Autophagy Regulates IL-23 Secretion and Innate T Cell Responses through Effects on IL-1 Secretion
Bibliographic record
Abstract
Autophagy controls IL-1β secretion by regulating inflammasome activation and by targeting pro-IL-1β for degradation. In this article, we show that inhibition of autophagy, either with the PI3K inhibitors 3-methyladenine, wortmannin, and LY294002 or with small interfering RNA against autophagy proteins augmented the secretion of IL-23 by human and mouse macrophages and dendritic cells in response to specific TLR agonists. This process occurred at the transcriptional level and was dependent on reactive oxygen species and IL-1R signaling; it was abrogated with an IL-1R antagonist or with IL-1-neutralizing Abs, whereas treatment with either rIL-1α or IL-1β induced IL-23 secretion. Dendritic cells treated with LPS and 3-methyladenine secreted enhanced levels of both IL-1β and IL-23, and supernatants from these cells stimulated the innate secretion of IL-17, IFN-γ, and IL-22 by γδ T cells. These data demonstrate that autophagy has a potentially pivotal role to play in the induction and regulation of inflammatory responses by innate immune cells, largely driven by IL-1 and its consequential effects on IL-23 secretion.
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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 | 0.000 |
| 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".