# Patent

## Title
Detection of biomarkers for neuropsychiatric disorders

## Abstract

Systems and methods provide a comprehensive high-throughput approach toward the sequential identification, prioritization, verification, and validation of etiologic factors in neuropsychiatric disorders, some of which can also be utilized as biomarkers for these illnesses. The systems and methods determine patterns of gene expression in various tissues from various samples under various experimental and non-experimental conditions, and uses the differences and similarities between the gene expression profiles observed under these conditions to delineate distinct gene expression profiles of risk and treatment of neuropsychiatric disorders.

## Background
RELATED APPLICATION
This application is a U.S. National Stage filing under 35 U.S.C. 371 from International Application No. PCT/2006/005533 filed 15 Feb. 2006, published in English as WO 2006/089062 A2 on 24 Aug. 2006, which claims the benefit of U.S. Provisional Application Ser. No. 60/653,217 filed 15 Feb. 2005, which applications and publication are incorporated herein by reference.


FIELD
The embodiments of the present invention relate to detection of biomarkers and the use of biomarkers. More specifically, the embodiments relate to systems and methods for detecting biomarkers for neuropsychiatric disorders and the use of selenium binding protein 1 (SELENBP1) as a biomarker.
BACKGROUND
Neuropsychiatric disorders may be etiologically complex and heterogeneous thereby making it difficult to identify risk factors for a particular neuropsychiatric disorder. Microarray techniques hold great promise for identifying risk factors for neuropsychiatric disorders such as schizophrenia (SZ) but have not yet generated widely reproducible results due to methodological differences between studies and the high risk of type I inferential errors.
Schizophrenia has a substantial genetic basis, but its biological underpinnings remain largely unknown. Early attempts to profile the expression of specific neurochemicals in blood and postmortem brain tissue detected several promising candidate risk factors for SZ that ultimately could not be substantiated. Subsequent progress in mapping the human genome increased the viability of candidate gene association studies. Most candidate genes have been targeted based on their expression within systems widely implicated in the disorder (e.g., dopamine and glutamate neurotransmitter systems), and this approach may be used for clarifying the nature of dysfunction within these recognized candidate pathways; however, it may not be optimal for identifying additional novel risk factors outside of these systems.
The advent of microarrays that can survey the entire expressed human genome has made it possible to simultaneously investigate the roles of several thousand genes in a disorder. Relative to traditional candidate gene studies predicated on existing disease models, microarray analysis is a less-constrained strategy that may foster the discovery of novel risk genes that otherwise would not come under study. Because gene expression can reflect both genetic and environmental influences, it may be particularly useful for identifying risk factors for a complex disorder such as SZ, which is thought to have a multifactorial polygenic etiology in which many genes and environmental factors interact. However, the simultaneous consideration of thousands of dependent variables also increases the likelihood of false-positive results. In short, microarrays hold great promise for identifying etiologic factors for SZ but run the risk of being too liberal and failing to provide replicable results.
Several groups have characterized gene expression profiles of SZ in postmortem tissue from the dorsolateral prefrontal cortex (DLPFC) of the brain, which has been consistently identified as dysfunctional in the illness. These studies have noted variable patterns of dysregulated gene expression in several domains, including G protein signaling, metabolism, mitochondrial function, myelination, and neuronal development. However, not all of these studies have reported significant alterations in each domain. Methodological differences, including ethnic and demographic disparities, alternative microarray platforms, and diverse methods of data analysis, as well as the high risk of false positives, have been cited as factors possibly contributing to this variability.
As a result, the gene expression profiles generated by existing methods are prone to the identification of false-positive results or risk factors that have little utility for diagnostic and predictive purposes.
SUMMARY
The embodiments of the present invention include systems and methods providing a comprehensive high-throughput approach toward the sequential identification, prioritization, verification, and validation of etiologic factors in neuropsychiatric disorders, some of which can also be utilized as biomarkers for these illnesses. The systems and methods determine patterns of gene expression in various tissues from various samples under various experimental and non-experimental conditions, and uses the differences and similarities between the gene expression profiles observed under these conditions to delineate distinct gene expression profiles of risk and treatment of neuropsychiatric disorders.



BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 illustrates the major components of a system for detecting biomarkers for neuropsychiatric disorders according to embodiments of the invention.
FIG. 2 is a flowchart illustrating an exemplary method for detecting biomarkers for neuropsychiatric disorders according to embodiments of the invention.
FIGS. 3A and 3B illustrate the expression of SELENBP1 protein in DLPFC of a control subject and a patient diagnosed with schizophrenia.
FIGS. 4A and 4B are graphs illustrating relationships between ontology terms identified by embodiments of the invention as overrepresented among genes differentially expressed in DLFPC in SZ.



