Impaired Glutamate Transport in a Mouse Model of Tau Pathology in Astrocytes
Bibliographic record
Abstract
Filamentous tau inclusions in neurons and glia are neuropathological hallmarks of tauopathies. The discovery of microtubule-associated protein tau gene mutations that are pathogenic for a heterogenous group of neurodegenerative disorders, called frontotemporal dementia and parkinsonism linked to chromosome-17 (FTDP-17), directly implicate tau abnormalities in the onset/progression of disease. Although the role of tau pathology in neurons in disease pathogenesis is well accepted, the contribution of glial pathology is essentially unknown. We recently generated a transgenic (Tg) mouse model of tau pathology in astrocytes by expressing the human tau protein under the control of the glial fibrillary acidic protein (GFAP) promoter. Both wild-type and FTDP-17 mutant GFAP/tau Tg animals manifest an age-dependent accumulation of tau inclusions in astrocytes that resembles the pathology observed in human tauopathies. We further demonstrate that both strains of Tg mice manifest compromised motor function that correlates with altered expression of the glial glutamate-aspartate transporter and occurs before the development of tau pathology. Subsequently, the Tg mice manifest additional deficits in neuromuscular strength that correlates with reduced expression of glutamate transporter-1 (GLT-1) and occurs concurrent with tau inclusion pathology. Reduced GLT-1 expression was associated with a progressive decrease in sodium-dependent glutamate transport capacity. Reductions in GLT-1 expression were also observed in corticobasal degeneration, a tauopathy with prominent pathology in astrocytes. Less robust changes were observed in Alzheimer's disease in which neuronal tau pathology predominates. Thus, these Tg mice recapitulate features of astrocytic pathology observed in tauopathies and implicate a role for altered astrocyte function in the pathogenesis of these disorders.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".