Microarray gene expression and cerebral cortical grey matter changes in treatment naive schizophrenia patients in Sri Lanka

Nishantha Kumarasinghe

MBBS

This thesis is submitted in partial fulfilment of the requirement for the

Degree of PhD in Behavioural Sciences in Relation to Medicine

Faculty of Health School of Medicine and Public Health

University of Newcastle

Australia.

March 2012

Statement of Originality

This thesis contains no material which has been accepted for the award of any other degree or diploma in any university or other tertiary institution and, to the best of my knowledge and belief, contains no material previously published or written by another person, except where due reference has been made in the text. I give consent to this copy of my thesis, when deposited in the University Library, being made available for loan and photocopying subject to the provisions of the Copyright Act 1966.

Nishantha Kumarasinghe

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Acknowledgement of Collaboration

I hereby certify that the work embodied in this thesis has been done in collaboration with other researchers. I have included as part of the thesis a statement clearly outlining the extent of collaboration, with whom and under what auspices.

Nishantha Kumarasinghe

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Acknowledgement of Authorship

I hereby certify that this thesis is in the form of a series of published papers of which I am the principal author. References for publications are included as an appendix.

Nishantha Kumarasinghe

I certify the contribution of the candidate (Nishantha Kumarasinghe) as the First Author of the above-mentioned publications.

Professor Ulrich Schall (Principal Supervisor)

Dr Paul A Tooney (Co-Supervisor)

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Acknowledgement of Contribution

Many people helped me to achieve my goal. I wish to place my appreciation on record for all of them.

I am grateful to my principal supervisor Professor Ulrich Schall for his guidance, dedication and generous time. His friendly personality, hospitality and warmth helped me to go through all the difficult times with ease and strengthened my commitment to work.

Next I would like to extend a warm thank you to my co-supervisor Dr. Paul A. Tooney for his guidance, kindness, generous support and dedication.

Also, my heartfelt thanks go to Prof John Rostas, who kindly responded to my initial inquiry in 2006.

My very sincere gratitude goes to Dr. Jayan Mendis and Dr. Gambheera Harischandra consultant psychiatrists from the Institute of Mental Health Angoda who supervised me and guided me during the diagnosis of disease at the time of patient inclusion, and administration of neuropsychological assessments. Further, I thank all the consultant psychiatrists and psychiatry post graduate trainees as well as medical officers at National Institute of Mental Health (Sri Lanka) who worked during the period of 2006-2008 for their generous support for the various aspects of this project.

My heartfelt gratitude goes to Prof. Surangi Yasawardene of the Department of Anatomy who provided the overall supervision of the local (Sri Lankan) component of this project. Her encouragement and support in various aspects of this project has been a great strength.

I am grateful to Senior Prof. Antoinette Perera, Professor of Family Medicine (Sri Lanka) who helped with the recruitment of healthy volunteers as controls, their selection and neuropsychological assessments, also for helping me with finer points in editing.

I am thankful to Dr Kanishka Suriyakumara for assisting me with MRI and genetic data collection and Mr Palitha Siriwardene, Dr Manthika Kodithuwakku and Dr

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Sajewanie Wickramasinghe for assisting me to administer neuropsychological assessment tools.

I am thankful to all the OPD nurses and supporting staff of the National Institute of Mental Health (Sri Lanka) for helping with phlebotomy and follow up procedures. Also to Psychiatric social workers attached to NIMH for assisting with follow up studies.

My heartfelt gratitude goes to Paul E. Rasser for training and assistance in application of Cortical Pattern Averaging method and also to Jessica Bergmann, Lilly Knechtel and Stewart Oxley who helped in various segments in the application of LONI method and Paul M. Thompson for casting his magnificent expert eye on MRI manuscript which is the foundation for chapter two of this thesis.

A warm thank you goes to Natalie Beveridge who assisted me with gene expression data analysis (SAM), qPCR procedures and biological pathway analysis (Ingenuity®) and to Erin Gardiner for helping me with the microarray procedures. I would also like to thank Professor Rodney Scott for access to his laboratory, the microarray scanner, and his laboratory’s expertise that allowed me complete the gene expression data analysis process.

Genetech Molecular laboratory (Sri Lanka) provided the low temperature storage facility for blood samples (for RNA) and the Radiology Department of Asiri Surgical Hospital (Sri Lanka) provided the MRI facility for this project. I am grateful to them.

Finally, I would like to acknowledge the services of all local and foreign colleagues of mine especially Tim Ehlkes and Mary-Claire from Centre for Brain and Mental Health Research (University of Newcastle Australia) who were so knowledgeable and passionate in the field of neuroscience. Tim kindly assisted me with final formatting, PDF conversion, printing, and submission also.

This project was funded by the World Bank through the IRQUE project, the Schizophrenia Research Institute, University of Newcastle, and University of Sri Jayewardenepura and received infrastructure support from the Australian Schizophrenia Research Bank, the Hunter Medical Research Institute, and New

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South Wales Health. Without their invaluable contribution, this project would not be a success.

I am ever grateful to my faculty for selecting me for the award of funds for this PhD through the IRQUE project.

Last but not least, I owe my wife Dammika and two sons, Randew and Dewmin for their patience. Without having their supportive atmosphere at home I would not have been able to dedicate my time in to this thesis during past seven years.

Nishantha Kumarasinghe

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CONTENTS TITLE PAGE i STATEMENT OF ORIGINALITY ii ACKNOWLEDGEMNET OF COLLABORATION iii ACKNOWLEDGEMNET OF AUTHORSHIP iv ACKNOWLEDGEMNET OF CONTRIBUTION v CONTENTS viii LIST OF FIGURES x LIST OF TABLES xi LIST OF SUPPLENTARY TABLES xi SUMMARY 1

1. INTRODUCTION 3

1.1. HISTORICAL OVERVIEW 3

1.2. CURRENT DIAGNOSTIC DEFINITION 5

1.3. HERITABILITY AND “CANDIDATE GENES” OF SCHIZOPHRENIA 6

1.4. BIOMARKERS AND “ENDOPHENOTYPES” OF THE DISORDER 7

1.5. CAPTURING GENE X GENE AND GENE X ENVIRONMENT INTERACTION 8

1.6. MICROARRAY GENE CHIP TECHNOLOGY 9

2. REVIEW OF GENE EXPRESSION FINDINGS IN SCHIZOPHRENIA 12

2.1. GENE EXPRESSION FINDINGS FROM POST MORTEM BRAINS 12

2.2. GENE EXPRESSION FINDINGS FROM PERIPHERAL TISSUES 22

2.3. GENE EXPRESSION FINDINGS FROM ANIMAL MODELS RESEARCH 24

2.4. IMPORTANCE OF GENE EXPRESSION RESEARCH IN TO PREVIOUSLY UNTREATED SCHIZOPHRENIA PATIENTS 26

3. CHANGES IN PBMC GENE EXPRESSION FOLLOWING TREATMENT WITH ANTIPSYCHOTIC MEDICATION IN TREATMENT NAÏVE PATIENTS WITH SCHIZOPHRENIA 29

3.1. INTRODUCTION 29

3.2. METHODS AND MATERIALS 30

3.2.1. PARTICIPANT RECRUITMENT AND COHORT CHARACTERISATION 30

3.2.2. RNA PURIFICATION FROM PBMC 32

3.2.3. MRNA EXPRESSION ARRAY ANALYSIS 33

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3.2.4. QUANTITATIVE REAL-TIME REVERSE TRANSCRIPTION PCR (Q-PCR) VALIDATION 34

3.2.5. PATHWAYS AND NETWORK ANALYSIS 35

3.3. RESULTS 35

3.3.1. CHANGES IN SYMPTOMS RATINGS AFTER ANTIPSYCHOTIC MEDICATION TREATMENT 35

3.3.2. GENE EXPRESSION CHANGES BEFORE AND AFTER ANTIPSYCHOTIC MEDICATION TREATMENT 35

3.3.3. QPCR VALIDATION OF DIFFERENTIALLY EXPRESSED GENES 36

3.3.4. INGENUITY PATHWAYS ANALYSIS OF DIFFERENTIALLY EXPRESSED GENES 37

3.4. DISCUSSION 44

3.5. STUDY LIMITATIONS 57

4. CEREBRAL CORTICAL GREY MATTER DEFICITS AND THEIR ASSOCIATIONS WITH AGE, PSYCHOPATHOLOGY, COGNITION AND TREATMENT RESPONSE 59

4.1. INTRODUCTION 59

4.2. METHODS AND MATERIALS 62

4.2.1. PARTICIPANTS’ RECRUITMENT AND COHORT CHARACTERISTICS 62

4.2.2. MRI PROCESSING: APPLICATION OF CORTICAL PATTERN AVERAGING 64

4.2.3. DATA ANALYSIS 65

4.3. RESULTS 65

4.3.1. NEUROPSYCHOLOGY 65

4.3.2. REGIONAL GREY MATTER DENSITY VARIANCE ACROSS BRODMANN AREAS 66

4.3.3. EVIDENCE OF SIGNIFICANT GLOBAL GREY MATTER REDUCTION IN PATIENTS 66

4.3.4. GREY MATTER REDUCTION VERSUS AGE AND THE DURATION OF ANTIPSYCHOTIC MEDICATION

THERAPY 67

4.3.5. SYMPTOM RATINGS AND COGNITIVE FUNCTIONS VERSUS REGIONAL GREY MATTER DENSITY 69

4.4. DISCUSSION 72

4.4.1. AGE EFFECTS ON GREY MATTER IN SCHIZOPHRENIA 73

4.4.2. GREY MATTER CORRELATES OF COGNITIVE IMPAIRMENT 73

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4.4.3. GREY MATTER CORRELATIONS OF PSYCHOPATHOLOGY 74

4.4.4. NEUROPATHOLOGY OF SCHIZOPHRENIA 74

4.4.5. HERITABILITY OF BRAIN MORPHOLOGY 74

5. EXPLORING ASSOCIATIONS OF PBMC GENE EXPRESSION AND GREY MATTER PATHOLOGY IN SCHIZOPHRENIA 76

5.1. INTRODUCTION 76

5.2. SUMMARY OF FINDINGS 76

5.3. ASSOCIATIONS OF GENE EXPRESSION, NEUROCOGNITIVE AND CLINICAL FINDINGS WITH REGIONAL

GREY MATTER DENSITY 78

5.3.1. GLOBAL PBMC GENE EXPRESSION AND GLOBAL CEREBRAL CORTICAL GREY MATTER DEFICIT 78

5.3.2. SUMMARY FOR INDIVIDUAL GENES 80

5.3.3. REGION OF INTEREST APPROACH: ANTERIOR CINGULATE 87

5.4. CONCLUSIONS AND STUDY LIMITATIONS 92

5.5. FUTURE DIRECTIONS 93

6. REFERENCES 95

LIST OF FIGURES

FIGURE 3.1: GENE EXPRESSION CHANGES IN SCHIZOPHRENIA PATIENTS BEFORE AND AFTER ANTIPSYCHOTIC DRUG TREATMENT, BY QPCR. 42 FIGURE 3.2: CHANGES TO THE EIF2 SIGNALLING PATHWAY IN PBMCS FROM PATIENTS WITH SCHIZOPHRENIA. 45 FIGURE 4.1: FACTOR LOADING SCORES FOR BRODMANN AREAS IN LEFT AND RIGHT CEREBRAL HEMISPHERES 68 FIGURE 4.2: PARAMETRIC MAPPING (THRESHOLD P<0.05 UNCORRECTED) OF GREY MATTER GROUP DIFFERENCES 70 FIGURE 4.3: PARAMETRIC MAPPING (THRESHOLD P<0.05 UNCORRECTED) OF GREY MATTER 71 FIGURE 5.1:STUDY OUTLINE. 78 FIGURE 5.2: SCATTER PLOT OF GLOBAL GENE EXPRESSION BY GLOBAL GREY MATTER REPORTING FACTOR SCORES FOR HEALTHY CONTROL SUBJECTS (CON) AND SCHIZOPHRENIA PATIENTS (SCZ). 80

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FIGURE 5.3: LATERAL AND MEDIAL VIEWS OF CEREBRAL CORRELATION MAPS OF INDIVIDUAL CANDIDATE GENE CONTRIBUTIONS TO GREY MATTER LOSS IN SCHIZOPHRENIA. 83 FIGURE 5.4: RPP21 CANDIDATE GENE EXPRESSION PREDICTED GLOBAL GREY MATTER CHANGES 88 FIGURE 5.5: GREY MATTER CORRELATIONS OF GENE DISC 1EXPRESSION IN RELATION TO ANTIPSYCHOTIC THERAPY. LEFT: EXPRESSION OF DISC1 90 FIGURE 5.6: SUMMARY OF THE FINDINGS INVOLVING DISC1 GENE EXPRESSION. 91

LIST OF TABLES

TABLE 2.1: GENE EXPRESSION STUDIES INTO POST MORTEM BRAIN TISSUE IN SCHIZOPHRENIA AND MOOD DISORDERS. 19 TABLE 2.2: GENE EXPRESSION STUDIES INTO PERIPHERAL BLOOD TISSUES IN SCHIZOPHRENIA AND BIPOLAR DISORDER. 27 TABLE 3.1: CHARACTERISTICS OF THE SCHIZOPHRENIA PATIENTS AND HEALTHY VOLUNTEERS 31 TABLE 3.2: DOWN REGULATED GENES IN SCHIZOPHRENIA PATIENTS BEFORE TREATMENT AND SIGNIFICANTLY UP REGULATED IN RESPONSE TO ANTIPSYCHOTIC DRUG TREATMENT BY ARRAY ANALYSIS (SZ = SCHIZOPHRENIA AND C = CONTROL; * - P VALUES DETERMINED USING SAM ANALYSIS). 39 TABLE 3.3: DIRECT COMPARISON OF GENE EXPRESSION IN PBMCS TAKEN FROM PATIENTS WITH SCHIZOPHRENIA BEFORE AND THEN AFTER ANTIPSYCHOTIC DRUG TREATMENT. (*SZ = SCHIZOPHRENIA AND C = CONTROL; ** - P VALUES DETERMINED USING SAM ANALYSIS) 47 TABLE 3.4: TOP RANKED BIOLOGICAL FUNCTIONS OVERREPRESENTED BY GENES DYSREGULATED IN SCHIZOPHRENIA BEFORE AND AFTER ANTIPSYCHOTIC DRUG TREATMENT. 48 TABLE 3.5: INGENUITY CANONICAL PATHWAY ANALYSIS. 48 TABLE 5.1: SPEARMAN CORRELATION STATISTICS FOR SIGNIFICANT (P<0.05 UNCORRECTED) ASSOCIATIONS OF GENE EXPRESSION WITH REDUCED MEAN GREY MATTER IN CORTICAL BRODMANN AREAS (BA) FOR PATIENTS PRIOR TO PHARMACOTHERAPY AND HEALTHY CONTROL SUBJECTS COMBINED 82

APPENDIX LIST OF PUBLICATIONS 115

LIST OF SUPPLEMENTARY TABLES

SUPPLEMENTARY TABLE 1: PRIMER SEQUENCES AND TAQMAN ASSAY DETAILS FOR QPCR 117

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SUPPLEMENTARY TABLE 2: DIFFERENTIALLY EXPRESSED GENES IN PBMCS FROM SCHIZOPHRENIA PATIENTS PRIOR TO ANTIPSYCHOTIC DRUG TREATMENT (CONTROL VERSUS BEFORE ANALYSIS) 118 SUPPLEMENTARY TABLE 3: DIFFERENTIALLY EXPRESSED GENES IN PBMCS FROM SCHIZOPHRENIA PATIENTS AFTER WITH ANTIPSYCHOTIC DRUG TREATMENT (CONTROL VERSUS AFTER ANALYSIS) 135 SUPPLEMENTARY TABLE 4:DIFFERENTIALLY EXPRESSED GENES IN PBMCS FROM SCHIZOPHRENIA PATIENTS THAT DID NOT CHANGE WITH ANTIPSYCHOTIC DRUG TREATMENT 138 SUPPLEMENTARY TABLE 5: TOP RANKED BIOLOGICAL FUNCTIONS OVERREPRESENTED BY GENES DYSREGULATED IN SCHIZOPHRENIA BEFORE AND AFTER ANTIPSYCHOTIC DRUG TREATMENT 141 SUPPLEMENTARY TABLE 6: COMPARISON OF CORTICAL GREY MATTER MEASURES. 143

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Summary

With an estimated heritability of 80%, molecular genetic research into schizophrenia has remained inconclusive. Recent large-scale genome-wide association studies only identified a small number of susceptibility genes with individually very small effect sizes. However, the variable expression of the phenotype is not well captured in diagnosis-based research as well as when assuming a “heterogenic risk model” (as apposed to a monogenic or polygenic model). Hence, the expression of susceptibility genes in response to environmental factors in concert with other disease promoting or protecting genes has increasingly attracted attention. Over the past decade, microarray gene expression research has been applied to post mortem brain tissue, peripheral tissues, and animal models of schizophrenia. Altered gene expression has been linked to presynaptic function, signalling, myelination, neural migration, cellular immune mechanisms, and response to oxidative stress consistent with multiple small effects of many individual genes. However, the majority of results are difficult to interpret due to small sample sizes (i.e. potential type-2 errors), confounding factors (i.e. medication effects) or lack of plausible neurobiological theory.

The current thesis investigated gene expression in peripheral blood mononuclear cells in a Sri Lankan cohort of drug treatment-naïve schizophrenia patients prior to introducing antipsychotic pharmacotherapy and again 6 to 8 weeks into treatment. Prior to introducing medication, 624 out of a total of 10,207 genes were found to be differently expressed (208 up- and 416 down-regulated) when compared to closely match healthy control subjects from the same communities. Differently expressed genes included new candidate genes of the disorder, such as AKT1, DISC1 and DGCR6. Patients significantly improved with antipsychotic pharmacotherapy of 200 mg/day chlorpromazine equivalents of risperidone or risperidone/haloperidol and abnormal expression was only confirmed for 106 genes (i.e. 6 up- and 100 down-regulated with 67 genes continued to show the same directional change in expression after treatment). These findings suggest a normalisation of the majority of altered gene expression

Summary 1

with treatment when compared to the more acute phase of illness at study entry. A pathway analysis of differentially expressed genes implicated dysregulation of biological functions, which are related to infectious diseases, inflammation and the immune system in patients with schizophrenia. Particularly AKT1 up-regulation prior to treatment was related to significant overrepresentation of altered genes in pathways that are triggered by growth factors and neurotrophic factors, but also respond to infections, including the EIF2 pathway, the mTOR and eIF4/p70S6K pathways.

The association of altered gene expression with cerebral grey matter pathology was then investigated with cortical pattern matching in high-resolution magnetic resonance imaging brain scans. The findings confirm widespread cerebral grey matter deficits in schizophrenia with grey matter deficits in the right dorsolateral prefrontal cortex as the strongest predictor of diagnosis. Symptom severity and treatment response were associated with regional grey matter deficits in older patients with a longer history of untreated illness, while significant structure/function associations with cognitive impairment in prefrontal and temporal cortices were found across all ages.

The expression of some of the candidate genes correlated with grey matter abnormalities. For instance, a higher expression of DGCR6 was associated with reduced grey matter in prefrontal, orbitofrontal, frontal, temporal, parietal and occipital areas. Moreover, DISC1 was found to be over-expressed in treatment- naïve patients while its expression normalised in the course of pharmacotherapy along with improving symptoms. DISC1 expression in patients also predicted grey matter deficits in right anterior cingulate cortex – a brain area strongly implicated in schizophrenia – along with grey matter deficits in various other associated brain regions.

While the results are promising and demonstrating the feasibility of linking in vivo peripheral schizophrenia candidate gene expression to in vivo measures of cerebral grey matter brain pathology, the findings should be interpreted with caution given the small sample size and when assuming the heterogeneous phenotype of the disorder.

Summary 2

1. Introduction

1.1. Historical overview

Emil Kraepelin pioneered this concept of schizophrenia about 130 years ago (as reviewed in Ion RM and Beer MD, 2002). Kraepelin originally termed the disorder ‘dementia praecox’ (meaning ‘early onset dementia’) by clustering or grouping symptoms in patients he saw in the mental asylums that existed at the time. He pioneered to classify schizophrenia subtypes and called them ‘clinical forms’ which reflect disease clusters of characteristic symptoms by taking into account the actual clinical presentation and the course of illness.

According to his original work, clinical forms delineated as dementia praecox simplex, hebephrenia, depressive dementia praecox, circular dementia praecox, agitated dementia praecox, periodic dementia praecox, catatonia, and paranoid dementia. Kraepelin, however, also recognised that there is no defining pathognomonic symptom of dementia praecox rather than a pattern of symptoms with variable expression amongst affected individuals. Well ahead of time he described characteristic cognitive deficits (such as irritability and alogia) in affected individuals that appeared to be present in variable expressions in all types of Schizophrenias.

Kraepelin further noted that the disorder is largely progressive (i.e. as in dementia) and postulated a distinct underlying cerebral neuropathology. With the limited research facilities at that time, however, he was unable to find a definite neuropathology.

One of the important observations of Kraepelin is the recognition of ‘dementia praecox’ as a distinct disease entity, which he clearly distinguished from ‘manic- depressive insanity’ based on the course of illness, prognosis as well as heredity. His original concepts are still valid in modern psychiatry although the terminology has been changed to schizophrenia and bipolar affective disorder, respectively (Fischer BA and Carpenter WT Jr, 2009).

Introduction 3

However, some current authors (Shao L and Vawter MP. 2008; Fischer BA and Carpenter WT Jr, 2009; Craddock N and Owen MJ, 2010) challenge that Kraepelin’s dichotomy should be revised as recent research suggest shared genetic aetiology of both schizophrenia and bipolar affective psychosis. This is on the basis of evidence from molecular genetics (linkage and association studies), neuropathological evidence and various longitudinal studies on phenomenology, cognitive deficits and brain morphometry of these complex major psychiatric disorders.

It was later Eugen Bleuler who introduced the term ‘schizophrenia’ with an emphasis on the various subtypes or “group of schizophrenias” (as reviewed in Shenton ME et al., 2001; Modestin J. et al., 2003) consisting of paranoid schizophrenia, catatonia, hebephrenia and simple schizophrenia.

Some of the Bleuler’s terminology is still present in today’s disease classification systems, such as the International Classification of Diseases version 10 (ICD – 10. 1994) and the Diagnostic Statistical Manual of Mental Disorders (DSM – IV. 1994). However, a recent re-examination of Bleuler’s “original samples’ data” by using DSM and ICD systems revealed some controversial diagnoses on some of his originally noted sub-types (Modestin J. et al., 2003).

Later, Kurt Schneider a German psychiatrist revisited Kraepelin and Bleuler’s phenomenology in the 1950s to describe so called “first rank symptoms” of schizophrenia. First rank symptoms include thought insertion, thought withdrawal, thought broadcasting, third person auditory hallucinations, voices giving running commentary about the patient’s actions, audible thoughts, somatic hallucinations, delusional perception and passivity feeling and Schneider argued that these symptoms distinguish schizophrenia from other forms of psychoses (Chandrasena R. 1983).

More recently, Karl Leonhard grouped the schizophrenia phenotype into three broader clusters and named it the group of systematic ‘schizophrenias’, the group of unsystematic or atypical schizophrenias and the group of cycloid psychoses (Bovi A. 1967; Perris C. 1990).

Introduction 4

1.2. Current diagnostic definition

The diagnosis of schizophrenia continues to rely solely on clinical phenomenology (DeLisi LE. 2008). This covers a broad range of heterogeneity in the signs and symptoms of the clinical presentation and is therefore commonly classified into phenotypically distinct subtypes. Contemporary phenomenology emphasises positive (acute), negative (chronic) and cognitive symptom dimensions (Lenzenweger MF. et al. 1996; Sivagnansundaram S. et al. 2003; Jablensky A. 2006 b). These symptoms may also show a quite variable expression during the course of illness. Symptom expression may also depend on the level of insight, stage of psychological disability and cognitive impairment, such as disordered thinking, impaired concentration, erratic behaviour, with substantial impact on social and occupational functioning (Sevy S. et al., 2004). Other factors include difficulty in rapport building, impaired verbal communication, drug and alcohol abuse and facts related to socio-economic and cultural background of the patient.

The DSM-IV defines schizophrenia by disturbances of perception, abnormal thought process and content, impaired affect, abnormal or even bizarre behaviour, and impaired communication lasting longer than 6 months. The manual defines five clinical subtypes of schizophrenia based on specific clinical symptom patterns:

(a) Paranoid subtype: The subject meets the basic criteria for Schizophrenia. Usually the patient is preoccupied with delusions or frequent auditory hallucinations. For this clinical subtype, disorganised speech and behaviour, inappropriate or flat affect or catatonic symptoms are less prominent.

(b) Disorganised subtype: These patients also meet the basic criteria for schizophrenia but also present with disorganised behaviour and speech, flat or inappropriate affect, without meeting the diagnostic criteria for catatonic subtype.

Introduction 5

(c) Catatonic subtype: In addition to the basic criteria for schizophrenia, these patients should present with at least two catatonic symptoms, such as stupor or immobility (i.e. catalepsy or waxy flexibility), aimless hyperactivity, mutism, marked negativism, peculiar behaviour (i.e. such as posturing, stereotypes, mannerisms or grimacing) and echolalia or echopraxia.

(d) Undifferentiated subtype: DSM–IV defines this as a subtype that is not fully meeting the criteria for the other phenomenological types (i.e. paranoid, disorganised or catatonic subtypes, respectively).

(e) Residual subtype: These patients have met the criteria for the paranoid, disorganised, catatonic or undifferentiated subtype at a time but do not meet any longer the corresponding criteria. These patients, however, may still present with residual symptoms, such as flattened affect, reduced speech output or lack of volition, or an attenuated form of at least two characteristic symptoms of schizophrenia, such as odd beliefs (i.e. delusions), distorted perceptions (i.e. hallucinations), odd speech (i.e. disorganized speech) or peculiarities in their behaviour (i.e. disorganized behaviour).

1.3. Heritability and “candidate genes” of schizophrenia

Schizophrenia is a disorder of low prevalence, affecting 0.7% to 1.4% of the population in a wide variety of geographic regions across the world (Jablensky A. et al., 1992). The heritability is high and estimated to contribute up to 83% in monozygotic twins (Cannon TD. et al., 1998; Sivagnansundaram S. et al., 2003; Lewis CM. et al., 2003; Jablensky A. 2006a). However, the molecular signatures of this disorder have not been identified despite large-scale genome-wide association studies involving up to 27,000 individuals (International Schizophrenia Consortium, 2009; Shi J. et al., 2009; Stefansson H. et al., 2009).

The largest genome-wide association study of schizophrenia was recently reported using 21,856 individuals of European ancestry in a discovery phase and 29,839 independent individuals in the replication phase (Ripke S. et al., 2011). Genome-wide significance was shown for association of seven loci in

Introduction 6

schizophrenia, five of which are new (1p21.3, 2q32.3, 8p23.2, 8q21.3 and 10q24.32-q24.33) and two of which had been previously reported (6p21.32-p22.1 and 18q21.2) (Ripke S. et al., 2011). This study also identified a strong association of the rs1625579 single nucleotide polymorphism within an intron of a putative primary transcript for miR137, a microRNA known to regulate neuronal development (Ripke S. et al., 2011).

1.4. Biomarkers and “endophenotypes” of the disorder

A main obstacle in understanding the genetics of the disorder is the clinical phenomenology of schizophrenia, which overlaps with other psychotic, affective and personality disorders. Hence, a heterogenic risk model as opposed to a monogenic or polygenic model probably accounts best for the complex clinical syndromes and diagnostic subtypes of schizophrenia as well as the variable expression of symptoms during the course of illness (Galter D. et al., 2007; Jablensky A. 1995).

For example, mismatch negativity, an event related potential that is a measure of attention, shows a consistent and robust deficit in schizophrenia but not in other psychoses such as bipolar I disorder (Umbricht D. and Krljes S. (2005); Salisbury DF. et al., 2002 and McCarley RW. et al., 1991; O'Donnell BF. et al., 2004 respectively). Furthermore, a pattern of regional grey and white matter pathology is emerging and is also associated with the clinical, neurocognitive and pathophysiological features of schizophrenia (Andreasen NC et al., 1997; Tsuang MT et al., 2005 and see the review below in Chapter Two).

Biomarkers or “endophenotypes” of the disorder have been proposed to facilitate more targeted neurogenetic research (McGuffin P. et al., 1987). Identification of biomarkers seems to be more promising in the areas of neurocognition, neurophysiology and brain imaging. Promising research revealed specific patterns of regional grey and white matter pathology that is also associated with the defining clinical, neurocognitive and pathophysiological signatures of this brain disorder (Andreasen NC et al., 2007; Tsuang MT et al., 2005).

Introduction 7

1.5. Capturing gene x gene and gene x environment interaction

Within a neurodevelopmental framework, the evolving phenotype is dependent on environmental factors, which impact on the normal trajectory of brain development. This “gene by environment interaction” is best captured by gene expression; that is the transcription of a gene’s DNA information into an mRNA copy to direct the synthesis of proteins via the genetic code. The transcription process itself reflects a major cellular mechanism of adaptation of cell function in its immediate environment at any given point in time. This also implies that gene expression data can capture mal-adaptation by identifying a signature of over and/or under-expressed quantities of RNA that is associated with a disease. Hence studies into gene expression have gained substantial interests in schizophrenia research since it holds the promise of identifying a signature of a pathological mechanism at the molecular level.

Introduction of gene expression profiling at the end of the 20th century lead to some great advances in the identification of disease that was not previously possible. Much of the success was seen in cancer where for example tumours originating from the same tissue that lead to completely distinct outcomes in different patients but were not distinguished by the available pathological examination techniques, could be identified by profiling the expression of mRNA in the biopsied tissue (Reis-Filho JS and Pusztai L. 2011; Li X et al., 2011; Ho DW et al., 2012).

This study aimed to investigate gene expression in patients with schizophrenia before and then after treatment with antipsychotic medication and to determine the utility of using gene expression profiling from peripheral blood mononuclear cells (PBMCs) for the purposes of assisting with the characterization and sub- typing of the schizophrenia syndrome.

Introduction 8

Whilst many studies have reported gene expression changes in several brain regions in subjects with schizophrenia (Mirnics K et al., 2000; Hemby SE et al., 2002, Hakak Y et al., 2001, Weidenhofer J et al., 2006, Bowden NA et al., 2008), the almost impossible access to biopsied brain tissue in schizophrenia patients makes this source of tissue unrealistic for the discovery of a molecular signature that may assist with unravelling the heterogeneity of this syndrome. In this regard, PBMC can be easily collected from patients who are rigorously phenotyped and followed up longitudinally with gene expression analyses to identify signatures that indicate subtypes and their prognosis, as well as treatment responses. (Kennedy JL. 1996; Tsuang MT et al., 2005; Yao Y et al., 2008; Huang N et al., 2008; Bartolomucci A et al., 2010).

1.6. Microarray gene chip technology

With recent technological advances it is now possible to analyse the expression of the entire genome (or “transcriptome”) in a single experiment. Historically, research has been limited to small numbers of genes when using northern blot, in situ hybridisation, or polymerase chain reaction (PCR) methods (Deb P et al., 1999; Yurov YB et al., 2001). On the other hand, “microarrays” provide a platform for the simultaneous screening of large number of genes from a variety of in vivo and post mortem tissues. This can extend up to the examination of gene expression in a single cell.

Microarrays were originally developed from “DNA microarrays” containing complementary DNAs (cDNAs) for particular genes spotted onto nylon membranes that were then used in combination with radioactive probes to determine the levels of mRNA for those genes.

In the mid 90’s, cDNAs were spotted onto glass microscope slides and this, coupled with fluorescent probes and improvements in scanning technologies, saw the development of DNA microarrays that significantly increased the number of genes that could be analysed in one single experiment. Over the past decade these technologies rapidly developed and expanded to allow the detection and characterisation of many genetic features including single nucleotide

Introduction 9

polymorphisms (SNPs), methylation status, alternative splicing of genes, the actual gene sequence, the actual number of gene copies referred to as copy number variations (CNVs; inherited, somatic, or de novo) as well as, for instance, loss of heterozygosity in cancer cells.

Most of the microarrays are produced by photolithographic in situ oligonucleotide synthesis on a solid surface (such as a glass microscope slide), microscopic beads and plastic or silicon based chips and all are sometimes referred to as “gene chips”. Today’s microarrays can feature a density >750,000 probes for genes or features per array. While this technology now provides an efficient way of screening vast numbers of genes in a single experiment, it also poses the risk of false positive findings. This is due to the larger number of tests, which are being performed without a prior hypothesis (i.e. each individual gene comparison constitutes a single statistical test). However, this method certainly has its place when aiming for discovery as long as the results are independently reproducible and the findings are critically evaluated with reference to other data (often post hoc) and in the context of existing theoretical models.

