New insights into the roles of sulfated glycosaminoglycans in islet amyloid polypeptide amyloidogenesis and cytotoxicity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it