IL-1α/IL-1R1 Expression in Chronic Obstructive Pulmonary Disease and Mechanistic Relevance to Smoke-Induced Neutrophilia in Mice
Bibliographic record
Abstract
BACKGROUND: Cigarette smoking is the main risk factor for the development of chronic obstructive pulmonary disease (COPD), a major cause of morbidity and mortality worldwide. Despite this, the cellular and molecular mechanisms that contribute to COPD pathogenesis are still poorly understood. METHODOLOGY AND PRINCIPAL FINDINGS: The objective of this study was to assess IL-1 α and β expression in COPD patients and to investigate their respective roles in perpetuating cigarette smoke-induced inflammation. Functional studies were pursued in smoke-exposed mice using gene-deficient animals, as well as blocking antibodies for IL-1α and β. Here, we demonstrate an underappreciated role for IL-1α expression in COPD. While a strong correlation existed between IL-1α and β levels in patients during stable disease and periods of exacerbation, neutrophilic inflammation was shown to be IL-1α-dependent, and IL-1β- and caspase-1-independent in a murine model of cigarette smoke exposure. As IL-1α was predominantly expressed by hematopoietic cells in COPD patients and in mice exposed to cigarette smoke, studies pursued in bone marrow chimeric mice demonstrated that the crosstalk between IL-1α+ hematopoietic cells and the IL-1R1+ epithelial cells regulates smoke-induced inflammation. IL-1α/IL-1R1-dependent activation of the airway epithelium also led to exacerbated inflammatory responses in H1N1 influenza virus infected smoke-exposed mice, a previously reported model of COPD exacerbation. CONCLUSIONS AND SIGNIFICANCE: This study provides compelling evidence that IL-1α is central to the initiation of smoke-induced neutrophilic inflammation and suggests that IL-1α/IL-1R1 targeted therapies may be relevant for limiting inflammation and exacerbations in COPD.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".