One of the early analytic approaches for DNA microarrays was based on computing dendrograms of pair-wise comparisons of gene expression (e.g. an individual index patient versus a matched healthy control subject) for a number of such paired comparisons within a single experiment. The resulting dendrogram represents the pattern of gene expression differences between patient and control data. Importantly, the measure taken here is the relative difference of gene expression in each individual pair as measured by a dye marker (e.g. patient sample red and control sample green) which indicates the relative expression difference within the red-green spectrum; that is a higher reading in the red spectrum indicates a higher expression in the patient sample relative to the matched control sample by x-fold and vice versa. Pooled samples (i.e. establishing a relative measure for the average of a sample) has also been used, however, lacks some control over potentially confounding variables, such as clinical features, duration of illness, age, gender, medication status, post mortem interval, etc.,

Introduction 10

which can be used as an independent variable (e.g. old versus young, male versus female, etc.) when computing dendrograms.

Earlier methods include Serial Analysis of Gene Expression or SAGE introduced by Velculescu VE et al., (1995) to describe the transcriptome in diseases like cancer in a wide range of organisms. More recent technological advances also allow for absolute expression measures, which, in turn, can be used to calculate correlations. For instance, when comparing U133plus2.0 Affymetrix microarray gene expression data of renal allograft biopsies with reverse transcriptase PCR (RT- PCR) data, Allanach K et al., (2008) reported significant correlations of most RT- PCR probes with the corresponding microarray data. Nevertheless, microarray data should always be confirmed by another method, such as real-time RT-PCR, which offers a well-accepted quantitative measure of gene expression.

With the availability of different suppliers of microarray chips (e.g. Affymetrix, Illumina, Agilent, etc.), inter-laboratory and cross-platform reliability testing has become possible. Such testing suggests excellent reliability between laboratories and chips (Mao X, et al., 2007). However, when comparing the two high-resolution microarray platforms using DNA copy number variance as an indicator on a collection of established human melanoma cell lines, Greshock J et al., (2007) reported Affymetrix's single nucleotide polymorphism microarrays offered better detection of dose-dependent changes in gene expression (also replicated by Mao X et al., 2007) while Agilent's 60-mer oligonucleotide microarray with probe design optimised for genomic hybridization seems to offer higher sensitivity and specificity (Greshock J et al., 2007; http: www.chem.agilent.com/Library/.../5988- 5063EN-72_WebReady.pdf).

As such, microarray gene chip technology has matured and developed into an important tool of molecular genetic research. The technology is constantly improving as well as the analytical tools capable of identifying the potential genetic signatures in large data sets. Notwithstanding, critical evaluation of each individual finding is still paramount, last but not least due to the statistical pitfalls of potential type-2 errors.

Introduction 11

2. Review of gene expression findings in schizophrenia

2.1. Gene expression findings from post mortem brains

An important target of investigations into gene expression is post mortem brain tissue, but one potential confounding factor is RNA degradation. To address this, Catts SV et al., (2005) quantified RNA over various post mortem intervals and observed good stability within the first 6 hours and acceptable RNA levels for up to 48 hours post mortem but a significant decline thereafter. Hence post-mortem studies are now generally required to cite the RNA Integrity Number (RIN) as an index for potential RNA degradation. Brain pH is a significant factor that affects the quality of the RNA obtained from port mortem tissue (Bahn H et al., 2001; Halim ND et al., 2008). Hence post mortem intervals and tissue pH are generally reported and commonly matched when comparing patient and control data. Other commonly employed matching criteria are age, gender, co-morbidity, and medication status. However, post-mortem interval should not be viewed as a constant measure across cases as the environment after death may vary across cases with complex impact on levels of autolysis.

Most gene expression studies targeted grey matter while some studies investigated gene expression in immunohistochemically characterised single cells (Bahn H et al., 2001). The latter approach is of particular interest since it is specific to individual cell types (Pietersen CY et al., 2009).

Common brain regions include (1) the prefrontal cortex which is implicated in planning complex cognitive behaviours, decision making and social cognition (Goldman-Rakic PS and Selemon LD, 1997; Pomarol-Clotet E et al., 2010; Yang Y and Raine A, 2009); (2) the temporal lobes, including entorhinal cortex, which serves as the main interface between hippocampus and neocortex (Hargreaves E et al., 2005); and (3) the hippocampus which forms a part of the limbic system and plays an important role in memory function and spatial information processing

Review of gene expression findings in schizophrenia 12

(Hargreaves E et al., 2005; Harrison PJ, 2004). All three-brain regions have been implicated in schizophrenia (Pantazopoulos H et al., 2010; Sivagnansundaram S et al., 2003) as well as interconnecting thalamic relay nuclei. For instance, Chu TT et al., (2009) screened the genome of laser-captured micro dissected neurones of the primary thalamic relay nucleus of the dorsolateral prefrontal cortex and found IGF1-mTOR-, AKT-, RAS-, VEGF-, Wnt- and immune-related signalling, eIF2- and proteasome-related genes were uniquely deregulated in schizophrenia (N=15) compared to patients with major depressive or bipolar disorder or healthy control subjects (N=15 each).

Animal models suggest that down regulation of oligodendrocyte transcripts in medial prefrontal cortex – thereby affecting myelinisation – is associated with impaired executive functions that are commonly seen in patients with schizophrenia (Gregg JR et al., 2009). The prefrontal Brodmann’s area (BA) 9 was also the first human brain region to be investigated by microarray analysis by Mirnics K et al., (2000). This study consisted of two groups of schizophrenia and control subjects with a total n of 11 for each matched pair that were matched for age, gender, post mortem interval, brain pH and brain storage time. Gene expression was determined using microarrays containing over 7,000 features. The authors observed a significant decrease in expression of genes involved in prefrontal pre-synaptic function and confirmed using in situ hybridisation that the presynaptic function genes coding for N-ethylmaleimide-sensitive factor and synapsin II were consistently down regulated. The group later also reported that the transcript encoding regulator of G-protein signalling 4 (RGS4) was the most consistently and significantly decreased in the prefrontal cortex (BA9) in schizophrenia (N=6) when compared to post mortem tissues of matched controls (N=6) or patients diagnosed with major depressive disorder (N=10) (Mirnics K et al., 2001; Mirnics K and Lewis DA. 2001). Decreased RGS4 expression was also verified across three cortical areas of ten subjects with schizophrenia with quantitative in situ hybridization. These findings suggest multiple small but synergistic effects on gene expression, linked to impaired nerve terminal function in schizophrenia.

Review of gene expression findings in schizophrenia 13

Further support for this notion is derived from several findings such as the reduced expression of the synaptophysin gene in layer II stellate neurons in entorhinal cortex (Hemby SE et al., 2002). Synaptophysin is an integral membrane protein of small synaptic vesicles in the brain and endocrine cells and appears to be involved in organising membrane components and it therefore is easy to see how these changes could cause dysregulation of the synapse in schizophrenia. More recently, consistent expression changes were also identified in gene sets associated with synaptic vesicle recycling, transmitter release and cytoskeleton dynamics by Maycox PR et al., (2009) in the anterior prefrontal cortex tissue of BA10 when using high-density microarrays (30,000 features) to investigate two large schizophrenia cohorts.

A year after the first microarray study in schizophrenia, reduced expression of five oligodendrocyte and myelin-related genes were identified by Hakak Y et al., (2001) in prefrontal cortex (BA46) in a cohort of 12 schizophrenia patients versus matched controls. Altered expression of myelination-related genes have also been reported in other brain areas (Tkachev D et al., 2007) such as CNP, MAG, OLIG2, and ErbB3 which were down-regulated throughout neocortex, hippocampus, caudate nucleus, and putamen (Katsel P et al., 2005). These findings are of particular relevance to schizophrenia, given that myelination is a key process of brain maturation.

In a well-controlled study of 14 schizophrenia brains, Arion D et al., (2007) identified over-expression of immune chaperone function-related genes in the same cortical region, BA 46). The authors used a study-specific DNA microarray platform with long oligonucleotides and multiple probes and replicates consisting of 1,800 genes. The authors carefully matched their 14 patients on gender, age, post-mortem interval, brain tissue pH, RNA integrity number, and tissue storage time with an equal number of healthy control subjects. Another study indicated higher activity of histone de-actylase 1 in BA10 and 46 of prefrontal cortex suggestive of transcriptional repression due to increased lysine de-acetylisation in histone and non-histone proteins (Sharma et al., 2008). Kim et al., (2007) initially identified changed expression of 70 genes of which PCR confirmed a significant Review of gene expression findings in schizophrenia 14

down regulation of Phospholipid Scramblase 4 (a gene coding for a protein involved in apoptosis and blood coagulation) and Empty Spiracles Homolog 2-Transcription Factor, which is involved in neurodevelopment. However, these expression changes were only observed in a cohort of suicide completers.

In a cross-study analysis of 7 gene expression microarrays, Choi KH et al., (2008) identified 110 differentially expressed transcripts across various brain regions in 163 individuals with and without psychosis. In the dorsolateral prefrontal cortex, PCR confirmed up-regulated Metallothionein genes (which code for proteins with an affinity to heavy metals and glucocorticoids) while neuropeptide-related genes were found to be down regulated. In an interesting study Narayan S et al., (2008) investigated duration of illness effects on gene expression in BA46 in three cohorts of matched pairs of schizophrenia and control subjects. The first cohort had duration of illness of less than 4 years (n = 8 matched pairs), the second cohort between 7-18 years (n = 14 matched pairs) and the third of more than 28 years (n = 8 matched pairs). Early in the progression of schizophrenia, dysfunction to genes involved in gene transcription and RNA processing, vesicle-mediated transport functions, and metal ion binding was identified whilst long-term illness was associated with inflammation and immune responses amongst other various biological functions (Kasai K et al., 2003). This suggested that gene expression changes as the disease progresses.

Tang B et al., (2009) reported that normal aging was significantly linked to abnormalities in pathways related to synaptic function, cell cycle/DNA damage and apoptosis. In contrast, aging in schizophrenia was significantly associated with fatty acid and steroid metabolism, but not with those functions associated with normal aging when investigating post-mortem BA46 tissue from 29 schizophrenia and 30 healthy subjects aged from 19 to 81 years. Investigating the same brain region in 27 schizophrenia subjects and 27 matched healthy controls, Narayan et al., (2009) found differential expression of genes particularly related to glycosphingolipid/sphingolipid metabolism and N- and O-linked glycan biosynthesis in schizophrenia. Expression decreases of seven genes associated with these pathways (i.e., UGT8, SGPP1, GALC, B4GALT6, SPTLC2, ASAH1, and Review of gene expression findings in schizophrenia 15

GAL3ST1) were validated by quantitative PCR in schizophrenia subjects with short- term illness while only one of these genes (i.e., SPTLC2) was differentially expressed in chronic schizophrenia. Moreover, the expression of five of these genes was also significantly positively correlated with age in schizophrenia but not in control subjects. The authors concluded that a disruption of the sphingolipid metabolism in the early phase of illness could result in widespread downstream effects involving myelination and oligodendrocyte function while the age-related effects may represent an adaptive response to disease progression or pharmacotherapy. The latter notion, however, was not supported by their data showing no correlation of gene expression levels with medication doses.

Torkamani A et al., (2010) investigated post mortem BA9 and BA46 tissue from a large sample of 101 subjects and also confirmed differential age-related gene expression. In particular, the expression of genes related to developmental processes (i.e., neurite outgrowth, neuronal differentiation, and dopamine-related cellular signalling) decreased with aging in normal subjects but not in schizophrenia subjects.

Temporal lobe neuropathology appears to be associated with some of the positive symptoms of schizophrenia (e.g. Sivagnansundaram S et al., 2003; Kasai et al., 2003), thus making the temporal cortex another prime target of molecular genetic research along with the prefrontal cortex. To this end, Hemby et al., (2002) reported decreased expression of genes associated with G-protein, glutamate and N-methyl-D-aspartate (NMDA) function in entorhinal cortex from post-mortem tissue. Indeed, NMDA dysfunction is highly relevant to schizophrenia psychopathology by mediating positive and negative symptoms of the disorder (Jentsch JD and Roth RH, 1999; Sivagnansundaram S et al., 2003). Aston et al., (2004) explored expression of 12,000 genes in post mortem middle temporal gyrus tissue that was collected from the brains of 12 schizophrenia patients and 14 matched healthy control subjects. The authors observed significantly reduced expression of genes associated with myelination (i.e. MAG, PLLP [TM4SF11], PLP1, and ERBB3). They also observed altered expression of genes involved in neurodevelopment (i.e. TRAF4, Neurod1, and Histone Deacetylase 3), the circadian Review of gene expression findings in schizophrenia 16

pacemaker gene (PER1) and several other regulator genes of chromatin function and signalling mechanisms. Also, genes related to immune function were recently reported to be differentially expressed in post mortem BA22 tissue of schizophrenia patients (Schmitt A et al., 2011).

More recently, Bowden NA et al., (2008) identified altered expression of genes involved in neurotransmission (linked to pre-synaptic functions), myelination and neurodevelopment when investigating post-mortem brain tissue derived from superior temporal gyrus of seven schizophrenia patients versus seven closely matched healthy control subjects. Interestingly, similarly altered expression profiles were also found in peripheral blood lymphocytes of schizophrenia patients (Bowden NA et al., 2006; see also below).

Subcortical brain structures that have been investigated that are implicated in schizophrenia include the hippocampus and amygdala. Along with volume reduction and impaired glutamate neurotransmission, impaired function of the hippocampus has been linked to psychopathology and cognitive impairment in schizophrenia (e.g. Goff DC and Coyle JT, 2001; Heckers S, 2001; Phillips ML et al., 2003). Chung C et al., (2003) observed increased expression of chondrex (or YKL- 40 which codes extracellular matrix glycoprotein involved in cell growth and migration), histamine-releasing factor, HERC2, and heat-shock 70. The histamine- releasing factor gene has been linked to negative symptoms, impaired learning and memory deficits in schizophrenia (Chung C et al., 2003; Goff DC and Coyle JT, 2001) but was not confirmed by real-time RT-PCR and neither were HERC2 and Heat-Shock 70. Increased expression of chondrex, on the other hand, was confirmed suggesting altered neuronal migration during neurodevelopment. In the amygdala, up-regulation of genes involved in presynaptic vesicle release (i.e. Piccolo, RIMS2, and RIMS3) in the cytomatrix active zone as well as changes in genes involved in neurotransmission and myelination were observed in subjects with schizophrenia (Weidenhofer J et al., 2006). In their follow up study, Weidenhofer J et al., (2009) showed that antipsychotic drug treatment was not likely to be the cause of the up-regulation of the genes involved in presynaptic release. Review of gene expression findings in schizophrenia 17

With hundreds of changes in gene expression being observed in different brain regions in schizophrenia, researchers more recently started to look for the mechanism(s) that might cause such widespread dysregulation of gene expression. One possibility that is gaining momentum is evidence for increased global expression of microRNAs, which are involved in the regulation of translation of schizophrenia-related mRNA in superior temporal gyrus and dorsolateral prefrontal cortex in schizophrenia (Beveridge NJ et al., 2010). Thus, one or several microRNAs could be causing major changes in gene expression in the brain in schizophrenia.

So far, the review has summarised the evidence for an array of altered gene expression in various brain regions implicated in schizophrenia (see Table 2.1). Taking these post-mortem findings together, these genes are likely to affect synaptic processes such as vesicle release and neurotransmitter signalling (reviewed by Eastwood SL and Harrison PJ, 2001) as well as neurodevelopmental processes such as myelination and neural migration. These are all very plausible changes that could lead to schizophrenia. However, post-mortem brain research is limited by the availability of suitable tissue, thus usually resulting in small sample sizes, and also relies on the quality and accuracy of existing medical records of deceased tissue donors. The use of peripheral tissues and cell samples for sources of RNA like blood has been investigated as an alternative to post mortem brain tissue. The choice of peripheral tissue may address some of these limitations but also raises the question of whether non-neural cells can inform about the brain (Matigian NA et al., 2008).

Review of gene expression findings in schizophrenia 18

Table 2.1: Gene expression studies into post mortem brain tissue in schizophrenia and mood disorders. Publication Source of Tissue Sample Characteristics Key Findings Mirnics et al., Prefrontal cortex (BA 9) 11 matched pairs of N-ethylmaleimide sensitive factor 2000 schizophrenia and control and synapsin II down-regulated subjects

Mirnics et al., Prefrontal cortex (BA 9) 6 schizophrenia patients, 6 Decreased transcript encoding 2001 matched controls and 10 regulator of G-protein signalling 4 healthy controls; replication gene in schizophrenia in another 10 schizophrenia patients

Hakak et al., Prefrontal cortex (BA 12 medicated and 4 Reduced expression of five 2001 46) unmediated schizophrenia oligodendrocyte and myelin-related patients and 12 controls genes

Hemby et al., Entorhinal cortex 8 schizophrenia patients and Reduced expression of synaptophysin 2002 (BA28/34) 9 matched controls gene in layer II stellate neurons

Chung et al., Hippocampus 7 schizophrenia patients and Increased expression of the chondrex 2003 8 controls gene in schizophrenia

Aston et al., Temporal cortex (BA21) 12 schizophrenia and 14 Significantly reduced expression of 2004 matched controls genes associated with myelination (i.e. MAG, PLLP [TM4SF11], PLP1, and ERBB3), neurodevelopment (i.e. TRAF4, Neurod1, and histone deacetylase 3), and the circadian pacemaker gene (PER1)

Katsel et al., Prefrontal, temporal 13 schizophrenia patients Significantly down-regulated 2005 and cingulate cortex, and 13 controls expression of 29 genes associated hippocampus, caudate with oligodendrocyte function and nucleus, putamen (BA8, myelination, particularly in 10, 44, 46, 23/31, hippocampus and cingulate cortex 24/32, 20, 21, 22, 36/28, 7 & 17)

Weidenhofer Amygdala 11 matched pairs of Up-regulated expression of genes in et al., 2006 schizophrenia patients and schizophrenia involved in the controls cytomatrix active zone, regulating membrane exocytosis 2, Regulating membrane exocytosis 3 and piccolo; in vitro analysis suggests antipsychotic drug treatment was unlikely to have caused the changes in RIMS2, RIMS3 and piccolo expression observed in the amygdala in schizophrenia (Weidenhofer et al., 2009)

Review of gene expression findings in schizophrenia 19

Tkachev et Prefrontal cortex (BA9) 15 matched pairs of Up-regulation of N-acetylaspartate- al., 2007 schizophrenia patients and related genes (ASPA, FOLH1, PGCP, controls and GRM3) in schizophrenia resulting in dysregulation of various enzymes involved in N-acetylaspartate metabolism

Arion et al., Prefrontal cortex 14 matched pairs of Expression changes in synaptic, 2007 (BA46) schizophrenia patients and oligodendrocyte, and signal controls transduction genes as well as immune/chaperone transcript up- regulation of SERPINA3, IFITM1, IFITM2, IFITM3, CHI3L1, MT2A, CD14, HSPB1, HSPA1B, and HSPA1A in the schizophrenia sample

Benes et al., Hippocampus 7 schizophrenia, 7 bipolar IL-1beta, (GRIK2/3), TGF-beta2, TGF- 2007 and 7 matched control betaR1, histone deacetylase 1 subjects (HDAC1), death associated protein (DAXX), and cyclin D2 (CCND2) were significantly up-regulated in schizophrenia, whereas PAX5, Runx2, LEF1, TLE1, and CCND2 were significantly down-regulated in bipolar disorder

Bowden et Temporal cortex (BA21) 7 schizophrenia patients and Altered expression of genes involved al., 2008 7 matched controls in neurotransmission, neurodevelopment, and presynaptic function and confirming previously reported changes in schizophrenia for PCLO, Ascbg1, FXYD1, and RGS4

Sharma et al., Prefrontal cortex 16 schizophrenia, 3 Confirming increased expression of 2008 schizoaffective, 18 bipolar histone deactylase 1 expression in and 27 matched controls schizophrenia

Choi et al., Prefrontal cortex Cross-study analysis of 7 Up-regulated metallothioneins 2008 (BA46/10, 8/9 & 6) gene expression microarrays (MT1E, MT1F, MT1H, MT1K, MT1X, that include both psychosis MT2A and MT3) and down-regulated and non-psychosis subjects neuropeptide (SST, TAC1 and NPY) in of >400 microarray samples BA46 of 56 psychosis versus 49 non- (163 individual subjects) on psychosis patients 3 different Affymetrix microarray platforms

Review of gene expression findings in schizophrenia 20

Narayan et Prefrontal cortex 26 schizophrenia patients Most pronounced altered gene al., 2008 (BA46) and 29 matched controls expression in the early phase of illness (<5 years since diagnosis of schizophrenia) affecting metal ion binding, RNA processing and vesicle- mediated transport, whereas long- term illness was associated with genes involved in inflammation, stimulus-response and immune functions; SAMSN1, CDC42BPB, DSC2 and PTPRE were consistently deregulated in schizophrenia

Maycox et al., Prefrontal cortex (BA9 28 schizophrenia patients 49 genes associated with synaptic 2009 & 10) and 23 matched controls vesicle recycling, transmitter release from two cohorts and cytoskeletal dynamics show same direction of significant disease- related change in expression across the two cohorts of schizophrenia patients

Tang et al., Prefrontal cortex 29 schizophrenia patients Aging in schizophrenia was 2009 (BA46) and 30 matched controls significantly associated with fatty acid and steroid metabolism, but not with those functions associated with normal aging

Narayan et Prefrontal cortex 27 schizophrenia patients Decreased expression of UGT8, al., 2009 (BA46) and 27 matched controls SGPP1, GALC, B4GALT6, SPTLC2, ASAH1, and GAL3ST1 in schizophrenia with short term illness; only SPTLC2 confirmed for chronic disease state

Torkamani et Prefrontal cortex (BA46 30 schizophrenia patients No normal age-related decreases in al., 2010 & 9) and 30 matched controls gene expression related to (BA46); 19 schizophrenia developmental processes, including patients and 26 matched neurite outgrowth, neuronal controls (BA9) differentiation, and dopamine- related cellular signalling in schizophrenia

Beveridge et Prefrontal (BA9) and 36 schizophrenia patients Significant schizophrenia-associated al., 2010 temporal cortex (BA21) and 30 matched controls increase in global microRNA miR-15 family expression in both brain regions targeting previously implicated schizophrenia-linked genes (i.e. RGS4, GRM7, GRIN3A, HTR2A, RELN, VSNL1, DLG4, DRD1 and PLXNA2)

Schmitt et al., Temporal cortex (BA22) 10 schizophrenia patients Significant down regulation of 2011 and 10 matched controls immune-modulator genes (i.e., PTGER4, ILS, EDG3, ILIA, LPL, and CFD) in schizophrenia

Review of gene expression findings in schizophrenia 21

2.2. Gene expression findings from peripheral tissues

In contrast to post-mortem brain tissue, peripheral blood mononuclear cells (PBMC) can be easily collected from large cohorts for gene expression studies. It also has the advantage that patients can be phenotyped and followed up longitudinally. PMBC gene expression studies have targeted putative risk factors for schizophrenia as well as potential biomarkers of treatment response and prognosis (Bartolomucci A et al., 2010; Huang N et al., 2008; Kennedy JL, 1996; Tsuang MT et al., 2005; Yao Y et al., 2008). There have also been two types of studies conducted; those using fresh PBMCs and those that have used PBMCs transformed into immortalised lymphoblastoid cell lines (LCLs). These cell transformations, however, may introduce a confounding error on gene expression profiles, thus making it potentially difficult to separate genuine disease effects from disease by cell culture interactions.

In 2006, Vawter MP et al., (2006) reported gene expression profiles from LCLs obtained from a multiplex schizophrenia pedigree containing five individuals with schizophrenia and nine unaffected family members. A total of nine genes were altered by array analysis, but only two of those, the neuropeptide Y receptor Y1 gene and the human guanine nucleotide-binding regulatory protein Go-alpha, were confirmed to be significantly decreased by RT-PCR.

Following on from this were three studies of fresh PBMCs. The first used PBMCs from 28 controls, 30 individuals with schizophrenia and 16 with bipolar disorder (Tsuang MT et al., 2005). This study showed that gene expression profiling of RNA from PBMCs could distinguish between schizophrenia and bipolar disorder. This was supported by the second study using PBMCs from sib-pairs with schizophrenia (N = 66 siblings) and bipolar disorders (N = 10 siblings) that also detected more than 2,000 genes with altered expression in schizophrenia. Interestingly, some of these genes were from functional groups involved in processes such as neurotransmission and presynaptic function (Middleton FA et al., 2005). In the third study, Bowden NA et al., (2006) observed altered expression of

Review of gene expression findings in schizophrenia 22

18 brain-related genes in PMBC derived from 14 drug-treated schizophrenia patients versus matched healthy control subjects. These studies suggested that gene expression changes in PBMCs might reflect changes in the brain. Indeed, Sullivan PF et al., (2006) compared gene expression profiles from PBMCs and various brain tissues, including the prefrontal cortex, and found significant overlap in the expression of genes in the blood and the brain, which, however does not mean that these peripheral gene expression changes also translate into potential effects on brain function.

The use of fresh PBMCs still does not mitigate antipsychotic drug treatment. In order to do this, studies need to be conducted in treatment-naïve participants. To this end, when comparing PBMC gene expression of 13 drug treatment-naïve schizophrenia patients with matched healthy control volunteers, Zvara A et al., (2005) found PCR-confirmed increased expression of the inwardly rectifying potassium channel (Kir2.3) and dopamine-3 receptor genes. Craddock RM et al. (2007) also showed prominent transcript changes in genes involved with cell cycle machinery, intracellular signalling, oxidative stress, and metabolism in schizophrenia patients (compared to controls), in a sample of six schizophrenia patients versus six control subjects by using human whole-genome microarray on freshly isolated T cell-derived RNA. Subsequent chromosomal location analysis of altered genes showed susceptibility clusters at 1p36, 1q42 and 6p22, which had been previously implicated with schizophrenia (Sivagnansundaram S et al., 2003).

Another line of research has focused on undifferentiated cell lines or blast cells derived from olfactory neuroepithelium showing deregulated cell cycle and phosphatidylinositol signalling in schizophrenia and bipolar I disorder, respectively (McCurdy RD et al., 2006). More recently, Glatt SJ et al., (2009) investigated alternatively spliced genes as potential biomarker in peripheral blood mononuclear cells of 13 schizophrenia, nine bipolar and eight healthy control subjects. Their preliminary finding suggests significant interactions between diagnostic group and exon identity, with 33 genes showing differential splicing patterns between schizophrenia and bipolar disorder. The group also reported

Review of gene expression findings in schizophrenia 23

deregulated ubiquitin proteasome pathways for psychosis and bipolar diagnostic groups in two independent samples (Bousman CA et al., 2010).

This line of research, however, stills lacks consistency and has not produced exclusive biomarkers or other signatures that may assist with diagnosis (see Table 1.1). This is mostly due to the heterogeneous nature of schizophrenia and the small sample sizes, which are prone to false-positive results. The few findings reported here also show little overlap with post-mortem reports, making extrapolations to impaired brain functions difficult. Nevertheless, gene expression studies of peripheral tissues gain momentum in schizophrenia research but are very much dependent on systematic research of transient (e.g. medication effects) and more stable illness effects (i.e. associated with susceptibility genes). Importantly, the in vivo nature of this type of research allows for longitudinal tissue collection in parallel with observing the clinical phenotype.

2.3. Gene expression findings from animal models research

With the advances of genetically modified animals, the effects of genome alterations on gene expression have become an important area of schizophrenia research. For instance, 22q11.2 deletion syndrome probably accounts for approximately 1% of all schizophrenia patients (Basset and Chow, 2008) whereas up to 35% of the individuals with the deletion are likely to develop schizophrenia.

Jurata LW et al., (2006) and Sivagnanasundaram S et al., (2007) investigated a mouse model by deleting 1 million base pair in a region of chromosome 16. The resulting Df1/+ mouse model carries a hemizygous deletion in a region, which corresponds to the human 22q11.2 region. The heterozygous (Df1/+) mice display deficits in prepulse inhibition, learning and memory, consistent with the schizophrenia phenotype (Blundell J et al., 2010).

Sivagnanasundaram S et al., (2007) identified 159 differentially expressed genes in heterozygous (Df1/+) mice, with 12 genes mapping to the deleted region and expressed in hippocampus. Moreover, 15 genes involve signal transduction, synaptic plasticity, neuronal differentiation, microtubule assembly, and an

Review of gene expression findings in schizophrenia 24

ubiquitin pathway relevant to hippocampus-mediated function as confirmed by PCR. Earlier Jurata LW et al., (2006) reported significantly reduced expression of COMT and PRODH genes in hippocampal dentate granule neurons.

Rodents with ventral hippocampus lesions have also been introduced as an animal model for schizophrenia (Lipska BK, 2004). Wong AH et al., (2005) showed that treatment with the antipsychotic haloperidol normalises the expression of those genes where the expression has been altered by the lesion. Intrapartum viral infection has also been linked to a higher prevalence of schizophrenia (Wright et al., 1995). When infecting mice with influenza virus at day 18 of pregnancy, altered expression of immunoglobulin domain (Ig), secreted semaphoring 3A (Sema3a), transferrin receptor 2 (Trfr2), and Vldlr were identified by microarray and confirmed with PCR in hippocampus, prefrontal cortex and cerebellum (Fatemi SH et al., 2008, 2009).

Isolation rearing has also been introduced to study the effects of an environmental insult on neurodevelopment. Genes related to GABA neurotransmission and synapse structure (Gabra4, Nsf, Syn2, and Dlgh1) were found to be over-expressed in prefrontal cortex of affected rat pups along with reduced extracellular glutamate levels and under-expression of transcripts related to glutamatergic transmission and synapse integrity in the adult animals (Murphy KJ et al., 2010).

Genetically modified animals have become an important tool when investigating the potential biological mechanisms of the pathogenesis of schizophrenia. Studying altered gene expression in these animal models is another logical step when considering the complexity of effects that are arising from single gene knockouts. However, due to the complex nature of the genetics of schizophrenia, single gene knockout animal models are highly unlikely to be representative of the schizophrenia syndrome. Thus to date, any animal model is limited to certain aspects of the disorder – such as individual aspects of micro or macro neuropathology or pathophysiology – with the complete clinical phenotype remaining an exclusive human phenomenon. Moreover, the effects of knocking out

Review of gene expression findings in schizophrenia 25

individual genes can also considerably differ between strains of mice; for instance, being potentially lethal in one strain and not having any discernable effect in another.

Moreover, gene expression changes have been reported across diagnostic categories. For instance, Kanazawa et al., (2008) reported up-regulated SELENBP1 in post-mortem dorsolateral prefrontal cortex of patients diagnosed with schizophrenia and bipolar disorder whereby the expression of SELENBP1 was significantly correlated with the presence of psychosis across diagnoses. Hence animal modelling may also need to take into account that syndrome-based diagnostic categories potentially represent a variable phenotype of a common pathological mechanism.

2.4. Importance of Gene expression research in previously untreated schizophrenia patients

This literature review mostly examined the data generated from patients previously treated with antipsychotic medication. That is one important drawback on their effort to identify a valid biomarker. It is currently accepted that the gene expression could be affected by medication treatment (Chen AC et al., 1998 and many studies described above). So, the quest to identify a ‘putative genetic signature’ or a ‘stable biomarker’ has become more difficult. In relation to this context this thesis work examines the gene expression in medication treatment naïve samples to avoid the medication confounds in data interpretation. This provided the theoretical background as well as the scientific justification to the experimental study, which is detailed in the next chapter.

