Inhibition of GSK3 by lithium, from single molecules to signaling networks
Bibliographic record
Abstract
For more than 60 years, the mood stabilizer lithium has been used alone or in combination for the treatment of bipolar disorder, schizophrenia, depression, and other mental illnesses. Despite this long history, the molecular mechanisms trough which lithium regulates behavior are still poorly understood. Among several targets, lithium has been shown to directly inhibit glycogen synthase kinase 3 alpha and beta (GSK3α and GSK3β). However in vivo, lithium also inhibits GSK3 by regulating other mechanisms like the formation of a signaling complex comprised of beta-arrestin 2 (βArr2) and Akt. Here, we provide an overview of in vivo evidence supporting a role for inhibition of GSK3 in some behavioral effects of lithium. We also explore how regulation of GSK3 by lithium within a signaling network involving several molecular targets and cell surface receptors [e.g., G protein coupled receptors (GPCRs) and receptor tyrosine kinases (RTKs)] may provide cues to its relative pharmacological selectivity and its effects on disease mechanisms. A better understanding of these intricate actions of lithium at a systems level may allow the rational development of better mood stabilizer drugs with enhanced selectivity, efficacy, and lesser side effects.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".