Investigations of Olfactory Stem Cells in Schizophrenia
Author Abrahamsen, Greger
Published 2009
Thesis Type Thesis (PhD Doctorate)
School School of Biomolecular and Physical and Physical Sciences
DOI https://doi.org/10.25904/1912/3043
Copyright Statement The author owns the copyright in this thesis, unless stated otherwise.
Downloaded from http://hdl.handle.net/10072/366642
Griffith Research Online https://research-repository.griffith.edu.au
Investigations of olfactory stem cells in schizophrenia
by
Greger Abrahamsen, B.Sc., M.Sc. (Hons)
School of Biomolecular and Physical Sciences Faculty of Science, Environment, Engineering and Technology Griffith University
A thesis submitted in fulfillment of the requirements for the degree of Doctor of Philosophy
September 2008 Acknowledgements I would like to thank Professor Alan Mackay-Sim for his patience, support and guidance, especially during the writing of this thesis. It has been an enormous privilege to have access to someone of your experience. Also, special thanks must go to Dr Wayne Murrell. Your advice, technical expertise and friendship throughout these years has been invaluable. Your support and willingness to sacrifice your time was greatly appreciated.
Many thanks to Dr. Carolyn Sue, Rachel Shepherd and Farrah-Yasmin Tate for sharing their technical expertise. I would also like to thank Dr. Tom Byrne for his advice on statistical analysis. Thanks also to Professor John McGrath, Professor Denis Crane and Associate Professor Dianne Watters for their help and guidance. I would also like to thank the other members of the lab: Ben Baldwin, Stacey Andersen, Dr Bernadette Bellette, Colm Cahill, Dr. Carla Toland, Dr. Andrew Wetzig, Dr Ian Hayward and Nick Matigian for their technical assistance and most importantly their friendship. You created a fun environment that made my work enjoyable. I also wish to express my sincere appreciation for my family.
Finally I would like to thank my wife Kathy, your love and support made this possible. Also to our son Jacob, for always making me smile.
II Abstract Schizophrenia is a severely debilitating psychiatric disorder with a complex aetiology where aberrant expression of multiple genes, concurrent with neurodevelopmental events, is believed to lead to the disorder. The ability to biopsy and culture olfactory neuroepithelium from patients with schizophrenia may provide a window into the processes underlying the altered brain development and function in the disorder. It has been established that neurogenesis, the formation of new neurons, occurs throughout life. There are several lines of evidence suggesting adult neurogenesis may be affected in schizophrenia. The functional relevance of neurogenesis in the adult mammalian brain remains unknown, however a disruption of neurogenesis during early development is consistent with the neurodevelopmental hypothesis of schizophrenia. The presence of a neural stem cell has been established in the olfactory neuroepithelium, which can give rise to various neural populations, providing a valuable source for studying various aspects of neurogenesis. The aim of this study was to investigate the utility of olfactory stem cell lines in studying the processes underlying altered brain development and brain functioning in schizophrenia. We also investigated the skin fibroblast cell model, to assess the utility of non-neural cells in studying schizophrenia. A mitochondrial dysfunction and increased oxidative stress have been linked to patients with schizophrenia, and have been proposed to be involved in the pathophysiology of the disorder. We compared gene expression, neural differentiation, mitochondrial function and focal adhesion in cells from patients with schizophrenia and healthy controls. Our results showed significant disruptions of signaling pathways important in adhesion, cell communication and signaling in the olfactory stem cell lines from the patients group, and no significant pathway disrupted in the skin fibroblasts. Though both cell models indicated a focal adhesion dysfunction in schizophrenia, we found that olfactory stem cell lines show signs of a more severe dysregulation when compared to skin fibroblasts, which may reflect the differing structural roles of fibroblasts compared to neural cells. There was also an impaired oxidative stress response in both cell models, and also a mitochondrial dysfunction in the olfactory stem cell lines, phenomena which support the hypothesis that mitochondrial dysfunction and oxidative stress are involved in schizophrenia pathophysiology. Significantly, differences in differentiation between patient and control groups were revealed. Altered neurogenesis in cells from patients with schizophrenia, support our other hypothesis that adult
III neurosgenesis is involved in schizophrenia pathophysiology. Though the details of adult neurogenesis, focal adhesion, mitochondrial function and oxidative stress remain to be elucidated in schizophrenia, these findings together with their proposed roles in brain function make them relevant to schizophrenia research. While we found that fibroblasts are not informative as a cell model for schizophrenia, we conclude that olfactory stem cell lines are a promising tool for studying neurobiological aspects of schizophrenia.
IV Content Acknowledgements ...... II Abstract ...... III Content ...... V List of Figures ...... IX List of Tables ...... X List of abbreviations ...... XI Statement of Originality ...... XIII Declaration of Work Performed ...... XIV Chapter 1: General introduction...... 1 1.1 Introduction ...... 2 1.2 The schizophrenic brain...... 3 1.2.1 Brain pathology ...... 4 1.2.2 Development of schizophrenia ...... 5 1.3 Adult neurogenesis ...... 6 1.3.1 Animal studies ...... 7 1.3.2 Human studies ...... 8 1.4 Adult neurogenesis in schizophrenia ...... 8 1.4.1 Animal studies ...... 9 1.4.2 Human studies ...... 11 1.4.3 Post-mortem studies ...... 13 1.4.4 Gene association ...... 15 1.5 Oxidative stress, mitochondria and schizophrenia ...... 17 1.5.1 Mitochondrial dysfunction ...... 18 1.5.2 Glutathione system ...... 19 1.5.3 Calcium signaling ...... 20 1.5.4 Nitric oxide synthase (NOS)...... 21 1.5.5 Role of mitochondria in the pathophysiology of schizophrenia ...... 22 1.6 Summary ...... 22 1.7 Cell models ...... 23 1.7.1 Olfactory epithelium ...... 23 1.7.1.1 Olfactory stem cells ...... 25 1.7.2 Skin fibroblasts ...... 26 1.8 Hypothesis and aims ...... 27 Chapter 2: General methods ...... 28 2.1 Tissue biopsy ...... 29 2.1.1 Tissue culture initiation ...... 30 2.1.2 Passaging ...... 30 2.1.3 Harvesting ...... 31 2.1.4 Cell counts using haemocytometer ...... 31 2.1.5 Protein estimation ...... 32 Chapter 3: Gene expression profiling on neurosphere-derived cells and skin fibroblasts in schizophrenia ...... 33 3.1 Introduction ...... 34 3.2 Methods ...... 34 3.2.1 Samples ...... 34 3.2.2 RNA extraction ...... 35 3.2.3 RNA labelling and microarray hybridization ...... 35
V 3.2.4 Data normalisation ...... 36 3.2.5 Data filtering ...... 36 3.3 Results ...... 37 3.3.1 Neurosphere-derived cells ...... 37 3.3.2 Skin fibroblasts ...... 54 3.3.3 Overlap in altered expression between neurosphere-derived cells and skin fibroblasts...... 57 3.4 Discussion ...... 59 3.4.1 Overview ...... 59 3.4.2 Neurosphere-derived cells ...... 59 3.4.3 Schizophrenia genes ...... 59 3.4.4 Focal adhesion ...... 61 3.4.5 Mitochondrial function ...... 63 3.4.6 Skin fibroblasts ...... 63 3.4.7 Summary ...... 64 Chapter 4: Mitochondrial function under oxidative stress in neurosphere-derived cells and skin fibroblasts ...... 66 4.1 Introduction ...... 67 4.2 Methods ...... 69 4.2.1 Samples ...... 69 4.2.2 LDH assay ...... 69 4.2.3 Annexin V ...... 70 4.2.4 ATP synthesis ...... 71 4.2.5 JC-1 assay ...... 72 4.2.6 Mitochondrial load ...... 72 4.2.7 Glutathione assay ...... 73 4.2.8 Statistical analysis ...... 73 4.3 Results ...... 73 4.3.1 Cell death ...... 73 4.3.1.1 LDH assay ...... 73 4.3.1.2 Annexin V ...... 75 4.3.2 Mitochondrial function ...... 79 4.3.2.1 ATP assay ...... 79 4.3.2.2 JC-1 ...... 79 4.3.2.3 Mitochondrial load ...... 84 4.3.2.4 Cell size ...... 87 4.3.3 GSH content ...... 90 4.3.4 Comparison of neurosphere-derived cells and skin fibroblast ...... 93 4.4 Discussion ...... 94 4.4.1 Overview ...... 94 4.4.2 Effect of oxidative stress on cell death in schizophrenia...... 94 4.4.3 Effect of oxidative stress on mitochondrial function in schizophrenia ...... 96 4.4.4 Summary ...... 97 Chapter 5: Olfactory neurosphere formation in schizophrenia ...... 99 5.1 Introduction ...... 100 5.2 Methods ...... 101 5.2.1 Samples ...... 101 5.2.2 Neurosphere assay ...... 101 5.2.3 Neurosphere density ...... 102
VI 5.2.4 Differentiation assay ...... 103 5.2.5 Immunocytochemistry ...... 103 5.2.5.1 Antibody staining ...... 103 5.2.5.2 Quantification ...... 103 5.2.6 Statistical analysis ...... 104 5.3 Results ...... 104 5.3.1 Neurosphere assay ...... 104 5.3.1.1 Neurosphere formation ...... 105 5.3.1.2 Neurosphere size...... 105 5.3.1.3 Neurosphere density ...... 107 5.3.2 Differentiation assay ...... 108 5.4 Discussion ...... 112 5.4.1 Overview ...... 112 5.4.2 Neurosphere formation in schizophrenia ...... 112 5.4.3 Differentiation of neurosphere-derived cells in schizophrenia...... 113 5.5 Summary ...... 115 Chapter 6: Focal adhesion in schizophrenia ...... 117 6.1 Introduction ...... 118 6.2 Methods ...... 119 6.2.1 Samples ...... 119 6.2.2 Adhesion assay ...... 119 6.2.3 Focal Adhesion Kinase ELISA assay ...... 120 6.2.3.1 Protein extraction ...... 120 6.2.3.2 FAK ELISA ...... 120 6.2.4 Focal adhesion complex ...... 121 6.2.4.1 Antibody staining ...... 121 6.2.4.2 Quantification ...... 122 6.2.5 Statistical analysis ...... 122 6.3 Results ...... 122 6.3.1 Adhesion assay ...... 122 6.3.2 Focal Adhesion Kinase ...... 124 6.3.3 Focal adhesion complex ...... 127 6.3.4 Cell size ...... 128 6.4 Discussion ...... 131 6.4.1 Overview ...... 131 6.4.2 Cell adhesion in schizophrenia ...... 131 6.4.3 Focal adhesion complex in schizophrenia ...... 132 6.4.4 Summary ...... 134 Chapter 7: General discussion ...... 136 7.1 Overview ...... 137 7.2 Mitochondrial function and oxidative stress in schizophrenia patients...... 137 7.2.1 Mitochondrial function ...... 138 7.2.2 Glutathione system ...... 140 7.3 Altered cell attachment in schizophrenia patients...... 141 7.3.1 Neurosphere formation and neurogenesis...... 143 7.4 Olfactory neurosphere derived cells as a cell model for schizophrenia...... 145 7.5 Confounding factors...... 146 7.5.1 Gene expression profiling of neurosphere-derived cells and skin fibroblasts in the study of schizophrenia ...... 146
VII 7.5.2 Olfactory neurosphere formation in schizophrenia ...... 146 7.5.3 Focal adhesion in schizophrenia ...... 147 7.6 Implications for further studies ...... 147 7.7 Clinical implications ...... 148 7.8 Summary ...... 148 Appendices ...... 150 Appendix 1A ...... 151 Appendix 1B ...... 173 Appendix 2 ...... 174 Appendix 3 ...... 177 References...... 178
VIII List of Figures Figure 1.2: The olfactory mucosa ...... 25 Figure 2.1: Haemocytometer...... 31 Figure 3.1: Identification of genes in neurosphere-derived cells with altered expression in schizophrenia...... 38 Figure 3.2: Supervised hierarchical clustering of gene expression from neurosphere-derived cells...... 39 Figure 3.2: KEGG Focal adhesion pathway map...... 41 Figure 3.3: Top five signalling and metabolic pathways dysregulated in neurosphere- derived cells ...... 42 Figure 3.4: Ingenuity® Integrin signalling pathway...... 45 Figure 3.5: Ingenuity® Ephrin receptor signalling pathway...... 47 Figure 3.6: Ingenuity® actin cytoskeleton signalling pathway...... 49 Figure 3.7: Ingenuity® oxidative phosphorylation metabolic pathway...... 51 Figure 3.8: Ingenuity® neuregulin signaling pathway...... 53 Figure 3.9: Identification of genes in skin fibroblasts with altered expression in schizophrenia...... 55 Figure 3.10: Supervised hierarchical clustering of gene ...... 56 Figure 3.11: Signalling and metabolic pathways which are dysregulated in skin fibroblasts...... 57 Figure 4.1: LDH release during peroxide treatment...... 75 Figure 4.2: Gating strategy...... 76 Figure 4.3: Apoptosis during oxidative stress...... 77 Figure 4.4: Apoptosis during oxidative stress...... 78 Figure 4.5: ATP synthesis in neurosphere-derived cells...... 79 Figure 4.6: JC-1 staining in neurosphere-derived cells...... 80 Figure 4.7: Mitochondrial membrane potential during oxidative stress...... 82 Figure 4.8: Mitochondrial membrane potential during oxidative stress...... 83 Figure 4.9: Mitochondrial load during oxidative stress...... 85 Figure 4.10: Mitochondrial load during oxidative stress...... 86 Figure 4.11: Cell size during oxidative stress...... 88 Figure 4.12: Cell size during oxidative stress...... 89 Figure 4.13: GSH content during oxidative stress ...... 91 Figure 4.14: GSH content during oxidative stress ...... 92 Figure 5.1: Formation of olfactory neurospheres ...... 104 Figure 5.2: Cumulative neurosphere formation...... 105 Figure 5.3: Average neurosphere diameter...... 106 Figure 5.5: Neurosphere cell density...... 108 Figure 5.6: Phenotype of cells after differentiation...... 109 Figure 5.7: Differentiation assay...... 111 Figure 6.1: Cell attachment rates...... 124 Figure 6.2: Phoshorylated FAK ELISA standard curve ...... 125 Figure 6.3: Phoshorylated FAK content...... 126 Figure 6.4: Expression of vinculin...... 127 Figure 6.5: Vinculin staining...... 128 Figure 6.6: Cell surface area...... 130
IX List of Tables Table 3.1: Pathways overrepresented in neurosphere-derived cells ...... 40 Table 3.2: Genes dysregulated in neurosphere-derived cell and previous schizophrenia studies...... 43 Table 3.3: Ingenuity® Integrin signalling pathway...... 44 Table 3.4: Ingenuity® Ephrin receptor signalling pathway...... 46 Table 3.5: Ingenuity® actin cytoskeleton signalling pathway...... 48 Table 3.6: Ingenuity® oxidative phosphorylation metabolic pathway. Genes ...... 50 Table 3.7: Ingenuity® neuregulin signaling pathway. Genes ...... 52 Table 3.8: Genes differently expressed in both cell models...... 58 Table 4.1: Summary of p-values from all experiments...... 93 Table 5.1: Differentiation assay statistics...... 110
X List of abbreviations ANOVA analysis of variance ATP Adenosine-5'-triphosphate Bcl-2 B-cell CLL/lymphoma 2 BDNF brain derived neurotrophic factor bFGF basic fibroblast growth factor BSA bovine serum albumin cDNA copy deoxyribose nucleic acid cRNA copy ribonucleic acid DAVID Database for Annotation, Visualization, and Integrated Discovery DISC1 Disrupted-in-schizophrenia 1 DMEM/F12 Dulbecco’s modified eagles medium/Ham’s nutrient mixture F-12 DMEM-ITS DMEM-supplemented with ITS DMSO dimethyl sulfoxide DNA deoxyribonucleic acid DNase deoxyribonuclease DSM IV diagnostic and statistical manual four EASE Expression Analysis Systematic Explorer EDTA ethylenediamine tetra acetic acid EGF epidermal growth factor FAK focal adhesion kinase FCS fetal calf serum FL1 fluorescent channel 1 FL1 fluorescent channel 2 FSC forward scatter GABA gamma-aminobutyric acid GABBR2 gamma-aminobutyric acid B receptor, 2 GABRE gamma-aminobutyric acid A receptor, epsilon GFAP glial fibrillary acidic protein GSH glutathione HBSS Hank’s balanced salt solution IgG immunoglobulin gamma
XI IgM immunoglobulin mu ITS insulin transferrin selenium IPA Ingenuity Pathway Analysis KEGG Kyoto encyclopedia for genes and genomes LDH lactate dehydrogenase mBC monochlorobimane mRNA messenger ribonucleic acid NF200 neurofilament 200 NGF nerve growth factor NGF/B27 Neurobasal medium - supplemented with NGF and B27 NGF/ITS DMEM/F12 - supplemented with NGF and ITS PBS phosphate buffered saline PCR polymerase chain reaction PFA paraformaldehyde PLL poly-L-lysine PLO poly-L-ornithine RELN reelin RGS4 regulator of G protein signaling 4 RNA ribonucleic acid RT-PCR reverse transcriptase PCR SEM standard error of the mean SDS sodium dodecyl sulfate SSC side scatter
XII Statement of Originality
The work in this thesis is original and has not been submitted for a degree or diploma in any university. The thesis contains no material which has been previously published by others except where due reference is given.
Greger Abrahamsen
XIII Declaration of Work Performed I declare that with the exception of the items indicated below, all work reported in this thesis was performed by me. Dr. Chris Perry, MD, performed all surgical procedures pertaining to the olfactory mucosa biopsies. Professor John McGrath, MD, PhD, FRANZCP, performed all surgical procedures pertaining to the skin biopsies. Dr. Richard McCurdy, PhD, cultured the olfactory mucosa biopsies and prepared the stocks of frozen neurosphere-derived cells. Dr. Francois Feron, PhD, cultured the skin biopsies and prepared the stocks of frozen skin fibroblasts. Dr. Bernadette Bellette, PhD, operated the flow cytometer for data collection. Dr. Carla Toland, PhD, wrote and ran the Acapella™ scripts for analyzing the immunocytochemistry from the differentiation experiments. Mr. James Longden, operated the Opera™ for confocal imagery. Wrote and ran the Acapella™ scripts for analyzing vinculin immunocytochemistry. Mr. Nick Matigian, performed RNA labeling, microarray hybridization and data normalizations. Ms. Rowena Cecil, culture the neurosphere-derived cells for the microarray study.
Greger Abrahamsen
XIV
Chapter 1: General introduction
1.1 Introduction Schizophrenia is a severe psychiatric disorder characterized by manifestations such as delusions, hallucinations, disorganized thought, incoherent speech and various cognitive impairments (American Psychiatric Association 1994). It is a disorder associated with a heavy psychological and social burden for both patients and their next of kin. Schizophrenia also incurs a high economic cost in terms of treatment and lost productivity. With an approximate prevalence of 1% worldwide it is a fairly common disorder, however in spite of decades of intensive research its cause remains unknown (Mowry and Nancarrow 2001). Structural and functional changes in a number of neocortical regions are associated with the symptoms of schizophrenia, and studies based on the fact that schizophrenia tends to aggregate in families have shown that it is partly caused by genetic factors (Impagnatiello et al. 1998; Mowry and Nancarrow 2001). It is thought that these genetic factors, together with early neurodevelopmental insults resulting in defective connectivity between a number of cortical regions, contribute to the etiology of the disorder (Ballmaier et al. 2002; Straub et al. 2002). The hypothesis that schizophrenia is caused by a single gene disorder has been discarded in favor of a multiple gene model. This model postulates that multiple genes of small or moderate effect cause the manifestations of the disorder as a result of interactions with each other and with non-genetic factors (Mowry and Nancarrow 2001).
As mentioned above, genetic susceptibility has been demonstrated to be a significant risk factor. The importance of genetic factors is demonstrated by comparing monozygotic and dizygotic twins: the concordance rate of schizophrenia is about 50% for the former and only 12% for the latter (Kendler et al. 1999; Kato et al. 2002). These statistics are not affected by adoption and demonstrate that inheritance plays a major role in schizophrenia (Kety et al. 1994). There are many plausible candidate regions in the genome which are currently under scrutiny, and dysregulation of several genes has been detected in schizophrenia (Hakak et al. 2001; Straub et al. 2002; Iwamoto and Kato 2006). It is believed these genetic risk factors together with early life events are involved in schizophrenia etiology. The neurodevelopmental hypothesis of schizophrenia postulates that a genetic predisposition together with developmental events are necessary for schizophrenia to develop (McGrath et al. 2003b). The olfactory epithelium has provided a window into changes in neural development in schizophrenia (Feron et al. 1999; Arnold et
2 al. 2001; McCurdy et al. 2006), and the recent discovery of an olfactory stem cell in the olfactory mucosa (Murrell et al. 2005), may provide a promising model for studying neurogenesis in schizophrenia (Matigian et al. 2008).
It is still unclear how these genetic defects link to the brain abnormalities and cognitive impairments observed in the disorder. Adult neurogenesis is believed to be involved in maintaining plasticity in the adult brain (Lledo et al. 2006). Neuronal plasticity is an ongoing adaptive structural and functional process that occurs in response to changing environmental stimulation. Connections are enhanced, duplicated or lessened. These events occur throughout life, but are particularly important during early development of the nervous system, when neuronal differentiation and growth results in synapse formation and elimination of unnecessary connections (Charron and Tessier-Lavigne 2005; Mattson 2007). It is therefore conceivable that altered or dysfunctional adult neurogenesis in high risk individuals, may result in altered plasticity and a permanent dysfunction in response to environmental stress, and is thus involved in the etiology of schizophrenia. Recent studies suggest adult neurogenesis may be a converging mechanism important for the pathophysiology of schizophrenia (Keilhoff et al. 2004; Lipska 2004; Lasky and Wu 2005; Eriksson 2006; Reif et al. 2006a; Toro and Deakin 2006). There is also accumulating evidence for an altered oxidative state and mitochondrial dysfunction in patients with schizophrenia (Reddy and Yao 1996; Ben-Shachar et al. 1999; Yao et al. 2000; Michel et al. 2004; Prabakaran et al. 2004), which fits well with the hypothesis that oxidative stress is involved in the pathophysiology of schizophrenia (Yao et al. 2001; Ben-Shachar 2002; Ben-Shachar and Laifenfeld 2004). In this review we bring together a discussion of the potential roles of oxidative stress, mitochondrial function and neurogenesis in the etiology of schizophrenia.
1.2 The schizophrenic brain There have been a large number of studies looking for differences in the brains of patients with schizophrenia, though some of the data generated has been contradictory as well as controversial. Post-mortem and imaging studies have shown increases in ventricular size, decrease in brain volume, and cortical surface abnormalities in schizophrenia (Harrison 1999; Fallon et al. 2003).
3 1.2.1 Brain pathology There are several studies showing structural abnormalities in the brains of patients with schizophrenia (Shenton et al. 2001; Fallon et al. 2003). However, multiple brain regions are implicated to varying extents. Recent reviews of MRI studies have collated the most replicated findings of altered neuroanatomy reporting increased ventricular volume, and decreased cortical and hippocampal volumes as some of the most reproduced brain pathologies of schizophrenia (Lawrie and Abukmeil 1998; Nelson et al. 1998; Whitworth et al. 1998; Zipursky et al. 1998; Wright et al. 2000; Shenton et al. 2001). These brain regions are involved in higher brain functions relevant for speech and thought, and deficits in these regions are associated with hallucinations, thought disorders and poor motivation (Bogerts 1993). Studies have also indicated increased neuronal density and decreased neural function, with unchanged number of neurons, in the prefrontal cortex of patients with schizophrenia (Pakkenberg 1993; Selemon et al. 1995; Selemon et al. 1998; Friedman et al. 1999). A recent study reported reduced volumes and neuron density as well as a reduction in total number of neurons in various subcortical regions of patients with schizophrenia (Kreczmanski et al. 2007). The reason for these seemingly contradictory findings could be attributed to regional differences or differences in methodologies and post mortem samples, though it is too early to conclude which findings are correct (Harrison 1999; Kreczmanski et al. 2007). Volume reductions of limbic system structures such as the amygdala, hippocampus and olfactory bulb has also been reported (Lawrie and Abukmeil 1998; Turetsky et al. 2000; Wright et al. 2000). Schizophrenia is also associated with cognitive impairments, and the prefrontal cortex is important in memory and cognition (Friedman et al. 1999; Nieoullon 2002) suggesting that these cellular changes are important in the pathophysiology of schizophrenia.
Methodological problems that overshadow the interpretations of some studies include the number of cases used in the study, post mortem time interval, and the duration of the disorder prior to analysis (Harrison 1999). Despite this, a pathology of schizophrenia has emerged where abnormalities are non-uniform and non-focal, which is in keeping with the proposed complexity of the disorder (Harrison 1999). A study by Faraone et al. looking at structural abnormalities in schizophrenic families, showed a correlation between structural brain abnormalities in relatives of patients with schizophrenia and schizophrenia (Faraone et al. 2003). They performed MRI on first degree adult nonpsychotic relatives of patients
4 with schizophrenia. The MRI analysis not only discriminated relatives of patients with schizophrenia and healthy controls based on deviant values on MRI measures of brain structures, but also non-psychotic relatives having two schizophrenic relatives from those having only one (Faraone et al. 2003). Similar results were also reported in studies with discordant twins, showing a correlation between discordant schizophrenia twins and reduced whole brain, hippocampus and ventricle volume (Rijsdijk et al. 2005; Ettinger et al. 2007). This demonstrates that heritable mechanisms have substantial impact on the disorder (Faraone et al. 2003; Van Den Bogaert et al. 2003; Rijsdijk et al. 2005; Ettinger et al. 2007).
The structural abnormalities seen in schizophrenia, as well as altered neuron density and numbers, are believed to be a result of disrupted neurodevelopment. The neurophil hypothesis of schizophrenia suggests that these differences may not necessarily be caused by apoptosis but atrophy (Selemon and Goldman-Rakic 1999). It proposes that atrophy of neuronal processes, not neuronal loss, causes the disturbances in prefrontal cognitive functioning in schizophrenia (Selemon and Goldman-Rakic 1999). Recent studies, using MRI technology, have demonstrated progressive changes in brain volume during the cause of the illness (Thompson et al. 2001; Thompson et al. 2005). When taking into account evidence that these regional differences exist in most first episode patients (Nopoulos et al. 1995; Fannon et al. 2000; Velakoulis et al. 2006), these structural changes therefore suggest an ongoing neurodevelopmental abnormality which leads to schizophrenia pathogenesis. Though there are many studies supporting a neurodevelopmental link, the pathophysiology of schizophrenia remains elusive.
1.2.2 Development of schizophrenia Findings from high-risk cohorts suggest that significant impairments in premorbid functioning, performance IQ, and aspects of visual and verbal learning are apparent before onset of psychosis which implicate the prefrontal regions (Brewer et al. 2005; Woodberry et al. 2008). It has been postulated that the disorder is an ongoing developmental process, as evidenced by the rate of symptom progression. This is supported by studies showing that length of period during which symptoms are allowed to progress without intervention, is a significant predictor of the long term outcome of pharmacological treatment (Ashe et al. 2001). Studies have also found that obstetric complications, low birth weight, perinatal
5 infections as well as physical and behavioral abnormalities are positively correlated to schizophrenia (Ashe et al. 2001). It is therefore believed that because of this, onset of the disorder predominantly occurs during adolescence, which is a period of heightened stress sensitivity in the brain. It is also possible that hormonal regulation of gene expression may trigger the onset of the disorder, because hormones can modulate latent genetic effects (Walker and Bollini 2002). Hormonal changes during puberty, such as altered neurosteroid biosynthesis and the demand to turn on adolescent levels of affect and thought, may therefore trigger faulty expression of genes which contributes to the brain dysfunction causing schizophrenia (Kozlovsky et al. 2002; Walker and Bollini 2002).
Deficits in olfactory identification are reported in schizophrenia, where the ability to identify certain odors is reduced with no significant change in olfactory sensitivity (Moberg et al. 2006; Brewer et al. 2007). These olfactory deficits can also precede the disorder (Brewer et al. 2003). It is possible that these olfactory identification deficits, which are present at first onset psychosis, are a reflection of neurodevelopmental abnormalities in schizophrenia where olfactory bulb and frontal lobe development is dysfunctional (Arnold et al. 2001; Brewer et al. 2003; Brewer et al. 2007). Lesions of the prefrontal cortex or dorsomedial thalamus significantly impair identification ability without affecting olfactory sensitivity, suggesting the apparent olfactory dysfunction could be a marker for frontal limbic abnormalities implicated in schizophrenia (Potter and Butters 1980; Jones-Gotman and Zatorre 1988; Brewer et al. 2007).
These developmental abnormalities have lead to the neurodevelopmental hypotheses, which postulates that factors resulting in neural development defects, together with maturational events, ultimately result in malfunction. These events are postulated to range from slow or latent viral infection to excessive synaptic pruning and drug abuse, and are thought to be necessary for schizophrenia to develop (Lieberman 1999; Ashe et al. 2001; McGrath et al. 2003b).
1.3 Adult neurogenesis The human brain has a limited ability to regenerate, demonstrated by its inability to completely recover from injury or disorder (Kozorovitskiy and Gould 2003). It was previously believed that any degree of recovery following brain damage was due to
6 processes involving existing neurons, though recent studies have shown this not to be the case (Eriksson et al. 1998; Kozorovitskiy and Gould 2003; Lledo et al. 2006). Postnatal or adult neurogenesis, the generation of new neurons in the adult nervous system, occurs throughout life and is believed to be supported by a population of neural stem cells (Alvarez-Buylla et al. 2000; Taupin and Gage 2002; Taupin 2006; Kitabatake et al. 2007; Zhao et al. 2008). Neural stem cells are self renewing, multipotent cells which generate neurons and glia in the nervous system. The identity of these neural stem cells remains unknown, however any cell that fulfills these criteria is by definition a neural stem cell (Taupin 2006). Since no marker to distinguish neural stem cells from progenitor cells is currently available, proliferating cells in the hippocampus and olfactory bulb are often referred to as neural progenitors (Elder et al. 2006). An unidentified neural stem cell is still believed to be present, because these neural progenitors must be replenished throughout life. It has been suggested that neurogenesis in the adult brain is involved in learning and memory, because changes in neurogenesis has been associated with changes in learning and memory tests (Elder et al. 2006; Taupin 2006; Bruel-Jungerman et al. 2007). Though neurogenesis occurs in areas involved in memory, no definitive causal link between neurogenesis, cognitive function and brain plasticity has been established (Bruel- Jungerman et al. 2007). Adult neurogenesis is a tightly regulated process and can be affected by various factors such as stress, stroke and physical activity (van Praag et al. 1999; Alonso et al. 2004; Taupin 2005; Dranovsky and Hen 2006).
1.3.1 Animal studies Reynolds and Weiss (1992) were the first to isolate and characterize undifferentiated cells from the striatum of adult mouse brain. They demonstrated that proliferating cells in the adult striatum of mice which were positive for nestin, an intermediate filament protein found in neuroepithelial stem cells, could develop cells with phenotypic characteristics of either neurons and astrocytes (Reynolds and Weiss 1992). Since then, multiple studies have investigated the presence of neural stem cells in the hippocampus and subventricular zone (Taupin 2006). It is now accepted that postnatal neurogenesis in the mammalian brain occurs primarily in the subventricular zone, hippocampus and the olfactory bulb (Altman 1969; Pencea et al. 2001; Bernier et al. 2002; Dayer et al. 2005; Lledo et al. 2006). Though postnatal neurogenesis in mammals has been reported in various other brain regions such as the amygdala, cortex and substantia nigra (Bernier et al. 2002; Lie et al. 2002; Zhao et al.
7 2003), controversy surrounds the presence of neural stem cells and postnatal neurogenesis in these regions (Kornack and Rakic 2001; Frielingsdorf et al. 2004). Although Gritti et al. and Palmer et al. reported the presence of multipotent cells in the hippocampus of adult mice (Gritti et al. 1996; Palmer et al. 1997), conflicting studies suggest neural progenitors rather than stem cells are responsible for neurogenesis in the hippocampus (Seaberg and van der Kooy 2002; Bull and Bartlett 2005). How these neural progenitors are supplied is not explained. Recent studies have lent support for the hypothesis that astrocytes from the subgranular zone are primarily responsible for the formation of new neurons in the adult hippocampus, by acting as slowly dividing stem cells generating neuroblast precursors, though this remains controversial (Seri et al. 2001; Filippov et al. 2003; Lledo et al. 2006; Merkle and Alvarez-Buylla 2006). Although the identity of neural stem cells remains elusive, their presence has been well established in mammals.
1.3.2 Human studies Postnatal neurogenesis in the adult human brain is believed to be restricted to the same regions as in other mammals, which is the subventricular zone, hippocampus and the olfactory bulb (Eriksson et al. 1998; Bedard and Parent 2004; Lledo et al. 2006). Eriksson et al. is the first study which found that new neurons are generated in the adult human brain (Eriksson et al. 1998). By combining the proliferative cell indicator BrdU and the mature neuron marker NeuN on post mortem brain samples, they were able to show that neurons are generated in the adult hippocampus (Eriksson et al. 1998). Postnatal neurogenesis in the olfactory bulb was demonstrated by Bedard and Parent (2004). By using Doublecortin, NeuroD and Nestin, which are expressed by immature neurons, in conjunction with various cell cycle markers they found new neurons are generated in the adult human olfactory bulb (Bedard and Parent 2004). Whether neural stem cells and postnatal neurogenesis are present outside these regions in the human brain is unclear.
1.4 Adult neurogenesis in schizophrenia A link between disrupted neurogenesis during early development and schizophrenia susceptibility was suggested as early as 1986 (Jakob and Beckmann 1986), and there is evidence to suggest adult neurogenesis is disrupted in schizophrenia (Eriksson 2006; Toro and Deakin 2006; Reif et al. 2007). Studies investigating neuroanatomical and neuropathologic abnormalities in patients with schizophrenia, found increased evidence of
8 abnormal development of the corpus callosum (Jakob and Beckmann 1986). Development of the corpus callosum is intimately linked to several regions, such as the hippocampal complex and the cingulate cortex, in which abnormalities are found in schizophrenia (Swayze et al. 1990). Abnormalities in the hippocampus of schizophrenics, a region of postnatal neurogenesis, could indicate a link between altered neurogenesis and schizophrenia during prenatal, postnatal or adult development. Correlating alleviation of cognitive impairments to altered cell proliferation in the hippocampus is currently premature, but it remains an interesting observation. Reports that neurogenesis in adult olfactory epithelium, a site of continuing neurogenesis, is altered in patients with schizophrenia (Feron et al. 1999; Arnold et al. 2001; McCurdy et al. 2006), fits the hypothesis of disrupted adult neurogenesis in the nervous system of patients with schizophrenia. It could be an indication that the disruption is generic, though currently there is insufficient evidence to support such a hypothesis. Only one published study has looked at neurogenesis in the adult brains in schizophrenia (Reif et al. 2006a), though several recent animal studies described later support the hypothesis that adult neurogenesis is altered in schizophrenia.
1.4.1 Animal studies Using a rat model, Lipska (2004) used tetrodotoxin to inactivate the ventral hippocampus at a critical time in the refinement of intracortical connections. The study found a number of behavioral changes associated with increased dopamine transmission, which is consistent with schizophrenia (Lipska 2004). Long-term altered proliferation in the hippocampus was also reported, but whether this was an increase or decrease is not noted. Whether the proliferating cells were of neural or glial origin was not stated (Lipska 2004). Treatment with methylazoxymethanol (MAM) during gestation has also lead to a schizophrenia-like phenotype in rats. Treatment with MAM, an inhibitor of mitosis, during gestation results in morphological changes as well as behavioral changes associated with schizophrenia (Balduini et al. 1991; Moore and Grace 1997; Flagstad et al. 2004; Flagstad et al. 2005; Penschuck et al. 2006). The MAM model also alters reelin expression, which is involved in neural plasticity, in the rat hippocampus (Weeber et al. 2002; Hoareau et al. 2006).
Likewise, altered reelin expression has been reported in the prefrontal cortex and hippocampus of patients with schizophrenia (Impagnatiello et al. 1998; Fatemi et al. 2000;
9 Guidotti et al. 2000; Knable et al. 2004). There are therefore several animal studies looking at the effect phenotype resulting from reduced reelin expression (Pappas et al. 2001; Pappas et al. 2003; Badea et al. 2007; Pillai and Mahadik 2008). Heterozygote reeler mice, which have reelin mutation, share several neurochemical and behavioral abnormalities with schizophrenia. Not only do they show a downregulation of both reelin mRNA and the translated proteins in the olfactory bulb and hippocampus, but also a decrease in the number of dendritic spines in cortical and hippocampal neurons compared to wild type mice (Pappas et al. 2001; Pappas et al. 2003; Badea et al. 2007). Though these anatomical differences are not unique for heterozygote reeler mice, they do provide insight into how a reelin deficiency would impact on neurodevelopment in schizophrenia.
Adult exposure to ketamine in humans results in symptoms reminiscent of schizophrenia (Krystal et al. 1994). Ketamine is a non-competitive N-methyl-D-aspartate (NMDA) receptor antagonist, and is believed to affect dopamine, serotonin and γ-aminobutyric acid (GABA) transmission which induce positive and negative symptoms associated with schizophrenia in humans (Krystal et al. 1994; Keilhoff et al. 2004). Disrupting either pathway can modulate symptoms of schizophrenia, and therefore are believed to be involved in the etiology of the disorder (Coyle 2006; Haleem 2006). A study by Keilhoff et al. found increased neurogenesis in the hippocampus of a ketamine rat model of schizophrenia (Keilhoff et al. 2004). They reported an increase in the number of hippocampal neurons in their animals, which also exhibited behavioral changes and a disruption of latent inhibition associated with schizophrenia, after adult exposure to ketamine (Keilhoff et al. 2004). The authors postulated that the newly formed neurons are incorrectly incorporated resulting in the observed change in behavior (Keilhoff et al. 2004). However no causative link between ketamine exposure and changes in neurogenesis was demonstrated.
Human epigenetic studies suggest low prenatal vitamin D is associated with increased risk of schizophrenia, by disrupting brain development (McGrath 1999; McGrath et al. 2003a; Mackay-Sim et al. 2004; McGrath et al. 2004). Rats exposed to low prenatal vitamin D levels develop altered brain morphology, changes in cell proliferation and protein expression as well as behavioral changes similar to that found in schizophrenia (Eyles et al. 2003; Becker et al. 2005; Eyles et al. 2006; Kesby et al. 2006; Almeras et al. 2007; Eyles
10 et al. 2007). The vitamin D model for schizophrenia therefore postulates that low prenatal vitamin D is a candidate risk factor for schizophrenia (Mackay-Sim et al. 2004; McGrath et al. 2004). The vitamin D model also suggests that neurogenesis is altered in schizophrenia (Mackay-Sim et al. 2004; Cui et al. 2007; Eyles et al. 2007). A recent study by Cui et al. on neurospheres generated from the subventricular zone of prenatal rats, found that neurosphere formation was significantly affected by vitamin D (Cui et al. 2007). Neurosphere generation is an in vitro assay used to study neural stem cells, and is an indicator of neural stem cell proliferation rate (Reynolds and Weiss 1996; Reynolds and Rietze 2005). Increased neurosphere formation in vitro as a result of low prenatal vitamin D, indicates vitamin D, directly or indirectly, plays a role in neural stem cell proliferation. This finding is also consistent with increased proliferation in the brains of animals exposed to low prenatal vitamin D levels, and suggests it may be related to the observed brain abnormalities associated with the model (Eyles et al. 2003; Ko et al. 2004; Cui et al. 2007). Whether brain abnormalities associated with schizophrenia are related to altered prenatal, postnatal or adult neural stem cell proliferation and neurogenesis remains to be reported. The vitamin D model for schizophrenia supports the hypothesis of altered neurogenesis in schizophrenia.
Animal studies have not demonstrated a causal link between altered neurogenesis and the observed behavioral and physical abnormalities. However, these studies do suggest that neurogenesis and neuronal integration in an adult brain is altered in schizophrenia because they propose long term disruption of neurogenesis is correlated with the behavioral abnormalities. Though the results from these animal models are interesting, they should be viewed with caution, because they fail to demonstrate whether the observed symptoms and cellular changes are generated independently by the treatments or whether they are linked to the pathology they mimic. These animal models of schizophrenia demonstrate that transient developmental insult at critical times of development can have long lasting effects on neurogenesis, which would fit well with the neurodevelopmental hypothesis of schizophrenia as a long term developing disorder.
1.4.2 Human studies Studies investigating prenatal neurogenesis in patients with schizophrenia are limited. Feron et al. compared olfactory neuroepithelium biopsies from 10 patients with
11 schizophrenia and 9 healthy controls, and found reduced attachment of the biopsies from the schizophrenia group (Feron et al. 1999). They also reported increased apoptosis and proliferation in the tissues from the schizophrenia group, indicating changes of olfactory neurogenesis (Feron et al. 1999). A dysfunction of olfactory neurogenesis in schizophrenia was also supported by Arnold et al., who reported differences in the ratio of neural populations from olfactory neuroepithelium biopsies from 13 patients with schizophrenia and 10 healthy controls (Arnold et al. 2001). They also identified significantly fewer neural precursors and an increase in immature and mature neural populations, suggesting differences in olfactory neurogenesis in schizophrenia (Arnold et al. 2001).
