Gene expression profiling following chronic NMDA receptor blockade‐induced learning deficits in rats
Bibliographic record
Abstract
Acute treatments with MK-801, a noncompetitive antagonist of the NMDA glutamate receptor, induce spatial memory deficits in rodents. In the present study, we developed a low-dose chronic MK-801 treatment regimen that induced persistent learning deficits (determined by the Morris water maze task) after administration of the drug (0.2 mg/kg) every 12 h for 14 days. To determine the impact of such a treatment, changes in mRNA expression were investigated in the hippocampi and striata of treated animals using a cDNA membrane array followed by Western blots. Genes whose expression levels were found to be most altered included preprolactin (downregulated) and mitogen-activated protein kinase (MAP kinase 1; upregulated) in the hippocampus, and acyl-CoA synthetase (downregulated) and apolipoprotein D (upregulated) in the striatum. Furthermore, MAP kinase 1 and proteosome subunit beta precursor was found to meet selection criteria for upregulation in both the hippocampus and striatum. Among other genes found to be most changed in the hippocampus were protein kinase C beta I and II, protein tyrosine phosphatase 1beta, neuropilin I and II, adenosine receptor A1, and metabotropic glutamate receptor 2/3. The impact of some gene expression alterations on their corresponding protein levels was studied next. In the hippocampus, protein kinase C beta I and II, protein tyrosine phosphatase, neuropilin I and II, adenosine receptor A, metabotropic glutamate receptor 2/3, and in the striatum phosphatidyl inositol 4 kinase, mitogen-activated protein kinase 1, adenylyl cyclase II, dopamine receptors 1A and 2, and cytochrome C oxidase subunit Va gene and protein expression levels were found to be highly correlated. These results suggest the potential involvement of several genes and proteins in the neuropharmacological effects of MK-801 and possibly the persisting cognitive deficits induced by this repeated drug treatment.
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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".