Some Bioinformatic Analyses of Human GDAP1 Gene Expression

Some bioinformatic analyses of human GDAP1 gene expression

Khloud Mubarak Algothmi

Thesis submitted, in fulfilment of the requirements for the degree of Doctor of Philosophy in Applied science.

University of Canberra

July 2015

Abstract

Charcot- Marie-Tooth (CMT) represents a group of genetic disorders, which cause damage in the peripheral nervous system. It was identified and described in 1886 by Jean-Martin Charcot, Pierre Marie and Howard Henry Tooth. It is the most common inherited disorder of the peripheral nervous system, and affects approximately 1 in every 2,500 people. A severe form of CMT has been linked to mutations in the coding region of Ganglioside-induced Differentiation Associated Protein (GDAP1), a member of the glutathione transferase (GST) family which is located in the outer membrane of mitochondria. GDAP1 mutations cause axonal, demyelinating and intermediate forms of CMT. In some cases the same mutation can cause different CMT phenotypes. The overall hypothesis for this thesis was, that changes in the expression in GDAP1 may lead to these phenotypic differences.

The methodology used to investigate the expression of human GDAP1 gene was a bioinformatic approach. The results demonstrated that in normal healthy tissues, GDAP1 had ubiquitous expression, particularly in neural and endocrine tissues. This pattern of expression was different to the expression of mouse GDAP1, where expression was predominantly in nervous tissues.

GDAP1 has mainly been studied in the context of peripheral neuropathies, based on its genetic linkage with CMT disease. In this study, the expression of GDAP1 was shown to be altered in some other diseases, such as brain cancers. Five out of six microarray studies found that glioblastoma expressed lower levels of GDAP1, than normal brain tissue. This was further verified by immunohistochemistry, showing weaker staining for GDAP1 in glioblastoma glial cell samples, compared to normal brain tissue.

Limited studies have investigated the transcriptional regulation of GDAP1. A comparison between the human and mouse GDAP1 5’ flanking regions for transcription factor binding was undertaken. Some differences in the transcription factor binding sites between human and mouse GDAP were found. These were further highlighted by microarray studies, focussing on some transcription factor families. In EGR family members, no impact on GDAP1 expression in mouse cells where EGR1 had been depleted was seen, whereas human GDAP1 was strongly upregulated, when WTAP (an EGR family member) was knocked down.

This project also investigated, whether genetic polymorphisms in the 5’ flanking region of the GDAP1 gene can alter GDAP1 expression. The study characterised a variable poly-A region, approximately -240bp upstream of the ATG. This region had a length varying between 11 and 15 A nucleotides. These results identified a novel SNP (G/A) in position -398 and also confirmed SNPs in positions -832 and -510. Transcription factors found lying under these SNPs were altered between the wild type and variant alleles. This variation could cause changes in the expression of GDAP1.

Finally, this project aimed to determine the potential of GDAP1L1 as a compensation for GDAP1 proteins in humans. Limited support was found supporting this hypothesis. The results showed an inverse relationship between the expression of GDAP1 and GDAP1L1 in 15% of the profiles investigated.

Taken together, this study presents new insights into the human GDAP1 expression. Evidence is provided of changes in GDAP1 expression in diseases other than CMT (such as cancers), and that common polymorphisms in the 5’ flanking region of GDAP1 may contribute to variation in GDAP1 expression. Also, the results show a difference in the expression patterns of human and mouse GDAP1 genes, supporting the need for further studies in human models, to support this hypothesis. Further work will be required to fully understand the implications of changes in GDAP1 expression in CMT disease.

Acknowledgement

Foremost, I would like to express my sincere gratitude to my supervisor, Dr Alison Shield for her continuous support of my PhD study and research. Her guidance helped me in all the time of research and writing of this thesis. Thanks to her patience, motivation, enthusiasm, and immense knowledge, I could not have had a better advisor and mentor for my PhD.

Besides my supervisor, I would like to thank the rest of my panel members: Dr Greg Kyle and Dr Luby Simson for their insightful comments.

My sincere thanks also go to Dr Joelle Vandermensbrugghe, for her encouragement. She was always willing to help and give her best suggestions. I would like to thank King Abdul Aziz University for providing me with King Abdullah scholarship.

I would like to thank my parents for supporting me spiritually throughout my life. Thanks to my husband, Haitham for all his patience and support. He was always there by my side; cheering me. This work would not have been accomplished without his help.

Last but not the least; I would like to thank my sister, my brother and my son. They were always supporting me and encouraging me with their best wishes.

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Table of Contents

Abstract ...... v

Acknowledgement ...... vii

List of Figures ...... xiv

List of Tables ...... xx

Chapter 1. Introduction ...... 1

1.1 The structure of the nervous system ...... 1

1.2 Charcot-Marie-Tooth disease...... 3

1.3 Mitochondrial network...... 9

1.5 GDAP1 Localization and Function ...... 12

1.6 GDAP1 phenotype and genotype...... 15

1.7 Regulation of gene expression ...... 29

1.8 Bioinformatic approaches to understanding gene expression ...... 29

1.9 Aims of this research ...... 30

Chapter 2. Materials and Methods ...... 31

2.1 Bioinformatics materials and methods...... 31

2.1.1 Bioinformatics tools used ...... 31

2.1.2 Data collection ...... 32

2.2 Molecular biology materials and methods ...... 39

2.2.1 Solutions, equipment, kits ...... 39

2.2.2 Human DNA samples ...... 41

2.2.3 Primer design ...... 41

2.2.4 PCR reaction optimisation ...... 42

2.2.5 Plasmid vectors ...... 44

2.2.6 Agarose Gel electrophoresis ...... 47

2.2.7 Gel purification ...... 47

2.2.8 Restriction digest ...... 47

2.2.9 Column purification ...... 48

2.2.10 A-Tailing ...... 48

2.2.11 Ligation ...... 48

2.2.13 Transformation using calcium chloride ...... 48

2.2.14 Plasmid purification ...... 49

2.2.15 Sequencing ...... 50

2.3 Immunohistochemistry ...... 50

2.4 Statistical analysis ...... 51

Chapter 3. Bioinformatic analysis of GDAP1 expression...... 53

3.1 Introduction ...... 53

3.2 Methodology ...... 54

3.2.1 Gene Expression Omnibus (GEO) data mining ...... 54

3.2.2 Oncomine data mining ...... 55

3.2.3 Immunohistochemistry studies for GDAP1 ...... 56

3.3. Results ...... 56

3.3.1 Expression of GDAP1 in normal tissues ...... 56

3.3.2 Expression of GDAP1 in selected tissues ...... 65

3.3.3 Oncomine datamining ...... 94

3.3.4 Immunohistochemical studies ...... 98

3.4 Discussion ...... 106

Chapter 4. Transcription factors and polymorphisms ...... 110

4.1 Introduction ...... 110

4.2 Methodology ...... 112

4.2.1 Bioinformatic analysis of the GDAP1 5’ flanking region ...... 112

4.2.2 Molecular biology methods ...... 113

4.3 Results ...... 114

4.3.1 Transcriptional regulation of the GDAP1 promoter ...... 114

4.3.2 Impact of Polymorphisms in GDAP1 regulation ...... 138

4.5 Discussion ...... 146

Chapter 5. Bioinformatic analysis for GDAP1L1 ...... 150

5.1 Introduction ...... 150

5.2 Methodology ...... 151

5.2.1 Gene Expression Omnibus (GEO) data mining ...... 151

5.2.2 Oncomine data mining ...... 152

5.3 Results ...... 152

5.3.1 Results for the gene expression Omnibus ...... 152

5.3.2 Oncomine data mining ...... 177

5.4 Discussion ...... 178

Chapter 6. General Discussion ...... 180

6.1 The expression of GDAP1 is ubiquitous ...... 180

6.2 The expression of GDAP1 is different in human and mouse ...... 181

6.3 GDAP1 transcription regulation ...... 182

6.4 Genetic variation in GDAP1 transcription ...... 183

6.5 GDAP1 gene has variable poly-A region ...... 184

6.6 Evidence for changes in GDAP1 expression in cancers ...... 185

6.7 Compensatory role for GDAP1L1 ...... 186

6.8 Limitations and Future direction ...... 187

Appendix ...... 189

Appendix 1 ...... 189

Abbreviations ...... 189

Appendix 2 ...... 191

Additional Material and method ...... 191

Appendix 3 ...... 198

A3.1 Search result for Human GDAP1 and Brain ...... 198

A3.2 Search result for Human GDAP1 and Epithelial cells ...... 204

A3.3 Search result for Human GDAP1 and Cell lines ...... 214

A3.4 Search result for Human GDAP1 and Breast ...... 240

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A3.5 GDAP1 expression in Gliomas ...... 246

A3.6 Search results for Oncomine data mining ...... 247

Appendix 4 ...... 255

A4.1 Transcriptional regulation of the human GDAP1 promoter ...... 255

A4.2 Transcriptional regulation of the mouse GDAP1 promoter ...... 268

A.4.3 Impact of Polymorphisms in GDAP1 regulation ...... 280

A4.4 PCR reaction optimisation ...... 282

A.4.5 Human and mouse GDAP1 gene alignment ...... 284

Appendix 5 Search result for Human GDAP1L1 and Brain...... 288

References ...... 294

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List of Figures

Figure 1.1: Schematic representation of PNS and CNS structure...... 3

Figure 1.2: Mitochondria are dynamic organelles constantly undergoing fusion and fission...... 11

Figure 1.3: Schematic representation of the GDAP1 protein with predicted domains (HD hydrophopic domain, TMD) and indication of the corresponding exons ...... 17

Figure 2.1: Human GDAP1 5’-flanking region sequence...... 37

Figure 2.2: Mouse GDAP1 5’-flanking region sequence...... 38

Figure 2.3: Agarose gel for PCR samples amplified, using Phusion Hot Star II High-Fidelity DNA Polymerase...... 43

Figure 2.4: Agarose gel for PCR samples amplified using iProof High-Fidelity Polymerase...... 44

Figure2.5: Diagram shows pGEM-T easy vector...... 45

Figure 2.6: Diagram shows pXPG vector...... 45

Figure 2.7: Chart showing cloning method using pGEM- T Easy vector...... 46

Figure 3.1: Molecular analysis for GDAP1. RT–PCR analysis of GDAP1 and GAPDH (control) transcripts in human (A) and mouse (B) tissues...... 54

Figure 3.2: The expression of GDAP1 in different normal mouse tissues...... 57

Figure 3.3: The expression of GDAP1 in normal healthy tissues for six dependent studies.. 60

Figure 3.4: The expression of GDAP1 in normal healthy tissues...... 61

Figure 3.5: The expression of GDAP1 in normal healthy tissues ...... 62

Figure 3.6: GDAP1 expression in cell lines derived from human tissues...... 63

Figure 3.7: Expression of GDAP1 in 60 cell lines ...... 64

Figure 3.8: GDAP1 expression in the brains of bipolar disorder patients ...... 70

Figure 3.9: GDAP1 expression in glial brain tumours ...... 71

Figure 3.10: GDAP1 expression in gliomas of different grades...... 72

Figure 3.11: GDAP1 expression in human breast tumours...... 73

Figure 3.12: The expression of GDAP1 in fractions of the MCF-7 cell line...... 74

Figure 3.13: GDAP1 expression in MCF-7 breast cancer cells treated with estradiol and cyclohexamide...... 75

Figure 3.14: GDAP1 expression in MCF-7 cell lines overexpressing constitutively active Raf- 1, constitutively active MEK, constitutively active c-erbB-2, or ligand-activatable EGFR.. .. 76

Figure 3.15: The expression of GDAP1 in MCF7 cells overexpressing XBP1...... 77

Figure 3.16: GDAP1 expression in LM2 breast cancer cells depleted for metadherin...... 78

Figure 3.17: GDAP1 expression in airway epithelial cells from children with asthma...... 79

Figure 3.18: The expression of GDAP1 in two non-small cell lung cancers...... 80

Figure 3.19: The expression of GDAP1 in normal ovarian surface epithelia and ovarian cancer epithelial cells...... 81

Figure 3.20: GDAP1 expression in K562 leukaemia cells treated with 1 µM imatinib for 24 hours...... 83

Figure 3.21: GDAP1 expression in HEK293 kidney cells expressing HCaRG ...... 84

Figure 3.22: GDAP1 expressions in HEK293 kidney cells...... 85

Figure 3.23: GDAP1 expression in RKO colon cancer cell line...... 86

Figure 3.24: GDAP1 expression in multiple myeloma MM1.S cells depleted for beta-catenin...... 87

Figure 3.25: GDAP1 expression in Jurkat CD4+ T cells following induction of Nef...... 88

