Human β-defensin-2 production upon viral and bacterial co-infection is attenuated in COPD
Bibliographic record
Abstract
Viral-bacterial co-infections are associated with severe exacerbations of COPD. Epithelial antimicrobial peptides, including human β-defensin-2 (HBD-2), are integral to innate host defenses. In this study, we examined how co-infection of airway epithelial cells with rhinovirus and Pseudomonas aeruginosa modulates HBD-2 expression, and whether these responses are attenuated by cigarette smoke and in epithelial cells obtained by bronchial brushings from smokers with normal lung function or from COPD patients. When human airway epithelial cells from normal lungs were infected with rhinovirus, Pseudomonas aeruginosa, or the combination, co-infection with rhinovirus and bacteria resulted in synergistic induction of HBD-2 (p<0.05). The combination of virus and flagellin replicated this synergistic increase (p<0.05), and synergy was not seen using a flagella-deficient mutant Pseudomonas (p<0.05). The effects of Pseudomonas aeruginosa were mediated via interactions of flagellin with TLR5. The effects of HRV-16 depended upon viral replication but did not appear to be mediated via the intracellular RNA helicases, retinoic acid-inducible gene-I or melanoma differentiation-associated gene-5. Cigarette smoke extract significantly decreased HBD-2 production in response to co-infection. Attenuated production was also observed following co-infection of cells obtained from healthy smokers or COPD patients compared to healthy controls (p<0.05). We conclude that co-exposure to HRV-16 and Pseudomonas aeruginosa induces synergistic production of HBD-2 from epithelial cells and that this synergistic induction of HBD-2 is reduced in COPD patients. This may contribute to the more severe exacerbations these patients experience in response to viral-bacterial co-infections.
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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".