GPI-1046 Increases Presenilin-1 Expression and Restores NMDA Channel Activity
Bibliographic record
Abstract
BACKGROUND: Previously we showed that 6-hydroxydopamine lesions of the substantia nigra eliminate corticostriatal LTP and that the neuroimmunolophilin ligand (NIL), GPI-1046, restores LTP. METHODS: We used cDNA microarrays to determine what mRNAs may be over- or under-expressed in response to lesioning and/or GPI-1046 treatment. Patch clamp recordings were performed to investigate changes in NMDA channel function before and after treatments. RESULTS: We found that 51 gene products were differentially expressed. Among these we found that GPI-1046 treatment up-regulated presenilin-1 (PS-1) mRNA abundance. This finding was confirmed using QPCR. PS-1 protein was also shown to be over-expressed in the striatum of lesioned/GPI-1046-treated rats. As PS-1 has been implicated in controlling NMDA-receptor function and LTP is reduced by lesioning we assayed NMDA mediated synaptic activity in striatal brain slices. The lesion-induced reduction of dopaminergic innervation was accompanied by the near complete loss of NDMA receptor-mediated synaptic transmission between the cortex and striatum. GPI-1046 treatment of the lesioned rats restored NMDA-mediated synaptic transmission but not the dopaminergic innervation. Restoration of NDMA channel function was apparently specific as the sodium channel current density was also reduced due to lesioning but GPI-1046 did not reverse this effect. We also found that restoration of NMDA receptor function was also not associated with either an increase in NMDA receptor mRNA or protein expression. CONCLUSION: As it has been previously shown that PS-1 is critical for normal NMDA receptor function, our data suggest that the improvement of excitatory neurotransmission occurs through the GPI-1046-induced up-regulation of PS-1.
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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 | 0.000 |
| 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 | 0.001 |
| 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".