Review of gene expression findings in schizophrenia 26

Table 2.2: Gene expression studies into peripheral blood tissues in schizophrenia and bipolar disorder. Publication Cell Type Sample Characteristics Key Findings Tsuang et al., Leukocytes 25 schizophrenia, 7 bipolar 8 putative biomarker genes (APOBEC3B, 2005 disorder and 10 healthy ADSS, ATM, CLC, CTBP1, DATF1, CXCL1, controls subjects with and S100A9) discriminated schizophrenia, micro-array RNA and real- bipolar disorder, and control samples time PCR data

Middleton et al., Leukocytes 33 gender and age- Most significant increases in 2005 matched discordant sibling schizophrenia-associated expression in pairs for schizophrenia and genes involved in immune and/or 5 pairs for bipolar disorder inflammatory function (e.g., CD14 antigen, chemokine receptor 1)

Zvara et al., Lymphocytes 13 drug-naïve/drug free Genes for dopamine receptor D2 (DRD2) 2005 schizophrenia patients and the inwardly rectifying potassium channel (Kir2.3) were over-expressed

Vawter et al., Lymphocytes Multiplex pedigree study of AGA expression levels showed suggestive 2006 5 haplotype-positive linkage to multiple markers in the individuals with haplotype; GALNT7expression levels schizophrenia 5 five showed linkage to regulatory loci at unaffected haplotype- 4q28.1 and in the haplotype region at negative controls 4q33−35.1

Bowden et al., Lymphocytes 14 schizophrenia and 14 18 genes with brain-related functions 2006 matched controls were altered in schizophrenia versus controls; 4 of which, endothelial differentiation gene 2 (Edg-2), ezrin- radixin-moesin phosphoprotein 50 (EBP50), myc-associated zinc finger protein (MAZ) and tumour necrosis factor receptor 2 (TNFR2), were confirmed by relative real-time PCR

McCurdy, 2006 Olfactory 10 schizophrenia, 8 bipolar Genes associated with vesicle-mediated neuroepithelium and 9 matched control transport, cell proliferation, subjects neurogenesis, apoptosis and in Iiositol phosphate metabolism/phosphatidylinositol signalling were most significantly deregulated in schizophrenia

Matigian et al., Lymphoblasts 16 schizophrenia patients No significantly different expression in 2008 and fibroblasts and 14 matched controls schizophrenia versus controls

Yao et al., 2008 Blood 30 first-admission Increased chemokine (C-X-C motif) mononuclear schizophrenia patients and legend 1 (CXCL1) expression in cells 26 healthy control subjects schizophrenia confirming (Tsuang et al., 2005)

Glatt et al., 2009 Blood 13 schizophrenia, 9 bipolar Significant interaction between mononuclear disorder, and 8 healthy diagnostic group and exon identity, with cells control subjects 33 genes showing differential splicing patterns between schizophrenia and bipolar disorder samples

Review of gene expression findings in schizophrenia 27

Bousman et al., Blood 24 schizophrenia, 23 Deregulated ubiquitin proteasome 2010 mononuclear bipolar disorder, 25 healthy pathways for psychosis and bipolar cells control subjects from two diagnostic groups in two independent independent samples samples which also correlate with (United States and Taiwan) positive symptoms (UBE2K, SIAH2, and USP2)

Review of gene expression findings in schizophrenia 28

3. Changes in PBMC gene expression following treatment with antipsychotic medication in treatment naïve patients with schizophrenia

3.1. Introduction

Microarray gene expression studies into schizophrenia are still inconsistent in their findings. While some findings have linked altered gene expression to presynaptic function, signalling, myelination, neural migration, cellular immune mechanisms, and response to oxidative stress, the majority of results are difficult to interpret due to small sample sizes (i.e. potential type-2 errors), confounding factors (i.e. medication effects) or lack of plausible neurobiological theory. By complementing structural genetic research into the origin of schizophrenia, microarray gene expression studies are likely to play an important role in the future when investigating gene/gene and gene/environment interactions.

Experimental animal models are particularly useful when studying changes of gene expression in genetically modified animals, including effects of environmental stressors on brain development. Post mortem brain research, on the other hand, relies on a scarce resource, which should not be wasted when alternative methods like in vivo tissue and animal models are available. Nevertheless, post-mortem human tissue research will remain an important prerequisite to validate findings of in vivo and animal models research.

However, longitudinal research into PMBC or other in vivo tissue holds the promise of identifying gene expression profiles which are associated with phenotype changes (e.g. acute versus chronic illness; prodromal versus established illness; etc.) and environmental factors (e.g. on and off medication treatment; contributing effects of substance abuse; etc.). In order to address the earlier discussed drug treatment confounds, the current thesis investigated gene expression in PMBCs in a Sri Lankan cohort of drug treatment-naïve schizophrenia patients prior to introducing antipsychotic pharmacotherapy and again 6 to 8

Changes in PBMC gene expression following treatment with antipsychotic medication 29 in treatment naïve patients with schizophrenia weeks into treatment. It is hypothesised that persistently altered gene expression in the acute as well as remitted phase of illness are more likely linked to candidate genes of the disorder whereas state-dependent gene expression should follow changes in symptoms and normalise with improving symptoms.

3.2. Methods and materials

3.2.1. Participant recruitment and cohort characterisation

Ethical and safety clearance for the study was obtained from human research ethics committees of University of Newcastle Australia, University of Sri Jayewardenepura Sri Lanka and National Institute of Mental Health (NIMH) Sri Lanka. The permit for transport of blood samples across Australian borders to the University of Newcastle was also obtained from Australian Quarantine and Inspection Service. Informed written consent was obtained from each participant.

Ten treatment-naïve schizophrenia patients meeting DSM-IV criteria for schizophrenia were recruited from the outpatients department at the NIMH during the period, January 2007 to July 2009. Previous antipsychotic pharmacotherapy, low global IQ (<70), a history of alcohol or illicit drug use, a neurological (e.g. epilepsy, traumatic brain injury) or chronic medical conditions (e.g. diabetes), pregnancy, claustrophobia, pacemakers, or metal implants were study exclusion criteria. Eleven matched healthy volunteers were recruited through the Family Practice Centre of the University of Sri Jayewardenepura (Sri Lanka).

For recruitment of controls, healthy volunteers were screened for a history of mental illness (including their first-degree biological relatives), a history of alcohol or illicit drug use, a neurological or chronic medical condition, low global IQ (<70), pregnancy, claustrophobia, pace makers, or metal implants and excluded from participation accordingly.

Patients were rated on the Brief Psychiatric Rating Scale (BPRS) at study inclusion and referred to standard clinical care (BPRS total scores are given in

Changes in PBMC gene expression following treatment with antipsychotic medication 30 in treatment naïve patients with schizophrenia

Table 3.1). Blood was collected from patients and controls into PAXgene Blood RNA tubes (approx. 2.5mL; PreAnalytiX) and then frozen at -80C.

Table 3.1: Characteristics of the schizophrenia patients and healthy volunteers (SZ= schizophrenia patients; C= healthy controls; R=Risperidone, H= Haloperidol; BPRS 1= total BPRS score before treatment; BPRS 2= total BPRS score after treatment)

Duratio n of Durati Family Illness Antipsyc on of Health Age History BPRS1 BPRS2 Age Gende at hotic medic y Patie (Yrs. of (total (total (yrs. Gender r inclusio Medicat ation contro nts ) Schizop score) score) ) n ion* (week ls hrenia$ (months s) ) SZ1 27 Male Present 12 R / H 6 47 36 C1 35 Female SZ2 65 Male Present 96 R / H 6 65 48 C2 38 Female SZ3 57 Male Present 18 R 6 59 34 C3 19 Female Not 53 44 SZ4 30 Male present 8 R / H 6 C4 51 Female SZ5 26 Male Present 7 R 7 42 37 C5 38 Male Not 48 36 SZ6 30 Male present 6 R 6 C6 19 Male Not 49 39 SZ7 22 Male present 6 R 7 C7 22 Male Femal 42 35 SZ8 31 e Present 8 R 6 C8 48 Female Femal Not 59 44 SZ9 47 e present 11 R 6 C9 27 Male SZ10 26 Male Present 12 R 6 35 38 C10 65 Male C11 31 Female

Changes in PBMC gene expression following treatment with antipsychotic medication 31 in treatment naïve patients with schizophrenia

Seven patients were commenced on 4 mg/day of risperidone and three patients received a combination of 2 mg/day of risperidone and 2 mg/day of haloperidol, thus equalling a total daily dose of 200 mg chlorpromazine equivalents (Woods SW, 2003) in both instances. Six to eight weeks into pharmacotherapy, patients underwent another clinical rating using BPRS (see Table 3.1). A second blood sample was collected at this time from patients into PAXgene Blood RNA tubes and frozen at -80C. Once all samples were collected they were transported to University of Newcastle on dry ice and stored briefly at -80C until processed. Table 3.1 summarises the characteristics of the schizophrenia patients and healthy volunteers.

3.2.2. RNA purification from PBMC

RNA was purified from PAXgene Blood RNA tubes at the Molecular Neurobiology Laboratory at the University of Newcastle Australia, using the PAXgene blood RNA Kit manual extraction method as described in the manufacturer’s instructions (PAXgene Blood RNA Kit Handbook 03/2009). In summary, PAXgene tubes were incubated for 2 hours at room temperature to ensure complete lysis of blood cells. Then the PAXgene RNA tubes were centrifuged for 10 minutes at 4000g and 4 mL of RNase-free water was added to the pellet, which was dissolved with vortexing. The sample was then centrifuged again for 10 minutes at 4000g and the pellet dissolved in 350ml BR1 suspension buffer (supplied with kit) with vortexing. After transferring to a 1.5mL micro centrifuge tube, 300mL of binding buffer and 40mL proteinase K was added then mixed and incubated for 10 minutes at 55C. The lysate was then passed through a PAXgene Shredder spin column for 3 minutes at maximum speed in a microfuge.

After transferring to a 1.5mL micro centrifuge tube, 300mL of binding buffer and 40mL proteinase K was added then mixed and incubated for 10 minutes at 55C. The lysate was then passed through a PAXgene Shredder spin column for 3 minutes at maximum speed in a microfuge. The flow through supernatant was transferred to a fresh 1.5mL micro centrifuge tube and vortexed with 350µL 100% ethanol (AnalaR grade).

Changes in PBMC gene expression following treatment with antipsychotic medication 32 in treatment naïve patients with schizophrenia

The PAXgene RNA spin column was placed into a fresh processing tube and the RNA eluted by adding 40µL of elution buffer BR5 (supplied with kit) with centrifugation for 1 min. The elution step was repeated and the sample incubated for 5 minutes at 65C, chilled and then stored at -20C till used for microarray analysis. RNA quantification was carried out by using Quant-iT RNA Assay Kit with the Invitrogen Qubit Fluorometer.

3.2.3. mRNA expression array analysis

The DNA bound to the column was digested with 80mL of a DNase I mixture (supplied with kit) at room temperature for 15 minute and the column washed again with BR3 as stated above followed by two washes with buffer BR4 (supplied with kit). Purified RNA was amplified and biotinylated using the TotalPrep Amplification kit (Ambion, ABI, Foster City, CA, USA) according to the manufacturer’s protocol. Briefly, 500ng RNA was reversed transcribed in a two- stage process using an oligo-dT primer that bears a T7 promoter. The resulting cDNA was column-purified and used as a template for in vitro transcription with T7 RNA polymerase and biotin-UTP. The amplified and biotinylated RNA product was likewise column purified prior to hybridisation to the array. Then 750ng labeled RNA was hybridised to commercial Illumina HT-12_V3 beadchips (~48,000 probes) according to the manufacturer’s protocol. Beadchips were then scanned using the Illumina BeadArray Reader (Illumina, San Diego, CA, USA). Expression data underwent quality control analysis and normalisation in Beadstudio V3.0 (Illumina).

Quality control was assessed by the Direct Hyb control plots within the GenomeStudio software. All control plots displayed expected values as per the Illumina specifications. Control measures included: hybridisation controls, negative and background controls, biotin-, low- and high-stringency controls, housekeeping gene intensities and average gene intensities. Data was background subtracted and normalized using the cubic spline method within GenomeStudio according to Illumina’s recommendations. Genes were considered expressed if fluorescence intensity was at least twice that of background.

Changes in PBMC gene expression following treatment with antipsychotic medication 33 in treatment naïve patients with schizophrenia

Differential expression analysis was then performed using the Significance Analysis of Microarrays (SAM) statistical analysis program full academic version 2.23(Larsson Oet al 2005; http://www-stat.stanford.edu/~tibs/SAM/).

SAM is capable of detecting the validity of genes that show significantly different expression according to a q-value - an adaptation of the p-value that is appropriate for multiple hypotheses testing (Jeffery IB et al., 2000), and denotes the lowest possible false discovery rate.

3.2.4. Quantitative real-time reverse transcription PCR (Q-PCR) validation

The altered expression of seven genes was confirmed by Q-PCR as previously described (described in Beveridge NJ et al., 2010) For three genes (i.e. AKT1, RXRAandMMP9) the validation was performed with oligonucleotide primers. Briefly, multiplex reverse transcription was performed on 500 ng of DNaseI- treated RNA using random hexamers. Reactions were performed using Superscript II reverse transcriptase in 1X first strand buffer according to the manufacturer’s instructions (Invitrogen). The validation of the remaining four genes (MAL, DISC1, DGCR6 and RPS25) was completed using TaqMan® Gene Expression Assays according to manufacturer’s instructions (Applied Biosystems, Foster City, California). The oligonucleotide primers and TaqMan assays used are shown in Supplementary Table 1 (see Appendix for supplementary tables). Q-PCR triplicate reactions were set up in a 96-well format with the epMotion 5070 automated pipetting system (Eppendorf, Hamburg, Germany) and carried out with the Applied Biosystems 7500 real-time PCR machine. Serial dilutions of cDNA were used as standards, and data were analysed with the relative quantitation method with efficiency correction. Relative mRNA expression was calculated as the ratio of the gene and the geometric mean of controls hydroxymethylbilane synthase (HMBS) and β-glucuronidase (GUSB).

Differential expression of a given miRNA or mRNA was determined by the difference between the mean ΔCt for the schizophrenia and control cohorts (ΔΔCt) expressed as a ratio (2-ΔΔCt) To determine the significance of any difference in

Changes in PBMC gene expression following treatment with antipsychotic medication 34 in treatment naïve patients with schizophrenia average expression in a given direction between the two cohorts, a paired one- tailed t-test was applied (Livak KJ and Schmittgen TD, 2001)

3.2.5. Pathways and Network Analysis

Lists of differentially expressed genes and their corresponding fold changes/p values were uploaded into Ingenuity Pathway Analysis (IPA) knowledge base v6.3 (Ingenuity Systems, Redwood City, CA, USA, www.ingenuity.com) to determine the biological implications of the altered expression.

3.3. Results

3.3.1. Changes in symptoms ratings after Antipsychotic medication treatment

This study is a subset of a larger cohort of 18 patients with schizophrenia and 18 matched healthy volunteers. We have previously reported on the clinical demographics of this larger cohort1 (Kumarasinghe et al., (2012), submitted). In summary patient’s symptom severity improved after pharmacotherapy as rated with the BPRS (total score: 46.3 [SD 9.1] at study entry versus 39.5 [SD 5.6] at follow-up; p=0.002), they also had a low expression of neurological soft signs (NES = 5.0) and displayed significant cognitive impairment (i.e. > 3 SD below healthy norms; Randolph 1998) as assessed by the RBANS.

3.3.2. Gene expression changes before and after antipsychotic medication treatment

Of the genome-wide expression profiling of 48,803 transcripts (25,202 genes) from PBMC from the schizophrenia patient cohort versus controls, SAM identified the expression of 10,207 genes. Further analysis revealed 208 up regulated and 416 down regulated genes (total of 624 genes) in patients prior to antipsychotic

1 Nishantha Kumarasinghe, Paul E. Rasser, Jayan Mendis, Jessica Bergmann, Lilly Knechtel, Stewart Oxley, Antoinette Perera, Paul M. Thompson, Paul A. Tooney, and Ulrich Schall, 2012 (Appendix: list of publications).

Changes in PBMC gene expression following treatment with antipsychotic medication 35 in treatment naïve patients with schizophrenia treatment when compared to controls (p<0.05; Supplementary Table S2). This list included schizophrenia-associated genes such as AKT1, DISC1 (both up- regulated) and DGCR6 (down-regulated).

Interestingly, after the 6-8 weeks of antipsychotic drug treatment when gene expression in the patients was compared back to the control samples, only 106 genes were deregulated (p<0.05), with 6 being up-regulated and 102 down regulated (Supplementary Table S3). When these genes were compared to the list of genes that were deregulated prior to treatment (Supplementary Table S2), 11 genes on the array that were significantly down regulated in schizophrenia compared to controls before treatment, returned to control levels after treatment, suggesting they changed in response to the antipsychotic medication (Table 3.2).

In addition, 63 genes remained down regulated before and after treatment compared to control levels (Supplementary table S2), whilst 4 genes, G6PD, F5, RNF144B and TIMP2, remained up regulated in schizophrenia despite treatment, in the array analysis (fold changes ranging from 1.4-2.8; p<0.03 for all genes). Gene expression was then directly compared in the schizophrenia patients before and then 6-8 weeks after commencement of antipsychotic treatment. Surprisingly, no genes were significantly down regulated by the treatment in patients after treatment, but 28 genes were shown to be up regulated at p<0.05(Table 3.3).

3.3.3. QPCR Validation of differentially expressed genes

A search in the Genetic Association Database (geneticassociationdb.nih.gov) and SzGene (Schizophrenia gene data base; www.schizophreniaforum.org) identified several differentially expressed genes that have previously been associated with schizophrenia. Six of these genes, AKT1, RXRA, MMP9, MAL, DISC1, and DGCR6, were selected for qPCR validation. An additional gene RPS25 was also selected on the basis of being significantly down regulated before treatment (control versus before treatment) and then significantly up regulated in response to treatment (before versus after analysis) suggesting it was changed by the medication.

Changes in PBMC gene expression following treatment with antipsychotic medication 36 in treatment naïve patients with schizophrenia

AKT1, RXRA and MMP9 were significantly up regulated in schizophrenia patients and then returned to control levels after treatment on the array. The qPCR in Figure 3.1 confirmed that AKT1 (1.6 fold up; p=0.028), RXRA (2.2 fold up; p=0.002) and MMP9 (1.8 fold up; p=0.008) were significantly up regulated in patients before treatment. After treatment, each of these three genes then showed a strong response to the medication and returned to control levels (Figure 3.1). DISC1 was up regulated in patients on the array before treatment and this was confirmed by QPCR (6 fold up; p=0.47). Interestingly, the QPCR suggested that DISC1 remained up regulated after the antipsychotic drug treatment in patients with schizophrenia (p=0.022).

RPS25 and DGCR6 were significantly down regulated in patients and then returned to control levels after treatment on the array. The QPCR (Figure 3.1) confirmed that RPS25 was significantly down regulated in patients before treatment (1.8 fold down, p=0.013), which brought the expression back to the control levels. DGCR6 showed a 1.3 fold trend towards down regulation (p=0.171), followed by a significant (p=0.049) increase in expression after treatment, which brought the expression back to the control levels (Figure 3.1).

MAL was significantly down regulated in patients and did not respond to treatment on the array analysis. Quantitative PCR showed that MAL was down regulated prior to treatment (1.8 fold down, p=0.0002) and while remaining lower than control levels. The level of expression was not significantly different after treatment (-1.24 fold down; p=0.111). Pearson’s correlation analysis confirmed that the array fold changes were highly correlated to the qPCR folds changes (r=0.9333; r2=0.8712). These results thus support the validation of the array data.

3.3.4. Ingenuity Pathways Analysis of differentially expressed genes

The lists of differentially expressed genes were submitted to Ingenuity Pathways Analysis (IPA). Analysis of the differentially expressed genes in patients prior to treatment revealed functions in inflammation, immune cell trafficking, infectious and respiratory disease, haematological system development and

Changes in PBMC gene expression following treatment with antipsychotic medication 37 in treatment naïve patients with schizophrenia function, cell-cell signalling, gene expression, protein synthesis, cell development and movement (Table 3.4; Supplementary Table S5).

Interestingly, IPA also detected alterations to biological functions related to inflammation and immune function in the lists of differentially expressed genes after treatment of patients when compared to controls (Table 3.5; Supplementary Table S5). However, a comparison of these inflammatory and immune functions, as shown in Table 3.5, it is clear that the treatment has stabilised gene expression and reduced the number of genes represented in each functional category. This accounts for the reduced the p value ranges from between 10-2 to 10-8 before treatment to 10-2 to 10-4 after treatment.

IPA canonical pathways analysis showed a significant representation of differentially expressed genes in the EIF2 (eukaryotic initiation factor-2) signalling, regulation of eIF4 and p70S6K, oxidative phosphorylation, mTOR signalling and mitochondrial dysfunction pathways in patients prior to treatment (Table 3.4).

These pathways also showed a significant representation of differentially expressed genes after treatment, but once again there were fewer genes represented and the p values where lower (Table 3.4). This is clearly represented in Figure 3.2 (A) and (B), which shows the EIF2 signalling pathway that is involved in the initiation of proteins synthesis.

Changes in PBMC gene expression following treatment with antipsychotic medication 38 in treatment naïve patients with schizophrenia

Table 3.2: Down regulated genes in schizophrenia patients before treatment and significantly up regulated in response to antipsychotic drug treatment by array analysis (SZ = Schizophrenia and C = Control; * - p values determined using SAM analysis). Control versus Before Before versus After Illumina ID Gene Name Entry Gene Name Fold change Fold change p value** p value* (SZ/C)* (SZ/C) ATP synthase, H+ transporting, mitochondrial F1 ILMN_2225887 ATP5EP2 -1.7757 0.0009 2.0463 0.0205 complex, epsilon subunit pseudo gene 2 Deoxyhypusine ILMN_1687279 DHPS -1.4451 0.0482 1.3038 0.0009 synthase

Linker for activation ILMN_2404625 LAT -1.7268 0.0009 2.2198 0.0009 of T cells

ILMN_1792528 LOC401206 -1.8277 0.0009 1.4970 0.0009

ILMN_1666323 LOC647251 -2.0339 0.0482 4.6130 0.0461

ILMN_1813175 LPHN1 Latrophilin 1 -1.8151 0.0143 1.9819 0.0009

Ribosomal protein ILMN_1754303 RPL30 -1.8669 0.0009 1.7061 0.0171 L30

Ribosomal protein ILMN_1746516 RPS25 -1.9534 0.0009 1.6822 0.0009 S25

tRNA-yW synthesizing ILMN_1736135 TYW1 protein 1 homolog (S. -1.6292 0.0482 2.0342 0.0203 cerevisiae)

Ubiquitin-fold ILMN_2110281 UFC1 modifier conjugating -1.6706 0.0196 1.4533 0.0145 enzyme 1

Ubiquinol- cytochrome c ILMN_2232936 UQCRH -1.7914 0.0009 1.4755 0.0148 reductase hinge protein

Changes in PBMC gene expression following treatment with antipsychotic medication 39 in treatment naïve patients with schizophrenia

Table 3.3: Direct comparison of gene expression in PBMCs taken from patients with schizophrenia before and then after antipsychotic drug treatment. (*SZ = Schizophrenia and C = Control; ** - p values determined using SAM analysis)

Fold Change p- Illumina ID Gene Symbol Entry Gene Name (SZ/C)* value** ILMN_2344956 ACP1 Acid Phosphatase 1, soluble 2.32 0.001

ILMN_2225887 ATP5EP2 2.05 0.021

Nitrogen permease regulator-like 3 (S. ILMN_1733581 C16orf35 1.89 0.001 cerevisiae)

ILMN_1707356 CFL2 Coiling 2 (muscle) 1.92 0.019

ILMN_1687279 DHPS Deoxyhypusine synthase 1.30 0.001

Eukaryotic translation initiation factor 3, ILMN_1715636 EIF3B 1.36 0.014 subunit B

ELAV (embryonic lethal, abnormal vision, ILMN_1678618 ELAVL3 2.20 0.022 Drosophila)-like 3 (Hu antigen C)

Essential meiotic endonuclease 1 homolog 2 ILMN_1771185 EME2 2.19 0.022 (S. pombe)

ILMN_1677483 EXOSC1 Exosome component 1 1.51 0.015

Transmembrane protein, adipocyte ILMN_1804938 GPR175 2.08 0.001 asscociated 1

ILMN_1776412 KRTAP10-11 Keratin associated protein 10-11 3.43 0.034

ILMN_2404625 LAT Linker for activation of T cells 2.22 0.001

ILMN_1776283 LGALS12 lectin, galactoside-binding, soluble, 12 2.19 0.022

ILMN_1792528 LOC401206 1.50 0.001

ILMN_1670589 LOC643933 2.46 0.025

ILMN_1705982 LOC645284 2.38 0.024

ILMN_1666323 LOC647251 4.61 0.046

ILMN_1711087 LOC648526 1.92 0.019

ILMN_1686060 LOC729777 3.03 0.030

ILMN_1813175 LPHN1 Latrophilin 1 1.98 0.001

ILMN_1765499 NTAN1 N-terminal asparagine amidase 5.71 0.057

ILMN_1652161 PNKD paroxysmal nonkinesigenic dyskinesia 2.75 0.028

ILMN_1767766 PRDX2 Peroxiredoxin 2 1.99 0.020

ILMN_1765518 RNASEH2C Ribonuclease H2, subunit C 2.13 0.021

ILMN_1754303 RPL30 Ribosomal protein L30 1.71 0.017

ILMN_1746516 RPS25 Ribosomal protein S25 1.68 0.001

Changes in PBMC gene expression following treatment with antipsychotic medication 40 in treatment naïve patients with schizophrenia

tRNA-yW synthesizing protein 1 homolog (S. ILMN_1736135 TYW1 2.03 0.020 cerevisiae)

Ubiquitin-fold modifier conjugating enzyme ILMN_2110281 UFC1 1.45 0.015 1

Ubiquinol-cytochrome c reductase hinge ILMN_2232936 UQCRH 1.48 0.015 protein

In Figure 3.2 (A), 16 genes including a number of EIF genes in this pathway were down regulated (green) and AKT1 was up regulated (red) in patients, whereas after treatment only four genes remained down regulated and AKT1 has returned to control levels as confirmed above in the QPCR analysis. Interestingly, AKT1 is also a member of the mTOR signalling pathway and the eIF4/p70S6K pathways, which show the same pattern of less dysregulation of gene expression and no change in AKT1 expression after treatment (data not shown).

These three pathways that contain AKT1 were also highlighted in the IPA analysis of differentially expressed genes from the direct comparison of patient samples before and after treatment (i.e. before versus after analysis), but once again there were fewer genes represented and the p values were lower.

Changes in PBMC gene expression following treatment with antipsychotic medication 41 in treatment naïve patients with schizophrenia

Figure 3.1: Gene expression changes in schizophrenia patients before and after antipsychotic drug treatment, by qPCR. (A) AKT1, RXRA and MMP9 each displayed increased expression in schizophrenia patients which returned to control levels upon drug treatment (p=0.028, p=0.002 and p=0.008 respectively). By qPCR, DISC1 also remained significantly elevated after drug treatment compared to controls (before treatment p=0.047, after p=0.022). (B) RPS25 displayed decreased gene expression in patients before treatment (p=0.013) then returned to control levels. DGCR6 was not significantly reduced before treatment (-1.31 fold down p=0.171) but expression was significantly higher after drug treatment, similar to control levels (p=0.049). (C) MAL displayed decreased expression in patients before treatment (p=0.0002) and while remaining lower than control levels (-1.24 fold) this was not below the threshold for significance after treatment (p=0.111). Bars are mean + SEM. Statistics is Mann- Whitney one-tailed tests. (D) Fold-changes obtained by microarray and qPCR are highly correlated (Pearson r=0.933, r2=0.8712, p=0.0002).

Changes in PBMC gene expression following treatment with antipsychotic medication 42 in treatment naïve patients with schizophrenia

IPA can also provide an indication of the likely activity of transcription regulators by comparing the expression on the array of genes that are known to be transcriptionally controlled by a particular regulator and then investigating the concordance of expression patterns.

This analysis suggested that 35 of 49 genes (including AKT1) on the array had expression direction changes consistent with inhibition of the transcription regulator MYC (v-myc myelocytomatosis viral oncogene homolog (avian); z-score - 2.886; p-value for overlap = 1.97x10-9) in the patient cohort prior to treatment.

The related neuroblastoma-derived myc called MYCN was also predicted to be inhibited in the patients prior to treatment, with 35 of 36 genes on the array having expression direction changes consistent with inhibition of this transcription regulator (v-myc myelocytomatosis viral related oncogene, neuroblastoma derived (avian); z-score -5.572; p-value of overlap = 3.36 x 10-20) and are inhibited in the patients prior to treatment.

This analysis also indicate the activation of the SPI1 (spleen focus forming virus (SFFV) proviral integration oncogene spi1) transcription regulator in patients before treatment, with 7 of 14 genes on the array having expression direction changes consistent with activation of this transcription regulator (z-score 2.882; p-value of overlap = 1.72x10-5). None of these transcription regulators were altered in any of the array analyses, suggesting that if they are indeed changed other mechanisms may be involved. The IPA analysis was repeated by restricting the gene lists to just those genes that were changed in the same direction both before and after treatment (i.e. 64 down regulated and 4 up regulated). This restricted list still detected overrepresentation of genes with roles in biological functions involved in inflammation, immune cell trafficking, infectious and respiratory disease, haematological system development and function, cell-cell signalling, gene expression, protein synthesis and cell development and movement (Supplementary Table S5).

It also predicted dysfunction of the EIF2 signalling, regulation of eIF4 and p70S6K, oxidative phosphorylation and mTOR signalling canonical pathways.

Changes in PBMC gene expression following treatment with antipsychotic medication 43 in treatment naïve patients with schizophrenia

3.4. Discussion

The diagnosis of schizophrenia remains in the realm of the psychiatrist who must consider the symptoms being expressed and their course in light of the diagnostic criteria (e.g., DSMIV). However, it is now widely recognised that schizophrenia is a very heterogeneous disorder with patients experiencing vast differences in symptomatology, as well as the severity and course of the illness. As such, schizophrenia is likely to be a spectrum or syndrome of related disorders and thus this heterogeneity has not assisted in rapidly advancing the understanding of the cause(s) of this disorder. Indeed, research in schizophrenia is hampered by this heterogeneity and would benefit by the development of methods to reliably identify the subtypes that make up the syndrome.

A number of studies have targeted peripheral tissue sources such as PBMCs in an attempt to develop gene expression profiles that might assist with the diagnosis or subtype classification of schizophrenia. These studies are hampered by the inability to distinguish between “state” versus “trait” effects and also many have been conducted on patients already receiving antipsychotic medication.

Indeed, PBMCs may also be a source of tissue to determine the likelihood of a favourable treatment response. In this regard, this study investigated gene expression changes in PBMCs in schizophrenia patients before and then 6-8 weeks after treatment with antipsychotic medication (risperidone alone or in combination with haloperidol). In the treatment naïve state, the patients exhibited greater dysregulation of gene expression (600+ genes) than after treatment.

There was a greater tendency towards down-regulation of gene expression in patients that was present before and after treatment. Also, there was less alteration to gene expression after treatment with antipsychotic medication, suggesting that risperidone and haloperidol are “normalising” the gene expression in the patients, returning their expression back to control levels.

Changes in PBMC gene expression following treatment with antipsychotic medication 44 in treatment naïve patients with schizophrenia

Figure part A

Figure 3.2: Changes to the EIF2 signalling pathway in PBMCs from patients with schizophrenia. IPA analysis of differentially expressed genes in PBMCs from schizophrenia patients before (part A; this page) and after (part B; on following page) treatment with antipsychotic drugs, compared to controls identified the EIF2 signalling pathway as the top canonical pathway containing the most significant

Changes in PBMC gene expression following treatment with antipsychotic medication 45 in treatment naïve patients with schizophrenia representation of differentially expressed genes. Before treatment, the schizophrenia-associated gene AKT1 was markedly up regulated (red) whilst many of the EIF genes were down regulated (green) suggesting disruption to the control of protein synthesis. The antipsychotic drug treatment reduced this widespread dysregulation in the pathway in the patients and returned the expression of a number of the genes including AKT1 back to control levels.

Changes in PBMC gene expression following treatment with antipsychotic medication 46 in treatment naïve patients with schizophrenia

Figure part B

Changes in PBMC gene expression following treatment with antipsychotic medication 47 in treatment naïve patients with schizophrenia

Table 3.4: Top ranked biological functions overrepresented by genes deregulated in schizophrenia before and after antipsychotic drug treatment (by Ingenuity Pathway Analysis).

Functional Category Control v Before Functional Category Control v After Diseases and Disorders p-value Molecules Diseases and Disorders p-value Molecules 2.61E-11 - 5.50E-04 - Inflammatory response* 92 Inflammatory Disease 7 1.99E-02 3.98E-02

3.49E-11 - Inflammatory 5.50E-04 - Infectious disease* 98 13 9.54E-03 response* 4.97E-02

3.49E-11 - Skeletal and Muscular 5.50E-04 - Respiratory disease* 58 2 1.84E-02 Disorders 2.67E-02

5.10E-05 - 5.50E-04 - Cancer 156 Infectious disease* 14 2.01E-02 2.67E-02

3.44E-04 - 5.50E-04 - Connective tissue disorder 82 Respiratory disease* 5 4.00E-03 2.67E-02 Molecular and Cellular Molecular and Cellular p-value Molecules p-value Molecules Functions Functions Cell-to-cell signalling and 9.96E-08 - Carbohydrate 5.50E-04 - 72 4 interaction* 2.13E-02 metabolism 3.55E-02

4.06E-07 - Cellular growth and 5.50E-04 - Gene expression* 20 7 2.20E-03 proliferation 4.93E-02

4.06E-07 - Nucleic acid 5.50E-04 - Protein synthesis* 54 5 3.53E-03 metabolism 3.55E-02

7.01E-07 - Small molecule 5.50E-04 - Cellular development* 80 9 2.01E-02 biochemistry 4.41E-02

1.08E-06 - 1.76E-03 - Cellular movement* 82 Antigen presentation 5 2.10E-02 4.84E-02 Physiological System Physiological System p-value Molecules Development and p-value Molecules Development and Function Function Haematological system 9.96E-08 - Haematological system 5.50E-04 - 94 16 development/function* 2.13E-02 development/function* 4.93E-02

9.96E-08 - Immune cell 1.76E-03 - Immune cell trafficking* 73 10 1.99E-02 trafficking* 4.84E-02

5.44E-06 - Cell-mediated immune 2.92E-03 - Haematopoiesis* 49 5 1.70E-02 response 3.11E-02

Lymphoid tissue structure 7.70E-06 - 2.92E-03 - 35 Haematopoiesis* 9 and development* 1.70E-02 4.84E-02

Lymphoid tissue 5.50E-05 - 2.92E-03 - Tissue development* 44 structure and 6 1.99E-02 3.55E-02 development*

*Functional categories assigned by IPA

Changes in PBMC gene expression following treatment with antipsychotic medication 48 in treatment naïve patients with schizophrenia

Table 3.5: Ingenuity canonical pathway analysis.

