NF-κB Links TLR2 and PAR1 to Soluble Immunomodulator Factor Secretion in Human Platelets
Bibliographic record
Abstract
The primary toll-like receptor (TLR)-mediated immune cell response pathway common for all TLRs is MyD88-dependent activation of NF-κB, a seminal transcription factor for many chemokines and cytokines. Remarkably, anucleate platelets express the NF-κB machinery, whose role in platelets remains poorly understood. Here, we investigated the contribution of NF-κB in the release of cytokines and serotonin by human platelets, following selective stimulation of TLR2 and protease activated receptor 1 (PAR1), a classical and non-classical pattern-recognition receptor, respectively, able to participate to the innate immune system. We discovered that platelet PAR1 activation drives the process of NF-κB phosphorylation, in contrast to TLR2 activation, which induces a slower phosphorylation process. Conversely, platelet PAR1 and TLR2 activation induces similar ERK1/2, p38, and AKT phosphorylation. Moreover, we found that engagement of platelet TLR2 with its ligand, Pam3CSK4, significantly increases the release of sCD62P, RANTES, and sCD40L; this effect was attenuated by incubating platelets with a blocking anti-TLR2 antibody. This effect appeared selective since no modulation of serotonin secretion was observed following platelet TLR2 activation. Platelet release of sCD62P, RANTES, and sCD40L following TLR2 or PAR1 triggering was abolished in the presence of the NF-κB inhibitor Bay11-7082, while serotonin release following PAR1 activation was significantly decreased. These new findings support the concept that NF-κB is an important player in platelet immunoregulations and functions.
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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) | 0.003 | 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".