DETAILED DESCRIPTION
In the following detailed description, reference is made to the accompanying drawings that form a part hereof, and in which is shown by way of illustration, specific embodiments in which the inventive subject matter may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice them, and it is to be understood that other embodiments may be utilized and that structural, logical, and electrical changes may be made without departing from the scope of the inventive subject matter. Such embodiments of the inventive subject matter may be referred to, individually and/or collectively, herein by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept if more than one is in fact disclosed.
The following description is, therefore, not to be taken in a limited sense, and the scope of the inventive subject matter is defined by the appended claims.
In the Figures, the same reference number is used throughout to refer to an identical component which appears in multiple Figures. Signals and connections may be referred to by the same reference number or label, and the actual meaning will be clear from its use in the context of the description.
The functions or algorithms described herein are implemented in hardware, and/or software in embodiments. The software comprises computer executable instructions stored on computer readable media such as memory or other types of storage devices. The term “computer readable media” is also used to represent software-transmitted carrier waves. Further, such functions correspond to modules, which are software, hardware, firmware, or any combination thereof. Multiple functions are performed in one or more modules as desired, and the embodiments described are merely examples. A digital signal processor, ASIC, microprocessor, or any other type of processor operating on a system, such as a personal computer, server, a router, or any other device capable of processing data including network interconnection devices executes the software.
Some embodiments implement the functions in two or more specific interconnected hardware modules or devices with related control and data signals communicated between and through the modules, or as portions of an application-specific integrated circuit. Thus, the example process flow is applicable to software, firmware, and hardware implementations.
Specific ranges, values, and embodiments provided below are for illustration purposes only and do not otherwise limit the scope of the invention, as defined by the claims.
As used herein, “selenium binding protein” includes polypeptides having at least 80%, e.g., at least 85%, 90%, 95%, or more amino acid sequence identity to sequences having NCBI Accession Nos. CAG33133, CAH70328, AAH32997, Q13228, P17563, AAH11202, NP003935, NP 033176, AAH74008, NP956864, NP543168, AAX43635, AAX31965, AAH56590, or AAH09084, and nucleic acid sequence encoding those polypeptides. In one embodiment, the selenium binding protein is selenium binding protein-1, e.g., human, rodent, for instance, rabbit, mouse, rat, mink or guinea pig, or nonhuman primate selenium binding protein-1.
FIG. 1 illustrates a system 100 for detecting potential biomarkers according to embodiments of the invention. In some embodiments, system 100 includes a statistical analysis system 120, a comparator 140, and an ontological identification system 160. In some embodiments, statistical analysis system 120 is a computerized system that receives two input files each of which contain gene expression data, performs a statistical analysis of the two files, and generates an output file that contains gene data representing genes that are differentially expressed between the two input files. In general, one of the input files (e.g. file 102 or 112) contains gene expression data for a control group not having a neuropsychiatric disorder of interest and the other input file (e.g. file 104 or 114) contains gene expression data for a group that has been diagnosed with the neuropsychiatric disorder of interest. Further, the input files generally contain gene expression data obtained from the same source type in the control group and the sample group. For example, the gene expression data may be obtained from central nervous system tissue from the control group and the sample group or from blood samples obtained from the control group and the sample group.
Statistical analysis system 120 in some embodiments uses a model that assumes multiplicative rather than additive noise. Additionally, in some embodiments, statistical analysis system may be designed to eliminate statistically significant outliers. Additionally, the statistical model used in some embodiments assumes a uniform background level that is estimated from both mismatch and perfect-match probe intensities. As noted above, the output of statistical analysis system 120 is a file containing gene data for differentially expressed genes in the two input files.
In particular embodiments, statistical analysis system 120 is the CORGON biostatistical analysis system. Further details on the operation of the CORGON biostatistical analysis system is found in Sasik, R., Calvo, E. & Corbeil, J. “Statistical Analysis of High-Density Oligonucleotide Arrays: A Multiplicative Noise Model” Bioinformatics 18, 1633-1640 (2002), which is hereby incorporated by reference.
Additionally, some embodiments employ an algorithm in which identification of differentially expressed genes is based on their statistical significance beyond a threshold of P=0.05. In these embodiments, the P value of each gene is determined by two-tailed unadjusted permutation testing of 100,000 permutations of sample labels for each gene. For each permutation, the t statistic was calculated from log(expression) values, and the P value was estimated as the fraction of permutations for which the absolute value of the t statistic was greater than or equal to the absolute value of the unpermuted t statistic. In particular embodiments, an implementation of a FOCUS algorithm is used. Further details on the FOCUS algorithm may be found in Cole, S. W., Galic, Z. & Zack, J. A. “Controlling false-negative errors in microarray differential expression analysis: a PRIM approach” (2003) Bioinformatics 19, 1808-1816.
The application of the FOCUS algorithm in conjunction with the statistical model used in some embodiments described above may be useful in reducing the occurrence of Type I errors (false positives).
In operation, to determine biomarkers for a neuropsychiatric disorder of interest, statistical analysis system 120 is executed twice, once for each of two sample types, central nervous system tissue and blood. Thus in one execution, an input file 102 having gene expression data from central nervous system tissue samples obtained from a control group and a second input file 104 having gene expression data from central nervous system tissue samples obtained from individuals having the neuropsychiatric disorder are supplied to the statistical analysis system 120 to produce output file 132 having differentially expressed gene data in the brain tissue samples of the two input groups. In some embodiments, input files 102 and 112 may be cell intensity (CEL) files from the National Brain databank (NBD) web site.
Various forms of central nervous system tissue may be used. In some embodiments, DLPFC tissue may be used. In alternative embodiments, other tissue samples from other brain regions may be used to obtain gene expression data. In further alternative embodiments, cerebrospinal fluid may be used to obtain gene expression data. The embodiments of the invention are not limited to a particular central nervous system component.
In a second execution, statistical analysis system 120 receives an input file 112 having gene expression data from blood samples obtained from a control group not having the neuropsychiatric disorder and a second input file 114 having gene expression data from blood samples obtained from individuals having the neuropsychiatric disorder and produces an output file 134 having differentially expressed gene data in the blood samples of the two input groups. In some embodiments, the blood samples comprise peripheral blood cells.
Comparator 140 comprises software that compares two files having differentially expressed gene data and produces an output file 150 comprising a set of differentially expressed gene data that is common in both the brain tissue samples and the blood samples. In some embodiments, the comparator software may be a spreadsheet style program that sorts and orders entries in order to perform a comparison. An example of such a program is the Microsoft Excel spreadsheet program available from Microsoft Corporation of Redmond Wash.
Ontological identification system 160 receives the common set of differentially expressed gene data 150 and applies a bioinformatic analysis algorithm to identify the ontologies (biological processes, molecular functions, and cellular components) linked to those genes. In some embodiments, the identified genes are placed in an output 170 using standardized gene ontology (GO) terms, such as terms recognized by the GO Consortium and defined in “Gene Ontology: tool for the unification of biology. The Gene Ontology Consortium (2000) Nature Genet. 25: 25-29. In some embodiments, Ontological identification system 160 compares the list of differentially expressed genes identified by the statistical analysis system 120 and comparator 140 with the list of all genes on a microarray and determines which of the standardized GO terms are more frequently represented than would be expected by chance based on the genes represented in the entire microarray. Further, in some embodiments, ontological identification system 160 calculates conditional P values, which allows for the discovery of significant terms even if they are small in size.
In particular embodiments, ontological identification system 160 comprises an implementation of the MicroArray Data Characterization and Profiling (MADCAP) algorithm. Further details on the MADCAP algorithm may be found in Lozach, J., Sasik, R., Ogawa, S., Glass, C. K. “MADCAP: MicroArray Data Characterization And Profiling. A tool for profiling lists of genes with different expression patterns” (2003) in European Conference on Computational Biology (Institut National de la Recherche Agronomique, Paris), p. GE24, which is hereby incorporated by reference.
Further details on the operation of the system described above will be described below with reference to FIG. 2. FIG. 2 illustrates flow diagrams of methods for determining biomarkers for neuropsychiatric disorders. The methods to be performed by the operating environment may include computer programs made up of computer-executable instructions. Describing the methods by reference to a flowchart enables one skilled in the art to develop such programs including such instructions to carry out the methods on suitable computers (the processor or processors of the computer executing the instructions from computer-readable media such as ROMs, RAMs, hard drives, CD-ROM, DVD-ROM, flash memory etc. The methods illustrated in FIG. 2 are inclusive of acts that may be taken by an operating environment executing an example embodiment of the invention. In the discussion below, nonlimiting examples of the execution of the method may be provided in order to provide further description of the method.
The method begins by performing statistical analysis on gene expression data obtained from brain tissue from a control group and from brain tissue from a group having a neuropsychiatric disorder to obtain a first set of differentially expressed genes (block 202). In some embodiments, the brain tissue may be DLPFC, however the embodiments are not limited to a particular type of brain tissue.
In particular embodiments, gene expression data were obtained from cRNA microarrays of fresh-frozen postmortem DLPFC tissue samples (50 mg) from 19 SZ patients and 27 nonpsychiatric control subjects in the National Brain Databank (NBD) maintained by the Harvard Brain Tissue Resource Center (HBTRC). Patients and controls were closely matched on gender (68% vs. 70% male; P=0.887) and mean age (57 vs. 56 yrs; P=0.955), and DLPFC samples were very similar in laterality (58% vs. 52% right hemisphere; P=0.875), mean pH (6.4 vs. 6.4; P=0.981), and mean postmortem interval (21 vs. 20 h; P=0.739). Ascertainment and diagnosis of these subjects according to Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria, preparation of brain tissue, extraction, purification and hybridization of RNA, quantification of expression levels on cRNA microarrays, and quality-control procedures were all performed at the Harvard Brain Tissue Resource Center by standard methods.
At the Harvard Brain Tissue Resource Center, ≈50 mg of fresh frozen brain tissue was obtained from the DLPFC of each subject, and RNA was extracted using a Totally RNA Kit (Ambion, Austin, Tex.). RNA samples were then purified using an RNeasy Kit (Qiagen, Valencia, Calif.). RNA quality of each sample was verified before hybridization by examining the state of 28S and 18S ribosomal RNA by gel electrophoresis on an Agilent 2100 bioanalyzer (Agilent Technologies, Palo Alto, Calif.). RNA quality was very similar between samples from SZ patients and control subjects, as determined by several indices, including the mean 28S:18S RNA ratio (1.11 vs. 1.07; P=0.790) and the mean 3′:5′ ratio of the RNA transcripts of the housekeeping genes G3PDH (1.57 vs. 1.44; P=0.407) and ACTB (2.42 vs. 2.33; P=0.728).
Each purified and quality-assured RNA sample (8 μg) was reverse-transcribed by using a SuperScript II double-stranded cDNA synthesis kit (Invitrogen) to yield complimentary cDNAs, which were then transcribed and amplified in vitro with an Enzo-IVT kit (Affymetrix) to yield biotin-labeled cRNAs of all mRNAs present in the brain tissue sample. Samples of cRNA were then fragmented and hybridized to a GeneChip Human Genome U95A or U133A array (Affymetrix), which was stained in a GeneChip Fluidics Station 400 (Affymetrix) and scanned twice in a DNA Microarray Scanner 2500 (Agilent Technologies). Scans were then visually inspected for artifacts such as edge effects or scratches, which were detected in <5% of samples. Samples affected with such artifacts were removed from the National Brain Databank (NBD) database.
Microarray suite 5.0 software (Affymetrix) was then used to assess the quality of the scans. For arrays with a rate of probe detection below 35%, the entire experimental procedure was repeated from the RNA extraction step forward. If the rate of probe detection was not improved above 35%, the gene expression data for that sample was not added to the NBD database. The rates of probe detection for the samples that we obtained from the NBD were essentially equivalent between the group of schizophrenia (SZ) patients and control subjects (45.8 vs. 45.9% present; P=0.913), and no sample had a rate of probe detection below 35%.
In some embodiments, expression levels of all genes identified as differentially expressed by the statistical analysis may then be examined in relation to antipsychotic and other medication use to determine whether the elevated frequency of such exposures among patients may account for group differences in gene expression.
The effects of anticonvulsant, antidepressant, and anxiolytic medications may be examined by comparing gene expression levels observed in treated and untreated groups with t tests for independent samples. Advancing beyond this scheme, antipsychotic medications may be evaluated in a more quantitative manner by converting daily dosages to a common metric (e.g., maximum effective dose) and examining correlations between this daily dose index and the expression level of each differentially expressed gene. Highly conservative family-wise corrections for multiple testing within each medication class may performed by using the Bonferroni correction
Additionally, a system executing the method performs statistical analysis on gene expression data obtained from blood samples from a control group and from blood samples from a group having the neuropsychiatric disorder to obtain a second set of differentially expressed genes (block 204).
In the execution of an embodiment, peripheral whole-blood samples (10 ml) were obtained from a separate set of 30 SZ patients and 24 nonpsychiatric control subjects. Patients and controls were of similar gender (40% vs. 58% male; P=0.180) but differed in mean age (34 vs. 42 yrs; P=0.014). All blood samples were collected into sterile violet-capped Vacutainer tubes (Becton Dickinson) containing K3 EDTA, temporarily stored at 4° C., and processed within 6 h of collection. Ascertainment and diagnosis of these subjects according to Diagnostic and Statistical Manual of Mental Disorders (DSM-IV, ref. 12) criteria; collection and preparation of blood samples; separation and lysis of peripheral blood cells (PBCs); extraction, purification, and hybridization of RNA; quantification of expression levels on cRNA microarrays; and quality-control procedures were all performed by standard methods.
For some embodiments, each sample was processed by separating plasma, buffy coat, and red blood cell layers through centrifugation. Plasma was discarded, red blood cells were ruptured with lysis buffer, and each mixture was centrifuged again, yielding a pellet of white blood cells, which was briefly washed in lysis buffer. TRIzol (1 ml; Invitrogen) was then applied to extract mRNA, which was then purified and assessed for quality as described above for DLPFC samples.
Each purified and quality-assured RNA sample (5 μg) was prepared for microarray, hybridized to either a GeneChip Human Genome U133A or U133 Plus 2.0 array, and scanned once using an Affymetrix GeneChip Scanner and Affymetrix gcos software, Ver. 1.1.1, according to the manufacturer's published protocols (Affymetrix).
The gene expression data for the blood samples may then be analyzed by statistical analysis system 160 as described above an in a manner similar to that of the brain tissue samples.
The system also compares first and second sets of differentially expressed genes to identify a common set of differentially expressed genes (block 206). For example, the list of genes differentially expressed in the blood of these patients and controls may be compared with the list of genes previously identified as differentially expressed in the DLPFC of SZ patients and controls in the NBD. Medication effects on gene expression levels may be examined as outlined above for DLPFC samples, and the effect of age (which differed between patients and controls) may be evaluated by correlation as well.
