Comparative gene expression analysis of blood and brain provides concurrent validation of SELENBP1 up-regulation in schizophrenia Stephen J. Glatta,b,c,d, Ian P. Everallb,c,e, William S. Kremena,c, Jacques Corbeilf,g, Roman Saˇ ´ sˇikh, Negar Khanlouc,e, Mark Hani, Choong-Chin Liewi, and Ming T. Tsuanga,c,j,k,l aCenter for Behavioral Genomics, Departments of cPsychiatry and gMedicine, hUniversity of California San Diego Cancer Center, and eHIV Neurobehavioral Research Center, University of California at San Diego, La Jolla, CA 92093; dVeterans Medical Research Foundation, San Diego, CA 92161; fDepartment of Anatomy and Physiology, Laval University, Quebec, PQ, Canada G1V 4G2; iChondroGene, Inc., Toronto, ON, Canada M3J 3K4; jDepartments of Epidemiology and Psychiatry, Harvard Institute of Psychiatric Epidemiology and Genetics, Boston, MA 02115; and kVeterans Affairs Healthcare System, San Diego, CA 92161 Communicated by Eric R. Kandel, Columbia University, New York, NY, September 1, 2005 (received for review July 28, 2005) Microarray techniques hold great promise for identifying risk come under study. Because gene expression can reflect both genetic factors for schizophrenia (SZ) but have not yet generated widely and environmental influences, it may be particularly useful for reproducible results due to methodological differences between identifying risk factors for a complex disorder such as SZ, which is studies and the high risk of type I inferential errors. Here we thought to have a multifactorial polygenic etiology in which many established a protocol for conservative analysis and interpretation genes and environmental factors interact. However, the simulta- of gene expression data from the dorsolateral prefrontal cortex of neous consideration of thousands of dependent variables also SZ patients using statistical and bioinformatic methods that limit increases the likelihood of false-positive results (7). In short, false positives. We also compared brain gene expression profiles microarrays hold great promise for identifying etiologic factors for with those from peripheral blood cells of a separate sample of SZ SZ but run the risk of being too liberal and failing to provide replicable results. patients to identify disease-associated genes that generalize across Several groups (8) have characterized gene expression profiles of tissues and populations and further substantiate the use of gene SZ in postmortem tissue from the dorsolateral prefrontal cortex expression profiling of blood for detecting valid SZ biomarkers. (DLPFC) of the brain, which has been consistently identified as Implementing this systematic approach, we: (i) discovered 177 dysfunctional in the illness (9). These studies have noted variable putative SZ risk genes in brain, 28 of which map to linked chro- patterns of dysregulated gene expression in several domains, in- mosomal loci; (ii) delineated six biological processes and 12 mo- cluding G protein signaling, metabolism, mitochondrial function, lecular functions that may be particularly disrupted in the illness; myelination, and neuronal development. However, not all of these (iii) identified 123 putative SZ biomarkers in blood, 6 of which studies have reported significant alterations in each domain. Meth- (BTG1, GSK3A, HLA-DRB1, HNRPA3, SELENBP1, and SFRS1) had odological differences, including ethnic and demographic dispari- GENETICS corresponding differential expression in brain; (iv) verified the ties, alternative microarray platforms, and diverse methods of data differential expression of the strongest candidate SZ biomarker analysis, as well as the high risk of false positives, have been cited (SELENBP1) in blood; and (v) demonstrated neuronal and glial as factors possibly contributing to this variability (7). expression of SELENBP1 protein in brain. The continued application To overcome the limitations of prior microarray studies of SZ, we of this approach in other brain regions and populations should have adopted a rigorous and systematic approach to sequentially facilitate the discovery of highly reliable and reproducible candi- identifying, prioritizing, verifying, and validating potential etiologic date risk genes and biomarkers for SZ. The identification of valid factors in SZ. Importantly, our approach is also very conservative due to three critical design features, including: (i) the application of peripheral biomarkers for SZ may ultimately facilitate early iden- statistical and bioinformatic methods that substantially reduce type tification, intervention, and prevention efforts as well. I error rates by using statistical significance criteria rather than fold-change values; (ii) the evaluation of potential confounds such microarray ͉ ontology as psychotropic medication use; and (iii) the comparison of gene expression profiles in two tissues (brain and blood) from two chizophrenia (SZ) has a substantial genetic basis (1), but its different samples. This approach has allowed us to identify numer- Sbiological underpinnings remain largely unknown. Early at- ous putative risk factors for SZ and further validate the use of gene tempts to profile the expression of specific neurochemicals in blood expression profiling of blood for detecting SZ biomarkers, which we and postmortem brain detected several promising candidate risk described in a pilot study earlier this year (10). The stringency of our factors for SZ (2, 3) that ultimately could not be substantiated (4, methods bolsters the validity of the results and increases their 5). Subsequent progress in mapping the human genome increased likelihood of generalizing to other samples, which should prove the viability of candidate gene association studies, which have since essential for advancing our understanding of the biological basis of SZ. The identification of valid peripheral biomarkers for SZ may proliferated (6). Most candidate genes have been targeted based on ultimately facilitate early identification, intervention, and preven- their expression within systems widely implicated in the disorder tion efforts as well. (e.g., dopamine and glutamate neurotransmitter systems), and this approach is essential for clarifying the nature of dysfunction within Methods these recognized candidate pathways; however, it may not be Design. We first acquired data from cRNA microarrays surveying optimal for identifying additional novel risk factors outside of these a vast portion of the expressed human genome in postmortem tissue systems. The advent of microarrays that can survey the entire expressed human genome has made it possible to simultaneously investigate Abbreviations: DLPFC, dorsolateral prefrontal cortex; GO, gene ontology; NBD, National the roles of several thousand genes in a disorder. Relative to Brain Databank; PBC, peripheral blood cell; SZ, schizophrenia. traditional candidate gene studies predicated on existing disease bS.J.G. and I.P.E. contributed equally to this work. models, microarray analysis is a less-constrained strategy that could lTo whom correspondence should be addressed. E-mail: [email protected]. foster the discovery of novel risk genes that otherwise would not © 2005 by The National Academy of Sciences of the USA www.pnas.org͞cgi͞doi͞10.1073͞pnas.0507666102 PNAS ͉ October 25, 2005 ͉ vol. 102 ͉ no. 43 ͉ 15533–15538 Downloaded by guest on September 24, 2021 from the DLPFC of SZ patients and nonpsychiatric control sub- determine whether the elevated frequency of such exposures jects. We analyzed the data with an innovative statistical tool that among patients would account for group differences in gene reduces the number of false positives relative to other methods and expression. applied a bioinformatic algorithm to simplify the interpretation of Following the method used by Iwamoto et al. (17), the effects of the ontologies represented by the differentially expressed genes. In anticonvulsant, antidepressant, and anxiolytic medications were addition, following the comparative tissue approach adopted by independently examined by comparing gene expression levels ob- Martin et al. (11) for studying breast cancer, we compared gene served in treated and untreated groups with t tests for independent expression profiles in DLPFC with those derived from peripheral samples. Advancing beyond this scheme, antipsychotic medications blood cells (PBCs) from a separate sample of SZ patients and were evaluated in a more quantitative manner by converting daily nonpsychiatric control subjects. This comparison allowed for iden- dosages to a common metric [maximum effective dose (18)] and tification of those genes whose differential expression in SZ gen- examining correlations between this daily dose index and the eralizes across tissues and populations and isolation of potential expression level of each differentially expressed gene. Highly peripheral biomarkers for SZ. The differential expression of the conservative family-wise corrections for multiple testing within strongest candidate SZ biomarker emerging from the microarray each medication class were performed by using the Bonferroni analyses (SELENBP1, which was significantly up-regulated in both correction. brain and blood in SZ) was then verified in PBCs by quantitative Ontological profiling. To assist in the biological and molecular char- RT-PCR. Finally, to demonstrate that SELENBP1 protein is acterization of the differentially expressed genes, we classified these expressed in brain and to preliminarily validate the differential genes using the MicroArray Data Characterization and Profiling