New insights into the roles of sulfated glycosaminoglycans in islet amyloid polypeptide amyloidogenesis and cytotoxicity
Bibliographic record
Abstract
Glycosaminoglycans (GAGs) are found in association with virtually all extracellular protein deposits related to amyloid diseases. Particularly, GAGs were shown to enhance fibrillogenesis of the islet amyloid polypeptide (IAPP), a peptide hormone whose aggregation is associated with Type-II diabetes pathogenesis. However, the exact molecular mechanism by which GAGs enhance IAPP amyloidogenesis remains unclear as well as the implications of cell surface GAGs in IAPP-mediated cytotoxicity. The aim of this study was to gain conformational and thermodynamics insights about GAGs-IAPP interactions as a function of IAPP protonation state and buffer ionic strength as well as to explore the roles of cell surface GAGs in IAPP cytotoxicity. Isothermal titration calorimetry revealed that protonation of residue His(18) increases the binding affinity of IAPP towards heparin and, in turn, strongly stimulates fibrillogenesis. Interaction of IAPP with heparin induces a random coil to helix conformational conversion and the helical intermediates could be on-pathway to amyloid fibrils formation. Using rat beta-cells INS-1 that were enzymatically treated with GAG lyases and a CHO cell line that is deficient in the biosynthesis of GAGs, we observed that the lack of GAGs at the plasma membrane does not prevent IAPP-induced toxicity, whereas the presence of soluble heparin in the cell media inhibits IAPP cytotoxicity. Overall, this study reinforces the postulate that sulfated GAGs are actively implicated in IAPP amyloidogenic process in vivo, where they could play a protective role by interacting with cytotoxic species and converting them into less culprit amyloid fibrils.
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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 | 0.001 |
| 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".