Role for the kinase SGK1 in stress, depression, and glucocorticoid effects on hippocampal neurogenesis
Bibliographic record
Abstract
Stress and glucocorticoid hormones regulate hippocampal neurogenesis, but the molecular mechanisms mediating these effects are poorly understood. Here we identify the glucocorticoid receptor (GR) target gene, serum- and glucocorticoid-inducible kinase 1 (SGK1), as one such mechanism. Using a human hippocampal progenitor cell line, we found that a small molecule inhibitor for SGK1, GSK650394, counteracted the cortisol-induced reduction in neurogenesis. Moreover, gene expression and pathway analysis showed that inhibition of the neurogenic Hedgehog pathway by cortisol was SGK1-dependent. SGK1 also potentiated and maintained GR activation in the presence of cortisol, and even after cortisol withdrawal, by increasing GR phosphorylation and GR nuclear translocation. Experiments combining the inhibitor for SGK1, GSK650394, with the GR antagonist, RU486, demonstrated that SGK1 was involved in the cortisol-induced reduction in progenitor proliferation both downstream of GR, by regulating relevant target genes, and upstream of GR, by increasing GR function. Corroborating the relevance of these findings in clinical and rodent settings, we also observed a significant increase of SGK1 mRNA in peripheral blood of drug-free depressed patients, as well as in the hippocampus of rats subjected to either unpredictable chronic mild stress or prenatal stress. Our findings identify SGK1 as a mediator for the effects of cortisol on neurogenesis and GR function, with particular relevance to stress and depression.
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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 | 0.000 | 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".