Inhibition of the TLR4 signalling pathway with TAK-242 reduces RSV infection and cytokine release in primary airway epithelial cells
Bibliographic record
Abstract
Abstract Respiratory syncytial virus (RSV) infection is the leading cause of hospitalisation in children worldwide, but there is still no vaccine or anti-viral treatment available. RSV has been implicated in the development of respiratory diseases such as asthma. Toll like receptor 4 (TLR4) has been well characterised in the immune responses to RSV. However, the role of TLR4 in RSV infection remains unclear. To study RSV in the lung epithelium, where RSV preferentially infects ciliated cells, we used a well-differentiated primary airway epithelial cell (WD-PAEC) model: a pseudostratified epithelium that produces mucus and beating cilia. We demonstrate in this physiologically relevant model that TLR4 is a pro-viral factor. Inhibition of TLR4 using TAK-242 significantly reduces RSV titres in WD-PAECs in a dose-dependent manner but has no effect on RSV growth kinetics in a range of immortalised respiratory-derived cell lines. Specific inhibition of a range of downstream effectors of TLR4 signalling in the WD-PAEC model identified p38 MAPK as a pro-viral factor, whereas inhibition of MEK1/2 significantly increased RSV titres. Our data demonstrate a role for TLR4 in RSV infection and highlight the importance of biologically relevant models to study virus-host interactions. Author summary Respiratory Syncytial Virus (RSV) can cause severe respiratory infection in young children and is responsible for approximately 200,000 deaths worldwide every year. Despite decades of research since the identification of this virus in the 1950s there is still no vaccine or treatment available. Advances in research have led to the development of cell cultures that are very similar to the cells that line human airways. These cultures provide an opportunity to study how viruses interacts with airway cells in a representative model and may provide insights that traditional research models have not yet been able to answer. Using this experimental model we show that a drug, TAK-242 which targets a pathogen recognition receptor on the surface of cells, reduces growth of RSV and dampens the immune response to infection in these airway cells. Our data demonstrate potential targets for RSV treatments and also highlight the importance of using relevant experimental models.
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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 | 0.001 |
| 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) | 0.003 | 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".