Morris Water Maze Training in Mice Elevates Hippocampal Levels of Transcription Factors Nuclear Factor (Erythroid-derived 2)-like 2 and Nuclear Factor Kappa B p65
Bibliographic record
Abstract
Research has identified several transcription factors that regulate activity-dependent plasticity and memory, with cAMP-response element binding protein (CREB) being the most well-studied. In neurons, CREB activation is influenced by the transcription factor nuclear factor kappa B (NF-κB), considered central to immunity but more recently implicated in memory. The transcription factor early growth response-2 (Egr-2), an NF-κB gene target, is also associated with learning and memory. Nuclear factor (erythroid-derived 2)-like 2 (Nrf2), an antioxidant transcription factor linked to NF-κB in pathological conditions, has not been studied in normal memory. Given that numerous transcription factors implicated in activity-dependent plasticity demonstrate connections to NF-κB, this study simultaneously evaluated protein levels of NF-κB, CREB, Egr-2, Nrf2, and actin in hippocampi from young (1 month-old) weanling CD1 mice after training in the Morris water maze, a hippocampal-dependent spatial memory task. After a 6-day acquisition period, time to locate the hidden platform decreased in the Morris water maze. Mice spent more time in the target vs. non-target quadrants of the maze, suggestive of recall of the platform location. Western blot data revealed a decrease in NF-κB p50 protein after training relative to controls, whereas NF-κB p65, Nrf2 and actin increased. Nrf2 levels were correlated with platform crosses in nearly all tested animals. These data demonstrate that training in a spatial memory task results in alterations in and associations with particular transcription factors in the hippocampus, including upregulation of NF-κB p65 and Nrf2. Training-induced increases in actin protein levels caution against its use as a loading control in immunoblot studies examining activity-dependent plasticity, learning, and memory.
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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 | 0.000 |
| 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 | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".