Molecular Expression Analyses of Mice Treated with Antipsychotic Drugs

Molecular Expression Analyses of Mice

Treated with Antipsychotic Drugs

Carlotta Elizabeth Duncan

A thesis submitted in fulfilment of the requirements

for the degree of Doctor of Philosophy

The University of New South Wales

January 2008

Supervisors: Professor Peter R. Schofield &

Professor Cynthia Shannon Weickert ABSTRACT Schizophrenia is a devastating psychiatric disorder that affects approximately 1% of the population. The main treatments for schizophrenia are antipsychotic drugs that target dopamine receptors, yet the underlying biological mechanisms through which they alleviate the symptoms of schizophrenia remain ill defined. In this study, we used microarray analysis to profile the expression changes of thousands of genes simultaneously, following antipsychotic drug treatment of mice. Mice were treated chronically (28 days), or for a novel intermediate time-point (7 days), with one of three antipsychotic drugs: clozapine, haloperidol or olanzapine. The use of three drugs enabled us to discern antipsychotic-specific effects co-regulated by multiple drugs, rather than the side effects of individual compounds. Transcript profiling and validation by quantitative PCR of whole brain tissue revealed antipsychotic drug regulation of genes in diverse biological pathways, including: dopamine metabolism, neuropeptide and second-messenger signalling, neurogenesis, synaptic plasticity, cell adhesion, myelination, and voltage-gated ion channels. The regulation of voltage-gated channels by antipsychotic drugs has been suggested previously by electrophysiological studies, although thorough analysis has not been undertaken in vivo. Therefore, the second aim of this study was to characterise the regional mRNA and protein expression of two genes altered by multiple APDs, the voltage-gated potassium channel -subunit (Kcna1) and voltage- gated potassium channel interacting protein (Kchip3). Regional characterisation and expression analyses were carried out by immunohistochemistry, in situ hybridisation, and Western blot analysis of mouse brain regions of interest to schizophrenia and its treatment. Following 7-day haloperidol treatment we observed up-regulation of Kcna1 in the striatum and dentate gyrus, with increased protein in the striatum, hippocampus and midbrain; and down-regulation of Kchip3 in the striatum, with decreased protein in the cortex, hippocampus and midbrain. These studies implicate voltage-gated potassium channels in the antipsychotic drug regulation of midbrain dopaminergic neuronal activity, adult neurogenesis and/or striatothalamic GABAergic neuronal inhibition. These findings indicate that regulation of potassium channels may underlie some of the mechanisms of action of antipsychotic drugs, and that voltage-gated ion channels may provide alternative drug targets for the treatment of schizophrenia.

This thesis is dedicated to my Grandpa Mark: a prolific researcher, groundbreaking surgeon, lover of fine food and wine, and an inspiration to everyone whose life he touched.

Mark B Coventry, M.D. 1913-1994

ii ACKNOWLEDGMENTS

First and foremost I would like to thank my supervisor, Prof. Peter Schofield, who despite running an institute has been a rock when I needed one. Thank you for the opportunity to earn my PhD in two engaging environments. Dr. Carol Dobson- Stone provided a fantastically pedantic critical review of the thesis and some great early morning chats. My lab colleagues, past and present, from the Schofield lab: Dr Renee Morris, for mouse brain regional and protein expertise; Dr John Kwok, the Western and weather king!; Marianne – special thank you for your support throughout my PhD and your persistence with orders; Erica, thanks for lots of chocolate and yoga!; Dr Jan Fullerton, Dr Clement Loy, Kerrie, Anna and Mel, thank you for providing a lively work atmosphere. I’d also like to thank my present and future colleagues in the Schizophrenia Research Laboratory: Debora and Inara for keeping the lab running smoothly and for last minute desperate orders for me! Duncan and Cami for their assistance with experiments in the last year; Dr Sinthuja Sivagnanasundaram for reading my literature review; and also Dr Jenny Wong and Shan, thank you all for your help this year and I look forward to working with you next year. Profs Halliday and Garner, as well as members of their labs, particularly Dr Scott Kim, Elias, Heather and Karen, provided useful advice and resources and helped to ease the transition while settling into POWMRI. I’d also like to thank Prof. George Paxinos for his expertise in discerning regional expression patterns for in situ hybridisation and for use of his cryostat, for which Peter also provided invaluable technical advice. Additionally, Dr Warren Kaplan at the Garvan Institute bioinformatics department gave technical help with microarray data analysis and he and his staff were very patient with this non-statistician.

An extra special thanks goes to (Dr) Agnes Luty, for suffering her PhD alongside me and providing inspiration and company during all those extra hours in the lab!

Thanks must also be given to my family and friends. Mum and dad I could not have done this without your support – fiscal and emotional! I hope your pride in my achievements will one day parallel mine in yours. Dave, thanks for your support

iii and understanding through this incredibly trying and exhausting year … you officially have your “comma-happy” girlfriend back! Andrew, you guided me into this field of interest, which I adore, and for that I am eternally grateful. I also enjoyed your retirement celebration and seeing the value and respect you have achieved in a lifetime in science, as my career is just beginning. James and Suzanne, your advice on my oral presentation skills was priceless, not to mention the wonderfully distracting conversations and dinners over the years…thank you! Liz and Gila, initially my colleagues in the lab and now eternal friends, I’m so proud of you both and look forward to the day when we can raise a cocktail to three “doctors”.

There have been four women scientists who have inspired me to a life in science and trained me for this role: my high school chemistry teacher, Ms Oswald, and physics teacher, Dr Huxley, who humoured my adolescent angst and gave me an early love for science; my honours supervisor Prof. Emma Whitelaw who inspired me with her passion for epigenetics and encouraged me to remain in research; and Prof. Cyndi Shannon-Weickert – I am infinitely grateful for your supervising the final year of my PhD. Your breadth of knowledge is awe-inspiring and you have taught me so much about neuroscience, schizophrenia, the research world and commitment to a cause. I look forward to helping you cure schizophrenia.

iv TABLE OF CONTENTS

ABSTRACT……………………………………………………………………..i Acknowledgements…………………………………………………………..iii Table of contents………………………………………………………………v List of abbreviations and symbols……………………………………….xiii List of publications arising from this thesis…………………………….xv

1. INTRODUCTION...... 1 1.1. Schizophrenia……………………………………………………....2 1.1.1. Classification……………………………………………………...... 2 1.1.1.1. History……………………………………………………...2 1.1.1.2. Onset and course…………………………………………...2 1.1.1.3. Clinical diagnosis.…………………………………………..3 1.1.1.4. Neurophysiological measures.……………………………...3 1.1.2. Neuropathology and imaging………………………………………4 1.1.2.1. Structural abnormalities……………………………………4 1.1.2.2. Cytoarchitectural abnormalities……………………………7 1.1.3. Proposed aetiological models………………………………………8 1.1.3.1. Neurodevelopmental hypothesis of schizophrenia………....8 1.1.3.2. Schizophrenia as a disorder of the synapse………………...9 1.1.4. Summary………………………………………………………….10 1.2. Neurochemistry and pharmacology in schizophrenia……..10 1.2.1. Neurotransmission occurs at the synapse…………………………10 1.2.2. History of neurochemistry and the treatment of schizophrenia…..11 1.2.2.1. Atypical versus conventional antipsychotic drugs………...13 1.2.3. The evolution of the dopamine hypothesis of schizophrenia……..15 1.2.3.1. Dopamine…………………………………………………15 1.2.3.2. Striatal dopamine hyperactivity underlies psychosis...……17 1.2.3.3. New support for dopamine hyperactivity in schizophrenia………………………………………………..18 1.2.3.4. A role for cortical dopamine hypoactivity………………...19

v 1.2.4. Glutamatergic dysfunction in schizophrenia……………………...21 1.2.4.1. Glutamate………………………………………………....21 1.2.4.2. NMDA receptor hypofunction in schizophrenia…………22 1.2.5. The present state of the field – a synthesis………………………..25 1.3. Molecular genetics of schizophrenia…………………………..27 1.3.1. Schizophrenia has a genetic predisposition……………………….27 1.3.2. Linkage and positional cloning……………………………………28 1.3.2.1. Neuregulin 1………………………………………………29 1.3.2.2. PPP3CC…………………………………………………..30 1.3.2.3. Dysbindin…………………………………………………31 1.3.2.4. G72/DAOA………………………………………………33 1.3.3. Cytogenetic abnormalities………………………………………...34 1.3.3.1. Microdeletions of 22q11…………………………………..34 1.3.3.2. DISC1 and partners………………………………………36 1.3.3.3. Other genes suggested through cytogenetic analysis……..37 1.3.4. Candidate genes and association studies………………………….38 1.3.4.1. COMT……………………………………………………38 1.3.4.2. ERBB4……………………………………………………40 1.3.4.3. GRM3…………………………………………………….41 1.3.4.4. BDNF……………………………………………………..41 1.3.4.5. GAD1…………………………………………………….42 1.3.4.6. RGS4……………………………………………………..42 1.3.4.7. AKT1……………………………………………………..43 1.3.5. Summary – schizophrenia susceptibility genes…………………...44 1.4. Gene expression profiling in schizophrenia………………….46 1.4.1. Techniques for detecting altered gene expression………………..46 1.4.1.1. Molecular biology in the 21st century…………………….46 1.4.1.2. Transcript profiling by microarray analysis………………47 1.4.1.3. Validation techniques……………………………………..49 1.4.2. Gene expression profiling analyses of tissue from schizophrenia patients……………………………………………….53 1.4.2.1. Analyses of gene expression in postmortem brain tissue…………………………………………………….53

vi 1.4.2.2. Analyses of molecular expression in schizophrenia peripheral tissue……………………………………………….59 1.4.3. Gene expression profiling in animal models……………………...61 1.4.3.1. Animal models of schizophrenia………………………….61 1.4.3.2. Animal models of antipsychotic drug treatment………….62 1.5. Specific aims of this thesis………………………………………65

2. MATERIALS & METHODS………………………………………………67 2.1. Materials……………………………………………………………68 2.1.1. Animals……………………………………………………………68 2.1.2. Drugs……………………………………………………………...68 2.1.3. Common chemicals and reagents………………………………...68 2.1.4. Solutions and buffers……………………………………………...69 2.1.5. Enzymes and enzyme buffers……………………………………..72 2.1.6. Microarray experiments…………………………………………..72 2.1.7. Molecular biology kits…………………………………………….73 2.1.8. Oligonucleotide primers…………………………………………..73 2.1.9. Antibodies…………………………………………………………76 2.1.10. Bacterial media and competent cells……………………………...77 2.1.11. Vectors…………………………………………………………….77 2.1.12. Radiochemicals…………………………………………………...77 2.1.13. Histological materials……………………………………………..77 2.2. Animal handling and treatment………………………………..78 2.2.1. Animal housing conditions……………………………………….78 2.2.2. Animal drug treatment……………………………………………78 2.2.3. Animal sacrifice…………………………………………………...79 2.2.3.1. Standard endpoint technique……………………………..79 2.2.3.2. Perfusion…………………………………………………..79 2.3. Histological methods……………………………………………..79 2.3.1. Mouse brain collection and preparation………………………….79 2.3.1.1. Whole brain extraction and storage………………………79 2.3.1.2. Fixed tissue preparation…………………………………..80 2.3.1.3. Mouse brain microdissection……………………………..80

vii 2.3.2. Mouse brain sectioning…………………………………………...82 2.3.2.1. Gelating coating of microscope slides……………………82 2.3.2.2. Fresh frozen cutting of mouse brain tissue……………….83 2.3.2.3. Fixed tissue cutting of mouse brain tissue………………...83 2.4. Basic molecular biology methods………………………………84 2.4.1. DNA extraction and precipitation………………………………..84 2.4.2. Polymerase chain reaction………………………………………..84 2.4.3. Agarose gel electrophoresis……………………………………….85 2.4.4. Construction of recombinant plasmids…………………………...85 2.5. Gene expression analysis………………………………………..88 2.5.1. RNA extraction and analysis……………………………………..88 2.5.1.1. RNA extraction from whole brain tissue…………………88 2.5.1.2. Purification of total RNA…………………………………88 2.5.1.3. Quantification and assessing integrity……………………89 2.5.2. Microarray analysis……………………………………………….89 2.5.2.1. Target preparation………………………………………..89 2.5.2.2. Microarray hybridisation…………………………………91 2.5.2.3. Data analysis………………………………………………93 2.5.3. Quantitative real-time RT-PCR (QPCR) analysis………………..95 2.5.3.1. DNase treatment and cDNA synthesis……………………95 2.5.3.2. Primer design……………………………………………...95 2.5.3.3. Polymerase chain reaction using SYBR Green polymerase…………………………………………………….96 2.5.3.4. Quantification analysis……………………………………96 2.5.4. In situ hybridisation………………………………………………..97 2.5.4.1. RNA probe generation……………………………………97 2.5.4.2. Radiolabeling of probe……………………………………98 2.5.4.3. Tissue preparation………………………………………...99 2.5.4.4. Probe hybridisation……………………………………….99 2.5.4.5. Visualisation and quantification…………………………100 2.6. Protein analysis………………………………………………….102 2.6.1. Protein extraction techniques……………………………………102 2.6.1.1. Protein extraction from whole brain tissue………………102

viii 2.6.1.2. Protein extraction from dissected brain regions…………102 2.6.2. Protein quantification……………………………………………102 2.6.3. Western blotting…………………………………………………103 2.6.3.1. SDS-PAGE, Western transfer and immunoblotting of whole brain lysates…………………………………………………..103 2.6.3.2. SDS-PAGE, Western transfer and immunoblotting of microdissected brain region lysates…………………………..104 2.6.3.3. Signal detection and quantification……………………...105 2.6.4. Immunohistochemistry of fixed mouse brain tissue……………..105 2.6.4.1. Tissue preparation……………………………………….105 2.6.4.2. Immunohistochemical procedure………………………..105 2.6.4.3. Nissl counterstain of fixed tissue sections………………...106 2.7. Statistical analysis……………………………………………….106

3. MOUSE ANTIPSYCHOTIC DRUG TREATMENT and TRANSCRIPT PROFILING of BRAIN TISSUE……………………..109 3.1. Introduction……………………..……………………………….110 3.1.1. Antipsychotic drugs……………………..……………………….110 3.1.2. Transcript profiling of animals treated with antipsychotic drugs……………………..…………………………..111 3.1.3. Aims of this chapter……………………..……………………….113 3.2. Results……………………..………………………………………113 3.2.1. Antipsychotic drug treatment……………………..……………..113 3.2.2. Microarray hybridisation and data analysis……..……………....117 3.3. Discussion……………………..………………………………….123 3.3.1. Response to antipsychotic drug treatment in mice……………...123 3.3.2. Effect of antipsychotic drug treatment on transcription in mice……………………..………………………...124 3.3.3. Study design……………………..………………………………125 3.3.4. Microarray data analytical techniques…………………………..126

ix 4. INTERMEDIATE ANTIPSYCHOTIC DRUG TREATMENT REGULATION of GENE and PROTEIN EXPRESSION in MOUSE WHOLE BRAIN TISSUE………………………………………………..129 4.1. Introduction………………………………………………………130 4.2. Results……………………………………………………………..131 4.2.1. Microarray bioinformatical analysis……………………………..131 4.2.2. Quantitative real-time RT-PCR validation……………………..137 4.2.3. Protein quantification by Western blot analysis…………………137 4.3. Discussion…………………………………………………………140 4.3.1. Study findings……………………..……………………………..140 4.3.2. Relevance of validated genes in schizophrenia treatment……….142 4.3.2.1. Antipsychotic drug effects on voltage-gated ion channels……………………..……………………………142 4.3.2.2. Genes up-regulated by multiple antipsychotic drugs……144 4.3.2.3. Genes down-regulated by multiple antipsychotic drugs……………………..…………………………………..146 4.3.3. Voltage-gated potassium channels in antipsychotic drug action…………………………..…………………………………..150

5. CHRONIC ANTIPSYCHOTIC DRUG TREATMENT REGULATION of GENE and PROTEIN EXPRESSION in MOUSE WHOLE BRAIN TISSUE…………………………………………………………………….157 5.1. Introduction………………………………………………………158 5.2. Results……………………………………………………………..159 5.2.1. Microarray bioinformatical analysis……………………………..159 5.2.2. Quantitative real-time RT-PCR validation……………………..163 5.2.3. Protein quantification by Western blot analysis…………………165 5.3. Discussion…………………………………………………………166 5.3.1. Chronic antipsychotic drug treatment verified altered genes…...166 5.3.2. Genes with long-term altered expression………………………..169 5.3.3. Genes in top interaction network………………………………..170 5.3.4. Genes with functional relevance…………………………………173 5.3.5. Comparison of microarray data analytical techniques…………..176

6. VOLTAGE-GATED POTASSIUM CHANNELS in the MECHANISM of ANTIPSYCHOTIC DRUG ACTION……………………………….181 6.1. Introduction………………………………………………………182 6.1.1. Role of voltage-gated potassium channels in neurotransmission………………………………………………….182 6.1.2. The dopamine hypothesis implicates specific brain regions of relevance to schizophrenia treatment……………………………...183 6.1.3. Aims of this chapter……………………………………………...184 6.2. Results……………………………………………………………..186 6.2.1. KCHIP3 localisation and expression in normal and haloperidol- treated mouse brain………………………………………………..186 6.2.1.1. Localisation of Kchip3 mRNA by in situ hybridisation…...186 6.2.1.2. Quantification of haloperidol-induced regional changes in Kchip3 mRNA by in situ hybridisation………………………...190 6.2.1.3. Localisation of KCHIP3 protein by immunohistochemistry……………………………………….192 6.2.1.4. Quantification of haloperidol-induced regional changes in KCHIP3 protein by Western blot analysis…………………..201

6.2.2. Kv1.1 localisation and expression in normal and haloperidol-treated mouse brain………………………………………………………..206 6.2.2.1. Localisation of Kcna1 mRNA by in situ hybridisation……206 6.2.2.2. Quantification of haloperidol-induced regional changes in Kcna1 mRNA by in situ hybridisation…………………………210

6.2.2.3. Localisation of Kv1.1 protein by immunohistochemistry………………………………………212 6.2.2.4. Quantification of haloperidol-induced regional changes in

Kv1.1 protein by Western blot analysis………………………221 6.3. Discussion…………………………………………………………225 6.3.1. Study design……………………………………………………..225 6.3.2. Localisation of KCHIP3 mRNA and protein expression in the adult mouse brain………………………………………………………...226

xi 6.3.3. Localisation of Kv1.1 mRNA and protein expression in the adult mouse brain………………………………………………………...229

6.3.4. Characterisation of Kv1.1 and KCHIP3 protein expression in the adult mouse brain………………………………………………….232

6.3.5. Regional regulation of Kv1.1 and KCHIP3 mRNA and protein in haloperidol-treated animals compared to controls………………...233

7. GENERAL DISCUSSION……………………………………………….239 7.1. Summary of results……………………………………………..240

7.2. Antipsychotic drug regulation of Kv channel subunits may alter dopamine neurotransmission in schizophrenia………..242 7.3. Antipsychotic drug regulation of genes involved in adult neurogenesis…………………………………………………..246

7.4. Antipsychotic drug regulation of Kv channels in the striatum may affect negative symptomatology in schizophrenia………250 7.5. Future directions…………………………………………………254 7.6. Final remarks…………………………………………………….257

REFEERENCES………………………………………………………………261

APPENDIX 1………………………………………………………………….324

xii ABBREVIATIONS USED IN THIS THESIS ac: anterior commissure AMPA: -amino-3-hydroxy-5-methylisoxazole-4-propionic acid APD: antipsychotic drug cAMP: cyclic adenosine monophosphate cDNA: complementary DNA cRNA complementary RNA CSF: cerebrospinal fluid CT: computerized tomography DA: dopamine DDC: dopamine decarboxlyase DNA: deoxyribonucleic acid dsDNA: double-stranded DNA DLPFC: dorsolateral prefrontal cortex EPS: extrapyramidal side effects FDR: false discovery rate fMRI: functional magnetic resonance imaging GABA: -amino butyric acid GOI: gene of interest GP: globus pallidus Glu: glutamate hr: hour(s) mGluR: metabotropic receptor min: minute(s) MRI: magnetic resonance imaging mRNA: messenger RNA NAA: N-acetyl aspartate NAc: nucleus accumbens NMDA: N-methyl-D-aspartic acid NT: neurotransmitter NT-R: neurotransmitter receptor PCP: phencyclidine PCR: polymerase chain reaction PET: positron emission tomography

xiii PFC: prefrontal cortex PPI: prepulse inhibition QPCR: quantitative real-time RT-PCR RNA: ribonucleic acid rRNA: ribosomal RNA RP: rank product RT: room temperature/ reverse transcriptase RT-PCR: reverse transcription polymerase chain reaction SDS: sodium dodecyl sulphate SGZ: subgranular zone SVZ: subventricular zone sec: second(s) SN: substantia nigra SNP: single nucleotide polymorphism SPECT: single photon emission tomography Th: thalamus TH: tyrosine hydroxylase UK: United Kingdom US: United States of America UTR: untranslated region VTA: ventral tegmental area WCST: Wisconsin card sorting test

xiv PUBLICATIONS ARISING FROM THIS THESIS

Manuscripts C.E. Duncan, A.F. Chetcuti & P.R. Schofield. ‘Co-regulation of genes in the mouse brain following treatment with clozapine, haloperidol or olanzapine implicates altered potassium channel subunit expression in the mechanism of antipsychotic drug action’ Revised manuscript submitted to Psychiatric Genetics.

Oral communications C.E. Duncan, A.F. Chetcuti & P.R. Schofield. ‘Identification of genes associated with schizophrenia using an animal model of antipsychotic drug action’ The Australian Society for Medical Research XIVth NSW Scientific Meeting Sydney, Australia. June 2005.

Poster presentations A.F. Chetcuti, C.E. Duncan & P.R. Schofield ‘Regulation of NEDD4 by clozapine, haloperidol and olanzapine in the mouse brain’. International Congress on Schizophrenia Research. Colorado Springs, CO, USA. April 2007.

C.E. Duncan, A.F. Chetcuti & P.R. Schofield. ‘Co-regulation of genes in the mouse brain following antipsychotic drug treatment’. The Australasian Society for Psychiatric Research, Annual Meeting. Sydney, Australia. December 2006.

C.E. Duncan, A.F. Chetcuti & P.R. Schofield. ‘Gene expression analysis of an animal model for the treatment of schizophrenia’. 27th Annual Conference on the Organisation and Expression of the Genome Lorne, Victoria, Australia. February 2006.

C.E. Duncan, A.F. Chetcuti & P.R. Schofield. ‘Identification of genes associated with schizophrenia using gene expression analysis of an animal model of antipsychotic drug action’. World Congress on Psychiatric Genetics. Boston, MA, USA. October 2005.

C.E. Duncan, A.F. Chetcuti & P.R. Schofield. ‘Identification and characterisation of genes associated with schizophrenia using an animal model of antipsychotic drug action’. 26th Annual Conference on the Organisation and Expression of the Genome. Phillip Island, Victoria, Australia. February 2005.

C.E. Duncan, A.F. Chetcuti & P.R. Schofield. ‘Identification of genes associated with schizophrenia using an animal model of antipsychotic drug action’. The 14th St Vincents & Mater Health Sydney Research Symposium. Garvan Institute, Sydney, Australia. September 2004.

xvi

“Let us understand what our own selfish genes are up to, because we may then at least have a chance to upset their designs, something that no other species has ever aspired to do.”

— Richard Dawkins (The Selfish Gene, 1976).

xvii

Chapter 1

INTRODUCTION

1 1.1 SCHIZOPHRENIA

1.1.1 Classification 1.1.1.1 History Schizophrenia is a major psychiatric disorder with a prevalence of approximately 1% throughout the world. The disorder was first described over a century ago by Emil Kraepelin who called it dementia praecox (early intellectual deterioration) and separated it symptomatically from manic-depressive illness (Kraepelin, 1909). In 1911, Eugen Bleuler redefined the disorder as “schizophrenia” (a splitting of the mind) as he recognised that it was characterised by thought disorder, rather than intellectual decline (Bleuler, 1911).

1.1.1.2 Onset and course Psychotic behaviour associated with schizophrenia is most commonly detected late in the second or early in the third decade of life. The course of schizophrenia includes a prodromal period of deterioration usually starting in early adolescence and characterised by social withdrawal (Kandel, 2000). This precedes the acute period of psychosis that is followed by a chronic phase, although relapse into the acute phase is common. The acute period is characterised by bizarre delusions, hallucinations and thought disorder known as the “positive” symptoms of psychosis as they represent the presence of distinct behaviours associated with schizophrenia (Kandel, 2000). Conversely, it is a deprivation of normal behaviours that is seen in the chronic phase. These “negative” symptoms include social isolation, decreased motivation and blunted emotional affect. Schizophrenia is also characterised by deficits in certain cognitive domains, particularly working and episodic memory, social cognition, processing speed, reasoning and attention (Nuechterlein et al., 2004). Social functioning in the chronic phase of schizophrenia is highly negatively correlated with the degree of cognitive deterioration (Green et al., 2004) and persistence of negative symptoms (Fenton & McGlashan, 1994).

1.1.1.3 Clinical diagnosis Currently, schizophrenia is defined by the criteria set out in the International Classification of Diseases, Tenth Edition (ICD-10) (1994a) and the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) (1994a; , 1994b). These have similar criteria for diagnosis, including that the possibility of a mood disorder with psychosis due to medical or non-medical drug use be excluded before schizophrenia is considered. DSM-IV is more rigorous, requiring six instead of one month of severe symptoms for a diagnosis of schizophrenia. Allowance is also made for the possibility of a combination of affective illness and schizophrenia, which is termed schizoaffective disorder. However many psychiatrists believe this is not a discrete illness but rather one subclassification within what is most likely a spectrum of schizophrenia disorders, across a dimension of symptom factors and developmental impairments (Dutta et al., 2007).

1.1.1.4 Neurophysiological measures There are a number of neurophysiological measures that are generally abnormal in patients with schizophrenia, particularly sensorimotor gating, event-related potentials and mismatch negativity (Wong & Van Tol, 2003). The most robust of these are sensorimotor gating abnormalities evident in prepulse inhibition (PPI) deficits (Fig. 1.1) first described thirty years ago in schizophrenic patients (Braff et al., 1978). The severity of the PPI deficit has recently been correlated with sex (more severe in females), medication status (more severe in untreated patients) and smoking (more severe in abstainers) (Swerdlow et al., 2006). PPI deficits are also found in patients with schizotypal personality disorder and in relatives of patients with schizophrenia (Cadenhead et al., 2000), supporting an underlying genetic aetiology within the schizophrenia spectrum.

NORMAL SCHIZOPHRENIA PULSE

STIMULUS PREPULSE INTENSITY STARTLE STARTLE

RESPONSE 30-500 ms

Figure 1.1 Prepulse inhibition (PPI) of startle response. In normal individuals, a reduction in startle response is seen when a stimulus is preceeded by a prepulse. Patients with schizophrenia do not show this prepulse inhibition in their startle response.

1.1.2 Neuropathology and imaging Many neuroimaging and neuropathological studies have been undertaken in the brains of schizophrenic patients. The major neuropathological abnormalities associated with schizophrenia have been found in first-episode patients and are distinct from those found in antipsychotic-drug treated animals, indicating that they may be a primary feature of the illness rather than a result of treatment or of disease progression (Harrison, 1999). Limitations apply to the replication of neuropathological findings due to different methodologies, inclusion parameters and the small sample sizes used in studies. Consequently, meta-analyses often provide the best assessment of pathological changes as they account for discrepancies between studies, and increase the effective sample size.

1.1.2.1 Structural abnormalities Computerised tomography (CT) and magnetic resonance imaging (MRI) have been used to discern major anatomical abnormalities in the brains of patients with schizophrenia. The most replicable finding of these imaging studies is an enlargement of lateral ventricles in schizophrenic brains that correlates with cognitive impairment (Johnstone et al., 1976). Separate meta-analyses have reported around 25-40% increase in total ventricular volume in patients with schizophrenia compared to controls (Lawrie & Abukmeil, 1998; Wright et al.,

4 2000). This is accompanied by a subtle decrease in total brain volume as confirmed by meta-analyses (Ward et al., 1996; Lawrie & Abukmeil, 1998). These imaging studies suggest that increased ventricle:brain ratio may be a faithful indicator of schizophrenia, although a meta-analysis of ventricle:brain ratio measurements has suggested that the effect is too small, relative to individual variation, to be of practical significance (Van Horn & McManus, 1992).

An extensive review of forty volumetric MRI studies indicates various changes in regional volumes in the brains of patients with schizophrenia compared to controls (Table 1.1). This analysis indicated that other than brain and ventricular volume changes, the prefrontal and temporal lobes and limbic regions are effected in schizophrenic brains (Lawrie & Abukmeil, 1998).

Table 1.1 Meta-analysis of median volume changes in brain imaging studies of patients with schizophrenia compared to controls. Adapted from

Lawrie & Abukmeil, 1998.

Brain region Number of studies Volume change Whole brain 29 -3% Lateral ventricles 29 +40% Grey matter 8 -4% Prefrontal lobe 10 -1 to -5.5% Temporal lobe 16 -1.5 to -3.5% Amygdala 6 -10% Hippocampus 7 -2.5 to -8.5% +: increased, -:decreased

The prefrontal and temporal lobes are the major brain regions examined as they are involved in thought processes that are believed to be compromised in schizophrenia. The frontal lobe is involved in reasoning, planning and speech; the temporal lobe is involved in the perception of auditory stimuli, memory and speech.

A meta-analysis of the frontal brain, using 22 imaging studies, concluded that the importance of the frontal lobe volume reduction in schizophrenia has been overstated (Zakzanis & Heinrichs, 1999). This group also conducted a meta- analysis on functional imaging studies that used positron emission tomography

5 (PET) during a neurocognitive test of executive function, and were able to determine that there is a relationship between duration of illness and decreased frontal physiological activity (Zakzanis & Heinrichs, 1999. This could indicate that deficits in the frontal brain are a secondary feature of the illness, are a result of neuroleptic treatment, or occur in only some phases of illness.

Zakzanis and colleagues also conducted a meta-analysis of structural and functional imaging studies, specifically of the temporal lobe to investigate if a schizophrenic variant with deficits in this region exists (Zakzanis et al., 2000). Their analysis of 57 studies using a variety of imaging techniques revealed a moderate prevalence of temporal lobe deficits in schizophrenia. Imaging studies suggest that any frontal or temporal lobe structural deficit is minimal and may be peripheral to the central illness or represent a small subgroup of patients only.

Limbic regions have also been associated with schizophrenia. Decreases in amygdala and hippocampus volumes have been detected by imaging studies (Table 1.1). The thalamus has also been associated with schizophrenia as it is involved in filtering sensory information between regions, believed to be important in the cognitive deficits seen in schizophrenia. A meta-analysis of eleven MRI studies showed that thalamic volume reductions are seen in schizophrenic patients in excess of that expected purely from decreased brain size (Konick & Friedman, 2001).

Imaging studies of monozygotic twins discordant for schizophrenia have been useful in supporting typical case-control imaging studies. These have confirmed that affected twins have increased lateral ventricular size (Suddath et al., 1990), increased ventricle:brain ratio (Ohara et al., 1998) and grey matter deficits (Hulshoff Pol et al., 2002) compared to their unaffected twin. That these pathologies are found in only one member of a monozygotic twin pair indicates that these aberrations are not be due solely to genetic predisposition or shared maternal enviroment.

6 1.1.2.2 Cytoarchitectural abnormalities In an extensive review of major neuropathological studies, selected neuronal abnormalities and altered synaptic connectivity were shown to be associated with schizophrenia (Harrison, 1999). Relatively robust findings were a decrease in neuronal size in the prefrontal cortex (PFC) and hippocampus and decreased neuronal number in the thalamus of schizophrenic brains. Immunohistochemical and gene expression studies revealed an overall reduction in synaptic connectivity, indicated by decreased presynaptic markers.

Two regions often investigated in neuropathological studies in schizophrenia research are the dorsolateral prefrontal cortex (DLPFC) and the hippocampus. Proton mass resonance spectroscopy studies have revealed that both the hippocampus and DLPFC have reduced levels of the neuronal marker N-acetyl aspartate (NAA), which is believed to reflect a decrease in the somal size of constituent neurons in these regions rather than a decrease in neuronal number (Callicott et al., 2000; Heckers, 2001). A reduction in somal size in layers III and V of the DLPFC has been observed in brains of patients with schizophrenia (Volk et al., 2000; Pierri et al., 2001; Chana et al., 2003). Decreased NAA in the hippocampus and PFC has been shown in chronically unmedicated patients, indicating that this is a characteristic phenotype of the disease rather than a result of drug treatment (Bertolino et al., 1998). This decrease is not correlated with the length of illness, suggesting that this is a primary neuropathological marker.

Some studies have revealed aberrant cerebral asymmetry in schizophrenic brains. A study of 14 postmortem schizophrenic brains revealed asymmetrical alterations in hippocampal neuronal size and shape, although not orientation, compared to control brains (Zaidel et al., 1997). Histochemical analysis of the entorhinal cortex has shown a shift of some neurons into deeper laminae, suggesting disturbances in cortical development (Akbarian et al., 1993a; Akbarian et al., 1993b). However, other studies have shown no abnormalities in neuronal placement in the cortex in schizophrenic brains (Akil & Lewis, 1997; Krimer et al., 1997).

7 1.1.3 Proposed aetiological models 1.1.3.1 Neurodevelopmental hypothesis of schizophrenia A failure to find any substantial evidence for neurodegeneration in the brains of patients with schizophrenia has led to the evolution of the neurodevelopmental hypothesis of schizophrenia. One of the characteristic hallmarks of a neurodegenerative disorder is the presence of a dense group of glial cells, which proliferate following the death of neuronal cells, a process known as gliosis. Gliosis has not been observed in the neuropathology of schizophrenia, except in cases of coincidental neuronal insult, which supports the concept that this is a neurodevelopmental, rather than a neurodegenerative disorder (Harrison, 1999). Neuropathological studies indicating altered neuronal migration, enlarged ventricles and cytoarchitectural abnormalities support the concept of a deficit in neuronal development in schizophrenia (Harrison, 1999).

The neurodevelopmental hypothesis in its earliest form posited that alterations in normal brain development during prenatal and early life lead to deficits in brain functioning that are revealed in early adulthood (Murray & Lewis, 1987; Weinberger, 1987). Synaptic pruning, which occurs during normal adolescent brain development, may be the event that reveals such neurodevelopmental deficits (Feinberg, 1982). Evidence for early neuronal insults in schizophrenia include an increased risk of minor physical anomalies (Lane et al., 1996) and longitudinal studies indicating people with schizophrenia had delayed language, social and motor skills as children (Walker, 1994). Major support for the neurodevelopmental hypothesis comes from epidemiological studies that suggest early life exposures as risk factors for schizophrenia (Table 1.2) (Lewis & Levitt, 2002).

An alternative theory for a neurodevelopmental aetiology of schizophrenia suggests that there may be a progressive neurodevelopmental deficit with changes in brain structure occurring later in life, a dynamic process implying that therapeutic intervention may be possible after the onset of psychotic symptoms (Woods, 1998). This alternate theory is supported by neuropathological studies

8 showing greater grey matter reductions in aged schizophrenic patients compared to controls (Hulshoff Pol et al., 2002).

Table 1.2 Epidemiological studies investigating risk factors in schizophrenia. Adapted from Lewis & Levitt, 2002. Risk factor Implication References Maternal starvation Nutrition important in normal brain (Susser et al., 1998; St Clair in 1st trimester development, especially folate et al., 2005) Maternal infection Viral infection interrupts normal brain (Mednick et al., 1988; in 2nd trimester function McGrath & Castle, 1995; Brown et al., 2000; Jablensky, 2000) Winter/spring birth A factor late in utero, perhaps vitamin D (Torrey et al., 1997; deficiency or infection, interrupts normal McGrath & Welham, 1999) brain function Urban birth Stress (environmental/social) in early (Mortensen et al., 1999) postnatal life disrupts brain development Obstetrical Existing fetal maldevelopment may cause (Geddes & Lawrie, 1995) complications labor delivery complications

1.1.3.2 Schizophrenia as a disorder of the synapse Recent evidence suggests that schizophrenia, in addition to having neurodevelopmental aetiology, may be a disorder of the synapse. Neuronal connectivity may be altered through neurotransmitter dysfunction (see Section 1.2); through an inherited altered expression or function of genes involved in synaptic plasticity and/or glial function (see Section 1.3); and through altered expression of other synaptic genes and genes involved in myelination (see Section 1.4). There is neuropathological evidence for this, with decreased abundance of some synaptic proteins, particularly those that localise to excitatory synapses, observed in the hippocampus of patients with schizophrenia compared to controls (Harrison & Eastwood, 2001). Neuroimaging has also revealed disrupted structural integrity of white matter in schizophrenic patients (Kubicki et al., 2005). The cause of these alterations has not been determined but could represent a change in synaptic number, or density, or a loss of vesicles released from each synaptic terminal.

1.1.4 Summary A century has passed since the definition of schizophrenia as a severe psychiatric disorder with social withdrawal and cognitive decline, yet still there is no definitive classification or hallmark pathology, which may reflect the complex nature of the disorder. Progress has been made in defining some of the contributions to susceptibility for schizophrenia, and in defining areas of its clinical manifestations, yet the underlying biological mechanism of the disorder remains unknown, leaving a cure for schizophrenia unattainable. This is unsatisfactory both to sufferers of the disorder, with suicide rates ten times higher in patients with schizophrenia than in the general population, and also due to the huge societal burden of the illness, with indirect costs estimated at $1.5 billion per annum in Australia (Smark, 2006).

Three areas of research that are currently being undertaken to increase our understanding of schizophrenia and are pertinent to this thesis are pharmacology, genetics and molecular expression analysis. Each of these areas will be discussed further with regard to the current literature.

1.2 NEUROCHEMISTRY & PHARMACOLOGY IN SCHIZOPHRENIA

1.2.1 Neurotransmission occurs at the synapse Neural cells communicate not through direct physical contact but rather through synapses – gaps between neuronal cells where chemical and electrical signalling are integrated. This phenomenon was first proposed by Ramon y Cajal in the beginning of the 20th century and comprehensively described by Young in the giant synapse of the frog neuromuscular junction.

During synaptic transmission, electrical signals are propagated down a neuronal axon by opening and closing of sodium and potassium channels, which alters cellular ion gradients (Hodgkin & Huxley, 1952). This culminates in

10 depolarisation of the presynaptic membrane, allowing calcium to flow into the terminal and causing synaptic vesicles carrying neurotransmitters to fuse to the presynaptic membrane. Following fusion, neurotransmitters are released and migrate across the synaptic cleft and bind to receptors on the dendrite or cell body of the post-synaptic neuron (Fig. 1.2). This elicits chemical or electrical signalling in the postsynaptic membrane and is the basis of neuronal communication.

There are two modes of postsynaptic signalling: ‘fast’ and ‘slow’. ‘Fast’ transmission refers to the binding of neurotransmitters to ligand-gated ion channels on the post-synaptic membrane, directly activating the ion channels and altering neuronal excitability through changes in ion concentrations in the cell.

Action potential Figure 1.2 Synaptic neurotransmission. An action potential propagates down the Pre-synaptic membrane neuronal axon culminating in neurotransmitter

NT release into the synaptic cleft, binding to Synaptic cleft postsynaptic receptors and downstream NT-R Post-synaptic signalling events. NT-R: neurotransmitter terminal receptor.

‘Slow’ neurotransmission or (neuromodulation) occurs when neurotransmitters bind and activate G-protein coupled receptors on the post-synaptic membrane. A cascade of downstream protein signalling events follow including activation of enzymes, such as adenlyl cyclase, that synthesise second messengers, like cAMP, culminating in altered neuronal excitability.

1.2.2 History of neurochemistry and the treatment of schizophrenia Much of what is known about the neurochemistry of schizophrenia comes from analysis of the sites of action of antipsychotic drugs. Since they were first

11 introduced in the 1950’s, antipsychotic drugs (APDs) have remained the most efficient and widely used treatment for the positive symptoms of schizophrenia (Kane, 1996), although the first of these compounds was discovered through serendipity.

Initially synthesised as a sedative, two French psychiatrists first prescribed chlorpromazine to schizophrenic patients in 1952 to reduce their agitation, yet found this agent also reduced the hallucinations and delusions associated with psychosis (Delay & Deniker, 1956). Chlorpromazine became the first prescribed antipsychotic drug and many antipsychotic compounds have since been synthesised that mimic its effect.

Also around this time the antipsychotic effect of reserpine, a derivative of the rauwolfia root used in India to treat insanity, was being explored. The Swedish pharmacologist Arvid Carlsson made some landmark observations about the mode of action of reserpine, showing that it altered the levels of dopamine in the brains of treated animals (Carlsson & Hillarp, 1956). Subsequently he showed that the antipsychotic drugs then in use – chlorpromazine and haloperidol – blocked dopamine receptors (Carlsson & Lindqvist, 1963). It was later shown that these ‘conventional’ antipsychotic drugs target the dopamine D2 receptor. D2-receptor occupancy is directly correlated with the amount of various APDs required for the treatment of schizophrenia and is both necessary and sufficient for reduction of positive symptoms (Seeman et al., 1975).

Dopamine D2 receptor blockade is also required for the action of newer or ‘atypical’ drugs although these have less affinity and show faster dissociation from the dopamine D2 receptor (Kapur & Remington, 2001). Atypical APDs also bind with varying affinities to the dopamine D4 receptor (Seeman et al., 1997), serotonin 5HT2A receptor and other neurotransmitter receptors, including histaminergic, muscarinic and 1-adrenergic receptors (Miyamoto et al., 2005) (Table 1.3).

12 Table 1.3 Properties of the two classes of antipsychotic drugs.

Class Receptor profile^ Side Effects Examples

Conventional/ Dopamine D2 > EPS; Chlorpromazine; First generation Dopamine D3/D4 > movement Haloperidol dopamine D1 = serotonin 5HT-2A disorders Aypical/ Second Serotonin 5HT-2A > Few EPS; Clozapine; generation dopamine D2 = dopamine D1/D3/D4 metabolic Risperidone; syndrome Olanzapine ^ Adapted from Miyamoto et al., 2005

APDs have well documented adverse effects. Imaging studies have shown that striatal D2-receptor occupancy following chronic antipsychotic drug treatment is directly correlated with extrapyramidal side effects (EPS) in patients treated with conventional APDs (Farde, 1992). These manifest as neurological movement disorders including pseudoparkinsonism, dystonia (muscle contraction), akathisia (restlessness) and tardive dyskinesia, an irreversible movement disorder (Tauscher et al., 2002). Recent receptor binding studies have shown that approximately 65- 70% dopamine D2 receptor occupancy is required for APD action, yet greater than 72% striatal D2-receptor occupancy induces EPS (Kapur et al., 2000).

Atypical antipsychotics have reduced EPS, possibly through their increased serotonin receptor: dopamine receptor binding ratios (Leucht et al., 2003); via their lower affinity for striatal dopamine D2 receptors; or through dopamine D4 receptor specificity (Seeman et al., 1997). However, other adverse effects are common, particularly those relating to the ‘metabolic syndrome’: weight gain (Allison et al., 1999), hyperglycemia, hypertension and insulin resistance (Newcomer, 2007).

1.2.2.1 Atypical versus conventional antipsychotic drugs Investigation into the efficacy of atypical APDs compared to conventional APDs in treating the symptoms of schizophrenia has been inconclusive, with many randomised trials conducted and two meta-analyses reviewing these trials reaching differing conclusions. Geddes et al. reviewed 52 clinical trials and concluded that atypical APDs were slightly more efficacious and better tolerated in patients compared to high dose conventional APDs, although not when

13 compared to low dose conventional drugs (Geddes et al., 2000). A subsequent meta-analysis found there to be two groups of atypical APDs – those that performed considerably better than conventional APDs and those that had similar efficacy, and that this may explain previous study discrepancies (Davis et al., 2003). Both meta-analyses found atypical antipsychotics to have better compliance and reduced EPS compared to conventional APDs, but higher risk of weight gain. Atypical APDs were also reported to be more efficacious than haloperidol as maintenance medication for schizophrenia on five clinical factors: positive symptoms, negative symptoms, thought disorder, impulsivity/hostility and anxiety/depression (Davis & Chen, 2003).

An attempt to rectify these inconclusive analyses is the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study, initiated by the National Institutes of Mental Health and conducted in multiple U.S. clinical sites using 1500 schizophrenic patients (Lieberman et al., 2005). The CATIE study evaluated the effect of four atypical antipsychotics compared to the conventional APD perphenazine. Efficacy rates were equal between the two APD types in patients that were compliant. However, more tellingly, there was an approximately 70% non-compliance rate for both atypical and conventional drugs highlighting the need for better drug targeting in schizophrenia treatment. Perphenazine was chosen specifically for its low potency and moderate side effect profile and patients with tardive dyskinesia were removed from study analysis, so this study fails to account for the high risk of this irreversible movement disorder and associated non-compliance in conventional APD treatment (Correll et al., 2004). The rate of metabolic syndrome, which greatly increases the risk of type II diabetes and cardiovascular disease, was approximately 2-fold higher in CATIE participants than the national average (McEvoy et al., 2005).

In addition to their antipsychotic action, there has been some debate over whether atypical APDs are more effective at reducing negative symptoms in patients with schizophrenia than conventional APDs (Gardner et al., 2005). One study found olanzapine had greater effect in negative symptoms in around half of

14 schizophrenic patients compared to haloperidol treatment (Tollefson et al., 1997). A meta-analysis of haloperidol and four atypical APDs confirmed that conventional and atypical APDs have comparable effects on reducing negative symptomatology, although two particular atypical APDs (olanzapine and risperidone) were slightly superior (Leucht et al., 1999). However, other meta- analyses have shown a moderate beneficial effect of atypical APDs on negative symptoms (Geddes et al., 2000; Davis et al., 2003), yet this apparent increase may be due to secondary effects of reduced EPS (Miyamoto et al., 2005). The lack of consensus may be due to clinical shortcomings in rating the extent of negative symptoms (Gardner et al., 2005).

The effect of treatment type on cognition was also investigated by the CATIE study, with a small yet significant improvement in function on neurocognitive testing in all schizophrenic patients independent of treatment group (Keefe et al., 2007). However these benefits are most likely too small to have significant effects on long-term outcome in schizophrenia (Heinrichs, 2007) and may even result simply from repeated exposure to these tasks rather than any clinically significant improvement (Goldberg et al., 2007).

1.2.3 The evolution of the dopamine hypothesis of schizophrenia 1.2.3.1 Dopamine Dopamine is a neurotransmitter involved in mediating movement, reward and cognition in the mammalian brain. It is synthesised in a two-step reaction from the amino acid L-tyrosine (Fig. 1.3) and stored in vesicles at the axonal terminal, or released from the dendrites, of dopaminergic neurons.

TH DDC L-tyrosine L-dopa Dopamine

Figure 1.3 Synthesis of dopamine from L-tyrosine occurs in the cytosol and is catalysed by the enzymes tyrosine hydroxylase (TH) and dopa decarboxylase (DDC).

15 Dopamine-containing neurons have well defined projections and sites of action in the mammalian brain (Fig. 1.4) (Siegel et al., 1999). The nigrostriatal pathway is the predominant dopaminergic pathway in the brain, with 80% of all dopamine found in the striatum. The nigrostriatal dopamine system controls movement and it is these neurons that degenerate in Parkinson’s disease. The mesolimbic pathway, with neurons projecting to the amygdala and nucleus accumbens mediates reward and motivational behaviour. Projections of dopaminergic neurons from the midbrain to the olfactory, frontal and cingulate cortical regions are important in cognition, particularly the domains of working memory, attention and other executive functions. Dopamine can also function as a hormonal modulator — when secreted from neurons in the arcuate nucleus of the hippocampus into blood vessels supplying the pituitary gland, dopamine inhibits the production of prolactin.

Cortex Nigrostriatal dopamine neurons Mesolimbic dopamine neurons Striatum Mesocortical dopamine neurons Limbic NAc

SN VTA

Figure 1.4 Major midbrain dopaminergic neuronal projections in the mammalian brain. The nigrostriatal dopamine pathway projects from the substantia nigra (SN) to the striatum, the mesocortical pathway from ventral tegmental area (VTA) to the cortical regions of the brain and the mesolimbic dopamine pathway projects from the VTA to the limbic regions of the brain and the nucleus accumbens (NAc) in the ventral striatum.

There are two main classes of receptors that bind dopamine following its release from vesicles during neurotransmission. The dopamine D1-like receptors (D1 and D5) elicit an excitatory effect on postsynaptic neurons, activating adenlyl cyclase, increasing intracellular cAMP and signalling downstream events. Conversely D2-

16 like receptors (D2, D3 and D4) inhibit adenlyl cyclase, dampening post-synaptic neuronal excitability. Dopamine D2-like receptors also function as autoreceptors mediating feedback on the presynaptic membrane. Dopamine receptors are differentially localised in dopaminergic neuronal projection regions of the brain (Table 1.4) (Siegel et al., 1999).

Table 1.4 Localisation of dopamine receptors in the mammalian brain. From Siegel et al., 1999. D1 D2 D3 D4 D5 Major Striatum, Hippocampus, Nucleus Hypothalamus, Frontal and regional olfactory hypothalamus, accumbens, nucleus motor cortex, expression tubercule, striatum, olfactory accumbens, brain stem, cortex cortex tubercule olfactory midbrain tubercule

1.2.3.2 Striatal dopamine hyperactivity underlies psychosis That hyperactivity of dopamine is pathological in schizophrenia patients has been the predominant neurochemical hypothesis since it was proposed by Van Rossum in the 1960’s (van Rossum, 1966), based on observations of monoamine receptor targeting by antipsychotic drugs (Carlsson & Lindqvist, 1963). Indirect support for the dopamine hypothesis came from the hallucinogenic properties of dopamine-altering compounds. It was observed that some Parkinson’s disease patients treated with the dopamine precursor L-DOPA manifested psychiatric symptoms (Jenkins & Groh, 1970; Tobias & Merlis, 1970; Goodwin, 1971). Also amphetamine, a dopamine receptor agonist, was known to induce schizophrenia- like psychosis in normal individuals (Connell, 1958; Angrist & Gershon, 1970) with mechanisms linked to catecholamine modulation (Snyder, 1972) and reversible by APD treatment (Angrist et al., 1974). The dopamine hypothesis of schizophrenia was initially supported by evidence of elevated levels of dopamine D2 receptors in the striatum of a subset of schizophrenia patients (Mita et al., 1986; Seeman, 1987).

17 1.2.3.3 New support for dopamine hyperactivity in schizophrenia Initial evidence of dopamine dysfunction in schizophrenia was either inferred from pharmacological modulation or evaluated in postmortem tissue, approaches that are inherently confounded by lifetime disease and medication. Advances in live imaging techniques have allowed dynamic measures of dopamine functioning during the course of schizophrenia.

Positron emission tomography (PET) uses a radiolabeled ligand tracer to quantify numbers of receptors within a brain region. Single photon emission computed tomography (SPECT) uses a similar theory to PET but with radiotracers that have longer half-lives, making it more readily available but providing less resolution. Several SPECT/PET studies have explored pre- and post-synaptic striatal dopamine hyperactivity in schizophrenia (reviewed in (Soares & Innis, 1999).

Presynaptic striatal dopamine metabolism in schizophrenia has been explored by numerous PET imaging studies using radiolabeled fluoro-DOPA. These have consistently found accumulation of this dopamine precursor at the synapse in the striatum of first episode and medicated schizophrenic patients indicating elevated DDC activity and excessive dopamine synthesis in this region (Reith et al., 1994; Hietala et al., 1995; Lindstrom et al., 1999; McGowan et al., 2004).

Abnormalities in striatal dopamine release were assessed by administration of amphetamine. Amphetamine induced psychosis in approximately 40% of patients and SPECT imaging showed a concomitant reduction in radiotracer binding to striatal D2-receptors, indicating increased endogenous dopamine release (Laruelle et al., 1996). This finding has since been replicated in another patient cohort (Abi-Dargham et al., 1998) and by a separate research group using PET (Breier et al., 1997).

Alterations in dopamine D2 receptor densities have also been explored. To assess the contribution of lifetime APD use, dopamine blockade by haloperidol was

18 imaged in both drug-naïve and treated schizophrenia patients using an agonist of dopamine D2 receptors (Wong et al., 1986). Striatal D2-receptor binding was comparable between both groups of schizophrenia patients although it was significantly greater than in control subjects. Other imaging studies of drug-naïve and first-episode schizophrenia patients have not seen any differences in dopamine D2-receptor densities in the striatum (Farde et al., 1990; Martinot et al., 1990; Nordstrom et al., 1995). A meta-analysis of these conflicting studies has indicated a 10-15% increase in dopamine D2 receptors in the striatum of schizophrenic patients (Zakzanis & Hansen, 1998). However this increase cannot be considered diagnostic nor is it as strong an effect as in postmortem tissue, implicating lifetime neuroleptic use as a confounding factor (Laruelle, 1998).

A major problem with these imaging studies is the use of radioligands that are noncompetitive agonists of dopamine D2 receptors, indicating that they do not consider possible abnormalities in the endogenous ligand, dopamine. Abi- Dargham and colleagues have since resolved this issue by administering an inhibitor of TH, thereby preventing dopamine synthesis and allowing analysis of baseline D2-receptor densities in schizophrenia (Abi-Dargham et al., 2000). Following dopamine depletion, SPECT imaging with a non-competitive D2- receptor radioligand confirmed increased numbers of striatal dopamine D2 receptors in first episode schizophrenic patients (Abi-Dargham et al., 2000). This study also showed that high synaptic dopamine levels predict response to APD treatment, indicating that these drugs will be most effective in hyperdopaminergic patients.

1.2.3.4 A role for cortical dopamine hypoactivity Around 15 years ago there was a paradigm shift in the dopamine hypothesis. It was proposed that subcortical dopamine hyperactivity, which underlies psychosis, coexists in schizophrenia with a deficit in cortical dopaminergic transmission, responsible for negative and cognitive symptoms in the illness (Davis et al., 1991). This was based on a number of previous observations highlighted by Weinberger and colleagues in a series of reports in the late 1980’s. They showed that

19 schizophrenic patients have reduced activation of the dorsolateral prefrontal cortex (DLPFC) during frontal lobe neurocognitive testing compared to controls (Weinberger et al., 1986). This deficit was independent of medication status (Berman et al., 1986), specific to prefrontal cortical functioning (Weinberger et al., 1988) and associated with plasma levels of the dopamine metabolite homovanillic acid, implicating deficits in monoamine neurotransmission (Berman et al., 1988).

The most direct evidence for cortical underactivity of dopamine in schizophrenia is a postmortem study revealing a reduction in the density of midbrain dopaminergic neurons innervating the DLPFC in schizophrenic brains compared to controls (Akil et al., 1999). Cortical dopamine neurotransmission is mediated predominantly through dopamine D1 receptors. A landmark PET study showed decreased D1-receptor binding in the cortex of untreated schizophrenic patients, in the absence of striatal abnormalities, which was correlated with severity of negative symptoms and cognitive function (Okubo et al., 1997). Subsequently, Abi-Dargham and colleagues showed an increase in the number of dopamine D1 receptors in the cortex of schizophrenic patients, presumably a compensatory mechanism for decreased dopaminergic transmission in this region (Abi- Dargham et al., 2002). Furthermore, D1-receptor density was inversely correlated with working memory function, a well-defined cognitive deficit in schizophrenia resulting from aberrant DLPFC activation (Manoach et al., 1999; Perlstein et al., 2001).

It has been proposed that there is an optimum level of dopamine at the cortical D1 receptors that will predict cognitive functioning (Goldman-Rakic et al., 2000), the so-called inverted U hypothesis (Fig.1.5). Dysfunctional activity at dopamine D1 receptors in the DLPFC may underlie cognitive deficits in schizophrenic patients (Abi-Dargham, 2004).

