The Flavanol (−)-Epigallocatechin 3-Gallate Inhibits Amyloid Formation by Islet Amyloid Polypeptide, Disaggregates Amyloid Fibrils, and Protects Cultured Cells against IAPP-Induced Toxicity
Bibliographic record
Abstract
Islet amyloid polypeptide (IAPP, amylin) is the major protein component of the islet amyloid deposits associated with type 2 diabetes. The polypeptide lacks a well-defined structure in its monomeric state but readily assembles to form amyloid. Amyloid fibrils formed from IAPP, intermediates generated in the assembly of IAPP amyloid, or both are toxic to β-cells, suggesting that islet amyloid formation may contribute to the pathology of type 2 diabetes. There are relatively few reported inhibitors of amyloid formation by IAPP. Here we show that the tea-derived flavanol, (-)-epigallocatechin 3-gallate [(2R,3R)-5,7-dihydroxy-2-(3,4,5-trihydroxyphenyl)-3,4-dihydro-2H-1-benzopyran-3-yl 3,4,5-trihydroxybenzoate] (EGCG), is an effective inhibitor of in vitro IAPP amyloid formation and disaggregates preformed amyloid fibrils derived from IAPP. The compound is thus one of a very small set of molecules which have been shown to disaggregate IAPP amyloid fibrils. Fluorescence-detected thioflavin-T binding assays and transmission electron microscopy confirm that the compound inhibits unseeded amyloid fibril formation as well as disaggregates IAPP amyloid. Seeding studies show that the complex formed by IAPP and EGCG does not seed amyloid formation by IAPP. In this regard, the behavior of IAPP is similar to the reported interactions of Aβ and α-synuclein with EGCG. Alamar blue assays and light microscopy indicate that the compound protects cultured rat INS-1 cells against IAPP-induced toxicity. Thus, EGCG offers an interesting lead structure for further development of inhibitors of IAPP amyloid formation and compounds that disaggregate IAPP amyloid.
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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 | 0.000 |
| 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".