Abstract Gene expression investigations within psychiatry have implicated a number of different genes that are associated with major psychiatric disorders, with the majority of these studies carried out in brains from patients with schizophrenia. In post-mortem brain studies, alterations in the expression of genes involved in oligodendrocyte functioning and myelina-tion, mitochondrial-related functions and energy metab-olism as well as synaptic structure and transmission have been identifi ed. To an extent these alterations refl ect changes in mRNAs and proteins previously reported in neuropathological investigations of psychi-atric disorders. The advent of assessing gene expres-sion changes in the blood of patients with psychiatric disorders may allow us to correlate these fi ndings to brain-related changes and hence to potentially identify biomarkers for detection, intervention and treatment. However, while microarray technology has opened the way for high-throughput gene expression analysis, a signifi cant number of methodological and technical questions still exist with their application. It is there-fore necessary that a stringent approach be adopted by researchers when designing gene expression studies and precaution taken as to the fi nal analysis and inter-pretation of the data generated. The aim of this chapter is to provide a balanced view of microarray investiga-tions in the blood and brain in major psychiatric disor-ders by highlighting the benefi ts and pitfalls of such studies in identifying candidate genes. The relation-ship between these alterations and endophenotypes in

identifying such biomarkers will be discussed as well as potential strategies to overcome weaknesses within studies and how to improve future investigations.

Keywords Psychiatry • microarray • schizophrenia • gene • endophenotype • expression

Abbreviations ACHR: Acetylcholine receptor; AMPA1: 2 2-aminomethyl phenylacetic acid; Apo: Apolipoprotein; ATP1A3: Na + /K + -ATPase alpha 3 subunit I; BDNF: Brain derived neurotrophic factor; BTG1: B-cell translocation gene 1; CAD: Carbamoyl-phosphate synthetase 2, aspartate transcarbamylase and dihydro-orotase; CCK: Cholecystokinin; CD14: Cell differen-tiation 14; CHI3L1: Chitinase 3 like 1; CNP: Cyclic nucleotide phosphodiesterase; dlPFC: Dorsolateral prefrontal cortex; DRD2: Dopamine receptor D2; EBV: Epstein Barr virus; EDGF: Epidermal derived growth factor; ER: Endoplasmic reticulum; FGF: Fibroblast growth factor; GABA: Gamma-amino butyric acid; GAD67: Glutamate decarboxylase-67; GAP-43: Growth associated protein-43; Giα1: G-protein inhibitory alpha1; GluR1–2: Glutamate receptor 1–2; GNA01: Guanine nucleotide binding protein alpha 1; GSK3A: Glycogen synthase kinase 3 alpha; HER3: Heregulin-3; HNRPA3: Heterogeneous nuclear ribonucleoprotein A3; HERC2: Heat domain and rcc1-like domain 2; HLA-DRB1 major: Histocompatibility complex DR beta 1; HML-2: Human macrophage lectin 2; HSPB1: Heat shock protein beta 1; SERPINA3: Alpha-1-antichymotrypsin A3; IFITM1: Interferon induced protein with tetratricopeptide repeats 1; Kir2.3: Potassium channel inward rectifying 2.3; LARS2: Leucyl-tRNA synthetase, mitochondrial; LCM: Laser capture microdissection; MAG: Myelin associated glycoprotein; MAL: Myelin and lymphocyte protein;

Chapter 42Gene Expression Changes and Potential Impact of Endophenotypes in Major Psychiatric Disorders

Gursharan Chana, Janet Kwok, Stephen J. Glatt, Ian P. Everall, and Ming T. Tsuang

G. Chana, J. Kwok, I. P. Everall, and M. T. TsuangDepartment of Psychiatry, University of California, San Diego, La Jolla, CA, 92093-0603

S. J. Glatt Department of Psychiatry and Behavioral Sciences, State University of New York, Syracuse, NY, 13210

M.S. Ritsner (ed.), The Handbook of Neuropsychiatric Biomarkers, Endophenotypes and Genes, 77© Springer Science + Business Media B.V. 2009

78 G. Chana et al.

MARCKS: Myristolated alanine-rich C-kinase substrate; TGF-beta 1: Transforming growth factor beta 1; erbB2: Erythroblastic leukemia viral oncogene homolog 2; MOBP: Myelin-oligodendrocyte basic protein; MBP: Myelin basic protein; MOG: Myelin-oligodendrocytic protein; MM: Mismatch; MT2A: Metallothionein 2A; NPY: Neuropeptide Y; Neurod1: Neurogenic differentiation 1; NRG-1: Neuregulin-1; NMDA1: N-methyl, d-aspartate 1; NSF: N-ethylmaleimide sensi-tive fusion protein; PEA-15: Phosphoprotein enriched in astrocytes 15; PDE4D: Phosphodiesterase 4D; PLP: Proteolipid protein; PM: Perfect match; PMI: Post-mortem interval; PMP22: Peripheral myelin protein 22; qRT-PCR: Quantitative real time PCR; RMA: Robust multichip analysis algorithm; SELENBP1: Selenium binding protein-1; SFRS1: Splicing factor, arginine/serine rich 1; SNP: Single nucleotide polymorphism; SPR: Sepiapterin reductase; SOX10: SRY-related homeobox gene 10; SMDF: Sensory and motor neuron derived factor; S100: Calcium binding protein A1; TGF-a: Transforming growth factor-alpha; TRAF4: Tumour necrosis factor receptor associated factor 4; VAMP-1: Vesicle associate membrane protein-1; XBP1: X box binding protein 1

Overview

The use of microarray technology to assess gene expression changes in major psychiatric disorders has increased rapidly over the past 2 decades. This increase has been fuelled primarily by a vast body of neuro-pathological literature failing to demonstrate consis-tent changes in candidate proteins in brains of patients with these disorders. Therefore, the premise behind gene expression investigations has been to identify candidate mRNAs and genes in the brains of patients which can then be validated and related at the protein level to provide us with clues as to the etiology of these disorders. This reversal in the traditional scientifi c pro-cess has allowed us to take a more global view of gene expression changes related to these polygenic psychi-atric disorders. However, while some interesting fi nd-ings have emerged from microarray investigations, they have unfortunately not always been replicated by independent research groups when using different

brain cohorts. This discrepancy in results may be partially attributed to inconsistencies in methodologies and platforms used for analysis; however, it is also largely a consequence of the heterogeneous nature of these disorders as well as general variability in gene expres-sion patterns between individuals that may in part result from lifestyle. Nevertheless, as microarray tech-nology and tools to assess gene expression data become more consistent we are now beginning to build up gene expression profi les for major psychiatric disorders. The construction of such profi les between disorders may eventually help in the separation of their overlap-ping clinical profi les and problems with differential diagnosis faced by clinicians.

