Rescue of Dysfunctional Autophagy Attenuates Hyperinflammatory Responses from Cystic Fibrosis Cells
Bibliographic record
Abstract
A hallmark feature of cystic fibrosis (CF) is progressive pulmonary obstruction arising from exaggerated host proinflammatory responses to chronic bacterial airway colonization. The mechanisms for these heightened inflammatory responses have been only partially characterized, hampering development of effective anti-inflammatory therapies. The aim of this study was to identify and validate novel dysfunctional processes or pathways driving the hyperinflammatory phenotype of CF cells using systems biology and network analysis to examine transcriptional changes induced by innate defense regulator (IDR)-1018, an anti-inflammatory peptide. IDR-1018 selectively attenuated hyperinflammatory cytokine production from CF airway cells and PBMCs stimulated with multiple bacterial ligands, including flagellin (FliC). Network analysis of CF cell transcriptional responses to FliC and IDR-1018 identified dysfunctional autophagy as the target of the peptide via modulation of upstream adenosine monophosphate-activated protein kinase (AMPK)-Akt signaling. After treatment with FliC, CF cells were found to have elevated levels of the autophagosome marker LC3-II, and GFP-LC3-transfected CF airway cells showed abnormal perinuclear accumulation of GFP(+) structures. In both instances, treatment of CF cells with IDR-1018 abolished the accumulation of LC3 induced by FliC. Furthermore, inhibition of autophagosome-lysosome fusion with bafilomycinA1 attenuated the anti-inflammatory and autophagosome-clearing effects of IDR-1018, as did a chemical inhibitor of Akt and an activator of AMPK. These findings were consistent with hypotheses generated in silico, demonstrating the utility of systems biology and network analysis approaches for providing pathway-level insights into CF-associated inflammation. Collectively, these data suggest that dysfunctional autophagosome clearance contributes to heightened inflammatory responses from CF transmembrane receptor mutant cells and highlight autophagy and AMPK-Akt signaling as novel anti-inflammatory targets 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) | 0.001 | 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) | 0.001 | 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".