Figure 3.26: GDAP1 expression in intestinal Caco-2 cells treated with conjugated linoleic acid...... 89

Figure 3.27: GDAP1 expression in IMR-90 fibroblasts in two and three dimensional collagen- glycosaminoglycan...... 90

Figure 3.28: GDAP1 expression in HeLa cells depleted for optineurin using RNAi knockdown ...... 91

Figure 3.29: GDAP1 expression in lymphoblast cell lines derived from subjects with nicotine dependence...... 92

Figure 3.30: GDAP1 expression in siRNA induced knockdown Wilms' tumour 1-associating protein (WTAP)...... 93

Figure 4.1: Histogram showing the frequency of transcription factor binding sites in the human GDAP1 5’FR...... 115

Figure 4.2: Histogram showing the frequency of transcription factor binding sites in the mouse GDAP1 5’FR...... 116

Figure 4.3: Frequency distribution between the mouse and the human transcription factor binding sites...... 116

Figure 4.4: Blast alignment of the human and mouse GDAP1 5’FR...... 123

Figure 4.5: The human GDAP1 sequence and location of SOX, EGR and YY1 TFBS...... 125

Figure 4.6: The mouse GDAP1 sequence and location of SOX, EGR and YY1 TFBS...... 127

Figure 4.7: The expression of GDAP1 in the depletion of SOX10 in Schwann cell line..... 130

Figure 4.8: The expression of GDAP1 in the overexpression of SOX7 and SOX17 in embryonic stem cell lines...... 131

Figure 4.9: The expression of GDAP1 in the retinas of Egr-1 deficient animals on post-natal days, 30 and 42 ...... 133

Figure 4.10: The expression of GDAP1 in RAR deficient F9 teratocarcinoma cells...... 134

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Figure 4.11: The expression of GDAP1 in HeLa cells after YY knockdown ...... 135

Figure 4.12: The expression of GDAP1 in E12.5 mouse hearts deficient in Irx3 and Irx5.. 136

Figure 4.13: The expression of GDAP1 in the cortex and basal ganglia from embryonic telencephalon of homozygous and heterozygous Dlx1/2 mutants ...... 137

Figure4.14: The expression of GDAP1 in the intestine of PLAGL2 knockout mice ...... 138

Figure 5.1: The expression of GDAP1L1 in normal healthy tissues...... 154

Figure 5.2: The expression of GDAP1L1 in 32 normal healthy tissues...... 155

Figure 5.3: The expression of GDAP1L1 in brain tissues of HIV infected patients...... 157

Figure 5.4: The expression of GDAP1L1, in portmortem brain tissues from HIV patients. 158

Figure 5.5: Comparison between the expression of GDA1P1 and GDAPL1 in bipolar disorder...... 159

Figure 5.6: The expression of GDAP1L1 in gliomas of different grades...... 160

Figure 5.7: Comparison between the expression of GDAP1 and GDAP1L1 in oligodendroglioma Grade II ...... 161

Figure 5.8: The expression of GDAP1L1 in LM2 breast cancer cells depleted for metadherin...... 162

Figure 5.9: Comparison between the expression of GDA1P1 and GDAPL1 in the metadherin depleted culture alone LM2 breast cancer cells...... 163

Figure 5.10: Comparison between the expression of GDA1P1 and GDAPL1 in the airway epithelial cells from children with asthma...... 164

Figure 5.11: The expression of GDAP1L1 in normal bronchial epithelial cells exposed to cigarette smoke for up to 24 hours...... 165

Figure 5.12: Comparison between the expression of GDA1P1 and GDAPL1 in normal bronchial epithelial cells exposed to cigarette smoke...... 166

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Figure 5.13: Comparison between the expression of GDAP1 and GDAP1L1 in normal ovarian surface epithelia and ovarian cancer epithelial cells...... 167

Figure 5.14: Comparison between the expression of GDAP1 and GDAP1L1 in HEK293 kidney cells expressing HCaRG...... 168

Figure 5.15: Comparison between the expression of GDAP1 and GDAP1L1 in HEK293 kidney cells...... 169

Figure 5.16: Comparison between the expression of GDAP1 and GDAP1L1 in Caco-2 cells treated with cis-12 CLA...... 170

Figure 5.17: Comparison between the expression of GDAP1 and GDAP1L1 in HeLa cells depleted for optineurin using RNAi knockdown...... 171

Figure 5.18: Comparison between the expression of GDAP1 and GDAP1L1 in lymphoblast cell lines derived from six subjects with active nicotine dependence...... 172

Figure 5.19: Comparison between the expression of GDAP1 and GDAP1L1 in Wilms' tumor 1-associating protein (WTAP) knockdown cells...... 174

Figure 5.20: Comparison between the expression of GDAP1 and GDAP1L1 in cells overexpressing SOX17...... 175

Figure 5.21: Comparison between the expression of GDAP1 and GDAP1L1 in cells overexpressing SOX7...... 176

Figure A2.1: PCR results for cycle in Table A2.3 runs on the gel. .…………………….....198

Figure A2.2: Agarose gel for PCR samples amplified using Phusion Hot Star II High-Fidelity DNA Polymerase. ……………………………………………………………….199

Figure A3.1: Expression of GDAP1, in the analysis of grades III and IV gliomas of various histologic types for the expression of GDAP1 ……………………………………………247

Figure A4.1: Agarose gel for PCR samples amplified using Phusion Hot Star II High-Fidelity DNA Polymerase………………….…………………………………….…………284

Figure A4.2: Agarose gel for PCR samples amplified using iProof High-Fidelity Polymerase………………………………………………………………………………..285

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List of Tables

Table 1.1: Summary of the different subtypes of CMT disease...... 4

Table 1.2: Summary of the typical characteristics for the different subtypes of CMT disease...... 6

Table 1.3: Ganglioside-Induced Differentiation Associated Protein 1disease linked mutations...... 18

Table 2.1: Websites programs used in this study...... 31

Table 2.2: Summary of the microarray data analysis software that was used to analyse the results of the microarray in the bioinformatics studies...... 33

Table 2.3: Frequently used buffers and solutions...... 39

Table 2.4: Enzymes used in the molecular method...... 39

Table 2.5: Equipment used in the molecular method...... 40

Table 2.6: Molecular kits, vectors, competent cells and reagent used in this study...... 40

Table 2.7: Primers that were used in the molecular study in this project ...... 41

Table 2.8: PCR cycling instructions used for amplifying GDAP1 gene...... 43

Table 2.9: Cycle instructions for sequencing...... 50

Table 3.1: Microarray experiments showing the expression of mouse GDAP1 in healthy normal tissues...... 56

Table 3.2: Microarray experiments showing the expression of human GDAP1 in healthy normal human tissues ...... 58

Table 3.3: Overview of systematic search for expression of GDAP1 in selected tissues...... 66

Table 3.4: Summary of the including cell line profiles that were found in the cell line search...... 67

Table 3.5: Summary of the epithelium GEO profiles included...... 68

Table 3.6: Summary of the 6 GEO profiles looking at K562 leukaemia cells treated with 1µM imantinib for 24 hours...... 82

Table 3.7: Summary of the studies found in Oncomine database...... 94

Table 3.8: Summary for the brain and CNS studies found in the Oncomine database...... 95

Table 3.9: Summary table for lung cancer studies found in the Oncomine database...... 96

Table 3.10: GDAP1 expression in the brain...... 99

Table 3.11: GDAP1 expression in the ovary...... 100

Table 3.12: GDAP1 expression in the pancreas...... 101

Table 3.13: GDAP1 expression in the prostate...... 102

Table 3.14: GDAP1 expression in the bladder...... 102

Table 3.15: GDAP1 expression in the testis...... 103

Table 3.16: GDAP1 expression in the skin...... 104

Table 3.17: GDAP1 expression in the pituitary...... 105

Table 4.1: Summary of the most repeated transcription factor binding sites found in the human and mouse 5’FR...... 118

Table 4.2: Summary of the transcription factors matrices that were searched for in the NCBI GEO profiles...... 124

Table 4.3: Summary of the SOX transcription factor binding sites found in the human GDAP1 gene...... 128

Table 4.4: Summary of the SOX transcription factor binding sites found in the mouse GDAP1 gene...... 129

Table 4.5: Summary of the EGR transcription factors found in the human GDAP1 gene. .. 131

Table 4.6: Summary of the EGR transcription factors found in the mouse GDAP1 gene ... 132

xxi

Table 4.7: Summary of the samples that were sequenced...... 139

Table 4.8: Summary of the Poly -A region found in this study...... 140

Table 4.9: The sequence results for SNPs in the GDAP1 5’FR...... 140

Table 4.10: Summary of the GDAP1 5’FR SNPs identified...... 143

Table 4.11: Predicted transcription factor binding sites found lying under the GDAP1 5-FR SNPs...... 145

Table 5.1: Summary table of microarray experiments showing the expression of human GDAP1L1 in healthy normal human tissues...... 153

Table 5.2: Comparison between XBP1, SOX and WTAP transcription factors in the GDAP1 and GDAP1L1 5’FR...... 176

Table 5.3: Summary for the expression of GDAP1 and GDAP1L1 in different tissues and cell lines...... 177 TableA2.1: Buffers and solution used in this study………….……………………………193

Table A2.2: One of the cycles used to amplify the PCR using iProof high fidelity polymerase………………………………………………………………………………….198

Table A2.3: One of the cycles used to amplify the PCR using the MangoTaq polymerase. ………………………..……………………………………………………………………199

Table A2.4: Summary of the number of PCR amplifications done in this study…………199

Table A3.1: Human GDAP1 and Brain…………………………………………………….200

Table A3.2: Human GDAP1 and Epithelial cells ………………………………………….206

Table A3.3: Human GDAP1 and cell lines ………………………………………………216

Table A3.4: Human GDAP1 and Breast …………………………………………………242

Table A3.5: Summary of all cancer studies found in Oncomine…………………………..248

Table A4.1: Transriptional factors in human GDAP1……………………………………..256

Table A4.2: Transriptional factors in mouse GDAP1……………………………………..269

TableA4.3: Polymorphisms ………………………………………………………..…….281

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TableA4.4: PCR cycling instructions used for amplifying GDAP1 gene………………..283

Table A5.1: Human GDAP1L1 and Brain…………………………………………..…...289

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Chapter 1. Introduction

Charcot–Marie–Tooth disease (CMT) is a common hereditary disease which affects 1 in 2,500 people (Berger et al., 2002, Skre, 1974). CMT refers to a group of genetic disorders which appear in children in their first or second decade of life, as functional decline in the lower legs and arms, caused by damage in the peripheral nerves (Berger et al., 2006). It has been commonly found that patients with CMT have foot deformities, resulting in gait impairments (De Sandre-Giovannoli et al., 2003, Senderek et al., 2003).

A severe recessive form of CMT (CMT4) has been linked with mutations in the Ganglioside- induced Differentiation Associated Protein (GDAP1) (Baxter et al., 2002, Cuesta et al., 2002, Nelis et al., 2002). GDAP1 is located in the outer membrane of the mitochondria, and is thought to play an important role in mitochondrial morphology (Niemann et al., 2005). The GDAP1 gene has been cloned, but the specific function of GDAP1 is uncertain (Shield et al., 2006). It is therefore difficult to understand how changes in this protein alter peripheral nerve function, to cause CMT disease.

New studies have found that GDAP1 is a regulator of the mitochondrial network; it helps mitochondrial fragmentation and is involved in mitochondrial fission processes (Niemann et al., 2005, Niemann et al., 2014). This research investigates the normal regulation of GDAP1, which will help us to understand how mutations in the GDAP1 gene contribute to CMT disease. This introductory chapter will describe the features of CMT disease and its phenotypes. It will then focus on the role of mitochondria in nerves, and how disruption of mitochondrial networks can lead to CMT disease. Current knowledge about GDAP1 function, as well as GDAP1 mutations causing CMT, and their resulting disease phenotypes, will also be introduced.

1.1 The structure of the nervous system

The nervous system is a highly complex communication network, which deals with information about an organism’s surrounding and interior condition. The nervous system is divided into two main parts: the central nervous system (CNS) and the peripheral nervous system (PNS) (Wagner et al., 2009). The CNS includes the brain and spinal cord; and its role is to process information (Rezaie et al., 2002). In contrast, the PNS contains mainly nerves that are sensory fibres, which connect the CNS to other parts of the body, thus distributing information (see Figure 1.1). The major cell type of nervous tissue are neurons and glial cells. Neurons are

communicating cells, which conduct electrical signals, and are also involved in the processing and conduction of electrically and chemically determined information (Barres and Raff, 1994).

