Differential Activation of Innate Immune Pathways by Distinct Islet Amyloid Polypeptide (IAPP) Aggregates
Bibliographic record
Abstract
Aggregation of islet amyloid polypeptide (IAPP) contributes to beta cell dysfunction in type 2 diabetes and islet transplantation. Like other amyloidogenic peptides, human IAPP induces macrophage IL-1β secretion by stimulating both the synthesis and processing of proIL-1β, a pro-inflammatory cytokine that (when chronically elevated) impairs beta cell insulin secretion. We sought to determine the specific mechanism of IAPP-induced proIL-1β synthesis. Soluble IAPP species produced early during IAPP aggregation provided a Toll-like-receptor-2- (TLR2-) dependent stimulus for NF-κB activation in HEK 293 cells and bone marrow-derived macrophages (BMDMs). Non-amyloidogenic rodent IAPP and thioflavin-T-positive fibrillar amyloid produced by human IAPP aggregation failed to activate TLR2. Blockade of TLR6 but not TLR1 prevented hIAPP-induced TLR2 activation, consistent with stimulation of a TLR2/6 heterodimer. TLR2 and its downstream adaptor protein MyD88 were required for IAPP-induced cytokine production by BMDMs, a process that is partially dependent on autoinduction by IL-1. BMDMs treated with soluble but not fibrillar IAPP provided a TLR2-dependent priming stimulus for ATP-induced IL-1β secretion, whereas late IAPP aggregates induced NLRP3-dependent IL-1β secretion by LPS-primed macrophages. Moreover, inhibition of TLR2 and depletion of islet macrophages prevented up-regulation of Il1b and Tnf expression in human IAPP-expressing transgenic mouse islets. These data suggest participation by both soluble and fibrillar aggregates in IAPP-induced islet inflammation. IAPP-induced activation of TLR2 and secretion of IL-1 may be important therapeutic targets to prevent amyloid-associated beta cell dysfunction.
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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".