McCurdy et al. compared olfactory neuroepithelium biopsies from 10 patients with schizophrenia and 9 healthy controls, investigating gene expression and cell proliferation (McCurdy et al. 2006). They found a significantly increased rate of mitosis in the schizophrenia group and altered expression of a number of genes related to cell cycle and neurogenesis in agreement with the previous study (Feron et al. 1999). McCurdy et al. also reported altered expression of RAD51L1, NCK2 and VIPR1 transcripts in olfactory neuroepithelium from patients with schizophrenia (McCurdy et al. 2006). NCK2 and VIPR1 are directly involved in cell proliferation so these changes were consistent with the observed increase of cell proliferation in the olfactory neuroepithelium cultures. RAD51L1 is involved in regulating the timing of the G1 phase of the cell cycle (Havre et al. 2000). This is in agreement with morphological changes seen in olfactory neuroepithelial tissue from patients with schizophrenia (Feron et al. 1999; Arnold et al. 2001; McCurdy et al. 2006), and fit with observed changes seen in RAD51L1, NCK2 and VIPR1 gene expression. They also reported altered expression of PTN and NTF5, which promote neural differentiation, and NPDC1 which is expressed in non-dividing neural cells as they start to differentiate (Berkemeier et al. 1991; Evrard et al. 2004; Jung et al. 2004). Decreased expression of these genes would affect neurogenesis in the olfactory epithelium indicating increased in vitro proliferation and survival of neural progenitors in olfactory neuroepithelium (McCurdy et al. 2006). This would suggest increased neurogenesis in schizophrenia, though the relationship between these changes in olfactory neurogenesis and the developing brain is unknown (Feron et al. 1999; Arnold et al. 2001; McCurdy et al. 2006). A link between schizophrenia and altered neurogenesis during brain development is
12 yet to be established, however the proliferation changes in neural tissue lend support for the hypothesis that the dynamics of adult neurogenesis is altered in schizophrenia.
One study has looked at adult neurogenesis in the brain of patients with schizophrenia (Reif et al. 2006a). They performed Ki-67 immunohistochemistry on post mortem hippocampal sections from patients with schizophrenia. Ki-67 is present in all active stages of cell proliferation, and was used as a measure of neurogenesis (Scholzen and Gerdes 2000; Reif et al. 2006a), though they did not distinguish between proliferation of neuronal and non- neuronal cells. They reported that neurogenesis was significantly reduced in patients with schizophrenia, and that this was not a result of medications prescribed (Reif et al. 2006a). The findings by Reif et al. are not in line with what was observed in the ketamine model by Keilhoff et al. (Keilhoff et al. 2004; Reif et al. 2006a), however, as mentioned, it is important to distinguish between animal models and human studies, because the ketamine model may not accurately mimic the pathophysiology of schizophrenia. The authors postulate that reduced neurogenesis might result in impaired memory formation and therefore be involved in cognitive impairment observed in patients with schizophrenia (Reif et al. 2006a). The finding by Reif et al. therefore provides relevant data in the quest to study adult neurogenesis in schizophrenia.
1.4.3 Post-mortem studies Because there is limited data available on adult neurogenesis in the schizophrenic brain, it is important to explore signaling and related pathways which might impact on neurogenesis. There are many published molecular studies showing various disruptions and abnormalities, though little clear evidence implicating adult neurogenesis has been produced. Here we will attempt to summarize findings from expression studies which relate to neurogenesis pathways.
Most of the published microarray studies using postmortem tissue have yielded both convergent and divergent profiles of altered gene expression, which is mainly attributed to differences in the studied cohorts (Mirnics et al. 2006). However there are several array studies showing dysregulation of genes related to multiple signaling pathways which could affect the dynamics of adult neurogenesis (Hakak et al. 2001; Aston et al. 2004; Prabakaran et al. 2004; Iwamoto et al. 2006). Though these genes may have different functions at
13 various stages of development, they suggest multiple signaling pathways are regulated differently in schizophrenia.
Increased NPAT and decreased CEP2 expression, which are associated with the function of cyclin-dependent protein kinases and centriole cohesion respectively, have been reported to be significantly changed in postmortem temporal cortex of patients with schizophrenia (Aston et al. 2004). NPAT is a downstream component of the cyclin E/Cdk2 signaling pathway and is required for the G1/S transition in the cell cycle (Ye et al. 2003). CEP2 is believed to be an important component in centromere cohesion during the interphase of the cell cycle (Mayor et al. 2000).
Hakak et al. reported altered expression of the neuregulin receptor Her3 (Hakak et al. 2001), which is involved in Schwann cell development and myelination (Goodearl et al. 2001; Fregien et al. 2005). Dysregulation of IGF1 is also reported in schizophrenia, which would effect differentiation of neural progenitor cells (Aberg et al. 2003; McCurdy et al. 2005; Higgs et al. 2006). Though these genes may not be involved in the same pathways, their functions converge in neurogenesis and differentiation.
The retinoid pathway, which is central in neurodevelopment, also regulates expression of genes dysregulated in schizophrenia (LaMantia 1999; Arnold and Rioux 2001; Rioux and Arnold 2005). Rioux and Arnold (2005) found that more granule cells of the dentate gyrus expressed the retinoic acid receptor (RAR) alpha in patients with schizophrenia. Retinoids are important in adult neurogenesis, where depletion of retinoic acid in adult mice results in significantly reduced neuronal differentiation in the dentate gyrus (Jacobs et al. 2006). Increased levels of RARγ7 mRNA in stellate cells of the entorhinal cortex of patients with schizophrenia has also been reported (Hemby et al. 2002). Altered expression of retinoid receptors could affect expression of proteins involved in neuronal plasticity, such as GAP43, Wnt and NCAM, which are also affected in schizophrenia (Barbeau et al. 1995; Sower et al. 1995; Miyaoka et al. 1999; Vawter 2000; Rioux and Arnold 2005).
Brain-derived neurotrophic factor (BDNF) supports neuronal survival, differentiation and synaptic plasticity in the adult brain, as well as being important in neurogenesis itself (Lee et al. 2002b; Farmer et al. 2004; Bramham and Messaoudi 2005; Bull and Bartlett 2005;
14 Scharfman et al. 2005). BDNF mRNA and protein levels are significantly reduced in the cerebral cortex, anterior cingulate cortex and hippocampus of patients with schizophrenia (Takahashi et al. 2000; Durany et al. 2001; Knable et al. 2002; Weickert et al. 2003; Hashimoto et al. 2005). Inhibition of BDNF secretion can negatively affect memory and hippocampal function (Egan et al. 2003), and a reduction in BDNF levels may help explain the observed pathology in schizophrenia.
The success of microarray technology has proved to be limited in schizophrenia studies. Although over 30 expression profile studies in schizophrenia have been performed, only few replicable candidate genes have been indicated (Iwamoto and Kato 2006). The reason for this lack of consistency is partly related to disorder heterogeneity as well as sample degradation, differences in post mortem tissues, gene expression platforms and the analytical approaches used (Iwamoto and Kato 2006; Mirnics et al. 2006). Though the gene expression profile in schizophrenia remains unclear, certain disrupted pathways rather than specific genes, have emerged with some consistency. These molecules are important in maintaining and regulating the proliferation and differentiation of neural precursors. As such their altered expression in schizophrenia supports the suggestion that a disruption of neural precursor cell proliferation and differentiation is present. It is therefore plausible to assume that changes in the expression in any or all of these genes would affect adult neurogenesis in the brain (Eriksson 2006; Toro and Deakin 2006; Reif et al. 2007). How altered expression of these genes would affect cell proliferation and neurogenesis is unclear. Dysregulation of genes involved in normal cell cycle mechanisms do suggest that cell proliferation is altered in neural tissues, as has been reported in vitro and in vivo (Feron et al. 1999; McCurdy et al. 2006; Reif et al. 2006a). These findings are not necessarily a reflection of neurogenesis, because the biopsies studied contain heterogeneous cell populations (Feron et al. 1999; McCurdy et al. 2006; Reif et al. 2006a). They are however interesting in the context of cell cycle dynamics in the schizophrenic nervous system.
1.4.4 Gene association SOX10, which is important in neurogenesis (Kim et al. 2003), also appears to be associated with schizophrenia. Disruption of its transcript expression and methylation status has been reported in postmortem brains of patients with schizophrenia (Iwamoto et al. 2005b; Dracheva et al. 2006; Iwamoto et al. 2006). SOX10 was also recently identified as
15 significantly associated with patients with schizophrenia in a Japanese cohort (Maeno et al. 2007). Because this is a transcription factor that promotes the survival of neural crest precursor cells, and is also responsible for terminal differentiation of oligodendrocytes, any irregularities in expression or function would conceivably influence brain development and function (Stolt et al. 2002; Mollaaghababa and Pavan 2003).
Disrupted-in-schizophrenia 1 (DISC1) is associated with schizophrenia in multiple human genetic studies and is highly expressed in the dentate gyrus of the hippocampus (Millar et al. 2000; Ekelund et al. 2001; Austin et al. 2004; Callicott et al. 2005; Zhang et al. 2006a). It is a multifunctional protein involved in extracellular signaling, neurite outgrowth, and neuronal migration (Morris et al. 2003). DISC1 polymorphisms have also been associated with cognitive function in schizophrenia, where a relationship with neurocognitive performance and DISC1 genotype is reported (Burdick et al. 2005; Cannon et al. 2005). Because DISC1is located in the limbic system and participates in neurite outgrowth (Miyoshi et al. 2003; Ozeki et al. 2003; Kamiya et al. 2006) as well as cortical development (Kamiya et al. 2005), it is suspected to play a role in neurogenesis (Eriksson 2006; Toro and Deakin 2006; Reif et al. 2007).
There are several reports linking neuregulin 1 with schizophrenia (Stefansson et al. 2002; Stefansson et al. 2003; Li et al. 2004a; Li et al. 2006). Neuregulin 1 is part of the Neuregulin family of growth and differentiation factors and is involved in a diverse range of brain development processes including cell–cell signalling, axon guidance, synaptogenesis, glial differentiation, myelination, and neurotransmission (Corfas et al. 2004; Esper et al. 2006). The enrichment of the neuregulin 1 receptor erB4 at postsynaptic densities, has lead to the proposal that neuregulin 1 is involved in neural plasticity through interactions with proteins at the glutamate synapse and neurotrophins (Garcia et al. 2000). Because neuregulin 1 is necessary for proliferation and migration of neuronal stem cells and precursors in the central nervous system, it is proposed to be important in regulating neurogenesis, and schizophrenia pathophysiology (Falls 2003; Lai and Feng 2004; Liu et al. 2005; Eriksson 2006; Toro and Deakin 2006)
Association studies have linked Notch4 and BDNF with schizophrenia (Shibata et al. 2006; Wang et al. 2006; Qian et al. 2007). The Notch signaling pathway is involved in a variety
16 of cellular functions in the developing and adult brain which include maintenance of stem cell self-renewal, proliferation, differentiation and apoptosis (Lasky and Wu 2005). A disruption of the Notch pathway in patients with schizophrenia could result in altered adult neurogenesis due to its role in differentiation and maintaining neuronal precursors (Oishi et al. 2004). There are several linkage studies looking at the Notch pathway and schizophrenia, which have had limited success in demonstrating a positive correlation. The discrepancy between findings may be due to differences in experimental and analytical approach (Imai et al. 2001; Ivo et al. 2006; Passos Gregorio et al. 2006; Shibata et al. 2006; Wang et al. 2006). Though a link between Notch and schizophrenia is yet to be firmly established, its involvement in neurogenesis makes it a prime candidate for involvement in a neurodevelopmental disorder such as schizophrenia (Lasky and Wu 2005). BDNF has also been identified as a schizophrenia susceptibility gene in genetic studies (Neves-Pereira et al. 2005; Qian et al. 2006). Because both Notch4 and BDNF are involved in multiple processes, the effect of their dysregulation is difficult to predict. However, it is likely that their altered expression would result in a functional disturbance of the affected brain, as is suggested in schizophrenia.
1.5 Oxidative stress, mitochondria and schizophrenia There are several studies reporting altered antioxidant levels in schizophrenia, which has lead to the hypothesis that patients with schizophrenia have an altered oxidative state, though the molecular basis for these changes remains unknown (Do et al. 2000; Yao et al. 2001; Nishioka and Arnold 2004; Zhang et al. 2005). Through their interaction with multiple signaling pathways, and their involvement in synaptic plasticity, reactive oxygen species could affect neurodevelopment in schizophrenia (Monks et al. 2006; Fialkow et al. 2007; Mattson 2007).
Oxidative phosphorylation is the leading source of reactive oxygen species, and a mitochondrial dysfunction can lead to altered ATP production and concentration of reactive oxygen species (Adam-Vizi and Chinopoulos 2006). A mitochondrial dysfunction, which could result in an abnormal energy state in the cells, is therefore hypothesized to be involved in the observed change in oxidative state in samples from patients with schizophrenia. Mitochondria are involved in essential developmental processes including the establishment of axonal polarity, regulation of neurite outgrowth, and synaptic plasticity
17 in the mature nervous system (Mattson 2007). Dysfunctional mitochondria could therefore have downstream effects on neurogenesis by altering proliferation and differentiation of precursor cells. Such a defect could also result in altered neuronal function and plasticity directly or by affecting astrocytes and second messenger pathways involved in neuronal cell function (Mattson 2007). It is postulated that these changes would result in the abnormalities characteristic of schizophrenia (Ben-Shachar et al. 1999; Do et al. 2000; Zhao and Flavin 2000; Mattson and Camandola 2001; Maurer et al. 2001; Yao et al. 2001; Mattiasson et al. 2003; Ranjekar et al. 2003; Ben-Shachar and Laifenfeld 2004; Prabakaran et al. 2004; Iwamoto et al. 2005a; Chinopoulos and Adam-Vizi 2006). Several studies have been undertaken in recent years in an attempt to describe mitochondrial function in schizophrenia. The majority of these studies utilized microarray technology to study gene expression in post-mortem brain samples, though some protein work has been done as well.
1.5.1 Mitochondrial dysfunction An extensive study by Prabakaran et al. looking at the gene and protein expression profiles of the prefrontal cortices, of 10 patients with schizophrenia and 10 controls, found significant differences in transcript and protein levels for genes related to oxidative phosphorylation (Prabakaran et al. 2004). Of significance was the downregulation of 26 subunits of the electron transport chain. The subunits in question are involved in complex I, complex III and complex IV respectively (Prabakaran et al. 2004). This is in agreement with another study which found significantly lower protein and transcript levels of complex I subunits in the prefrontal cortex and blood platelets of patients with schizophrenia (Ben- Shachar et al. 1999; Karry et al. 2004). Downregulation of the electron transport chain is associated with an increase in reactive oxygen species (Sipos et al. 2003). These findings could be explained by reports of reduced mitochondrial density in various brain regions of patients with schizophrenia (Uranova et al. 2001). The study by Prabakaran et al. included medicated and non-medicated patients, and found no concordance between medication and the observed changes (Prabakaran et al. 2004).
A recent study has been published by Iwatomo et al., analyzing the gene expression profiles of the prefrontal cortex from 35 patients with schizophrenia and 35 healthy controls from a different cohort (Iwamoto et al. 2005a). The data from Iwamoto et al. suggested significantly altered expression of genes related to oxidative phosphorylation in their
18 patient group. However they found a correlation between medication state and altered expression of mitochondrial genes, which does not agree with the findings of Prabakaran et al. (Prabakaran et al. 2004; Iwamoto et al. 2005a). They suggested this discrepancy was due to the drugs used in the cohort having either a side effect or a normalizing effect on mitochondria (Iwamoto et al. 2005a). Though the power of the study by Iwatomo et al. is greater, the reason for this discrepancy remains unknown, and warrants further studies to verify either finding.
Lowered expression of mitochondrial genes does not in itself imply a chronic disruption of ATP production, but could indicate a higher susceptibility to stress. A mitochondrial dysfunction in schizophrenia would also tie in with the calcium signaling hypothesis of schizophrenia, which has been used to explain many molecular abnormalities observed in schizophrenia (Lidow 2003). Mitochondria sequester and regulate intracellular calcium levels, which is critical for neural function (Gunter et al. 2004; Chinopoulos and Adam- Vizi 2006; Rimessi et al. 2008). Though oxidative stress is also known to induce apoptosis, low levels of oxidative stress may induce cell proliferation (Tatton and Olanow 1999; Han et al. 2003; Klein and Ackerman 2003). It might therefore be possible to observe increased proliferation rates during lowered expression of mitochondrial genes.
1.5.2 Glutathione system Reduced levels and synthesis of glutathione (GSH) have been reported in the cerebral spinal fluid and plasma of patients with schizophrenia (Do et al. 2000; Zhang et al. 2005; Gysin et al. 2007). GSH is the predominant low-molecular weight thiol within mammalian cells, which in the reduced state serves as the most abundant intracellular thiol-based antioxidant controlling the cellular redox state (Das and White 2002). Do et al. found decreased levels of GSH in cerebrospinal fluid and the prefrontal cortex of patients with schizophrenia, which could result in degenerative processes in dopaminergic terminals (Do et al. 2000). Exposing cultured cortical neurons from mice to dopamine, causes a significant decrease in GSH levels in schizophrenia. This also causes a reduction of neuronal processes in culture, which fits with synaptic spine reduction reported in schizophrenia (Grima et al. 2003). A similar dysfunction has also been reported in skin fibroblasts from patients with schizophrenia, where GSH synthesis is significantly reduced when the cells are exposed to oxidative stress (Gysin et al. 2007). These studies
19 demonstrate a dysfunction in the glutathione system which may impact on synaptic plasticity in schizophrenia.
An impairment of the glutathione system fits well with other reports of increased oxidative stress in plasma from patients with schizophrenia (Mukherjee et al. 1996; Yao et al. 1998; Do et al. 2000; Akyol et al. 2002). A systemic defect in the glutathione system would be associated with an impaired antioxidant defense and would therefore imply increased oxidative stress in patients with schizophrenia. Earlier studies reported an increase or a decrease in glutathione peroxidase activity in plasma from patients with schizophrenia, depending on the schizophrenia sub-group studied (Yao et al. 2001). Glutathione peroxidase activity is affected by, among other things, levels of oxidized GSH (GSSG). Altered glutathione peroxidase activity in plasma from patients with schizophrenia is therefore supported by the findings that altered GSH levels also exist. It is therefore possible that the suggested increase in oxidative stress found in plasma from patients with schizophrenia is caused by dysregulation of the redox system and the glutathione system in particular. A study by Akyol et al. observed no change in glutathione peroxidase activity in plasma from patients with schizophrenia, but they found evidence of increased oxidative stress based on the activity of other antioxidants (Akyol et al. 2002). This is not consistent with increased glutathione peroxidase activity in plasma as described in Yao et al. (2001), and suggests that other factors also are involved in causing an altered redox state in patients with schizophrenia. Variation in measured glutathione peroxidase activity between cohorts are further underlined in a study by Zhang et al., where they found an increase in glutathione peroxidase levels in plasma (Zhang et al. 1998).
A dysfunctional GSH response agrees with the dopamine hypothesis of schizophrenia where excessive dopamine results in cognitive impairments and physiologic disturbances observed in schizophrenia (Davis et al. 1991). Dopamine is a major source of reactive oxygen species in the brain and can reduce GSH levels in cells (Shukitt-Hale et al. 1997; Rabinovic and Hastings 1998; Cohen 2000).
1.5.3 Calcium signaling In addition to altered ATP production, mitochondrial dysfunction would affect mitochondrial calcium accumulation and disrupt intracellular calcium signaling (Duchen
20 2000; Duchen 2004a). The notion of a mitochondrial dysfunction therefore fits the hypothesis that a calcium signaling dysfunction is involved in the pathology of schizophrenia. Calcium transport in mitochondria has several functions including regulating ATP synthesis by activating intra-mitochondrial metabolic reactions (Territo et al. 2001; Gunter et al. 2004; Chinopoulos and Adam-Vizi 2006). Calcium signaling is important in controlling multiple cell functions such as neuronal circuits, gene expression, hormone secretion and muscle contraction and is therefore linked to schizophrenia etiology, and extensively reviewed elsewhere (Gunter et al. 2004; Chinopoulos and Adam-Vizi 2006; Rimessi et al. 2008). It has therefore been hypothesized that a calcium signaling dysfunction is involved in the pathophysiology of schizophrenia. There are several studies implicating a disruption of calcium signaling in schizophrenia (Eyles et al. 2002; Lidow 2003; James et al. 2004). Calcium signaling is a common feature of various proteins reported to be affected in schizophrenia, and it is proposed that it could cause histological and cognitive deficits associated with the disorder (Lidow 2003). A link between calcium signaling and neuron generation has been demonstrated, where increased calcium signaling results in a corresponding increase in neurogenesis in the neocortex of rats (Weissman et al. 2004).
1.5.4 Nitric oxide synthase (NOS) NOS synthesizes nitric oxide and is an important signaling molecule through its interaction with various proteins involved in production of reactive oxygen species, adult neurogenesis and neural plasticity (Holscher 1997; Yao et al. 2004; Matarredona et al. 2005; Sunico et al. 2005). There are several reports that the NOS pathway is altered in schizophrenia. Yao et al. reported a significant increase in nitric oxide levels in the brains of patients with schizophrenia (Yao et al. 2004). A NOS polymorphism has been linked with schizophrenia in a case control study which is proposed to have an impact on prefrontal cortex function (Yao et al. 2004; Reif et al. 2006b). Inhibiting NOS increases adult neurogenesis in the subventricular zone of mice and NOS exposure decreases neurogenesis and promotes gliogenesis in rats, therefore supporting the hypothesis that nitric oxide inhibits neurogenesis in the subventricular zone (Moreno-Lopez et al. 2004; Covacu et al. 2006; Romero-Grimaldi et al. 2006). Neurogenesis was not affected in any other region of the hippocampus, and the proliferating neural precursors express epidermal growth factor receptor (Romero-Grimaldi et al. 2006). The authors therefore postulate that nitric oxide
21 inhibits neurogenesis by modulating expression of epidermal growth factors. A recent in vitro study using neurosphere assays verified this hypothesis by demonstrating that NOS inhibition promoted neurosphere formation and growth (Torroglosa et al. 2007). A link between nitric oxide and BDNF in the role of synaptic remodeling has been proposed because they both control production and activity of each other (Moreno-Lopez and Gonzalez-Forero 2006). Because BDNF levels are also altered in schizophrenia, it supports the hypothesis that the NOS pathway is affected in schizophrenia.
1.5.5 Role of mitochondria in the pathophysiology of schizophrenia No causative effect for the observed change in mitochondria and related metabolisms in schizophrenia has been identified. Altered oxidative state in schizophrenia, fits well with the hypothesis that oxidative stress and mitochondrial dysfunction is involved in the pathophysiology of schizophrenia (Reddy and Yao 1996; Yao et al. 2000; Yao et al. 2001; Michel et al. 2004; Prabakaran et al. 2004; Wang et al. 2006). Though a connection between the pathophysiology of schizophrenia and mitochondrial dysfunction is supported, it does not appear to be the underlying cause for the disorder. Because any mitochondrial dysfunction in schizophrenia is likely to be subtle, it could help explain any changes in adult neurogenesis that are also observed. Normal complex I activity has been shown to be critical in differentiation and functional activity of brain cells (Papa et al. 2004), and because mitochondria make the major contribution to the cell's energy requirement, abnormal mitochondrial function would impact on adult neurogenesis. This together with calcium signaling dysfunction and oxidative stress have become recurring themes in schizophrenia research.
1.6 Summary Though the functional significance of adult neurogenesis remains unclear, its proposed role in brain plasticity and cognitive function makes it relevant to schizophrenia research. The hypothesis that adult neurogenesis is disrupted in patients with schizophrenia is gaining support. Though there is little direct evidence of disrupted adult neurogenesis in schizophrenia, accumulating indirect evidence indicates that it is affected. Dysregulation of multiple genes important in neurogenesis from various studies, and the recurrence of pathways thought to be important in neurogenesis, suggests that this process is affected. Evidence of mitochondrial dysfunction and oxidative stress, which would affect the
22 proliferation and differentiation of neural stem cells as well as synaptic plasticity, together with subtle neurodevelopmental and anatomic anomalies in patients with schizophrenia consistent with developmental events involving neurogenesis, supports a contribution to schizophrenia pathophysiology. Studying adult neurogenesis presents technical hurdles and is limited by availability of brain samples. A different approach is therefore necessary. By performing in vitro studies of neurogenesis or by analyzing isolated proliferating and differentiating neural progenitor cells, one could overcome the inherent weakness in any molecular signal data related to adult neurogenesis from post-mortem brain biopsies. Though the functional significance of neurogenesis in the adult brain remains unclear, a dysfunction could result in altered brain function, causing the cognitive impairments seen in schizophrenia. Establishing a causal link between changes in adult neurogenesis and schizophrenia may prove to be a challenging task, but needs to be explored.
1.7 Cell models Brain samples are used in investigating neural growth abnormalities and the processes underlying neurotransmitter receptor-mediated functions in schizophrenia. However their use is limited by the availability of tissue from patients and properly matched controls (Mahadik and Mukherjee 1996a). Linking altered processes observed in brain studies with schizophrenia is also problematic because many of the key processes underlying early brain development are not easily studied in vivo, and hence not enabling a distinction to be made between changes in neurodevelopmental events and changes caused by years of illness and treatment of the disorder. (Mahadik and Mukherjee 1996a). By the time schizophrenia becomes clinically apparent, most of the central processes in brain development are long finished, further confounding the problem. Adult brain neurogenesis might be used to investigate fundamental aspects of the neurodevelopmental hypothesis, but harvesting neural precursors from the living human brain is neither ethical nor practical. The solution is to find a more easily accessible peripheral tissue sample that could serve as a cell culture model of schizophrenia. In this thesis two such models are presented: olfactory stem cells and skin fibroblasts.
1.7.1 Olfactory epithelium The olfactory epithelium is a pseudostratified epithelium in the olfactory mucosa, composed primarily of supporting cells, neurons and basal cells (Figure 1.2). It is known
23 that throughout adult life the olfactory epithelium retains a continuous capacity for self renewal and neurogenesis, and that sensory neurons are continuously replaced. Cultured olfactory epithelium can give rise to cells which express markers of olfactory basal cells, neuronal precursors, as well as markers of developing and mature neurons and glia (Mackay-Sim and Kittel 1991; Pixley 1992; Caggiano et al. 1994; MacDonald et al. 1996; Murrell et al. 1996; Newman et al. 2000). Cells from the olfactory epithelium are responsive to various growth factors, and can therefore be manipulated to study the mechanisms of neuronal development in the brain (DeHamer et al. 1994; Farbman and Buchholz 1996; MacDonald et al. 1996; Newman et al. 2000). Biopsy of the olfactory mucosa is a simple and minimally invasive procedure that does not affect olfaction in the patient, it provides a useful and readily available tissue to study neurodevelopmental and neurodegenerative diseases (Feron et al. 1998).
24
Figure 1.2: The olfactory mucosa. The structure of the olfactory epithelium and lamina propria in the olfactory mucosa (Gray and Williams 1989).
Several neurodegenerative and neurodevelopmental disorders are associated with altered olfactory function (Hawkes et al. 1998; Brewer et al. 2003; Hawkes 2006; Moberg et al. 2006; Brewer et al. 2007; Pentzek et al. 2007; Berg 2008), among them schizophrenia where changes in molecular phenotype is also reported (Arnold et al. 1998; Smutzer et al. 1998). Olfactory biopsies from patients with schizophrenia have also shown altered attachment and proliferation in vitro, indicating that abnormalities in schizophrenia are also manifested in the olfactory mucosa (Feron et al. 1999).
1.7.1.1 Olfactory stem cells Recently a study by Murrell et al. demonstrated that multipotent cells reside in the olfactory mucosa (Murrell et al. 2005). In their study, they showed that neurospheres derived from olfactory mucosa biopsies, gave rise to several neural populations including neurons, astrocytes and oligodendrocytes. Expanding primary neurospheres on plastic later gave rise to multipotent neurospheres, demonstrating the potency of these neurospheres is it did not diminish after proliferation (Murrell et al. 2005). These neurospheres also gave rise to non-
25 neuronal lineages in both in vitro and in vivo experiments (Murrell et al. 2005). By showing that the olfactory mucosa-derived neurospheres are able to differentiate into both neuronal and non-neuronal lineages, they demonstrated that stem-cell like cells exist in the olfactory mucosa (Murrell et al. 2005).
Because the olfactory mucosa-derived neurospheres can give rise to various neuronal populations, and have shown multipotency after spending generations in culture, they provide a valuable source for studying various aspects of neurogenesis. By culturing these neurospheres, an in vitro model for studying schizophrenia in neural tissue will be available.
1.7.2 Skin fibroblasts Cultured non-neuronal cells have previously been used for studying biochemical processes underlying the neuropathophysiology associated with several neurodevelopment and neurodegenerative diseases. The use of non-neuronal cells, specifically cultured fibroblasts, has benefited the study of relevant metabolic processes in Lesch-Nyhan syndrome, Tay- Sacks disease and Alzheimer’s disease among others (Mahadik and Mukherjee 1996a; Connolly 1998).
The rationale for studying fibroblast in neurological disorders is based on the idea that even though the disorders manifests in the central nervous system, cultured fibroblasts from neurologically impaired patients retain the genetic background of the patient and may therefore mirror changes within the central nervous system (Connolly 1998). Several studies have shown that cultured skin fibroblasts from patients with schizophrenia exhibit altered morphology, cell adhesiveness and growth, together with membrane abnormalities (Mahadik et al. 1994; Mukherjee et al. 1994; Ramchand et al. 1994; Miyamae et al. 1998; Gysin et al. 2007). These findings support the assumption that neurological disorders may lead to abnormalities in the brain as well as in peripheral organs.
The guiding hypothesis when studying non-neuronal tissues is that schizophrenia may be systemic, manifesting in peripheral tissues as well as the central nervous system. Fibroblasts are easily obtained from peripheral tissue and can be manipulated and stored using simple cell culture procedures, producing homogeneous cultures and providing a reproducible and reliable cellular model. Fibroblasts are grown from small skin biopsies
26 taken from the patient, making the sample collection non-invasive and simple. After several passages in culture skin fibroblast cultures are free of state-related changes and effects of diet, hormones and drugs (Mahadik and Mukherjee 1996a; Connolly 1998). This is in contrast to the difficulties encountered when using brain samples. However the utility of a non-neural derived cell model, such as skin fibroblasts, in studying neurological disorders has recently been questioned due to lack evidence of significant disease-related gene expression changes associated with disease pathology (Matigian et al. 2008).
1.8 Hypothesis and aims The driving hypothesis in this thesis is that schizophrenia is a neurodevelopmental disease that leads to cellular changes in peripheral tissues. These changes relate to parameters of neurodevelopment such as attachment, cell proliferation, cell death, and differentiation. Two cell models are used in this thesis: skin fibroblasts and olfactory stem cell lines. It was hypothesized that these models would reveal an impaired oxidative stress response and mitochondrial function, as well as changes in cell proliferation and neural differentiation. We also hypothesized that these cell models would reveal changes in gene expression, and that the olfactory stem cell lines would provide better insight into processes related to schizophrenia, due to their neural origin. These hypotheses will be tested in the following experiments: 1 Micorarray analysis on skin fibroblasts and olfactory stem cell lines from patients and controls to reveal differential gene expression. This will also prepare us for further investigations of oxidative stress, attachment and differentiation (Chapter 3). 2 Assessing cell death and mitochondrial function in both cell models under various culture conditions to assess whether mitochondrial and oxidative stress response is impaired in schizophrenia (Chapter 4). 3 By manipulating the culture conditions of olfactory stem cell lines, we investigate proliferation and differentiation of neural-derived tissue to test whether neurogenesis is disrupted in schizophrenia (Chapter 5). 4 Investigating adhesion in both cell models, to elucidate altered attachment observed in tissues and cells (Chapter 6).
27
Chapter 2: General methods
2.1 Tissue biopsy Neurosphere-derived cells and skin fibroblasts from the same individuals were used in this study. Sex- and age-matched samples from 10 control subjects and 9 patients with schizophrenia were used in the study. One out of 10 patients declined a nasal biopsy. One of the control neurosphere cell lines and two of the skin fibroblast cell lines developed significantly impaired proliferation rates at various stages of this study, believed to be related to failed liquid nitrogen storage. Thus, samples available for the mitochondria, neurosphere and focal adhesion study were 9 control and 9 patient neurosphere-derived cell lines and 8 control and 9 patient skin fibroblast cell lines. Patients with schizophrenia were diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) and tissues from healthy normal subjects were used as control (i.e. who were screened to ensure the absence of a psychotic disorder). All biopsies were obtained with the written informed consent of the subjects, and the procedures were carried out in accordance with the ethics committees of the Park Centre for Mental Health and Griffith University, and according to guidelines of the National Health and Medical Research Council of Australia.
Skin fibroblasts cultures were established by Dr. Francois Feron. Human skin fibroblast cultures used in this study were derived from skin biopsies collected by Dr John McGrath (Queensland Center for Mental Health Research, The Park Centre for Mental Health) from the upper arm. Skin biopsies were placed on ice in Dulbecco’s Modified Eagle Medium/Ham F-12 (D-MEM/F12) (GibcoBRL) supplemented with 10 % fetal bovine serum (FBS) (GibcoBRL), 1% Streptomycin/Penicillin (GibcoBRL). The tissue was then incubated for 30 min at 37°C in 2.4 U/ml dispase II (Boehringer Mannheim, Germany). Using a dissection microscope the dermis was separated from the epidermis, and sliced at 200 m using a McIlwain tissue chopper. Each slice of dermis was plated in separate Petri dishes, covered with a sterile 13 mm-diameter glass coverslip and fed with a serum- containing DMEM. After 2 to 3 days, fibroblasts started to grow out of the explant and proliferate. When the culture reached confluency, the cells were passaged and grown in 75 cm2 flasks to obtain large quantities of cells.
Olfactory neursosphere cultures were established by Dr. Richard McCurdy, from olfactory mucosa biopsies collected by Dr. Chris Perry (Department of Otolaryngology, Princess
29 Alexandra Hospital). The neurosphere-derived cells lines were established from olfactory mucosa biopsies from which primary neurospheres were generated as described previously (Murrell et al. 2005). Briefly, the lamina propriae and olfactory epithelium were separated with 2.4units/ml dispase II (Boehringer Mannheim, Germany), and digested with 0.25 mg/ml collagenase H (Sigma, C8501). Cells were then grown on plastic in DMEM/F12 with 10 % FBS and 1% Streptomycin/Penicillin. When reaching confluency, the cultures were harvested (Chapter 2.1.3) and cultured on 6-well plastic plates pretreated with 1µg/cm2 poly-L-lysine (Sigma, P6282). After 18 and 24 hours, spheres were collected which upon subsequent culturing on plastic, proliferated as neurosphere-derived cell.
Cells were stored in liquid nitrogen with 90% FBS and 10% dimethyl sulfoxide. Frozen aliquots were then used in this study.
2.1.1 Tissue culture initiation Tissue culturing was performed in a laminar flow culture hood in aseptic conditions. D- MEM/F12 containing 10 % FBS and 1% Streptomycin/Penicillin (GibcoBRL) was used for culturing. 75 cm2 culture flasks were used for initiating cultures from thawed fibroblast cell lines. Tissue cultures were incubated at 37˚C and 5% CO2 in a Sanyo MCO-175M O2/CO2 incubator.
2.1.2 Passaging Cell lines were passaged into 75 cm2 or 175 cm2 culture flasks Nunclon®. Cell lines were simultaneously passaged into 75 cm2 culture flasks to retain growing cell lines for later experiments. Cell lines were not passaged more than 5 times for any of the experiments.
The media was aspirated, and the cultures were washed 3 times with 10ml of Hanks Balanced Salt Solution (HBSS) (JRH Biosciences), leaving a small amount of HBSS in the culture flask after the final aspiration before adding 0.5 ml of trypsin. The culture flask was incubated at 37˚C for 5 minutes. Trypsin (JRH Biosciences) was inactivated by addition of FBS-containing medium (at least 1 ml medium for each 0.1 ml trypsin) and the solution was collected in a 10 ml centrifuge tube. The cells were pelleted by centrifugation for 5 minutes at 300g followed by aspiration of the supernatant. The cell pellet was resuspended
30 in fresh medium and resuspended cells were transferred into prepared culture flask. Tissue cultures were incubated at 37˚C and 5% CO2.
2.1.3 Harvesting Normal transcription of mRNA is slowed down when the culture reaches the stationary phase. Consequently, lower mRNA yields are obtained from confluent cultures. By harvesting cultures when they reached 80 % and 90 % confluency, we obtained optimal mRNA yield and allowed the cultures to be in similar growth phases. Confluency of the cultures was assessed using an inverted microscope. Harvesting was performed as described in chapter 2.1.2, with the following modifications. A cell count was performed (see chapter 2.1.4) on resuspended cells, before being centrifuged for 5 minutes at 300 g and replated in a culture flasks.
2.1.4 Cell counts using haemocytometer The haemocytometer was set up, ensuring the coverslip was resting on the raised supports and adhered to the haemocytometer. The cell suspension was resuspended and 100 μl of sample was mixed with an equal volume of trypan blue to identify dead cells, which were excluded from the count. The sample was then injected into the chamber and, under an inverted microscope, the number of cells in the four corner squares was counted.
Figure 2.1: Haemocytometer.
For an undiluted sample, the total number of cells per ml of sample is given by the formula: Cells/ml = Average (Cell count per corner square) x 104
31 2.1.5 Protein estimation Protein estimation was performed using the DC Protein Assay (Bio-Rad, USA), which is a colorimetric assay based on the Lowry method (Lowry et al. 1951). The assay was performed in a 96-well plate, where 5 μl of samples, standards and blank (lysis buffer) were loaded in triplicate wells. Firstly Reagent AX was prepared by mixing 20 μl of Reagent S (confidential composition) for each ml of Reagent A (confidential composition). 25 μl of Reagent AX was then added to each well, mixed and allowed to stand for 1 minute. 200 μl of Reagent B (confidential composition) was then added to each well and the plate was incubated at room temperature for 15 minutes. The absorbance was then read at 750nm using a Synergy™ 2 microplate reader with Gen5™ data analysis software (BioTek Instruments, Inc). The absorbance of the BSA standards was used to make a standard curve, from which the protein concentrations of the sample dilutions, and thus the sample lysate, were extrapolated.
32
Chapter 3: Gene expression profiling on neurosphere-derived cells and skin fibroblasts in schizophrenia
3.1 Introduction The goal of this study was to test the hypothesis of altered adhesion, neurogenesis and mitochondrial function in schizophrenia. This was done by establishing global expression profiles for neurosphere-derived cells and skin fibroblasts from patients with schizophrenia and controls. Microarray technology is a tool which allows a high-throughput gene expression profile to be determined for a sample. This gives the advantage that gene expression related to many biological pathways can be analysed simultaneously. This can be useful when studying disorders where the etiology is not fully understood, as the technology may generate insights into novel pathways and interactions associated with the disorder. As outlined in Chapter 1, previous studies have identified multiple pathways involved in neurogenesis, differentiation and mitochondrial function in schizophrenia (Hakak et al. 2001; Prabakaran et al. 2004; McCurdy et al. 2006; Maeno et al. 2007). Previous studies have also reported altered adhesion of olfactory biopsies and skin fibroblasts in patients with schizophrenia (Mahadik et al. 1994; Miyamae et al. 1998), which has lead to a hypothesis of dysfunctional focal adhesion in schizophrenia. It was hypothesized that gene expression profiling of neurosphere-derived cells and skin fibroblasts would reveal pathways which show global expression changes in both cell types, and pathways which are limited to neural derived neurospheres. Microarray analysis has previously been performed on non-neuronal cell lines from patients with schizophrenia, however their value to biological psychiatry remains largely unproven (Matigian et al. 2008). Our group is the first to explore gene expression profiles on adult stem cells from patients with schizophrenia. We also have skin fibroblasts obtained from the same patients, enabling us to compare gene expression profiles of neuronal and non-neuronal tissue in schizophrenia.
3.2 Methods 3.2.1 Samples In this chapter, neurosphere-derived cell lines were grown by Rowena Cecil. Neurosphere- derived cells and skin fibroblasts were grown to approximately 80% confluency as described in Chapter 2.1.3. Confluency of the cultures was assessed using an inverted microscope and harvesting was performed as described in Chapter 2.1.3, with the following modifications. Pelleted cells were washed with HBSS, aspirated and stored at -80˚C for 30
34 minutes prior to RNA extraction. Culturing and RNA extractions were performed twice for each sample, which were used as biological replicates for each cell line.
