An isoform‐specific role of FynT tyrosine kinase in Alzheimer's disease
Bibliographic record
Abstract
Alzheimer's disease (AD) is the leading cause of dementia in old age and is characterized by the accumulation of β-amyloid plaques and neurofibrillary tangles (NFT). Recent studies suggest that Fyn tyrosine kinase forms part of a toxic triad with β-amyloid and tau in the disease process. However, it is not known whether Fyn is associated with the pathological features of AD in an isoform-specific manner. In this study, we identified selective up-regulation of the alternative-spliced FynT isoform with no change in FynB in the AD neocortex. Furthermore, gene ontology term enrichment analyses and cell type-specific localization of FynT immunoreactivity suggest that FynT up-regulation was associated with neurofibrillary degeneration and reactive astrogliosis. Interestingly, significantly increased FynT in NFT-bearing neurons was concomitant to decreased FynB immunoreactivity, suggesting an involvement of alternative splicing in NFT formation. Furthermore, cultured cells of astrocytic origin have higher FynT to FynB ratio compared to those of neuronal origin. Lastly, primary rat mixed neuron-astrocyte cultures treated with Aβ25-35 showed selective up-regulation of FynT expression in activated astrocytes. Our findings point to an isoform-specific role of FynT in modulating neurofibrillary degeneration and reactive astrogliosis in AD. Fyn kinase is known to interact with β-amyloid and tau, and contributes to Alzheimer's disease pathogenesis. In this study, it is shown that the alternatively spliced FynT isoform is specifically up-regulated in the AD neocortex, with no change in FynB isoform. The increased FynT correlated with markers of neurofibrillary degeneration and reactive astrogliosis. In primary mixed cultures, treatment with amyloid peptides specifically up-regulated FynT in activated astrocytes. This study points to altered alternative splicing as a potential pathogenic mechanism in AD.
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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) | 0.000 | 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 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".