Control v Before Control v After Ingenuity Canonical Pathways p-value Ratio p-value Ratio EIF2 Signalling 9.26 x 10-26 40/199 (0.201) 5.27 x 10-10 11/199 (0.055)

Regulation of eIF4 and p70S6K Signalling 3.81 x 10-14 25/170 (0.147) 4.65 x 10-04 5/170 (0.029)

Oxidative Phosphorylation 2.90 x 10-12 23/160 (0.144) 1.38 x 10-04 5/160 (0.031)

mTOR Signalling 2.17 x 10-09 22/201 (0.109) 1.38 x 10-03 5/201 (0.025)

This study showed that RXRA, MAL, DISC1 and RPS25 were significantly altered in PBMCs in patients prior to treatment (i.e. treatment naïve state). RPS25 a gene coding for a ribosomal protein has not been reported to be associated with schizophrenia before. On the other hand, RXRA was implicated in sib-pairs with schizophrenia where a copy number variation disrupted the RXRA gene (Lee CH et al., 2010). MAL, a gene coding for myelin and lymphocyte protein was down- regulated in the patients with schizophrenia which is in accordance with a down- regulation in MAL in the prefrontal cortex (BA46) in a post-mortem study of schizophrenia (Hakak Y., et. al., 2001). MAL has also been reported to be deregulated in major depressive disorder (Aston C et al., 2005), so this change may be common to several psychiatric conditions. Detection of myelin-associated gene changes in PBMCs in schizophrenia was also reported in a study comparing schizophrenia patients and their unaffected and medication-free siblings to controls (Glatt SJ et al., 2011), where MBP (myelin basic protein) was shown to be up regulated in schizophrenia compared to controls. In this study, MBP was also up regulated on the array prior to treatment of the patients but returned to control levels.

DISC1 (Disrupted in Schizophrenia 1) is a known genetic candidate for schizophrenia that has been considered as a key susceptibility gene (Morris JA et al., 2003; Ozeki Y et al., 2004; Millar JK et al., 2005;Hennah W and Porteous D, 2009; Sawamura N, Sawa A., 2006). This gene has been implicated in neurodevelopment, cognition and other mental disorders (Brandon NJ et al., 2004;

Changes in PBMC gene expression following treatment with antipsychotic medication 49 in treatment naïve patients with schizophrenia

Duan X et al., 2007 and many others). Ishizuka K et al., (2006) also emphasized the role of DISC1 in cerebral cortical development and others have described this gene’s functions in synaptogenesis and sensory perception (Hennah W and Porteous D., 2009) as well as the molecular mechanisms of schizophrenia (Matsuzaki S and Tohyama M., 2007). Interestingly, a post-mortem study reported increased expression of specific isoforms of DISC1 in the hippocampus in medication exposed schizophrenia patients (Nakata et al., 2009), which is consistent with the increased expression of DISC1 prior to and after treatment in this study. This provides additional evidence that brain-related gene changes can be detected in PBMCs in schizophrenia (Bowden NA et al., 2006) and may also be showing a steady pattern of expression through the antipsychotic medication. However, whether these changes seen in single genes or in small groups of genes will assist in the identification of suitable biomarkers for diagnostic purposes remains to be determined, possibly by replication studies.

Research into gene expression has more recently placed less emphasis on individual gene changes and more on sets of genes and the biological functions and pathways they affect. To this end, the IPA pathway analysis in this study identified enrichment of genes with altered expression in schizophrenia that contribute to biological functions related to immune function, inflammation and infectious diseases. Indeed, epidemiological studies have analysed data from collections dating back many decades and provided evidence for increased risk of developing schizophrenia in people exposed to infectious agents such as rubella and influenza either in-utero or during early life (reviewed in Brown AS, 2009). In the laboratory, models of maternal infection in mice lead to changes in brain structure and cytoarchitecture, elevated cytokine levels and the development of behavioural and cognitive changes that are equated to those observed in patients with schizophrenia (reviewed in Patterson, 2009). The data presented here suggests that the biological functions related to immunity, inflammation and infectious disease are present in treatment-naïve patients with schizophrenia, that the treatment with risperidone alone or in combination with haloperidol can correct the changes in these pathways to a certain degree by ‘normalising’ gene expression,

Changes in PBMC gene expression following treatment with antipsychotic medication 50 in treatment naïve patients with schizophrenia but cannot quite compensate for all changes. This would suggest that the signatures of immune dysfunction generated in PBMCs and brain tissue, are likely to be somewhat reflective of the actual pre-treatment state of the individual, providing weight to the infection/immune hypothesis of schizophrenia.

By the microarray analysis Takahashi et al., (2010) also identified, almost similer set of genes deregulated. Interestingly, only significant difference in the expression of the 14 genes identified in treatment naïve patients was the DRD2 and Kir3 when compared to genes highlighted in a set of treatment naïve patients by Zvara et al. (2005). However, three genes down-regulated prior to treatment in the later study, ABCF1 (ATP-binding cassette sub-family F member 1), BTBD11 (BTB/POZ domain-containing protein 11), and BCL11B (B-cell CLL/lymphoma 11B zinc finger protein), were also down-regulated in PBMCs from schizophrenia patients in two other studies, with one additional report also showing down- regulation of ABCF1. The first study showed down-regulation of ABCF1 in recent onset schizophrenia patients that were medicated compared to healthy controls (van Beveren et al., 2012). The second study of 92 medicated schizophrenia patients and 111 healthy controls reported down-regulation of ABCF1, BTBD11, and BCL11B, with all three genes confirmed to be down-regulated in a second cohort consisting of 29 treatment-naïve schizophrenia patients (de Jong et al., 2012). Finally, recently completed a study of PBMCs isolated from 114 medicated patients with schizophrenia/schizoaffective disorder and 80 non-psychiatric controls from the Australian Schizophrenia Research Bank (Loughland et al., 2010) and showed that ABCF1, BTBD11, and BCL11B were down-regulated by microarray analysis (Gardiner et al., 2012). Whilst the SAM analysis of the gene expression data from the current study suggested that these three genes returned to control levels after treatment, individual students t-tests conducted on the microarray expression data confirmed a significant reduction for ABCF1 and a trend towards down-regulation for BCL11B after treatment, suggesting these two genes are down-regulated in PBMCs in schizophrenia prior to treatment and remain unchanged after treatment.

Changes in PBMC gene expression following treatment with antipsychotic medication 51 in treatment naïve patients with schizophrenia

The BTBD11 gene, which appears to respond to antipsychotic drug treatment, is part of the BTB/POZ domain-containing protein family thought to function as transcription factors in different signaling pathways (Liu et al., 2011). BCL11B, a transcription factor with roles in driving commitment to the T cell lineage during hematopoiesis (Rothenberg 2012), is known to be involved in the development of layer 5 cortical neurons (Chen et al., 2008) and was recently linked to brain- derived neurotrophic factor (BDNF) signaling (Tang et al., 2011). Interestingly, ABCF1 is located in the 6p21.32-p22.1 major histocompatibility complex locus reported to be associated with schizophrenia (Purcell et al., 2009; Shi et al., 2009; Stefansson et al., 2009; Ripke et al., 2011). ABCF1 was discovered in experiments that identified it as a gene that responded to tumour necrosis factor treatment of synoviocytes isolated from healthy controls and patients with rheumatoid arthritis and expressed in the brain (Richard et al., 1998). It was also reported to be associated with autoimmune pancreatitis (Ota et al., 2007) and thus may play a role in inflammation, a process gaining more attention in schizophrenia research.

The IPA and GSEA analyses conducted in this study identified enrichment of genes with altered expression in schizophrenia that contribute to biological functions related to immune function, inflammation and infectious diseases. These findings are in accordance with the study mentioned above by de Jong et al. (2012) that also identified enrichment of genes with altered expression in schizophrenia to pathways such as Cell-mediated Immune Response, Antigen Presentation, Haematological System Development and Function, Inflammatory Response, Infectious Disease and Immune Cell Trafficking (de Jong et al., 2012). What is pertinent to this current study is that these pathways were also identified as deregulated in the follow-up cohort of antipsychotic-free schizophrenia patient.

Epidemiological studies have analysed data from collections dating back many decades and provided evidence for increased risk of developing schizophrenia in people exposed to infectious agents such as rubella and influenza either in utero or during early life (reviewed in Brown 2011). The 6p21.32-p22.1 major histocompatibility complex locus (Ripke et al., 2011) and the interleukin (IL)-1 gene complex (Xu and He 2010) are associated with schizophrenia. Furthermore,

Changes in PBMC gene expression following treatment with antipsychotic medication 52 in treatment naïve patients with schizophrenia studies have shown increased levels of soluble IL-1b, IL-2R, IL-6, IL-8 and IL-18 protein in serum (Zhang et al., 2004; Schmitt et al., 2005; Potvin et al., 2008; Bresee and Rapaport 2009; Garcia-Miss Mdel et al., 2010; Kunz et al., 2011; Garcia- Rizo et al., 2012; Palladino et al., 2012; Xiu et al., 2012) and IL-1b, IL-6 and IL-8 mRNA in the dorsolateral prefrontal cortex (Fillman et al. 2012) in people with schizophrenia. The GSEA reported here shows enrichment for genes in interleukin pathways, IL-1R, IL-2, IL-2Rb, IL-6 and IL-10, in schizophrenia patients prior to treatment. Furthermore, evidence suggests that IL-6 may have a neuroprotective effect by activating the mitogen-activated protein kinase (MAPK) pathway (Wang et al. 2009) that was one of the top ranked gene sets enriched in the patients prior to treatment identified by GSEA. The MAPK pathway is linked to the activity of the glutamate NMDA receptor, which is implicated in schizophrenia and both have roles in the brain in long-term potentiation, learning and memory, neurotoxicity and oxidative stress (Haddad 2005). In addition, antipsychotic drug treatment is known to affect the MAPK signalling pathway (Molteni et al., 2009), however the exact role of the MAPK signalling pathway in the development of schizophrenia remains to be determined.

The data presented herein suggests that the biological functions related to immunity, inflammation and infectious diseases are present in treatment-naïve patients with schizophrenia and that antipsychotic pharmacotherapy can partially compensate for these changes in these pathways by ‘normalising’ gene expression. This would suggest that the signatures of immune dysfunction generated in PBMCs and brain tissue, are likely to be reflective of the actual pre-treatment states, providing evidence for the infection/immune hypothesis of schizophrenia. To this end, IL32 was down-regulated in this study of PBMCs both prior to and after treatment and interestingly, IL32 was also shown to be down-regulated in the left superior temporal cortex in post-mortem brains from schizophrenia subjects who received treatment (Schmitt et al., 2011). Whether this proinflammatory cytokine has a role in the development of schizophrenia remains to be determined.

Further evidence for immune/infection hypothesis for schizophrenia was recently provided by the large study of PBMCs isolated from 112 patients with

Changes in PBMC gene expression following treatment with antipsychotic medication 53 in treatment naïve patients with schizophrenia schizophrenia and 76 non-psychiatric controls from the Australian Schizophrenia Research Bank that was conducted by this laboratory (Gardiner et. al., 2011). This study showed that 33 microRNA (miRNA) were down-regulated in schizophrenia patients and that some of the biological functions of the genes targeted by these miRNA are related to infectious diseases and the role of the immune system (Gardiner et. al., 2011) in previously medication treated patients. Furthermore, 17 of the down-regulated miRNAs are transcribed from a single imprinted locus at the maternally expressed DLK1-DIO3 region on chromosome 14q32. Interestingly, AKT1 that is up-regulated in this current study, which normalises upon treatment, is located in the 14q32 region and is predicted to be targeted (according to miRGen http://www.diana.pcbi.upenn.edu/cgi-bin/miRGen/v3/Targets.cgi) by four miRNAs that were down-regulated in PBMCs in schizophrenia, including one located in the imprinted region. Whether these miRNA are down regulated prior to treatment is yet to be determined, but one could speculate that this might contribute to the up regulation of AKT1 observed in schizophrenia patients before treatment.

The evidence for a role of AKT1 in schizophrenia has been building with a number of studies suggesting association of genetic variants with schizophrenia, but there are also several negative reports of non-association in patients with schizophrenia who had various levels of medication exposure (Balu DT and Cole GT, 2011). Post-mortem studies suggest reduction in the levels of AKT1 protein in the prefrontal cortex, hippocampus and lymphoblast cell lines in schizophrenia (Emamian ES, 2004) whilst a second study identified reductions of AKT1 mRNA levels in the prefrontal cortex (Thiselton DL et al., 2008). AKT1 deficiency in schizophrenia has also been associated with impairment of hippocampal plasticity and function (Balu DT and Cole GT, 2011). Beaulieu JM et al., (2005) reported, regulation of AKT1 signalling by dopamine D2 receptors in mice and another study of AKT1-deficientmice suggested that AKT1 function is important for dopaminergic neurotransmission and dopamine-dependent behaviours mostly implicated in schizophrenia (Emamian et al., 2004).

Changes in PBMC gene expression following treatment with antipsychotic medication 54 in treatment naïve patients with schizophrenia

Whilst the literatures suggest there is AKT1-deficiency in the brain and lymphoblast cells in schizophrenia, our data suggests that PBMCs that are not EBV- transformed and were isolated prior to treatment with antipsychotic medication have an increase in AKT1 expression. In addition, the up-regulation of AKT1 expression was corrected by risperidone or a combination of risperidone and haloperidol treatment for 6-8 weeks, providing additional evidence for a link between dopamine D2 receptors and AKT1. Whether particular (single nucleotide polymorphism) SNPs in AKT1 gene were present in the current cohort of patients and contribute to these results was not determined. Nevertheless, what is striking about the data presented here is that three of the top five ranked canonical pathways identified by IPA that had significant enrichment of genes with altered expression patterns before treatment of patients with antipsychotic drugs all contain AKT1 and all remained significantly overrepresented by the altered gene expression, in our sample who have not been exposed to the antipsychotic medication before.

Each of these pathways, EIF2 signaling, regulation of eIF4 and p70S6K and mTOR signaling, have roles in regulating protein synthesis that is required for cell growth, cell survival and development (Carter CJ, 2007). In the brain, these signaling cascades respond to neuregulin and growth factors such as brain derived neurotrophic factor (BDNF) (Carter CJ, 2007), they respond to glutamate via NMDA receptors, all of which have been implicated in schizophrenia. They respond to hormones, cytokines and stressors such as viral infections (Carter CJ, 2007). There is also evidence that the AKT1/mTOR signaling pathway is a target of DISC1 (Kim et al., 2009), which was also up regulated in this study.

In this study it was not possible to determine if these pathways are altered initially as a ‘state’ effect rather than a ‘trait’ effect. One could see how the stress of becoming psychotic may have induced widespread shutdown of the EIF2 pathway for example and that the increase in AKT1 expression might be an attempt to correct this situation. As each of the patients did show improvement upon treatment with risperidone alone or in combination with haloperidol, the

Changes in PBMC gene expression following treatment with antipsychotic medication 55 in treatment naïve patients with schizophrenia schizophrenia patients might have been less stressed leading to a return to control levels for many of the EIF genes by this study in the EIF2 pathway (Figure 3.2) and a consequent reduction in the AKT1 activation. One must also keep in mind the small size of the cohort used in this study. Even with this limitation, there were however 67 genes that remained deregulated in the same direction after treatment with medication and these genes were still significantly overrepresented in the EIF2, the regulation of eIF4 and p70S6K and the mTOR signalling pathways. So for the purposes of developing biomarkers for schizophrenia, the data suggest changes to gene expression highlighting particular biological pathways that are relevant to schizophrenia pathology can be detected in PBMCs and that these pathways are affected by antipsychotic medication.

Whilst caution should be exercised when interpreting the data from this small cohort of schizophrenia patients, the study provides evidence that AKT1 and its associated biological pathways; EIF2, mTOR and regulation of eIF4 and p70S6K pathways are deregulated in schizophrenia prior to treatment with antipsychotic drugs. Upon treatment, as the patients’ symptoms improved there was a noticeable correction of the gene expression back to control levels which included that of AKT1. However, after treatment the three biological pathways were still overrepresented by genes with significantly altered expression. Whether these signatures can or the individual changes in gene expression can be developed into biomarkers for schizophrenia diagnosis or treatment response remains to be determined in larger cohort studies.

In summary, these results provide further evidence for infection and immune dysfunction as a likely risk factor for the development of schizophrenia. Gene expression signatures could be identified that are not affected by antipsychotic medication and that justifies a role of infectious diseases and immune dysfunction in the development of schizophrenia. This may be useful in the development of a biological means for diagnosis or classification of the schizophrenia syndrome. However, how these signatures of altered gene expression are related to cerebral brain pathology will be investigated with cortical pattern matching in high-

Changes in PBMC gene expression following treatment with antipsychotic medication 56 in treatment naïve patients with schizophrenia resolution magnetic resonance imaging brain scan. Some of the differently expressed genes, on the other hand, are novel findings (see the Supplementary Table S1). This is very significant when considering the treatment naïve nature of the patient sample of the current study. However, the usefulness of these newly emerging genes and their biological role needs to be examined by repeat studies using much larger cohorts.

3.5. Study Limitations

A major limitation of the study was the low sample size meaning it was difficult to interpret and make generalisations about the data without the affects of possible type 1 and 2 errors. As a consequence, the protocol was changed to the use of PaxGene RNA collection tubes to stabilise the RNA for transport to Australia where it was purified by the candidate. The reason for the small sample size was the loss of RNA samples from six patients and five controls during the data collection period as a result of the degradation of RNA during the purification and subsequent storage before they were transported from Sri Lanka to Australia. This initial loss of samples left little time/funding to collect additional participants for the study in Sri Lanka.

This left the candidate with a small cohort of ten previously untreated schizophrenia patients and eleven healthy controls. The approach used in this project to limit the impact of the small sample size was by performing multivariate permutation testing using the SAM analysis (for more details see Pérez-Santiago J et al., 2012). A low sample size has been a common drawback in a number of gene expression studies in schizophrenia (as discussed in the review above).

However, a limitation that was not controlled for in this smaller cohort was the participants were not matched in pairs for gender, but they were a subset of the larger cohort (eighteen schizophrenia patients and eighteen matched controls), which is described, in the next chapter. This gender bias could have impacted on the differences in gene expression between schizophrenia patients and controls that were observed in the study. Indeed, it is well known that males generally Changes in PBMC gene expression following treatment with antipsychotic medication 57 in treatment naïve patients with schizophrenia experience symptoms earlier than females, their severity is often worse as is the overall outcome for the male patients compared to females. Since the cohort was small, no separate analyses of neuropsychological data was conducted. Instead the larger group (eighteen matched pairs) was analysed for their symptom and neuropsychology ratings and the results are presented in the next Chapters Four and Five. It is clear that additional participants need to be recruited into the gene expression part of the study so that firm conclusions can be made about this preliminary data.

Changes in PBMC gene expression following treatment with antipsychotic medication 58 in treatment naïve patients with schizophrenia

4. Cerebral cortical grey matter deficits and their associations with age, psychopathology, cognition and treatment response

4.1. Introduction

Since Kraepelin’s introduction of dementia praecox(Kraepelin E, 1899) as a disease entity (see historical overview 1.1), the diagnosis of schizophrenia continues to be based on patient introspection and behavioural observation (see 1.2). Diagnostic criteria now assign different weights to certain aspects of the psychopathology, such as positive (or psychotic) symptoms (i.e. hallucinations and delusions), negative symptoms (e.g. social withdrawal, lack of motivation, and impaired communication), and – more recently – cognitive symptoms (Keefe RS and Fenton WS, 2007). However, the diagnosis of schizophrenia is still biologically ill-defined and too broad to effectively support neurobiological research. Hence, the concept of intermediate phenotypes (or “end phenotypes”) (Gottesman II and Gould TD, 2003) has emerged as an alternative to diagnostic classification alone (see 1.4).

Kraepelin already related the neuropathology of dementia praecox to clinical features (Kraepelin E, 1899). Brain imaging now allows us to relate the morphology and function of the living brain to signs and symptoms of the disorder (Liddle PF, 1987). The current investigation will explore this association of structural brain measures with psychopathology in a sample of schizophrenia patients with a very short history of pharmacological treatment.

Most consistently reported in schizophrenia are enlarged lateral ventricles and grey matter reductions in the medial temporal lobes (e.g. in the hippocampus and amygdala), thalamus, prefrontal cortex, superior temporal cortex (reviewed in Wright et al., 2000; Honea RA et al., 2005; Shenton ME et al., 2001; Levitt JJ et al., 2010; Meyer-Lindenberg A, 2010) and the cerebellum (Andreasen NC and Pierson

Cerebral cortical grey matter deficits and their associations with age, 59 psychopathology, cognition and treatment response

R, 2008; Rasser PE et al., 2010). Grey matter deficits may emerge in the prodromal phase (Pantelis C et al., 2003). They typically manifest at the onset of the disorder (Steen RG et al., 2006) and further progress in the course of illness (Weinberger DR, 2002). Grey matter deficits have also been reported in close biological relatives, such as twins and siblings (Hulshoff Pol et al., 2004; Hulshoff Pol HE et al., 2006; Honea RA et al. 2008). Hence, structural brain changes in people with schizophrenia are increasingly accepted as an intermediate phenotype of the disorder (Glahn DC et al., 2008; Bearden CE et al., 2007; Prasad KM and Keshavan MS, 2008; Kaymaz N and van Os J, 2009).

Regional grey matter structural abnormalities have been increasingly reported in schizophrenia (Zipursky RB et al., 1992; El-Sayed M et al., 2010; Fischer BA et al., 2012). Regions include, prefrontal and temporal cortices (Fischer BA et al., 2012 and many others), parietal cortex (Whitford TJ et al., 2005; El-Sayed M et al., 2010; Collin G et al., 2012) and occipital cortex (El-Sayed M et al., 2010; Collin G et al., 2012). Grey matter changes have been reported in relation to various features (i.e. cognitive impairment) and psychopathology (including pathophysiology and genetics) of the disorder (e.g. Minatogawa-Chang TMet al., 2009, Schiffer B et al., 2010; Schall U et al., 2003, Hammer TB et al., 2012; Wylie KP, Tregellas JR., 2010, Palaniyappan L, Liddle PF., 2012; Fornito A et al., 2009; Harms MP et al., 2010).

Supported by brain imaging research, sub-grouping of patients with schizophrenia into sub-syndromes based on psychopathology is one way to derive more homogeneous patient groups (Jablensky A 2006 b). Nenadic and colleagues, for instance, reported different patterns of regional grey matter pathology in diagnostic sub-groups. The medial temporal cortex and cerebellum appears to be more affected in the disorganised sub-syndrome while abnormalities in the superior temporal cortex appear to be associated with the paranoid/hallucinatory sub-syndrome and the prefrontal cortex, together with the thalamus, with the negative sub-syndrome (Nenadic I et al., 2010).

Cognitive impairment affecting working/verbal memory, cognitive control/attention, and face/emotion processing in schizophrenia has also emerged as a promising intermediate phenotype of the disorder (Gur RE et al., 2007; Rasetti

Cerebral cortical grey matter deficits and their associations with age, 60 psychopathology, cognition and treatment response

R and Weinberger DR 2011). Cognition has been extensively studied with conventional neuropsychological tests and functional brain imaging, but associations with abnormal brain structure have been largely limited to volumetric measures, such as voxel-based morphometry, and findings are inconsistent (Antonova E et al., 2005; Rusch N et al., 2007; Wolf RC et al., 2008; Bonilha L et al., 2008).

Volumetric methods, however, may be less sensitive to quantifying grey matter and accurately mapping associations with disease features. To address these limitations, methods such as Cortical Pattern Matching (CPM) have been developed to generate highly accurate surface maps of grouped grey matter data (Thompson PM et al., 2004). Using this method, Rasser and colleagues found significant correlations of reduced regional grey matter thickness with reduced left- hemispheric prefrontal/frontal and bilateral parietal blood oxygenation level- dependent (BOLD) activation in first-episode schizophrenia patients (Rasser PE et al., 2005) when the patients performed an MRI-adapted Tower of London visual- spatial planning task (Schall U et al., 2003).

Using FreeSurfer (http://surfer.nmr.mgh.harvard.edu) and conventional neuropsychological tests, Hartberg and colleagues found that task-specific cognitive performance correlated with regional cortical grey matter thickness (Hartberg CB et al., 2010). Impaired verbal learning was associated with reduced grey matter thickness in right temporal, and right superior and middle frontal cortex, impaired executive function with left temporal and superior frontal cortex, and verbal IQ with insula, frontal and occipital cortex in both hemispheres. Diagnostic specificity, however, was only confirmed for associations of verbal IQ with regional grey matter deficits in the right temporo-occipital junction and left middle occipital gyrus.

Like many other studies, however, the previous study investigated patients with often-long histories of pharmacotherapy, which has been identified, to potentially contribute to grey matter deficits in schizophrenia (Ho BC et al., 2011; Thompson PM et al., 2009; Navari S and Dazzan P, 2009). The effect may vary depending on the type of antipsychotic used (i.e. typical antipsychotics have been

Cerebral cortical grey matter deficits and their associations with age, 61 psychopathology, cognition and treatment response associated basal ganglia volumes increase while atypicals appear to reverse this effect (reviewed by Smieskova R et al., 2009). Also, evidence from primate brain studies (e.g. Dorph-Petersen KA et al., 2005; Ho BC et al., 2011) suggests that global and regional volume reduction is associated with long-term antipsychotic medication exposure. Hence, antipsychotic pharmacotherapy should be considered a potential confounding effect when investigating brain function/structure associations. However, grey matter deficits are already present in the prodromal phase (Pantelis C et al., 2003) and in the first episode of psychosis (Narr KL et al., 2005a; Narr KL et al., 2005b) prior to introducing antipsychotic drug treatment. While these findings suggest the presence of a disease-related neuropathology around the onset of illness, its association with disease phenomenology at various stages of brain maturity (i.e. late adolescence/young adulthood versus mature adulthood) remains unclear. Hence, the pattern of regional grey matter deficits in the cerebral cortex, its interaction with age (Nenadic I et al., 2011; Kubota M et al., 2011), and associations with the pattern of clinical symptoms, neurocognitive deficits, neurological symptoms (i.e. motor coordination and motor sequencing) (Liddle 1987) as well as response to pharmacotherapy (MitelmanSA et al., 2007; Hartberg CB et al., 2010; Nenadic I et al., 2010) will be investigated in detail.

It is hypothesised that regional grey matter deficits are present in prefrontal, frontal, temporal and parietal cortical areas of patients diagnosed with schizophrenia with very little exposure to antipsychotic pharmacotherapy. It is further hypothesised that the degree of regional grey matter deficits predicts the severity of psychopathology and cognitive/neurological impairment as well as treatment response (i.e. poor response with more pronounced grey matter deficits) particularly in mature patients with a longer history of untreated psychosis.

4.2. Methods and materials

4.2.1. Participants’ recruitment and cohort characteristics

Cerebral cortical grey matter deficits and their associations with age, 62 psychopathology, cognition and treatment response

The Human Research Ethics Committees of the Universities of Newcastle (Australia), Sri Jayewardenepura (Sri Lanka) and the National Institute of Mental Health (NIMH) Sri Lanka approved the study. Patients gave informed written consent.

Eighteen schizophrenia patients (32.2 years [SD 14.3], 13 males and 5 females; meeting DSM-IV criteria for schizophrenia (Castle DJ et al., 2006) were recruited from the NIMH in the period January 2007 to July 2009. Previous antipsychotic pharmacotherapy, low global IQ (<70), a history of alcohol or illicit drug use, a neurological (e.g. epilepsy, traumatic brain injury) or chronic medical condition (e.g. diabetes), pregnancy, claustrophobia, pacemakers, or metal implants were the main study exclusion criteria. Nine patients had a family history of a schizophrenia spectrum disorder.

Eighteen pair-wise age (±2 years) and gender-matched healthy volunteers (31.9 years [SD 14.3]) were recruited through the Family Practice Centre of the University of Sri Jayewardenepura (Sri Lanka). Healthy volunteers were screened for a history of mental illness (including their first-degree biological relatives), a history of alcohol or illicit drug use, a neurological or chronic medical condition, low global IQ (<70), pregnancy, claustrophobia, pace makers, or metal implants and excluded from study participation accordingly.

Patients were rated on the Brief Psychiatric Rating Scale (BPRS) at study inclusion and referred to standard clinical care. Fourteen patients were commenced on 4 mg/day of risperidone and four patients received a combination of 2 mg/day of risperidone and 2 mg/day of haloperidol, thus equalling a total daily dose of 200 mg chlorpromazine equivalents (Woods SW, 2003) in both instances.

Six to twelve weeks into pharmacotherapy, patients underwent another clinical rating (BPRS) together with neuropsychological testing (i.e. Figure Copy, Figure Recall, Semantic Fluency, Digit Span, and Coding from the Repeatable Battery for the Assessment of Neuropsychological Status [RBANS] (Randolph C, 1998; Strunk KK et al., 2010) as well as an assessment of neurological soft signs

Cerebral cortical grey matter deficits and their associations with age, 63 psychopathology, cognition and treatment response and handedness (Neurological Evaluation Scale [NES](Buchanan RW and Heinrichs DW, 1989). At that time, high-resolution T1 MRI data were also collected on a 1.5 Tesla General Electrics Signa Exite whole body magnetic resonance scanner at the Asiri Surgical Hospital (Colombo, Sri Lanka). A three-dimensional spoiled gradient recalled (SPGR) pulse sequence with the following parameters was employed: 1 mm coronal slices without gaps, flip angle of 45°, 30 ms repetition and 6 ms echo time, 240 mm field of view, with an acquisition matrix of 256 x 192.

4.2.2. MRI processing: application of cortical pattern averaging

Processing of each participant’s three-dimensional high resolution structural MRI consisted of a preliminary correction of radio frequency bias (Sled JG, 1998) followed by a transformation to ICBM space (Mazziotta J et al., 2001) using a 12- parameter affine transformation. Skull striping was conducted using the Brain Extraction Tool (Smith SM, 2002) with the subsequent mask then used in the application of a secondary and more stringent radio frequency bias correction (Sled JG et al., 1998), followed by tissue classification (Zhang Y et al., 2001).

A preliminary mask of each hemisphere for each subject was extracted from a hemispheric cortical surface model (MacDonald D, 1994), followed by manually editing (blind to diagnosis) to exclude non-cerebral tissue and to delineate the boundaries for models of the cerebral hemispheres for each subject. Also blind to diagnosis, 31 sulci were identified and manually traced (Sowell ER et al., 2004) onto each subject’s cerebral hemispheres. The accuracy of inter-rater reliability of sulcal line tracing was determined using a set of six archived reference brains achieving < 2.5 mm deviation from all reference landmarks. Cortical pattern matching was then applied to align gyral features for each subject after a deformation to a geometric average target set of sulci (Thompson PM et al., 2004).

For each vertex of a subject’s cortical surface model, a kernel or radius 15 mm was used together with their tissue-classified volume to determine the grey matter density. This measure of grey matter was used to calculate the ratio of grey to non- grey matter voxels within the kernel. Parametric statistical maps of grey matter group differences and associations with age were calculated and permutation-

Cerebral cortical grey matter deficits and their associations with age, 64 psychopathology, cognition and treatment response tested for each hemisphere in order to correct the p-value by randomly assigning covariates across the subjects and estimating the chance that the overall surface area of supra threshold statistics (i.e. p<0.05) could have been obtained by chance in null data (Thompson PM et al., 1997; Thompson PM et al., 2003).

4.2.3. Data Analysis

Three dimensional structure of regional grey matter variance as defined by a deformable Brodmann area atlas (Rasser PE et al., 2004; Rasser PE et al., 2005) was assessed by Principal Component Analysis and factor scores extracted for each subject representing the global grey matter measure. Parametric statistics were used to test group differences (One-way ANOVA) and associations between continuous variables (Pearson correlation coefficient and linear regression) when assumptions of normal distribution and variance homogeneity in the data were met. Otherwise, nonparametric tests were employed accordingly (e.g. Wilcoxon test and Spearman’s rho). Statistical significance was tested two-sided at α=0.05 if not stated otherwise.

4.3. Results

4.3.1. Neuropsychology

Patients improved clinically with pharmacotherapy as rated on the BPRS (total score: 46.3 [SD 9.1] at study entry versus 39.5 [SD 5.6] at follow-up; p=0.002) in both symptom domains (positive symptoms: 20.3 [SD 4.8] versus 17.0 [SD 3.3]; p=0.001; negative symptoms: 8.0 [SD 1.9] versus 6.7 [SD 1.8]; p=0.002). BPRS total scores significantly correlated with the subset of positive (rho=0.86; p<0.001) and negative BPRS symptoms ratings (rho=-0.55; p<0.05), respectively.