One recent area of interest in gene expression studies in psychiatry has been to assess changes in mRNA tran-scripts in the blood of patients. These blood-based gene expression studies are potentially very powerful as they may enable the identifi cation of potential biomarkers which could ultimately be used as diagnostic and prog-nostic indicators. While the discovery of such markers would greatly advance psychiatry, the realization of this may take some time to achieve. This chapter will begin by giving a brief discussion of the study design of microarray investigations prior to covering current gene expression fi ndings in the blood and also in the brain in major psychiatric disorders. The impact of endopheno-types on these gene expression changes will also be discussed together with their potential role in helping to identify biomarkers. Finally, we will look at technical problems associated with gene expression studies and how solving these will help to get us closer to the goal of identifying biomarkers for psychiatric disorders.

Introduction

Historically, diagnoses of psychiatric disorders have always been problematic due to overlapping symptom-atology. This differential diagnosis can be illustrated by the Kraepelinian dichotomy. Kraepelin’s original classifi cation separated schizophrenia and bipolar dis-order into two distinct disease entities. However, with advances in diagnosis and genetic studies it is clear that the profi les of these two disorders demonstrate signifi cant overlap. Such overlap makes the differen-tial diagnoses of these psychiatric disorders diffi cult. An ideal scenario to overcome this diffi culty would be

42 Gene Expression Changes and Potential Impact of Endophenotypes in Major Psychiatric Disorders 79

to have a distinct genetic profi le for each disorder. This would not only clarify and aid diagnoses but would also allow us to screen at-risk individuals for develop-ing these disorders. While this ideal may not be achiev-able in the near future, the use of high-throughput genetic technology such as microarrays are greatly facilitating the building of gene expression profi les and identifi cation of biomarkers.

Locating candidate genes for major psychiatric disorders is an intrinsically diffi cult task; psychiatric disorders are not simple Mendelian traits, and have complex genetic architecture. Endophenotypes are her-itable traits that have specifi c genes associated with them (genes that may or may not already be identifi ed), yet are not directly related to symptoms of the disease or disorder in question and are not typically considered in the diagnosis and screening. These specifi c pheno-types have been suggested to bear a closer relationship to the etiological processes than the actual diagnostic measures,1 indicating that the link between genes and endophenotypes is stronger than that between genes and the phenotype. Recent research into the genetics of schizophrenia now consider endophenotypes to aid our understanding of this polygenic disorder with probable multiple etiologies.2 However, with a lack of consistent changes in genes and their expression levels in schizo-phrenia and major psychiatric disorders they remain as clinically defi ned disorders.

Endophenotypes for major psychiatric disorders include neuroanatomical, neurophysiological, bioche-mical, endocrinological, cognitive and psychological distinctions, and will be discussed in more details in the following sections. Due to these endophenotypes having the potential to alter (or be altered by) gene expression it may be warranted to consider their infl u-ence when designing gene expression studies and also interpreting data gathered from them. In doing so it is feasible that certain knowledge of endophenotypes will help us to better understand and categorize genes into various biological, cellular and molecular pro-cesses related to major psychiatric disorders.

Designing Microarray Investigations

One of the main limitations of designing post-mortem gene expression investigations is the availability of well characterized and matched brain samples from

candidate regions of interest. Once this obstacle has been overcome many steps can be taken when design-ing gene expression studies in order to reduce the rates of false discovery. Attempting to control for potential confounds in blood-based investigations present over-lapping and different sets of external variables that can infl uence gene expression. The most obvious variable that can add power to a gene expression study is the number of patient samples available for investigation. As previously mentioned, however, this is often lim-ited in post-mortem studies by the availability of tis-sue, with competition for samples between researchers. For post-mortem investigations, matching of clinical and demographic variables that are known to affect RNA quality and hence gene expression fi ndings should be attempted. When this is not achievable the variable should be included as a covariate in the fi nal analysis. These variables include factors such as age, gender, agonal period and cause of death, medication histories, history of drug abuse, pH and post-mortem interval (PMI) (for review see Bunney et al.3). Ideal matching of all or even half of these parameters is however, rarely achieved. For blood-based gene expression studies there are even more variables that can signifi cantly affect gene expression. These include, but are not limited to; diet, smoking, time of last meal, time of day, any medications used, and frequency of exercise.4 These variables may also affect post-mortem brain changes but to a lesser extent than blood which is a much more dynamic changing substrate. Some con-trol over these variables for blood based investigations can potentially be achieved by the researcher, i.e. patients could be asked to give a fasting blood sample fi rst thing in the morning before eating. Additional questionnaires pertaining to the patient’s lifestyle can also be administered in order to glean as much infor-mation as possible regarding potential infl uences on gene expression changes. For blood-based gene expres-sion studies it is also worthwhile attempting the recruit-ment of fi rst-degree relatives. This has the potential for differentiating disease-related genes from medication-related genes or genes that change in response to chronic mental illness.