3.2.2 RNA extraction In this chapter, RNA extractions from neurosphere-derived cell lines were performed by Rowena Cecil. Total RNA was extracted using QIAGEN RNeasy mini kit for isolation of total RNA from animal cells, which works by RNA binding to a silica-gel-based membrane using a high-salt buffer system. The protocol used was described by the manufacturer. Cells were lysed using Buffer RLT containing 10 l -Mercaptoethanol ( -ME) per 1 ml. The solution was passed 10 times through a 25-gauge needle with an RNase free syringe to homogenize the sample. One volume of 70% ethanol was added to the homogenate and mixed before the solution was transferred to an RNeasy mini column. The sample was centrifuged for 15 seconds at 14,000 rpm and sample was then washed once with Buffer RW1 followed by DNase 1 treatment for 15 minutes using an RNase-free DNase set (QIAGEN). The sample was subsequently washed once with Buffer RW1, followed by two washes with buffer RPE containing 4 volumes of 99 % ethanol. Total RNA was then eluted with 50 μl RNase-free H2O and stored at -80°C.
3.2.3 RNA labelling and microarray hybridization RNA labeling and microarray hybridization was performed by Nick Matigian. All RNA preparations were quantified using a Nanodrop (Thermo Scientific), then checked with an Agilent 2100 Bioanalyser (RNA Nano kit) where only RNA with RNA integrity number (RIN) > 9 were accepted for RNA amplification. Illumina Human-Refseq8 v2 BeadChip (Illumina, Inc.) human whole-genome expression arrays were used in this study. A total of 0.5 μg RNA from each sample was amplified using the Ambion Illumina RNA amplification kit with biotin UTP labeling (Ambion, Inc). The amplification kit uses T7 oligo(dT) primer to generate single stranded cDNA. Second strand synthesis was then performed to generate double-stranded cDNA, followed by column purification of the samples. In vitro transcription using T7 RNA polymerase was done for 4 hours on the column purified samples to synthesize biotin-labeled cRNA. The cRNA was then column purified prior to quantifying size and yield using an Agilent 2100 Bioanalyser (RNA Nano kit). A total of 0.75 μg of cRNA was hybridized for each array using standard Illumina protocols, using streptavidin-Cy3 (Amersham, Piscataway, NJ, USA) for detection. Slides
35 were then scanned on an Illumina Beadstation and analyzed using BeadStudio (Illumina, Inc).
3.2.4 Data normalisation Data normalizations were performed by Nick Matigian. BeadStudio was imported into R/BioConductor using the readBead function from the BeadExplorer package (Elvidge 2006). Background adjustment and quantile normalization (Bolstad et al. 2003) was performed using algorithms within the Affymetrix package (Irizarry et al. 2003) (function: bg.adjust and ormalize.quantiles). The normalized data was exported out off R/BioConductor using write.beadData function for further analysis.
3.2.5 Data filtering Normalised data was imported into and analysed using Genespring GX 7.3.1. software (Agilent Technologies). Genes were initially filtered using Illumina® detection p-value. The detection p-value represents the confidence that a given transcript is expressed above the control signal. A detection p-value ≥ 0.99 was required in 75% of samples within the disease group and control group respectively, for a gene to be deemed present on the array. Genes present within both groups were then combined and analysed statistically for differences in gene expression. Standard parametric 2-way ANOVA (p≤0.05), with Benjamini-Hochberg multiple testing procedure (Benjamini and Hochberg 1995), was performed on the data with disease state and replicate as the main effect. ANOVA was used to identify genes differently expressed between control and schizophrenia samples. Data was then analysed using Database for Annotation, Visualization, and Integrated Discovery 2.0 (DAVID) (http://david.abcc.ncifcrf.gov/) and Ingenuity Pathway Analysis (IPA) 6.0 (Ingenuity® Systems). DAVID was used to analyse which KEGG pathways were overrepresented in the genes differently expressed in the schizophrenia group. DAVID pathway analysis shows which pathways are statistically overrepresented in the gene list, and provide us with the biological theme of the data. The data is then ranked according to an EASE score, which ranks pathways based on whether the number of genes in a pathway is overrepresented in the data set compared to the total number of genes in each pathway (Hosack et al. 2003). The EASE score is calculated using the distribution of the Jackknife Fisher exact probability distribution, and provides a robust analysis of pathways. An EASE score cutoff of 0.05 was applied. IPA uses Fisher Exact Probability to calculate whether
36 pathways are overrepresented in the data set. IPA 6.0 also provides a more detailed breakdown of pathways. This allows us to identify relationships, functions and pathways relevant for understanding the processes occurring in our samples.
3.3 Results 3.3.1 Neurosphere-derived cells 9853 genes were expressed in the neurosphere-derived cells, of which 3742 genes were expressed significantly differently in the schizophrenia group. The genes were differently expressed due to disease state, replicate or as a result of an interaction between disease state and replicates. By comparing these lists, genes that were differently expressed due to a replicate effect or an interaction between the disease state and replicate, were excluded (Figure 3.1). Though these genes excluded genes that may still represent valuable insight, there is a distinct possibility that their altered expression is due to biological variation in the experiment rather than disease state. Of the 3742 genes identified in the ANOVA, 1505 genes had a replicate effect and 277 genes showed a replicate and disease state interaction. 993 genes were significantly differently expressed exclusively as a result of disease state (Appendix 1A). These genes were then used to generate a hierarchical condition tree to see how well the gene list separates the two patient groups (Figure 3.2), where genes are clustered together or apart according to the similarity of their gene expression. We see two major clusters of schizophrenia samples and control samples with the exception of a few outliers. The presence of outliers may reflect heterogeneity within the groups. The majority of the samples clustered together with similar disease states, showing that the data filtering resulted in good quality data which differentiates the schizophrenia samples and control samples well.
37 First parameter (Disease state) test. Second parameter (Replicate) test.
993 1505 277
36
78 282
571
Interaction between parameters Disease state and Replicate.
Figure 3.1: Identification of genes in neurosphere-derived cells with altered expression in schizophrenia. Venn diagram of 2-way ANOVA data from neurosphere-derived cells. The two parameters tested were Disease state (red circle), representing the effect of schizophrenia on gene expression, and Replicate (green circle), representing the effect of the replicate number on gene expression. The diagram also shows genes which were found to demonstrate an interaction between replicates and disease state (blue circle). Using the Venn diagram, we excluded genes whose changed gene expression may be a result of other parameters than disease state. 993 genes were differently expressed in the schizophrenia group as a result of disease state only (Appendix 2).
38
Figure 3.2: Supervised hierarchical clustering of gene expression from neurosphere-derived cells. A cluster analysis, using a standard correlation, was carried out on the 993 genes found to have significantly different expression as a result of disease state (Appendix 1A). Each branch of the tree represents a sample, whose disease state is denoted by the status bar. Blue=Control, Red=schizophrenia. The samples are branched according to the similarity of their gene expression, where branch length between to samples is a representation of their dissimilarity. Each branch contains a heat map of the 993 genes, showing whether they are up regulated (yellow) or downregulated (blue). The figure shows that schizophrenia samples and control samples are clustering well according to their disease state.
Using DAVID we identified the most overrepresented pathway in neurosphere-derived cells from the schizophrenia group, which was the ErbB signaling pathway where 14 genes were significantly dysregulated (Table 3.1). The focal adhesion pathway was also one of the most overrepresented pathways in the data set, with 20 genes dysregulated (Table 3.1). The KEGG focal adhesion pathway is a combination of multiple signaling pathways, and shows how they interact to regulate focal adhesion. The genes showing altered expression in our data set suggest disruption of focal adhesion complex, with potential significant implications to cell function (Figure 3.2).
39
Table 3.1: Pathways overrepresented in neurosphere-derived cells .Pathways overrepresented in neurosphere-derived cells from DAVID 2.0 analysis.
We also found 8 genes involved in mTOR signaling, and various cancers as the most overrepresented pathways (Table 3.1). A disruption of the ErbB signaling and focal adhesion pathways, in conjunction with mTOR signaling suggests a disruption in cell-cell communication, migration and growth.
40
Figure 3.2: KEGG Focal adhesion pathway map. Genes surrounded by a red ring are dysregulated in neurosphere-derived cells from the schizophrenia group. From DAVID 2.0 analysis.
41
The gene list was then analysed using IPA 6.0. IPA provides a more detailed breakdown of pathways in the data set. This allows us to identify relationships, functions and pathways relevant for understanding the processes occurring in our samples. Of particular interest, of the genes statistically different between disease states there were 31 genes previously reported to be differently expressed in the brains of patients with schizophrenia, or associated with schizophrenia (Table 3.2). These include reelin (RELN) and RSG4, genes which are well established as being associated with schizophrenia (Impagnatiello et al. 1998; Guidotti et al. 2000; Fatemi et al. 2001; Mirnics et al. 2001; Williams et al. 2004; Colantuoni et al. 2008).
Figure 3.3: Top five signalling and metabolic pathways dysregulated in neurosphere-derived cells. The top x-axis shows –log(p- values), represented by the blue bars. The threshold line represents p=0.05. The bottom x-axis shows ratio of genes as a proportion of all genes present in a pathway. When analysed using Ingenuity Pathway Analysis software, Integrin signalling and oxidative phosphorylation were among the 5 pathways most overrepresented in the data set. From IPA 6.0 analysis (Ingenuity® Systems, www.ingenuity.com).
42
Table 3.2: Genes dysregulated in neurosphere-derived cell and previous schizophrenia studies. Genes previously associated with schizophrenia or dysregulated in the brains of patients with schizophrenia, which are dysregulated in neurosphere-derived cells from the schizophrenia group. Green denotes changes which agree with previous findings. Red denotes changes which disagree with previous findings. From IPA 6.0 analysis (Ingenuity® Systems, www.ingenuity.com).
IPA identified the Integrin signaling and Ephrin receptor signaling pathways as the most overrepresented pathways in the data set (Figure 3.3). Of 192 genes identified in the Integrin signaling pathway, 22 had significantly altered expression in this study (Figure 3.4, Table 3.3). Of 184 genes in the Ephrin signaling pathway, 20 genes had significantly altered expression in this study (Figure 3.5, Table 3.4). The actin cytoskeleton signaling pathway was also overrepresented in this data (Appendix 1B). Of 218 genes in the actin cytoskeleton signaling pathway, 17 genes had significantly altered expression in this study (Figure 3.6, Table 3.5). Of note is dysregulation of FAK and vinculin. FAK is involved in the integrin, ephrin and actin cytoskeleton signaling pathways (Table 3.3, 3.4 and 3.5) while vinculin is involved in the integrin and ephrin signaling pathways (Table 3.3 and 3.4). Of particular interest to our hypothesis of a mitochondrial dysfunction (Chapter 1.5), oxidative phosphorylation was also overrepresented in the data (Figure 3.7, Table 3.6). The Neuregulin signaling pathway, which has been linked to schizophrenia, was also
43 overrepresented in the data (Figure 3.8, Table 3.7). However neither of these pathways rates as one of the most disrupted pathways (Appendix 1B).
Table 3.3: Ingenuity® Integrin signalling pathway. Genes in the Ingenuity® Integrin signalling pathway differently expressed in neurosphere-derived cells. Genes are arranged according to altered expression from highest to lowest fold change. From IPA 6.0 analysis (Ingenuity® Systems, www.ingenuity.com).
44
Figure 3.4: Ingenuity® Integrin signalling pathway. Genes from the gene list (Appendix 1A) which are downregulated (green) or upregulated (red) in neurosphere-derived cells. Green and red objects show complexes with subunits down or unregulated respectively. From IPA 6.0 analysis (Ingenuity® Systems, www.ingenuity.com).
45
Table 3.4: Ingenuity® Ephrin receptor signalling pathway. Genes in the Ingenuity® Ephrin receptor signalling pathway differently expressed in neurosphere-derived cells. Genes are arranged according to altered expression from highest to lowest fold change. From IPA 6.0 analysis (Ingenuity® Systems, www.ingenuity.com).
46
Figure 3.5: Ingenuity® Ephrin receptor signalling pathway. Genes from the gene list (Appendix 1A) which are downregulated (green) or up regulated (red) in neurosphere- derived cells. Green and red objects show complexes with subunits down or upregulated respectively. From IPA 6.0 analysis (Ingenuity® Systems, www.ingenuity.com).
47
Table 3.5: Ingenuity® actin cytoskeleton signalling pathway. Genes in the Ingenuity® actin cytoskeleton signalling pathway differently expressed in neurosphere-derived cells. Genes are arranged according to altered expression from highest to lowest fold change. From IPA 6.0 analysis (Ingenuity® Systems, www.ingenuity.com).
48
Figure 3.6: Ingenuity® actin cytoskeleton signalling pathway. Genes or complex subunits in the actin cytoskeleton signalling pathway that are dysregulated in neurosphere- derived cells (Appendix 1A). Green denotes genes or complex subunit genes that are downregulated. Red denotes genes or complex subunits genes that are upregulated (Table 3.5). From IPA 6.0 analysis (Ingenuity® Systems, www.ingenuity.com).
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Table 3.6: Ingenuity® oxidative phosphorylation metabolic pathway. Genes in the Ingenuity® oxidative phosphorylation metabolic pathway differently expressed in neurosphere-derived cells. Genes are arranged according to altered expression from highest to lowest fold change. From IPA 6.0 analysis (Ingenuity® Systems, www.ingenuity.com).
50
Figure 3.7: Ingenuity® oxidative phosphorylation metabolic pathway. Genes or complex subunits in the oxidative phosphorylation pathway that are dysregulated in neurosphere- derived cells (Appendix 1A). Green denotes genes or complex subunit genes that are downregulated. Red denotes genes or complex subunits genes that are upregulated (Table 3.6). From IPA 6.0 analysis (Ingenuity® Systems, www.ingenuity.com).
51
Table 3.7: Ingenuity® neuregulin signaling pathway. Genes in the Ingenuity® neuregulin signaling pathway differently expressed in neurosphere-derived cells. Genes are arranged according to altered expression from highest to lowest fold change. From IPA 6.0 analysis (Ingenuity® Systems, www.ingenuity.com).
Figure 3.8: Ingenuity® neuregulin signaling pathway. Genes or complex subunits in the neuregulin signaling pathway that are dysregulated in neurosphere-derived cells (Appendix 1A). Green denotes genes or complex subunit genes that are downregulated. Red denotes genes or complex subunits genes that are upregulated (Table 3.7). From IPA 6.0 analysis (Ingenuity® Systems, www.ingenuity.com). 3.3.2 Skin fibroblasts Of 9634 genes expressed in the skin fibroblasts, 1062 genes were significantly differently expressed. The 1062 genes were analysed as in Chapter 3.3.1 (Figure 3.9). Of the 1062 genes identified in the ANOVA, 743 genes were the result of a replicate effect and 42 genes showed a replicate and disease state interaction. 110 genes were significantly differently expressed exclusively as a result of schizophrenia (Appendix 2). These genes were then used to generate a hierarchical condition tree to see how well the gene list separates the two groups (Figure 3.10).
First parameter (Disease state) test. Second parameter (Replicate) test.
110 743 42
26
3 90
48
Interaction between parameters Disease state and Replicate.
Figure 3.9: Identification of genes in skin fibroblasts with altered expression in schizophrenia. Venn diagram of 2- way ANOVA data from skin fibroblasts. The two parameters tested were Disease state (red circle), representing the effect of schizophrenia on gene expression, and Replicate (green circle), representing the effect of the replicate number on gene expression. The diagram also shows genes which were found to demonstrate an interaction between replicates and disease state (blue circle). Using the Venn diagram, we excluded genes whose changed gene expression may be a result of other parameters than disease state. 110 genes were differently expressed in the schizophrenia group as a result of disease state only (Appendix 2).
55
Figure 3.10: Supervised hierarchical clustering of gene expression from skin fibroblasts. A cluster analysis, using a standard correlation, was carried out on the 110 genes found to have significantly different expression as a result of disease state (Appendix 2). Each branch of the tree represents a sample, whose disease state is denoted by the status bar. Blue=Control, Red=schizophrenia. Each branch contains a heat map of the 110 genes, showing whether they are up regulated (yellow) or downregulated (blue). The figure shows schizophrenia samples and control samples are clustering well according to their disease state.
In contrast to the data based on neurosphere-derived cells, pathway analysis using DAVID 2.0, as in Chapter 3.3.1, found no pathways significantly overrepresented in the data. Though the transcript levels of some genes involved in integrin and mitochondrial function were significantly different, the absence of altered expression of other genes in these pathways suggests the pathways are not significantly dysregulated. This suggests that pathways identified in neurosphere-derived cells were not dysregulated in skin fibroblasts, or that their dysregulation was less pronounced. Analysis in IPA 6.0 confirmed these findings, where the Hypoxia signalling in the cardiovascular system was the only significantly overrepresented pathway in the data (Figure 3.11). This was as a result of an upregulation of ATF2 and UBE2M (Appendix 2).
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Figure 3.11: Signalling and metabolic pathways which are dysregulated in skin fibroblasts. The top x-axis shows – log(p-values), represented by the blue bars. The threshold line represents p=0.05. The bottom x-axis shows ratio of genes as a proportion of all genes present in a pathway. When analysed using Ingenuity Pathway Analysis software, Hypoxia signalling in the cardiovascular system is the only overrepresented pathway in the data. From IPA 6.0 analysis (Ingenuity® Systems, www.ingenuity.com).
3.3.3 Overlap in altered expression between neurosphere-derived cells and skin fibroblasts. Of genes found to show significantly different expression between healthy controls and the schizophrenia group, only 12 genes were significantly different in both tissues. However, the most consistent direction in fold change of expression in these genes is in the opposite direction, when comparing skin fibroblasts with neurosphere-derived cells (Table 3.8). Only fold change of UXT was in the same direction (Table 3.8). Both data sets had an overrepresentation of genes in Hypoxia signalling in the cardiovascular system (Figure 3.11, Appendix 1B), however there is no agreement with regards to the change in expression levels between the two disease models (Table 3.8).
57
Table 3.8: Genes differently expressed in both cell models. Genes differently expressed between healthy controls and the schizophrenia group in both neurosphere-derived cells and skin fibroblasts. One gene, UXT, showed fold change in the same direction
58
3.4 Discussion 3.4.1 Overview We have profiled global gene expression of neursophere derived cells and skin fibroblasts from patients with schizophrenia. In the neurosphere-derived cells we identified several metabolic and signalling pathways which were dysregulated in the schizophrenia group (Table 3.1 and Figure 3.3). Of interest was the significant overrepresentation of genes involved in focal adhesion, namely the integrin, ephrin and actin cytoskeleton signalling pathways in the data (Table 3.3, 3.4 and 3.5). We also reported changed expression of genes involved in the oxidative phosphorylation metabolic pathway (Table 3.6) and neuregulin signalling pathway (Table 3.7). Of particular interest were the dysregualtion of several schizophrenia-related genes (Table 3.2). In skin fibroblasts there was no significant overrepresentation of any metabolic or signalling pathways (Figure 3.11). The overlap of genes differently expressed between the two cell models showed little agreement, and was less than what was expected by chance (Table 3.8).
3.4.2 Neurosphere-derived cells The gene expression profiles of neurosphere-derived cells provided good separation between the two disease states. We see nice clustering of samples to their respective groups. Many genes were expressed significantly differently in samples from the schizophrenia group, and support the hypothesis that adhesion, neurogenesis and mitochondrial function are altered in schizophrenia. Their fold change ranges from 0.01 to 16, and are involved in multiple pathways. We found the focal adhesion, Integrin signalling, Ephrin receptor signalling and oxidative phosphorylation pathways to be overrepresented in the data. There is also dysregulation of a number of genes previously reported to be changed in the brains of patients with schizophrenia, or associated with schizophrenia. This supports the use of the olfactory neurosphere-derived cells to model schizophrenia. These results suggest that disease pathophysiology may be reflected in a neurally derived cell culture.
3.4.3 Schizophrenia genes Of particular interest is the altered expression of RELN and RGS4, and also the RELN receptor LRP8 (Appendix 1A). As RELN and RGS4 are well established susceptibility genes in schizophrenia research, their altered expression in neurosphere-derived cells is of particular interest (Impagnatiello et al. 1998; Guidotti et al. 2000; Mirnics et al. 2001;
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Chowdari et al. 2002; Williams et al. 2004; Buckholtz et al. 2007; Carter 2007; Lang et al. 2007). RELN and RGS4, involved in neuronal signalling and migration (Kawauchi and Hoshino 2008), show large fold change in expression levels, -4.095 and -3.049 fold change respectively, which agree with previous reports of reduced expression levels in the prefrontal cortex of patients with schizophrenia (Impagnatiello et al. 1998; Guidotti et al. 2000; Mirnics et al. 2001; Colantuoni et al. 2008). Though there are other RELN receptor molecules, a downregulation of LRP8 (-1.543 fold change) lend support for a disruption of the RELN signaling pathway (Hiesberger et al. 1999; Trommsdorff et al. 1999; Cariboni et al. 2005).
The overrepresentation of the ErbB4 and neuregulin signaling pathway in DAVID and IPA respectively, was of particular interest, as neuregulin has been linked with schizophrenia in several linkage studies (Stefansson et al. 2002; Stefansson et al. 2003; Li et al. 2004a; Li et al. 2006). Though neither neuregulin 1 or any ErbB receptors demonstrated significant changes in transcript levels (Appendix 1A), dysregulation of the ErbB4 ligands HBEGF and DCN (Santra et al. 2000; Matsuda et al. 2002) (Table 3.7) indicates the neuregulin signaling pathway may be affected by changes upstream of the ErbB4 receptors. No studies to date have reported altered expression of neuregulin 1 in schizophrenia, however a dysregulation of the ErbB4/neuregulin signaling pathways could have significant consequences for multiple cell functions such as growth, differentiation, cell–cell signaling, axon guidance, synaptogenesis, glial differentiation, myelination, and neurotransmission (Garratt et al. 2000; Buonanno and Fischbach 2001; Falls 2003; Esper et al. 2006; Britsch 2007). As the ErbB4/neuregulin signaling pathways are necessary for proliferation and migration of neuronal stem cells and precursors in the central nervous system, it is possible these dysregualtions could impact on regulation of neurogenesis in schizophrenia pathophysiology (Garratt et al. 2000; Buonanno and Fischbach 2001; Falls 2003; Corfas et al. 2004; Esper et al. 2006; Britsch 2007; Mei and Xiong 2008).
We also see altered GABRE expression, a gamma-aminobutyric acid (GABA) receptor and a drug target for schizophrenia treatment. A disruption of these genes is thought to be involved in modulating a GABA and synaptic signaling deficiency in schizophrenia (Buckholtz et al. 2007; Lang et al. 2007), and suggests similar mechanisms may be present in the neurosphere-derived cells of the schizophrenia group. Though these are only a few of
60 the genes reported to be associated with schizophrenia, they fit in with recent hypotheses suggesting schizophrenia is a result of a complex interplay between various signaling pathways (Carter 2007; Lang et al. 2007). Many of the genes reported associate with schizophrenia, including RELN and RGS4, have direct or downstream effects on eIF2- alpha kinases, which may affect synaptic plasticity (Pfeiffer and Huber 2006; Carter 2007).
Altered levels of GABA biosynthesis is believed to alter synaptic plasticity in schizophrenia (Akbarian et al. 1995; Akbarian and Huang 2006; Lang et al. 2007; Reynolds and Harte 2007). In addition to dysregulation of RELN, we saw significant upregulation of 2 GABA receptors, GABBR2 (+5.15 fold change) and GABRE (+1.96 fold change), as well as an overrepresentation of genes in the G-protein receptor signaling pathway (Appendix 1A, Appendix 1B). Though GABA is a comparatively weak signal in our data set, which could be explained by the cells being homogeneous undifferentiated cells, it does present an interesting observation when considering previous findings in schizophrenia.
There is also an overrepresentation of genes involved in the mTOR and PI3K/AKT signaling pathways (Table 3.1, Appendix 1B), which are downstream of RELN in the signaling cascade (Jossin and Goffinet 2007). These pathways also have down stream effects on GSK-3 and the Wnt signaling pathway, which are dysregulated in the array (Appendix 1A, Appendix 1B) and implicated in schizophrenia pathophysiology (Miyaoka et al. 1999; Beasley et al. 2001; Beasley et al. 2002). This demonstrates a significant aggregation of altered expression of pathways related to growth differentiation and survival of neurons in our data. RELN requires integrin receptors to regulate synaptic plasticity in the cerebral cortex (Dulabon et al. 2000; Rodriguez et al. 2000; Belvindrah et al. 2007; Groc et al. 2007), thus linking in to another of the most overrepresented pathways in the data; the focal adhesion pathway.
3.4.4 Focal adhesion A disruption of focal adhesion pathways was one of the most significant signals observed in our data set (Table 3.1). This was also verified when applying the Fisher Exact Probability algorithm of IPA 6.0, where the Integrin and Ephrin signaling pathways are among the most overrepresented pathways. Both these pathways interact with each other
61 and are involved in regulating focal adhesion and cell migration (Davy and Robbins 2000; Schatzmann et al. 2003; Martinez and Soriano 2005; Bourgin et al. 2007). Both pathways showing significant overrepresentation in dysregualted genes may well be a reflection of their interaction. The Ephrin signaling pathway is important for normal development and maturation of the hippocampus, and also for the formation of neural networks (Gao et al. 1996; Tanaka et al. 2004; Martinez and Soriano 2005; Rodenas-Ruano et al. 2006). This is particularly interesting given the neurodevelopmental aspect of schizophrenia. The Integrin ITGA8 is one of several extracellular matrix (ECM) receptors which are involved in controlling multiple cell functions (Zutter 2007), and it is highly upregulated (Table 3.3). ECM signaling affects neural plasticity, which is proposed to be part of schizophrenia etiology (Dityatev et al. 2006; Daskalakis et al. 2008).
FAK and Vinculin, involved in the Integrin signaling pathway, are also critical components in assembly of the focal adhesion complex (Figure 3.4, Appendix 1A). Focal Adhesion Kinase (FAK) is a non-receptor proteintyrosine kinase that localizes to focal adhesions and plays a central role in differentiation and migration among other things. FAK is activated by phosphorylation events in response to several stimuli, subsequently activating downstream signaling events in the cell (Romer et al. 2006; Vadali et al. 2007; Tilghman and Parsons 2008). Vinculin is an important component in focal adhesion, and acts as an intracellular linker protein allowing interaction between integrins and the cytoskeleton (Miyamoto et al. 1995; Yamada and Miyamoto 1995; Romer et al. 2006). Disrupted regulation of the actin cytoskeleton was also observed (Figure 3.6, Table 3.5) which through its interaction with integrins and the extracellular matrix (Brakebusch and Fassler 2003; Thiery 2003; Wiesner et al. 2005) would affect focal adhesion in the cells. A down- regulation of these genes would affect assembly of focal adhesion complexes, suggesting cell adhesion, interaction, migration and differentiation are affected (Schatzmann et al. 2003; Xie et al. 2003; Romer et al. 2006; Vadali et al. 2007; Tilghman and Parsons 2008). These are essential processes in neural tissue for normal development and function. They would also have downstream effects on signaling within the cell, and agrees with the hypothesis of dysfunctional neurogenesis and focal adhesion in schizophrenia.
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3.4.5 Mitochondrial function The Oxidative phosphorylation pathway also showed significant disruption in the neurosphere-derived cells from the schizophrenia group (Appendix 1A). A majority of the dysregulated genes encoded subunits for Complex I and ATPase (Figure 3.7, Table 3.6). There are multiple studies reporting disruption of complex I in the oxidative phosphorylation pathway in the brains of patients with schizophrenia (Ben-Shachar et al. 1999; Bubber et al. 2004; Karry et al. 2004; Prabakaran et al. 2004). Though this is a comparatively weak signal, it is still an interesting observation in light of previous reports of a mitochondrial dysfunction in schizophrenia. It also fits with our hypothesis of a mitochondrial dysfunction in these cells (Chapter 1.5), warranting further functional confirmation. As Integrins can modulate mitochondrial function (Zhang et al. 1995; Gilmore et al. 2000; Matter and Ruoslahti 2001; Reginato et al. 2003; Martin and Vuori 2004), it is possible that observed dysregulation in oxidative phosphorylation genes may be related to changes seen in the Integrin signaling pathway.
3.4.6 Skin fibroblasts Genes differently expressed in skin fibroblasts from the schizophrenia group were not clustered around any specific pathway. We found no genes previously reported as schizophrenia candidate genes. As discussed in Chapter 3.4.1.2, there are reports of reduced attachment in skin fibroblasts from patients with schizophrenia. However the expression profiles for our samples give no obvious indication of dysfunctional pathways related to adhesion (Appendix 2). The reason we do not see a clear disruption of the focal adhesion pathway, as would be expected if the cells have disrupted attachment, may be due to the dysfunction not being expressed at the time of RNA extraction. When analyzing this data in conjunction with previous reports on attachment rates of skin fibroblasts from patients with schizophrenia patients (Miyamae et al. 1998), we hypothesize the attachment dysfunction in skin fibroblasts is not manifested under these culture conditions. This could explain why we see no evidence of an attachment dysfunction of these cells when grown to 80% confluency.
When comparing the expression profiles between the two tissues, we see little overlap with pathways significantly overrepresented, and poor overlap with genes differently expressed in the data from neurosphere-derived cells. The overlapping genes show no agreement in
63 fold change, and their small number is less than what is expected by chance. Compared to the neurosphere-derived cells, the gene expression profile of skin fibroblasts provided little insight into the neurobiological correlates of schizophrenia. Little converging expression data in non-neural tissue was also reported by Matigian et al. who looked at gene expression in skin fibroblasts and lymphoblastoid cell lines from schizophrenic patients (Matigian et al. 2008).
3.4.7 Summary The overlap of differently expressed genes between the two cell models tested was small, and had poor correlation. The gene expression of skin fibroblasts from the schizophrenia group did not produce any significant data suggesting a schizophrenia etiology. This agrees with a recent study by Matigian et al. (2008) comparing the global expression profile of non-neuronal lineages between patients with schizophrenia and healthy controls. They reported little differences between the schizophrenia group and healthy controls in both skin fibroblasts and lymphoblastoid cells, casting doubt over the utility of non-neuronal cell models in studying schizophrenia etiology (Matigian et al. 2008). They also proposed a neural-derived cell model would be better for studying gene expression changes in schizophrenia (Matigian et al. 2008).
Gene expression in neurosphere-derived cells identified focal adhesion, and several related pathways, as the most overrepresented. Focal adhesion is therefore an interesting candidate for further experimentation. Previous reports of altered attachment of cells from patients with schizophrenia support such a research direction. Dysfunctional focal adhesion, and the dysregulation of the identified schizophrenia candidate genes, would have downstream effects on differentiation and signalling. By studying various aspects of the neurosphere- derived cell system, specifically sphere formation and differentiation, we propose to compare neurogenesis between schizophrenia and healthy controls in vitro. Though disruption of the oxidative phosphorylation pathway is a comparatively weak signal in the neurosphere-derived cell data set, it does lend support for previous reports of mitochondrial dysfunction in schizophrenia, and therefore warrants further investigation.
The number of overlapping genes between neurosphere-derived cells and skin fibroblasts was less than what would be expected by chance, and as neurosphere-derived cells are of
64 neural origin, we believe them to be a more valuable and relevant model for studying differences in schizophrenia. This supports the hypothesis that a neural-derived cell model, such as olfactory stem cells, is a better alternative for studying gene expression changes in schizophrenia.
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Chapter 4: Mitochondrial function under oxidative stress in neurosphere-derived cells and skin fibroblasts
4.1 Introduction Mitochondria are organelles located within eukaryotic cells and are involved in several important metabolic processes. They are responsible for the majority of energy production within tissues and are involved in regulating cell cycle via their involvement in apoptosis and calcium homeostasis (Marcinek 2004; Shadel 2004). Mitochondrial function is therefore critical for normal cell function. If a mitochondrial dysfunction exists in patients with schizophrenia, it is possible that an abnormal energy state exists in the cells as a result, which ultimately affects neural function and plasticity (Chapter 1.5). There is also evidence of dysfunctional antioxidant system in patients with schizophrenia, where impaired antioxidant defense has been implicated in disease etiology (Yao et al. 1999; Do et al. 2000; Kuloglu et al. 2002; Grima et al. 2003; Gysin et al. 2007).
The specific hypothesis to be tested here is that cells from patients with schizophrenia are more susceptible to oxidative stress, and that mitochondrial function is altered. Therefore this study aimed to establish: 1) cell survival under normal culture conditions and oxidative stress to assess their oxidative state, 2) mitochondrial function under normal culture conditions and oxidative stress. In particular, the cells’ antioxidant capacity will be assessed by measuring cell survival and glutathione levels under various oxidative stress conditions.
By measuring the cytotoxic effect of H2O2 on neurosphere-derived cells and skin fibroblasts from patients with schizophrenia, we can assess their ability to survive oxidative stress, their oxidative state and antioxidant capacity. This was done measuring lactate dehydrogenase (LDH) release and Annexin V staining. LDH is a soluble cytosolic enzyme located in the cytoplasm, which is released into the culture medium following loss of membrane integrity as a result of either apoptosis or necrosis. Though LDH is an indirect measure, it allows us to perform large scale analysis on the effects of various treatments on cell death. The tolerable H2O2 concentrations found using the LDH assay, was then used as a basis for H2O2 treatment in all preceding experiments. Annexin V stains phosphatidylserine. In normal live cells, phosphatidylserine is located on the cytoplasmic surface of the cell membrane. In apoptotic cells the phosphatidylserine is translocated from the inner to the outer leaflet of the plasma membrane, therefore exposing phosphatidylserine to the external cellular environment (van Engeland et al. 1998). Annexin V is a phospholipid-binding protein that has a high affinity for phosphatidylserine and by using Annexin V labelled with a fluorophore we can identify apoptotic cells by
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Annexin V binding to phosphatidylserine (Koopman et al. 1994; van Engeland et al. 1998; Steensma et al. 2003; Okuda et al. 2006; Kerr 2008; Palma et al. 2008). Though both the LDH and Annexin V assay measure cell death, Annexin V measures the percentage of both preapoptotic and apoptotic cells in the population. This allowed for a higher resolution when studying the effect of H2O2 treatment on cell death.
To assess the antioxidant capacity in the cells from patients with schizophrenia, we measured glutathione content. Glutathione (GSH) content was measured using monochlorobimane (mBCl), which is a non-fluorescent substrate what binds rapidly to glutathione (GSH). This occurs through a reaction catalysed by glutathione S-transferase, forming fluorescent adducts (Hulbert and Yakubu 1983). GSH provides a large portion of the intracellular reducing power, and assessing intracellular GSH levels therefore provides an assessment of intracellular oxidative stress (Dickinson and Forman 2002; Sebastia et al. 2003; Webb et al. 2006). Impairment of the GSH system has previously been reported in schizophrenia (Do et al. 2000; Zhang et al. 2005; Gysin et al. 2007). By measuring total GSH levels in the in neurosphere-derived cells and skin fibroblasts under normal culture conditions and under oxidative stress, we assess the antioxidant capacity of these cells in schizophrenia.
To identify a respiratory chain dysfunction in patients with schizophrenia, we measured various parameters related to mitochondrial function. Mitochondrial function in cells from schizophrenic patients was assessed via testing various parameters associated with mitochondrial functions such as mitochondrial membrane potential and mitochondrial load. A change in ATP synthesis is an indication of mitochondrial dysfunction, and is hypothesised to be involved in several neurological disorders (Hanna and Nelson 1999). Impaired respiratory chain function is also reported in the brains of schizophrenia (Maurer et al. 2001), which would affect ATP synthesis and fits with the hypothesis that mitochondrial dysfunction is involved in schizophrenia etiology. This allowed us to determine whether there is reduced respiratory dysfunction in the neurosphere-derived cells. We also aimed to determine whether they were more susceptible to oxidative stress, by observing the impact of oxidative stress on respiratory chain function in schizophrenia patients. Another measure of mitochondrial function is mitochondrial membrane potential. An intact mitochondrial membrane potential is necessary for oxidative phosphorylation to
68 take place, and by measuring the membrane potential using flow cytometry we obtain more detailed information regarding the mitochondrial membrane potential of a cell population (Cossarizza et al. 1993). JC-1 is a cell permeant monomer, which in the presence of a mitochondrial membrane potential form aggregates resulting in a shift in fluorescence emission. The emission changes from 527nm to 590nm as the probe binds to mitochondria with an intact membrane potential. The ratio between fluorescence at 527nm and 590nm is a measure of mitochondrial membrane potential in the cell (Cossarizza et al. 1993; Salvioli et al. 1997). Another measure of mitochondrial function is intracellular mitochondrial load. Mitochondrial load in cells has been shown to change during oxidative stress (Lee et al. 2002a). The aim of this experiment was to assess mitochondrial load in neurosphere- derived cells and skin fibroblasts under normal culture conditions and under oxidative stress. If the mitochondrial load is different in the cells from the schizophrenia group, it would indicate a mitochondrial dysfunction in the samples. It would also impact on the analysis of mitochondrial membrane potential, as altered mitochondrial load would impact on JC-1 staining. Mitochondrial load was assessed using a MitoTracker® probe from Invitrogen® which is not affected by mitochondrial membrane potential. This study will compare mitochondrial function in neurally derived and non neural cells from patients with schizophrenia and controls.
4.2 Methods 4.2.1 Samples Neurosphere-derived cells and skin fibroblasts were grown to approximately 80% confluency as described in Chapter 2. For skin fibroblasts, only 8 of the control samples were successfully grown. Confluency of the cultures was assessed using an inverted microscope and harvesting was performed as described in Chapter 2.1.3. Cell concentrations were quantified using a hemacytometer as described in Chapter 2.1.4.
4.2.2 LDH assay Neurosphere-derived cells and skin fibroblasts were grown in 6-well plates until confluency reached approximately 80%. They were then treated with 0 mM, 0.5 mM, 1 mM, 1.5 mM,
2 mM and 2 mM H2O2 respectively, for 2 hours at 37°C protected from light. LDH release was assessed using CytoTox 96® Non-Radioactive Cytotoxicity Assay (Promega, G1780) following manufacturers’ instructions. Briefly, 250μl/well from each sample was added to a
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V-bottomed 96-well plate. Remaining cells in the culture plates were then ruptured with 10 μl of Lysis Solution (0.8% Triton® X-100 in water) per 100 μl of medium to measure maximum LDH, followed by 45 minute incubation in a humidified chamber at 37˚C, 5% CO2. The V-bottomed 96-well plate was centrifuged at 250 x g for 4 minutes. Then, 50 μl aliquots from all the wells were transferred to a fresh 96-well flat-bottom white matrix plate. Colour formation was initiated by addition of 50 μl Substrate solution to each well of the plate. The plate was covered with foil to protect it from light, and incubated at room temperature for 30 minutes. The reaction was then stopped with addition of 50 μl Stop Solution to each well. Bubbles were removed, and absorbance at 490nm recorded. Cell death was then assessed by calculating the percentage of LDH released when the cells were treated with H2O2 from total LDH released. Triplicate measurements were taken for each sample.
4.2.3 Annexin V Neurosphere-derived cells and skin fibroblasts were grown in 6-well plates until confluency reached approximately 80%. They were then treated with H2O2, at 37°C protected from light, according to the experiment. Two experiments were performed. A concentration trial experiment measured the effect of various concentration of H2O2 on cell survival over a 2 hour incubation period. Cells were treated with 0 mM H2O2, 1 mM H2O2 or 2 mM H2O2 respectively. Time trial experiments were performed to measure the effect of H2O2 on cell survival over various incubation times. Cells were treated with 1 mM H2O2 for 0 hours, 2 hours and 24 hours respectively. Following H2O2 treatment the cells were washed 3 times in HBSS and incubated with trypsin for 5 minutes at 37°C, followed by staining with Annexin V conjugated to Alexa Fluor 647 (Invitrogen™, A23204) as per the manufacturer’s instructions. Briefly, harvested cells were washed in cold PBS and supernatant discarded. Cells were then resuspend in 100μl annexin-binding buffer (10 mm
HEPES, 140 mM NaCl, and 2.5 mm CaCl2, pH7.4), at a cell concentration of approximately 1×105 cells/ml. This was followed by addition of 5 μl annexin V conjugate and incubation at room temperature for 15 minutes. Incubation was stopped by adding 400 μl of annexin-binding buffer. Samples were stored on ice protected from light until flow cytometric analysis. Samples were analysed on a FACSAria™ flow cytometer equipped with a 633nm trigon laser (Becton Dickinson, San Jose, IL, USA). The FACSAria™ flow cytometer was operated by Dr. Bernadette Bellette. Parameters for forward scatter (FSC),
70 side scatter (SSC) together with fluorescence channel 1 (FL1, 660nm ± 20nm) were measured. Experiments were performed 3 times. The data was analysed using FACSDiva™ software. Cells were first discriminated to exclude debris and dead cells using FSC and SSC parameters (Figure 4.2). Unstained cells were then discriminated (Figure 4.2), and percentage of Annexin V positive cells were recorded. Experiments were performed 3 times. Cells were first discriminated to exclude debris and dead cells using FSC and SSC parameters (Figure 4.2). Unstained events were then discriminated (Figure 4.2).