Optimum

Schizophrenia

Normal range memory Working performance Cortical dopamine levels/ D1 receptor activation

Figure 1.5 Inverted U hypothesis. Postulates under or overactivity of dopamine at D1 receptors in the prefrontal cortex can have deleterious effects on working memory function. From Goldman-Rakic et al., 2000.

Mesocortical dopamine hypoactivity in schizophrenia has implications for dopamine regulation in other pathways. Haloperidol-induced dopamine turnover in the nucleus accumbens is increased following lesion of mesocortical dopaminergic projections in rats (Rosin et al., 1992). Furthermore, reduced activity of the DLPFC in schizophrenic patients during a cognitive task has been inversely correlated with striatal dopamine uptake through dopamine D2 receptors (Meyer-Lindenberg et al., 2002).

These studies show that cortical dopamine hypoactivity is integrally linked to subcortical dopamine hyperactivity and may explain the concurrence of these in the brain of patients with schizophrenia, strengthening a role for widespread dopamine dysfunction in the pathogenesis of schizophrenia.

1.2.4 Glutamatergic dysfunction in schizophrenia 1.2.4.1 Glutamate Glutamate, the major excitatory neurotransmitter in the brain, is a non-essential amino acid that cannot cross the blood brain barrier. It is therefore made within the brain in a tripartite system between the synaptic terminal, where it is synthesised from glutamine and stored in vesicles; the postsynaptic terminal

21 where it binds its receptors; and glial cells, where it is taken up following neurotransmission, converted back into glutamine and transported back to the synaptic terminal (Fig. 1.6).

synaptic Presynaptic vesicles terminal

Astrocytic endfoot Postsynaptic terminal ionotropic receptors mGluR

Figure 1.6 The tripartite synapse Glutamatergic transmission relies on pre- and post-synaptic neurons synapsing with glial cells that facilitate glutamate metabolism.

Glutamate binds both ionotropic receptors for fast neurotransmission and metabotropic receptors for neuromodulation. The ionotropic glutamate receptors are named for the first compounds that were found to bind to them – -amino-3- hydroxy-5-methylisoxazole-4-propionic acid (AMPA), N-methyl-D-aspartic acid (NMDA) and kainate – although the endogenous glutamate ligand binds each with a much higher affinity than these agonists. Metabotropic receptors (mGluR) bind glutamate with varying affinities and all effect downstream signalling events that often lead to modulation of the ionotropic receptors. Glutamate, as well as its receptors, are localised throughout the CNS as the majority of neurons in the mammalian cortex use glutamate as a neurotransmitter.

1.2.4.2 NMDA receptor hypofunction in schizophrenia Glutamate was first associated with schizophrenia through pharmacological observations of antagonists to the NMDA receptor: phencyclidine (PCP) and ketamine. PCP was developed as an animal anaesthetic in the 1950’s but when tested on surgical patients it elicited thought disorder, social withdrawal and blunted affect that mimicked the symptoms of schizophrenia (Luby et al., 1959). Controlled clinical studies have since shown that administration of PCP to

22 humans mimics the positive and negative symptoms of schizophrenia, with chronic administration also leading to some of the frontal lobe-specific cognitive deficits seen in the illness (Jentsch & Roth, 1999). Ketamine administration has a similar effect, inducing features of positive and negative schizophrenic symptoms and cognitive deficits in normal individuals (Krystal et al., 1994). Additionally it has been shown to exacerbate psychosis (Lahti et al., 2001), negative symptoms and cognitive deficits in schizophrenia patients (Malhotra et al., 1997b), with elevation of psychotic symptoms ameliorated by clozapine treatment (Malhotra et al., 1997a).

Direct evidence for glutamate dysregulation in schizophrenia has also been seen, with decreased glutamate levels in the cerebrospinal fluid (CSF) of patients (Kim et al., 1980). Psychosis induced by the dopamine receptor agonist, amphetamine, also leads to decreased CSF glutamate levels indicating an interplay between dopamine and glutamate dysfunction in schizophrenia (Kim et al., 1981).

These clinical and pharmacological findings contributed to the hypothesis that there is underactivity of glutamate (Kim et al., 1980) at the NMDA receptor (Olney & Farber, 1995) in patients with schizophrenia and that this may be mediated in part through aberrant dopamine subcortical (Carlsson & Carlsson, 1990) and cortical (Jentsch & Roth, 1999) transmission, although the primary deficit has not been determined.

Studies into glutamatergic abnormalities in schizophrenic patients, through altered neuronal innervation or changes in receptor density, have been inconclusive. In vivo imaging support for the NMDA hypofunction model has been constrained by a lack of suitable PET or SPECT ligands for the NMDA receptor (Bressan & Pilowsky, 2000). Postmortem studies of glutamate markers in brains of schizophrenia patients have been undertaken, with the caveat that findings may be influenced by lifetime illness and neuroleptic use. These studies indicate decreased presynaptic glutamate markers and decreased expression of NMDA, AMPA and kainate receptor subunits in the hippocampus (as reviewed

23 in (Harrison et al., 2003). Conversely, in the frontal cortex there is evidence for increased ionotropic glutamate receptors (Deakin et al., 1989) and altered NMDA receptor subunit gene expression (Akbarian et al., 1996), consistent with elevated glutamate responsiveness in the presence of signalling deficits (Goff & Coyle, 2001).

Pharmacological animal models support a role for glutamate hypoactivity at the NMDA receptors in the pathology of schizophrenia. Mice treated chronically with PCP or MK-801, another NMDA receptor antagonist, show spatial learning impairments (Mandillo et al., 2003), hyperactivity and social interaction impairments (Sams-Dodd, 1998) that are improved by treatment with clozapine (Hashimoto et al., 2005). These effects are mediated solely through NMDA receptor blockade as mice with dramatically reduced expression of this receptor display hyperactivity, repetitive movements and social deficits that are not exacerbated with PCP or MK-801 treatment, yet are ameliorated with APD treatment (Mohn et al., 1999). Cognitive deficits may result from a decrease in dopamine D1 receptors in the prefrontal cortex after administration of these NMDA antagonists (Healy & Meador-Woodruff, 1996).

Accordingly, modulation of glutamate in patients with schizophrenia may have therapeutic benefits and there is clinical evidence that glycine and d-cycloserine — binding at the glycine site and activating the NMDA receptor — may reduce long-term symptoms of schizophrenia (Javitt et al., 1994) and may be useful as APD adjunctive treatment for patients with schizophrenia (Heresco-Levy et al., 1996; Tsai et al., 1998). However, a recent multi-site placebo-controlled study refuted these findings, reporting no improvement in negative and cognitive symptoms in patients with schizophrenia following glycine or d-cycloserine treatment (Buchanan et al., 2007).

There is good pharmacological evidence for dysregulated glutamatergic transmission in the brains of patients with schizophrenia, in particular when altered dopamine transmission is also considered.

1.2.5 The present state of the field – a synthesis Although dysfunctions in other neurotransmitter systems have certainly been implicated, it is an interplay between aberrant glutamate and dopamine transmission in the cortex, striatum, thalamus and midbrain of schizophrenic patients that may be responsible for some of the core deficits in the illness (Carlsson et al., 1999; Stone et al., 2007). Specifically, decreased innervation of mesocortical dopamine neurons on the prefrontal cortex reduces activation of glutamatergic projections to the substantia nigra. This is thought to decrease action of inhibitory neurons in the midbrain, which increases dopaminergic neuronal projections to the striatum, leading to psychosis (Fig. 1.7).

Nigrostriatal

PFC dopamine neurons

Mesolimbic/cortical 6 DRD1 dopamine neurons 4 DA neurons DRD21 Corticonigral/striatal glutamate neurons

Striatum DA3 Striatothalamic DRD21 Th NAc GABA neurons 7 5 GABA Glu DA2

SN VTA

Figure 1.7 The dopamine-glutamate dysfunction hypothesis of schizophrenia. Projections of glutamate, dopamine and GABA are indicated in the regions of interest. Neurochemical dysregulation in schizophrenia is indicated in red. Adapted from Carlsson et al., 1999. DA: dopamine, DR: dopamine receptor, GABA: - aminobutyreic acid, Glu: glutamate, NAc: nucleus accumbens, PFC: prefrontal cortex, SN: substantia nigra, Th: thalamus, VTA: ventral tegmental area, : decreased, : increased, +: activation, –: inhibition. . 1Mita et al., 1986; Seeman et al., 1987, 2 Rosin et al., 1992, 3Laruelle et al., 1996; Abi-Dargham et al., 1998; Breier et al., 1997, 4Meyer- Lindberg et al., 2002; Akil et al. 1999, 5Kegeles et al., 2000, 6Abi-Dargham et al., 2002, 7Carlsson, 1988.

25 Corticostriatal dopamine interactions have been demonstrated in a seminal imaging study where reduced activation of the DLPFC in schizophrenia patients during a cognitive task was inversely correlated with striatal dopamine uptake (Meyer-Lindenberg et al., 2002).

The strongest evidence for the interplay between dopamine and glutamate is from SPECT imaging studies showing that reduced activation of midbrain NMDA receptors by acute ketamine administration increases striatal dopamine release in normal individuals, comparable to that observed during amphetamine- challenge of patients with schizophrenia (Kegeles et al., 2000). This finding supports the hypothesis that glutamate hypoactivity at midbrain NMDA receptors (possibly acting through GABA-inhibitory neurons), underlies striatal dopamine hyperactivity in schizophrenic patients.

Additionally, dopamine and glutamate interact directly in the striatum on dendritic spines of GABAergic medium spiny neurons. These neurons contain NMDA recptors as well as dopamine D1 (in striatonigral GABAergic projections) and D2 receptors (in striatothalamic projections) that have opposing effects on corticostriatal glutamate (Gerfen et al., 1990). Dopamine D1 receptor stimulation on striatonigral GABAergic interneurons activates glutamatergic transmission through NMDA receptors whereas dopamine D2 receptor stimulation on striatothalamic GABAergic inhibits corticostriatal glutamatergic neurotransmission (Laruelle et al., 2003). The effect of dopamine D2 receptors on corticostriatal glutamate transmission is relevant to the pathophysiology of schizophrenia, as excess striatal D2-receptors would further impair glutamatergic neurotransmission, whereas striatal D2-receptor blockade would enhance cortical glutamate transmission. Furthermore, aberrant striatothalamic GABAergic neurotransmission may lead to negative symptomatology in schizophrenia (Carlsson, 1988).

Further support for the synthesis of glutamate and dopamine in schizophrenia pathogenesis is a study in which non-human primates treated with PCP showed

26 deficits in frontal lobe cognitive behaviour (Jentsch et al., 1997). This cognitive impairment inversely correlated with dopamine function in the prefrontal and prelimbic cortex and was ameliorated by treatment with clozapine, which increased frontal dopamine release, supporting a link between deficits in both neurotransmitter systems in schizophrenia.

In summary, observations of pharmacological modulations in schizophrenia combined with in vivo imaging studies have supported the hypothesis of aberrant dopaminergic neurotransmission in schizophrenia and indicated that this may be in part due to underlying deficits in glutamate transmission.

1.3 MOLECULAR GENETICS OF SCHIZOPHRENIA

1.3.1 Schizophrenia has a genetic predisposition Family studies have shown that the risk of developing schizophrenia is higher in relatives of schizophrenic patients than in the general population prevalence and increases proportionally to the amount of genes shared with the proband (Table 1.5) (Gottesman, 1991).

Table 1.5 Evidence for genetic contribution to schizophrenia Adapted from Gottesman, 1991. Relationship to schizophrenic patient Relatedness Risk General population 0 1% Third-degree (e.g. Cousin) 0.125 2% Second-degree (e.g. Neice/nephew) 0.25 2-6% First-degree (e.g. Sibling/ child) 0.5 6-17% Dizygotic twin 0.5 17% Monozygotic twin 1 ~50%

Twin studies support genetic susceptibility by comparison of the concordance rate of disease in monozygotic twins, who share all of their genomes and dizygotic twins, that share on average half of their genomes. Twin studies have been conducted for over seventy years in schizophrenia research and show that the concordance between twin pairs is 50% in monozygotic twins compared to

27 around 17% for dizygotic twins (Gottesman, 1991). These concordance rates suggest a fairly strong genetic contribution to the disorder. However, it could still be argued that schizophrenia is a result of prenatal insult or postnatal “nurture” effects. Adoption studies have been used to refute this objection. In a group of Danish adoptees it was found that biological parents of an adopted child suffering schizophrenia had a ten-fold higher prevalence of the disorder than was seen in biological relatives of non-schizophrenic adoptees (Kety & Ingraham, 1992).

These studies have been invaluable in proving that schizophrenia has a strong genetic contribution with a heritability of approximately 80% (Cardno et al., 1999), indicating that while there will also be environmental and epigenetic influences in the development of schizophrenia, it has a high genetic contribution. Similar to many other human diseases, schizophrenia is a complex disorder that likely involves multiple genes each with a small, yet additive, effect on disease susceptibility (Sullivan et al., 2003). A number of techniques have been used in an attempt to elucidate the specific genes involved in schizophrenia.

1.3.2 Linkage and positional cloning Due to the recombination of chromosomes that occurs during meiosis, portions of chromosomal DNA that are located proximally to each other are normally inherited together. By locating physical markers that are consistently associated with a disease phenotype, regions of DNA that may be involved in susceptibility can be isolated. The physical markers used for linkage analysis include single nucleotide polymorphisms (SNPs) and nucleotide repeat microsatellite markers.

Linkage studies in schizophrenia have been conducted in many populations and have isolated a number of chromosomal loci that may contain genes involved in schizophrenia. Schizophrenia susceptibility regions with repeated replication that have been examined by meta-analysis with strong evidence for linkage include chromosomes 1q21-q22, 1q42, 5q21-p23, 6p24-p22, 6q21-p25, 8p22-p21, 10p15-11, 10q23-q24, 13q32-q34 and 22q11-q12 (Lewis et al., 2003; Owen et al., 2005). A number of genes have been suggested as candidate genes in these

28 regions as well as in other regions of the human genome (O'Donovan et al., 2003). Positional cloning in the regions of high linkage has revealed some candidate genes for schizophrenia susceptibility, in particular neuregulin 1 and calcineurin A on chromosome 8p, dysbindin on chromosome 6p and D-amino acid oxidase activator on chromosome 13q, discussed further below.

1.3.2.1 Neuregulin 1 The deCODE Genetics Group was one of at least six teams to find linkage with schizophrenia and chromosome 8p. Further analysis of this area in their Icelandic family cohort identified a significant association between schizophrenia and a core haplotype of five SNPs and two microsatellite markers within the 5’ end of the neuregulin 1 (NRG1) gene (Stefansson et al., 2002). The association of this seven-marker haplotype with schizophrenia was replicated by the same group in a Scottish population (Stefansson et al., 2003). A meta-analysis of 14 subsequent NRG1 association studies reported a small but significant association with this initial haplotype in schizophrenia patients from Caucasian populations, although not Han Chinese schizophrenics who have association with a separate NRG1 haplotype at the 3’ region of the NRG1 gene (Tosato et al., 2005).

Neuregulin 1 is a large and transcriptionally complex gene with multiple isoforms and promoter regions. The schizophrenia-associated SNPs so far identified in NRG1 are non-protein coding but may influence gene expression via disruption of transcription factor binding sites. Recent evidence suggests there may be increased transcription of NRG1 immunoglobulin-containing type I isoform in the hippocampus (Law et al., 2006) and altered ratios of other NRG1 isoforms in the DLPFC of patients with schizophrenia (Hashimoto et al., 2004). The latter study also showed up-regulation of NRG1 type I mRNA in the DLPFC, although this correlated with lifetime APD treatment indicating it may be a result of the action of drugs used to treat schizophrenia rather than a primary pathological deficit.

Animal models have been used to conduct functional studies of neuregulin 1 (Nrg1) in an attempt to uncover its involvement in schizophrenia (Stefansson et al.,

29 2002). As neuregulin can act as a neuronal signalling molecule, it was proposed that schizophrenia pathogenesis might result from the interaction between some of the Nrg1 isoforms upon release from synaptic vesicles and their receptors (ErbB2, ErbB3 and ErbB4) on the post-synaptic membrane. Complete ablation of the Nrg1 gene in mice is embryonic lethal, but Nrg1+/- mice display hyperactivity, and sensorimotor gating deficits, measured using prepulse inhibition (PPI; as discussed in Section 1.1.1.4) compared to wild type littermates (Stefansson et al., 2002). These behavioural phenotypes were also seen in the ErbB4+/- knockouts, but not the other receptor knockouts. Interestingly, in this initial study both hyperactivity and PPI deficits found in the heterozygous mice were reversed by administration of clozapine. Hyperactivity of Nrg1+/- mice has recently been independently replicated with a thorough behavioural phenotyping paradigm although these authors were unable to replicate PPI deficits (Karl et al., 2007). Other researchers exploring behavioural phenotypes in Nrg1 mutant mice have reported aversion to social novelty with no social or working memory deficits (O'Tuathaigh et al., 2007). These behavioural studies indicate Nrg1 mouse models may be valid as models for some aspects of schizophrenia symptomatology despite the fact that haploinsufficiency of Nrg1 does not have good construct validity (see Section 1.4.3.1 for description of schizophrenia animal models).

Recent functional studies show that Nrg1+/- mice have reduced NMDA receptor functioning, reversible with clozapine administration (Bjarnadottir et al., 2007) and there is evidence through conditional knockout models that NRG1/ErbB4 signalling is crucial for the normal development of myelin sheaths in CNS neurons (Roy et al., 2007). Therefore genetic variation in Nrg1 may be involved in glutamatergic transmission dysfunction and/or white matter deficits in patients with schizophrenia.

1.3.2.2 PPP3CC Also on chromosome 8p is PPP3CC, a gene encoding an isoform of the catalytic subunit of the calcium/calmodulin dependent protein phosphatase, calcineurin A. The first genetic association of PPP3CC with schizophrenia was in a study of

30 over 400 affected families from diverse ethnicities (Gerber et al., 2003). A subsequent negative association study was published in a Japanese cohort (Kinoshita et al., 2005) Positive replication has recently been made in a Taiwanese population, in which the associated haplotype was also shown to decrease PPP3CC expression in lymphoblasts of schizophrenia patients (Liu et al., 2007). This dysregulation has also been seen in postmortem tissue, with decreased expression of PPP3CC mRNA and calcineurin A in the hippocampal formation of patients with schizophrenia (Eastwood et al., 2005). Another study found no change in calcineurin protein in the DLPFC or hippocampus of patients with schizophrenia although the expression of the catalytic subunit A was not measured independently in this analysis (Kozlovsky et al., 2006).

An animal model, with PPP3CC knockout specifically in the forebrain, shows schizophrenia-like behavioural abnormalities including hyperactivity, impaired working memory, social withdrawal and sensorimotor gating deficits (Miyakawa et al., 2003). This biological support for calcineurin A dysregulation in schizophrenia, combined with knowledge about its function indicates it may be involved in altered glutamatergic signalling or synaptic plasticity in patients with schizophrenia (Eastwood et al., 2005).

1.3.2.3 Dysbindin Straub and colleagues conducted linkage analysis and association studies within the major schizophrenia susceptibility locus 6p24-22 and found a strong association to a region containing the gene encoding dystrobrevin-binding protein (DTNBP1), also called dysbindin, in an Irish cohort (Straub et al., 2002). Further characterisation of this association in their cohort revealed a 2-SNP haplotype in DTNBP1 associated with schizophrenia (van den Oord et al., 2003). These groups speculate that dysbindin, which may form part of the dystrophin protein complex found in post-synaptic densities and is known to colocalise with GABA-receptor subtypes in the mouse brain, may play a role in both signalling and synaptic plasticity. An association with schizophrenia and genetic variation in DTNBP1 has been independently replicated in various populations, albeit

31 indicating different risk haplotypes. Meta-analysis of 14 association studies, including three negative studies, indicates that DTNBP1 has the strongest support yet found for a schizophrenia susceptibility gene (Williams et al., 2005).

In situ hybridisation experiments revealed that dysbindin is expressed in multiple brain regions, including the hippocampus, frontal and temporal cortex, basal ganglia and midbrain in normal brain (Weickert et al., 2004). In patients with schizophrenia, DTNBP1 expression was decreased in multiple layers of the DLPFC, and possibly midbrain, in a manner that correlated with DTNBP1 genotype, supporting a role for this gene in schizophrenia susceptibility (Weickert et al., 2004).

The function of dysbindin, and therefore its role in schizophrenia, is not well characterised. Dysbindin is found in hippocampal presynaptic neurons where its expression is decreased in patients with schizophrenia and inversely correlated with glutamate transporter expression (Talbot et al., 2004). Furthermore, overexpression of dysbindin in primary cortical neuronal culture increased basal glutamate levels and glutamate release as well as inducing expression of a synaptic protein marker, synaptosome-associated protein 25 (SNAP25), which promotes neuronal survival (Numakawa et al., 2004). In a separate study in PC12 cells, down-regulation of DTNBP1 correlated with increased SNAP25 expression and dopamine release (Kumamoto et al., 2006). These functional characterisation studies indicate dysbindin may play a role in multiple systems thought to be dysfunctional in the brains of patients with schizophrenia, including dopamine and glutamate signalling (see Section 1.2) and presynaptic functioning (see Section 1.4.2.1).

There is evidence that DTNBP1 dysregulation in schizophrenia may impact upon the degree of negative symptoms (Fanous et al., 2005; DeRosse et al., 2006) and cognitive functioning in schizophrenia, with recent clinical studies indicating that general cognitive ability is influenced by DTNBP1 genotype (Burdick et al.,

32 2006) and that the high-risk schizophrenia haplotype confers greater cognitive decline in patients (Burdick et al., 2007).

1.3.2.4 G72/DAOA A number of linkage studies have implicated 13q32-34 as a major susceptibility region in schizophrenia. Chumakov and colleagues conducted association studies on a 5 Mb distal portion of this region in a French Canadian cohort and found two loci significantly associated with schizophrenia including a region containing the genes G72 and G30 on opposing DNA strands, the latter of which is untranslated (Chumakov et al., 2002). This association was replicated by the same group in a Russian population (Chumakov et al., 2002). The authors speculated that G72, which is a primate-specific gene expressed in brain tissue, may be involved in higher-order functioning of the human brain. Independent replication of this association has been carried out and a recent meta-analysis of eleven genetic association studies confirmed an association with markers in the locus containing G72/G30 and schizophrenia, although the associated allele varied among studies (Detera-Wadleigh & McMahon, 2006).

A yeast two-hybrid screen revealed an interaction between G72 and D-amino acid oxidase (DAO), which was subsequently found to contain a genetic polymorphism associated with schizophrenia in the original French Canadian cohort (Chumakov et al., 2002) and in other populations (Schumacher et al., 2004). DAO is involved in the oxidation of D-serine, a modulator of NMDA receptors and subsequent studies have revealed alterations in serum levels of D- serine in schizophrenic subjects (Hashimoto et al., 2003a). It is hypothesised that alterations in DAO or its binding partner G72, now called DAO activator (DAOA), may be involved in the changes in glutamatergic transmission seen in schizophrenic patients (see Section 1.2.4).

33 A review of the original Chumakov study concluded that it was the first finding of a susceptibility gene for schizophrenia with direct links to a known mechanism of pathogenesis in the disorder (Cloninger, 2002).

1.3.3 Cytogenetic abnormalities A unique opportunity for identifying genes involved in complex disorders comes from the study of rare chromosomal translocations. This kind of genetic mutation means that disease susceptibility is found in a Mendelian pattern which allows easy identification of genes disrupted by the translocation that segregate with illness. Cytogenetic analyses of schizophrenic patients have revealed many chromosomal abnormalities in schizophrenia patients (reviewed in (MacIntyre et al., 2003) and some candidate genes have been suggested from these regions.

1.3.3.1 Microdeletions of 22q11 The most common chromosomal abnormality seen in schizophrenic patients is a microdeletion at 22q11, within a region first discovered to be abnormal in patients with Velocardial Facial Syndrome (reviewed in (Bassett et al., 2000). It has been estimated that this microdeletion is found in 2% of schizophrenic patients (Karayiorgou et al., 1995), a significant increase over 0.025% general population prevalence of the deletion. A number of genes have been analysed due to their presence at the 22q11 locus.

COMT Catechol-O-methyltransferase (COMT) has had a longstanding functional association with schizophrenia prior to the cytogenetic association of location on chromosome 22q11. COMT is an enzyme involved in the metabolism of dopamine and other catecholamines upon their release from synaptic vesicles. It was hypothesised in the 1960’s that defects in catecholamine methylation may play a role in schizophrenia. This functional association to COMT led to at least a dozen studies of the level of this enzyme in the erythrocytes of schizophrenic patients. Although some studies reported an increased COMT activity in

34 schizophrenics compared to controls, most revealed no significant difference (reviewed in (Floderus et al., 1981).

The location of COMT on chromosome 22q11 has lead to reinvestigation of the gene using molecular genetics techniques as discussed in Section 1.3.4.1.

PRODH Liu and colleagues defined a 1.5 Mb ‘schizophrenia critical region’ within the 22q11 microdeletion and found SNPs in the gene encoding proline dehydrogenase (PRODH) that were preferentially transmitted to schizophrenia probands in three separate cohorts: a South African case-control and two US family studies (Liu et al., 2002b). PRODH association has only been independently replicated once out of a dozen attempts. This is reflected in a recent meta-analysis concluding no significant association between variation in PRODH and schizophrenia (Li & He, 2006).

One study has identified a subgroup of hyperprolinemic schizophrenia patients in which all subjects with mutations or deletions in PRODH had increased proline levels, and these researchers suggest that hyperprolinemia may have excitotoxic effects through potentiation of glutamatergic transmission (Jacquet et al., 2002).

Mice that are homozygous for a nonsense mutation in Prodh have increased plasma proline levels and significant decreases in the levels of certain neurotransmitters in the hypothalamus and frontal cortex (Gogos et al., 1999). Observation of these mice during behavioural testing revealed significant prepulse inhibition (PPI) deficits in Prodh-/- mice compared to wild-types although no differences were seen in habituation of startle response, nor in a test of exploratory behaviour (Gogos et al., 1999). Changes in sensorimotor gating in the mutant mice were hypothesised to result from increased proline levels, potentiating glutamatergic transmission.

35 Other candidate genes at 22q11 Liu and colleagues analysed the second region of linkage they found within the 1.5 Mb ‘schizophrenia critical region’ (Liu et al., 2002a). The most promising candidate in this region was ZDHHC8, encoding a protein involved in posttranslational modifications that is predominantly expressed in the cortex and hippocampus of the mammalian brain. A subsequent study found an intronic SNP within ZDHHC8 that was associatied with schizophrenic patients in the US and South African cohorts (Mukai et al., 2004). This variation affected mRNA splicing leading to incorrect intron retention when the risk form of the variant was present. Genetic association between ZDHHC8 and schizophrenia has been independently replicated in Chinese and German family based studies (Chen et al., 2004b; Faul et al., 2005) although not in other Asian or Caucasian cohorts. Functional analysis of mouse knockouts of this gene show sex-specific behavioural abnormalities: female Zdhhc8-/- mice display deficits in PPI, locomotor activity and reduced sensitivity to a non-NMDA receptor activator, suggesting possible disruptions to glutamatergic transmission (Mukai, 2004).

1.3.3.2 DISC1 and partners Perhaps the most convincing candidate gene for schizophrenia was associated through cytogenetic analysis of a Scottish pedigree carrying a t(1;11) translocation (St Clair et al., 1990) that segregates in a highly significant pattern with 18 family members affected by mental illness (Blackwood et al., 2001). This translocation interrupts three genes – two novel genes called Disrupted in Schizophrenia 1 and 2 (DISC1 and DISC2), the latter of which is a non-coding RNA that may regulate DISC1 expression (Millar et al., 2000a); and TRAX, an intergenic splice product of DISC1 that suppresses its translation (Millar et al., 2000a; Millar et al., 2000b). Wild-type DISC1 encodes a protein with multiple isoforms that vary in their regional expression in the brain where it promotes neurite outgrowth, particularly in the cortex (Kamiya et al., 2005). The result of possible DISC1 truncation in these patients is unknown, but studies suggest it may lead to haploinsufficiency (Millar et al., 2005) and ultimately, deficits in neuronal migration (Kamiya et al., 2005).

Subsequent independent association studies have confirmed that DISC1 is a susceptibility gene for major mental illness including schizophrenia, schizoaffective disorder, bipolar affective disorder and depression in multiple population cohorts (Hennah et al., 2003; Hodgkinson et al., 2004; Callicott et al., 2005; Thomson et al., 2005; Zhang et al., 2006). There is also evidence that genetic variation in DISC1 may be associated with cognitive ability in the general population (Hodgkinson et al., 2004; Burdick et al., 2005; Callicott et al., 2005; Thomson et al., 2005) and with delusions in schizophrenia (DeRosse et al., 2007).

DISC1 is involved in multiple protein-protein interactions that may be of potential interest in schizophrenia research. Indeed, one of the binding partners of DISC1 is phosphodiesterase 4B (PDE4B), which is encoded by a gene that has been localised to a chromosomal breakpoint in a proband and cousin with schizophrenia (Millar et al., 2005). PDE4B is involved in cAMP signalling and its hippocampal neuronal co-localisation with DISC1 may indicate a role in memory and learning for this interaction (Millar et al., 2005). DISC1 also binds to NUDEL, a protein involved in neuronal migration (Brandon et al., 2004). Interestingly, the mutant translocation in DISC1 removes this binding site. There is recent evidence to suggest that the expression of NUDEL, as well as LIS1 and FEZ1, genes encoding other DISC1 binding partners, may be down-regulated in the prefrontal cortex of schizophrenic patients (Lipska et al., 2006b).

1.3.3.3 Other genes suggested through cytogenetic analysis Neuronal PAS domain protein 3 (NPAS3) is at the breakpoint of a reciprocal translocation found in a mother and her child with schizophrenia (Pickard et al., 2006b). Mouse knockouts of this gene have decreased hippocampal neurogenesis in the subgranular zone and behavioural abnormalities including PPI deficits, locomotor hyperactivity and delayed learning (Pieper et al., 2005). Genetic association studies in karyotypically normal patients are required to implicate this gene in susceptibility to schizophrenia.

37 Multiple chromosomal rearrangements were found in a single patient with schizophrenia and mental illness, including disruption to GRIK4, a kainite ionotropic glutamate receptor that provides a strong functional candidate for schizophrenia susceptibility (Pickard et al., 2006a). Genetic studies were subsequently conducted by the same group in a Scottish mental illness cohort and revealed significant association of a three SNP haplotype with both schizophrenia and bipolar disorder, strengthening genetic evidence for glutamate dysfunction in schizophrenia and further implicating shared aetiologies across the spectrum of major mental illness.

1.3.4 Candidate genes and association studies Linkage studies are often complemented by association studies, in which polymorphic differences in candidate genes are analysed in an attempt to ascertain genotypes and haplotypes that are associated with increased disease susceptibility. For complex diseases where heterogeneity can be an issue, these studies use population cohorts consisting of family triads (an affected proband and their parents) and sibling pairs in addition to traditional case-control studies. Often, a transmission disequilibrium test is used to detect alleles that are preferentially transmitted to the schizophrenic offspring. These techniques are used to detect genes of small to moderate effect. Some genes that are found in the major susceptibility regions isolated in linkage analysis have been subsequently used as candidate genes in association studies.

An online database of genetic analyses cites over 1800 candidate genes that have been investigated for association with schizophrenia (Allen et al., 2007). Genes for which there is strong evidence for association are those already discussed above as well as genes suggested through their biological function and/or altered gene expression, discussed further below.

1.3.4.1 COMT COMT has a longstanding biological association with schizophrenia (as discussed in Section 1.3.3.1). Initial genetic studies analysed association with a SNP in the

38 COMT coding region that changes enzymatic activity. This polymorphism leads to a valine (Val) to methionine (Met) amino acid change at codon 108/158 (short/long form) of the protein, where the valine form is associated with higher enzymatic activity, due to the thermolability of Met-108 at physiological temperatures (Lotta et al., 1995). Genetic association studies of this functional polymorphism have typically been inconclusive with three meta-analyses each revealing no significant association between schizophrenia and the COMT genotype in case-control association studies (Glatt et al., 2003; Fan et al., 2005; Munafo et al., 2005). However, significant association with the Val/Val genotype was detected in a meta-analysis of the small number of family-based studies (Glatt et al., 2003).

Alternative COMT polymorphisms, other than the activity-altering Val/Met substitution, may reveal greater associations with schizophrenia. Li and colleagues found association with a five-marker haplotype, including Val158 (Li et al., 2000), and Shifman and colleagues used an Ashkenazi Jewish population in the largest association study to date to identify a different five-SNP haplotype that was significantly associated with schizophrenic patients (Shifman et al., 2002). The latter haplotype included the Val/Met polymorphism and also two SNPs in untranslated regions of the gene, implicating gene expression dysregulation. Subsequent analyses have shown that the risk haplotype is associated with reduced COMT mRNA expression in normal adult human cortex (Bray et al., 2003). This finding was not replicated in the DLPFC of schizophrenic patients (Matsumoto et al., 2003), although higher enzymatic activity of the COMT-Val allele in normal human brain tissue has been seen (Chen et al., 2004a).

Despite the conflicting genetic data regarding the role of the Val/Met polymorphism in schizophrenia, evidence for a functional role of altered COMT activity in the aetiology of the disorder is accumulating. In the Wisconsin Card Sorting Test (WCST), a measure of executive function, cognition and working memory that is employed in psychiatric testing, the Val/Val COMT genotype conferred decreased ability in this test compared to the other genotypes in both

39 schizophrenics and controls (Egan et al., 2001). This decreased performance was linked by functional magnetic resonance imaging (fMRI) analysis to abnormalities in PFC neuronal function in the Val/Val patients. This suggests that increased activity of the COMT enzyme may result in excess dopamine metabolism in the PFC, compromising its function. Recent imaging analyses have replicated the finding of decreased cortical activation during cognitive tasks in patients with schizophrenia and have also shown that this allele is associated with reduced cortical grey matter density (McIntosh et al., 2007). A meta-analysis of twelve imaging studies during the WCST indicates a small but significantly increased ability in Met/Met individuals in the general population, although not in patients with schizophrenia (Barnett et al., 2007).

Additionally, interactions between COMT genotype and environmental risk factors have been reported to contribute to schizophrenia susceptibility. Findings from a New Zealand birth cohort study indicated that COMT genotype is associated with a 4.5-fold increased risk of developing schizophrenia following heavy cannabis use in early adolescence (Caspi et al., 2005). While most chronic users never developed psychosis, the majority of those that became psychotic carried the low activity Met encoding COMT allele.

These multiple lines of converging evidence suggest that COMT is a strong functional candidate for schizophrenia, yet highlight the difficulty of examining susceptibility in a genetically complex and clinically heteregenous population.

1.3.4.2 ERBB4 ErbB4, the major receptor for neuregulin in the brain, has been examined for genetic association to schizophrenia, with a haplotype conferring risk identified in Caucasian schizophrenia patients in one out of four UK cohorts examined in a single study (Norton et al., 2006). This finding has been replicated in an independent UK cohort (Benzel et al., 2007), an Israeli case-control study (Silberberg et al., 2006) and Caucasian and African-American schizophrenia patients in a US family-based study (Nicodemus et al., 2006). ErbB4 is

40 ubiquitously expressed in grey matter in primate brain (Thompson et al., 2007). ErbB4 has multiple isoforms and schizophrenia-associated SNPs increase the expression of multiple splice variants in the DLPFC of patients with schizophrenia (Silberberg et al., 2006; Law et al., 2007).

1.3.4.3 GRM3 Following initial genetic association of the metabotropic gluatamate receptor 3 gene (GRM3) with schizophrenia (Marti et al., 2002), association studies have found significantly associated SNPs in Japanese, German and US case-control and family-based cohorts (Fujii et al., 2003; Egan et al., 2004; Nicodemus et al., 2007). However, no association was identified in different cohorts within the same ethnic populations in separate studies (Marti et al., 2002; Tochigi et al., 2006). A recent meta-analysis of these genetic studies concluded no significant association with GRM3 and schizophrenia (Albalushi et al., 2007).

Some functional studies have been conducted to investigate the possible role of GRM3 variation in schizophrenia. GRM3 risk alleles from the US family study were shown to impact synaptic glutamate levels via down-regulation of glutamate transporter mRNA, and also to affect hippocampal and cortical cognitive functioning (Egan et al., 2004). Neuropsychological testing in normal individuals confirmed that polymorphisms in GRM3 differentially influence prefrontal cortical function in the general population (Marenco et al., 2006). Additionally, clinical studies indicate that variation in this gene may predict negative symptomatology improvement after olanzapine treatment (Bishop et al., 2005).

1.3.4.4 BDNF Brain-derived neurotrophic factor (BDNF) mediates glutamate transmission in pyramidal neurons and is reduced at the mRNA and protein expression levels in schizophrenia (Weickert et al., 2003). The receptor for BDNF, tyrosine kinase B, is also reduced in multiple cortical layers in the DLPFC of patients with schizophrenia (Weickert et al., 2005b).

41 An amino-acid changing common polymorphism in BDNF, Val66Met, is known to alter the activity-dependent secretion of BDNF (Egan et al., 2003). Genetic analyses have revealed that the low activity Val allele is associated with schizophrenia in a Scottish cohort and psychosis in Spanish families (Neves- Pereira et al., 2005; Rosa et al., 2006) but no association in other population patient cohorts. A recent meta-analysis of these studies concluded no significant association with the Val66Met polymorphism and schizophrenia (Kanazawa et al., 2007).

BDNF facilitates long-term potentiation of memory through its expression in the hippocampus. The Val66Met polymorphism affects memory performance in normal individuals that may reflect the integrity of neurons and synaptic connections in the hippocampus (Egan et al., 2003). Patients with schizophrenia showed similar genotype-dependent hippocampal effects. Other imaging studies have also shown that the BDNF Val allele is associated with decreased hippocampal volume (Szeszko et al., 2005).

Polymorphisms other than Val66Met have also been assessed for their association to schizophrenia and two separate meta-analyses reveal small but significant association with a C270T polymorphism in the 5’ region of BDNF, although the function of this polymorphism is not known (Watanabe et al., 2007; Zintzaras, 2007).

1.3.4.5 GAD1 GAD1 encodes two isoforms of the glutamic acid decarboxylase enzyme involved in GABA synthesis in the brain. Decreased expression of GAD-67, the longer isoform, has been seen in the brains of patients with schizophrenia (see Section 1.4.2.1). The initial GAD1 association study in schizophrenia was negative (De Luca et al., 2004). There is recent evidence that association of genetic variation at the GAD1 locus and schizophrenia may be dependent upon DNA methylation status at the GAD1 promoter (Akbarian & Huang, 2006) and interactions with polymorphisms in the COMT locus (Straub et al., 2007).

1.3.4.6 RGS4 After an initial suggestion of the possible role of RGS4 in schizophrenia by transcript profiling (see Section 1.4.2.1), association studies located a four-SNP haplotype that was associated with schizophrenia in multiple populations situated in the 5’ region of the RGS4, consistent with its altered expression in brain tissue (Chowdari et al., 2002). Many association studies have followed with mixed results and two recent meta-analyses have been conducted: one was inconclusive, probably due to population heterogeneity (Talkowski et al., 2006), and the other found that there was no single variant in RGS4 that was significantly associated with schizophrenia (Li & He, 2006).

1.3.4.7 AKT1 Protein analysis suggested AKT1 as a candidate gene in schizophrenia due to its decreased expression in schizophrenia lymphocytes and brain tissue (see Section 1.4.2.2). Association studies were conducted to elucidate a core haplotype for susceptibility. Using a collection of US and French family cohorts, a three-SNP haplotype was found to be preferentially transmitted to schizophrenic offspring (Emamian et al., 2004). Replication of association between AKT1 and schizophrenia has since been made in UK, Japanese, Iranian and Australian populations (Ikeda et al., 2004; Schwab et al., 2005; Bajestan et al., 2006; Norton et al., 2007) although negative associations have also been reported (Ohtsuki et al., 2004; Ide et al., 2006; Liu et al., 2006; Turunen et al., 2007). Surprisingly, significant under-transmission of this haplotype to schizophrenic patients in multiple affected Irish families was detected, and lower expression of AKT1 protein was localised to the prefrontal cortex of patients with schizophrenia (Thiselton et al., 2007). As yet no meta-analysis of association between AKT1 variation and schizophrenia has been conducted.

43 The group that found the original genetic association have also created a homozygous knockout mouse. This mouse shows no overt behavioural phenotype, although amphetamine-induced PPI deficit was found at lower concentrations of amphetamine than required to detect this phenotype in wild- type mice, suggesting a role for Akt1 signalling in dopamine regulation (Emamian et al., 2004).

1.3.5 Summary — schizophrenia susceptibility genes In the past five years, considerable advances have been made in defining the molecular genetics of schizophrenia. There are now over a dozen genes with strong evidence for contribution to schizophrenia susceptibility (Table 1.6) identified through linkage and positional cloning, cytogenetic analysis, association studies and expression analyses (see Section 1.4.2). Despite these molecular genetic advances, there are no disease-associated polymorphisms yet associated with schizophrenia that lead to altered protein products or expression levels (perhaps with the recent exception of NRG1) that may aid in our understanding of the aetiology of schizophrenia. Nor are there genes with common variants in all patient populations, indicating that there are still genetic mutations to uncover in understanding the inherited susceptibility to schizophrenia.

44 Table 1.6 Schizophrenia susceptibility candidate genes. Adapted from (Straub & Weinberger, 2006).

Chromosomal Association Biological Altered expression in schizophrenia a b c Gene linkage studies plausibility brain tissue

Revealed by linkage and positional cloning

DTNBP1 Yes 17 positive; Strong mRNA in hippocampus, cortex, 23 negative basal ganglia, midbrain, protein in hippocampal neurons NRG1 Yes 25 positive; Strong mRNA type I in DLPFC, altered 15 negative isoform ratios in the hippocampus DAAO/ Yes 23 positive; Strong No G72 7 negative PPP3CC Yes 4 positive; Weak mRNA and protein in hippocampus 5 negative

Revealed by cytogenetic analysis

DISC1 Yes 11 positive; Weak No 5 negative COMT Yes 20 positive; Strong No 48 negative PRODH Yes 4 positive; Weak No 9 negative

Revealed by association studies

ERBB4 No 6 positive; Strong mRNA in DLPFC 4 negative BDNF No 7 positive; Strong mRNA and protein in DLPFC 21 negative GRM3 No 5 positive; Strong Unknown 7 negative

d Revealed by molecular expression analyses

RGS4 Yes 14 positive; Strong mRNA in DLPFC 11 negative AKT1 No 5 positive; Moderate protein in lymphocytes, frontal 4 negative cortex, hippocampus GAD1 No 3 positive; Strong GAD67 mRNA in PFC 5 negative a From a meta-analysis of genome wide linkage studies (Lewis et al., 2003) or in an area of chromosomal rearrangement in schizophrenia b From SczGene database (http://www.schizophreniaforum.org/res/sczgene/) c d see individual descriptions for references As described in section 1.4.2 : increased, : decreased, DLPFC: dorsal lateral prefrontal cortex

45 1.4 GENE EXPRESSION PROFILING IN SCHIZOPHRENIA

1.4.1 Techniques for detecting altered gene expression 1.4.1.1 Molecular biology in the 21st century At the turn of this century there was a dramatic breakthrough in molecular biology with the sequencing of the human genome (Lander et al., 2001; Venter et al., 2001) and then the genomes of a couple dozens of other animals, and countless plant and microbial genomes. One of the most remarkable observations of comparative genomics – that is between genomes – is that there are relatively few human genes compared to the predicted number of proteins and to our perceived complexity. This observation has put more emphasis on the intermediary molecule, mRNA, in this post-genomics or “functional” genomics era (Lockhart & Winzeler, 2000). Instead of complexity arising from the number of genes, it arises from alternative splicing, which creates multiple transcripts and proteins from a single gene; and from non-protein coding regulatory RNAs. Each mRNA transcript can be described thoroughly by traditional techniques: Northern hybridisation for transcript size and relative abundance for multiple transcripts with the same sequence; in situ hybridisation for single transcript localization and relative abundance; RNase protection assays to characterise alternative splicing of a gene; and RT-PCR to quantify the relative or absolute abundance of transcripts of a known sequence.

However, to assay the complexity of the transcribed genome (transcript profiling) under various conditions and disease states requires high throughput technology, the most widely used being DNA microarrays (Hoheisel, 2006). DNA microarrays are applied to medical research as diagnostic tools, for example in discerning different tumor gene expression signatures and courses of action, as therapeutic tools, to identify drug targets; and as exploratory tools, to assay gene expression and to probe pathogenetic origins of disease state.

46 1.4.1.2 Transcript profiling by microarray analysis The basic principle of the microarray technique is the hybridisation of labelled single-stranded nucleic acids targets to immobilised complementary single- stranded nucleic acid probes, in a way that can be quantitatively assessed (Gillespie & Spiegelman, 1965). Three main formats of array have been developed: nylon macroarrays, cDNA spotted microarrays and synthesised oligonucleotide microarrays (Table 1.7).

Table 1.7 Comparison of arrays formats used in transcript profiling Macroarray cDNA Microarray Oligonucleotide GeneChip® Surface Nylon membrane Silicon glass chip Silicon glass chip Probe Sample cDNA Long cDNAs Synthesised ~25 nucleotide oligomers Target; number cDNA ; two cDNA ; two Fragmented cRNA; one samples samples sample Label Radioactive Cy5/Cy3 Biotinylated enzyme isotope fluorescent dye Probe Spotted Spotted Photolithographic synthesis application Density Low High High Transcript 500-5000 6-20,000 Up to 100,000 number Data Semi-quantitative Semi-quantitative, Quantitative, absolute comparative Reproducibility Fair Poor Good Equipment Non-specialised Scanner required All specialised ($1M+) (~$100K) Cost per array $200-$1000 $100-$300 $1500-$2000

DNA macroarrays use a radiollabeled target to bind to up to 5000 transcripts. With the development of high throughput microarrays this format has become largely defunct although it is still a reproducible and cost-effective technique (Pongrac et al., 2002).

47 cDNA microarrays utilise a comparative hybridisation process where two samples are each labelled with a characteristic signal (Cy5/Cy3 dyes are often employed) and the up- or down-regulation of gene expression is measured by comparative intensity of the two signals once equal amounts of labelled cDNA have been hybridised to the chip (Schena et al., 1995).

Synthetic oligonucleotide microarrays allow simultaneous quantification of the signal from up to 100,000 transcripts for one sample, with comparisons between samples performed after array analysis. Transcript abundance is comprehensively assessed by the binding of target sample to multiple oligomers (approximately 25 nucleotides in length) corresponding to the 3’ region of the transcript and the absence of target binding to mismatch probes containing single nucleotide substitutions. The most commonly used commercial microarrays are GeneChip® arrays produced by Affymetrix®, which, aside from use in quantifying genomewide mRNA expression, have applications for SNP analysis of human genetic variation and mutation detection in disease states.

The major differences between cDNA microarrays and GeneChips are cost, reproducibility and comparative versus absolute quantification (Table 1.7). Both produce large lists of dysregulated transcripts that require normalisation, data analysis and validation by an alternative method.

The software to cope with normalisation and data analysis is being constantly optimised and improved, yet it remains the most technically limited part of microarray technology (Hoheisel, 2006). Affymetrix® provides users with GeneChip Operating Software (GCOS) which allows normalisation to a control baseline and uses basic algorithms to annotate fold-change. Other techniques include rank product (RP), a powerful statistical method for low replicate microarray analysis that uses a non-parametric t-statistic (Breitling et al., 2004) and hierarchical Bayes modelling. RP analysis was originally developed for cDNA two-channel microarrays (Lonnstedt & Speed, 2002) and later refined for use in high density oligonucleotide arrays (Smyth, 2004) that use a moderated t-

48 statistic and an empirical Bayesian approach. These more sophisticated analytical tools allow the approximation of false discovery rates and significance levels for each transcript.

Microarray technology has provided unprecedented opportunities to explore molecular expression in complex brain tissue. However, it is essentially a screening tool and all transcript profiling analyses require validation by alternative techniques before the functional consequences of gene expression changes (protein altering or regulatory) are defined (Bunney et al., 2003).

1.4.1.3 Validation techniques Gene expression changes detected by microarray analysis can be validated by a number of techniques, including: in situ hybrisation, which allows visualisation of regional and cellular expression; RNase protetction assays, useful for determining intron/exon boundaries; and Northern blotting, which facilitates alternative transcript analysis. However, the most common technique for verifying changes across multiple transcripts is real-time quantitative RT-PCR (QPCR), a highly sensitive although not highly reproducible technique (described in Fig. 1.8) (Bustin, 2000).

The underlying technique of QPCR requires the transformation of gene expression (mRNA) into a more stable form (cDNA), which is readily amplifiable using the polymerase chain reaction (PCR) technique. Quantitative PCR differs from non-quantitative PCR in that the amount of signal is detected during the exponential amplification phase of PCR cycling, rather than the presence or absence of signal detected at the end of cycling (Fig. 1.9) (Higuchi et al., 1992). The quantity of cDNA present during this exponential phase is proportional to the initial amount present (Higuchi et al., 1993). This allows quantification of cDNA copy number of any gene of interest in a given sample.

RNA isolation and purification

Two-step One-step Reverse transcription for cDNA synthesis reaction: reaction: (using oligodT, random or specific primers) RNA to RNA to cDNA to PCR PCR

Polymerase Chain Reaction experiment performed in triplicate with fluorescent polymerase

Data analysis

Relative gene expression Absolute gene expression

Normalisation to housekeeping gene Standard curve construction from serial dilutions

Averaging of biological/technical replicates, gene expressions calculated

Statistical analysis

Figure 1.8 Flowchart of quantitative real-time RT-PCR procedure. Total RNA is synthesised into cDNA by reverse transcription (RT) and then amplified in the presence of fluorescent labeling using polymerase chain reaction (PCR) to detect a signal that is proportional to the amount of cDNA present. Gene expression is quantified either by relative measures, using a housekeeping gene or by absolute measures, using comparison to a standard curve of serial dilutions of cDNAs of known concentration included within the experiment.

Fluorescence Plateau phase

Log-linear

Ct exponential phase

Baseline

C –S1 C –S2 PCR cycle t t Figure 1.9 Real-time quantitative RT-PCR profile. The sigmoidal graphs indicate baseline detection limit, exponential amplification, where the threshold is set for quantitative real-time analysis; whereas the plateau phase endpoint is used for non- quantitative PCR amplicon detection. Changes in expression between two samples are measurable by Ct value. Ct: threshold cycle, S1: sample 1, S2: sample 2.

There are two main types of chemistry used for fluorescent labeling in QPCR: DNA binding dyes (eg. SYBR Green I) and hydrolysis probes (eg. Taqman) (Fig. 1.10). By emitting maximum fluorescence after intercalation into dsDNA, SYBR Green I allows detection of signal during all cycling phases: denaturation, annealing and extension, allowing optimization of QPCR conditions (Wittwer et al., 1997). Taqman probes use chemistry known as fluorescence resonance energy transfer (FRET). Oligonucleotides complementary to the DNA of interest are labeled with a fluorophore and quencher that are stable while in close proximity until cleavage of the fluorophore by DNA polymerase during the amplification phase results in fluorescence emission during each PCR cycle. The main advantage of using hydrolysis probes is that sequence specificity of oligonucleotides allows high specificity in detecting the gene expression of interest (Valasek & Repa, 2005).

(A) (B)

S S Denaturation DNA S S primer

Primer F Q S SYBR annealing Green I Q F Fluoro

polymerase

Q S S S F flurophore Extension F Q quencher S S S F

Q 480 520

Figure 1.10 Fluorescent chemistries used to quantify gene expression in real-time RT-PCR. (A) Sybr Green I dye intercalation into dsDNA during QPCR with fluorescence absorption (480 nm) and emission (520 nm) wavelengths (B) Taqman hydrolysis probe uses FRET technology with a fluorophore and quencher contained on the probe in close proximity until the polymerase cleaves the probe during amplification (adapted from Valasek & Repa, 2005)

There are also two methods to quantify gene expression following QPCR – relative and absolute quantification. Relative quantification measures expression of the gene of interest (GOI) compared to a standard curve created from a calibrator or housekeeping gene. This is limited by the quality or variability of the housekeeping gene of choice and it is now recommended that multiple control genes be used for normalisation (Vandesompele et al., 2002). Absolute quantification measures gene expression compared to internal standards of known concentration, which requires the generation and precise measurement of individual standards for each GOI (Bustin, 2000). For most gene expression analyses, relative quantification between samples is sufficient.

52 1.4.2 Gene expression profiling analyses of tissue from schizophrenic patients Due to the genetic heterogeneity of complex diseases like schizophrenia, the power of linkage studies in localising genes involved in the disorder is limited. An alternative technique in complex disease gene discovery is the use of microarray technology to analyse altered gene expression that may be the result of mutations within susceptibility genes and their regulatory regions, or the downstream effects of such mutations. Microarrays are a valuable tool in neuroscience due to their ability to assess global expression in a system as molecularly complex as the brain (Geschwind, 2000). The use of microarrays in psychiatric research has the benefit of simultaneously studying the expression of thousands of genes in an unbiased approach that can be followed by standard molecular biology techniques (Mirnics et al., 2001a), although the cellular heterogeneity of brain tissue may result in only small (<2-fold) detectable changes in gene expression (Mirnics, 2001).

A unique initiative in psychiatric microarray gene expression research has been the creation of the Stanley Medical Research Institute brain collection, initially 35 brains each from patients with schizophrenia, bipolar disorder and depression as well as matched controls (Torrey et al., 2000). This brain collection is accessible to researchers applying to undertake molecular expression studies, including about a dozen microarray studies to date, with all resultant data freely accessible on the internet (www.stanleyresearch.org).

1.4.2.1 Analyses of gene expression in postmortem brain tissue In transcript profiling of postmortem brain tissue there are a few factors that may bias gene expression, in particular postmortem interval, age, agonal state and most importantly RNA quality (Lipska et al., 2006a). In schizophrenia there is the added confounds of illness state at the time of death and treatment duration before death. Similar to genetic studies, postmortem expression studies have not been uniformly replicated, perhaps reflecting the heterogeneity of schizophrenia “disorders”.

53 Mirnics and colleagues have pioneered studies of transcriptional profiling in schizophrenia, characterising gene expression in postmortem brain tissue from the DLPFC of six patients with schizophrenia and controls matched for age, sex, postmortem interval and brain pH (Mirnics et al., 2000). cDNA microarrays were used to analyse 7800 genes from over 250 gene groups, with approximately 5% of genes differentially expressed in schizophrenia. Two schizophrenic patients were not taking medication at the time of death, indicating that the changes may be disease-specific and not the result of treatment, although lifetime neuroleptic use remains a potential confound. To address this, these researchers also analysed regional brain expression of monkeys treated chronically with haloperidol and concluded none of the transcriptional changes identified in schizophrenia patients reflected haloperidol-induced changes (Mirnics et al., 2000).

Presynaptic and synaptic function Hierarchical clustering of the DLPFC cDNA microarray data revealed a group of genes involved in pre-synaptic functioning that were consistently altered in brains of patients with schizophrenia. In particular, the decreased expressions of N- ethylmaleimide-sensitive factor (NSF) and synapsin II (SYN2) were verified using in situ hybridisation of sections from the PFC (Mirnics et al., 2000). Dysregulation of genes involved in presynaptic function was similarly detected in a microarray study of pooled tissue from the Stanley brain bank, a novel approach aimed to minimise individual variation (Vawter et al., 2001). Vawter and colleagues found downregulation of proteins involved in synaptic signalling in the PFC and temporal cortex but not the cerebellum, which supports the use of this technique. In a separate pooled study, Vawter and colleagues confirmed dysregulation of synaptic proteins in the PFC (Vawter et al., 2002).

