Atypical Activation of the Unfolded Protein Response in Cystic Fibrosis Airway Cells Contributes to p38 MAPK-Mediated Innate Immune Responses
Bibliographic record
Abstract
Inflammatory lung disease is the major cause of morbidity and mortality in cystic fibrosis (CF); understanding what produces dysregulated innate immune responses in CF cells will be pivotal in guiding the development of novel anti-inflammatory therapies. To elucidate the molecular mechanisms that mediate exaggerated inflammation in CF following TLR signaling, we profiled global gene expression in immortalized human CF and non-CF airway cells at baseline and after microbial stimulation. Using complementary analysis methods, we observed a signature of increased stress levels in CF cells, specifically characterized by endoplasmic reticulum (ER) stress, the unfolded protein response (UPR), and MAPK signaling. Analysis of ER stress responses revealed an atypical induction of the UPR, characterized by the lack of induction of the PERK-eIF2α pathway in three complementary model systems: immortalized CF airway cells, fresh CF blood cells, and CF lung tissue. This atypical pattern of UPR activation was associated with the hyperinflammatory phenotype in CF cells, as deliberate induction of the PERK-eIF2α pathway with salubrinal attenuated the inflammatory response to both flagellin and Pseudomonas aeruginosa. IL-6 production triggered by ER stress and microbial stimulation were both dependent on p38 MAPK activity, suggesting a molecular link between both signaling events. These data indicate that atypical UPR activation fails to resolve the ER stress in CF and sensitizes the innate immune system to respond more vigorously to microbial challenge. Strategies to restore ER homeostasis and normalize the UPR activation profile may represent a novel therapeutic approach to minimize lung-damaging inflammation 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".