision repair (BER) initiated by alkyladenine DNA glycosylase (AAG). Despite its role in DNA repair, AAG-initiated BER promotes cytotoxicity in a process dependent on poly (ADP-ribose) polymerase-1 (PARP-1); a NAD+-consuming enzyme activated by strand break intermediates of the AAG-initiated repair process. Importantly, PARP-1 activation has been previously linked to impaired glycolysis and mitochondrial dysfunction. However, whether alkylation affects cellular metabolism in the absence of AAG-mediated BER initiation is unclear. To address this question, we temporally profiled repair and metabolism in wild-type and Aag−/− cells treated with the alkylating agent methyl methanesulfonate (MMS). We show that, although Aag−/− cells display similar levels of alkylation-induced DNA breaks as wild type, PARP-1 activation is undetectable in AAG-deficient cells. Accordingly, Aag−/− cells are protected from MMS-induced NAD+ depletion and glycolysis inhibition. MMS-induced mitochondrial dysfunction, however, is AAG-independent. Furthermore, treatment with FK866, a selective inhibitor of the NAD+ salvage pathway enzyme nicotinamide phosphoribosyltransferase (NAMPT), synergizes with MMS to induce cytotoxicity and Aag−/− cells are resistant to this combination FK866 and MMS treatment. Thus, AAG plays an important role in the metabolic response to alkylation that could be exploited in the treatment of conditions associated with NAD+ dysregulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".