Investigating the Interactions of a Cyclic Peptide Hormone Somatostatin and Its Derivatives on Amyloid-Beta Aggregation
Bibliographic record
Abstract
We investigated the effects of cyclic peptides somatostatin, its isomer d-Trp8-somatostatin, and marketed somatostatin derivatives octreotide and lanreotide on Aβ42 aggregation and cytotoxicity in mouse hippocampal HT22 cells. The aggregation kinetic studies show that all the cyclic peptides were able to reduce Aβ42 fibrillogenesis at 1, 5, 10, and 25 μM. The native cyclic peptide somatostatin exhibited superior inhibition compared to other cyclic peptides (91% inhibition at 25 μM) and exhibited greater inhibition compared to the reference agent orange G (86% inhibition at 25 μM), whereas the corresponding isomer d-Trp8-somatostatin exhibited 74% inhibition at 25 μM. The marketed drugs octreotide and lanreotide exhibited a similar inhibition profile (∼54% inhibition at 25 μM). Electron microscopy and immunoblotting experiments also demonstrate their antiaggregation properties. Furthermore, the cyclic peptides were not toxic to mouse hippocampal neuronal HT22 cells and exhibited cell viability ranging from 89 to 98.7% at 10 μM. Strikingly, the cyclic peptides somatostatin, d-Trp8-somatostatin, octreotide, and lanreotide were able to rescue mouse hippocampal neuronal HT22 cells from Aβ42-mediated cytotoxicity (cell viability: 71.7-83.8% at 10 μM). The marketed cyclic peptide drugs octreotide and lanreotide exhibited superior activity (cell viability: 83.8% and 81%, respectively) in preventing Aβ42-induced cytotoxicity compared to somatostatin and d-Trp8-somatostatin. Computational studies were able to identify the potential interaction sites of cyclic peptides in the Aβ42 hexamer assembly. Our studies demonstrate the ability of these cyclic peptides to interact with Aβ42 and reduce Aβ42-induced toxicity, highlighting the potential of marketed drugs octreotide and lanreotide in drug repurposing for Alzheimer's disease.
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.
How this classification was reachedexpand
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 | 0.003 |
| 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 teacher head, 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".