eIF4A inhibition prevents the onset of cytokine-induced muscle wasting by blocking the STAT3 and iNOS pathways
Bibliographic record
Abstract
Cachexia is a deadly muscle wasting syndrome that arises under conditions linked to chronic inflammation, such as cancer. Cytokines, including interferon γ (IFNγ), tumor necrosis factor α (TNFα) and interleukin-6 (IL-6), and their downstream effectors such as Signal Transducer and Activator of Transcription 3 (STAT3), have been shown to play a prominent role in muscle wasting. Previously, we demonstrated that Pateamine A (PatA), a compound that targets eukaryotic initiation factor 4A (eIF4A), could prevent muscle wasting by modulating the translation of the inducible Nitric Oxide Synthase (iNOS) mRNA. Here we show that hippuristanol, a compound that impedes eIF4A in a manner distinct from PatA, similarly inhibits the iNOS/NO pathway and cytokine-induced muscle wasting. Furthermore, we show that hippuristanol perturbs the activation of the STAT3 pathway and expression of STAT3-gene targets such as IL-6. The decreased activation of STAT3, which resulted from a decrease in STAT3 protein expression, was due to the inhibition of STAT3 translation as there were no changes in STAT3 mRNA levels. These effects are likely dependent on the inhibition of eIF4A activity since we observed similar results using PatA. Our results identify the inhibition of eIF4A-responsive transcripts, such as STAT3, as a viable approach to alleviate cachexia.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".