Auxiliary Glial cells are much more numerous than neurons. Glial cells afford support and nutrition to neurons, maintain their stability, and contribute in signal transmission (Brophy and Shen, 2009). In addition, mature glial cells assist in the development of myelin sheaths that enwrap axons (Jessen and Mirsky, 2005).There are three types of corroborative or auxiliary cells in the CNS which are: astrocytes, microglia and oligodendrocytes (Jessen and Mirsky, 2005). In the PNS, Schwann cells constitute the major neuroglial component (Berthold and Carlstedt, 1977).

Myelination of the CNS is carried out by oligodendrocytes, while the PNS is myelinated by Schwann cells. Both cell types form myelin sheaths by making myelin, a membrane that is rich with lipid, which continually wraps around the axon (Barres and Raff, 1994). Oligodendrocytes can myelinate different axons and several internodes per axon, whereas Schwann cells myelinate a single internode in a single axon (Poliak and Peles, 2003) (Figure 1.1). Myelination permits the fast transmission of electrical signals in the direction of their target cell. This form of communication is likely due to the exclusive myelin structure created around axons (Barres and Raff, 1994). Conduction velocities of unmyelinated axons of the same diameter are up to ten times slower, compared to myelinated axons(Squire, 2002).

Early in development, Schwann cell precursors are reliant on axonal signals for existence: axons and Schwann cells in the PNS are functionally and anatomically linked and control each other (Jessen, 2004). In mature nerves, myelinating Schwann cells affect axonal properties, such as the axon organelle content, and rates of axonal transport (Arroyo and Scherer, 2000, Edgar and Garbern, 2004). Therefore, the role of Schwann cells and the associated axon is firmly regulated, and a failure of this complex system can lead to peripheral neuropathies, such as CMT.

Figure 1.1: Schematic representation of PNS and CNS structure. Myelinating glial cells, oligodendrocytes in the central nervous system (CNS), or Schwann cells in the peripheral nervous system (PNS) , form the myelin sheath by enwrapping their membrane several times around the axon. Myelin covers the axon at intervals (internodes), leaving bare gaps — the nodes of Ranvier. Oligodendrocytes can myelinate different axons and several internodes per axon, whereas Schwann cells myelinate a single internode in a single axon (Brewer et al., 2014). 1.2 Charcot-Marie-Tooth disease

The clinical characteristics of Charcot–Marie–Tooth disease (CMT) were first described by Jean–Martin Charcot, Pierre Marie and Howard Henry Tooth in 1886 (Sturtz et al., 1992). CMT causes muscle weakness and atrophy, predominantly in the lower extremities, distal sensory loss, and skeletal deformities (De Sandre-Giovannoli et al., 2003, Senderek et al., 2003). CMT normally appears in patients’ first or second decade of life, and is usually detected as a problem in the legs and feet, such as reflexive foot deformity (Claramunt et al., 2005). CMT patients also show characteristics such as weakness and atrophy in the distal leg muscles. In most patients, hands are also affected in the later stage of the disease (Sevilla et al., 2003). Some patients suffer from loss of major nerve fibres (De Sandra et al, 2003).

Based on its neuropathological and electrophysiological features, CMT can be divided into two main subtypes: the demyelinating and the axonal forms (Baxter et al., 2002, Cuesta et al., 2002). The demyelinating form is characterized by reduced nerve conduction velocities (NCV) less than 38m/sec, which are caused by partial or complete loss of the myelin sheath. This type

3 of pathology includes CMT1, CMT3, and CMT4. The axonal forms are characterized by a reduced compound muscle action potential (CMAP) (Cuesta et al., 2002). In the axonal phenotype, the formation of Schwann cell ‘onion bulbs’ (additional layers of Schwann cells and collagen around an axon) are common, while nerve conduction velocities are normal at 100m/sec (Baxter et al., 2002, Berger et al., 2002, Cassereau et al., 2009). The axonal types include CMT1 and CMT2 (De Sandre-Giovannoli et al., 2003). The categorization of CMT into demyelinating and axonal forms is clinically very useful. In some cases, the nerve damage is so severe that it can impede diagnosis of CMT subtypes.

In addition to the electrophysiology and anatomical pathology classifications, CMT subtypes are classified as per their mode of inheritance into; autosomal dominant (CMT1, CMT2), autosomal recessive (CMT3, CMT4) and X-linked (CMT1X) subtypes. There are also intermediate forms which display features of both types; CMTDI and CMTRI are examples of the intermediate forms (Lupski et al., 2010). The dominantly inherited forms CMT1 and CMT2 are common in CMT patients, accounting for more than two-thirds of all cases of CMT (Berger et al., 2002) . The autosomal recessive forms CMT3 and CMT4 are very rare. The X-linked dominant form CMT1X represents the second most common form of CMT (Patzko and Shy, 2011). These different sub-types are summarised in Table 1.1 below.

Table 1.1: Summary of the different subtypes of CMT disease. The first column shows the different types of CMT disease. The second and third columns show the two different phenotypes, the demyelinating and axonal. The fourth to the seventh columns show the pattern of inheritance: autosomal dominant, the autosomal recessive, the x-linked and the intermediate form.

The different

linked

types of form

Axonal

recessive

dominant

CMT Autosomal Autosomal

Intermediate Intermediate

Demyelinating

CMT1 X X X

CMT1X X X

CMT2 X X

CMT3 X

CMT4 X X

CMTDI X(Dominant)

CMTRI X(Recessive)

Autosomal recessive forms of CMT can occur in both demyelinating and axonal phenotypes (Suter and Scherer, 2003), so classification into demyelinating and axonal forms is not absolute, see table 1.1 which summarises the characteristics of the different subtypes of CMT. Intermediate forms show structures of both demyelinating and axonal forms (Suter and Scherer, 2003).

CMT is extremely heterogeneous, with more than 40 loci and 16 genes identified as being responsible for the pathological phenotypes (Espinós et al., 2012). In CMT disease, mutations in several different genes can cause similar disease phenotypes (Berger et al., 2006, Niemann et al., 2006). For example mutations in both GDAP1 and YARS genes cause an intermediate form of CMT, which is described by moderately reduced nerve conduction velocity with minor myelin deficits (Berger et al., 2006). These gene code proteins have quite different functions. The YARS gene encodes the enzyme tyrosyl- tRNA synthetase which is important for the synthesis of proteins (Jordanova et al., 2006). GDAP1 appears to be involved in mitochondrial network morphology (Niemann et al., 2005). The genes known to be linked to CMT are shown in Table 1.2 with their specific phenotype. Different mutations of the same gene can result in different disease phenotypes; for example mutations in GDAP1 have been identified as the cause of axonal, intermediate and demyelinating forms of CMT (Berger et al, 2006; Nieman et al, 2006).

Table 1.2: Summary of the typical characteristics for the different subtypes of CMT disease (the autosomal dominant, the-linked, the autosomal recessive demyelinating, the autosomal dominant, the autosomal recessive and the dominant intermediate) (Reilly and Shy, 2009). The first column shows the type of CMT, the second column shows gene locus and the third column shows the specific phenotype.

Type Gene/locus Specific phenotype

Autosomal dominant CMT1(AD CMT1)

CMT 1A Dup 17p CMT1 (PMP22)

PMP22 (point CMT1 / DSN / CHN / HNPP mutation)

CMT 1B MPZ CMT1/ DSN / CHN / intermediate / CMT2

CMT 1C LITAF CMT1

CMT 1D EGR2 CMT1 / DSN / CHN

CMT 1 NEFL CMT2 but can have slow MCVs in CMT1 range +/− early onset severe disease

X-linked CMT1 (CMT 1X)

CMT 1X GJB1 Intermediate +/− patchy MCVs / male MCVs < female MCVs

Autosomal recessive demyelinating (CMT4)

CMT4A GDAP1 CMT1 or CMT2 usually early onset and severe / vocal cord and diaphragm paralysis described / rare AD CMT2 families described

CMT4B1 MTMR2 Severe CMT1 / facial/bulbar/focally folded myelin

CMT4B2 MTMR13 Severe CMT1 / glaucoma/focally folded myelin

CMT4C KIAA1985 Severe CMT1 / scoliosis/cytoplasmic expansions (SH3TC2)

CMT4D NDRG1 Severe CMT1 / gypsy/deafness/tongue atrophy (HMSNL) CMT4E EGR2 Classic CMT1 / DSN / CHN

CMT4F PRX CMT1 / more sensory/focally folded myelin

CMT4H FGD4 CMT1

CMT4J FIG4 CMT1

CCFDN CTDP1 CMT1 / gypsy / cataracts / dysmorphic features

HMSN Russe 10q22-q23 CMT1

Autosomal dominant CMT2 (AD CMT 2)

CMT 2A KIF1Bb Classic CMT2

CMT 2A MFN 2 CMT2 / usually severe / optic atrophy

CMT 2B RAB7 CMT2 with predominant sensory involvement and sensory complications

CMT 2C 12q23 - q24 CMT2 with vocal cord and respiratory involvement

CMT 2D GARS CMT2 with predominant hand wasting / weakness or dHMN-V

CMT 2E NEFL CMT2 but can have slow MCVs in CMT1 range +/- early onset severe disease

CMT 2F HSP27 CMT2 or dHMN-II (HSPB1)

CMT 2G 12q12-q13.3 CMT2

CMT 2L HSP22 CMT2 or dHMN-II (HSPB8)

CMT 2 MPZ CMT1/ DSN / CHN / intermediate / CMT2

CMT 2 3q13.1 CMT2 with proximal involvement (HMSNP)

Autosomal recessive CMT 2

AR CMT2A LMNA CMT2 proximal involvement and rapid progression described / also causes muscular dystrophy / cardiomyopathy / lipodystrophy

AR CMT2B 19q13.1-13.3 Typical CMT2

AR CMT2 GDAP1 CMT1 or CMT2 usually early onset and severe / vocal cord and diaphragm paralysis described / rare AD CMT2 families described

Dominant intermediate CMT (DI-CMT)

DI-CMTA 10q24.1-25.1 CMT

DI-CMTB DNM2 CMT

DI-CMTC YARS CMT

Hereditary neuralgic amyotrophy (HNA)

AD = autosomal dominant; AR = autosomal recessive; CTDP1 = CTD phosphatise subunit 1; Del = deletion; Dup = duplication; EGR2 = early growth response 2; FGD4 = FYVE; RhoGEF and PH domain containing 4; FIG4 = FIG 4 homolog; GARS = glycyl-tRNA synthetase; GDAP1 = ganglioside- induced differentiation-associated protein 1; GJB1 = Gap junction protein beta1; HSP 22 = heat shock 22kDa protein 8; HSP 27 = heat shock 27kDa protein 1; KIF1Bß = kinesin family member 1B-ß; LITAF = lipopolysaccharide-induced tumournecrosis factor; LMNA = lamin A/C; DMN2 = dynamin 2; MFN2 = mitofusin 2; MPZ- myelin protein zero; MTMR13 = myotubularin-related protein 13; MTMR2 = myotubularin-related protein 2; NDRG1= N-myc downstream-regulated gene 1; NEFL = neurofilament, light polypeptide 68kDa; PMP-22 = peripheral myelin protein 22; PRX = periaxin; RAB7 = RAB7, member RAS oncogene family; SEPT9 = septin 9; SH3TC2 = SH3 domain and tetratricopeptide repeats 2; YARS = tyrosyl-tRNA synthetase. EGR2, early growth response 2; FGD4, actin filament-binding protein frabin; FIG4, FIG4 homolog SAC1 lipid phosphatase domain- containing; GDAP1, ganglioside-induced differentiation-associated protein 1; HINT1, histidine triad nucleotide-binding protein 1; HK1, hexokinase 1; HSPB1, heat-shock 27 kDa protein 1; LMNA, lamin

8 a/C; LRSAM1, leucine rich repeat and sterile alpha motif 1; MED25, mediator complex subunit 25; MFN2, mitofusin 2; MTMR2, myotubularin related protein 2; MTMR13, myotubularin-related protein13; NDRG1, N-myc downstream-regulated Gene; NEFL, neurofilament light chain; PRX, periaxin; SH3TC2, SH3 domain and tetratricopeptides repeats 2. 1.3 Mitochondrial network

Various neuropathies, including CMT1 are caused by mutations in mitochondrial dynamic factors, reinforcing the notion that neurons are particularly prone to defects in mitochondrial dynamics, a process described in the following section.

Mitochondria are membrane-bound organelles found in most eukaryotic cells. These structures are sometimes described as "the powerhouse of the cell", because they generate most of the cell's supply of adenosine triphosphate (ATP), used as a source of chemical energy (Figure 1.2) (Herzig and Martinou, 2008, Wagner, 2009). A continuous process of fusion and fission events is crucial to maintain the mitochondrial morphology at a given moment, the mitochondrial functionality, and to allow transport within a cell (Frazier et al, 2006).