Additional support for synaptic function and neurotransmitter alterations in schizophrenia has come from a single-cell gene expression study. Using a high- density antisense RNA array system, Hemby and colleagues analysed mRNA expression pattern in single neurons isolated from the entorhinal cortex of schizophrenia and matched control brains (Hemby et al., 2002). They found

54 decreased expression of ten synaptic protein markers of which three – encoding synaptophysin, and two synaptosome associated proteins (SNAP-23 and SNAP-25) – had confirmed altered expression by Northern blot. Decreased protein expression of synaptobrevin/vesicle associated membrane protein (VAMP), but not four other synaptic markers (including synaptophysin and SNAP-25), was detected by immunoreactivity in the prefrontal cortex of patients with schizophrenia (Halim et al., 2003). These studies indicate decreased expression of various synaptic proteins in the brains of patients with schizophrenia. Although the specific markers vary in different cohorts, these studies provide evidence for altered synaptic function in the cortex in patients with schizophrenia.

RGS4 The initial DLPFC cDNA array study also offered the first suggestion of a candidate gene for schizophrenia susceptibility identified through altered expression. The regulator of GTP-binding protein signalling gene (RGS4) was significantly downregulated in the DLPFC of all but one of the patients with schizophrenia, a finding verified using in situ hybridisation (Mirnics et al., 2001c). Subsequent studies have confirmed RGS4 mRNA downregulation (Bowden et al., 2007) as well as decreased RGS4 protein (Erdely et al., 2006) in cortical regions in schizophrenia. RGS4 is located on chromosome 1q21-22, which is one of the major schizophrenia susceptibility loci as defined by previous linkage studies (Lewis et al., 2003). Interestingly, 70 other genes localised to this chromosomal linkage region were not significantly altered in the initial microarray study. Confirmation of RGS4 contribution to linkage at this locus has now been provided by genetic association studies (see Section 1.3.4.6).

RGS4 is in a family of around 25 proteins that are crucial in the inactivation of G-protein mediated responses in eukaryotes. This family of proteins are involved in regulating postsynaptic signalling following neurotransmitter release at the synapse (Hepler, 1999). Taken together with the finding of altered presynaptic functioning and neuronal signalling genes, these microarray data suggest

55 molecular dysfunction at the synapse in schizophrenia patients that may affect synaptic formation and maturation during adolescence, leading to altered neuronal signalling in early adulthood (Mirnics et al., 2001b). In particular the enzymatic activity of RGS4 is regulated by calcium/calmodulin protein kinases. This is interesting, given the genetic association of calcineurin A in schizophrenia (see Section 1.3.2.2), and may implicate RGS4 as a central mediator of calcium signalling at the synapse (Levitt et al., 2006).

Alternatively, RGS4 has been implicated in stress-related response, so its down- regulation in schizophrenic patients may indicate involvement in a secondary pathway rather than a primary etiological change (Chowdari et al., 2002).

Metabolic pathways In the same DLPFC brain tissue used in presynaptic and RGS4 associations, Middleton and colleagues chose specific metabolic genes for transcript profiling analysis (Middleton et al., 2002). Of the 71 metabolic pathways examined by cDNA microarray, five were found to have consistently significant reductions in gene expression: the mitochondrial malate shuttle system, alanine and aspartate metabolism, ornithine and polyamine metabolism, ubiquitin metabolism as well as genes involved in regulation of the transcarboxylic acid (TCA) cycle. By comparison with in situ hybridisation in the PFC of haloperidol-treated monkeys, this group was able to determine whether or not the changes they saw in human DLPFC tissue were affected by neuroleptic treatment. Expression of MDH1, a gene involved in the mitochondrial malate shuttle system and TCA cycle that was decreased in the DLPFC of schizophrenia patients, was increased in the haloperidol-treated monkeys compared to controls. These findings suggest that MDH1 down-regulation in postmortem schizophrenic brains is not the result of APD treatment but rather that the malate shuttle system or TCA cycle may be a target of the antipsychotic drugs used to treat schizophrenia. A separate array study that profiled expression of single neurons from the dentate gyrus supported altered expression of genes involved in the malate shuttle system, in ubiquitin metabolism and in proteasome function (Altar et al., 2005).

Myelination A separate microarray analysis of DLPFC tissue from 12 schizophrenic patients and 12 matched controls found genes altered in multiple pathways, but particularly significant changes in genes involved in the production and maintenance of myelin, the structural component that ensheaths neurons faciliating fast neurotransmission (Hakak, 2001). This is supported by a microarray study reporting downregulation of myelination-related genes in cortical brain tissue from patients with schizophrenia (Aston et al., 2004). Additionally, analysis of gene expression using differential display PCR with prefrontal cortex from the Stanley brain collection reported the downregulation of oligodendrocyte developmental proteins and most known myelin-associated proteins (Tkachev et al., 2003). It is hypothesised that oligodendrocyte-axon interactions, mediated by changes in myelin regulation, may be involved in the cytoarchitectural changes seen in schizophrenia (Hakak et al., 2001). Furthermore, myelin abnormalities may coexist with altered metabolism of markers of neuronal integrity in the PFC that predominantly affects glutamatergic transmission (Tkachev et al., 2007).

GABA related genes Other major pathways altered in the DLPFC of schizophrenic patients were involved in GABA signalling gene dysregulation, and in particular GAD67 mRNA was up-regulated (Hakak et al., 2001). This supported previous findings of altered GAD67 mRNA and protein expression in this region, although typically GAD67 is downregulated in patients with schizophrenia (Sherman et al., 1991; Akbarian et al., 1995b). GAD67 is one isoform of the GAD1 gene that synthesizes GABA and has been implicated in schizophrenia susceptibility (see Section 1.3.4.4). It is believed that decreased GAD67 mRNA may be the result of reduced transcription in a subset of GABA neurons in the PFC, rather than a decreased number of GABA neurons (Volk et al., 2000; Hashimoto et al., 2003b). Antipsychotic drug use as a possible confounding factor in these studies has been

57 explored using gene expression analysis of haloperidol-treated monkeys, although this does not account for the varying drug types, dose regimens and lifetime neuroleptic use in schizophrenic patients.

Additionally, altered levels of GABA receptor subunit mRNA have been reported in the brains of patients with schizophrenia. There are two main types of GABA receptors in the brain: GABAA receptors mediate fast inhibition transmission, whereas GABAB receptors are modulatory in function (Blum & Mann, 2002).

Dysregulation of GABAA receptor subunit mRNAs has been seen in the PFC of patients with schizophrenia in some studies (Huntsman et al., 1998; Ohnuma et al., 1999) but not others (Akbarian et al., 1995a). It is hypothesised that GAD67 deficit in a subclass of neurons in the DLPFC of patients with schizophrenia, in combination with altered GABA receptor subunit expression, may relate to executive function and working memory deficits characteristic of this disorder (Lewis et al., 2005).

Interestingly, one of the most replicated findings in this field is decreased mRNA and protein expression of the neuron developmental marker, reelin, in the cortex, hippocampus, caudate nucleus and cerebellum of patients with schizophrenia (Impagnatiello et al., 1998; Fatemi et al., 2005). Reelin facilitates normal neuronal patterning in the developing brain by inhibiting aberrant neuronal migration, so its decreased expression has provided molecular support for the neurodevelopmental hypothesis of schizophrenia (see Section 1.1.3.1) (Sawa & Snyder, 2002).

Meta-analysis of multiple studies of postmortem brains from the Stanley Neuropathological Consortium confirms deficits of reelin and GAD67 in the hippocampal formation and cortex of patients with schizophrenia with expression in other brain regions not explored (Torrey et al., 2000). In the cortex, reelin mRNA is decreased in GABAergic neurons (Impagnatiello et al., 1998) and this deficit coexists with decreased GAD67 mRNA in the cortex of schizophrenic patients and also bipolar patients with psychosis, but not other mental illnesses

58 (Guidotti et al., 2000). This supports a role for deficits in GABAergic signalling in the manifestation of psychotic disturbance. Furthermore, reelin down-regulation within neurons in the hippocampus of patients with schizophrenia has recently been correlated with decreased synaptic protein markers (Eastwood & Harrison, 2006) indicating that dysregulation of reelin may affect various pathogenic pathways in schizophrenia. There is recent evidence that the mechanism for reelin down-regulation may be increased reelin promoter methylation, seen in the cortex of patients with schizophrenia in two independent studies (Abdolmaleky et al., 2005; Grayson et al., 2005). This hypermethylation and gene down-regulation is associated with increased activity of DNA methyltransferase I (Dnmt1) (Noh et al., 2005) suggesting that epigenetic mechanisms may be involved in altered expression of multiple genes in schizophrenia.

1.4.2.2 Analyses of molecular expression in schizophrenia peripheral tissue Another approach in schizophrenia research is the use of peripheral tissue, namely blood, in gene expression analysis. This approach was simultaneously taken by two separate groups, one profiling protein expression (Emamian et al., 2004) and one profiling gene expression (Vawter et al., 2004) in lymphocytes of schizophrenic patients compared to unaffected controls. It is suggested that if changes in gene expression between controls and patients with schizophrenia can be identified in peripheral tissues this would provide a valuable and more readily available diagnostic technique. Lymphocytes also have the advantage of being a single cell type and this may provide more useful data compared to heterogenous brain tissue.

Vawter and colleagues used a microarray chip of 1280 genes highly expressed in the brain (a neuroarray) to analyse lymphocyte samples from five control subjects and five patients with schizophrenia from a large, multiplex family (Vawter et al., 2004). Nine genes were differentially expressed and they analysed three of these using QPCR. Neuropeptide Y receptor type 1 (NPY1R) and human guanine nucleotide-binding regulatory protein G0 alpha (GNAO1) were verified as

59 significantly decreased in schizophrenic lymphocytes. A trend for increased expression in schizophrenic patients was seen for the cytosolic malate dehydrogenase gene (MDH1), the same gene decreased in schizophrenia DLPFC tissue but increased in DLPFC of haloperidol-treated monkeys (Middleton et al., 2002), indicating that peripheral tissue analysis in schizophrenia may reveal APD- specific as well as pathogenic gene dysregulation.

AKT1 Emamian and colleagues used a proteomics approach to screen for changes in levels of protein kinases in lymphocytes from schizophrenic patients compared to controls, with only v-akt murine thymoma viral oncogene homolog 1 (AKT1) showing a significant difference in expression (Emamian et al., 2004). Further analysis showed that this isoform of the protein threonine kinase, AKT, was decreased in the hippocampus and frontal cortex of patients with schizophrenia compared to matched controls, whereas the expression level of AKT2 and AKT3 were unchanged. Subsequent genetic association studies implicate AKT1 as a susceptibility gene for schizophrenia (see Section 1.3.4.7)

A functional consequence of decreased AKT1 expression in schizophrenia is the decreased phosphorylation of glycogen synthetase kinase (GSK3), a well- characterised substrate of AKT1, found in both lymphocytes and frontal cortex from schizophrenia patients. Also, decreased phosphorylation of AKT1 and GSK3 was seen in the cerebral cortex and hippocampus of haloperidol-treated mice, suggesting the Akt-GSK3 signalling pathway is a target of current schizophrenia treatments (Emamian et al., 2004).

A convergent genetic and functional genomics approach was taken in another study of peripheral tissue in families with known genetic linkage to loci of schizophrenia susceptibility. This study was intended to highlight candidate genes within the loci as well as interacting genes from other chromosomal regions (Middleton et al., 2005). Interestingly, Nrg1 mRNA was increased in leukocytes of patients, supporting the use of peripheral tissue in detecting genes associated with

60 schizophrenia. Another study used gene expression in blood leukocytes to show that peripheral tissue could be used in biological classification of schizophrenia and also confirmed alteration of genes implicated in myelination and neurotransmitter receptor regulation, supporting consistencies between brain and peripheral tissue gene expression patterns (Bowden et al., 2006).

It is important to note that changes in gene expression detected in tissue from patients with schizophrenia can be the result of either DNA mutations, adaptation to the disease process, or treatment (Pongrac et al., 2002). These three causes are separable by, respectively, genetic association studies, biological function analysis and expression analysis of animals treated with APDs.

1.4.3 Gene expression profiling in animal models 1.4.3.1 Animal models of schizophrenia Rationale The development of animal models for mental illness is difficult due to the complexity of the human brain. This is particularly so for psychiatric illness, which often manifests subjectively. In schizophrenia this is confounded by the lack of understanding and indeed consensus on the major neuropathological abnormalities associated with the disorder (Lipska & Weinberger, 2000). However, these obstacles must be overcome due to the importance of such models in testing new hypotheses of schizophrenia aetiology, in uncovering mechanisms of symptomatology and in suggesting and verifying new treatment strategies (Lipska & Weinberger, 2000). Animal models that have been developed in schizophrenia research to date have been focused on three main areas: neurodevelopment, neurochemistry and genetics. Three main criteria are considered important in developing animal models for schizophrenia: construct validity, which requires a sound theoretical rationale for development; predictive validity, which refers to how well the animals performs in tests considered to mimic that seen in schizophrenia (see below); and face validity, which describes how accurately an animal reproduces the various symptoms associated with schizophrenia, this being the hardest criteria to fulfil (Marcotte et al., 2001).

Behavioural paradigms There are a number of behaviours that experimental animal models exhibit that are considered to mimic some of the symptoms of schizophrenia: hyperactivity, reduced social interaction, reduced cognitive ability (particularly in memory tasks), anxiety and prepulse inhibition (PPI) deficits.

PPI is a measure of sensorimotor gating, a neurophysiological measure that is deficit in schizophrenia (as described in Section 1.1.1.4). PPI is measurable in rodents and is one of the main indicators of animal models for schizophrenia. Behavioural studies following pharmacological, neurodevelopmental or genetic manipulation of animals have shown deficits in PPI or in habituation of the startle response or hyperactivity in some animals and these behavioural phenotypes are believed to model aspects of schizophrenia in humans (Swerdlow & Geyer, 1998).

1.4.3.2 Animal models of antipsychotic drug treatment Given the inherent difficulties in modelling a psychiatric disorder in animals, another way of exploring dysfunction in schizophrenic brains is through analysis of the mechanism of antipsychotic drugs (APDs), the mainstay treatment for this disorder. Data from animal APD gene expression studies may also improve efficacy and specificity of these compounds.

There have been a number of previous gene expression studies of rodents treated with antipsychotic drugs at equivalent doses to the human therapeutic ranges. This type of analysis is presumed to reveal alterations in genes involved in the pharmacological function of APDs in treating psychosis and compensating for genes or pathways involved in the biochemical pathology of schizophrenia (Thomas, 2006). Gene expression alterations occur downstream of dopamine D2 receptor binding through the activation of immediate early genes, like c-fos, with the regional and cellular pattern of activation differing between different APDs (Miller, 1990; Rogue & Vincendon, 1992; MacGibbon et al., 1994).

62 Comparative analyses between previous animal studies is confounded by a number of variables, including drugs and concentrations used, administration method and duration, transcript profiling experimental method, tissue type and validation method (Table 1.8). Prior to the commencement of this thesis study, three animal APD treatment and transcript profile studies had been published. All three studies indicated effects of APD treatment, administered acutely or chronically, on genes and pathways involved in synaptic function (Chong et al., 2002; Kontkanen et al., 2002; MacDonald et al., 2005). That these results are convergent to the finding of synaptic dysfunction in transcript profiling of patients with schizophrenia (reviewed in Section 1.4.2.1) provides validity for rodent APD treatment studies, while other differentially regulated genes arising from these paradigms may function in novel pathways or be part of the adverse effects of individual drugs (Thomas, 2006). During this thesis period a further nine studies have been published in this area. All published antipsychotic animal treatment studies and their findings for gene regulation are discussed further in chapter 3.1.

63 Table 1.8 Transcript proﬁling studies of brain tissue from rodents treated with antipsychotic drugs. Drug(concentration); duration, Validation Housekeeping Other Validated Publication Animal tissue administration Transcript proﬁling method method gene techniques genes Published prior to thesis commencement Chong et al., 2002 Rat striatum Haloperidol (2 mg/kg); 28d, i.p injection cDNA array ~1,176 genes None N/A Western None blot, 1 gene

Kontkanen et al., Rat cortex Haloperidol (1mg/kg), clozapine (25mg/kg); cDNA array ~1,176 genes In situ N/A None 10 of 35 2002 17d, i.p. injection hybridisation (29%)

Thomas et al., 2003 Mouse striatum & Clozapine (7.5 mg/kg), haloperidol (4mg/kg); PCR-based ~17,000 transcripts QPCR and HPRT ISH, 2 10 of 12 frontal cortex 24h/5d/12d/14d, i.p. injection Northern blot genes (83%)

Published during the course of this thesis… Chong et al., 2004 Rat striatum Haloperidol (2mg/kg), clozapine (30 mg/kg); Differential display ~2,400 Northern beta-actin IHC, 1 gene 1 of 1 28d, i.p. injection genes blot, 1 gene Sondhi et al., 2005 Rat striatum Clozapine (20mg/kg); 28d, oral gavage cDNA array ~1,176 genes QPCR cyclophilin IHC, 1 gene 1 of 1

Feher et al., 2005 Rat cortex Haloperidol (0.05mg/ kg); 96h/ 28d, i.p. cDNA array ~ 8,000 genes QPCR beta-actin None Qualitatively injection assessed Chen et al., 2005 Rat frontal cortex Risperidone (1 mg/kg); 7d/14d/21d/28d, i.p. cDNA array ~ 1,536 genes QPCR GAPDH, None 8 of 17 injection cyclophilin A, (47%) 18S rRNA MacDonald et al., Rat frontal cortex Clozapine (8/20 mg/kg), haloperidol (0.2/0.5 Oligonucleotide array~ 8,800 QPCR beta-actin None 4 of 5 (80%) 2005 mg/kg); 26d, i.p. injection genes Iwata et al., 2005 Mice frontal cortex Haloperidol (2 mg/kg); 14d, i.p. injection PCR-based, 500 transcripts None N/A None None

Fatemi et al., 2006 Rat frontal cortex Olanzapine (2 mg/kg); 21d, i.p. injection Oligonucleotide array ~ QPCR beta-2- WB, 4 4 of 6 (66%) 23,000 transcripts microglobulin genes

Mehler-Wex et al., Mouse frontal cortex Haloperidol (1 mg/kg), clozapine (10 mg/kg); Oligonucleotide array ~ QPCR 18S rRNA None 6 of 7 (86%) 2006 31d oral, gavage 12,000 transcripts Le-Niculescu et al., Mouse PFC, NAC, Clozapine (2.5 mg/kg); 24h, i.p. injection Oligonucleotide array ~ None N/A None None 2007 VT, amygdala, CP, 39,000 transcripts HIP d: day, h:hour, i.p: interperitoneal; s.c subcutaneous, PFC: prefrontal cortex, CP: caudate putamen, NAC: nucleus accumbens, VT: ventral tegmentum, HIP: hippocampus, ISH: in situ hybridisation, IHC: immunohistochemistry, WB: western blot. 1.5 Specific aims of this thesis Pharmacological treatment of any disorder requires a validated drug target and a clinical target of desired outcome (Hyman & Fenton, 2003). In schizophrenia, the latter is hard to define due to the difficulties in classifying a symptomatologically diverse disorder. As there are no unique proteins that are altered in the brains of most patients with schizophrenia (Harrison & Weinberger, 2005) a validated drug target has not been found. Instead, antipsychotic drugs (APDs) used to treat the disorder were discovered through serendipity and while they treat the psychotic symptoms of the illness in most patients, as well as some negative and cognitive symptoms, some individuals remain treatment refractory and all APDs have adverse effects that lead to non-compliance and relapse (Lieberman et al., 2005).

In order to better treat schizophrenia, scientists are attempting to define the disorder through genetic aetiology, with a handful of associations in the past few years and rapid advances in sequencing technology; through neurochemistry, with multiple neurotransmitters now associated; through technological advances in neuroimaging; and through molecular expression analysis to understand the pathological abnormality and provide a drug target (Hyman, 2000). In support of the latter aim, this study seeks to carry forward the work of transcript profiling in animal models of antipsychotic drug action in order to better characterise the biochemical pathways that lead to the efficacy of APDs, which remain the only known treatment for schizophrenia. Further to previous studies, we aim to understand the regulation of molecular expression by multiple APDs in an attempt to converge upon drug targets and to perhaps understand some of the underlying pathology in schizophrenia that is treated by APDs. In order to achieve this, there are two main aims of this thesis.

Firstly, we aimed to use relatively new microarray technology to profile gene expression changes in mouse brains following treatment with one of three APDs. Our goal was to profile gene expression changes occurring at a chronic time- point (28 days), the traditional study length in animal pharmacological profiling, and also at a novel intermediate time-point (7 days) given the recent clinical

65 evidence suggesting maximal effect of APDs at this time. We specifically aimed to look at genes that were regulated by multiple APDs as these may reveal treatment-specific regulation rather than changes pertaining to adverse effect of each compound.

Secondly, we aimed to regionally define verified changes from Aim 1 while exploring the role of voltage-gated potassium channel (Kv) gene regulation in the mechanism of APD action, via studies of the regulation of two genes – voltage- gated potassium channel 1-subunit (Kcna1) encoding Kv1.1 and Kv channel interacting protein 3 (KChip3). Specifically, we aimed to look at cellular localisation and regional expression of these genes and their protein products and the quantification of regional changes occurring in haloperidol-treated mice in regions of interest in schizophrenia research.

Chapter 2

MATERIALS & METHODS

67 2.1 MATERIALS

2.1.1 Animals For all animal experiments described within, adult (8-week old) male C57Bl/6 mice were purchased from the Animal Resource Centre in Perth, Australia. Experiments were conducted in animal housing facilities at the Garvan Institute of Medical Research (AEEC #03/09) and at the Prince of Wales Medical Research Institute (ACEC# 07/22A). These studies had appropriate ethics committee approvals.

2.1.2 Drugs Clozapine and haloperidol were purchased from Sigma Aldrich (MI, USA). Olanzapine was purchased in the form of Zyprexa® powder for injection from Eli Lily (Sydney, Australia). 0.9% sodium chloride for injection (Baxter Healthcare, Illinois, USA) was used to treat control animals. Intraperitoneal (i.p.) injections were made using 1ml sterile latex-free syringes with 25G (0.5x25 mm) hypodermic needles (Becton Dickinson Medical, Singapore).

2.1.3 Common chemicals and reagents

Absolute ethanol ((CH3)2OH), chloroform (CHCl3) methanol (CH3OH) and isopropanol ((CH3)2CHOH) were purchased from Ajax Finechem (Taren Point,

NSW, Australia). Acetic acid (CH2COOH), acetone ((CH3)2CO), agar, ammonium acetate (NH4CH2COOH), ammonium persulfate (APS), ampicillin, bacto-tryptone, bacto-yeast, bovine serum albumin (BSA), 3,3,5,5- tetrabromophenolsulphonephthlein (bromophenol blue), chromium (III) potassium sulfate dodecahydrate (KCr(SO4)2.12H2O), DAB chromogen stain, dextran sulfate sodium salt polymer, DL-dithiothreitol (DTT), diethyl

® pyrocarbonate (DEPC), ethylene glycol (C2H6O2), Ficoll -400, formaldehyde

(H2CO), gelatin from porcine skin, glycerol, hydrogen chloride (HCl), magnesium acetate (C4H6MgO4), magnesium chloride (MgCl2), magnesium sulphate (MgSO4), methylanolamine hydrochloride (C6H15NO3), polyvinylpyrrolidone (PVP), potassium acetate (CH3COOK), salmon sperm

68 DNA, sodium dodecyl sulfate (SDS), sodium hydroxide (NaOH), sodium phosphate monobasic anhydrous (NaH2PO4), sodium thiosulfate pentahydrate

® (Na2S2O3·5H2O), sucrose, total yeast RNA, Triton X-100, Trizma base powder,

Tween 20, xylenes (C8H10) and yeast tRNA were purchased from Sigma- Aldrich (St Louis, MI, USA). Phenol:chloroform:isoamyl alcohol (25:24:1), DEPC-treated water, 0.5M ethylene diamine tetraacetic acid (EDTA) pH 8, 5M sodium chloride (NaCl), RNase Zap® were purchased from Applied Biosystems, formerly Ambion Inc. (Austin, TX, USA). DPX mountant, 30% hydrogen peroxide (H2O2) and sodium azide (NaN3) were purchased from BDH Laboratory Supplies (Poole, UK). TRIzol® Reagent, UltraPure Agarose, Isopropyl--D-thiogalactopylanoside (IPTG), 5-bromo-4-chloro-3-indoyl-B-D- galactopyranoside (X-gal) were purchased from Invitrogen (Carlsbad, CA,

USA). Ethidium bromide, phenol (C6H5OH) and N’-tetramethylethylenediamine

(TEMED; C6H16N2) were purchased from Amresco®, Inc. (Solon, OH, USA). - mercaptoethanol was purchased from Promega (Fitchburg, WI, USA). Sterile water and 0.9% sodium chloride solution were obtained from Baxter Health Care Pty. Ltd. (Old Toongabie, NSW, Australia).

2.1.4 Solutions and buffers Agarose gel loading buffer 0.25% bromophenol blue, 0.25% xylene cyanol and 0.3% glycerol in deionised water Cryoprotectant 30% (w/v) glycerol, 30% (w/v) ethylene glycol, 0.2X PBS, 0.1% (v/v) sodium azide in deionised water DEPC-treated water Diethylpyrocarbonate (DEPC) was added to deionised water to a final concentration of 0.1%, shaken vigorously and allowed to stand overnight then autoclaved to inactivate DEPC. Diluent for immunnohistochemical washes 0.5% BSA, 0.3% Triton-X in 1X PBS

69 Fluidics antibody solution 1X MES stain buffer, 2 mg/mL BSA, 0.1 mg/mL Goat IgG antibody, 3 μg/mL goat anti-streptavidin biotinylated IgG in deionised water. Fluidics SAPE stain solution 1X MES stain buffer, 2 mg/mL BSA, 10 μg/mL streptavidin phycoerythrin (SAPE) in deionised water. Fragmentation buffer (5X) 200 μM Tris acetate (pH 8.1), 150 μM magnesium acetate, 500 μM potassium acetate in DEPC-treated water. Solution was filtered through a 0.2 μM filter, aliquoted and stored at RT. Gelatin subbing solution (5% (w/v)) 5g gelatin was dissolved by heating in 250 ml deionised water, then 750 ml cold deionised water was added and 0.5g chromium (III) potassium sulfate dodecahydrate was stirred in. Solution was stored at 4°C maximum overnight. Before use bubbles were removed using a sterile transfer pipette. Hybridisation buffer (2X) for microarrays 1X MES buffer, 2 M sodium chloride, 0.04 M EDTA, 0.02 % Tween-20 in DEPC-treated water, shielded from the light and stored at 4°C. Laemmli loading buffer (3X) 6% SDS, 30% glycerol, 150 μM Tris-HCl (pH6.8) and 0.006% bromophenol blue in deionised water stored at 4°C. MES buffer (12X) for use in microarray analysis 6.5% (w/v) MES hydrate and 19.3% (w/v) MES sodium salt were dissolved in DEPC-treated water, pH 6.6, 0.2 μm filtered, shielded from the light and stored at 4°C. MOPS buffer (10X ) was purchased from Sigma-Aldrich (MI,USA) Paraformaldehyde fixative 4% (w/v) paraformaldehyde was dissolved in PBS by heating with constant stirring until dissolved and then brought back to RT before use Phosphate-buffered saline (PBS)

300mM NaCl, 20 mM Na2HPO4 and 3mM Na2HPO4.H20 in deionised water, pH 7.4

70 RNA sample buffer 50% v/v deionised formamide, 16% v/v formaldehyde, 5% v/v glycerol with 0.01% w/v bromophenol blue,1X MOPS buffer. SDS reducing loading buffer 0.0625M Tris-HCl (pH6.8), 10% glycerol, 2% SDS, 0.5% 2-mercaptoethanol, 1% (w/v) bromophenol blue in deionised water SSC buffer (20X) 3M sodium chloride, 0.3M sodium citrate in deionised water. (Purchased from Ambion) Stain buffer (2X) for microarrays 2X MES stock buffer, 1.85 M sodium chloride, 0.01% Tween-20 in DEPC- treated water, filtered through a 0.2 μm filter, shielded from light and stored at 4°C Thionine 0.02% thionine added to 0.4% (w/v) sodium acetate in deionised water, made up to pH 4.5 using glacial acetic acid. The solution was stirred well and filtered, then stored in the dark. Transfer buffer (10X) 10.5% glycine, 2.25% Tris(hydroxymethyl)aminomethane in deionised water. Tris acetate EDTA (TAE) buffer 40mM Tris-acetate, 5.7% (v/v) glacial acetic acid and 0.01M EDTA in deionised water. Tris borate EDTA (TBE) buffer (10X) 0.89M Tris, 0.89M borate and 0.02M EDTA in deionised water. Tris-buffered saline (TBS) 8.7% sodium chloride, 0.1M Tris(hydroxymethyl)aminomethane in deionised water, pH 7.4 Tris buffer pH 7.5 was purchased from Applichem Biochem (Darmstadt, Germany) Tris-glycine running buffer (5X) 15g Tris base, 72g glycine and 50mL 10% SDS made up to 1L with deionised water, pH 8.9

71 Tris (1M) pH8.0 was purchased from Ambion Wash buffer A: Non-stringent wash buffer 3X SSPE, 0.01% Tween-20 in DEPC-treated water, filtered through a 0.2 μm filter and stored at RT. Wash buffer B: Stringent wash buffer 1X MES stock buffer, 0.026 M sodium chloride, 0.01% Tween-20 in DEPC- treated water, filtered through a 0.2 μm filter, shielded from light and stored at 4°C.

2.1.5 Enzymes and enzyme buffers Platinum® Taq DNA polymerase (EC 2.7.7.7) was used to amplify DNA by PCR and Platinum® SYBR® Green qPCR SuperMix-UDG was used as DNA polymerase in all real-time quantitative RT-PCR experiments. Both enzymes were purchased from Invitrogen. For RNA work, DNA was eliminated using RQ1 RNase-free DNase I endonuclease (EC 3.1.21.1) and associated 10X reaction buffer from Promega corporation (Fitchburg, WI, USA). To remove unbound riboprobe during in situ hybridisation, sections were treated with RNase A purchased from Boehringer Ingelheim (North Ryde, NSW, Australia; EC 2.11.27.5). SuperScript reverse transcriptase and Superscript III reverse transcriptase (EC 2.7.7.49), used in cDNA synthesis from RNA, and RNase H (EC 3.1.13.2) to degrade RNA after synthesis were purchased from Invitrogen. Restriction endonucleases (EC 2.11.21.4) NdeI and SacII were used to linearise the vector before transcription of insert to be used for riboprobe in in situ hybridisation. These were supplied by New England Biolabs (Beverly, MA, USA) and used in conjunction with their NEBuffer #4. T7 and SP6 RNA polymerases (EC 2.7.7.6) were purchased from Promega Corporation. Complete ™ EDTA- free protease inhibitor cocktail was purchased from Roche Molecular Biochemicals (Basel, Switzerland).

2.1.6 Microarray experiments Affymetrix GeneChip Test3 microarrays were used to test the quality of the pooled cRNA probes before hybridisation to Affymetrix GeneChip Mouse

72 Genome 430 2.0 Array microarrays containing 45,101 transcripts purchased from Affymetrix (Santa Clara, CA, USA). Microarrays were washed using a semi- automated GeneChip Fluidics Station 400 (Affymetrix) and hybridisation was quantified using an Affymetrix GeneChip Scanner 3000.

2.1.7 Molecular biology kits The QIAquick® gel extraction kit (QIAGEN, Hilden, Germany) was used to purify PCR products to be subcloned into vectors. The RNeasy® Mini kit (QIAGEN) was used to purify RNA extracted from whole brain tissue. The RC DC Protein Assay kit (Bio-Rad Laboratories, CA, USA) was used to quantify protein from brain lysates. The Wizard Plus SV Minipreps DNA Purification System (Promega Corporation) was used to prepare recombinant DNA plasmids. BigDye Terminator Kit (Applied Biosystems) was used to sequence DNA. Superscript II Double-Stranded cDNA Synthesis Kit was used to synthesise cDNA from total RNA pooled from mouse brains to be used in microarray analysis and Superscript III First-strand Synthesis System for RT-PCR was used to synthesise cDNA from mouse brain RNA for quantitative real-time RT- PCR analysis. Both cDNA synthesis kits were purchased from Invitrogen (Carlsbad, CA, USA). The GeneChip® IVT Labeling Kit was used to make and label cRNA to be used for hybridisation to microarray chips, was obtained from Affymetrix. ECL Plus detection kit (Amersham Biosciences, NJ, USA) was used to detect horseradish-peroxide conjugated secondary antibodies following Western blotting. An antisense probe to -actin from the Mouse Internal Standards (Applied Biosystems) was used as an internal control during in situ hybrisation experiments. All riboprobes used in these experiments were synthesized using Riboprobe® Combination System – T7/SP6 RNA polymerase (Promega Corporation).

2.1.8 Oligonucleotide primers For subcloning into pGEM-TEasy vector, two oligonucleotide primers were used: - SP6-polymerase extension: TATTTAGGTGACACTATAG - T7-polymerase extension: TAATACGACTCACTATAGGG

Table 2.1 Oligonucleotide primers used to create riboprobes for in situ hybridisation Forward primer sequence Reverse primer sequence Size T Construct an (5'-3') (5'-3') (bp) (°C) AACCATGGAAGCGATGGC AAACTAGTCACGCTCA Kv1.1 241 66 AGGTGTGCATTAA CAGGGAGGGGCTT AACCATGGTTCCTCTGGCT AAACTAGTCTCTTTGTC Kchip3 250 66 TGGCCAAGGAA CCCTTGGAGTTTC T : annealing temperature used. an

Table 2.2 Oligonucleotide primer sequences used to amplify cDNAs from whole mouse brain treated with antipsychotic drugs for one week.

Forward primer sequence Reverse primer sequence Size T Gene an (5'-3') (5'-3') (bp) (°C) AACTTTGGCATTGTGGAAGG TCATCATACTTGGCAGGTTTC GAPDH 272 58 G TC Kcna1 AGTATCCCCGATGCTTTC GGTCACTGTCAGAGGCTAAG 250 58 Numbl GGGAGGGATGTTGACTCTGT TTCCTTTATTGGCTGTCCTTG 165 65 S100a9 TTACTTCCCACAGCCTTTGC TCAGACAAATGGTGGAAGCA 230 62 AK036626 TTGAACCCCTACCCTCCTCT GAGCCAAAATGTCCTGCTTC 208 62 Plp GTGTTCTCCCATGGAATGCT GTTTAAGGACGGCGAAGTTG 186 64 Rtn4 TTACGTTGGTGCCTTGTTCA ATTCTGCTTTGCGCTTCAAT 191 60 Dclk1 ACAAGGGGACTCCTCTCCAT CCCGTGACCGTTTCTTTTTA 212 64 Serpini1 GCTGTTCCTCTCCAAAGCTG CCTCAAAGTCATGGCCACTT 236 65 Sdfr1 AAAAACTTGCGCCAGAGAAA GGCATGCTTTAGACGGTCAT 160 62 Sypl TTGTGGTTGGTGAGTTCCTC AAGCATTTCCTCCCCAAAGT 206 62 Mal TCCCTGACTTGCTCTTCGTT TGTGGCTGCCCTATTTTACC 156 64 Kns2 CAGCTGGAGGAGGAGAAGA ACTACTGTGCTGCTGCTGGA 174 64 Cspg3 GTAGGGTGTGAACCCTGGT TTGTGCGTGTGTTGGAGAAT 179 60 Nedd4 GGATGATGGATTCGGAAAAA TCGTCAAAGGATTCGTAGGG 205 60 Rapgef4 GGATCCTTCCAGAAACCACA TATTAGCTTGAGCGGCATCC 233 63 Bat2 AGCCCTGGACCTCCTAATTC CAGAAACTGGCCCTTGAGAG 195 64 Kchip3 CTGGAGCATGTGGAGAGGTT CAGGATTGAGGCTTCTCTGG 198 64 -catenin CCAGTTCCCTCTTCAGGACA ATGCTCCATCATAGGGTCCA 176 62 Kcnab1 GACCTCTCTCCAATCGCTGA CCTTTTTGCTGTAGGGCTTG 223 64 T : annealing temperature used. an

74 Table 2.3 Oligonucleotide primer sequences used to amplify cDNAs from whole mouse brain treated with antipsychotic drugs for 28 days. Size T Gene Forward primer sequence (5'-3') Reverse primer sequence (5'-3') an (bp) (°C) TCATCATACTTGGCAGGTTTC GAPDH AACTTTGGCATTGTGGAAGGG 272 58 TC TGGGAATGGGTCAGAAGGACT -actin GGTCATCTTTTCACGGTTGGC 227 58 C TCAAACAGGAAGACAGACGTA Ubc AACTAAGACACCTCCCCCATC 100 56 CC Gabra1 AAAAGCGTGGTTCCAGAAAA CGATTTTGCTGACGCTGTTA 220 63

Nedd4 GGATGATGGATTCGGAAAAA TCGTCAAAGGATTCGTAGGG 205 60

Syt1 CAGACGCAGAAAGGCTTCTT GCTTTGAAGTTCCGTTCGAG 263 60

Cspg3 GTAGGGTGTGAACCCTGGT TTGTGCGTGTGTTGGAGAAT 179 60

Serpini1 GCTGTTCCTCTCCAAAGCTG CCTCAAAGTCATGGCCACTT 236 65

Kns2 CAGCTGGAGGAGGAGAAGAA ACTACTGTGCTGCTGCTGGA 174 64

Kcna1 AGTATCCCCGATGCTTTC GGTCACTGTCAGAGGCTAAG 250 58

Kchip3 CTGGAGCATGTGGAGAGGTT CAGGATTGAGGCTTCTCTGG 198 64 CCTGAAAACTCGTGAAGGGAA GGCAGACCTTTACAGGACAC Gnb1 303 58 TG GA Napb GCTCCTCACATTGTCTTCTGG AACCACCATGTCCGACAAAT 109 63

Anp32a ATGTTGAGGGCTACGTGGAG GGTTCTCGTTTTCGCTTCTG 241 60

Dnmt1 GCAACATCCTGGACAGACAC GGGGCTCAAAGGGTTAAAAA 210 56

Scn2b GTCCACGTTTCCCTGTGAGT CGCATCAGCAAGCTTCAATA 249 58

Sod1 TGTGACTGCTGGAAAGAACG CACCTTTGCCCAAGTCATCT 131 64

Fgfr1 TGCCAAGACGGTGAAGTTCA CAATTCGGTGGTCAGGCTTA 100 63

Cldn5 AGATCCTGGGGGCACTAGAT AGGAAGGCAACCCCTCTAAG 237 58

Grm5 CAAACTTCCCGATTTATTTGG TCAACAGGAAGGTGAGCAGA 227 58

Vegfb GGCTTAGAGCTCAACCCAGA GTGAAGCAGGGCCATAAAAG 104 58

Ddc TTTGTGCTACGCTTTGCTGT ATTTCACGAAGACGGAGTGG 205 57

Grm2 AACCCAGCGTAGACCCTCTT ACACGGGGCATAGCTACAAG 192 57

Mbp TCACACACGAGAACTACCCATT GCTGTCTCTTCCTCCCTTCC 102 57 GAGTAGGGAAGGGAGCCAA Tac2 CGTGACATGCACGACTTCTT 235 57 C Gad1 GAACAACCATGGTGGGCTAC GCCGATGATTCTGGTTCTGT 166 58

T : annealing temperature used. an

75 2.1.9 Antibodies During microarray hybridisation two antibodies were used: - Goat IgG antibody (I28320; Sigma-Aldrich, MI, USA) - Goat anti-streptavidin biotinylated IgG (BA-0500; Vector Laboratories, Burlingame, CA, USA) Five primary antibodies were used for protein expression studies. - Rabbit polyclonal anti-NEDD4 IgG (ab14592: Abcam, Cambridge, UK) was raised against the WW2 domain of rat NEDD4 corresponding to amino acids 395-462 - Rabbit polyclonal anti-CSEN IgG (ab41717: Abcam) was raised against a peptide surrounding amino acid 117 of human CSEN (aka KCHIP3). - Rabbit polyclonal anti-Kv1.1 IgG (sc-25680: Santa Cruz Biotechnology Inc., CA, USA) was raised against amino acids 426-475 in the C-terminus

of human Kv1.1. - Goat polyclonal anti-DREAM IgG (sc-9309: Santa Cruz Biotechnology Inc.) was raised against a peptide mapping near the C-terminus of human DREAM (aka KCHIP3). - Mouse monoclonal anti--actin IgG1 (ab6276: Abcam) was raised against the cytoplasmic N-terminal portion of frog -actin.

For detection of protein bands by Western blotting, two secondary antibodies were required: - HRP-conjugated goat anti-rabbit IgG (ab6721: Abcam) - HRP-conjugated sheep anti-mouse IgG (Silenus Laboratories, VIC, Australia). Before immunohistochemical analysis tissue was blocked in: - Normal goat serum (Vector Laboratories) - Normal rabbit serum (Vector Laboratories) For detection of signal following immunohistochemical analysis two secondary antibodies were required: - Biotinylated anti-rabbit IgG (H+L) (Vector Laboratories) - Biotinylated anti-goat IgG (H+L) (Vector Laboratories)

2.1.10 Bacterial media and competent cells For cloning of recombinant plasmids, One Shot Top10 chemically competent cells were purchased pre-made from Invitrogen (Carlsbad, CA, USA) LB (Luria Broth) 1%(w/v) bacto-tryptone, 0.5%(w/v) bacto-yeast extract. 0.5% (w/v) NaCl made up in deionised water and autoclaved. LB Agar (Luria agar) 1.5% (w/v) agar in LB, autoclaved.

2.1.11 Vectors The plasmid vector pGEM®-T Easy Vector System was obtained from Promega Corporation.

2.1.12 Radiochemicals 35S-UTP, used to label RNA probes prior to in situ hybridisation on mouse brain tissue sections, was purchased from GE Healthcare – Lifesciences Division (Uppsala, Sweden). 14C standards used in in situ hybridisation experiments were purchased from American Radiolabeled Chemicals, Inc. (St Louis, MO, USA).

2.1.13 Histological materials Tissue sectioning was performed on a Zeiss Microm HM560 cryostat machine (Carl Zeiss, CA, USA) using a microtome knife type C (Reichert-Jung, Heidelberg, Germany). One-end frosted microscope slides (Sail Brand, China) were used for tissue sectioning prior to in situ hybridisation. 12-well cell culture plates (Corning Inc., NY, USA) were used to float sections for immunohistochemistry.

77 2.2 ANIMAL HANDLING & TREATMENT

2.2.1 Animal housing conditions Mice were housed in a temperature- (22°C) and humidity- (52%) controlled environment with a 12 h day/night cycle and maintained with standard chow and water ad libitum during one-week initial quarantine and subsequent treatment periods. Animal studies were approved by the Garvan Institute of Medical Research/ St Vincent's Hospital Animal Experimentation Ethics Committee (NSW, Australia, AEEC #03/09) and the Prince of Wales Medical Research Institute (NSW, Australia, ACEC #07/22A). All mice were 8-10 weeks of age at the commencement of treatment and were weighed before each injection.

2.2.2 Animal drug treatment Clozapine and haloperidol were dissolved in glacial acetic acid, diluted with 0.9% saline, adjusted to pH 5.5 with sodium hydroxide (1M) and brought to a final concentration of 1 mg/ml for clozapine and 0.1 mg/ml for haloperidol. Olanzapine was dissolved in 0.9% saline solution to a final concentration of 1mg/ml. During a trial period to determine optimal dosage for the atypical antipsychotic drugs, clozapine (10 mg/kg) and olanzapine (10 mg/kg) were administered via daily intraperitoneal (i.p.) injection for seven days at 11am each day after daily assessment of mouse body weights.

During the intermediate and chronic timepoint treatment studies, mice were weighed daily before treatment. Clozapine (10 mg/kg), olanzapine (10 mg/kg) or haloperidol (1 mg/kg) were administered via daily i.p. injection for seven days for the intermediate study and 28 days for the chronic study. Control mice received i.p. injections once daily of 10 ml/kg sterile 0.9% saline solution for the period of study. Mice were injected daily at 11 am and monitored twice daily for adverse reactions.

78 2.2.3 Animal sacrifice 2.2.3.1 Standard endpoint technique Five hours after the last injection on the 7th or 28th day of treatment, mice were euthanased under 4% halothane anaesthetia to allow cardiac puncture and collection of approximately 1 ml blood from each animal. During the trial period blood samples taken from the treated animals were assayed for serum concentration for clozapine (by Central Sydney Area Health Service, Royal Prince Alfred Hospital, Sydney, Australia) and olanzapine (by SouthPath, Adelaide, Australia).

2.2.3.2 Perfusion For collection of fixed tissue, mice were transcardially perfused with 4% paraformaldehyde, dissolved in PBS. After euthanasia, mice were positioned ventrodorsally and limbs pinned to foam board, to allow access to the peritoneum. A midline incision was made and the skin peeled back. The musculo-peritoneal tissue was grasped below the sternum and a small 2-3 mm incision made to reveal the organs. Surgical scissors were used to separate the ribs from the diagram allowing full access to the heart. The left ventricle was punctured using a 25g needle (3/4”) and the right atrium was incised to release perfusate. 0.9% saline solution was perfused through ventricle for approximately 5 min, until organs became pale and tail twitched. This was followed by a 10 min perfusion of 4% paraformaldehyde fixative solution.

2.3 HISTOLOGICAL METHODS

2.3.1 Mouse brain collection and preparation 2.3.1.1 Whole brain extraction and storage Mice were killed by cervical dislocation and the tissue and bone surrounding the brain were carefully cut and peeled away. The remaining intact brain was lifted from the skull. Olfactory bulbs and cerebellum were detached and remaining

79 brain was transferred to a 2 mL sterile tube, which was immediately snap frozen in liquid nitrogen and stored at -80°C until required.

2.3.1.2 Fixed tissue preparation Following perfusion (section 2.2.3.2) whole brain was removed from the skull and post-fixed in 4% paraformaldehyde for 16-18 h at 4°C. The brain was then transferred to a 50 mL centrifuge tube containing 25 mL of 30% (w/v) sucrose overnight at 4°C. During this incubation, gauze was placed over the floating brain in solution within the tube to encourage the brain to sink through the sucrose gradient in order to replace all remaining water. The brain was then removed from the solution, washed twice in 1X PBS and stored at -80°C.

2.3.1.3 Mouse brain microdissection Anatomically defined brain dissections were performed on six mice treated with saline and six haloperidol-treated animals, following the procedure described by Lazar and Blum (Lazar & Blum, 1992). Mice were killed by cervical dislocation, decapitated and the tissue and bone surrounding the brain were carefully cut and peeled away. The remaining intact brain was lifted from the skull and transferred to cold saline for 1-3 min, then transferred to an ice-cooled glass plate lined with saline-soaked Whatmann paper. The olfactory bulbs were removed (dissection 1, Fig. 2.1) and a coronal cut was made with a single-edged razor blade 1 mm caudal to the frontal pole (dissection 2, Fig. 2.1). This coronal slab through the frontal pole was placed onto the glass plate caudal face up, with the forceps minor of the corpus callosum and the start of the anterior commissure (ventrally) visible (corresponding to bregma 1.98mm, Paxinos & Franklin, 2001; Fig. 2.1b). Vertical cuts were made laterally using the corpus callosum as the ventro-lateral boundary point (dissections 3,4; Fig. 2.1) and one horizontal cut using the base of the corpus callosum/ lateral ventricles as the ventro-medial boundary points (dissection 5, Fig. 2.1). This dissection encompassed the entire infralimbic and portions of anterior cingulate cortex and was termed the “prefrontal cortex”.

Figure 2.1 Mouse brain anatomical microdissection diagram Numbered dissections made to collect prefrontal cortex, striatum, ventral tegmental area, substantia nigra and hippocampus were performed as indicated. Shaded lines denote regions already removed but added for spatial reference. (a) and (c) are transverse images of whole mouse brain (b) corresponds to bregma 1.98 mm, (d) corresponds to bregma 0.38 mm (Paxinos & Franklin, 2001) and (e) is a sagittal image of whole brain and (f) corresponds to bregma -3.28 mm (Paxinos & Franklin, 2001).

After removing the most anterior slice, the remaining brain was inverted with the ventral side facing up and two other coronal cuts were made: one rostral and one caudal to the olfactory tubercules (dissection 6 and 7, Fig. 2.1). This slice was laid out on the cold plate rostral side upwards (corresponding to bregma 1.70 mm, Paxinos & Franklin, 2001; Fig. 2.1d), the tubercules were cut using the dorsal boundary of the lateral olfactory track and the lateral septum was removed by dissecting the tissue medial to the corpus collosum. The caudate putamen was extracted by gently separating the striated tissue remaining between the encapsulating fibres of the corpus callosum and internal capsule (dissections 8 and 9, Fig. 2.1).

The cerebellum was detached (arrow 10, Fig. 2.1) from remaining brain by severing the cerebellar peduncles and the brain stem and thalamus were scooped out from under the remaining telencephalon passing rostral to the superior colliculi and including the mamilliary nuclei. The midbrain was isolated from this tissue by making two cuts at 30° angles to the neuroaxis, one rostral to the superior colliculi (dissection 11, Fig. 2.1) and one caudal to the inferior colliculi (dissection 12, Fig. 2.1). The coronally sliced midbrain was laid caudal side upwards exposing the aqueduct and cerebral peduncles (corresponding to bregma -3.28mm, Paxinos & Franklin, 2001; Fig. 2.1f). Firstly, the ventral tegmental area was separated by triangular cuts with the apex at the cerebral aqueduct and each lateral base defined as the medial edge of the cerebral peduncles (dissections 13 and 14; fig 1d). The two halves of the substantia nigra were subsequently transected by making a horizontal cut dorsal to the cerebellar peduncles while passing ventral to the cerebral aqueduct (dissection 15, Fig. 2.1).

The remaining brain was hemisected and the hippocampus, now medially exposed, was removed in one piece by elevation and transection of its fimbrial border, by lifting it away whole from the surrounding white matter and subsequently detaching it from the cortex.

All sections of interest removed during the microdissections were immediately snap-frozen on aluminium foil on dry ice and stored at -80°C.

2.3.2 Mouse brain sectioning 2.3.2.1 Gelatin coating of microscope slides Approximately 2500 clear glass microscope slides, 1-1.2 mm thick, and frosted at one end (Sail Brand, China) were gelatinised for fresh frozen cutting of mouse brain tissue. Slides were placed in metal racks, soaked in 80% ethanol overnight, then rinsed three times in deionised water. Slides were dipped in gelatin subbing solution (0.5% w/v) and incubated at 50°C overnight. The gelatin subbing

82 procedure was repeated once more and then dried slides were removed from racks and stacked back into their slide boxes.

2.3.2.2 Fresh frozen tissue cutting of mouse brain tissue Brains were removed from the freezer, placed on dry ice and mounted onto cryostat pedestals, with coronal plane horizontal to the pedestal and olfactory region down, using Tissue-Tek optimal cutting temperature (OCT) compound (Sakura FineTek Co. Ltd, Tokyo, Japan). Serial 14 μm-thick coronal sections were cut using the Mouse Brain in Stereotaxic Coordinates atlas (Paxinos & Franklin, 2001) as a guide. Sections were cut through the midbrain/ventral hippocampus (Bregma -3.64mm to -2.70mm), hippocampus (Bregma -2.70mm to -1.82mm), striatum (Bregma +0.50 mm to +1.42 mm) and prefrontal cortex (Bregma +1.42mm to +2.10mm) and mounted onto gelatinized slides, with two serial sections per slide. A slide was also collected at the border of each region and was stained with a Nissl marker to ascertain correct sectioning. Approximately 100 slides were collected per mouse brain. Sections were dried onto slides for 1-2 min at RT and stored at -80°C. This procedure was carried out for twelve haloperidol-treated and twelve saline-treated mouse brains.

2.3.2.3 Fixed tissue cutting of mouse brain tissue Brains were mounted onto cryostat pedestals as described in section 2.3.2.2. Serial 40 μm sections were cut through the entire brain and floated in 12-well Corning plates with 3 mL of cryoprotectant, containing 0.1% sodium azide as an antibacterial agent, in each well. Brain regions of interest, as designated by the Mouse Brain in Stereotaxic Coordinates atlas (Paxinos & Franklin, 2001), were contained in separate plates: midbrain/ventral hippocampus (Bregma -3.64mm to -2.70mm), hippocampus (Bregma -2.70mm to -1.82mm), striatum (Bregma +0.50 mm to +1.42 mm) and prefrontal cortex (Bregma +1.42mm to +2.10mm). Plates were stored at -20°C. This procedure was carried out for six haloperidol- treated and six saline-treated mouse brains, although sections from three brains from each treatment group were pooled, with brains labeled HAL P1-3 and HAL X (pooled), SAL P1-3 and SAL X (pooled).

2.4 BASIC MOLECULAR BIOLOGY METHODS

2.4.1 DNA extraction and precipitation DNA was extracted by addition of equal volume phenol: chloroform: isoamyl alcohol (25:24:1), vortexed and transferred to an Eppendorf Phase Lock Gel (Hamburg, Germany). Tubes were subjected to centrifugation for 2 min at 12,000 g and the aqueous upper phase was isolated.

DNA was precipitated by addition of 0.5 volume of 7.5 M ammonium acetate and 2.5 volumes ice cold 100% ethanol and incubation overnight at -20°C. After centrifugation for 15 min at 12 000 g at 4°C, the pellet was washed in 70% ethanol by repeated inversion and subjected by centrifugation again at 12,000 g for 10 min at 4°C. Ethanol was removed and the pellet was dried in a vacuum for approximately 5 min before being diluted in an appropriate volume of water.

2.4.2 Polymerase chain reaction Polymerase chain reaction (PCR) was used for amplification of specific sequences of genomic DNA or cDNA. Oligonucleotide primers (typically 19-24 nucleotides in length) were annealed to the complementary strands of DNA and extended towards each other using the thermostable, heat activated enzyme Platinum Taq polymerase. By repeated cycles of template denaturation, primer annealing and strand extension, the copies of amplified sequence increased exponentially (Sambrook et al., 1989). PCR was used to amplify products ranging from 100 bp to 300 bp.

PCR amplification was performed on a DNA Engine Tetrad 2 Peltier thermal cycler (Bio-Rad Laboratories) using the following amplification profile: 1 cycle of

5 min at 94°C; 35-40 cycles of 15 sec at 94°C (denaturation), 15 sec at Tan (optimum annealing temperature), 30 sec at 72°C (extension); and then 1 cycle for 2 min at 72°C. The repeated cycles of denaturation, annealing and extension

84 were preceded by a denaturation period to activate the Taq polymerase and followed by a final extension cycle. Approximately 100 ng of template DNA was amplified in 1X PCR buffer with 0.5U Platinum Taq DNA polymerase, 3 mM

MgCl2, 200 nmol each deoxynucleotide triphosphate (dATP, dCTP, dGTP, dTTP) and 200 nM final concentration of each forward and reverse oligonucleotide primer.

2.4.3 Agarose gel electrophoresis Agarose gel electrophoresis was used to visually detect DNA and determine fragment sizes (Sambrook et al., 1989). Agarose gels were prepared by boiling of 1% (w/v) agarose powder in 1 TAE buffer. Ethidium bromide was subsequently added to the molten agarose at a final concentration of 0.5 μg/mL. Gels were cast in horizontal trays and allowed to set at room temperature. DNA samples were mixed with 1/10 volume of agarose gel loading buffer prior to loading into wells. A 100 bp DNA ladder (New England Biolabs, MA, USA) was run on each gel to identify fragment sizes. 1% agarose gels were run at 100V for approximately 40 min in 1XTAE buffer. DNA was visualized using an UV transilluminator (International Biotechnologies Inc., New Haven, CT, USA) and digital images were reproduced onto thermal paper using a Mitsubishi Video Copy Processor (Mitsubishi Electrical Corporation, Tokyo, Japan).

