Analysis of Proteinase-activated Receptor 2 and TLR4 Signal Transduction
Bibliographic record
Abstract
Proteinase-activated receptor 2 (PAR2), a seven-transmembrane G protein-coupled receptor, is activated at inflammatory sites by proteolytic cleavage of its extracellular N terminus by trypsin-like enzymes, exposing a tethered, receptor-activating ligand. Synthetic agonist peptides (AP) that share the tethered ligand sequence also activate PAR2, often measured by Ca2+ release. PAR2 contributes to inflammation through activation of NF-kappaB-regulated genes; however, the mechanism by which this occurs is unknown. Overexpression of human PAR2 in HEK293T cells resulted in concentration-dependent, PAR2 AP-inducible NF-kappaB reporter activation that was protein synthesis-independent, yet blocked by inhibitors that uncouple Gi proteins or sequester intracellular Ca2+. Because previous studies described synergistic PAR2- and TLR4-mediated cytokine production, we hypothesized that PAR2 and TLR4 might interact at the level of signaling. In the absence of TLR4, PAR2-induced NF-kappaB activity was inhibited by dominant negative (DN)-TRIF or DN-TRAM constructs, but not by DN-MyD88, findings confirmed using cell-permeable, adapter-specific BB loop blocking peptides. Co-expression of TLR4/MD-2/CD14 with PAR2 in HEK293T cells led to a synergistic increase in AP-induced NF-kappaB signaling that was MyD88-dependent and required a functional TLR4, despite the fact that AP exhibited no TLR4 agonist activity. Co-immunoprecipitation of PAR2 and TLR4 revealed a physical association that was AP-dependent. The response to AP or lipopolysaccharide was significantly diminished in TLR4(-/-) and PAR2(-/-) macrophages, respectively, and SW620 colonic epithelial cells exhibited synergistic responses to co-stimulation with AP and lipopolysaccharide. Our data suggest a unique interaction between two distinct innate immune response receptors and support a novel paradigm of receptor cooperativity in inflammatory responses.
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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) | 0.002 | 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".