Mitochondrial fusion is a coordinated process that allows the fusion of the outer and the inner mitochondrial membrane (Chan, 2006a). This leads to the exchange of lipids, proteins and mitochondrial DNA (mtDNA) (Chan, 2006a). Thereby, mitochondrial fusion lowers the risk that mutations in the mtDNA accumulate within a single organelle. Such an accumulation of mutated mtDNA and dysfunctional gene products could in turn interfere with mitochondrial functionality, and lead to more damage (Chan, 2006a). To avoid the accumulation of damaged mitochondrial material, fission continuously generates smaller mitochondrial units. If the separated mitochondrial unit is unable to fuse back, it can be degraded by the process of autophagy (Twig et al., 2008b). Thus, continuous fusion and fission events maintain a homogenous mitochondrial population within a cell and allow quality control (Twig et al., 2008b).

Mitochondrial fragmentation becomes most prominent during apoptosis (Herzig and Martinou, 2008). The mitochondrial fragmentation occurs during or prior to the release of pro-apoptotic factors such as cytochrome C. Although mitochondrial fission and the release of pro-apoptotic factors from mitochondria can be separated (Frazier et al, 2006), a tight association between the mitochondrial morphology and the apoptotic progression has been demonstrated in various studies (Herzig and Martinou, 2008).

Increased mitochondrial fusion blocks or delays the intrinsic apoptotic pathway (Herzig and Martinou, 2008). Loss of fission resulting in elongated mitochondria, as the fusion process is still ongoing, is also protective against the induction of apoptosis (Herzig and Martinou, 2008). Cells with fragmented mitochondria, caused by increased fission or decreased fusion, are more susceptible to apoptotic stimuli (Herzig and Martinou, 2008).

However, mitochondrial fission is not always destructive or deleterious. Fission is needed to allow mitochondrial inheritance into the daughter cells (Yaffe, 1999). In addition, fission influences mitochondrial transport and distribution in neuronal cells (Niemann et al., 2006). The overexpression of the fission factor Drp1 (Dynamin-related protein1, or Dlp1; Dnm1 in yeast) conducts more mitochondria into dendrites, and increases synapse formation in cultured hippocampal neurons (Li et al., 2004). Similar effects of Bcl-xL are Drp1-dependent (Li et al., 2008). This suggests that mitochondrial fission supports neural differentiation and maintenance (Frank, 2006; Niemann et al., 2006).

Detmer and Chan speculated, that due to the extreme dimensions of neurons, especially long peripheral nerves, they are especially vulnerable to alterations in the tight regulation of mitochondrial fusion and fission. This dependence of neurons probably stems from their high energy demands and the special importance of proper mitochondrial distribution: mitochondria are concentrated in several neuronal regions, including pre- and postsynaptic sites (Detmer and Chan, 2007). Several proteins involved in the regulation of mitochondrial dynamics have been found to cause peripheral neuropathies, when the cognate genes are mutated. Mutations in the mitochondrial fusion factors, MFN2 (mitofusin 2) and OPA1 (optical atrophy 1), lead to the neurodegenerative disorders: Charcot- Marie-Tooth disease (CMT) 2A and autosomal dominant optical atrophy (ADOA), respectively (; Chan, 2006a).

Mutations in GDAP1 are known to lead to axonal, intermediate and demyelinating forms of CMT. As the following study focused on GDAP1 and the disease-causing mechanisms of GDAP1 mutations, this protein will be discussed in detail in the next section.

Figure 1.2: Mitochondria are dynamic organelles constantly undergoing fusion and fission. Depicted are selected factors that are known to influence the mitochondrial morphology. (Wagner, 2009). BAX = (Bcl-2-associated X protein), BAC= (Bacterial artificial chromosomes), MFN1 = (mitofusin 1), MFN2 = (mitofusin 2), OPA1= (optic atrophy 1), Drp1= (Dynamin-related protein 1), MTP18= Mitochondrial protein 18 and Mff= (Mitochondrial Fission Factor).

1.4 Ganglioside-induced Differentiation Associated Protein (GDAP1)

GDAP1 is a protein of 41 kDa, containing 358 amino acids (Baxter et al., 2002). The corresponding gene is 23,728 base pairs long, and contains six exons (Cassereau et al., 2009). GDAP1 was originally described in a study by Liu et al. (1999) , who found ten different types of mRNA, all up-regulated after the transfection of GD3 synthase cDNA into a mouse neuroblastoma cell line (Neura2a), resulting in cellular differentiation to a neuronal cell line. These cDNAs were all named ‘Ganglioside-induced Differentiation Associated Protein’ (GDAP), to reflect this relationship. However, this name does not follow the usual conventions for naming a gene, where the name reflects membership of the gene family (Liu et al., 1999). GDAP1 expression was also found to be higher in neural differentiated P19 cells, compared to the undifferentiated cells. Liu et al (1999) also found that GDAP1 expression in murine brain reaches the highest levels at adulthood. It was concluded from this study that GDAP1 could be playing a role in a signal transduction pathway responsible for neural differentiation (Liu et al., 1999), although no additional studies have showed the involvement of GDAP1 in such a pathway.

In humans, mutations in GDAP1 have been linked with several forms of CMT (Baxter et al, 2002; Cuesta et al 2002). Baxter et al (2002) mapped candidate genes for CMT disease, using

11 genetic linkage and found a relationship between CMT and GDAP1, which is located on the 8 q21.1. Mutations in this gene have been linked with CMT, and can cause the early onset of CMT with either the demyelinating or the axonal phenotypes (Birouk et al., 2003, Baxter et al., 2002, Nelis et al., 2002, De Sandre-Giovannoli et al., 2003). There are more than 30 mutations in the GDAP1 gene which are sub-classified as either CMT4A or CMT2K (Medicine, 2013).

Mutations were first found in GDAP1, by studying genetic linkage in consanguineous Spanish families and Tunisian families with autosomal recessive CMT (Cuesta et al., 2002, Baxter et al., 2002). Other European studies confirmed the relationship between GDAP1 and CMT as an autosomal recessive disease (Ammar et al., 2003, Stojkovic et al., 2004), and since then, further studies have revealed dominant and sporadic inherited forms of disease that are linked to GDAP1 (Chung et al., 2008, Claramunt et al., 2005, Xin et al., 2008). After screening for the GDAP1 gene in 43 index patients, 39 of them with CMT2, and 4 with intermediate CMT, Crimella et al. (2010) found a GDAP1 mutation frequency of 27% in the dominant families analysed. This suggested that the involvement of GDAP1 in dominant CMT2 could be higher than expected. To increase our understanding of GDAP1 and how it might contribute towards CMT pathology, we need further information on the cellular role of GDAP1, as well as how the GDAP1 genotypes relate to the CMT phenotypes. Mutations in the GDAP1 gene are linked with a large variety of CMT phenotypes (Chung et al., 2008) such as axonal CMT (Cuesta et al., 2002), an intermediate phenotype of CMT (Nelis et al., 2002, Senderek et al., 2003), and the demyelinating form of CMT (Baxter et al., 2002). The phenotype is dependent on loss of GDAP1 expression in Schwann cells, with mild impairment of the mitochondrial transport in axons (Cuesta et al., 2002).

1.5 GDAP1 Localization and Function

To understand how mutations in GDAP1 contribute towards CMT pathology, we need to understand its localisation and function. GDAP1 expression has been seen in different regions of the CNS, including cerebellum, spinal cord, cortex, olfactory bulb and peripheral nerves, as determined by Western blot analysis (Niemann et al., 2005), reverse transcription polymerase chain reaction (RT-PCR) (Pedrola et al., 2005), and immunohistochemistry (Pedrola et al., 2008). Much of these data have been obtained in murine tissues, although Cuesta et al. (2002) used RT-PCR to study GDAP1 mRNA levels in 22 human tissues; and their study suggested that human GDAP1 has higher expression in the central nervous tissues than in the peripheral nervous tissue.

In a murine model, Niemann et al (2005) also found that GDAP1 was expressed in the dorsal root ganglia and the sciatic nerve. Niemann et al (2005) demonstrated in mice that both neurons and Schwann cells express GDAP1; changes in these cells are thought to be important for the development of CMT (section 1.1). By contrast, Pedrola et al. (2005) found GDAP1 expression only in neurons, and did not identify GDAP1 in cultured rat Schwann cells.

The function of the GDAP1 protein is uncertain; however, it is structurally related to the glutathione S-transferase (GST) enzyme family (Marco et al., 2004). There is evidence that GDAP1 contains structural features of glutathione S-transferases (Marco et al., 2004, Shield et al., 2006). It is still unclear if GDAP1 has glutathione S-transferase activity, as for recombinant GDAP1, no glutathione S-transferase activity was detected in vitro (Shield et al., 2006).Computer analysis revealed that the GDAP1 protein demonstrates a high degree of homology with an N-terminal thioredoxin fold domain (GST-N) and C-terminal substrate- binding domain (GST-C). The GST family consists of cytosolic, mitochondrial and microsomal proteins, which are found in eukaryotes and prokaryotes (Sheehan et al., 2001). The main role of GSTs is conjugation of glutathione to many endogenous and xenobiotic substrates (Sheehan et al., 2001). Inspite of the likeness to GST proteins, evolutionary studies have shown special features in GDAP1, that are absent in canonical GSTs (Marco et al., 2004). These features are shared by GDAP1-like1 (GDAP1L1: a GDAP1 paralog) and a homologous protein from Drosophila melanogaster, suggesting that these three proteins belong to a novel class of GST like proteins (Marco et al., 2004). The novel features in GDAP1 are an enlarged domain between the GST domains, comprising of two additional α-helices and a C-terminal transmembrane domain, which anchors into the mitochondrial outer membrane (Marco et al., 2004, Shield et al., 2006, Niemann et al., 2005, Pedrola et al., 2008). Cross-linkage studies of recombinant GDAP1 showed that GDAP1 has a dimeric structure comparable to other cytosolic GSTs (Shield et al, 2006). Co-immunoprecipitation studies by Ruegg et al (2009) confirmed the capacity of GDAP1 to form dimers. There has been limited evidence of GST activity or glutathione-binding ability (Pedrola et al, 2005; Shield et al, 2006); however Wagner (2009) recently demonstrated GST activity, using a truncated GDAP1, expressed by a eukaryotic baculovirus system.

The GDAP1 protein is localised to the outer membrane of mitochondria (Nieman et al, 2005). The necessary information for proper organelle targeting is contained within the carboxyl group terminal part of the polypeptide (Niemann et al, 2005). Studies using protease digest

13 have found, that GDAP1 is integrated in the mitochondrial outer membrane (MOM) with the N-terminal part having the GST domains oriented towards the cytosol (Niemann et al., 2005). The specific topology of GDAP1 in MOM targeting has been studied by Wagner et al. (2009). They revealed that GDAP1 shows a single transmembrane domain, whereas the second N- terminal hydrophobic domain is set in the cytoplasm, determining GDAP1 as a tail anchored protein (Wagner et al., 2009). The transmembrane domain (TMD) and the adjacent basic amino acids were found to be fundamental for mitochondrial targeting and membrane insertion. MOM targeting is essential for the fission role of GDAP1, while alterations in the GDAP1 transmembrane domain do not impact fission activity. Nevertheless, the TMD-bordering basic amino acids, that have been shown to be interacting in MOM targeting, and the cytosolic hydrophobic domain, are essential for GDAP1-induced fission (Wagner et al., 2009).

Niemann et al. (2005) speculated, that GDAP1 influences the balance between fused and fragmented mitochondria. They found that over expression of GDAP1 induces fragmentation of mitochondria without inducing apoptosis, affecting overall mitochondrial activity, or interfering with mitochondrial fusion (Niemann et al., 2005). GDAP1 knockdown by RNA interference, results in a tubular mitochondrial morphology (Niemann et al., 2005). The over expression of GDAP1 was found to play a role in mitochondrial fragmentation in COS-7 cells (Niemann et al., 2005; Pedrola et al., 2008). The mitochondrial fusion proteins, mitofusin 1 and 2 could balance GDAP1induced fission. The latter activity was also strongly reduced for disease-associated GDAP1 point mutations (Niemann et al., 2005). The fission capabilities of CMT-associated GDAP1 mutants differ, dependent on the type of genetic inheritance (Niemann et al., 2005). Recessive inherited GDAP1 mutations have reduced fission activity, while dominantly inherited mutations impair mitochondrial fusion (Niemann et al., 2005, Pedrola et al., 2005, Wagner et al., 2009). The most severe forms, with onset of disease within the first decade of life, are caused by mutations leading to a premature stop codon or within the C-terminal mitochondrial and peroxisomal targeting domain (Wagner et al., 2009, Cassereau et al., 2011, Kabzinska et al., 2011, Huber et al., 2013).