4.2.4 ATP synthesis Neurosphere-derived cells were grown in 75cm2 flasks until confluency reached approximately 80%. They were then treated with 0 mM H2O2 or 1 mM H2O2 for 2 hours at 37°C protected from light. Cells were washed 3 times in HBSS and trypsinised for 5 minutes at 37°C, followed by 3 washes in HBSS. ATP synthesis was then measured as described previously (Shepherd et al. 2006). The amount of protein was determined using the Dc Protein Assay Kit (Bio-Rad, Hercules, CA, USA) as described in Chapter 2.1.5. Cells were resuspended in buffer containing 150 mM KCl, 25 mM Tris-HCl; pH 7.6, 2 mM
EDTA pH 7.4, 10 mM KPO4 pH 7.4, 0.1 mM MgCl2 and 0.1% (w/v) BSA at a concentration of 1mg protein per ml of buffer. ATP synthesis was initiated by the addition of 250 μl of the cell suspension to 750 μl of substrate buffer (10 mM malate, 10 mM pyruvate, 1 mM ADP, 40 μg/ml digitonin and 0.15 mM adenosine pentaphosphate). Cells were incubated at 37°C for 10 min. At 0 and 10 min, 50 μl aliquots of the reaction mixture were withdrawn and quenched in 450 μl of boiling quenchin buffer (100 mM Tris-HCl, 4 mM EDTA pH 7.75) for 2 min. The sample was then diluted 1/10 in quenching buffer. The quantity of ATP was measured in a luminometer (Wallac Victor 2, PerkinElmer, Massachusetts, USA) using the ATP Bioluminescence Assay Kit (Roche Diagnostics, Basel, Switzerland) following the manufacturer’s instructions (Shepherd et al. 2006). Briefly, 50 μl of each sample was loaded into a black 96-well microtitre plate. Using an automated injector, 50 μl of luciferase reagent was added to the each sample and measured after a 1s delay and integrated for 1 to 10s. The blank (no ATP) was subtracted from the raw data, and ATP concentrations were calculated based on a standard curve generated with the supplied ATP standard. Triplicate measurements were taken for each time sample.
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4.2.5 JC-1 assay Neurosphere-derived cells and skin fibroblasts were grown in 6-well plates until confluency reached approximately 80%. They were then treated with H2O2, at 37°C protected from light, as described in chapter 4.2.3. Following H2O2 treatment the cells were incubated with 1µg/ml JC-1 (Sigma T4069) in DMEM/F12, 10%FBS, 1% Streptomycin/Penicillin for 15 minutes. Cells were washed 3 times in HBSS and trypsinised for 5 minutes at 37°C. Cells were then resuspended in DMEM/F12, 10%FBS, 1% Streptomycin/Penicillin and protected from light until analysis on FACSAria™ flow cytometer equipped with a 488nm octagon laser with FACSDiva™ software (Becton Dickinson, San Jose, IL, USA). The FACSAria™ flow cytometer was operated by Dr. Bernadette Bellette. Parameters for forward scatter (FSC), side scatter (SSC) together with fluorescence channel 1 (FL1, 530nm ± 30nm. Green) and fluorescence channel 2 (FL2; 585nm ± 42nm. Red) were measured. The data was analysed using FACSDiva™ software. Compensation settings for JC-1, to correct for spillage of green and red fluorescence, were usually FL1-FL2: 4% and FL2-FL1: 50%. The experiment was performed 3 times. Cells were first discriminated to exclude debris and dead cells using FSC and SSC parameters (Figure 4.2). Unstained events were then discriminated (Figure 4.2).
4.2.6 Mitochondrial load Neurosphere-derived cells and skin fibroblasts were grown in 6-well plates until confluency reached approximately 80%. They were then treated with H2O2 as described in chapter
4.2.3. Following H2O2 treatment the cells were incubated with 75mM Mitotracker Red 580 (Invitrogen, M-22425) in DMEM/F12, 10%FBS, 1% Streptomycin/Penicillin for 15 minutes. Cells were washed 3 times in HBSS and trypsinised for 5 minutes at 37°C. Cells were then resuspended in DMEM/F12, 10%FBS, 1% Streptomycin/Penicillin and protected from light until analysis on FACSAria™ flow cytometer equipped with a 633nm trigon laser with FACSDiva™ software (Becton Dickinson, San Jose, IL, USA). The FACSAria™ flow cytometer was operated by Dr. Bernadette Bellette. Parameters for forward scatter (FSC), side scatter (SSC) together with fluorescence channel 1 (FL1, 660nm ± 20nm) were measured. Experiments were performed 3 times. Cells were first discriminated to exclude debris and dead cells using FSC and SSC parameters (Figure 4.2). Unstained events were then discriminated (Figure 4.2), and mean Mitotracker Red 580 fluorescence was measured. Relative mitochondrial load of the cell population was
72 determined, using FACSDiva™ software , by normalising the readings against a calibrator sample. Mean FSC readings were used to compare the size of the cells in each experiment.
4.2.7 Glutathione assay Neurosphere-derived cells and skin fibroblasts were grown in 6-well plates until confluency reached approximately 80%. They were then treated with H2O2 as described in chapter
4.2.3. Following H2O2 treatment the cells were incubated with 40µM mBCl (Sigma, 69899) in DMEM/F12, 10%FBS, 1% Streptomycin/Penicillin for 15 minutes. Cells were washed 3 times in HBSS and trypsinised for 5 minutes at 37°C. Cells were then resuspended in DMEM/F12, 10%FBS, 1% Streptomycin/Penicillin and protected from light until analysis on FACSAria™ flow cytometer equipped with a 407nm trigon laser with FACSDiva™ software (Becton Dickinson, San Jose, IL, USA). The FACSAria™ flow cytometer was operated by Dr. Bernadette Bellette. Parameters for forward scatter (FSC), side scatter (SSC) together with fluorescence channel 1 (FL1, 450nm ± 40nm) were measured. Experiments were performed 3 times. Cells were first discriminated to exclude debris and dead cells using FSC and SSC parameters (Figure 4.2). Unstained events were then discriminated (Figure 4.2), and mean mBCl fluorescence was measured. Relative GSH content of the cell population was determined, using FACSDiva™ software , by normalising the readings against a calibrator sample.
4.2.8 Statistical analysis As described in Chapter 2.1, the samples sizes for these experiments were 9 control and 9 patient neurosphere-derived cell lines and 8 control and 9 patient skin fibroblast cell lines. For each assay, triplicate measurements were averaged, and the averages for the control group and schizophrenia group was compared statistically by two-way ANOVA. ANOVA was performed using SPSS© 15.0 software. Data are expressed as mean over three experiments ± SEM. α = 0.05 was chosen to assign significance in all statistical tests.
4.3 Results 4.3.1 Cell death 4.3.1.1 LDH assay A dose response curve was generated for the effect of on LDH for both cell types (Figure
4.1). Cell death as a result of H2O2 treatment was not different between the schizophrenia
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and control groups in either cell type. Based on this study, a treatment with 1 mM H2O2 was assessed to be a tolerable dose and 2 mM H2O2 to be a toxic dose (Figure 4.1). These two H2O2 concentrations were then subsequently used for oxidative stress treatment. There was a significant effect of H2O2 treatment on neurosphere-derived cells (F5,12 = 23.765, p ≤
0.001) and skin fibroblasts (F5,11 = 36.344, p ≤ 0.001) where there was an increase in cell death with increasing H2O2 concentrations (Figure 4.1). When neurosphere-derived cells and skin fibroblasts were incubated with increasing concentrations of H2O2, there was a significant increase in cell death for both cell types (Figure 4.1). There was no significant difference in the mean level of cell death between the schizophrenia and control groups in neurosphere-derived cells (F1,16 = 0.155, p = 0.699) or skin fibroblasts (F1,15 = 0.288, p = 0.599) in the experiment.
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Figure 4.1: LDH release during peroxide treatment. A and B: Mean cell death ± SEM, as measured by LDH released into the media as a percentage of total LDH. A: Neurosphere-derived cells treated with H2O2 (p = 0.699). B: Skin fibroblasts treated with H2O2 (p = 0.599). There is no significant difference in response to varying H2O2 concentrations between the groups in either cell type.
4.3.1.2 Annexin V
We found no difference in cell death as a result of H2O2 treatment different between the schizophrenia and control groups in either cell type (Figure 4.3 and Figure 4.4). This
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confirms the findings from the LDH assay that cell death is not different as a result of H2O2 treatment. We tested the effect of H2O2 on Annexin V staining in order to determine whether there was a dose response of the H2O2 treatment. When comparing the effect of various treatment periods of 1 mM H2O2 (Figure 4.3), there was a significant effect of the treatment on neurosphere-derived cells (F2,15 = 18.966, p ≤ 0.001) and skin fibroblasts (F2,14
= 10.523, p = 0.002). There was an increase in cell death with increasing H2O2 concentrations (Figure 4.3).There was no significant difference in the mean level of cell death between the schizophrenia and control groups in neurosphere-derived cells (F1,16 =
0.447, p = 0.513) or skin fibroblasts (F1,15 = 0.053, p = 0.822) in the experiment. When comparing the effect of various H2O2 concentrations (Figure 4.4), there was a significant effect of the treatment on neurosphere-derived cells (F2,15 = 46.360, p ≤ 0.001) and skin fibroblasts (F2,14 = 27.458, p ≤ 0.001). There was an increase in cell death with increasing incubation times with H2O2 (Figure 4.4).There was no significant difference in the mean level of cell death between the schizophrenia and control groups in neurosphere-derived cells (F1,16 = 0.190, p = 0.669) or skin fibroblasts (F1,15 = 0.483, p = 0.497) in the experiment.
Figure 4.2: Gating strategy. Gating strategy used when analysing flow cytometry data. A: Dot plot showing FSC vs SSC. The cells outside the polygon were discriminated to exclude debris. Events within the polygon were defined as cells. B: Unstained events were then discriminated.
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Figure 4.3: Apoptosis during oxidative stress. A and B: Mean apoptosis ± SEM, as measured by Annexin V staining, when cells were treated with 1 mM H2O2 for 0, 2 and 24 hours respectively. A: Neurosphere-derived cells treated with H2O2 (p = 0.513). B: Skin fibroblasts treated with H2O2 (p = 0.822). There is no significant difference in response to varying treatments times with 1 mM H2O2 between the schizophrenia and control groups in either cell type.
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Figure 4.4: Apoptosis during oxidative stress. A and B: Mean apoptosis ± SEM, as measured by Annexin V staining, when cells were treated for 2 hours with 0 mM, 1 mM and 2mM H2O2 respectively. A: Neurosphere-derived cells treated with H2O2 (p = 0.669). B: Skin fibroblasts treated with H2O2 (p = 0.467). There is no significant difference in response to varying concentrations of H2O2 between the schizophrenia and control groups in either cell type.
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4.3.2 Mitochondrial function 4.3.2.1 ATP assay There was no significant difference in ATP synthesis between the schizophrenia and control groups in neurosphere-derived cells under normal culture conditions and when exposed to 1mM H2O2 for 2 hours (Figure 4.5). The mean ATP synthesis ± SEM for the two groups was as follows: control, 28.950 ± 2.411 untreated and 7.025 ± 1.653 H2O2 treated. n = 9; schizophrenic, 30.145 ± 1.117 untreated and 5.365 ± 0.611 H2O2 treated, n =
9 (Figure 4.5). To determine whether there was a dose response of the H2O2 treatment, we tested the effect of H2O2 on ATP synthesis. There was a significant effect of the H2O2 treatment on the neurosphere-derived cells (F1,16 = 449.383, p ≤ 0.001), where there was a decrease in ATP synthesis observed when cell were treated with H2O2. There was no significant difference in ATP synthesis between the schizophrenia and control groups (F1,16 = 0.007, p = 0.935).
Figure 4.5: ATP synthesis in neurosphere-derived cells. Mean ATP synthesis ± SEM measured in neurosphere- derived cells when treated for 2 hours with 0 mM and 1 mM H2O2 respectively (p = 0.935). There is no significant difference in ATP synthesis between the schizophrenia and control groups.
4.3.2.2 JC-1 Mitochondrial membrane potential was assessed using the fluorophore JC-1, which aggregates to mitochondria with a membrane potential. Any change in fluorescence between the FL1 (530nm) and FL2 (585nm) channels therefore represents changes in
79 membrane potential of the cells’ mitochondrial population. The gating strategy was based on the fluorescence of an untreated sample. The majority of cells in untreated samples had a high fluorescence in both FL1 (530nm) and FL2 (585nm) channels. Following some H2O2 treatments there was a significant reduction in percentage of cells with high FL2 (585nm) fluorescence in the cell populations (Figure 4.6). The mitochondrial membrane potential was compared by assessing the percentage of cells with high FL2 (585nm) fluorescence in the cell populations (Figure 4.6).
Figure 4.6: JC-1 staining in neurosphere-derived cells. A and C: JC-1 staining in untreated neurosphere-derived cells. B and D: JC-1 staining in neurosphere derived cells treated with 2 mM H2O2 for 2 hours. C and D: Dot plots from flow cytometry analysis of untreated and treated samples respectively. The data were gated based on high and low fluorescence in FL1 (530nm, green) and FL2 (585nm, red). A and C: The majority of the cell population from the untreated sample has high fluorescence in FL1 (530nm, green) and FL2 (585nm, red), indicating a healthy cell population with energized mitochondria. B and D: Upon H2O2 treatment there was a significant reduction in FL2 (585nm, red) fluorescence, indicating cells have less energized mitochondria as a result of the treatment. Mitochondrial membrane potential was assessed by comparing the percentage of cells having high FL2 (585nm, red) fluorescence in the cell populations.
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Loss of mitochondrial membrane potential as a result of H2O2 treatment was not different between the schizophrenia and control groups in either cell type (Figure 4.7 and Figure 4.8).
We tested the effect of H2O2 on mitochondrial membrane potential, as measured by JC-1 staining, to determine whether there was a dose response of the H2O2 treatment. When comparing the effect of various treatment periods of 1 mM H2O2 (Figure 4.7), there was a significant effect of the treatment on neurosphere-derived cells (F2,15 = 47.447, p ≤ 0.001) and skin fibroblasts (F2,14 = 62.114, p ≤ 0.001). There was a decrease in mitochondrial membrane potential when cells were exposed to longer incubation with H2O2 (Figure 4.7).There was no significant difference in the mean level of mitochondrial membrane potential between the schizophrenia and control groups in neurosphere-derived cells (F1,16 =
0.574, p = 0.460) or skin fibroblasts (F1,15 = 0.343, p = 0.567) in the experiment. When comparing the effect of various H2O2 concentrations (Figure 4.8), there was a significant effect of the treatment on neurosphere-derived cells (F2,15 = 45.250, p ≤ 0.001) and skin fibroblasts (F2,14 = 27.603, p ≤ 0.001). There was a decrease in membrane potential when the cells were exposed to increasing H2O2 concentrations (Figure 4.8).There was no significant difference in the mean level of mitochondrial membrane potential between the schizophrenia and control groups in neurosphere-derived cells (F1,16 = 2.974, p = 0.104) or skin fibroblasts (F1,15 = 0.124, p = 0.730) in the experiment.
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Figure 4.7: Mitochondrial membrane potential during oxidative stress. A and B: Mean mitochondrial membrane potential ± SEM, as measured by JC-1 aggregation, when cells were treated with 1 mM H2O2 for 0, 2 and 24 hours respectively. A: Neurosphere-derived cells treated with H2O2 (p = 0.460). B: Skin fibroblasts treated with H2O2 (p = 0.567). There is no significant difference in response to varying treatments times with 1 mM H2O2 between the schizophrenia and control groups in either cell type.
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Figure 4.8: Mitochondrial membrane potential during oxidative stress. A and B: Mean mitochondrial membrane potential ± SEM, as measured by JC-1 aggregation, when cells were treated for 2 hours with 0 mM, 1 mM and 2mM H2O2 respectively. A: shows neurosphere-derived cells treated with H2O2 (p = 0.104). B: show skin fibroblasts treated with H2O2 (p = 0.730).There is no significant difference in response to varying concentrations of H2O2 between the schizophrenia and control groups in either cell type.
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4.3.2.3 Mitochondrial load Change in mitochondrial load was different between the schizophrenia and control groups when neurosphere-derived cells were exposed to toxic H2O2 levels, but not for any other treatment or for skin fibroblasts (Figure 4.9 and Figure 4.10). We tested the effect of H2O2 on mitochondrial load to determine whether there was a dose response of the H2O2 treatment. When comparing the effect of various treatment periods of 1 mM H2O2 on mitochondrial load (Figure 4.9), there was a significant effect of the treatment on neurosphere-derived cells (F2,15 = 5.965, p = 0.012) and skin fibroblasts (F2,14 = 86.137, p ≤ 0.001). The mitochondrial load of the neurosphere-derived cells fluctuates when exposed to longer incubation with H2O2 (Figure 4.9). There was no significant difference in the mean level of mitochondrial load between the schizophrenia and control groups in neurosphere- derived cells (F1,16 = 0.148, p = 0.706) or skin fibroblasts (F1,15 = 0.060, p = 0.811) in the experiment. When comparing the effect of various H2O2 concentrations on mitochondrial load (Figure 4.10), there was a significant effect of the treatment on neurosphere-derived cells (F2,15 = 68.104, p ≤ 0.001) and skin fibroblasts (F2,14 = 77.838, p ≤ 0.001). There was an increase in mitochondrial load of the cells with increasing H2O2 concentrations (Figure 4.10). There was a significant difference in the mean level of mitochondrial load between the schizophrenia and control groups in neurosphere-derived cells (F1,16 = 4.579, p = 0.048) but not skin fibroblasts (F1,15 = 0.023, p = 0.882) in the experiment.
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Figure 4.9: Mitochondrial load during oxidative stress. A and B: Mean relative mitochondrial load ± SEM, as measured by Mitotracker Red 580 staining, when cells were treated with 1 mM H2O2 for 0, 2 and 24 hours respectively. A: Neurosphere-derived cells treated with H2O2 (p = 0.706). B: Skin fibroblasts treated with H2O2 (p = 0.811).There is no significant difference in response to varying treatments times with 1 mM H2O2 between the schizophrenia and control groups in either cell type.
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Figure 4.10: Mitochondrial load during oxidative stress. A and B: Mean relative mitochondrial load ± SEM, as measured by Mitotracker Red 580 staining, when cells were treated for 2 hours with 0 mM, 1 mM and 2mM H2O2 respectively. A: Neurosphere-derived cells treated with H2O2 (p = 0.048). B: Skin fibroblasts treated with H2O2 (p = 0.882).There was a significant difference in response to varying concentrations of H2O2 between the schizophrenia and control groups in neurosphere-derived cells but not skin fibroblasts.
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4.3.2.4 Cell size There was no significant difference in average cell size, as measured by forward scatter (FSC), between neurosphere derived cells and skin fibroblasts from the schizophrenia and control groups. We tested the effect of H2O2 on cell size to determine whether there was a dose response of the H2O2 treatment. When comparing the effect of various treatment periods of 1 mM H2O2 on cell size (Figure 4.11), there was no significant effect of the treatment on neurosphere-derived cells (F2,15 = 3.281, p = 0.066). In contrast there was a significant difference in cell size of skin fibroblasts with vaeying treatment periods 1 mM
H2O2 (F2,14 = 21.230, p ≤ 0.001). There was no difference in cell size when the cells were exposed to longer incubation with H2O2 (Figure 4.11). There was no significant difference in the mean level of cell size between the schizophrenia and control groups in neurosphere- derived cells (F1,16 = 0.776, p = 0.391) or skin fibroblasts (F1,15 = 0.470, p = 0.503) in the experiment. When comparing the effect of various H2O2 concentrations on cell size (Figure
4.12), there was no significant effect of the treatment on neurosphere-derived cells (F2,15 =
0.821, p = 0.459) but in the skin fibroblasts (F2,14 = 22.931, p ≤ 0.001). There was an increase in cell size of neurosphere-derived cells when treated with increasing H2O2 concentrations (Figure 4.12).There was no significant difference in the mean level of cell size between the schizophrenia and control groups in neurosphere-derived cells (F1,16 =
1.904, p = 0.187) or skin fibroblasts (F1,15 = 0.014, p = 0.908) in the experiment.
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Figure 4.11: Cell size during oxidative stress. A and B: Mean relative cell size ± SEM, as measured by forward scatter (FSC), when cells were treated with 1 mM H2O2 for 0, 2 and 24 hours respectively. A: Neurosphere-derived cells treated with H2O2 (p = 0.391). B: Skin fibroblasts treated with H2O2 (p = 0.503).There was no significant difference in response to H2O2 between the schizophrenia and control groups in either cell type.
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Figure 4.12: Cell size during oxidative stress. A and B: Mean relative cell size ± SEM, as measured by forward scatter (FSC), when cells were treated for 2 hours with 0 mM, 1 mM and 2mM H2O2 respectively. A: Neurosphere-derived cells treated with H2O2 (p = 0.187). B: Skin fibroblasts treated with H2O2 (p = 0.908).There was no significant difference in response to concentrations of H2O2 between the schizophrenia and control groups in either cell type.
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4.3.3 GSH content There was reduced GSH content in both neurosphere-derived cells and skin fibroblasts from the schizophrenia group when exposed to long term toxic H2O2 treatment, but not when exposed to toxic H2O2 levels (Figure 4.13 and Figure 4.14). We tested the effect of
H2O2 on GSH content to determine whether there was a dose response of the H2O2 treatment. When comparing the effect of various treatment periods of 1 mM H2O2 on GSH content (Figure 4.13), there was a significant effect of the treatment on neurosphere-derived cells (F2,15 = 18.943, p ≤ 0.001) but not the skin fibroblasts (F2,14 = 3.421, p = 0.062). There was an increase in GSH content when the cells are exposed to a longer incubation with
H2O2 (Figure 4.13). There was a significant difference in the mean level of GSH content between the schizophrenia and control groups in neurosphere-derived cells (F1,16 = 5.546, p
= 0.032) and skin fibroblasts (F1,15 = 16.130, p = 0.001) in the experiment. When comparing the effect of various H2O2 concentrations on GSH content (Figure 4.14), there was a significant effect of the treatment on neurosphere-derived cells (F2,15 = 7.054, p =
0.007) but not skin fibroblasts (F2,14 = 3.424, p = 0.062). There was a significant change in
GSH content of neurosphere-derived cells when treated with increasing H2O2 concentrations (Figure 4.14). There was no significant difference in the mean level of GSH content between the schizophrenia and control groups in neurosphere-derived cells (F1,16 =
1.236, p = 0.283) or skin fibroblasts (F1,15 = 1.852, p = 0.194) in the experiment.
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Figure 4.13: GSH content during oxidative stress A and B: Mean relative GSH content ± SEM, as measured by mBCl staining, when cells were treated with 1 mM H2O2 for 0, 2 and 24 hours respectively. A: Neurosphere-derived cells treated with H2O2 (p = 0.032). B: Skin fibroblasts treated with H2O2. (p = 0.001).There was a significant difference in response to varying treatments times with 1 mM H2O2 between the schizophrenia and control groups in both cell types.
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Figure 4.14: GSH content during oxidative stress. A and B: Mean relative GSH content ± SEM, as measured by mBCl staining, when cells were treated for 2 hours with 0 mM, 1 mM and 2mM H2O2 respectively. A: Neurosphere-derived cells treated with H2O2 (p = 0.283). B: Skin fibroblasts treated with H2O2 (p = 0.194). There is no significant difference in response to varying concentrations of H2O2 between the schizophrenia and control groups in either cell type.
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4.3.4 Comparison of neurosphere-derived cells and skin fibroblast There was a difference in total GSH content between the schizophrenia groups and the controls in both neurosphere-derived cells and skin fibroblasts when they were treated with
H2O2 for 24 hours (Figure 4.13). Reduced mitochondrial load in cells from the schizophrenia group was only observed in neurosphere-derived cells when treated with a toxic concentration of H2O2 (Figure 4.10). There was no difference between the schizophrenia and control groups in any other parameter for both cell types.
Table 4.1: Summary of p-values from all experiments. Summary of p-values from statistical analysis performed on the data. Time: Analysis performed after various treatment periods of 1 mM H2O2. Concentration: Analysis performed after treatment various H2O2 concentrations for 2 hours. Both cell types showed impaired GSH levels when treated with H2O2 for 24 hours. There was a significant increase in the mitochondrial load of neurosphere-derived cells, but not skin fibroblasts. Green indicates statistically a significant difference between the schizophrenia and control group.
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4.4 Discussion 4.4.1 Overview We have performed various functional assays to assess the effect of oxidative stress and mitochondrial function in neurosphere-derived cells and skin fibroblasts from the schizophrenia group. We found no evidence of an altered energy state in cells from the schizophrenia group, in either normal culture conditions or under oxidative stress (Table 4.1). There was also no significant difference in cell death under any of the assayed culture conditions (Table 4.1). This would suggest there is no functional difference between the cells as a result of dysfunctional mitochondrial function or antioxidant capacity. However, the GSH content of both cell lines from the schizophrenia group was significantly lower than that of control cells when incubated with H2O2 for 24 hours (Table 4.1). There was also a significant increase in the mitochondrial load of neurosphere-derived cells from the schizophrenia group when exposed to a toxic dose of H2O2 (Table 4.1), which was not the result of changes in cell size (Figure 4.1), suggesting some dysfunction in the mitochondria and GSH system.
4.4.2 Effect of oxidative stress on cell death in schizophrenia There was no significant difference in cell death under normal culture conditions, which fits with a previous report of unaltered cell death in the olfactory neuroepithelium in schizophrenia (McCurdy et al. 2006), or during peroxide treatment in either cell line. This was confirmed by both LDH assay and Annexin V staining (Figure 4.1, 4.3 and 4.4). The latter experiment involved multiple H2O2 concentrations and incubation times. We previously hypothesized that a mitochondrial dysfunction in cells from patients with schizophrenia would result in them being more susceptible to oxidative stress (Chapter 1.5). Our hypothesis was based on previous reports of impaired oxidative state in schizophrenia, where disruptions in oxidative phosphorylation and antioxidants were indicated (Do et al. 2000; Yao et al. 2004; Dietrich-Muszalska et al. 2005; Kropp et al. 2005; Zhang et al. 2005). These present findings do not support our hypothesis of increased susceptibility to oxidative stress.
We did observe a significant difference in GSH content after 24 hour incubation with H2O2, which was observed in both cell lines (Figure 4.13). Reduced levels and synthesis of GSH have been previously reported in the cerebral spinal fluid and plasma of patients with
94 schizophrenia (Do et al. 2000; Zhang et al. 2005). Reduced levels of GSH and GSH synthesis has also been reported in fibroblasts from patients with schizophrenia upon oxidative stress (Gysin et al. 2007).
GSH is the predominant low-molecular weight thiol within mammalian cells, which in the reduced state serves as the most abundant intracellular thiol-based antioxidant controlling the cellular redox state (Das and White 2002). Though there was no significant difference in GSH content under normal culture conditions and response under toxic H2O2 concentrations, the effect of a 24 hour exposure does suggest an impairment of the oxidative stress response of patients with schizophrenia. The manifestation of impaired GSH response in both neural-derived and non-neural derived cells, would suggest this may be a systemic phenomenon. The altered response in the GSH system did not translate into increased cell death, because impaired oxidative stress did not result in increased apoptosis rates in the schizophrenia cell lines (Figure 4.1, 4.3 and 4.4).
The altered response from 24 hour exposure indicates this impairment would manifest under conditions related to schizophrenia pathophysiology, where brain specific insults leading to oxidative stress could result in abnormal synapse formation (Reddy and Yao 1996; Grima et al. 2003; Gysin et al. 2007). The functional implications of impaired long- term antioxidant potential of these cells are hard to predict, but it is reasonable to assume multiple cell functions would be affected as a result.
Unaltered cell death during oxidative stress indicates the effect of the altered GSH response does not significantly affect the cell ability to survive. This fits with the morphology observed in the schizophrenic brain, which does not bear signs of severe oxidative stress (Madrigal et al. 2006). When comparing the global gene expression profiles of these cells there was no clear indication of an impaired antioxidant defense mechanism under normal culture conditions (Chapter 3). The observed effect is therefore likely related to signaling mechanisms regulating the antioxidant defense mechanisms of the cell.
A GSH deficiency has been linked with a reduction in synaptic processes in neurons, and is consistent with reports of reduced dendritic spine densities in the brain of patients with schizophrenia (Garey et al. 1998; Glantz and Lewis 2000; Grima et al. 2003). These
95 abnormalities would also be consistent with dysfunctions of the focal adhesion signaling pathways, as reported in Chapter 3 (Thiery 2003; Wiesner et al. 2005; Romer et al. 2006), implying abnormal development and synapse formation is the converging outcome of these dysfunctions.
4.4.3 Effect of oxidative stress on mitochondrial function in schizophrenia Of the mitochondrial function assays performed in this study, only neurosphere-derived cells from the schizophrenia group demonstrated any significant mitochondrial dysfunction. We did not observe a significant difference in reduction of mitochondrial membrane potential, as previously described in cultured cortical neurons of mice (Grima et al. 2003). Though excessive dopamine induces oxidative stress (Grima et al. 2003), the different response from H2O2 treatment on mitochondrial membrane potential suggest a stress response mechanisms are initiated. Only when the neurosphere-derived cells were exposed to high concentrations of H2O2 was there a significant difference between the control and schizophrenia cell lines. This difference in response was only apparent when measuring the mitochondrial load of the cells, where toxic H2O2 concentrations resulted in significantly more mitochondria in the schizophrenia cell lines with no difference in cell size (Figure 4.10 and Figure 4.12). There was no other sign of disruption in energy metabolism in the cells, as measured by ATP synthesis and mitochondrial membrane potential (Figure 4.5, 4.7 and 4.8), indicating no energy impairment in the schizophrenia cell lines when compared to controls. The significant increase in mitochondrial load observed in neurosphere-derived cells from the schizophrenia group, suggests a significant increase in mitochondrial biogenesis during severe oxidative stress. No differences in mitochondrial function were detected in the skin fibroblasts, indicating this is a tissue-specific phenomenon.
The regulation of mitochondrial biogenesis involves a complex network of signalling and regulatory pathways which is not fully elucidated (Attardi and Schatz 1988; Neupert 1997; Nisoli et al. 2004a). The increase observed in mitochondrial load in neurosphere-derived cells is probably either the result of a compensatory reaction to the oxidative stress, or there is a dysfunction in the mechanisms regulating mitochondrial biogenesis. The change in mitochondrial load of the cell population, with no change in overall mitochondrial membrane potential, indicates the increased mitochondrial load does not change the cell’s ability to generate ATP (Figure 4.8 and 4.10). This suggests the increased mitochondrial
96 load may be the result of a mechanism to compensate for increased loss of mitochondrial membrane integrity (Nisoli et al. 2004a). This results in maintenance of ATP synthesis at a level comparable to that of the control cell lines.
Reduced GSH levels prevent inhibition of complex I in the oxidative phosphorylation pathway which occurs during oxidative stress through a nitric oxide-mediated pathway (Clementi et al. 1998; Hsu et al. 2005; Maneiro et al. 2005). Reduced GSH levels in conjunction with altered mitochondrial load could therefore be related to complex I inhibition in the neurosphere-derived cells. Though these changes were not observed during the same experimental conditions to when this interaction has been reported(Clementi et al. 1998; Hsu et al. 2005; Maneiro et al. 2005), such an interaction between GSH and complex I is relevant because a disruption of complex I is reported in the brains of patients with schizophrenia (Ben-Shachar et al. 1999; Bubber et al. 2004; Karry et al. 2004; Prabakaran et al. 2004). Although there was not a strong signal of a complex I dysfunction in the array study to directly support this interaction in our system (Chapter 3), a dysfunction in the GSH response in conjunction with a complex I dysfunction might signal an increase in mitochondrial biogenesis to maintain energy homeostasis in the cells (Nisoli et al. 2004a; Nisoli et al. 2004b). This phenomenon was however only observed in the neurosphere- derived cells, whereas the skin fibroblasts only showed altered levels GSH in response to oxidative stress. No difference in cell death in either cell type was demonstrated which suggests that there is no obvious indication of a complex I dysfunction in the skin fibroblasts. Though we found no evidence of altered mitochondrial function in the skin fibroblasts, we did observe an impaired GSH response. The reason for this discrepancy is unclear, but shows there are functional differences between neural-derived and non-neural tissue in schizophrenia.
4.4.4 Summary Using a variety of assays, we have evidence of a limited dysfunction in oxidative stress response and mitochondrial function in cells from patients with schizophrenia in vitro. Our hypothesis of increased susceptibility to oxidative stress in the schizophrenia groups was not supported, as there was no increase in cell death as a result of the various oxidative stress treatments. However we did find a difference in response to oxidative stress in both cell types from the schizophrenia group, where cell lines from the schizophrenia group had
97 an impaired antioxidant response. This was manifested by lower levels of GSH after long- term exposure to H2O2. The impaired GSH response in both cell types suggests this may be a systemic phenomenon in schizophrenia, and fits with previous reports of impaired GSH response in schizophrenia. We did not find an obvious difference in the energy state of cells from the schizophrenia group, however there was a significant increase in mitochondrial load in neurosphere-derived cells from the schizophrenia group when these cells were exposed to a toxic concentration of H2O2. Though this was only observed in the neural-derived cell model, it supports our hypothesis of a mitochondrial dysfunction in schizophrenia. Though we have not shown any obvious interaction between a hypothesized complex I dysfunction and GSH in our data, which would cause the observed change in mitochondrial function in the neurosphere-derived cell, it is prudent to assume this may be the case (Clementi et al. 1998; Hsu et al. 2005; Maneiro et al. 2005). If there is a complex I dysfunction in these cells under oxidative stress, we hypothesize that the dysfunction is only present in neural tissue from patients with schizophrenia.
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Chapter 5: Olfactory neurosphere formation in schizophrenia
5.1 Introduction Olfactory neurospheres contain multipotent stem cells that can differentiate into neurons and glia (Murrell et al. 2005). Their ability to differentiate into neural populations provides us with an in vitro cell model to study neurogenesis in neurodegenerative and neurological disorders. It is hypothesized that adult neurogenesis is disrupted in schizophrenic patients, supported by studies where the increased proliferation and altered ratios of neural precursors and neural populations in the olfactory neuroepithelium has been reported (Feron et al. 1999; Arnold et al. 2001; McCurdy et al. 2006). Indications are that neurogenesis in the olfactory neuroepithelium may be increased in schizophrenia, which supports the hypothesis that adult neurogenesis is disrupted in the brain of patients with schizophrenia (Eriksson 2006; Toro and Deakin 2006; Reif et al. 2007). Olfactory biopsies have also shown reduced attachment to plastic, but not glass (Feron et al. 1999; McCurdy 2005), indicating possible dysfunctional adhesion in vivo. By studying the dynamics of neurosphere formation and the differentiation of olfactory neurosphere-derived cells, we aim to investigate whether in vitro neurogenesis is disrupted in schizophrenia. This chapter aims to establish whether: 1) neurosphere formation is altered in samples from schizophrenic patients, 2) whether the ability of neurosphere-derived cells to differentiate into neurons and glia are different in samples from schizophrenic patients.
Adult neural stem cells are characterised by their ability to self renew and differentiate into neurons, astrocytes and oligodendrocytes (Reynolds and Weiss 1992; Weiss et al. 1996). Due to the lack of any specific marker for adult neural stem cells, their ability to form neurospheres has been the assay of choice when detecting and studying stem cell proliferation (Reynolds and Weiss 1992; Reynolds and Rietze 2005). By growing adult neural stem cells under specific growth conditions, they can be differentiated into multiple cell types, including neurons, providing us with an in vitro cell model for neurogenesis (Murrell et al. 2005; Zhang et al. 2006b; Xu et al. 2007). To assess neurosphere formation in schizophrenia we will perform the neurosphere assay on these cells, where the number, size and cell densities of the neursopheres formed are compared. We will also perform differentiation assays on these cells where the number of differentiated neurones and glia under different differentiation conditions are compared. This will allow us to compare the dynamics of neurosphere formation and the effect of extrinsic factors in neural
100 differentiation in schizophrenia. Olfactory neursopheres therefore present us with a promising in vitro model for studying neurogenesis in schizophrenia.
5.2 Methods 5.2.1 Samples Neurosphere-derived cells were grown as described in Chapter 2. Additionally, cells were allowed to grow to confluency, at which time the media was changed to DMEM-ITS for 3 days. Confluency of the cultures was assessed using an inverted microscope. Harvesting was performed as described in chapter 2.1.3, and cell concentrations were quantified using a hemacytometer as described in chapter 2.1.4.
5.2.2 Neurosphere assay Harvested neurosphere-derived cells were plated onto 24-well plates (Nunc™, Denmark) coated with 0.85μg/cm2 poly-L-lysine (PLL) at 37°C for 3 hours. Cells were seeded at a density of 10,000 cell/cm2, and grown in DMEM-ITS with 50 ng/ml epidermal growth factor (EGF) and 25 ng/ml basic fibroblast growth factor 2 (bFGF). Medium was changed every 2 days by aspirating and replacing media with fresh DMEM-ITS/EGF/FGF. At which time detached neurospheres were counted and measured in the aspirated media using a micrometer (10 mm/100 scale) eyepiece. A neurosphere was defined as an aggregate at least 70 μm in diameter, spherical and optically dense. The neurosphere assay was performed twice, and 3 wells were established for each sample in both experiments. Neurospheres collected from every harvest were used to compare the formation of neurospheres across the experiment. Floating neurospheres were harvested during media changes, and the cumulative total of neurospheres formed during the experiment was calculated. This was done by adding the number of neurospheres harvested every day. As an example, the cumulative number of neurosphere at 8 days in vitro, include the number of neurospheres harvested at 2 days, 4 days and 6 days in vitro. This was done for each time point and cell line in each experiment, giving us the cumulative neurosphere formation over 14 days in vitro. This was repeated for each of the two experiments. The size of neurospheres collected from every harvest was used to compare the size of neurospheres across the experiment. Floating neurospheres were harvested during media changes, and the average size of the neurospheres formed from each cell line was calculated. This was done for each time point and cell line in each experiment, giving us the average size of the
101 neurospheres formed every 2 days during the experiment. This was repeated for each of the two experiments.
5.2.3 Neurosphere density To assess cell density of the olfactory neurospheres we harvested neurospheres and determined their cell density, via nuclear staining, and analysed via 3D microscopic imaging. The harvested neurospheres were centrifuged at 300 x g for 5 minutes, and aspirated. Neurospheres were then fixed with 4% paraformaldehyde (PFA) in PBS for 10 minutes. The neurospheres were then washed and incubated with 10 µg/ml of 7-AAD in PBS for 30 minutes at room temperature protected from light. Samples were then centrifuged at 300 x g for 5 minutes, aspirated and resuspended in 90% glycerol in PBS before being mounted on a coverslip. Z-stacks of the neurospheres were generated using AxioImager™Z1 with an ApoTome™ module (Carl Zeiss Inc., Germany). The ApoTome™ uses grid projection to remove out-of-focus information and improves signal to noise ratio, allowing acquisition of optical sections for 3D reconstructions (Bauch and Schaffer 2006). The images were analysed using AxioVision™ software (Carl Zeiss Inc., Germany). By analysing nucleic density of 3 neurospheres for each cell line, the density of nuclei was assessed.
Due to the labour intensive nature of 3D reconstructions and the number of samples in the study, the neurosphere cell density was only assessed at one time point. The density of nuclei in the neurospheres was used as a measure of cell density within the neurospheres. We were unable to take clear images through the entire neurospheres because of increased signal-to-noise ratio and loss of signal. Cell density within the neurospheres was therefore assessed using the optical dissector principle on the neurosphere mid-section (Nyengaard 1999). Nuclei were not counted if they were only partially present in the section. Nuclear density was calculated using the formula: Nv = Q/((Section area) x Thickness), where Nv = nuclear density, Q = number of nuclei exclusively in the section, Section area = area of neurosphere z-slice and Thickness = thickness of section. Neurosphers generated at 6 days in vitro were chosen to compare cell density of neurospheres, because their formation was at its highest rate at 6 and 8 days in vitro (Figure 5.2) and they also showed a difference in mean size (Figure 5.3). This allowed us to asses whether the decreased neurosphere size was the result of reduced density, or cell numbers.
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5.2.4 Differentiation assay Harvested neurosphere-derived cells were plated onto 96-well plates (MatriCal, USA) coated with poly-L-ornithine (PLO) and laminin, at a concentration of 100,000 cell/cm2. Plates were coated with 28µg/cm2 PLO overnight at 37C, washed 3 times with HBSS then coated with 2.9µg/cm2 laminin for 3 hours at 37°C. Plates were washed and stored in HBSS before the assay was initiated. Cells were then cultured in DMEM-ITS + 50 ng/ml neural growth factor (NGF) (NGF/ITS)or Neurobasal medium with 0.5 x B-27 and 50 ng/ml NGF (NGF/B27). Media were changed every 2 days and cultures were fixed after 6 days in vitro. Cells were fixed with 4% PFA in PBS for 10 minutes, then washed and stored in PBS + 0.1% NaAzide at 4°C. For both differentiation experiments, 3 wells were established for each cell line and the experiments performed three times.
