NF-κB p50 subunit knockout impairs late LTP and alters long term memory in the mouse hippocampus
Bibliographic record
Abstract
BACKGROUND: Nuclear factor kappa B (NF-κB) is a transcription factor typically expressed with two specific subunits (p50, p65). Investigators have reported that NF-κB is activated during the induction of in vitro long term potentiation (LTP), a paradigm of synaptic plasticity and correlate of memory, suggesting that NF-κB may be necessary for some aspects of memory encoding. Furthermore, NF-κB has been implicated as a potential requirement in behavioral tests of memory. Unfortunately, very little work has been done to explore the effects of deleting specific NF-κB subunits on memory. Studies have shown that NF-κB p50 subunit deletion (p50-/-) leads to memory deficits, however some recent studies suggest the contrary where p50-/- mice show enhanced memory in the Morris water maze (MWM). To more critically explore the role of the NF-κB p50 subunit in synaptic plasticity and memory, we assessed long term spatial memory in vivo using the MWM, and synaptic plasticity in vitro utilizing high frequency stimuli capable of eliciting LTP in slices from the hippocampus of NF-κB p50-/- versus their controls (p50+/+). RESULTS: We found that the lack of the NF-κB p50 subunit led to significant decreases in late LTP and in selective but significant alterations in MWM tests (i.e., some improvements during acquisition, but deficits during retention). CONCLUSIONS: These results support the hypothesis that the NF-κ p50 subunit is required in long term spatial memory in the hippocampus.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".