Recently, it has been found that GDAP1 also plays a role in peroxisomal fission by the import receptor pex 19 (Huber et al., 2013). GDAP1 silencing in the SH-SY5Y cell line induces abnormal distribution of the mitochondrial network, reduces the contact between mitochondria and endoplasmic reticulum (ER), and alters the mobilization of mitochondria towards plasma membrane, upon depletion of ER-Ca2+ stores (Pla-Martin et al., 2013). The study done by Pla

–Martin et al (2013) suggested, that the pathophysiology of GDAP1-related CMT neuropathies may be associated with abnormal distribution and movement of mitochondria throughout the cytoskeleton towards the ER and subplasmalemmal microdomains, resulting in a decrease in store-operated Ca2+ entry (SOCE) activity and impaired SOCE-driven Ca2+ uptake in mitochondria. GDAP1 cooperates with the vesicle-organelle trafficking proteins RAB6B and caytaxin, which suggests that GDAP1 may affect the mitochondrial movement within the cell (Pla-Martin et al., 2013). Recently, it has been confirmed that GDAP1 is involved in the control of the intracellular antioxidant glutathione content and mitochondrial activity, suggesting a contribution of oxidative stress in the pathogenesis of CMT4A (Noack et al., 2012). In additional investigation Niemann et al. (2014) found a novel mechanistic link between the GDAP1-family of proteins and oxidized glutathione-associated stress. GDAP1 effects the peroxisomal morphology (Huber et al., 2013). Niemann et al. (2014) knocked out GDAP1 from mice models, mimicking severe forms of CMT disease caused by GDAP1 mutations. The mice developed a late-onset peripheral neuropathy with a reduced nerve conduction velocity and hypomyelination.

1.6 GDAP1 phenotype and genotype

Initially, it was thought that GDAP1 related CMT had only the axonal CMT phenotype, but it has now been shown to have demyelinating, intermediate or axonal forms of CMT phenotype (Ben Othmane et al., 1993, Baxter et al., 2002, Cuesta et al., 2002, Niemann et al., 2006). There are 58 mutations in the GDAP1 gene, which are sub-classified as either CMT4A or AR- CMT2K (Medicine, 2013, Cassereau, 2014). CMT4A is the most common recessive subtype of demyelinating CMT (Baxter et al., 2002). AR-CMT2K is an axonal recessive subtype of CMT; GDAP1 also causes a rare dominant subtype called AD-CMT2K (Cuesta et al., 2002). An intermediate recessive subtype of GDAP1 induced CMT, CMT RIA has a mixed axonal and demyelinating phenotype (Senderek et al., 2003, Kabzinska et al., 2006).

Of the 58 GDAP1 mutations that have been defined (Cassereau, 2014), many of them are nonsense mutations leading to truncated proteins (Noak, 2012). There are also frame shift mutations, missense mutations and splice mutations in the introns three and four (Wagner, 2009). Figure 1.3 shows the location of the 26 missense mutations described so far, while Table 1.3 summarises all GDAP1 mutations reported to date.

In some patients with AR-CMT that is associated with GDAP1, the characteristics of CMT are very severe (Birouk, 2009, Nelis et al., 2002). The age of onset is early in childhood; with hypotonia at birth and delayed progress of early motor milestones seen in some patients (Birouk et al, 2003and Claramunt et al, 2005). Patients also develop foot defects and disabilities affecting the hands and feet towards the end of their first decade, followed by association of proximal muscles in the lower limbs, causing loss of autonomy (Birouk et al., 2003, Sevilla et al., 2003). As the disease progresses significant proximal upper limb weakness develops and laryngeal and respiratory muscles can become involved (Sevilla et al, 2008).

Studies in patients with AD GDAP1 mutations and sporadic GDAP1 mutations show that just one copy of the mutated GDAP1 protein is enough to cause disease (Claramunt et al., 2005). Interestingly the phenotype of GDAP1 induced disease results in both CMT clinical subtypes: some patients show demyelination, while others have a loss of myelinated axons without signs of demyelination (Baxter et al., 2002, Cuesta et al., 2002, Nelis et al., 2002). For example R12Q cause demyelination and R120W cause axonal. These phenotypes are not associated with specific GDAP1 mutations, since the same mutation can give different phenotypes in separate cases; for example R239F (Nelis et al., 2002, Ammar et al., 2003). Patients that develop CMT associated with GDAP1 mutations also have some unique characteristics. For example, some patients who developed CMT in their late teens also developed a hoarse voice and vocal cord paresis (Sevilla et al., 2003). We hypothesise, that there might be common SNPs in the promoter region, causing alternate expressions of GDAP1, which contribute towards these variations.

Figure 1.3: Schematic representation of the GDAP1 protein with predicted domains (HD hydrophopic domain, TMD) and indication of the corresponding exons. CMT-causing mutations and their phenotypes: intermediate, red; demyelinating, blue; axonal, black (Noack, 2011)

Table 1.3: Ganglioside-Induced Differentiation Associated Protein 1disease linked mutations. The columns show the name of the mutation and the position of the mutation in GDAP1 gene, the phenotype of the mutation and the age of onset, what ethnic group the mutation was found in, and the reference for the mutation. (A=axonal; AD= autosomal dominant inheritance; DM =demyelinating; DS= reported in different studies; I= intermediate; IN/DEE= Insertion/Deletion) NA= data not available; SS= reported in the same family.

DNA change Reported Type Location Age of Phenotype Ethnicity Variant Reference(s) onset (cDNA) (years) Remarks and Rare feature

c.27_28del 2 times Deletion Exon 1 1-5 A Italian Sporadic Crimella et al. (2010) (SS) Speculative p.(Gly10Glufs* 21-30 15)

c.92G>A 4 times Substitution Exon 1 1 DM Tunisian Consanguinity Baxter et al. (2002)

(SS) p.(Trp31*)

c.101C>G once Substitution Exon 1 6-10 A Italian Sporadic Crimella et al. (2010)

p.(Ser34Cys)

c.102C>G 2 times Substitution Exon 1 3-8 A Turkish Consanguinity Sahin-Calapoglu et (SS) al. (2009)

c.172_173delins once IN/DEE Exon 2 < 1 A Spanish Vocal cord paresis, Sevilla et al. (2008) TTA Diaphragmatic p.(Pro59Alafs* paralysis 4)

18 c.174_176delins 2 times Insertion/Deleti Exon 2 1-2 DM Turkish Consanguinity Auer-Grumbach et al. TGTG (SS) on (2008)

p.(Pro59Valfs* 4)

c.233C>T 10 times Substitution Exon 2 1-5 DM Moroccan Hoarse voice Bouhouche et al. (2007a) (SS) p.(Pro78Leu) <2

c.295C>T 8 times Substitution Exon 2 2 A Italian Vocal cord paresis Moroni et al. (2009) (SS) p.(Gln99*)

c.311-1G>A 2 times Substitution Intron 2 3 A Poland Vocal cord paresis, Kabzinska et al. Spanish Diaphragmatic (2005) (DS) paralysis Sevilla et al. (2008)

c.332C>A Once Substitution Exon 3 7 AD Korean AD Chung et al. (2011)

p.(Pro111His)

c.341_344del 3 times Deletion Exon 3 1 A/DM Spanish - Claramunt et al. (2005) Ammar et al. (DS) p.(Glu114Alafs Italian (2003) *32) German

c.347T>C 6 times Substitution Exon 3 5 A Poland - Kabzinska et al. (2006a) (SS) p.(Met116Thr)

19 c.347T>G 12 times Substitution Exon 3 1-7 A/DM Italian Pyramidal feature Di Maria et al. (2004)

(DS) p.(Met116Arg) 5-6 A/DM Biancheri et al. (2006)

c.349dup 4 times Duplication Exon 3 <2 A/DM Turkish - Senderek et al. (2003) (SS) p.(Tyr117Leufs *13) c.358C>T 28 times Substitution Exon 3 Different A/DM Spanish Pyramidal feature Claramunt et al. age (2005) (DS) p.(Arg120Trp) onset, Belgium from Sivera et al. (2010) Asympto Austrian Ammar et al. (2003) matic to French 50 years. Zimon et al. (2011) American Vital et al. (2012) Italian c.359G>A 2 times Substitution Exon 3 DM Japanese Consanguinity Boerkoel et al. (2003)

(SS) p.(Arg120Gln) 1-5 Facial paralysis Vocal cord paresis c.364C>A Substitution Exon 3 2.5 A/I Italian - Moroni et al. (2009)

p.(Gln122Lys) c.368A>G 8 times Substitution Exon 3 Asympto A Finland - Zimon et al. (2011) matic, 1- (SS) p.(His123Arg) 5, 6-10, Tunisian

11-20, 31- 40

c.373C>T 4 times Substitution Exon 3 < 1 year A/DM Italian Consanguinity Moroni et al (2009)

(DS) p.(Arg125*) 1-5 Pakistanis Fusco et al. (2011)

c.389C>G Once Substitution Exon 3 1-5 A Poland - Kabzinska et al. (2005) p.(Ser130Cys)

c.439del 4 times Deletion Exon 3 NA DM Iranian Consanguinity Georgiou et al. (2006) (SS) p.(Thr147Leufs *5) c.4453333G>T 2 times Substitution Exon 3 3 DM Turkish Consanguinity Parman et al. (2004)

(SS) p.(Asp149Tyr)

c.458C>T 4 times Substitution Exon 3 A Polish Consanguinity Kabzinska et al. (2007) (DS) p.(Pro153Leu) 1-5 DM Turkish Hoarse voice Auer-Grumbach et al. (2008)

c.467C>G 6 times Substitution Exon 3 6-10 A Polish Hand/finger tremor, Zimon et al. (2011) Hoarse voice (DS) p.(Ala156Gly) 11-20

c.469A>C Once Substitution Exon 3 1-5 A Spanish Sporadic Claramunt et al. (2005) P.(Thr157Pro) Optic atrophy

c.482G>A 8 times Substitution Exon 3 1-5 DM Tunisian Consanguinity Baxter et al. (2002)

(DS) p.(Arg161His) DM/I Ammar et al. (2003)

c.485-2A>G 4 times Substitution Intron 3 1-5 A Algerian Consanguinity De Sandre- Giovannoli et al. (SS) RNA changes DM (2003) r.485_579del

c.487C>T 34 times Substitution Exon 4 < 1 year A Spanish Consanguinity, Claramunt et al. MFN2 in (2005) (DS) p.(Gln163*) 1-5 DM Costa Rica heterozygous state Cuesta et al. (2002) Vocal cord paresis, Diaphragmatic Boerkoel et al. (2003) paralysis, Hoarse Sevilla et al. (2008), voice, Facial paralysis Cassereau et al. (2011)

Sevilla et al. (2003)

c.507T>C 10 times Substitution Exon 4 < 1 year DM Peru Consanguinity Boerkoel et al. (2003)

(SS) 1-5 Costa Rica Facial paralysis, Vocal cord paresis, Diaphragmatic paralysis

c.507T>G 11 times Substitution Exon 4 1-5 A Italian - Senderek et al. (2003)

(DS) 6-10 DM Turkish Di Maria et al. (2004)

German

c.533A>G Once Substitution Exon 4 NA NA Chinese - Zhang et al. (2004)

p.(Asn178Ser)

c.558del 4 times Deletion Exon 4 <1 A French Vocal cord paresis, Stojkovic et al. Diaphragmatic (2004) (SS) p.(Ile186Metfs* paralysis 20)

c.571C>T 4 times Substitution Exon 4 1-5 A Czech - Barankova et al. (2007) (DS) p.(Arg191*) 6-10 NA Austrian Auer-Grumbach et al (2008) c.579+1G>A 2 times Substitution Intron 4 1-5 A German - Senderek et al. (2003) (SS) DM

c.581C>G 38 times Substitution Exon 5 1-5 DM Tunisian Consanguinity Cuesta et al. (2002)

(DS) p.(Ser194*) <1 A Moroccan Hoarse voice, Sevilla et al. (2003) Vocal cord paresis, A/DM Spanish Diaphragmatic Claramunt et al. paralysis (2005)

Sevilla et al. (2008)

Baxter et al (2002)

Birouk et al. (2003)

Bouhouche et al. (2007b)

Nelis et al (2002)

Azzedine et al (2003)

c.652C>G 2 times Substitution Exon 5 11-20 A/DM Korean - Chung et al. (2008)

