Glutathione S‐transferase is a novel target for mood stabilizing drugs in primary cultured neurons
Bibliographic record
Abstract
Oligonucleotide microarray technology was used to analyze gene expression profiles after chronic treatment with the mood stabilizing drug valproate at a therapeutically relevant concentration in primary cultured rat cerebral cortical cells. We discovered that valproate regulates expression of 28 genes, including three isoenzymes (M1, A3 and A4) of glutathione S-transferase (GST), an important protective factor against oxidative stress. Because previous studies in our laboratory found that chronic valproate treatment protected cultured neurons against oxidative stress, further experiments on the regulation of GST were performed. Regulation of GST M1, GST A3 and GST A4 was verified using northern blotting hybridization. Chronic valproate treatment increased mRNA levels of M1 and A4, but decreased the A3 mRNA level dose-dependently, indicating further complexities in the regulation of GST by valproate. The level of GST M1 protein and GST activity were also increased by chronic valproate treatment. In addition, chronic treatment with lithium, another commonly prescribed mood stabilizer, also increased levels of GST M1 mRNA and protein. The present findings suggest that regulation of GST M1, and possibly GST A4, may mediate the anti-oxidative effects of valproate treatment, and regulation of GST may be involved in the mood stabilizing effect of valproate and lithium.
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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) | 0.001 | 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".