Inhibition of glycogen synthase kinase 3 by lithium, a mechanism in search of specificity
Bibliographic record
Abstract
Inhibition of Glycogen synthase kinase 3 (GSK3) is a popular explanation for the effects of lithium ions on mood regulation in bipolar disorder and other mental illnesses, including major depression, cyclothymia, and schizophrenia. Contribution of GSK3 is supported by evidence obtained from animal and patient derived model systems. However, the two GSK3 enzymes, GSK3α and GSK3β, have more than 100 validated substrates. They are thus central hubs for major biological functions, such as dopamine-glutamate neurotransmission, synaptic plasticity (Hebbian and homeostatic), inflammation, circadian regulation, protein synthesis, metabolism, inflammation, and mitochondrial functions. The intricate contributions of GSK3 to several biological processes make it difficult to identify specific mechanisms of mood stabilization for therapeutic development. Identification of GSK3 substrates involved in lithium therapeutic action is thus critical. We provide an overview of GSK3 biological functions and substrates for which there is evidence for a contribution to lithium effects. A particular focus is given to four of these: the transcription factor cAMP response element-binding protein (CREB), the RNA-binding protein FXR1, kinesin subunits, and the cytoskeletal regulator CRMP2. An overview of how co-regulation of these substrates may result in shared outcomes is also presented. Better understanding of how inhibition of GSK3 contributes to the therapeutic effects of lithium should allow for identification of more specific targets for future drug development. It may also provide a framework for the understanding of how lithium effects overlap with those of other drugs such as ketamine and antipsychotics, which also inhibit brain GSK3.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".