Spatiotemporal proteomic profiling of cellular responses to NLRP3 agonists
Bibliographic record
Abstract
Nucleotide-binding domain and leucine-rich repeat pyrin-domain containing protein 3 (NLRP3) is an innate immune sensor that forms an inflammasome in response to various cellular stressors. Gain-of-function mutations in NLRP3 cause autoinflammatory diseases and NLRP3 signalling itself exacerbates the pathogenesis of many other human diseases. Despite considerable therapeutic interest, the primary drivers of NLRP3 activation remain controversial due to the diverse array of signals that are integrated through NLRP3. Here, we mapped subcellular proteome changes to lysosomes, mitochondrion, EEA1-positive endosomes, and Golgi caused by the NLRP3 inflammasome agonists nigericin and CL097. We identified several common disruptions to retrograde trafficking pathways, including COPI and Shiga toxin-related transport, in line with recent studies. We further characterized mouse NLRP3 trafficking throughout its activation using temporal proximity proteomics, which supports a recent model of NLRP3 recruitment to endosomes during inflammasome activation. Collectively, these findings provide additional granularity to our understanding of the molecular events driving NLRP3 activation and serve as a valuable resource for cell biological research. We have made our proteomics data accessible through an open-access Shiny browser to facilitate future research within the community, available at: https://harperlab.connect.hms.harvard.edu/inflame/. We will display anonymous peer review for this manuscript on pubpub.org (https://harperlab.pubpub.org/pub/nlrp3/) rather than a traditional journal. Moreover, we invite community feedback on the pubpub version of this manuscript, and we will address criticisms accordingly.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
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".