5.2.5 Immunocytochemistry 5.2.5.1 Antibody staining Fixed cells were blocked and permeabilised in blocking solution of 2% bovine serum albumin (BSA), 0.1% triton X-100 and 0.1% NaAzide in PBS for 30 minutes at room temperature. Primary antibodies were applied in blocking solution (2% BSA, 0.1% triton X-100, 0.1% NaAzide, PBS) for 1 hour at room temperature. Primary antibodies were β- Tubulin III (1:1000, Covance, PRB-435P), Neurofilament 200 (NF200) (1:200, Sigma, N0142), Nestin (1:200, Sapphire Biosciences, ab6320), CNPase (1:200, Sapphire Biosciences, ab6319) and glial fibrillary acidic protein (GFAP) (1:500, Sapphire Biosciences, ab7260). Cells were then washed 3 times in blocking solution. Secondary antibodies; goat anti-mouse IgG conjugated with Alexa Fluor 488 (Invitrogen™, A11001), goat anti-rabbit IgG conjugated with Alexa Fluor 647 (Invitrogen™, A11009), were applied at 10 μg/ml for 1 hour at room temperature followed by two washes in PBS. The cell nuclei were stained with DAPI (5 μg/ml, Invitrogen™, D1306) in 0.1% NaAzide and PBS for 10 minutes followed by another wash in 0.1% NaAzide/PBS.
5.2.5.2 Quantification The differentiation experiment resulted in 324 wells requiring analysis, from 18 cell lines, 3 wells per cell line, 2 experimental conditions all repeated 3 times. This required an automated analysis which was achieved using an Opera™ confocal imaging reader (Evotec
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Technologies, USA). 20 images were taken by James Longden from each well resulting in 6480 images. The images were then analyzed and quantified by Dr. Carla Toland using Acapella™ image analysis software (Perkin Elmer®, USA). The software counted and identified cells by their DAPI nuclear staining. It then analyzed and counted the immunofluoresence for each of the 5 respective cell-type markers.
5.2.6 Statistical analysis As described in Chapter 2.1, the samples sizes for these experiments were 9 control and 9 patient neurosphere-derived cell lines. For replicate experiments, triplicate measurements for each cell line in each experiment were averaged, and the averages compared statistically by two-way ANOVA. Otherwise, triplicate measurements for each cell line were averaged, and the averages compared statistically by performing a one-way ANOVA. ANOVA was performed using SPSS© 15.0 software. Data are expressed as mean ± SEM. α = 0.05 was chosen to assign significance in all statistical tests.
5.3 Results 5.3.1 Neurosphere assay When cultured under neurosphere-forming conditions for at least 2 days (Chapter 5.2.2), olfactory neurosphere-derived cells from human olfactory mucosa formed neurospheres. After 4 days in vitro we found detached neurospheres which were harvested, counted and measured (Figure 5.1). This was done every second day until the experiment was ended at 14 days in vitro, at which time the cultures had become sparsely populated with no neurospheres observed.
Figure 5.1: Formation of olfactory neurospheres. A-C: Formation of spherical and optically dense olfactory neurospheres forming in epidermal growth factor (EGF) and basic fibroblast growth factor (bFGF) on plastic wells coated with PLL. A and B: Formation and expansion of star like structures, from which neurospheres are formed. C: Harvested neurospheres after detaching in the neurosphere assay. Scale bar = 100 µm.
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5.3.1.1 Neurosphere formation The average cumulative neurosphere formation was similar between the two groups and similar between replicates. Cumulative neurosphere formation from the control and schizophrenia group was compared (Figure 5.2). Neurospheres were collected every second day at each change of medium. Detached neurospheres were first observed after 4 days in vitro, and cultures had stopped producing them after 14 days in vitro (Figure 5.2) ANOVA indicated there was no significant difference between the two groups in the mean number of cumulative neurospheres formed (F1,16 = 0.382, p = 0.545). There was no significant difference within the replicates (F1,16 = 0.118, p = 0.735).
Figure 5.2: Cumulative neurosphere formation. Average cumulative neurosphere formation ± SEM for each day in vitro, over 14 days in vitro. Neurospheres started detaching after 4 days in vitro, and neurosphere formation seized after 14 days in vitro. There is no significant difference in cumulative neurosphere formation between schizophrenia and controls (p = 0.545)
5.3.1.2 Neurosphere size The size of neurospheres formed was different between the two groups, where the schizophrenia cell lines tended to form smaller neurospheres (Figure 5.3). The size of neurospheres collected from every harvest was compared (Figure 5.3). The diameter of neurospheres collected every second day at each change of medium (Chapter 5.3.11) was
105 measured. ANOVA indicated there was a significant difference between the two groups in the mean diameter of neurospheres formed (F1,16 = 6.550, p = 0.021). There was no significant variation within the replicates (F1,16 = 0.212, p = 0.652).
Figure 5.3: Average neurosphere diameter. Average neurosphere diameter ± SEM of neurospheres formed. Neurospheres started detaching after 4 days in vitro, and neurosphere formation seized after 14 days in vitro. There is a significant difference in average neurosphere size between schizophrenia and controls (p = 0.021).
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5.3.1.3 Neurosphere density
Figure 5.4: Nuclear staining of neurosphere. A z-stack slice through a neurosphere stained with 7-AAD.Bright spots represent stained nuclei in the neurosphere. Scale bar = 10 µm.
At 6 days in vitro olfactory neurospheres generated from schizophrenia cell lines had significantly higher cell density when compared to control cell lines (Figure 5.5). The data are presented as 10-4 cells per µm3. The mean ± SEM for the two groups was as follows: control, 1.154 ± 0.096, n = 9; schizophrenic, 1.705 ± 0.172, n = 9 (Figure 5.5). ANOVA indicated there was a significant difference in the average cell densities of neurospheres formed between the two groups (F1,16 = 8.223, p = 0.012).
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Figure 5.5: Neurosphere cell density. Average cell density of neurospheres formed after 6 days in vitro ± SEM. ANOVA indicated a significantly higher average cell density in olfactory neurospheres generated from the schizophrenia group when compared to the control group (p = 0.012).
5.3.2 Differentiation assay When neurosphere-derived cells were grown with NGF/ITS or NGF/B27 for 6 days on PLO and laminin, there were cells expressing NF200, β-Tubulin III, nestin, CNPase and GFAP (Figure 5.6). There was a significant difference in phenotype between the schizophrenia group and control group in the differentiation assays (Table 5.1 and Figure 5.7) Data is presented in Table 5.1 as the percentage of cells imaged which stained for the respective marker for the control group (n = 9) and the schizophrenia group (n = 9). In the NGF/ITS assay there was significantly less CNPase (p = 0.021) positive cells in the schizophrenia group (Table 5.1). In the NGF/B27 assay there were significantly less β-Tubulin III positive cells (p = 0.007) and significantly more NF200 (p = 0.044) and GFAP (p = 0.016) positive cells (Table 5.1) in the schizophrenia group.
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Figure 5.6: Phenotype of cells after differentiation. Differentiation of neurosphere-derived cells for 6 days in vitro. A, D, G, J and M shows nuclei labeled with DAPI (blue). B: Cells labeled with NF200 (green). E: Cells labeled with β-Tubulin III (red). H: Cells labeled with nestin (green). K: Cells labeled with GFAP (red). N: Cells labeled with CNPase (green). C,F, J, L and O: Merged images of DAPI labeled nuclei (blue) and the cell staining (green or red).Scale bar = 10 µm.
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Table 5.1: Differentiation assay statistics. Statistical comparisons of the differentiation assay. The data are presented as mean percentage of cells positive ± SEM. The p-values were obtained by one-way ANOVA, comparing the effect of disease state on the respective cell-markers from schizophrenic patients and controls. Green indicates a statistically significant difference between the groups was found in the assay.
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Figure 5.7: Differentiation assay. A and B: Data from the differentiation assays. Staining for each marker tested is shown as mean percentage of cells positive ± SEM. A: For cells grown in NGF/ITS there was a significant difference in number of cells positive for CNPase (p = 0.021) between the schizophrenia groups and controls. B: For cells grown in NGF/B27 there was a significant difference in number of cells positive for NF200 (p = 0.044), β-Tubulin III (p = 0.007) and GFAP (p = 0.016) between the schizophrenia and controls groups.
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5.4 Discussion 5.4.1 Overview By measuring various parameters related to neurosphere formation, differences in neural stem cell dynamics and neurogenesis in these cells from schizophrenic patients can be assessed. The neurosphere assay therefore provides an in vitro measure of neurogenesis and neural stem cell dynamics (Reynolds and Weiss 1992; Reynolds and Rietze 2005; Jensen and Parmar 2006). The main finding in this present study was no significant difference in cumulative neurosphere formation between cell lines from schizophrenic patients and controls (Figure 5.2). However, a significant difference in the size and density of neurospheres formed in the schizophrenia cell lines was demonstrated, where they tended to form significantly smaller and more densely packed neurospheres. The differentiation assays showed cell lines from the schizophrenia groups had altered phenotype when compared to controls. In the NGF/ITS assay there were significantly fewer CNPase positive cells while the NGF/B27 assay resulted in significantly less β-Tubulin III positive, and significantly more NF200 and GFAP positive cells (Table 5.1).
5.4.2 Neurosphere formation in schizophrenia There are limitations to the interpretation of the neurosphere assay because of the heterogenous nature of neurospheres and the variability of the assay in different culture conditions (Jensen and Parmar 2006). Neurospheres contain a mixed population consisting of neural stem cells, proliferating neural progenitors and postmitotic neurons and glia (Suslov et al. 2002; Parmar et al. 2003; Wetzig 2007). Because neurospheres consist of a mixed cell population, the neurosphere assay is assessing the dynamics of this mixed population of precursor and neural stem cells. Neurosphere formation is therefore not a direct measure of stem cell frequency or proliferation. The sensitivity of the assay to variations in neurosphere culture conditions such as cell density, growth factors and passaging, poses a problem for inter-study comparison (Tropepe et al. 1999; Arsenijevic et al. 2001; Caldwell et al. 2001; Morshead et al. 2002; Irvin et al. 2003; Jensen and Parmar 2006; Wetzig 2007). This does not pose a problem in our study because culture conditions were similar in all the experiments. This reproducibility was confirmed by the fact that we saw no replicate effect on neurosphere formation (Chapter 5.3.1.1 and Chapter 5.3.1.2). In our study, the neurosphere assay is a measure of neural stem cell and progenitor cell proliferation, where the number and density of spheres formed was indicated. This is a
112 measure, but certainly only an indirect measure, of what could be occurring to contribute to disease pathophysiology.
The changes observed in neurosphere formation in the schizophrenia group suggest that neurogenesis in schizophrenia may be disrupted. The tendency of the cell lines from the schizophrenia group to form smaller and denser spheres suggests there is a disruption of cell-cell interaction and adhesion and fits with altered adhesion in schizophrenia (Mahadik et al. 1994; Barbeau et al. 1995; Feron et al. 1999). These observations could also be explained by increased cell proliferation rates (Feron et al. 1999; McCurdy et al. 2006), resulting in more but smaller cells in the neurospheres from schizophrenic patients. These changes in neurosphere size and cell density therefore indicate changes in integrin and actin cytoskeleton signaling in these cells as well as possible cell cycle alterations (Brakebusch and Fassler 2003; Wiesner et al. 2005; McCurdy et al. 2006), when grown under sphere forming conditions. Though the increased cell density and smaller size of neurospheres from the schizophrenia group agree with increased cell proliferation rates, the microarray data (Chapter 3) did not suggest any significant changes in cell cycle signalling in these cells. These data do however support a dysfunction of the actin cytoskeleton as reported in Chapter 3. Though these changes in cell density and neurosphere size are not directly comparable to the array data (Chapter 3) due to differences in cell states, they both point to changes in the focal adhesion pathway in olfactory neurospheres. This suggests cell-cell interaction, focal adhesion and actin cytoskeleton of olfactory neurons are disrupted.
5.4.3 Differentiation of neurosphere-derived cells in schizophrenia It is assumed the percentage of stem cells in neurospheres are low, with estimates of 0.5% or less having been reported (Wetzig 2007). The majority of cells in neurospheres therefore consist of neural progenitors, neurons and glia (Suslov et al. 2002; Parmar et al. 2003; Wetzig 2007). Differentiation of neurons and glia in the adult brain is regulated by symmetric and asymmetric cell division of progenitors and neural stem cells, in order to amplify the appropriate cell numbers (Noctor et al. 2008). Neural differentiation of these stem cells is regulated by multiple transcription factors, which act by inhibiting alternative cell fates and promoting lineage specific differentiation (Morrison 2001). Neurotrophins, which are growth factors regulating differentiation, proliferation, and synaptic plasticity in
113 the nervous system, are also used to manipulate differentiation of these cells in vitro (Arevalo and Wu 2006; Hempstead 2006).
Both differentiation assays generated a high percentage of nestin positive cells (50%-100%), which is an intermediate filament expressed in neural stem cells and progenitors This is in agreement with previous reports stating that there is a large pool of progenitors in neurosphere-derived cultures (Figure 5.7 and Table 5.1) (Valtz et al. 1991; Tohyama et al. 1992; Mujtaba et al. 1998; Messam et al. 2000; Wetzig 2007). The calculated percentages of positive cells suggests that many nestin positive cells are also likely to be double positive, which must be the case in the NGF/ITS assay where all the cells were nestin positive(Figure 5.7). Cells double-positive for nestin and neuronal or glial markers may indicate these cells retain early neuronal or glial characteristics, and that they are in a very early state of differentiation (Rieske et al. 2007; Yamaguchi et al. 2008).
The NGF/ITS assay generated differentiation of both neurons and glia, where β-Tubulin III and CNPase staining were the most pronounced markers with a few cells expressing GFAP and neurofilament 200 (Figure 5.7 and Table 5.1). β-Tubulin III is a neuronal marker expressed in neurons and neuronal precursors (Ferreira and Caceres 1992; Roskams et al. 1998; Maurer et al. 2008) while CNPase is expressed by mature oligodendrocytes (Edwards and Braun 1988; Thompson 1992). There are also a few cells expressing GFAP (Figure 5.7 and Table 5.1) which indicates there are precursors differentiating into astrocytes (Krum and Rosenstein 1999).
A similar differentiation pattern was observed in the NGF/B27 assay, where double staining with nestin was indicated. However, using NGF/B27 resulted in a much higher percentage of neurofilament 200 (NF200) positive cells (Figure 5.7 and Table 5.1). Neurofilaments are only found in mature neurons (Lee et al. 1993; Lee and Cleveland 1996) indicating cell lines from the schizophrenia group had increased neuronal differentiation under these conditions. Lower levels of CNPase positive cells in the schizophrenia group were only observed in the NGF/ITS assay, while the NGF/B27 resulted in significantly more GFAP and neurofilament with fewer β-Tubulin III positive cells (Figure 5.7 and Table 5.1). Though β-Tubulin III expression is expressed by neurons and neuronal precursors, there are reports of β-Tubulin III expression in glial precursors
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(Katsetos et al. 2001; Katsetos et al. 2002; Katsetos et al. 2003). However as these glial precursors are neoplastic, it is unlikely this would impact on our study (Katsetos et al. 2003). The increase in neurofilament 200 and GFAP positive cells therefore indicates that neuronal maturation of neurosphere-derived cells from the schizophrenia groups is promoted by NGF/B27, as seen by reduced CNPase positive cells.
The B-27 medium supplement is specifically formulated to improve neuron growth and differentiation (Brewer et al. 1993; Brewer 1995; Svendsen et al. 1995), which would be expected to improve neuron differentiation rates when compared to ITS supplemented media. The presence of cells positive for neuronal and glial markers after the NGF/B27 differentiation assay confirm these cells are, in addition to stem cells, a mix of neuronal and glial progenitors (Figure 5.7 and Table 5.1) (Mujtaba et al. 1998; Messam et al. 2000; Murrell et al. 2005). It is also conceivable there is a population of bipotent progenitors, capable of differentiating into either neurons or glia.
These changes of in vitro differentiation are consistent with observed differences of neuronal population in olfactory neuroepithelium (Arnold et al. 2001). As these cells were differentiated under controlled culture conditions, it is prudent to assume the differences are caused by dysfunctional intrinsic factors regulating differentiation. This shows that the cell lines from the schizophrenia group are more responsive to the extrinsic signals in the differentiation assay, and that the dynamics of neurogenesis is altered in schizophrenia,
5.5 Summary We have shown that there are differences in olfactory neurosphere formation and differentiation in schizophrenia. Neurospheres formed from cell lines in the schizophrenia group tend to be smaller and more densely packed (at 6 days in vitro). Though there are limitations to the finding regarding neurosphere cell density, because we were unable to assess cell density of neurospheres from all time points, this is an interesting result because it suggests a significant disruption of neurosphere dynamics in schizophrenia. It suggests there are changes in the actin cytoskeleton and adhesion in these cells when grown in neurosphere-forming conditions. We have also shown differences in differentiation into both neuronal and glial cell types in the schizophrenia group. Though this is an in vitro system, and the differentiation assay does not necessarily reflect what occurs in vivo, it
115 confirms our hypothesis that olfactory neurogenesis is disrupted in schizophrenia. We have therefore shown that the dynamics of in vitro neurogenesis is disrupted in schizophrenia.
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Chapter 6: Focal adhesion in schizophrenia
6.1 Introduction Focal adhesion complexes are large protein aggregates consisting of at least 32 structural and signalling components (Miyamoto et al. 1995; Yamada and Miyamoto 1995). These complexes act by linking the intracellular cytoskeleton to the extracellular matrix, allowing the cells to integrate and respond to extracellular stimuli (Romer et al. 2006).
Previously we reported significant disruption in the focal adhesion signalling pathway of neurosphere-derived cells from the schizophrenia group (Chapter 3). There are also reports of altered adhesion in schizophrenia (Mahadik et al. 1994; Barbeau et al. 1995; Feron et al. 1999). The specific hypothesis to be tested here is that cells from patients with schizophrenia have a disruption of the focal adhesion pathway, resulting in impaired adhesion. The cells’ ability to adhere and repulse other cells is regulated through complex intracellular signalling pathways (Thiery 2003; Kruger et al. 2005; Halloran and Wolman 2006; Romer et al. 2006; Benvenuti and Comoglio 2007). By measuring the cells’ ability to attach, we can assess whether the composition of the focal adhesion complex and composition of cell surface molecules are changed in cells from patients with schizophrenia. Fibronectin and collagen are extracellular matrix components that promotes focal adhesion formation through engagement of integrins on the cell surface (Pierschbacher and Ruoslahti 1984; Zhang et al. 2006c). By growing the cells on both fibronectin and collagen IV respectively, we can assess interaction of integrins with collagen and, specifically α5β1- integrin, with fibronectin on the cell surface.
A disruption of FAK transcript expression was reported in Chapter 3 which could affect cell adhesion in the neurosphere-derived cells. Focal Adhesion Kinase (FAK) is a non- receptor protein tyrosine kinase that localizes to focal adhesions. FAK plays a central role in multiple cellular functions such as differentiation, migration, cell death and cell cycle. FAK is activated by phosphorylation events in response to several stimuli and subsequently activating downstream signaling events (Romer et al. 2006; Vadali et al. 2007; Tilghman and Parsons 2008). Vinculin, an intracellular linker protein, is another important component of the focal adhesion complex and facilitates interaction between integrins and the cytoskeleton (Miyamoto et al. 1995; Yamada and Miyamoto 1995; Romer et al. 2006). In Chapter 3 we demonstrated reducecd transcript levels in neurosphere-derived cells from the
118 schizophrenia group. We therefore hypothesise that focal adhesion is impaired in cells from patients with schziophrenia.
The aim of this study was to examine the hypothesis presented above using functional assessment of cell adhesion and quantification of proteins in the focal adhesion complex. The experiments, which compared cells from patients with schizophrenia and controls, were: 1) cell attachment rates to two extracellular matrices in order to assess focal adhesion, 2) quantification of FAK and vinculin by ELISA and by quantitative immunocytochemical analysis. This study also compared neurosphere-derived cell and skin fibroblasts from patients with schizophrenia and controls.
6.2 Methods 6.2.1 Samples Neurosphere-derived cells and skin fibroblasts were grown to approximately 80% confluency as described in Chapter 2. Of skin fibroblasts, only 8 control samples were grown successfully. Harvesting was performed as described in Chapter 2.1.3, and cell concentrations were assessed using a hemacytometer as described in Chapter 2.1.4.
6.2.2 Adhesion assay Crystal violet is a cellular stain. The assay measures the amount of crystal violet which has accumulated into fixed cells which have attached to the surface of the culture plate at the time of the assay. The dye is then dissolved from the cells to provide an estimate of attachment (Thompson et al. 1993; Rosfjord et al. 1999). By comparing the ratio of cells attached to a common calibrator sample, we get a relative measure of cell adhesion.
Harvested cells (Chapter 6.2.1) were plated onto a 96-well precoated plate at a concentration of 45,000 cell/cm2 in DMEM/F12, 10%FBS and 1% Streptomycin/Penicillin. Plates were coated with 5 µg/cm2 collagen IV or 10 µg/cm2 fibronectin respectively, for 24 hours at room temperature. Excess harvested cells from each experiment were used for protein extraction (Chapter 6.2.3.1). The plates were then washed 3 times with HBSS before being used in the adhesion assay. Plated cells were allowed to attach for 2 hours at 37°C before unattached cells were carefully washed off and aspirated. Shorter incubation times resulted in dislodgement of the attached cells during the washing steps of the protocol.
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The cells were then fixed and stained with 50 µl of 0.05% crystal violet in 25% MeOH for
10 minutes. The plates were then gently rinsed with dH2O three times, and allowed to air dry. The dye was then dissolved by addition of 100µl of 0.1M NaCitrate in 50% EtOH, and absorbance was read at 540nm using a Synergy™ 2 microplate reader with Gen5™ data analysis software (BioTek Instruments, Inc). Values were normalised to a control sample within each assayed 96-well plate. The experiments were performed three times with triplicate measurements.
6.2.3 Focal Adhesion Kinase ELISA assay FAK plays a central role in multiple cellular functions and we previously reported disruption in the transcript expression of FAK (Chapter 3), which would affect cell adhesion in the neurosphere-derived cells. Here we study the levels of phosphorylated FAK in neurosphere-derived cells and skin fibroblasts from patients with schizophrenia.
6.2.3.1 Protein extraction Protein extraction was performed in the following manner. Harvested cells were washed twice with HBSS, and the supernatant was aspirated. The cells were then lysed by addition of 400 µl of lysis buffer (25 mM Tris HCl, pH: 7.6; 150 mM NaCl; 1% NP-40, 2 mM
EDTA; 50 mM NaF; 30 mM Na4P2O7; 0.2 mM Na3PO4; 0.1% SDS; 0.5% deoxycholate; 1 x Protease Inhibitor Cocktail (Roche, cat# 11 697 498 001)), and left for 30 minutes on ice. The lysates were transferred to microcentrifuge tubes and centrifuged at 13,000 rpm for 10 min at 4°C. The clear lysate supernatant was transferred to clean microcentrifuge tubes and stored at -80°C. The amount of protein was determined using the Dc Protein Assay Kit (Bio-Rad, Hercules, CA, USA) as described in Chapter 2.1.5. Each sample from the three experiments (Chapter 6.2.2) was measured in triplicate.
6.2.3.2 FAK ELISA Phosphorylated FAK in the protein extracts was measured using PhosphoDetect™ FAK (pTyr397) ELISA Kit (Chalbiochem®, CBA062). The protocol used was as described by the manufacturer. Briefly, samples and the supplied phosphorylated FAK standard were diluted with Standard Diluent Buffer to a total volume of 50 μl in the 96-well assay plate. 50 μl of rabbit anti-FAK (pTyr397) detector antibody was then added to each well. The plate was then covered and incubated for 2 hours at room temperature. The wells were then
120 aspirated and washed 4 times using the supplied Wash Buffer. 100 μl of anti-Rabbit IgG- HRP working solution was then added to each well. The plate was covered and incubated for 30 minutes at room temperature, followed by 4 washes using the supplied washing buffer. Colour development was initiated with the addition of 100 μl TMB to the wells. The plate was then incubated for 25 minutes at room temperature, in the dark. The reaction was stopped by addition of 100 μl stop solution to the wells. Absorbance was read at 450 nm and blanked against the lysis buffer. The concentration of Phosphorylated FAK was extrapolated from the standard curve, and the readings normalized against the protein concentration samples (Chapter 6.2.3.2). The triplicate sample for each cell line collected (Chapter 6.2.2) was measured once.
6.2.4 Focal adhesion complex By using vinculin as a measure of the focal adhesion complex, we can quantify and assess the focal adhesion complexes in cells by quantitative immunocytochemical analysis (Weber et al. 2002; Melton et al. 2007).
6.2.4.1 Antibody staining Excess harvested cells (Chapter 6.2.2) were plated onto a 96-well precoated plate at a concentration of 45,000 cell/cm2 in DMEM/F12, 10%FBS and 1% Streptomycin/Penicillin. Plates were coated with collagen IV or fibronectin as described in Chapter 6.2.2. Plated cells were allowed to grow for 2 hours at 37°C before unattached cells were carefully washed off and aspirated. The cells were then fixed with 4% PFA in PBS for 10 minutes, then washed and stored in PBS + 0.1% NaAzide at 4°C. Fixed cells were blocked and permeabilised in 2% bovine serum albumin (BSA), 0.1% triton X-100 and 0.1% NaAzide in PBS for 30 minutes at room temperature. Mouse anti-Vinculin (1:800, Sigma, V9131) was applied in blocking solution (2% BSA, 0.1% triton X-100, 0.1% NaAzide, PBS) for 1 hour at room temperature. Cells were then washed 3 times in blocking solution. Secondary antibody; goat anti-mouse IgG conjugated with Alexa Fluor 488 (Invitrogen™, A11001) was applied at 10 μg/ml for 1 hour at room temperature, followed by two washes in PBS. The cell nuclei were stained with DAPI (5 μg/ml, Invitrogen™, D1306) in 0.1% NaAzide and PBS for 10 minutes and the cells washed again. Each well was imaged three times, and morphometric analysis was performed as described previously (Weber et al. 2002). Briefly, the punctate expression of vinculin was counted in all the cells in each image. The data is
121 expressed as punctates per cell. Cells were imaged using an Opera™ confocal imaging reader (Evotec Technologies, USA), and the images quantified using Acapella™ image analysis software (Perkin Elmer®, USA) by James Longden. Triplicate wells for each cell line were assayed, analyzing 3 images from each well.
6.2.4.2 Quantification The focal adhesion staining experiment resulted in 210 wells requiring analysis, from 35 cell lines, 3 wells per cell line and 2 experimental conditions. This required an automated analysis which was achieved using an Opera™ confocal imaging reader (Evotec Technologies, USA). 20 images were taken by James Longden from each well resulting in 6480 images. The images were then analyzed and quantified by James Longden using Acapella™ image analysis software (Perkin Elmer®, USA). The software counted and identified cells by their DAPI nuclear staining. It then analyzed and counted the immunofluoresence of the vinculin, where aggregates of vinculin staining which were above background indicated a focal adhesion. The vinculin staining was also used to assess relative the cell areas in the images analyzed, enabling comparison of cell size between the experimental conditions.
6.2.5 Statistical analysis As described in Chapter 2.1, the samples sizes for these experiments were 9 control and 9 patient neurosphere-derived cell lines and 8 control and 9 patient skin fibroblast cell lines. Replicate measurements across experiments were averaged, and the means of the two groups were compared statistically by one-way ANOVA. One-way ANOVA was performed using SPSS© 15.0 software. Data is expressed as mean ± SEM. α = 0.05 was chosen to assign significance in all statistical tests.
6.3 Results 6.3.1 Adhesion assay There was a significant increase in cell adhesion in both neurosphere-derived cells and skin fibroblasts from the schizophrenia group when grown for 2 hours on fibronectin and collagen. Relative attachment of cells in the two groups was obtained by comparing the ratio of crystal violet absorbance for each sample to a calibrator sample in each experiment, which normalized the data. For the neurosphere-derived cells, the mean ± SEM for the two
122 groups was as follows: control, 1.423 ± 0.075 fibronectin and 1.595 ± 0.165 collagen. n = 9; schizophrenic, 1.915 ± 0.086 fibronectin and 2.110 ± 0.156 collagen, n = 9 (Figure 6.1). There was a significant increase in attachment rates of neurosphere-derived cells from the schizophrenia group when grown on both fibronectin (F1,16 = 17.907, p = 0.001) and collagen (F1,16 = 5.038, p = 0.039). For the skin fibroblasts, the mean ± SEM for the two groups was as follows: control, 0.999 ± 0.055 fibronectin and 1.070 ± 0.060 collagen. n = 8; schizophrenic, 1.446 ± 0.125 fibronectin and 1.641 ± 0.120 collagen, n = 9 (Figure 6.1). There was a significant increase in attachment rates of skin fibroblasts from the schizophrenia group when grown on both fibronectin (F1,15 = 16.488, p = 0.001) and collagen (F1,15 = 23.931, p ≤ 0.001).
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Figure 6.1: Cell attachment rates. A and B: Mean relative cell attachment ± SEM, as measured by crystal violet staining, when cells were allowed to attach for 2 hours to fibronectin and collagen respectively. A: Attachment of neurosphere-derived cells (Fibronectin p = 0.039, Collagen p = 0.001). B: Attachment of skin fibroblasts. There was significant more attachement in the schizophrenia group to both extracellular matrixes in both cell types (Figure B: Fibronectin p = 0.001, Collagen IV p ≤ 0.001).
6.3.2 Focal Adhesion Kinase There was a significant decrease in the levels of phosphorylated FAK in neurosphere- derived cells, but not skin fibroblasts, from the schizophrenia group when cells are grown under normal culture conditions. A standard curve was used to ensure the samples were
124 within the concentration range of the standard curve, and to extrapolate the concentration of phosphorylated FAK in the protein samples (Figure 6.2). The normalized data is presented as Units/mg protein, where one unit is defined as the amount of FAK (pTyr397) autophosphorylated from 300 pg total FAK. For the neurosphere-derived cells, the mean ± SEM for the two groups was as follows: control, 22.361 ± 1.785 Units/mg protein, n = 9; schizophrenic, 10.705 ± 1.683 Units/mg protein, n = 9 (Figure 6.3). There was a significant decrease in levels of phosphorylated FAK in neurosphere-derived cells from the schizophrenia group compared to controls (F1,16 = 22.567, p ≤ 0.001). For the skin fibroblasts, the mean ± SEM for the two groups was as follows: control, 12.583 ± 1.807 Units/mg protein, n = 8; schizophrenic, 9.767 ± 1.082 Units/mg protein, n = 9 (Figure 6.3). There was no significant difference in levels of phosphorylated FAK in skin fibroblasts from the schizophrenia group compared to controls (F1,16 = 1.884, p = 0.190).
Figure 6.2: Phoshorylated FAK ELISA standard curve. Standard curve of phosphorylated FAK ELISA. The assay was optimized in order to ensure the samples were within the range of the standard curve.
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Figure 6.3: Phoshorylated FAK content. A and B: Mean phosphorylated FAK content ± SEM. A: phosphorylated FAK content for neurosphere-derived cells (p ≤ 0.001). B: phosphorylated FAK content for skin fibroblasts (p = 0.190). Neurosphere-derived cells from the schizophrenia group contain significantly less phosphorylated FAK that of the control group. There was no significant difference in levels of phosphorylated FAK in skin fibroblasts of the schizophrenia group and controls.
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6.3.3 Focal adhesion complex There was no significant difference in vinculin staining in either neurosphere-derived cells or skin fibroblasts from the schizophrenia group when grown for 2 hours on fibronectin and collagen. For the neurosphere-derived cells, the mean number of focal adhesions ± SEM for the two groups was as follows: control, 4.356 ± 0.813 fibronectin and 8.248 ± 1.987 collagen, n = 9; schizophrenic, 3.421 ± 0.567 fibronectin and 4.296 ± 0.907 collagen, n = 9 (Figure 6.5). There was no significant difference in vinculin staining of neurosphere- derived cells from the schizophrenia group when grown on either fibronectin (F1,16 = 0.450, p = 0.512) or collagen (F1,16 = 3.537, p = 0.080). For the skin fibroblasts, the mean ± SEM for the two groups was as follows: control, 8.218 ± 1.358 fibronectin and 8.268 ± 1.030 collagen. n = 8; schizophrenic, 5.615 ± 0.545 fibronectin and 5.979 ± 1.180 collagen, n = 9 (Figure 6.5). ). There was no significant difference in vinculin staining of skin fibroblasts from the schizophrenia group when grown on either fibronectin (F1,15 = 3.162, p = 0.097) or collagen (F1,15 = 2.135, p = 0.166).
Figure 6.4: Expression of vinculin. A-C: Vinculin staining of neurosphere-derived cells. A: DAPI staining (blue). B: Expression of vinculin (green). C: Merged image. The punctate vinculin expression identifies focal adhesions in the cells. Scale bar = 10 µm.
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Figure 6.5: Vinculin staining. A and B: Mean number of punctate vinculin staining ± SEM, as measured by immunofluorescence. A: Vinculin staining in neurosphere-derived cells (Fibronectin p = 0.512, Collagen p = 0.080). B: Vinculin staining in skin fibroblasts (Fibronectin p = 0.097, Collagen p = 0.166). There was no significant difference in vinculin staining between the two groups in either cell type.
6.3.4 Cell size There was no significant difference in relative cell area in either neurosphere-derived cells or skin fibroblasts from the schizophrenia group when grown for 2 hours on fibronectin and collagen. For the neurosphere-derived cells, the mean relative cell area ± SEM for the two groups was as follows: control, 93.562 ± 9.409 fibronectin and 119.205 ± 26.816 collagen, n = 9; schizophrenic, 133.061 ± 36.354 fibronectin and 82.072 ± 13.869 collagen, n = 9
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(Figure 6.5). There was no significant difference in relative cell area of neurosphere- derived cells from the schizophrenia group when grown on either fibronectin (F1,16 = 1.106, p = 0.309) or collagen (F1,16 = 1.513, p = 0.236). For the skin fibroblasts, the mean relative cell area ± SEM for the two groups was as follows: control, 108.595 ± 17.292 fibronectin and 102.273 ± 15.927 collagen. n = 8; schizophrenic, 165.098 ± 60.367 fibronectin and 220.105 ± 105.611collagen, n = 9 (Figure 6.5). ). There was no significant difference in relative cell area of skin fibroblasts from the schizophrenia group when grown on either fibronectin (F1,15 = 0.899, p = 0.358) or collagen (F1,15 = 1.378, p = 0.259).
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Figure 6.6: Cell surface area. A and B: Mean relative cell area ± SEM, as measured by immunofluorescence. A: Relative cell surface in neurosphere-derived cells (Fibronectin p = 0.309, Collagen p = 0.236). B: Relative cell surface are in skin fibroblasts (Fibronectin p = 0.358, Collagen p = 0.259). There was no significant difference relative cell surface are between the two groups in either cell type.
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6.4 Discussion 6.4.1 Overview We have performed various assays relating to focal adhesion in neurosphere-derived cells and skin fibroblasts from patients with schizophrenia. We noted differences between cells from patients and controls on some of the measures. There was a significant increase in cell attachment of both neurosphere-derived cells and skin fibroblasts from the schizophrenia group, when grown on fibronectin and collagen (Figure 6.1), with no significant difference in relative cell area in either experimental condition (Figure 6.6). Neurosphere-derived cells from patients with schizophrenia grown under normal culture conditions have significantly lower levels of phosphorylated FAK, indicating a dysregulation of downstream signalling pathways which would affect attachment. This was not the case in skin fibroblasts, though there was a trend showing reduced levels of phosphorylated FAK (Figure 6.3). This agrees with reduced FAK transcript levels reported in neurosphere-derived cells but not skin fibroblasts (Chapter 3). When assessing vinculin staining, we found no significant difference in either cell model when the cells were grown on fibronectin or collagen (Figure 6.5), indicating similar numbers of focal adhesions between the two groups.
6.4.2 Cell adhesion in schizophrenia We found a significant increase in cell adhesion of cells from the schizophrenia group in neurosphere-derived cells and skin fibroblasts (Figure 6.1), showing significantly improved adhesion to fibronectin and collagen. No difference in cell area (Figure 6.6) indicates these differences were not due to changes in cell size that would confound the crystal violet assay.
Cell adhesion is regulated by complex interactions between the extracellular matrix, adhesins and the actin cytoskeleton of the cell (Thiery 2003; Kruger et al. 2005; Wiesner et al. 2005; Halloran and Wolman 2006; Romer et al. 2006; Benvenuti and Comoglio 2007). This increase in attachment is not in agreement with our hypothesis and previous reports of decreased attachment rates in olfactory epithelial biopsies and skin fibroblasts from patients with schizophrenia (Miyamae et al. 1998; Feron et al. 1999). This may reflect the complexity of cell adhesion, which was also observed by McCurdy 2005 when attempting to repeat the explant attachment experiment by Feron et al. (Feron et al. 1999; McCurdy 2005). Olfactory biopsies from patients with schizophrenia have demonstrated significantly different attachment to plastic, but not glass surfaces (Feron et al. 1999; McCurdy 2005).
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The sensitivity of cell attachment to cell-cell interactions and properties and composition of the extracellular matrix may explain these observed differences (Thiery 2003; Berrier and Yamada 2007; Zaidel-Bar et al. 2007). Differences in experimental strategy could also explain the discrepancy of previous reports of reduced attachment, and our findings. These studies used different attachment times, and findings using olfactory epithelial biopsies are not directly comparable due to significantly more complex and heterogenous cell populations (Miyamae et al. 1998; Feron et al. 1999; McCurdy 2005). Though this data shows an opposite effect on cell adhesion to what was hypothesised, it suggests a significant difference in a process critical for brain development and function.
The micorarray study was not performed on cells grown on either fibronectin or collagen, however it indicated an increase in α8-integrin expression when the neurosphere-derived cells from the schizophrenia group are grown under normal culture conditions (Table 3.3). This data supports this finding since when these cells are exposed to fibronectin and collagen, there is a significant change in their attachment. Though increased attachment to fibronectin would suggest an increase in integrin interactions on the cell surface leading to increased attachment, the complexity of cell adhesion precludes any such conclusion to be made without more data (Akiyama et al. 1995; Thiery 2003; Abdollahi et al. 2005; Wiesner et al. 2005; Berrier and Yamada 2007; Zaidel-Bar et al. 2007).
One of the pathways significantly overrepresented in the neurosphere-derived array was actin cytoskeleton signalling (Appendix 1B). Though it did not rate as one of the most significantly overrepresented pathways (Figure 3.3), there were 17 genes in this pathway with altered transcript levels. Disrupted regulation of the actin cystoskeleton would affect its interaction with integrins and the extracellular matrix (Brakebusch and Fassler 2003; Thiery 2003; Wiesner et al. 2005), which again would affect adhesion. Though the complexity of altered cell adhesion in these cells has not been fully explored, we have shown significant dysfunction of adhesion in neurosphere-derived cells and skin fibroblasts from schizophrenic patients.
6.4.3 Focal adhesion complex in schizophrenia There is no significant difference in number of focal adhesion complexes, as measured by vinculin staining, in either neurosphere-derived cells or skin fibroblasts from the
132 schizophrenia group on either extracellular matrix. We also found significantly lower levels of phosphorylated FAK in neurosphere-derived cells but not skin fibroblasts from the schizophrenia group (Figure 6.3). This agrees with findings from the microarray study (Chapter 3), in which FAK transcripts were significantly lower in neurosphere-derived cells, but not skin fibroblasts, from the schizophrenia group.
Reduced levels of phosphorylated FAK could indicate an impairment of focal adhesion assembly, suggesting a dysfunction of cell attachment. Miyamae et al. reported a significant decrease in attachment rates of fibroblasts from patients with schizophrenia to fibronectin, when compared to controls (Miyamae et al. 1998), They also found no significant difference in the amount of intracellular FAK (Miyamae et al. 1998). This again highlights the differences in expression patterns between the two cell models as reported in the micorarray study, where we found little significant overlap in transcripts differently expressed. Though the levels of FAK transcripts and phosphorylated FAK are consistent, they are not measuring the same. Reduced FAK transcripts indicate dysfunctional regulation, while reduced phosphorylated FAK indicate impaired activation (Ben Mahdi et al. 2000; Chatzizacharias et al. 2008). These findings are however consistent with a dysfunction of FAK regulation and function, which could affect neural plasticity and function in schizophrenia (Girault et al. 1999; Nikolic 2004).
Reduced levels of phosphorylated FAK in neurosphere-derived cells is a significant finding, as it indicates these cells have impaired focal adhesion assembly and disruption of downstream signalling events, but that this is not the case in the skin fibroblasts from the schizophrenia group (Romer et al. 2006; Vadali et al. 2007; Tilghman and Parsons 2008). Previous findings indicate focal adhesions are dysfunctional when the cells are exposed to fibronectin for up to 30 minutes (Miyamae et al. 1998), however we observed increased cell attachment with no difference in the number of focal adhesions after 2 hours suggesting the fibroblasts ability to adhere improves over time (Figure 6.1 and Figure 6.5).
