Hippocampal SGK1 promotes vulnerability to depression: the role of early life adversity, stress, and genetic risk
Bibliographic record
Abstract
Serum and Glucocorticoid-regulated Kinase 1 (SGK1) is elevated in hippocampal neurons following glucocorticoid exposure and in peripheral blood of depressed patients. However, its mechanistic role in psychopathology and its relevance to the human brain are unknown. To address this gap, we investigated human postmortem brain tissue and found higher SGK1 expression in the hippocampus of depressed suicide decedents compared to healthy subjects who died of natural causes. We observed the highest levels of SGK1 in subjects with reported early life adversity (ELA) - a major risk factor for psychiatric disorders. To determine potential genetic factors underlying increased SGK1 in the hippocampus, we computed expression-based polygenic risk scores (ePRS) for a large population sample from the ABCD study and found that a collection of genetic variants associated with high hippocampal SGK1 expression predicts depression severity and moderates associations between ELA, depressive symptoms, and suicide attempts. Similar to the human brain, hippocampal SGK1 expression was increased in mouse models of ELA, adult chronic stress, and chronic corticosterone exposure, and hippocampal-specific knockdown of SGK1 conferred resilience to stress-induced behavior abnormalities. To test SGK1 as a potential therapeutic target, we injected mice with the small molecule inhibitor, GSK650394, and found that pharmacological inhibition conferred stress resilience, increased adult hippocampal neurogenesis, and rescued stress-induced dentate gyrus hyperactivity. Our cross-species findings reveal a novel role for hippocampal SGK1 in stress resilience, highlight an interaction between ELA and SGK1 on depression and suicide risk, and establish for the first time a functional role for SGK1 in stress-induced psychopathology.
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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".