Activating the NLRP3 Inflammasome Using the Amyloidogenic Peptide IAPP
Bibliographic record
Abstract
In addition to several other extracellular substances, phagocytosis of amyloid-forming peptides can perturb cellular homeostasis, leading to activation of the cytoplasmic innate immune receptor NLRP3. Once triggered, NLRP3 forms an inflammasome complex that ultimately cleaves pro-IL-1β and pro-IL-18 into their mature, secreted forms. Here we describe a protocol by which one type of amyloidogenic peptide, islet amyloid polypeptide (IAPP, otherwise known as amylin) can be prepared and used to stimulate myeloid cells in vitro to engage the NLRP3 inflammasome. Methods for measuring the ensuing inflammasome activation are also described. Although initially soluble, IAPP monomers rapidly aggregate in solution to form oligomers and subsequently insoluble amyloid fibrils. More work is required to examine how this transition influences inflammasome activation for different types of amyloid. The course of amyloid formation and corresponding inflammatory capacity of these pre-fibrillar species following uptake also requires further examination, and we hope that our protocols are useful in these endeavors. While these protocols are restricted to examination of synthetic IAPP, isolation of IAPP aggregates from human and transgenic mouse pancreas will be required to definitively determine the proinflammatory effects of endogenous IAPP oligomers and fibrils.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".