Patients rated on average 5.0 (SD 1.2) on the Neurological Evaluation Scale (NES) consistent with a low expression of neurological soft signs (Buchanan RW and Heinrichs DW, 1989). By contrast, neuropsychological testing (RBANS) indicated significant cognitive impairment (i.e. > 3 SD below healthy norms; Randolph C, 1998) in Figure Copying (mean 8.1 [SD 3.0]), Figure Recall (4.3

Cerebral cortical grey matter deficits and their associations with age, 65 psychopathology, cognition and treatment response

[SD1.8]), Semantic Fluency (11.7 [SD 3.3]), Digit Span (6.2 [SD 1.7]), and Coding (14.2 [SD 5.7]). All but one patient was right-handed according to NES handedness scores.

4.3.2. Regional grey matter density variance across Brodmann areas

Regional grey matter density across all Brodmann areas in both hemispheres was highly inter-correlated with a single factor derived from a Principal Component Analyses explaining 54.7% of grey matter variance in healthy subjects and 51.3% in patients. The relative contribution to global grey matter in each group was assessed via factor loading scores calculated for individual Brodmann areas. All but one factor loading score (-0.06 in right anterior cingulate cortex [BA26] in healthy subjects) were positive, consistent with a synergistic inter- relationship of regional grey matter variance (Figure 4.1).

In healthy subjects, particularly high factor loading scores of > 0.8 were recorded bilaterally in left and right frontal lobes (BA6, 8, 9 & 10), extending into the left posterior parietal cortex (BA7),, right primary somatosensory cortex (BA1- 3), right primary motor cortex (BA4), right visual association cortex (BA19) and right Boca’s area (BA45)(Figure 4.1 A).

By comparison, patients presented with high factor loading scores (> 0.8) bilaterally in left and right frontal lobes (BA6, 8, 9, 10, 11, 44, 45, & 47), visual association cortex (BA19), and angular gyrus (BA39), extending into the left somatosensory association cortex (BA5), left middle temporal gyrus (BA21), left dorsal anterior and posterior cingulate (BA31 & 32), left dorsolateral prefrontal cortex (BA46), right primary somatosensory cortex (BA1-3), right occipital cortex (BA18), right rostral superior middle temporal area (BA38), and right supramarginal gyrus (BA40)(Figure 4.1 B).

4.3.3. Evidence of significant global grey matter reduction in patients

Group differences in global cortical grey matter were assessed by extracting factor scores for all Brodmann areas in both hemispheres via Principle Component Analysis of the combined sample.

Cerebral cortical grey matter deficits and their associations with age, 66 psychopathology, cognition and treatment response

When compared to the matched healthy subjects, global grey matter was significantly reduced in schizophrenia subjects (ANOVA: F(1,34)=4.84; p=0.035; ε2=0.15; (Figure 4.2 A). Permutation testing further confirmed the regional pattern of reduced grey matter in schizophrenia at p=0.031 for the left and p=0.006 for the right hemisphere. Moreover, permutation testing also confirmed a highly significant association of reduced regional grey matter with ageing for both hemispheres in both groups, respectively (p<0.006; Figure 4. A&C), thus significantly predicting global grey matter (Linear Regression: F(1,34)=13.67; p=0.001; ε2=0.30).

4.3.4. Grey matter reduction versus age and the duration of antipsychotic medication therapy

Pearson correlation analysis further confirmed significant associations of global grey matter with age in healthy subjects (r=-0.63; p<0.01) and patients (r=- 0.48; p<0.05). A median split for age indicates significantly reduced global grey matter in young schizophrenia patients only (i.e. ≤ 26.5 years) when compared to their age-matched healthy control subjects (ANOVA: F(1,17)=5.44; p=0.032) and not for older patients (i.e. > 26.5 years; F(1,17)<1.0;Figure 4. B).

Duration of illness (18.4 months [SD 29.1]) was highly correlated with age in patients (r=-0.69; p<0.01) and did not statistically differentiate from age-related effects on grey matter. Moreover, duration (8.4 weeks [SD 1.9]) of antipsychotic medication was not associated with global or regional grey matter measures in patients.

At a regional level, cortical grey matter was significantly reduced in schizophrenia (ANOVA: p<0.05 uncorrected) compared to the control group bilaterally in the left and right frontal/dorsolateral prefrontal cortex (BA8, 9, 10, 11 & 45), superior temporal cortex (BA22), anterior cingulate (BA32), left entorhinal area (BA28), right superior parietal lobule (BA5), right angular gyrus (BA39), right supramarginal area (BA40), and right dorsolateral prefrontal cortex (BA44, 46 & 47) (please see Supplementary Table 6 for more details).

Cerebral cortical grey matter deficits and their associations with age, 67 psychopathology, cognition and treatment response

Figure 4.1: Factor loading scores for Brodmann areas in left and right cerebral hemispheres: Factor loading scores for Brodmann areas in left and rights cerebral hemispheres of 18 schizophrenia patients (B) and 18 pair-wise age and gender-matched healthy volunteers (A). Factor loading scores were derived from Principal Component Analyses and represent the relative contribution of each Brodmann area to global grey matter variance in each group. Regional grey matter in prefrontal, frontal, parietal, occipital and left middle temporal cortex (red) – versus, for instance, right anterior cingulate cortex (blue) – largely determine global grey matter in healthy subjects. In patients, however, global grey matter is less determined by prefrontal and frontal grey matter consistent with a more widespread brain pathology in schizophrenia (Figure 4.2(A).

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Brodmann area map

Cerebral cortical grey matter deficits and their associations with age, 68 psychopathology, cognition and treatment response

However, only the right dorsolateral prefrontal cortex (BA46) emerged in a stepwise binary logistic regression as the brain area that is significantly discriminating both groups (F (1,34)=13.37; p=0.001; ε2=0.28) with an accuracy of 75% correct diagnostic classification. There was also a significant interaction of group by age for grey matter in right BA46 (F (1,33)=9.24; p=0.001; ε2=0.36).

However, group differences remained significant when including age as a covariate (F (1,33)=14.88; p=0.001; ε2=0.31).

4.3.5. Symptom ratings and cognitive functions versus regional grey matter density

Reduced regional grey matter correlated with more severe symptom expression rated at study entry as BPRS total score (Figure 4.2 B) in left and right posterior cingulate (BA23), right anterior cingulate (BA24), and left perirhinal cortex (BA35) (rho=-0.56 to -0.59; p<0.05).

When taking into account the directed nature of the hypothesised relationship and accepting one-sided testing at p<0.05, reduced grey matter in left and right posterior cingulate (BA23), right retrosplenial area (BA26), left occipital cortex (BA18), and right Boca’s area (BA45) correlated with the level of positive symptom expression (rho=-0.44 to -0.52). When splitting the sample according to median age, these associations of regional grey matter deficits with positive symptoms were only confirmed for the older patients (> 26.5 years; rho< -0.72; p<0.05) and not for the younger patients (≤ 26.5 years). There were no significant associations with negative symptom expression.

Moreover, reduced regional grey matter in the right ventro-temporal cortex (BA20) predicted poor Semantic Fluency performance (rho=0.59; p<0.05) while reduced grey matter in right dorsolateral prefrontal cortex (BA46) predicted poor Digit Span performance (rho=0.64; p<0.01).

Cerebral cortical grey matter deficits and their associations with age, 69 psychopathology, cognition and treatment response

21

Figure 4.2:

Parametric mapping (threshold p<0.05 uncorrected) of grey matter group differences: (A) Parametric mapping (threshold p<0.05 uncorrected) of grey matter group differences (18 schizophrenia patients versus 18 pair-wise age gender-matched healthy control subjects. Permutation testing confirms reduced grey matter (blue) in schizophrenia in left (p=0.031) and right hemisphere (p=0.006). (B) Correlation maps (threshold p<0.05 uncorrected) of grey matter by psychopathology ratings on the Brief Psychiatric Rating Scale suggest an association of reduced regional grey matter with more severe symptoms in patients. Please see text for further statistical and anatomical details.

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Brodmaan area map

Cerebral cortical grey matter deficits and their associations with age, 70 psychopathology, cognition and treatment response

Figure 4.3:

Parametric mapping (threshold p<0.05 uncorrected) of grey matter: Parametric mapping (threshold p<0.05 uncorrected) of grey matter by age in left and right cerebral hemispheres of 18 schizophrenia patients (C) and 18 pair-wise age and gender-matched healthy volunteers (A). Permutation testing confirms reduced grey matter with ageing (blue) in both groups and both hemispheres (P≤0.006). (B) Cerebral grey matter was globally reduced in schizophrenia (p=0.035; dotted lines represent standardised means for the control [CON] and patient group [SCZ]), particularly in young patients (i.e. <26.5 years = median split) versus their age-matched control peers (p=0.032). Solid lines represent quadratic regressions graphs of global grey matter by age for each group (blue = CON; green = SCZ) as well as for the combined sample (black).

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Brodmaan area map

This pattern of structural/functional associations was confirmed for both

Cerebral cortical grey matter deficits and their associations with age, 71 psychopathology, cognition and treatment response patient subsamples when analysing the data after median age split (i.e. 26.5 years; rho< 0.72; p<0.05). However, there were no significant associations of regional grey matter with Figure Recall and Coding performance, or with neurological soft signs (NES).

4.4. Discussion

Consistent with previous reports (Wright IC et al., 2000; Honea RA et al., 2005; Mitelman SA et al., 2007; Shenton ME et al., 2001; Levitt JJ et al., 2010; Meyer- Lindenberg A, 2010; Nenadic I et al., 2010), the results confirm reduced regional grey matter with medium to large effects sizes in cingulate cortex and various temporal, parietal, frontal and prefrontal brain areas with cortical pattern matching in previously untreated schizophrenia patients. These cross-sectional findings are consistent with recent reports showing that cerebral grey matter losses occur in a relatively limited period of time around the onset of illness with little progression thereafter (Bose SK et al., 2009; Kubota M et al., 2011) with some progression thereafter (Thompson PM et al., 2001; van Haren NE et al., 2007; Ho BC et al., 2011).

Medication effects on brain volumes vary considerably and seem to depend on the type of medication and duration of treatment (Dazzan P et al., 2005; Thompson PM et al., 2009; Ho BC et al., 2011). Haloperidol, for instance, seems to be associated with more profound grey reduction whereas some atypical antipsychotics appear to reverse this effect (Dazzan P et al., 2005; Scherk H, Falkai P., 2006; Hulshoff Pol HE 2001). Quetiapine has been found to increase cerebral grey matter and striatal volumes following short periods of intervention (Stip E et al., 2009; Chua SE et al., 2009). Hence, confounding medication effects on grey matter measures cannot be ruled out completely

4.4.1. Age effects on grey matter in schizophrenia

Cerebral cortical grey matter deficits and their associations with age, 72 psychopathology, cognition and treatment response

Previous studies suggested an impact of duration of untreated illness on grey matter pathology (Malla AK et al., 2011) whereas atypical neuroleptics appear to slow down this process (Dazzan P et al., 2005; Stip E et al., 2009; Chua SE et al., 2009). The current data, however, does not allow exploring the respective effects of age and duration of untreated illness due to the high correlation of these two variables.

4.4.2. Grey matter correlates of cognitive impairment

Cognitive impairment has been consistently linked to grey matter deficits in prefrontal and temporal brain areas across (Hartberg CB et al., 2010; Minatogawa- Chang TM et al., 2009). Functionally, regional grey matter in ventral, temporopolar and hippocampal areas correlated with poor verbal (Semantic Fluency) and poor visuospatial (Figure Copying) performance while grey matter deficits in right dorsolateral prefrontal cortex were correlated with impaired working memory (Digit Span) in patients. Grey matter deficits in right dorsolateral prefrontal cortex were also the strongest statistical predictor of schizophrenia in the current study.

4.4.3. Grey matter correlations of psychopathology

The current study suggests associations of reduced grey matter in Boca’s area and cingulate and occipital cortex with psychotic symptoms while reduced grey matter in cingulate, somatosensory, visual, motor, and parietal language areas is predictive of poor response to pharmacotherapy (coded by BPRS). Particularly, reduced grey matter in anterior and posterior cingulate cortex correlated with the severity of psychopathology prior to any interventions as rated on the BPRS (Fornito A et al., 2008; Venkatasubramanian G, 2010). Positive symptoms were associated with reduced grey matter in posterior cingulate cortex and right Boca’s area while no structure/function associations were confirmed for negative symptoms and neurological symptoms (NES). Previous reports suggested association of widespread grey matter reduction with symptom profiles (i.e. psychomotor poverty, disorganization and reality distortion (i.e. in the early phase of illness; Whitford TJ et al., 2005) which was not confirmed in the current sample

Cerebral cortical grey matter deficits and their associations with age, 73 psychopathology, cognition and treatment response due to the relatively low expression and minimal variance of negative and neurological symptom ratings in the patient sample.

4.4.4. Neuropathology of schizophrenia

Notably, grey matter variance in (right) anterior cingulate was statistically independent from global grey matter variance in healthy subjects. This brain area is involved in conflict monitoring thereby supporting goal-directed behaviour (Van Veen V and Carter C, 2002). In patients, grey matter deficits in anterior cingulate cortex correlated with global grey matter deficits and severity of psychotic symptoms (McGuire et al., 1998). Neuropathology in this brain region has been strongly implicated in schizophrenia (Fornito A et al., 2009; Venkatasubramanian G 2010). The current data suggests that the deficits in anterior cingulate cortex are not distinct or independent of the more global and widespread grey matter pathology as it occurs in schizophrenia.

Nevertheless, the findings confirm that regional cerebral grey matter deficits are correlated with psychopathology and cognitive impairment in schizophrenia, thus supporting the notion of cerebral grey matter deficits as an intermediate phenotype of the disorder. While these findings obtained here with Cortical Pattern Matching in previously untreated patients are consistent with previous reports using other methods (Wright IC et al., 2000; Honea RA et al., 2005; Mitelman SA et al., 2007; Nenadic I et al., 2010), the current sample lacks the power for more detailed structure/function analyses with the appropriate α adjustment for multiple testing.

4.4.5. Heritability of brain morphology

Schizophrenia is a highly heritable disorder (Matthysse SW and Kidd KK, 1976). Likewise, cerebral grey matter morphology – particularly in frontal and parieto- occipital association cortex as well as Boca’s and Wernicke’s areas – is strongly genetically influenced (Thompson PM et al., 2002). Hence, a pathogenetic mechanism leading to schizophrenia may also predominantly affect the structural integrity of those brain areas with a high level of heritability. Our healthy participant data indeed suggest a high level of inter-correlated cerebral grey

Cerebral cortical grey matter deficits and their associations with age, 74 psychopathology, cognition and treatment response matter variance between brain areas that also have a relatively high degree of morphological heritability.

In schizophrenia, the genetic determination of the regional grey matter formation may be disturbed in the prodromal phase of the illness, thereby resulting in the observed grey matter deficits in frontal, sensorimotor, linguistic and parieto-occipital areas along with functional impairment in a region-specific pattern as shown here and by others (Antonova E et al., 2004; Crespo-Facorro B et al., 2007). This pattern of grey matter pathology in schizophrenia may also reflect disturbed connectivity amongst the affected brain areas, which functionally impacts on the normal integration of information processes, such as sensorimotor integration within and across modalities. Hence, future studies should investigate grey and white matter pathology in schizophrenia concurrently and longitudinally.

Cerebral cortical grey matter deficits and their associations with age, 75 psychopathology, cognition and treatment response

5. Exploring associations of PBMC gene expression and grey matter pathology in schizophrenia

5.1. Introduction

A major limitation of integrating the clinical, neurocognitive, brain imaging, and gene expression data is the small sample size relative to the large amount of possible tests when exploring potential associations of gene expression data with the schizophrenia phenotype.

Therefore, the statistical analysis primarily followed a stepwise data reduction strategy with the aim of identifying a pattern of associations of genes – which are differently expressed in schizophrenia – with reduced regional grey matter, clinical symptoms and cognitive impairment. It is also important to stress that the approach taken here is not based on biological models as presented in Chapter Three of this thesis. It rather employs statistical analyses to identify associations of a selected sample of schizophrenia candidate genes with brain pathology as identified in the previous section using regional cerebral grey matter reduction in schizophrenia. Global effects from a combination of selected genes as well as the effects of individual genes in relation to treatment effects and brain morphology will be explored.

Given the nature of this statistical approach, which is blind to biology, some findings are necessarily not always consistent to those reported earlier. However, we believe that this analysis will reveal interesting associations that could be the subject of future research.

5.2. Summary of findings

Figure 5.1 provides an illustration of the conceptual framework of the current thesis. Its first important finding is that cerebral grey matter thickness measures

Exploring associations of PBMC gene expression and grey matter pathology in 76 schizophrenia inter-correlate across brain regions, particularly for those brain areas where the morphology appears to be under strong genetic control. Most of these brain areas are also affected by schizophrenia (i.e. grey matter reduced compared to matched control subjects) suggestive of a pathological process that is interfering with their genetically determined morphology. There was also some preliminary evidence that regional grey matter reduction correlated with cognitive impairment. For instance, with BA20 (inferior temporal gyrus), believed to play a part in high-level visual processing and recognition memory and 46 (Dorsolateral prefrontal cortex), which, thought to be involved with sustaining attention and working memory. There is also, association of positive symptom ratings and the treatment response with BA23 (posterior singulate cortex). More details on above brain areas are given in the previous chapter.

A similar degree of uniformity was also confirmed for a selection of 21 schizophrenia candidate genes or genes involved with brain development or other brain functions, which were differentially expressed in the schizophrenia patients who were participating in the current study. As for regional grey matter reduction, differential gene expression was also highly inter-correlated, thus resulting in a single factor (extracted by Principle Component Analysis), which explained 60% of gene expression variance and also significantly discriminated the two groups. While this approach is not biologically valid in terms of the functional relationship of these genes, it nevertheless suggests that altered gene expression in schizophrenia affects a set of genes rather than individual genes in isolation.

This interpretation is biologically plausible and consistent with the degree of genetic heterogeneity found in schizophrenia (Garver DL et al., 1989; Pulver AE et al., 2000; Jablensky A, 2006). However, many more genes were found to be differentially expressed in the current schizophrenia sample while even more genes have been identified by others (e.g., Jablensky A, 2006; Sullivan PF et al., 2006a and more recently Yue WH et al.;) or were not covered by the current arrays. However, some recent genome wide association studies involving larger numbers of samples have generated more biologically plausible gene lists (Wray RM, Visscher PM, 2010; Bergen SE, Petryshen TL, 2012).

Exploring associations of PBMC gene expression and grey matter pathology in 77 schizophrenia

Symptoms & Cognitive Impairment

Regional grey matter deficits

Brain Pathology

Antipsychotic Brain development Medication/ Environment RNA

Micro RNA Gene expression

DNA

Figure 5.1: The current study investigated gene expression and clinical symptoms (before and after commencing antipsychotic treatment), in vivo grey matter pathology by MRI brain scans, and neurocognitive impairment in a Sri Lankan cohort of previously treatment-naïve schizophrenia patients.

Nevertheless, this pattern of inter-correlation suggests that a few, or perhaps even a single biological mechanism may be responsible for the here-observed uniform changes in gene expression.

Exploring associations of PBMC gene expression and grey matter pathology in 78 schizophrenia

5.3. Associations of gene expression, neurocognitive and clinical findings with regional grey matter density

5.3.1. Global PBMC gene expression and global cerebral cortical grey matter deficit

Reduced gene expression in schizophrenia was confirmed (p<0.01 uncorrected) for COG2, DGCR6, EIF2B2, MAL, RGS10, RPL12, RPL24, RPP21, RPS12, RPS4X1, and RPS4X2 whereas AKT1, CSF2RA, DISC1, FHOD1, HGS, MBP, MMP9, PPM1F, RXRA, and SLC26A8 showed increased expression in schizophrenia which is in accordance with the SAM analysis conducted on the microarray data and presented in Chapter Three where the function and relation to schizophrenia of a number of these genes was discussed. These changes in gene expression were highly inter-correlated and a single factor score for global gene expression explaining 60.0% of variance and significantly discriminating the two groups (F (1,19)=145.4; p<0.001; ε2=0.88) was extracted by PCA. Of the group- discriminating genes, the expression of AKT1, DISC1, FHOD1, RGS10, RPL12, RPL24, RPS12, and RPS4X2 significantly changed (p<0.05 uncorrected) in the course of pharmacotherapy towards the gene expression levels found in healthy subjects. However, these gene expression changes did not inter-correlate. Global gene expression was not associated with global grey matter changes (Figure 5.2). There was also little overlap with grey matter deficits in Brodmann areas when testing for regional associations of altered gene expression with grey matter changes (Table 5.1).

The expression of some individual genes, however, correlated global grey matter density (Figure 5.3). For instance, reduced RPP21 expression correlated significantly (Spearman; uncorrected) with reduced grey matter (rs=0.59; p=0.02) in the combined group.

This association was confirmed in the control group (rs=0.76; p<0.03) but not in patients (rs=0.29) as with any of the other here selected candidate genes. On the other hand, increased expression of CSF2RA (rs=0.91; p=0.002) and PPM1F

(rs=0.74; p<0.04) correlated with globally increased grey matter in healthy subjects.

Exploring associations of PBMC gene expression and grey matter pathology in 79 schizophrenia

Moreover, gene expression also significantly correlated (p<0.05 uncorrected) with reduced cortical grey matter in some Brodmann areas for the combined group (Table 5.1). The pattern of associations (Spearman correlation analysis; p<0.05 uncorrected) with regional grey matter reductions will be summarised for individual genes.

5.3.2. Summary for individual genes

(i) DGCR6 (DiGeorge Syndrome Critical Region gene 6) is associated with various psychiatric phenotypes, including schizophrenia (Liu H et al., 2002; Chakravarti A, 2002). The gene is located on 22q11 and is involved in neural migration during brain development. An expression increase of DGCR6 was associated with reduced grey matter in prefrontal, orbitofrontal, frontal,

Figure 5.2: Scatter plot of global gene expression by global grey matter reporting factor scores for healthy control subjects (CON) and schizophrenia patients (SCZ).

Exploring associations of PBMC gene expression and grey matter pathology in 80 schizophrenia

temporal, parietal and occipital areas (Table 5.1). This association was confirmed for most Brodmann areas in healthy subjects as well as in patients for left BA9 in the prefrontal cortex and right BA1, 2,3 and 4 in the motor and somatosensory cortex. Contrary to this association, however, DGCR6 expression was reduced in patients prior to pharmacotherapy (i.e. below the level of expression of healthy subjects).

(ii) DISC1 (Disrupted in Schizophrenia 1) is a known schizophrenia candidate gene (Morris JA et al., 2003; Ozeki Y et al., 2004; Hennah W and Porteous D, 2009; Duan X et al., 2007). The current study suggests higher expression of DISC1 in schizophrenia patients together with reduced grey matter in right BA24 (anterior cingulate) while DISC1 expression normalised in the course of pharmacotherapy. By contrast, the reverse association was confirmed in healthy subjects bilaterally for BA10, 38 and 44, left BA7, 9, 19, 32, 39, 40, 44 and 46, and right BA10, 18, 25, 45 and 47; that is reduced grey matter in all cortical lobes with reduced DISC1 expression.

Exploring associations of PBMC gene expression and grey matter pathology in 81 schizophrenia

Table 5.1: Spearman correlation statistics for significant (p<0.05 uncorrected) associations of gene expression with reduced mean grey matter in cortical Brodmann areas (BA) for patients prior to pharmacotherapy and healthy control subjects combined; *BA24 & 25 are showing a reversed association; #gene expression changes towards normal levels in the course of pharmacotherapy; Arrows indicate direction of gene expression in relation to predominantly grey matter reduction. Corresponding Brodmann areas affected in both hemispheres are highlighted. Brodmann areas with reduced grey matter in schizophrenia patients versus healthy control subjects are underlined and direction of gene expression in schizophrenia patients (SZ) prior to neuroleptic treatment versus healthy control subjects (C) is indicated (↓ or ↑).

Candidate genes Left hemisphere Right hemisphere Expression SZ vs. C 1,2,3,4,5,6,7,9,11,18,19, DGCR6↑ 1,2,3,4,7,17,19,35,39 ↓ 21,39,44

DISC1↑ 24 ↑#

HGS↑ 25,27 ↑

MBP↑ 25,35,36 32,45,46,47 ↑

RPS12↑ 5,21 26,27 ↓#

RPS4X1↑ 20 ↓

SLC26A8↑ 23,30,32,35 24,32 ↑

MMP9↓ 5,7,9,11,20,21 7,20,21,*24,27 ↑

PPM1F↓ 11 11,20 ↑

RGS10↓ 24,46 ↓#

RPL12↓ 24 ↓#

RPP21↓ 23,40 1,2,3,5,6,17,22,31,43,46 ↓

RPS4X2↓ 24 ↓#

1,2,3,5,7,9,10,*25,43, RXRA↓ ↑ 44,46

Exploring associations of PBMC gene expression and grey matter pathology in 82 schizophrenia

Figure 5.3:

Lateral and medial views of cerebral correlation maps of individual candidate gene contributions to grey matter loss in schizophrenia. Reference: Brodmann area map and deformable Brodmann atlas

Exploring associations of PBMC gene expression and grey matter pathology in 83 schizophrenia

(iii) Increased expression of HGS (Hepatocyte Growth Factor-regulated Tyrosine Kinase Substrate) was associated with schizophrenia and reduced grey matter in left BA25 and 27. This association was confirmed in healthy subjects and also included right BA25 and 32 in the cingulate. However, this association of increased HGS expression with reduced grey matter was not confirmed for other brain areas in patients.

(iv) MBP (Myelin Basic Protein gene) has been suggested as a potential susceptibility gene for schizophrenia (Baruch K et al., 2009) with abnormal protein levels described for the entorhinal cortex of post-mortem schizophrenia brains (Parlapani E et al., 2009). The current study suggests increased MBP expression that was particularly associated with right prefrontal grey reduction (e.g. BA45, 46 and 47) in the combined group. MBP expression was also increased in the schizophrenia cohort where expression increase significantly correlated with reduced grey matter in left BA35 & 36 in perirhinal cortex but not with right- prefrontal region.

(v) RPS12 (Ribosomal Protein S12) has been previously linked to colorectal cancers. For the combined data set of the current study, increased RPS12 was associated with grey matter reduction in left BA5 (somatosensory cortex), left BA21 (middle temporal gyrus), and right BA26 & 27 (cingulate gyrus and rostral part of parahippocampal gyrus). This association was not confirmed in healthy subjects alone while the reversed association (i.e. decreased RPS12 expression with reduced grey matter) was confirmed in patients for BA6, 7, 17, 18, 19 & 22 in both hemispheres as well as for left BA21, 26 & 40 and right BA 43 & 44. This finding is also consistent with reduced expression of RPS12 in schizophrenia, which recovered in the course of antipsychotic treatment.

(vi) Increased expression of RPS4X1 (Ribosomal Protein S4, X-linked) was associated with reduced grey matter in left inferior temporal gyrus (BA20) in the combined data set. In patients, this association was also found in the somatosensory cortex (BA5) and the left dorsolateral prefrontal cortex (BA9 & 43). In healthy subjects, this association was reversed for left anterior cingulate (BA39) and right orbitofrontal cortex (BA11 & 47).

Exploring associations of PBMC gene expression and grey matter pathology in 84 schizophrenia

(vii) SLC26A8 is a member of Solute Carrier Family 26 or Sulphate Anion Transporter genes, which mediate chloride, sulphate and oxalate transport. The expression of this gene was increased in patients compared to healthy control subjects. Pharmacotherapy did not alter the expression of SLC26A8. In the combined group, increased expression of SLC26A8 was associated with reduced grey matter in cingulate (Table 5.1). In healthy subjects, this association was not confirmed for this brain area but for the left middle temporal gyrus (BA21) while increased expression of SLC26A8 in patients correlated with reduced grey matter in the right somatosensory cortex (BA5) and right temporopolar region (BA38).

(viii) MMP9 (Matrix Metallopeptidase 9) plays a crucial role in local proteolysis of the extracellular matrix and in leukocyte migration. Functional polymorphism of this gene has been linked to schizophrenia (Rybakowski JK et al., 2009). Across the two groups in the current study, lower MMP9 expression correlated with reduced grey matter in the parietal lobe (BA5 & 7) and middle and inferior temporal gyrus (BA20 and 21). Other areas included right parahippocampal gyrus (BA27) and left prefrontal areas (BA9 and 11) while grey matter reduction in the right anterior cingulate (BA24) correlated with increased MMP9 gene expression. In healthy subjects, the association of reduced gene expression with reduced grey matter was confirmed for left prefrontal cortex (BA8 and 9), left perirhinal cortex (BA35), right prefrontal cortex (BA10), posterior entorhinal cortex (BA28) and parahippocampal gyrus (BA36), parietal (BA40) and visual cortex (BA19) and temporal lobe (BA20, 21 and 22). In patients, this association was confirmed for prefrontal (BA9), parietal (BA5) and inferior sensorimotor cortex (BA43) in the left hemisphere. However, patients presented with increased MMP9 expression that was only found to be associated with decreased grey matter in BA24 for both groups combined.

(ix) Overexpression of PPMIF (Protein Phosphatase Mg2+/Mn2+ Dependent 1F) has been shown to mediate caspase-dependent apoptosis (Jaiyen Y et al., 2009) of human mononuclear cells. In the current study, patients presented with increased gene expression prior to treatment whereas decreased gene expression correlated with prefrontal grey matter reduction in BA11 in the

Exploring associations of PBMC gene expression and grey matter pathology in 85 schizophrenia combined group. This association with BA11 – but also with grey matter reduction in the right temporopolar region – was confirmed for the patient sample while reduced grey matter in left perirhinal cortex (BA35) and right inferior temporal (BA20) and supramarginal gyrus (BA40) correlated with low PPMIF expression in healthy subjects.

(x) RGS10 (Regulator G-protein Signalling 10) inhibits neuronal signal transduction by increasing the GTPase activity of G-protein alpha subunits. Various reports have linked this gene to schizophrenia (e.g. Mirnics K et al., 2001; Hishimoto A et al., 2004). The current study suggests reduced RGS10 expression with reduced grey matter in right anterior cingulate (BA24) and right dorsolateral prefrontal cortex (BA46) for the combined sample. In patients, RGS10 expression was also found to be reduced compared to controls, which recovered in the course of pharmacotherapy (Table 5.1).

(xi) As for RGS10, RPL12 (Ribosomal Protein L12) expression was reduced in schizophrenia patients and correlated with reduced grey matter in right anterior cingulate (BA24). RPL12 expression recovered with clinical improvements and pharmacotherapy.

(xii) RPP21 (Ribonuclease P/MRP 21kDa subunit) appears to be associated with schizophrenia (Glessner JT and Hakonarson H, 2009). The current studies suggest lower expression of this gene to be associated with global grey matter reduction (Figure 5.4), particularly in the right hemisphere (i.e. prefrontal, frontal, parietal, occipital and temporal cortex; Table 5.1). Patients presented with lower RPP21 gene expression at study entrance and study completion when compared to controls. In patients, lower expression also correlated with reduced grey matter in BA5 (somatosensory cortex) and BA17 (primary visual cortex) in the right hemisphere whereas in healthy controls such associations were predominantly found in the left hemisphere (e.g. BA21, 22, 23, 30, 32, 40, 43, 44 and 46) with some associations in right BA4, 39 and 43. RPP21 gene expression was also reduced with ageing (rs=-0.50; p=0.02).

Exploring associations of PBMC gene expression and grey matter pathology in 86 schizophrenia

(xiii) Right anterior cingulate (BA24) grey matter reduction correlated with low RPS4X2 (Ribosomal Protein S4 gene; X-linked) gene expression that recovered with pharmacotherapy. RXRA (Retinoid X Receptor Alpha gene) has previously been linked to schizophrenia (Wallen-Mackenzie A et al., 2003).

Mirroring the areas identified for the right-hemispheric associations of reduced grey matter with low RPP21 expression, reduced RXRA expression correlated with reduced grey matter in the corresponding brain areas of the left hemisphere in the current study. This was also confirmed in patients for left BA9, 44 & 46 and extended to the right hemisphere thereby affecting right BA1, 2, 3, 4 and 39. Healthy control subjects also presented with some RXRA gene expression by grey matter correlations, such as left BA7 and 32 and right BA37.

In general terms, however, global grey matter pathology was not associated with this signature of altered candidate gene expression (Figure 5.2). This finding may indicate that altered gene expression, as measured here in lymphocytes, is not directly related to the regional grey matter reduction as it was confirmed for schizophrenia patients in the current study. However, the small sample size does not allow the detection of small to medium sized effects, which in turn is the likely magnitude for any gene expression associations with grey matter changes.

This interpretation is also supported by the inconsistent pattern of individual gene expression associations with regional grey matter changes. For instance, the current study suggests a correlation of increased DGCR6 expression with reduced grey matter whereas patients present with reduced DGCR6 expression when compared to healthy control subjects. On the other hand, ‘this pattern of gene expression by brain pathology correlations’ (see also MMP9, PPMIF and SLC26A8) affected corresponding cortical regions on both hemispheres, thus rendering a pure chance finding unlikely.

5.3.3. Region of interest approach: anterior cingulate

Structural and functional associations of anterior cingulate region (BA24) has been repeatedly reported in schizophrenia (Yücel M et al., 2002; Riffkin Jet al., 2005;Baiano M et al., 2007;Fornito A et al., 2009; Venkatasubramanian G, 2010).

