Defective regulation of the eIF2-eIF2B translational axis underlies depressive-like behavior in mice and correlates with major depressive disorder in humans
Bibliographic record
Abstract
Major depressive disorder (MDD) is a significant cause of disability in adults worldwide. However, the underlying causes and mechanisms of MDD are not fully understood, and many patients are refractory to available therapeutic options. Impaired control of brain mRNA translation underlies several neurodevelopmental and neurodegenerative conditions, including autism spectrum disorders and Alzheimer's disease (AD). Nonetheless, a potential role for mechanisms associated with impaired translational control in depressive-like behavior remains elusive. A key pathway controlling translation initiation relies on the phosphorylation of the α subunit of eukaryotic initiation factor 2 (eIF2α-P) which, in turn, blocks the guanine exchange factor activity of eIF2B, thereby reducing global translation rates. Here we report that the expression of EIF2B5 (which codes for eIF2Bε, the catalytic subunit of eIF2B) is reduced in postmortem MDD prefrontal cortex from two distinct human cohorts and in the frontal cortex of social isolation-induced depressive-like behavior model mice. Further, pharmacological treatment with anisomycin or with salubrinal, an inhibitor of the eIF2α phosphatase GADD34, induces depressive-like behavior in adult C57BL/6J mice. Salubrinal-induced depressive-like behavior is blocked by ISRIB, a compound that directly activates eIF2B regardless of the phosphorylation status of eIF2α, suggesting that increased eIF2α-P promotes depressive-like states. Taken together, our results suggest that impaired eIF2-associated translational control may participate in the pathophysiology of MDD, and underscore eIF2-eIF2B translational axis as a potential target for the development of novel approaches for MDD and related mood disorders.
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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 | 0.001 | 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) | 0.001 | 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".