(SS) p.(Gln218Glu) 21-30 c.656T>A Once Substitution Exon 5 1-5 A Italian - Moroni et al. (2009)

p.(Val219Asp) c.656T>G Once Substitution Exon 5 1-5 AD Korean - Chung et al. (2011)

p.(Val219Gly) c.668T>A 2 times Substitution Exon 5 1-5 DM/I Lebanese - De Sandre- Giovannoli et al. (SS) p.(Leu223*) (2003) c.678A>T 2 times Substitution Exon 5 6-10 A Italian - Crimella et al. (2010)

(SS) p.(Arg226Ser) 31-40

c.679A>G 2 times Substitution Exon 5 1-5 A Bulgaria - Kabzinska et al. (2010) (SS) p.(Asn227Asp) 6-10

c.692C>T 6 times Substitution Exon 5 6-10 A Amish - Xin et al. (2008)

(SS) p.(Pro231Leu) North American c.694+24C>T 8 times Substitution Intron 5 1-5 Turkish Consanguinity Sahin-Calapoglu et al. (2009) (SS) p.(=) Hoarse voice

c.715C>T 28 times Substitution Exon 6 1-5 A Czech Consanguinity Ammar et al. (2003)

(DS) p.(Leu239Phe) 6-10 DM Polish Sporadic Bara´nkova´ et al (2007) German Hand/finger tremor Kabzin´ska et al Bulgarian (2006)

Austria Rougeot et al (2008)

Italian Auer-Grumbach et al. (2008)

Moroni et al (2009)

c.719G>A 3 times Substitution Exon 6 6-10 A French - Cassereau et al. (2009) (SS) p.(Cys240Tyr) 21-10

41-50

25 c.767A>G 4 times Substitution Exon 6 <1 NA Chinese - Zhang et al. (2004)

(DS) p.(His256Arg) AD Korean Chung et al. (2011)

Taiwan Lin et al. (2011)

c.786del 4 times Deletion Exon 6 1-5 DM/I Turkish - Nelis et al. (2002)

(SS) p.(Phe263Leufs *22) c.805G>A Once Substitution Exon 6 1-5 A Italian - Crimella et al. (2010)

p.(Gly269Arg) c.811G>A Once Substitution Exon 6 1-5 DM Belgian - Ammar et al. (2003)

p.(Gly271Arg) c.817C>G Once Substitution Exon 6 1-5 A Polish Hand/finger tremor, Kabzinska et al. Hoarse voice (2010) p.(Arg273Gly) c.821C>T 2 times Substitution Exon 6 Asympto A Italian - Zimon et al. (2011) matic (SS) p.(Pro274Leu) I 41-50 c.836A>G 14 times Substitution Exon 6 1-5 A Turkish Consanguinity Rougeot et al (2008)

(DS) p.(Tyr279Cys) 10-6 Hoarse voice

11-20

Sahin-Calapoglu et al. (2009) c.844C>T 13 times Substitution Exon 6 1-5 A German Consanguinity Nelis et al (2002) (DS) p.(Arg282Cys) 10-6 DM Croatian Ammar et al (2003)

Turkish Senderek et al (2003) Kabzinska et al. Poland (2010)

Spanish c.845G>A Once Substitution Exon 6 <1 - Taiwan -

p.(Arg282His) Lin et al. (2011)

c.862dup 7 times Duplication Exon6 <1 A Vocal cord paresis, Cuesta et al. (2002) Facial paralysis, (DS) p.(Thr288Asnfs 1-5 Spanish Diaphragmatic Sevilla et al. (2003) *3) paralysis, Hoarse Claramunt et al. voice (2005)

Sevilla et al. (2008) c.891C>G 2 times Substitution Exon 6 1-5 A Italian -

(SS) p.(Asn297Lys) I Moroni et al. (2009) c.929G>A 2 times Substitution Exon 6 1-5 A Moroccan Vocal cord paresis, Diaphragmatic (SS) p.(Arg310Gln) Azzedine et al. (2003)

paralysis, Hoarse voice c.980G>A 4 times Substitution Exon 6 1-5 A Poland - Kotruchow et al. (2011) (SS) p.(Gly327Asp) DM

1.7 Regulation of gene expression

Genes translate into proteins and proteins direct cell function (Clay et al., 2011). Every phase in the stream of information from DNA to RNA to protein, offers the cell a control point for self-regulating its functions, by regulating the amount and type of proteins it produces. At any specified time, the amount of a specific protein in a cell indicates the equilibrium between its synthetic and degradative biochemical pathways (Clay et al., 2011). Thus, control of these processes plays a critical role in determining what proteins are present in a cell and in what amounts. Regulation of gene expression indicates the control of the amount, and timing of appearance of the functional product of a gene (Wang et al., 2014). Control of expression is dynamic to allow the cell to produce the gene products it needs, when it needs them. This gives the cells flexibility to adjust to the changes in environment, external signals, and damage to the cell. Regulation of the gene expression includes a wide range of mechanisms which are used by cells to increase or decrease the specific gene products; for example transcription factor binding sites and polymorphisms. There are approximately 11 million human genetic polymorphisms (Buckland, 2006). The number of these sequence variants, that have a functional effect, is uncertain. However, it seems that the majority of those that impact on disease are found in the amino acid coding regions of genes, or affect the regulation of gene expression—these are called rSNPs (Buckland, 2006). Current data propose that about half the genes have at least one common rSNP linked with them, which may alter their expression (Knight, 2005). In vitro studies suggest, that most rSNPs lie within the core and proximal promoter regions of genes. However, it is not clear how the majority of these impact on transcription, as they are not found in any of the known transcription factor binding sites (Frodsham and Hill, 2004). Studying the impact of changes in gene expression can help us to better understand the variation in disease phenotype.

1.8 Bioinformatic approaches to understanding gene expression

Bioinformatics approaches can be used to study the regulation of genes. Through such analysis, the maximum amount of existing information can be derived from previous work on a research topic and, consequently, unnecessary duplication of experiments can be prevented (Leenaars et al., 2012); for example it can be difficult to obtain human tissues to investigate the expression of a single gene of interest.

There are many different bioinformatic tools to look at the expression of the genes: one of the most useful tools in this area are curated by the National Centre of Biotechnology (NCBI). The NCBI Gene Expression Omnibus (GEO) is a publically available source of microarray, next- generation sequencing, and other high-throughput functional genomic data submitted by the scientific community (Edgar et al., 2002). Microarray analysis methods allow researchers to explore the expression of a large number of genes, to evaluate the overall state of a cell or organism. By analyzing microarray data, we can investigate the expression of the gene of interest. Gene expression profiling is the simultaneous measurement of the cellular concentration of altered messenger RNAs. Another useful tool to study the expression of gene in cancer profiles is Oncomine database, which allows the researcher to compare the expression of different genes, compared to normal control cells or compare the expression to different cancers. The Human Protein Atlas is also one of the tools to investigate the expression of the gene using immunohistochemistry images.

1.9 Aims of this research

This study will investigate the expression of human GDAP1. Most studies investigating the expression of GDAP1 were based on neural tissues of rodents. Limited studies investigated the expression of GDAP1 in human tissues. Also the majority of GDAP1 studies have focused on mutations in the coding region of the GDAP1 gene; this study will focus on the promoter region.

The aims of this study are to

 Investigate how GDAP1 is expressed in healthy and non-healthy human tissues.  Define the core regulatory regions of GDAP1, by comparing transcription factor binding, between the human and the mouse GDAP1 5’ flanking region.  Assess whether common polymorphisms in the GDAP1 5 flanking region alter GDAP1 expression, including determining the length of the poly-A region located at -242.  Test whether GDAP1L1 expression is altered, in response to changes in GDAP1 expression in human tissues.

Chapter 2. Materials and Methods

2.1 Bioinformatics materials and methods

2.1.1 Bioinformatics tools used

The websites that were used throughout this thesis are listed in Table 2.1.

Table 2.1: Websites programs used in this study.

The first column shows the website or the program name; the second column shows the function of the tool and the third column shows the URL used. URL is the uniform resource locator, also known as web address.

Program Function of tool URL

Gene Expression Omnibus Expression levels of the http://www.ncbi.nlm.nih.gov/ge genes of interest oprofiles

Oncomine Expression levels of https://www.oncomine.org/resou genes of interest in rce cancer profiles.

Human Protein Atlas Expression profiles for http://www.proteinatlas.org/ genes of interest in protein level.

MatInspector Prediction of http://www.genomatix.de transcription factors binding sites.

GenBank human GDAP1sequence http://www.ncbi.nlm.nih.gov/nu ccore/NG_008787.2

HapMap project Polymorphism database. http://hapmap.ncbi.nlm.nih.gov/

dbSNP Polymorphism database. http://www.ncbi.nlm.nih.gov/SN P/

Primer3 Primers design. http://frodo.wi.mit.edu/primer3/

NEB Cutter2 Restriction enzyme http://www.tools.neb.com/NEBc binding site prediction. utter2/

NEB Double digest finder To choose the right https://www.neb.com/tools-and- temperature for the resources/interactive- enzymes. tools/double digest finder?

Unigene EST profiles http://www.ncbi.nlm.nih.gov/uni gene

NCBI Blast, nucleotide To align human GDAP1 http://blast.ncbi.nlm.nih.gov/Bla sequence and mouse GDAP1. st.cgi?PAGE_TYPE=BlastSearc h&BLAST_SPEC=OGP__9606 __9558 Quantity one 4.6.5 1-D Analysis software ( for BioRad gel dox, gel photos)

2.1.2 Data collection

The expression of GDAP1 and GDAP1L was searched for in three different databases. These included the Gene Expression Omnibus (GEO) profiles at the National Centre for Biotechnology Information (NCBI). Oncomine cancer profiles (Compendia Bioscience, Ann Arbor, MI) and the Human Protein Atlas (Uhlen et al., 2010). These databases contained microarray experiments (GEO and Oncomine) or protein immunohistochemistry (Human Protein Atlas).

2.1.2.1 Using Gene Expression Omnibus (GEO) profiles

The Gene Expression Omnibus (GEO) is a public repository of microarray data (Edgar et al., 2002, Barrett et al., 2013). Data was collected from GEO using specific search terms in two stages. To ensure high quality data, criteria for exclusion and inclusion of datasets were applied. Expression changes were then statistically analysed by t-test or ANOVA where relevant. Details of statistical analysis are given in section 2.4, and specific details of search terms are given in the results chapter.

Using microarray analysis used in GEO database techniques allows researchers to examine the expression state of a large number of genes in a single experiment. However these large data sets can be difficult to analyse. Data analysis of the microarray is a vital part of the experiment.

Each microarray study comprises multiple microarrays, each giving tens of thousands of data points. Since the volume of data is growing exponentially, as microarrays grow larger, the analysis becomes more challenging. In general, the greater the volume of data, the more are the chances of erroneous results. Handling such large volumes of data requires high end computational infrastructure, and programs that can handle multiple data formats. There are already programs available for microarray data analysis on various platforms. However, due to rapid development, diversity in microarray technology, and different data formats, there is always scope for more comprehensive and complete microarray data analysis.

The most common methods used for data analysis include; Robust Multi-array Average (RMA), Affymetrix® Microarray Suite software 5 (MAS5), Gene Chip Operating System analysis (GCOS) and Gene Chip Robust MultichiP (GC-RM). These methods may then be normalised by microarray analysis; for example by using logarithms used by datasets. The microarray data analysis software selected for our study are listed in Table 2.2 below. This table also provides a definition or discretion of the analysis. At times, it was difficult to find specific details of the analysis of a study, and therefore difficult to directly compare data. For example the difference between RMA cultured expression (3) and expression estimate (4) could not be distinguished. Therefore, we have therefore used the description given by the author of the data, and aligned a numeric key to each of the descriptions observed (see Table 2.2). This numeric key is used in figures of microarray data in chapters three and five.

Table 2.2: Summary of the microarray data analysis software that was used to analyse the results of the microarray in the bioinformatics studies.

The data analysis of the microarray is a vital part of the experiment, since it gives an idea about the results of the experiment. The first column shows the name of the microarray analysis, the second column shows the description of the analysis, the third column shows the techniques that were used, and the final column shows the number given to the analysis. ( This number is used in the study charts.)

Microarray Description Techniques used Given analysis number

RMA Normalization approach that does Normalized signal 1 not take advantage of mismatch intensity spots, but still must summarize the perfect matches through median Express-calculated 2 (Irizarry et al., 2003). signal intensity (log2 transformed)

Calculated expression 3

Analysed Data 4

Log (base 2) of the 5 ratio of the median

Expression estimate 6

Quantile Normalised 25 Signal Intensity (Quantile normalization makes the empirical distribution of probe intensities the same for every chip).

Signal intensity 26

Log2 signal 29

GCOS Genechip Operating System Calculated Signal 8 analysis software supplied by intensity Affymetrix (Affymetrix, 2014).