To address the question of endophenotypes in the design of blood-based gene expression investigations, this can theoretically be carried out in two ways. The fi rst method would be to pre-sort patients into different categories based on their clinical or perhaps biological profi le prior to conducting the gene expression analysis

80 G. Chana et al.

or alternatively by carrying out post-hoc analysis of gene expression data by different endophenotypes. Ideally, pre-sorting would be the chosen method as it would set the framework for the microarray investiga-tion. For example, patients could be separated on the basis of structural brain changes observed, such that patients with certain structural brain abnormalities would be grouped and compared to those lacking the abnormality. Separating patients on the basis of their genotype represents another pre-sorting approach. The use of single nucleotide polymorphisms (SNPs) found to confer susceptibility to schizophrenia or other major psychiatric disorders could be utilized. However, with SNP studies in major psychiatric disorders being rela-tively controversial and inconsistent this may not be a reliable way to separate patient groups until putative risk-conferring variants are widely replicated. While a post-hoc analysis of endophenotypes on observed gene expression changes would be easier to carry out, it would be more diffi cult to establish a connection between the already-determined changes in gene expression to the endophenotypes of interest. However, to properly utilize the potential impact of endopheno-types in gene expression investigations, the endophe-notypes must serve to steer the experimentation, so that the alterations in gene expression changes can be attributed to the specifi c intermediary phenotypes. In fact, current research into the genetics of psychiatric disorders tends to lean towards evaluating the predic-tive power of an endophenotype conditioned upon the liability of schizophrenia, rather than looking at the predictive power of an endophenotype given the liabil-ity of schizophrenia (Lenzenweger, http://www.schizo-phreniaforum.org/for/live/transcript.asp?liveID = 46). In this manner, current research does not address the utility of endophenotypes.

In order to pre-sort patients, questionnaires may be administered to group subjects prior to gene expres-sion analysis using microarrays. During screening, the patients could be pre-sorted into different groups according to the specifi c phenotype exhibited, which would allow us to observe the impact of the patients’ specifi c endophenotypes on the changes in the expres-sion of their genes (or vice versa). In this manner, the pathway from genotype to phenotype would be observed. Such endophenotypic parameters could include surveying the patients according to the afore-mentioned distinctions: neuroanatomical, neurophysi-ological, biochemical, endocrinological, cognitive and

psychological. Patients may also be categorized according to their clinical profi les, utilizing their medi-cal records to determine the effect of certain diseases, events or medical conditions on the genetic suscepti-bility for the psychiatric disorder. Examples of the latter are prenatal hypoxia or a low APGAR at birth or a family history of schizophrenia. An important consid-eration in dividing patient groups is the reliability of the data used to separate these endophenotype sub-populations. If consistent and reliable data on specifi c parameters cannot be obtained it maybe more prudent not to make the division.

Choosing a Platform

Selecting a microarray platform for a gene expression study depends largely on the aims of the investigation. For major psychiatric disorders, the majority of investi-gators have tended to employ high-throughput micro-array gene chips that encompass the whole human genome. This approach is warranted due to a lack of information regarding etiological mechanisms for major psychiatric disorders. Nevertheless, a fairly broad choice still exists when selecting a genome-wide chip for screening patient brain or blood samples. While a detailed technical dissection of different microarray platforms is beyond the scope of this review, it is worth-while considering the major strengths and weaknesses of the various arrays. Many of the microarray studies in psychiatry have utilized gene chips produced by Affymetrix (Santa Clara, USA). However, other main competitors for the whole human gene chip market include Codelink, Agilent, Applied Biosystems and Illumina.

The primary reason for this early adoption of Affymetrix gene chips was due to the highly automated process by which they are manufactured. This process comprises the use of highly specifi c photolithographic masks and a solid phase DNA synthesis to construct 25 bases probes on the surface of the chip.5 Other plat-forms such as Agilent, Codelink and Illumina utilize 60–70 bases probe sets which are more sensitive in detecting mRNA signal than the 25mer probe sets used by Affymetrix. However with this increased sensitivity potentially comes an increase in false-positive fi nd-ings. Affymetrix increase their specifi city of detection by including 11 probe sets which are averaged following

42 Gene Expression Changes and Potential Impact of Endophenotypes in Major Psychiatric Disorders 81

analysis of the chip post-hybridization. Also contained on the chip are mismatch (MM) probe sets. These probe sets are identical to the perfect match (PM) probes with the exception of a single base difference located in a central position. The MM probes serve as controls for specifi c hybridization and facilitate back-ground correction due to cross-hybridization signals.5 The newer Affymetrix U133 plus 2.0 platform has been utilized by more recent investigations in psychiatry. This gene chip comprises probes for analysis of over 47,000 transcripts, including the entire human genome and provides a comprehensive way to assess global gene expression changes. This chip has recently been superseded by the Affymetrix Exon arrays that are also capable of assessing gene expression of splice variants by surveying multiple probes for each exon in the genome. This novel technology will help to better understand the contribution of individual variants to overall gene expression and hence better relate to gene function. With the inclusion of alternative splicing events, the number of features per microarray chip has jumped from approx. 470,000 to 2–3 million. This large increase in data necessitate the evolution of pro-grams used to read chip data and also to assess differ-ential gene expression between groups.

However, with the further development of microar-ray technology, Affymetrix’s competitors are also begin-ning to produce highly reproducible arrays for analyzing whole human genome expression. For instance Ilumina’s latest chip sets are also produced in a highly automated way, utilizing a randomly self-assembled silica bead pool with attached oligonucleotide probes that are recorded on specifi c locations on a patterned substrate.6 Due to the small size of the bead used for this procedure, the density of these arrays can be up to 40,000 times greater than that of spotted arrays.7

Another deciding factor for choosing a microarray platform is how well gene expression changes detected on the platform can be validated by quantitative real time PCR (qRT-PCR). This validation of microarray fi ndings using qRT-PCR is of major importance given the enhanced sensitivity of detection by this technique and therefore this process adds confi dence in gene expression changes observed on microarrays.

