Amyloid β (Aβ) Peptide Directly Activates Amylin-3 Receptor Subtype by Triggering Multiple Intracellular Signaling Pathways
Bibliographic record
Abstract
The two age-prevalent diseases Alzheimer disease and type 2 diabetes mellitus share many common features including the deposition of amyloidogenic proteins, amyloid β protein (Aβ) and amylin (islet amyloid polypeptide), respectively. Recent evidence suggests that both Aβ and amylin may express their effects through the amylin receptor, although the precise mechanisms for this interaction at a cellular level are unknown. Here, we studied this by generating HEK293 cells with stable expression of an isoform of the amylin receptor family, amylin receptor-3 (AMY3). Aβ1-42 and human amylin (hAmylin) increase cytosolic cAMP and Ca(2+), trigger multiple pathways involving the signal transduction mediators protein kinase A, MAPK, Akt, and cFos. Aβ1-42 and hAmylin also induce cell death during exposure for 24-48 h at low micromolar concentrations. In the presence of hAmylin, Aβ1-42 effects on HEK293-AMY3-expressing cells are occluded, suggesting a shared mechanism of action between the two peptides. Amylin receptor antagonist AC253 blocks increases in intracellular Ca(2+), activation of protein kinase A, MAPK, Akt, cFos, and cell death, which occur upon AMY3 activation with hAmylin, Aβ1-42, or their co-application. Our data suggest that AMY3 plays an important role by serving as a receptor target for actions Aβ and thus may represent a novel therapeutic target for development of compounds to treat neurodegenerative conditions such as Alzheimer disease.
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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 | 0.001 |
| 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) | 0.002 | 0.001 |
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".