Next, the system identifies gene ontologies from the common set of differentially expressed genes (block 208). In some embodiments, this may be performed by an ontological identification system 160, including systems using the MADCAP algorithm. MADCAP compares the list of differentially expressed genes identified by statistical analysis system with the list of all genes on a microarray and determines which of the standardized GO terms are more frequently represented than would be expected by chance based on the genes represented in the entire microarray.
In some embodiments, genes may be classified according to the standardized ontology terms recognized by the Gene Ontology Consortium (GOC). The gene ontology (GO) classification system established and maintained by the GOC describes gene products in three domains, including Biological Process, Cellular Component, and Molecular Function. The grouping of genes based on ontological dimensions further increases the likelihood of identifying true-positive results for genes that exist within and influence the activity of a family of functionally related genes or an extended gene system that may change in synchrony in the disease state.
Ontological classifications and relationships of genes may be analyzed with the MicroArray Data Characterization and Profiling (MADCAP) algorithm, which is a tool for unsupervised statistical analysis of gene lists under the three separate GOs of the GOC. The algorithm finds all statistically significant ontology “terms” (of the >18,000 terms recognized by the GOC) and the genes that represent them, and creates a graphical output of the ontology highlighting significant terms (e.g. see FIGS. 4A and 4B). The input into MADCAP consists of two lists of genes: a reference list and a selected list. The reference list typically contains all genes detected as expressed in the corgon-analyzed microarray dataset. The selected list of genes represents a smaller group of genes selected from the reference list according to some criterion, in some embodiments, the genes that were identified by the statistical analysis system as significantly differentially expressed in the DLPFC of SZ patients and control subjects. MADCAP then identifies which, if any, biological processes, cellular components, and molecular functions are significantly represented in the selected set of genes. Each gene can be linked to several ontology terms, either directly or through daughter GO terms. The reference gene list defines a reference ontology graph as determined by connections of individual genes to ontology terms and connections of terms to other terms. The selected genes define a subgraph of the reference graph. To identify the biological processes represented by the selected genes, MADCAP searches for terms linked to an unusually high number of selected genes. Specifically, starting from the root term of the subgraph and using the known structure of the graphs, we calculate the probability for each immediate daughter term (a value) that it be linked to as many or more selected genes than if the same number of genes was selected from the reference list at random. If the selected genes had been selected by a specific biological process (or processes) rather than at random, the process itself can be detected by an unusually large number of selected genes.
In some embodiments, the method includes verifying the set of differentially expressed gene data obtained from blood samples of the control group and the group having the neuropsychiatric disorder (block 210).
In the execution of a particular embodiment, the level of mRNA expression of the strongest candidate biomarker gene (SELENBP1, which was significantly up-regulated in both DLPFC and PBCs in SZ) was quantified in PBCs by RT-PCR. Total blood RNA isolated by the TRIzol method was reversed-transcribed into single-stranded cDNA by using a High-Capacity cDNA Archive Kit (Applied Biosystems) in a 100-μl reaction. Each sample of cDNA (2 ng) was then mixed with SYBR green master mix (Qiagen, Valencia, Calif.) and primers in a 20-μl reaction. Forward and reverse primers were designed by using PRIMERQUEST (Integrated DNA Technologies, Coralville, Iowa). PCR amplification was performed by using DNA Engine Opticon (MJ Research, Cambridge, Mass.). An automatically calculated melting point dissociation curve was examined to ensure specific PCR amplification and the lack of primer-dimer formation in each well. The comparative Ct equation (Applied Biosystems) was used to calculate relative fold changes between patient and control samples. Briefly, gene expression levels were represented as 2t−DΔC, where DΔCt={[ΔCt(single sample)]−[mean ΔCt(control samples)]}, ΔCt={[Ct(target gene)]−[Ct(ACTB)]}, and ACTB is the housekeeping gene coding for β actin.
In some embodiments, the method includes verifying the set of differentially expressed gene data obtained from the brain tissue of the control group and from the group having the neuropsychiatric disorder (block 212). In some embodiments, the verification include analyzing immunohistochemistry for the sample.
In the execution of a particular embodiment, paraffin wax-embedded DLPFC brain tissue sections (10 μm) were treated with citrate buffer, microwaved for 10 min, and exposed for 24 h at 4° C. to the mouse antiselenium binding protein monoclonal antibody (1:250 dilution; MBL International, Woburn, Mass.). Antibody was detected by using the mouse monoclonal Vectastain ABC kit and the 3,3′ diaminobenzidine substrate for peroxidase (Vector Laboratories). Sections were counterstained with hematoxylin, visualized on a Zeiss microscope, and analyzed by using IMAGE-PRO PLUS software (Media Cybernetics, Silver Spring, Md.).
Results of the Execution of Particular Embodiments
Gene Expression in DLPFC. Application of the CORGON and Focus algorithms described above to the brain gene expression data from 19 SZ patients and 27 control subjects in the NBD identified 177 genes that were differentially expressed in the DLPFC of the two groups. Of these, 111 genes were up-regulated, and 66 were down-regulated in SZ. The Affymetrix probe number, accession number, gene symbol, gene product, and chromosomal locus of each differentially expressed gene, as well as its fold-change difference in expression between SZ patients and control subjects and the corresponding P value, are provided in Table 4 below. Anticonvulsant-treated subjects showed significant down-regulation of six genes and up-regulation of one gene relative to untreated subjects, whereas anxiolytic treatment increased the expression of two genes and decreased the expression of two others. Antidepressant treatment influenced the expression of many more genes, with 10 showing significant upregulation and 7 showing significant down-regulation with treatment. Daily dosage of antipsychotic medication had a significant positive impact on the expression of 13 genes but was not significantly related to down-regulation of any genes. The significance of all of these medication effects was abolished by Bonferroni correction for multiple testing.
The 177 differentially expressed genes were then profiled by MicroArray Data Characterization and Profiling, and 25 were found to be linked to one or more overrepresented ontologies. Thirteen of the 25 genes represented six Biological Process GO terms. FIG. 4A is a graph showing the relationships between biological process ontology terms identified as overrepresented among genes differentially expressed in DLPFC in SZ. The most populated term within this ontology was Energy Pathways (P=0.007), which described the involvement of six genes shown in Table 1 below. In addition, 12 Molecular Function GO terms were represented by 18 genes. FIG. 4B is a graph showing the relationships between molecular function ontology terms identified as overrepresented among genes differentially expressed in DLPFC in SZ. The most populated term in this ontology was oxidoreductase activity (P=0.031), which described the function of seven genes and which are shown in Table 2 below. Three of these genes (NDUFA2, NDUFB5, and NDUFC1) were also classified as having NADH dehydrogenase activity. Genes representing terms in both the Biological Process and Molecular Function ontologies included ACOX1, COX7C, COX17, CNN3, NDUFA2, and NMT1, of which three genes (ACOX1, COX7C, and NDUFA2) had oxidoreductase activity within energy pathways. The remaining 152 differentially expressed genes were linked to GO terms that were not significantly overrepresented.
Gene Expression in PBCs. Application of the CORGON and Focus algorithms to the gene expression data from a separate sample of 30 SZ patients and 24 control subjects from Taiwan identified 123 genes that were differentially expressed in PBCs from the two groups. Of these, 67 genes were up-regulated and 56 down-regulated in SZ. The Affymetrix probe number, accession number, gene symbol, gene product, and chromosomal locus of each differentially expressed gene, as well as its fold-change difference in expression between SZ patients and control subjects and the corresponding P value, are provided in Table 5 below. Eight genes increased and 10 genes decreased significantly in expression level with age. Anticonvulsant-treated subjects differed from untreated subjects in the expression of only one gene, which was up-regulated; however, antidepressant treatment was associated with significant up-regulation of 15 genes and significant down-regulation of two others. The effects of age and these classes of medication did not remain significant after correction for multiple testing. Remarkably, anxiolytic treatment was associated with significant upregulation of 34 genes and down-regulation of another 40. Differential expression of 12 of these genes [including CSDA, EPB42, FBXO9, FKBP8, GSK3A HBA1 (two transcripts), HBA2, HBB (two transcripts), HLA-B, and UBB) remained significant after correcting for multiple testing. Daily dosage of antipsychotic medication was linearly related to the expression of only one gene (G0S2), which also remained statistically significant after corrections for multiple testing were applied.
Comparing the list of 123 genes differentially expressed in PBCs with that obtained from DLPFC identified six genes common to both. These six genes are detailed in Table 3 below. BTG1, HRNPA3, and SFRS1 were significantly up-regulated in the DLPFC in SZ but significantly down-regulated in PBCs from the other sample of SZ patients; the reverse pattern of differential expression was observed for GSK3A, which was the only one of these six genes to show a significant relationship to psychotropic medication (i.e., anticonvulsant) use. In contrast, SELENBP1 was significantly up-regulated in both tissues from the two samples of SZ patients. HLA-DRB1 was significantly downregulated in both DLPFC and PBCs in SZ; however, different probe sets (corresponding to different transcripts of the same gene) were associated with the illness in the two tissues.
SELENBP1 was identified as the strongest candidate biomarker among all genes differentially expressed in SZ, because it was the only gene for which identical probe sets indicated significant differential expression in a similar direction in both brain and blood in SZ. This significant up-regulation was substantiated by RT-PCR in PBCs from a randomly selected subset of the same SZ patients (n=21) and controls (n=18) that were profiled by microarray analysis. A highly significant (P=0.003) 2.2-fold increase in SELENBP1 was observed in PBCs of SZ patients by RT-PCR, which closely corresponded to the significant 2.0-fold up-regulation of the gene observed by microarray.
Protein Expression in DLPFC. Granular cytoplasmic staining of SELENBP1 protein was observed in a proportion of neurons and glia in DLPFC tissue from each of the four control subjects and four SZ patients. A representative example of the antibody-staining pattern observed in controls is shown in FIG. 3A, whereas a representative example of the antibody-staining pattern observed in patients is shown in FIG. 3B. Arrows 310 and 320 indicate antibody staining of cytoplasm in different cell types, with arrow 310 indicating glia and arrow 320 indicating neurons. Compared with control tissue, the intensity and ratio of glial/neuronal SELENBP1 antibody staining was noticeably increased in DLPFC tissue from at least three of the four SZ patients. The enhanced intraglial staining of SELENBP1 antibody in samples from SZ patients was most notable in a perinuclear rim of increased expression. No staining was observed in any cell when the primary antibody was omitted.
Analysis of Results
In some embodiments, a protocol for analysis and interpretation of gene expression microarray data includes using the CORGON and Focus algorithms to limit type I errors and MicroArray Data Characterization and Profiling to identify the ontologies represented by differentially expressed genes. Application of embodiments to gene expression data from DLPFC in SZ identified 177 genes, 6 biological processes, and 12 molecular functions that may be considered as high-priority targets for gene association analyses and hypothesis-driven functional studies. Twenty-eight of these genes also code to chromosomal loci strongly implicated in SZ by linkage analysis (Table 4) and are thus particularly attractive candidates. These include four genes (ACOX1, NDUFA2, SUCLG1, and TAPBP) that were also linked to significantly overrepresented GO terms, and SFRS1 and HLA-DRB1, which were differentially expressed in both DLPFC and PBCs in SZ. These genes provide reference points for the fine-mapping and positional cloning of putative SZ risk loci.
Application of embodiments of the invention resulted in GO terms overrepresented in DLPFC in the present study that were generally related to neurotransmitter systems not typically implicated in SZ (e.g., GABA receptor activity), neuronal processes not specific to a given neurotransmitter system (e.g., regulation of action potential), or biological processes not specific to the nervous system (e.g., energy pathways). More precisely, genes related to energy metabolism were predominant among those differentially expressed in DLPFC in SZ. Furthermore, at least four of the genes implicated in the results described above are involved in electron transfer, and three of these are also part of the NADH dehydrogenase complex located in the inner mitochondrial membrane. These data suggest that dysfunction within particular pathways or processes, but perhaps not necessarily in specific genes, might be important in the etiology of SZ.
Additionally, each of the 177 genes differentially expressed in DLPFC of SZ patients is a promising candidate risk gene. Also, the 123 genes differentially expressed in PBCs from such patients can be considered putative biomarkers for the illness. Six of the putative biomarker genes identified above in PBCs were also differentially expressed in the brain in SZ. Among these were BTG1, which regulates cell proliferation, and three genes (GSK3A, HNRPA3, and SFRS1) that regulate RNA splicing or transcription. The genes noted above may be considered secondary candidate SZ biomarkers to SELENBP1 and HLADRB1, which showed altered expression in the same direction in both DLPFC and PBCs (up- and down-regulation, respectively). Furthermore, HLA-DRB1 maps to the MHC region on chromosome 6p21.3, which is a prime candidate locus for SZ (22), and SELENBP1 maps to chromosome 1q21-22, a locus that has been strongly linked to SZ in some but not most genome-wide linkage studies.
Additionally, the utility of SELENBP1 as a potential peripheral biomarker was substantiated by RT-PCR verification of its up-regulation in PBCs. The altered levels of SELENBP1 transcripts measured in DLPFC and PBCs also translated into observable consequences at the functional level, because analyses suggests that expression of SELENBP1 protein was denser in glia and less dense in neurons of the DLPFC in SZ.
Thus various embodiments of the invention use SELENBP1 in various methods. For example, in some embodiments, a method to diagnose a neuropsychiatric disorder comprises detecting or determining the amount or level of selenium binding protein expression in cells in a physiological sample of a first mammal. An increase in the amount or level of selenium binding protein expression in the cells of the first mammal relative to the amount of level of selenium binding protein expression in cells of a mammal not having a psychotic mental disorder is indicative of a psychotic mental disorder in the first mammal. The physiological sample may be blood, including peripheral blood cells.
In further embodiments, a method to determine a mammal at risk of a neuropsychiatric disorder includes comparing the amount or level of selenium binding protein expression in cells in a physiological sample from a first mammal to the amount or level of selenium binding protein expression in cells from the first mammal at an earlier point in time or to the amount or level of selenium binding protein expression in cells of a mammal not having a neuropsychiatric disorder. An increase in the amount or level of selenium binding protein expression in the cells of the first mammal over time or relative to the mammal not having a neuropsychiatric disorder is indicative that the first mammal is at risk of a neuropsychiatric disorder. The physiological sample may be blood, including peripheral blood cells.
In still further embodiments, a method to determine the risk of progression of a neuropsychiatric disorder in a mammal includes comparing the amount or level of selenium binding protein expression in cells in a physiological sample from a mammal having a neuropsychiatric disorder to the amount or level of selenium binding protein expression in cells from the mammal at an earlier point in time. An increase in the amount or level of selenium binding protein expression over time is indicative that the mammal is at risk of progression of the neuropsychiatric disorder. The physiological sample may be blood, including peripheral blood cells.
In yet further embodiments, a method to determine whether an agent inhibits selenium binding protein expression in cells of a mammal includes