2.4.4 Construction of Recombinant Plasmids Inserts to be subcloned into plasmids were amplified by PCR and run on an agarose gel, as described in section 2.4.3. A band corresponding to the size of the insert of interest was excised from the gel, purified using the QIAquick® gel extraction kit (QIAGEN) and diluted in DEPC-treated water to a final concentration of ~5 ng/μL. Inserts were ligated into the plasmid pGEM®-T Easy Vector (Fig. 2.2) using 3’A overhangs created by the Taq polymerase in a 1:3 insert:vector molar ratio in ligation buffer. Ligation buffer contained 10X ligation buffer (Promega) with 300 mM Tris-HCl pH 7.8, 100 mM MgCl2, 100 mM DTT, 10 mM ATP, 3U of T4 DNA ligase. Similarly, control DNA provided with the pGEM®-T Easy Vector System (Promega) was ligated into vector to provide a

85 positive control as well as a ligation set up with no insert DNA, serving as a negative control. Ligations were incubated overnight at 4°C.

Apa I Xmn I Sca I Aat II Sph I BstZ I Nco I

AmpR Nae I Not I Sac II EcoR I 3015 bp MCS Spe I EcoR I Not I lacZ BstZ I Pst I Sal I Nde I Sac I BstX I Nsi I

SP6

Figure 2.2 pGEM®-T Easy Vector (Promega). This cloning vector contains an ampicillin resistance gene and a lacZ gene encoding the -galactosidase enzyme, which is induced by IPTG to catalyse the cleavage and oxidation of X-gal to produce an insoluble blue product. There are various restriction enzyme sites around the vector as well as collected at the multiple cloning site (MCS) where the insert is subcloned into the vector, disrupting lacZ and leading to white bacterial colonies that can be selected for insert sequencing.

One Shot Top10 chemically competent cells (Invitrogen) were rapidly thawed and 50 μL aliquots were added to the 10μL ligation product. Tubes were kept on ice for 20 min and then heat-shocked for 25 sec at 42°C to allow uptake of plasmid DNA, followed by 2 min on ice. Using aseptic technique, 900 μL Luria broth (LB) was added to cells and 50 μL, 100 μL and 200 μL of this mixture were spread onto LB agar plates containing 50 μg/mL ampicillin, 40 μg/mL X-gal and 100 mM IPTG. For the positive and negative control ligations, 100 μL of

86 cells were plated. Cells were incubated overnight at 37°C and transformations screened by presence of white colonies on plate.

For each transformation, 12 colonies were streaked out onto separate LB agar plates using a sterile pipette tip. The plates were incubated overnight at 37°C to amplify clones. The used tip was suspended in 50 μL deionised water. 5 μL of the colony resuspension was used as a template for PCR with SP6 and T7 primers (section 2.1.8) and run on an agarose gel to ensure insertion of products.

A transformed amplified clone from PCR-positive colonies was picked from each agar plate and inoculated in 2 mL LB containing 100 μg/mL ampicillin with aeration and shaking overnight at 37°C. Small-scale preparation of plasmid DNA was then performed using the Wizard Plus SV Minipreps DNA Purification System. Bacterial cultures were pelleted by centrifugation at 13,000 g for 3 min and resuspended in Cell Resuspension Solution. The cells were then lysed in Cell Lysis Solution and endonucleases and other proteins released during this process were inactivated by the addition of Alkaline Protease Solution. High molecular weight DNA and cellular debris were precipitated by the addition of Neutralisation Solution and bacterial lysate was collected following centrifugation at 13 000 g for 10 min. Plasmid DNA was purified from bacterial lysate using microcentrifugation to elute clear lysate through the Wizard Plus SV Minipreps Spin Column. After two washes with Wash Solution diluted with 95% ethanol, the plasmid DNA was eluted in 50 μL of nuclease-free water by centrifugation at 13 000 g for 1 min.

To ensure presence of insert, 5 μL of plasmid DNA product was cut with SpeI and NcoI restriction enzymes in NEB buffer 2 at 37°C for 3 hours and digest was run on gel. To determine direction of insert, 1 μL miniprep plasmid DNA was added to BigDye Termination mix in sequencing buffer, ethanol precipitated, dehydrated, shielded from light and sequenced by Angela Collins at The Ramaciotti Centre at the University of New South Wales using an Applied Biosystems 3730 DNA Analyser. Remaining plasmid DNA was stored at -20°C.

87 2.5 GENE EXPRESSION ANALYSIS

2.5.1 RNA extraction and analysis 2.5.1.1 RNA extraction from whole brain tissue Total RNA was extracted from mouse whole brain tissue in TRIzol Reagent (Invitrogen, Melbourne, Australia). Thawed whole brains were homogenized in 3 mL TRIzol Reagent solution (1 ml/100 mg tissue) for 5 min using a polypropylene hand homogeniser (Sigma-Aldrich) and kept at RT for 5 min. Tissue was delipidated by addition of 600 μL chloroform, inversion 50 times and then incubation at RT for 3 min. Samples were then subjected to centrifugation at 12,000 g for 15 min at 4°C. The aqueous phase was separated and 1.5 mL isopropanol was added, tubes were inverted 50 times again and left at RT for 10 min to precipitate RNA. (The phenol layer was stored at -20°C for protein extraction, section 2.6.1.1). RNA was pelleted by centrifugation at 12,000 g for 15 min at 4°C. The pellet was washed by addition of 75% ethanol and centrifugation at 7,500 g for 5 min at 4°C. Ethanol was aspirated off and the pellet was allowed to dry at RT for 2-3 h. RNA was dissolved in 250 μL of RNase-free water, aliquoted and stored at -70°C.

2.5.1.2 Purification of total RNA Total RNA from whole mouse brains was purified using spin columns from the RNeasy® Mini kit (QIAGEN, Hilden, Germany). This procedure enriches the sample for mRNA by shearing contaminating genomic DNA and removing low molecular weight RNA (15-20% total RNA including 5S, 5.8S rRNA and tRNAs). 100μg of total RNA was adjusted to 100 μL using RNase-free water. 350 μL lysis buffer RLT was added, samples were vortexed and 250 μL 100% ethanol was mixed into solution by pipetting up and down. Samples were applied to spin columns and subjected to centrifugation at 12,000 g for 30 sec at RT. Columns were transferred to a new collection tube, 500 μL buffer RPE (diluted in 95% ethanol) was applied to the column membrane and columns were subjected to centrifugation at 12,000 g for 30 sec at RT. This final buffer addition was repeated and columns were subjected to centrifugation at 12,000 g for 2 min at

88 RT. Columns were placed in a new tube and subjected to centrifugation at 12,000 g for 1 min at RT to avoid transfer of buffer into final product. Columns were then transferred to a centrifuge capped tube and 50 μL RNase-free water was used to elute RNA with centrifugation at 12,000 g for 1 min at RT. Elution was repeated in a further 50 μL RNase-free water. Purified RNA was quantified, integrity was analysed and remaining RNA was stored at -70°C.

2.5.1.3 Quantification and assessing integrity of RNA The concentration of total RNA samples was determined by measuring the absorbance of 1/50 and 1/100 dilutions of purified total RNA at 260 nm using a GeneQuant UV spectrophotometer (Pharmacia Biotech, Sydney, NSW, Australia). RNA quality was checked by visualization of pre- and post-purification RNA samples, with 5 μg RNA made up to 10 μL in DEPC-treated water and diluted in 20 μL RNA sample buffer with ethidium bromide added for nucleic acid detection. Samples were subjected to electrophoresis on a 0.8% formaldehyde-denaturing agarose gel (FAGE) in MOPS buffer at 100V for 10 min then 3 h at 50V. RNA was visualized using UV as in section 2.4.3.

2.5.2 Microarray analysis Transcript profiling was carried out in accordance with the Affymetrix GeneChip Expression Analysis Technical manual in a procedure detailed below and represented in Figure 2.3.

2.5.2.1 Target preparation For both the 7-day and 28-day microarray studies, high quality purified total RNA samples were prepared from 64 mice, 16 from each treatment group (saline, clozapine, haloperidol or olanzapine) using the technique described in section 2.5.1. Within each treatment group, eight individual mouse total RNA samples were randomly selected and pooled together to form two separate pools.

For both study groups, 25 μg pooled purified total RNA was used as a template to generate high-fidelity cDNA using Superscript II reverse Transcriptase

89 (Invitrogen), which was modified at the 3’ end to contain an initiation site for T7- RNA polymerase. RNA and oligoDT primer (50 μM) were initially incubated for 10 min at 72°C and then chilled on ice to avoid reannealing. A first-strand buffer mix (containing 10 mM DTT, 125 μM each dNTP) was then added and samples were incubated for 2 min at 45°C. First-strand cDNA was synthesized by addition of 2U SuperScript II reverse transcriptase and incubation at 45°C for 1 hr. Samples were placed on ice to inhibit further synthesis. The complementary strand of cDNA was synthesized by addition of 1X second-strand buffer mix (containing 5μM each dNTP, 1U E.Coli DNA ligase, 1U E.Coli DNA-polymerase, 1U RNase H) and incubation at 16°C for 2 h. 1U of T4 DNA polymerase was subsequently added with incubation for a further 5 min, then tubes were placed on ice and 10 μL of 0.5 M EDTA was added to inhibit further synthesis. Double- stranded cDNA was cleaned up using phenol/chloroform separation and ethanol precipitation as described in section 2.4.1. Purified cDNA was diluted in 12 μL of DEPC-treated water.

Biotinylated cRNA was transcribed in vitro from the purified double stranded cDNA using the GeneChip® IVT Labeling Kit (Affymetrix, Santa Clara, USA). To achieve this, 50% of the purified cDNA was added, in order, to 1X IVT Labeling buffer, 1X IVT Labeling NTP mix and 1X IVT Labeling Enzyme mix to a final volume of 40 μL. Samples were inverted, subjected to centrifugation briefly to collect contents at the bottom of the tube and incubated in an epMastercycler thermal cycler (Eppendorf, Sydney, Australia) for 16 hr at 37°C. Half of the biotinylated cRNA was then purified using the RNeasy® Mini kit (QIAGEN, Hilden, Germany) as described in Section 2.5.1.2, except when eluting RNA from spin columns two washes of 30 μL of RNase-free water were used. Following purification, cRNA samples were fragmented in 1X fragmentation buffer with incubation at 94°C for 35 min and transferal to ice. Purified and unpurified cRNA were run on a 0.8% agarose gel as described in Section 2.4.

90 2.5.2.2 Microarray hybridisation For both treatment studies (7-days and 28-days) microarray cRNA probes were prepared for each separate pooled sample in all four groups (control, clozapine, haloperidol, olanzapine). These cRNA probes were firstly hybridised to Affymetrix GeneChip Test3 microarrays. The quality of each sample was assessed using the 3'/5' ratios for mouse housekeeping genes glyceraldehyde-3- phosphate dehydrogenase (GAPDH) and -actin.

Following quality control assessment, cRNA probes were hybridised to Affymetrix GeneChip Mouse Genome 430 2.0 Array microarrays following the protocol outlined in the Affymetrix GeneChip Expression Analysis Technical manual. For both test and Mouse Genome array experiments, microarrays were equilibrated to room temperature before use. Hybridisation cocktail (1X) was prepared from 20X Eukaryotic hybridisation controls, 2X hybridisation buffer, control oligo B2 (final concentration 0.05 nM), herring sperm DNA (final concentration 100 μg/mL), BSA (final concentration 0.5 mg/mL) and 10% (v/v) DMSO made up in nuclease-free water. For the test arrays, 5 μg of fragmented cRNA was resuspended in 1X hybridisation cocktail. For the Mouse Genome arrays, 15 μg of fragmented cRNA was resuspended in 1X hybridisation cocktail. Samples were heated for 99°C for 5 min, 45°C for 5 min followed by centrifugation at 13 000 g for 5 min to pellet insoluble material. Meanwhile, 1X hybridisation buffer was loaded into the microarrays, which were slowly rotated at 45°C for 10 min in the Affymetrix hybridisation oven to moisten the probe surface. Buffer solution was then removed and replaced with 80 μL hybridisation cocktail for the Test arrays and 200 μL hybridistation cocktail for the Mouse Genome arrays. Microarrays were hybridized with rotation in the Affymetrix hybridisation oven at 45°C for 16 h at 60 rpm.

1. Total RNA preparation and purification

2. Oligo dT primer hybridization and first strand cDNA synthesis

3. Second strand cDNA synthesis and clean up

4. Biotin labeling of uridine trinucleotide phosphate (U) and amplification of antisense cRNA

5. Clean up and fragmentation of cRNA

6. Hybridisation to microarray chip

7. Washing

8. Scanning of microarray chip

Figure 2.3 Transcript profiling. Outline of procedure for mouse brain RNA target labelling and hybridisation to Affymetrix oligonucleotide microarray chips, followed by washing and scanning of chips as described in Section 2.5.2.

Following overnight hybridisation, microarrays were washed using a semi- automated GeneChip Fluidics Station 400 (Affymetrix) using the fluidics wash program EukGE-WS2v5 (Affymetrix). Each microarray was washed and stained using the following protocol: Post hybridisation wash #1 (10 cycles of 2 mixes/cycle with Wash Buffer A at 30°C), post hybridisation wash #2 (6 cycles of 15 mixes/cycle with Wash Buffer B at 50°C), first stain (5 min incubation with Fluidics SAPE solution at 35°C), post stain wash (10 cycles of 4 mixes/cycle with Wash Buffer A at 30°C), second stain (5 min incubation with Fluidics Antibody solution at 35°C), third stain (5 min incubation with Fluidics SAPE solution at 35°C) and then final wash (15 cycles of 4 mixes/cycle with Wash Buffer A at 35°C). Following the washing/staining procedure the microarray hybridisation was quantified using an Affymetrix GeneChip Scanner 3000 (Affymetrix).

2.5.2.3 Data analysis Two separate analytical techniques were utilised. For the 7-day study, GeneChip Operating Software (GCOS) v1.2 software was used to determine fluorescent signal intensities of 45,101 transcripts contained on the Affymetrix GeneChip Mouse Genome 430 2.0 Array. The raw intensity values were normalised to the mean intensity of all probe sets. Genes were initially filtered for a "present" call as assigned by the GCOS software for each of the hybridised microarray chips and selected for further analysis if they were called as "present" on all eight microarrays. Differentially expressed genes were determined by calculating the mean signal intensity from the two array chips for each gene in each group (control, clozapine-, haloperidol- or olanzapine-treated). The fold change signal-ratio was then calculated for control versus treatment groups and used to rank genes based on the magnitude of fold difference. Genes were further selected that had 1.5-fold difference in expression between treatment and control groups.

For the chronic time-point study, microarray data analysis was carried out by Warren Kaplan in the Bioinformatics department at the Garvan Institute of

93 Medical Research. The data were first normalised using the robust multichip average (RMA) method which has three parts: background subtraction to remove non-specific fluorescence that affect distinct regions of each microarray, quantile normalisation to ensure that the distribution of intensity data for each array is the same and thus comparable among arrays and a median polish step that combines the multiple expression measurements from each perfect match probe into a single expression measurement for each Probe Set (Irizarry et al., 2003a). Next, the data were analysed using the Rank Product method, a powerful statistical method for low replicate microarray analysis (Breitling et al., 2004). This method compares the rank of each transcript in the two samples and performs a permutation procedure to determine false discovery rates (FDR). We set a stringent FDR threshold of 0.05 to identify transcripts altered by drug treatment compared to control, indicating less than 1 in 20 transcripts would be false positives (Storey & Tibshirani, 2003) and fold-change of >1.5 to ascertain biologically relevant changes (Breitling et al., 2004).

Given the high false discovery rate associated with microarray data analysis we undertook further gene expression analysis to verify the observed altered gene expression. In order to choose genes for further analysis, thorough bioinformatics searches were conducted. For the 7-day analysis we looked for genes that were in schizophrenia linkage regions (according to meta-analysis of genome-wide linkage studies by Lewis et al., 2003), genes that were expressed in brain tissue in regions of interest from U133A human microarray data analysis Genatlas database (Frezal, 1998), biological function as revealed on Gene Cards (www.genecards.org) and whether there was prior schizophrenia association by conducting PubMed searches using “schizophrenia OR antipsychotic AND gene X” in May 2005. For the chronic analysis, Ingenuity Pathways Analysis 3.1 (Ingenuity Systems, Redwood City, CA, USA. www.ingenuity.com) was used to describe major networks of interest, define top pathways of interaction and for literature review information for each candidate gene.

2.5.3 Quantitative real-time RT-PCR (QPCR) analysis The differential gene expression detected by microarray analysis was validated using real-time quantitative RT-PCR analysis of cDNA from total RNA extracted previously from haloperidol- and saline-treated whole mouse brains (see Fig. 1.8 for procedure).

2.5.3.1 DNase treatment and cDNA synthesis Genomic DNA contamination was removed from total RNA using RQ1 RNase- free DNase (Promega, Sydney, Australia). Briefly, 2.75 μg total RNA was adjusted to 7 μL in nuclease-free water. 1 μL RQ1 RNase-free DNase reaction buffer (400 mM Tris-HCl pH 8.0, 100 mM MgSO4, 10 mM CaCl2) was added followed by 2 U RQ1 RNase-free DNase enzyme. The reaction was incubated at 37°C for 30 min, then terminated with 1 μL RQ1 Stop solution and DNase inactivated by incubation at 65°C for 30 min.

Reverse transcription was performed using 2 μg of total RNA and the Superscript III First-Strand Synthesis System for RT-PCR (Invitrogen). Briefly, RNA, oligoDT primer (50 μM) and 10 mM dNTP mix were initially incubated for 5 min at 65°C and then chilled on ice to avoid reannealing. cDNA was synthesised by addition of first-strand buffer mix made up in nuclease-free water (final concentrations of 1X reverse transcription buffer, 10 mM DTT, 125 μM each dNTP) with 40 U RNaseOut RNA inhibitor and 200U of Superscript III Reverse Transcriptase (RT) and incubation at 50°C for 50 min. The reaction was terminated by inactivation of RT enzyme at 85°C for 5 min.

2.5.3.2 Primer design PCR primers were designed using Primer3 (Rozen & Skaletsky, 2000), with specific requirements for amplification by SYBR® Green polymerase of amplicon length 80-250 bp and approximately 50% GC content. Oligonucleotides were synthesised by Sigma Genosys (Sydney, Australia) (Tables 2.2 & 2.3).

2.5.3.3 Polymerase chain reaction using SYBR Green polymerase All QPCR dilutions and reactions were done in UV-irradiated irrigated water (Baxter Healthcare). The following procedure was undertaken for each gene of interest (GOI). Serial dilutions of DNA standards of known concentration were used for each gene to quantitate the number of mRNA copies in the unknown samples. Test cDNA was created by mixture of equal amounts of each mouse cDNA prepared as in Section 2.5.2.1 and diluted 2/5. Standard cDNA was prepared with 8 μL diluted test cDNA amplified by PCR with primers for the GOI and Taq polymerase (Section 2.4.2). From a total of 200 μL PCR product, 10 μL was analysed by electrophoresis on a 1% agarose gel to ensure correct amplicon size (Section 2.4.3). 1 μL was quantified using a spectrophotomoter. The remaining PCR product was precipitated, purified and resuspended in 15 μL water. Serial dilutions of these products were made with 1/1000 to 1/1,000,000 dilutions to be run with samples in order to create a standard curve with known concentrations. Prepared sample cDNA was diluted 2/5 and added to a PCR reaction mixture containing 1X Platinum® SYBR® Green QPCR SuperMix- UDG (Invitrogen) and forward and reverse primers for the GOI (200 nM final concentration each). Amplification conditions were as follows: uracil-DNA glycosylase treatment at 50°C for 2 min, denaturation at 95°C for 2 min followed by 45 cycles of amplification (denaturation at 95°C for 5 s, annealing for 15 s, and extension at 72°C for 15 s). SYBR Green I fluorescence intensity was measured at the end of the annealing step. Following amplification, samples were dissociated by incremental heating between 72°C and 99°C, at a rate of 0.2°C/s. During this dissociation, SYBR Green I fluorescence was constantly measured and a melting curve was created to verify amplification of correctly sized amplicon. Amplification was performed in a Rotor-Gene 3000 PCR machine (Corbett Research, Sydney, Australia).

2.5.3.4 Quantification analysis

Quantitative PCR critical threshold (Ct) values for each sample were determined using Rotor-Gene v5.0.37 software (Corbett Research). Amplification was

96 performed in triplicate for each gene and amplicon was quantified by comparison with the standards of known concentration.

For the 7-day study, analysis was undertaken on 10 randomly selected cDNA samples (from a total of 16) for each treatment group (clozapine, haloperidol and olanzapine) and controls. The relative expression of the gene of interest (GOI) for each sample was expressed as a ratio of the number of GOI mRNA copies to the number of copies of GAPDH mRNA, giving a normalised expression value.

For the chronic study, analysis was undertaken on 14 randomly selected cDNA samples for each treatment group in an attempt to increase statistical power. Both GAPDH mRNA and ubiquitin c (Ubc) mRNA were amplified and the geometric mean of the copy numer of these housekeeping genes was used to normalize the mRNA copies for each GOI (Vandesompele et al., 2002).

2.5.4 In situ hybridisation In situ hybrisation of radiolabeled probes to fresh frozen mouse brain cryostat-cut sections (14 μm) was carried out according to the protocol described by Whitfield and colleagues (Whitfield et al., 1990). All equipment was autoclaved, soaked for two nights in DEPC-treated water and autoclaved again to ensure absence of RNase enzymes. All solutions were RNase-free and made up in DEPC-treated water.

2.5.4.1 RNA probe generation Approximately 250 bp fragments, chosen to have approximately 50% GC content and corresponding to a transcribed portion of the genes Kv1.1 and KChip3 were subcloned into the multiple cloning site of pGEM®-T Easy Vector using the method described in section 2.4.3 (Fig. 2.2).

Following cloning, amplification and sequencing of recombinant plasmids (Section 2.4.4), a clone containing the riboprobe insert of interest was further

97 amplified using midi-preps and resequenced for presence and direction of insert by Lofstrand Labs Ltd (Gaithersburg, MD, USA).

2.5.4.2 Radiolabeling of probe 35S-labeled riboprobes were synthesized using the clones described in 2.5.3.1. - actin antisense riboprobe, synthesized from mouse loading control template (Applied Biosystems), was used as an internal control in in situ hybrisation experiments using the T7 promoter and polymerase. For KCHIP3 synthesis with the T7 polymerase yielded the antisense strand when the vector was linearised with NdeI. Synthesis with the SP6 polymerase yielded the sense strand when the vector was linearised with SacII. For Kv1.1 riboprobe, synthesis with the T7 polymerase yielded the sense strand when the vector was linearised with NdeI. Synthesis with the SP6 polymerase yielded the antisense strand when vector was linearised with SacII.

To produce riboprobes 200 ng of linearised plasmid was labelled using 200 μCi of [35S]UTP and 5 units of DNA polymerase (SP6 or T7) in a mixture from the Riboprobe® Combination System (Promega) containing: 1 mM each of rATP, rCTP, rGTP, 10 mM dithiothreitol, 40 U RNAse inhibiting enzyme (RNasin) in transcription buffer. After incubation for 15 min in a 37°C water bath, the vector was digested with 3 U DNase for 30 min at 37°C. The reaction was stopped by addition of 0.5 M EDTA (final concentration 0.025M) and made up to 100 μL in DEPC-treated water. Specific activity and other measurements were calculated by comparison of incorporated radioactivity, measured by washing filter paper containing 1 μL sample in NaH2PO4, to unincorporated radioactivity, measured as the dry counts of 1 μL of sample.

The labelled riboprobe was then precipitated in ethanol in the presence of yeast tRNA and ammonium acetate for more than 16 h at -20°C. After precipitation the probes were pelleted by centrifugation for 20 min at 4°C, washed in 70% ethanol, re-spun and vacuum dried for 5 min. Probes were diluted in 50 μL DEPC-treated water and the yield and concentration was calculated.

98 2.5.4.3 Tissue preparation Fresh frozen 14 μm sections (2 per slide) from haloperidol- and saline- treated mouse brains (cut as described in Section 2.3.2.2) were prepared for hybridisation as detailed by Whitfield and colleagues (1990). Slides were taken from -80°C freezer, placed tissue-side up on aluminium foil and allowed to come to RT for 20 min before being placed in metal racks. For antisense riboprobe hybridisation 88 slides were prepared from 22 mice (11 haloperidol-treated and 11 controls) with four sections per mouse gathered sequentially for each probe, corresponding approximately to Bregma +1.70 mm for prefrontal cortex, Bregma +1.10 mm for striatum, Bregma -2.06 mm for dorsal hippocampus and Bregma -3.08 mm for ventral hippocampus/ midbrain (Paxinos & Franklin, 2001). For sense probes, six slides were chosen from the brain of one saline-treated mouse, corresponding approximately to Bregma +0.70 mm.

Slides were fixed in 4% formaldehyde in 1X PBS for 5 min at RT and rinsed twice in 1X PBS. Tissue was rinsed in 0.1M triethanolamine-HCl (pH 8.0) and acetylated for 10 min at RT in 0.25% (v/v) acetic anhydride added to a bone-dry container and made up in 0.1M triethanolamine-HCl (pH 8.0). Slides were rinsed twice in 2X SSC at RT and then dehydrated in increasing percentage ethanol (1 min each in 70%, 80%, 95% for 2 min and 100% for 1 min), delipidated in chloroform for 5 min at RT, rinsed in ethanol (1 min each in 100%, 95%) and air-dried by tilting the rack at about 30° tissue-side down.

2.5.4.4 Probe hybridisation In situ hybrisation of probes to tissue and washing of tissue following probe hybridisation was carried out according to protocol described by Whitfield and colleagues (1990). The hybridization oven was preheated at 55°C and humidified by incubation of two 2L containers of DEPC-treated water. Hybridisation cocktail was prepared from heat denatured 2X hybridisation buffer, 50% formamide, 0.1% sodium thiosulphate, 0.1 M dithiothreitol and 0.1% sodium dodecyl sulphate. 2X hybrisation buffer contained 1200 nM NaCl, 20 mM Tris- HCl, 0.04% (w/v) Ficoll 0.04% (w/v) bovine serum albumin, 0.04% (w/v) PVP,

99 2 mM EDTA (pH 8), 0.02% (w/v) salmon sperm DNA, 0.1% (w/v) total yeast RNA, 0.01% (w/v) yeast tRNA and 20% dextran sulfate and was heat denatured before use for 10 min at 85°C. Hybridisation cocktail was vortexed and left to settle for 1 h at RT. Incubation chambers were prepared by lining polypropylene incubation containers with a sheet of Whatmann 3MM filter paper (Sigma- Aldrich, MI, USA) saturated in 4X SSC/ 50% formamide. This solution was also used to fill two falcon tube lids inverted in the corners of the chamber to ensure a humid environment. Prepared slides were laid tissue-side up on top of saturated filter paper in the incubation chambers. Probes were heat denatured for 5 min at 85°C and added to the hybridisation cocktail to a final concentration of 5 ng/mL. Approximately 3 x 105 dpm of riboprobe section (in 40 uL of hybridisation cocktail) was pipetted onto the appropriate mouse brain tissue and a coverslip was placed on the slide to keep sections moist. In situ hybridisation was carried out overnight at 55°C.

The following day coverslips were removed by soaking the slides in 2X SSC at RT for 30 min and serial dipping of each slide in five 2X SSC containers. Slides were re-racked and washed twice more in 2X SSC. To remove unbound riboprobe, sections were treated with RNase A (20 mg/L) for 30 min at RT in a buffer containing 0.5M NaCl, 0.01M Tris (pH 8), 1mM EDTA in DEPC-treated water and washed in RNase A- free buffer for 30 min at RT. Slides were washed in 2X SSC at RT. To increase stringency and remove non-specific binding, slides were washed in decreasing salts and increasing temperature. They were covered in pre-equilibrated 2X SSC and placed in a shaking water bath at 50°C for 1 h. Slides were then incubated in pre-equilibrated 0.2X SSC at 55°C for 1 h followed by 1 h at 60°C. Sections were dehydrated in increasing alcohol washes containing 300 mM ammonium acetate and air-dried by tilting.

2.5.4.3 Visualisation and quantification For visualisation, riboprobe-hybridised slides were laid down in a film cassette, within a border of blank slides. Slides were placed closely abridging each other to minimise movement and apposed with 14C standards to BioMax Kodak film

100 (Sigma-Aldrich, MI, USA). Slides were allowed to expose for one day (-actin), two days (Kv1.1) or five days (KChip3). Autoradiographic films generated from in situ hybridisation experiments were visualised using a CanoScan 8600F scanner (Canon, Tokyo, Japan) at 300 dpi and 500% magnification, with images optimised using PhotoStudio 4 software, with ideal contrast and brightness corrected identically for all images (ArcSoft, Fremont, CA, USA). Optical densities were interpolated along a 14C standard curve (results in nCi/g of 14C) and densitometric measurements were made using NIH Image v1.63 software developed by Wayne Rasband at the National Institutes of Health. Samples of the regions of interest (ROI) are provided (Fig 2.4).

(A) (B)

(C)

CA1

DG CA4

Fig 2.4 Regions of interest (ROI) for in situ hybrisation. Autoradiographic images from NIH Image software v1.63 of mouse brain sections with ROIs outlines in bold for (A) prefrontal cortex, (B) striatum, and (C) hippocampus, sampled from the dentate gyrus (DG), CA1,CA3 and CA4 (hilar region) hippocampal fields.

101 2.6 PROTEIN ANALYSIS

2.6.1 Protein extraction techniques 2.6.1.1 Protein extraction from whole brain tissue Protein from 7-day antipsychotic drug treated mouse brains (6 clozapine, 6 haloperidol, 6 olanzapine and 4 saline) and 28-day antipsychotic drug treated mouse brains (6 per treatment and control) was precipitated from the phenol layer of the TRIzol product (section 2.5.1.1) and dissolved in 10% sodium dodecyl sulfate (SDS).

2.6.1.2 Protein extraction from dissected brain regions Protein extraction from regional lysates was optimized in consideration of the small amount of tissue used (10 mg). This involved extracting with two solutions: solution 1 was 0.05% Triton-X100 in TBS with Complete ™ EDTA- free protease inhibitor cocktail facilitating inhibition of chymotrypsin, thermolysin, papain, pronase and trypsin. Solution 2 contained 0.05 M Tris pH 8, 2% SDS in TBS with Complete ™ EDTA-free protease inhibitor cocktail.

For protein extraction, tissues were homogenised in microcentrifuge tubes with solution 1 (100μl/10mg) using a hand-held homogeniser for 5 min. Following centrifugation at 16,000g at 4°C for 15 min, lysate was removed and allocated fraction 1. Remaining tissue was resuspended in solution 2 (100μl/10mg) and homogenized for a further 5 mins. Following centrifugation at RT at 16,000 g for 15 min, lysate was collected and allocated as fraction 2. Both fractions were quantified as detailed in section 2.6.2, aliquoted and stored at -20°C.

2.6.2 Protein quantification Protein concentrations were assayed using the RC DC Protein Assay kit by comparison with BSA standards of known concentration. Optical density was measured at 650 nm using SpectraMax250 plate reader with SoftmaxPro v1.1 software (Molecular Devices Corp, CA, USA).

102 2.6.3 Western blotting 2.6.3.1 SDS-PAGE, Western transfer and immunoblotting of whole brain lysates Western blotting was used to measure protein expression in whole brain lysates of three genes: Kcna1 encoding Kv1.1, Kchip3 and Nedd4. Approximately 20 μg of protein samples prepared from whole mouse brain tissue were heated at 50°C for 10 min, placed on ice for 5 min and subjected to centrifugation briefly to collect contents. 4X loading buffer was added and samples were loaded into wells. A Protein marker, broad range (2-12 kDa) (New England Biolabs) was loaded onto each gel to identify protein band sizes. For Kv1.1 and NEDD4, SDS-PAGE was performed using a NuPAGE 4-12% BisTris Gel (Invitrogen) in MES SDS- PAGE Running buffer (Invitrogen) followed by transfer to a a Hybond N+- transfer nitrocellulose membrane (Bio-Rad Laboratories) using the XCell SureLock Mini-Cell Western Transfer apparatus (Invitrogen) in accordance with the manufacturer’s instructions. For KCHIP3 SDS-PAGE was performed using a Criterion Cell 10% Tris-HCl gel (Bio-Rad Laboratories) in 1X Tris- glycine running buffer for 60 min at 200 V. Following electrophoresis, gels were soaked in 1X transfer buffer for 15 min before transfer to a Hybond N+-transfer membrane (Bio-Rad Laboratories) using the Criterion Blotter system (Bio-Rad Laboratories) for 22 min at 100 V.

Immunoblots were blocked for 1 h at RT with agitation in 5% skim milk powder and appropriate buffer, as indicated below. Blots were then incubated with primary antibody overnight at 4°C with agitation. A 1:300 dilution of rabbit anti-

Kv1.1 IgG was used to detect Kv1.1 protein levels. A 1:250 dilution of rabbit anti- CSEN IgG was used to detect KCHIP3 protein levels. A 1:5000 dilution of rabbit anti-NEDD4 IgG was used to detect NEDD4 protein levels. To bind the secondary antibody, blots were incubated for 1 h at RT with agitation in a 1:10,000 dilution of biotinylated goat anti-rabbit IgG. After each step in this process Kv1.1 and KCHIP3 immunoblots were washed in 0.1% Tween-20/TBS buffer and NEDD4 immunoblots were washed in 0.05% Triton-X100/TBS buffer at RT. To normalise the amount of protein loaded, onto each lane

103 membranes were additionally blotted with an antibody to -actin. Following incubation with all antibodies, Kv1.1 and KCHIP3 immunoblots were washed for 30 mins with three solution changes in 0.1% Tween-20/TBS buffer and NEDD4 immunoblots were washed in 0.05% Triton-X100/TBS buffer.

2.6.3.2 SDS-PAGE, Western transfer and immunoblotting of microdissected brain region lysates

For the region-specific analyses on Kv1.1 and KCHIP3, Western blots were carried out as described above except that KCHIP3 was used at a dilution of 1:200. Also, the BioRad Western blotting and transfer system was employed for both Kv1.1 (with 20 μg protein per sample) and KCHIP3 (with 25 μg protein per sample). Criterion Cell 10% Tris-Hcl gels run in 1X Tris-glycine running buffer were used to separate protein samples for binding to KCHIP3 antibody and Criterion Cell 10% Bis-Tris gels (Bio-Rad Laboratories) in 1X MES SDS- PAGE Running Buffer were used to separate protein samples for binding to

Kv1.1 antibody. In both cases, electrophoretic separation of proteins was undertaken for 60 mins at 200 V and Western transfer for 22 mins at 100 V.

Immunoblots were blocked for 1 h at RT with agitation in 5% skim milk powder in TBS/0.1% Tween-20. An overnight incubation at 4°C with a 1:300 dilution of rabbit anti-Kv1.1 IgG was used to detect Kv1.1 protein levels. A 1:200 dilution of rabbit anti-CSEN (ab41717) was used to detect KCHIP3 protein levels, with 2 h incubation at RT followed by overnight at 4°C. To bind the secondary antibody, blots were incubated for 1 h at RT with agitation in a 1:10,000 dilution of biotinylated goat anti-rabbit IgG. Following incubation with each antibody,

Kv1.1 and KCHIP3 immunoblots were washed for 30 min with three solution changes in 0.1% Tween-20/TBS buffer at RT. Following the final wash, bands were visualized and quantified as described in section 2.6.3.3.

To normalize the amount of protein loaded onto each gel, following visualization of the first primary antibody, blots were incubated for 5 min at RT with agitation in a 1:10,000 dilution of a mouse monoclonal anti--actin IgG1. To bind the

104 secondary antibody, blots were incubated for 1 h at RT with agitation in a 1:5000 dilution of HRP-conjugated sheep anti-mouse IgG antibody. Once again after each step in this process Kv1.1 and KCHIP3 immunoblots were washed in 0.1% Tween-20/TBS buffer.

2.6.3.3 Signal detection and quantification Following the Western antibody binding procedure, protein bands were detected using the ECL Plus detection kit. Visualisation of chemiluminescent bands, with background removed, was undertaken using the Molecular Imager ChemiDoc XRS System (Bio-Rad Laboratories) with QuantityOne v4.5.1 analysis software used for quantification (Bio-Rad Laboratories). This procedure (electrophoresis, transfer, antibody staining and detection) was completed in duplicate for each protein of interest.

2.6.4 Immunohistochemistry of fixed mouse brain tissue 2.6.4.1 Tissue preparation Fixed 40 μm sections of interest (three per mouse per region) were removed from cryoprotectant and separated into a 12-well Corning plate containing 1X PBS. One plate was prepared per antibody – goat polyclonal anti-human DREAM

IgG (KCHIP3) and rabbit polyclonal anti-human Kv1.1 IgG (Santa Cruz Biotechnology Inc.) – and per region – midbrain/ventral hippocampus, hippocampus, striatum and prefrontal cortex. Sections were floated in 3 mL per well of 1X PBS and washed twice for 1 hr with shaking at 4°C to remove cryoprotectant.

2.6.4.2 Immunohistochemical procedure All washes and incubations for the immunohistochemical staining of tissue took place on a shaking platform at RT unless otherwise indicated. Serums, antibodies and Avidin Biotin Complex (ABC) were made up in diluent. Sections were initially washed in 1X PBS for 5 min at RT. Endogenous peroxidase was blocked by incubation for 20 min in 3:1 methanol/3% hydrogen peroxide, and was followed by three washes in 1X PBS for 5 min each. Blocking of tissue sections to

105 prevent non-specific binding was achieved by incubation for 1 h in 10% normal goat serum (Kv1.1) or 10% normal rabbit serum (KCHIP3), immediately followed by incubation with primary antibody — goat polyclonal anti-human

DREAM IgG (KCHIP3) or rabbit polyclonal anti-human Kv1.1 IgG — overnight (16 h) at 4°C with agitation.

The following day, sections were washed three times for 5 min each in 1X PBS. For detection of signal following immunohistochemical sections were incubated in secondary antibodies — biotinylated anti-rabbit IgG (H+L) (Kv1.1) and biotinylated anti-goat IgG (H+L) (KCHIP3) — for 60 min. During this incubation, ABC was made by dilution of 2 drops (~100 μL) reagent A and 2 drops reagent B in 10 mL diluent, thorough vortexing and incubation at RT for 30 min. Following secondary antibody incubation, sections were washed three times for 5 min each in 1X PBS and then incubated in prepared ABC for 1 h. Sections were washed again for 5 min each in 1X PBS and then DAB chromogen stain was applied to sections for 1.5 min (KCHIP3) or 3 min (Kv1.1). Following staining, sections were washed twice and then floated in 1X PBS and transferred onto labeled slides before being air-dried overnight.

2.6.4.3 Nissl counterstaining of fixed tissue sections To aid in visualization of tissue stained in immunohistochemistry a Nissl counterstain was used that specifically labels nucleic acid, giving intensely stained cell nuclei. After fixture to slides, sections were dehydrated by washing for 2 min each in 70%, 95% and 100% ethanol. Slides were left in a second wash of 100% ethanol for 30 min before transfer to thionine for 2.5 min. Sections were washed for 1 min each in 95%, 100%, 100% ethanol before soaking in highly hydrophobic xylenes for at least 5 min. Slides were dipped again briefly in fresh xylenes and then coverslipped using DPX mounting medium.

2.7 Statistical analysis For all analytical techniques, treatment and control group averages, standard deviations and differences in means were analysed using the statistical commands

106 in Microsoft Excel X for Mac (2001). The null hypothesis that the means of the control and treatment groups were equal was tested using a Student’s t test with unequal variance and rejected when p<0.05, indicating less than 5% chance that the null hypothesis is correct, a statistically significant assumption. For QPCR analysis the Gapdh normalised expression levels for the gene of interest was statistically compared between APD-treated and control animals using a two- tailed Student's t test with unequal variance. For whole brain regional analysis, the -actin normalised expression level for the protein of interest was statistically compared between APD-treated and control animals using a two-tailed Student's t test with unequal variance. For the regional Western blot analysis, given that we knew the direction of expected change, a one-tailed Student's t test with unequal variance was used on protein expression levels normalised to -actin and expressed as an averaged ratio of total protein expression levels in saline-treated animals. For in situ hybridization, STATISTICA version 5.5 (Statsoft Inc., Tulsa, OK, USA) was used to analyse densitometry values. To determine probability associated with differences in means in densitometry signal, a one-tailed Student's t test with unequal variance was used. To determine the overall effect of drug treatment in our regions of interst, the average mRNA densitometric values per mouse per region per treatment group were compared using repeated measures analysis of variance (ANOVA) with the null hypothesis that there were no mean differences between treatments. For all experiments, statistical significance was set at p <0.05.

107 108

Chapter 3

MOUSE ANTIPSYCHOTIC DRUG TREATMENT and TRANSCRIPT PROFILING of BRAIN TISSUE

109 3.1 INTRODUCTION

3.1.1 Antipsychotic drugs There are two main classes of antipsychotic drugs (APDs), classified by their neurotransmitter receptor binding profiles. Haloperidol is a conventional APD with high affinity for the dopamine D2 receptor (Seeman, 1980). Clozapine and olanzapine are both atypical APDs with broader neurotransmitter receptor binding profiles than conventional APDs, interacting at dopamine, serotonin, - adrenergic, histaminergic and muscarinic receptors (Schultz & Andreasen, 1999) (Table 3.1).

Table 3.1 Neurotransmitter receptor binding profiles. Relative affinities of the three antipsychotic drugs used in this study (Adapted from Schultz & Andreasen, 1999).

Receptor Clozapine Haloperidol Olanzapine Dopamine D1 ++ +++ +++ Dopamine D2 ++ ++++ +++ Serotonin 5-HT1A + - - Serotonin 5-HT2A +++ + ++++ 1-adrenergic +++ ++ +++ 2-adrenergic +++ - - Histamine H1 ++++ - ++++ Muscarine M1 +++++ - +++++

Haloperidol belongs to the class of drugs known as butyrophenones (Fig. 3.1A).

Like all conventional APDs, high levels of D2-receptor occupancy by haloperidol are associated with the main adverse effect of this class of APDs, extrapyramidal side effects (Farde, 1992), manifesting as neurological movement disorders (Tauscher et al., 2002). These side effects require concomitant medication in half of patients prescribed this neuroleptic and lead to high rates of non-compliance (Kane et al., 2007). Clozapine was introduced in 1989 as the first atypical APD belonging to the dibenzodiazepine family of drugs (Fig. 3.1B). Clozapine has the highest effect size in reducing symptoms of schizophrenia of all APDs (Davis et al., 2003) and provides therapy for approximately 70% of otherwise treatment- refractory patients (Jonsson & Walinder, 1995). However, it is generally used as a last resort in the clinic due to its high rate of adverse effects, particularly risk of

110 agranulocytosis (loss of white blood cells) that develops in about 1% of patients (Gardner et al., 2005). Olanzapine is a thienobenzodiazepine and is structurally similar to clozapine (Fig. 3.1C). Olanzapine has been used to treat schizophrenia since 1997 and it was the most well tolerated of the five APDs tested in the clinical APD trial CATIE study (detailed in Section 1.2.2.1) (Lieberman et al., 2005). Clozapine and olanzapine are associated with reduced extrapyramidal side effects compared to conventional APDs (Leucht et al., 2003), yet they have other adverse effects such as dramatic weight gain (Allison et al., 1999) and insulin resistance leading to a high risk of metabolic syndrome (McEvoy et al., 2005; Newcomer, 2007).

(A) Cl

OH N

F O

(B) N (C) N N Cl N N N

N H N H S Cl

Figure 3.1 Chemical structures of common antipsychotic drugs. (A) haloperidol, (B) clozapine and (C) olanzapine.

3.1.2 Transcript profiling of animals treated with antipsychotic drugs As reviewed in Section 1.4.3.2, there have been a number of transcript profiling studies of brain tissue derived from APD-treated rodents. These have revealed that the APD regulation of genes in diverse biological pathways, including many already suggested through transcript profiling of schizophrenia brain tissue.

111 Genes involved in signal transduction, cell communication, metabolism and transport were altered following chronic olanzapine treatment (Fatemi et al., 2006) and chronic risperidone administration altered genes involved in neurotransmission and synaptic plasticity (Chen & Chen, 2005). Transcript profiling of mice treated acutely with clozapine revealed changes in genes involved in neurotransmission, signalling, neuronal and glial cell development and function, transcription factors, and enzymatic regulators, in multiple schizophrenia-associated brain regions (Le-Niculescu et al., 2007). A 2-week study of haloperidol treatment in mice revealed regulation of proteolytic processing genes in the frontal cortical tissue (Iwata et al., 2005). Transcript profiling analyses and validation studies have also reported the regulation of specific genes by APD treatment. Increased synapsin II expression was seen after chronic haloperidol treatment (Chong et al., 2002). Chronic clozapine treatment resulted in increased mRNA and protein expression of nexin, a glial-derived neuroprotector (Chong et al., 2004) and, in a separate study, the up-regulation of glucose-dependent insulinotropic peptide (Sondhi et al., 2005).

If additional functional or associative evidence is not provided for expression changes seen in animal APD studies, then it is probable that changes seen after treatment with individual compounds may pertain to their adverse effects (Thomas, 2006). One way to ensure disease-specific changes is by looking at the transcript profile from multiple treatment studies and to compare expression changes regulated by multiple drugs. However, due to the different methodologies, treatment times and regions studied, this is not possible with the current literature (see Section1.4.3.2). In an attempt to normalise these variables, a few researchers have investigated the expression of genes regulated by multiple APDs within a single study paradigm. Gene expression analysis of rat frontal cortex after acute treatment with clozapine or haloperidol showed co-regulation of genes related to synaptic function (Kontkanen et al., 2002). Two-week treatment of mice with the same APDs revealed altered expression of genes involved in metabolism, calcium homeostasis and signal transduction (Thomas et al., 2003). A chronic APD treatment study supported a role for clozapine and

112 haloperidol in synaptic plasticity and protein phosphorylation in the rat frontal cortex (MacDonald et al., 2005). Genes in these pathways were also altered by acute and chronic treatment with risperidone (Feher et al., 2005). These multiple drug regulation studies may uncover common biochemical pathways in APD action.

3.1.3 Aims of this chapter Previous studies have shown gene expression changes in rodent brains following acute or chronic treatment with APDs, with single or multiple compounds. We aimed to further characterise these changes using whole-genome transcript profiling to explore coregulation of genes after treatment with a conventional APD, haloperidol, and two atypical APDs, clozapine and olanzapine. This study was commenced in 2004 when only three rodent APD treatment transcript profile studies had been published (Chong et al., 2002; Kontkanen et al., 2002; Thomas et al., 2003) and was conducted at two timepoints: a novel intermediate timepoint of 7 days, as well as at a chronic timepoint of 28 days. Clozapine, haloperidol and olanzapine have distinct receptor profiles, and distinct side effect profiles, and yet all act to alleviate the psychotic symptoms of schizophrenia. As all APDs bind dopamine D2 receptors (Seeman et al., 1975) this effect is undoubtedly mediated through dopamine and other neurotransmitters, yet the underlying molecular mechanisms of therapeutic action following neurotransmitter modulations remain undefined. By treating mice with three APDs and assaying transcriptional changes coregulated by these drugs through microarray analysis we aim to better understand the mechanisms of these drugs in treating schizophrenia.

3.2 RESULTS

3.2.1 Antipsychotic drug treatment Trials and serum level evaluations

113 The concentration of APDs to be used in animal treatment studies was determined by a 7-day trial evaluation for clozapine and olanzapine. The dosage used for haloperidol treatment (1 mg/kg/day) was based entirely on previous studies as we were unable to assay the serum levels locally and there is extensive literature on use of this conventional APD in treating animals (Arnaiz et al., 1999; Kontkanen et al., 2002; Simosky et al., 2003; Emamian et al., 2004).

The clozapine dosage selected (10 mg/kg/day) was based on endpoint serum drug concentrations following the 7-day trial period (Fig. 3.2A), which were at the low end of human therapeutic dosage (100 ng/mL). This dose was low compared to concentrations used in a previous animal profiling study (Kontkanen et al., 2002) yet was high range compared to another APD treatment study (Simosky et al., 2003). We found higher doses than 10 mg/kg had adverse effects on treated animals, including sedation and dramatic weight loss. Olanzapine dose trials were based on a previous study (Schreiber et al., 1999) and the dosage was selected at 10 mg/kg/day based upon endpoint serum drug concentrations of 40.4-50.7 ng/mL (within the human therapeutic range of 5-75 ng/mL) after the 7-day trial period (Fig. 3.2B).

Animal treatment studies –7 days and 28 days Following the initial trial period, mice were weighed and injected i.p. once daily for 7 days (intermediate time-point) or for 28 days (chronic time-point) with one of three APDs or saline. During the course of treatment, average body weights for haloperidol-treated mice were similar to that for control animals, increasing by around 10% of their body weight during the month (Fig. 3.3). However, atypical APD-treated animals showed a different profile, with clozapine- and olanzapine- treated mice losing about 5% of their body weight during the first week of study. This may be due to the sedation effect, observed after treatment with these higher dosages (10 mg/kg compared to 1 mg/kg for haloperidol treatment). By the end of the first week, the mice were gaining weight again at a similar rate to control animals although it is possible that transcriptional changes due to this weight loss may still be apparent at this timepoint.

114

Figure 3.2 Atypical antipsychotic drug dosage trials. Endpoint serum drug concentrations were determined after one week trial with varying dosages of (A) clozapine (B) olanzapine collected and analysed as described in Section 2.2.2. Shaded red area covers human therapeutic concentration ranges.

115 114% Clozapine Olanzapine 112% Haloperidol Saline 110%

108%

106%

104%

102%

100%

98%

96%

94%

Average mouse body weight compared to starting weight weight compared to starting weight mouse body Average 92%

90% 12345678910111213141516171819202122232425262728 Day of study

Figure 3.3 Mouse body weight ﬂuctuations during antipsychotic drug treatment study. Body weight of mice, in grams, was collected daily just prior to treatment with saline, clozapine (10 mg/kg/day), haloperidol (1 mg/kg/day) or olanzapine (10 mg/kg/day). Plots represent average of 18 mice per treatment group with 95% conﬁdence interval, expressed as a percentage of initial study weight. 3.2.2 Microarray hybridisation and data analysis Transcript profiling study design Prior to microarray analysis, we undertook statistical analyses to determine the best way to increase statistical power of the microarray experiments while minimising cost, which was substantial at the study start time. The process of maximising power while minimising cost is essentially the same as subgrouping in process control (Fig. 3.4), which shows that for a fixed number of chips, to decrease the biological (within experiment) variance it is always better to increase the number of replicates, to minimise between chip variance.

2 + 2 /n 2 = b w c c

Figure 3.4 Subgrouping equation. Where 2c is the total chip variance, 2b is between chip variance, 2w is within chip variance, c is number of chips and n is number of pooling replicates.

A previous study in the lab, that investigated RNA transcript profile following sodium valproate treatment, had used four mice pooled onto three chips (Chetcuti et al., 2006). For this study we wanted to increase the number of drugs we used but had to decrease chip number to two per treatment group to contain cost. Data modelling suggested that we would require 7.5 biological replicates to have equal between chip variance as in the three-chip study. Additionally, Peng and colleagues (2003) have indicated that maximum power from a minimal number of chips is achieved by equal pooling of each biological replicate (in this case genetically inbred mice). Therefore, for this study treatment we used equal amounts of RNA from eight mice, pooled onto each microarray. Analysis of gene expression profiles in brain RNA was undertaken using whole-genome Affymetrix GeneChip microarrays.

Evaluation of total RNA quality Following drug treatment studies, animals were sacrificed and brains were removed, snap-frozen in liquid nitrogen and stored at -80°C. Total RNA was

117 extracted from whole brains, purified and checked for integrity (Fig. 3.5) and concentration.

Mouse 1 Mouse 2 Mouse 3 Mouse 4 Mouse 5 Mouse 6 Mouse 7

T P T P T P T P T P T P T P

gDNA

rRNA

28S

18S

Figure 3.5 Total RNA from whole mouse brains of treated mice pre- and post-purification. Photograph of electophoresis on a 0.8% formaldehyde agarose gel of RNA visualised for integrity of 18S and 28S ribosomal RNA (rRNA). Faint high molecular weight bands are most likely contaminating genomic DNA (gDNA) and are reduced in the purification step. RNA lost during the purification step (such as for mouse # 3) was re-purified from total RNA. T: total RNA, P: purified total RNA.

Following purification of total RNA, we pooled equal amounts of RNA from eight mice into one sample for preparation to target probes on oligonucleotide microarrays by complementary hybridisation. In order to amplify the target, cDNA was synthesised from pooled RNAs and then transcribed in vitro to create a cRNA copy. cRNA was then purified to enrich for mRNA and remove genomic DNA. Purified cRNA was then fragmented into 30-250 bp fragments to target oligonucleotide probes (Fig. 3.6).

Before transcript profiling on whole mouse genome chips we used Affymetrix Test3 chips, containing probe sets to the 3’ end, 5’ end and the middle of housekeeping genes, to ensure quality of samples and controls. By comparing the

118 intensity of 3’ and 5’ probe sequences from the same gene, the degradation of mRNA was measured.

Pooled cRNA Sample 1 Sample 2

Figure 3.6 cRNA visualisation for microarray target preparation. Photograph of electrophoresis on a 0.8% formaldehyde agarose gel of cRNA made by in vitro transcription (IVT) of cDNA synthesised from pooled whole mouse brain total RNA which is subsequently purified, enriched for mRNA (clean) and fragmented (frag). During the purification step high molecular weight species (DNAs) and low molecular weight species (tRNAs and 5.8S, 5S rRNA) are removed and only 30-250 bp RNA species remain following fragmentation.

As the in vitro transcription method of RNA amplification transcribes from the polyA tail at the 3’ end of the transcript, the 3’/5’ ratios usually exceed 1 but by how much depends on the degradation of mRNA. In general, GAPDH cRNA target had good 3’/5’ ratios, averaging approximately 1.0 (Table 3.2). The 3’/5’ ratios for -actin cRNA were higher, indicating some degradation of this product, and also more variability between samples, indicating that -actin may not be a good housekeeping gene for this study. If GAPDH ratios were 2, the samples were re-prepared from the RNA pooling stage. The ratio of intensities and variability between samples improved for Affymetrix MouseGenome 430 2.0 Arrays compared to the test chips, probably due to higher amounts of starting material and increased accuracy and reproducibility in the subsequently larger number of transcripts.

119

Table 3.2 3'/5' ratios for housekeeping genes in array analysis. Intensity of target hybridisation to probes from the 3' and 5' region of housekeeping genes on Affymetrix test and whole mouse genome chips for 8 pooled RNA samples/ time-point.

Test 3.0 chips Mouse 430 chips Target sample -actin GAPDH -actin GAPDH 7-day Saline 1 2.93 1.05 2.42 1.02 Saline 2 3.50 1.04 3.17 0.98 Clozapine 1 2.28 1.11 2.16 0.93 Clozapine 2 #1 7.46 2.21 Clozapine 2 #2 3.65 1.32 3.08 1.20 Haloperidol 1 4.66 1.60 3.83 1.50 Haloperidol 2 3.95 1.57 4.11 1.35 Olanzapine 1 4.00 1.45 3.32 1.15 Olanzapine 2 #1 5.69 1.97 Olanzapine 2 #2 4.25 1.25 3.59 1.14

Average 3.652 1.299 3.321 1.178 SD 0.756 0.226 0.631 0.200

28-day Saline 1 3.48 1.23 3.14 1.18 Saline 2 2.73 1.04 3.31 0.85 Clozapine 1 2.47 0.82 3.48 1.08 Clozapine 2 2.98 1.12 2.59 0.88 Haloperidol 1 4.76 1.13 3.59 1.12 Haloperidol 2 #1 20.14 4.41 Haloperidol 2 #2 4.54 1.52 3.64 1.36 Olanzapine 1 2.24 0.97 2.25 0.98 Olanzapine 2 2.74 1.23 2.5 1.08

Average 3.243 1.133 3.063 1.066 SD 0.943 0.208 0.542 0.165 SD: standard deviation, #1,#2 indicate that a second RNA preparation was used when #1 preparation failed GAPDH ratio test

Transcript profiling of whole brain tissue from mice treated with clozapine, haloperidol or olanzapine For the 7-day treatment study, data analysis using GeneChip Operating Software (GCOS) revealed a total of 11,941 out of 45,101 transcripts showed greater than 1.5-fold change by at least one APD compared to saline-treated controls, as assessed by average intensity of hybridisation for each transcript on

120 two treatment arrays compared to the baseline standard of each of the two saline treatment control arrays (Fig. 3.7).

