Somatostatin slows Aβ plaque deposition in aged <i> APP <sup>NL-F/NL-F</sup> </i> mice by blocking Aβ aggregation in a neprilysin-independent manner
Bibliographic record
Abstract
ABSTRACT The molecular underpinnings that govern the endoproteolytic release of the amyloid beta peptide (Aβ) from the amyloid precursor protein (APP) are now quite well understood. The same cannot be said for the events that precipitate the aggregation and amyloid deposition of Aβ in Alzheimer’s disease (AD). The 14-amino-acid cyclic neuroendocrine peptide somatostatin (SST-14) has long been thought of as playing a role, foremost by controlling the expression of the Aβ clearing enzyme neprilysin, and more recently by directly interacting with Aβ oligomers. Missing have been in vivo data in a relevant Aβ amyloidosis model. Here we addressed this shortcoming by crossing App NL-F/NL-F mice with Sst -deficient mice of identical genetic background to assess if and how the presence of Sst influences key pathological hallmarks of Aβ amyloidosis that develop in App NL-F/NL-F mice after 10 months of age. Surprisingly, we found that Sst had no influence on whole brain neprilysin transcript, protein or activity levels, an observation that cannot be accounted for by a compensatory upregulation of the Sst paralog, cortistatin (Cort), that we observed in 15-month-old Sst-deficient mice. The absence of Sst did lead to a subtle but significant increase in the density of cortical Aβ amyloid plaques. Follow-on western blot analyses of whole brain extracts indicated that Sst interferes with early steps of Aβ assembly that manifest in Sst null brains through the appearance of SDS-stable smears of 55- 150 kDa. As expected, no effect of Sst on tau steady-state levels or its phosphorylation were observed. Results from this study are easier reconciled with an emerging body of data that point toward Sst affecting Aβ amyloid plaque formation through direct interference with Aβ aggregation rather than through its effects on neprilysin expression.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".