Exploring associations of PBMC gene expression and grey matter pathology in 87 schizophrenia

Figure 5.4: RPP21 candidate gene expression predicted global grey matter changes (CON: control subjects, SCZ: schizophrenia subjects).

Exploring associations of PBMC gene expression and grey matter pathology in 88 schizophrenia

In the current study, BA24 appears to be a key region showing a consistent pattern of inter-correlated grey matter reduction across the brain. In the combined sample (i.e. patients and controls), grey matter reduction in right BA24 was significantly correlated (rs=0.39 to rs=0.71; p<0.05 to p<0.001 uncorrected) with grey matter reduction in 11 Brodmann areas in the left hemisphere (i.e., BA23, 24, 26, 27, 28, 29, 30, 31, 32, 33 and 35) and 5 areas in the right hemisphere (i.e., BA23, 26, 29, 31, 32 and 39). These inter-correlations were not confirmed for the control group but in the patient group, thereby affecting 13 Brodmann areas in the left hemisphere (i.e., BA8, 18, 23, 24, 26, 27, 28, 29, 30, 32, 33, 35 and 40) and 11 areas in the right hemisphere (i.e., BA8, 19, 23, 26, 27, 29, 30, 31, 32, 33 and 35). This finding may suggest a diagnosis-specific pattern of wide-spread grey matter pathology in frontal, parietal, occipital, middle temporal and perirhinal cortices, as well as dorsal and posterior cingulate cortex in both hemispheres, that is linked to reduced grey matter in right anterior cingulate cortex in schizophrenia.

When employing this index region approach, reduced grey matter in right BA24 was most consistently associated with altered gene expression of six candidate genes (i.e., DISC1, SLC26A8, MMP9, RGS10, RPL12, and RPS4X2). The expression of these genes together explained 68.5% of grey matter variance of right BA24 in the combined sample (F[6,8]=2.9; p=0.08). Specific to diagnosis, however, was only DISC1 (Figure 5.5), explaining 57.9% of the right BA24 grey matter variance (F[1,13]=17.9; p=0.001) or 60.2% of the variance specifically in patients (F[1,5]=7.6; p=0.04).

Exploring associations of PBMC gene expression and grey matter pathology in 89 schizophrenia

Figure 5.5: Grey matter correlations of gene DISC 1expression in relation to antipsychotic therapy. Left: Expression of DISC1 (in patients prior to pharmacotherapy) predicts grey matter deficits in right anterior cingulate cortex Right: Level of grey matter deficits in right BA24 also predicts level of positive symptoms (rs= -0.65; P<0.02). Mean 95% confidence intervals are presented

This association of the these six candidate genes was also consistent with the pattern of up- or down-regulation when patients were compared to healthy control subjects prior to pharmacotherapy. Moreover, the expression of three of these genes (i.e. DISC1, RGS10, and RPL12) normalised in the course of neuroleptic treatment. Grey matter deficits in right BA24 also correlated with symptom expression (particularly positive symptoms) at study entry as well as when re- assessed 5 to 8 weeks into treatment. The findings in relation to DISC1 and anterior cingulate cortex are summarised in Figure 5.6.

However, right BA24 was not a brain region with confirmed grey matter reduction when compared to healthy control subjects (Table 5.1). Also, this region did not show any significant associations with cognitive performance measures in patient sample, though it had been implicated previous studies (e.g. Szeszko PR et al., 2007)

Exploring associations of PBMC gene expression and grey matter pathology in 90 schizophrenia

Symptoms and Treatment Response

Regional grey matter deficits

Anterior Cingulate Cortex

Pharmaco- Brain development therapy DISC1

Micro RNA Gene expression

DNA

Figure 5.6: Summary of the findings involving DISC1 gene expression. DISC1was found to be over-expressed in treatment-naïve patients. DISC1 expression normalised in the course of pharmacotherapy along with improving symptoms. DISC1 expression in patients also predicted grey matter deficits in right anterior cingulate cortex, along with grey matter deficits in various other associated brain regions

Ageing, on the other hand, correlated significantly with right BA24 grey matter reduction in patients only (Table 5.1). This finding may indicate that grey matter loss in BA24 may also constitute an effect of duration of untreated illness when considering its high correlation of age in patients only.

Exploring associations of PBMC gene expression and grey matter pathology in 91 schizophrenia

5.4. Conclusions and study limitations

The current study is probably the first of its kind investigating brain function and structure as well as their associations with gene expression in a relatively small sample of previously treatment-naïve schizophrenia patiens. The findings indicate that regional cerebral grey matter deficits are correlated with psychopathology and cognitive impairment in schizophrenia. These results are supporting the notion of cerebral grey matter deficits as an intermediate phenotype of the disorder. The study also indicates that quantitative brain maps have the potential to better capture schizophrenia heterogeneity. However, the current study lacks power to support the detection of other potential associations of gene expression with the schizophrenia phenotype, particularly when assuming a heterogenic risk model with an unknown number of relatively small but synergistic effects.

Notably, this PhD project has suffered from limitations due to complex logistical problems when conducting the RNA collection in Sri Lanka (also described earlier in Chapter Three). Whilst these were resolved by the refinement of the methodology and the subsequent microarray data generated underwent analysis using multiple corrections in the SAM computer software package, the sample size remained relatively small, particularly in relation to the large number of variables investigated in this thesis. In addition, the lack of matching of the two groups of participants in the analysis of PBMC gene expression by microarrays did not allow us to execute more statistically sound pair-wise analysis and thus exposes the data to gender bias. Since it was not possible to collect another pair- wise matched cohort at the time, one other possibility would have been to pool samples in the controls and compare the patients to the pooled samples, however this introduces its own limitations. In spite of this limitation, the approach did examine the expression of genes from PBMCs in patients both before and then again after treatment of the same participants. As such the main variable in that analysis is the change in treatment that showed there to be a generalised reduction in the differences in gene expression between schizophrenia and controls, after the six weeks of treatment.

Exploring associations of PBMC gene expression and grey matter pathology in 92 schizophrenia

Nevertheless, the study has clearly demonstrated as a proof of concept that combining molecular genetics with sophisticated brain imaging methodology can guide research into the neurobiology of schizophrenia beyond the limitations of a purely diagnostic approach when defining the phenotype.

5.5. Future Directions

The research presented in Chapter Four and Five suggests that regional cerebral grey matter deficits correlate to some extent with psychopathology and cognitive impairment in schizophrenia. These findings support the notion of cerebral grey matter deficits as an intermediate phenotype of the disorder.

The data presented in Chapter Three further suggest that biological functions related to immunity, inflammation and response to infection are activated in treatment-naïve schizophrenia patients. Most interestingly, antipsychotic pharmacotherapy appears to partially ‘normalise’ gene expression in the identified biological pathways. This would suggest that the genetic signatures of immune response are likely to reflect pre-treatment states, thus providing some evidence for an activation of the immune system in acute schizophrenia. These findings are new and consistent with recent reports showing that at least a subsample of schizophrenia patients can be tested positive for autoantibodies of brain tissue (Goldsmith CA, Rogers DP. 2008) However, whether this finding can be generalised is subject to on going investigations.

Nevertheless, schizophrenia is still considered the heterogeneous disorder without a defining pathophysiology. Two approaches may be considered for future research. One approach may be to take the current study as a preliminary study and repeat the investigation in a much larger sample for confirmation. Considering the scale of such a project, which may require hundreds of participants, a more targeted examining single nucleotide polymorphisms or SNPs to test a specific hypothesis (for example, to examine the role of AKT1 that is linked to the immune/infection hypothesis of schizophrenia) may be more feasible. Another suggestion would be to look at patients’ gene expression with other antipsychotic

Exploring associations of PBMC gene expression and grey matter pathology in 93 schizophrenia drugs to see if they also have the same effect as the current study. If they all cause normalisation of gene expression it might suggest that all antipsychotics have the ability to change the immune signature. Another approach may be to test the microRNA (miRNA) expression before and after treatment with antipsychotics. Recently published results on Peripheral Blood Mononuclear Cell miRNA expression (Gardener et al., 2011) also suggest an immune signature in Schizophrenia.

Whatever the approach, however, the findings will yield bits and pieces of information necessary to fill in the gaps in the current knowledge base of schizophrenia and may finally lead to a biologically plausible discovery of the aetiology of the disorder.

Exploring associations of PBMC gene expression and grey matter pathology in 94 schizophrenia

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Appendix

List of Publications

Conference Presentations:

• Poster: Gene expression analysis of schizophrenia patients. (2010) Displayed at 11th Schizophrenia conference Sydney Australia (First Author).

• Abstract: Kumarasinghe N. et al., (2010) Aust. Nz. J. Psychiatry 44 Suppl. 1, A52 (First Author).

• Oral: Gene expression in Schizophrenia in the course of Antipsychotic pharmacotherapy. (2011) 3rd European conference on Schizophrenia Research, Berlin (First Author).

• Oral: Association of cerebral cortical grey matter deficits, cognitive neurological measures and symptom ratings in schizophrenia. (2011) 1st international conference on Psychology and Allied Professionals Sri Lanka (First Author).

• Oral: Cerebral cortical grey matter and their association with associated factors in previously never medicated patients in Sri Lanka. Presented at annual academic sessions 2012 (May), Sri Lanka Collage of Psychiatrists (First Author)

Oral: Finding biological means for diagnosis or classification of the schizophrenia by using PBMC microarrays, has been accepted for an oral presentation at the SAAP3 (South Asian Association of Physiologists) meeting on the 09th November 2012 (First Author)

Journal Articles (Peer Reviewed):

• Kumarasinghe N, Tooney PA, Schall U (2012). Finding the needle in the haystack: a review of microarray gene expression research into schizophrenia. Aust N Z J Psychiatry. 46(7): 598-610 (First Author)

• Nishantha Kumarasinghe, Paul E. Rasser, Jayan Mendis, Jessica Bergmann, Lilly Knechtel, Stewart Oxley, Antoinette Perera, Paul M. Thompson, Paul A. Tooney,

Appendix 115

and Ulrich Schall. (2012) Age effects on cerebral grey matter in schizophrenia and their associations with psychopathology, cognition and treatment response in previously untreated patients. Submitted to the International Journal of Neuropsychopharmacology: Under review: manuscript ID: IntJNP-12-0004 (First Author).

• Nishantha Kumarasinghe, Ulrich Schall, Natalie Beveridge, Erin Gardiner, Rodney Scott, Surangi Yasawardene, Antoinette Perera, Jayan Mendis, Kanishka Suriyakumara and Paul Tooney. (2012) Effects of Antipsychotic Medication on Peripheral Blood Mononuclear Cell Gene Expression in Previously Untreated Schizophrenia Patients in Sri Lanka: Article in press: Int J Neuropsychopharmacol. 27:1-21 (First Author)

Appendix 116

Supplementary Table 1: Primer sequences and TaqMan Assay details for qPCR

Assay Type Oligo Name Target Sequence Primers AKT1-F AKT1 5'- CACCACCTGACCAAGATGACAGCA -3' AKT1-R AKT1 5'- AGATCATGGCACGAGGCCGC -3' RXRA-F RXRA 5'- AGCCGGGAAGGTTCGCTAAGCTC -3' RXRA-R RXRA 5'- GCCAGAACGGGTGGGCACAAA -3' MMP9-F MMP9 5'- CGACGTCTTCCAGTACCGAGAGAAA -3' MMP9-R MMP9 5'- CACTCCGGGAACTCACGCGC -3'

Target Assay ID TaqMan DISC1 Hs00962131_m1 DGCR6 Hs00606390_mH MAL Hs00707014_s1 RPS25 Hs01568661_g1

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Supplementary Table 2: Differentially expressed genes in PBMCs from schizophrenia patients prior to antipsychotic drug treatment (Control versus Before analysis) Downregulated Genes

Fold Change Illumnia ID Gene ID Entrez Gene Name p-value* (SZ/CTL ) ATP-binding cassette, sub-family F (GCN20), ILMN_1763875 ABCF1 member 1 -1.351 0.023 ILMN_2396672 ABLIM1 actin binding LIM protein 1 -2.234 0.012 ILMN_1731610 ABLIM1 actin binding LIM protein 1 -2.017 0.034 ILMN_1785424 ABLIM1 actin binding LIM protein 1 -1.452 0.034 ILMN_1708672 ACAT2 acetyl-CoA acetyltransferase 2 -1.553 0.012 ILMN_2103841 AIP aryl hydrocarbon receptor interacting protein -1.276 0.029 ILMN_1716053 AK2 adenylate kinase 2 -1.559 0.014 asparagine-linked glycosylation 8, alpha-1,3- ILMN_1685413 ALG8 glucosyltransferase homolog (S. cerevisiae) -1.439 0.014 ILMN_1722102 ANAPC11 anaphase promoting complex subunit 11 -1.578 0.034 ILMN_1710979 ANKRD39 ankyrin repeat domain 39 -1.452 0.048 APEX nuclease (multifunctional DNA repair enzyme) ILMN_1661886 APEX1 1 -1.287 0.020 ILMN_1722491 APRT adenine phosphoribosyltransferase -1.295 0.012 ATP synthase, H+ transporting, mitochondrial F1 ILMN_1756674 ATP5EP2 complex, epsilon subunit pseudogene 2 -1.839 0.001 ATP synthase, H+ transporting, mitochondrial F1 ILMN_2225887 ATP5EP2 complex, epsilon subunit pseudogene 2 -1.776 0.001 ATP synthase, H+ transporting, mitochondrial Fo ILMN_1676393 ATP5G1 complex, subunit C1 (subunit 9) -1.381 0.041 ATP synthase, H+ transporting, mitochondrial Fo ILMN_1660577 ATP5G2 complex, subunit C2 (subunit 9) -1.378 0.014 ATP synthase, H+ transporting, mitochondrial Fo ILMN_1666372 ATP5H complex, subunit d -1.43 0.036 ATP synthase, H+ transporting, mitochondrial Fo ILMN_1726603 ATP5I complex, subunit E -1.716 0.029 ATP synthase, H+ transporting, mitochondrial Fo ILMN_1812638 ATP5L complex, subunit G -2.079 0.001 ATP synthase, H+ transporting, mitochondrial Fo ILMN_1679188 ATP5S complex, subunit s (factor B) -3.267 0.023 ILMN_1809027 ATP5SL ATP5S-like -1.188 0.048 ATPase, H+ transporting, lysosomal 31kDa, V1 ILMN_1798485 ATP6V1E1 subunit E1 -1.217 0.041 ATPase, H+ transporting, lysosomal 14kDa, V1 ILMN_2099783 ATP6V1F subunit F -1.407 0.025 ILMN_1724480 AXIN2 axin 2 -1.963 0.048 ILMN_2179837 BANF1 barrier to autointegration factor 1 -1.402 0.023 ILMN_1808059 BCAS4 breast carcinoma amplified sequence 4 -1.808 0.001 ILMN_2325506 BCAS4 breast carcinoma amplified sequence 4 -1.718 0.001 branched chain amino-acid transaminase 2, ILMN_1695110 BCAT2 mitochondrial -1.391 0.013 ILMN_1796113 BCDIN3D BCDIN3 domain containing -1.625 0.034

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ILMN_1665761 BCL11B B-cell CLL/lymphoma 11B (zinc finger protein) -1.907 0.020 ILMN_1669663 BCR breakpoint cluster region -1.759 0.036 ILMN_1772713 BMS1 BMS1 homolog, ribosome assembly protein (yeast) -1.615 0.014 ILMN_1782633 BOLA2 bolA homolog 2 (E. coli) -1.817 0.001 ILMN_1659343 BOLA2 bolA homolog 2 (E. coli) -1.679 0.001 ILMN_1786658 BOLA3 bolA homolog 3 (E. coli) -2.028 0.001 ILMN_2343010 BOLA3 bolA homolog 3 (E. coli) -1.785 0.001 ILMN_1705066 BTBD11 BTB (POZ) domain containing 11 -1.277 0.034 ILMN_1659762 BTF3 basic transcription factor 3 -1.415 0.041 ribosome production factor 2 homolog (S. ILMN_1664167 BXDC1 cerevisiae) -2.113 0.034 ILMN_1662470 C10orf35 chromosome 10 open reading frame 35 -2.081 0.034 ILMN_1786759 C11orf10 chromosome 11 open reading frame 10 -1.677 0.001 ILMN_1812191 C12orf57 chromosome 12 open reading frame 57 -1.824 0.014 ILMN_1751559 C16orf30 transmembrane protein 204 -1.558 0.048 ILMN_1719224 C17orf45 C17orf76 antisense RNA 1 (non-protein coding) -1.462 0.048 ILMN_1763688 C17orf49 chromosome 17 open reading frame 49 -1.462 0.009 ILMN_2201533 C17orf61 chromosome 17 open reading frame 61 -1.833 0.048 ILMN_1672554 C17orf81 chromosome 17 open reading frame 81 -2.081 0.041 ILMN_2043615 C17orf90 chromosome 17 open reading frame 90 -1.314 0.034 ILMN_2182531 C18orf55 chromosome 18 open reading frame 55 -1.64 0.048 ILMN_1671374 C19orf53 chromosome 19 open reading frame 53 -1.515 0.023 ILMN_2082130 C1orf123 chromosome 1 open reading frame 123 -1.373 0.012 complement component 1, q subcomponent binding ILMN_1668996 C1QBP protein -1.567 0.023 ILMN_2356311 C21orf51 family with sequence similarity 165, member B -2.1 0.025 ILMN_1774584 C2orf28 chromosome 2 open reading frame 28 -1.51 0.009 ILMN_1708906 C2orf29 chromosome 2 open reading frame 29 -1.46 0.029 NADH dehydrogenase (ubiquinone) 1 alpha ILMN_2354515 C3orf60 subcomplex, assembly factor 3 -1.431 0.014 ILMN_2380588 C6orf108 chromosome 6 open reading frame 108 -1.887 0.013 ILMN_1651987 C6orf129 coiled-coil domain containing 167 -1.685 0.012 ILMN_2391765 C6orf48 chromosome 6 open reading frame 48 -1.657 0.001 NADH dehydrogenase (ubiquinone) 1 alpha ILMN_1659524 C6orf66 subcomplex, assembly factor 4 -2.075 0.048 ILMN_1700028 C9orf156 chromosome 9 open reading frame 156 -1.315 0.034 ILMN_1709043 C9orf46 chromosome 9 open reading frame 46 -1.569 0.023 calcium channel, voltage-dependent, gamma ILMN_1779043 CACNG6 subunit 6 -2.089 0.048 ILMN_1726574 CACYBP calcyclin binding protein -1.98 0.009 ILMN_1714599 CAMLG calcium modulating ligand -1.583 0.012 ILMN_1725071 CCDC12 coiled-coil domain containing 12 -1.397 0.001 ILMN_1811264 CCDC32 chromosome 15 open reading frame 57 -1.432 0.014 ILMN_1662318 CCDC59 coiled-coil domain containing 59 -1.727 0.025 ILMN_2232166 CCDC90B coiled-coil domain containing 90B -1.596 0.041 ILMN_1752394 CCNB1IP1 cyclin B1 interacting protein 1, E3 ubiquitin protein -2.235 0.014

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ligase ILMN_1715131 CCR7 chemokine (C-C motif) receptor 7 -1.525 0.048 ILMN_1695025 CD2 CD2 molecule -1.704 0.001 ILMN_2208903 CD52 CD52 molecule -2.423 0.012 ILMN_1746565 CD6 CD6 molecule -1.437 0.023 ILMN_1710017 CD79B CD79b molecule, immunoglobulin-associated beta -1.599 0.048 ILMN_2328666 CD83 CD83 molecule -2.634 0.025 ILMN_2354191 CD8B CD8b molecule -2.32 0.009 ILMN_1711573 CD96 CD96 molecule -1.607 0.048 ILMN_2335398 CECR5 cat eye syndrome chromosome region, candidate 5 -1.561 0.048 ILMN_1695645 CETN2 centrin, EF-hand protein, 2 -1.411 0.036 CKLF-like MARVEL transmembrane domain ILMN_1710124 CMTM8 containing 8 -1.656 0.029 ILMN_1776993 COG2 component of oligomeric golgi complex 2 -1.585 0.034 ILMN_1810334 COMMD7 COMM domain containing 7 -1.221 0.048 COP9 constitutive photomorphogenic homolog ILMN_1764431 COPS6 subunit 6 (Arabidopsis) -1.212 0.041 COX17 cytochrome c oxidase assembly homolog ILMN_2187718 COX17 (S. cerevisiae) -1.768 0.012 ILMN_1652207 COX4I1 cytochrome c oxidase subunit IV isoform 1 -1.313 0.034 ILMN_1798189 COX7C cytochrome c oxidase subunit VIIc -2.773 0.041 ILMN_1656920 CRIP1 cysteine-rich protein 1 (intestinal) -1.558 0.036 ILMN_1813256 CRIPT cysteine-rich PDZ-binding protein -1.561 0.048 ILMN_1712390 CUTA cutA divalent cation tolerance homolog (E. coli) -1.399 0.012 CWC15 spliceosome-associated protein homolog ILMN_1713482 CWC15 (S. cerevisiae) -1.764 0.009 ILMN_1731619 DAD1 defender against cell death 1 -1.394 0.012 ILMN_1776005 DC2 DC2 -1.554 0.048 ILMN_1683194 DCN decorin -2.64 0.034 ILMN_1756220 DDX18 DEAD (Asp-Glu-Ala-Asp) box polypeptide 18 -1.686 0.023 ILMN_1747162 DDX47 DEAD (Asp-Glu-Ala-Asp) box polypeptide 47 -1.82 0.034 ILMN_1791396 DGCR6 DiGeorge syndrome critical region gene 6 -1.495 0.013 ILMN_1743432 DGUOK deoxyguanosine kinase -1.49 0.001 ILMN_1752967 DHPS deoxyhypusine synthase -1.445 0.048 DKFZp761P0 ILMN_1757872 423 Hypothetical Protein -1.394 0.034 ILMN_1698258 DNAJC8 DnaJ (Hsp40) homolog, subfamily C, member 8 -1.4 0.034 ILMN_1658259 DRG1 developmentally regulated GTP binding protein 1 -1.361 0.048 ILMN_1741780 DUSP28 dual specificity phosphatase 28 -1.402 0.041 ILMN_1772796 DYNLL2 dynein, light chain, LC8-type 2 -1.603 0.041 ILMN_1805922 EBPL emopamil binding protein-like -1.585 0.036 ILMN_1726169 EDF1 endothelial differentiation-related factor 1 -1.481 0.009 ILMN_1694587 EEF1B2 eukaryotic translation elongation factor 1 beta 2 -2.258 0.023 ILMN_2318725 EEF1B2 eukaryotic translation elongation factor 1 beta 2 -2.022 0.013 ILMN_2262288 EEF1G eukaryotic translation elongation factor 1 gamma -1.727 0.009 ILMN_1702105 EFS embryonal Fyn-associated substrate -2.362 0.013

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ILMN_1803846 EIF1 eukaryotic translation initiation factor 1 -1.417 0.020 eukaryotic translation initiation factor 2B, subunit 2 ILMN_1713380 EIF2B2 beta, 39kDa -1.771 0.034 ILMN_1739847 EIF3D eukaryotic translation initiation factor 3, subunit D -1.346 0.014 ILMN_1739257 EIF3E eukaryotic translation initiation factor 3, subunit E -2.282 0.013 ILMN_1689446 EIF3G eukaryotic translation initiation factor 3, subunit G -1.454 0.023 ILMN_2192693 EIF3M eukaryotic translation initiation factor 3, subunit M -1.418 0.034 ILMN_1801421 EMD emerin -1.397 0.036 ILMN_1797074 EMG1 EMG1 nucleolar protein homolog (S. cerevisiae) -1.609 0.013 ILMN_1781795 ERH enhancer of rudimentary homolog (Drosophila) -2.588 0.041 ILMN_2323048 ERP29 endoplasmic reticulum protein 29 -1.378 0.023 ILMN_2251184 ERP29 endoplasmic reticulum protein 29 -1.314 0.009 ILMN_1775542 FAIM3 Fas apoptotic inhibitory molecule 3 -1.335 0.020 ILMN_1712431 FAM113B family with sequence similarity 113, member B -1.498 0.041 ILMN_2153466 FAM50B family with sequence similarity 50, member B -1.587 0.048 ILMN_1779813 FAM96B family with sequence similarity 96, member B -1.383 0.009 Finkel-Biskis-Reilly murine sarcoma virus (FBR- ILMN_1664614 FAU MuSV) ubiquitously expressed -1.441 0.048 ILMN_1719205 FBL fibrillarin -1.547 0.001 ILMN_2223941 FBLN5 fibulin 5 -3.028 0.029 ILMN_1664922 FLNB filamin B, beta -1.522 0.012 ILMN_2091412 FLT3LG fms-related tyrosine kinase 3 ligand -1.745 0.001 ILMN_1679640 FXR1 fragile X mental retardation, autosomal homolog 1 -2.262 0.048 ILMN_1756469 GAMT guanidinoacetate N-methyltransferase -1.728 0.001 phosphoribosylglycinamide formyltransferase, phosphoribosylglycinamide synthetase, ILMN_1679476 GART phosphoribosylaminoimidazole synthetase -1.49 0.048 ILMN_1694780 GCHFR GTP cyclohydrolase I feedback regulator -1.482 0.013 ILMN_1769383 GIMAP5 GTPase, IMAP family member 5 -1.453 0.001 ILMN_1776678 GIMAP7 GTPase, IMAP family member 7 -3.134 0.048 ILMN_1796177 GIPC1 GIPC PDZ domain containing family, member 1 -1.647 0.020 ILMN_1729170 GPA33 glycoprotein A33 (transmembrane) -1.443 0.048 ILMN_2383305 GPATCH4 G patch domain containing 4 -1.604 0.048 ILMN_1780368 GPR18 G protein-coupled receptor 18 -1.638 0.023 ILMN_1720799 GPSN2 trans-2,3-enoyl-CoA reductase -1.287 0.020 ILMN_1739497 GTF2H5 general transcription factor IIH, polypeptide 5 -4.37 0.001 GTPase, very large interferon inducible pseudogene ILMN_1668526 GVIN1 1 -1.317 0.014 granzyme A (granzyme 1, cytotoxic T-lymphocyte- ILMN_1779324 GZMA associated serine esterase 3) -1.596 0.041 ILMN_1710734 GZMK granzyme K (granzyme 3; tryptase II) -2.051 0.048 ILMN_1705570 H2AFY2 H2A histone family, member Y2 -1.516 0.041 ILMN_1654319 HAPLN3 hyaluronan and proteoglycan link protein 3 -2.009 0.023 ILMN_2091454 HBM hemoglobin, mu -1.618 0.041 ILMN_1656977 HIBCH 3-hydroxyisobutyryl-CoA hydrolase -2.561 0.034 ILMN_1652762 HIC2 hypermethylated in cancer 2 -1.502 0.034

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ILMN_2165753 HLA-A29.1 major histocompatibility antigen HLA-A29 -2.411 0.025 major histocompatibility complex, class II, DP alpha ILMN_1772218 HLA-DPA1 1 -1.371 0.020 ILMN_1689655 HLA-DRA major histocompatibility complex, class II, DR alpha -1.274 0.041 ILMN_1810274 HOXB2 homeobox B2 -2.556 0.001 ILMN_2355971 HSD17B10 hydroxysteroid (17-beta) dehydrogenase 10 -1.461 0.020 ILMN_1686367 HSPA8 heat shock 70kDa protein 8 -1.62 0.041 ILMN_1766713 HSPD1 heat shock 60kDa protein 1 (chaperonin) -1.946 0.029 ILMN_1786823 ICAM2 intercellular adhesion molecule 2 -1.283 0.001 ILMN_1722282 IL17REL interleukin 17 receptor E-like -1.84 0.029 ILMN_1778010 IL32 interleukin 32 -1.439 0.025 ILMN_1745172 ILF2 interleukin enhancer binding factor 2, 45kDa -1.413 0.009 ILMN_2409062 ISCU iron-sulfur cluster scaffold homolog (E. coli) -1.404 0.013 ILMN_2387636 ITGB4BP eukaryotic translation initiation factor 6 -1.377 0.014 ILMN_1699160 ITK IL2-inducible T-cell kinase -1.748 0.029 ILMN_1664756 KPNA4 karyopherin alpha 4 (importin alpha 3) -1.675 0.048 ILMN_2404625 LAT linker for activation of T cells -1.727 0.001 ILMN_1691539 LAT linker for activation of T cells -1.447 0.009 ILMN_1679185 LEF1 lymphoid enhancer-binding factor 1 -2.109 0.001 ILMN_1774375 LOC284422 -2.01 0.023 ILMN_1680208 LOC284821 -1.478 0.034 ILMN_1695261 LOC285176 -1.896 0.048 ILMN_1657612 LOC285900 -1.926 0.001 ILMN_1688127 LOC341457 -1.865 0.001 ILMN_1801795 LOC347544 -1.556 0.013 ILMN_1762189 LOC387686 -1.647 0.041 ILMN_1674983 LOC387841 -1.877 0.001 ILMN_1660376 LOC387841 -1.493 0.009 ILMN_1716382 LOC387882 -1.846 0.023 ILMN_2050112 LOC388524 -1.367 0.014 ILMN_1748827 LOC388564 -1.844 0.029 ILMN_1754990 LOC388720 -1.465 0.001 ILMN_1704315 LOC389435 -1.788 0.029 ILMN_1739335 LOC400948 -1.711 0.014 ILMN_1792528 LOC401206 -1.828 0.001 ILMN_1700316 LOC440055 -2.006 0.001 ILMN_1677076 LOC441771 -1.823 0.048 ILMN_1695598 LOC441775 -1.31 0.041 ILMN_1804415 LOC57228 -1.701 0.048 ILMN_1673753 LOC642033 -2.075 0.001 ILMN_1651403 LOC642161 -2.405 0.001 ILMN_1715926 LOC642210 -1.297 0.029 ILMN_1663416 LOC642250 -2.47 0.048 ILMN_1793287 LOC642755 -1.415 0.029

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ILMN_1661306 LOC643433 -1.512 0.014 ILMN_1789809 LOC644511 -1.741 0.012 ILMN_1701696 LOC644863 -1.583 0.013 ILMN_1815124 LOC645317 -1.83 0.023 ILMN_1659771 LOC645683 -1.636 0.001 ILMN_1772888 LOC645688 -1.439 0.025 ILMN_1732328 LOC646200 -1.644 0.048 ILMN_1673738 LOC646483 -1.462 0.048 ILMN_1666323 LOC647251 -2.034 0.048 ILMN_1811063 LOC649447 -1.386 0.009 ILMN_1715436 LOC650799 -2.239 0.020 ILMN_1687805 LOC651894 -1.856 0.023 ILMN_1683597 LOC652071 -1.355 0.025 ILMN_1702783 LOC652595 -1.511 0.014 ILMN_1672961 LOC652624 -1.4 0.029 ILMN_1667932 LOC652726 -1.534 0.012 ILMN_1651899 LOC653314 -1.366 0.029 ILMN_1725981 LOC654189 -1.728 0.048 ILMN_1755808 LOC654194 -1.833 0.048 ILMN_1691949 LOC728554 -1.874 0.009 ILMN_1654313 LOC730995 -1.88 0.041 ILMN_1656662 LOC731365 -1.588 0.014 ILMN_1703102 LOC731777 -1.614 0.034 ILMN_1794927 LOC90925 -1.69 0.012 ILMN_1813175 LPHN1 latrophilin 1 -1.815 0.014 ILMN_1662417 LRPPRC leucine-rich PPR-motif containing -1.557 0.029 ILMN_1795464 LTA lymphotoxin alpha (TNF superfamily, member 1) -1.543 0.034 ILMN_1703132 LYRM2 LYR motif containing 2 -1.443 0.048 ILMN_2084353 M6PR mannose-6-phosphate receptor (cation dependent) -1.322 0.041 ILMN_1798663 MAB21L2 mab-21-like 2 (C. elegans) -2.762 0.048 ILMN_2320330 MAL mal, T-cell differentiation protein -2.134 0.001 ILMN_2327860 MAL mal, T-cell differentiation protein -2.054 0.029 ILMN_1656913 MDH1 malate dehydrogenase 1, NAD (soluble) -1.592 0.029 ILMN_1751956 MGST3 microsomal glutathione S-transferase 3 -1.593 0.012 ILMN_1691090 MPV17 MpV17 mitochondrial inner membrane protein -1.537 0.025 ILMN_2316540 MRPL11 mitochondrial ribosomal protein L11 -1.415 0.048 ILMN_2189424 MRPL20 mitochondrial ribosomal protein L20 -1.785 0.036 ILMN_1706326 MRPL33 mitochondrial ribosomal protein L33 -1.684 0.001 ILMN_1700477 MRPL43 mitochondrial ribosomal protein L43 -2.389 0.020 ILMN_2258774 MRPL43 mitochondrial ribosomal protein L43 -1.394 0.048 ILMN_2097421 MRPL51 mitochondrial ribosomal protein L51 -2.037 0.029 ILMN_1813682 MRPL53 mitochondrial ribosomal protein L53 -2.463 0.001 ILMN_1722905 MRPS11 mitochondrial ribosomal protein S11 -1.268 0.034 ILMN_1802553 MRPS24 mitochondrial ribosomal protein S24 -1.272 0.048

