CX3CR1 is dysregulated in blood and brain from schizophrenia patients Aurélie Bergona,b, Raoul Belzeauxc,d,e, Magali Comtef, Florence Pelletierc,d, Mylène Hervéc,d, Erin J Gardinerg-j, Natalie J Beveridgeg-j, Bing Liug,h,k, Vaughan Carrj,l,m, Rodney J Scottg-j, Brian Kellyg,h,i, Murray J Cairnsg-j, Nishantha Kumarasingheg-j,n, Ulrich Schallg-j, Olivier Blino, José Boucrautc,d, Paul A Tooneyg-j, Eric Fakraf,p, and El Chérif Ibrahimc,d, * aINSERM, TAGC UMR_S 1090, 13288 Marseille Cedex 09, France bAix Marseille Université, TAGC UMR_S 1090, 13288 Marseille Cedex 09, France cAix Marseille Université, CNRS, CRN2M UMR 7286, 13344 Marseille Cedex 15, France dFondaMental, Fondation de Recherche et de Soins en Santé Mentale, 94000 Créteil, France eAP-HM, Hôpital Sainte Marguerite, Pôle de Psychiatrie Universitaire Solaris, 13009 Marseille, France fAix-Marseille Université, CNRS, Institut de Neurosciences de la Timone UMR 7289, 13005 Marseille, France gSchool of Biomedical Sciences and Pharmacy and School of Medicine and Public Health, Faculty of Health, The University of Newcastle, University Drive, Callaghan, NSW 2308 Australia hCentre for Translational Neuroscience and Mental Health, The University of Newcastle, Callaghan, NSW 2308 Australia iHunter Medical Research Institute, New Lambton Heights, NSW 2305Australia CX3CR1 mRNA in schizophrenia jSchizophrenia Research Institute, Darlinghurst, NSW 2010 Australia kKids Cancer Alliance, Cancer Institute NSW, Sydney, Australia lSchool of Psychiatry, University of New South Wales, Randwick, NSW 2301 Australia mDepartment of Psychiatry, Monash University, Clayton, VIC 3168 Australia nUniversity of Sri Jayewardenepura, Nugegoda, and National Institute of Mental Health, Angoda, Sri Lanka oCIC-UPCET et Pharmacologie Clinique, Hôpital de la Timone, 13005 Marseille, France pCHU de Saint-Etienne, Pôle de Psychiatrie, 42100 Saint-Etienne, France * Corresponding author at: Aix Marseille Université, CNRS, CRN2M UMR 728651, Bd Pierre Dramard, 13344 Marseille Cedex 15, France. Tel: +33 (0)4 91 69 89 56; fax: +33 (0)4 91 69 89 20. Email address: [email protected] (E. C. Ibrahim) 2 CX3CR1 mRNA in schizophrenia Abstract The molecular mechanisms underlying schizophrenia remain largely unknown. Although schizophrenia is a mental disorder, increasing evidence indicates that inflammatory processes driven by diverse adverse environmental factors play a significant role. Since gene expression studies have been conducted on both brain and blood samples, it is possible to investigate convergent signatures that may reveal players of the interaction between the immune and nervous systems in schizophrenia pathophysiology. We conducted 2 meta-analyses of microarray gene expression data concerning schizophrenia (N=474) and healthy (N=485) control-matched samples from brain and blood tissues. Then, we assessed whether significantly dysregulated genes in schizophrenia could be shared between blood and brain. To validate our findings, we tested the expression of one top gene candidate by RT-qPCR on a cohort of schizophrenia subjects stabilized by atypical antipsychotic monotherapy (N=29) and matched healthy controls (N=31). Brain and blood meta-analyses converged to indicate that inflammation was the major biological process associated with schizophrenia and that the chemokine receptor CX3CR1 was significantly underexpressed in schizophrenia patients both in blood and brain. We confirmed such differential expression, in schizophrenia patients compared to healthy controls, in our own validation cohort. The dysregulated expression of a chemokine receptor could be the hallmark of specific subsets of immune cells participating in schizophrenia pathophysiology. Current knowledge on CX3CR1 gain or lack of function is discussed for speculation on a role in psychiatric disorders. Keywords CX3CR1 schizophrenia mRNA inflammation transcriptomics meta-analysis 3 CX3CR1 mRNA in schizophrenia 1. Introduction Schizophrenia (SCZ) is a complex and devastating brain disorder with an elusive etiology. Although heritability estimates for SCZ is close to 70 % (Lichtenstein et al., 2009; Sullivan et al., 2003), intense investigations over the past 2 decades to define conserved genetic variations