CX3CR1 is dysregulated in blood and brain from schizophrenia patients

https://doi.org/10.1016/j.schres.2015.08.010Get rights and content

Abstract

The molecular mechanisms underlying schizophrenia remain largely unknown. Although schizophrenia is a mental disorder, there is increasing evidence to indicate that inflammatory processes driven by diverse environmental factors play a significant role in its development. With gene expression studies having been conducted across a variety of sample types, e.g., blood and postmortem brain, it is possible to investigate convergent signatures that may reveal interactions between the immune and nervous systems in schizophrenia pathophysiology. We conducted two meta-analyses of schizophrenia microarray gene expression data (N = 474) and non-psychiatric control (N = 485) data from postmortem brain and blood. Then, we assessed whether significantly dysregulated genes in schizophrenia could be shared between blood and brain. To validate our findings, we selected a top gene candidate and analyzed its expression by RT-qPCR in a cohort of schizophrenia subjects stabilized by atypical antipsychotic monotherapy (N = 29) and matched controls (N = 31). Meta-analyses highlighted inflammation as the major biological process associated with schizophrenia and that the chemokine receptor CX3CR1 was significantly down-regulated in schizophrenia. This differential expression was also confirmed in our validation cohort. Given both the recent data demonstrating selective CX3CR1 expression in subsets of neuroimmune cells, as well as behavioral and neuropathological observations of CX3CR1 deficiency in mouse models, our results of reduced CX3CR1 expression adds further support for a role played by monocyte/microglia in the neurodevelopment of schizophrenia.

Introduction

Schizophrenia (SCZ) is a complex and devastating brain disorder with an unknown etiology. Although heritability for SCZ is estimated to be close to 70% (Lichtenstein et al., 2009, Sullivan et al., 2003), extensive investigations over the past two decades to define conserved genetic variations among thousands of SCZ samples has not led to definitive results (Gratten et al., 2014). Gene expression profiling has been proposed as an alternative strategy to identify the possible causes of the disease and to understand gene–environment interactions (Mitchell and Mirnics, 2012). The transcriptional landscape can be considered an intermediate phenotype between genomic sequence variability and complex traits that may help to reveal disruptions to biological pathways that underlie the progressive decline from normality to a psychiatric pathology. In fact, the high throughput transcriptome profiling experiments conducted with DNA microarrays identified several molecular processes in SCZ such as myelination, synaptic transmission, metabolism, ubiquitination and immune function (Kumarasinghe et al., 2012, Mistry et al., 2013b). Historically, as for all investigated psychiatric diseases, postmortem brain samples have been considered as the gold standard material to profile SCZ (Arion et al., 2007, Barnes et al., 2011, Chen et al., 2013, Hagihara et al., 2014, Mistry et al., 2013a, Mistry et al., 2013b, Perez-Santiago et al., 2012, Saetre et al., 2007, Schmitt et al., 2011, Torkamani et al., 2010). However, such tissue presents many limitations including access and collection of large sample sizes, tissue preparation and conservation, and antemortem diagnosis. With the aim of developing biomarkers, blood samples have been increasingly utilized because they are easily obtained and allow longitudinal follow-up of gene expression some of which is also correlated in brain tissue (Mamdani et al., 2013, Woelk et al., 2011).

Many transcriptomic microarray studies in SCZ have been made available in public domains such as the Gene Expression Omnibus (GEO) from NCBI (Barrett et al., 2013), ArrayExpress from EBI (Rustici et al., 2013), and the Stanley Medical Research Institute (SMRI) online genomics database (Higgs et al., 2006). Raw data from both postmortem and blood SCZ studies can be easily retrieved from these databases for use in a meta-analysis with enough sample size and sensitivity for the identification of differentially expressed genes and biological processes. With increasing numbers of analyzed samples, it is important to apply normalization procedures that will balance effects that may arise from the heterogeneity in tissue regions, microarray platform, and sample quality that could collectively deteriorate the meta-analysis. Indeed, different methods have been proposed and discussed to conduct meta-analysis (Chang et al., 2013, Chen et al., 2011, Conlon et al., 2007, Lopez et al., 2008, Phan et al., 2012, Schurmann et al., 2012, Seita et al., 2012, Stevens and Doerge, 2005, Tian and Suarez-Farinas, 2013, Warnat et al., 2005).

In the present study, we conducted a meta-analysis to explore whether a common gene expression profile exists across various brain regions that is shared with blood samples and distinguishes SCZ from healthy individuals. To achieve this goal, we used microarray data from SCZ and matched control cohorts publicly available sources, as well as data made available from the Gardiner et al. (2013), and Kumarasinghe et al. (2013) publications (Gardiner et al., 2013, Kumarasinghe et al., 2013). To validate our analysis, we tested the expression of a gene candidate on a cohort of stabilized SCZ patients and healthy controls by RT-qPCR. Taken together, our work pinpoints a biological process and potentially specific cell populations for future experiments investigating SCZ pathophysiology.

Section snippets

Microarray datasets

Microarray datasets were selected on the basis of whether they used either postmortem brain or blood tissue (i.e., cell lines were excluded), the availability of raw data, and information on covariates (i.e., age and sex for blood samples, as well as pH and postmortem interval (PMI) for brain tissues). The treatment status and the acute or remitted status of patients were not taken into account for the selection of datasets. Each dataset was comprised of neuropathologically normal and SCZ

Meta-analysis in postmortem brain

We first compiled a brain cohort from 204 SCZ patients and 212 normal controls by pooling data from 10 studies (Table 1, Table 2). Brain regions investigated included the prefrontal, frontal and temporal cortices, cerebellum, hippocampus, striatum and thalamus. Analysis of the demographic variables identified a significant difference in PMI and pH, but not age and gender between the SCZ subjects and controls (Table 2). After normalization and the removal of underexpressed probes and batch

Discussion

This study has presented for the first time a large-scale evaluation of the consistency of overlap between peripheral blood and postmortem brain gene expression in SCZ with the aim to discover transcriptional biomarkers of the disorder. A decade ago, Glatt et al. (2005) were the first to compare the gene expression profiles of blood and brain. Although they used a small cohort of SCZ cases and controls, their pioneering work has paved the way for using gene expression to identify biomarkers in

Conflict of interest

The authors have no conflicts of interest to disclose.

Contributors

R. Belzeaux, E. Fakra and E. C. Ibrahim designed the study. A. Bergon performed the bioinformatics work. M. Comte and E. Fakra recruited study subjects and provided the clinical data concerning the validation cohort. F. Pelletier, M. Hervé and E. C. Ibrahim performed the experiments. A. Bergon, R. Belzeaux and E. C. Ibrahim undertook the statistical analysis. E. J. Gardiner, N. J. Beveridge, B. Lui, V. Carr, R. J. Scott, B. Kelly, M. J. Cairns, Nishantha Kumarasinghe, Ulrich Schall, and P. A.

Role of the funding source

This work was supported by research grants from Bristol-Myers Squibb Company & Otsuka Pharmaceutical Company, the Aviesan Neuroscience, Cognitive Science and Psychiatry Multi-agency Thematic Institute (ITMO), and Pierre Houriez Foundation. Gene expression data analyzed here from the Gardiner et al., 2013 publication were provided by the Neurobehavioral Genetics Unit (Chief Investigators: Vaughan Carr, Rodney Scott, Brian Kelly, Paul Tooney, Murray Cairns; Associate Investigators: Christopher

Acknowledgments

The authors are thankful to Jeanne Hsu for editing the manuscript.

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