The fi nal and perhaps most important decision in choosing a microarray platform lies in the ability of the researcher to compare their data on gene expres-sion changes with what has been generated by other research groups. A number of studies to date, looking

at cross-platform comparisons, have unfortunately seen a very low level of correlation in gene expression,8,9 even with mRNA samples derived from relatively homogenous tissue sources, such as transformed cell lines. This has led to the conclusion that gene expres-sion data cannot be combined reliably between plat-forms. Knowledge of this result has often led researchers to choose platforms on the basis of what past investigations have utilized. While the logic behind this choice is concrete, it does to a certain extent preclude an informed decision based on merits of indi-vidual platforms. Furthermore, recent comparisons of cross-platform correlations have yielded more concor-dant data,10–13 suggesting that either the technology, or the scientists’ ability to harness it, is improving.

Post-mortem Brain Gene Expression Investigations

Early post-mortem gene expression studies of the brain in schizophrenia utilized pooled samples of RNA in order to identify gene expression changes between control and patient groups. While these studies were cost-effi cient using a small number of microarray chips, they masked many of the signifi cant gene expression differences that may have existed between groups but which may have been hidden by within-group heterogeneity and/or variability. Therefore, cur-rent post-mortem gene expression investigations tend to use one gene chip per sample when carrying out their analysis. Mirnics et al. (2000), were the fi rst to conduct a high-throughput gene expression study for schizophrenia that compared 250 functional gene groups between matched pairs of patients and controls. While differences in specifi c genes were not consistent between the pairs they did observe down-regulation of functional gene groups. The most convincing of their fi ndings related to a down-regulation of genes encod-ing pre-synaptic proteins.14 Included in this list were the genes for N-ethylmaleimide sensitive factor and synapsin II. These changes were subsequently vali-dated by in situ hybridization. Interestingly, regulator of G protein signaling 4 (RGS4), a gene that has received much attention from family-based genetic association studies in schizophrenia was also shown to be dysregulated. Since this fi rst study by Mirnics many different post-mortem microarray investigations for

82 G. Chana et al.

schizophrenia have been carried out in a number of dif-ferent brain regions. The majority of these have focused on schizophrenia and gene expression changes in the dorsolateral prefrontal cortex (dlPFC). The main rea-son for the dlPFC being chosen as a candidate region is due to the relatively consistent cognitive and func-tional defi cits seen in patients with schizophrenia,15–18 functions that are associated with the dlPFC. The study design and main fi ndings of these investigations are summarized in Table 42.1 below.

Looking at the post-mortem microarray studies, one of the most consistent fi ndings has been reductions in genes related to myelin structure and oligodendrocyte functioning in the prefrontal cortex in schizophrenia,19–22 with reductions also being demonstrated in bipolar dis-order and major depressive disorder.20,23,24

While demyelination has not been observed in schizophrenia or bipolar disorder, alterations in oligo-dendrocyte numbers and associated proteins have been demonstrated histopathologically25–28 together with alterations in white matter visualized via diffusion ten-sor imaging.29–31 Given the close relationship between oligodendrocytes and axons, and their role in maintain-ing effi cient neurotransmission, it seems logical that alterations in oligodendrocytes or myelin proteins could cause disruption in normal transmission and hence potentially lead to dysfunctional circuitry in schizophre-nia and other major mental illnesses. Genes related to metabolism and mitochondrial pathways are another ontological group that have been found to be reduced in expression in schizophrenia by a number of different studies20,32–35 and have also been observed in bipolar dis-order.34 Gene expression changes related to mitochon-drial genes that are involved in energy pathways were initially reported by Middleton et al., in 2002 in schizo-phrenia. This investigation demonstrated reductions in transcripts associated with regulation of ornithine, polyamine metabolism, the mitochondrial malate shut-tle system, the tricarboxyclic acid cycle, and amino-acid and ubiquitin metabolism. While the investigations that followed have also shown alterations in mitochondrial and metabolism related genes there has been no overlap in gene lists between studies. Nevertheless, reductions in metabolically related genes, especially in the prefron-tal cortex of schizophrenic subjects fi ts in well with reductions in prefrontal activity shown via functional neuroimaging studies in patients performing working memory tasks.17,36 One potential confounder of the mito-chondrial gene expression fi ndings observed in both

bipolar disorder and schizophrenia maybe agonal-pH state. In a recent study by Vawter and colleagues 28% of genes related to mitochondrial function were found to be differentially expressed between control brains at low and high pH respectively.37

Other relatively consistent changes that have been observed in post-mortem gene expression investigations in schizophrenia and bipolar disorder have been altera-tions in synaptic and synaptic-related genes together with genes encoding neurotransmitters and their associ-ated proteins.14,19,38–41 Of interest, these fi ndings relate to reductions in synaptic proteins such as SNAP-25 and vesicle associate membrane protein-1 (VAMP-1) observed in the PFC of schizophrenics42–44 as well as reductions in GAD-67,45–47 a key enzyme involved in the synthesis of gamma amino butyric acid (GABA). While it is worthwhile relating mRNA changes to these protein alterations reported by post-mortem investigations it should be noted that it has been the inconsistencies in post-mortem neuropathological fi ndings that have led investigators to turn to microarray technology. A point to note here is that microarray investigations have also been relatively inconsistent in their fi ndings, and while these inconsistencies may be related to methodological differences it is likely more associated with the complex nature of the disorders under investigation. These uncer-tainties highlight the strength of microarray analyses in general, which is their ability to generate novel hypo-theses to be tested subsequently, rather than to provide ultimate validation of existing biological theories of a given mental illness.

Post-mortem microarray investigations for major depressive disorder have been less numerous within psychiatry but are beginning to increase in number. One interesting fi nding has been by Evans et al., in 2004 who demonstrated dysregulation of fi broblast growth factor (FGF) genes.48 This study reported reductions in FGF2, a neuroprotective FGF and also increases in FGF9, also known as glial growth factor. Taken together these fi ndings may represent a key neurotrophic system that is dysregulated in major depressive disorder. Directly correlated to this fi nding we also observed reductions in FGF2 and an upregulation of FGF9 in vitro in human fetal brain aggregates exposed to cortisol.49 As hypercortisolemia is a defi ning feature in MDD, our fi ndings may corroborate a role for FGFs in MDD etiology. Replication of post-mortem fi ndings and expansion of in vitro experiments are necessary before a causative role for FGFs in MDD can be achieved.

