The scaffolding and activation of NLRP3 on acidic vesicles depends on their biophysical properties and is independent of intermediate filaments
Bibliographic record
Abstract
Inflammasomes are nucleated by receptors that become activated upon cellular stresses, including ionic dyshomeostasis. Rather than forming in the cytosol, recent evidence suggests that inflammasomes are nucleated at specific sites in the cell, including on cytoskeletal polymers and the membrane surfaces of organelles. The NLRP3 inflammasome, which is formed upon the loss of cytosolic K+, had been proposed to form on intermediate filaments as well as on vesicles along the endocytic pathway. To determine the necessary requirement of either mechanism, we used vimentin knockout macrophages that do not have intermediate filaments and compared the formation and function of NLRP3 inflammasomes. We report that vimentin was dispensable for the activation of caspase-1, IL-1β cleavage and release, and inflammatory responses in mice attributed to the inflammasome. Instead, NLRP3 was recruited to PI(3,5)P2, PI(4)P- and LAMP1-positive compartments undergoing osmotic swelling. Swelling of these compartments was dependent on the V-ATPase, the inhibition of which curtailed NLRP3 recruitment and inflammasome activation. Similarly, decreasing the hydrostatic pressure on these vesicles prevented NLRP3 recruitment, IL-1β release and pyroptosis. The results suggest that NLRP3 is activated by biophysical features of acidic organelles in the endocytic pathway.
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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) | 0.000 | 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 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".