    
    
        a) administering an agent to a mammal; and
        b) detecting or determining whether the agent inhibits selenium binding protein expression in cells in a physiological sample of the mammal.
    
    


In further embodiments, a method to determine whether an agent inhibits or treats a neuropsychiatric disorder includes:

    
    
        a) administering an agent to a mammal having a neuropsychiatric disorder; and
        b) detecting or determining whether the agent inhibits selenium binding protein expression in cells in a physiological sample of the mammal. A decrease in the amount or level of selenium binding protein expression in the cells is indicative of an agent useful to inhibit or treat the neuropsychiatric disorder.
    
    


The embodiments described above provide systems and methods for analysis of gene expression microarray data to detect potential biomarkers for a neuropsychiatric disorder and for using biomarkers detected as a result of the analysis. Application of the embodiments resulted in 177 putative SZ risk genes in DLPFC, 28 of which map to chromosomal loci linked to the disorder; delineated 6 biological processes and 12 molecular functions that may be particularly disrupted in the illness; identified 123 putative SZ biomarkers in PBCs, 6 of which had corresponding differential expression in DLPFC; verified the up-regulation of the strongest candidate SZ biomarker (SELENBP1) in PBCs; and demonstrated an altered pattern of expression of SELENBP1 protein in DLPFC in SZ. While the discussion above has focused on SZ, those of skill in the art will appreciate that the systems and methods described above may be applied to other brain regions (e.g., both implicated and nonimplicated structures, to identify ubiquitous and region-specific changes) and populations (e.g., bipolar disorder patients, to establish disease-specific changes) in order to facilitate the discovery of highly reliable and reproducible candidate risk genes and biomarkers for SZ and other neuropsychiatric disorders.
In the foregoing Detailed Description, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments have more features than are expressly recited in each claim. Thus the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate embodiment.
The foregoing descriptions of specific embodiments of the present invention have been presented for purposes of illustration and description. The embodiments presented are not intended to be exhaustive or to limit the invention to the particular forms disclosed. It should be understood that one of ordinary skill in the art can recognize that the teachings of the detailed description allow for a variety of modifications and variations that are not disclosed herein but are nevertheless within the scope of the present invention. Accordingly, it is intended that the scope of the present invention be defined by the appended claims and their equivalents, rather than by the description of the embodiments.
The Abstract is provided to comply with 37 C.F.R. §1.72(b) to allow the reader to quickly ascertain the nature and gist of the technical disclosure. The Abstract is submitted with the understanding that it will not be used to limit the scope of the claims.







TABLE 1







Biological Process ontology terms overrepresented by genes differentially expressed in DLPFC in SZ











Ontology Term
p
Gene Symbol
Gene Product
Gene Fold-Change (p) in SZ





ATP-Dependent Proteolysis
0.022
CRBN
cereblon
1.08 (0.01660)


Circulation
0.023
LPL
lipoprotein lipase
1.28 (0.02680)




RYR2
ryanodine receptor 2 (cardiac)
1.14 (0.01030)




SRI
sorcin
1.20 (0.02480)


Energy Pathways
0.007
ACOX1
acyl-Coenzyme A oxidase 1, palmitoyl
1.20 (0.00480)




COX17
COX17 homolog, cytochrome c oxidase assembly protein (yeast)
1.10 (0.01780)




COX7C
cytochrome c oxidase subunit VIIc
1.07 (0.04030)




GLP1R
glucagon-like peptide 1 receptor
−1.09 (0.03080) 




NDUFA2
NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 2, 8 kDa
1.09 (0.02260)




SUCLG1
succinate-CoA ligase, GDP-forming, alpha subunit
1.06 (0.02870)


Muscle Contraction
0.041
CNN3
calponin 3, acidic
1.37 (0.01660)




RYR2
ryanodine receptor 2 (cardiac)
1.14 (0.01030)




SRI
sorcin
1.20 (0.02480)




SSPN
sarcospan (Kras oncogene-associated gene)
1.21 (0.04060)


Protein-Lipoylation
0.004
NMT1
N-myristoyltransferase
1.09 (0.00980)


Regulation of Action
0.013
SRI
sorcin
1.20 (0.02480)


Potential
















TABLE 2







Molecular Function ontology terms overrepresented by genes differentially expressed in DLPFC in SZ















Gene Fold-Change


Ontology Term
p
Gene Symbol
Gene Product
(p) in SZ





Beta-Catenin Binding
0.041
APC
adenomatosis polyposis coli
1.14 (0.02510)


Calpain Activity
0.009
CAPN1
calpain 1, (mu/l) large subunit
−1.10 (0.02210) 




CAPNS1
calpain, small subunit 1
−1.10 (0.01740) 


Copper Ion Transporter Activity
0.016
COX17
COX17 homolog, cytochrome c oxidase assembly protein (yeast)
1.10 (0.01780)


Electron Donor Activity
0.027
ACOX1
acyl-Coenzyme A oxidase 1, palmitoyl
1.20 (0.00480)


GABA Receptor Activity
0.012
GABRA2
gamma-aminobutyric acid (GABA) A receptor, alpha 2
1.22 (0.01050)




GABRB1
gamma-aminobutyric acid (GABA) A receptor, beta 1
1.16 (0.04360)


Glycylpeptide
0.048
NMT1
N-myristoyltransferase
1.09 (0.00980)


N-Tetradecanoyltransferase


Activity


MHC Protein Binding
0.048
TAPBP
TAP binding protein (tapasin)
−1.09 (0.02930) 


NADH Dehydrogenase Activity
0.019
NDUFA2
NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 2, 8 kDa
1.09 (0.02260)




NDUFB5
NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 5, 16 kDa
1.09 (0.02910)




NDUFC1
NADH dehydrogenase (ubiquinone) 1, subcomplex unknown, 1, 6 kDa
1.07 (0.00900)


Oxidoreductase Activity
0.031
ACOX1
acyl-Coenzyme A oxidase 1, palmitoyl
1.20 (0.00480)




COX7C
cytochrome c oxidase subunit VIIc
1.07 (0.04030)




HSD17B12
hydroxysteroid (17-beta) dehydrogenase 12
1.11 (0.03900)




NDUFA2
NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 2, 8 kDa
1.09 (0.02260)




NDUFB5
NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 5, 16 kDa
1.09 (0.02910)




NDUFC1
NADH dehydrogenase (ubiquinone) 1, subcomplex unknown, 1, 6 kDa
1.07 (0.00900)




P4HA1
procollagen-proline, 2-oxoglutarate 4-dioxygenase, alpha polypeptide I
1.12 (0.03640)


Peroxisome Targeting Signal
0.031
PEX5
peroxisomal biogenesis factor 5
−1.12 (0.00890) 


Receptor Activity


Phosphoserine Phosphatase
0.009
PSPHL
phosphoserine phosphatase-like
−2.14 (0.04420) 


