Interleukin-10 Inhibits Lipopolysaccharide-induced Tumor Necrosis Factor-α Translation through a SHIP1-dependent Pathway
Bibliographic record
Abstract
Production of the proinflammatory cytokine TNFα by activated macrophages is an important component of host defense. However, TNFα production must be tightly controlled to avoid pathological consequences. The anti-inflammatory cytokine IL-10 inhibits TNFα mRNA expression through activation of the STAT3 transcription factor pathway and subsequent expression of STAT3-dependent gene products. We hypothesized that IL-10 must also have more rapid mechanisms of action and show that IL-10 rapidly shifts existing TNFα mRNA from polyribosome-associated polysomes to monosomes. This translation suppression requires the presence of SHIP1 (SH2 domain-containing inositol 5'-phosphatase 1) and involves inhibition of Mnk1 (MAPK signal-integrating kinase 1). Furthermore, activating SHIP1 using a small-molecule agonist mimics the inhibitory effect of IL-10 on Mnk1 phosphorylation and TNFα translation. Our data support the existence of an alternative STAT3-independent pathway through SHIP1 for IL-10 to regulate TNFα translation during the anti-inflammatory response.
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) | 0.000 | 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 | 0.001 |
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".