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ILMN_1689800 MRTO4 mRNA turnover 4 homolog (S. cerevisiae) -1.642 0.034 ILMN_1775170 MT1X metallothionein 1X -1.62 0.023 nascent polypeptide-associated complex alpha ILMN_2167617 NACA subunit -1.435 0.012 N(alpha)-acetyltransferase 20, NatB catalytic ILMN_2354140 NAT5 subunit -1.808 0.025 NADH dehydrogenase (ubiquinone) 1 alpha ILMN_1784286 NDUFA1 subcomplex, 1, 7.5kDa -1.952 0.020 NADH dehydrogenase (ubiquinone) 1 alpha ILMN_1737738 NDUFA12 subcomplex, 12 -1.415 0.036 NADH dehydrogenase (ubiquinone) 1 alpha ILMN_1759729 NDUFA8 subcomplex, 8, 19kDa -1.365 0.023 NADH dehydrogenase (ubiquinone) 1 beta ILMN_2117330 NDUFB2 subcomplex, 2, 8kDa -1.838 0.013 NADH dehydrogenase (ubiquinone) 1 beta ILMN_1807397 NDUFB5 subcomplex, 5, 16kDa -1.457 0.013 NADH dehydrogenase (ubiquinone) 1 beta ILMN_1661170 NDUFB8 subcomplex, 8, 19kDa -1.228 0.012 NADH dehydrogenase (ubiquinone) Fe-S protein 7, ILMN_1669966 NDUFS7 20kDa (NADH-coenzyme Q reductase) -1.256 0.048 NADH dehydrogenase (ubiquinone) Fe-S protein 8, ILMN_1794132 NDUFS8 23kDa (NADH-coenzyme Q reductase) -1.655 0.041 ILMN_1749011 NECAP2 NECAP endocytosis associated 2 -1.253 0.020 neural precursor cell expressed, developmentally ILMN_2058070 NEDD8 down-regulated 8 -1.285 0.036 ILMN_1691506 NGRN neugrin, neurite outgrowth associated -1.498 0.048 NHP2 non-histone chromosome protein 2-like 1 (S. ILMN_1709809 NHP2L1 cerevisiae) -1.606 0.012 NHP2 non-histone chromosome protein 2-like 1 (S. ILMN_1763460 NHP2L1 cerevisiae) -1.348 0.034 ILMN_1704305 NIP7 nuclear import 7 homolog (S. cerevisiae) -1.594 0.036 ILMN_1775011 NOL10 nucleolar protein 10 -1.781 0.014 ILMN_1750052 NOL14 NOP14 nucleolar protein homolog (yeast) -1.403 0.041 ILMN_1815479 NOLA3 NOP10 ribonucleoprotein homolog (yeast) -1.38 0.020 ILMN_1684210 NPAL3 NIPA-like domain containing 3 -1.455 0.025 ILMN_1720468 NPSR1 neuropeptide S receptor 1 -2.376 0.036 nudix (nucleoside diphosphate linked moiety X)-type ILMN_2330243 NUDT1 motif 1 -1.961 0.014 nudix (nucleoside diphosphate linked moiety X)-type ILMN_1778347 NUDT2 motif 2 -1.913 0.034 nudix (nucleoside diphosphate linked moiety X)-type ILMN_1665192 NUDT6 motif 6 -1.764 0.012 ILMN_1771903 NUP37 nucleoporin 37kDa -1.582 0.023 ILMN_1738681 NUP62 nucleoporin 62kDa -1.371 0.048 oligonucleotide/oligosaccharide-binding fold ILMN_1789186 OBFC1 containing 1 -1.394 0.048 ILMN_1700306 OCIAD2 OCIA domain containing 2 -2.029 0.001 ILMN_1748591 ODC1 ornithine decarboxylase 1 -1.461 0.048 ILMN_1662174 ORMDL3 ORM1-like 3 (S. cerevisiae) -1.765 0.009 platelet-activating factor acetylhydrolase 1b, ILMN_1748093 PAFAH1B3 catalytic subunit 3 (29kDa) -1.409 0.036

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ILMN_1723793 PALB2 partner and localizer of BRCA2 -1.726 0.014 ILMN_1667022 PASK PAS domain containing serine/threonine kinase -1.94 0.013 ILMN_1754858 PASK PAS domain containing serine/threonine kinase -1.932 0.048 ILMN_1788024 PCID2 PCI domain containing 2 -1.75 0.012 ILMN_2306540 PDE9A phosphodiesterase 9A -3.117 0.001 ILMN_1745806 PEMT phosphatidylethanolamine N-methyltransferase -1.343 0.020 ILMN_1717855 PFDN1 prefoldin subunit 1 -1.509 0.001 ILMN_1789944 PGAM5 phosphoglycerate mutase family member 5 -2.144 0.013 ILMN_1795285 PHF15 PHD finger protein 15 -1.262 0.020 ILMN_1704537 PHGDH phosphoglycerate dehydrogenase -1.497 0.048 phosphatidylinositol glycan anchor biosynthesis, ILMN_1774949 PIGP class P -1.724 0.041 ILMN_1790309 PINX1 PIN2/TERF1 interacting, telomerase inhibitor 1 -2.591 0.012 ILMN_2093343 PLAC8 placenta-specific 8 -1.74 0.029 ILMN_1805827 PPA1 pyrophosphatase (inorganic) 1 -1.717 0.023 ILMN_2326737 PPIE peptidylprolyl isomerase E (cyclophilin E) -1.534 0.009 ILMN_2366391 PRDX1 peroxiredoxin 1 -2.207 0.001 ILMN_2366388 PRDX1 peroxiredoxin 1 -1.562 0.009 ILMN_1692473 PRMT1 protein arginine methyltransferase 1 -1.635 0.001 ILMN_1729142 PRR6 centromere protein V -1.686 0.001 proteasome (prosome, macropain) subunit, beta ILMN_1764794 PSMB2 type, 2 -1.328 0.013 proteasome (prosome, macropain) subunit, beta ILMN_1666409 PSMB6 type, 6 -1.457 0.001 protein tyrosine phosphatase-like A domain ILMN_1743065 PTPLAD1 containing 1 -1.432 0.029 ILMN_1694147 PUS3 pseudouridylate synthase 3 -2.43 0.013 ILMN_1799015 PXMP2 peroxisomal membrane protein 2, 22kDa -1.403 0.025 retinoic acid receptor responder (tazarotene ILMN_1701613 RARRES3 induced) 3 -1.633 0.001 ILMN_1749009 REXO2 REX2, RNA exonuclease 2 homolog (S. cerevisiae) -1.714 0.048 ILMN_1668559 RGS10 regulator of G-protein signaling 10 -1.9 0.001 ILMN_1786388 RNF113A ring finger protein 113A -1.556 0.048 ILMN_1747192 RNF125 ring finger protein 125 -2.171 0.012 ILMN_2322498 RORA RAR-related orphan receptor A -1.452 0.034 ILMN_1687922 RP9 retinitis pigmentosa 9 (autosomal dominant) -2.165 0.009 ILMN_1770339 RPAIN RPA interacting protein -1.348 0.020 ILMN_2154566 RPL10A ribosomal protein L10a -1.752 0.001 ILMN_1808041 RPL10A ribosomal protein L10a -1.509 0.025 ILMN_2114876 RPL11 ribosomal protein L11 -1.492 0.020 ILMN_1653469 RPL12 ribosomal protein L12 -1.818 0.001 ILMN_2116366 RPL12 ribosomal protein L12 -1.48 0.009 ILMN_2160388 RPL24 ribosomal protein L24 -1.845 0.001 ILMN_1656807 RPL27 ribosomal protein L27 -1.775 0.029 ILMN_1713086 RPL27A ribosomal protein L27a -1.419 0.014 ILMN_1754303 RPL30 ribosomal protein L30 -1.867 0.001

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ILMN_1663799 RPL32 ribosomal protein L32 -1.86 0.023 ILMN_2400143 RPL32 ribosomal protein L32 -1.621 0.020 ILMN_1756360 RPL35A ribosomal protein L35a -1.724 0.001 ILMN_2189936 RPL36AL ribosomal protein L36a-like -1.667 0.014 ILMN_2343775 RPL38 ribosomal protein L38 -1.533 0.001 ILMN_1765043 RPL38 ribosomal protein L38 -1.362 0.014 ILMN_1737015 RPL39 ribosomal protein L39 -1.693 0.048 ILMN_1752285 RPL4 ribosomal protein L4 -1.599 0.020 ILMN_1712155 RPL6 ribosomal protein L6 -1.567 0.001 ILMN_1705908 RPL7L1 ribosomal protein L7-like 1 -1.33 0.023 ILMN_1709880 RPLP0 ribosomal protein, large, P0 -1.563 0.048 ILMN_1755733 RPLP2 ribosomal protein, large, P2 -1.725 0.025 ILMN_2186597 RPP21 ribonuclease P/MRP 21kDa subunit -1.463 0.001 ILMN_1740587 RPS11 ribosomal protein S11 -1.394 0.036 ILMN_1782621 RPS12 ribosomal protein S12 -1.554 0.001 ILMN_2338785 RPS14 ribosomal protein S14 -1.733 0.013 ILMN_1666635 RPS14 ribosomal protein S14 -1.41 0.048 ILMN_2219134 RPS15 ribosomal protein S15 -1.374 0.013 ILMN_1787949 RPS15A ribosomal protein S15a -1.902 0.029 ILMN_1651850 RPS16 ribosomal protein S16 -1.632 0.001 ILMN_2207539 RPS17 ribosomal protein S17 -2.088 0.023 ILMN_1784717 RPS19 ribosomal protein S19 -1.572 0.013 ILMN_2177965 RPS19BP1 ribosomal protein S19 binding protein 1 -1.367 0.034 ILMN_2218277 RPS2 ribosomal protein S2 -1.544 0.013 ILMN_1701596 RPS20 ribosomal protein S20 -2.137 0.001 ILMN_1746516 RPS25 ribosomal protein S25 -1.953 0.001 ILMN_2160819 RPS27 ribosomal protein S27 -1.919 0.029 ILMN_1660498 RPS27 ribosomal protein S27 -1.619 0.023 ILMN_2048326 RPS27A ribosomal protein S27a -1.51 0.009 ILMN_1694742 RPS29 ribosomal protein S29 -1.579 0.009 ILMN_1810577 RPS4X ribosomal protein S4, X-linked -1.955 0.001 ILMN_2166831 RPS4X ribosomal protein S4, X-linked -1.843 0.001 ILMN_1657204 SAE1 SUMO1 activating enzyme subunit 1 -1.463 0.025 ILMN_2258816 SAMD3 sterile alpha motif domain containing 3 -1.719 0.034 ILMN_1728298 SBK1 SH3-binding domain kinase 1 -1.73 0.025 ILMN_1749213 SDF2L1 stromal cell-derived factor 2-like 1 -1.422 0.029 secretion regulating guanine nucleotide exchange ILMN_1760049 SERGEF factor -1.565 0.009 ILMN_1771801 SIRPG signal-regulatory protein gamma -1.692 0.013 ILMN_2383058 SIRPG signal-regulatory protein gamma -1.684 0.020 ILMN_1760109 SLAMF6 SLAM family member 6 -1.574 0.029 solute carrier family 25 (mitochondrial carrier; ILMN_1679949 SLC25A23 phosphate carrier), member 23 -1.455 0.001 ILMN_1781231 SLC25A38 solute carrier family 25, member 38 -1.585 0.023

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ILMN_2222880 SLC25A42 solute carrier family 25, member 42 -1.441 0.001 SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily b, ILMN_1758823 SMARCB1 member 1 -1.492 0.029 small nucleolar RNA host gene 5 (non-protein ILMN_2200659 SNHG5 coding) -2.104 0.034 small nuclear ribonucleoprotein polypeptides B and ILMN_1774661 SNRPB B1 -1.342 0.012 ILMN_1690706 SNRPB2 small nuclear ribonucleoprotein polypeptide B -1.528 0.034 small nuclear ribonucleoprotein D2 polypeptide ILMN_2369785 SNRPD2 16.5kDa -1.774 0.001 ILMN_2067370 SNRPF small nuclear ribonucleoprotein polypeptide F -1.887 0.001 ILMN_1678966 SNRPF small nuclear ribonucleoprotein polypeptide F -1.874 0.041 ILMN_2372082 SNRPN small nuclear ribonucleoprotein polypeptide N -1.804 0.048 ILMN_1656537 SNRPN small nuclear ribonucleoprotein polypeptide N -1.669 0.014 ILMN_1660000 SNURF SNRPN upstream reading frame -1.652 0.034 ILMN_1662438 SOD1 superoxide dismutase 1, soluble -2.174 0.023 ILMN_1789244 SOX8 SRY (sex determining region Y)-box 8 -1.358 0.041 sparc/osteonectin, cwcv and kazal-like domains ILMN_1656287 SPOCK2 proteoglycan (testican) 2 -1.425 0.048 ILMN_2286334 SR140 -1.474 0.034 signal recognition particle 14kDa (homologous Alu ILMN_2234758 SRP14 RNA binding protein) -1.901 0.023 synovial sarcoma translocation gene on ILMN_1796407 SS18L2 chromosome 18-like 2 -1.648 0.012 ILMN_1657796 STMN1 stathmin 1 -1.715 0.009 ILMN_1785570 SUSD3 sushi domain containing 3 -1.692 0.041 TAF12 RNA polymerase II, TATA box binding ILMN_1653367 TAF12 protein (TBP)-associated factor, 20kDa -1.413 0.009 ILMN_1749478 TCEAL3 transcription elongation factor A (SII)-like 3 -1.527 0.041 ILMN_2415926 THOC3 THO complex 3 -2.104 0.001 ILMN_1654609 TIGA1 -1.835 0.012 ILMN_1694259 TINP1 -1.775 0.048 transmembrane emp24 protein transport domain ILMN_1719316 TMED3 containing 3 -1.467 0.009 ILMN_1704024 TMEM160 transmembrane protein 160 -1.469 0.012 ILMN_1760245 TMEM42 transmembrane protein 42 -1.999 0.029 ILMN_1757391 TMEM50B transmembrane protein 50B -1.369 0.048 ILMN_1812392 TMSB10 thymosin beta 4, X-linked -1.653 0.009 ILMN_2414325 TNFAIP8 tumor necrosis factor, alpha-induced protein 8 -1.8 0.048 translocase of outer mitochondrial membrane 22 ILMN_1714623 TOMM22 homolog (yeast) -1.955 0.020 translocase of outer mitochondrial membrane 7 ILMN_1674069 TOMM7 homolog (yeast) -2.339 0.048 ILMN_1764851 TP53RK TP53 regulating kinase -1.637 0.020 ILMN_1789614 TPT1 tumor protein, translationally-controlled 1 -1.532 0.036 ILMN_1780397 TRAF3IP3 TRAF3 interacting protein 3 -1.396 0.009 ILMN_1747058 TRAPPC2L trafficking protein particle complex 2-like -1.456 0.012

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ILMN_1814650 TRAPPC4 trafficking protein particle complex 4 -1.536 0.009 ILMN_1714700 TRIB2 tribbles homolog 2 (Drosophila) -1.505 0.023 ILMN_2223805 TSGA14 centrosomal protein 41kDa -2.071 0.001 ILMN_1718907 TSHZ1 teashirt zinc finger homeobox 1 -1.701 0.029 ILMN_1678140 TTC4 tetratricopeptide repeat domain 4 -1.462 0.048 ILMN_1695731 TUBG1 tubulin, gamma 1 -1.456 0.048 tRNA-yW synthesizing protein 1 homolog (S. ILMN_1736135 TYW1 cerevisiae) -1.629 0.048 ILMN_1779177 U2AF1L4 U2 small nuclear RNA auxiliary factor 1-like 4 -1.805 0.009 ILMN_1785179 UBE2G2 ubiquitin-conjugating enzyme E2G 2 -2.288 0.014 ILMN_1810474 UBE2I ubiquitin-conjugating enzyme E2I -1.359 0.029 ILMN_1781986 UCRC -2.725 0.001 ILMN_2110281 UFC1 ubiquitin-fold modifier conjugating enzyme 1 -1.671 0.020 ILMN_2232936 UQCRH ubiquinol-cytochrome c reductase hinge protein -1.791 0.001 ubiquinol-cytochrome c reductase, complex III ILMN_1666471 UQCRQ subunit VII, 9.5kDa -1.617 0.041 ILMN_1797384 UROS uroporphyrinogen III synthase -1.312 0.034 ILMN_1664537 USP11 ubiquitin specific peptidase 11 -1.521 0.020 ILMN_1729816 VDAC3 voltage-dependent anion channel 3 -1.487 0.020 ILMN_1679978 WBSCR16 Williams-Beuren syndrome chromosome region 16 -1.6 0.041 ILMN_1697348 WBSCR22 Williams Beuren syndrome chromosome region 22 -1.364 0.036 ILMN_1658289 WDR54 WD repeat domain 54 -1.351 0.025 ILMN_1665887 WDR61 WD repeat domain 61 -1.481 0.020 ILMN_1745343 ZMAT2 zinc finger, matrin-type 2 -1.497 0.020 ILMN_1757627 ZMYND19 zinc finger, MYND-type containing 19 -1.542 0.048 ILMN_1798533 ZNF22 zinc finger protein 22 (KOX 15) -1.704 0.034 ILMN_1652754 ZNF428 zinc finger protein 428 -2.197 0.012 ILMN_2093775 ZNF831 zinc finger protein 831 -2.936 0.023 ILMN_1812478 ZNHIT3 zinc finger, HIT-type containing 3 -1.821 0.014 Upregulated Genes Fold Change Illumnia ID Gene ID Entrez Gene Name p-value* (SZ/CTL ) ATP-binding cassette, sub-family A (ABC1), ILMN_1766054 ABCA1 member 1 2.068 0.048 ATP-binding cassette, sub-family C ILMN_1706531 ABCC5 (CFTR/MRP), member 5 1.816 0.041 ILMN_2232177 ACTN1 actinin, alpha 1 1.633 0.014 ILMN_1708348 ADAM8 ADAM metallopeptidase domain 8 1.515 0.025 ILMN_1752247 AKAP13 A kinase (PRKA) anchor protein 13 1.486 0.034 ILMN_2148847 AKIRIN2 akirin 2 1.574 0.048 ILMN_2388507 AKT1 v-akt murine thymoma viral oncogene homolog 1 1.375 0.010 ILMN_1680996 ALOX5 arachidonate 5-lipoxygenase 1.676 0.023 ILMN_2078697 ALPK1 alpha-kinase 1 1.722 0.023 adhesion molecule, interacts with CXADR ILMN_1778723 AMICA1 antigen 1 1.466 0.006

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ILMN_1672356 ANKRD13D ankyrin repeat domain 13 family, member D 1.73 0.041 ILMN_1763837 ANPEP alanyl (membrane) aminopeptidase 1.951 0.041 amyloid beta (A4) precursor protein-binding, ILMN_2320513 APBB3 family B, member 3 1.617 0.041 anterior pharynx defective 1 homolog B (C. ILMN_1767816 APH1B elegans) 1.573 0.036 ATG16 autophagy related 16-like 2 (S. ILMN_1664644 ATG16L2 cerevisiae) 2.173 0.048 ATPase, H+ transporting, lysosomal 38kDa, V0 ILMN_1795826 ATP6V0D1 subunit d1 1.338 0.006 ILMN_1693394 BCKDK branched chain ketoacid dehydrogenase kinase 1.319 0.013 ILMN_1718982 BEST1 bestrophin 1 2.021 0.034 ILMN_2233279 C19orf31 1.338 0.041 ILMN_1667966 C1orf24 family with sequence similarity 129, member A 1.689 0.010 ILMN_1695157 CA4 carbonic anhydrase IV 1.993 0.029 ILMN_2192281 CARD8 caspase recruitment domain family, member 8 1.519 0.023 ILMN_1722622 CD163 CD163 molecule 1.836 0.041 ILMN_1673363 CD97 CD97 molecule 1.531 0.034 ILMN_1714592 CDA cytidine deaminase 2.292 0.041 carcinoembryonic antigen-related cell adhesion ILMN_1743570 CEACAM3 molecule 3 1.707 0.048 ArfGAP with RhoGAP domain, ankyrin repeat ILMN_1812618 CENTD3 and PH domain 3 2.013 0.041 ILMN_2394381 CLN3 ceroid-lipofuscinosis, neuronal 3 1.363 0.006 cysteine-rich secretory protein LCCL domain ILMN_1790689 CRISPLD2 containing 2 2.093 0.014 colony stimulating factor 2 receptor, alpha, low- ILMN_2376455 CSF2RA affinity (granulocyte-macrophage) 1.864 0.006 ILMN_2371280 CSF3R colony stimulating factor 3 receptor (granulocyte) 1.584 0.006 ILMN_1743032 CTSS cathepsin S 1.941 0.048 ILMN_1728478 CXCL16 chemokine (C-X-C motif) ligand 16 1.612 0.036 ILMN_1791847 DAPK2 death-associated protein kinase 2 2.073 0.048 ArfGAP with SH3 domain, ankyrin repeat and PH ILMN_1690963 DDEF1 domain 1 1.508 0.048 ILMN_1676632 DISC1 disrupted in schizophrenia 1 2.15 0.029 ILMN_1789642 DNAJC5 DnaJ (Hsp40) homolog, subfamily C, member 5 1.564 0.034 ILMN_2275098 DTX2 deltex homolog 2 (Drosophila) 1.306 0.041 ILMN_1781285 DUSP1 dual specificity phosphatase 1 1.442 0.048 ILMN_1734288 DUSP18 dual specificity phosphatase 18 1.485 0.029 dysferlin, limb girdle muscular dystrophy 2B ILMN_1810420 DYSF (autosomal recessive) 1.876 0.048 ILMN_1662741 EDG4 lysophosphatidic acid receptor 2 2.205 0.036 ILMN_1761463 EFHD2 EF-hand domain family, member D2 1.346 0.010 ILMN_1736048 ELL elongation factor RNA polymerase II 1.657 0.023 v-ets erythroblastosis virus E26 oncogene ILMN_1720158 ETS2 homolog 2 (avian) 1.468 0.041 ILMN_1778226 EXTL3 exostoses (multiple)-like 3 1.984 0.023 ILMN_1709233 F5 coagulation factor V (proaccelerin, labile factor) 2.811 0.010

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ILMN_1713402 FAM160A2 family with sequence similarity 160, member A2 1.301 0.041 ILMN_1744508 FAM53C family with sequence similarity 53, member C 1.471 0.048 ILMN_2365091 FCAR Fc fragment of IgA, receptor for 2.346 0.036 ILMN_1705302 FCGRT Fc fragment of IgG, receptor, transporter, alpha 1.633 0.041 ILMN_1654571 FCHO1 FCH domain only 1 1.55 0.025 ILMN_1772686 FGD3 FYVE, RhoGEF and PH domain containing 3 1.502 0.029 Gardner-Rasheed feline sarcoma viral (v-fgr) ILMN_2368318 FGR oncogene homolog 1.339 0.048 ILMN_1651776 FHOD1 formin homology 2 domain containing 1 1.573 0.001 ILMN_1707286 FLJ22662 1.482 0.041 ILMN_2194852 FLJ46309 1.605 0.048 ILMN_1726222 FLOT2 flotillin 2 1.393 0.034 ILMN_1844692 FOXO3 forkhead box O3 1.444 0.029 ILMN_1681703 FOXO3 forkhead box O3 2.077 0.025 ILMN_2092118 FPR1 formyl peptide receptor 1 1.387 0.034 frequently rearranged in advanced T-cell ILMN_1736180 FRAT1 lymphomas 2.111 0.048 ILMN_2347949 G6PD glucose-6-phosphate dehydrogenase 1.45 0.010 ILMN_1670926 GALNAC4S-6ST 1.564 0.029 ILMN_2364110 GBA glucosidase, beta, acid 1.474 0.029 ILMN_1728698 GDE1 glycerophosphodiester phosphodiesterase 1 1.432 0.034 ILMN_1735155 GLB1 galactosidase, beta 1 1.447 0.048 ILMN_1652631 GLIPR2 GLI pathogenesis-related 2 1.281 0.023 ILMN_1727043 GLT25D1 glycosyltransferase 25 domain containing 1 1.33 0.006 ILMN_1765941 GPR97 G protein-coupled receptor 97 2.861 0.048 ILMN_1724250 GRN granulin 1.547 0.036 ILMN_1791771 HCK hemopoietic cell kinase 1.547 0.014 hepatocyte growth factor-regulated tyrosine ILMN_1715994 HGS kinase substrate 1.359 0.001 ILMN_1670302 HK3 hexokinase 3 (white cell) 1.654 0.006 ILMN_2087646 HLX H2.0-like homeobox 2.114 0.048 ILMN_2212763 ICAM3 intercellular adhesion molecule 3 1.486 0.006 ILMN_1815445 IDS iduronate 2-sulfatase 1.992 0.010 ILMN_1662524 IL8RA chemokine (C-X-C motif) receptor 1 1.61 0.034 ILMN_1680397 IL8RB chemokine (C-X-C motif) receptor 2 1.745 0.041 ILMN_2094061 IMPA2 inositol(myo)-1(or 4)-monophosphatase 2 1.566 0.025 IMP (inosine 5'-monophosphate) dehydrogenase ILMN_1676515 IMPDH1 1 1.53 0.001 ILMN_1808299 IQSEC1 IQ motif and Sec7 domain 1 1.609 0.034 integrin, alpha 5 (fibronectin receptor, alpha ILMN_1792679 ITGA5 polypeptide) 1.406 0.048 integrin, alpha X (complement component 3 ILMN_2254635 ITGAX receptor 4 subunit) 1.945 0.001 integrin, beta 2 (complement component 3 ILMN_2175912 ITGB2 receptor 3 and 4 subunit) 1.273 0.048 ILMN_1732609 KIAA1539 KIAA1539 1.912 0.006

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ILMN_1763640 KIAA1602 NCK-associated protein 5-like 1.697 0.048 ILMN_1852022 KIAA1881 perilipin 4 3.442 0.029 ILMN_1692785 KLHL21 kelch-like 21 (Drosophila) 1.606 0.013 ILMN_1673282 LAMP2 lysosomal-associated membrane protein 2 1.374 0.048 leukocyte immunoglobulin-like receptor, ILMN_1716983 LILRA2 subfamily A (with TM domain), member 2 1.869 0.048 leukocyte immunoglobulin-like receptor, subfamily B (with TM and ITIM domains), ILMN_2406132 LILRB3 member 3 1.646 0.001 leukocyte immunoglobulin-like receptor, subfamily B (with TM and ITIM domains), ILMN_1784884 LILRB3 member 3 1.844 0.023 ILMN_1767377 LOC153561 2.229 0.025 ILMN_1724822 LOC399744 2.209 0.025 ILMN_1670410 LOC641710 2.517 0.036 ILMN_1712999 LOC642103 3.702 0.041 ILMN_1789086 LOC642334 2.982 0.048 ILMN_1752798 LOC642780 2.967 0.006 ILMN_1656530 LOC642788 2.779 0.048 ILMN_1731907 LOC643313 2.266 0.036 ILMN_1660227 LOC646434 1.921 0.048 ILMN_1657348 LOC650909 1.665 0.006 ILMN_1738648 LOC729008 putative apoptosis-related protein 2-like 2.105 0.029 ILMN_2127605 LRP3 low density lipoprotein receptor-related protein 3 1.916 0.006 lymphotoxin beta receptor (TNFR superfamily, ILMN_1667476 LTBR member 3) 1.606 0.041 v-yes-1 Yamaguchi sarcoma viral related ILMN_1781155 LYN oncogene homolog 1.411 0.041 ILMN_1652490 MANSC1 MANSC domain containing 1 1.886 0.014 ILMN_1779010 MAP3K3 mitogen-activated protein kinase kinase kinase 3 1.899 0.006 ILMN_1738749 MAST3 microtubule associated serine/threonine kinase 3 1.655 0.029 ILMN_2331544 MBP myelin basic protein 1.437 0.023 ILMN_1792682 MCTP2 multiple C2 domains, transmembrane 2 2.353 0.029 ILMN_1688775 METRNL meteorin, glial cell differentiation regulator-like 1.545 0.048 ILMN_1743232 MICALCL MICAL C-terminal like 2.249 0.029 ILMN_1750429 MKNK1 MAP kinase interacting serine/threonine kinase 1 1.665 0.041 ILMN_1678170 MME membrane metallo-endopeptidase 1.839 0.034 ILMN_1717207 MMP25 matrix metallopeptidase 25 1.724 0.023 matrix metallopeptidase 9 (gelatinase B, 92kDa ILMN_1796316 MMP9 gelatinase, 92kDa type IV collagenase) 3.107 0.006 ILMN_1792910 MNT MAX binding protein 1.516 0.025 ILMN_1776515 MPPE1 metallophosphoesterase 1 1.428 0.029 ILMN_2214678 MXD1 MAX dimerization protein 1 1.804 0.025 ILMN_1781184 MYBPC3 myosin binding protein C, cardiac 2.073 0.014 myeloid differentiation primary response gene ILMN_1738523 MYD88 (88) 1.567 0.048 ILMN_1722872 MYH9 myosin, heavy chain 9, non-muscle 1.424 0.029

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ILMN_1681239 MYO1F myosin IF 2.138 0.001 ILMN_1758963 NADK NAD kinase 1.582 0.025 ILMN_1760189 NAIP NLR family, apoptosis inhibitory protein 3.207 0.023 ILMN_2260082 NAIP NLR family, apoptosis inhibitory protein 3.714 0.023 ILMN_1726392 NIN ninein (GSK3B interacting protein) 1.587 0.010 ILMN_1758735 NLRP12 NLR family, pyrin domain containing 12 2.047 0.023 ILMN_1716105 NLRP12 NLR family, pyrin domain containing 12 2.098 0.010 ILMN_2366719 NSFL1C NSFL1 (p97) cofactor (p47) 1.468 0.014 ILMN_1705783 NXF1 nuclear RNA export factor 1 1.357 0.041 ILMN_1740010 PCNX pecanex homolog (Drosophila) 1.418 0.048 pyruvate dehydrogenase phosphatase regulatory ILMN_1667034 PDPR subunit 2.231 0.048 6-phosphofructo-2-kinase/fructose-2,6- ILMN_1653292 PFKFB4 biphosphatase 4 1.737 0.023 ILMN_2058795 PGCP plasma glutamate carboxypeptidase 2.01 0.014 ILMN_1794165 PGD phosphogluconate dehydrogenase 1.664 0.025 ILMN_1808047 PHC2 polyhomeotic homolog 2 (Drosophila) 2.311 0.041 ILMN_2241953 PILRA paired immunoglobin-like type 2 receptor alpha 1.766 0.006 pleckstrin homology domain containing, family M ILMN_1683980 PLEKHM2 (with RUN domain) member 2 1.214 0.048 ILMN_2059535 PPM1F protein phosphatase, Mg2+/Mn2+ dependent, 1F 1.449 0.006 ILMN_1769091 PRCP prolylcarboxypeptidase (angiotensinase C) 1.592 0.023 ILMN_1801105 PRKCD protein kinase C, delta 1.363 0.034 ILMN_1660364 PSCD4 cytohesin 4 1.495 0.023 ILMN_2154115 PSD4 pleckstrin and Sec7 domain containing 4 1.403 0.014 proline-serine-threonine phosphatase interacting ILMN_1703327 PSTPIP1 protein 1 1.864 0.010 ILMN_1741727 QPCT glutaminyl-peptide cyclotransferase 1.614 0.041 ILMN_1712705 RAB40C RAB40C, member RAS oncogene family 1.239 0.010 Ras association (RalGDS/AF-6) domain family ILMN_1709683 RASSF2 member 2 1.553 0.048 ILMN_1668065 RBED1 ELMO/CED-12 domain containing 3 1.481 0.034 regulatory factor X, 2 (influences HLA class II ILMN_2396287 RFX2 expression) 1.993 0.034 ILMN_1752526 RNF144B ring finger protein 144B 1.709 0.025 ILMN_1687315 RXRA retinoid X receptor, alpha 1.529 0.029 ILMN_2183409 SCARB1 scavenger receptor class B, member 1 1.799 0.041 sema domain, immunoglobulin domain (Ig), transmembrane domain (TM) and short ILMN_1702787 SEMA4A cytoplasmic domain, (semaphorin) 4A 1.87 0.023 sema domain, immunoglobulin domain (Ig), transmembrane domain (TM) and short ILMN_1672589 SEMA4B cytoplasmic domain, (semaphorin) 4B 1.436 0.010 ILMN_1815656 SERINC3 serine incorporator 3 1.4 0.001 ILMN_1665384 SH3BP5L SH3-binding domain protein 5-like 1.79 0.001 ILMN_1765493 SHKBP1 SH3KBP1 binding protein 1 1.756 0.029 solute carrier family 11 (proton-coupled divalent ILMN_1741165 SLC11A1 metal ion transporters), member 1 2 0.023

