Fibrinogen primes the microglial NLRP3 inflammasome and propagates pro-inflammatory signaling via extracellular vesicles: Implications for blood-brain barrier dysfunction
Bibliographic record
Abstract
The brain's response to acute injury is characterized by increased permeability of the blood-brain barrier (BBB) and pro-inflammatory microglia signaling, both of which have been linked to poor cognitive outcomes and neurological disease. The damaged BBB has increased leakiness, allowing serum proteins like fibrinogen into the brain, which interacts with local cells in a deleterious manner. At the same time, in response to injury, microglia demonstrate increased NLRP3 inflammasome activity and heightened release of pro-inflammatory cytokines. The relationship between increased fibrinogen uptake and microglial inflammasome signaling in the injured brain has not been well described. In this work, we investigate fibrinogen mediated NLRP3 inflammasome priming of BV-2 cells and primary adult microglia and propose a role for extracellular vesicles (EVs) as propagators of this interaction. Following exposure to fibrinogen microglia significantly upregulate transcription of IL-1β, IL-6, NLRP3 and other pro-inflammatory cytokines which was sustained by repeated fibrinogen exposure. Inhibition of fibrinogen mediated NLRP3 signaling was achieved at the transcriptional and assembly level using cannabidiol (CBD) and the NLRP3 inhibitor MCC950, respectively. EVs released following NLRP3 priming carry IL-1β, IL-18 mRNA and fibrinogen, propagate inflammatory signaling and can be detected in the circulation following BBB disruption in a preclinical stroke model. In conclusion, the interplay between fibrinogen extravasation, microglial NLRP3 signaling, and EV release can perpetuate chronic pro-inflammatory signaling and represents a novel method of inflammatory propagation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".