B-Raf kinase blockade protects airway epithelial cells against respiratory syncytial virus infection and modulates interferon responses
Bibliographic record
Abstract
Background Respiratory syncytial virus (RSV) is the primary cause of hospitalisation due to acute bronchiolitis and viral pneumonia in infants and young children. Recently, a maternal RSV vaccine (Pfizer's Abrysvo) has been approved to protect infants from birth up to 6 months of age. However, there is currently no vaccine or antiviral therapy against RSV for children aged >6 months. Therefore, there is an urgent need for novel antiviral therapies against RSV infection for young children. Methods We hypothesised that blocking a host protein called B-Raf kinase would inhibit RSV replication and protect airway epithelial cells against infection. We investigated the in vitro effects of dabrafenib, a US Food and Drug Administration-approved B-Raf kinase inhibitor, against RSV. Human airway epithelial cell lines and primary nasal epithelial cells were infected with RSV and treated with dabrafenib. Real-time PCR, plaque assay, quantitative mass spectrometry, ELISA and immunofluorescence were performed. Results Dabrafenib impaired RSV infection and replication (p=0.0003), while protecting cells against RSV-induced lytic cell death (p<0.0001). Proteomics and PCR analyses revealed that dabrafenib decreased the expression of the interferon-stimulated genes IFIT1 (p<0.0001) and ISG15 (p<0.0001) and corresponding proteins in airway epithelial cells. Therapeutic treatment with dabrafenib differentially modulated the release of type I and III interferons. Conclusions Collectively, our data indicate that B-Raf kinase is involved in RSV replication, interferon-stimulated gene induction, and type I and III interferon release in airway epithelial cells following infection. We propose that repurposing dabrafenib as a host-directed antiviral against RSV may be valuable in reducing disease pathogenesis associated with RSV infection.
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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) | 0.001 | 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".