Activation of TLR4 by viral glycoproteins: A double-edged sword?
Bibliographic record
Abstract
Recognition of viral infection by pattern recognition receptors is paramount for a successful immune response to viral infection. However, an unbalanced proinflammatory response can be detrimental to the host. Recently, multiple studies have identified that the SARS-CoV-2 spike protein activates Toll-like receptor 4 (TLR4), resulting in the induction of proinflammatory cytokine expression. Activation of TLR4 by viral glycoproteins has also been observed in the context of other viral infection models, including respiratory syncytial virus (RSV), dengue virus (DENV) and Ebola virus (EBOV). However, the mechanisms involved in virus-TLR4 interactions have remained unclear. Here, we review viral glycoproteins that act as pathogen-associated molecular patterns to induce an immune response via TLR4. We explore the current understanding of the mechanisms underlying how viral glycoproteins are recognized by TLR4 and discuss the contribution of TLR4 activation to viral pathogenesis. We identify contentious findings and research gaps that highlight the importance of understanding viral glycoprotein-mediated TLR4 activation for potential therapeutic approaches.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".