CDADC1 is a vertebrate-specific dCTP deaminase that metabolizes gemcitabine and decitabine to prevent cellular toxicity
Bibliographic record
Abstract
Cancer therapy is limited by resistance to standard-of-care chemotherapeutic and/or by treatment-associated toxicity. Identifying molecular mechanisms that modulate cellular toxicity is crucial for enhancing treatment efficacy. We characterize CDADC1, a vertebrate-specific orphan enzyme, as an unprecedented eukaryotic dCTP deaminase. CDADC1 catalyzes the conversion of dCTP into dUTP. While bacteria use this activity to sustain proliferation, CDADC1 evolved independently and is not required for mammalian cell proliferation, as demonstrated in cell lines and by the normal growth and standard lifespan of Cdadc1-deficient mice. However, we uncover a role of CDADC1 in metabolizing nucleotide analogs gemcitabine and decitabine. Gain- and loss-of-function assays in cancer cell lines, along with ectopic mouse models of pancreatic cancer, show that CDADC1 reduces these drugs’ efficacy. By the same token, Cdadc1 −/− mice are hypersensitive to gemcitabine. Mechanistically, CDADC1 deaminates the active triphosphate form of gemcitabine and decitabine, rendering them susceptible to inactivation by deoxyuridine triphosphatase. In contrast, the dCMP deaminase DCTD contributes to cell proliferation and promotes gemcitabine and decitabine toxicity. Thus, CDADC1 underpins a previously unrecognized mechanism of intrinsic chemoresistance in cancer cells and has a nonredundant role in protecting from gemcitabine toxicity. CDADC1 reveals a clinically relevant metabolic pathway that might be exploited to enhance the efficacy of deoxycytidine analogs but calls for assessing CDADC1 status to avoid lethal toxicities.
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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".