Adenosine kinase and ADAL coordinate detoxification of modified adenosines to safeguard metabolism
Bibliographic record
Abstract
RNA contains diverse post-transcriptional modifications, and its catabolic breakdown yields numerous modified nucleosides requiring correct processing, but the mechanisms remain unknown. Here, we demonstrate that three RNA-derived modified adenosines, N 6 -methyladenosine (m 6 A), N 6 , N 6 -dimethyladenosine (m 6,6 A), and N 6 -isopentenyladenosine (i 6 A), are sequentially metabolized into inosine monophosphate (IMP) to mitigate their intrinsic cytotoxicity. After phosphorylation by adenosine kinase (ADK), they undergo deamination by adenosine deaminase-like (ADAL). In Adal knockout mice, N 6 -modified adenosine monophosphates (AMPs) accumulate and allosterically inhibit AMP-activated protein kinase (AMPK), dysregulating glucose metabolism. Furthermore, ADK deficiency, linked to human inherited disorders of purine metabolism, elevates levels of the three modified adenosines, resulting in early lethality in mice. Mechanistically, excessive m 6 A, m 6,6 A, and i 6 A impair lysosomal function by interfering with lysosomal membrane proteins, thereby disrupting lipid metabolism and causing cellular toxicity. Through this nucleotide metabolism pathway and mechanism, cells detoxify modified adenosines, linking modified RNA metabolism to human disease.
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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 | 0.000 |
| 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".