Amyloid-β Peptides Stimulate the Expression of the p75NTR Neurotrophin Receptor in SHSY5Y Human Neuroblastoma Cells and AD Transgenic Mice
Bibliographic record
Abstract
The progression toward end-stage Alzheimer's disease (AD) in the aging brain is driven by accumulating amyloid-beta (Abeta)(1-42) oligomers that is accompanied by the downregulation of the Trk A neurotrophin receptor and by either upregulation or at least maintenance of the p75 neurotrophin receptor (p75(NTR)), which can be stimulated by the accumulating Abeta(1-42) peptides. Here we show that Abeta(1-42) and its active fragment Abeta(25-35), but not Abeta(42-1), can at least double the level of p75(NTR) receptors in the membranes of model SH-SY5Y human neuroblastoma cells. We also show that p75(NTR) is upregulated in the hippocampi of two strains of AD transgenic mice. Specifically, the level of the p75(NTR) receptor in the hippocampal membranes from 12-15 month old AD-triple transgenic mice (3xTg-AD) harboring PS1 (M146V), AbetaPP (Swe), and tau (P301L) was nearly twice that in hippocampal membranes from age-matched wild-type mice. Similarly, the level of p75 (NTR) receptor in 7 month-old B6.Cg-Tg AD mice harboring PSEN1dE9 and AbetaPP (Swe) was also increased above the level in the corresponding wild-type mice. This increase correlated with the age-dependent rise in Abeta(1-42) levels in the AD mice. Thus, it appears that it could be the accumulating Abeta(1-42) that increases or at least prevents the downregulation of p75 (NTR) receptors in key parts of AD brains. It is possible that when the Abeta (1-42) accumulation reaches a critical level in the brain on the way to late-onset AD, the Abeta (1-42) induced p75 (NTR) receptor signaling starts a vicious cycle that accelerates AD development because of the activated receptors' recently shown ability to stimulate Abeta(1-42) production.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".