We reported no difference in the number of focal adhesion complexes, in either neurosphere-derived cells or skin fibroblasts from the schizophrenia group on either extracellular matrix. However, we did observe a trend for reduced focal adhesions in both assay conditions for both cell types (Figure 6.5). The increased attachment rates after 2
133 hours incubation could have indicated that cells from the schizophrenia group contain more focal adhesions, therefore attaching better than the controls. This was found to not be the case, indicating the increased attachment rates are the result of a different mechanism. The composition of the focal adhesion is important in focal adhesion function (Akiyama 1996; Hynes 2002; Thiery 2003; Romer et al. 2006), and the protein levels of vinculin in the focal adhesion could provide insight into the increased cell attachment in schizophrenia.
Increased attachment could be the result of not only an increase in integrins, but also other adhesins such as cadherins and selectins, which would affect cell attachment (Schwartz et al. 1995; Akiyama 1996; Hynes 2002; Thiery 2003; Romer et al. 2006). It is therefore possible focal adhesion complexes in the cells from the schizophrenia groups attach better than those of the control group, resulting in the improved attachment (DiMilla et al. 1993; Akiyama 1996; Hynes 2002; Thiery 2003; Romer et al. 2006). This could also affect cell detachment and migration, where the increased attachment strength of the focal adhesions impairs cell migration and detachment (DiMilla et al. 1993; Melton et al. 2007). Though integrins are the primary binding partner for fibronectin (Akiyama et al. 1995; Abdollahi et al. 2005), the dynamic nature of focal adhesion composition makes it difficult to extrapolate details of the adhesion dysfunction for the schizophrenia groups in this study (Miyamoto et al. 1995; Yamada and Miyamoto 1995; Romer et al. 2006)
6.4.4 Summary We have found significant disruption in focal adhesion in cells from patients with schizophrenia, specifically cell attachment and levels of phosphorylated FAK. Significantly, these findings confirm the disrupted transcript expression of the integrin signalling pathway, where altered FAK and integrin expression was reported (Chapter 3). A dysfunction in the focal adhesion complex in cells from patients with schizophrenia, their cell-cell communication and downstream signalling would be altered. Though both cell models demonstrate changes in cell adhesion, the neurosphere-derived cells also show significant dysregulation of phosphorylated FAK under normal culture conditions. This was not the case in the skin fibroblasts from the schizophrenia group, suggesting the altered focal adhesion observed when the cells were exposed to collagen and fibronectin are not related to FAK signalling. Based on previous studies cell adhesion also appears to change over time, indicating the observed change in adhesion is the result of dysfunctional focal
134 adhesion signalling. This study into focal adhesion in schizophrenia, comparing neurosphere-derived cells and skin fibroblasts, supports the hypothesis that there are systemic alterations in regulation of focal adhesion in the disease. We also found that the neurosphere-derived cells show signs of a more prevalent dysregulation when compared to skin fibroblasts, which may be a reflection of the requirements of differing structural roles of fibroblasts compared to a neurally derived cell line. Though the exact mechanisms of this dysfunction have not been elucidated, it would result in changes of critical processes of brain development and function in schizophrenia.
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Chapter 7: General discussion
7.1 Overview Olfactory neurospheres contain multipotent stem cells and can differentiate into neurons and glia. Their ability to proliferate and differentiate into neuronal and glial populations in vitro provides an ideal model for studying neurogenesis and neurodevelopment in schizophrenia. This window into neurodevelopment allows us to study aspects of the neurobiology of schizophrenia, without the same difficulties associated in acquiring sufficient and reliable post-mortem brain samples. We also included skin fibroblasts from the same individuals in order to compare the two cell models, and to identify dysfunctions which manifested in both neural and non-neural tissues. The main findings of this study were: 1. Significant differences between patients with schizophrenia and healthy controls in global transcript levels in neurosphere-derived cells, where focal adhesion and related pathways were overrepresented. Few significant changes in global transcript levels in skin fibroblasts, with poor overlap between the two cell types (Chapter 3). 2. No significant difference between patients with schizophrenia and healthy controls in mitochondrial function or cell death of neurosphere-derived cells and skin fibroblasts. Changes in mitochondrial load and the glutathione (GSH) response during oxidative stress in neurosphere-derived cells from the schizophrenia group. Altered GSH response to oxidative stress was also observed in the skin fibroblasts (Chapter 4). 3. No difference in the number of neurospheres generated. Neurospheres generated from cell lines in the schizophrenia group were smaller and denser. Differences in differentiation were observed in neurosphere-derived cells from the schizophrenia group (Chapter 5). 4. Increased attachment of neurosphere-derived cells and skin fibroblasts when grown on either collagen IV or fibronectin coated plates, though no difference in the number of focal adhesions. Significantly lower levels of phosphorylated focal adhesion kinase (FAK) in neurosphere-derived cells, but not skin fibroblasts (Chapter 6).
7.2 Mitochondrial function and oxidative stress in schizophrenia patients. Our initial hypothesis was of an impaired mitochondrial function and oxidative stress response in schizophrenia. We observed a small but significant signal for a mitochondrial
137 dysfunction and changes in oxidative stress response in this study, with differences in mitochondrial biogenesis and GSH synthesis in response to oxidative stress. This agrees with previous reports of changes to mitochondrial function and GSH response in schizophrenia (Do et al. 2000; Ben-Shachar 2002; Karry et al. 2004; Prabakaran et al. 2004; Iwamoto et al. 2005a; Zhang et al. 2005; Gysin et al. 2007), and supports our hypothesis of altered mitochondrial function and oxidative stress response.
7.2.1 Mitochondrial function Increased mitochondrial load in neurosphere-derived cells from patients with schizophrenia, indicating increased mitochondrial biogenesis, without any significant measurable change in mitochondrial function or cell size, implies a change in mitochondrial function and regulation in these cells. Mitochondria show significant plasticity with regards to external shape, size and their distribution within cells (Nisoli et al. 2004a). Mitochondrial proliferation usually results from changes in bioenergetics, where increased energy demand initiates signaling cascades for mitochondrial biogenesis (Nisoli et al. 2004a). A significant increase in biogenesis when exposed to toxic levels of H2O2, suggests either that these neural-derived cells are under significantly more stress than the control cell lines, or that the transcriptional control of biogenesis is dysfunctional. Because we observed no significant difference in mitochondrial membrane potential in the schizophrenia group, an indicator of mitochondrial function and energy state of the cells, there was no suggestion of an increase in mitochondrial function. We therefore propose this increase in mitochondrial biogenesis is an indication of impaired oxidative phosphorylation in the mitochondria or impaired transcriptional control of biogenesis.
An impairment of oxidative phosphorylation, as seen in dysregulation of some oxidative phosphorylation transcripts, is consistent with multiple studies reporting dysfunctional complex I function in post-mortem tissues from patients with schizophrenia (Ben-Shachar et al. 1999; Maurer et al. 2001; Karry et al. 2004; Prabakaran et al. 2004; Iwamoto et al. 2005a). Complex I is the first enzyme in oxidative phosphorylation and impaired function would therefore influence downstream ATP synthesis (Ben-Shachar et al. 2004; Adam-Vizi and Chinopoulos 2006). There are also reports that the activity of other oxidative phosphorylation complexes is altered (Prince et al. 1999; Maurer et al. 2001), though complex I is the most consistent finding. Dopamine toxicity in schizophrenia has been
138 suggested to involve complex I, where increased dopamine levels are implicated in the proposed mitochondrial and complex I dysfunctions (Ben-Shachar et al. 2004). Oxidative stress induced by dopamine toxicity causes a reduction of mitochondrial membrane potential in cortical neurons in mice (Grima et al. 2003). However we found no direct indication of impaired complex I function or oxidative phosphorylation, as measured by the mitochondrial membrane potential, pointing to changes in other mechanisms regulating mitochondrial biogenesis.
Though disruption of mitochondrial transcript levels were limited in the neurosphere- derived cells, the arrays measured in vitro differences in transcript expression, and may not reflect what occurs in the brain. Altered transcript levels of some mitochondrial genes may be significant when considering previous reports of a mitochondrial dysfunction in the brain (Ben-Shachar et al. 1999; Maurer et al. 2001; Karry et al. 2004; Prabakaran et al. 2004; Iwamoto et al. 2005a). Mitochondrial function can be modulated by integrins through regulation of Bcl-2 proteins (Zhang et al. 1995; Gilmore et al. 2000; Matter and Ruoslahti 2001; Reginato et al. 2003; Martin and Vuori 2004). It is therefore plausible the differences we see in mitochondrial function may be related to differences seen in transcript levels of integrin signaling pathway genes, though we have no direct evidence of the impact of the integrin dysfunction on mitochondrial function in the neurosphere-derived cells. A mitochondrial dysfunction in schizophrenia may involve upstream regulators of mitochondrial function, which includes the integrin signaling pathway which was dysregulated in the neurosphere derived cells. The apparent absence of both a dysfunction in the integrin signaling pathway and a mitochondrial dysfunction in skin fibroblasts from patients with schizophrenia could therefore be linked.
Mitochondrial biogenesis can regulate morphology and plasticity of dendritic spines and synapses as well as being regulated by glutamate and synaptic plasticity (Rintoul et al. 2003; Li et al. 2004b; Liu and Shio 2008). A dysfunction of mitochondrial biogenesis in neurosphere-derived cells would therefore fit with the hypothesis of a mitochondrial dysfunction in schizophrenia. Altered mitochondrial biogenesis could lead to neural death and disruption of normal neural function, through disruption of calcium homeostasis, and synaptic activity and development (Hastings et al. 1996; Seyfried et al. 1999; Do et al. 2000; Grima et al. 2003; Gunter et al. 2004; Chinopoulos and Adam-Vizi 2006; Rimessi et
139 al. 2008). Dysfunctional mitochondria during early development could therefore result in abnormal synaptic formation. It could also result in abnormal neural function and synapse formation in the adult brain, which fits with the neurodevelopmetnal hypothesis of schizophrenia (McGrath et al. 2003b; Rehn and Rees 2005). Interestingly, mitochondrial function and transcript levels in skin fibroblasts from the schizophrenia group were not different to that of the control group. This indicates the dysfunction is limited to neural- derived tissue, which impacts on the utility of skin fibroblasts as a model for studying schizophrenia.
7.2.2 Glutathione system We found no evidence of increased oxidative stress in these cells, as previously indicated in plasma from schizophrenic patients (Mukherjee et al. 1996; Yao et al. 1998; Do et al. 2000; Akyol et al. 2002). However, we did find evidence of an altered oxidative stress response in the glutathione system in cells from the schizophrenia group. The impaired GSH response in both cell models gives clear indication of a dysfunction in the glutathione system. It also agrees with previous studies reporting an impaired GSH response to oxidative stress in fibroblasts from patients with schizophrenia (Gysin et al. 2007). An impaired oxidative stress response is a significant finding, as it fits with the hypothesis of increased oxidative stress in schizophrenia pathophysiology.
Dopamine is a major source of reactive oxygen species in the brain which reduce mitochondrial function and GSH levels in cells (Shukitt-Hale et al. 1997; Rabinovic and Hastings 1998; Cohen 2000; Ben-Shachar et al. 2004). Impaired GSH response in schizophrenia therefore fits with the dopamine hypothesis of schizophrenia, and may represent a temporal mode for disease pathogenesis (Davis et al. 1991). The result of impaired oxidative stress response in the brain could lead to increased vulnerability to viral infections, inflammation and obstetrical complications, which are known to increase oxidative stress in the brain and are known environmental risk factors for schizophrenia (Benes 1997; Yao et al. 2001; Rapoport et al. 2005; Rehn and Rees 2005; Robertson et al. 2006). It is conceivable that a dysfunctional oxidative stress response is amplified during physiological stress, with downstream implications on neural development and subsequent function in the developing and adult brain (Radley and Morrison 2005; Fuchs et al. 2006).
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Because both cell models displayed an impaired oxidative stress response, it is not limited to neural-derived tissue. Differences in oxidative stress response between the two cell models, with regards to GSH and mitochondria, suggest the neruosphere-derived cells are more sensitive to oxidative stress. This implies that the mechanisms underlying a mitochondrial dysfunction in schizophrenia is limited to neural-derived tissue (Wallace 2001; Duchen 2004b; Zeviani and Di Donato 2004; Mattson 2007). Transcript expression data in neurosphere-derived cells showed significant differences in transcript levels of oxidative phosporylation genes as well as several genes and putative pathways implicated in schizophrenia. The skin fibroblasts indicated no significant dysregulation in any pathways, suggesting that disease pathology in neural-derived tissues may be more relevant to the neurobiology of schizophrenia.
7.3 Altered cell attachment in schizophrenia patients. Disruptions of focal adhesion signalling seen in the neurosphere-derived cells, specifically integrin, ephrin and actin cytoskeleton signaling, would have a severe impact on not only neural development and function, but also synapse formation, cell interactions and the cell cytoskeleton (Girault et al. 1999; Thiery 2003; Nikolic 2004; Kruger et al. 2005; Wiesner et al. 2005; Halloran and Wolman 2006; Romer et al. 2006; Benvenuti and Comoglio 2007). Of particular interest are the reduced levels of focal adhesion kinase mRNA and levels of phosphorylated focal adhesion kinase in neurosphere-derived cells, as this enzyme also regulates neural migration and microtubule organisation, in addition to focal adhesion and actin cytoskeleton organisation (Xie et al. 2003). This is consistent with a dysfunction of focal adhesion kinase regulation and function in these cells, which could affect neural plasticity and function in schizophrenia (Girault et al. 1999; Nikolic 2004).
Impaired expression of focal adhesion kinase could also affect several downstream signalling events such as PI3 kinase activity (Romer et al. 2006; Vadali et al. 2007; Tilghman and Parsons 2008). PI3-kinase is a regulator of GSK-3 activation, another candidate for schizophrenia pathophysiology important in maintaining structural plasticity (Kozlovsky et al. 2001; Emamian et al. 2004; Kozlovsky et al. 2005; Lovestone et al. 2007). GSK-3 is a component of the Wnt signaling pathway, involved in regulating neuronal function and synaptic plasticity, and is also dysregulated in the neurosphere- derived cells. Dysregulation of this pathway is also implicated in schizophrenia
141 pathophysiology (Miyaoka et al. 1999; Beasley et al. 2001; Beasley et al. 2002), and suggests dysfunctional regulation of neuronal function and synaptic plasticity.
Increased attachment in neurosphere-derived cells and skin fibroblasts does not agree with previous reports of reduced attachment in schizophrenia (Mahadik et al. 1994; Barbeau et al. 1995; Feron et al. 1999). The increased cell attachment is also curious, because it is not an intuitive result when considering the transcript data of either cell types. The transcript levels measured would suggest impaired cell attachment in neurosphere-derived cells, and normal attachment in the skin fibroblasts from patients with schizophrenia. This could indicate that a dysfunction in focal adhesion signaling is modulated by extrinsic signals. Whether these genes are consistently downregulated in neurosphere-derived cells, or are a result of a dysfunctional regulatory mechanism in schizophrenia, remains to be elucidated. Because the number of focal adhesions were not different between the groups, increased attachment can be the result of differences in actin cytoskeleton and focal adhesion complexes (Akiyama 1996; Schatzmann et al. 2003; Lynch et al. 2005; Lee et al. 2006; Romer et al. 2006). The increased attachment does not intuitively agree with reports of increased neural density in the prefrontal cortex, as increased attachment could lead to impaired neuronal migration to the PFC (Pakkenberg 1993; Selemon et al. 1995; Selemon et al. 1998; Friedman et al. 1999). It is however a significant finding as it demostrates that neural-derived cells from patients with schizophrenia have a significant dysfunction in focal adhesion.
The differences in adhesion indicate a significant dysfunction of cell interaction with the extracellular matrix in both neural-derived and non-neural cells from patients with schizophrenia. We therefore hypothesize that this attachment dysfunction in schizophrenia has developmental ramifications, where disruptions in the focal adhesion signaling pathway alter the cells’ dynamics of adherence and connection. Such a finding therefore clearly points to a mechanism whereby crucial processes would be impacted both in developmental events and in the integrity and plasticity of neural networks (Girault et al. 1999; Schatzmann et al. 2003; Nikolic 2004; Romer et al. 2006; Chatzizacharias et al. 2008). This could explain the structural abnormalities observed in brains of schizophrenia patients, where a dysfunction in adhesion could result in increased neuron density and disrupted neural function (Lawrie and Abukmeil 1998; Nelson et al. 1998; Whitworth et al. 1998;
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Zipursky et al. 1998; Friedman et al. 1999; Kreczmanski et al. 2007). This would also impact on plasticity of the adult brain in schizophrenia, through its disruption of neurogenesis (Eriksson 2003; Radley and Morrison 2005; Eriksson 2006; Mirescu and Gould 2006; Toro and Deakin 2006; Reif et al. 2007; Zhao et al. 2008).
7.3.1 Neurosphere formation and neurogenesis. The differences observed in olfactory neurosphere formation and neural differentiation supports the hypothesis of dysfunctional neurogenesis in schizophrenia. Though differences in neurosphere formation do not necessarily reflect an impairment in vivo (Jensen and Parmar 2006), they do demonstrate that differences in neurogenesis can occur in schizophrenia. Because differentiation was performed under controlled culture conditions with defined media, it is prudent to assume these differences in olfactory neurogenesis are caused by dysfunctional intrinsic factors regulating differentiation rather than extrinsic factors (Arnold et al. 2001). Observations of altered neural populations in the olfactory epithelium could therefore be due to differences in intrinsic regulation rather than failure to gain trophic support (Arnold et al. 2001). Differences in intrinsic regulation are also indicated by the transcript expression of neurosphere-derived cells, with significant differences in multiple signalling and metabolic pathways regulating proliferation, migration and differentiation.
The dysregulation of the reelin (RELN) and RGS4 genes would also support such a dysfunction in schizophrenia (Impagnatiello et al. 1998; Guidotti et al. 2000; Mirnics et al. 2001; Chowdari et al. 2002; Williams et al. 2004; Buckholtz et al. 2007; Carter 2007; Lang et al. 2007; Kawauchi and Hoshino 2008). Their regulation of neuronal signalling, migration and synaptic plasticity would indicate disruptions of neurogenesis and neurodevelopment in schizophrenia (Guidotti et al. 2000; Frotscher et al. 2003; Pfeiffer and Huber 2006; Carter 2007; Kawauchi and Hoshino 2008). Dysregulation of RELN, RGS4 and GABRE become particularly relevant when considering the differences observed in olfactory neurogenesis. RELN is synthesised and secreted by gabaergic neurons in the cortex and hippocampus, and regulates dendritic plasticity by binding to integrin receptors present at dendrites and dendritic spines (Guidotti et al. 2000; Costa et al. 2001; Toro and Deakin 2006). RELN appears to play an important role in allowing neuron precursors to migrate radially in the olfactory bulb and fully differentiate (Hack et al. 2002). RGS4
143 regulates G-protein coupled neurotransmitter receptors, which are involved in synaptic plasticity as well as modulating mood and behaviour, and is therefore an interesting candidate for schizophrenia etiology (Levitt et al. 2006; Buckholtz et al. 2007; Carter 2007; Marshall 2008). Though we did not assay synapse formation, the dysregulation of these genes would suggest a dysfunction in synapse formation and differentiation in these cells. The altered transcript levels for these and other genes in neurosphere-derived cells have recently been validated in our lab by Dr. Yongjun Fan (Appendix 3). Altered transcript expression of RELN, RGS4, GABRE, SLC1A1 and LAMA was confirmed, and consistent with differences seen in the array data (Appendix 3). Transcript expression of LRP8 and PARVB was not significantly different, but showed trends in the directions indicated by the array data (Appendix 3).
In the NGF/B27 differentiation assay we saw significantly fewer cells expressing β-Tubulin III in cell lines from the schizophrenia group, however the assay also resulted in significantly more cells expressing neurofilament 200 when compared to controls. A trend of fewer cells expressing β-Tubulin III and no difference in cells expressing neurofilament 200 in cell lines from the schizophrenia group, suggest different extrinsic signals affect the dysfunction observed in these differentiation assays. An increase in neurofilament 200 positive cells indicates an increase in fully differentiated neurons, and does not agree with the reduced RELN and RGS4 expression seen in neural precursors from mice cultured in serum, which resulted in reduced differentiation of neurons in mice (Hack et al. 2002). However the results from both differentiation assays could suggest that complex intrinsic mechanisms are causing the altered differentiation of neursophere-derived cells in schizophrenia.
Altered cell density of olfactory neurospheres from schizophrenia cell lines could be a manifestation of the observed differences in the focal adhesion and actin cytoskeleton signaling pathway transcript levels measured in neurosphere-derived cells. Though this is in agreement with the transcript levels of genes in the focal adhesion pathways of neurosphere-derived cells in the schizophrenia cell lines, the mechanism causing the altered cell density is not known. The transcript data indicates reduced expression of focal adhesion transcripts, which could result in reduced adhesion in the neurosphere-derived cells from the schizophrenia group. This is contrary to our observations of the neurospheres
144 containing smaller more tightly packed cells. The observed dysfunction in expression of focal adhesion signaling genes may therefore be temporal, or masked by other adhesion mechanisms, resulting in the differences observed with cell attachment and morphology. Significantly, cells in the cell attachment and mitochondrial function experiments showed no difference in cell size. It is therefore likely the difference in cell size observed in the nerusopheres is a result of the culture conditions, where extrinsic factors in the neurosphere assay induces the altered cell response. This is likely a reflection of the complexity of cell adhesion and actin cytoskeleton interactions (Yamada and Miyamoto 1995; Akiyama 1996; Kruger et al. 2005; Wiesner et al. 2005; Halloran and Wolman 2006).
Actin cytoskeleton signalling was one of the pathways significantly overrepresented in the genes differently expressed in neurosphere-derived cells from patients with schizophrenia. Disrupted regulation of the actin cytoskeleton would affect the cell-cell interaction as well as cell morphology (Brakebusch and Fassler 2003; Thiery 2003; Wiesner et al. 2005). This is supported by the higher cell density in neurospheres from the schizophrenia group, showing that these cells are smaller. These differences in focal adhesion and actin cytoskeleton signaling may contribute to the reported differences of microstructure and neural density reported in cortex and hippocampus respectively (Pakkenberg 1993; Selemon et al. 1995; Selemon et al. 1998; Friedman et al. 1999; Hoptman et al. 2002). Changes in focal adhesion and actin cytoskeleton signaling are consistent with the neurodevelopmental hypothesis of schizophrenia where postnatal or adult events, such as dysfunctions in neurogenesis and synaptic plasticity, result in development of schizophrenia (McGrath et al. 2003b; Eriksson 2006; Toro and Deakin 2006; Reif et al. 2007).
7.4 Olfactory neurosphere derived cells as a cell model for schizophrenia. In contrast to the olfactory neurosphere-derived cells, gene expression profiling of skin fibroblasts from the schizophrenia group did not reveal any disease-related pathways altered in schizophrenia. Skin fibroblasts did show functional differences in this and other studies (Mahadik et al. 1991; Mahadik et al. 1994; Mukherjee et al. 1994; Ramchand et al. 1994; Mahadik and Mukherjee 1996; Miyamae et al. 1998; Zhang et al. 1998; Gysin et al. 2007), but they have provided little converging data with regards to gene expression differences and differences in the central nervous system in schizophrenia (Matigian et al.
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2008). Neurosphere-derived cells from patients with schizophrenia demonstrated significant functional difference, including differences in neurogenesis, as well as displaying a gene expression profile with significant signal differences in pathways putatively associated with schizophrenia etiology (Arnold et al. 2001; Prabakaran et al. 2004; Williams et al. 2004; McCurdy et al. 2006; Buckholtz et al. 2007; Carter 2007; Lang et al. 2007; Reynolds and Harte 2007). Gene expression and functional data from the neurosphere-derived cells indicate a converging outcome of dysfunctional neurogenesis, synaptic plasticity and stress related signalling in schizophrenia (Harrison and Weinberger 2004; Knable et al. 2004; Cariboni et al. 2005; Toro and Deakin 2006; Carter 2007; Lang et al. 2007; Reif et al. 2007). It can be concluded here, in agreement with Matigian et al., that fibroblasts are not informative as a cell model for schizophrenia (Matigian et al. 2008).
7.5 Confounding factors. The data from this study provides some new and exciting findings that provide interesting future directions. There are however limitations which should be considered when interpreting the data.
7.5.1 Gene expression profiling of neurosphere-derived cells and skin fibroblasts in the study of schizophrenia The array studies were done with biological replicates, however increasing the cohort size is still recommended. Schizophrenia is a heterogeneous disorder, so caution should be applied with regards to identifying common disease pathophysiology in gene expression using relatively small sample sizes. There are multiple array studies investigating global transcript expression in schizophrenia, but reproducibility of results between studies has so far been less than impressive. By increasing the sample size we would improve the robustness of our findings, and may help elucidate the heterogeneity of the disorder. Validating the transcript levels of some of these genes using quantitative RT-PCR has been done in our lab (Dr. Yongjun Fan, Appendix 3), however time constraints prevented validation of transcript expression for the focal adhesion signaling pathways.
7.5.2 Olfactory neurosphere formation in schizophrenia The neurosphere assay with multiple cell lines is very labour intensive, but it would be preferable to perform the assay over multiple generations. An even better analysis of
146 density would be to perform 3D image reconstructions for all the time points, to better assess neurosphere density. This would allow us to see whether the difference in size and density are constant, increased or reduced. Growing the neurospheres over multiple generations selects for multipotent and self renewing cells (Wetzig 2007), which would tell us whether the differences in cell-cell interactions are limited to progenitors or are generic in the culture system. One would also attempt to look at the phenotype ratios and 3D architecture within the neurospheres to assess their proliferation dynamics. Furthermore, expanding differentiation studies to obtain more terminally differentiated cells, and improve on the resolution of expressed phenotype by testing for more cell markers is recommended.
7.5.3 Focal adhesion in schizophrenia The attachment assay did not include a standard curve because the cells intended for a standard curve did not attach sufficiently, and subsequently washed off during the assay. However a relative comparison of cell attachment between patients and control cell lines was possible.
7.6 Implications for further studies There are several important issues regarding the dysfunctions identified in this thesis. Are the observed dysfunctions causal or compensatory in the etiology of schizophrenia? For example, do the protein levels of these differently expressed genes reflect the changes seen in transcript levels? Do the changes in focal adhesion signaling and neural differentiation reflect dysfunctions in brain development and function? Does the mitochondrial dysfunction and altered oxidative stress response affect neural development or is it the downstream result of other dysfunctional mechanisms? How are these dysfunctions affected by different extrinsic signals?
Some of these questions can be investigated using small interfering RNA, whereby the expression of integrin, focal adhesion kinase and vinculin is inhibited in control cell lines. Together with western blots and ELISA, to test the effect on protein levels and their activity, we would assess whether these molecules are central in the altered cell functions seen in the patient group. Also assessing the size and protein content of focal adhesions, as well as the attachment strength of the cells, would allow us to better understand the increase in cell
147 attachment and what role focal adhesions play. Together with measurements of RELN protein levels, this would tell us whether these enzymes are central in the observed phenotype of the schizophrenia cell lines. Using various activity assays looking at the activity of complex I and glutathione system enzymes under stress, we would better understand the altered mitochondrial and oxidative stress response in neural-derived and non-neural tissue. This would help elucidate the apparent absence of a mitochondrial dysfunction in non-neural tissue. Also, testing the effect of oxidative stress and various culture conditions on gene expression, could allow us to better understand the interactions of the dysregulated signaling pathways. This may also shed some light on the mechanisms behind these dysfunctions. Studying differentiation of olfactory stem cell cultures, comparing the impact of various growth and transcription factors on the differentiation of schizophrenia and control cell lines, could elucidate the differences observed in neural differentiation. These studies could provide valuable insight into neurodevelopmental abnormalities in schizophrenia.
7.7 Clinical implications Continued investigations of differentially expressed genes and dysregulated pathways associated with schizophrenia may provide researchers and clinicians with biological markers for the disease. The necessity for olfactory biopsies makes the olfactory stem cell model unpleasant to impose upon patients, however it may help confirm difficult diagnoses. With future studies into the expression profile of schizophrenia using the olfactory stem cell model, information could be provided regarding the severity of the illness or even identifying and diagnosing high-risk individuals prior to onset of symptoms. It may also help guide the development of treatments tailored to the individual patient. By better understanding the metabolic differences that occur in, or cause schizophrenia, new targets for the development of new pharmacological targets that are both more effective while minimizing unpleasant side effects are possible (Sawa and Snyder 2003).
7.8 Summary The altered expression of disease-related in neurosphere-derived cells from patients with schizophrenia indicates that the molecular differences found in the olfactory stem cell model may reflect some of the differences reported in the brain. Of particular interest were the notable disruptions in focal adhesion signaling pathways, including the integrin and
148 ephrin signaling pathways. The difference between neural-derived and non-neural tissue was most pronounced in the microarray analysis, where the neural-derived tissue indicated disease pathology with significant complexity, while the skin fibroblasts displayed differences in cell adhesion but no significant change in gene expression. There was also an impaired oxidative stress response in both cell models, and also a mitochondrial dysfunction in the olfactory stem cell lines, which agrees with the hypothesis that mitochondrial dysfunction and oxidative stress oxidative stress is involved in schizophrenia pathophysiology. We found differences in neural differentiation of olfactory stem cell lines, showing altered neurogenesis in cells from schizophrenia patients. Though skin fibroblasts exhibit functional differences and have provided some insight into schizophrenia biology, a neural-derived cell model such as olfactory stem cells enables us to manipulate and study differences in the dynamics of neural tissue development and function in vitro. We conclude fibroblasts are not informative as a cell model for schizophrenia, and that olfactory stem cell cultures provide a novel and promising tool for studying neurobiological aspects of schizophrenia. These new data therefore have provided new insight into the pathophysiology of schizophrenia.
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Appendices
Appendix 1A Appendix 1A contains the gene list from the neurosphere-derived cell microarray study. The appendix contains genes significantly differently expressed exclusively as a result of schizophrenia.
