Polyphenolic Gallotannins 1,3,6-Tri-O-galloyl-β-<scp>d</scp>-glucose and Corilagin Attenuate IAPP Amyloid Formation and Cytotoxicity by Primarily Targeting Secondary Nucleation
Bibliographic record
Abstract
Protein misfolding and subsequent aggregation into insoluble amyloid deposits are associated with various diseases, including Alzheimer's disease, systemic amyloidosis, and type 2 diabetes mellitus (T2DM). In T2DM, the peptide hormone islet amyloid polypeptide (IAPP), which regulates glucose homeostasis, aggregates in the pancreas, forming soluble cytotoxic aggregates and amyloid fibrils that contribute to β-cell dysfunction and death. Thus, the inhibition of IAPP aggregation consists of a promising strategy for treating T2DM. Natural gallotannins are potential amyloid modulators, though their effects on amyloid self-assembly are not fully understood. This study examines two gallotannins, 1,3,6-tri-O-galloyl-β-d-glucose (β-TGG) and corilagin, and their inhibitory effects on IAPP aggregation. Using thioflavin T fluorescence, atomic force microscopy, and circular dichroism, it was found that the gallotannins delay IAPP self-assembly and reduce the length and quantity of amyloid fibrils. Despite structural similarity, corilagin exhibited markedly higher antiaggregative activity at lower concentrations compared to β-TGG. Peptide monomer-gallotannin interactions were further investigated using all-atom explicit solvent molecular dynamics simulations, providing valuable insight into the binding of both gallotannins to monomeric IAPP. Furthermore, corilagin provided significant cytoprotective effects against IAPP-induced cytotoxicity and membrane damage in pancreatic β-cells. Mechanistic analysis revealed that corilagin exerts its effects primarily by inhibiting secondary nucleation and facilitating off-pathway aggregation into cytocompatible proteospecies. Together, these findings highlight the potential of both gallotannins in inhibiting amyloid self-assembly and inspiring the development of antiaggregative agents.
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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) | 0.000 | 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".