Bioinformatics-driven identification and prioritization of PTSD targets based on published multi-omic data

No novel therapeutic targets for post-traumatic stress disorder (PTSD) have been successfully advanced in over two decades, despite substantial unmet clinical need. High-throughput genomic and transcriptomic studies have generated large pools of candidate targets, yet many lack mechanistic relevance, clinical applicability, or druggability. We developed a systematic, biologically rationalized prioritization framework to identify high-confidence CNS-relevant PTSD targets.

Methods

A three-phase quantitative prioritization strategy was applied to 2,467 initial candidate targets derived from PTSD transcriptomic datasets. Phase 1 identified targets expressed in CNS tissues that replicated in independent cohorts, showed consistent differential expression in PTSD CNS tissues, and had concordant direction of effect. Phase 2 advanced targets with moderate or strong CNS disease associations using DisGeNET scores. Phase 3 ranked targets using a composite pathogenicity score incorporating drug trial data, predicted loss-of-function intolerance, and protein-protein interaction network connectivity.

Results

Phase 1 reduced 2,467 candidates to 177 targets enriched for PTSD-relevant traits such as irritability, emotional symptoms, and insomnia. Phase 2 refinement yielded 55 targets with strong CNS phenotypic associations. Phase 3 prioritization identified 20 top-ranked targets with robust PTSD brain association and high CNS pathogenicity, implicating neurotransmitter systems, neurite structural regulation, and protein homeostasis.

Discussion

This three-phase prioritization framework enables efficient de-risking of PTSD target discovery, focusing resources on the most promising and biologically relevant candidates. The approach is adaptable to other poorly understood CNS disorders and may help overcome decades-long stagnation in PTSD therapeutic innovation.