42 Gene Expression Changes and Potential Impact of Endophenotypes in Major Psychiatric Disorders 83

Tab

le 4

2.1

Po

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m m

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vest

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in m

ajor

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ain

gene

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fi ndi

ngs

Pref

ront

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x (B

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5MD

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(60

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e Y

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and

bip

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dis

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6(10

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84 G. Chana et al.

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row

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n-43

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iα1

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lpha

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eous

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lear

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3); H

ER

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t dom

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and

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A-D

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1 (m

ajor

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plex

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bet

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ctin

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HSP

B1

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t sho

ck p

rote

in b

eta

1); S

ER

PIN

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ha-1

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mot

ryps

in A

3); I

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M1

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erfe

ron

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ced

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with

tet

ratr

icop

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e re

peat

s 1)

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AR

S2 (

leuc

yl-t

RN

A s

ynth

etas

e, m

itoch

ondr

ial)

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AG

(m

yelin

ass

ocia

ted

glyc

opro

tein

); M

AL

(m

yelin

and

lym

phoc

yte

prot

ein)

; M

AR

CK

S (m

yris

tola

ted

alan

ine-

rich

C-k

inas

e su

bstr

ate)

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GF

-bet

a 1

(Tra

nsfo

rmin

g gr

owth

fac

tor

beta

1);

erbB

2 er

ythr

obla

stic

leu

kem

ia v

iral

onc

ogen

e ho

mol

og 2

); M

OB

P

(mye

lin-o

ligod

endr

ocyt

e ba

sic

prot

ein)

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P (

mye

lin b

asic

pro

tein

);M

OG

(m

yelin

-olig

oden

droc

ytic

pro

tein

); M

T2A

(m

etal

loth

ione

in 2

A);

Neu

rod1

(ne

urog

enic

dif

fere

ntia

tion

1); N

MD

A1

(N-m

ethy

l, d-

aspa

rtat

e 1)

; NSF

(N

-eth

ylm

alei

mid

e se

nsiti

ve f

usio

n pr

otei

n); P

EA

-15

(pho

spho

prot

ein

enri

ched

in a

stro

cyte

s 15

); P

LP

(pr

oteo

lipid

pro

tein

); P

MP

22

(per

iphe

ral m

yelin

pro

tein

22)

; SE

LE

NB

P1

(sel

eniu

m b

indi

ng p

rote

in-1

); S

FR

S1 (

splic

ing

fact

or, a

rgin

ine/

seri

ne r

ich

1); S

OX

10 (

SRY

-rel

ated

hom

eobo

x ge

ne 1

0); S

100

(cal

cium

bi

ndin

g pr

otei

n A

1); T

GF

-a (

tran

sfor

min

g gr

owth

fac

tor-

alph

a); T

RA

F4

(tum

our

necr

osis

fac

tor

rece

ptor

ass

ocia

ted

fact

or 4

);

Tab

le 4

2.1

(c

ontin

ued)

Bra

in r

egio

nSa

mpl

ePl

atfo

rmM

ain

gene

exp

ress

ion

fi ndi

ngs

42 Gene Expression Changes and Potential Impact of Endophenotypes in Major Psychiatric Disorders 85

Blood Based Gene Expression Investigations

Due to the use of blood for gene expression analysis being a relatively new concept, the number of blood-based microarray investigations for major psychiatric disorders are not as numerous as those for post-mortem brain. The main fi ndings so far from these investiga-tions are summarized in Table 42.2 above. Half of these studies have focused on assessing gene expres-sion changes in lymphoblastoid cells cultured from patients with schizophrenia and bipolar disorder. The earliest one of these studies was that by Kakiuchi et al in 2003. The authors of this study reported a reduction in expression in XBP1 in a pair of twins discordant for bipolar disorder versus a control twin pair. XBP1 is a pivotal gene in the endoplasmic reticulum (ER) stress response and therefore is of interest in the light of elevation in stress response molecules such as cortisol in patients with bipolar disorder. In addition, they found that a polymorphism substitution at position 116 (C—G) in the promoter region of XBP1 lymphoblasts derived from Japanese bipolar patients conferred a reduced ER stress response that was rescued by treat-ment with the mood stabilizer valproate.50 More recently, they extended their fi nding by demonstrating that lithium treatment also was more effective in

patients with the 116C allele as opposed to patients homozygous for 116G.51 While this fi nding directly relates the codon substitution to a known mood-stabi-lizing agent, validation of this interesting fi nding in a separate larger independent patient cohort is warranted. In the largest microarray study of lymphoblastoid cells so far by Vawter et al., 2004 it was found that the expression of NPY and GNA01 were reduced in schizophrenia as well as an increase in the mitochon-drial-related gene MDH1.52 Changes in NPY have been observed by some post-mortem investigations in schizophrenia and bipolar disorder and are therefore of interest.53–55 It should be noted that even for this study sample numbers were relatively small with only fi ve schizophrenic and nine control subjects. Moreover, studies that utilized lymphoblastoid cell lines utilized Epstein Barr virus (EBV) to transform PBMCs, an effect which in itself could lead to changes in gene expression.

