Dose-Dependent Effects of IL-17 on IL-13–Induced Airway Inflammatory Responses and Airway Hyperresponsiveness
Bibliographic record
Abstract
The Th2 cytokine IL-13 regulates several aspects of the asthmatic phenotype, including airway inflammation, airway hyperresponsiveness, and mucus production. The Th17 cytokine IL-17A is also implicated in asthma and has been shown to both positively and negatively regulate Th2-dependent responses in murine models of allergic airways disease. Our objective in this study was to better understand the role of IL-17 in airway inflammation by examining how IL-17 modifies IL-13-induced airway inflammatory responses. We treated BALB/c mice intranasally with IL-13 or IL-17 alone or in combination for 8 consecutive days, after which airway hyperresponsiveness, inflammatory cell influx into the lung, and lung chemokine/cytokine expression were assessed. As expected, IL-13 increased airway inflammation and airway hyperresponsiveness. IL-13 also increased numbers of IL-17-producing CD4(+) and γδ T cells. Treating mice with a combination of IL-13 and IL-17 reduced infiltration of IL-17(+) γδ T cells, but increased the number of infiltrating eosinophils. In contrast, coadministration of IL-13 with a higher dose of IL-17 decreased all IL-13-induced inflammatory responses, including infiltration of both IL-17(+)CD4(+) and γδ T cells. To examine the inhibitory activity of IL-17-expressing γδ T cells in this model, these cells were adoptively transferred into naive recipients. Consistent with an inhibitory role for γδ T cells, IL-13-induced infiltration of eosinophils, lymphocytes, and IL-17(+)CD4(+) T cells was diminished in recipients of the γδ T cells. Collectively, our data indicate that allergic airway inflammatory responses induced by IL-13 are modulated by both the quantity and the cellular source of IL-17.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".