CRISPR-Cas9-Mediated Intersectional Knockout of Glycogen Synthase Kinase 3β in D2 Receptor–Expressing Medial Prefrontal Cortex Neurons Reveals Contributions to Emotional Regulation
Bibliographic record
Abstract
Glycogen synthase kinase 3β (GSK3β) activity is regulated by dopamine D2 receptor signaling and can be inhibited by psychoactive drugs in a D2 receptor-dependent manner. However, GSK3β is ubiquitously expressed in the brain, and D2 receptor-expressing cells are distributed as a mosaic in multiple cortical regions. This complicates the interrogation of GSK3β functions in cortical D2 cells in a circuit-defined manner using conventional animal models. We used a CRISPR-Cas9-mediated intersectional approach to achieve targeted deletion of GSK3β in D2-expressing neurons of the adult medial prefrontal cortex (mPFC). Isolation and analysis of ribosome-associated RNA specifically from mPFC D2 neurons lacking GSK3β demonstrated large-scale translatome alterations. Deletion of GSK3β in mPFC D2 neurons revealed its contribution to anxiety-related, cognitive, and social behaviors. Our results underscore the viability of an intersectional knockout approach to study functions of a ubiquitous gene in a network-defined fashion while uncovering the contribution of GSK3β expressed in mPFC D2 neurons in the regulation of behavioral dimensions related to mood and emotions. This advances our understanding of GSK3β action at a brain circuit level and can potentially lead to the development of circuit selective therapeutics.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".