Protein Profiles Associated With Context Fear Conditioning and Their Modulation by Memantine
Bibliographic record
Abstract
Analysis of the molecular basis of learning and memory has revealed details of the roles played by many genes and the proteins they encode. Because most individual studies focus on a small number of proteins, many complexities of the relationships among proteins and their dynamic responses to stimulation are not known. We have used the technique of reverse phase protein arrays (RPPA) to assess the levels of more than 80 proteins/protein modifications in subcellular fractions from hippocampus and cortex of mice trained in Context Fear Conditioning (CFC). Proteins include components of signaling pathways, several encoded by immediate early genes or involved in apoptosis and inflammation, and subunits of glutamate receptors. At one hour after training, levels of more than half the proteins had changed in one or more fractions, among them multiple components of the Mitogen-activated protein kinase, MAPK, and Mechanistic Target of Rapamycin, MTOR, pathways, subunits of glutamate receptors, and the NOTCH pathway modulator, NUMB homolog (Drosophila). Levels of 37 proteins changed in the nuclear fraction of hippocampus alone. Abnormalities in levels of thirteen proteins analyzed have been reported in brains of patients with Alzheimer's Disease. We therefore further investigated the protein profiles of mice treated with memantine, a drug approved for treatment of AD. In hippocampus, memantine alone induced many changes similar to those seen after CFC and altered the levels of seven proteins associated with Alzheimer's Disease abnormalities. Lastly, to further explore the relevance of these datasets, we superimposed responses to CFC and memantine onto components of the long term potentiation pathway, a process subserving learning and memory formation. Fourteen components of the long term potentiation pathway and 26 proteins interacting with components responded to CFC and/or memantine. Together, these datasets provide a novel view of the diversity and complexity in protein responses and interactions following normal learning.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".