Activity


Troponin C Binding
0.030
CNN3
calponin 3, acidic
1.37 (0.01660)
















TABLE 3







Six genes differentially expressed in both DLPFC and PBCs in SZ












Affymetrix
Accession
Gene
Gene
Chromosomal
Fold-Change (p) in SZ













Probe Number
Number
Symbol
Product
Locus
DLPFC
PBCs





200920_s_at
AL535380
BTG1
B-cell translocation gene 1, anti-proliferative
12q22
1.14 (0.04020)
−1.36 (0.00008)


202210_x_at
NM_019884
GSK3A
glycogen synthase kinase 3 alpha
19q13.2
−1.09 (0.02490)  
  1.59 (0.00044)


209728_at
BC005312
HLA-DRB1
major histocompatibility complex, class II,
6p21.3
−1.17 (0.04220)  
NS*





DR beta 1


209312_x_at
U65585
′′
major histocompatibility complex, class II,
′′
NS*
−1.27 (0.00007)





DR beta 1


215193_x_at
AJ297586
′′
major histocompatibility complex, class II,
′′
NS*
−1.33 (0.00006)





DR beta 1


211929_at
AA527502
HNRPA3
heterogeneous nuclear ribonucleoprotein A3
2q31.2
1.15 (0.01410)
−2.12 (0.00004)


214433_s_at
NM_003944
SELENBP1
selenium binding protein 1
1q21-q22
1.16 (0.04510)
  1.95 (0.00093)


211784_s_at
BC006181
SFRS1
splicing factor, arginine/serine-rich 1
17q21.3-q22
1.12 (0.02460)
−1.71 (0.00005)





(splicing factor 2, alternate splicing factor)





*NS probe was not differentially expressed on the microarray used to profile the indicated tissue













TABLE 4







Genes differentially expressed in DLPFC in SZ


















Fold-



Affymetrix
Accession
Gene
Gene
Chromosomal
Change


Probe Number
Number
Symbol
Product
Locus*
in SZ
p
















210764_s_at
AF003114
CYR61
cysteine-rich, angiogenic inducer, 61
1p31-p22
1.39
0.02990


201445_at
NM_001839
CNN3
calponin 3, acidic
1p22-p21
1.37
0.01660


208859_s_at
AI650257
ATRX
alpha thalassemia/mental retardation syndrome X-linked
Xq13.1-q21.1
1.36
0.00980





(RAD54 homolog, S. cerevisiae)


215338_s_at
AI688640
NKTR
natural killer-tumor recognition sequence
3p23-p21
1.33
0.00280


216563_at
X80821
ANKRD12
ankyrin repeat domain 12
18p11.22
1.32
0.01470


216609_at
AF065241
TXN
thioredoxin
9q31
1.32
0.01980


208993_s_at
AW340788
PPIG
peptidyl-prolyl isomerase G (cyclophilin G)
2q31.1
1.30
0.03260


209655_s_at
AI803181
TM4SF10
transmembrane 4 superfamily member 10
Xp11.4
1.30
0.01260


203548_s_at
BF672975
LPL
lipoprotein lipase
8p22*
1.28
0.02680


217820_s_at
NM_018212
ENAH
enabled homolog (Drosophila)
1q42.12
1.28
0.00620


211997_x_at
NM_005324
H3F3B
H3 histone, family 3B (H3.3B)
17q25
1.26
0.00280


214212_x_at
AI928241
PLEKHC1
pleckstrin homology domain containing, family C
14q22.1
1.26
0.04640





(with FERM domain) member 1


218930_s_at
NM_018374
FLJ11273
hypothetical protein FLJ11273
7p21.3
1.26
0.01520


202619_s_at
AI754404
PLOD2
procollagen-lysine, 2-oxoglutarate 5-dioxygenase
3q23-q24
1.25
0.02280





(lysine hydroxylase) 2


202412_s_at
AW499935
USP1
ubiquitin specific protease 1
1p32.1-p31.3
1.23
0.04030


209069_s_at
BC001124
H3F3B
H3 histone, family 3B (H3.3B)
17q25
1.23
0.00480


207014_at
NM_000807
GABRA2
gamma-aminobutyric acid (GABA) A receptor, alpha 2
4p12
1.22
0.01050


204964_s_at
NM_005086
SSPN
sarcospan (Kras oncogene-associated gene)
12p11.2
1.21
0.04060


212649_at
AL079292
DHX29
DEAH (Asp-Glu-Ala-His) box polypeptide 29
5q11.2
1.21
0.04200


215450_at
W87901
SNRPE
small nuclear ribonucleoprotein polypeptide E
1q32
1.21
0.03350


217936_at
AW044631
ARHGAP5
rho GTPase activating protein 5
14q12*
1.21
0.03890


208920_at
AV752215
SRI
sorcin
7q21.1
1.20
0.02480


209024_s_at
AI472757
SYNCRIP
synaptotagmin binding, cytoplasmic RNA interacting protein
6q14-q15
1.20
0.01850


209600_s_at
S69189
ACOX1
acyl-Coenzyme A oxidase 1, palmitoyl
17q24-q25*
1.20
0.00480


212044_s_at
BE737027
RPL27A
hypothetical protein MGC10850
11p15
1.20
0.02780


218490_s_at
NM_018443
ZNF302
zinc finger protein 302
19q13.11
1.20
0.03440


200943_at
NM_004965
HMGN1
high-mobility group nucleosome binding domain 1
21q22.3
1.19
0.00920


222035_s_at
AI984479
PAPOLA
poly(A) polymerase alpha
14q32.31
1.19
0.02600


203202_at
AI950314
HRB2
HIV-1 rev binding protein 2
12q21.1
1.18
0.02550


203628_at
H05812
IGF1R
insulin-like growth factor 1 receptor
15q26.3
1.18
0.03650


205475_at
NM_007281
SCRG1
scrapie responsive protein 1
4q31-q32
1.18
0.02860


218859_s_at
NM_016649
C20orf6
chromosome 20 open reading frame 6
20p12.1*
1.18
0.03300


201965_s_at
NM_015046
KIAA0625
senataxin
9q34.13
1.17
0.01070


203549_s_at
NM_000237
LPL
lipoprotein lipase
8p22*
1.17
0.04020


208990_s_at
AF132362
HNRPH3
heterogeneous nuclear ribonucleoprotein H3 (2H9)
10q22
1.17
0.01960


210970_s_at
AF235049
IBTK
inhibitor of Bruton agammaglobulinemia tyrosine kinase
6q14.1
1.17
0.04700


212179_at
AW157501
C6orf111
chromosome 6 open reading frame 111
6q16.3*
1.17
0.04900


212689_s_at
AA524505
JMJD1A
jumonji domain containing 1A
2p11.2*
1.17
0.01230


214352_s_at
BF673699
KRAS2
v-Ki-ras2 Kirsten rat sarcoma 2 viral oncogene homolog
12p12.1
1.17
0.00330


221960_s_at
AI189609
RAB2
RAB2, member RAS oncogene family
8q12.1
1.17
0.02270


201129_at
NM_006276
SFRS7
splicing factor, arginine/serine-rich 7, 35 kDa
2p22.1
1.16
0.03180


201918_at
AI927944
FLJ10618
hypothetical protein FLJ10618
3q23
1.16
0.04000


207010_at
NM_000812
GABRB1
gamma-aminobutyric acid (GABA) A receptor, beta 1
4p12
1.16
0.04360


209763_at
AL049176
CHRDL1
chordin-like 1
Xq23
1.16
0.03340


212368_at
AA972711
ZNF292
zinc finger protein 292
6q15*
1.16
0.04620


213792_s_at
AA485908
INSR
insulin receptor
19p13.3-p13.2
1.16
0.01300


214433_s_at
NM_003944
SELENBP1
selenium binding protein 1
1q21-q22
1.16
0.04510


218183_at
NM_013399
C16orf5
chromosome 16 open reading frame 5
16p13.3
1.16
0.03280


218595_s_at
NM_018072
FLJ10359
hypothetical protein FLJ10359
1q43
1.16
0.01920


202258_s_at
U50532
PFAAP5
phosphonoformate immuno-associated protein 5
13q12-q13
1.15
0.02060


211929_at
AA527502
HNRPA3
heterogeneous nuclear ribonucleoprotein A3
2q31.2
1.15
0.01410


219819_s_at
NM_014018
MRPS28
mitochondrial ribosomal protein S28
8q21.1-q21.2
1.15
0.00580


200616_s_at
BC000371
KIAA0152
KIAA0152 gene product
12q24.31
1.14
0.02490


200920_s_at
AL535380
BTG1
B-cell translocation gene 1, anti-proliferative
12q22
1.14
0.04020


201138_s_at
BG532929
SSB
Sjogren syndrome antigen B (autoantigen La)
2q31.1
1.14
0.00570


201637_s_at
NM_005087
FXR1
fragile X mental retardation, autosomal homolog 1
3q28
1.14
0.04690


201831_s_at
BE875592
VDP
vesicle docking protein p115
4q21.1
1.14
0.02890


202324_s_at
NM_022735
ACBD3
acyl-Coenzyme A binding domain containing 3
1q42.12
1.14
0.04460


203526_s_at
M74088
APC
adenomatosis polyposis coli
5q21-q22
1.14
0.02510


207557_s_at
NM_001035
RYR2
ryanodine receptor 2 (cardiac)
1q42.1-q43
1.14
0.01030


208663_s_at
AI652848
TTC3
tetratricopeptide repeat domain 3
21q22.2
1.14
0.04010


209049_s_at
BC001004
PRKCBP1
protein kinase C binding protein 1
20q13.12
1.14
0.03340


212332_at
BF110947
RBL2
retinoblastoma-like 2 (p130)
16q12.2*
1.14
0.04480


216039_at
D38503
PMS2L1
postmeiotic segregation increased 2-like 1
7q22.1
1.14
0.03510


217975_at
NM_016303
WBP5
WW domain binding protein 5
Xq22.2
1.14
0.02620


200944_s_at
NM_004965
HMGN1
high-mobility group nucleosome binding domain 1
21q22.3
1.13
0.02050


201773_at
NM_015339
ADNP
activity-dependent neuroprotector
20q13.13
1.13
0.01290


203133_at
NM_006808
SEC61B
Sec61 beta subunit
9q22.32-q31.3
1.13
0.00260


208921_s_at
L12387
SRI
sorcin
7q21.1
1.13
0.02490


210588_x_at
L32610
HNRPH3
heterogeneous nuclear ribonucleoprotein H3 (2H9)
10q22
1.13
0.04050


