Molecular signatures of hyperexcitability and lithium responsiveness in bipolar disorder patient neurons provide alternative therapeutic strategies
Bibliographic record
Abstract
ABSTRACT Bipolar disorder (BD) is a multifactorial psychiatric illness affecting about 1% of the world population. The first line treatment, lithium (Li), is effective in only a subset of patients and its mechanism of action remains largely elusive. In the present study, we used iPSC-derived neurons from BD patients responsive (LR) or not (LNR) to lithium and combined electrophysiology, calcium imaging, biochemistry, transcriptomics, and phosphoproteomics to report mechanistic insights into neuronal hyperactivity in BD, and Li’s mode of action. We show a selective rescue of neuronal hyperactivity by Li in BD LR neurons through changes in Na + currents. The whole transcriptome sequencing revealed altered gene expression in BD neurons in pathways related to glutamatergic transmission, and Li selectively altered those involved in cell signaling and ion transport/channel activity. We found the therapeutic effect of Li in BD LR patients was associated with Akt signaling and confirmed that an Akt activator mimics Li effect in BD LR neurons. Further, we showed that AMP-activated protein kinase (AMPK) reduces neural network activity and sodium currents in BD LNR patients. These findings suggest the potential for novel treatment strategies in BD, such as Akt activators in BD LR cases, and the use of AMPK activators for BD LNR patients.
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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) | 0.002 | 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".