Clozapine 1 array hybridisation intesity

Saline 1 array hybridisation intenstity Figure 3.7 Rocket graph of comparative transcript intensities for treated vs control arrays. Log distribution of transcript intensities allows visualisation of transcripts (red dots) up- or down-regulated in treatment group at a pre-determined fold- change rate, indicated by parallel lines. This analysis was carried out for each array compared to control.

Further analysis was undertaken to identify genes that were regulated by multiple APDs, which was approximately 37% of the dysregulated transcripts (Fig. 3.8). Specifically, 4,404 of the 11,735 transcripts decreased by drug treatment were altered by at least two APDs and 90 of the 206 transcripts up-regulated by drug treatment were altered by at least two APDs. In total, 249 transcripts were co- regulated by all three APDs in this analysis.

121

(A) (B) Clozapine

Olanzapine Clozapine 48 74 Olanzapine 12

67 15 1 2

0 458 234 46 3 4104

6861 Haloperidol Haloperidol

Figure 3.8 Venn diagrams of 7-day APD treatment regulated transcripts. Transcripts regulated (union) and co-regulated (intersection) by microarray analysis of whole brain RNA. (A) Up-regulated genes and (B) down-regulated genes are significantly altered (p< 0.05) by at least 1.5-fold change.

For the chronic treatment study, robust multichip average (RMA) normalisation (Irizarry et al., 2003b) and Rank Product analysis (Breitling et al., 2004) were used to determine significantly regulated transcripts (with false discovery rate < 0.05) that were altered by at least 1.5-fold change in haloperidol and clozapine treatment groups (Fig. 3.9). We chose a 2-fold change cut-off for the olanzapine treatment group due to excessive regulation of transcripts by this treatment. This analysis was much more stringent than our 7-day analysis and revealed only 243 genes regulated by APD treatment – 175 genes were increased and 68 genes were decreased. One gene was upregulated by all three APDs and nine genes were regulated by two APDs.

122

(A) (B) Clozapine

Olanzapine Clozapine 0 4 Olanzapine 0 167 1 0 2 3 22 0 0 0 1

Haloperidol 43

Haloperidol

Figure 3.9 Venn diagrams of chronic APD treatment regulated genes. Transcripts regulated (union) and co-regulated (intersection) by microarray analysis of whole brain RNA. (A) Up-regulated genes and (B) down-regulated genes are significantly altered (p< 0.05) by 1.5-fold change (>2-fold change for excessively regulating olanzapine treatment).

3.3 DISCUSSION

3.3.1 Response to antipsychotic drug treatment in mice During mouse treatment with APDs we determined body weights daily. Haloperidol-treated animals showed no observable response to treatment and remained a similar body size to controls, yet clozapine- and olanzapine-treated mice appeared to be sedated after treatment and lost weight during the first week of the study. Although body weight reductions normalised after one week, these mice were still considerably lighter than saline- or haloperidol-treated mice after 28 days. The decreased body weight of clozapine, although not haloperidol, has been subsequently noted in a mouse study by researchers who also measured food intake and surprisingly found increased consumption by clozapine-treated animals compared to the other groups (Mehler-Wex et al., 2006). That the clozapine-treated mice (and perhaps olanzapine-treated mice in our study) eat

123 more food yet do not put on weight contrasts to the findings in schizophrenia patients, where clozapine and olanzapine treatment is associated with the most dramatic weight gain of all of the APDs (Allison et al., 1999). It has been suggested that this discrepancy may be due to hormonal differences in rodents with female olanzapine-treated rats exhibiting hyperphagia and associated weight gain, increased adiposity and insulin resistance (Cooper et al., 2005) yet similarly treated male rats showed only enhanced adiposity, without hyperphagia, weight gain or other metabolic abnormalities (Cooper et al., 2007). Rodent sex-specific metabolic effects are consistent with our study, which used male mice with no induced weight gain. Future studies would ideally measure food consumption and home cage activity.

3.3.2 Effect of antipsychotic drug treatment on transcription in mice Our transcript profiling study of animals treated with APDs revealed many genes that were altered by treatment with individual drugs, as well as transcripts that were regulated by multiple APDs. In the 7-day treatment study there is an obvious disparity in the number of down-regulated transcripts for each treatment group, with clozapine having very few altered transcripts compared to olanzapine and particularly haloperidol. In the 28-day study there is a clear bias towards olanzapine regulation of transcription. These findings may be a result of the dose used for each APD compared to other studies using the same administration technique (see Table 1.8 for comprehensive list). The dosage used in this study was mid-range for clozapine (10 mg/kg) compared to other similar reports ranging from 2.5-30 mg/kg with average dosage of 15.5 mg/kg (Kontkanen et al., 2002; Thomas et al., 2003; Chong et al., 2004; MacDonald et al., 2005; Le- Niculescu et al., 2007). The dosage used in this study was mid-range for haloperidol (1 mg/kg) compared to other similar studies with dosages ranging from 0.05-4 mg/kg and averaging 1.47 mg/kg (Chong et al., 2002; Kontkanen et al., 2002; Thomas et al., 2003; Chong et al., 2004; Feher et al., 2005; Iwata et al., 2005; MacDonald et al., 2005). The dosage used in this study was maximal for olanzapine (10 mg/kg) compared to other reports using 2 mg/kg (Fatemi et al.,

124 2006) and 10 mg/kg (Schreiber et al., 1999; Chong et al., 2002; Kontkanen et al., 2002; Wang et al., 2004; MacDonald et al., 2005; Sondhi et al., 2005).

Despite this, we have identified many transcripts down-regulated by multiple APDs. Interestingly, more transcripts were up-regulated by both atypical APDs (clozapine and olanzapine) than between either/both atypical APDs and the typical APD haloperidol in the 7-day treatment group and more transcripts were also regulated by clozapine and olanzapine than with haloperidol in 28-day treatment study. These results may reflect the similar chemical structure of these APDs and indicate genes involved in atypicality of clozapine and olanzapine compared to haloperidol.

3.3.3 Study design In this animal treatment transcript profiling study we chose to pool RNA from genetically identical mice that had undergone the same treatment onto a single microarray chip. This technique has been used successfully in other rodent APD- treatment transcript profiling studies (Chen & Chen, 2005; Mehler-Wex et al., 2006; Le-Niculescu et al., 2007). As discussed in Section 3.2, pooling, particularly with equal contribution from each RNA sample as in this study, is a statistically valid method for maximising power while minimising the cost of microarray analysis (Peng et al., 2003).

We also chose to use whole brain tissue (with cerebellum and olfactory bulbs removed) in our microarray study as this provides an unbiased approach to screen for major changes in gene expression pertaining to a disorder without a definitive neuropathology and also allows for the widespread nature of neuronal interaction. Selection of whole brain tissue may lead to more false negatives: real changes in expression in one region may be diluted by minimal changes in other regions, or opposing changes in different regions may lead to no observable change in the whole brain tissue. Indeed, with the cellular complexity of brain tissue, even regional analyses have increased likelihood of false negatives due to dilution of cell-specific changes (Mirnics et al., 2001b). Previous studies have not

125 used a whole brain profiling technique, instead choosing to focus on specific regions with the advantage of reducing false negatives. Yet these studies have chosen either the striatum, a region targeted by APDs but never studied by gene expression profiling in schizophrenia tissue, or the frontal cortex, an area that has been consistently associated with schizophrenia yet is more highly evolved in humans and has substantially different development (Kennedy & Dehay, 1993) and neurochemistry (Khan et al., 1998) than in rodents. In both of these regions of prior rodent APD treatment studies, comparisons and correlations with human studies are therefore constrained. Furthermore, microarray studies have shown that within a mouse there are few transcriptional changes between cortical, midbrain and cerebellar mouse brain regions (approximately 0.5% differentially regulated genes) (Sandberg et al., 2000). However, another study did find altered regional expression of some genes, between the cortex and hypothalamus, in particular indicating a cis-regulating mechanism specifying gene expression (Boon et al., 2004). This highlights the limitations of microarray analysis (Geschwind, 2000) but also indicates that altered transcription in one region may be detected by whole brain analysis where the rest of the tissue has relatively homogeneous expression.

3.3.4 Microarray data analytical techniques In our 7 day study we used an average fold-change method for identifying differentially expressed genes. Fold-change does have weaknesses in the arbitrary cut-off value and inability to describe significance of observed changes, yet it is intuitively appealing as small fold-changes, no matter how significant, are unlikely to be biologically relevant (Breitling et al., 2004). Another study has found approximately 75% validation rate with genes altered above 1.4-fold change (Morey et al., 2006). Furthermore, the majority of animal APD treatment array studies that have been published to date use fold-change (Chong et al., 2002, Kontkanen et al., 2002, Thomas et al., 2003, Sondhi et al., 2005, Feher et al., 2005, Mehler-Wex et al., 2006, Fatemi et al., 2006).

126 In our 28-day study, to determine genes significantly regulated by chronic APD treatment we used Rank Product analysis, a powerful statistical method for low replicate microarray analysis (Breitling et al., 2004). This method allowed us to ascribe a false discovery rate (FDR) to each transcript allocated as differentially expressed in the microarray analysis. This describes the number of false positives within a chosen significance level. In this case FDR < 0.05 would indicate that less than 1 in every 20 genes were false positives and the remaining 19 were true positives (Storey & Tibshirani, 2003). We further narrowed this group to include transcripts that had greater than 1.5-fold change in at least two treatment groups compared to saline to determine biologically relevant transcripts that would be verifiable using other molecular biology techniques (Breitling et al., 2004). As far as we are aware, this stringent analytical technique has not previously been applied to APD-treated rodent transcript profiling studies although the normalisation tool we used has been previously applied to a study using hierarchial clustering for significance analysis (MacDonald et al., 2005).

The comparative relevance of the analytical techniques we applied will be discussed further in Section 5.3, after QPCR validation analysis results at each time-point are described.

127

128

Chapter 4

INTERMEDIATE ANTIPSYCHOTIC DRUG TREATMENT REGULATION of GENE and PROTEIN EXPRESSION in MOUSE WHOLE BRAIN TISSUE

129 4.1 INTRODUCTION

Previously published studies of gene expression alterations by APDs in rodents have focused on either acute or chronic dysregulation (see Section 3.1.2). Our study is unique in that a 7 day intermediate time point of APD treatment was used. Most studies use a chronic treatment time-point due to the dogma of a “delayed-onset” of antipsychotic drug action in treating the positive symptoms of schizophrenia. The electrophysiological evidence for delayed onset of antipsychotic action is known as the ‘depolarisation-block’ hypothesis (Grace & Bunney, 1986). This follows observation that acute haloperidol treatment increases spontaneous firing of midbrain dopamine neurons, compared to decreased spontaneous action after 21 days in rats indicating overexcitation and depolarisation of midbrain dopamine neurons (Grace & Bunney, 1986).

However, there have recently been a number of studies to refute this dogma. A meta-analysis of clinical studies reported a greater improvement in positive symptoms in patients with schizophrenia in the first week of APD treatment than in any subsequent week (Agid et al., 2003). A follow up meta-analysis replicated this finding and also showed that improvement in psychotic symptom scores in the first month was more substantial than in the remainder of the year (Leucht et al., 2005). These studies indicate that clinically relevant changes in brain biochemistry resulting from APD administration may occur earlier than originally thought, indicating an “early onset” of antipsychotic action (Agid et al., 2006).

Given the clinical relevance of an intermediate timepoint on the mechanisms of antipsychotic drug treatment we aimed to look at gene expression altered after 7 days in animals treated with three APDs, an approach not previously undertaken. Our study aimed to identify gene expression changes that occur at this novel time-point. That is, after acute effects have stabilised, yet before deleterious effects of chronic medication, such as tardive dyskinesia and metabolic syndrome appear in rodents (Waddington, 1990; Coccurello et al., 2006).

130

4.2 RESULTS

4.2.1 Microarray bioinformatical analysis Gene expression profiling revealed many transcripts regulated by 7-day treatment with multiple APDs (Section 3.2.2). Due to the high number of transcripts down- regulated by haloperidol, a 2-fold change cutoff was chosen for further analysis of this drug regulation. Altered transcripts were from 79 known genes, with 64 genes down-regulated and 15 genes up-regulated by APD treatment. Bioinformatic analysis of genes regulated by multiple APDs determined their expression, chromosomal location of their human homologue, and biological function (Table. 4.1).

Altered transcripts encode proteins that function in transcription and translation regulation, signal transduction, protein metabolism, cytoskeletal function, neurogenesis and synaptic transmission. Nineteen genes had human homologues in linkage regions of schizophrenia susceptibility and twelve genes had previously been associated with schizophrenia or APD treatment. Twenty candidate genes were chosen for further expression analysis based on their regulation by multiple APDs, human homologue chromosomal location, pattern of expression and relevant neurobiological function (Table 4.1). All candidate genes had greater than 1.5-fold change in expression in at least two APD treatment groups compared to saline-treated controls in the microarray analysis.

131 Table 4.1A Bioinformatic analysis of genes up-regulated by microarray analysis. Fold-change differences in treated mouse arrays are indicated with known expression, function and prior SZ association. Genes chosen for further validation by QPCR are denoted with a tick. Microarray fold-change‡ Chromosomal QPCR Gene Cloz Hal Ola Location^ Expression pattern* Biological function Prior SZ association validation Upregulated by drug treatment in microarray analysis ATP7a 2.736 1.920 2.092 Xq21.1 Ubiquitous, integral Unknown None membrane Bicd2 1.671 1.360 2.385 9q22.31 Ubiquitous Microtubule movement None Dclk1 2.338 1.297 2.928 13q13.3 Brain Neuronal development None Elys 2.124 1.259 2.780 1q44 Blood, nucleus Unknown None Gh1 90.916 1.771 52.737 17q24 Ubiquitous Hormone None Kcna1 1.625 1.129 1.653 1q44 Brain, heart Membrane excitability mRNA expression by chronic cloz treatment (Sondhi et al., 2005) Luc7l2 2.223 0.943 2.804 7q34 Ubiquitous Unknown None Mamdc1 2.330 1.203 2.071 14q21.3 Ubiquitous, integral Unknown None membrane Mapre2 1.809 1.168 1.850 18q21.1 CNS Microtubule associated None R3hdm 1.963 1.256 2.728 2q21.3 Brain esp fetal Unknown None Rgs16 1.585 0.879 1.765 1q25.3 brain, thyroid, retina G-protein signaling None AK036626 2.181 1.297 1.966 3q25.32 CNS Unknown None Sdfr1 1.932 0.901 2.258 15q24.1 PFC, amygdala Unknown None Ssbp2 1.585 0.879 1.765 5q14.1 Ubiquitous, nuclear Transcription None Taf15 2.239 0.950 2.115 17q12 Ubiquitous, nuclear Transcription None ‡ Bold indicates > 1.5 fold-change in APD treated versus control arrays; *: expression pattern from Genatlas of human U133A microarray data (Frezal, 1998) ^: Bold indicates schizophrenia susceptibility region as deﬁned by meta-analysis of genome wide linkage studies ( Lewis et al., 2003), or by an area of chromosomal rearrangement in schizophrenia SZ: schizophrenia, CNS: central nervous system, CSF: cerebrospinal ﬂuid, PFC: prefrontal cortex, : increased, : decreased, cloz:clozapine Table 4.1B Bioinformatic analysis of genes down-regulated by microarray analysis. Fold-change differences in treated mouse arrays are indicated with known expression, function and prior SZ association. Genes chosen for further validation by QPCR are also indicated.

Microarray fold-change‡ Chosen for Chromosomal QPCR Gene Cloz Hal Ola Location^ Expression pattern Biological function Prior SZ association validation Downregulated by drug treatment in microarray analysis Anapc5 -1.554 -4.015 -1.998 12q24.31 Ubiquitous Ubiquitination None Araf1 -1.437 -3.332 -1.824 Xp11.3 Ubiquitous Signal transduction None Arl2bp -1.577 -3.306 -2.413 16q13 Ubiquitous G-protein, enzyme None Bat2 -1.625 -3.364 -3.605 6p21.33 Ubiquitous, PFC Inﬂammation None BM88 -1.287 -3.600 -1.929 11p15.5 Neuron-speciﬁc Neuronal differentiation None Bpgm -1.407 -2.589 -1.808 7q33 Bone marrow; placenta Oxygen regulation None Bzw1 -1.287 -2.774 -1.508 2q33.1 Low in brain Translation None Cap2 -1.572 -3.567 -1.879 6p22.3 Brain, skeletal muscle Unknown none Catnb -1.490 -4.070 -1.899 3p22.1 Ubiquitous Synapse biogenesis, APDs protein in rat midbrain, vesicle transport hippocampus, prefrontal cortex and striatum (Alimohamad et al., 2005a,b); cytosolic protein in hippocampus in SZ (Cotter et al., 1998); No change PFC in SZ (Beasley et al., 2001)

Ccnl2 -2.564 -4.095 -3.263 1p36.33 Ubiquitous, PFC Transcription None Cspg3 -1.803 -5.560 -1.414 19p13.11 Brain, astrocytes Neuronal development None Dnajb6 -1.187 -3.964 -1.521 7q36.3 Brain, amygdala, PFC Molecular chaperone None Eif4a1 -1.727 -4.743 -2.131 17p13.1 Ubiquitous Translation None Fus -1.506 -3.989 -1.567 16p11.2 Ubiquitous, nuclear Nucleic acid binding None Gabra1 -1.055 -4.089 -1.502 5q34 Brain, postsynaptic Neurotransmission mRNA expression in SZ PFC membrane (Hakak et al., 2001) Downregulated by drug treatment in microarray analysis, continued Gdi2 -1.879 -5.281 -1.826 10p15.1 Ubiquitous GTPase activity None Gucy1b3 -1.504 -2.981 -1.627 4q32.1 PFC, amygdala, Enzyme None hypothalamus Hcfc1 -1.330 -2.376 -2.487 Xq28 Ubiquitous Transcription None Hnrpa1 -1.292 -3.296 -1.556 12q13.13 Ubiquitous Transcription None Hnrpu -1.626 -3.628 -1.738 1q44 Ubiquitous, nuclear Transcription None Hrmt1l2 -2.079 -4.627 -2.589 19q13.33 Brain Transcription None Hspca -1.661 -7.584 -2.030 14q32.32 Ubiquitous, cytoplasmic Molecular chaperone None Hspcb -2.146 -7.315 -3.087 6p21.1 Ubiquitous, cytoskeletal Molecular chaperone None Kcnab1 -1.320 -5.756 -1.625 3q25.31 Brain, heart Neuronal excitability None Kchip3 -1.189 -3.422 -2.338 2q11.1 Brain Neuronal excitability, None transcription Klc1/Kns2 -1.464 -5.959 -2.979 14q32.32 Amygdala, PFC, Microtubule associated mRNA in SZ PFC (Hakak et al., 2001) thalamus Lancl2 -1.386 -3.402 -1.502 7p11.2 Testes Unknown None Laptm4a -1.543 -3.305 -1.763 2p24.1 Ovaries, testies Integral membrane, None transport Mal -1.834 1.366 -3.249 2q11.1 Glial cells Myelination mRNA in SZ PFC (Hakak et al., 2001), mRNA in BP blood (Middleton et al., 2005)

Meg3/Gtl2 -1.929 -4.010 -2.470 14q32.2 Brain, ovaries Non-coding RNA None Mgea5 -1.631 -3.160 -1.381 10q24.32 Brain Protein metabolism None Mrpl9 -1.966 -4.632 -3.286 1q21.3 Ubiquitous Mitochondrial protein None synthesis Nap1l1 -1.789 -4.768 -1.828 12q21.2 Ubiquitous Nucleosome assembly None Ncoa4 -2.032 -4.182 -2.220 10q11.23 Testes Cell growth/ maintenance None Downregulated by drug treatment in microarray analysis, continued Nedd4 -1.542 -4.287 -2.000 15q21.3 Ubiquitous Neuronal development/ None plasticity Nnp1 -1.573 -4.992 -2.533 21q22.3 Ubiquitous rRNA regulation None Nt5c2 -1.261 -2.908 -1.572 10q24.33 Blood, PFC, amygdala, Enzyme None thyroid Numbl -1.653 -3.605 -3.138 19q13.2 Ubiquitous Neurogenesis None Pcp4l1 -1.516 -3.201 -2.999 1q23.3 CNS Unknown None Pkia -1.703 -3.592 -1.510 8q21.12 Brain Enzyme inhibitor None Plp1 -1.569 -4.595 -2.107 Xq22.2 CNS Myelin production and mRNA in SZ PFC, NC in hal-treated maintenace monkeys (Pongrac et al., 2002); mRNA in SZ temporal cortex (Aston et al., 2004); mutation in male chinese SZ (Qin et al., 2005)

Pmm1 -1.546 -4.452 -2.797 22q13.2 PFC, amygdala, Protein synthesis None hypothalamus Ppp1r1c -1.591 -3.808 -1.654 2q31.3 Ubiquitous Signal transduction None Prp19 -1.506 -4.666 -2.118 11q12.2 Ubiquitous, PFC Ubiquitination None Rab18 -1.904 -3.942 -1.721 10p21.1 Ubiquitous, PFC, GTPase activity None amygdala Rab40c -1.572 -3.567 -1.879 16p13.3 Ubiquitous, pons, GTPase activity None amygdala, heart Rapgef4/ -1.653 -4.438 -1.866 2q31.1 Amygdala, PFC Neurotransmission None Epac2 Rtn4 -1.464 -4.362 -1.516 2p16.1 Brain, adipocytes Neuronal development/ mRNA in SZ frontal cortex (Novak et plasticity al., 2002)

S100a9 -2.549 -1.711 -2.549 1q21.3 Blood Inflammation protein in SZ CSF (Poltorak et al., 1995); mRNA in SZ & BP blood (Tsuang et al., 2005) Downregulated by drug treatment in microarray analysis, continued Scoc -1.544 -3.027 -1.310 4q31.1 Amygdala, Unknown None hypothalamus Scrna1 -1.815 -3.363 -1.911 7p15.1 Brain, cytoplasm Unknown None Serpini1 -1.464 -4.141 -1.516 3q26.1 Amygdala, PFC, Synaptic plasticity mRNA in SZ PFC (Hakak et al., 2001) occipital lobe Sfsr1 -1.510 -3.517 -1.470 17q23.2 Ubiquitous, nuclear Transcription None Sh3bgrl -1.691 -4.318 -1.504 Xq21.1 Heart, muscle Unknown None Spop -1.432 -3.722 -2.004 17q21.33 Ubiquitous, PFC Unknown None Sypl -1.366 -3.605 -2.035 7q22.3 Ubiquitous, synapse Vesicle transport None Syt1 -2.261 -5.836 -1.614 12q21.2 Synaptic vesicles Neurotransmission mRNA in SZ temporal cortex (Sokolov et al., 2000) Tde2/ -1.982 -5.682 -1.495 6p22.31 CNS, glutamatergic Unknown None Tms2 neurons Tmp21 -1.418 -2.512 -1.756 14q24.3 Ubiquitous Protein traffiking None Tra1 -2.684 -6.086 -2.174 12q23.3 Dendritic cells Molecular chaperone None Ube2d3 -1.660 -3.641 -1.910 4q24 Ubiquitous Ubiquitination ubiquitin metabolism genes in SZ PFC (Middleton et al., 2002) Ube2g2 -1.966 -4.632 -3.286 21q22.3 Ubiquitous Ubiquitination ubiquitin metabolism genes in SZ PFC (Middleton et al., 2002) Zwint -1.392 -3.333 -1.543 10q21.1 Low in brain Mitosis None ‡ Bold indicates > 1.5-fold change (clozapine and olanzapine) or > 2-fold change in treated arrays compared to controls (haloperidol) ^: Bold indicates schizophrenia susceptibility region as defined by meta-analysis of genome wide linkage studies ( Lewis et al., 2003), or by an area of chromosomal rearrangement in schizophrenia *: expression pattern from Genatlas of human U133A microarray data (Frezal J, 1998) SZ: schizophrenia, BP: bipolar affective disorder, APD: antipsychotic drugs, CNS: central nervous system, CSF: cerebrospinal fluid, PFC: prefrontal cortex, : increased, : decreased 4.2.2 Quantitative real-time PCR validation A total of 13 out of 20 genes altered by microarray analysis had a statistically significant change in gene expression by one or more APDs as determined by real-time quantitative RT-PCR (QPCR). Statistically significant up-regulation by drug treatment was found for two known genes: potassium voltage-gated channel, shaker-related subfamily, member 1 (Kcna1) and doublecortin-like kinase I (Dclk1); and for one expressed sequence tag (AK036626) (Fig. 4.1A). Ten genes were statistically significantly down-regulated by drug treatment (Fig. 4.1B). These are calgranulin B (S100a9); rap guanine nucleotide exchange factor (GEF) 4 (Rapgef4); myelin and lymphocyte protein (Mal); neural precursor cell expressed, developmentally downregulated gene 4 (Nedd4); HLA-B associated transcript 2

(Bat2); -catenin; Kv channel-interacting protein 3 (KChip3); numb-like (Numbl); potassium voltage-gated channel shaker-related subfamily member 1 (Kcnab1); and chondroitin sulfate proteoglycan 3 (Cspg3).

4.2.3 Protein quantification by Western blot analysis To determine whether changes in gene expression following APD treatment in our animal model were translated into protein changes, three genes with verified regulation by multiple APDs were selected for further analysis: Kcna1 (encoding

Kv1.1), Kchip3 and Nedd4. Western blot analysis of protein samples from APD- treated animals showed significant up-regulation of Kv1.1 by haloperidol compared to controls (Fig. 4.2A). This was true for both protein species, at 57 and

59 kDa, comprising the recognised doublet for Kv1.1 cell surface protein expression (Deal et al., 1994). No change in whole brain Kv1.1 protein expression was detected following clozapine or olanzapine treatment. KCHIP3 protein was significantly decreased in whole brain lysates after both haloperidol and olanzapine treatment compared to controls (Fig. 4.2B), with no change detected in the clozapine treatment group. Western blot analysis also showed significant down-regulation of NEDD4 protein following olanzapine treatment (Fig. 4.2C), with haloperidol treatment effecting no change in whole brain NEDD4 levels. Proteins from clozapine-treated animals were not assayed, as Nedd4 gene expression change was not verified by QPCR analysis.

137 Figure 4.1 Quantitative RT-PCR validation Genes found to (A) * be significantly (A) upregulated and (B) downregulated by multiple 4 * antipsychotic drug treatments in 7-day microarray analysis were 3.5 * * * explored using this alternative gene expression quantification 3 * * * technique. Columns represent the mean expression of eight 2.5 * * * * individual mouse RNAs/ treatment group over three replication 2 * analyses, normalised to GAPDH expression. Asterisks denote a 1.5 * significant change between antipsychotic-treated and saline-treated 1 whole brain mRNA expression with *: p <0.05, **: p< 0.01, ***: p<0.0001. Error bars indicate 95% confidence interval. 0.5 0 Mean normalised expression ratio (B) Dclk1 AK036626 Rgs16 Sdfr1 Kcna1 2.5 Saline Clozapine Haloperidol Olanzapine

2.0

1.5 * 1.0 * * * * * * * * * * * * * * * * * * 0.5 * Mean normalised expression ratio Mean normalised expression

0.0

Syt1 Plp1 Rtn4 Sypl Kns2 Tde2 Mal Bat2 Nedd4 Kchip3 Numbl Cspg3 S100a9 Rapgef2 Gabra1 -catenin Serpini1 Kcnab1 (A) Saline Haloperidol Kv1.1 (57-59 kDa)

-actin (42 kDa)

(B) 3.0 *

1.5 Kv1.1

protein level protein level 0.0 Saline Clozapine Haloperidol Olanzapine

0.8 * *

KCHIP3 0.4 protein level 0.0 Saline Clozapine Haloperidol Olanzapine

(E) Saline Olanzapine

NEDD4 (115 kDa)

-actin (42 kDa)

(F) 2.0 1.5 1.0

rotein level rotein level *

p * 0.5 0.0

NEDD4 Saline Haloperidol Olanzapine

Figure 4.2 Western blot analysis and quantiﬁcation of whole brain protein lysates. (A), (B) Kv1.1 protein (~ 57-59 kDa) (C), (D) KCHIP3 protein (~ 29 kDa) and (E), (F) NEDD4 protein (~ 115 kDa). (A), (C), (E) Immunoblots showing bands for proteins of interest in control and APD-treated animals. -actin (~42 kDa) was used to control equal protein loading. (B), (D), (F) Graphs showing mean protein expression, measured by optical density, in control (n= 4) and antipsychotic drug-treated animals (n= 6/group) normalised to -actin protein expression. Error bars are SEM. *= p < 0.05. 4.3 DISCUSSION

4.3.1 Study findings Using bioinformatic analysis we identified twenty candidate genes that were regulated by multiple APDs in our intermediate (7 day) microarray study, were expressed in the brain, had relevant functions and/or had a previous association with schizophrenia. This convergent approach will emphasise gene regulation that is most relevant to symptom pathology in the disorder (Thomas, 2006). By QPCR analysis we verified the dysregulation of 65% of the candidate genes – 4 up-regulated and 9 down-regulated by multiple APDs.

Some of the genes that were regulated by APD treatment in our study have been previously implicated in similar studies or in studies of postmortem tissue from schizophrenia patients (Table 4.2) (Higgs et al., 2006; Elashoff et al., 2007). This demographic data was gathered and statistical analyses defined significance as p<0.01; FC>1.3. In particular, the expression of Dclk, S100a9 (Calgranulin B), Bat2, KChip3 and Kcnab1 in postmortem brain tissue has been shown to be dependent on medication status in patients and this study provides additional evidence for their regulation by APDs. Furthermore, both Kcna1 and S100a9 have previously shown similar regulation by chronic APD treatment in animals (Sondhi et al., 2005; Fatemi et al., 2006) as we see in this intermediate treatment study. Acute (24-hour) APD treatment also revealed alterations in Mal, KChip3 and Kcnab1 (Le-Niculescu et al., 2007). Cspg3 has been previously shown to up- regulated in rat frontal cortex after 21 days olanzapine treatment at a much lower dose than used in this study (Fatemi et al., 2006). This is the opposite regulation to what we see for higher dose olanzapine after 7-day treatment in whole mouse brain, indicating that APD regulation may change over time for Cspg3.

We supported findings of APD regulation of three genes by analysis of protein expression for Kv1.1, KCHIP3 and NEDD4. It is recognised that gene expression changes from microarray studies do not necessarily translate into protein changes, with concordance particularly low in brain tissue (Mirnics et al., 2006).

140 Table 4.2 Previous evidence for dysregulation of verified candidate genes in this 7-day study by APD treatment and/or in postmortem brain tissue expression analyses. Human postmortem brain analysis^ Gene Previous animal APD treatment studies mRNA expression change Psychiatric drug effect* Genes upregulated in this study Kcna1 chronic clozapine treatment (Sondhi et al., 2005); atypical APD DLPFC in BP and SZ patients (Bahn, 2007) Lithium BP pimozide blocks Kv1.1 (Zhang et al., 2003) Dclk1 — DLPFC in BP (Stanley consortium) Lifetime APD in BP; valproate in SZ; lithium in SZ Genes downregulated in this study S100a9 chronic olanzapine treatment (Fatemi et al., 2006) DLPFC in BP (Stanley consortium), in SZ Lifetime APD in SZ & BP (Stanley array) Rapgef4 — - MS in SZ, lithium in BP Mal acute clozapine and PCP treatment (Le-Niculescu, 2007) DLPFC in SZ (Hakak et al., 2001), DLPFC in No effect found BP & MD (AltarC, 2007), DLPFC in SZ (Stanley array) Nedd4 — DLPFC in BP (Sklar, 2007) MS in SZ Bat2 — - Lifetime APD & AD in SZ -catenin APDs protein in rat midbrain, hippocampus, prefrontal cortex DLPFC in BP & MD (AltarB, 2007) No effect found and striatum (Alimohamad et al., 2005a,b); Hal in vitro (Sutton et al., 2007) Kchip3 acute clozapine and PCP treatment (Le-Niculescu et al., 2007) DLPFC in BP & MD (AltarB, 2007) Lifetime APD in BP Kcnab1 by acute clozapine, by acute PCP treatment (Le-Niculescu et DLPFC in BP (Stanley array & consortium), APD, MS in BP; APD, MS, al., 2007) DLPFC in SZ (Stanley array) lithium in SZ; valproate in SZ Cspg3 chronic olanzapine (Fatemi et al., 2006) DLPFC in MD (AltarB, 2007; AltarC, 2007) No effect found Numbl — DLPFC in BP (Dobrin, 2007), DLPFC in MD No effect found (AltarB, 2007) : decreased expression, : increased expression, SZ: schizophrenia patients, BP: bipolar affective disorder patients, MD: major depression patients, APD: antipsychotic drugs, AD: antidepressants, MS: mood stabilisers, DLPFC: dorsolateral prefrontal cortex, PCP: phencylcidine ^ Stanley Medical Research Institute online genomics database of a meta-analysis of 12 array studies using the Consortium collection of 15/15/15 SZ/BP/MD brains and Array collection of 35/35/35 SZ/BP/MD brains. Name in brackets indicates specific study showing altered change, if not in array or consortium meta- analyses. * Demographic data provided by Stanley Medical Research Institute, significance set at p<0.05; FC>1.3 From the list of validated dysregulated genes, we chose Kcna1, KChip3and Nedd4 for further expression analysis, as we had verified their transcriptional regulation by multiple APDs and commercial antibodies were available for their protein products. Using Western blot analysis of whole brain lysates we were able to confirm translation of gene expression changes into significant protein expression changes after haloperidol (Kv1.1 and KCHIP3) and olanzapine (KCHIP3 and NEDD4) treatment.

An important caveat of the work described thus far is the use of whole brain tissue to analyse molecular expression changes, the disadvantages of which are outlined in Section 3.3.3. An attempt to define regional expression for Kv1.1 and KCHIP3 and their regulation by APDs is made in Chapter 6.

4.3.2 Relevance of validated genes in schizophrenia treatment 4.3.2.1 Antipsychotic drug effects on voltage-gated ion channel genes Kcna1 and Kcnab1

Voltage-gated potassium (Kv) channels modulate the electrical activity of neurons and are comprised of pore-forming -subunits and auxiliary -subunits that regulate Kv channel activity (Rettig et al., 1994). In this study we found APD regulation of two genes encoding subunits of the Kv1 channel: potassium voltage- gated channel, shaker-related subfamily, member 1 (Kcna1) and beta member 1

(Kcnab1). Kcna1 encodes Kv1.1, one of seven subunits that is highly expressed in some regions of the adult rat brain, particularly the midbrain, cerebellum, cerebral cortex, and hippocampus (Beckh & Pongs, 1990). Kcnab1 encodes three isoforms, of which Kv1 is the major species in the rodent nervous system. Kcnab1 is highly expressed in the hippocampus and caudate putamen, as well as in the dentate gyrus, thalamic nuclei, neocortex and cerebellum (Rettig et al., 1994).

Immunohistochemical experiments reveal that Kv1 and Kv1.1 colocalise in neurons of the rat brain, particularly in the molecular layer of the dentate gyrus and interneurons in CA1-CA3 hippocampal fields (Rhodes et al., 1997). Kv1 and

Kv1.1 interact in vitro and co-injection of these subunit mRNAs in Xenopus oocytes

142 showed that this interaction results in modulation of the Kv channel gating properties (Rettig et al., 1994).

KChip3

Our study also found alterations in Kv channel-interacting protein 3 (KChip3), with decreased mRNA and protein following 7-day APD treatment. KCHIP3 is the third annotated member of a family of Kv channel-interacting proteins that modulate Kv4 -subunits and increase A-type Kv currents, important in neuronal excitability in the brain (An et al., 2000). KCHIP3 is highly expressed throughout the brain, where it has three distinct functions and three associated pseudonyms: Calsenilin, DREAM and KCHIP3 (Buxbaum, 2004). KCHIP3 is localised to human chromosome 2q11.1, a region linked to schizophrenia susceptibilty (Lewis et al., 2003).

KCHIP3 was also called calsenilin due to its binding of both calcium and presenilin, the latter of which is associated with early-onset familial Alzheimer’s disease (AD) (Buxbaum et al., 1998). Calsenilin exhibits presenilin-dependent apoptosis and amyloidogenesis in rat neuronal cell culture (Jo et al., 2003; Jo et al., 2004). Additionally, calsenilin mRNA and protein are upregulated in the brains of sporadic AD patients compared to controls (Jin et al., 2005). KCHIP3 is also known as DREAM as it binds the dynorphin response element (DRE) in the promoter of prodynorphin, a precursor to the opiod neuropeptide dynorphin, where it acts as a transcriptional repressor or DRE antagonistic modulator (DREAM) (Carrion et al., 1998).

In an attempt to confirm these alternate functions for calsenilin in the brain, mouse knockouts have been created. Dream-/- mice have increased prodynorphin mRNA expression in the spinal cord and exhibit reductions in both acute and chronic pain sensitivity, indicating a role for DREAM in modulating nociception (Cheng et al., 2002). In a separate study, with deletion of a different targeted genomic locus, Kchip3-/- mice were shown to have reduced -amyloid peptide production, supporting a role in amyloidogenesis (Lilliehook et al., 2003). They

143 also displayed enhanced long-term potentiation in the dentate gyrus and a concomitant reduction in A-type Kv current, which supports the assignment of this protein as KCHIP3 (Lilliehook et al., 2003). In these mice, reductions in dynorphin seen in the Dream-/- mice were not replicated, although behavioural studies revealed decreased pain sensitivity consistent with a role in nociception. Given the various neuronal functions of KCHIP3, its regulation by multiple APDs could have multiple effects in the brains of APD-treated mice, although regional analysis will be required to ascertain the role important to its regulation.

Nedd4 Neural precursor cell expressed, developmentally down-regulated gene 4 (Nedd4) mRNA and protein were decreased in whole mouse brain by 7-day APD treatment in our study. Nedd4 is highly expressed in granular cells of the olfactory bulb, cerebellum (Kumar et al., 1997), hippocampus and cortical layers II, IV in adult mouse brain (Allen, 2006). NEDD4 interacts with some of the pore-forming

-subunits of neuronal voltage-gated sodium channels (Nav) in Xenopus oocytes, resulting in Nav channel inhibition that is dependent upon the enzymatic activity of NEDD4 (Fotia et al., 2004). Nav channels are crucial for neuronal cell development and plasticity as they control action potential initiation in excitable membranes (Catterall, 2000).

4.3.2.2 Genes up-regulated by multiple antipsychotic drugs Dlck1 The doublecortin–like kinase I gene (Dclk1) was up-regulated by all three APDs in our microarray analysis. This up-regulation was confirmed by QPCR analysis, with haloperidol showing statistically significant regulation. The doublecortin– like kinase (Dclk) gene encodes two main isoforms that are both highly expressed in rodent brain. The long form of the protein (DCLK1) is expressed in fetal brain and has high homology to doublecortin (DCX), which is a microtubule-associated protein involved in neuronal migration (Francis et al., 1999). The short form of the protein is mainly expressed in adult CNS and lacks the DCX domain. This isoform is further subdivided into two splice variants: CARP, which lacks protein

144 kinase activity; and DCLK-short, also called CPG16 (Burgess & Reiner, 2002). CARP regulates neuronal cell survival (Kruidering et al., 2001) and its expression is induced by treatment of rats with kainate, dopamine and cocaine (Berke et al., 1998). Recent studies have supported a role for DCLK1 in neurogenesis, neuronal migration, and axonal wiring in the developing mouse brain (Weimer & Anton, 2006). DCLK1 has been described as a microtubule associated protein with a role in cell fate determination during neurogenesis (Shu et al., 2006). DCLK1-deficient mice, with the CPG-16 and CARP isoforms only, have a decreased corpus callosum, indicating fibre tract abnormalities. RNAi knockdown of Dclk in mice indicates developmental deficits in neuronal migration (Koizumi et al., 2006), specifically of cortical interneurons (Friocourt et al., 2007). Mutated mice with a deletion of all DCLK1 isoforms except CARP are normal (Deuel et al., 2006). However, when they are combined with doublecortin knockouts they exhibit abnormal axonal and dendritic development, as well as deficits in synaptic vesicle transport (Deuel et al., 2006). This last function, pertaining to synaptic transmission, may be most relevant to regulation of Dclk1 by APDs in adult brains.

AK036626 Our microarray analysis revealed that the expressed sequence tag AK036626 was up-regulated by clozapine and olanzapine and to a lesser extent haloperidol. This up-regulation by all three APDs was confirmed using QPCR analysis. Subsequent sequence analysis of AK036626 revealed that this transcript contains the third exon of Schwannomin interacting protein I (Schip1). Schwannomin is a tumour supressor protein that SCHIP1 was found to interact with during a yeast two-hybrid screen (Goutebroze et al., 2000). SCHIP1 is highly expressed in human brain, skeletal muscle and heart, where it co-localises with actin at the cytoskeleton and with some isoforms of schwannomin beneath the cytoplasmic membrane in vitro (Goutebroze et al., 2000).

145 4.3.2.3 Genes down-regulated by multiple antipsychotic drugs S100a9 Our microarray analysis showed that all three APDs decreased the expression of S100 calcium-binding protein A9 (S100a9) compared to controls, with clozapine and olanzapine showing greater than 2.5-fold change. Using QPCR analysis we verified the down-regulation of S100a9 by clozapine and olanzapine. S100A9, also known as calgranulin B, has previously been suggested as a biomarker for schizophrenia as it is up-regulated in the blood of patients with schizophrenia (Tsuang et al., 2005). A separate study found a decrease in the level of calgranulin B in the cerebrospinal fluid of schizophrenic patients with the effect of haloperidol treatment reported to be neglible (Poltorak et al., 1995). However, a transcript profiling study of rats treated chronically with APDs found increased S100a9 following olanzapine treatment (Fatemi et al., 2006). This previous chronic APD treatment study, in combination with our 7-day analysis, indicates that calgranulin B may be transiently regulated by atypical APD treatment and therefore not a suitable biomarker candidate. Genes encoding members of the S100 family of calcium-binding proteins including S100a9 are clustered on human chromosome 1q21, a locus linked to schizophrenia susceptibility (Lewis et al., 2003).

Rapgef4 Rap guanine nucleotide exchange factor (GEF) 4 (Rapgef4) was downregulated more than 1.5-fold by all three APDs in our microarray analysis. By QPCR analysis, olanzapine-treated animals showed a statistically significant decrease in expression compared to controls. RAPGEF4, also known as CGEF2, is found in developing and mature brain tissue, in particular the cerebral cortex and hippocampus where it binds to cAMP (Kawasaki et al., 1998). Our finding that Rapgef4 is regulated by APDs is interesting given the suggestion that CGEF2 may modulate cAMP functioning in brain regions implicated in schizophrenia.

146 Mal In this study, myelin and lymphocyte protein (Mal) was down-regulated by both atypical APDs in the microarrays, with decreased expression verified in olanzapine-treated animals by QPCR analysis. MAL is an integral membrane protein that is expressed in oligodendrocytes and Schwann cells in the nervous system, where it is a structural component of compact myelin (Frank et al., 1998). Previous studies have implicated MAL dysregulation in human neurological disorders. MAL was one of five oligodendrocyte-expressed genes that was significantly down-regulated in the dorsolateral prefrontal cortex of medicated schizophrenic patients compared to controls (Hakak et al., 2001). Similarly, MAL was one of eight genes encoding structural components of myelin that was found to be decreased in the temporal cortex of patients with major depressive disorder (Aston et al., 2005). The finding that Mal is regulated by APDs may indicate a role for drug treatment in the alteration of MAL in schizophrenic brains.

Bat2 Our microarray results show that all three APDs down-regulate HLA-B associated transcript 2 (Bat2) expression. In particular, treatment with olanzapine and haloperidol resulted in more than 3-fold down-regulation. QPCR analysis verified a trend for down-regulation across all treatment groups, reaching statistical significance for olanzapine-treated animals. BAT2 is highly expressed in the embryonic central nervous system (Schneiders et al., 2005). Bat2 is widely expressed in the brain of 8-week old male C57Bl/6 mice with high expression in the hippocampus and molecular cortical layers (Allen, 2006). There is some evidence that certain alleles of Bat2 may provide protection against human autoimmune disorders as there are associations between Bat2 polymorphisms and susceptibility to rheumatoid arthritis (Singal et al., 2000). This is interesting given that the literature has noted a long-standing inverse correlation between schizophrenia and rheumatoid arthritis. A meta-analysis of over 30,000 schizophrenic patients found that these patients had less than one-third the rate of rheumatoid arthritis than that seen in the general population (Oken & Schulzer, 1999). Bat2 is localised within the conserved class III region of the

147 major histocompatibility complex at human chromosome 6p21.33, a region associated with schizophrenia susceptibility (Lewis et al., 2003).

Numbl Our transcript profiling analysis revealed that all three APDs decreased Numb- like (Numbl) expression in mouse brain compared to controls. This trend was confirmed by QPCR analysis, with clozapine-treated animals showing statistically significant down-regulation. In Drosophila, Numb is important in determining daughter cell fate during cell division in the sensory nervous system (Uemura et al., 1989). There are two partially redundant Numb homologues in vertebrates, Numb and Numbl. Numbl is highly expressed in the cytoplasm of cells in the developing mouse nervous system as well as in adult brain tissue (Zhong et al., 1997). In the brains of C57Bl/6 8-week-old male mice, Numbl is expressed mostly in the hippocampus, suggesting this would be a region of interest for further study (Allen, 2006). Both Numb and Numbl encode proteins that are important in determining cell fate during neurogenesis in the mammalian nervous system (Petersen et al., 2004). They function by directing cell polarity in radial glia, which is important in maintaining neurogenesis and supporting neurite migration (Rasin et al., 2007). Mice with postnatal silencing of Numb and Numbl in subventricular zone radial glia show enlarged ventricles through disruptions to cellular integrity at the lateral wall and abnormalities in neurogenesis (Kuo et al., 2006). A recent genetic study revealed association between a polyglutamine repeat in NUMBL and schizophrenia in patients from Brazilian and Danish cohorts (Passos Gregorio et al., 2006).

Cspg3 Chondroitin sulphate proteoglycan 3 (Cspg3) was down-regulated by all three APDs in our study. In particular, clozapine and haloperidol treatment decreased Cspg3 expression by greater than 1.5-fold change compared to controls. The down-regulation of Cspg3 by all three APDs was verified using QPCR analysis, with clozapine and olanzapine treatments reaching a statistically significant difference. CSPG3, also known as neurocan, is a large protein with brain-specific

148 expression in rats (Rauch et al., 1991) and humans (Prange et al., 1998). Neurocan binds neurons and cell adhesion molecules, inhibiting neuronal adhesion and neurite growth in vivo (Friedlander et al., 1994), and is up-regulated after injury in the mammalian CNS (Properzi et al., 2003). Neurocan expression is developmentally regulated in the rat brain, with widespread expression in embryonic neuronal tissue (Engel et al., 1996). In early postnatal rat brain neurocan is highly expressed in prospective white matter with some neuronal expression (Rauch et al., 1991). In adult brain tissue, neurocan is expressed predominantly in molecular and granular cell layers, as a truncated form of the protein (Rauch et al., 1991). Neurocan is highly expressed in the adult cortex and hippocampus (Allen, 2006), suggesting these as regions for further analysis for Cspg3 dysregulation by APDs.

-catenin In our microarray study, -catenin mRNA was down-regulated by 7-day haloperidol and olanzapine treatment, a result that we verified in olanzapine- treated animals by QPCR analysis. -catenin has many known functions in mammalian cells, including cell adhesion and signalling through the Wnt pathway. Particularly relevant to its regulation by APDs may be its suggested involvement in the organisation of synaptic vesicles (Bamji et al., 2003). Conditional knockout of -catenin in hippocampal pyramidal neurons in vivo resulted in morphological changes at the synapse, including a reduction in the synaptic vesicle reserve pool at the presynaptic terminal, which is required during long-term synaptic transmission (Bamji et al., 2003). The involvement of -catenin in schizophrenia pathophysiology has been considered previously. Immunohistochemical analysis revealed a decrease in diffuse -catenin staining in the intraneuronal cytosol, with no change in the membrane-bound form, in the hippocampus of schizophrenic patients (Cotter et al., 1998).

Validated genes regulated by multiple APDs in this study are involved in neurogenesis, cell adhesion, myelination, cAMP-regulated signalling, synaptic

149 plasticity and voltage-gated ion channels, the latter of which will be explored in further detail.

4.3.3 Voltage-gated potassium channels in antipsychotic drug action In this study we found specific effects of multiple APDs on three genes regulating voltage-gated potassium channels – Kcna1, Kcnab1 and KChip3. Part of the mechanism of action of currently prescribed APDs may be the modulation of neuronal voltage-gated potassium (Kv) channels that are integral for neuronal electrical activity and neurotransmission (Trimmer & Rhodes, 2004).

(A) (B) Action potential Action potential

2+ Kv channel Na Nav channel opens closes Pre-synaptic membrane 0 mV K+

2+ Resting state Ca

Synaptic cleft DA Nav channel Post- opens DRD2 synaptic terminal

Figure 4.3 Voltage-gated potassium (Kv) channels are integral for neurotransmission. Diagrammatical representation of the (A) electrical and (B) chemical propagation of an action potential down an axon, culminating in voltage-gated sodium (Nav) channels opening and elevation of membrane potential from resting state. This causes intracellular calcium levels to increase and vesicles containing neurotransmitters, such as dopamine (DA), to fuse with the pre-synaptic membrane. Neurotransmitters are then released into the synaptic cleft and bind to receptors on the post-synaptic membrane, like dopamine D2 receptors (DRD2) in the striatum, where signal is propagated in the post-synaptic terminal. Once maximum potential is attained,

Nav channels close and Kv channels open, with potassium ions effluxing from the presynaptic membrane to facilitate resetting of resting state at negative electrical potential.

150 In particular, Kv channels are crucial for resetting electrical potential after a neuron fires. This resetting facilitates repeated neuronal activity that modulates neurotransmitter release (Fig. 4.3).

There are more than 70 Kv channel subunit genes (the KCN family) and these encode a superfamily of twelve Kv channels (Kv1-Kv12) (Li et al., 2006). Kv channel -subunits are numbered after the channel designation, so Kv1.1 is the

1-subunit of the Kv1 channel and is encoded by the KCNA1 gene, whereas Kv1 is the 1-subunit of these channels encoded by KCNAB1. The channel pore is formed from four heterogenous or homogenous -subunits, each consisting of six transmembrane domains, with cytoplasmic or transmembrane auxiliary - subunits interacting at the cytoplasmic pore opening or through the cell membrane to alter channel gating properties (Trimmer & Rhodes, 2004) (Fig. 4.4).

Extracellular II III I IV

Figure 4.4 Voltage-gated Cytoplasmic K+

potassium channel structure.

Kv channels produce two main types of current, termed delayed rectifier and A- type currents. The delayed rectifier channels are present on axons at synaptic terminals and juxtaparanodal to the nodes of Ranvier where they are involved in action potential propagation (Wang et al., 1993) (Fig. 4.5).

TERMINAL SOMA AXON DENDRITES

Figure 4.5 Neuronal localisation of Kv channels. Delayed rectifier channels (pink cylinders) and A-type transient current producing channels (blue cylinders) are differentially localised in neuronal cells. Adapted from Dodson & Forsythe, 2004.

151 A-type currents are present postsynaptically at somatodendritic regions to receive synaptic input and inhibit action potential back propagation as well as in presynaptic terminals to control action potential initiation and neurotransmission (reviewed in Pongs, 1999).

Delayed rectifier channels produce current with only a minor effect during the first action potential although these raise the threshold for further action potentials, reducing aberrant neuronal firing (Dodson & Forsythe, 2004). In contrast, A-type or fast-inactivating currents activate upon membrane depolarisation, generating a transient conductance charge that facilitates neurotransmission (Dodson & Forsythe, 2004).

Kv1.1 -subunits usually form delayed rectifier current channels, although Kv1 interaction results in a switch to A-type current (Rettig et al., 1994). In this study of 7-day APD treatment we see increased Kv1.1 mRNA and protein expression with decreased Kv1 mRNA expression. If this is occurring in the same cells, these transcriptional changes would presumably increase the delayed rectifier current, reducing aberrant neuronal firing. Alternatively, if increased Kv1.1- containing channels were interacting with Kv1 subunits this would increase A- type current.

We also see decreased KCHIP3 mRNA and protein in this 7-day APD treatment study. KCHIP3 interacts with Kv4 channels, which produce the A-type current. However, KCHIP3 modulation inhibits the characteristic fast-inactivation of these currents (An et al., 2000), suggesting decreased KCHIP3 expression would release inhibition on A-type currents. Increased A-type currents would prolong action potential activation, thereby facilitating neurotransmitter release (reviewed in Pongs, 1999). In order to ascertain the neuronal networks that dampening of aberrant neuronal firing and facilitation of increased neurotransmission are acting upon, it is important to explore the regional expression changes of these channel subunits following APD administration. Regional expression experiments in 7-day haloperidol treated mice are detailed in Chapter 6.

152

The modulation of Kv channels by APDs has been explored previously using electrophysiological techniques (Ogata et al., 1989; Zhou et al., 2006). Of particular relevance is the inhibition of Kv1.1 channel current upon treatment of chinese hamster ovary cells with pimozide, a typical APD (Zhang et al., 2003).

This is contradictory to our findings of an up-regulation of Kv1.1 mRNA and protein following APD treatment. When co-expressed with Kv1 the Kv1 current inhibition was reversed (Zhang et al., 2003). Our data suggest that this may be due to opposing regulation of these subunit gene expressions by APDs. Electrophysiological studies with haloperidol have found a suppression of sodium currents (Ito et al., 1997) and currents from Kv1.1 and Kv1.4 channels in rat ganglion cells (Akamine et al., 2002), the latter of which would be consistent with decreased KChip3 mRNA expression. A separate study in Xenopus oocytes, however, found minimal inhibition of these channel subtypes by haloperidol (Suessbrich et al., 1997).

There are many reasons why Kv channels may be regulated by APDs, but two main areas that may have functional relevance are discussed here: altered neurogenesis in Kv1.1 deficient animal models and the role for KCHIP3 in midbrain dopaminergic neuron activity.

Mutations in human KCNA1 cause autosomal dominant episodic ataxia by decreasing potassium current and affecting channel assembly and trafficking (Rea et al., 2002). This disorder is modelled by homozygous knockout of Kcna1 in mice, which causes recurrent epileptic seizures (Smart et al., 1998; Rea et al., 2002). This hyperexcitability is also seen in the endogenous mutant megencephaly mouse (mceph) which has a mutation in Kcna1, leading to truncated Kv1.1 protein (Petersson et al., 2003) and enlarged hippocampus and ventral cortex (Persson et al., 2007). This additional phenotype is caused by a hippocampal-specific doubling of neurons, implicating Kv1.1 in neurogenesis and neuronal survival

(Almgren et al., 2007). That APDs modulate Kv1.1, an important factor in neurogenesis, is particularly interesting, given accumulating evidence for altered

153 adult neurogenesis in schizophrenia (Reif et al., 2007). However, the possible effect of APD treatment on neurogenesis remains controversial (Halim et al., 2004; Toro & Deakin, 2007). Mceph-/- mutants also have increased BDNF mRNA expression (Lavebratt et al., 2006) indicating Kv1.1 may be a negative regulator of this neurotrophic factor. The up-regulation of Kcna1 by APDs in this study is interesting given that BDNF is down-regulated by chronic haloperidol treatment in the rat hippocampus (Lipska et al., 2001; Bai et al., 2003). The importance of this neurotrophic factor in schizophrenia is indicated by the finding of decreased BDNF mRNA and protein in the cortex of schizophrenia patients (Weickert et al., 2003).

As a further note, behavioural tests have indicated antisense inhibition of Kv1.1 impairs associative memory (Meiri et al., 1997) and that transient expression of

Kv1.1 mRNA in the hippocampus is linked to associative learning (Kourrich et al., 2005).