211930_at
AW080932
HNRPA3
heterogeneous nuclear ribonucleoprotein A3
2q31.2
1.13
0.02060


212519_at
AL518159
UBE2E1
ubiquitin-conjugating enzyme E2E 1 (UBC4/5 homolog, yeast)
3p24.2*
1.13
0.03680


216399_s_at
AK025663
ZNF291
zinc finger protein 291
15q24
1.13
0.00080


218435_at
NM_013238
DNAJD1
DnaJ (Hsp40) homolog, subfamily D, member 1
13q14.1
1.13
0.01580


55692_at
W22924
ELMO2
engulfment and cell motility 2 (ced-12 homolog, C. elegans)
20q13
1.13
0.03650


202299_s_at
NM_006402
HBXIP
hepatitis B virus x interacting protein
1p13.3*
1.12
0.04400


207543_s_at
NM_000917
P4HA1
procollagen-proline, 2-oxoglutarate 4-dioxygenase
10q21.3-q23.1
1.12
0.03640





(proline 4-hydroxylase), alpha polypeptide I


209180_at
U49245
RABGGTB
rab geranylgeranyltransferase, beta subunit
1p31
1.12
0.02210


211784_s_at
BC006181
SFRS1
splicing factor, arginine/serine-rich 1 (splicing factor 2,
17q21.3-q22*
1.12
0.02460





alternate splicing factor)


211828_s_at
AF172268
TNIK
TRAF2 and NCK interacting kinase
3q26.2
1.12
0.04840


221613_s_at
AL136598
ZA20D3
zinc finger, A20 domain containing 3
15q25.1
1.12
0.04060


200020_at
NM_007375
TARDBP
TAR DNA binding protein
1p36.22
1.11
0.04070


200063_s_at
BC002398
NPM1
nucleophosmin (nucleolar phosphoprotein B23, numatrin)
5q35
1.11
0.03260


202266_at
NM_016614
TTRAP
TRAF and TNF receptor associated protein
6p22.3-p22.1*
1.11
0.04520


202301_s_at
BE396879
FLJ11021
similar to splicing factor, arginine/serine-rich 4
12q24.31
1.11
0.02420


209059_s_at
AB002282
EDF1
endothelial differentiation-related factor 1
9q34.3
1.11
0.02090


214053_at
AW772192
ERBB4
v-erb-a erythroblastic leukemia viral oncogene homolog 4 (avian)
2q33.3-q34
1.11
0.04040


217869_at
NM_016142
HSD17B12
hydroxysteroid (17-beta) dehydrogenase 12
11p11.2
1.11
0.03900


222216_s_at
AK026857
MRPL17
mitochondrial ribosomal protein L17
11p15.5-p15.4
1.11
0.01650


200977_s_at
AF090891
TAX1BP1
tax1 (human T-cell leukemia virus type I) binding protein 1
7p15
1.10
0.03050


201812_s_at
NM_019059
TOMM7
translocase of outer mitochondrial membrane 7 homolog (yeast)
7p15.3
1.10
0.04140


203870_at
BE856374
USP46
ubiquitin specific protease 46
4q12
1.10
0.04410


203880_at
NM_005694
COX17
COX17 homolog, cytochrome c oxidase assembly protein (yeast)
3q13.33
1.10
0.01780


208726_s_at
BC000461
EIF2S2
eukaryotic translation initiation factor 2, subunit 2 beta, 38 kDa
20pter-q12*
1.10
0.03110


208895_s_at
BG530850
DDX18
DEAD (Asp-Glu-Ala-Asp) box polypeptide 18
2q14.1*
1.10
0.04930


217724_at
AF131807
PAI-RBP1
PAI-1 mRNA-binding protein
1p31-p22
1.10
0.01610


201157_s_at
AF020500
NMT1
N-myristoyltransferase 1
17q21.31
1.09
0.00980


201606_s_at
BE796924
PWP1
nuclear phosphoprotein similar to S. cerevisiae PWP1
12q23.3
1.09
0.04390


203621_at
NM_002492
NDUFB5
NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 5, 16 kDa
3q26.33
1.09
0.02910


209224_s_at
BC003674
NDUFA2
NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 2, 8 kDa
5q31*
1.09
0.02260


217898_at
NM_020154
C15orf24
chromosome 15 open reading frame 24
15q14
1.09
0.01910


217907_at
NM_014161
MRPL18
mitochondrial ribosomal protein L18
6q25.3
1.09
0.00370


218123_at
NM_017835
C21orf59
chromosome 21 open reading frame 59
21q22.1
1.09
0.00130


200728_at
BE566290
ACTR2
actin-related protein 2 homolog (yeast)
2p14
1.08
0.04030


217774_s_at
NM_016404
HSPC152
hypothetical protein HSPC152
11q13.1
1.08
0.02850


217940_s_at
NM_018210
FLJ10769
hypothetical protein FLJ10769
13q34
1.08
0.01660


218142_s_at
NM_016302
CRBN
cereblon
3p26.2
1.08
0.01660


201134_x_at
NM_001867
COX7C
cytochrome c oxidase subunit Vllc
5q14
1.07
0.04030


203478_at
NM_002494
NDUFC1
NADH dehydrogenase (ubiquinone) 1, subcomplex unknown,
4q28.2-q31.1
1.07
0.00900





1, 6 kDa


217491_x_at
AF042165
COX7CP1
cytochrome c oxidase subunit Vllc pseudogene 1
13q14-q21
1.07
0.03650


217874_at
NM_003849
SUCLG1
succinate-CoA ligase, GDP-forming, alpha subunit
2p11.2*
1.06
0.02870


200021_at
NM_005507
CFL1
cofilin 1 (non-muscle)
11q13
−1.06
0.04670


203929_s_at
AI056359
MAPT
microtubule-associated protein tau
17q21.1
−1.07
0.04770


211780_x_at
BC006163
DCTN1
dynactin 1 (p150, glued homolog, Drosophila)
2p13
−1.07
0.03780


213867_x_at
AA809056
ARHGEF10
rho guanine nucleotide exchange factor (GEF) 10
8p23
−1.07
0.02110


201864_at
NM_001493
GDI1
GDP dissociation inhibitor 1
Xq28
−1.08
0.02220


202508_s_at
NM_003081
SNAP25
synaptosomal-associated protein, 25 kDa
20p12-p11.2*
−1.08
0.02980


202752_x_at
NM_012244
SLC7A8
solute carrier family 7 (cationic amino acid transporter, y+ system),
14q11.2*
−1.08
0.01790





member 8


203103_s_at
NM_014502
PRP19
PRP19/PSO4 homolog (S. cerevisiae)
11q12.2
−1.08
0.01750


206213_at
NM_003394
WNT10B
wingless-type MMTV integration site family, member 10B
12q13
−1.08
0.04540


214626_s_at
AK026548
GANAB
glucosidase, alpha; neutral AB
11q12.3
−1.08
0.01490


221966_at
AA813194
RH98400
chromosome 11 hypothetical protein ORF4
11cen-q22.3*
−1.08
0.01840


35265_at
U31501
FXR2
fragile X mental retardation, autosomal homolog 2
17p13.1
−1.08
0.00660


200758_s_at
AI361227
NFE2L1
nuclear factor (erythroid-derived 2)-like 1
17q21.3
−1.09
0.01890


202210_x_at
NM_019884
GSK3A
glycogen synthase kinase 3 alpha
19q13.2
−1.09
0.02490


202801_at
NM_002730
PRKACA
protein kinase, cAMP-dependent, catalytic, alpha
19p13.1
−1.09
0.04400


203906_at
AI652645
IQSEC1
IQ motif and Sec7 domain 1
3p25.2*
−1.09
0.04350


205331_s_at
NM_016606
C5orf19
chromosome 5 open reading frame 19
5q31*
−1.09
0.01970


207366_at
NM_002251
KCNS1
potassium voltage-gated channel, delayed-rectifier,
20q12
−1.09
0.04360





subfamily S, member 1


208390_s_at
U01104
GLP1R
glucagon-like peptide 1 receptor
6p21*
−1.09
0.03080


208829_at
AF029750
TAPBP
TAP binding protein (tapasin)
6p21.3*
−1.09
0.02930


214354_x_at
T91506
SFTPB
surfactant, pulmonary-associated protein B
2p12-p11.2*
−1.09
0.02470


219521_at
NM_018644
B3GAT1
beta-1,3-glucuronyltransferase 1 (glucuronosyltransferase P)
11q25
−1.09
0.04350


220465_at
NM_024988
FLJ12355
hypothetical protein FLJ12355
19q13.11
−1.09
0.00970


221560_at
AB049127
MARK4
MAP/microtubule affinity-regulating kinase 4
19q13.3
−1.09
0.02540


200001_at
NM_001749
CAPNS1
calpain, small subunit 1
19q13.12
−1.10
0.01740


200639_s_at
NM_003406
YWHAZ
tyrosine 3-monooxygenase/tryptophan 5-monooxygenase
8q23.1
−1.10
0.03720





activation protein, zeta polypeptide


200752_s_at
NM_005186
CAPN1
calpain 1, (mu/l) large subunit
11q13
−1.10
0.02210


202676_x_at
NM_006712
FASTK
FAST kinase
7q35
−1.10
0.04340


202806_at
NM_004395
DBN1
drebrin 1
5q35.3
−1.10
0.02130


203618_at
AB023167
FAIM2
Fas apoptotic inhibitory molecule 2
12q13
−1.10
0.02870


204947_at
NM_005225
E2F1
E2F transcription factor 1
20q11.2
−1.10
0.00300


204980_at
NM_004898
CLOCK
clock homolog (mouse)
4q12
−1.10
0.04370


209229_s_at
BC002799
KIAA1115
KIAA1115
19q13.42
−1.10
0.01650


210975_x_at
BC000377
FASTK
FAST kinase
7q35
−1.10
0.03390


211677_x_at
AF062733
IGSF4B
immunoglobulin superfamily, member 4B
1q21.2-q22
−1.10
0.02190


214903_at
AF070580
FLJ42519
CDNA FLJ42519 fis, clone BRACE3000787
1q32
−1.10
0.03390


