Reelin-Related Disturbances in Depression: Implications for Translational Studies
Bibliographic record
Abstract
The finding that reelin expression is significantly decreased in mood and psychotic disorders, together with evidence that reelin can regulate key aspects of hippocampal plasticity in the adult brain, brought our research group and others to study the possible role of reelin in the pathogenesis of depression. This review describes recent progress on this topic using an animal model of depression that makes use of repeated corticosterone (CORT) injections. This methodology produces depression-like symptoms in both rats and mice that are reversed by antidepressant treatment. We have reported that CORT causes a decrease in the number of reelin-immunopositive cells in the dentate gyrus subgranular zone (SGZ), where adult hippocampal neurogenesis takes place; that down-regulation of the number of reelin-positive cells closely parallels the development of a depression-like phenotype during repeated CORT treatment; that reelin downregulation alters the co-expression of reelin with neuronal nitric oxide synthase (nNOS); that deficits in reelin might also create imbalances in glutamatergic and GABAergic circuits within the hippocampus and other limbic structures; and that co-treatment with antidepressant drugs prevents both reelin deficits and the development of a depression-like phenotype. We also observed alterations in the pattern of membrane protein clustering in peripheral lymphocytes in animals with low levels of reelin. Importantly, we found parallel changes in membrane protein clustering in depression patients, which differentiated two subpopulations of naïve depression patients that showed a different therapeutic response to antidepressant treatment. Here, we review these findings and develop the hypothesis that restoring reelin-related function could represent a novel approach for antidepressant therapies.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".