MAS5 Sensitive and selective algorithm Calculated Signal 7 that used for identifying intensity differentially expressed genes MAS 5.0 19 (Pepper et al., 2007) Calculated by MAS 5 30 or GCOS software

GC-RMA An improved form of RMA that is Calculated Signal 9 able to use the sequence-specific intensity, log2 probe affinities of the GeneChip transformed probes to attain more accurate gene expression values (Robson, 2003). Calculated Signal 12 intensity

Normalized signal 11

Normalized signal 10 log2

Log2 ratio The logarithm to the base 2, Normalized 15 defined as log2 (T/R) where T is the gene expression level in the testing sample, R is the gene expression level in the reference sample. So, it is dividing the gene expression in the samples wanted to test by the gene expression of the reference or control sample. (Cui, 2013).

Mean signal These methods allow the detection 18 intensity and/or correction of spatially systematic artifacts in microarray data (Colantuoni et al., 2002).

UNF_VALUE One of the Log-like transformation Value 20

Value methods (Jung et al., 2011) 22

Cubic spline Very popular model for Normalized 27 interpolation (illumina, 2011).

PLIER Probe Logarithmic Intensity Error Normalized signal 16 Estimation is a multi-array normalization method developed by Affymetrix (Lockstone, 2011)

dChip expression A software that pre-processing of 31 value Affymetrix microarrays (Blangiardo and Richardson, 2008).

2.1.2.2 Using Oncomine

Oncomine is a “cancer microarray database and web-based data-mining platform, aimed at facilitating discovery from genome-wide expression analyses” (Rhodes, et al 2004). The

Oncomine database has been collected from cancer patient genomes; standardized, annotated and analysed using t-test or ANOVA by Compendia Bioscience. Experimental details and sample facts are collected from supplemental data and by direct correspondence with authors of published work; and added to the database to drive new analyses. The data are normalized and analysed using standard protocols, and presented to the end-user of Oncomine through a web-based interface. The expression of GDAP1 and GDAP1L1 was investigated using this database, so we put the gene of interest in the search box, then chose the criteria that was to be applied. (Some criteria was chosen for inclusion and exclusion. For example, cancer verses

35 normal was chosen to investigate the expression, mRNA was chosen for the data type, p-value 1E-4 was chosen, number two was chosen for the fold change and the gene rank was chosen to be in the top 10%. )

2.1.2.3 Using the Human Protein Atlas

The Human Protein Atlas is a publicly accessible database, containing high-resolution immunohistochemistry (IHC) images of; 44 normal human tissues, 20 different cancer types, and 46 human cell lines (Berglund et al., 2008, Pontén et al., 2008, Uhlen et al., 2010). For GDAP1, two types of different antibodies have been used: HPA014266 and HPA024334 (Atlas, 2012). The HPA014266 antibody was been chosen in this study, because the IHC stain shows more clarity on tissue sections making it easier for analysis.

2.1.2.4 Theoretical promoter analysis Using Genomatix software

MatInspector is a Genomatix software tool, utilizing a large library of matrix descriptions for transcription factor binding sites, to locate matches in DNA sequences. This allowed a comparison between the human and mouse 5’FR as well as analysis for the potential impact of polymorphisms in the 5’FR. MatInspector is fast and has been shown to produce superior results. It assigns a quality rating to matches and thus allows quality-based filtering and selection of matches.

Transcription factor (TF) binding sites were predicted in the 5’-flanking sequence of approximately 2000bp upstream, the start codon of human and mouse GDAP1 gene. As inclusion criterions, core sequence similarity was set at 0.75 and matrix similarity was set at 0.8. The sequences used for analysis are given below (see Figures 2.1 and 2.2). The HCBI genbank reference numbers are (NG_008787.1) and (JN953299.1) for human and mouse respectively.

36 atccaagctt gcaggatgta tggagaaaag tgttgtgttt gagggaccag ggctttattg tcaggacttt ttcagctgca ggtgacacat ggcttatcac gagtactgga atttattcaa atatttagga tgatttagta ttaaggttgg tgcaaaagta agcgtggtgt ttgctattga aggtaatgga atagaaaact aggaatctga aggtggataa ttaaaaaatg aagttctgaa gaggaatggg agaggaggat tatatgggta gttgtaaaaa ttttgagacc tacagagaga ggatgaaatg taatataatg ccctcctaat gtcagtgaag gggcttttac acatatgttt ttatcaatgg ctacattcca ggcaataccc taggtgcttt atatatatcc tttcttgaat tcccacagcc atctgggtag gcatataatt tttatgtatc cattttttac acacaatgaa gtcaaagctc aagaaagctc aagttagggt agagctagaa tttaaacccc actctgtccg atttcaaagt tttcctcttt ccgctcttac tcttaagtgt gaactgataa aaacaatttt ggcaaggaat tcaccttttc tatagcactt gaattaactg aaggatataa aagacattat gaaatcttaa aaaattatga aagaattaca gagagtaaca aatgaagtac tgtaaaggca aggaagaacc tcatgagatt ttgtcttttc caggggccct tctctgacct gggttggctg cctgcccctc ctttctcata gtactctgtg ttcaccctaa gacagttctc atctttattg tgattgtcta tttccttggt cgtctctact gccagattat atcttgagtg ttagatatta gatttcacac atctttatat taccaggagc cagcacaatg tctggaacag tgtaggtgct caataaatat ttgccagata aattatacag gcatttattc tatggtcata tttatacata ttactgaaag agatttttaa aatcaactta ctatatgatg ccaaattttt aaagacatac aataataact agactaatat aaaattatga ttctgactat ggttataaca gaatgtttat aagacaactg aacctgtgac taagactgag tgcctacaac gtgacagggt gttctgagtg tttaacattt tattattaat ttttcacaaa aatcttatga gggaaacaat aaaattatta ttcttactta gaaaatggaa aaaccaggat atgaacttag gcagtactgt tgtagaattt ttgtgtttaa caactaggct attttatatc gccattgttt attagctgtt tatgacaaaa acagtttttt tgcatgactc catagtggta tttttttttc ttttacaaag cacagtttaa ttatcaaaat caagtactta ctgtttatac aagactattc tctaatctac agcagttagt caaatttgtg gaattagtcc actaatgtca tagtagcatc ttctaatgaa agctcataga tccttttcct gttgctttgc ttgtcgaccg gaacttcctt tccaacaaag gaacaggctc caagagcaac cctcagtatc ttgggaaatt gctgctttta tacctgagca acctccaaac cgaagagtaa tttgctatca tcattcttcc ttactgccct tcataaccag ggtctcatat tttttatttt ttcttctaaa aaaaaaaaca aacccaaaaa acaaccgttc aattgcacct cccaggtgca ctcccaggct tgccaggggc tttccagtcg cagaccccgc gtgttcgcag actctgccgc cggcgaaact acatttccca gcgggccgcg cgccctcctt ccggcaggta cccctcaaaa cccggaaacg ccttgcgggg cagtgtggga gggagaagtc cagggcggac aggctgggcg cacccgtgct cgcgcacccc aagatggctg agaggcagga agagcagaga gggagcccat ggat

Figure 2.1: Human GDAP1 5’-flanking region sequence. The sequence starting approximately 2000bp upstream of the start codon for human GDAP1 gene (NG_008787.1). The start codon is highlighted in green.

A- Mouse GDAP1 sequence The mouse GDAP1 sequence that was used in Genomatix software is shown in figure 2.2 below. The length of the sequence is 2000 bp from the start codon (atg) which is highlighted; the reference number of this sequence in the NCBI is JN953299.1. gcttttcttt attgtttcta cttctatttt tagatcttgg atgatttttt ttcaattcctt cacctgttt tgttgtgttt tcctgtaatt cttttgggga tttttgtgtt tcctctttaa ggacttttac atgtttagca gtgtatttct tttttttttt tccattttta ttaggtattt agctcattta catttccaat gctataccaa aagtccccca tacccaccca cccccactcc cctacccccc ccactccccc tttttggccc tggcgttccc ctgttctggg gcatataaag tttgtgtgtc caatgggcct ctctttccag tgatggccga ctaggccatc ttttgataca tatgcagcta gagtcaagag ctctggggta ctggttagtt cataatgttg ttccacctat agggttgcag atccctttag ctccttgggt actttctcta gctcctccat tgggagccct gtgatccatc cattagctga ctgtgagcat ccacttctgt gtttgctagg ccccggcata gtctcacaag agacagctac atctgggtcc tttcgataaa atcttgctag tgtatgcaat ggtatgccca tggtatttct ttaagtgagt tattaatgtc cttcttaaaa tcctctacca gaatcatgag atatgatttt acatccgaat cttgctgttt ttggtgtgta gggctatcca agacttgctg gatgaaggca cgaaggtacc ttgtccaaga aggtctgttg cttctgtggc ctgtgtgctc tcctgcatgg acctccctca gatggacccc agataaaaaa tgtcgatcac acctgaattc caagggctgg gcccttgctg taggcaagcc cttcttttgt ggggaaggta cacagaggac tgagggtcag ctcttcctac tggctgagga tgaaggccca aaatgatcct gtccaagaag ctctgttgct tctgaggccc gtgatctcct gtgcggcccc cttctgagag accccctgaa tagaaaatca agataaaaat tcttaagaag agtgctctct agttcgcatt tgcatttgcc tgacttccac atactggtca tagaacccct ttgttataga gcagctttgt catggcacta atccttggca accctgtcca cagtgagtct ttggtcagat catgtcttgg tcacatgatg ccgatgtcag tgtgaatgaa tgtcttcctc tttttcctta tatgtatatt atttccaaat cctacctgat tagtctttat gtgcatctgc actccagaac gggacgtcag acaatctgag ctgccatgtg gttgctggaa atttaactta ggacctttgg aagagctcct aaccaccaac tcatctctcc agacccataa agacactatt tttaaggcct tgtgagtagt attgccattt cgtttactgt cttatttatc tactcagttc tcacaaaaag cttacagatt aaataaaata atctctattt caaaaatgaa aacagcagga catgatctca ggtatttctg atatgaacat tcacacttaa tcaataggca cttttacatt accattgttg atttatttgt aaaatacata ctttgtattc cattgtaata tattctaatg aaaattaatt gatacttaat tttatttctt tgctctctag aactctccca cctaagtaaa aaccctgaat gtcttgtgaa actggtgctt ttatgcccaa gcaacgtcca aggcccagga gtattatttg ctgttatcat ttttccttgc cttaaaagcc agacagtcat attttagaaa atttctctac ctaaaagtca tcccattaca tctcacagat gatctcttaa gggcctcagc cgactaggca gcacagcatc cttggggaga aactacattt cccagaagcc agttcgaccg tagaggaagt cctttacgca acagggggcc ttgagaaacc aaggatgctt gctgcagcaa gtgtctgcag agagcaggcc aacccataaa gctgcctgag tcctttcatg cgaaatggct Figure 2.2: Mouse GDAP1 5’-flanking region sequence. The sequence starts approximately 2000bp upstream of the start codon for human GDAP1 gene (JN953299.1). The start codon is highlighted in green.

2.2 Molecular biology materials and methods

2.2.1 Solutions, equipment, kits

The solutions, equipment, kits and computer programs that were used in the molecular studies are summarized in Tables 2.7, 2.8, 2.9 and 2.10 below.

Table 2.3: Frequently used buffers and solutions.

Solution Company

Ampicillin Astral Scientific Australia 50 mg/ml in ddH2O Luria Bertani (LB) broth Astral Scientific Australia

LB- broth + 15 g/L agar Astral Scientific Australia

Super Optimal Broth (SOC) New England BioLabs

50xTAE BioRad

Agarose AmResco

5-bromo-4-chloro-3-indolyl-beta-D- Thermo galacto-pyranoside (X- Gal) Isopropyl-beta-D-thiogalactopyranoside Thermo (IPTG) (purchased as powder)

Table 2.4: Enzymes used in the molecular method.

Enzymes Company

EcoRI New England BioLabs KpnI New England BioLabs NcoI New England BioLabs Hind III New England BioLabs CIP (Calf-Intestinal Alkaline Phosphate) New England BioLabs

Table 2.5: Equipment used in the molecular method.

Equipment Brand and model

Centrifuge CT15T, Versatile Refrigerated centrifuge, Technomp Incubator 37oC Labmaster

Shaking incubator, 37oC 210 rpm Bioline

UV-transilluminator Spectroline

PCR apparatus Mstercycler, Thermalcycler, Eppendrof

Autoclave Atherton

Gel electrophoresis BioRad

Geldoc Bio-Rad Geldoc XR system, serial number #7658316

Nano drop Spectrophotometer ND-1000

Nano drop ND-1000V3.7.1 Thermo Scientific

Table 2.6: Molecular kits, vectors, competent cells and reagent used in this study (Protocols are given in Appendix two).

