IL-1 Receptor Regulates microRNA-135b Expression in a Negative Feedback Mechanism during Cigarette Smoke–Induced Inflammation
Bibliographic record
Abstract
Although microRNA-135b (miR-135b) is known to be associated with cancer, with recent work showing that it is massively induced in the pulmonary tissues of mice challenged with nanoparticles suggests a critical role for this microRNA in mediating inflammatory response. In this study, we investigated the expression and function of miR-135b in mice exposed to cigarette smoke or nontypeable Haemophilus influenzae (NTHi). Exposure to both cigarette smoke and NTHi elicited robust lung inflammation, but increased miR-135b expression was observed only in the lungs of cigarette smoke-exposed mice. Using IL-1R 1 knockout mice, we show that miR-135b expression is IL-1R1 dependent. A series of in vitro experiments confirmed the role of IL-1R1 in regulating miR-135b expression. In vitro activation of the IL-1R1 pathway in mouse embryonic fibroblast (NIH3T3) and lung epithelial (FE1) cells resulted in increased miR-135b, which was blocked by IL-1R1 antagonists or small interfering RNA-mediated silencing of IL-1R1 expression. Overexpression of mature miR-135b in NIH3T3 cells (pEGP-mmu-mir-135b) resulted in the suppression of endogenous levels of IL-1R1 expression. pEGP-mmu-miR-135b cells transiently transfected with luciferase reporter vector containing the 3'UTR of mouse IL-1R1 showed reduced luciferase activity. Finally, we demonstrate that miR-135b targets IL-1-stimulated activation of Caspase-1, the IL-1R1 downstream activator of IL-1β leading to suppressed synthesis of the active form of IL-1β protein. These results suggest that miR-135b expression during cigarette smoke-induced inflammation is regulated by IL-1R1 in a regulatory feedback mechanism to resolve inflammation.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".