Major Depressive Disorder (MDD) frequently follows a recurrent trajectory of episodes and remissions, often culminating in treatment-resistance. Molecular differences defining state-specific changes during episode and remission have been explored. However, progressive differences–defined here as cross-sectional linear trends across clinical stages from first to recurrent episodes or remissions, reflecting increasing illness burden over time–remain poorly understood, limiting sustained therapeutic outcomes. Here, we analyzed RNA-seq data from postmortem sgACC to identify progressive differences across MDD episodes or remission relative to state-specific differences, using an integrative assessment of molecular and cellular specificity, genetic-risk, disease-comorbidity and potential therapeutic targets. Differential expression analysis showed greater overlap between progressive and state-specific differences during remission than episode. Pathway enrichment highlighted disruptions in extracellular-matrix pathways shared by state-specific and progressive episodes, while metabolic and catalytic pathways were restored during remission. Cell-type-specific analyses showed that progressive changes were linked to superficial-layer intra-telencephalic neurons, whereas state-specific changes were enriched in pyramidal neuron subtypes and deeper layer SST-positive interneurons. Genome-wide association-informed enrichment analysis further linked these transcriptomic changes to genetic risk factors and symptom dimensions. Anhedonia was associated with both state-specific episode and progressive-remission signatures, suggesting that it is a persistent trait-like feature of MDD. Finally, an integrative pharmacological analysis revealed shared molecular mechanisms between pro-disease and therapeutic targets, highlighting pleiotropic effects of key pathways depending on disease state and dosage. Together, these findings provide a novel perspective on biological underpinnings of MDD progression over episodes or remissions and identify pharmacological targets that account for pathological and/or compensatory/therapeutic processes.
Crisis support teams’ technological openness and learning attitudes toward the AI based virtual patient system crisis support VR
BackgroundAgainst the backdrop of escalating global humanitarian crises, innovative didactic simulations are becoming increasingly important. A promising alternative to traditional classroom-based didactics for learning psychological