Tauopathy in the <i>APP</i> <sub>swe</sub> / <i>PS1</i> <sub>ΔE9</sub> mouse model of familial Alzheimer’s disease
Bibliographic record
Abstract
Abstract Despite compelling evidence that the accumulation of amyloid-beta (Aβ) promotes cortical MAPT (tau) aggregation in familial and idiopathic Alzheimer’s disease (AD), murine models of cerebral amyloidosis are not considered to develop tau-associated pathology. The absence of neurofibrillary lesions in amyloidosis mice remains a challenge for the amyloidocentric paradigm of AD pathogenesis. It has resulted in the generation of transgenic mice harboring mutations in their tau gene, which may be inappropriate for studying a disease with no known TAU mutations, such as AD. Here, we have used APP swe / PS1 ΔE9 mice to show that tau pathology can develop spontaneously in murine models of familial AD. Tauopathy was abundant around Aβ deposits, with Gallyas- and thioflavin-S-positive perinuclear inclusions accumulating in the APP swe / PS1 ΔE9 cortex by 18 months of age. Age-dependent increases in Gallyas signal correlated positively with binding levels of the paired helical filament (PHF) ligand [ 18 F]Flortaucipir, in all brain areas examined. Sarkosyl-insoluble PHFs were visualized by electron microscopy. Tandem mass tag proteomics identified sequences of hyperphosphorylated tau in transgenic mice, along with signs of RNA missplicing, ribosomal dysregulation and disturbed energy metabolism. Human frontal gyrus tissue was used to validate these findings, revealing primarily quantitative differences between the tauopathy observed in AD patient vs. transgenic mouse tissue. Levels of tau mRNA were not different between APP swe / PS1 ΔE9 and littermate control animals. As physiological levels of endogenous, ‘wild-type’ tau aggregate secondarily to Aβ in transgenic mice, this study demonstrates that amyloidosis is both necessary and sufficient to drive tauopathy in experimental models of familial AD.
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 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) | 0.000 | 0.001 |
| 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 | 0.001 |
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".