Recently peripheral blood microarray fi ndings have implicated a SMDF, a splice variant of NRG-1 in patients with schizophrenia. This fi nding for NRG1 refl ects previous genetic association studies that have found a susceptibility for schizophrenia to be associ-ated with NRG1 polymorphisms.56,57 More recently, evidence for reduced NRG1 levels in the PFC of brains of patients with schizophrenia and unipolar depression

Table 42.2 Blood-based microarray investigations in major psychiatric disorders

Source Sample Platform Main gene expression fi ndings

Lymphoblastoid cells 1C; 2B U95Av2 Reduced expression of ER stress response gene XBP1 in affected twins50

Lymphoblastoid cells 9C; 5S Custom array (1127) Reduced expression of NPY, GNA01, MDH1 in schizophrenia52

Lymphoblastoid cells 2B cDNA array (2400) Increased expression of alpha1B-AR in bipolar prior to lithium treatment. Chronic lithium treatment reduced alpha1B-AR, ACHR, PDE4D, SPR in lithium responders88

Peripheral blood sample 33S; 5B U133A Increased expression of NRG-1 variant SMDF in patients with schizophrenia89

Peripheral blood sample 10C; 13S Custom array (3200) Increased expression of DRD2 and Kir2.3 in drug naïve schizophrenic subjects90

Peripheral blood sample 24(17) C; Custom array (3200) Increased expression of SELENBP1, GSK3alpha in 30 (30S); schizophrenia; Reduced expression of BTG1, HLADRB1, (7)B HNRPA3, SFRS1 in schizophrenia63,69

Peripheral blood sample 21 C; 21B U133A 2.0 Reduced expression of electron transfer chain genes in bipolar cultured lymphocytes following glucose deprivation versus controls with increased expression67

ACHR (acetylcholine receptor); DRD2 (dopamine receptor D2); ER (endoplasmic reticulum); GNA01 (guanine nucleotide binding protein alpha 1); Kir2.3 (potassium channel inward rectifying 2.3); MDH1 (malate dehydrogenase 1); NPY (neuropeptide Y); NRG-1 (neuregulin-1); PDE4D (phosphodiesterase 4D); SPR (sepiaterin reductase); SMDF (sensory and motor neuron derived factor); XBP1 (X box binding protein 1)

86 G. Chana et al.

has also emerged.58 While some studies have failed to demonstrate association of NRG1 to schizophrenia,59–61 recent meta-analyses have supported a relationship.57,62 In a comparative blood and brain gene expression study we demonstrated that the gene for selenium binding protein-1 (SELENBP1) was upregulated in both compartments in schizophrenia.63 In a follow-up qRT-PCR study looking at a separate brain cohort, our group also observed increased SELENBP1 expression in the dlPFC of patients with schizophrenia. Interes-tingly, the expression of SELENBP1 was signifi cantly correlated with psychosis in a combined sample of schizophrenia and bipolar disorder patients regardless of their diagnosis.64

However, while increased expression in SELENBP1 in the DLPFC in schizophrenia may be present, the functional relevance of this upregulation remains to be elucidated. One potential mechanism may be related to its potential role as a neurogenic factor evi-denced by two investigations demonstrating its ability to promote neurite outgrowth in the rat cerebral cor-tex65 as well as co-localization with actin in growing tips of human neuroblastoma SY5Y cells.66 Given that reductions in synaptic and dendritic arbors have been observed in the brains of patients with schizophrenia, elevated SELENBP1 may play a compensatory role in restoring neuronal connectivity and functioning. Further work to better defi ne this tentative mechanism is being carried out.

Finally, in a recent study by Naydenov et al., reduc-tion in the expression of electron transport chain genes in lymphocytes isolated from patients with bipolar dis-order following glucose deprivation was seen.67 This reduction was in contrast to an upregulation seen in normal controls. This fi nding indicates that patients with bipolar disorder may lack the ability to correct functional defi cits in the blood and hence potentially also within the brain.

Methodological Considerations for Psychiatric Microarray Investigations

An innate diffi culty in conducting microarray investi-gations in the brain is the gene expression diversity that will exist between cell types and various brain regions.68 While many of the studies listed above in Table 42.1 defi ne Brodmann areas for sampling, the

proportion of neurons and glia that make up the fi nal constituent cells for RNA extraction remain largely unknown. This is due to variation in the proportion of gray and white matter sampled. A potential way around this variation is by the use of laser capture microdis-section (LCM) to extract out individual cell types and analyze their gene expression separately. While the collection of targeted cells may appear to be more advantageous compared to homogenizing macroscopic blocks of brain tissue LCM is not without confounders. One of these is that it is very labor-intensive to gener-ate enough cells for RNA extraction and subsequent gene expression analysis. Secondly, dissection of indi-vidual cells from brain tissue inevitably leads to sampling of some of the interstitial neuropil, with the extent of this being variable between cases. Nevertheless, with the development of more automated techniques for LCM and increased precision this method represents a novel approach to assess gene expression changes in specifi c brain cell populations in major psychiatric dis-orders and brain-related disorders. This may also extend to the assessment of sub-populations of neu-rons and glia, i.e. assessment of gene expression changes in interneuronal populations in BA9. Other challenges that face brain microarray studies include (1) variability introduced by genetic diversity, (2) effects of disease treatment on gene expression, (3) differential diagnoses, (4) co-morbidity with other dis-orders, (5) variation in age, PMI, pH, and drug abuse between groups in a cohort, (6) limited sample sizes with a limited number of samples yielding high-quality RNA for investigation, and (7) variability in platform types and methods for hybridization.68

With the exception of PMI all of these factors also affect blood-based gene expression investigations. Further confounding variables for blood based studies, as previously mentioned, include diet, exercise, smok-ing, time of last meal and perhaps most importantly the patients’ immune status. All these factors can affect gene expression in the blood and hence matching and normalization where possible should be attempted when designing such studies. Just as with brain microarray investigations, blood contains several different cell types within its constituents. These cell types fall primarily into three categories; erythrocytes, leukocytes and thrombocytes. With leukocytes com-prising the immune component of blood, focus has been cast on assessing gene expression in this cellular sub-population. However, even within this cell category

