Distinct actions of oligomers and fibrils of islet amyloid polypeptide on Toll-like receptor 2 and NLRP3 inflammasome activation (INM7P.423)
Bibliographic record
Abstract
Abstract Aggregation of islet amyloid polypeptide (IAPP) to form amyloid contributes to beta cell dysfunction in type 2 diabetes. Human but not non-amyloidogenic rodent IAPP activates TLR2 and NLRP3 to induce IL-1β secretion by islet macrophages. It is unclear which form of IAPP aggregate induces IL-1β and whether this pathway is relevant to disease pathogenesis. We therefore evaluated the contributions of soluble and fibrillar IAPP to IL-1β secretion by bone marrow-derived macrophages (BMDMs) and assessed the effects of IL-1 receptor antagonist (IL-1Ra) on glucose homeostasis in transgenic mice with beta cell expression of human IAPP. BMDMs treated with soluble but not fibrillar IAPP provided a TLR2-dependent priming stimulus for ATP-induced IL-1β secretion, suggesting that pre-fibrillar species interact with TLR2. Fibrillar but not soluble IAPP aggregates were required for IL-1β secretion in LPS-primed BMDMs and are therefore likely the principal stimulus for NLRP3. Moreover, IL-1Ra (50 mg/kg/d for 8 weeks) improved glucose tolerance in obese human IAPP transgenic mice (AUC=1360±110 vs. 880±70; p<0.05) with no effect in wild-type littermates. These data provide the first in vivo evidence that IAPP-induced islet dysfunction in type 2 diabetes is mediated by IL-1, and suggest that both soluble and fibrillar IAPP aggregates participate in this process. Islet inflammation and amyloid formation are therefore important therapeutic targets to improve beta cell function in type 2 diabetes.
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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 | 0.001 |
| 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".