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Transl Psychiatry. 2018 Jun 6;8(1):112. doi: 10.1038/s41398-018-0158-y.

Networks of blood proteins in the neuroimmunology of schizophrenia.

Author information

1
Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC, USA. clark_jeffries@med.unc.edu.
2
Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA.
3
Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland.
4
Laboratory of Neurogenomic Biomarkers, Centre for Integrative Biology, and Microsoft Research, Centre for Computational Systems Biology, University of Trento, Trento, Italy.
5
Hotchkiss Brain Institute, Department of Psychiatry, University of Calgary, Calgary, AB, Canada.
6
Departments of Psychiatry and Biobehavioral Sciences and Psychology, UCLA, Los Angeles, CA, USA.
7
Department of Psychiatry, UCSD, San Diego, CA, USA.
8
Department of Psychology, Yale University, New Haven, CT, USA.
9
Department of Psychiatry, Zucker Hillside Hospital, Long Island, NY, USA.
10
Department of Psychiatry, UCSF and San Francisco VA Healthcare System, San Francisco, CA, USA.
11
Department of Psychiatry, Harvard Medical School at Beth Israel Deaconess Medical Center and Massachusetts General Hospital, Boston, MA, USA.
12
Department of Psychiatry, Center for Behavioral Genomics UCSD, San Diego, CA, USA.
13
Departments of Psychology and Psychiatry, Emory University, Atlanta, GA, USA.

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.

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