217419_x_at
AK021586
AGRN
agrin
1p36.33
−1.10
0.03320


219400_at
NM_003632
CNTNAP1
contactin associated protein 1
17q21
−1.10
0.03850


221901_at
BF516072
KIAA1644
KIAA1644 protein
22q13
−1.10
0.03390


47560_at
AI525402
LPHN1
latrophilin 1
19p13.2
−1.10
0.02260


203151_at
AW296788
MAP1A
microtubule-associated protein 1A
15q13-qter
−1.11
0.02680


203831_at
NM_014925
KIAA1002
KIAA1002 protein
12q13.3
−1.11
0.00700


205325_at
NM_014759
PHYHIP
phytanoyl-CoA hydroxylase interacting protein
8p21.3*
−1.11
0.04680


208823_s_at
BE787860
PCTK1
PCTAIRE protein kinase 1
Xp11.3-p11.23
−1.11
0.03640


211240_x_at
AB002382
CTNND1
catenin (cadherin-associated protein), delta 1
11q11
−1.11
0.01540


217766_s_at
NM_014313
SMP1
small membrane protein 1
1p36.11
−1.11
0.01340


220974_x_at
NM_030971
BA108L7.2
similar to rat tricarboxylate carrier-like protein
10q24.32
−1.11
0.00260


221535_at
AL136897
FLJ11301
hypothetical protein FLJ11301
3q29
−1.11
0.00450


203244_at
NM_000319
PEX5
peroxisomal biogenesis factor 5
12p13.3
−1.12
0.00890


203264_s_at
NM_015185
ARHGEF9
Cdc42 guanine nucleotide exchange factor (GEF) 9
Xq11.2
−1.12
0.03980


207629_s_at
NM_004723
ARHGEF2
rho/rac guanine nucleotide exchange factor (GEF) 2
1q21-q22
−1.12
0.04820


209435_s_at
BC000265
ARHGEF2
rho/rac guanine nucleotide exchange factor (GEF)
1q21-q22
−1.12
0.00820


217637_at
R25692
Unknown
full length insert cDNA clone ZE05A03
20q13.1
−1.12
0.04500


212252_at
AA181179
CAMKK2
calcium/calmodulin-dependent protein kinase kinase 2, beta
12q24.2
−1.13
0.03980


212699_at
BE222801
SCAMP5
secretory carrier membrane protein 5
15q23
−1.13
0.04090


204893_s_at
NM_004799
ZFYVE9
zinc finger, FYVE domain containing 9
1p32.3
−1.14
0.04400


211934_x_at
W87689
GANAB
glucosidase, alpha; neutral AB
11q12.3
−1.14
0.01130


212285_s_at
AW008051
AGRN
agrin
1p36.33
−1.14
0.00740


217991_x_at
NM_018070
SSBP3
single stranded DNA binding protein 3
1p32.3
−1.14
0.01450


218952_at
NM_013271
PCSK1N
proprotein convertase subtilisin/kexin type 1 inhibitor
Xp11.23
−1.14
0.03060


205508_at
NM_001037
SCN1B
sodium channel, voltage-gated, type I, beta
19q13.1
−1.15
0.00770


209728_at
BC005312
HLA-DRB1
major histocompatibility complex, class II, DR beta 1
6p21.3*
−1.17
0.04220


219724_s_at
NM_014796
KIAA0748
KIAA0748 gene product
12q13.2
−1.21
0.00310


203337_x_at
NM_004763
ITGB1BP1
integrin beta 1 binding protein 1
2p25.2
−1.29
0.01930


217427_s_at
X75296
HIRA
HIR histone cell cycle regulation defective homolog A (S. cerevisiae)
22q11.2*
−1.32
0.00940


205048_s_at
NM_003832
PSPHL
phosphoserine phosphatase-like
7q11.2
−2.14
0.04420





*Implicated in SZ by meta-analysis of genome-wide linkage studies (22)













TABLE 5







Genes differentially expressed in PBCs in SZ













Affymetrix




Fold-



Probe
Accession
Gene
Gene
Chromosomal
Change


Number
Number
Symbol
Product
Locus
in SZ
p
















210746_s_at
M30646
EPB42
erythrocyte membrane protein band 4.2
15q15-q21
2.35
0.00053


211560_s_at
AF130113
ALAS2
aminolevulinate, delta-, synthase 2
Xp11.21
2.34
0.00111





(sideroblastic/hypochromic anemia)


213524_s_at
NM_015714
G0S2
putative lymphocyte G0/G1 switch gene
1q32.2-q41
2.28
0.00146


219672_at
NM_016633
ERAF
erythroid associated factor
16p11.2
2.13
0.00029


209890_at
AF065389
TM4SF9
transmembrane 4 superfamily member 9
4q23
2.12
0.00203


203691_at
NM_002638
PI3
protease inhibitor 3, skin-derived (SKALP)
20q12-q13
2.05
0.00001


211781_x_at
BC006164
Unknown
hypothetical protein MGC13219
Unknown
2.04
0.00007


219630_at
NM_005764
MAP17
membrane-associated protein 17
1p33
2.01
0.00011


205950_s_at
NM_001738
CA1
carbonic anhydrase I
8q13-q22.1
1.99
0.00049


210395_x_at
AF116676
MYL4
myosin, light polypeptide 4, alkali; atrial, embryonic
17q21-qter
1.99
0.00003


204466_s_at
BG260394
SNCA
synuclein, alpha (non A4 component of amyloid precursor)
4q21
1.95
0.00119


214433_s_at
NM_003944
SELENBP1
selenium binding protein 1
1q21-q22
1.95
0.00093


201178_at
NM_012179
FBXO7
F-box protein 7
22q12-q13
1.91
0.00025


211475_s_at
AF116273
BAG1
BCL2-associated athanogene
9p12
1.89
0.00175


206834_at
NM_000519
HBD
hemoglobin, delta
11p15.5
1.86
0.00132


210088_x_at
M36172
MYL4
myosin, light polypeptide 4, alkali; atrial, embryonic
17q21-qter
1.84
0.00004


202219_at
NM_005629
SLC6A8
solute carrier family 6 (neurotransmitter transporter, creatine),
Xq28
1.83
0.00026





member 8


218872_at
NM_017899
TSC
hypothetical protein FLJ20607
12q24.22
1.83
0.00019


201161_s_at
NM_003651
CSDA
cold shock domain protein A
12p13.1
1.81
0.00240


202387_at
NM_004323
BAG1
BCL2-associated athanogene
9p12
1.80
0.00130


204419_x_at
NM_000184
HBG2
hemoglobin, gamma G
11p15.5
1.80
0.00501


204467_s_at
NM_000345
SNCA
synuclein, alpha (non A4 component of amyloid precursor)
4q21
1.80
0.00256


216054_x_at
X58851
MYL4
myosin, light polypeptide 4, alkali; atrial, embryonic
17q21-qter
1.79
0.00002


204187_at
NM_006877
GMPR
guanosine monophosphate reductase
6p23
1.77
0.00011


220757_s_at
NM_025241
UBXD1
UBX domain containing 1
19p13
1.77
0.00434


202947_s_at
NM_002101
GYPC
glycophorin C (Gerbich blood group)
2q14-q21
1.73
0.00752


202201_at
NM_000713
BLVRB
biliverdin reductase B (flavin reductase (NADPH))
19q13.1-q13.2
1.70
0.00390


204848_x_at
NM_000559
HBG1
hemoglobin, gamma A
11p15.5
1.69
0.00779


202859_x_at
NM_000584
IL8
interleukin 8
4q13-q21
1.68
0.00347


207827_x_at
L36675
SNCA
synuclein, alpha (non A4 component of amyloid precursor)
4q21
1.68
0.00061


211546_x_at
L36674
SNCA
synuclein, alpha (non A4 component of amyloid precursor)
4q21
1.68
0.00008


200844_s_at
BE869583
PRDX6
peroxiredoxin 6
1q25.1
1.66
0.00251


202364_at
NM_005962
MXI1
MAX interactor 1
10q24-q25
1.66
0.00005


204505_s_at
NM_001978
EPB49
erythrocyte membrane protein band 4.9 (dematin)
8p21.1
1.65
0.00214


214273_x_at
AV704353
C16orf35
chromosome 16 open reading frame 35
16p13.3
1.65
0.00131


217748_at
NM_015999
ADIPOR1
adiponectin receptor 1
1p36.13-q41
1.65
0.00138


220807_at
NM_005331
HBQ1
hemoglobin, theta 1
16p13.3
1.62
0.00439


206207_at
NM_001828
CLC
Charcot-Leyden crystal protein
19q13.1
1.61
0.00009


210638_s_at
AF176704
FBXO9
F-box protein 9
6p12.3-p11.2
1.61
0.00001


202074_s_at
NM_021980
OPTN
optineurin
10p13
1.60
0.00048


202210_x_at
NM_019884
GSK3A
glycogen synthase kinase 3 alpha
19q13.2
1.59
0.00044


205592_at
X77737
SLC4A1
solute carrier family 4, anion exchanger, member 1
17q21-q22
1.59
0.00400





(erythrocyte membrane protein band 3, Diego blood group)


221479_s_at
AF060922
BNIP3L
BCL2/adenovirus E1B 19 kDa interacting protein 3-like
8p21
1.56
0.00543


201052_s_at
BG029917
PSMF1
proteasome (prosome, macropain) inhibitor subunit 1 (PI31)
20p13
1.55
0.00652


40850_at
L37033
FKBP8
FK506 binding protein 8, 38 kDa
19p12
1.52
0.00462


217882_at
NM_018447
LOC55831
30 kDa protein
3p25.3
1.51
0.00863


205863_at
NM_005621
S100A12
S100 calcium binding protein A12 (calgranulin C)
1q21
1.50
0.00018


208949_a_at
BC001120
LGALS3
lectin, galactoside-binding, soluble, 3 (galectin 3)
14q21-q22
1.47
0.00157


215499_at
AA780381
MAP2K3
mitogen-activated protein kinase kinase 3
17q11.2
1.47
0.00001


203966_s_at
NM_021003
PPM1A
protein phosphatase 1A (formerly 2C), magnesium-dependent,
14q23.1
1.46
0.00013





alpha isoform


210183_x_at
AF112222
PNN
pinin, desmosome associated protein
14q21.1
1.45
0.00028


