Rhinovirus-bacteria coexposure synergistically induces CCL20 production from human bronchial epithelial cells
Bibliographic record
Abstract
Exacerbations of chronic obstructive pulmonary disease are triggered by viral or bacterial pathogens, with human rhinovirus (HRV) and nontypeable Hemophilus influenzae (NTHI) among the most commonly detected pathogens. Patients who suffer from concomitant viral and bacterial infection have more severe exacerbations. The airway epithelial cell is the initial site of viral and bacterial interactions, and CCL20 is an epithelial chemokine that attracts immature dendritic cells to the airways and can act as an antimicrobial. As such, it contributes to innate and adaptive immune responses to infection. We used primary cultures of human bronchial epithelial cells and the BEAS-2B cell line to examine the effects of bacterial-viral coexposure, as well as each stimulus alone, on epithelial expression of CXCL8 and, in particular, CCL20. HRV-bacterial coexposure induced synergistic production of CXCL8 and CCL20 compared with the sum of each stimulus alone. Synergistic induction of CCL20 did not require viral replication and occurred with two different HRV serotypes that use different viral receptors. Synergy was also seen with either NTHI or Pseudomonas aeruginosa. Synergistic induction of CCL20 was transcriptionally regulated. Although NF-κB was required for transcription, it did not regulate synergy, but NF-IL-6 did appear to contribute. Among MAPK inhibitors studied, neither SB203580 nor PD98059 had any effect on synergy, whereas U0126 prevented synergistic induction of CCL20 by HRV and bacteria, apparently via “off-target” effects. Thus bacterial-viral coexposure synergistically increases innate immune responses compared with individual infections. We speculate that this increased inflammatory response leads to worse clinical outcomes.
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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 | 0.001 |
| 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) | 0.002 | 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".