Cigarette smoke-initiated autoimmunity facilitates sensitisation to elastin-induced COPD-like pathologies in mice
Bibliographic record
Abstract
It is currently not understood whether cigarette smoke exposure facilitates sensitisation to self-antigens and whether ensuing auto-reactive T cells drive chronic obstructive pulmonary disease (COPD)-associated pathologies. To address this question, mice were exposed to cigarette smoke for 2 weeks. Following a 2-week period of rest, mice were challenged intratracheally with elastin for 3 days or 1 month.Rag1−/−,Mmp12−/−, andIl17a−/−mice and neutralising antibodies against active elastin fragments were used for mechanistic investigations. Human GVAPGVGVAPGV/HLA-A*02:01 tetramer was synthesised to assess the presence of elastin-specific T cells in patients with COPD. We observed that 2 weeks of cigarette smoke exposure induced an elastin-specific T cell response that led to neutrophilic airway inflammation and mucus hyperproduction following elastin recall challenge. Repeated elastin challenge for 1 month resulted in airway remodelling, lung function decline and airspace enlargement. Elastin-specific T cell recall responses were dose dependent and memory lasted for over 6 months. Adoptive T cell transfer and studies in T cells deficientRag1−/−mice conclusively implicated T cells in these processes. Mechanistically, cigarette smoke exposure-induced elastin-specific T cell responses were matrix metalloproteinase (MMP)12-dependent, while the ensuing immune inflammatory processes were interleukin 17A-driven. Anti-elastin antibodies and T cells specific for elastin peptides were increased in patients with COPD. These data demonstrate that MMP12-generated elastin fragments serve as a self-antigen and drive the cigarette smoke-induced autoimmune processes in mice that result in a bronchitis-like phenotype and airspace enlargement. The study provides proof of concept of cigarette smoke-induced autoimmune processes and may serve as a novel mouse model of 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 | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".