DAMP Molecule S100A9 Acts as a Molecular Pattern to Enhance Inflammation during Influenza A Virus Infection: Role of DDX21-TRIF-TLR4-MyD88 Pathway
Bibliographic record
Abstract
Pathogen-associated molecular patterns (PAMPs) trigger host immune response by activating pattern recognition receptors like toll-like receptors (TLRs). However, the mechanism whereby several pathogens, including viruses, activate TLRs via a non-PAMP mechanism is unclear. Endogenous "inflammatory mediators" called damage-associated molecular patterns (DAMPs) have been implicated in regulating immune response and inflammation. However, the role of DAMPs in inflammation/immunity during virus infection has not been studied. We have identified a DAMP molecule, S100A9 (also known as Calgranulin B or MRP-14), as an endogenous non-PAMP activator of TLR signaling during influenza A virus (IAV) infection. S100A9 was released from undamaged IAV-infected cells and extracellular S100A9 acted as a critical host-derived molecular pattern to regulate inflammatory response outcome and disease during infection by exaggerating pro-inflammatory response, cell-death and virus pathogenesis. Genetic studies showed that the DDX21-TRIF signaling pathway is required for S100A9 gene expression/production during infection. Furthermore, the inflammatory activity of extracellular S100A9 was mediated by activation of the TLR4-MyD88 pathway. Our studies have thus, underscored the role of a DAMP molecule (i.e. extracellular S100A9) in regulating virus-associated inflammation and uncovered a previously unknown function of the DDX21-TRIF-S100A9-TLR4-MyD88 signaling network in regulating inflammation during 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 | 0.000 |
| 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".