Transcriptional profiles of antidepressant resistance across the corticolimbic pathway of chronically stressed mice.

Summary

Approximately one-third of individuals with major depressive disorder experience treatment-resistant depression (TRD), which is poorly understood at the molecular level. A preclinical model using mice showed that prior treatment with fluoxetine (FLX) enhanced responsiveness to ketamine (KET) in some mice, but not in non-responders. This suggests that failed treatments can alter the brain's molecular landscape, influencing future treatment outcomes. Identifying gene networks linked to antidepressant resistance may help develop new therapies for TRD.

Study at a glance

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. However, this priming effect was absent in non-responders, despite identical treatment regimes, suggesting a transcriptional divergence in both the NAc and PFC in 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 antidepressant resistance 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.