Fold Genbank Gene name Description Change NM_052988 CDK10 cyclin-dependent kinase (CDC2-like) 10 -1.136 NM_004512 IL11RA interleukin 11 receptor, alpha 1.318 NM_172373 ELF1 E74-like factor 1 (ets domain transcription factor) 1.222 NM_001453 FOXC1 forkhead box C1 -1.279 NM_006621 AHCYL1 S-adenosylhomocysteine hydrolase-like 1 1.098 NM_015299 KIAA0323 KIAA0323 1.162 NM_020194 C2orf33 chromosome 2 open reading frame 33 1.231 NM_016122 CCDC41 coiled-coil domain containing 41 -1.188 NM_145263 SPATA18 spermatogenesis associated 18 homolog (rat) 1.308 NM_001007 RPS4X ribosomal protein S4, X-linked 1.209 NM_005486 TOM1L1 target of myb1 (chicken)-like 1 1.521 NM_003028 SHB Src homology 2 domain containing adaptor protein B -1.351 NM_018441 PECR peroxisomal trans-2-enoyl-CoA reductase 1.260 NM_207644 C22orf36 chromosome 22 open reading frame 36 1.299 NM_005370 RAB8A RAB8A, member RAS oncogene family -1.099 NM_002961 S100A4 S100 calcium binding protein A4 1.202 NM_017707 DDEFL1 development and differentiation enhancing factor-like 1 1.444 NM_012245 SNW1 SNW domain containing 1 1.078 NM_005654 NR2F1 nuclear receptor subfamily 2, group F, member 1 -1.727 NM_001013848 EXOC6 exocyst complex component 6 -1.280 NM_025189 ZNF430 zinc finger protein 430 1.293 NM_031210 C14orf156 chromosome 14 open reading frame 156 -1.133 NM_006392 NOL5A nucleolar protein 5A (56kDa with KKE/D repeat) -1.161 NM_198580 SLC27A1 solute carrier family 27 (fatty acid transporter), member 1 1.364 NM_006278 ST3GAL4 ST3 beta-galactoside alpha-2,3-sialyltransferase 4 1.188 NM_006299 ZNF193 zinc finger protein 193 -1.138 NM_014652 IPO13 importin 13 -1.178 NM_006377 UNC13B unc-13 homolog B (C. elegans) -1.230 NM_013372 GREM1 gremlin 1, cysteine knot superfamily, homolog (Xenopus laevis) 2.562 NM_018083 ZNF358 zinc finger protein 358 1.259 NM_012456 TIMM10 translocase of inner mitochondrial membrane 10 homolog (yeast) -1.639 NM_014957 DENND3 DENN/MADD domain containing 3 1.298 NM_177436 CSE1L CSE1 chromosome segregation 1-like -1.233 NM_014018 MRPS28 mitochondrial ribosomal protein S28 -1.151 solute carrier family 25 (mitochondrial carrier; oxoglutarate carrier), member NM_003562 SLC25A11 -1.138 11 NM_015141 GPD1L glycerol-3-phosphate dehydrogenase 1-like -1.163 NM_003311 PHLDA2 pleckstrin homology-like domain, family A, member 2 -1.595 NM_052873 C14orf179 chromosome 14 open reading frame 179 1.121
151
NM_015261 NCAPD3 non-SMC condensin II complex, subunit D3 -1.361 solute carrier family 25 (mitochondrial carrier; adenine nucleotide NM_001151 SLC25A4 -1.351 translocator), member 4 NM_003582 DYRK3 dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 3 -1.144 NM_018229 C14orf108 chromosome 14 open reading frame 108 -1.164 NM_000993 RPL31 ribosomal protein L31 1.065 NM_175902 FLJ22222 hypothetical protein FLJ22222 1.234 NM_013300 C12orf24 chromosome 12 open reading frame 24 -1.222 NM_015409 EP400 E1A binding protein p400 1.129 NM_004941 DHX8 DEAH (Asp-Glu-Ala-His) box polypeptide 8 -1.140 guanine nucleotide binding protein (G protein), alpha inhibiting activity NM_002069 GNAI1 1.218 polypeptide 1 NM_014577 BRD1 bromodomain containing 1 1.167 NM_016048 ISOC1 isochorismatase domain containing 1 -1.167 NM_003193 TBCE tubulin folding cofactor E -1.122 NM_172169 CAMK2G calcium/calmodulin-dependent protein kinase (CaM kinase) II gamma 1.178 NM_018442 IQWD1 IQ motif and WD repeats 1 1.106 NM_001017956 OS9 amplified in osteosarcoma 1.175 CTD (carboxy-terminal domain, RNA polymerase II, polypeptide A) NM_004715 CTDP1 1.155 phosphatase, subunit 1 NM_030796 ECOP EGFR-coamplified and overexpressed protein 1.157 NM_007184 NISCH nischarin 1.229 solute carrier family 35 (UDP-glucuronic acid/UDP-N-acetylgalactosamine NM_015139 SLC35D1 -1.389 dual transporter), member D1 NM_004586 RPS6KA3 ribosomal protein S6 kinase, 90kDa, polypeptide 3 1.178 NM_032246 MEX3B ring finger and KH domain containing 3 -1.587 NM_021830 PEO1 progressive external ophthalmoplegia 1 -1.215 NM_001004 RPLP2 ribosomal protein, large, P2 1.085 NM_031903 MRPL32 mitochondrial ribosomal protein L32 -1.114 NM_138288 C14orf147 chromosome 14 open reading frame 147 1.135 NM_020347 LZTFL1 leucine zipper transcription factor-like 1 1.241 NM_139321 ATRN attractin 1.157 NM_017548 CDV3 CDV3 homolog (mouse) 1.154 NM_015345 DAAM2 dishevelled associated activator of morphogenesis 2 1.830 sema domain, immunoglobulin domain (Ig), short basic domain, secreted, NM_001005914 SEMA3B 1.885 (semaphorin) 3B NM_212552 BOLA3 bolA homolog 3 (E. coli) -1.199 NM_000610 CD44 CD44 molecule (Indian blood group) 1.501 NM_021129 PPA1 pyrophosphatase (inorganic) 1 -1.124 NM_002593 PCOLCE procollagen C-endopeptidase enhancer 2.099 NM_004704 RRP9 RRP9, small subunit (SSU) processome component, homolog (yeast) -1.190 NM_016103 SAR1B SAR1 gene homolog B (S. cerevisiae) -1.155 NM_002047 GARS glycyl-tRNA synthetase -1.094 solute carrier family 25 (mitochondrial carrier; adenine nucleotide NM_001152 SLC25A5 -1.185 translocator), member 5 NM_006893 LGTN ligatin 1.165 NM_032424 KIAA1826 KIAA1826 -1.119 NM_018022 TMEM51 transmembrane protein 51 -1.464 NM_012088 PGLS 6-phosphogluconolactonase 1.155 NM_001642 APLP2 amyloid beta (A4) precursor-like protein 2 1.245 NM_194430 ANG ribonuclease, RNase A family, 4 1.329
152
NM_015523 REXO2 REX2, RNA exonuclease 2 homolog (S. cerevisiae) -1.068 NM_014880 CD302 CD302 molecule 1.675 NM_018992 KCTD5 potassium channel tetramerisation domain containing 5 -1.161 NM_017722 TRMT1 TRM1 tRNA methyltransferase 1 homolog (S. cerevisiae) -1.107 NM_012337 CCDC19 coiled-coil domain containing 19 -1.152 NM_021260 ZFYVE1 zinc finger, FYVE domain containing 1 1.143 NM_030666 SERPINB1 serpin peptidase inhibitor, clade B (ovalbumin), member 1 1.522 NM_004934 CDH18 cadherin 18, type 2 2.612 Finkel-Biskis-Reilly murine sarcoma virus (FBR-MuSV) ubiquitously NM_001997 FAU 1.121 expressed (fox derived); ribosomal protein S30 NM_006994 BTN3A3 butyrophilin, subfamily 3, member A3 1.288 NM_138384 MTG1 mitochondrial GTPase 1 homolog (S. cerevisiae) -1.359 NM_020773 TBC1D14 TBC1 domain family, member 14 1.123 NM_014709 USP34 ubiquitin specific peptidase 34 1.115 NM_177453 PAQR3 progestin and adipoQ receptor family member III -1.143 NM_032740 SFT2D3 SFT2 domain containing 3 1.299 NM_206962 PRMT2 protein arginine methyltransferase 2 1.242 NM_015292 FAM62A family with sequence similarity 62 (C2 domain containing), member A -1.127 NM_024656 GLT25D1 glycosyltransferase 25 domain containing 1 -1.182 5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase/IMP NM_004044 ATIC -1.130 cyclohydrolase NM_001439 EXTL2 exostoses (multiple)-like 2 -1.202 NM_006982 CART1 cartilage paired-class homeoprotein 1 2.849 NM_014729 TOX thymocyte selection-associated high mobility group box 1.593 NM_019012 PLEKHA5 pleckstrin homology domain containing, family A member 5 1.479 NM_001560 IL13RA1 interleukin 13 receptor, alpha 1 1.156 NM_020762 SRGAP1 SLIT-ROBO Rho GTPase activating protein 1 1.335 NM_007008 RTN4 reticulon 4 -1.125 NM_006824 EBNA1BP2 EBNA1 binding protein 2 -1.196 NM_015392 NPDC1 neural proliferation, differentiation and control, 1 -1.186 NM_016569 TBX3 T-box 3 (ulnar mammary syndrome) 1.331 NM_001002913 PTRH1 peptidyl-tRNA hydrolase 1 homolog (S. cerevisiae) -1.199 NM_152480 C19orf23 chromosome 19 open reading frame 23 -1.253 NM_017811 UBE2R2 ubiquitin-conjugating enzyme E2R 2 1.309 NM_002780 PSG4 pregnancy specific beta-1-glycoprotein 4 7.605 NM_020365 EIF2B3 eukaryotic translation initiation factor 2B, subunit 3 gamma, 58kDa -1.163 NM_014369 PTPN18 protein tyrosine phosphatase, non-receptor type 18 (brain-derived) -1.148 NM_014933 SEC31A SEC31 homolog A (S. cerevisiae) 1.107 NM_001018104 FAHD1 fumarylacetoacetate hydrolase domain containing 1 -1.198 NM_170726 ALDH4A1 aldehyde dehydrogenase 4 family, member A1 -1.220 solute carrier family 25 (mitochondrial carrier; ornithine transporter) member NM_014252 SLC25A15 -1.259 15 NM_133503 DCN decorin 2.159 NM_002802 PSMC1 proteasome (prosome, macropain) 26S subunit, ATPase, 1 -1.101 NM_144584 C1orf59 chromosome 1 open reading frame 59 -1.393 NM_017686 GDAP2 ganglioside induced differentiation associated protein 2 -1.190 NM_001004019 FBLN2 fibulin 2 1.903 NM_002823 PTMA prothymosin, alpha (gene sequence 28) 1.390 NM_153211 C18orf17 chromosome 18 open reading frame 17 1.508
153
NM_002546 TNFRSF11B tumor necrosis factor receptor superfamily, member 11b (osteoprotegerin) 1.315 NM_181050 AXIN1 axin 1 1.131 NM_052837 SCAMP3 secretory carrier membrane protein 3 -1.153 NM_032221 CHD6 chromodomain helicase DNA binding protein 6 1.169 NM_033649 FGF18 fibroblast growth factor 18 1.680 NM_003156 STIM1 stromal interaction molecule 1 1.141 NM_017857 SSH3 slingshot homolog 3 (Drosophila) 1.201 NM_000146 FTL ferritin, light polypeptide 1.062 NM_024014 HOXA6 homeobox A6 -1.176 NM_024661 CCDC51 coiled-coil domain containing 51 -1.193 NM_012074 DPF3 D4, zinc and double PHD fingers, family 3 1.832 NM_001269 SNHG3-RCC1 regulator of chromosome condensation 1 1.354 NM_183063 RNF7 ring finger protein 7 -1.238 NM_133491 SAT2 spermidine/spermine N1-acetyltransferase 2 1.207 NM_006534 NCOA3 nuclear receptor coactivator 3 1.155 NM_015905 TRIM24 tripartite motif-containing 24 -1.136 NM_014324 AMACR alpha-methylacyl-CoA racemase -1.414 NM_199173 BGLAP bone gamma-carboxyglutamate (gla) protein (osteocalcin) 1.131 NM_024569 MPZL1 myelin protein zero-like 1 1.187 NM_153812 PHF13 PHD finger protein 13 1.166 NM_004285 H6PD hexose-6-phosphate dehydrogenase (glucose 1-dehydrogenase) 1.396 NM_006432 NPC2 Niemann-Pick disease, type C2 -1.122 NM_014779 TSC22D2 TSC22 domain family, member 2 1.211 NM_014837 SMG7 Smg-7 homolog, nonsense mediated mRNA decay factor (C. elegans) -1.116 NM_001733 C1R complement component 1, r subcomponent 2.605 NM_017772 TBC1D22B TBC1 domain family, member 22B -1.161 NM_058246 DNAJB6 DnaJ (Hsp40) homolog, subfamily B, member 6 -1.181 NM_030767 AKNA AT-hook transcription factor 1.291 NM_000907 NPR2 natriuretic peptide receptor 1.241 NM_015282 CLASP1 cytoplasmic linker associated protein 1 1.123 NM_145320 OSBPL3 oxysterol binding protein-like 3 1.365 NM_015509 NECAP1 NECAP endocytosis associated 1 -1.142 NM_000955 PTGER1 prostaglandin E receptor 1 (subtype EP1), 42kDa -1.527 NM_017822 C12orf41 chromosome 12 open reading frame 41 -1.080 NM_002332 LRP1 low density lipoprotein-related protein 1 (alpha-2-macroglobulin receptor) 1.419 NM_024580 EFTUD1 elongation factor Tu GTP binding domain containing 1 -1.193 NM_152549 CCDC112 coiled-coil domain containing 112 -1.203 NM_152755 MGC40499 PRotein Associated with Tlr4 1.191 NM_022072 NSUN3 NOL1/NOP2/Sun domain family, member 3 -1.172 NM_005536 IMPA1 inositol(myo)-1(or 4)-monophosphatase 1 1.271 NM_000987 RPL26 ribosomal protein L26 1.192 NM_145230 ATP6V0E2 ATPase, H+ transporting V0 subunit e2 -1.287 NM_006912 RIT1 Ras-like without CAAX 1 -1.253 NM_014309 RBM9 RNA binding motif protein 9 -1.271 NM_018159 NUDT11 nudix (nucleoside diphosphate linked moiety X)-type motif 11 -1.185 NM_022044 SDF2L1 stromal cell-derived factor 2-like 1 -1.198 NM_006680 ME3 malic enzyme 3, NADP(+)-dependent, mitochondrial 1.365
154
NM_001002926 TWISTNB TWIST neighbor -1.135 NM_017585 SLC2A6 solute carrier family 2 (facilitated glucose transporter), member 6 -1.477 NM_002180 IGHMBP2 immunoglobulin mu binding protein 2 -1.190 NM_002355 M6PR mannose-6-phosphate receptor (cation dependent) -1.178 NM_024594 PANK3 pantothenate kinase 3 -1.131 NM_015329 KIAA0892 KIAA0892 1.215 NM_182507 KRT80 keratin 80 -1.848 NM_144728 DUSP10 dual specificity phosphatase 10 1.644 NM_198400 NEDD4 neural precursor cell expressed, developmentally down-regulated 4 1.254 NM_001025071 RPS14 ribosomal protein S14 1.125 NM_153698 C9orf21 chromosome 9 open reading frame 21 1.281 NM_001003703 ATP5J ATP synthase, H+ transporting, mitochondrial F0 complex, subunit F6 -1.115 NM_001014431 AKT1 v-akt murine thymoma viral oncogene homolog 1 1.135 NM_002486 NCBP1 nuclear cap binding protein subunit 1, 80kDa -1.143 NM_004811 LPXN leupaxin 1.515 NM_058182 C21orf51 chromosome 21 open reading frame 51 1.271 NM_018380 DDX28 DEAD (Asp-Glu-Ala-Asp) box polypeptide 28 -1.117 NM_014813 LRIG2 leucine-rich repeats and immunoglobulin-like domains 2 1.158 NM_183382 RNF13 1.139 cysteine conjugate-beta lyase; cytoplasmic (glutamine transaminase K, NM_004059 CCBL1 -1.192 kyneurenine aminotransferase) NM_017626 DNAJB12 DnaJ (Hsp40) homolog, subfamily B, member 12 1.202 NM_019049 FLJ20054 hypothetical protein FLJ20054 1.160 NM_018113 LMBR1L limb region 1 homolog (mouse)-like 1.256 protein tyrosine phosphatase-like (proline instead of catalytic arginine), NM_014241 PTPLA -1.326 member A NM_130469 JDP2 jun dimerization protein 2 1.259 NM_007364 TMED3 transmembrane emp24 protein transport domain containing 3 -1.200 solute carrier family 1 (neuronal/epithelial high affinity glutamate transporter, NM_004170 SLC1A1 -2.331 system Xag), member 1 NM_024692 CLIP4 CAP-GLY domain containing linker protein family, member 4 1.230 NM_033301 RPL8 ribosomal protein L8 -1.481 NM_015414 RPL36 ribosomal protein L36 -1.403 NM_004655 AXIN2 axin 2 (conductin, axil) 2.147 TAF6-like RNA polymerase II, p300/CBP-associated factor (PCAF)- NM_006473 TAF6L -1.124 associated factor, 65kDa NM_003573 LTBP4 latent transforming growth factor beta binding protein 4 1.473 NM_012176 FBXO4 F-box protein 4 1.283 NM_001613 ACTA2 actin, alpha 2, smooth muscle, aorta -1.603 NM_020320 RARS2 arginyl-tRNA synthetase 2, mitochondrial (putative) 1.154 NM_000628 IL10RB interleukin 10 receptor, beta 1.234 NM_004273 CHST3 carbohydrate (chondroitin 6) sulfotransferase 3 -1.280 NM_033661 WDR4 WD repeat domain 4 -1.353 NM_002101 GYPC glycophorin C (Gerbich blood group) 1.787 NM_000110 DPYD dihydropyrimidine dehydrogenase 1.360 NM_001008697 TFIP11 tuftelin interacting protein 11 1.281 NM_012470 TNPO3 transportin 3 -1.116 NM_019026 TMCO1 transmembrane and coiled-coil domains 1 -1.104 NM_145006 SUSD3 sushi domain containing 3 -2.288 NM_015000 STK38L serine/threonine kinase 38 like -1.745
155
NM_002797 PSMB5 proteasome (prosome, macropain) subunit, beta type, 5 -1.095 NM_002937 RNASE4 ribonuclease, RNase A family, 4 1.445 NM_014342 MTCH2 mitochondrial carrier homolog 2 (C. elegans) -1.263 NM_002868 RAB5B RAB5B, member RAS oncogene family -1.092 NM_032375 AKT1S1 AKT1 substrate 1 (proline-rich) -1.170 NM_005886 KATNB1 katanin p80 (WD repeat containing) subunit B 1 -1.159 NM_014450 SIT1 signaling threshold regulating transmembrane adaptor 1 2.369 NM_022551 RPS18 ribosomal protein S18 1.122 NM_017939 FLJ20718 HEAT repeat containing 3 -1.185 NM_005391 PDK3 pyruvate dehydrogenase kinase, isozyme 3 -1.689 NM_016310 POLR3K polymerase (RNA) III (DNA directed) polypeptide K, 12.3 kDa -1.266 NM_017946 FKBP14 FK506 binding protein 14, 22 kDa -1.182 NM_022447 PAPD5 PAP associated domain containing 5 -1.130 1-acylglycerol-3-phosphate O-acyltransferase 7 (lysophosphatidic acid NM_153613 AGPAT7 -1.236 acyltransferase, eta) NM_001031703 TMEM103 transmembrane protein 103 -1.109 NM_000612 INS-IGF2 insulin-like growth factor 2 (somatomedin A) 3.754 NM_020825 CRAMP1L Crm, cramped-like (Drosophila) 1.180 NM_004717 DGKI diacylglycerol kinase, iota -2.288 NM_032603 LOXL3 lysyl oxidase-like 3 -1.484 NM_014300 SEC11A SEC11 homolog A (S. cerevisiae) 1.149 NM_033416 IMP4 IMP4, U3 small nucleolar ribonucleoprotein, homolog (yeast) -1.196 NM_016324 ZNF274 zinc finger protein 274 -1.266 NM_015526 CLIP3 CAP-GLY domain containing linker protein 3 1.282 NM_002093 GSK3B glycogen synthase kinase 3 beta -1.233 NM_001961 EEF2 eukaryotic translation elongation factor 2 1.153 NM_001183 ATP6AP1 ATPase, H+ transporting, lysosomal accessory protein 1 -1.163 NM_007270 FKBP9 FK506 binding protein 9, 63 kDa 1.225 NM_014165 C6orf66 chromosome 6 open reading frame 66 -1.267 NM_005857 ZMPSTE24 zinc metallopeptidase (STE24 homolog, S. cerevisiae) -1.153 NM_001797 CDH11 cadherin 11, type 2, OB-cadherin (osteoblast) 1.177 NM_002977 SCN9A sodium channel, voltage-gated, type IX, alpha subunit -1.397 NM_002067 GNA11 guanine nucleotide binding protein (G protein), alpha 11 (Gq class) -1.103 NM_148957 TNFRSF19 tumor necrosis factor receptor superfamily, member 19 2.763 caspase 1, apoptosis-related cysteine peptidase (interleukin 1, beta, NM_033294 CASP1 2.284 convertase) NM_000308 CTSA cathepsin A 1.277 NM_144658 DOCK11 dedicator of cytokinesis 11 1.680 NM_006548 IGF2BP2 insulin-like growth factor 2 mRNA binding protein 2 1.133 NM_015571 SENP6 SUMO1/sentrin specific peptidase 6 1.113 NM_013319 UBIAD1 UbiA prenyltransferase domain containing 1 -1.205 NM_032026 TATDN1 TatD DNase domain containing 1 1.130 NM_001020 RPS16 ribosomal protein S16 1.071 NM_033251 RPL13 ribosomal protein L13 -1.348 NM_015559 SETBP1 SET binding protein 1 1.952 NM_032534 KRBA1 KRAB-A domain containing 1 1.177 NM_001030007 AP1G1 adaptor-related protein complex 1, gamma 1 subunit 1.151 NM_024825 PODNL1 podocan-like 1 1.410 NM_198540 B3GNT8 UDP-GlcNAc:betaGal beta-1,3-N-acetylglucosaminyltransferase 8 1.321
156
NM_014830 ZBTB39 zinc finger and BTB domain containing 39 -1.198 NM_003732 EIF4EBP3 eukaryotic translation initiation factor 4E binding protein 3 1.430 NM_005417 SRC v-src sarcoma (Schmidt-Ruppin A-2) viral oncogene homolog (avian) 1.178 NM_000824 GLRB glycine receptor, beta -1.458 NM_020164 ASPH aspartate beta-hydroxylase 1.349 xylosylprotein beta 1,4-galactosyltransferase, polypeptide 7 NM_007255 B4GALT7 -1.183 (galactosyltransferase I) NM_145686 MAP4K4 mitogen-activated protein kinase kinase kinase kinase 4 1.242 NM_003809 TNFSF12 tumor necrosis factor (ligand) superfamily, member 12 1.216 NM_031488 L3MBTL2 l(3)mbt-like 2 (Drosophila) -1.127 NM_014389 PELP1 proline, glutamic acid and leucine rich protein 1 -1.127 NM_004697 PRPF4 PRP4 pre-mRNA processing factor 4 homolog (yeast) -1.168 NM_004281 BAG3 BCL2-associated athanogene 3 -1.181 NM_014925 R3HDM2 R3H domain containing 2 1.186 NM_020148 SPIRE1 spire homolog 1 (Drosophila) -1.167 NM_182916 TRNT1 tRNA nucleotidyl transferase, CCA-adding, 1 -1.227 NM_003283 TNNT1 troponin T type 1 (skeletal, slow) 2.118 NM_001733 C1R complement component 1, r subcomponent 2.605 NM_018287 ARHGAP12 Rho GTPase activating protein 12 1.299 NM_002727 SRGN serglycin -2.049 NM_018561 USP49 ubiquitin specific peptidase 49 1.264 NM_004711 SYNGR1 synaptogyrin 1 1.522 NM_033280 SEC11C SEC11 homolog C (S. cerevisiae) -1.366 UDP-N-acetyl-alpha-D-galactosamine:polypeptide N- NM_003774 GALNT4 1.147 acetylgalactosaminyltransferase 4 (GalNAc-T4) NM_015396 ARMC8 armadillo repeat containing 8 -1.198 NM_014884 SFRS14 splicing factor, arginine/serine-rich 14 -1.167 NM_032921 AGXT2L2 alanine-glyoxylate aminotransferase 2-like 2 1.286 NM_005003 NDUFAB1 NADH dehydrogenase (ubiquinone) 1, alpha/beta subcomplex, 1, 8kDa -1.135 NM_005026 PIK3CD phosphoinositide-3-kinase, catalytic, delta polypeptide 1.428 NM_198567 C5orf25 chromosome 5 open reading frame 25 1.158 NM_033300 LRP8 low density lipoprotein receptor-related protein 8, apolipoprotein e receptor -1.543 NM_004053 BYSL bystin-like -1.209 NM_001002034 FAM109B family with sequence similarity 109, member B 1.175 NM_032331 ECE2 hypothetical protein MGC2408 -1.422 NM_199129 Kua ubiquitin-conjugating enzyme variant Kua -1.263 NM_015097 CLASP2 cytoplasmic linker associated protein 2 -1.346 NM_001025080 CD47 CD47 molecule 1.245 NM_001012752 ABI1 abl-interactor 1 1.126 NM_018442 IQWD1 IQ motif and WD repeats 1 1.106 NM_006246 PPP2R5E protein phosphatase 2, regulatory subunit B', epsilon isoform -1.126 NM_018412 ST7 suppression of tumorigenicity 7 -1.143 NM_016390 C9orf114 chromosome 9 open reading frame 114 -1.157 NM_002166 ID2 inhibitor of DNA binding 2, dominant negative helix-loop-helix protein 1.870 NM_030932 DIAPH3 diaphanous homolog 3 (Drosophila) -1.550 NM_001001522 TAGLN transgelin -2.203 NM_014909 VASH1 vasohibin 1 1.475 NM_000877 IL1R1 interleukin 1 receptor, type I 1.961 NM_002925 RGS10 regulator of G-protein signalling 10 1.294
157
NM_006295 VARS valyl-tRNA synthetase -1.225 NM_018294 CWF19L1 CWF19-like 1, cell cycle control (S. pombe) -1.241 NM_001376 DYNC1H1 dynein, cytoplasmic 1, heavy chain 1 -1.124 NM_004623 TTC4 tetratricopeptide repeat domain 4 -1.099 NM_199246 CCNG1 cyclin G1 1.251 NM_006209 ENPP2 ectonucleotide pyrophosphatase/phosphodiesterase 2 (autotaxin) 2.641 NM_014935 PLEKHA6 pleckstrin homology domain containing, family A member 6 3.091 NM_000159 GCDH glutaryl-Coenzyme A dehydrogenase -1.212 NM_152999 STEAP2 six transmembrane epithelial antigen of the prostate 2 1.842 NM_017883 WDR13 WD repeat domain 13 1.139 NM_000981 RPL19 ribosomal protein L19 1.097 NM_015160 PMPCA peptidase (mitochondrial processing) alpha -1.160 NM_006559 KHDRBS1 KH domain containing, RNA binding, signal transduction associated 1 1.197 NM_032486 DCTN5 dynactin 5 (p25) -1.143 NM_005952 MT1X metallothionein 1X -1.475 NM_015335 MED13L thyroid hormone receptor associated protein 2 1.267 NM_012230 POMZP3 POM (POM121 homolog, rat) and ZP3 fusion 2.149 NM_006260 DNAJC3 DnaJ (Hsp40) homolog, subfamily C, member 3 -1.399 NM_019600 KIAA1370 KIAA1370 1.206 NM_001012479 GRN granulin 1.231 NM_005045 RELN reelin -4.950 NM_152271 LONRF1 LON peptidase N-terminal domain and ring finger 1 1.206 NM_080632 UPF3B UPF3 regulator of nonsense transcripts homolog B (yeast) -1.182 NM_000719 CACNA1C calcium channel, voltage-dependent, L type, alpha 1C subunit 1.316 NM_024052 C17orf39 chromosome 17 open reading frame 39 1.198 NM_013233 STK39 serine threonine kinase 39 (STE20/SPS1 homolog, yeast) -1.221 NM_006823 PKIA protein kinase (cAMP-dependent, catalytic) inhibitor alpha -1.876 NM_005864 EFS embryonal Fyn-associated substrate 1.521 NADH dehydrogenase (ubiquinone) Fe-S protein 8, 23kDa (NADH- NM_002496 NDUFS8 -1.183 coenzyme Q reductase) NM_006999 POLS polymerase (DNA directed) sigma 1.156 NM_006973 ZNF32 zinc finger protein 32 1.163 NM_001294 CLPTM1 cleft lip and palate associated transmembrane protein 1 -1.170 NM_013316 CNOT4 CCR4-NOT transcription complex, subunit 4 -1.106 NM_018256 WDR12 WD repeat domain 12 -1.195 NM_174938 FRMD3 FERM domain containing 3 -2.004 NM_006303 JTV1 JTV1 gene -1.172 NM_031426 C9orf58 chromosome 9 open reading frame 58 -1.225 NM_005792 MPHOSPH6 M-phase phosphoprotein 6 1.525 NM_001964 EGR1 early growth response 1 1.621 NM_002064 GLRX glutaredoxin (thioltransferase) 1.348 NM_001610 ACP2 acid phosphatase 2, lysosomal -1.134 NM_032412 C5orf32 chromosome 5 open reading frame 32 -1.302 NM_018208 ETNK2 ethanolamine kinase 2 1.672 NM_002618 PEX13 peroxisome biogenesis factor 13 -1.227 NM_016306 DNAJB11 DnaJ (Hsp40) homolog, subfamily B, member 11 -1.164 NM_018250 INTS9 integrator complex subunit 9 -1.138 NM_153713 LIX1L Lix1 homolog (mouse)-like -1.200
158
NM_152387 KCTD18 potassium channel tetramerisation domain containing 18 1.202 NM_144629 RFTN2 raftlin family member 2 2.553 NM_000214 JAG1 jagged 1 (Alagille syndrome) -2.105 NM_175047 PILRB paired immunoglobin-like type 2 receptor beta -1.221 NM_001625 AK2 adenylate kinase 2 -1.188 NM_017420 SIX4 sine oculis homeobox homolog 4 (Drosophila) 1.351 NM_007125 UTY ubiquitously transcribed tetratricopeptide repeat gene, Y-linked 1.289 NM_003199 TCF4 transcription factor 4 1.399 NM_152523 CCNYL1 cyclin Y-like 1 -1.404 NM_018486 HDAC8 histone deacetylase 8 1.223 NM_002540 ODF2 outer dense fiber of sperm tails 2 -1.189 NM_003324 TULP3 tubby like protein 3 1.204 NM_003759 SLC4A4 solute carrier family 4, sodium bicarbonate cotransporter, member 4 -2.155 NM_001734 C1S complement component 1, s subcomponent 1.851 NM_004182 UXT ubiquitously-expressed transcript 1.159 NM_016823 CRK v-crk sarcoma virus CT10 oncogene homolog (avian) -1.179 NM_203364 CAPRIN1 cell cycle associated protein 1 -1.130 NM_080476 PIGU phosphatidylinositol glycan anchor biosynthesis, class U -1.247 NM_152871 FAS Fas (TNF receptor superfamily, member 6) 1.364 NM_020311 CXCR7 chemokine (C-X-C motif) receptor 7 6.394 NM_016277 RAB23 RAB23, member RAS oncogene family -1.381 NM_025176 KIAA0980 KIAA0980 protein 1.443 NM_006653 FRS3 fibroblast growth factor receptor substrate 3 1.266 NM_014292 CBX6 chromobox homolog 6 -1.127 NM_022461 AZI2 5-azacytidine induced 2 1.234 NM_207344 SPRYD4 SPRY domain containing 4 -1.245 NM_015449 C1orf43 chromosome 1 open reading frame 43 1.097 NM_014941 MORC2 MORC family CW-type zinc finger 2 -1.151 NM_199050 C21orf25 chromosome 21 open reading frame 25 -1.233 NM_014649 SAFB2 scaffold attachment factor B2 -1.104 NM_015364 LY96 lymphocyte antigen 96 2.302 NM_078468 BCCIP BRCA2 and CDKN1A interacting protein -1.161 NM_020313 CIAPIN1 cytokine induced apoptosis inhibitor 1 -1.156 NM_152655 ZNF585A zinc finger protein 585A 1.338 NM_015525 IBTK inhibitor of Bruton agammaglobulinemia tyrosine kinase 1.135 NM_003844 TNFRSF10A tumor necrosis factor receptor superfamily, member 10a 1.671 NM_002168 IDH2 isocitrate dehydrogenase 2 (NADP+), mitochondrial 1.417 NM_144653 BTBD14A BTB (POZ) domain containing 14A -1.439 NM_033500 HK1 hexokinase 1 -1.163 NM_022778 CCDC21 coiled-coil domain containing 21 -1.475 NM_001393 ECM2 extracellular matrix protein 2, female organ and adipocyte specific 2.188 NM_014220 TM4SF1 transmembrane 4 L six family member 1 -1.590 NM_001116 ADCY9 adenylate cyclase 9 1.264 NM_153214 FLJ37440 hypothetical protein FLJ37440 2.411 NM_014963 SBNO2 strawberry notch homolog 2 (Drosophila) 1.287 NM_016422 RNF141 ring finger protein 141 -1.427 NM_020300 MGST1 microsomal glutathione S-transferase 1 -1.639
159
CTD (carboxy-terminal domain, RNA polymerase II, polypeptide A) small NM_005730 CTDSP2 1.306 phosphatase 2 NM_004474 FOXD2 forkhead box D2 1.592 NM_018390 PLCXD1 phosphatidylinositol-specific phospholipase C, X domain containing 1 -1.387 NM_025201 PLEKHQ1 pleckstrin homology domain containing, family Q member 1 1.255 NM_032025 EIF2A eukaryotic translation initiation factor 2A, 65kDa 1.201 NM_001004431 METRNL meteorin, glial cell differentiation regulator-like 1.583 pterin-4 alpha-carbinolamine dehydratase/dimerization cofactor of NM_032151 PCBD2 -1.339 hepatocyte nuclear factor 1 alpha (TCF1) 2 NM_004127 GPS1 G protein pathway suppressor 1 -1.095 NM_014517 UBP1 upstream binding protein 1 (LBP-1a) -1.116 NM_144671 FAM109A family with sequence similarity 109, member A 1.235 NM_013299 SAC3D1 SAC3 domain containing 1 -1.279 NM_004727 SLC24A1 solute carrier family 24 (sodium/potassium/calcium exchanger), member 1 1.175 NM_144617 HSPB6 heat shock protein, alpha-crystallin-related, B6 -2.062 NM_004322 BAD BCL2-antagonist of cell death 1.158 NM_013434 KCNIP3 Kv channel interacting protein 3, calsenilin 2.279 NM_016245 HSD17B11 hydroxysteroid (17-beta) dehydrogenase 11 1.621 NM_153645 NUP50 nucleoporin 50kDa -1.290 NM_002812 PSMD8 proteasome (prosome, macropain) 26S subunit, non-ATPase, 8 -1.195 NM_007233 TP53AP1 TP53 activated protein 1 1.269 NM_005252 FOS v-fos FBJ murine osteosarcoma viral oncogene homolog 2.344 NM_015529 MOXD1 monooxygenase, DBH-like 1 2.272 solute carrier family 25 (mitochondrial carrier; adenine nucleotide NM_001636 SLC25A6 1.268 translocator), member 6 ST6 (alpha-N-acetyl-neuraminyl-2,3-beta-galactosyl-1,3)-N- NM_013443 ST6GALNAC6 1.275 acetylgalactosaminide alpha-2,6-sialyltransferase 6 NM_152644 FAM24B family with sequence similarity 24, member B -1.447 NM_006367 CAP1 CAP, adenylate cyclase-associated protein 1 (yeast) -1.147 NM_002874 RAD23B RAD23 homolog B (S. cerevisiae) 1.187 NM_004728 DDX21 DEAD (Asp-Glu-Ala-Asp) box polypeptide 21 -1.238 NM_000848 GSTM2 glutathione S-transferase M2 (muscle) 1.464 NM_203284 RBPJ recombination signal binding protein for immunoglobulin kappa J region 1.185 NM_024303 ZSCAN5 zinc finger and SCAN domain containing 5 1.265 NM_145739 OSBPL6 oxysterol binding protein-like 6 -1.486 NM_003400 XPO1 exportin 1 (CRM1 homolog, yeast) -1.144 NM_004586 RPS6KA3 ribosomal protein S6 kinase, 90kDa, polypeptide 3 1.178 NM_002117 HLA-C major histocompatibility complex, class I, C 1.857 NM_021927 GUF1 GUF1 GTPase homolog (S. cerevisiae) -1.427 NM_024336 IRX3 iroquois homeobox protein 3 3.146 NM_001009937 SLC25A26 solute carrier family 25, member 26 1.228 NM_012102 RERE arginine-glutamic acid dipeptide (RE) repeats 1.228 NM_018467 MDS032 uncharacterized hematopoietic stem/progenitor cells protein MDS032 1.189 NM_001008703 C6orf1 chromosome 6 open reading frame 1 -1.261 NM_153367 C10orf56 chromosome 10 open reading frame 56 1.313 NM_022089 ATP13A2 ATPase type 13A2 -1.389 NM_033396 TNKS1BP1 tankyrase 1 binding protein 1, 182kDa 1.301 NM_032444 BTBD12 BTB (POZ) domain containing 12 -1.269 NM_014960 ARSG arylsulfatase G -1.292 NM_175744 RHOC ras homolog gene family, member C -1.199
160
NM_005177 ATP6V0A1 ATPase, H+ transporting, lysosomal V0 subunit a1 -1.319 NM_138737 HEPH hephaestin 2.130 NM_004425 ECM1 extracellular matrix protein 1 1.499 NM_194428 DHX34 DEAH box polypeptide 34 -1.307 NM_013441 RCAN3 Down syndrome critical region gene 1-like 2 -1.292 NM_033160 ZNF658 zinc finger protein 658 1.787 NM_004085 TIMM8A translocase of inner mitochondrial membrane 8 homolog A (yeast) -1.376 NM_001009186 CCT6A chaperonin containing TCP1, subunit 6A (zeta 1) -1.159 NM_001003679 LEPR leptin receptor -1.730 NM_004901 ENTPD4 ectonucleoside triphosphate diphosphohydrolase 4 -1.536 NM_000355 TCN2 transcobalamin II; macrocytic anemia 1.517 NM_006708 GLO1 glyoxalase I -1.166 NM_001002017 HCFC1R1 host cell factor C1 regulator 1 (XPO1 dependent) 1.593 NM_017832 C9orf6 chromosome 9 open reading frame 6 -1.195 2-oxoglutarate and iron-dependent oxygenase domain containing 1; synonyms: TPA1, FLJ10826, KIAA1612; TPA1, termination and NM_001031707 OGFOD1 -1.227 polyadenylation 1, homolog; Homo sapiens 2-oxoglutarate and iron- dependent oxygenase domain containing 1 (OGFOD1), mRNA. NM_024706 ZNF668 zinc finger protein 668 1.137 NM_015251 ASCIZ ATM/ATR-Substrate Chk2-Interacting Zn2+-finger protein 1.143 NM_001008394 EID3 EP300 interacting inhibitor of differentiation 3 1.445 NM_015429 ABI3BP ABI gene family, member 3 (NESH) binding protein -1.761 NM_032777 GPR124 G protein-coupled receptor 124 1.649 NM_004958 FRAP1 FK506 binding protein 12-rapamycin associated protein 1 -1.121 NM_183049 TMSB4X thymosin-like 3 -1.071 NM_032985 SEC23B Sec23 homolog B (S. cerevisiae) -1.295 NM_004461 FARSA phenylalanyl-tRNA synthetase, alpha subunit -1.242 NM_004047 ATP6V0B ATPase, H+ transporting, lysosomal 21kDa, V0 subunit b -1.202 NM_012405 ICMT isoprenylcysteine carboxyl methyltransferase -1.211 NM_017530 LOC55565 hypothetical protein LOC55565 1.261 NM_020192 C7orf36 chromosome 7 open reading frame 36 1.111 NM_000788 DCK deoxycytidine kinase -1.449 NM_031266 HNRPAB heterogeneous nuclear ribonucleoprotein A/B -1.181 NM_006675 TSPAN9 tetraspanin 9 1.341 NM_018164 C12orf11 chromosome 12 open reading frame 11 -1.208 NM_001033046 C17orf62 chromosome 17 open reading frame 62 1.218 NM_001356 DDX3X DEAD (Asp-Glu-Ala-Asp) box polypeptide 3, X-linked 1.092 NM_053276 VIT vitrin 2.354 NM_018121 C10orf6 chromosome 10 open reading frame 6 -1.163 NM_006519 DYNLT1 dynein, light chain, Tctex-type 1 -1.183 NM_016022 APH1A anterior pharynx defective 1 homolog A (C. elegans) 1.169 NM_021135 RPS6KA2 ribosomal protein S6 kinase, 90kDa, polypeptide 2 1.243 NM_004251 RAB9A RAB9A, member RAS oncogene family 1.284 NM_020925 CACHD1 cache domain containing 1 1.949 NM_014278 HSPA4L heat shock 70kDa protein 4-like -1.422 NM_032823 C9orf3 chromosome 9 open reading frame 3 1.322 NM_032300 TCHP trichoplein, keratin filament binding -1.199 NM_004693 KRT75 keratin 75 1.377 NM_138387 G6PC3 glucose 6 phosphatase, catalytic, 3 -1.172
161
NM_153702 ELMOD2 ELMO/CED-12 domain containing 2 -1.245 NM_032350 C7orf50 hypothetical protein MGC11257 -1.148 NM_016582 SLC15A3 solute carrier family 15, member 3 1.399 NM_003491 ARD1A ARD1 homolog A, N-acetyltransferase (S. cerevisiae) -1.188 NM_001849 COL6A2 collagen, type VI, alpha 2 1.493 NM_001765 CD1C CD1c molecule 2.441 NM_002576 PAK1 p21/Cdc42/Rac1-activated kinase 1 (STE20 homolog, yeast) 1.186 NM_199133 LOC134145 hypothetical protein LOC134145 -1.160 NM_138571 HINT3 histidine triad nucleotide binding protein 3 -1.364 NM_203341 SEP15 15 kDa selenoprotein 1.100 NM_001009555 SH3D19 SH3 domain protein D19 1.307 tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, NM_003405 YWHAH -1.189 eta polypeptide NM_152776 GK5 glycerol kinase 5 (putative) -1.250 NM_080744 SRCRB4D scavenger receptor cysteine rich domain containing, group B (4 domains) -1.311 NM_007200 AKAP13 A kinase (PRKA) anchor protein 13 1.259 NM_152756 RICTOR rapamycin-insensitive companion of mTOR 1.136 NM_003863 DPM2 dolichyl-phosphate mannosyltransferase polypeptide 2, regulatory subunit -1.171 NM_005711 EDIL3 EGF-like repeats and discoidin I-like domains 3 -3.067 NM_003659 AGPS alkylglycerone phosphate synthase -1.253 NM_016516 VPS54 vacuolar protein sorting 54 homolog (S. cerevisiae) 1.185 NM_024754 PTCD2 pentatricopeptide repeat domain 2 -1.211 NM_003310 TSSC1 tumor suppressing subtransferable candidate 1 -1.155 NM_001032 RPS29 ribosomal protein S29 -1.044 NM_152391 PQLC3 PQ loop repeat containing 3 1.319 NM_032731 TXNL5 thioredoxin-like 5 -1.138 NM_013433 TNPO2 transportin 2 (importin 3, karyopherin beta 2b) -1.110 NM_021570 BARX1 BarH-like homeobox 1 -3.021 NM_005086 SSPN sarcospan (Kras oncogene-associated gene) 1.384 NM_004520 KIF2A kinesin heavy chain member 2A -1.229 NM_001845 COL4A1 collagen, type IV, alpha 1 -1.672 NM_005659 UFD1L ubiquitin fusion degradation 1 like (yeast) -1.351 NM_053041 COMMD7 COMM domain containing 7 -1.208 NM_018837 SULF2 sulfatase 2 2.127 NM_032852 ATG4C ATG4 autophagy related 4 homolog C (S. cerevisiae) -1.224 NM_032638 GATA2 GATA binding protein 2 1.311 NM_153831 PTK2 PTK2 protein tyrosine kinase 2 -1.198 NM_001005340 GPNMB glycoprotein (transmembrane) nmb 1.685 NM_001620 AHNAK AHNAK nucleoprotein 1.132 NM_152410 PACRG PARK2 co-regulated -1.198 NM_003373 VCL vinculin -1.117 NM_033089 ZCCHC3 zinc finger, CCHC domain containing 3 1.206 NM_020438 DOLPP1 dolichyl pyrophosphate phosphatase 1 -1.229 NM_001999 FBN2 fibrillin 2 (congenital contractural arachnodactyly) 2.731 NM_058216 RAD51C RAD51 homolog C (S. cerevisiae) -1.292 NM_170672 RASGRP3 RAS guanyl releasing protein 3 (calcium and DAG-regulated) 2.744 NM_015496 KIAA1429 KIAA1429 1.135 NM_015343 DULLARD dullard homolog (Xenopus laevis) 1.227
162
