Transcriptional Profiles of Antidepressant Resistance Across the Corticolimbic Pathway of Chronically Stressed Mice
Bibliographic record
Abstract
ABSTRACT Treatment-resistant depression (TRD), defined by unsuccessful response to multiple antidepressants, affects approximately one-third of individuals with major depressive disorder, yet its underlying molecular mechanisms remain poorly understood. Here, we developed a preclinical model of TRD in which mice exposed to chronic social defeat stress were sequentially treated with fluoxetine (FLX) and ketamine (KET), allowing behavioral stratification into antidepressant responsive and non-responsive mice. RNA sequencing of the nucleus accumbens (NAc) and prefrontal cortex (PFC) revealed transcriptional signatures associated with treatment outcomes. Prior exposure to FLX exerted a priming effect that facilitated molecular and behavioral responsiveness to KET in a subset of animals in both the NAc and PFC. However, this priming effect was absent in non-responders, despite identical treatment regimes, suggesting a transcriptional divergence underlying differential outcomes. Gene co-expression network analysis identified modules enriched for differentially expressed genes unique to stress-susceptible and FLX-KET nonresponsive mice, as well as modules overlapping with both stress susceptibility and antidepressant resistance. These findings suggest that failed antidepressant treatment can shape the brain’s molecular landscape in a way that influences subsequent treatment outcomes, and that resistance arises not simply from treatment failure but from an absence of adaptive molecular priming. This work provides insight into the gene networks contributing to antidepressant non-response and highlights a mechanistic framework for modeling TRD in preclinical systems. By identifying molecular correlates of sequential pharmacological resistance, our findings may inform the development of novel therapeutic strategies for individuals with TRD.
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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 | 0.001 |
| 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".