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solute carrier family 16, member 5 ILMN_1755649 SLC16A5 (monocarboxylic acid transporter 6) 1.699 0.014 solute carrier family 24 (sodium/potassium/calcium exchanger), member ILMN_2370738 SLC24A4 4 1.741 0.041 solute carrier family 24 (sodium/potassium/calcium exchanger), member ILMN_1675391 SLC24A4 4 2.347 0.041 ILMN_1715969 SLC25A37 solute carrier family 25, member 37 1.494 0.048 ILMN_1755843 SLC26A8 solute carrier family 26, member 8 2.285 0.001 solute carrier family 2 (facilitated glucose ILMN_1775708 SLC2A3 transporter), member 3 1.456 0.041 solute carrier family 31 (copper transporters), ILMN_1758938 SLC31A2 member 2 1.55 0.014 ILMN_1745778 SLC45A4 solute carrier family 45, member 4 1.737 0.041 ILMN_2391976 SLC45A4 solute carrier family 45, member 4 1.813 0.025 solute carrier organic anion transporter family, ILMN_1706261 SLCO3A1 member 3A1 1.745 0.023 ILMN_1781468 SMAP2 small ArfGAP2 2.381 0.036 syntrophin, beta 2 (dystrophin-associated protein ILMN_1808374 SNTB2 A1, 59kDa, basic component 2) 1.409 0.013 ILMN_1672834 SSH2 slingshot homolog 2 (Drosophila) 1.465 0.041 signal transducer and activator of transcription 3 ILMN_2410986 STAT3 (acute-phase response factor) 1.597 0.048 signal transducer and activator of transcription ILMN_1684034 STAT5B 5B 1.402 0.048 signal transducer and activator of transcription 6, ILMN_1763198 STAT6 interleukin-4 induced 1.28 0.041 ILMN_1651692 STK10 serine/threonine kinase 10 1.334 0.025 ILMN_2159453 STXBP2 syntaxin binding protein 2 1.518 0.034 sulfotransferase family, cytosolic, 1A, phenol- ILMN_2404795 SULT1A1 preferring, member 1 1.381 0.041 ILMN_1720623 SYTL3 synaptotagmin-like 3 1.819 0.006 T-cell, immune regulator 1, ATPase, H+ ILMN_1711994 TCIRG1 transporting, lysosomal V0 subunit A3 1.834 0.014 ILMN_1667883 THOC5 THO complex 5 1.467 0.010 ILMN_2393169 THOC5 THO complex 5 1.498 0.048 ILMN_2358560 TIAM2 T-cell lymphoma invasion and metastasis 2 2.519 0.034 ILMN_1749078 TIMP2 TIMP metallopeptidase inhibitor 2 2.332 0.029 ILMN_1736597 TKT transketolase 1.436 0.025 ILMN_1722981 TLR5 toll-like receptor 5 1.949 0.048 ILMN_1750961 TM6SF1 transmembrane 6 superfamily member 1 1.719 0.048 ILMN_1709334 TM9SF1 transmembrane 9 superfamily member 1 1.305 0.041 ILMN_1692754 TMEM49 vacuole membrane protein 1 1.478 0.029 ILMN_1803811 TRIB1 tribbles homolog 1 (Drosophila) 1.563 0.029 ILMN_1813625 TRIM25 tripartite motif containing 25 1.59 0.023 ILMN_1746704 TRIM8 tripartite motif containing 8 1.405 0.023 ILMN_1697160 TTC7A tetratricopeptide repeat domain 7A 2.291 0.048 ILMN_1807596 UBAP1 ubiquitin associated protein 1 1.269 0.041

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ILMN_1735052 ULK1 unc-51-like kinase 1 (C. elegans) 1.46 0.048 ILMN_1705144 ULK1 unc-51-like kinase 1 (C. elegans) 1.561 0.010 ILMN_1782538 VIM vimentin 1.501 0.023 ILMN_1804935 VNN3 vanin 3 3.086 0.034 vacuolar protein sorting 8 homolog (S. ILMN_1678268 VPS8 cerevisiae) 1.439 0.041 ILMN_1687592 WWC3 WWC family member 3 1.571 0.010 xeroderma pigmentosum, complementation ILMN_1790807 XPC group C 1.43 0.048 ILMN_1803564 YIPF1 Yip1 domain family, member 1 1.513 0.014 ILMN_1673604 YIPF3 Yip1 domain family, member 3 1.465 0.029 ILMN_1771627 ZMIZ1 zinc finger, MIZ-type containing 1 1.616 0.010 ILMN_1668185 ZNF282 zinc finger protein 282 1.446 0.041 ILMN_1701875 ZYX zyxin 1.67 0.023 ILMN_2371169 ZYX zyxin 1.877 0.029 SZ = CTL = Control; * Schizophrenia -. p values determined using SAM Analysis

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Supplementary Table 3: Differentially expressed genes in PBMCs from schizophrenia patients after with antipsychotic drug treatment (Control versus After analysis)

Downregulated Gene Fold Change Illumnia ID Gene ID Entrez Gene Name p-value* (SZ/CTL) ILMN_1731610 ABLIM1 actin binding LIM protein 1 -2.226 0.038 ILMN_1780806 ANKRD36B ankyrin repeat domain 36B -1.878 0.046 ILMN_1722491 APRT adenine phosphoribosyltransferase -1.344 0.001 ATP synthase, H+ transporting, mitochondrial ILMN_1676393 ATP5G1 Fo complex, subunit C1 (subunit 9) -1.287 0.02 ATP synthase, H+ transporting, mitochondrial ILMN_1660577 ATP5G2 Fo complex, subunit C2 (subunit 9) -1.385 0.001 ILMN_1808059 BCAS4 breast carcinoma amplified sequence 4 -1.775 0.001 ILMN_2325506 BCAS4 breast carcinoma amplified sequence 4 -1.709 0.001 ILMN_1782633 BOLA2 bolA homolog 2 (E. coli) -1.64 0.001 ILMN_1659343 BOLA2 bolA homolog 2 (E. coli) -1.509 0.031 ILMN_1786759 C11orf10 chromosome 11 open reading frame 10 -1.351 0.02 ILMN_1812191 C12orf57 chromosome 12 open reading frame 57 -1.918 0.001 ILMN_1671374 C19orf53 chromosome 19 open reading frame 53 -1.496 0.02 ILMN_1774584 C2orf28 chromosome 2 open reading frame 28 -1.435 0.038 ILMN_1790461 C6orf125 chromosome 6 open reading frame 125 -2.34 0.02 ILMN_2391765 C6orf48 chromosome 6 open reading frame 48 -1.474 0.001 ILMN_2081335 C7orf44 chromosome 7 open reading frame 44 -2.622 0.02 ILMN_1811264 CCDC32 chromosome 15 open reading frame 57 -1.45 0.02 ILMN_1715131 CCR7 chemokine (C-C motif) receptor 7 -1.814 0.001 ILMN_1792538 CD7 CD7 molecule -1.443 0.038 cutA divalent cation tolerance homolog (E. ILMN_1712390 CUTA coli) -1.788 0.038 cutA divalent cation tolerance homolog (E. ILMN_2311989 CUTA coli) -1.354 0.031 ILMN_1815115 CYC1 cytochrome c-1 -1.401 0.02 dual-specificity tyrosine-(Y)-phosphorylation ILMN_1794588 DYRK2 regulated kinase 2 -1.68 0.001 ILMN_1805922 EBPL emopamil binding protein-like -1.743 0.028 eukaryotic translation elongation factor 1 ILMN_2262288 EEF1G gamma -1.717 0.02 ILMN_1794522 EIF5A eukaryotic translation initiation factor 5A -1.417 0.031 EMG1 nucleolar protein homolog (S. ILMN_1797074 EMG1 cerevisiae) -1.575 0.028 ILMN_1779813 FAM96B family with sequence similarity 96, member B -1.311 0.031 Finkel-Biskis-Reilly murine sarcoma virus ILMN_1664614 FAU (FBR-MuSV) ubiquitously expressed -1.478 0.031 ILMN_1794510 FLJ32255 hypothetical LOC643977 -1.591 0.001 ILMN_2091412 FLT3LG fms-related tyrosine kinase 3 ligand -1.539 0.028

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ILMN_1784709 GNPDA1 glucosamine-6-phosphate deaminase 1 -1.366 0.001 ILMN_1780368 GPR18 G protein-coupled receptor 18 -1.693 0.028 ILMN_1742166 GRWD1 glutamate-rich WD repeat containing 1 -1.722 0.001 ILMN_1786823 ICAM2 intercellular adhesion molecule 2 -1.247 0.02 ILMN_1778010 IL32 interleukin 32 -1.653 0.031 ILMN_1745172 ILF2 interleukin enhancer binding factor 2, 45kDa -1.477 0.02 ILMN_1690139 KIAA0748 KIAA0748 -1.492 0.046 ILMN_1679185 LEF1 lymphoid enhancer-binding factor 1 -1.694 0.02 ILMN_1795243 LOC220433 -3.148 0.001 ILMN_1688127 LOC341457 -1.509 0.001 ILMN_1674983 LOC387841 -1.743 0.001 ILMN_1748827 LOC388564 -1.96 0.038 ILMN_1688749 LOC400963 -1.316 0.001 ILMN_1673753 LOC642033 -2.222 0.001 ILMN_1659771 LOC645683 -1.71 0.001 ILMN_1811063 LOC649447 -1.295 0.031 ILMN_1672755 LOC649548 -1.787 0.031 ILMN_1667932 LOC652726 -1.657 0.046 ILMN_1700674 LOC728481 -1.763 0.031 ILMN_1691949 LOC728554 -1.646 0.038 ILMN_2320330 MAL mal, T-cell differentiation protein -1.697 0.001 ILMN_1728360 MED29 mediator complex subunit 29 -1.753 0.038 ILMN_1751956 MGST3 microsomal glutathione S-transferase 3 -1.522 0.001 ILMN_1802553 MRPS24 mitochondrial ribosomal protein S24 -1.359 0.031 NADH dehydrogenase (ubiquinone) 1 beta ILMN_1661170 NDUFB8 subcomplex, 8, 19kDa -1.283 0.001 neural precursor cell expressed, ILMN_2058070 NEDD8 developmentally down-regulated 8 -1.216 0.046 ILMN_2110252 NPM3 nucleophosmin/nucleoplasmin 3 -1.85 0.031 ILMN_1689342 NUBP1 nucleotide binding protein 1 -1.386 0.028 ILMN_1700306 OCIAD2 OCIA domain containing 2 -1.719 0.02 ILMN_2306540 PDE9A phosphodiesterase 9A -2.525 0.028 ILMN_1717855 PFDN1 prefoldin subunit 1 -1.305 0.031 PIN2/TERF1 interacting, telomerase inhibitor ILMN_1790309 PINX1 1 -2.596 0.031 ILMN_1733666 PLDN pallidin homolog (mouse) -1.579 0.038 polymerase (DNA-directed), epsilon 4 (p12 ILMN_1660063 POLE4 subunit) -1.587 0.001 polymerase (RNA) II (DNA directed) ILMN_1720542 POLR2I polypeptide I, 14.5kDa -2.544 0.02 ILMN_2366388 PRDX1 peroxiredoxin 1 -1.362 0.046 ILMN_1692473 PRMT1 protein arginine methyltransferase 1 -1.564 0.038 ILMN_1729142 PRR6 centromere protein V -1.43 0.031 proteasome (prosome, macropain) subunit, ILMN_1764794 PSMB2 beta type, 2 -1.281 0.028

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protein tyrosine phosphatase, receptor type, ILMN_1804279 PTPRC C -1.727 0.04 protein tyrosine phosphatase, receptor type, ILMN_1672417 PTPRCAP C-associated protein -1.74 0.001 ILMN_1748578 RAD21 RAD21 homolog (S. pombe) -1.377 0.02 retinoic acid receptor responder (tazarotene ILMN_1701613 RARRES3 induced) 3 -1.51 0.001 ILMN_1786388 RNF113A ring finger protein 113A -2.044 0.02 ILMN_1812327 RNF19A ring finger protein 19A -1.482 0.031 ILMN_2079386 RPL22 ribosomal protein L22 -1.346 0.031 ILMN_2160388 RPL24 ribosomal protein L24 -1.42 0.02 ILMN_1756360 RPL35A ribosomal protein L35a -1.424 0.038 ILMN_1765043 RPL38 ribosomal protein L38 -1.336 0.001 ILMN_1712155 RPL6 ribosomal protein L6 -1.465 0.02 ILMN_1689725 RPLP1 ribosomal protein, large, P1 -2.244 0.04 ILMN_1701596 RPS20 ribosomal protein S20 -1.765 0.04 ILMN_1738243 RPS29 ribosomal protein S29 -1.797 0.031 ILMN_1694742 RPS29 ribosomal protein S29 -1.448 0.031 ILMN_1760714 RPS3 ribosomal protein S3 -1.267 0.04 ILMN_2166831 RPS4X ribosomal protein S4, X-linked -1.486 0.028 secretion regulating guanine nucleotide ILMN_1760049 SERGEF exchange factor -1.666 0.031 small nuclear ribonucleoprotein D2 ILMN_2369785 SNRPD2 polypeptide 16.5kDa -1.466 0.02 transmembrane emp24 protein transport ILMN_1719316 TMED3 domain containing 3 -1.454 0.001 ILMN_1667716 TMEM101 transmembrane protein 101 -1.525 0.031 ILMN_1704024 TMEM160 transmembrane protein 160 -1.32 0.028 ILMN_1812392 TMSB10 thymosin beta 4, X-linked -1.468 0.028 ILMN_1740185 TPMT thiopurine S-methyltransferase -1.516 0.038 ILMN_1747058 TRAPPC2L trafficking protein particle complex 2-like -1.53 0.001 ILMN_1814650 TRAPPC4 trafficking protein particle complex 4 -1.41 0.028 ILMN_1718907 TSHZ1 teashirt zinc finger homeobox 1 -1.554 0.038 ILMN_1779177 U2AF1L4 U2 small nuclear RNA auxiliary factor 1-like 4 -1.652 0.038 vesicle-associated membrane protein 8 ILMN_2190084 VAMP8 (endobrevin) -1.558 0.02 ILMN_1700147 VPREB3 pre-B lymphocyte 3 -1.722 0.001 ILMN_1809866 WDR74 WD repeat domain 74 -1.395 0.038

Upregulated Genes

Entrez Gene Name Fold Change Illumnia ID Gene ID p-value* (SZ/CTL) ATPase, H+ transporting, lysosomal 16kDa, V0 ILMN_1789005 ATP6V0C subunit c 1.417 0.038 ILMN_1709233 F5 coagulation factor V (proaccelerin, labile factor) 2.8 0.028 ILMN_2347949 G6PD glucose-6-phosphate dehydrogenase 1.396 0.02 ILMN_1752526 RNF144B ring finger protein 144B 1.602 0.02

Appendix 137

ILMN_1746588 TALDO1 transaldolase 1 1.45 0.028 ILMN_1749078 TIMP2 TIMP metallopeptidase inhibitor 2 1.827 0.02

Note: SZ = Schizophrenia, CTL = Control; * - p values determined using SAM Analysis.

Appendix 138

Supplementary Table 4: Differentially expressed genes in PBMCs from schizophrenia patients that did not change with antipsychotic drug treatment

DOWNREGULATED GENES

Control v Before Control v After

Gene ID Entrez Gene Name Fold Fold Change p-value* Change p-value* SZ/CTL SZ/CTL ABLIM1 actin binding LIM protein 1 -2.23 0.012 -2.23 0.038 APRT adenine phosphoribosyltransferase -1.30 0.012 -1.34 0.001 ATP synthase, H+ transporting, mitochondrial ATP5G1 -1.38 0.041 -1.29 0.020 Fo complex, subunit C1 (subunit 9) ATP synthase, H+ transporting, mitochondrial ATP5G2 -1.38 0.014 -1.38 0.001 Fo complex, subunit C2 (subunit 9) BCAS4 breast carcinoma amplified sequence 4 -1.81 0.001 -1.78 0.001 BOLA2 bolA homolog 2 (E. coli) -1.68 0.001 -1.64 0.001 C11orf10 chromosome 11 open reading frame 10 -1.68 0.001 -1.35 0.020 C12orf57 chromosome 12 open reading frame 57 -1.82 0.014 -1.92 0.001 C19orf53 chromosome 19 open reading frame 53 -1.51 0.023 -1.50 0.020 C2orf28 chromosome 2 open reading frame 28 -1.51 0.009 -1.44 0.038 C6orf48 chromosome 6 open reading frame 48 -1.66 0.001 -1.47 0.001 CCDC32 chromosome 15 open reading frame 57 -1.43 0.014 -1.45 0.020 CCR7 chemokine (C-C motif) receptor 7 -1.52 0.048 -1.81 0.001 CUTA cutA divalent cation tolerance homolog (E. coli) -1.40 0.012 -1.35 0.031 EBPL emopamil binding protein-like -1.59 0.036 -1.74 0.028 eukaryotic translation elongation factor 1 EEF1G -1.73 0.009 -1.72 0.020 gamma EMG1 nucleolar protein homolog (S. EMG1 -1.61 0.013 -1.57 0.028 cerevisiae) FAM96B family with sequence similarity 96, member B -1.31 0.031 -1.38 0.009 Finkel-Biskis-Reilly murine sarcoma virus (FBR- FAU -1.44 0.048 -1.48 0.031 MuSV) ubiquitously expressed FLT3LG fms-related tyrosine kinase 3 ligand -1.74 0.001 -1.54 0.028 GPR18 G protein-coupled receptor 18 -1.64 0.023 -1.69 0.028 ICAM2 intercellular adhesion molecule 2 -1.28 0.001 -1.25 0.020 IL32 interleukin 32 -1.44 0.025 -1.65 0.031 ILF2 interleukin enhancer binding factor 2, 45kDa -1.41 0.009 -1.48 0.020 LEF1 lymphoid enhancer-binding factor 1 -2.11 0.001 -1.69 0.020 LOC341457 -1.86 0.001 -1.51 0.001 LOC387841 -1.49 0.009 -1.74 0.001 LOC388564 -1.84 0.028 -1.96 0.038 LOC642033 -2.08 0.001 -2.22 0.001 LOC645683 -1.64 0.001 -1.71 0.001 LOC649447 -1.39 0.009 -1.30 0.031 LOC652726 -1.53 0.012 -1.66 0.046 LOC728554 -1.87 0.009 -1.65 0.038

Appendix 139

MAL mal, T-cell differentiation protein -2.13 0.001 -1.70 0.001 MGST3 microsomal glutathione S-transferase 3 -1.59 0.012 -1.52 0.001 MRPS24 mitochondrial ribosomal protein S24 -1.27 0.048 -1.36 0.031 NADH dehydrogenase (ubiquinone) 1 beta NDUFB8 -1.23 0.012 -1.28 0.001 subcomplex, 8, 19kDa neural precursor cell expressed, NEDD8 -1.29 0.036 -1.22 0.046 developmentally down-regulated 8 OCIAD2 OCIA domain containing 2 -2.03 0.001 -1.72 0.020 PDE9A phosphodiesterase 9A -3.12 0.001 -2.52 0.028 PFDN1 prefoldin subunit 1 -1.51 0.001 -1.31 0.031 PINX1 PIN2/TERF1 interacting, telomerase inhibitor 1 -2.59 0.012 -2.60 0.031 PRDX1 peroxiredoxin 1 -1.56 0.009 -1.36 0.046 PRMT1 protein arginine methyltransferase 1 -1.64 0.001 -1.56 0.038 PRR6 centromere protein V -1.69 0.001 -1.43 0.031 proteasome (prosome, macropain) subunit, PSMB2 -1.33 0.013 -1.28 0.028 beta type, 2 retinoic acid receptor responder (tazarotene RARRES3 -1.63 0.001 -1.51 0.001 induced) 3 RNF113A ring finger protein 113A -1.56 0.048 -2.04 0.020 RPL24 ribosomal protein L24 -1.84 0.001 -1.42 0.020 RPL35A ribosomal protein L35a -1.72 0.001 -1.42 0.038 RPL38 ribosomal protein L38 -1.36 0.014 -1.34 0.001 RPL6 ribosomal protein L6 -1.57 0.001 -1.46 0.020 RPS20 ribosomal protein S20 -2.14 0.001 -1.77 0.039 RPS29 ribosomal protein S29 -1.58 0.009 -1.45 0.031 RPS4X ribosomal protein S4, X-linked -1.95 0.001 -1.49 0.028 secretion regulating guanine nucleotide SERGEF -1.56 0.009 -1.67 0.031 exchange factor small nuclear ribonucleoprotein D2 polypeptide SNRPD2 -1.77 0.001 -1.47 0.020 16.5kDa transmembrane emp24 protein transport TMED3 -1.47 0.009 -1.45 0.001 domain containing 3 TMEM160 transmembrane protein 160 -1.47 0.012 -1.32 0.028 TMSB10 thymosin beta 4, X-linked -1.65 0.009 -1.47 0.028 TRAPPC2L trafficking protein particle complex 2-like -1.46 0.012 -1.53 0.001 TRAPPC4 trafficking protein particle complex 4 -1.54 0.009 -1.41 0.028 TSHZ1 teashirt zinc finger homeobox 1 -1.70 0.028 -1.55 0.038 U2AF1L4 U2 small nuclear RNA auxiliary factor 1-like 4 -1.80 0.009 -1.65 0.038

UPREGULATED GENES

Control v Before Control v After Fold p- Gene ID Entrez Gene Name Fold Change p- Change value SZ/CTL value* SZ/CTL * F5 coagulation factor V (proaccelerin, labile factor) 2.81 0.010 2.80 0.028 G6PD glucose-6-phosphate dehydrogenase 1.45 0.010 1.40 0.020 RNF144b ring finger protein 144B 1.71 0.025 1.60 0.020

Appendix 140

TIMP2 TIMP metallopeptidase inhibitor 2 2.33 0.028 1.83 0.020

Note: SZ = Schizophrenia, CTL = Control; * - p values

determined using SAM Analysis.

Control v Before Control v After Entrez Gene Name Fold Change Fold Change p-value* p-value* SZ/CTL SZ/CTL coagulation factor V (proaccelerin, labile factor) 2.81 0.010 2.80 0.028 glucose-6-phosphate dehydrogenase 1.45 0.010 1.40 0.020 ring finger protein 144B 1.71 0.025 1.60 0.020 TIMP metallopeptidase inhibitor 2 2.33 0.028 1.83 0.020

Appendix 141

Supplementary Table 5: Top ranked biological functions overrepresented by genes dysregulated in schizophrenia before and after antipsychotic drug treatment

IPA Dysregulat IPA Before v IPA IPA Functio ed and Functio After Control v Control v Functional Functional nal unchanged nal Direct Before After Category Category Categor by Catego Compariso y treatment ry n # #

# M M # M o o M ol Disease Diseas Diseases Diseases l l p- ol p- e s and es and and and p-value e p-value e value ec value c Disorder Disord Disorders Disorders c c ul ul s ers u u es e l l s e e s s

Connec 2.61E- 5.50E- 8.98E- Inflamma 2.29E-04 tive Inflammator 11 - Inflammator 04 - 04 - 92 7 tory - 2.59E- 5 tissue 1 y response* 1.99E- y Disease 3.98E- 8.98E- Disease 02 disorder 02 02 04 s 3.49E- 5.50E- Inflamma 8.98E- 2.29E-04 Infectious 11 - Inflammator 04 - 1 tory Genetic 04 - 98 - 4.27E- 9 4 disease* 9.54E- y response* 4.97E- 3 response disorder 8.98E- 02 03 02 * 02 Skeletal 3.49E- 5.50E- Infectio 8.98E- Skeletal and and 2.29E-04 Respiratory 11 - 04 - us 04 - 58 Muscular 2 Muscular - 1.73E- 2 1 disease* 1.84E- 2.67E- disease 8.98E- Disorders Disorder 02 02 02 * 03 s 5.10E- 5.50E- 8.98E- Antimicr 2.91E-03 Neurolo 05 - 15 Infectious 04 - 1 04 - Cancer obial - 4.27E- 2 gical 4 2.01E- 6 disease* 2.67E- 4 8.98E- response 02 disease 02 02 02 Skeletal 3.44E- 5.50E- 8.98E- Connective 2.91E-03 muscle 04 - Respiratory 04 - 2 04 - tissue 82 5 Cancer - 4.95E- and 2 4.00E- disease* 2.67E- 1 8.98E- disorder 02 disorder 03 02 02 s

Molecul Molecu Molecular Molecular ar and lar and and and Cellular Cellular Cellular Cellular Functio Functio Functions Functions ns ns Cell-to-cell 9.96E- 5.50E- Amino 8.98E- Carbohydrat Anitgen 7.37E-04 signaling 08 - 04 - acid 04 - 72 e 4 presenta - 3.71E- 4 1 and 2.13E- 3.55E- metabol 8.98E- metabolism tion 02 interaction 02 02 ism 04 4.06E- 5.50E- 8.98E- Cellular 7.37E-04 Gene 07 - 04 - Cell Cell 04 - 20 growth and 7 - 4.83E- 8 4 expression 2.20E- 4.93E- death death 3.59E- proliferation 02 03 02 02

Appendix 142

Post- 4.06E- 5.50E- 8.98E- Cellular 7.37E-04 translati Protein 07 - Nucleic acid 04 - 04 - 54 5 moveme - 4.83E- 8 onal 2 synthesis 3.53E- metabolism 3.55E- 3.88E- nt 02 modific 03 02 02 ation Cell-to- Small 7.01E- 5.50E- cell 8.98E- Cellular Small 2.91E-03 molecul 07 - 04 - signaling 04 - developmen 80 molecule 9 - 3.99E- 6 e 3 2.01E- 4.41E- and 2.05E- t biochemistry 02 bioche 02 02 interactio 02 mistry n 1.08E- 1.76E- 1.80E- Cellular 2.91E-03 Cellular Cellular 06 - Antigen 03 - 1 03 - 82 5 develop - 4.83E- develop 4 movement 2.10E- presentation 4.84E- 0 4.32E- ment 02 ment 02 02 02

Physiol Physiol Physiologic ogical ogical Physiologic al System System System al System Developme Develop Develo Developme nt and ment pment nt and Function and and Function Functio Functio n n Haemato logical Haematologi 9.96E- Haematologi 5.50E- 1.80E- system 7.37E-04 Tissue cal system 08 - cal system 04 - 1 1 03 - 94 develop - 4.83E- morphol 1 developmen 2.13E- developmen 4.93E- 6 0 1.80E- ment 02 ogy t/function* 02 t/function* 02 03 and function* 9.96E- 1.76E- Immune 1.98E- 7.37E-04 Tissue Immune cell 08 - Immune cell 03 - 1 cell 03 - 73 - 4.83E- 7 Develop 3 trafficking* 1.99E- trafficking* 4.84E- 0 traffickin 4.14E- 02 ment 02 02 g* 02 Renal Cardiova and scular Urologic 5.44E- Cell- 2.92E- 2.69E- system 2.91E-03 al Haematopoi 06 - mediated 03 - 03 - 49 5 develop - 4.83E- 3 system 1 esis* 1.70E- immune 3.11E- 2.69E- ment 02 develop 02 response 02 03 and ment fucntion and function Lymphoid tissue 7.70E- 2.92E- Embryon Embryo 4.77E- 2.91E-03 structure 06 - Haematopoi 03 - ic nic 03 - 35 9 - 3.99E- 3 2 and 1.70E- esis* 4.84E- develop develop 4.77E- 02 developmen 02 02 ment ment 03 t* Lymphoid Lympho 5.50E- tissue 2.92E- id tissue 4.77E- Tissue 2.91E-03 05 - structure 03 - Haemato structur 03 - developmen 44 6 - 4.83E- 8 2 1.99E- and 3.55E- poiesis e and 4.77E- t 02 02 developmen 02 develop 03 t* ment*

Appendix 143

Supplementary Table 6: Comparison of cortical grey matter measures.

Standard deviation in parenthesis) for individual Brodmann areas (BA) of the left and right hemisphere between 18 schizophrenia patients (SCZ) and 18 closely matched healthy control subjects (CON). Significant group differences bolded (df=1,34; * denotes Bonferroni-corrected P<0.0014).

Left hemisphere Right hemisphere

BA C SZ F P C SZ F P

1-3 0.23 (0.03) 0.21 (0.03) 3.5 .07 0.24 (0.03) 0.22 (0.03) 3.1 .09

4 0.25 (0.03) 0.24 (0.02) 2.7 .11 0.25 (0.03) 0.24 (0.03) 2.6 .12

5 0.26 (0.03) 0.25 (0.03) 2,1 .16 0.26 (0.03) 0.25 (0.02) 4.6 .04

6 0.29 (0.03) 0.27 (0.02) 3.7 .06 0.30 (0.03) 0.27 (0.02) 9.4 .00

7 0.28 (0.03) 0.27 (0.02) 2.0 .17 0.29 (0.02) 0.27 (0.02) 2.9 .10

8 0.29 (0.03) 0.27 (0.02) 4.6 .04 0.29 (0.03) 0.26 (0.03) 5.2 .03

9 0.31 (0.02) 0.29 (0.03) 5.3 .03 0.31 (0.03) 0.29 (0.02) 8.3 .01

10 0.32 (0.02) 0.31 (0.02) 3.9 .06 0.33 (0.02) 0.31 (0.02) 7.0 .01

11 0.32 (0.02) 0.31 (0.02) 2.0 .16 0.33 (0.02) 0.32 (0.03) 4.2 .05

17 0.27 (0.02) 0.26 (0.02) 1.9 .18 0.29 (0.02) 0.27 (0.03) 3.2 .08

18 0.27 (0.02) 0.26 (0.02) 0.4 .53 0.27 (0.02) 0.26 (0.03) 1.1 .30

19 0.30 (0.02) 0.30 (0.02) 1.6 .22 0.30 (0.02) 0.29 (0.02) 2.2 .14

20 0.30 (0.02) 0.29 (0.03) 1.0 .32 0.30 (0.02) 0.29 (0.03) 0.6 .45

21 0.31 (0.02) 0.30 (0.02) 1.3 .26 0.31 (0.02) 0.30 (0.02) 2.0 .16

22 0.34 (0.03) 0.31 (0.03) 4.9 .04 0.34 (0.03) 0.31 (0.03) 9.5 .00

23 0.28 (0.02) 0.27 (0.02) 0.7 .42 0.29 (0.02) 0.28 (0.02) 0.2 .65

24 0.25 (0.02) 0.25 (0.03) 0.6 .45 0.27 (0.02) 0.26 (0.02) 2.9 .10

25 0.35 (0.02) 0.34 (0.03) 1.0 .33 0.35 (0.03) 0.34 (0.03) 2.8 .10

26 0.18 (0.02) 0.18 (0.02) 0.0 .84 0.19 (0.02) 0.19 (0.02) 0.0 .88

27 0.33 (0.02) 0.32 (0.03) 1.0 .33 0.33 (0.03) 0.34 (0.03) 0.1 .72

28 0.34 (0.02) 0.33 (0.03) 4.5 .04 0.35 (0.03) 0.35 (0.02) 0.9 .36

29 0.23 (0.03) 0.22 (0.03) 1.8 .19 0.24 (0.03) 0.22 (0.03) 3.2 .08

30 0.31 (0.02) 0.29 (0.03) 3.4 .07 0.32 (0.03) 0.30 (0.03) 1.1 .30

31 0.35 (0.02) 0.34 (0.02) 2.0 .17 0.36 (0.02) 0.34 (0.03) 2.7 .11

Appendix 144

32 0.33 (0.02) 0.31 (0.03) 4.5 .04 0.34 (0.02) 0.32 (0.03) 5.3 .03

33 0.23 (0.02) 0.22 (0.03) 2.3 .14 0.24 (0.02) 0.22 (0.03) 3.9 .06

35 0.33 (0.02) 0.33 (0.03) 0.1 .73 0.34 (0.02) 0.33 (0.02) 0.4 .52

36 0.39 (0.03) 0.39 (0.03) 0.2 .69 0.39 (0.02) 0.39 (0.02) 0.3 .59

37 0.32 (0.02) 0.31 (0.02) 1.5 .23 0.32 (0.02) 0.31 (0.02) 2.6 .11

38 0.32 (0.04) 0.30 (0.04) 1.9 .18 0.33 (0.03) 0.31 (0.05) 1.2 .28

39 0.32 (0.02) 0.31 (0.03) 2.5 .12 0.32 (0.02) 0.31 (0.03) 4.8 .04

40 0.30 (0.03) 0.29 (0.02) 1.7 .20 0.31 (0.02) 0.29 (0.02) 7.1 .01

43 0.29 (0.04) 0.28 (0.03) 1.0 .33 0.31 (0.04) 0.29 (0.03) 3.4 .08

44 0.35 (0.04) 0.33 (0.03) 2.6 .12 0.37 (0.03) 0.35 (0.03) 4.7 .04

45 0.29 (0.03) 0.27 (0.02) 4.3 .05 0.31 (0.03) 0.29 (0.02) 5.6 .02

46 0.32 (0.02) 0.31 (0.02) 2.3 .14 0.33 (0.02) 0.31 (0.02) 13.3 .00*

47 0.29 (0.03) 0.28 (0.03) 1.9 .17 0.32 (0.03) 0.30 (0.03) 5.9 .02

Appendix 145