Aside from a possible function for Kv1.1 in hippocampal-related behaviour and neurogenesis, there is evidence to suggest a direct correlation between KChip3 mRNA expression levels in dopaminergic neurons in the substantia nigra and densities of the A-type potassium channels that control spontaneous electrical activity of these midbrain dopaminergic neurons (Liss et al., 2001). Spontaneous activity is tuned by an intrinsic pacemaker and triggers dopamine release in projections from the midbrain. The modulation of spontaneous dopamine neuronal activity by Kchip3 mRNA is particularly interesting given the depolarisation block hypothesis of APD action. This hypothesis follows observations that there is increased activity of midbrain dopaminergic neurons following acute APD treatment that causes hyperexcitablity and decreased activity of these neurons in chronic APD treatment (Grace et al., 1997).

Kchip3 mRNA and protein regulation by multiple APDs in this study may pertain to increased striatal dopamine release in schizophrenic patients, which is directly correlated with psychosis and is blocked by APD treatment (Abi-Dargham et al.,

154 2000). Furthermore, KChip3 regulation by multiple APDs may play a role in altering mesocortical dopamine neuronal firing, which is believed to underlie the cognitive deficits in schizophrenia (Abi-Dargham, 2004). Alternatively, KChip3 regulation may represent part of the mechanism of action of the extrapyramidal side effects of these drugs, which have been associated with depolarisation of the nigrostriatal dopamine system (Grace et al., 1997).

In order to explore the various functional roles of Kv channel regulation by multiple APDs we must first better define this regulation in our animal model.

Characterisation of Kv1.1 and KChip3 mRNA and protein localisation as well as quantification of expression regulation by haloperidol, is carried out in Chapter 6.

155 156

Chapter 5

CHRONIC ANTIPSYCHOTIC DRUG TREATMENT REGULATION of GENE and PROTEIN EXPRESSION in MOUSE WHOLE BRAIN TISSUE

157 5.1 INTRODUCTION

While there is clinical evidence that the onset of action of APDs in treating the symptoms of schizophrenia occurs earlier than originally thought, these drugs are used chronically in patients. Some of the long-term effects of APDs are hypothesised to occur through eliciting adaptive changes in postsynaptic terminal gene expression (Hyman & Nestler, 1996). Evidence for this is the early activation of transcriptional regulators, like c-fos, after APD treatment in rodents (Miller, 1990), in which dopamine D2-receptor activation links immediate neurochemical effects with long-term transcriptional regulation (Thomas, 2006). Interestingly, the regional protein expression of immediate early genes differs between the two classes of APDs (Fibiger, 1994). For example, c-fos is highly expressed in the striatum and nucleus accumbens after acute haloperidol treatment of rats (MacGibbon et al., 1994). Following acute clozapine treatment, c-fos is also expressed in the striatum (MacGibbon et al., 1994), as well as in the PFC (Deutch & Duman, 1996) and thalamic nuclei (Deutch et al., 1995), where haloperidol does not induce expression. The differential regional expression of immediate early genes may explain the long-term differences in the clinical efficacy of typical and atypical antipsychotics in treating schizophrenia. It may also explain differences in their adverse effects like tardive dyskinesia, which has a 5-fold increased risk after chronic treatment with conventional compared to atypical APDs (Remington, 2007).

There have been a number of previous studies examining the transcriptional profile of brain tissue from rodents treated chronically with one or two APDs (Table 1.8). Chronic treatment time-points have been chosen to investigate long- term gene expression changes effected by APDs. Chronic APD treatment in rodents also causes some adverse effects, like symptoms of the metabolic syndrome seen in rodents treated for 20 days with olanzapine (Coccurello et al., 2006) and acute EPS-like movements in rodents treated for 21 days with haloperidol (Egan et al., 1996). Yet these time-points avoid adverse effects of longer treatment like tardive dyskinesia-like movements that appear after 30

158 weeks conventional APD treatment in rats (Egan et al., 1996). The possibility that altered gene expression may be causing these effects must be considered in transcript profiling studies (Thomas, 2006). However, the use of multiple conventional and atypical APDs, which have different adverse effect profiles, can be used to discriminate between adverse side effects and antipsychotic-specific therapeutic effects.

In this chapter we aim to explore the genes regulated by 28-day treatment of mice with multiple APDs in our microarray study, and to validate the regulation of some of these candidate genes.

5.2 RESULTS

5.2.1 Microrray bioinformatical analysis Gene expression profiling analysis of the data generated in Chapter 3 was undertaken using a stringent cut-off (p< 0.05 and fold change> 1.5). This analysis revealed transcripts altered by chronic (28 days) treatment with multiple APDs (Fig. 3.9). Due to the excessive transcriptional regulation by olanzapine in this study, a 2-fold change cut-off was chosen for further analysis of this treatment group. In total, 175 genes were up-regulated and 68 genes down-regulated by APDs using this study paradigm and these had diverse subcellular localisations and functions (For full list of altered genes, see Appendix 1). Once the normalisation of the arrays was complete, we ran the data through Ingenuity Pathways Analysis software – a program that creates networks from altered genes based on published interaction results. From the networks created in Ingenuity we selected the top network for each treatment group (based on the number of genes altered in microarrays) and merged them into one network (Fig. 5.1).

159

Figure 5.1 Ingenuity Pathway Analysis merged top network. Published interactions between transcripts altered by chronic APD treatment appear in networks, which are ranked by number of genes changed. The top ranked network from each APD treatment was merged to form a single network of interacting proteins. Pink circles represent genes up-regulated by APDs. Blue circles are genes down-regulated by APDs. Grey rectangles are genes not regulated by APDs in this study, although interacting with regulated genes. AKT1: v-akt murine thymoma viral oncogene homolog 1; ANP32A: acidic leucine-rich nuclear phosphoprotein 32A; BDNF: brain-derived neurotrophic factor; CACNA1B: calcium channel, voltage-dependent, N type, -1B subunit; CSPG3: chondroitin sulphate proteoglycan 3; DNMT1: DNA methyltransferase 1; GNB1: G-protein 1 subunit; HRAS: v-ras Harvey rat sarcoma viral oncogene homolog; KCNJ5: potassium inwardly-rectifying channel, subfamily J, member 5; NAPB: N-ethylmaleimide-sensitive factor attachment protein, ; SCN2B: sodium channel, voltage-gated, type II, ; SOD1: superoxide dismutase 1; SLC12A5: solute carrier family 12, (potassium-chloride transporter) 5; TNR: tenascin R; SYT1: synaptogamin 1.

160 Table 5.1 Candidate genes chosen for QPCR analysis due to their regulation in chronic antipsychotic drug treatment microarray analysis (false discovery rate< 0.05, fold change > 1.5).

Mouse Clozapine Haloperidol Olanzapine Entrez FDR- FDR- FDR- Subcellular Biological function and/or prior Gene Gene ID FC^ value FC^ value FC^ value Family location Linkage* association with SZ or APD treatment Genes also regulated by 7-day antipsychotic drug treatment in this project Nedd4 17999 0.147 0.973 -1.342 0.036 -0.042 0.923 enzyme cytoplasm Altered in 7-day APD treatment study Gabra1 14394 -1.429 0.038 ion plasma Altered in 7-day APD treatment study ; channel membrane mRNA in SZ PFC (Hakak et al., 2001) Cspg3 13004 -1.101 0.046 -0.102 0.756 -0.762 0.06 other unknown Altered in 7-day APD treatment study Serpini1 20713 0.518 0.382 -1.01 0.041 0.218 0.646 other extracellular Altered in 7-day APD treatment study ; space mRNA in SZ PFC (Hakak et al., 2001) Kns2 16593 -0.01 0.87 -1.415 0.014 0.409 0.334 other cytoplasm Altered in 7-day APD treatment study Syt1 20979 -2.714 0.004 transporter cytoplasm Altered in 7-day APD treatment study Genes in the top network in Ingenuity Pathway Analysis Dnmt1 13433 -0.039 0.486 -0.215 0.773 1.134 0.04 enzyme nucleus in cortical GABA neurons in SZ (Veldic et al., 2005) Scn2b 72821 -1.201 0.023 0.595 0.179 -1.148 0.028 ion plasma 11q23.3 Voltage-gated sodium channel -subunit channel membrane Napb 17957 0.187 0.779 -1.605 0.014 -0.277 0.655 transporter cytoplasm Interacts with ionotropic AMPA receptor, GRIA2 Gnb1 14688 1.607 0.06 1.576 0.061 2.589 0.023 enzyme plasma Activates Akt1 Bommakanti et al., 1998); membrane induced by Nrg1 (Fu et al.,1999); by clozapine (Le-Niculescu et al., 2007) Sod1 20655 1.264 0.011 0.361 0.175 1.189 0.007 enzyme cytoplasm SOD1 by clozapine & olanzapine in vitro (Bai et al., 2002; Li et al., 1999) Anp32a 11737 1.268 0.009 -0.685 0.039 1.131 0.01 other nucleus 15q23.3 ubiquitously expressed apoptosis/ signaling protein Genes with functional relevance to schizophrenia or its treatment Tac2 21334 0.397 0.687 0.181 0.818 1.276 0.044 other extracellular Neuromodulator; Suggested new APD space action target (Meltzer et al., 2004) Grm5 108071 0.034 0.989 -1.037 0.038 -0.372 0.446 G-protein plasma mGluR5 gene linked to SZ (Devon et al., coupled membrane 2001); mGluR5 KO mouse SZ behavioural receptor phenoypes (Kinney et al., 2003)

Gad1 14415 -1.233 0.017 enzyme cytoplasm Proposed SZ susceptibility gene (see section 1.3.4.5) Mbp 17196 -0.115 0.707 -1.012 0.037 0.027 0.993 other extracellular 18q23.3 myelination protein; catatonic SZ space (Rimon et al., 1986); female SZ (Chambers et al., 2004); NC CSF (Steiner et al., 2006) Fgfr1 14182 -0.086 0.783 -0.047 0.931 1.135 0.034 kinase plasma Growth factor associated with membrane neurogenesis (Weickert et al., 2005) Vegfb 22340 -0.317 0.393 -0.281 0.318 1.185 0.046 growth extracellular Growth factor stimulates neurogenesis factor space (Sun et al., 2006); Grm2 108068 -0.086 0.58 0.096 0.715 1.197 0.037 G-protein plasma mGluR2 gene implicated in SZ (Joo et al., coupled membrane 2001) receptor

Ddc 13195 0.617 0.024 -0.261 0.762 1.06 0.022 enzyme cytoplasm mRNA haloperidol (Buckland et al., 1992); NC (Vernaleken et al., 2006), activity male SZ (Gunder et al., 2003)

Cldn5 12741 0.419 0.079 0.15 0.974 1.155 0.005 other plasma 22q11.21 Regulates BBB permeability; 3 +ve genetic membrane association studies with SZ (Ye et al., 2005; Wei et al., 2005; Sun et al., 2004)

^ FC is log transformed, so ±0.6 is approximately equal to 1.5-fold difference in antipsychotic treatment compared to control. Bold indicates p < 0.05. * linkage of human homologue to a chromosomal loci implicated in schizophrenia linkage meta-analysis (Lewis et al., 2003) FC: fold-change, FDR: false discovery rate, SZ: schizophrenia, APD: antipsychotic drug, : increased, : decreased, NC: no change, BBB: blood brain barrier, mGluR: metabotropic glutamate receptor, KO: knockout, +ve: positive 5.2.2 Quantitative real-time PCR validation From the list of 243 genes altered in the 28-day treatment microarray analysis, 21 genes were chosen to undergo validation by QPCR (Table 5.1). These genes were selected for one of three factors: their regulation by multiple APDs, detected by microarray analysis, at the 7-day time-point (6 genes); their co-regulation by multiple APDs within the top network of altered genes, as defined by Ingenuity Pathway Analysis software (6 genes) (Fig. 5.1); or their functional association with schizophrenia (9 genes). The expression of these genes, and of Kcna1 and KChip3, was explored by QPCR at this 28-day time-point.

Of the six genes altered by microarray analysis at both 7 days and 28 days, three showed validated gene expression changes by QPCR: neural precursor cell expressed, developmentally downregulated gene 4 (Nedd4), chondroitin sulfate proteoglycan 3 (Cspg3) and kinesin light chain gene 1 (Kns2) (Fig. 5.2A). Additionally, although its regulation was not detected by microarray analysis at this chronic time-point, KChip3 mRNA was altered by haloperidol treatment in QPCR analysis, indicating its expression may be more chronically regulated. Kcna1 mRNA was not altered in whole brains of mice following 28-day treatment, indicating its regulation at 7 days may be transient (Fig. 5.2A).

Six genes that were regulated by multiple APDs in the top Ingenuity network were analysed by QPCR, with validated regulation of four of these genes: sodium channel, voltage-gated, type II, beta (Scn2b), G-protein 1 subunit (Gnb1), superoxide dismutase 1 (Sod1) and acidic leucine-rich nuclear phosphoprotein 32 family member A (Anp32a) (Fig. 5.2B).

From reading the literature regarding schizophrenia and its treatment, we also chose some functionally relevant candidates for validation from the list of altered transcripts to analyse by QPCR. Of the nine genes we chose for their biological function, only three genes were found to be regulated by APDs in our QPCR analysis: neurokinin B (Tac2), vascular endothelial growth factor B (Vegfb) and dopamine decarboxylase (Ddc) (Fig. 5.2C).

163 (A)1.6 Saline Clozapine Haloperidol Olanzapine 1.4

1.2 DOWN

* UP 1.0 * * * * 0.8 *

0.6 *

0.4

Normalised mRNA expression Normalised mRNA 0.2

0.0 Nedd4 Gabra1 Cspg3 Serpini Kns2 Kchip3 Syt1 Kcna1 (B) 2.0 ** 1.8 Saline Clozapine Haloperidol Olanzapine ** * * 1.6 *

1.4 DOWN *

1.2 UP 1.0 * 0.8 * 0.6 * 0.4 Normalised mRNA expression Normalised mRNA 0.2 0.0 Dnmt1 Scn2b Napb Gnb1 Sod1 Anp32a

1.4 DOWN * **

1.2 UP 1.0 * 0.8

0.6

0.4 Normalised mRNA expression Normalised mRNA 0.2

0.0 Tac2 Grm5 Gad1 Mbp Fgfr1 Vegfb Grm2 Ddc Cldn5

Figure 5.2 Quantitative RT-PCR validation of chronic time-point microarrays. Genes significantly (p< 0.05; fold-change> 1.5) down-regulated (left side) or up-regulated (right side) by multiple antipsychotic drug treatments in chronic time-point microarray analysis (A) Genes also regulated in 7-day treatment study. (B) Genes in top Ingenuity network. (C) Genes with possible functional significance in schizophrenia (where only treatment groups with altered expression in microarrays were tested). Columns represent the mean expression (95% confidence interval) of 12 individual mice per treatment group over three replication analyses, normalised to the geometric mean of Ubc and GAPDH mRNA expression. Asterisks denote a significant change between antipsychotic- treated and saline-treated mRNA expression. *= p <0.05, **= p< 0.01 5.2.3 Protein quantification by Western blot analysis We chose to look further at the regulation of Nedd4 by chronic APDs as it was also regulated at 7 days. In particular, we showed that its transcriptional downregulation was translated into a trend for reduced levels of NEDD4 protein by haloperidol (p=0.067) treatment and olanzapine treatment (p=0.075) although not for clozapine treatment (Fig. 5.3).

(A) Saline Haloperidol

NEDD4 ~115 kDa -actin ~42 kDa

Saline Olanzapine

NEDD4 ~115 kDa

-actin ~42 kDa

(B)

Figure 5.3 Western blot analysis and quantification of whole brain protein lysates after chronic antipsychotic drug treatment. (A) Immunoblots showing bands for NEDD4 (~ 115 kDa) in control and APD-treated animals. -actin (~42 kDa) was used to control equal protein loading. (B) Graph showing mean protein expression, measured by optical density of immunoblot bands, in control (n= 5) and antipsychotic drug-treated animals (n= 6/group) normalised to -actin protein expression. No significant differences were observed.

165 5.3 DISCUSSION

5.3.1 Chronic antipsychotic drug treatment verified altered genes. Since the 7-day APD experiment was conducted (Chapter 4), the trend in the field was to use more than one housekeeping gene to normalise QPCR expression results (Vandesompele et al., 2002). Like others, we found -actin was not a good housekeeping gene as its expression was altered by high doses of antipsychotic drugs administered chronically (Chiba et al., 2006). Instead, we used the geometric mean of Gapdh mRNA and ubiquitin C (Ubc) mRNA to normalise gene expression for our genes of interest at this chronic treatment time-point. These genes were not regulated by APD analysis during trial QPCR studies using the 28-day treatment mouse brain cDNAs (data not shown).

We chose 23 candidate genes for further validation by QPCR analysis based on: their regulation at both 7 and 28days, their position in the top network of altered genes as designated by Ingenuity Pathway Analysis software, or their biological association with schizophrenia. The regulation of 11 of these genes was validated by QPCR analysis. One replication caveat is that pooled RNA was used for the microarray yet individual RNAs for QPCR, which could have been a confounding factor. Interestingly there were two overlaps: Syt1 and Cspg3, included here as genes previously altered by 7-day APD treatment, are also in the Ingenuity top network. Cspg3 down-regulation in the microarray analysis was validated by QPCR analysis. Syt1 was significantly down-regulated by microarray analysis with haloperidol treatment, although a non-significant trend was seen by QPCR, indicating there may not have been enough statistical power to detect an association. Some of the validated candidate genes have been previously implicated in similar studies or in studies of postmortem tissue from schizophrenic patients (Table 5.2) (Higgs et al., 2006; Elashoff et al., 2007). Gnb1, Sod1, Anp32a and Ddc have previously shown the same regulation in other APD treatment studies that we see in this chronic treatment study (Buckland et al., 1992; Li et al., 1999; Bai et al., 2002; Le-Niculescu et al., 2007).

166 Table 5.2 Previous evidence for dysregulation of veriﬁed candidate genes in this 28-day study by APD treatment and/or in postmortem brain tissue expression analyses. Human postmortem brain analysis^ Gene Previous APD animal treatment studies mRNA expression change Psychiatric drug effect* Genes regulated in 7-day and 28-day treatment arrays Nedd4 — DLPFC in BP (Sklar, 2007) mood stabilisers in SZ Cspg3 chronic ola (Fatemi et al., 2006) DLPFC in MD (AltarB, 2007; AltarC, 2007) No effect found Kns2 chronic risperidone (Chen et al., 2005) DLPFC in BP, MD (AltarC, 2007); DLPFC in SZ (Hakak et lifetime APD in BP, lifetime APD in SZ al., 2001) Kchip3 acute clo & PCP (Le-Niculescu et al., DLPFC in BP, MD (AltarB, 2007) lifetime APD in BP 2007)

Genes altered in Ingenuity network 1 Scn2b - DLPFC in BP (Stanley consortium) lifetime APD & valproate in SZ, lithium in SZ, valproate in BP Gnb1 acute clo & PCP (Le-Niculescu et al., DLPFC in SZ, BP (Sklar, 2007; AltarC, 2007); DLPFC in SZ, lifetime APD & MS in BP, AD in SZ, 2007) BP (AltarA, 2007); cerebellum in SZ, BP (Feinberg, 2007) mood stabilisers in SZ Sod1 atypical APDs (Bai et al., 2002) & ola DLPFC in BP, MD (AltarC, 2007) valproate in SZ in PC12 cells (Li et al., 1999) Anp32a acute PCP (Le-Niculescu et al., 2007) DLPFC in BP (Stanley consortium) & MD (AltarC, 2007) lifetime APD in BP, lifetime APD in SZ

Genes with prior functional signiﬁcance in SZ Tac2 - DLPFC in BP (Stanley consortium) & SZ, MD (AltarB, 2007) lifetime APD in BP Vegfb - DLPFC in SZ, BP (Sklar, 2007; AltarC, 2007); DLPFC in BP valproate in BP, lifetime APD in SZ (AltarB, 2007) Ddc chronic hal (Buckland et al., 1992) DLPFC in BP (Chen, 2007) & SZ (AltarB, 2007), DLPFC in AD & valproate in SZ SZ (Dobrin, 2007) : decreased expression, : increased expression, clo: clozapine, hal: haloperidol, ola: olanzapine, PCP: phencyclidine, SZ: schizophrenia, BP: bipolar affective disorder, MD: major depression, APD: antipsychotic drugs, AD: antidepressants, DLPFC: dorsolateral prefrontal cortex ^ Stanley Medical Research Institute online database of a meta-analysis of 12 array studies using the Consortium collection of 15/15/15 SZ/BP/MD brains and Array collection of 35/35/35 SZ/BP/MD brains. Name in brackets indicates speciﬁc study showing altered change, if not in array or consortium meta-analyses.

* Demographic data provided by Stanley Medical Research Institute, signiﬁcance set at p<0.05; FC>1.3 Kchip3 expression was increased in a previous study of acute clozapine treatment (Le-Niculescu et al., 2007) and in this study we find down-regulation of Kchip3 with clozapine treatment after 7 days, yet no change at 28 days indicating Kchip3 may be dynamically regulated. In this study with haloperidol treatment we see down-regulated Kchip3 after 28 days and decreased KCHIP3 mRNA and protein after 7 days, indicating that there may be differential regulation of this gene between conventional and atypical APDs at the chronic time-point. Cspg3 has also been previously shown to increase in rat frontal cortex after 21-day olanzapine treatment, albeit at a much lower dose than used in this study (Fatemi et al., 2006). This is the opposite regulation to what we observed for higher dose olanzapine treatment at 7 and 28 days in whole mouse brain.

All genes with validated expression changes in this study have previously shown altered expression in postmortem tissue from patients with major mental illness (Table 5.2). Five genes showed altered expression in the DLPFC from patients with schizophrenia. Gnb1, Tac2 and Ddc showed the same regulation in some schizophrenia postmortem studies that we saw in animals treated with APDs perhaps indicating an effect of APD treatment in their differential postmortem regulation (AltarA, 2007; Chen, 2007; Dobrin, 2007; Feinberg, 2007). Gnb1, Ddc and Vegfb showed the opposite regulation in some schizophrenia postmortem studies to the changes that we saw in animals treated with APDs, perhaps indicating a normalising and therapeutic effect of APD treatment (AltarB, 2007; AltarC, 2007; Sklar, 2007). Lifetime APD treatment was found to be a statistically significant confounding factor in the analysis of these transcripts in postmortem brain tissue for seven genes. For Kns2 and Anp32a, the lifetime APD levels correlated negatively in bipolar disorder patients and positively in schizophrenia patients, which may suggest disease-specific factors interact differentially with neuroleptic treatment. For Scn2b, Vegfb and Tac2, the lifetime APD regulation correlated with that seen in our study of chronic APD treatment in mice, supporting our findings, whereas for KChip3 and Gnb1 the effect was in the opposite direction, indicating that other factors may contribute to their regulation in human brain tissue.

168

5.3.2 Genes with long-term altered expression Our first approach for choosing genes that may be important in chronic APD action was to validate those genes that were also altered in the 7-day analysis, and therefore may show a long-term regulation pattern. Three genes showed validated regulation at both 7-day and 28-day APD treatment analyses – Nedd4, Cspg3 and Kchip3. The trend for decreased expression of NEDD4 protein was also detected for haloperidol and olanzapine treatment, the latter of which is consistent with decreased expression of protein in the 7-day study. The chronic down-regulation of NEDD4 and KCHIP3 is further evidence for the regulation of voltage-gated ion channels by APDs as both of these proteins interact with voltage-gated ion channels and alter their properties.

Nedd4 is highly expressed in granular cells of the olfactory bulb, cerebellum (Kumar et al., 1997), hippocampus and cortical layers II, IV in adult mouse brain

(Allen, 2006). NEDD4 interaction inhibits Nav channels (Catterall, 2000) so presumably its decreased expression will have an activating effect, facilitating the flow of sodium ions into the presynaptic terminal during neurotransmission and allowing faster depolarisation of the membrane (see Fig. 4.3). Sodium channel currents are important in the firing of nigrostriatal dopamine neurons (Surmeier & Kitai, 1993) and in the dopamine altering regulation of cocaine (Zhang et al., 1998).

KCHIP3 is localised throughout the brain as part of a family of Kv channel- interacting proteins that modulate Kv4 -subunits and increase A-type Kv currents, important in neuronal excitability in the brain (An et al., 2000). Kchip3 mRNA is inversely correlated with electrical activity of midbrain DA neurons (Liss et al., 2001) so its long-term decreased expression in APD treatment may facilitate increased neurotransmission. Both KChip3 and Nedd4 regulation by long- term APD treatment may play a role in altering dopamine neurotransmission in the basal ganglia in the brains of patients with schizophrenia.

169

Chronic olanzapine treatment was shown to cause decreased expression of Cspg3, an effect also seen in our 7-day treatment studies with clozapine. Cspg3 is highly expressed during rodent brain development (Engel et al., 1996) and inhibits axonal growth during the CNS injury response in the adult brain (Properzi et al., 2003). Cspg3 transcription in astrocytes is sensitive to neuronal excitability (Schwarzacher et al., 2006). Its expression in the hippocampus and cortex of adult brain tissue (Allen, 2006) indicates that Cspg3 may be important in regions associated with schizophrenia, in response to altered neuronal activity following long-term atypical APD treatment.

Kns2 showed verified regulation by haloperidol in this chronic study despite its regulation by multiple APDs not being verified using QPCR in the 7-day analysis. This may indicate that its expression is increasing to a detectable level during chronic treatment or that this 28-day study (using twelve individual mouse brains for QPCR analysis, rather than the eight used during 7-day analysis) has increased power to detect such a change, which was the aim of increasing the replicates. Kinesin light chain 1 (Kns2) is a microtubule-associated protein that has been associated with Alzheimer’s disease through its role in transporting amyloid precursor protein in neurons (Andersson et al., 2007). A genetic association study in a case-control cohort using pathologically confirmed late onset Alzheimer’s disease patients has shown association between Alzheimer’s disease and an intronic polymorphism within KNS2, which may affect transcript splicing or regulation (Dhaenens et al., 2004). Additionally, a role for amyloid precursor protein mutations in schizophrenia has been proposed, although only a handful of schizophrenic patients have been reported with mutations in this gene (Arnholt et al., 1993; Jonsson et al., 1995).

5.3.3 Genes in top interaction network Our second approach for choosing genes that may be important in APD action was to verify those regulated within the top network of interacting altered genes as defined by Ingenuity Pathway Analysis (Fig. 5.1). Half of the genes chosen

170 from their top ranking in Ingenuity Pathway Analysis and regulation by multiple APDs have linkage to regions of susceptibility in schizophrenia (Table 5.1) (Lewis et al., 2003). This top network is also of note for including two proteins that are reported to be dysregulated in the brains of patients with schizophrenia: BDNF (see Section 1.3.4.4) and AKT1 (see Sections 1.3.4.7 and 1.4.2.2). While these proteins were not altered by chronic APD treatment in this study, they interact with proteins that were regulated in this study and have thus become the focus for candidate gene analysis.

Scn2b Sodium channel, voltage-gated, type II, beta (Scn2b) was downregulated by multiple APDs in this study, providing further evidence for the role of voltage- gated ion channel regulation in APD action. Like other voltage-gated ion channel -subunits, Scn2b modulates the gating properties of channels formed from voltage-gated sodium channel (Nav) -subunits (Catterall, 2000) with their co- expression required for physiological sodium current activity in vitro (Isom et al., 1995). Unlike other -subunits, these molecules additionally function in cell- adhesion, and Scn2b expands the plasma membrane size when co-expressed with

Nav -subunits in vitro by increasing the fusion of intracellular vesicles (Isom et al., 1995). In a rat model of neuropathic pain, Scn2b is down-regulated in the spinal cord, which as well as reducing the hyperexcitability associated with neuropathic pain may promote axonal growth and neurite migration to facilitate synaptogenesis (Blackburn-Munro & Fleetwood-Walker, 1999). It has also been suggested that SCN2B may be involved in demyelinating disorders as it is localised to human chromosomal regions linked to disorders with altered myelination and neurological deficits and it has a similar protein structure to a component of the myelin sheath (Eubanks et al., 1997). The previous association of Scn2b down-regulation with increased synaptic plasticity and myelination is of interest given the detection in human schizophrenia postmortem brain tissue of altered transcription of genes involved in these pathways (see Section 1.4.2.1). This may support a role for enhanced synaptic plasticity and myelination in APD treatment.

171

Gnb1 The regulation of the G-protein 1 subunit (Gnb1) by all APDs in this study has interesting implications for their mechanism of action. Gnb1 encodes G1, a - subunit of the heterotrimeric G-protein complex that functions in cellular signalling. G1 is highly expressed in the cortex, CA1/CA2 hippocampal fields and in the midbrain of adult male mice (Allen, 2006). Interestingly, G1 is up- regulated by overexpression of Nrg1 (Fu et al., 1999) and activates AKT1 kinase activity in vitro (Bommakanti et al., 2000). Increased expression of an isoform of NRG1 has been seen in the cortex of patients with schizophrenia although it was found to correlate with APD levels (Hashimoto et al., 2004). Our study supports this finding as G1, up-regulated by Nrg1, is increased by APD treatment. Furthermore, AKT1 is decreased in the brains of patients with schizophrenia (see Section 1.4.2.2) so up-regulation of G1 by APDs may function to reverse this pathological deficit. The expression of G1 in regions associated with schizophrenia and its interaction with two schizophrenia susceptibility genes indicates that its regulation by all three APDs should be further studied to investigate its role in reversing pathogenically altered pathways in this disorder.

Sod1 The upregulation of superoxide dismutase 1 (Sod1) by all three APDs in this study provides good validation for our technique as this regulation has been seen previously following olanzapine and clozapine treatment in cell culture (Li et al., 1999; Bai et al., 2002). It has also been shown that treatment of cells with atypical APDs, when administered after withdrawal of serum, had a neuroprotective effect (Bai et al., 2002). Our study supports these findings by showing regulation of Sod1 by chronic treatment with atypical APDs, and additionally haloperidol, in the whole mouse brain. This may implicate a neuroprotective effect for APDs in the brains of patients with schizophrenia. However, since microarray studies using postmortem brain tissue from patients with schizophrenia indicate SOD1 mRNA is still downregulated after a lifetime of APD treatment (AltarC, 2007), additional antioxidant treatment may be required for therapy. This is supported by

172 transcript profiling studies revealing down-regulation of genes altered in multiple metabolism pathways in the brains of patients with schizophrenia (Middleton et al., 2002).

Anp32a Acidic leucine-rich nuclear phosphoprotein 32 family member A (Anp32a) was the only gene in our chronic treatment study with altered expression by all three APDs in the microarray data analysis. The upregulation of Anp32a was validated for the atypical APDs by QPCR analysis, with a trend to increase for haloperidol treatment (p=0.07). ANP32A interacts with ataxin-1, which is the protein product of the gene mutated in patients with spinocerebellar ataxia 1, an autosomal dominant disease associated with motor neuron degeneration (Matilla et al., 1997). Transcript profiling studies of brain tissue from these patients reveals co- regulation of Anp32a and Bdnf, which is also altered in the brains of patients with schizophrenia (see Section 1.3.4.4). This interaction with BDNF occurs in the pons, however Anp32a expression is also detectable in the granular layers of the adult mouse hippocampus (Allen, 2006) which may pertain more to its regulation by APDs, although ANP32A protein interactions have not been characterised in this region.

5.3.4 Genes with functional relevance Our third approach for choosing genes that may be important in APD action was to verify those that had previously been associated with schizophrenia pathogenesis. This convergent approach will emphasise gene regulation that is most relevant to symptom pathology in the disorder (Thomas, 2006).

Tac2 Neurokinin B (Tac2) is decreased by chronic atypical APD treatment in this profiling study, with validated down-regulation by olanzapine. Neurokinin B is in a family of tachykinin neuropeptide neurotransmitters and is a ligand to the NK3 receptor, which is expressed throughout the human and rodent brain (Mileusnic et al., 1999), with high expression in the middle and deeper layers of the PFC

173 (Tooney et al., 2000). The regulation of neurokinin B by APDs in this study is interesting as a clinical trial of patients with schizophrenia and schizoaffective disorder showed that an antagonist to NK3, osanetant, was as effective as haloperidol in reducing both positive and negative clinical symptoms after 6 weeks treatment, yet had dramatically reduced EPS and increased compliance (Meltzer et al., 2004). Furthermore, NK3 receptor antagonism is not associated with adverse effects of APDs, like tardive dyskinesia and weight gain. NK3 knockout mice have reduced cognitive ability in learning and memory tasks, indicating that NK3 antagonism may alter deficits in these domains in patients with schizophrenia (Siuciak et al., 2007). NK3 receptor modulation regulates dopamine, GABA and serotonin transmission in various brain regions including the midbrain and striatum, so it may have a number of unpredictable effects in treating the symptoms of schizophrenia (Meltzer & Prus, 2006). Since the initial positive clinical trial, osanetant has been withdrawn from the market due to unpublished negative effects in a follow-on study, although other NK3 receptor antagonists are being tested for adjunctive therapy in schizophrenia (Meltzer & Prus, 2006). Our study indicates that NK3 receptor modulation by down- regulation of the ligand, neurokinin B, may already be an effect of atypical APD treatment, supporting the investigation of NK3 receptor antagonists in the treatment of schizophrenia.

Vegfb Vascular endothelial growth factor B (Vegfb) was up-regulated by chronic haloperidol treatment in this study. Like CSPG3, VEGFB is increased in the brain following injury, although with a neuroprotective function (Nag et al., 2002). VEGFB typically functions in blood vessel growth (Olofsson et al., 1996), however recent studies of Vegfb knockout mice showing reduced neuroproliferation in the subgranular and subventricular zones, indicate that VEGFB also plays a role in adult neurogenesis (Sun et al., 2006). One neuropathological study has indicated increased neurogenesis in the dentate gyrus following haloperidol in the gerbil brain (Dawirs et al., 1998) although more often atypical APDs and not conventional APDs are associated with modulation of adult neurogenesis

174 (Wakade et al., 2002; Wang et al., 2004). This study supports a role for neuroproliferative growth factors in the mechanism of conventional APD action.

Ddc Dopamine decarboxylase (Ddc) is a key enzyme in the synthesis of dopamine (Fig. 1.3). In this study, we see up-regulation of Ddc by chronic treatment with atypical APDs, with validated regulation in olanzapine-treated mice. Decreased DDC activity in the caudate of male schizophrenic patients after haloperidol treatment for over one month has been previously reported (Grunder et al., 2003). However acute haloperidol treatment and brain imaging of [18F]DOPA (a substrate of DDC) was unsuccessful in correlating DDC activity with physiological response, although this study may have had too little statistical power (Vernaleken et al., 2006). Our study indicates that Ddc may be chronically regulated by APDs. DDC has been examined as a candidate gene in schizophrenia susceptibility in four genetic association studies (Allen et al., 2007). All four studies found no association in case-control cohorts, although an association between age of onset for schizophrenia in males and DDC genotype was reported (Borglum et al., 2001). Interestingly, PCP and LSD, psychomimetic drugs acting respectively through NMDA-R antagonism and serotonergic modulation, also increase Ddc expression in rodent brain, particularly in the striatum, nucleus accumbens, hippocampus and cerebellum (Buckland et al., 1997). These first three regions have prior neurochemical and neuropathological association with schizophrenia and may be regions to pursue for APD regulation of Ddc in the brain. Postmortem expression analyses of brains from patients with schizophrenia have shown that the number of DDC-positive neurons is decreased in the striatum, particularly the nucleus accumbens (Ikemoto, 2004) yet no change in DDC mRNA is seen in the substantia nigra (Ichinose et al., 1994). Our study indicates that altered expression of DDC in patients with schizophrenia may be the result of chronic APD treatment.

175 5.3.5 Comparison of microarray data analytical techniques For our microarray study of whole brain tissue following 7- and 28-day treatment of mice with APDs we used a number of bioinformatic analytical techniques to define candidate genes of interest. These gave us varying successes in discerning false positives as assessed by QPCR analysis. For the 7-day analysis we used the GeneChip Operating Software (GCOS) provided by Affymetrix, which normalises each array to a designated control baseline array and uses basic algorithms to annotate fold-change. Normalisation is required when multiple arrays are used to reduce non-biological variance (Bolstad et al., 2003). The use of fold-change for microarray statistical analysis was the common method at the time we conducted this analysis and was used by other similar studies (Chong et al., 2002, Kontkanen et al., 2002, Thomas et al., 2003, Sondhi et al., 2005, Feher et al., 2005, Mehler-Wex et al., 2006). Fold-change is a relative measure and so estimates are independent of amount of RNA hybridized (Irizarry et al., 2003a). We chose a cutoff of 1.5- fold change average between the two treatment arrays compared to the two saline arrays. Other studies have found approximately 75% validation rate (or 25% false positive rate) with genes altered above 1.4-fold change (Morey et al., 2006). Fold-change analysis does have weaknesses in the arbitrary cutoff value and inability to describe the significance of observed changes, yet it is intuitively appealing as small fold-changes no matter how significant are unlikely to be biologically relevant (Breitling et al., 2004). Using this 1.5-fold change cutoff we found thousands of transcripts altered by olanzapine treatment, and thus increased the stringency for this treatment group to 2-fold change. This list of regulated genes was narrowed to a group of 20 candidate genes based on a functional genomics approach (Lockhart & Winzeler, 2000), accounting for tissue and regional expression patterns, chromosomal location of human homologues, published literature of biological functions and/or prior associations with schizophrenia. Using QPCR analysis we validated expression changes in 13 of the 20 candidate genes – a 65% validation rate. This is fairly consistent with other animal APD treatment transcript profiling studies that have reported between 29% and 86% validated genes using alternative approaches to microarray data analysis (Kontkanen et al., 2002; Thomas et al., 2003; Chen &

176 Chen, 2005; MacDonald et al., 2005; Fatemi et al., 2006; Mehler-Wex et al., 2006). Given our comparable validation rate, we believe the use of rudimentary analysis, arbitrary fold change cutoff and a functional genomics approach appropriately captured significant changes in gene expression in our 7-day study.

With the increased use of microarray technology, software to cope with normalisation and data analysis is being constantly optimized and improved, yet it remains the most limiting part of microarray technology (Hoheisel, 2006). In order to take advantage of the most current analytical techniques we acquired the expertise of a bioinformatician to analyse the microarray data from the 28-day treatment study. The 28-day microarray data underwent robust multichip average (RMA) normalisation, which provides better sensitivity in discerning true false positives than the Affymetrix method (Irizarry et al., 2003b) and rank product (RP) analysis, a powerful statistical method for low replicate microarray analysis that approximates false discovery rates (FDR) for each transcript on the array (Breitling et al., 2004). To define statistically significant transcripts we chose an arbitrary cut-off FDR< 0.05, indicating that in 100 changed transcripts, 95 could be expected to be true positives and less than 5 would be false positives (see Storey & Tibshirani, 2003). We additionally used a 1.5-fold change cut-off to discern biologically relevant, and replicable, transcripts. This stringent analysis yielded a much smaller list of altered transcripts than the 7-day analysis, on which we then used three techniques to filter for candidate genes, with varying success.

Firstly, we chose six genes that were also altered in the 7-day analysis and were able to validate the altered expression of three of these by QPCR– a 50% validation rate. Next, we chose genes according to their ranking on the software analytical tool Ingenuity Pathway Analysis, which provided interaction information based on the published literature about the list of altered genes, and ranked these interactions according to the number of genes in each network. We combined the top ranking network in each drug treatment and chose genes altered by multiple treatments within this network. Using this technique we validated four out of six candidate genes by QPCR giving us the highest

177 validation rate, 67%. When including the two genes also regulated at 7-days APD treatment, Syt1 and Cspg3, the validation rate is five out of eight, or 63%. The third technique was to choose genes that had published association to schizophrenia or its treatment. Using this approach we validated only 3 out of 9 genes – a 33% validation rate, indicating sophisticated network programs to be more reliable at discerning true positives than hand-picking genes.

Grouping the 28-day treatment candidate genes together, it is hard to interpret the discrepancy between expected false positives of 5% and the actual false positive rate of 52% of our 21 candidate genes. QPCR is a sensitive, yet not highly replicable, technique (Bustin, 2000) and other gene expression validation techniques may be required to highlight more true positives in this group. In particular, in situ hybridisation may be more successful in validating altered genes and also provides regional and cellular characterisation of transcriptional changes found by microarray analysis of whole brain tissue. This technique is used in Chapter 6 for two genes altered in this study.

Following the 28-day analysis using more sophisticated analytical tools, we additionally carried out FDR estimations using RP analysis for the 7-day treatment microarray data set. For the candidate genes we had chosen for further analysis, the majority (18/20) had an FDR < 0.3 and the remaining two an FDR < 0.6. This indicates that we would expect 12 true positives from the 18 genes (with less than 6 false positives) and 0.8 true positives from the remaining 2 genes (with less than 1.2 false positives) (see Storey & Tibshirani, 2003). This is remarkably consistent with the further biological analysis we conducted to determine these false positives, using QPCR analysis, that verified 13 of 20 genes as true positives.

These different bioinformatical approaches have highlighted the value of using complementary techniques for microarray data analysis. The sophisticated network program analytical approach was most successful at picking true positives in the stringently analysed 28-day treatment arrays, yet this validation

178 rate was the same as that achieved through a convergent functional genomics approach with rudimentary data analysis for the 7-day treatment arrays. Microarray analysis is essentially a non-hypothesis driven technique, which is particularly valuable in schizophrenia – a genetically multifactorial disorder with no definitive pathological, neurochemical or aetiological definition (Bunney et al., 2003; Mirnics et al., 2006). However, a scientific brain is still required to interpret the significance of the vast quantity of data output from microarray analysis (Lockhart & Winzeler, 2000).

179 180

Chapter 6

VOLTAGE-GATED POTASSIUM CHANNELS in the MECHANISM of ANTIPSYCHOTIC DRUG ACTION

181 6.1 INTRODUCTION

6.1.1 Role of voltage-gated potassium channels in neurotransmission

Voltage-gated potassium (Kv) channels are crucial elements for neurotransmission as they facilitate repeated neuronal firing (Trimmer & Rhodes, 2004). They are composed of tetrameric integral membrane -subunits and auxiliary -subunits that modulate the channel gating properties. Kv channels are localised throughout the neuron: in dendrites, along unmyelinated axons and myelinated axons at the nodes of Ranvier and at the synaptic terminal, where they propagate electrical signalling and chemical neurotransmission (reviewed in Dodson & Forsythe, 2004).

There are a dozen Kv channels in the brain, encoded by over 70 subunit genes, which can be subclassed into two groups based upon their electrical activity

(Trimmer & Rhodes, 2004). The Kv4 class of channels elicit A-type, fast inactivating currents involved in the initiation and prolonging of action potentials that triggers neurotransmitter release (Pongs, 1999). Kv channel interacting proteins (KCHIPs) modulate the activation properties of Kv4 channels (An et al.,

2000). In contrast, the Kv1 class of channels produce delayed rectifier currents that raise the membrane potential threshold to inhibit aberrant neuronal firing

(Dodson & Forsythe, 2004), except when in the presence of the Kv1 subunit when they switch to A-type current (Rettig et al., 1994).

In our 7-day APD study we found mRNA and protein regulation of Kv1.1 and

KCHIP3, with transcriptional effects on Kv1, that suggest drug treatment may be increasing both the delayed rectifier current and the A-type current, reducing aberrant neuronal firing and prolonging neurotransmitter release (as discussed in Section 4.3.3). These changes were particularly prominent in the haloperidol treatment group. Transcriptional alterations in 28-day APD treated mice suggest the A-type current modulation and prolonged neurotransmitter release may also occur with chronic APD treatment. However, the significance of these changes must be considered in the regional context in which they occur in the brain.

182

Kv channels are mostly expressed in neuronal cells in the mammalian brain, yet regional expression of channel subtypes are not well defined due to the complexity of subunit composition and oligomerisation (Trimmer & Rhodes,

2004). Kv1.1-containing channels are expressed in neurons in the midbrain, cortex, hippocampus and cerebellum (Beckh & Pongs, 1990), with Kv1 subunit interaction occurring in the hippocampus (Rhodes et al., 1997). Kchip3 is expressed in the striatum, amygdala, throughout the hippocampal formation, in deeper layers of the cortex, in the piriform cortex and in granular layers of the cerebellum (Xiong et al., 2004) and KCHIP3 is highly expressed in thalamic nuclei and in the midbrain (Hammond et al., 2003).

Brain regions of interest to Kv channel regulation by APDs have been suggested through functional studies and animal models. Electrophysiological studies have shown that KChip3 mRNA is inversely correlated with firing activity of midbrain dopamine neurons, which has been suggested to be of potential relevance in APD treatment (Buxbaum, 2004). Kcna1 gene disruption in mice leads to an enlarged hippocampus and ventral cortex (Persson et al., 2007), resulting from increased hippocampal neurogenesis (Almgren et al., 2007). Increased hippocampal neurogenesis has been seen in rodents treated chronically with antidepressants (Malberg et al., 2000) and may be a mechanism of action of antidepressant drugs in treating major depression where it is also considered to possibly have a causative role (reviewed in (Sahay & Hen, 2007). There have been some recent suggestions for modulation of adult neurogenesis by APDs (Halim et al., 2004; Toro & Deakin, 2007).

6.1.2 The dopamine hypothesis implicates specific brain regions of relevance to schizophrenia treatment When exploring the mechanism of APD action in the brain it is important to consider that all APDs are antagonists at dopamine D2 receptors, albeit with varying binding affinities (Seeman et al., 1975). This has lead to the dopamine hypothesis of schizophrenia, which was first suggested in the 1960’s. This

183 postulates a hyperexcitability of dopamine in the striatum is responsible for psychotic symptoms of schizophrenia, whereas an underactivity of dopamine in cortical and overactivity in limbic projections, in conjunction with glutamatergic and GABA abnormalities in cortical and thalamic projections, are responsible for the long-term negative symptoms and cognitive decline in patients with schizophrenia (See Section 1.2). The synthesis of current neurotransmitter dysfunction hypotheses of schizophrenia pathogenesis was detailed in Section 1.2.5 and summarised in Figure 1.7.

The dopamine-glutamate dysfunction hypothesis for schizophrenia pathogenesis suggests brain regions that may be targeted by APDs. Specifically, the substantia nigra and ventral tegmental area in the midbrain are the origins of proposed pathogenic projections to the prefrontal cortex, striatum and limbic regions (particularly the ventral striatum). Dopamine D2 receptors in the striatum are known targets of APDs. Furthermore, APDs are known to induce the expression of a group of transcription factors called immediate early genes in the striatum, nucleus accumbens and prefrontal cortex (Fibiger, 1994; MacGibbon et al., 1994; Deutch & Duman, 1996) suggesting immediate target regions of these drugs.

6.1.3 Aims of this chapter By treating mice with multiple APDs and transcript profiling whole brain tissue from these animals we found several genes involved in voltage-gated ion channel activity were regulated at both intermediate (7 day) and chronic (28 day) time- points (Table 6.1).

Table 6.1 Genes involved in the regulation of voltage-gated ion channels are altered by intermediate and chronic treatment with multiple APDs in this study. Voltage-gated ion channel 7 days 28 days Calcium channel Scn2b Sodium channel Nedd4 Nedd4 Potassium channel Kcna1; Kcnab1; KChip3 KChip3 : increased, : decreased

184 The aim of this chapter is to characterise the regional expression of two altered genes and their protein products that are subunits of Kv channels (Kv1.1 and KCHIP3) and to explore their regulation by APDs. The majority of the changes in Table 6.1 occurred in haloperidol treated animals, for which we also saw the largest protein regulation of Kv1.1 and KCHIP3 (Section 4.2.3). Therefore, we undertook a further animal study of 7-day haloperidol treatment to anatomically localise the molecular expression changes in potassium channel related mRNAs. We focused on the hippocampus as both proteins are expressed in this region and

Kv1.1 deficient animals have altered hippocampal neurogenesis. We also focused on regions of dopaminergic neuronal projection: namely, the substantia nigra, ventral tegmental area, prefrontal cortex and striatum, given the role for Kchip3 transcription in the activity of these neurons and implications of these regions in schizophrenia pathogenesis and treatment.

185 6.2 RESULTS

6.2.1 KCHIP3 localisation and mRNA expression in normal and haloperidol-treated mouse brain 6.2.1.1 Localisation of Kchip3 mRNA by in situ hybridisation To localise Kchip3 gene expression in our mouse brain tissues of interest we generated a riboprobe, a complementary nucleic acid probe that is isotopically labelled to detect the fixed tissue mRNA. To avoid cross hybridisation with other transcripts we carried out database and BLAST searches on the KChip3 sequence. This revealed that there are three other family members to KCHIP3 (Pruunsild & Timmusk, 2005) with high homology to base pairs 119-889, that is, within the protein-coding region of Kchip3 mRNA. Therefore, we selected an appropriate probe sequence within the 3’ untranslated region (UTR). Using the recommendations of Whitfield and colleagues (1990) we PCR amplified a 250 bp segment of cDNA (NM_019789; bps 1110-1359) located 221 bp downstream from the stop codon with approximately 50% GC content. This 250bp segment was subcloned into the pGEM®-T Easy plasmid vector. To generate riboprobes, the construct was linearised by restriction enzyme digest and transcribed using either SP6- or T7- polymerases and a 35S-UTP radiolabelled nucleotide to produce sense (SP6-pol) and antisense (T7-pol) riboprobes (Table 6.2).

Table 6.2 Kchip3 riboprobe radioactivity. Probe synthesis Post-purification Amount Specific Incorporated synthesised Activity Yield Concentration Probe (%) (μg) (cpm/μg) (%) (ng/μL) Kchip3 antisense 65.9 0.170 1.43E+09 92.2 3.14 Kchip3 sense 63.1 0.163 1.41E+09 88.8 2.89 Specific activity is measured in counts per minute (cpm) per μg of probe

Antisense Kchip3 riboprobe was hybridised to the tissue sections of interest, which were subsequently washed and put to film to visualise KChip3 mRNA expression in the prefrontal cortex (Fig. 6.1A), striatum (Fig. 6.1B), hippocampus (Fig.

186 6.21C) and midbrain (Fig. 6.1D). Sense Kchip3 riboprobe was used as a control and resulted in a barely detectable background signal (Fig. 6.1E).

Kchip3 mRNA is highly expressed in the piriform cortex and frontal cortical layers IV/V and VIb, with mid-level expression in layers II - VIa and barely detectable expression in the molecular cortical layer I. KChip3 has a very specific patterning in the caudate putamen, with hybridisation in the matrix but not the striosomes. There is detectable Kchip3 expression in the amygdala, substantia nigra pars compacta, dentate gyrus and throughout the hippocampal fields. Kchip3 mRNA has strikingly specific expression in the thalamic nuclei, particularly the ventral posteromedial and posterolateral thalamic nuclei, the lateral posterior thalamic nuclei and dorsal lateral geniculate nuclei (Fig. 6.1C) and the medial geniculate nucleus in more caudal regions (Fig. 6.1D). Kchip3 mRNA expression is low in the rest of the thalamus, the hypothalamus, the corpus callosum and cingulum.

187 (A) (B)

I II/III IV/V IV/V Striosomes VIa VIb VIb CC Striatal CPu matrix Pir Acb rf PirPiP r Pir (E) Tu

Cg CA1/CA2

LP DLG CA3 DG MHb VPM MGN VPL Amy VPMpc SNc Pir CA3

Fig. 6.1 Kchip3 mRNA expression as detected by in situ hybridisation. Autoradiographic images of Kchip3 antisense riboprobe hybridisation to saline-treated adult male inbred mouse coronal brain sections corresponding to (A) prefrontal cortex (Bregma 1.70 mm, Paxinos & Franklin, 2001), (B) striatum (Bregma 1.10 mm) (C) hippocampus (Bregma -2.06 mm). (D) midbrain (Bregma -3.16 mm) (E) Kchip3 sense riboprobe hybridisation. Cortical layers are indicated in roman numerals. Acb: nucleus accumbens, Amy: amygdala, CA1-CA3: hippocampal fields, CC: corpus callosum, Cg: cingulum, CPu: caudate putamen, DG: dentate gyrus, DLG: dorsal lateral geniculate, LP: lateral posterior thalamic nucleus MGN: medial geniculate nucleus, MHb: medial habenular nucleus, Pir: piriform cortex, rf: rhinal fissure, SNc: substantia nigra pars compacta, VPL: ventral posterolateral thalamic nucleus, VPM: ventral posteromedial thalamic nucleus, VPPC: ventral posteromedial parvocellular nucleus. 6.2.1.2 Quantification of haloperidol-induced regional changes in Kchip3 mRNA by in situ hybridisation Using NIH imaging software we sampled the PFC, striatum and four regions in the hippocampal formation for hybridisation of radiolabelled probe (as indicated in Fig. 2.4). We were unable to examine the regulation of Kchip3 in the midbrain due to the small number of sections (4 of 22 mouse brains) that were accurately attained from this region and the resultantly low statistical power. By comparison of the average Kchip3 riboprobe hybridisation signal from sampled regions to 14C standards, we quantified mRNA expression in each region in each mouse brain. When taking all sampled regions together, the level of Kchip3 mRNA in haloperidol treated animals was less than in controls (Fig. 6.2). This decrease did not reach statistical significance (p=0.283), indicating that brain regions other than those sampled in this analysis may be contributing to whole brain down- regulation of Kchip3 by 7-day haloperidol treatment as previously detected by microarray analysis and QPCR analysis, or that the in situ hybridisation technique holds less power to detect an association. Statistical analyses of the expression values of individual brain regions did however reveal significantly decreased Kchip3 mRNA in the striatum in haloperidol-treated mice compared to controls (t=1.83, df=19, p=0.041) (Fig. 6.3). Kchip3 mRNA expression was not significantly regulated in other regions of analysis.

190 0.19

0.185

0.18

0.175

0.17

Kchip3 mRNA (μCi/g) mRNA Kchip3 0.165

0.16 Saline Haloperidol

Figure 6.2 In situ hybridisation detected KChip3 mRNA expression in saline and haloperidol treated mouse brain regions of interest. Analysis of variance (ANOVA) statistical analysis was made of all regions sampled by in situ hybridisation in mice treated with saline (n= 8) and haloperidol (n=9). Error bars represent standard error of the mean.

0.25 SALINE HALOPERIDOL

0.20

* 0.15

0.10 Kchip3 mRNA (μCi/g) mRNA Kchip3

0.05

0.00 PFC Striatum DG CA4 CA3 CA1

Figure 6.3 KChip3 mRNA quantiﬁcation by in situ hybridisation. Mean optical density reading from Kchip3 mRNA signal per anatomic region sampled from autoradiographic images of saline (n=8) and haloperidol (n=9) treated mouse brains. Regional differences in KChip3 mRNA levels in haloperidol-treated animals are shown with error bars denoting standard error of the mean. Asterisk denotes p< 0.05 signiﬁcance. PFC: prefrontal cortex, DG: dentate gyrus, CA1, CA3 and CA4: hippocampal formation subregions. 6.2.1.3 Localisation of KCHIP3 protein by immunohistochemistry We next analysed the regional expression of KCHIP3 protein using a commercially available antibody. KCHIP3 has a distinctive pattern of staining in cell bodies and neuropil in the frontal cortex, particularly in the medial region near the pial surface (Fig. 6.4B) and in the deeper cortical layers. In the deeper cortical layers (Fig. 6.4C) there is very specific and highly intense labelling of some layer VIb neurons (black arrowhead) and their projections at the cingulum compare to adjacent deep cortical layer cells with similar morphology (open arrowhead) that appear negative for KChip3 expression (Fig. 6.4D). In the medial region (Fig. 6.4E), Kchip3 is notably expressed in the neuropil in layer I, with labelled fibres from the brain stem or cortical layers as well as vertical projections of glial cells from the pial surface into the upper cortical layers (black arrow) (Fig. 6.4F). Although there is no apparent labelling of some neurons in the middle cortical layers (open arrowhead; Fig. 6.4E) there is specific labelling of medium- sized neurons and projections in the middle cortical layers (black arrowhead; Fig. 6.4E)

Figure 6.4 Immunohistochemical staining of mouse frontal cortex with - KCHIP3 and Nissl counterstain. Representative brightfield photomicrographs of 40 μm sections taken from the frontal cortex of immunohistochemically stained, paraformaldehyde-fixed brain tissue from a saline-treated mouse. (A) No primary antibody negative control in cortical tissue. (B) Infralimbic and cingulate cortex with boxes indicating regions of higher magnification. (C) Cingulum and deeper cortical layers, with higher resolution of boxed area in (D). (E) Medial cortex with subsequent higher magnification of boxed regions at pial surface in (F) and higher cortical layers in (G). Cortical layers are denoted with roman numerals. p:pial surface. Scale bar represents 40 μm. Black arrowhead: immunolabelled neuron. Open arrowhead: unlabelled neuron.