42 Gene Expression Changes and Potential Impact of Endophenotypes in Major Psychiatric Disorders 87

several cell types exist; neutrophils, eosinophils, baso-phils, lymphocytes, monocytes and macrophages. As these cell types possess varied roles within the blood, this would be refl ected in their gene expression profi les. Furthermore, based on the immune status of an indi-vidual (i.e. immunosupressed due to infection) different proportions of these cell types may be present. While this signifi cant variability in gene expression paints a rather bleak picture for blood-based microarray inves-tigations to identify biomarkers for disease, specifi c patterns of gene expression have nevertheless been observed in blood samples of patients with schizophre-nia, bipolar disorder and controls.69,70 In an attempt to circumnavigate problems with cell variability in blood samples, some researchers have chosen to transform and culture B-lymphocytes into lymphoblastoid cells lines using Epstein Barr virus prior to microarray anal-ysis (see Table 42.2). While these studies have the advantage of assessing gene expression effects in a relatively homogenous cell population free from the temporal state of individuals, effects of exposure to virus and chromosomal alterations during culture must be considered as a confounder.71

Cross-Platform Comparison and Ontological Analysis

A signifi cant amount of variability lies in gene expression data generated from different microar-ray platforms and in the tools used for analysis. More recent studies have demonstrated better correlation between platforms, however even a 10% divergence will lead to differences in thousands of transcripts between platforms. A contributing element to this plat-form variability lies in the relative sensitivity of the platforms in detecting low abundance transcripts. The sensitivity of platforms in detecting low abundance transcripts is determined to a large extent by the length of oligonucleotide probes present on the array. Probes with a longer length i.e. 60–70 bases are more sensi-tive than probes with a 25-base sequence. One prob-lem with this longer probe length is that it is more prone to cross react with other transcripts and hence its specifi city may be reduced. Due to these relative incon-sistencies it is important that changes in gene expres-sion levels detected via microarrays be validated by qRT-PCR.

Post-hoc analysis of microarray data using onto-logical tools represents another area for potential error in generating false-positive and false-negative results. Firstly, analysis of individual chip image fi les are com-pleted with programs such as robust multichip analysis algorithm (RMA). RMA consists of a three-step pro-cedure involving background adjustment, quantile normalization and fi nally summarizing gene intensi-ties across probes. The next step in the analysis is determining a suitable cut-off for genes differentially regulated. Most researchers will use fold-change rela-tive to controls as their cut-off, however the value cho-sen for this is often arbitrary and tends to vary from study to study based largely on the genes of interest. Given that major psychiatric disorders have complex genetic etiologies this method of fi ltering may lead to increased generation of false-positive and false-nega-tive results. Another way in which researchers select a cut-off may be based on the signifi cance level of the gene differing between control and disease groups. This is typically set to a threshold of p = 0.05. While this method may also be seen as subjective it repre-sents a relatively more unbiased approach to choosing a cut-off, even though the relevance of the integer maybe questionable. While it maybe worthwhile attempting to adjust this scientifi cally dogmatic p-value threshold it is diffi cult to arrive at a good rational for a particular correction approach i.e. if a Bonferroni cor-rection were applied to the data one could argue to divide the p-value by the number of genes being inves-tigated. In the case of whole genome arrays this would eliminate most gene expression changes and would be over-exclusive.

Once gene lists are fi ltered ontological programs such as GoSurfer, GoMiner, OntoExpress, DAVID, Metacore and Ingenuity can be utilized to separate gene lists according to biological, cellular and molecular classification. This ontological profiling shifts the emphasis of microarray fi ndings away from single genes and instead focus is placed on categories where a signifi cant number of genes have been dysregulated. However, variations in databases and methods used for this step also exist and hence potentially introduce another layer of complexity. For example the probabil-ity of gene A being included in process Z depends on how much information the particular company who maintains an ontological program has curated about the function of gene A, furthermore very little infor-mation is know about certain genes so they will never

88 G. Chana et al.

populate a process. Thus molecular processes or networks generated by ontological programs are biased to genes for which there is more published data. Nevertheless, these programs allow us to take a more global view of gene expression changes and to hypoth-esize about certain mechanistic pathways that maybe causatively involved. Standardization of fi ltering and also ontological profi ling will inevitably lead to less variability between research groups and therefore increase the robustness of fi ndings.

Gene Expression and Endophenotypes

Schizophrenia and other major psychiatric disorders have demonstrated defi nite genetic origins, but unlike simpler diseases that follow Mendelian frameworks, their genetic architecture cannot be deconstructed as simply. Microarray investigations provide effective high-throughput gene expression analysis, however, utilizing the potential impact of endophenotypes on gene expression may enhance our ability to tease out disease-related genes and identifying potential bio-markers. Endophenotypes are heritable traits that are exhibited by those with the disorders, but are not directly symptomatic, nor are they normally associated with the disorders in terms of clinical diagnoses. Recent research gives strong evidence that endopheno-types may play a strong role in aiding the screening of patients for psychiatric disorders, especially because they are easier to analyze than the typical phenotypes, and because they may be strongly linked with the etiol-ogy of the psychiatric disorders (Flint & Munafo, 2007), Psychiatric disorders such as schizophrenia have very complex genetic architecture, due to the multiple genetic sources that individually alter suscep-tibility by a small degree while interacting with each other and the environment.72

Examples of endophenotypes and the potential identifi cation of biomarkers can be observed in a number of scientifi c articles. Flint and Munafo (2007) identi-fi ed six different categories of endophenotypes that can correlate with the disorder: anatomical, develop-mental, electrophysiological, metabolic, sensory and psychological/cognitive. Indeed, differences in neuro-anatomical structure have been attributed to schizo-phrenia and bipolar disorder. The genetic risk of schizophrenia was associated with distributed gray

matter volume defi cits in the bilateral fronto-striato-thalamic and left lateral temporal regions, whereas those with bipolar disorder demonstrated a genetic liability linked to gray matter defi cits only in the right anterior cingulate gyrus and ventral striatum.73 Genetic risk for the disorders is thus associated with gray matter structural endophenotypes, and so determination of the genetic basis of this change in gray matter may shed light on the genetic root of schizophrenia.

