Endoplasmic reticulum stress regulates chemokine production in cystic fibrosis airway cells through STAT3 modulation
Bibliographic record
Abstract
Endoplasmic reticulum (ER) stress has been recognized to play an important role in chronic inflammatory diseases such as cystic fibrosis (CF), and targeting ER stress may be useful for alleviating damaging neutrophilic inflammation in CF airways. Cellular models were used in conjunction with data from a recent CF genome-wide association study (GWAS) meta-analysis to determine modulators of ER stress-mediated inflammation. Surprisingly, cells undergoing ER stress during inflammatory stimulation showed reduced interleukin 8 (IL-8) and CXCL1 secretion (P < .001). Neutralization of CXCL1 and IL-8 reduced neutrophil chemotaxis >50% to supernatants from IL-1β-stimulated CF airway epithelial cells (P < .01). The clinical importance of these chemokines was validated by association of CXCL1 and IL8 polymorphisms with changes in lung disease severity in patients with CF (n = 6365; IL8, P = .001; CXCL1, P = .001), confirming that targeting these chemokine pathways could help improve lung disease. We determined that production of these chemokines was partially controlled by ER stress in a signal transducer and activator of transcription 3 (STAT3)-dependent manner, whereby ER stress inhibited STAT3 activation. Our findings support a role for CXCL1 and IL-8 in CF lung disease severity and identify STAT3 as a modulating pathway. Targeting these pathways may help improve health outcomes in CF.
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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 | 0.000 |
| 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) | 0.001 | 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 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".