IL-6 trans-signaling in cystic fibrosis bronchial cells potentiates TNF-α-driven ICAM-1 expression
Bibliographic record
Abstract
Introduction Pseudomonas aeruginosa is gram-negative bacillus that causes chronic airway infections, leading to severe pulmonary inflammation in cystic fibrosis. This bacterial infection is frequently associated with a massive recruitment of neutrophils and an abnormal increase in production of inflammatory cytokines. Among these cytokines, interleukin (IL)-6 has both anti- and pro-inflammatory properties able to signal through classic and trans-signaling pathways, respectively. Furthermore, IL-6 is known to be upregulated in CFTR-deficient bronchial cell lines in the presence of Pseudomonas aeruginosa-derived filtrates and in Pulmonary Exacerbations (PEx). In this study, we aimed to determine whether IL-6 trans-signaling could contribute to neutrophilic inflammation leading to lung tissue damage during PEx of people with CF (pwCF). Methods sIL-6Ra expression was measured by ELISA in plasma samples from pwCF at baseline and during exacerbations. IL-6 signalling was investigate in CF and non-CF cell lines using immunoblotting of STAT3 phosphorylation. ICAM-1 cell surface expression was determined using flow cytometry. Results We show that pwCF had higher sIL-6Rα levels in their plasma during PEx, suggestive of IL-6 trans-signaling. Furthermore, we show that a CF bronchial cell line is hyper-responsive to both classic and trans-signaling, with the higher levels of activation occurring during trans-signaling when compared to two non-CF cell lines. Discussion Our data unveiled that ICAM-1, which promotes neutrophil adhesion, is upregulated by the combination of TNF-α and IL-6 signaling in CF bronchial cells. Interestingly, soluble IL-6R (sIL-6Rα) protects IL-6 from degradation by bacterial proteases. Therefore, we suggest that strategies which target IL-6 trans-signaling may alleviate ICAM-1 mediated neutrophil adhesion and reduce subsequent lung damage in PEx.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".