Enhanced accumulation of phosphorylated α‐synuclein in double transgenic mice expressing mutant β‐amyloid precursor protein and presenilin‐1
Bibliographic record
Abstract
A recent report showed that the accumulation of alpha-synuclein (alpha-syn) was detected in the brains of one-third of Alzheimer's disease and Down syndrome patients. However, the relationship between amyloid-beta protein (Abeta) and alpha-syn remains unclear. We analyzed the relation between the mutation of presenilin-1 (PS-1) and the pathological features of beta-amyloidosis and alpha-synucleinopathy. We generated doubly transgenic mice overexpressing mutant beta-amyloid precursor protein (betaAPP; Tg2576) and mutant PS-1 (PS1L286Vtg; line 198) and analyzed 19 double Tg betaAPP(+)/PS(+) mice at 5-23 months (young to old), 23 age-matched single Tg betaAPP(+)/PS(-) mice, and 11 non-Tg littermates. Immunohistochemical comparison was performed in these three groups by counting the area and the number of alpha-syn- or phosphorylated alpha-syn (palpha-syn)-positive dystrophic neurites per plaque (ASPDN, pASPDN). The acceleration of Abeta pathology was found with earlier onset and exaggerated numbers in double Tg betaAPP(+)/PS(+) compared with single Tg betaAPP(+)/PS(-) mouse brains. The accumulation of ASPDN and pASPDN was also accelerated in double Tg betaAPP(+)/PS(+) compared with single Tg betaAPP(+)/PS(-) mouse brains, especially in pASPDN. The number and area of alpha-syn and palpha-syn, and the ratio of palpha-syn positive neurites were significantly higher in double Tg betaAPP(+)/PS(+) than in single Tg betaAPP(+)/PS(-) mouse brains in middle-aged and old groups. Additional overexpression of mutant PS-1 accelerated Abeta-induced alpha-synucleinopathy and further facilitated the phosphorylation of alpha-syn, suggesting a direct association between mutant PS-1 and phosphorylation of alpha-syn.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".