Cognitive Enhancing Treatment with a PPARγ Agonist Normalizes Dentate Granule Cell Presynaptic Function in Tg2576 APP Mice
Bibliographic record
Abstract
Hippocampal network hyperexcitability is considered an early indicator of Alzheimer's disease (AD) memory impairment. Some AD mouse models exhibit similar network phenotypes. In this study we focused on dentate gyrus (DG) granule cell spontaneous and evoked properties in 9-month-old Tg2576 mice that model AD amyloidosis and cognitive deficits. Using whole-cell patch-clamp recordings, we found that Tg2576 DG granule cells exhibited spontaneous EPSCs that were higher in frequency but not amplitude compared with wild-type mice, suggesting hyperactivity of DG granule cells via a presynaptic mechanism. Further support of a presynaptic mechanism was revealed by increased I-O relationships and probability of release in Tg2576 DG granule cells. Since we and others have shown that activation of the peroxisome proliferator-activated receptor gamma (PPARγ) axis improves hippocampal cognition in mouse models for AD as well as benefitting memory performance in some humans with early AD, we investigated how PPARγ agonism affected synaptic activity in Tg2576 DG. We found that PPARγ agonism normalized the I-O relationship of evoked EPSCs, frequency of spontaneous EPSCs, and probability of release that, in turn, correlated with selective expression of DG proteins essential for presynaptic SNARE function that are altered in patients with AD. These findings provide evidence that DG principal cells may contribute to early AD hippocampal network hyperexcitability via a presynaptic mechanism, and that hippocampal cognitive enhancement via PPARγ activation occurs through regulation of presynaptic vesicular proteins critical for proper glutamatergic neurotransmitter release, synaptic transmission, and short-term plasticity.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".