Akt and AMPK activators rescue hyperexcitability in neurons from patients with bipolar disorder
Bibliographic record
Abstract
BACKGROUND: Bipolar disorder (BD) is a multifactorial psychiatric illness affecting ∼1% of the global adult population. Lithium (Li), is the most effective mood stabilizer for BD but works only for a subset of patients and its mechanism of action remains largely elusive. METHODS: In the present study, we used iPSC-derived neurons from patients with BD who are responsive (LR) or not (LNR) to lithium. Combined electrophysiology, calcium imaging, biochemistry, transcriptomics, and phosphoproteomics were employed to provide mechanistic insights into neuronal hyperactivity in BD, investigate Li's mode of action, and identify alternative treatment strategies. FINDINGS: conductance. Whole transcriptome sequencing in BD neurons revealed altered gene expression pathways related to glutamate transmission, alterations in cell signalling and ion transport/channel activity. We found altered Akt signalling as a potential therapeutic effect of Li in LR neurons from patients with BD, and that Akt activation mimics Li effect in LR neurons. Furthermore, the increased neural network activity observed in both LR & LNR neurons from patients with BD were reversed by AMP-activated protein kinase (AMPK) activation. INTERPRETATION: These results suggest potential for new treatment strategies in BD, such as Akt activators in LR cases, and the use of AMPK activators for LNR patients with BD. FUNDING: Supported by funding from ERA PerMed, Bell Brain Canada Mental Research Program and Brain & Behavior Research Foundation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".