title: Biomarkers for combat-related PTSD: focus on molecular networks from high-dimensional data.
abstract: Posttraumatic stress disorder (PTSD) and other deployment-related outcomes originate from a complex interplay between constellations of changes in DNA, environmental traumatic exposures, and other biological risk factors. These factors affect not only individual genes or bio-molecules but also the entire biological networks that in turn increase or decrease the risk of illness or affect illness severity. This review focuses on recent developments in the field of systems biology which use multidimensional data to discover biological networks affected by combat exposure and post-deployment disease states. By integrating large-scale, high-dimensional molecular, physiological, clinical, and behavioral data, the molecular networks that directly respond to perturbations that can lead to PTSD can be identified and causally associated with PTSD, providing a path to identify key drivers. Reprogrammed neural progenitor cells from fibroblasts from PTSD patients could be established as an in vitro assay for high throughput screening of approved drugs to determine which drugs reverse the abnormal expression of the pathogenic biomarkers or neuronal properties.
authors.affiliation: Department of Psychiatry, University of California, San Francisco, CA, USA ; Mental Health Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA.
authors.forename: Thomas C
authors.initials: TC
authors.lastname: Neylan
authors.affiliation: Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, NY, USA.
authors.forename: Eric E
authors.initials: EE
authors.lastname: Schadt
authors.affiliation: Department of Psychiatry, James J. Peters Veterans Affairs Medical Center, Bronx, NY, USA ; Department of Psychiatry and Neurobiology, Mount Sinai School of Medicine, New York, NY, USA.
authors.forename: Rachel
authors.initials: R
authors.lastname: Yehuda
keywords: Computational Biology; PTSD; gene expression; genomics; proteomics; risk factors