204018_x_at
NM_000558
HBA1
hemoglobin, alpha 1
16p13.3
1.43
0.00001


211699_x_at
AF349571
HBA1
hemoglobin, alpha 1
16p13.3
1.43
0.00003


217756_x_at
NM_005770
SERF2
small EDRK-rich factor 2
15q15.3
1.43
0.00132


200633_at
NM_018955
UBB
ubiquitin B
17p12-p11.2
1.41
0.00031


217414_x_at
V00489
HBA2
hemoglobin, alpha 2
16p13.3
1.40
0.00008


201285_at
NM_013446
MKRN1
makorin, ring finger protein, 1
7q34
1.39
0.00044


209116_x_at
M25079
HBB
hemoglobin, beta
11p15.5
1.38
0.00002


211745_x_at
BC005931
HBA2
hemoglobin, alpha 2
16p13.3
1.38
0.00005


217232_x_at
AF059180
HBB
hemoglobin, beta
11p15.5
1.36
0.00005


214271_x_at
AA281332
RPL12
ribosomal protein L12
9q34
1.35
0.00001


209458_x_at
AF105974
HBA1
hemoglobin, alpha 1
16p13.3
1.34
0.00012


221700_s_at
AF348700
UBA52
ubiquitin A-52 residue ribosomal protein fusion product 1
19p13.1-p12
1.33
0.00005


214290_s_at
AI313324
H2AFO
H2A histone family, member O
1p36.13-q24.1
1.30
0.00089


211696_x_at
AF349114
HBB
hemoglobin, beta
11p15.5
1.29
0.00002


217977_at
NM_016332
SEPX1
selenoprotein X, 1
16p13.3
1.29
0.00265


214414_x_at
T50399
HBA1
hemoglobin, alpha 1
16p13.3
1.26
0.00076


211911_x_at
L07950
HLA-B
major histocompatibility complex, class I, B
6p21.3
−1.21
0.00038


209619_at
K01144
CD74
CD74 antigen (invariant polypeptide of major histocompatibility
5q32
−1.24
0.00010





complex, class II antigen-associated)


200871_s_at
NM_002778
PSAP
prosaposin (variant Gaucher disease and variant metachromatic
10q21-q22
−1.27
0.00003





leukodystrophy)


209312_x_at
U65585
HLA-DRB1
major histocompatibility complex, class II, DR beta 1
6p21.3*
−1.27
0.00007


200904_at
X56841
HLA-E
major histocompatibility complex, class I, E
6p21.3
−1.28
0.00003


208980_s_at
M26880
UBC
ubiquitin C
12q24.3
−1.28
0.00037


212363_x_at
AU145192
ACTG1
actin, gamma 1
17q25
−1.30
0.00045


205237_at
NM_002003
FCN1
ficolin (collagen/fibrinogen domain containing) 1
9q34
−1.33
0.00055


215193_x_at
AJ297586
HLA-DRB1
major histocompatibility complex, class II, DR beta 1
6p21.3
−1.33
0.00006


201368_at
U07802
ZFP36L2
zinc finger protein 36, C3H type-like 2
2p22.3-p21
−1.35
0.00018


200634_at
NM_005022
PFN1
profilin 1
17p13.3
−1.36
0.00001


200866_s_at
M32221
PSAP
prosaposin (variant Gaucher disease and variant metachromatic
10q21-q22
−1.36
0.00015





leukodystrophy)


200920_s_at
AL535380
BTG1
B-cell translocation gene 1, anti-proliferative
12q22
−1.36
0.00008


200772_x_at
BF686442
PTMA
prothymosin, alpha (gene sequence 28)
2q35-q36
−1.37
0.00032


208438_s_at
NM_005248
FGR
Gardner-Rasheed feline sarcoma viral (v-fgr) oncogene homolog
1p36.2-p36.1
−1.38
0.00001


219505_at
NM_017424
CECR1
cat eye syndrome chromosome region, candidate 1
22q11.2
−1.38
0.00001


200742_s_at
BG231932
CLN2
ceroid-lipofuscinosis, neuronal 2, late infantile
11p15
−1.41
0.00001





(Jansky-Bielschowsky disease)


200743_s_at
NM_000391
CLN2
ceroid-lipofuscinosis, neuronal 2, late infantile
11p15
−1.41
0.00001





(Jansky-Bielschowsky disease)


205898_at
U20350
CX3CR1
chemokine (C-X3-C motif) receptor 1
3p21
−1.41
0.00002





isoform, 1, cardiac muscle


213738_s_at
AI587323
ATP5A1
ATP synthase, H+ transporting, mitochondrial F1 complex,
18q12-q21
−1.44
0.00008





alpha subunit,


211991_s_at
M27487
HLA-DPA1
major histocompatibility complex, class II, DP alpha 1
6p21.3
−1.48
0.00002


212192_at
AI718937
KCTD12
potassium channel tetramerisation domain containing 12
13q22.3
−1.49
0.00005


207419_s_at
NM_002872
RAC2
ras-related C3 botulinum toxin substrate 2 (rho family, small GTP
22q13.1
−1.51
0.00092





binding protein Rac2)


211921_x_at
AF348514
PTMA
prothymosin, alpha (gene sequence 28)
2q35-q36
−1.52
0.00002


204912_at
NM_001558
IL10RA
interleukin 10 receptor, alpha
11q23
−1.54
0.00006


208743_s_at
BC001359
YWHAB
tyrosine 3-monooxygenase/tryptophan 5-monooxygenase
20q13.1
−1.54
0.00003





activation protein, beta polypeptide


205292_s_at
NM_002137
HNRPA2B1
heterogeneous nuclear ribonucleoprotein A2/B1
7p15
−1.59
0.00001


203037_s_at
NM_014751
MTSS1
metastasis suppressor 1
8p22
−1.60
0.00002


203104_at
NM_005211
CSF1R
colony stimulating factor 1 receptor, formerly McDonough feline
5q33-q35
−1.63
0.00001





sarcoma viral (v-fms) oncogene homolog


56256_at
AA150165
SIDT2
SID1 transmembrane family, member 2
11q23.3
−1.63
0.00001


201393_s_at
NM_000876
IGF2R
insulin-like growth factor 2 receptor
6q26
−1.66
0.00015


211784_s_at
BC006181
SFRS1
splicing factor, arginine/serine-rich 1 (splicing factor 2,
17q21.3-q22
−1.71
0.00005





altemate splicing factor)


214617_at
AI445650
PRF1
perforin 1 (pore forming protein)
10q22
−1.71
0.00001


203741_s_at
NM_001114
ADCY7
adenylate cyclase 7
16q12-q13
−1.73
0.00002


213475_s_at
AC002310
ITGAL
integrin, alpha L (antigen CD11A (p180), lymphocyte function-
16p11.2
−1.73
0.00001





associated antigen 1; alpha polypeptide)


208988_at
BE675843
FBOXL11
F-box and leucine-rich repeat protein 11
11q13.2
−1.75
0.00001


213872_at
BE465032
C6orf62
chromosome 6 open reading frame 62
6p22.2
−1.75
0.00014


218396_at
NM_017684
VPS13C
vacuolar protein sorting 13C (yeast)
15q22.2
−1.79
0.00002


209007_s_at
AF267856
NPD014
NPD014 protein
1p36.13-p35.1
−1.80
0.00001


201218_at
N23018
CTBP2
C-terminal binding protein 2
10q26.13
−1.86
0.00003


203113_s_at
NM_001960
EEF1D
eukaryotic translation elongation factor 1 delta
8q24.3
−1.86
0.00118





(guanine nucleotide exchange protein)


202663_at
AI005043
WASPIP
Wiskott-Aldrich syndrome protein interacting protein
2q31.1
−1.89
0.00001


215646_s_at
R94644
CSPG2
chondroitin sulfate proteoglycan 2 (versican)
5q14.3
−1.91
0.00008


202230_s_at
NM_006387
CHERP
calcium homeostasis endoplasmic reticulum protein
19p13.1
−1.94
0.00001


201798_s_at
NM_013451
FER1L3
fer-1-like 3, myoferlin (C. elegans)
10q24
−1.95
0.00001


212070_at
AL554008
GPR56
G protein-coupled receptor 56
16q13
−2.02
0.00001


204516_at
BG390306
ATXN7
ataxin 7
3p21.1-p12
−2.05
0.00001


214683_s_at
AI251890
CLK1
CDC-like kinase 1
2q33
−2.07
0.00002


201088_at
NM_002266
KPNA2
karyopherin alpha 2 (RAG cohort 1, importin alpha 1)
17q23.1-q23.3
−2.08
0.00002


208810_at
AF080569
DNAJB6
DnaJ (Hsp40) homolog, subfamily B, member 6
7q36.3
−2.09
0.00001


211929_at
AA527502
HNRPA3
heterogeneous nuclear ribonucleoprotein A3
2q31.2
−2.12
0.00004


213906_at
AW592266
MYBL1
v-myb myeloblastosis viral oncogene homolog (avian)-like 1
8q22
−2.22
0.00020


203132_at
NM_000321
RB1
retinoblastoma 1 (including osteosarcoma)
13q14.2
−2.46
0.00005


209006_s_at
AF247168
NPD014
NPD014 protein
1p36.13-p35.1
−2.67
0.00001


221768_at
AV705803
SFPQ
splicing factor proline/glutamine rich (polypyrimidine tract
1p34.3
−2.73
0.00051





binding protein associated)


202817_s_at
NM_005637
SS18
synovial sarcoma translocation, chromosome 18
18q11.2
−7.24
0.00002

## Claims

1. A method to diagnose schizophrenia, comprising:
detecting an amount or level of selenium binding protein expression in a sample of cells from a human suspected of having schizophrenia;
determining the amount or level of selenium binding protein expression in said cells; and
comparing said amount or level to that in a corresponding sample from a human not having schizophrenia, wherein an increase in selenium binding protein expression is indicative of schizophrenia.

2. The method of claim 1, wherein the sample is blood.

3. The method of claim 1 wherein the cells are peripheral blood cells.

4. The method of claim 1 wherein detecting an amount or level of selenium binding protein expression comprises measuring protein.

5. The method of claim 1 wherein detecting an amount or level of selenium binding protein expression comprises measuring RNA.

6. The method of claim 1 wherein the selenium binding protein is selenium binding protein-1 (SELENBP-1).

7. The method of claim 1 wherein detecting an amount or level of selenium binding protein expression in a sample of cells comprises detecting the amount or level of selenium binding protein-1 expression in blood.