NM_004892 SEC22B SEC22 vesicle trafficking protein homolog B (S. cerevisiae) -1.171 NM_005828 WDR68 WD repeat domain 68 -1.174 NM_032797 AIFM2 apoptosis-inducing factor, mitochondrion-associated, 2 1.483 NM_021219 JAM2 junctional adhesion molecule 2 3.227 NM_030954 RNF170 ring finger protein 170 1.244 NM_021105 PLSCR1 phospholipid scramblase 1 1.439 NM_006905 PSG1 pregnancy specific beta-1-glycoprotein 1 5.221 NM_181676 PPP2R2B protein phosphatase 2 (formerly 2A), regulatory subunit B, beta isoform -4.149 NM_001012734 AGPAT4 1-acylglycerol-3-phosphate O-acyltransferase 4 1.626 NM_021235 EPS15L1 epidermal growth factor receptor pathway substrate 15-like 1 -1.292 NM_053067 UBQLN1 ubiquilin 1 -1.299 NM_016546 C1RL complement component 1, r subcomponent-like 1.707 NM_015171 XPO6 exportin 6 -1.171 NM_024517 PHF2 PHD finger protein 2 1.224 NM_012341 GTPBP4 GTP binding protein 4 -1.203 NM_006221 PIN1 protein (peptidylprolyl cis/trans isomerase) NIMA-interacting 1 -1.153 NM_006884 SHOX2 short stature homeobox 2 1.666 NM_175061 JAZF1 JAZF zinc finger 1 1.329 NM_015419 MXRA5 matrix-remodelling associated 5 8.364 NM_016548 GOLPH2 golgi phosphoprotein 2 1.431 NM_022748 TNS3 tensin 3 1.649 NM_138432 SDSL serine dehydratase-like -1.372 NM_032989 BAD BCL2-antagonist of cell death 1.215 NM_018464 ZCD1 zinc finger, CDGSH-type domain 1 -1.368 NM_016210 C3orf18 chromosome 3 open reading frame 18 1.266 NM_016482 C9orf78 chromosome 9 open reading frame 78 -1.086 NM_203318 MYO18A myosin XVIIIA 1.292 NM_003969 UBE2M ubiquitin-conjugating enzyme E2M (UBC12 homolog, yeast) -1.133 ataxia telangiectasia mutated (includes complementation groups A, C and NM_000051 ATM 1.205 D) NM_002721 PPP6C protein phosphatase 6, catalytic subunit -1.092 NM_152385 FLJ31438 hypothetical protein FLJ31438 -1.248 NM_018225 SMU1 smu-1 suppressor of mec-8 and unc-52 homolog (C. elegans) -1.156 NM_015140 TTLL12 tubulin tyrosine ligase-like family, member 12 -1.229 NM_006170 NOL1 nucleolar protein 1, 120kDa -1.192 NM_005731 ARPC2 actin related protein 2/3 complex, subunit 2, 34kDa -1.140 NM_020300 MGST1 microsomal glutathione S-transferase 1 -1.639 NM_015480 PVRL3 poliovirus receptor-related 3 1.234 NM_130438 DYRK1A dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 1A 1.109 NM_014683 ULK2 unc-51-like kinase 2 (C. elegans) 1.402 NM_017622 C17orf59 chromosome 17 open reading frame 59 -1.192 NM_000153 GALC galactosylceramidase 1.225 NM_002116 HLA-A major histocompatibility complex, class I, A 1.438 NM_005596 NFIB nuclear factor I/B -1.504 NM_014556 EVC Ellis van Creveld syndrome 1.368 NM_032839 DIRC2 disrupted in renal carcinoma 2 -1.119 NM_013401 RAB3IL1 RAB3A interacting protein (rabin3)-like 1 1.559 NM_014847 UBAP2L ubiquitin associated protein 2-like -1.135
163
NM_199342 CCDC23 coiled-coil domain containing 23 1.323 NM_001015053 HDAC5 histone deacetylase 5 1.361 NM_003588 CUL4B cullin 4B 1.381 NM_001029863 C6orf120 chromosome 6 open reading frame 120 1.180 NM_153047 FYN FYN oncogene related to SRC, FGR, YES -1.198 NM_022061 MRPL17 mitochondrial ribosomal protein L17 -1.266 NM_012260 HACL1 2-hydroxyacyl-CoA lyase 1 -1.236 NM_181724 TMEM119 transmembrane protein 119 4.751 NM_006148 LASP1 LIM and SH3 protein 1 -1.135 NM_015689 DENND2A DENN/MADD domain containing 2A 4.394 NM_024578 OCEL1 occludin/ELL domain containing 1 1.220 NM_152621 SGMS2 sphingomyelin synthase 2 -1.488 NM_152330 FRMD6 FERM domain containing 6 1.232 SWI/SNF related, matrix associated, actin dependent regulator of chromatin, NM_003075 SMARCC2 1.239 subfamily c, member 2 NM_017443 POLE3 polymerase (DNA directed), epsilon 3 (p17 subunit) -1.238 NM_032928 TMEM141 transmembrane protein 141 -1.143 NM_130386 COLEC12 collectin sub-family member 12 6.752 NM_001001551 C9orf103 chromosome 9 open reading frame 103 1.473 NM_0181170 BRWD2 bromodomain and WD repeat domain containing 2 1.150 NM_014291 GCAT glycine C-acetyltransferase (2-amino-3-ketobutyrate coenzyme A ligase) -1.304 NM_003099 SNX1 sorting nexin 1 1.376 NM_015057 MYCBP2 MYC binding protein 2 1.246 NM_052849 CCDC32 coiled-coil domain containing 32 -1.181 NM_003973 RPL14 ribosomal protein L14 1.449 NM_021101 CLDN1 claudin 1 -6.452 NM_024899 CEP76 centrosomal protein 76kDa -1.168 NM_003312 TST thiosulfate sulfurtransferase (rhodanese) -1.214 NM_002737 PRKCA protein kinase C, alpha 1.290 NM_019063 EML4 echinoderm microtubule associated protein like 4 1.168 NM_006634 VAMP5 vesicle-associated membrane protein 5 (myobrevin) 1.282 NM_015384 NIPBL Nipped-B homolog (Drosophila) 1.192 NM_003038 SLC1A4 solute carrier family 1 (glutamate/neutral amino acid transporter), member 4 -1.504 NM_024570 RNASEH2B ribonuclease H2, subunit B 1.275 NM_003760 EIF4G3 eukaryotic translation initiation factor 4 gamma, 3 -1.142 NM_173647 RNF149 ring finger protein 149 1.157 NM_015271 TRIM2 tripartite motif-containing 2 1.547 NM_024065 PDCL3 phosducin-like 3 -1.319 NM_018648 NOLA3 nucleolar protein family A, member 3 (H/ACA small nucleolar RNPs) -1.172 NM_002303 LEPR leptin receptor -2.045 NM_001500 GMDS GDP-mannose 4,6-dehydratase -1.242 NM_173576 MKX mohawk homeobox 3.788 NM_014899 RHOBTB3 Rho-related BTB domain containing 3 1.575 NM_015946 PELO pelota homolog (Drosophila) -1.271 NM_019084 CCNJ cyclin J 1.328 NM_001031734 MGC19604 similar to RIKEN cDNA B230118G17 gene -1.214 NM_199187 LOC646723 keratin 18 -3.058 NM_173849 GSC goosecoid -1.786
164
NM_021249 SNX6 sorting nexin 6 -1.316 NM_138386 LOC92345 hypothetical protein BC008207 -1.232 NM_017882 CLN6 ceroid-lipofuscinosis, neuronal 6, late infantile, variant -1.339 NM_006703 NUDT3 nudix (nucleoside diphosphate linked moiety X)-type motif 3 -1.134 NM_014624 S100A6 S100 calcium binding protein A6 1.085 NM_020039 ACCN2 amiloride-sensitive cation channel 2, neuronal 1.648 NM_199203 Kua-UEV ubiquitin-conjugating enzyme E2 variant 1 -1.328 NM_138781 LOC113386 -1.242 NM_002783 PSG7 pregnancy specific beta-1-glycoprotein 7 6.054 NM_015918 POP5 processing of precursor 5, ribonuclease P/MRP subunit (S. cerevisiae) -1.238 NM_014893 NLGN4Y neuroligin 4, Y-linked 1.584 NM_014604 TAX1BP3 Tax1 (human T-cell leukemia virus type I) binding protein 3 -1.101 NM_139322 ATRN attractin 1.224 NM_005631 SMO smoothened homolog (Drosophila) 1.689 NM_001839 CNN3 calponin 3, acidic -1.144 NM_015959 TXNDC14 thioredoxin domain containing 14 -1.175 twist homolog 1 (acrocephalosyndactyly 3; Saethre-Chotzen syndrome) NM_000474 TWIST1 1.963 (Drosophila) NM_002087 GRN granulin 1.266 NM_004850 ROCK2 Rho-associated, coiled-coil containing protein kinase 2 1.166 NM_173517 VKORC1L1 vitamin K epoxide reductase complex, subunit 1-like 1 -1.287 NM_033557 YIF1B Yip1 interacting factor homolog B (S. cerevisiae) -1.403 NM_015935 KIAA0859 KIAA0859 -1.174 NM_000553 WRN Werner syndrome -1.155 NM_032861 SERAC1 serine active site containing 1 1.487 NM_015153 PHF3 PHD finger protein 3 1.164 NM_005147 DNAJA3 DnaJ (Hsp40) homolog, subfamily A, member 3 -1.160 NM_006597 HSPA8 heat shock 70kDa protein 8 -1.170 NM_001512 GSTA4 glutathione S-transferase A4 1.226 NM_025215 PUS1 pseudouridylate synthase 1 -1.133 NM_014714 IFT140 intraflagellar transport 140 homolog (Chlamydomonas) 1.575 NM_001032280 TFAP2A transcription factor AP-2 alpha (activating enhancer binding protein 2 alpha) 1.300 NM_015208 ANKRD12 ankyrin repeat domain 12 1.186 NM_001024630 RUNX2 runt-related transcription factor 2 2.154 NM_177972 TUB tubby homolog (mouse) 1.406 NM_023934 FUNDC2 FUN14 domain containing 2 -1.340 NM_001755 CBFB core-binding factor, beta subunit -1.196 NM_001015508 PURG purine-rich element binding protein G -1.170 NM_147162 IL11RA interleukin 11 receptor, alpha 1.456 NM_024955 FOXRED2 FAD-dependent oxidoreductase domain containing 2 -1.385 NM_001750 CAST calpastatin 1.320 NM_030922 NIPA2 non imprinted in Prader-Willi/Angelman syndrome 2 -1.151 NM_001834 CLTB clathrin, light chain (Lcb) -1.136 NM_000735 CGA glycoprotein hormones, alpha polypeptide -3.125 NM_017798 YTHDF1 YTH domain family, member 1 -1.085 NM_198129 LAMA3 laminin, alpha 3 1.834 NM_018166 C1orf78 chromosome 1 open reading frame 78 1.259 NM_003062 SLIT3 slit homolog 3 (Drosophila) 2.670
165
NM_016447 MPP6 membrane protein, palmitoylated 6 (MAGUK p55 subfamily member 6) -1.242 NM_173505 ANKRD29 ankyrin repeat domain 29 2.581 NM_013976 GCDH glutaryl-Coenzyme A dehydrogenase -1.250 NM_001015072 UFSP1 inactive Ufm1-specific protease 1 -1.464 NM_001792 CDH2 cadherin 2, type 1, N-cadherin (neuronal) -2.179 NM_001895 CSNK2A1 casein kinase 2, alpha 1 polypeptide -1.230 NM_030579 CYB5B cytochrome b5 type B (outer mitochondrial membrane) -1.152 NM_005628 SLC1A5 solute carrier family 1 (neutral amino acid transporter), member 5 1.227 NM_001009182 SIP1 survival of motor neuron protein interacting protein 1 -1.290 NM_006644 HSPH1 heat shock 105kDa/110kDa protein 1 -1.263 NM_016016 SLC25A39 solute carrier family 25, member 39 -1.152 NM_032683 FKSG24 hypothetical protein MGC12972 -1.309 NM_021170 HES4 hairy and enhancer of split 4 (Drosophila) -2.141 NM_001002257 LYCAT lysocardiolipin acyltransferase -1.274 NM_004454 ETV5 ets variant gene 5 (ets-related molecule) -1.404 NM_145167 PIGM phosphatidylinositol glycan anchor biosynthesis, class M -1.145 NM_004280 EEF1E1 eukaryotic translation elongation factor 1 epsilon 1 -1.266 thyroid hormone receptor, alpha (erythroblastic leukemia viral (v-erb-a) NM_003250 THRA 2.062 oncogene homolog, avian) NM_001024959 ARPC4 actin related protein 2/3 complex, subunit 4, 20kDa -1.138 myeloid/lymphoid or mixed-lineage leukemia (trithorax homolog, NM_006818 MLLT11 -1.776 Drosophila); translocated to, 11 NM_024331 C20orf121 chromosome 20 open reading frame 121 -1.211 NM_144692 LOC148137 -1.230 NM_016551 TM7SF3 transmembrane 7 superfamily member 3 -1.224 NM_001009608 C20orf94 chromosome 20 open reading frame 94 -1.414 NM_152755 MGC40499 PRotein Associated with Tlr4 1.191 NM_006327 TIMM23 translocase of inner mitochondrial membrane 23 homolog (yeast) -1.135 NM_031477 YPEL3 yippee-like 3 (Drosophila) 1.502 NM_015239 AGTPBP1 ATP/GTP binding protein 1 -1.156 NM_001031699 MOXD1 monooxygenase, DBH-like 1 3.056 NM_032410 HOOK3 hook homolog 3 (Drosophila) -1.230 NM_175636 RUNX1T1 runt-related transcription factor 1; translocated to, 1 (cyclin D-related) 1.983 NM_078626 CDKN2C cyclin-dependent kinase inhibitor 2C (p18, inhibits CDK4) 2.312 NM_000671 ADH5 alcohol dehydrogenase 5 (class III), chi polypeptide 1.275 NM_144970 CXorf38 chromosome X open reading frame 38 -1.351 NM_032168 WDR75 WD repeat domain 75 -1.116 NM_004955 SLC29A1 solute carrier family 29 (nucleoside transporters), member 1 -1.479 NM_018264 TYW1 tRNA-yW synthesizing protein 1 homolog (S. cerevisiae) -1.129 NM_001334 CTSO cathepsin O 1.431 NM_145918 CTSL1 cathepsin L1 1.602 NM_015252 EHBP1 EH domain binding protein 1 1.426 NM_015294 TRIM37 tripartite motif-containing 37 -1.244 NM_148923 CYB5A cytochrome b5 type A (microsomal) 1.634 NM_152244 SNX11 sorting nexin 11 -1.289 NM_020824 ARHGAP21 Rho GTPase activating protein 21 1.168 NM_018689 KIAA1199 KIAA1199 2.743 NM_004891 MRPL33 mitochondrial ribosomal protein L33 -1.181 NM_001822 CHN1 chimerin (chimaerin) 1 3.585
166
NM_025000 C2orf37 chromosome 2 open reading frame 37 -1.653 NM_004882 CIR CBF1 interacting corepressor 1.205 NM_018428 UTP6 UTP6, small subunit (SSU) processome component, homolog (yeast) -1.152 NM_003596 TPST1 tyrosylprotein sulfotransferase 1 1.371 NM_020453 ATP10D ATPase, Class V, type 10D -1.534 NM_024513 FYCO1 FYVE and coiled-coil domain containing 1 1.252 NM_001031746 C10orf72 chromosome 10 open reading frame 72 2.717 NM_030978 ARPC5L actin related protein 2/3 complex, subunit 5-like -1.217 NM_014976 PDCD11 programmed cell death 11 -1.252 NM_178025 GGTL3 Gamma-glutamyltransferase-like 3 1.424 NM_005606 LGMN legumain -1.299 NM_032276 RHBDD1 rhomboid domain containing 1 -1.199 NM_025203 C2orf44 chromosome 2 open reading frame 44 -1.340 alanyl (membrane) aminopeptidase (aminopeptidase N, aminopeptidase M, NM_001150 ANPEP 1.514 microsomal aminopeptidase, CD13, p150) NM_152892 DKFZp434K1815 hypothetical protein DKFZp434K1815 -1.244 NM_138557 TLR4 Toll-like receptor 4 -1.715 NM_002079 GOT1 glutamic-oxaloacetic transaminase 1, soluble (aspartate aminotransferase 1) -1.215 NM_001326 CSTF3 cleavage stimulation factor, 3' pre-RNA, subunit 3, 77kDa -1.227 NM_203422 LOC221091 similar to hypothetical protein 4.121 NM_004541 NDUFA1 NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 1, 7.5kDa -1.104 NM_024093 C2orf49 chromosome 2 open reading frame 49 -1.171 NM_174925 LOC205251 -1.362 NM_015127 CLCC1 chloride channel CLIC-like 1 -1.217 NM_145731 SYNGR1 synaptogyrin 1 1.370 NM_002690 POLB polymerase (DNA directed), beta -1.156 NM_002048 GAS1 growth arrest-specific 1 3.677 NM_000088 COL1A1 collagen, type I, alpha 1 -1.079 NM_006431 CCT2 chaperonin containing TCP1, subunit 2 (beta) -1.182 NM_015225 KIAA0367 KIAA0367 -2.151 NM_012325 MAPRE1 microtubule-associated protein, RP/EB family, member 1 -1.244 NM_004328 BCS1L BCS1-like (yeast) -1.224 NM_006896 HOXA7 homeobox A7 -1.264 NM_005020 PDE1C phosphodiesterase 1C, calmodulin-dependent 70kDa -2.088 NM_020141 C1orf119 chromosome 1 open reading frame 119 1.165 NM_003846 PEX11B peroxisomal biogenesis factor 11B -1.151 NM_182480 COQ6 coenzyme Q6 homolog, monooxygenase (S. cerevisiae) -1.188 NM_000678 ADRA1D adrenergic, alpha-1D-, receptor -1.238 NM_001207 BTF3 basic transcription factor 3 1.275 NM_001013714 LOC440993 2.053 NM_144736 C2orf56 chromosome 2 open reading frame 56 -1.133 NM_005028 PIP5K2A phosphatidylinositol-4-phosphate 5-kinase, type II, alpha -1.357 NM_002946 RPA2 replication protein A2, 32kDa -1.330 NM_000418 IL4R interleukin 4 receptor -1.468 NM_015609 C1orf144 chromosome 1 open reading frame 144 -1.189 NM_001551 IGBP1 immunoglobulin (CD79A) binding protein 1 1.322 NM_032195 SON SON DNA binding protein 1.223 NM_024561 NARG1L NMDA receptor regulated 1-like 1.504
167
NM_194272 RBPMS2 RNA binding protein with multiple splicing 2 2.980 NM_015544 TMEM98 transmembrane protein 98 1.594 NM_006638 RPP40 ribonuclease P 40kDa subunit -1.332 NM_003619 PRSS12 protease, serine, 12 (neurotrypsin, motopsin) 2.021 NM_020676 ABHD6 abhydrolase domain containing 6 -1.233 NM_015444 TMEM158 transmembrane protein 158 1.748 NM_152400 C4orf32 chromosome 4 open reading frame 32 -1.412 NM_001384 DPH2 DPH2 homolog (S. cerevisiae) -1.220 NM_018270 C20orf20 chromosome 20 open reading frame 20 1.097 NM_025250 TTYH3 tweety homolog 3 (Drosophila) 1.400 NM_175624 RAB3IP RAB3A interacting protein (rabin3) -1.825 NM_025149 FLJ20920 hypothetical protein FLJ20920 1.412 NM_032494 ZC3H8 zinc finger CCCH-type containing 8 -1.225 NM_001020825 NR3C1 nuclear receptor subfamily 3, group C, member 1 (glucocorticoid receptor) 1.448 NM_004380 CREBBP CREB binding protein (Rubinstein-Taybi syndrome) 1.226 NM_138717 PPT2 palmitoyl-protein thioesterase 2 -1.404 NM_012098 ANGPTL2 angiopoietin-like 2 3.950 NM_015187 KIAA0746 KIAA0746 protein -2.083 NM_014637 MTFR1 mitochondrial fission regulator 1 -1.225 NM_018206 VPS35 vacuolar protein sorting 35 homolog (S. cerevisiae) -1.107 NM_145791 MGST1 microsomal glutathione S-transferase 1 -1.658 NM_003638 ITGA8 integrin, alpha 8 5.564 NM_003298 NR2C2 nuclear receptor subfamily 2, group C, member 2 -1.297 NM_001017405 LOC730744 macrophage erythroblast attacher -1.096 NM_023935 C20orf116 chromosome 20 open reading frame 116 -1.160 NM_006166 NFYB nuclear transcription factor Y, beta 1.822 NM_004589 SCO1 SCO cytochrome oxidase deficient homolog 1 (yeast) -1.181 NM_144641 PPM1M protein phosphatase 1M (PP2C domain containing) 1.277 NM_002543 OLR1 oxidized low density lipoprotein (lectin-like) receptor 1 -2.326 NM_020117 LARS leucyl-tRNA synthetase -1.203 NM_005613 RGS4 regulator of G-protein signalling 4 -3.049 NM_001024594 C1orf53 chromosome 1 open reading frame 53 -1.287 NM_014580 SLC2A8 solute carrier family 2, (facilitated glucose transporter) member 8 -1.414 NM_012334 MYO10 myosin X 1.352 NM_007288 MME membrane metallo-endopeptidase 3.771 NM_014048 MKL2 MKL/myocardin-like 2 1.291 NM_003873 NRP1 neuropilin 1 1.607 NM_005458 GABBR2 gamma-aminobutyric acid (GABA) B receptor, 2 5.150 NM_133493 CD109 CD109 molecule 1.849 NM_018290 PGM2 phosphoglucomutase 2 -1.502 NM_015073 SIPA1L3 signal-induced proliferation-associated 1 like 3 -1.391 NM_016417 GLRX5 glutaredoxin 5 homolog (S. cerevisiae) -1.230 NM_014391 ANKRD1 ankyrin repeat domain 1 (cardiac muscle) -7.463 NM_024122 APOO apolipoprotein O -1.190 NM_025217 ULBP2 UL16 binding protein 2 -1.980 phosphodiesterase 4A, cAMP-specific (phosphodiesterase E2 dunce NM_006202 PDE4A 1.840 homolog, Drosophila) NM_152882 PTK7 PTK7 protein tyrosine kinase 7 1.897
168
NM_138957 MAPK1 mitogen-activated protein kinase 1 -1.242 NM_001945 HBEGF heparin-binding EGF-like growth factor -3.322 NM_014920 ICK intestinal cell (MAK-like) kinase 1.302 NM_005224 ARID3A AT rich interactive domain 3A (BRIGHT-like) 1.306 NM_005154 USP8 ubiquitin specific peptidase 8 1.190 cadherin, EGF LAG seven-pass G-type receptor 2 (flamingo homolog, NM_001408 CELSR2 -1.727 Drosophila) NM_004776 B4GALT5 UDP-Gal:betaGlcNAc beta 1,4- galactosyltransferase, polypeptide 5 1.380 NM_001017530 PRR5 proline rich 5 (renal) 1.952 NM_018207 TRIM62 tripartite motif-containing 62 -1.845 NM_018210 FLJ10769 hypothetical protein FLJ10769 1.166 NM_003171 SUPV3L1 suppressor of var1, 3-like 1 (S. cerevisiae) -1.155 NM_003002 SDHD succinate dehydrogenase complex, subunit D, integral membrane protein 1.171 NM_002768 CHMP1A chromatin modifying protein 1A 1.157 NM_001905 CTPS CTP synthase -2.016 NM_019555 ARHGEF3 Rho guanine nucleotide exchange factor (GEF) 3 1.780 NM_138289 ACTRT1 actin-related protein T1 -1.527 NM_203284 RBPJ recombination signal binding protein for immunoglobulin kappa J region 1.185 NM_019613 WDR45L WDR45-like 1.203 NM_032036 FAM14A family with sequence similarity 14, member A 1.280 NM_198514 NHLRC2 NHL repeat containing 2 -1.318 NM_004699 FAM50A family with sequence similarity 50, member A -1.145 NM_182627 WDR53 WD repeat domain 53 -1.164 NM_033331 CDC14B CDC14 cell division cycle 14 homolog B (S. cerevisiae) 1.549 NM_002961 S100A4 S100 calcium binding protein A4 1.202 NM_024769 ASAM adipocyte-specific adhesion molecule 1.849 NM_001024644 XCR1 chemokine (C motif) receptor 1 -1.377 NM_024666 FLJ11506 hypothetical protein FLJ11506 -1.225 NM_144604 NHN1 conserved nuclear protein NHN1 -1.170 NM_019040 ELP4 elongation protein 4 homolog (S. cerevisiae) -1.222 NM_033254 BOC Boc homolog (mouse) 2.435 NM_014869 IQSEC1 IQ motif and Sec7 domain 1 1.313 NM_020813 ZNF471 zinc finger protein 471 -1.374 NM_017990 PDPR pyruvate dehydrogenase phosphatase regulatory subunit 1.411 NM_138458 WDR92 monad -1.269 NM_018079 SRBD1 S1 RNA binding domain 1 -1.208 NM_031302 GLT8D2 glycosyltransferase 8 domain containing 2 1.866 NM_024308 MGC4172 short-chain dehydrogenase/reductase -1.484 NM_145113 MAX MYC associated factor X -1.368 NM_206909 PSD3 pleckstrin and Sec7 domain containing 3 1.627 NM_002133 HMOX1 heme oxygenase (decycling) 1 1.903 NM_138455 CTHRC1 collagen triple helix repeat containing 1 2.851 NM_020442 VARS2 valyl-tRNA synthetase 2, mitochondrial (putative) -1.238 NM_147223 NCOA1 nuclear receptor coactivator 1 1.346 similar to HIV TAT specific factor 1; cofactor required for Tat activation of NM_001013680 LOC401233 5.124 HIV-1 transcription NM_181716 PRR6 proline rich 6 -1.381 NM_006039 MRC2 mannose receptor, C type 2 1.559 NM_014892 RBM16 RNA binding motif protein 16 1.153
169
NM_017947 MOCOS molybdenum cofactor sulfurase -2.062 NM_181866 ACOT7 acyl-CoA thioesterase 7 -1.546 NM_005598 NHLH1 nescient helix loop helix 1 -1.357 NM_153336 PSTK phosphoseryl-tRNA kinase -1.366 NM_173490 TMEM171 transmembrane protein 171 1.788 NM_032166 ATRIP ATR interacting protein -1.389 NM_016505 ZCCHC17 zinc finger, CCHC domain containing 17 -1.215 NM_015544 TMEM98 transmembrane protein 98 1.594 NM_198552 FAM89A family with sequence similarity 89, member A 2.062 NM_017593 BMP2K BMP2 inducible kinase 1.423 NM_004003 CRAT carnitine acetyltransferase -1.277 NM_199485 C20orf24 chromosome 20 open reading frame 24 -1.147 NM_001951 E2F5 E2F transcription factor 5, p130-binding -1.490 NM_003289 TPM2 tropomyosin 2 (beta) -1.071 NM_003505 FZD1 frizzled homolog 1 (Drosophila) 1.519 NM_032780 TMEM25 transmembrane protein 25 2.525 NM_018138 TBCCD1 TBCC domain containing 1 -1.182 NM_181696 PRDX1 peroxiredoxin 1 -1.148 NM_004815 ARHGAP29 Rho GTPase activating protein 29 2.196 NM_021203 SRPRB signal recognition particle receptor, B subunit -1.294 NM_012118 CCRN4L CCR4 carbon catabolite repression 4-like (S. cerevisiae) -1.653 NM_023072 ZSWIM4 zinc finger, SWIM-type containing 4 -1.479 NM_003918 GYG2 glycogenin 2 3.182 NM_015265 SATB2 SATB homeobox 2 1.462 NM_002912 REV3L REV3-like, catalytic subunit of DNA polymerase zeta (yeast) 1.737 NM_020663 RHOJ ras homolog gene family, member J -1.582 NM_004069 AP2S1 adaptor-related protein complex 2, sigma 1 subunit -1.188 NM_012091 ADAT1 adenosine deaminase, tRNA-specific 1 -1.279 NM_003494 DYSF dysferlin, limb girdle muscular dystrophy 2B (autosomal recessive) -3.378 NM_014904 RAB11FIP2 RAB11 family interacting protein 2 (class I) 1.459 NM_015179 RRP12 ribosomal RNA processing 12 homolog (S. cerevisiae) -1.323 NM_001033568 RHOT1 ras homolog gene family, member T1 -1.225 NM_006851 GLIPR1 GLI pathogenesis-related 1 (glioma) -1.953 NM_002414 CD99 CD99 molecule 1.266 NM_000437 PAFAH2 platelet-activating factor acetylhydrolase 2, 40kDa -1.285 NM_007348 ATF6 activating transcription factor 6 -1.236 NM_000505 F12 coagulation factor XII (Hageman factor) -1.789 protein tyrosine phosphatase, non-receptor type 13 (APO-1/CD95 (Fas)- NM_080685 PTPN13 1.585 associated phosphatase) NM_007106 UBL3 ubiquitin-like 3 1.288 NM_078628 MSL3L1 male-specific lethal 3-like 1 (Drosophila) -1.294 NM_175569 XG Xg blood group 2.001 NM_000971 RPL7 ribosomal protein L7 1.339 NM_194281 C18orf37 chromosome 18 open reading frame 37 -1.647 NM_000900 MGP matrix Gla protein -3.876 NM_001001991 DKFZP586H2123 regeneration associated muscle protease 1.712 NM_017897 OXSM 3-oxoacyl-ACP synthase, mitochondrial -1.208 NM_014828 TOX4 TOX high mobility group box family member 4 1.196
170
NM_177422 EIF2C3 eukaryotic translation initiation factor 2C, 3 -1.221 NM_004521 KIF5B kinesin family member 5B -1.399 NM_020223 FAM20C family with sequence similarity 20, member C 1.700 NM_001006 RPS3A ribosomal protein S3A 1.131 NM_018087 TMEM48 transmembrane protein 48 -1.805 NM_001089 ABCA3 ATP-binding cassette, sub-family A (ABC1), member 3 -1.969 NM_001686 ATP5B ATP synthase, H+ transporting, mitochondrial F1 complex, beta polypeptide -1.157 NM_016206 VGLL3 vestigial like 3 (Drosophila) 1.565 NM_152737 RNF182 ring finger protein 182 -1.916 NM_001198 PRDM1 PR domain containing 1, with ZNF domain 3.867 NM_015055 SWAP70 SWAP-70 protein 1.491 NM_018346 RSAD1 radical S-adenosyl methionine domain containing 1 -1.127 NM_145057 CDC42EP5 CDC42 effector protein (Rho GTPase binding) 5 1.545 NM_021990 GABRE gamma-aminobutyric acid (GABA) A receptor, epsilon 1.969 NM_003581 NCK2 NCK adaptor protein 2 1.286 NM_015009 PDZRN3 PDZ domain containing RING finger 3 1.756 NM_177953 DYNLRB1 dynein, light chain, roadblock-type 1 1.166 NM_000487 ARSA arylsulfatase A 1.584 NM_152272 CHMP7 CHMP family, member 7 -1.221 NM_001543 NDST1 N-deacetylase/N-sulfotransferase (heparan glucosaminyl) 1 1.460 NM_003882 WISP1 WNT1 inducible signaling pathway protein 1 3.087 NM_014947 FOXJ3 forkhead box J3 1.134 NM_006329 FBLN5 fibulin 5 2.464 NM_004554 NFATC4 nuclear factor of activated T-cells, cytoplasmic, calcineurin-dependent 4 1.885 NM_013388 PREB prolactin regulatory element binding -1.206 NM_032102 SFRS2B splicing factor, arginine/serine-rich 2B -1.133 NM_018112 TMEM38B transmembrane protein 38B -2.128 NM_022754 SFXN1 sideroflexin 1 -1.353 NM_182703 ANKDD1A ankyrin repeat and death domain containing 1A 1.795 NM_138957 MAPK1 mitogen-activated protein kinase 1 -1.242 Cbp/p300-interacting transactivator, with Glu/Asp-rich carboxy-terminal NM_133467 CITED4 -2.410 domain, 4 NM_138376 TTC5 tetratricopeptide repeat domain 5 -1.193 NM_001124 ADM adrenomedullin 1.430 NM_001004346 MTHFD2L methylenetetrahydrofolate dehydrogenase (NADP+ dependent) 2-like -1.605 NM_014862 ARNT2 aryl-hydrocarbon receptor nuclear translocator 2 2.083 dolichyl-phosphate (UDP-N-acetylglucosamine) N- NM_001382 DPAGT1 -1.183 acetylglucosaminephosphotransferase 1 (GlcNAc-1-P transferase) NM_012433 SF3B1 splicing factor 3b, subunit 1, 155kDa 1.167 NM_002589 PCDH7 protocadherin 7 -6.024 NM_173674 DCBLD1 discoidin, CUB and LCCL domain containing 1 -1.770 NM_001003680 LEPR leptin receptor -2.273 NM_181894 GRIA3 glutamate receptor, ionotrophic, AMPA 3 2.738 NM_015453 THUMPD3 THUMP domain containing 3 -1.161 matrix metallopeptidase 2 (gelatinase A, 72kDa gelatinase, 72kDa type IV NM_004530 MMP2 2.680 collagenase) NM_152760 FLJ30934 hypothetical protein FLJ30934 -1.506 NM_001003828 PARVB parvin, beta -1.520 NM_003186 TAGLN transgelin -1.127
171
NM_024766 C2orf34 chromosome 2 open reading frame 34 -1.410 NM_003812 ADAM23 ADAM metallopeptidase domain 23 -2.336 NM_001880 ATF2 activating transcription factor 2 -1.261 NM_016510 SCLY selenocysteine lyase -1.381 NM_024630 ZDHHC14 zinc finger, DHHC-type containing 14 -1.425 NM_022455 NSD1 nuclear receptor binding SET domain protein 1 -1.468 NM_001238 CCNE1 cyclin E1 -1.580 NM_020856 TSHZ3 teashirt family zinc finger 3 1.895 NM_181504 PIK3R1 phosphoinositide-3-kinase, regulatory subunit 1 (p85 alpha) 1.421 NM_080870 DPCR1 diffuse panbronchiolitis critical region 1 -1.585 NM_003392 WNT5A wingless-type MMTV integration site family, member 5A 2.146 NM_152490 B3GALNT2 beta-1,3-N-acetylgalactosaminyltransferase 2 -1.639 NM_005433 YES1 v-yes-1 Yamaguchi sarcoma viral oncogene homolog 1 1.255 NM_020440 PTGFRN prostaglandin F2 receptor negative regulator 2.938 NM_002717 PPP2R2A protein phosphatase 2 (formerly 2A), regulatory subunit B, alpha isoform -1.192 NM_032645 RAPSN receptor-associated protein of the synapse -1.529 NM_080629 COL11A1 collagen, type XI, alpha 1 -16.667 NM_001032279 RCE1 RCE1 homolog, prenyl protein peptidase (S. cerevisiae) -1.195 NM_003387 WIPF1 WAS/WASL interacting protein family, member 1 1.907 NM_001101 ACTB actin, beta -1.096 NM_001440 EXTL3 exostoses (multiple)-like 3 1.483 NM_022164 TINAGL1 tubulointerstitial nephritis antigen-like 1 -5.650
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Appendix 1B Appendix 1B contains the list of pathways identified by IPA as being significantly overrepresented, in the genes found to be dysregualted in the neurosphere derived cells from patients with schizophrenia. The ratio represents the ratio of genes in pathway dysregulated to total number of genes in the pathway. α = 0.05 was chosen to assign significance.
Pathway p-value Ratio Integrin Signaling 0.0000 0.115 Ephrin Receptor Signaling 0.0001 0.109 VEGF Signaling 0.0001 0.130 B Cell Receptor Signaling 0.0006 0.074 NRF2-mediated Oxidative Stress Response 0.0007 0.117 Leukocyte Extravasation Signaling 0.0008 0.094 ERK/MAPK Signaling 0.0015 0.094 IGF-1 Signaling 0.0017 0.109 PTEN Signaling 0.0020 0.098 Insulin Receptor Signaling 0.0020 0.083 IL-4 Signaling 0.0023 0.118 Calcium Signaling 0.0044 0.064 p53 Signaling 0.0047 0.081 Cell Cycle: G1/S Checkpoint Regulation 0.0047 0.117 G-Protein Coupled Receptor Signaling 0.0060 0.085 GM-CSF Signaling 0.0071 0.081 Axonal Guidance Signaling 0.0071 0.073 Oxidative Phosphorylation 0.0093 0.070 IL-2 Signaling 0.0102 0.113 Amyloid Processing 0.0102 0.096 NF-κB Signaling 0.0105 0.070 PDGF Signaling 0.0107 0.108 Neurotrophin/TRK Signaling 0.0120 0.082 Actin Cytoskeleton Signaling 0.0123 0.078 Neuregulin Signaling 0.0123 0.110 Aryl Hydrocarbon Receptor Signaling 0.0132 0.079 PI3K/AKT Signaling 0.0132 0.089 FGF Signaling 0.0182 0.083 SAPK/JNK Signaling 0.0204 0.065 EGF Signaling 0.0214 0.128 Estrogen Receptor Signaling 0.0229 0.085 Chemokine Signaling 0.0251 0.107 Ceramide Signaling 0.0355 0.114 Inositol Phosphate Metabolism 0.0380 0.052 Regulation of Actin-based Motility by Rho 0.0447 0.120 Natural Killer Cell Signaling 0.0457 0.064 Huntington's Disease Signaling 0.0468 0.056 Wnt/β-catenin Signaling 0.0479 0.109
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Appendix 2 Appendix 2 contains the gene list from the skin fibroblast microarray study. The appendix contains genes significantly differently expressed exclusively as a result of schizophrenia.
Gene Fold Genbank Description name Change NM_00101498 LAT linker for activation of T cells 1.273 8 NM_007049 BTN2A1 butyrophilin, subfamily 2, member A1 -1.238 NM_003076 SMARCD1 SWI/SNF related, matrix associated, actin dependent regulator of chromatin 1.222 NM_025115 C8orf41 chromosome 8 open reading frame 41 1.137 NM_030974 SHARPIN SHANK-associated RH domain interactor 1.166 NM_013301 CCDC106 coiled-coil domain containing 106 1.236 NM_181093 SCYL3 SCY1-like 3 (S. cerevisiae) -1.202 NM_201398 FLAD1 FAD1 flavin adenine dinucleotide synthetase homolog (S. cerevisiae) 1.168 NM_203453 PPAPDC2 phosphatidic acid phosphatase type 2 domain containing 2 -1.203 NM_023924 BRD9 bromodomain containing 9 1.114 NM_006384 CIB1 calcium and integrin binding 1 (calmyrin) 1.145 NM_032269 CCDC135 coiled-coil domain containing 135 1.333 NM_004792 PPIG peptidylprolyl isomerase G (cyclophilin G) -1.183 NM_002496 NDUFS8 NADH dehydrogenase (ubiquinone) Fe-S protein 8, 23kDa 1.157 NM_020895 GRAMD1A GRAM domain containing 1A 1.275 NM_014339 IL17RA interleukin 17 receptor A -1.399 NM_020470 YIF1A Yip1 interacting factor homolog A (S. cerevisiae) 1.230 NM_003130 SRI sorcin -1.217 NM_004182 UXT ubiquitously-expressed transcript 1.141 NM_022735 ACBD3 acyl-Coenzyme A binding domain containing 3 -1.145 NM_014053 FLVCR1 feline leukemia virus subgroup C cellular receptor 1 1.172 NM_018036 ATG2B chromosome 14 open reading frame 103 -1.241 NM_006351 TIMM44 translocase of inner mitochondrial membrane 44 homolog (yeast) 1.256 NM_016647 C8orf55 chromosome 8 open reading frame 55 1.300 NM_030811 MRPS26 mitochondrial ribosomal protein S26 1.199 NM_032301 FBXW9 F-box and WD repeat domain containing 9 1.235 NM_020531 C20orf3 chromosome 20 open reading frame 3 -1.138 NM_001880 ATF2 activating transcription factor 2 1.152 NM_00102986 C6orf120 chromosome 6 open reading frame 120 -1.182 3 NM_015382 HECTD1 HECT domain containing 1 -1.256 NM_002835 PTPN12 protein tyrosine phosphatase, non-receptor type 12 -1.164 NM_005275 GNL1 guanine nucleotide binding protein-like 1 1.127 NM_00101298 AAK1 AP2 associated kinase 1 -1.408 7 NM_006703 NUDT3 nudix (nucleoside diphosphate linked moiety X)-type motif 3 1.167 NM_004238 TRIP12 thyroid hormone receptor interactor 12 -1.172 NM_022641 CSHL1 chorionic somatomammotropin hormone 1 (placental lactogen) 1.329 NM_032166 ATRIP ATR interacting protein 1.280 NM_001178 ARNTL aryl hydrocarbon receptor nuclear translocator-like -1.250 NM_177405 CECR1 cat eye syndrome chromosome region, candidate 1 1.245
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NM_175875 SIX5 sine oculis homeobox homolog 5 (Drosophila) 1.239 NM_206909 PSD3 pleckstrin and Sec7 domain containing 3 -1.818 NM_024685 BBS10 Bardet-Biedl syndrome 10 -1.185 NM_020738 KIDINS220 kinase D-interacting substrate of 220 kDa -1.190 NM_030819 GFOD2 glucose-fructose oxidoreductase domain containing 2 1.306 NM_014311 SMUG1 single-strand-selective monofunctional uracil-DNA glycosylase 1 1.114 NM_173852 KRTCAP2 keratinocyte associated protein 2 1.194 NM_002906 RDX radixin -1.170 NM_00103170 TMEM103 transmembrane protein 103 1.141 3 NM_203288 RP9 retinitis pigmentosa 9 (autosomal dominant) 1.155 NM_002967 SAFB scaffold attachment factor B 1.155 NM_016498 MTP18 mitochondrial protein 18 kDa 1.522 NM_201274 M-RIP myosin phosphatase-Rho interacting protein -1.193 NM_006513 SARS seryl-tRNA synthetase 1.196 NM_012111 AHSA1 AHA1, activator of heat shock 90kDa protein ATPase homolog 1 (yeast) 1.116 NM_015907 LAP3 leucine aminopeptidase 3 -1.379 NM_021727 FADS3 fatty acid desaturase 3 1.398 NM_173691 C9orf75 chromosome 9 open reading frame 75 1.313 NM_138462 ZMYND19 zinc finger, MYND-type containing 19 1.270 NM_207106 UCHL5IP UCHL5 interacting protein 1.257 NM_004747 DLG5 discs, large homolog 5 (Drosophila) 1.132 NM_175744 RHOC ras homolog gene family, member C 1.135 NM_003969 UBE2M ubiquitin-conjugating enzyme E2M (UBC12 homolog, yeast) 1.143 NM_021633 KLHL12 kelch-like 12 (Drosophila) -1.205 NM_018135 MRPS18A mitochondrial ribosomal protein S18A 1.168 NM_024630 ZDHHC14 zinc finger, DHHC-type containing 14 1.381 NM_031989 PCBP2 poly(rC) binding protein 2 1.264 NM_152506 C21orf129 chromosome 21 open reading frame 129 1.235 NM_001930 DHPS deoxyhypusine synthase 1.170 NM_024038 C19orf43 chromosome 19 open reading frame 43 1.114 NM_178867 SFXN4 sideroflexin 4 1.249 NM_005642 TAF7 TAF7 RNA polymerase II, TATA box binding protein (TBP)-associated factor -1.155 NM_145037 FAM55C family with sequence similarity 55, member C -1.408 NM_080867 SOCS4 suppressor of cytokine signaling 4 -1.176 NM_001667 ARL2 ADP-ribosylation factor-like 2 1.225 NM_015565 ZNF294 zinc finger protein 294 -1.200 NM_022838 ARMCX5 armadillo repeat containing, X-linked 5 -1.235 NM_016049 C14orf122 chromosome 14 open reading frame 122 1.354 NM_001015 RPS11 ribosomal protein S11 1.076 NM_001190 BCAT2 branched chain aminotransferase 2, mitochondrial 1.142 NM_138401 FAM125A family with sequence similarity 125, member A 1.137 NM_014188 SSU72 SSU72 RNA polymerase II CTD phosphatase homolog (S. cerevisiae) 1.126 TMEM183 NM_138391 transmembrane protein 183A 1.131 A NM_002601 PDE6D phosphodiesterase 6D, cGMP-specific, rod, delta 1.135 mitochondria-associated protein involved in granulocyte-macrophage colony- NM_016069 Magmas 1.234 stimulating factor signal transduction NM_031288 ZNHIT4 zinc finger, HIT type 4 1.220
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NM_014413 EIF2AK1 eukaryotic translation initiation factor 2-alpha kinase 1 1.201 NM_032848 C12orf52 chromosome 12 open reading frame 52 1.243 NM_014633 CTR9 Ctr9, Paf1/RNA polymerase II complex component, homolog (S. cerevisiae) -1.282 NM_019045 WDR44 WD repeat domain 44 -1.326 NM_020205 OTUD7B OTU domain containing 7B 1.204 NM_014034 ASF1A ASF1 anti-silencing function 1 homolog A (S. cerevisiae) -1.261 NM_017916 PIH1D1 PIH1 domain containing 1 1.194 NM_00100431 FCRL6 Fc receptor-like 6 1.284 0 NM_007017 SOX30 SRY (sex determining region Y)-box 30 1.301 NM_005249 FOXG1 forkhead box G1 1.323 NM_022736 MFSD1 major facilitator superfamily domain containing 1 -1.190 NM_002014 FKBP4 FK506 binding protein 4, 59kDa 1.402 NM_012394 PFDN2 prefoldin subunit 2 1.197 NM_145911 ZNF23 zinc finger protein 23 (KOX 16) -1.311 NM_032276 RHBDD1 rhomboid domain containing 1 1.286 NM_001812 CENPC1 centromere protein C 1 -1.321 NM_016258 YTHDF2 YTH domain family, member 2 -1.138 NM_012079 DGAT1 diacylglycerol O-acyltransferase homolog 1 (mouse) 1.332 NM_016221 DCTN4 dynactin 4 (p62) -1.178 NM_145080 NSMCE1 non-SMC element 1 homolog (S. cerevisiae) 1.188 NM_032287 LDOC1L leucine zipper, down-regulated in cancer 1-like -1.230 NM_198044 ZDHHC16 zinc finger, DHHC-type containing 16 1.124 NM_015324 KIAA0409 KIAA0409 1.221 NM_021183 RAP2C RAP2C, member of RAS oncogene family -1.242 NM_014612 FAM120A family with sequence similarity 120A 1.253
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Appendix 3 Appendix 3 contains the list of genes differently expressed in neurosphere-derived cells from patients with schizophrenia in the microarray study, which has been validated in our lab. The transcript levels of the genes were assessed using quantitative RT-PCR, which was performed by Dr. Yongjun Fan. Green denotes that the RT-PCR data is in agreement with the findings from the microarray study (Illumina).
Illumina RT-PCR Fold Fold Name Genbank p-value change p-value change RELN NM_005045 0.028 -4.950 0.023 -7.060 RGS4 NM_005613 0.006 -3.049 0.002 -3.450 GABRE NM_021990 0.002 1.969 0.008 2.004 LRP8 NM_033300 0.030 -1.543 0.550 -1.155 SLC1A1 NM_004170 0.035 -2.331 0.036 -2.165 PARVB NM_1003828 0.001 -1.520 0.760 -1.046 LAMA3 NM_198129 0.012 1.834 0.038 1.836
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