S100A8, S100A9 and S100A12 activate airway epithelial cells to produce <scp>MUC</scp>5<scp>AC</scp> via extracellular signal‐regulated kinase and nuclear factor‐<i>κ</i>B pathways
Bibliographic record
Abstract
Airway mucus hyperproduction is a common feature of chronic airway diseases such as severe asthma, chronic obstructive pulmonary disease and cystic fibrosis, which are closely associated with neutrophilic airway inflammation. S100A8, S100A9 and S100A12 are highly abundant proteins released by neutrophils and have been identified as important biomarkers in many inflammatory diseases. Herein, we report a new role for S100A8, S100A9 and S100A12 for producing MUC5AC, a major mucin protein in the respiratory tract. All three S100 proteins induced MUC5AC mRNA and the protein in normal human bronchial epithelial cells as well as NCI-H292 lung carcinoma cells in a dose-dependent manner. A Toll-like receptor 4 (TLR4) inhibitor almost completely abolished MUC5AC expression by all three S100 proteins, while neutralization of the receptor for advanced glycation end-products (RAGE) inhibited only S100A12-mediated production of MUC5AC. The S100 protein-mediated production of MUC5AC was inhibited by the pharmacological agents that block prominent signalling molecules for MUC5AC expression, such as mitogen-activated protein kinases, nuclear factor-κB (NF-κB) and epidermal growth factor receptor. S100A8, S100A9 and S100A12 equally elicited both phosphorylation of extracellular signal-regulated kinase (ERK) and nuclear translocation of NF-κB/degradation of cytosolic IκB with similar kinetics through TLR4. In contrast, S100A12 preferentially activated the ERK pathway rather than the NF-κB pathway through RAGE. Collectively, these data reveal the capacity of these three S100 proteins to induce MUC5AC production in airway epithelial cells, suggesting that they all serve as key mediators linking neutrophil-dominant airway inflammation to mucin hyperproduction.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".