among thousands of SCZ samples did not provide, consensual replications or validations (Gratten et al., 2014). As an alternative strategy to identify the possible causes of the disease, gene expression profiling has been proposed to fill the gap between genetic variation and environmental players that overlay an epigenetic layer of susceptibility factors (Mitchell and Mirnics, 2012). Therefore the transcriptional landscape can be considered as an intermediate phenotype between genomic sequence variability and complex traits that may help in revealing the biological pathways whose perturbations underlie the progressive sliding 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). Initially, as for all investigated psychiatric diseases, postmortem brain samples have been considered as a 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, b; Perez-Santiago et al., 2012; Saetre et al., 2007; Schmitt et al., 2011; Torkamani et al., 2010). However, such tissue presents many limitations for sample size collection and access, tissue preparation and conservation, and antemortem diagnosis. Therefore, with the aim of developing biomarkers, blood collection has been considered. It is easily obtained and allows the longitudinal follow-up of many genes whose expression is also correlated in the brain tissue (Mamdani et al., 2013; Woelk et al., 2011). 4 CX3CR1 mRNA in schizophrenia Many transcriptomic microarray studies in SCZ were 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). One can thus easily retrieve raw data from both postmortem and blood SCZ studies, and combine data as a meta-analysis to increase both sample size and sensitivity for the identification of differentially expressed genes and biological processes. Of course, in order to keep the benefit of increasing the number of the analyzed samples, it is important to apply normalization procedures that will balance the effects that may arise from the heterogeneity in tissue region, microarray platform, 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 by whether a common paradigm could exist in various brain regions and be shared with blood to distinguish SCZ from healthy individuals. To achieve this goal, we used samples from publicly available microarray data involving SCZ and matched control cohorts, 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 procedure, we tested the expression of a gene candidate on a cohort of stabilized SCZ patients and healthy controls by RT-qPCR. Altogether, our work allows us to pinpoint a biological process and potential specific cell populations for future experiments investigating SCZ physiopathology. 5 CX3CR1 mRNA in schizophrenia 2. Materials and methods 2.1 Microarray Datasets Microarray datasets were selected on the basis of use of either brain or blood tissue (i.e. cell lines were excluded), availability of raw data and information on covariates such as age and sex for blood as well as pH and postmortem interval (PMI) for brain tissues. The treatment and the acute or remitted status of patients were not taken into account for the selection of datasets. Each dataset comprises cohorts of neuropathologically normal subjects and SCZ subjects, as diagnosed and reported in their respective studies (Table 1). Sources for data include GEO (http://www.ncbi.nlm.nih.gov/geo), ArrayExpress (http://www.ebi.ac.uk/arrayexpress/), and the Stanley database (https://www.stanleygenomics.org/). GEO and ArrayExpress studies were identified by extensive manual and combinatorial keyword searches (schizophrenia, psychosis, control, human, brain, blood, microarray) until November 1st 2014. Although additional datasets were retrievable (including some microarrays with custom design and not pangenomic), many represent repeated runs of the samples from the same subjects, so we selected datasets that do not allow overlap between samples and/or individuals. Two additional studies of whole blood were obtained from the investigators (Gardiner et al., 2013; Kumarasinghe et al., 2013). Altogether, ten datasets relied on different postmortem brain areas (prefrontal, frontal and temporal cortex, cerebellum, hippocampus, striatum and thalamus) and five on blood tissue (whole blood and PBMCs) (see Table 1 for details