192 [Insert Fig. 6.4] [071194-07]

193 [Insert Fig. 6.5] [071194-07]

194 KCHIP3 has a very specific labelling pattern in the striatum (Fig. 6.5). The intensely labelled layer VIb neurons seen in the PFC are also detected in the lateral cortex (Fig. 6.5B). In the dorsolateral striatum (Fig. 6.5C), there is labelling of some neuronal somata (black arrowheads) and processes as well as in the cytoplasm of morphologically separable cells (white arrowheads). There is sporadic immunostaining of glial cell-like cytoplasm in the corpus callosum (Fig. 6.5D). KCHIP3 exhibits differential white and grey matter expression in the striatum (Fig. 6.5E) with marked labelling in neuropil and cell bodies in the caudate putamen and a relative absence of immunostaining in the fibres of internal capsule, except for sporadic fibre immunolabelling (white arrow; Fig. 6.5F). This differential immunostaining is also apparent in the intense staining of the nucleus accumbens compared to anterior commissure (Fig. 6.5G). KCHIP3 is labelled in cell bodies of most neurons in the nucleus accumbens (black arrowhead) as well as dendrites and other projections in the neuropil (white arrowhead; Fig. 6.5H, I) compared to an absence of labelling in the white matter of the anterior commissure.

Figure 6.5 Immunohistochemical staining of mouse striatum and nucleus accumbens with -KCHIP3 and Nissl counterstain. Representative brightfield photomicrographs of 40 μm sections taken from various regions of immunohistochemically stained, paraformaldehyde-fixed brain tissue from a saline- treated mouse. (A) No primary antibody negative control in striatal tissue. (B) Corpus callosum and surrounding caudate putamen and deeper cortical layers with boxed areas indicating regions subsequently magnified with higher resolution of the corpus callosum (C) and dorsolateral caudate putamen (D). (E) Striatum with higher magnification in (F). (G) Nucleus accumbens surrounding the anterior commissure, with higher resolution at two vertical layers highlighting neuron cell bodies (H) and fibre tracts (I). ac: anterior commissure, NAc: nucleus accumbens. Scale bar represents 40 μm. Black arrowhead: immunolabelled neuron. White arrowhead: immunolabelled neuronal cytoplasm. White arrow: immunolabelled glial cell cytoplasm.

195 KCHIP3 has restricted immunolocalisation in the hippocampal formation. In the dentate gyrus it is expressed in the outer granular cell layer (Fig. 6.6A) with only minimal cytoplasmic labelling in the hilar region. There is intense immunolabelling of specific neurons along the subgranular zone (black arrowheads; Fig. 6.6B). Other than that, there is minimal immunolabelling in the hippocampal fields and neuropil in the polymorphic layer with most neuronal cell bodies unlabelled or with faint labelling (open arrowheads; Fig. 6.6D,E) and there is sporadic neuronal immunolabelling of cells in the pyramidal layer of the CA3 hippocampal field (black arrowhead; Fig. 6.6E).

KCHIP3 is most abundantly expressed in the midbrain region, with intense labelling in dopamine neuron cell bodies and projections in the substantia nigra and ventral tegmental area (Fig. 6.7B). KCHIP3 is expressed in the substantia nigra pars compacta and reticulata with immunostaining in dopamine neuron cell bodies and projections (black arrowheads; Fig. 6.7C) and minimal staining in the glial cells (open arrowheads; Fig. 6.7D). In the ventral tegmental area (Fig. 6.7E) there is distinct immunolabelling in dopamine neuronal somata (black arrowheads) as well as intense immunostaining in apical dendrites (black arrows) and the surrounding neuropil with an absence of staining in the nucleolus. Glial cells have minimal staining (open arrowheads; Fig. 6.7F). The density of cells in this midbrain region is apparent from the difference in number of cells and morphology in the deeper and more superficial layers. There is some minimal background immunolabelling detectable in the midbrain region (Fig. 6.7A), which indicates that the chromogen staining time could have been reduced for this region.

Overall, there were no qualitative differences in KCHIP3 immunolabelling between saline- and haloperidol-treated animals in our sampled regions. Western blot analysis was used to quantify protein levels in these regions in saline- and haloperidol- treated animals.

196

[Insert Fig. 6.6] [071194-04]

197 [Insert Fig. 6.6] [071194-04]

198 [Insert Fig. 6.7] [071194-03]

199 [Insert Fig. 6.7] [071194-03]

200 6.2.1.4 Quantification of haloperidol-induced regional changes in KCHIP3 protein expression by Western blot analysis Western blotting with anti-KCHIP3 antibody revealed multiple bands in protein lysates from different brain regions (Fig. 6.8). These bands are detected mostly in the SDS-extracted fraction so this was the subject of further analysis. Compared to a standard curve of known molecular weight markers, KCHIP3 bands were approximately 108 kDa, 80 kDa, 68 kDa, 55 kDa, 34 kDa and 27 kDa. In order to determine the identity of multiple bands on the Western blot we conducted database searches with the amino acid sequence corresponding to our antibody (around residue 117 of human CSEN, the pseudonym of KCHIP3). SWISS- PROT has four isoforms of mouse CSEN of which two contained our sequence of interest – Q9QXT8-1 with a molecular weight of 29.5 kDa and Q9QXT8-1 of 32.7 kDa. Ensembl had an additional 26.4 kDa isoform (KCNIP3-4) that contained the appropriate antigen sequence and was unique by the presence of a transmembrane domain. With the inherent variability of protein size detection by Western blot we can assume the 27 kDa we detected is the transmembrane- containing isoform and that the 34 kDa band is either of the two SWISS-PROT detected isoforms. No higher molecular weight species were identified by database searches.

The major KCHIP3 protein species in our mouse brain tissue were 27 kDa, 55 kDa and 108 kDa (Fig. 6.9). Previous studies have identified oligomers of

KCHIP3 in vitro, with a dimer species that has Kv channel interacting function and a tetramer species that binds DNA (Osawa et al., 2001). By denaturing our protein before Western blotting with increasing concentrations of urea we were able to reduce the presence of the 55 kDa band in a dose-dependent manner, while increasing the 27 kDa band (Fig. 6.9). Gradients from trendlines indicate an inversely proportional relationship between these species. The 108 kDa band was not decreased by urea titration, in fact showing a slight increase. This indicates the 55 kDa band is likely to be a dimer of KCHIP3 and that the 27 kDa protein is likely to be the monomer of KCHIP3 detectable in mouse brain.

201 0.05% Triton- extracted protein 2% SDS- extracted protein Size KCHIP3 marker bands (kDa) Saline Haloperidol Saline Haloperidol (kDa)

131.3 — — 108

89.1 — — 80

— 68

— 55

41.7 —

— 34 31.4 — — 27

-actin

Figure 6.8 Representative Western blot of KCHIP3 antibody binding to hippocampal protein lysates. Each lane contains approximately 25 μg of protein lysate, with two consecutively extracted fractions from brain regions of 6 mice per treatment group (saline and haloperidol). Approximate band sizes of multiple bands were determined by their position on a log-transformed standard curve created using comparative migration of protein standards of known molecular weight. Urea (M) 1.6 "dimer" monomer "tetramer" 0 0.6 1.2 2 3 6 KCHIP3 1.4 108 kDa y = 0.0218x + 1.1451 2 1.2 R = 0.0676

1.0 y = 0.0367x + 0.5282 0.8 R2 = 0.8991 56 kDa 0.6 y = -0.0369x + 0.4911 2

KCHIP3 normalised expression 0.4 R = 0.4072

27 kDa 0.2

0.0 -actin 0123456 [Urea] (M)

Fig 6.9 Urea titration to examine KCHIP3 oligomeristation (A) The densities of multiple bands on KCHIP3 Western blots were quantiﬁed following addition of increasing concentrations of Urea into the loading buffer of protein lysate from a saline-treated control mouse brain. -actin shows equal protein loading. (B) Plot equations and R-squared values denoted for three prime bands of interest. "Tetramer"~ 108 kDa band, "Dimer" ~ 56 kDa band and monomer of 29 kDa. Expression is normalised to -actin expression detected on the same blots. The proposed dimer and monomer forms of KCHIP3 protein were expressed at comparatively different levels in mouse brain regions of interest in this study (Table 6.3). Accounting for the relatively different sizes of each region, the 27 kDa monomer was expressed highly in the substantia nigra and ventral tegmental area, with fair expression in the hippocampus and striatum and no detectable expression in the prefrontal cortex. The 55 kDa band was present in all analysed regions with highest expression in the substantia nigra and striatum.

Table 6.3 Relative abundance of KCHIP3 in mouse brain regions. KCHIP3 protein levels, normalised to -actin expression, for each region analysed by Western blot.

Molecular weight species PFC Str SN VTA Hipp 27 kDa monomer 0.7 1.4 1.0 0.6 55 kDa dimer 0.5 1.7 1.9 0.2 0.2 PFC: prefrontal cortex, Str: striatum, SN: substantia nigra, VTA: ventral tegmental area, Hipp: hippocampus

In our regional analysis of KCHIP3 protein in saline and haloperidol treated animals, we quantified the expression of all bands except the 80 kDa band, which was found at too low abundance to facilitate accurate detection. We detected a significant decrease in the 55 kDa proposed dimer species in the PFC, ventral tegmental area and hippocampus by Western blot analysis (Fig. 6.10A). KCHIP3 levels in the substantia nigra and striatum were unchanged. The highly abundant 27 kDa monomer protein was unchanged by drug treatment (Fig. 6.10B). No other KCHIP3-immunoreactive band exhibited a significant change in expression between haloperidol and saline-treated animals in this study (Fig. 6.10C-E).

204 (A) 1.50 (B) Saline Haloperidol 1.4 Saline Haloperidol 1.25 ** * ** 1.2 1.00 1.0 0.8 0.75 0.6 0.50 0.4

0.25 0.2

KCHIP3 dimer protein level 0.0 0.00

PFC Striatum SN VTA Hipp KCHIP3 monomer protein level Striatum SN Hipp (C) (D) (E) 1.4 Saline Haloperidol 2.0 1.4 Saline Haloperidol Saline Haloperidol 1.2 1.2 1.5 1.0 1.0 0.8 0.8 1.0 0.6 0.6 0.4 0.5 0.4 0.2 0.2

0.0 – 68 kDa band KCHIP3 level 0.0 0.0 – 35 kDa band KCHIP3 level KCHIP3 level – 108 kDa band KCHIP3 level Striatum SN Hipp PFC Striatum SN VTA Hipp PFC Striatum SN Hipp Figure 6.10 KCHIP3 regional protein quantiﬁcation and haloperidol regulation. Graphs represent mean optical density, and standard error of the mean, of bands detected by Western blot analysis from protein extracted using 2% SDS after microdissection of brains from mice treated with saline or haloperidol for one week. Values represent an average KCHIP3 protein expression of 6 mice per treatment group, normalised to actin, expressed as a ratio to total normalised saline-treated protein amount. (A) 55 kDa band is the proposed dimer (B) 27 kDa band is the highest abundance monomer (C) 108 kDa band (D) 68 kDa band (E) 34 kDa band. PFC: prefrontal cortex, SN: substantia nigra, VTA: ventral tegmental area, Hipp: hippocampus. Asterisks denote a signiﬁcant change between haloperidol- and saline-treated protein level *p < 0.05. **p <0.01 6.2.2 Kv1.1 localisation and expression in normal and haloperidol- treated mouse brain 6.2.2.1 Localisation of Kcna1 mRNA by in situ hybridisation Kcna1 riboprobe preparation to characterise the expression of Kcna1 mRNA

(encoding Kv1.1) in the mouse brain was carried out similarly to that described for Kchip3 (Section 6.2.1.1). In earlier studies, a riboprobe sequence containing part of the 5’UTR and exon 1 from the Kcna1 mRNA was used to detect Kv1.1 mRNA expression (Wang et al., 1994) but a BLAST search revealed that portions of this sequence had >90% sequence homology to other Kv channel -subunit mRNAs. We subsequently found a region immediately upstream to this sequence, contained completely within the 5’UTR, that is specific to Kcna1 mRNA. We PCR amplified a 240 bp segment of cDNA that was 128 bp upstream from the protein-coding start site (NM_010595; 1720-1960 bp). This 240 bp segment was subcloned into the pGEM®-T Easy plasmid vector. To generate riboprobes, the construct was linearised by restriction enzyme digest and transcribed using either T7- or SP6- polymerases and a 35S-UTP radiolabelled nucleotide to produce sense (T7-pol) and antisense (SP6-pol) riboprobes (Table 6.4).

Table 6.4 Kv1.1 riboprobe radioactivity. Probe synthesis Post-purification Amount Specific Incorporated synthesised Activity Yield Concentration Probe (%) (μg) (cpm/μg) (%) (ng/μL) Kv1.1 antisense 60.1 0.155 1.48E+09 90.0 2.79 Kv1.1 sense 45.3 0.117 1.60E+09 86.7 2.03 Specific activity is measured in counts per minute (cpm) per μg of probe

Antisense Kcna1 riboprobe was hybridised to the tissue sections of interest, which were subsequently washed and put to film to visualise Kcna1 mRNA expression in the PFC (Fig. 6.11A), striatum (Fig. 6.11B), hippocampus (Fig. 6.11C) and midbrain (Fig. 6.11D). Sense Kcna1 riboprobe was used as a control and resulted in a very low background hybridisation signal (Fig. 6.11E).

206 Kcna1 mRNA is highly expressed in the piriform cortex, the dentate gyrus granular cell layer and the hippocampal CA3 field, with a mid-level of expression in the polymorphic layer or hilus of the dentate gyrus. In the cortex, there is marginally more Kcna1 mRNA in cortical layers II and IV/V, with a mid-level of hybridisation in the other layers. Kcna1 mRNA has fairly uniform and low expression in the striatum and low to background levels in the corpus callosum. Kcna1 mRNA has specific expression in ventromedial and ventral posteromedial thalamic nuclei. Kcna1 expression is not discernibly detected in the midbrain regions sampled in this analysis, although the distinctive dentate gyrus and CA3 hippocampal field hybridisation is detected in the ventral hippocampus at the level of the midbrain. Further in situ hybridisation analysis does not include the midbrain region as this may not have been sampled accurately or Kcna1 may have no detectable expression there.

207 (A) (B)

I II III I IV-V VI II III IV-V VI CC Str

Pir Pir

(E)

Cg CA1/CA2 CA3 h DG DG VPM CA1 VM CA3

Figure 6.11 Kcna1 mRNA expression as detected by in situ hybridisation. Autoradiographic images of Kcna1 antisense riboprobe binding to (A) prefrontal cortex (Bregma 1.70 mm, Paxinos & Franklin, 2001), (B) striatum (Bregma 1.10 mm) (C) hippocampus (Bregma -2.06 mm) (D) midbrain (Bregma -3.16 mm) (E) Kcna1 sense riboprobe hybridisation. Cortical layers are indicated in roman numerals. CA1-CA3: hippocampal fields, Cg: cingulum, DG: dentate gyrus, h: hilus of the dentate gyrus, Pir: piriform cortex, VM: ventromedial thalamic nuclei, VPL: ventral posterolateral thalamic nuclei.

6.2.2.2 Quantification of haloperidol-induced regional changes in Kcna1 mRNA by in situ hybridisation To quantify Kcna1 mRNA by in situ hybridisation, we sampled the PFC, striatum and four regions in the hippocampus (as indicated in Fig. 2.4) using NIH imaging software. By comparison of the average Kcna1 riboprobe hybridisation signal from sampled regions to the 14C standards we were able to quantify Kcna1 mRNA expression in each region in each mouse brain. When taking all sampled regions together, the level of Kcna1 mRNA in haloperidol treated animals was greater than in controls (Fig. 6.12). This increase was not significant (p=0.371), indicating brain regions other than those sampled in this analysis may be contributing to whole brain up-regulation of Kcna1 previously detected by microarray analysis and QPCR, or that in situ hybridisation is a less powerful quantitative tool. Statistical analyses of these expression values per region revealed significantly increased Kcna1 mRNA in the striatum (t=-1.81, df=19, p=0.043) and in the CA4 hippocampal subfield (the hilar region) (t=-2.22, df=18, p=0.020) in haloperidol- treated mice compared to controls (Fig. 6.14). Kcna1 mRNA expression was not significantly regulated in other regions of analysis.

210 0.34

0.33

0.32

0.31

Kcna1 mRNA (μCi/g) Kcna1 mRNA 0.30

0.29 * Saline Haloperidol

Figure 6.12 In situ hybridisation detected differential Kcna1 mRNA expression in saline and haloperidol treated mouse brain regions of interest. Analysis of variance (ANOVA) statistical analysis was made of all regions sampled by in situ hybridisation in mice treated with saline (n= 8) and haloperidol (n=9). Error bars represent standard error of the mean.

0.65 SALINE

HALOPERIDOL 0.55

0.45

0.35 * Kcna1 mRNA (μCi/g) Kcna1 mRNA

0.25 *

0.15 PFC Striatum DG CA4 CA3 CA1

Figure 6.13 Kcna1 mRNA quantiﬁcation by in situ hybridisation. Mean optical density reading from Kcna1 mRNA signal per anatomic region sampled from autoradiographic images of saline (n=8) and haloperidol (n=9) treated mouse brains. Regional differences in Kcna1 mRNA levels in haloperidol-treated animals are shown with error bars denoting standard error of the mean. Asterisk denotes p < 0.05 signiﬁcant changes. PFC: prefrontal cortex, DG: dentate gyrus, CA1, CA3 and CA4: hippocampal formation regions. 6.2.2.3 Localisation of Kv1.1 protein by immunohistochemistry

Kv1.1 immunohistochemical analysis revealed that this neuronal potassium channel subunit is expressed in various subcellular locations throughout the brain. In the frontal cortex, Kv1.1 is highly expressed in the neuropil without differential expression patterns through the cortical layers (Fig. 6.14B).

Kv1.1 is expressed throughout the striatum (Fig. 6.15A), found in the neuropil and cell bodies of a subset of large (>40 μm) neurons (black arrowheads; Fig. 6.15B) and also in the fibre bundles of myelinated axons (white arrow; Fig. 6.15B), yet there is a marked absence of staining around the glial cell nuclei in white matter tracts (open arrowhead; Fig. 6.15B). This pattern is repeated in the nucleus accumbens (Fig. 6.15D) with Kv1.1 in discrete neuronal cell bodies, (black arrowheads; Fig. 6.15E) and in fibres (white arrow; Fig. 6.15E) but not glial cells (open arrowhead; Fig. 6.15E) in the anterior commissure.

Kv1.1 is most abundant in the hippocampus, where it is highly expressed in granular cells of the dentate gyrus, and pyramidal cells of the CA4 and CA3 regions (Fig. 6.16). Interestingly there is a marked absence of staining in the inner molecular layer of the dentate gyrus despite intense staining in the hilus and hippocampal CA4 field (Fig. 6.16B). A panoramic picture of the dentate gyrus hilar region captures intense staining of subgranular zone neurons (black arrowheads; Fig. 6.16C) with various morphologies, as well as in the perikarya of hilar region neurons (white arrowhead; Fig. 6.16C). Kv1.1 has distinct expression in the hippocampal fields with particularly high expression in pyramidal cell layers of the CA3 region (Fig. 6.16E). Cell bodies are visibly labelled, as well as and apical dendrites extending into the polymorphic layer where there is sporadic somata labelling and extensive neuropil labelling (Fig. 6.16E). High resolution imaging shows intense Kv1.1 labelling in every neuronal cell in the CA3 pyramidal cell layer and labelling of apical and basal dendrites, (black arrows; Fig. 6.16F) projecting into the mossy fibre layer.

212 [Insert Fig. 6.14]

213 [Insert Fig. 6.14]

214 [Insert Fig. 6.15]

215 [Insert Fig. 6.15]

216 [Insert Fig. 6.16]

217 [Insert Fig. 6.16]

218 [Insert Fig. 6.17] [071194-06]

219 [Insert Fig. 6.17] [071194-06]

220 Kv1.1 is abundantly expressed in the cell bodies and neuropil of the substantia nigra (Fig. 6.17B). Expression of Kv1.1 is localised to perikarya of dopamine neurons in the substantia nigra pars compacta (Fig. 6.17C) with intensive staining of apical dendrites in the substantia nigra pars reticulata (Fig. 6.17D). Similar staining, in dopamine cell bodies and surrounding neuropil, is detected in the ventral tegmental area (VTA), along with a lack of glial cell labelling in some cells (black arrow) but not others (open arrow) (Fig. 6.17E). Also the neuronal staining is more disparate in the VTA, with varying levels of Kv1.1 and a marked absence of labelling in nucleoli (open arrowhead; Fig. 6.17F). Overall, there were no qualitative differences in Kv1.1 immunolabelling between saline-and haloperidol- treated animals in our sampled regions. Western blot analysis was used to quantitate protein levels in these regions in saline- and haloperidol- treated animals.

6.2.2.4 Quantification of haloperidol-induced regional changes in

Kv1.1 protein expression by Western blot analysis

Regional Western blots of Kv1.1 confirmed the presence of a 57/59 kDa dimer as seen in previous studies (Deal, 1994), where the higher molecular weight species likely corresponds to a palmitoylated form of Kv1.1 (Gubitosi-King, 2005). Our protein extraction method was able to separate these two molecular weight species (Fig. 6.18). Triton X-100 (0.05%) protein extraction revealed the presence of the lower molecular weight protein, approximately 57 kDa and the higher 59 kDa Kv1.1 band. A second protein fraction, extracted with 2% SDS, contained only the higher molecular weight species. We also saw additional bands to the 57/59 kDa dimer on the Western blot: a higher molecular weight dimer primarily expressed in the SDS-extracted fraction and a lower weight band of approximately 44 kDa expressed in both fractions. The higher molecular weight bands have previously been attributed to grey matter specific cross-reactivity (Coleman et al., 1999). The lower molecular weight band has not been published, nor does it correspond to any annotated protein with sequence similarity to the antibody (raised against 50 amino acid peptide in C-terminus of human Kv1.1). We presume this minor species is also due to cross reactivity of the antibody.

221 Size 0.05% Triton- extracted 2% SDS- extracted marker (kDa) Saline Haloperidol Saline Haloperidol Bands (kDa) 112 —

— 75-80

60.4— — 59 — 57 — 44

31.7 —

-actin

Figure 6.18 Representative Western blot of Kv1.1 antibody binding to protein lysates from the hippocampus. Each lane contains approximately 20 μg of protein lysate, with two consecutively extracted fractions from brain regions of 6 mice per treatment groups (saline and haloperidol). Approximate band sizes of multiple bands were determined by their position on a log-transformed standard curve created using comparative migration of protein standards of known molecular weight. The two molecular weight species of the Kv1.1 dimer were present at different levels in our regions of interest (Table 6.5). Accounting for the relatively different sizes of each region, both bands were most highly expressed in the substantia nigra. The 59 kDa band was also expressed in the hippocampus, PFC and striatum whereas the 57 kDa band had low abundance in other regions and was not detectable in the prefrontal cortex.

Table 6.5 Relative abundance of Kv1.1 in mouse brain regions. Protein levels, normalised to -actin expression, for each region analysed by Western blot.

Molecular weight species PFC Str SN VTA Hipp 59 kDa band 1.8 1.1 2.7 0.5 2.0 57 kDa band 0.4 1.8 0.6 0.3 PFC: prefrontal cortex, Str: striatum, SN: substantia nigra, VTA: ventral tegmental area, Hipp: hippocampus

The two molecular weight species of Kv1.1 also had differential expression in haloperidol-treated brains compared to control brains (Fig. 6.19). The 57 kDa species (Triton-X100 extraction only) was significantly increased in the ventral tegmental area and hippocampus in haloperidol-treated mice compared to controls (Fig. 6.19A). The Triton-X100 extracted 59 kDa species was significantly increased in the striatum and hippocampus of haloperidol-treated mice compared to controls (Fig. 6.19B). The SDS-extracted Kv1.1 protein was not significantly increased in our regional expression analysis (Fig. 6.19C).

223 (A) 2.5 Saline Haloperidol 2.0 * *

1.5

1.0 – 57 kDa band

Kv1.1 protein level Kv1.1 protein level 0.5

0.0 Striatum SN VTA Hipp (B)

2.00 * Saline Haloperidol 1.75 * 1.50 1.25 1.00 0.75 0.50 0.25 (0.05% Triton-X100 (0.05% Triton-X100 fraction) 0.00 Kv1.1 protein level – 59 kDa band Kv1.1 protein level PFC Striatum SN VTA Hipp

1.00

0.75

0.50

(2% SDS fraction) 0.25

0.00

Kv1.1 protein level – 59 kDa band Kv1.1 protein level Striatum SN VTA Hipp

Figure 6.19 Kv1.1 regional protein quantiﬁcation and regulation by haloperidol. Graphs represent mean optical density, and standard error of the mean, of bands detected by Western blot analysis from protein extracted by microdissection of brains from mice treated with saline or haloperidol for one week. Values represent an average expression of 6 mice per treatment group, normalised to -actin, expressed as a ratio to total normalised saline treated protein amount. Note that not all Kv1.1 isoforms were expressed in the PFC. (A) Kv1.1 57 kDa protein from Triton X-100 extracted lysates. (B) Kv1.1 59 kDa protein from Triton X-100 extracted lysates. (C) Kv1.1 59 kDa protein from SDS-extracted lysates. PFC: prefrontal cortex, SN: substantia nigra, VTA: ventral tegmental area, Hipp: hippocampus. Asterisks denote a signiﬁcant change between haloperidol-treated and saline-treated protein level at p < 0.05. 6.3 DISCUSSION

6.3.1 Study design In this study we aimed to explore region-specific expression changes in voltage- gated potassium (Kv) channel subunits previously detected by microarray and QPCR analysis on whole brain tissue. By in situ hybridisation, immunohistochemistry and Western blot analysis of microdissected brain regions we examined the region-specific protein and mRNA changes for Kv1.1 and KCHIP3. We focused on regions with high expression of these proteins and those that are important in dopamine transmission as all APDs target dopamine D2 receptors (Seeman et al., 1975). Specifically, we looked at the origin of dopamine neurons in the midbrain, separated into substantia nigra and ventral tegmental area, and the regions of projection of dopamine neurons — the striatum and prefrontal cortex. We also examined the hippocampus for multiple reasons: it is highly expressed in our genes of interest; it is a major contributor to whole brain protein and mRNA measurements; it is the site of neuronal proliferation in a

Kv1.1 mutant mouse (Almgren et al., 2007); and it has been implicated in schizophrenia pathogenesis (Harrison, 2004).

In order to define the regional regulation of these Kv channel subunits by APD treatment we wanted to focus on one APD to characterise any modulations. When reviewing the results from Chapter 4 we found that microarray analysis detected a large decrease for Kchip3 expression in the haloperidol-treated group, with a moderate decrease in the olanzapine-treated group (Table 4.1). Only a small change was detected for Kcna1 expression (encoding Kv1.1) in the haloperidol treatment group, with moderate increases for clozapine and olanzapine (Table 4.1). Conversely, by QPCR analysis Kcna1 was upregulated by all three APDs, whereas Kchip3 was changed by clozapine and olanzapine treatment but not haloperidol (Fig. 4.2). Western blot analysis revealed Kv1.1 was increased 2.5-fold by haloperidol treatment, with no change detected in other treatment groups while KCHIP3 protein expression was decreased 1.5-fold in the olanzapine-treatment group and decreased 2-fold in the haloperidol-treatment

225 group (Fig. 4.3). Additionally, Kchip3 is regulated at 28 days by haloperidol, as indicated by QPCR analysis (Fig. 5.2). Given the dramatic regulation of KCHIP3 and Kv1.1 proteins by haloperidol and their direct biological/functional interaction we chose to focus on this treatment group to detect regional effects on

Kv channel subunits. We undertook a further 7-day haloperidol treatment study to collect brain tissue to undergo sectioning for in situ hybridisation and immunohistochemistry, and microdissection for regional Western blot analysis. Future analysis may wish to take into account the regional effects of atypical

APDs on Kv channel subunit mRNA and protein expression.

6.3.2 Localisation of KCHIP3 mRNA and protein expression in the adult mouse brain

Kv channels are encoded by over 70 genes in the human and have mainly neuronal expression in the mammalian brain (Trimmer & Rhodes, 2004). Their regional localisation has been previously explored, although Kv channels remain underdefined compared to other voltage-gated ion channels due to the complexity of their subunit composition (Trimmer & Rhodes, 2004). In this study we aimed to use in situ hybridisation and immunohistochemistry of saline-treated wild-type mice to add to the accumulating body of knowledge of mRNA and protein localisation for KCHIP3, which interacts with Kv4 channels, and Kv1.1, an -subunit of the Kv1 channels.

There have previously been a number of studies using in situ hybridisation to detect Kchip3 expression. Previous in situ hybridisation analyses have reported high Kchip3 expression in the piriform cortex and hippocampus (including all pyramidal cell hippocampal field areas and the granular layer of the dentate gyrus) as well as hybridisation in all layers of the cortex except the molecular layer and weak expression in the globus pallidus and striatum (Spreafico et al., 2001; Pruunsild & Timmusk, 2005). These findings are very similar to ours, although we did not specifically examine Kchip3 mRNA expression in the globus pallidus. However, our in situ hybridisation allowed a more specific analysis of cortical expression, with particularly high Kchip3 expression in layer VIb and high

226 expression also in layer IV/V. This specific cortical expression has been previously detected in a study of Kchip3 gene knockout and replacement with a cassette containing the -gal gene that remained under control of the Kchip3 promoter (Lilliehook et al., 2003). This gene knockout study also detected expression in the dentate gyrus and piriform cortex.

By immunohistochemistry we detected that high cortical layer VIb Kchip3 mRNA expression was translated into robust immunolabelling of neurons in this deep cortical layer in both the medial and lateral cortex. Layer VI is a heterogenous group of cells, with layer VIb blending into the white matter and guiding axons to and from the cortex, particularly in corticothalamic circuitry (Killackey & Sherman, 2003).

There was a highly distinctive pattern of Kchip3 expression in the lateral posterior and ventral posterior thalamic nuclei and geniculate nuclei, yet virtually no expression in adjacent paracentral or reticular thalamic nuclei. Expression of Kchip3 in thalamic nuclei has been reported previously, although not with such comprehensive and specific detection (Xiong et al., 2004; Pruunsild & Timmusk, 2005). The thalamus is involved in relaying sensory perception from the periphery to the cortex, with the Kchip3-expressing nuclei specifically important in relaying somatosensory (the ventral posterolateral and posteromedial nuclei) and auditory perception (the medial geniculate nucleus) (Amaral, 2000). This is particularly interesting given that schizophrenia is classified by defects in these perceptions, with a functional imaging study of patients undergoing auditory and visual hallucinations revealing aberrant activation of thalamic nuclei (Silbersweig et al., 1995). Alternatively, the expression of Kchip3 in regions of sensory perception could be related to the alternative function of its encoded protein DREAM, which is modulates pain perception (Cheng et al., 2002). Evidence for the latter explanation is the highly specific hybridisation in the ventral posteromedial parvocellular nucleus, associated with perception of taste but also with oral pain (Lenz et al., 1997).

227 Further to previous in situ hybridisation analyses, we detected remarkably specific Kchip3 expression in the matrix, but not the striosomes, in the caudate putamen. This specificity of Kchip3 mRNA detection in the striatum was translated into KCHIP3 protein localisation throughout the grey matter of the striatum, as detected by immunohistochemistry, which showed labelling of neuropil and neurons in the caudate putamen, with virtually no KCHIP3 expression in the white matter fibre tracts. The different striatal tissue compartments of Kchip3 mRNA expression have specialised functions. The striosomes relay information to the limbic regions of the brain like the nucleus accumbens, important in reward mediated behaviour (White & Hiroi, 1998), whereas the matrix relates to sensorimotor regions of the brain (Berretta et al., 1997). The striosomes have previously been linked to antipsychotic-induced immediate early gene expression (Grande et al., 2004) and psychostimulant-induced cellular toxicity (Granado et al., 2008), yet we see lower expression of Kchip3 mRNA in these compartments. The matrix is the predominant origin of striatopallidal neurons, although some afferents are seen in the striosomes (Gimenez-Amaya & Graybiel, 1990). This indicates Kchip3 mRNA expression may be located preferrentially in the striatopallidal GABAergic neuronal nuclei, with translated protein expression in their cell bodies and along the afferents, producing more extensive immunolabelling.

There has been only one previous study of KCHIP3 protein localisation in the brain, which found KCHIP3 highly expression in the piriform cortex, moderate immunolabelling in thalamic nuclei, expression in the dentate gyrus granular cell layer although no immunolabelling in pyramidal cells, and light immunostaining in the striatum (Hammond et al., 2003). Our immunohistochemical analysis supports the finding of low KCHIP3 expression in the hippocampus, except for immunolabelling of some subgranular zone cells and granular cells in the dentate gyrus, although we do see sporadic pyramidal cell immunolabelling in the CA3 hippocampal field. Also in contrast to this previous study we found remarkably specific KCHIP3 expression in the striatum as discussed above, with high expression in the grey matter and virtually no expression in white matter tracts.

228 We also detected a very strong signal for KCHIP3 in the midbrain region. High- resolution imaging shows KCHIP3 immunolabelling in dopaminergic neurons in the substantia nigra and the ventral tegmental area, specifically in the cytoplasm and projections of these neurons. Non-neuronal cells were unlabelled. In this study, KCHIP3 expression in dopaminergic neurons in the midbrain as well as the in situ hybridisation of Kchip3 detected in the substantia nigra, supports a role for Kchip3 in modulation of midbrain dopaminergic neurons (Liss et al., 2001).

6.3.3 Localisation of Kv1.1 mRNA and protein expression in the adult mouse brain

The protein localisation of Kv1.1 has been previously explored by immunohistochemistry. These studies have mainly focused on the region of high

Kv1.1 expression, the hippocampus. Expression has been noted in the middle third layer of the dentate gyrus (Rhodes et al., 1997), and interneurons in the hilar region (Wang et al., 1994). We noted an absence of Kv1.1 expression in the inner molecular layer, and specific labelling of hilar neurons that is consistent with these previous reports, although we also saw expression in subgranular zone neurons. In the CA3 region we saw strong Kv1.1 immunolabelling in the pyramidal cell layer with expression detected in projections into the hippocampal mossy fibre layer.

Electron microscopy of Kv1.1 expression has shown that it is localised along the axon and with synaptic vesicles at or near synaptic zones in the hippocampal

CA3 region, which indicates a role for Kv1.1 expression in modulating neurotransmitter release in this region (Wang et al., 1994). Interestingly, Kv1.1 is also translated locally at dendrites from mRNA transported from the neuronal somata in the CA1 hippocampal subfield (Raab-Graham et al., 2006). These subcellular localisation reports are consistent with our Kv1.1 immunolabelling in the white matter tracts, neuropil and dendrites.

In addition to localisation in the hippocampus, Kv1.1 expression in the basal ganglia has been explored, with immunohistochemical detection in the substantia

229 nigra pars reticulata, caudate putamen and globus pallidus (Rhodes et al., 1997).

In this study we localise high expression of Kv1.1 in the substantia nigra pars compacta and reticulata to both dopaminergic neurons and surrounding neuropil, as well as in the perikarya of some ventral tegmental area dopaminergic neurons. This high level of expression indicates Kv1.1-containing channels most likely regulate the electrophysiological current of these dopaminergic neurons.

However, despite the strong Kv1.1 protein in the midbrain, we were unable to localise mRNA in this region by in situ hybridisation. This could indicate low mRNA in dopaminergic neurons, and that the protein is coming from the terminals afferents of neurons from other regions. However, the localisation of

Kv1.1 protein to the perikarya of dopamine neurons refutes this conclusion.

Instead, the Kv1.1 antibody may have cross-reacted to another protein in this region, although previous analyses of Kv1.1 suggests true localisation and perhaps a very stable protein exists in this region leading to high expression. Alternatively, our in situ hybridisation probe may not have accurately detected the Kcna1 transcript present in the midbrain neurons, possibly due to alternative tertiary structure or stability of the mRNA in the region.

In the caudate putamen, we observed Kv1.1 expression in the axon bundles projecting through the striatum and in the anterior commissure, with specific neuronal labelling in the caudate putamen and nucleus accumbens. Presumably this dense Kv1.1 neuronal expression is occurring on GABAergic medium spiny projection neurons, which comprise over 90% of the striatal cell type (DeLong,

2000) and is therefore inhibitory in nature. Kv1.1 detection throughout the white matter in the brain is consistent with its localisation at the nodes of Ranvier on myelinated axons (Wang et al., 1993).

In the cingulate cortex and infralimbic cortex (which we term “prefrontal”) we noted Kv1.1 expression in the neuropil of all layers, with higher magnification showing distinct neuronal somata labelling in layer II and in the deeper cortical layers. The accuracy of our immunohistochemical localisation is supported by previous publication of this pattern of Kv1.1 expression in the neocortex, where

230 the neuronal cell body labelling was reported to be pyramidal in origin (Wang et al., 1994).

In contrast to the well-studied localisation of Kv1.1 protein expression, there have been few previously published attempts to characterise Kcna1 mRNA expression

(encoding Kv1.1) in the brain. Northern blot analysis of regional mouse brain extracts detected moderate Kcna1 expression in the caudate nucleus, hippocampus and thalamus, with low expression in the amygdala and hypothalamus (Leicher et al., 1996). Previous in situ hybridisation studies have focused on Kcna1 mRNA expression in the hippocampal region, with Kcna1 mRNA most highly expressed in the dentate gyrus, CA3 hippocampal field and entorhinal cortex (Wang et al., 1994). Subsequent analyses localised strong Kv1.1 protein expression in CA3 pyramidal cells and their projections, particularly in Schaffer collaterals synapsing in the CA1 region (Monaghan et al., 2001). Our study supports these findings. Compared to the specific in situ hybridisation pattern of Kchip3, we see relatively ubiquitous expression of Kcna1 in the mouse brain, consistent with grey and white matter expression. The notable exceptions are the strong signals from the dentate gyrus and CA3 hippocampal field and although we did not explore the entorhinal cortex we saw high Kcna1 expression in the rostral part of this structure, the piriform cortex. Similarly to Kchip3 in situ hybridisation, we saw specific immunolabelling of thalamic nuclei involved in somatosensory perception by Kcna1, but also the hypothalamic ventromedial nucleus, which is associated with satiety and sociosexual behaviour (Harding & McGinnis, 2003).

The high levels of expression for both Kcna1 and Kchip3 mRNA in the piriform cortex is interesting given this region is involved in sensory perception, deficits in which may underlie the auditory and visual hallucinations of schizophrenia psychosis (Freedman et al., 1991). Previous studies have shown that psychostimulant drugs induce c-fos expression in the piriform cortex of rats (Lillrank et al., 1996; Sharp, 1997). cFos is a transcription factor that is also altered in regions believed to be important in APD treatment in schizophrenia

231 (see Section 5.1). Furthermore, electroconvulsive therapy, which has been used historically and currently to treat psychiatric disorders (as reviewed in (George et al., 2007), may act via altered transcription of genes in the dentate gyrus and piriform cortex (Sun et al., 2005). While this convergent evidence suggests that the piriform cortex may be a region warranting further analysis, its phylogenetically conserved primary function in receiving input from the olfactory bulb (Price, 1973) indicates its possible role in schizophrenia aetiology is limited.

6.3.4 Characterisation of Kv1.1 and KCHIP3 protein expression in the adult mouse brain

By Western blot analysis we detected multiple molecular weight species of Kv1.1 and KCHIP3 in different regions in the saline-treated mouse brain. Interestingly, we found the two molecular weight species of Kv1.1 comprising the previously published dimer (Deal et al., 1994) to be differentially extracted, as the higher molecular weight band was less readily soluble. This is consistent with the higher molecular weight species being the palmitoylated form of the Kv1.1 protein, which is more tightly associated to the lipid membrane, as previously suggested (Gubitosi-Klug et al., 2005).

Our regional protein analysis also revealed alterations in a band we believe corresponds to a dimer of KCHIP3, as it is the approximate molecular weight of the published KCHIP3 dimer (Osawa et al., 2001) and was reduced by increased titration of the denaturant, urea. KCHIP3 is also known as DREAM and calsenilin and diverse functions have been ascribed to each of these pseudonyms in the mammalian brain. Yet it is the dimerised form of the KCHIP3 protein that is believed to associate with potassium channels subunits (Osawa et al., 2001), so it is this potassium channel regulatory function that may be targeted by APDs in this study.

We observed regional differences in expression of the multiple molecular weight species of Kv1.1 and KCHIP3 proteins in control animals in our brain regions of interest. The KCHIP3 monomer and more readily soluble Kv1.1 band were not

232 detectable in the PFC. In contrast, both molecular weight species of Kv1.1 and KCHIP3 were robustly expressed in the substantia nigra (when protein amount was normalised), suggesting that they have a direct role in dopaminergic neurotransmission.

6.3.5 Regional regulation of Kv1.1 and KCHIP3 mRNA and protein in haloperidol-treated animals compared to controls. By in situ hybridisation we detected altered transcription of Kchip3 and Kcna1 in specific brain regions following 7-day haloperidol treatment of mice. The difference detected in regions sampled by this analysis – the caudate putamen (striatum), PFC and four samples within the hippocampus – did not account for mRNA differences previously detected for Kchip3 and Kcna1 by microarray and QPCR analysis in the whole brain of haloperidol-treated animals. This may indicate reduced sensitivity to detect a difference despite the use of a large number of replicates. Alternatively, microarray hybridisation and QPCR analysis are both techniques that analyse denatured mRNA, whereas in situ hybridisation localises mRNA in its native form and any tertiary structure may interfere with hybridisation. Another possibility is that we may not have included the important region of regulation of these transcripts in the brain, although we covered most of the previously associated areas of transcriptional change.

By Western blot analysis we detected more dramatic regional differences in both

Kv1.1 and KCHIP3 protein in haloperidol-treated mice compared to controls.

For Kv1.1, both molecular weight species (pre- and post-translationally modified) were regulated by haloperidol in different regions. For KCHIP3, the proposed dimer, but not the monomer form, was regulated by haloperidol. These findings are integrated into region-specific alterations in Kv channel subunit expression in this discussion.

Striatum In our regional mRNA and protein analysis studies we saw the greatest regulation of Kv channel subunits in the striatum. This is perhaps not surprising given that

233 the striatum has the highest concentration of dopamine D2-receptors that are the immediate target of APDs (Seeman et al., 1975) and that immediate early genes, like c-Fos, have increased transcription in the striatum following APD treatment (MacGibbon et al., 1994). Two targets of these induced transcription factors may be Kcna1 (encoding Kv1.1) and Kchip3, which are both altered by haloperidol treatment as detected by in situ hybridisation.

Characterisation of Kchip3 by in situ hybridisation shows that it is exclusively expressed in the striatal matrix, which indicates it is most likely expressed in the nuclei of GABAergic medium spiny neurons with striatopallidal projections (Gimenez-Amaya & Graybiel, 1990). KCHIP3 protein is not as compartmentatlised as the mRNA expression, which is consistent with protein expression not only in the cell body but also in the afferents of these GABAergic neurons that project throughout the striatum. The effect of striatal Kchip3 mRNA downregulation in striatal GABAergic neurons, and decreased expression of KCHIP3 in striatopallidal projections of these neurons is discussed further in Section 7.4.

By in situ hybridisation and Western blot analysis in this regional study, we detected a significant increase in Kv1.1 mRNA and the Kv1.1 higher molecular weight protein, specifically in the striatum. This indicates increased Kcna1 transcription and increased presence of the palmitoylated, and presumably active, membrane-bound Kv1.1 subunit on the membranes of striatal neurons. Kv1.1- containing channels usually produce delayed rectifier current that dampens neuronal hyperexcitability. Kv1.1 loss-of-function mutations in humans leads to episodic ataxia (Adelman et al., 1995) and Kv1.1 knockout mice have prolonged seizures (Wenzel et al., 2007) supporting a role for Kv1.1-containing channels in dampening neuronal hyperexcitablity. Therefore, increased striatal Kv1.1 expression by APDs as seen in this study may serve to dampen aberrant hyperexcitablity, such as that which occurs in the hyperdopaminegic state in the striatum of patients with schizophrenia (as reviewed in Sections 1.2.3.2 & 1.2.3.3).

234 Alternatively, Kv1.1-containing channels can switch from delayed rectifier to produce A-type currents upon interaction with the Kv1 subunit (Rettig et al., 1994). In our 7-day whole brain microarray analysis we found down-regulation of

Kv1 mRNA although this was not confirmed by QPCR analysis of haloperidol- treated animals. In this regional analysis, increased Kv1.1, interacting with Kv1 to produce A-type currents, may prolong action potentials of striatal neurons, triggering neurotransmitter release (as reviewed in (Pongs, 1999). In order to decipher these two possible functions of Kv1.1, the expression of Kv1 in the striatum after 7-day haloperidol treatment could be assayed by Western blot analysis.

It must be noted that when dissecting the striatal region for Western blot analysis, we included the ventral striatum in the striatal tissue. The contribution of the nucleus accumbens to APD regulation of Kv1.1 protein expression must be considered. The nucleus accumbens receives input from midbrain dopamine neurons and mesolimbic dopaminergic projections are thought to be hypereractive in schizophrenia, leading to the positive symptomatology of the disorder (see Fig. 6.1). Increased expression of the palmitoylated form of Kv1.1 would increase the Kv1.1-containing channels in this region which, as discussed above, may produce delayed rectifier current that dampens hyperexcitability or A-type current that triggers neurotransmitter release in this region. Presumably the former function would be most effective in reversing the hyperactivity of mesolimbic dopaminergic neurons in the brains of patients with schizophrenia.

However, our sampling during in situ hybridisation analysis did not include the ventral striatal region so transcriptional changes of both Kcna1 and Kchip3 must be occurring in the caudate putamen. The ventral striatum may be targeted in further mRNA expression studies. The implication for increased A-type current, through Kchip3 or Kcna1 regulation, and increased neurotransmitter release in regard to the regulation of GABAergic neuronal activity is discussed further in Section 7.4.

235 Hippocampus We detected increased mRNA expression of Kcna1 in the CA4 or hilar region of the hippocampus by in situ hybridisation following haloperidol treatment. This was translated into increased protein expression of both Kv1.1 molecular weight species, indicating that pre- and post- translationally modified Kv1.1 may be important in the action of APDs in this region. The CA4 hippocampal region has previously been specifically implicated as a site of molecular expression changes in the brains of patients with schizophrenia (as reviewed in (Harrison, 2004). In particular, GAD-67 mRNA-positive neurons and Reelin-positive neurons are decreased in the CA4 although not other hippocampal fields in patients with schizophrenia (Fatemi et al., 2000; Heckers et al., 2002).

Our immunohistochemical studies suggest Kv1.1 is expressed in subgranular zone neurons at various stages of maturation. The regulation of Kv1.1 in this region of adult hippocampal neurogenesis is of interest given hippocampal enlargement from increased neuronal proliferation in an endogenous mouse mutant with truncated Kv1.1 protein (Almgren et al., 2007). The relevance of Kv1.1 regulation in the hippocampus by APDs, as it pertains to neurogenesis, is discussed further in Section 7.3.

By regional Western blot analysis, we also detected a 2-fold decrease in a band corresponding to the molecular weight of KCHIP3 dimerised protein in the hippocampus after haloperidol treatment. The regulation of hippocampal Kchip3 mRNA was not detected by in situ hybridisation in this study, indicating transcription is not changing, but rather the ability of the KCHIP3 protein to oligomerise. This provides support for APD regulation of functional Kv channel subunits, and therefore altered neuronal excitability, in the hippocampus. The hippocampus is important to the pathophysiology of schizophrenia (as reviewed in (Harrison, 2004), yet has not been previously associated with APD regulation. Future animal APD treatment studies would benefit from assessing mRNA and protein regulation, as well as electrophysiological modulation, in the hippocampus.

236

Midbrain Unfortunately we were unable to assess transcriptional differences in the midbrain by haloperidol treatment, as we did not obtain enough of these sections for detection by in situ hybridisation. Given the importance of dopamine regulation in the treatment of schizophrenia, it is impossible to draw conclusions about the involvement of Kv channels in this regulation without assessment of transcription in the origin of dopamine neurons. However, Western blot analysis revealed increased Kv1.1 protein levels, specifically the non-palmitoylated form, in the ventral tegemental area (VTA). It is possible that this more readily soluble form of the Kv1.1 protein, is produced in dopamine neurons in the ventral tegmental area and transported up to the nucleus accumbens, which was part of the striatal tissue acquisition for which we detected increased expression of the mature, membrane-bound Kv1.1 protein.

Furthermore, we detected decreased expression of KCHIP3 protein in the VTA by Western blot analysis. Kchip3 mRNA levels have previously been inversely correlated with the activity of midbrain dopamine neurons (Liss et al., 2001). If decreased KCHIP3 protein in the VTA is the result of decreased transcription of Kchip3 in mesocortical dopaminergic neuronal projections, then haloperidol could be altering the firing of these neurons that are projecting to the PFC. KCHIP3 is expressed in the nucleus, soma and in axonal projections (Zaidi et al., 2002). Therefore, decreased KCHIP3 protein expression detected in the PFC in our study may derive from downregulated mRNA in dopaminergic neurons originating from the VTA. Underactivity of mesocortical dopaminergic projections is proposed to contribute to cognitive decline in patients with schizophrenia (Abi-Dargham, 2004). No other mRNA or protein changes in Kv1.1 or Kchip3 are detected in the prefrontal cortical tissue.

Implications for the APD regulation of KCHIP3 in regions of midbrain dopaminergic projection are discussed further in Section 7.2. However, further analysis of dopaminergic neuronal transcription of Kcna1 and Kchip3 in the VTA,

237 by in situ hybridisation of histologically characterised midbrain tissue, is required to ascertain the role of Kv channel transcription in dopamine regulation by haloperidol.

238

Chapter 7

GENERAL DISCUSSION

239 7.1 SUMMARY of RESULTS

Schizophrenia is a debilitating psychiatric disorder that is both genetically and clinically complex. Although there have been many advances in our understanding of the illness since it was first clinically described over a century ago, the underlying cause(s) of this debilitating disorder remain unknown. Researchers are using neuropathological, neurochemical, genetic and molecular expression techniques to investigate the pathogenesis of schizophrenia. Another avenue is to explore the action of therapeutic pharmacological agents in order to better define or treat schizophrenia.

In this study, we examined the effect of antipsychotic drug (APD) administration on gene expression profiles in the rodent brain. Animal studies involved 7- and 28-day treatment paradigms with one of three APDs: clozapine, haloperidol or olanzapine, and saline treatment as control. Transcript profiling of whole brain tissue extracted from these animals was undertaken and the data analysed using a number of different techniques. These analyses revealed mRNA transcripts that were co-regulated by multiple APDs at each time-point. Using stringent cut-offs we narrowed the APD treatment regulated mRNA transcripts to those encoding 79 known genes in the 7-day analysis and 243 known genes in the 28-day analysis.

Using bioinformatic analysis and convergent functional genomics techniques we isolated candidate genes to undergo further validation by QPCR. In the 7-day treatment group we validated 13 out of 20 candidate genes as being regulated by APD treatment. These genes were regulated by multiple APDs and were highly expressed in the brain. They encode proteins involved in various biological processes, including neurogenesis, cell adhesion and four genes involved in voltage-gated ion channel regulation. In the 28-day treatment group we validated 10 out of 21 candidate genes as being regulated by APD treatment. Candidate genes were either additionally regulated by 7-day APD treatment in this study, were involved in a protein interaction network including BDNF and AKT1 (two

240 schizophrenia susceptibility genes, see Section 1.3.4.5 & 1.3.4.7), or had previously been associated with schizophrenia or its treatment. Validated APD- regulated genes encoded proteins involved in neuroprotection and neurogenesis, neuronal excitability and neurotransmission.

By Western blot analysis, the expression of three genes – Kchip3, Kcna1 (encoding

Kv1.1) and Nedd4 – were demonstrated to translate to protein expression changes in the 7-day APD treatment group. NEDD4 was also analysed by Western blot in the 28-day treatment group, although no significant protein regulation was detected. KCHIP3 and Kv1.1 are both involved in voltage-gated potassium channel regulation that is integral to neuronal excitability and neurotransmission. Given that we were able to validate translation of mRNA regulation into protein changes for these Kv channel genes in the 7-day treatment group, a relatively novel time-point for rodent APD treatment analyses, we decided to explore the regional expression and regulation of Kv1.1 and KCHIP3 by 7-day treatment with haloperidol.

By in situ hybridisation we localised Kchip3 and Kcna1 mRNA in various brain regions, particularly the frontal cortex, striatum, hippocampus and midbrain. By immunohistochemistry we localised KCHIP3 and Kv1.1 protein expression to neurons and projections within these regions. Kv1.1 has mostly neuronal expression and is found fairly ubiquitously throughout the brain in cell bodies, axons and termini, with particularly high expression in the hippocampal dentate gyrus and CA3 field. KCHIP3 has a more specific mRNA and protein expression pattern. It is localised exclusively in the grey matter regions of the brain, with particularly high expression in dopaminergic neurons in the midbrain. Following 7-day treatment with haloperidol, we detected increased Kcna1 mRNA in the caudate putamen and the CA4 or hilar region of the dentate gyrus. Kchip3 mRNA was decreased in the striatum after 7-day haloperidol treatment, as detected by in situ hybridisation. By Western blot analysis of microdissected brain regions we detected a band corresponding to a dimer of KCHIP3 that was decreased in the prefrontal cortex (PFC), ventral tegmental area (VTA) and hippocampus after 7-

241 day haloperidol treatment. Both bands of the published Kv1.1 dimer were increased by haloperidol treatment. The more soluble Kv1.1 protein was increased in the VTA and hippocampus, whereas that likely to correspond to the post-translationally modified and membrane-bound Kv1.1 protein was increased in the striatum and the hippocampus.

Characterisation of mRNA and protein in mouse brains and regulation analysis after haloperidol treatment have indicated that Kv channels may play a role in the mechanism of action of APDs in modulating multiple areas of dysregulation in schizophrenia. The possible role for Kv channel regulation of dopamine activity in midbrain projections, adult neurogenesis in the subventricular zone and striatal GABAergic neurons will be discussed below.

7.2 ANTIPSYCHOTIC DRUG REGULATION of Kv CHANNEL SUBUNITS may alter DOPAMINE NEUROTRANSMISSION in SCHIZOPHRENIA

One of the key areas of schizophrenia research arises from the knowledge that all antipsychotic drugs are dopamine receptor antagonists, leading to the dopamine hypothesis of schizophrenia, which posited striatal hyperactivity of dopamine leads to schizophrenia psychosis (see Section 1.2.3.2). As more neurochemical studies were undertaken, and technology advanced to provide direct evidence, this hypothesis was updated to include an underactivity of dopamine in cortical regions, in combination with glutamate and GABA deficits underlying the long- term negative and cognitive deficits in schizophrenia (Carlsson, 1999; as discussed in Section 1.2.5). Over the decades, the direct and indirect pharmacological and neuroimaging evidence has indicated that dysfunctional dopamine neurotransmission underlies the pathogenesis of schizophrenia.

