Increased excitability of dentate gyrus mossy cells occurs early in life in the Tg2576 model of Alzheimer’s disease
Bibliographic record
Abstract
BACKGROUND: Hyperexcitability in Alzheimer's disease (AD) is proposed to emerge early and contribute to disease progression. The dentate gyrus (DG) and its primary cell type, granule cells (GCs) are implicated in hyperexcitability in AD. Hence, we hypothesized that mossy cells (MCs), important regulators of GC excitability, contribute to early hyperexcitability in AD. Indeed, MCs and GCs are linked to hyperexcitability in epilepsy. METHODS: Using the Tg2576 model of AD and WT mice (~ 1 month-old), we compared MCs and GCs electrophysiologically and morphologically, assessed the activity marker c-Fos, Aβ expression and a hippocampal- and MC-dependent memory task that is impaired at 3-4 months of age in Tg2576 mice. RESULTS: Tg2576 MCs had increased spontaneous excitatory events (sEPSP/Cs) and decreased spontaneous inhibitory currents (sIPSCs), increasing the excitation/inhibition ratio. Additionally, Tg2576 MC intrinsic excitability was enhanced. Consistent with in vitro results, Tg2576 MCs showed enhanced c-Fos protein expression. Tg2576 MCs had increased intracellular Aβ expression, suggesting a reason for increased excitability. GCs showed increased excitatory and inhibitory input without changes in intrinsic properties, consistent with effects of increased MC activity. In support, increased GC activity was normalized by an antagonist of MC input to GCs. Also in support, Tg2576 MC axons showed sprouting to the area of GC dendrites. These effects occurred before an impairment in the memory task, suggesting they are extremely early alterations. CONCLUSIONS: Alterations in Tg2576 MCs and GCs early in life suggest an early role for MCs in increased GC excitability. MCs may be a novel target to intervene in AD pathophysiology at early stages.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".