Pre-existing human rhinovirus infection modulates host response to secondary bacterial infections
Bibliographic record
Abstract
Secondary bacterial infections following viral infections of the airways are well documented and are associated with increased severity of respiratory disease compared to virus or bacterial infections alone. Human rhinovirus (HRV) infections are the most common causes of exacerbations in individuals with chronic airways diseases such as asthma and COPD. Moreover, bacterial colonization is commonly found in the airways of patients experiencing exacerbations of these chronic airways diseases and linked to increased severity and duration of these exacerbations. The mechanisms underlying the increased prevalence of secondary bacterial infections and the association with more severe outcomes following viral infections is not known. It has been suggested that viral infection of the airways cause dysregulation of innate host defense mechanisms, such as, impaired antimicrobial peptide expression of the airways. Antimicrobial peptides are key components of the innate immune response after infection and are important in efficient clearance of microbial colonization to prevent infection. We sought to determine whether HRV modulates the innate host defense response to secondary bacterial infections of the airways. Studies performed using primary human bronchial epithelial cells (HBECs). Cells grown in monolayer to confluence (80-90%). Antibiotics and Hydrocortisone were removed from the media 48 h and 24 h prior to infection respectively. On day of infection, cells were stimulated with purified HRV-16 or bacteria ( H. influenzae/P. aeruginosa ) alone, or treated in combination of HRV-16 then subsequently bacteria. TLR5 agonists (Flagella) also used alone and in combination with HRV-16. The protein and mRNA levels of different antimicrobial peptides (β-defensin, LL-37, lysozyme, lactoferrin, and SLPI) measured using ELISA (R&D Systems) and real-time RT-PCR (Applied Biosystems), respectively. Preliminary data indicate supernatants from HRV-16/bacterial co-infection resulted in synergistic trend in β-defensin levels compared to HRV and bacteria alone. A synergistic increase in β-defensin levels was also seen with TLR5 agonist when combined post HRV-16 infection compared to TLR5 or HRV-16 alone. Minimum inhibitory concentration results showed β-defensin (1mg/ml), Lysozyme (30µg/ml), Lactoferrin (1mg/ml) and LL-37 (460µg/ml) were able to inhibit growth of NTHi and PAO. The data provide the first demonstration that there is a dysregulation of antimicrobial levels in HRV infected HBECs when encountered with a secondary bacterial infection. This provides evidence to why individuals with chronic airways diseases have a prolonged and more severe disease state than normal individuals. It could also lead to a new targeted therapy of people with diseases such as asthma or COPD to decrease severity of exacerbations.
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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) | 0.002 | 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".