Matrix Metalloproteinase-9 Protects Islets from Amyloid-induced Toxicity
Bibliographic record
Abstract
Deposition of human islet amyloid polypeptide (hIAPP, also known as amylin) as islet amyloid is a characteristic feature of the pancreas in type 2 diabetes, contributing to increased β-cell apoptosis and reduced β-cell mass. Matrix metalloproteinase-9 (MMP-9) is active in islets and cleaves hIAPP. We investigated whether hIAPP fragments arising from MMP-9 cleavage retain the potential to aggregate and cause toxicity, and whether overexpressing MMP-9 in amyloid-prone islets reduces amyloid burden and the resulting β-cell toxicity. Synthetic hIAPP was incubated with MMP-9 and the major hIAPP fragments observed by MS comprised residues 1-15, 1-25, 16-37, 16-25, and 26-37. The fragments 1-15, 1-25, and 26-37 did not form amyloid fibrils in vitro and they were not cytotoxic when incubated with β cells. Mixtures of these fragments with full-length hIAPP did not modulate the kinetics of fibril formation by full-length hIAPP. In contrast, the 16-37 fragment formed fibrils more rapidly than full-length hIAPP but was less cytotoxic. Co-incubation of MMP-9 and fragment 16-37 ablated amyloidogenicity, suggesting that MMP-9 cleaves hIAPP 16-37 into non-amyloidogenic fragments. Consistent with MMP-9 cleavage resulting in largely non-amyloidogenic degradation products, adenoviral overexpression of MMP-9 in amyloid-prone islets reduced amyloid deposition and β-cell apoptosis. These findings suggest that increasing islet MMP-9 activity might be a strategy to limit β-cell loss 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) | 0.001 | 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".