Bioinformatics-Driven Identification and Prioritization of PTSD Targets Based on Published Multi-omic Data

Mark Zervas^*Allyson GageMagali Haas

• Cohen Veterans Bioscience, Cambridge, United States

Efforts to uncover mechanisms underlying posttraumatic stress disorder (PTSD) have yielded an expanding candidate target pool from genomic and transcriptomic data. However, not all candidates are disease-causing, related to pathological mechanisms, clinically relevant, nor druggable. Systematically identifying and prioritizing high-confidence, high-impact targets in the central nervous system is required to de-risk resource-intensive experimental validation of disease mechanisms and accelerate development of novel treatments. We describe a novel 3-phased, biologically rationalized, and quantitative prioritization strategy to identify and rank order PTSD associated targets based on confidence of association to PTSD and CNS-relevant pathogenicity. Phase 1 was designed to identify and advance targets associated with PTSD through expression in CNS tissues. Putative targets derived from transcriptomic analyses of PTSD were evaluated for: 1. Replication in independent cohorts, 2. Observation of differential expression in PTSD CNS tissues, and 3. Demonstration of consistent direction of effect. This resulted in 177 targets for advancement. Phase 1-selected targets were supported by enrichment for PTSD relevant traits including irritability, emotional symptoms, and insomnia. Phase 2 targets were advanced with additional evidence of association to pathological CNS phenotypes. DisGeNET gene-disease association scores applied to Phase 1-selected targets were assigned a confidence score indicating a target was associated to CNS-relevant pathology using criteria for moderate or strong evidence of disease association. Phase 2 advanced 55 of the 177 targets, which were enriched for CNS phenotypic abnormalities. Phase 3 enabled target prioritization with a composite pathogenicity score, which included metrics derived from drug trial databases, predicted loss-of-function intolerance, and connectivity within a protein-protein interaction network defined by PTSD-associated targets. 55 targets were prioritized by the sum of Phase 2 and Phase 3 scores, where top-ranked targets had strong evidence to support both association with PTSD in brain and high pathogenicity estimates in a CNS-relevant context. Biologically, top-ranked targets implicated transmitter systems, structural regulation of neurites, and protein homeostasis. Future work is required to experimentally validate the utility of our prioritized PTSD targets and to demonstrate the general applicability of this methodology. We anticipate the three-phased approach will enable efficient de-risking of PTSD and other poorly understood CNS disorders.