Networks of blood proteins in the neuroimmunology of schizophrenia

• Type of Material: Article
• Authors: Clark D. Jeffries, University of North Carolina; Diana O. Perkins, University of North Carolina; Margot Fournier, Lausanne University Hospital (CHUV); Kim Q. Do, Lausanne University Hospital (CHUV); Michel Cuenod, Lausanne University Hospital (CHUV); Ines Khadimallah, Lausanne University Hospital (CHUV); Enrico Domenici, University of Trento; Jean Addington, University of Calgary; Carrie E. Bearden, UCLA; Kristin S. Cadenhead, UCSD; Tyrone D. Cannon, Yale University; Barbara A. Cornblatt, Zucker Hillside Hospital; Daniel H. Mathalon, UCSF and San Francisco VA Healthcare System; Thomas H. McGlashan, Yale University; Larry J. Seidman, Harvard Medical School at Beth Israel Deaconess Medical Center and Massachusetts General Hospital; Ming Tsuang, Center for Behavioral Genomics UCSD; Elaine Walker, Emory University; Scott Woods, Yale University
• Language: English
• Date: 2018-12
• Publisher: Nature Publishing Group: Open Access Journals - Option B
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2018
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1038/s41398-018-0158-y
• Title of Journal or Parent Work: Translational Psychiatry
• ISSN: 2158-3188
• Volume: 8
• Issue: 1
• Research Categories: Psychology, General; Psychology, Psychobiology

Abstract

Levels of certain circulating cytokines and related immune system molecules are consistently altered in schizophrenia and related disorders. In addition to absolute analyte levels, we sought analytes in correlation networks that could be prognostic. We analyzed baseline blood plasma samples with a Luminex platform from 72 subjects meeting criteria for a psychosis clinical high-risk syndrome; 32 subjects converted to a diagnosis of psychotic disorder within two years while 40 other subjects did not. Another comparison group included 35 unaffected subjects. Assays of 141 analytes passed early quality control. We then used an unweighted co-expression network analysis to identify highly correlated modules in each group. Overall, there was a striking loss of network complexity going from unaffected subjects to nonconverters and thence to converters (applying standard, graph-theoretic metrics). Graph differences were largely driven by proteins regulating tissue remodeling (e.g. blood-brain barrier). In more detail, certain sets of antithetical proteins were highly correlated in unaffected subjects (e.g. SERPINE1 vs MMP9), as expected in homeostasis. However, for particular protein pairs this trend was reversed in converters (e.g. SERPINE1 vs TIMP1, being synthetical inhibitors of remodeling of extracellular matrix and vasculature). Thus, some correlation signals strongly predict impending conversion to a psychotic disorder and directly suggest pharmaceutical targets.