The Ability of BDNF to Modify Neurogenesis and Depressive-Like Behaviors Is Dependent upon Phosphorylation of Tyrosine Residues 365/367 in the GABA<sub>A</sub>-Receptor γ2 Subunit
Bibliographic record
Abstract
Brain-derived neurotrophic factor (BDNF) is a potent regulator of neuronal activity, neurogenesis, and depressive-like behaviors; however, downstream effectors by which BDNF exerts these varying actions remain to be determined. Here we reveal that BDNF induces long-lasting enhancements in the efficacy of synaptic inhibition by stabilizing γ2 subunit-containing GABA(A) receptors (GABA(A)Rs) at the cell surface, leading to persistent reductions in neuronal excitability. This effect is dependent upon enhanced phosphorylation of tyrosines 365 and 367 (Y365/7) in the GABA(A)R γ2 subunit as revealed using mice in which these residues have been mutated to phenyalanines (Y365/7F). Heterozygotes for this mutation exhibit an antidepressant-like phenotype, as shown using behavioral-despair models of depression. In addition, heterozygous Y365/7F mice show increased levels of hippocampal neurogenesis, which has been strongly connected with antidepressant action. Both the antidepressant phenotype and the increased neurogenesis seen in these mice are insensitive to further modulation by BDNF, which produces robust antidepressant-like activity and neurogenesis in wild-type mice. Collectively, our results suggest a critical role for GABA(A)R γ2 subunit Y365/7 phosphorylation and function in regulating the effects of BDNF.
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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 | 0.000 |
| 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".