A role for Akt and glycogen synthase kinase-3 as integrators of dopamine and serotonin neurotransmission in mental health
Bibliographic record
Abstract
Mental illnesses, such as bipolar disorder, attention-deficit/hyperactivity disorder, depression and schizophrenia are a major public health concern worldwide. Several pharmacologic agents acting on monoamine neurotransmission are used for the management of these disorders. However, there is still little understanding of the ultimate molecular mechanisms responsible for the therapeutic effects of these drugs or their relations with disease etiology. Here I provide an overview of recent advances on the involvement of the signalling molecules Akt and glycogen synthase kinase-3 (GSK3) in the regulation of behaviour by the monoamine neurotransmitters dopamine (DA) and serotonin (5-HT). I examine the possible participation of these signalling molecules to the effects of antidepressants, lithium and antipsychotics, as well as their possible contribution to mental disorders. Regulation of Akt and GSK3 may constitute an important signalling hub in the subcellular integration of 5-HT and DA neurotransmission. It may also provide a link between the action of these neurotransmitters and gene products, like disrupted in schizophrenia 1 (DISC1) and neuregulin (NRG), that are associated with increased risk for mental disorders. However, changes in Akt and GSK3 signalling are not restricted to a single disorder, and their contribution to specific behavioural symptoms or therapeutic effects may be modulated by broader changes in biologic contexts or signalling landscapes. Understanding these interactions may provide a better understanding of mental illnesses, leading to better efficacy of new therapeutic approaches.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".