The translation inhibitor pateamine A prevents cachexia-induced muscle wasting in mice
Bibliographic record
Abstract
Cachexia, or muscle-wasting syndrome, is one of the major causes of death in patients affected by diseases such as cancer, AIDS and sepsis. However, no effective anti-cachectic treatment is currently available. Here we show that a low dose of pateamine A, an inhibitor of translation initiation, prevents muscle wasting caused by the cytokines interferon γ and tumour necrosis factor α or by C26-adenocarcinoma tumours. Surprisingly, although high doses of pateamine A abrogate general translation, low doses selectively inhibit the expression of pro-cachectic factors such as inducible nitric oxide synthase. This selectivity depends on the 5′UTR of inducible nitric oxide synthase messenger RNA (mRNA) that, unlike the 5′UTR of MyoD mRNA, promotes the recruitment of inducible nitric oxide synthase mRNA to stress granules, where its translation is repressed. Collectively, our data provide a proof of principle that nontoxic doses of compounds such as pateamine A could be used as novel drugs to combat cachexia-induced muscle wasting. Cachexia, or muscle-wasting syndrome, is often observed in patients with cancer or sepsis, and no specific treatment of cachexia is currently available. In this study, Di Marcoet al.show that low doses of pateamine A, an inhibitor of translation initiation, prevent cachexia in a mouse model of the 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".