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Biomarkers in an Animal Model for Revealing Neural, Hematologic, and Behavioral Correlates of PTSD
Published on: October 10, 2012
Dynamic gene expression signatures predict psychotherapy response in PTSD
Changxin Xu1, Heather N Bader1,2, Janine D Flory1,2
1James J. Peters VA Medical Center, Bronx, NY, USA.
Abstract:
Static blood-based biomarkers provide limited insight into psychiatric illness and recovery, largely because they capture only a single, unperturbed snapshot of inherently dynamic biological systems. In this study, we applied a dynamic, longitudinal dual-state approach to characterize peripheral blood mononuclear cell (PBMC) transcriptional trajectories associated with psychotherapy outcomes in post-traumatic stress disorder (PTSD). PBMC transcriptional profiles were generated for veterans with PTSD (n = 46, 21, and 17, respectively) and controls (n = 26, 26, and 23) at pre-treatment, post-treatment, and follow-up. The PTSD cohort with profiles across all three timepoints was divided into responders (n = 6) and non-responders (n = 11) based on their Clinician-Administered PTSD Scale for DSM-5 (CAPS-5) trajectories. All subjects were profiled in both unstimulated and dexamethasone-stimulated states. At pre-treatment, unstimulated responders and non-responders exhibited pronounced transcriptional differences exceeding those observed between PTSD and controls. Longitudinally, unstimulated responders showed a marked shift toward control-like profiles, whereas non-responders retained persistent divergence. Pathway analyses identified a prominent temporal signal within Class C/3 G-protein-coupled receptor pathways driven largely by bitter taste receptors (TAS2Rs), along with subgroup-specific regulation of GABAB, opioid, and NMDA receptor signaling. Dexamethasone-induced responses further differentiated subgroups and showed opposing associations with recovery trajectories, implicating glucocorticoid sensitivity as a potential predictor of treatment outcomes. These findings indicate that biological responsiveness itself constitutes a measurable phenotype of recovery, and that dynamic functional profiling of living immune cells can reveal clinically relevant trajectories and candidate mechanisms not accessible through static biomarkers.
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