Restoring striatal WAVE-1 improves maze exploration performance of GluN1 knockdown mice
Bibliographic record
Abstract
Abstract NMDA receptors are important for cognition and are implicated in neuropsychiatric disorders. GluN1 knockdown (GluN1KD) mice have reduced NMDA receptor levels, striatal spine density deficits, and cognitive impairments. However, how NMDA depletion leads to these effects is unclear. Since Rho GTPases are known to regulate spine density and cognition, we examined the levels of RhoA, Rac1, and Cdc42 signaling proteins. Striatal Rac1-pathway components are reduced in GluN1KD mice, with Rac1 and WAVE-1 deficits at 6 and 12 weeks of age. Concurrently, medium spiny neuron (MSN) spine density deficits are present in mice at these ages. To determine whether WAVE-1 deficits were causal or compensatory in relation to these phenotypes, we intercrossed GluN1KD mice with WAVE-1 overexpressing (WAVE-Tg) mice to restore WAVE-1 levels. GluN1KD-WAVE-Tg hybrids showed rescue of striatal WAVE-1 protein levels and MSN spine density, as well as selective behavioral rescue in the Y-maze and 8-arm radial maze tests. GluN1KD-WAVE-Tg mice expressed normalized WAVE-1 protein levels in the hippocampus, yet spine density of hippocampal CA1 pyramidal neurons was not significantly altered. Our data suggest a nuanced role for WAVE-1 effects on cognition and a delineation of specific cognitive domains served by the striatum. Rescue of striatal WAVE-1 and MSN spine density may be significant for goal-directed exploration and associated long-term memory in mice.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".