Novel mechanism of action for the mood stabilizer lithium
Bibliographic record
Abstract
BACKGROUND: Bipolar Disorder (BD) is associated with a decrease in cellular resilience. Despite the half a century old discovery of lithium's efficacy for the treatment of BD, its exact mechanisms remain elusive. Accumulating data suggest that lithium's cytoprotective properties involve the modulation of several UPR proteins, such as GRP78. Mesencephalic astrocyte-derived neurotrophic factor (MANF) is an endoplasmic reticulum resident protein that regulates proteostasis through directly interacting with GRP78. The purpose of this study was to determine whether lithium increases MANF expression using cellular and rodent models and, if so, to elucidate the cellular mechanisms of action. PROCEDURE: Mouse striatal neuroblasts were treated with PBS, lithium, or lithium + Activator Protein-1 (AP-1) inhibitor for 24-72 hours. Once cells were harvested, mRNA was extracted. In vivo experiments included, intraperitoneal injections of lithium or saline to male Sprague Dawley rats twice daily for 14 consecutive days. Following drug treatment, brain tissue was isolated, and mRNA was extracted from various regions. MANF gene expression was measured using RT-qPCR. RESULTS: In vitro studies showed lithium-treated cells displayed a significant increase in MANF mRNA expression compared to controls. In contrast, cells treated with lithium and AP-1 inhibitor showed no increase in expression. Similarly, in vivo studies revealed that lithium-treated rats compared to controls had a significant increase in MANF expression in the PFC and striatum. CONCLUSION: Taken together, these data suggest that lithium's therapeutic mechanism involves the maintenance of ER homeostasis via increased MANF gene expression mediated by the AP-1 transcription factor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".