IL-17A Contributes to Lung Fibrosis in a Model of Chronic Pulmonary Graft-versus-host Disease
Bibliographic record
Abstract
BACKGROUND: Chronic pulmonary graft-versus-host disease (cpGVHD) after hematopoietic cell transplant (HCT) manifests as progressive airway and parenchymal lung fibrosis. On the basis of our prior data, mice that undergo allogeneic HCT with Tbet-knockout donors (AlloTbet) have increased lung Th17 cells and IL-17A and develop fibrosis resembling human cpGVHD. The role of IL-17A in posttransplant pulmonary fibrosis remains incompletely understood. We hypothesized that IL-17A is necessary for development of murine cpGVHD in this model. METHODS: AlloTbet mice received weekly intraperitoneal anti-IL-17A or IgG (200 μg/mouse) starting 2 weeks post-HCT and were sacrificed after week 5. Histologic airway and parenchymal fibrosis were semiquantitatively graded in a blinded fashion. Lung cells and proteins were measured by flow cytometry, ELISA, and multicytokine assays. RESULTS: Anti-IL-17A modestly decreased airway and parenchymal lung fibrosis, along with a striking reduction in pulmonary neutrophilia, IL-6, MIP-1α, MIP-1β, CXCL1, and CXCL5 in AlloTbet mice. Additionally, anti-IL-17A decreased CCL2, inflammatory monocytes and macrophages, and Th17 cells. CONCLUSIONS: In the setting of murine AlloHCT with Tbet donors, IL-17A blockade decreases fibrotic features of cpGVHD. This may be mediated by the observed reduction in neutrophils or specific lung monocyte and macrophage populations or alternatively via a direct effect on fibroblasts. Collectively, our results further suggest that anti-IL-17A strategies could prove useful in preventing alloimmune-driven fibrotic lung diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".