Since the pharmacological dopamine hypothesis has evolved, the knowledge of how APDs may modulate dysfunctional dopamine neurotransmission has also

242 expanded. The major way in which APDs are known to modulate dopaminergic transmission is through blockade of dopamine receptors at post-synaptic densities, with the efficacy of APDs correlated with their dopamine D2 receptor striatal antagonism (Seeman et al., 1975). APDs are not believed to alter the number of dopamine receptors in the brain, despite the increased number of dopamine D2- receptors in the striatum of patients with schizophrenia (Mita et al., 1986; Seeman, 1987).

There is also evidence that APDs alter the activity of midbrain dopamine neurons in rodents. The release of neurotransmitter by dopaminergic neurons is mainly controlled by spontaneous electrical activity, the frequency of which is tuned by an intrinsic pacemaker (Grace & Bunney, 1984; Kang & Kitai, 1993). Acute treatment with conventional APDs increases the spontaneous activity of substantia nigra and VTA dopaminergic neurons (Hand et al., 1987). Acute treatment with atypical APDs increases the spontaneous activity of VTA dopaminergic neurons only (Hand et al., 1987; Stockton & Rasmussen, 1996). Conversely, long-term treatment with APDs decreases spontaneous activities of the same neurons (White & Wang, 1983; Stockton & Rasmussen, 1996). These electrophysiological studies indicate that both classes of APDs alter the electrical activity of midbrain dopaminergic neurons.

In midbrain dopaminergic neurons pacemaker activity is set mainly by endogenous current, which in turn is under the control of A-type potassium channels (Connor & Stevens, 1971; Grace, 1991). In the mammalian brain, these channels can be formed from Kv4 channels (Serodio et al., 1996) or Kv1 channels containing either Kv1.4 -subunits (Stuhmer et al., 1989) or Kv1.1 -subunits interacting with Kv1 -subunits (Rettig et al., 1994). Recent evidence suggests the Kv4 -subunits (KCHIPs) also regulate pacemaker activity, with Kchip3 mRNA expression inversely correlated with spontaneous activity of striatonigral dopaminergic neurons (Liss et al., 2001). Unfortunately, we were unable to assess Kchip3 or Kcna1 mRNA levels in the midbrain region, which makes it difficult to ascertain the role for Kchip3 regulation by APDs in controlling dopaminergic

243 neuronal activity. However, analysis of regional protein regulation by haloperidol suggests the midbrain as an important regulatory region.

Analysis of KCHIP3 protein levels in the midbrain in our study indicated no significant alteration by haloperidol treatment in the substantia nigra. However, we did find decreased expression of KCHIP3 protein in the VTA, and in the PFC in haloperidol-treated mice. We propose that this decreased protein expression may arise from decreased Kchip3 mRNA in the nucleus of mesocortical dopaminergic neurons in the VTA. This is then translated to decreased KCHIP3 in two known subcellular localisations: somatodendritically in the VTA, supported by perikarya immunolocalisation and decreased immunoreactivity in this region; and at the presynaptic terminal in the prefrontal cortex, supported by KCHIP3 immunolocalisation in the neuropil (Dodson & Forsythe, 2004) and decreased immunoreactivity in the frontal cortex. Electrophysiological studies indicate that decreased Kchip3 mRNA would increase the spontaneous firing rate of mesocortical dopaminergic neurons (Liss et al., 2001). Therefore, down- regulation of Kchip3 by APDs may be compensatory to the hypoactivity of mesocortical dopaminergic neuronal projections that are associated with working memory deficits in schizophrenia (see Section 1.2.3.4).

There is electrophysiological evidence indicating that haloperidol, clozapine and olanzapine all increase the firing rate of mesocortical dopaminergic neurons that synapse in the prefrontal cortex (Gessa et al., 2000), which seems to be a requirement of effective APD action. Our regional studies suggest that down- regulation of Kchip3 may be part of the mechanism of APDs in altering mesocortical dopaminergic neuronal activity. That Kchip3 is also downregulated at 28 days in this study suggests its regulation may be part of the chronic mechanism of APD action.

Interestingly, midbrain Kv1.1 regulation by haloperidol may also have implications for APD action. Delayed rectifier currents produced by Kv1 channels raise the threshold for action potentials after the first generation,

244 thereby dampening hyperexcitability (Barrett & Barrett, 1982; Dodson & Forsythe, 2004). With 7-day haloperidol treatment, we see increased expression of the soluble Kv1.1 protein in the VTA and of the membrane-bound protein in the striatum (tissue acquisition of which included the nucleus accumbens). The differential soluble Kv1.1 protein in the VTA could be in transit to the presynaptic terminal in the ventral striatum, particularly the nucleus accumbens, if produced within mesolimbic dopaminergic neurons. Increased expression of delayed rectifier current at the presynaptic terminal at the nucleus accumbens may dampen hyperexcitability of mesolimbic neurons, reducing striatal dopamine release that is excessive in schizophrenia (Abi-Dargham et al., 1998). In this way,

APD-treatment induced changes in Kv1.1 may act in concert with striatal dopamine D2-receptor blockade to decrease the psychotic symptoms of schizophrenia.

There has been a long-standing hypothesis about the therapeutic action of APDs known as the “delayed-onset” hypothesis, that posits APDs take several weeks to have a physiological effect in the brain and a clinical effect in patients (Grace et al., 1997). The evidence for this is the depolarisation-block hypothesis, in which immediate action of haloperidol increases the spontaneous activity of midbrain dopaminergic neurons, yet after repeated treatment (greater than 21 days) these neurons are over-activated and blocked from further activity (Grace & Bunney, 1986). The delayed-onset hypothesis proposes that the clinical outcome of depolarisation block of mesolimbic neurons reduces positive symptoms, whereas depolarisation block of nigrostriatal neurons induces extrapyramidal side effects in patients with schizophrenia (Grace et al., 1997). Recently, clinical studies have refuted this hypothesis, with evidence that APDs reduce psychotic symptoms in patients within the first week of treatment (Agid et al., 2006) rather than in the few weeks it takes for depolarisation block. Our studies indicate that transient Kv1.1 regulation by APDs, in dampening hyperexcitability of mesolimbic dopaminergic neurons, may be involved in producing these early effects on psychotic symptoms.

245 In summary, we propose that the Kv channel subunit regulation by APDs effects two projections of dopaminergic neurons from the VTA: Kchip3 downregulation, evidenced by decreased KCHIP3 protein in the VTA and prefrontal cortex, increases the spontaneous activity of mesocortical neurons; whereas Kv1.1 upregulation, evidenced by increased Kv1.1 protein in the VTA and the striatum, dampens hyperexcitability of mesolimbic neurons. Both of these neuronal activity regulations would have beneficial effects in the brains of patients with schizophrenia.

7.3 ANTIPSYCHOTIC DRUG REGULATION of GENES INVOLVED in ADULT NEUROGENESIS

It has recently become widely accepted that adult neurogenesis occurs in the mammalian brain, a notion supported by two seminal studies showing neurogenesis in the subgranular zone (SGZ) of the dentate gyrus and the subventricular zone (SVZ) in adult rodents (Kaplan & Hinds, 1977) and in adult humans (Eriksson et al., 1998). Neurons that proliferate in the SVZ migrate to the olfactory bulb (Lois & Alvarez-Buylla, 1993) and possibly into the frontal association area in primates (Gould et al., 1999). The majority of new cells in the SGZ of hilus of dentate gyrus differentiate into granular neurons and migrate into the dentate gyrus granular cell layer (Cameron et al., 1993). There are four main stages to neurogenesis: proliferation, migration, differentiation and survival of neuronal cells (Toro & Deakin, 2007). In this study we found APD regulation of genes encoding proteins that function in each of these stages.

During our Kv channel subunit analysis we detected APD-induced regulation of multiple subunits of Kv1.1 and KCHIP3 in numerous brain regions. Interestingly, both molecular weight species of Kv1.1 protein were increased in the hippocampus and in situ hybridisation detected increased Kv1.1 mRNA in the CA4 or hilar region of the dentate gyrus following 7-day haloperidol treatment. Furthermore, immunohistochemical analysis in our study suggests that within the

246 hilar region, Kv1.1 is expressed in the subgranular zone, the site of adult hippocampal neurogenesis. These findings are particularly interesting given that

Kv1.1 has previously been implicated as a factor involved in neurogenesis through animal studies. An endogenous Kcna1 mouse mutant has been described with a brain overgrowth phenotype (Donahue et al., 1996). The megencephaly (Mceph) mouse has a frame-shift deletion in the Kcna1 gene (Petersson et al., 2003) causing early truncation of the Kv1.1 protein, which is expressed in the brain but rapidly degraded (Persson et al., 2005). Mceph-/- mice exhibit recurrent seizures and have 3-fold increased hippocampal neuronal proliferation and survival, resulting in an enlarged hippocampus and ventral cortex (Almgren et al., 2007). Additionally, the Mceph-/- mice have increased BDNF expression (Lavebratt et al., 2006), which has been implicated in schizophrenia (see Section 1.3.4.4) and is thought to be a key regulator of hippocampal neurogenesis (Lee et al., 2002) modulated by antidepressant treatment (Dias et al., 2003). Interestingly, the megencephaly phenotype is rescued, and BDNF levels decreased, after treatment with carbamazepine (Lavebratt et al., 2006) an anti-epileptic drug that blocks Nav channels (Macdonald & Kelly, 1995) and may have some use in treating schizophrenia (Leucht et al., 2007). Our study provides further localisation support for a role for Kv1.1 in adult neurogenesis and also indicates that Kv1.1 hippocampal regulation is a target of haloperidol treatment.

During our whole brain expression study analyses, we found other genes involved in neurogenesis to be altered by multiple APDs. After 7-day treatment, we found that all three APDs increased expression of double-cortin like kinase (Dclk). This gene was not differentially regulated by microarray analysis in the 28-day APD treatment groups so may represent transient regulation. Different isoforms of this protein are involved in cell fate determination (Shu et al., 2006), neuronal migration (Francis et al., 1999) and neuronal cell survival (Kruidering et al., 2001). Numb-like (Numbl) was also down-regulated in our 7-day treatment microarray analysis study by all three APDs, particularly clozapine. Neurogenesis is asymmetric, in that neuronal progenitor cells divide to produce one daughter cell that will develop into a neuron and one daughter cell that will remain a

247 neuroprogenitor. NUMBL is important in maintaining the neuroprogenitor population of cells during proliferation and differentiation (Petersen et al., 2004) and also in supporting neuronal migration (Rasin et al., 2007) in the developing brain. Mice with postnatal silencing of Numb and Numbl in SVZ radial glia show enlarged ventricles and abnormal neurogenesis (Kuo et al., 2006). In the adult mouse, Numbl is mostly expressed in the hippocampus (Allen, 2006), suggesting it may function in adult hippocampal neurogenesis as well as during development.

In our chronic APD candidate gene expression analysis, we detected up- regulation of the vascular endothelial growth factor B gene (Vegfb) by 28-day haloperidol treatment in mice. Recent studies of Vegfb knockout mice show reduced neuroproliferation in the SGZ and SVZ, with recovery of neuroproliferation upon addition of exogenous VEGFB in adult mice, indicating this growth factor stimulates neurogenesis in the adult brain (Sun et al., 2006).

Further work is required to link Dclk, Numbl and Vegfb with adult neurogenesis, however these findings do suggest that genes involved in neuronal proliferation, differentiation, migration and survival may be regulated by APDs. These changes, and the hippocampal regulation of Kv1.1 by haloperidol in this study, are particularly interesting with accumulating evidence for altered adult neurogenesis in schizophrenia. A key study has reported decreased neuronal proliferation in the adult human dentate gyrus in schizophrenia (Reif et al., 2006). Interestingly, this study examined ten patients on APD treatment and three patients without treatment at the time of death, and although the correlation between treatment status and neuronal proliferation was non- significant (p=0.11), the trend suggests with more statistical power that an association may be made. Additionally, decreased expression of markers of neurogenesis have been observed in the hippocampus of patients with schizophrenia. Immunohistochemical studies of the hippocampal hilar region have shown a dramatic reduction in neural cell adhesion marker (NCAM) (Barbeau et al., 1995) as well as increased expression of fibroblast growth factor receptor (FGFR1) (Gaughran et al., 2006), suggesting altered synaptic plasticity

248 and neurogenesis in this region. These changes may be reflected by hippocampal dysfunction in memory and learning tasks in patients with schizophrenia (Reif et al., 2007).

Further support for altered adult neurogenesis in schizophrenia is the function of a number of genes that have been proposed as susceptibility factors in the disorder, or have altered expression in brains of patients. Reelin is a protein involved in neuronal migration and differentiation that has altered expression in the brains of patients with schizophrenia (see Section 1.4.2.1). Reelin is mutated in the reeler mouse that has disrupted SGZ neurogenesis during development (Stanfield & Cowan, 1979) and has also been shown to have reduced and disorganised adult neurogenesis (Won et al., 2006).

Additionally, reduced expression of the growth factor BDNF has been detected in the brains of patients with schizophrenia, and there is some evidence for a genetic association between BDNF and the disorder (see Section 1.3.4.4). The dysregulation of BDNF in the brain of patients with schizophrenia indicates altered survival of newly born neurons in this disorder (Reif et al., 2007). Injection of BDNF into the hippocampus of adult mice results in increased granule cell neurogenesis (Scharfman et al., 2005). However, Bdnf expression is decreased by chronic APD treatment in the hippocampus and PFC in a rat model of schizophrenia (Lipska et al., 2001). Although this indicates increased neurogenesis via induction of BDNF is not likely in chronic APD treatment, it may be more of a target of short-term APD action.

The latest gene of interest to adult neurogenesis and schizophrenia is DISC1, one of the major schizophrenia susceptibility genes (see Section 1.3.3.2). Downregulation of DISC1 causes enhanced neuronal development through increased repetitive firing of neurons and accelerated synaptogenesis of adult- born neurons, implicating a role for DISC1 in the regulation of adult neurogenesis (Duan et al., 2007).

249 Given the accumulating evidence for altered neurogenesis in schizophrenia, there have been numerous studies investigating markers of neuronal proliferation following APD treatment in rodents, although this remains a controversial field. In the SVZ, increased neurogenesis has been seen after chronic treatment with atypical APDs including olanzapine (Wakade et al., 2002). In the SGZ, low-dose clozapine, although not haloperidol or high-dose clozapine, increased neurogenesis (Halim et al., 2004). Additionally, chronic olanzapine treatment (although not 7-day treatment) has been found to increase neurogenesis in the SGZ (Kodama et al., 2004). However, another study found no evidence for hippocampal neuronal proliferation following acute or chronic clozapine or haloperidol treatment (Schmitt et al., 2004). Chronic olanzapine treatment, but not haloperidol, stimulated adult neurogenesis in the frontal cortex (Wang et al.,

2004). These previous studies indicate a greater effect on hippocampal Kv1.1 expression may be seen in an atypical APD treatment study, as most studies show chronic haloperidol treatment is not associated with adult neurogenesis. However, one neuropathological study has shown increased neurogenesis in the dentate gyrus following acute haloperidol treatment in the gerbil brain (Dawirs et al., 1998) and another study has shown increased SVZ neurogenesis after 14-day haloperidol treatment in rats (Kippin et al., 2005). Therefore, previous studies indicate a transient effect of haloperidol on mammalian adult neurogenesis. Our study supports a transient role for intermediate time-point haloperidol treatment in altering the transcription of regulators of neurogenesis in the adult mammalian brain.

7.4 ANTIPSYCHOTIC DRUG REGULATION of Kv CHANNELS in the STRIATUM may affect NEGATIVE SYMPTOMATOLOGY in SCHIZOPHRENIA

Thalamic glutamatergic neurons are under inhibitory control from pallidothalamic GABAergic neurons and under excitatory control from corticothalamic glutamatergic neurons. It has been hypothesised that negative symptomatology in schizophrenia may result from cortical glutamate deficiency

250 in corticothalamic pathways decreasing excitation of thalamocortical glutamate neurons (Carlsson et al., 1999) (Fig. 7.1). Furthermore, there is electrophysiological evidence in rat neuronal culture that dopamine, acting through D2 receptors specifically on striatopallidal GABAergic medium spiny neurons, reduces Kv current and stabilises the resting potential of these neurons (Francis et al., 1999). Overactivity of dopamine in the striatum in schizophrenia (see Section 1.2.3.2) may over-stabilise striatopallidal GABAergic neurons, leading to doplarisation block and decreased GABA release into the globus pallidus. This in turn would increase excitation of pallidothalamic GABAergic neurons, leading to increased inhibition of thalamic glutamate neurons. Therefore hyperdopaminergia in the striatum and hypoglutamatergia in the cortex may combine to reduce activity of thalamic neurons and produce negative symptomatology in schizophrenia (Carlsson et al., 1999).

As all APDs are striatal D2-receptor antagonists, reduced dopamine action in the striatum may reverse some of the over-stabilisation of striatopallidal GABAergic neurons leading to increased excitatory drive on thalamic glutamatergic neurons.

We propose that APD regulation of striatal Kv channels serves to further increase

Kv current in this region to counteract the over-stability of GABAergic neurons.

By in situ hybridisation and Western blot analysis we observed a significant increase in Kcna1 mRNA and the Kv1.1 higher molecular weight protein in the striatum after haloperidol treatment. This indicates increased Kcna1 transcription in GABAergic medium spiny neurons (the major cell population in this region) and increased presence of the palmitoylated, and presumably active, membrane- bound Kv1.1 subunit expressed on these neurons. Increased Kv current may reverse the dopamine-induced stabilisation of striatal GABAergic neurons that has been previously observed (Francis et al., 1999), further increasing excitatory drive on thalamocortical glutamatergic neurons.

It is important to note that in our regional analysis of haloperidol-treated mice, we also saw decreased Kchip3 mRNA in the striatum, which is presumably

251 occurring in the nucleus of GABAergic medium spiny neurons as these are the major cell type in this region. However, this decreased Kchip3 mRNA was not translated to decreased protein expression. In fact we observed a non-significant increase in KCHIP3 monomer and dimer expression in haloperidol-treated animals by Western blot analysis. KCHIP3 augments Kv current by increasing the density of Kv4-containing at the plasma membrane (An et al., 2000). Therefore increased KCHIP3 protein expression in the striatum may also increase Kv current in this region.

In combination then, the actions of APDs on Kv1.1 and KCHIP3 expression in the striatum, as observed in mice in this study, may serve to increase Kv current and release over-stabilisation of striatopallidal GABAergic neurons. Increased GABA in the pallidum would inhibit pallidothalamic GABAergic neurons, thereby increasing the excitatory drive of thalamocortical glutamatergic neurons and reducing negative symptomatology in patients with schizophrenia (Fig. 7.1).

PFC Corticothalamic/ thalamocortical Glu glutamate DA; neurons Striatum DRD2 Glu Striatopallidal/ pallidothalamic Th GABA GABA neurons GABA GP

Figure 7.1 Neurochemical deficits in schizophrenia and proposed affects of APDs on the corticostriatothalamic circuitry in schizophrenia. APDs are known to block striatal D2-receptors and we propose that they also increase activity of striatothalamic GABAergic pathways, over-inhibiting thalamic neuronal projections and increasing excitatory drive on thalamocortical glutamate neurons. Proposed APD effects are indicated by red arrows. Adapted from Carlsson et al., 1999. DA: dopamine, GABA: -amino butyric acid, GP: globus pallidus, Glu: glutamate, PFC: prefrontal cortex, Th:thalamus.

252

There is some debate in the field as to the efficacy of APDs, particularly conventional type, in alleviating the long-term negative symptoms of schizophrenia (reviewed in Section 1.2.2.1). A meta-analysis of clinical studies showed haloperidol and four atypical APDs have comparable effects on reducing negative symptomatology (Leucht et al., 1999), although others find atypical APDs to be moderately superior than conventional types in treating negative symptoms (Davis et al., 2003). The improvement in negative symptomatology in schizophrenia patients may occur within the first week of treatment, as it is part of the criteria for the Brief Psychiatric Rating Scale that is known to be improved early in APD action (Agid et al., 2006). We hypothesise that striatal regulation of

Kv channel subunits by haloperidol may act in concert, early in APD treatment, to increase the firing rate of striatal GABAergic neurons, increasing excitatory drive on cortical glutamate neurons. This is proposed to result in reduced negative symptoms in patients with schizophrenia.

In accordance with our hypothesis, Kv channels may be therapeutic targets for drugs that will better treat negative symptoms in schizophrenia. This is consistent with preliminary reports suggesting carbemazepine and lamotrigine, antiepileptic drugs that inhibit voltage-gated sodium channels (Macdonald & Kelly, 1995), may be effective at reducing the negative and affective symptoms in schizophrenia when used adjunctively with APDs (Hosak & Libiger, 2002). Inhibition of sodium channels could be expected to have a similar physiological effect as enhancing the activity of Kv channels. However, recent reviews of lamotrigine and carbamazapine in schizophrenia treatment conclude no current evidence for their use and suggest more extensive studies are required to confirm an association (Premkumar & Pick, 2006; Leucht et al., 2007).

253

7.5 FUTURE DIRECTIONS

Completion of the characterisation of haloperidol-induced regulation of Kv1.1 and KCHIP3, and their encoded genes, in the mouse brain requires the quantification of midbrain gene expression. This could be undertaken by first performing immunohistochemistry on the previously collected mouse brain sections with an antibody to TH, enabling detection of the characteristic dopaminergic neuronal pattern of the substantia nigra pars compacta that defines the midbrain region. Alternatively, we could undertake whole brain microdissections of the substantia nigra pars compacta and VTA, as carried out for protein lysate collection for regional Western blot analysis. QPCR analysis of RNA extracted from these microdissected regions, from saline- and haloperidol- treated mice, would facilitate mRNA quantification of Kcna1 and Kchip3 in the midbrain. Differences detected as a result of haloperidol treatment would direct future regional analyses and aid in interpretation of the data collected in Chapter 6 of this thesis. Midbrain mRNA quantification through either of these methods would complete the extensive characterisation and differential expression analysis in haloperidol-treated mouse brains.

One of the immediate questions that would then arise is whether there is a different Kv channel subunit pattern of expression in mice treated with atypical APDs than with typical APDs? Our microarray and QPCR studies in whole brain tissue indicated that Kchip3 and Kcna1 were similarly regulated by clozapine, haloperidol and olanzapine. However, given the body of evidence suggesting different modes of action in conventional and atypical APDs in switching on early gene expression, and the claims they may have differential clinical outcomes in patients with schizophrenia, confirmation of regional Kv channel regulation by atypical APDs would support a role for regulation of Kv channels as a central mechanism mediating antipsychotic action.

254 Another question that arises in this rodent study is how transient Kv channel regulation is, and whether other modes of altering neuronal excitability are valuable in chronic APD treatment? It is possible that after Kv1.1 and KCHIP3 regulation has altered the intrinsic excitability of target neurons, that the expression of other ion channel subunits may be modified during chronic treatment. For example, in the 28-day study we saw altered gene expression of two subunits of voltage-gated sodium channels: Scn2b and Nedd4. Using gene expression profiles from previously published animal APD treatment studies, as well as that gleaned from postmortem analyses of tissue from patients treated with differing APDs, a time-line of APD regulation of ion channels may be constructed that will decipher the importance of this potential mode of regulation in schizophrenia treatment.

The next major project following on from this animal-based thesis is the characterisation and quantification of Kv1.1 and KCHIP3, and their encoded genes, in brain tissue of patients with schizophrenia. Regions of interest for human schizophrenia tissue expression analysis, utilising molecular expression techniques similar to those used in our animal study, are suggested by this and previous studies. Kcna1 has previously been shown to be up-regulated by APD treatment in the striatum (Sondhi et al., 2005), as it is in this study, suggesting an area of interest to look for compensatory changes in schizophrenia brains. Kchip3 is increased by acute clozapine treatment in the ventral tegmentum and caudate putamen in mice (Le-Niculescu et al., 2007), suggesting KChip3 may be dynamically regulated over the course of treatment in the striatum.

In adding to these previously published studies, it would be interesting to evaluate the expression of the Kv channel subunits in brain regions relevant to schizophrenia that have shown to be regulated by APDs in this study. In particular, we could expand on the findings of our study to examine the expression of Kv1.1 in the hippocampus of patients with schizophrenia that are on APD treatment or in neuroleptic naïve patient tissue. Additionally, the indication for KCHIP3 regulation of dopaminergic neuronal firing activity and in

255 striatopallidal GABAergic activity suggests regions and cells of particular interest for further analysis. For example, preliminary transcript profiling studies have indicated that both Kchip3 and Kcna1 are differentially expressed in the DLPFC in schizophrenic patients (AltarA, 2007; Bahn, 2007). This is interesting given we found both Kv1.1 and KCHIP3 proteins to be altered in the mouse PFC after haloperidol treatment. The quantification of protein expression in the human DLPFC may be of interest to ascertain whether haloperidol treatment is leading to, or compensating for, altered expression of Kcna1 and Kchip3 in schizophrenic brain tissue. The characterisation and expression analysis of Kv channel subunits in the brains of patients with schizophrenia would aid in our understanding of a role for these regulators of neuronal excitability in schizophrenia and its treatment.

The inclusion of neuroleptic-naïve patients is crucial in deciphering compensatory APD-induced or real pathological effects, however neuroleptic- naïve schizophrenia patients are very rare. Prior knowledge of the direction of effect in non-pathological mammalian brain tissue, provided by this study, may be useful when evaluating molecular expression in schizophrenic brain tissue. If changes in Kchip3 or Kcna1 expression were detected in schizophrenia tissue that were compensatory in their regulation by APDs, it would be interesting to conduct association studies on these genes in schizophrenia patient cohorts. This would aid in elucidating whether Kv channel subunit dysregulation, corrected by APDs, is part of the underlying aetiology of schizophrenia.

As an interesting side experiment, during the course of this thesis study a genetic analysis revealed that a CAG trinucleotide repeat polymorphism in NUMBL is associated with schizophrenia in Brazilian and Danish cohorts (Passos Gregorio et al., 2006). In particular, the 18 repeat allele was found more often in patients with schizophrenia. Our transcript profiling study revealed 7-day treatment with clozapine, haloperidol and olanzapine all decreased the expression of Numbl in mouse whole brain tissue, with validated down-regulation in the clozapine- treated group by QPCR analysis. That NUMBL mutations may be associated

256 with schizophrenia provides evidence that APD regulation of Numbl may be compensating for a component of the underlying pathology of schizophrenia. NUMBL is a factor in determining daughter cell fate during neurogenesis. In order to explore the significance of its regulation by APDs, analysis of Numbl expression in schizophrenia brain tissue should be explored. In parallel, cell culture studies to reveal the effect of the 18 CAG trinucleotide repeat polymorphism on NUMBL protein function could be examined. Trinucleotide repeat mutations are known as dynamic mutations as they exhibit anticipation, which is the occurrence of the generational accumulation of trinucleotide repeats, where the number of repeats is inversely proportional to the onset age and correlates with symptom severity in patients. Trinucleotide repeat expansions have been shown to result in a range of neurological disorders. For example, expanded polyglutamine tracts in the Huntingtin protein are causative of Huntington’s chorea in patients Huntington (1993). In schizophrenia there is some evidence for anticipation, specifically the earlier onset of illness in subsequent generations (reviewed in (Margolis et al., 1999). It would be interesting to correlate the features of dynamic mutations with patients in the schizophrenia NUMBL genetic association study, and to explore the pathological effects of polyglutamine tracts in NUMBL protein.

7.6 FINAL REMARKS

Schizophrenia is a devastating and debilitating disorder that strikes individuals in the prime of their lives leading to psychosis, rapid degradation in social functioning, and cognitive decline. Antipsychotic drugs have remained the mainstay treatment for schizophrenia, since they were first introduced in the 1960’s, as they are effective in reducing psychosis in most patients. However, these treatments do not greatly lessen the long-term burden of schizophrenia in patients or their carers and are associated with severe adverse effects leading to non-compliance and relapse into psychosis. The introduction of antipsychotic drugs has meant an end to the inhumane treatment of psychotic patients in

257 mental institutes, yet due to the unsatisfactory results of antipsychotic drug treatment around 15% of mentally ill people are homeless and around 40% of patients with schizophrenia attempt suicide during illness.

This study has attempted to uncover some of the underlying mechanisms of action of antipsychotic drugs, particularly in relation to altered molecular expression in the brain. Our studies of a novel 7-day treatment time-point have indicated transient effects of antipsychotic drugs on the transcription and translation of voltage-gated potassium channel subunits and regulatory proteins.

These effects on Kv channels may function in the mechanism of antipsychotic action by altering dopaminergic or GABAergic neurotransmission, or the potential for neurogenesis, in the brains of patients with schizophrenia. In understanding some of the underlying biological mechanisms in currently used treatments, such as regulation of Kv current, it is hoped that we can create better drug targets. Additionally, insight into the mechanism of action of the drugs that treat some of the symptoms of schizophrenia may increase our understanding as to the aetiology of these symptoms, in what is a genetically and clinically heterogenous disorder with no currently defined pathogenesis.

258

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323 APPENDIX 1 GENES ALTERED IN 28-DAY MICROARRAY ANALYSIS AT FALSE DISCOVERY RATE<0.05 & FOLD-CHANGE > 1.5

Biological function and/or Mouse Clozapine Haloperidol Olanzapine Entrez FDR- FDR- FDR- GeneGene ID FC^ value FC^ value FC^ value FamilySubcellular location Linkage* prior SZ or APD association AATK 11302 -0.085 0.503 -0.039 0.577 1.052 0.015 kinase cytoplasm ABCG4 192663 -0.032 0.596 0.146 0.744 1.548 0.027 transporter plasma membrane 11q23.3 grey matter expressed cholesterol protein ABHD5 67469 0.057 0.326 0.085 0.518 1.028 0.015 enzyme cytoplasm ACTL6B 83766 -0.307 0.229 -0.155 0.371 1.069 0.022 other nucleus ADCY3 104111 0.107 0.593 0.098 0.29 1.037 0.004 enzyme plasma membrane AHCYL1 229709 -1.725 0.043 enzyme cytoplasm 1p13.3 inositol phospholipid signaling

ALG5 66248 0.01 0.951 0.025 0.733 1.171 0.048 enzyme cytoplasm ANAPC7 56317 0.279 0.186 0.267 0.636 1.032 0.013 other unknown ANAPC11 66156 -0.191 0.645 0.747 0.44 -1.129 0.029 enzyme unknown ANP32A 11737 1.268 0.009 -0.685 0.039 1.131 0.01 other nucleus 15q23.3 ubiquitously expressed apoptosis/ signaling protein AOF2 99982 0.102 0.746 -0.357 0.468 1.013 0.029 other nucleus AP1GBP1 217030 0.396 0.386 -0.357 0.718 1.185 0.046 other cytoplasm AP1S2 108012 -0.094 0.938 -1.229 0.02 -0.077 0.718 transporter cytoplasm AP2A2 11772 0.194 0.289 -0.019 0.686 1.291 0.02 transporter cytoplasm APC2 23805 -0.29 0.133 0.135 0.648 1.354 0.013 other cytoplasm ARF5 11844 0.073 0.955 0.344 0.586 1.183 0.04 transporter cytoplasm ARFRP1 76688 0.501 0.067 0.001 0.387 1.252 0.01 enzyme unknown ASS 11898 0.024 0.986 -0.239 0.346 1.029 0.038 enzyme cytoplasm ATF2 11909 0.063 0.773 -1.715 0.036 -0.571 0.648 transcription nucleus regulator ATN1 13498 -0.051 0.613 -0.001 0.654 1.233 0.006 other nucleus AXIN1 12005 0 0.609 -0.023 0.89 1.114 0.022 other cytoplasm AXIN2 12006 -0.1 0.484 0.042 0.13 1.009 0.001 other cytoplasm AXL 26362 0.123 0.235 -0.169 0.071 1.225 0.005 kinase plasma membrane AZIN1 54375 0.349 0.465 -1.03 0.049 0.202 0.858 enzyme cytoplasm B3GALT6 117592 0.101 0.364 -0.003 0.861 1.195 0.004 enzyme unknown BAX 12028 0.031 0.299 -0.05 0.694 1.506 0.003 other cytoplasm BCS1L 66821 0.139 0.073 0.181 0.234 1.136 0.003 other cytoplasm BNIP2 12175 -0.173 0.359 0.283 0.466 -1.216 0.04 other cytoplasm BTRC 12234 0.012 0.531 0.065 0.704 1.021 0.018 enzyme cytoplasm C6ORF1 106672 -0.033 0.989 -0.241 0.148 1.08 0.034 other unknown CACNA2D2 56808 0.279 0.247 0.409 0.383 1.039 0.035 ion channel unknown voltage-gated calcium channel - subunit gene CACNB4 12298 0.014 0.811 -1.417 0.025 -0.593 0.286 ion channel plasma membrane voltage-gated calcium channel - subunit gene – no interaction with CACNA2D2 CADPS 27062 -0.01 0.755 -1.035 0.027 -0.317 0.301 other plasma membrane CAMK2A 12322 -0.172 0.276 -0.647 0.016 -0.397 0.369 kinase cytoplasm CCNT1 12455 -0.036 0.987 -0.165 0.976 1.011 0.037 other nucleus CDC42 12540 0.11 0.873 -1.106 0.024 -0.194 0.589 enzyme cytoplasm CDKN2D 12581 0.715 0.126 -0.33 0.554 1.012 0.041 transcription nucleus regulator CDO1 12583 0.233 0.551 0.011 0.74 1.027 0.035 enzyme cytoplasm CHST2 54371 -0.38 0.076 -0.036 0.504 -1.132 0.029 enzyme extracellular space CKMT1B12716 1.385 0.021 kinase cytoplasm CLCN2 12724 0.005 0.839 -0.083 0.945 1.047 0.02 ion channel plasma membrane CLDN5 12741 0.419 0.079 0.15 0.974 1.155 0.005 other plasma membrane 22q11.21 regulates BBB permeability; 3 +ve genetic association studies with SZ (Ye et al., 2005; Wei et al., 2005; Sun et al., 2004)

CLDN10 58187 0.327 0.414 -0.397 0.278 1.06 0.044 other plasma membrane COL6A1 12833 -0.032 0.703 0.078 0.327 1.212 0.021 other extracellular space COPS6 26893 0.235 0.41 0.142 0.973 1.233 0.034 other nucleus CORO1C 23790 0.211 0.541 0.093 0.854 1.097 0.024 other cytoplasm COX8A 12868 1.097 0.002 enzyme cytoplasm CSNK1D 104318 0.276 0.339 -0.205 0.598 1.127 0.016 kinase cytoplasm CSNK1G2 103236 0.073 0.932 0.04 0.639 1 0.017 kinase cytoplasm CSPG3 13004 -1.101 0.046 -0.102 0.756 -0.762 0.06 other unknown altered in 7-day APD treatment study CYB5R1 72017 0.202 0.188 -0.003 0.543 1.01 0.006 enzyme unknown CYP26A1 13082 -0.089 0.164 0.202 0.244 -1.035 0.009 enzyme cytoplasm DAPK3 13144 0.022 0.772 -0.047 0.869 1.026 0.011 kinase cytoplasm DDC 13195 0.617 0.024 -0.261 0.762 1.06 0.022 enzyme cytoplasm mRNA haloperidol (Buckland et al., 1992); activity haloperidol (Grundy et al., 2002); NC (Vernaleken et al., 2006)

DDX17 67040 0.09 0.925 -1.031 0.03 0.137 0.899 enzyme nucleus DHPS 330817 -0.034 0.371 0.04 0.316 1.042 0.007 enzyme cytoplasm DLG4 13385 0.084 0.712 -1.182 0.002 0.035 0.791 kinase plasma membrane DLGAP3 242667 -0.453 0.199 0.063 0.704 1.13 0.018 other cytoplasm DNAJA2 56445 0.262 0.682 -1.053 0.028 -0.03 0.865 enzyme nucleus DNAJC11 230935 -0.989 0.162 -0.097 0.471 -1.478 0.047 other unknown DNM1 13429 -0.899 0.095 -1.002 0.023 0.404 0.349 enzyme cytoplasm DNMT1 13433 -0.039 0.486 -0.215 0.773 1.134 0.04 enzyme nucleus in cortical GABA neurons in SZ (Veldic et al., 2005) DPYSL4 26757 0.129 0.298 -0.26 0.311 1.024 0.024 enzyme cytoplasm DRAP1 66556 0.228 0.616 -0.325 0.519 1.171 0.043 transcription nucleus regulator DSCR1L1 53901 0.634 0.143 -0.211 0.157 1.07 0.025 other unknown inhibits calcineurin signaling DUSP14 56405 0.036 0.933 -0.578 0.221 1.025 0.025 phosphatase unknown DUSP16 70686 0.222 0.332 0.054 0.749 1.136 0.026 phosphatase nucleus EEF1A2 13628 0.076 0.518 -0.064 0.807 1.691 0.015 translation cytoplasm regulator EFTUD2 20624 -1.32 0.045 enzyme nucleus EIF2B4 13667 0.21 0.749 -0.486 0.042 1.098 0.008 translation cytoplasm regulator EIF4E2 26987 0.173 0.372 0.016 0.905 1.217 0.012 translation cytoplasm regulator ELF2 69257 0.457 0.159 -0.17 0.717 1.01 0.004 transcription nucleus regulator ENAH 13800 0.039 0.948 -1.344 0.022 -0.098 0.621 other cytoplasm ERCC5 22592 0.351 0.02 0.015 0.867 1.086 0.002 enzyme nucleus EXOSC4 109075 0.32 0.064 -0.12 0.876 1.114 0.001 enzyme nucleus FAM50A 108160 0.365 0.291 -0.052 0.895 1.117 0.045 other nucleus FBP2 14120 -0.044 0.899 0.291 0.348 -1.124 0.045 phosphatase cytoplasm FBXO6 50762 0.024 0.596 -0.124 0.807 1.078 0.005 enzyme cytoplasm FBXO9 71538 0.533 0.175 -0.321 0.21 1.067 0.012 enzyme cytoplasm FDFT1 14137 -0.331 0.666 0.244 0.517 -1.109 0.047 enzyme cytoplasm FGFR1 14182 -0.086 0.783 -0.047 0.931 1.135 0.034 kinase plasma membrane growth factor associated with neurogenesis (Weickert et al., 2005) FHL2 14200 0.192 0.29 -0.186 0.862 1.016 0.041 other nucleus GABRA1 14394 -1.429 0.038 ion channel plasma membrane altered in 7-day APD treatment study GABRB3 14402 -0.088 0.821 -1.002 0.017 -0.224 0.495 ion channel plasma membrane GABRD 14403 -0.003 0.86 -0.045 0.355 1.495 0.028 ion channel plasma membrane GAD1 14415 -1.233 0.017 enzyme cytoplasm proposed SZ susceptibility gene (see Section 1.3.4.5) GADD45GIP1 102060 0.133 0.156 -0.003 0.944 1.047 0.007 other nucleus GBF1 107338 -0.061 0.52 -0.27 0.093 1.206 0.02 other cytoplasm GCAT 26912 0.117 0.394 0.064 0.744 1.012 0.002 enzyme cytoplasm GDI2 14569 -0.207 0.624 -1.293 0.032 -0.345 0.418 other cytoplasm 10p15.1 GGA3 260302 -0.063 0.599 -0.304 0.159 1.114 0.017 transporter cytoplasm GLTSCR2 68077 0.252 0.047 0.051 0.713 1.229 0.002 other cytoplasm GNB1 14688 1.607 0.06 1.576 0.061 2.589 0.023 enzyme plasma membrane activates Akt1 (Murgul et al., 1998); induced by Nrg1 (Fu et al., 1999); cortical expression GNG4 14706 0.453 0.17 -0.146 0.76 1.012 0.04 enzyme plasma membrane GOLGA3 269682 0.342 0.122 -0.212 0.905 1.117 0.033 transporter cytoplasm GPSM1 67839 0.111 0.568 -0.215 0.739 1.666 0.017 other cytoplasm GRM2 108068 -0.086 0.58 0.096 0.715 1.197 0.037 G-protein plasma membrane mGluR2 gene implicated in SZ coupled (Joo et al., 2001) receptor GRM5 108071 0.034 0.989 -1.037 0.038 -0.372 0.446 G-protein plasma membrane mGluR5 gene implicated in SZ coupled (Devon et al., 2001); mGluR5 receptor knockout mouse SZ behavioural phenoypes (Kinney et al., 2003)

GUK1 14923 0.472 0.112 -0.155 0.759 1.278 0.018 kinase cytoplasm HBP1 73389 0.359 0.064 0.1 0.827 1.11 0.016 transcription nucleus regulator HDAC4 208727 0.258 0.243 0.246 0.491 1.023 0.034 transcription nucleus regulator HES5 15208 -0.052 0.914 -0.006 0.935 1.039 0.01 other nucleus HK1 15275 -0.392 0.526 -1.034 0.028 0.81 0.04 kinase cytoplasm HMGA1 15361 -0.192 0.624 -0.205 0.331 1.467 0.026 transcription nucleus regulator HNRPA1 15382 0.832 0.172 0.334 0.789 1.312 0.039 other nucleus HNRPL 15388 0.362 0.232 0.053 0.497 1.056 0.002 other nucleus HOM-TES-103 320678 0.035 0.029 0.049 0.836 1.099 0.004 other unknown HOXA5 15402 -0.531 0.43 0.858 0.11 -1.073 0.048 transcription nucleus regulator HOXC6 15425 -0.443 0.199 0.034 0.766 -1.237 0.041 transcription nucleus regulator HRAS 15461 0.083 0.857 0.042 0.815 1.052 0.042 enzyme plasma membrane HS6ST1 50785 0.144 0.398 0.054 0.992 1.22 0.018 enzyme extracellular space HSP90AB1 15516 -0.469 0.253 -1.338 0.012 -0.005 0.795 other cytoplasm HSP90B1 22027 0.073 0.968 -1.53 0.05 -0.425 0.505 other plasma membrane HSPA5 14828 -0.229 0.583 0.06 0.816 -1.079 0.022 other cytoplasm HTRA2 64704 0.139 0.574 0.02 0.599 1.108 0.005 peptidase cytoplasm ICAM5 15898 0.037 0.856 -0.221 0.632 1.22 0.023 other plasma membrane ICMT 57295 -0.045 0.885 -0.086 0.799 1.165 0.037 enzyme cytoplasm IL15RA 16169 -0.22 0.234 -0.024 0.765 -1.405 0.041 transmembrane plasma membrane receptor ILVBL 216136 0.245 0.28 -0.155 0.841 1.164 0.013 enzyme cytoplasm INTS7 77065 0.419 0.126 -0.214 0.674 1.037 0.032 other unknown IPO4 75751 0.019 0.801 -0.136 0.945 1.009 0.029 transporter nucleus 14q11.2 vitamin D receptor translocation

ITPKA 228550 0.036 0.863 0.004 0.975 1.058 0.032 kinase cytoplasm ITSN1 16443 -0.43 0.309 0.355 0.924 -1.078 0.035 other cytoplasm KCNT1 227632 0.126 0.374 -0.217 0.607 1.112 0.012 ion channel unknown KIAA0553 237943 -0.109 0.374 -0.18 0.041 1.304 0.001 other unknown KIAA0802 68617 -0.031 0.996 -1.155 0.043 0.107 0.883 other unknown KIAA1267 76719 0.122 0.544 -0.421 0.098 1.05 0.03 other nucleus KIF1A 16560 -0.337 0.356 -1.109 0.016 0.478 0.2 other cytoplasm KLF3 16599 -0.409 0.326 0.184 0.741 -1.228 0.047 transcription nucleus regulator KLHL17 231003 0.064 0.742 -0.031 0.648 1.307 0.037 other cytoplasm KNS2 16593 -0.01 0.87 -1.415 0.014 0.409 0.334 other cytoplasm altered in 7-day APD treatment study LCAT 16816 -0.05 0.873 0.042 0.749 1.134 0.038 enzyme extracellular space LDLR 16835 0.222 0.091 -0.01 0.895 1.198 0.001 transporter plasma membrane LIN7B 22342 0.757 0.11 0.359 0.38 1.57 0.007 other cytoplasm LTBP3 16998 0.118 0.814 0.159 0.488 1.05 0.01 other unknown LZTR1 66863 0.145 0.696 -0.154 0.382 1.21 0.007 transcription nucleus regulator MAFG 17134 0.108 0.189 -0.063 0.521 1.323 0.005 transcription nucleus regulator MAN2A2 140481 -0.28 0.179 -0.005 0.333 -1.141 0.029 enzyme cytoplasm MAPK1 26413 0.268 0.896 -0.908 0.045 0.233 0.81 kinase cytoplasm MAPRE1 13589 -0.135 0.706 -0.055 0.77 1.008 0.011 other cytoplasm MAPRE3 100732 -0.033 0.862 -0.077 0.987 1.097 0.022 enzyme cytoplasm MARCKS 17118 0.114 0.8 -1.104 0.027 -0.453 0.215 other plasma membrane MATK 17179 -0.018 0.965 -0.571 0.042 1.013 0.028 kinase cytoplasm MATR3 17184 -1.223 0.049 other nucleus MBP 17196 -0.115 0.707 -1.012 0.037 0.027 0.993 other extracellular space 18q23.3 myelination protein; catatonic SZ (Ramon et al., 1986); SZ (Chambers et al., 2004); NC CSF (Steiner et al., 2006)

MCM7 17220 0.467 0.238 0.162 0.891 1.327 0.044 enzyme nucleus MGC17330 216505 0.456 0.114 0.106 0.644 1.184 0.008 other unknown MYH9 17886 -0.256 0.53 0.208 0.932 -1.448 0.039 other cytoplasm MYR8 244281 0.021 0.787 -0.066 0.424 1.077 0.011 other cytoplasm N4BP1 80750 0.163 0.426 0.191 0.87 1.399 0.005 other cytoplasm ubiquitinylated by NEDD4 NADK 192185 0.325 0.472 -0.559 0.445 1.161 0.037 kinase unknown NAPB 17957 0.187 0.779 -1.605 0.014 -0.277 0.655 transporter cytoplasm interacts with ionotropic AMPA receptor, GRIA2 NCOR2 20602 -0.042 0.914 -0.191 0.229 1.039 0.021 transcription nucleus regulator NCSTN 59287 0.116 0.68 0.003 0.883 1.034 0.001 peptidase plasma membrane 1q23.3 NDUFA6 67130 0.715 0.074 -0.018 0.446 1.093 0.013 enzyme cytoplasm NEDD4 17999 0.147 0.973 -1.342 0.036 -0.042 0.923 enzyme cytoplasm altered in 7-day APD treatment study NEU1 18010 0.169 0.357 0.065 0.998 1.133 0.026 enzyme cytoplasm NFRKB 235134 0.286 0.152 0.008 0.59 1.2 0.015 transcription nucleus regulator NR0B2 23957 -0.33 0.231 0.247 0.973 -1.266 0.043 ligand- nucleus dependent nuclear receptor

NUDT16L1 66911 0.144 0.489 -0.037 0.583 1.419 0.009 other unknown OPLAH 75475 0.223 0.964 -0.039 0.996 1.124 0.005 enzyme unknown PABPN1 54196 0.388 0.224 0.201 0.621 1.356 0.021 other nucleus PDPR 319518 0.218 0.505 -0.206 0.431 1.111 0.047 enzyme unknown PDRG1 68559 0.382 0.276 0.001 0.909 1.515 0.027 other cytoplasm PEX19 19298 -0.032 0.827 -0.344 0.097 1.031 0.045 other cytoplasm 1q23.3 PFN1 18643 0.417 0.557 0.356 0.519 1.149 0.04 other cytoplasm PFTK1 18647 -0.124 0.375 -1.014 0.009 -0.221 0.389 kinase nucleus PGD 110208 -0.001 0.981 -0.016 0.729 1.173 0.05 enzyme cytoplasm PHF17 269424 0.051 0.741 -0.097 0.493 1.012 0.025 other nucleus PIGC 67292 0.367 0.077 -0.019 0.589 1.138 0.009 enzyme cytoplasm PIM3 223775 -0.027 0.971 -0.009 0.865 1.045 0.002 kinase unknown PMM129858 1.181 0.044 enzyme cytoplasm POP5 117109 0.632 0.179 -0.174 0.363 1.136 0.049 enzyme nucleus PPM1F 68606 -0.002 0.838 -0.045 0.575 1.289 0.015 phosphatase cytoplasm PPP2R5D 21770 0.049 0.559 0.068 0.444 1.147 0.016 phosphatase nucleus PRDX4 53381 0.325 0.199 -0.241 0.412 1.284 0.038 enzyme cytoplasm PREB 50907 0.138 0.354 1.046 0.009 -0.121 0.717 transcription nucleus regulator PRKRA 23992 0.158 0.167 -0.016 0.72 1.06 0.029 other cytoplasm PSMD13 23997 0.752 0.223 -0.022 0.844 1.12 0.045 peptidase cytoplasm PSME1 19186 0.048 0.281 -0.478 0.066 1.038 0.02 other cytoplasm PTBP1 19205 0.01 0.933 -0.188 0.487 1.182 0.032 enzyme nucleus PTGES2 96979 0.156 0.573 0.067 0.205 1.021 0.005 transcription cytoplasm regulator PTGFRN 19221 -0.264 0.231 -0.175 0.403 -1.172 0.047 other plasma membrane PTOV1 84113 -0.291 0.676 -0.385 0.211 1.167 0.034 other nucleus PTPMT1 66461 0.366 0.328 -0.308 0.64 1.233 0.026 phosphatase nucleus PTPN5 19259 0.237 0.817 -0.133 0.794 1.166 0.018 phosphatase cytoplasm RAB4A 19341 -0.051 0.824 -0.359 0.118 1.012 0.021 enzyme cytoplasm RAMP1 51801 0.414 0.361 -0.271 0.795 1.101 0.011 transporter plasma membrane RAMP2 54409 0.001 0.862 0.082 0.657 1 0.033 other plasma membrane RASGRF1 19417 -0.343 0.673 -1.152 0.037 0.047 0.923 other cytoplasm RCE1 19671 0.2 0.619 0.036 0.697 1.018 0.028 peptidase cytoplasm REV1L 56210 0.403 0.06 -0.056 0.653 1.144 0.042 enzyme nucleus 2q11.2 DNA mismatch repair RGS12 71729 -0.029 0.802 -0.111 0.752 1.254 0.018 other nucleus ROR1 26563 -0.431 0.348 -0.028 0.741 -1.073 0.013 kinase plasma membrane RORA 19883 -0.56 0.124 0.375 0.341 -1.09 0.038 ligand- nucleus dependent nuclear receptor

RUFY1 216724 0.317 0.34 -0.015 0.738 1.266 0.035 transporter cytoplasm S100A13 20196 0.096 0.572 -0.011 0.43 1.244 0.033 other cytoplasm SCAMP2 71742 -0.014 0.641 0.095 0.546 1.241 0.009 transporter cytoplasm SCD2 20250 -1.873 0.008 enzyme cytoplasm SCN2B 72821 -1.201 0.023 0.595 0.179 -1.148 0.028 ion channel plasma membrane 11q23.3 voltage-gated sodium channel - subunit SDC4 20971 0.579 0.12 0.199 0.406 1.392 0.026 other plasma membrane SDCBP 53378 0.141 0.825 -0.894 0.032 -0.195 0.702 enzyme plasma membrane SERINC1 56442 0.112 0.824 -1.734 0.034 -0.39 0.513 transporter plasma membrane SERPINI1 20713 0.518 0.382 -1.01 0.041 0.218 0.646 other extracellular space altered in 7-day APD treatment study SFRS2 20382 0.1 0.171 -0.203 0.787 -0.642 0.049 other nucleus SFRS9 108014 0.408 0.239 -0.015 0.694 1.124 0.038 enzyme nucleus SGK 20393 0.136 0.986 -0.893 0.038 -0.212 0.566 kinase cytoplasm 6q23.2 interacts with N4BP1 SGTA 52551 -0.054 0.763 -0.486 0.228 1.047 0.03 other cytoplasm SIN3B 20467 0.315 0.324 0.305 0.197 1.121 0.026 other nucleus SLC11A2 18174 0.177 0.805 0 0.651 1.176 0.004 transporter plasma membrane SLC12A5 57138 -1.241 0.004 transporter plasma membrane SLC1A6 20513 0.208 0.396 0.037 0.577 1.166 0.009 transporter plasma membrane glutamate transporter SLC25A19 67283 0.132 0.448 0.034 0.952 1.013 0.047 transporter cytoplasm SLC25A28 246696 0.084 0.838 -0.364 0.161 1.08 0.034 other unknown SLC6A6 21366 0.419 0.415 -0.327 0.252 1.232 0.02 transporter plasma membrane sodium/chloride dependent neurotransmitter transporter family – taurine transport

SMARCAL1 54380 0.368 0.399 0.13 0.984 1.098 0.014 enzyme unknown SND1 56463 0.212 0.222 0.069 0.254 1.129 0.007 enzyme nucleus SNF8 27681 0.348 0.213 0.275 0.367 1.132 0.023 enzyme nucleus SOD1 20655 1.264 0.011 0.361 0.175 1.189 0.007 enzyme cytoplasm SOD1 by clozapine & olanzapine in vitro (Bai et al., 2002; Li et al., 1999) SPG21 27965 0.025 0.326 0.02 0.91 1.059 0.003 enzyme plasma membrane SPR 20751 0.319 0.131 -0.007 0.903 1.044 0.011 enzyme cytoplasm SREBF2 20788 0 0.96 -0.361 0.401 1.117 0.043 transcription nucleus regulator SRXN1 76650 0.476 0.119 -0.034 0.957 1.086 0.016 enzyme unknown STRN3 94186 -0.228 0.666 -1.573 0.006 -0.241 0.484 other nucleus SUDS3 71954 0.073 0.691 0.067 0.448 1.028 0.034 other nucleus SURF4 20932 0.107 0.75 0.066 0.75 1.054 0 other cytoplasm SYT1 20979 -2.714 0.004 transporter cytoplasm altered in 7-day APD treatment study TAC2 21334 0.397 0.687 0.181 0.818 1.276 0.044 other extracellular space neuromodulator; Suggested new APD action target (Meltzer et al., 2004) TH1L 57314 0.286 0.306 -0.074 0.869 1.137 0.005 other unknown TIMM44 21856 0.355 0.598 0.257 0.704 1.214 0.023 transporter cytoplasm TOMM7 66169 0.416 0.302 -0.103 0.765 1.229 0.044 transporter cytoplasm TOP3B 21976 -1.13 0.081 0.415 0.745 -1.517 0.039 enzyme nucleus TPM1 22003 0.692 0.135 -0.315 0.601 1.136 0.023 other cytoplasm TRFP 56771 0.101 0.361 0.103 0.675 1.366 0.014 transcription nucleus regulator TRIM37 68729 -0.226 0.697 -1.217 0.008 0.061 0.772 other cytoplasm UBQLN4 94232 0.086 0.886 -0.09 0.606 1.334 0.015 other cytoplasm UCRC 66152 0.349 0.235 -0.164 0.897 1.001 0.027 enzyme cytoplasm ULK1 22241 -0.013 0.976 0.074 0.472 1.177 0.019 kinase cytoplasm USP2 53376 -0.511 0.283 -1.168 0.015 0.007 0.97 peptidase cytoplasm USP20 74270 0.301 0.823 -0.077 0.509 1.249 0.016 peptidase cytoplasm VEGFB 22340 -0.317 0.393 -0.281 0.318 1.185 0.046 growth factor extracellular space growth factor stimulates neurogenesis (Sun et al., 2006); Acts through activation of AKT1 (Silvestre et al., 2003)

WARS 22375 -0.406 0.393 -0.452 0.086 1.204 0.049 enzyme cytoplasm ZA20D1 229603 0.155 0.95 -0.062 0.668 1.416 0.042 peptidase cytoplasm ZNF38 22697 0.242 0.037 -0.045 0.308 1.187 0.005 transcription nucleus regulator ^ FC is log transformed, so ±0.6 is approximately equal to 1.5-fold difference in antipsychotic treatment compared to control * linkage of human homologue to a chromosomal loci implicated in schizophrenia linkage meta-analysis (Lewis et al., 2003) Bold indicates genes with p-val less than 0.05. FC: fold-change, FDR: false discover rate, SZ: schizophrenia, APD: antipsychotic drug, : increased, : decreased, NC: no change, BBB: blood brain barrier, mGluR: metabotropic glutamate receptor