As already mentioned, endophenotype examination may follow either a post-hoc analysis, once the gene expression changes have been evaluated in patients with major psychiatric disorders, or endophenotypes could also be used to pre-sort patients into different groups. Both methods would ultimately attempt to fur-ther categorize and differentiate psychiatric disorders into their genetic composition in order to improve diagnosis and thus treatment. An interesting approach would be to run all patient samples blind to diagnosis followed by separation of profi les by certain endophe-notypes where data is available. In relation to blood gene expression investigations this could be done by analysis of a number of different physiological and biological factors including assessment of altered sin-gle nucleotide polymorphisms (SNPs). By separating out on the basis of endophenotypes and then compar-ing these profi les to those generated by clinical symp-tomatology common and distinguishing genes of interest between endophenotypes and psychiatric disorders may be found. When correlated with clinical data this may enable the identifi cation of a set or sets of genes that can be used for diagnosis and prognosis; i.e. patient X is a diabetic characterized by a group of dysregulated genes which are in turn related to a more frequent occurrence of psychosis. If such data is made available to a clinician it would allow them to conduct a more individualized diagnosis and treatment plan as well as enabling screening of potentially affected fam-ily members. However, as already mentioned this causative link for major psychiatric disorders is likely to rely on a number of different gene clusters.

Future Directions

Microarray investigations assessing gene expression for major psychiatric disorders in the brain have greatly increased our power to detect candidate genes for these

42 Gene Expression Changes and Potential Impact of Endophenotypes in Major Psychiatric Disorders 89

disorders. Nevertheless, signifi cant problems exist in terms of methodological and technical issues with microarrays. Probably the biggest problem facing microarray studies is the need for standardization. This includes further standardization of diagnosis, collec-tion and processing of samples, platform design and evolution in consistency, laboratory techniques and tools used for analysis. Many problems exist within these individual categories, the details of which have been discussed elsewhere.3,68,71 Problems associated with the differential diagnosis of major psychiatric dis-orders are a consequence of overlapping phenotypes between these disorders. A lack of consistent biomark-ers for major psychiatric disorders makes differential diagnosis for the trained psychiatrist a diffi cult task. The solution to this problem may potentially come at the end of the evolution of microarray investigations in the blood, whereby enough data would have been generated to reliably identify biomarkers and hence aid in differentiating between disorders.

With regard to sample collection in microarray investigations, approaches for the collection of brain material for post-mortem study and RNA extraction are well established. For blood-based investigations a number of different methods exist for the isolation of leukocytes and extraction of RNA. These different methodologies lead to differential sampling of sub-types of cells and hence signifi cant contribution of gene expression differences, an effect mirrored in brain studies where different proportions of neurons and glia may be sampled from cortical and sub-cortical regions. Isolation of specifi c lymphocyte types in blood-based studies may be achievable by the use of techniques such as immunolabelling with beads for specifi c cell-surface markers and in brain investigations via LCM. However, such techniques are relatively ineffi cient in obtaining suffi cient numbers of cells in order to yield enough RNA for microarray investigation. Nonetheless, as these techniques evolve a better isolation of indi-vidual cell populations will be achieved and will lead to a greater sensitivity in detecting gene expression changes in the blood and brain of patients with major psychiatric disorders.

Another confounding variable touched upon earlier is the effects of medications on gene expression changes. These effects occur in both blood and brain and hence require consideration and inclusion of rele-vant controls. In studies generating lymphoblastoid cells from patients, treatment of these cells with atypical

antipsychotics followed by assessment of gene expres-sion effects may help in separating disease-related versus medication-related genes. However, as already mentioned above the process of cell immortalization by infection with EBV may critically alter the cell’s gene expression pattern and therefore cloud results. In vitro and in vivo investigation represent another pos-sible route for investigating medication related effects. Some investigations have already demonstrated these effects by alterations in transcripts in rats and cell lines exposed to typical and atypical antipsychotics.74–77 The most straightforward way to assess medication effects is to study fi rst-episode or unmedicated patients; how-ever these subjects are diffi cult to identify and study.

As already mentioned, genetic studies of major psy-chiatric disorders may indeed be enhanced by using endophenotypes. Since these endophenotypes are con-sidered to be simpler and more prominent, researchers in the fi eld of behavioral science are able to design clearer experiments. By pre-sorting patients according to endophenotype, the scope of the analysis is greatly narrowed; instead studying a large population of schizophrenic patients and performing wide-range microarray investigations, samples of patients can then be studied according to the neuroanatomical, behav-ioral, biochemical etc. correlates. This in turn can be related to clinical phenotype and may be a marker indicative of diagnosis or prognosis.

Conclusion

With the advent of blood-based gene expression inves-tigations in patients with major psychiatric disorders, we may have moved one step closer to identifying potential biomarkers. However, care must be taken in the initial stages of these studies to avoid over-inter-pretation of such generated data and the creation of white elephants that cannot be replicated. By imple-menting careful study design many of these potential confounders can be reduced or accounted for. Further still, separation of specifi c cell populations for study including both blood and brain compartments will allow us to glean more knowledge as to the mechanism of gene dysregulation. The fi eld of microarray plat-form design and development is also very fast-moving. With microarray platforms continuing to evolve the ability to detect splice variants of transcripts is now a

90 G. Chana et al.

reality as well as incorporating single nucleotide polymorphism (SNP) analysis for a more detailed understanding of gene-based expression changes. This then also can be related to other epigenetic mechanisms that have the potential to infl uence gene expression changes and will allow us to not only understand gene expression changes but also what in-built mechanisms may be regulating them. The ability to mine and extract useful data requires standardization in analysis techniques and the free distribution of data to all researchers. An example of this can be seen in the Stanley Foundation’s online genomics database (https://www.stanleygenomics.org/) whereby study results from numerous investigators and platforms have been summarized. Finally, analyzing the tran-scriptome using microarrays must be coupled to other evolving fi elds such as proteomics and metabolomics. Adopting such an approach will greatly facilitate our search for biomarkers in psychiatry as it provides a multi-pronged approach for identifi cation and validation purposes. These techniques will also allow us to look more closely at endophenotypes and their common and specifi c contributions to major psychiatric disorders.

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