Kits name Purpose of kit company

QIAprep spin Miniprep kit Plasmid Purification Qiagen using a Microcentrifuge Wizard®Plus SV Minipreps Plasmid Purification Promega DNA Purification System

QIAquick gel extraction Gel purification Qiagen

Wizard® SV Gel and PCR Gel purification Promega Clean-Up System

QIAquick PCR Purification Column purification Qiagen

Wizard® SV Genomic DNA Column purification Promega Purification System

iProof High-Fidelity DNA DNA amplification BioRad Polymerase amplification

MangoTaq DNA Polymerase DNA amplification Bioline (no proof reading Taq) Phusion Hot Star II DNA amplification Finnzymes (Thermo High-Fidelity DNA amplification Fisher Scientific) Polymerase pGEM T-EASY vector A protocol for subcloning PCR Promega products using T vectors NEB 5- alpha competent Subcloning host New England BioLabs E.coli high efficiency

EZivision DNA visualisation New England BioLabs

2.2.2 Human DNA samples The genomic DNA samples were studied with the approval of the Human Ethics Committee of the Australian National University and were obtained, with informed consent, from normal healthy blood. As reference for GDAP1, synthesised sequence was ordered from DNA2.0 and used (see Figure 2.1). DNA2.0 is an online company known for providing gene synthesis and protein engineering, see Appendix A2.2.1for the detail of using the DNA2.0.

2.2.3 Primer design

The 5’FR of human GDAP1 was amplified using computer designed primers. Ten forward primers were designed using the New England Biolabs (NEB) website. The primers were requested every 200bp and are listed in Table 2.7. Primers were designed to include restriction sites for subcloning Hind III for forward primers, and NcoI for reveres primers. The primers were purchased from ‘Gene Works, Australia’.

Table 2.7: Primers that were used in the molecular study in this project. The first column shows the primer’s name and the second column the primer’s sequence.

Primer name Primer’s sequence

R33NCO1 CTgCCATgggCTCCCTCTCTgCTCTTCCT

F-1865HIND3 CTgAAgCTTggTgCAAAAgTAAgCgTggT

F-1599HIND3 CTgAAgCTTTTgAATTCCCACAgCCATCT

F-1414HIND3 CTgAAgCTTTggCAAggAATTCACCTTTT

F-1097HIND3 CTgAAgCTTATATTACCAggAgCCAgCAC

F-888HIND3 CTgAAgCTTTgATgCCAAATTTTTAAAgACA

F-837HIND3 CTgAAgCTTCAACgTgACAgggTgTTCTg

F-626HIND3 CTgAAgCTTTTTTgCATgACTCCATAgTggT

F-403HIND3 CTgAAgCTTgAACAggCTCCAAgAgCAAC

F-144HIND3 CTgAAgCTTCCggCgAAACTACATTTCC

F-63HIND3 CTgAAgCTTCAgTgTgggAgggAgAAgTC

2.2.4 PCR reaction optimisation

To amplify the GDAP1 promoter region, we trialled several DNA polymerase including iProof, MangoTaq and Phusion, which were all high fidelity polymerases. First iProof High-Fidelity DNA Polymerase was trialled, using different annealing temperatures (58o C, 60oC) with different annealing times (between 10 and 30 seconds). The iProof protocol suggested the use of two buffers, HF and GC and inclusion of DMSO; all of these conditions were applied (details are given in Appendix A2.2.2.3). We were unable to get a clear band of the appropriate size (see Figure 2.4).

A similar experience occurred with Mango Taq DNA Polymerase, tried with different annealing temperatures (54oC, 58oC) and the inclusion of DMSO. Finally, Phusion Hot Start II High-Fidelity DNA Polymerase was tried with different annealing temperatures and cycle conditions. This gave a band of 1bp which was the expected size (see Figure 2.3).

Phusion Hot Star II High-Fidelity DNA Polymerase was subsequently used for the PCR with the cycle conditions given in Table 2.2. The 20 μl reaction contained: 4 μL 5x Phusion HF Buffer, 0.4 μL 10mM dNTPs, 0.5 μL Forward primers (F-), 0.5 μL Reverse primer (R33) and 0.2 μL Phusion Hot Start II High-Fidelity DNA polymerase. As a control only the master mix was used. The Eppendorf Master Cycler ARCBS-SA PCR machine was used.

Table 2.8: PCR cycling instructions used for amplifying GDAP1 gene. (Adapted from the Thermoscientific website).

Cycle step 2 steps protocol Cycles

Temperature Time

Initialisation 98°C 30s 1

Denaturation 98°C 5-10s 25–35

Annealing - -

Extension 72°C 15–30 s

Final extension 72°C 5–10 min 1

Hold 4°C4°C hold

Figure 2.3: Agarose gel for PCR samples amplified, using Phusion Hot Star II High-Fidelity DNA Polymerase. Samples were amplified with the forward primer F-888 and reverse primer R33 and was run on agarose gel. The first column from the left shows the 1 Kb DNA ladder, the second column shows no template control. The third to eighth columns show PCR products from six different blood donors.

Figure 2.4: Agarose gel for PCR samples amplified using iProof High-Fidelity Polymerase. Samples were amplified with the forward primer F-888 and reverse primer R33 and was run on 1.5% agarose gel. The first column from the left shows the 1 KbDNA ladder, the second column shows no template control. The third to seventh columns show PCR products from five different blood donors. 2.2.5 Plasmid vectors

In this study two different vectors were used; the pGEM-T easy vector (Figure 2.5) and the pXPG plasmid vector (Figure 2.6). The pGEM-T easy vector was used to obtain legible sequencing results. It has previously been difficult to determine the length of the poly-A region, due to its heterozygosity; subcloning allows homozygous samples to be inserted into the pGEM -T easy vector. The pXPG vector was intended to be used to do an expression analysis for the GDAP1 5’FR. This vector utilises a luciferase reporter system. The cloning method using the pGEM- T Easy vector is shown in Figure 2.7 below.

Figure2.5: Diagram shows pGEM-T easy vector. The DNA with an A-Tail is easily inserted in the vector due to the T-overhang of the vector. Different targets for restriction enzymes exist (Promega, 2012).

Figure 2.6: Diagram shows pXPG vector. The vector contains a luciferase gene (Luc+) and multiple cloning sites for promoter elements (Bert et al., 2000).

Figure 2.7: Chart showing cloning method using pGEM- T Easy vector.

2.2.6 Agarose Gel electrophoresis

Agarose gel electrophoresis was used to analyse both, the PCR products and restriction digest. Gels were made with 1.5 gm agarose and TAE buffer. The 50x TAE was first diluted to 1x with distilled de-ionized water, and then mixed with an appropriate amount of agarose. This suspension was heated until all the agarose was dissolved. When the gel was solidified, it was immersed in the gel electrophoresis tank in 1xTAE. A 1kb DNA ladder was used to determine band sizes in unknown samples. All samples were mixed with blue EZ-vision three DNA Dye 8 Buffer to see the different band sizes under UV light source. The gel ran at 80 volts for approximately 1.5 hours. Geldoc was used to visualise and check the bands, with the geldocXR computer programme used to analyse the results.

2.2.7 Gel purification

After gel electrophoresis, DNA bands of the appropriate size were cut out of the agarose gel and purified. Two different gel purification kits were used: QIAquick PCR Purification or Promega Wizard® SV Gel and PCR Clean-Up System. Detailed protocols for these kits are given in Appendix two.

Briefly cut gel slices were dissolved in a buffer, at 50-65oC, to bind the DNA. The solution was then applied to a column and centrifuged, binding the DNA to a membrane. The column was then washed and the DNA eluted with 30 μL of water or buffer (10mM Tris Cl, pH 8.5).

2.2.8 Restriction digest

Restriction digests were used to prepare DNA for subcloning, and also to identify plasmid clones of interest in preparation for sequencing. The restriction enzyme EcoRI was used for screening the clones of the pGEM-T easy vector. As shown in Figure 2.5, EcoRI cuts on either sides of the subcloned insert. The GDAP1 sequence was checked for EcoRI cut sites, using the NEB enzyme cutter tool. Digest reaction was incubated for three hours at 37o C.

For subcloaning into pXPG, digests were performed using 1 volume of HindIII and NcoI in 2 volumes of Buffer 4, in a final reaction volume of 20 μl. The reaction was incubated for three hours at 37°C. Subclones were screened for inserts, using restriction digests with KpnI or HindIII with NcoI. After digestion, the restriction enzymes HindIII and NcoI were heat inactivated at 80˚C for 20 minutes.

2.2.9 Column purification

After restriction digest, the pXPG vector was placed at 4˚C to cool down for 30 minutes. Then 2 μL of Calf-Intestinal Alkaline Phosphate (CIP) was added, and the mixture incubated at 37˚C for 60 minutes.

The pXPG vector was column purified, using the column purification protocol for the QIAquick gel purification kit. This protocol was basically the same as that described in section 2.2.7, with the exception of the dissolving step. In the third step, Buffer PB was added to the sample and the mixture was applied to the column. The DNA was then bound to the column by centrifuging at 13000 rpm for 1 minute. The bound mixture was washed with Buffer PE and eluted with 30 μL Buffer EB (10mM Tris·Cl, pH 8.5). Further details are given in Appendix two.

2.2.10 A-Tailing

For ligation into pGEM-T, easy vector A-Tailing is needed. This is because the high-fidelity DNA polymerase creates a blunt end on the PCR fragments (Promega, 2012). After gel purification of the PCR products, 6.5 μL of DNA and 3.5 μL of a mastermix were combined. The mastermix consisted of 2 μL 5 x MangoTaq colourless reaction buffer, 0.3 μL 50 mM

MgCl2, 0.2 μL 10mM dATP and 5 units of MangoTaq polymerase (5 U/μL). The reaction mixture was incubated at 70˚C for 15-30 minutes.

2.2.11 Ligation

For pGEM-T Easy vector ligations, 5 μL 2x rapid ligation buffer, 1 μL pGEM-T easy vector (50 ng/μL), 3 μL of A-tailed PCR product and 1 μL T4 DNA ligase were combined. The reaction was incubated overnight at 4˚C.

For the pXPG vector ligation reaction, after the column purification, a 7 fold molar surplus (7μL) of the insert was added to 1 μL of the vector (10-100 ng) with 1 μl of T4 DNA ligase and 1 μl of the appropriate reaction buffer (total volume 10 μl). The reaction was incubated overnight at 4˚C.

2.2.13 Transformation using calcium chloride

For transformation with the ligation products, containing the pXPG vector, 50 μl of CaCl2 competent Dh5 α E. coli cells were thawed on ice. 1 μl of the ligation reaction or plasmid DNA

48 were added and incubated on ice for ten minutes. Then a heat shock was performed for 45 seconds at 42°C. 1 ml of LB-medium was added and the samples were incubated by shaking (150 rpm) for 1 hour at 37°C. 40 μl from the LB media was plated on LB-culture plates containing the appropriate antibiotic (Ampicillin100 μg/mL). The plates were incubated overnight at 37°C. As negative control, an open, but empty vector was transformed. As control for the activity of the antibiotics, untransformed bacteria were plated.

For the transformation with the ligation products containing the pGEM-T easy vector, JM109 High Efficiency Competent E. coli cells were used. The transformation mixture consisted of 2 μL of ligation products with 50 μL JM109 High Efficiency Competent E. coli cells. The mixture was incubated on ice for 20 minutes, heat-shocked at 42˚C for 45 seconds and then placed back on ice for 2 minutes. 950 μL of SOC or LB medium was added and the mixture was placed in a shaking incubator (150 rpm) at 37˚C for 1.5 hours. 100 μL of each transformation culture was plated on LB/ampicillin/ IPTG/X-Gal plates. The plates were incubated overnight at 37˚C. (The IPTG and X-Gal are reagents that colour the colonies containing the insert white, and the colonies without insert, blue.)

2.2.14 Plasmid purification

An overnight culture was made, by picking colonies of insert from agar plates and growing them in 5ml of LB broth containing ampicillin. The cells were harvested and DNA purified with plasmid purification kits from either QIAGEN or Promega (the details for both protocols are given in Appendix two).

When using the QIAGEN kit, the cells were harvested at 6000 x g/minute at 4˚C for 15 minutes. The cells were resuspended in 250 μL Buffer P1, and transferred to a microcentrifuge tube. 250 μL Buffer P2 and 350 μL Buffer N3 was added to the cells. The mixture was centrifuged for 10 minutes at 13,000 rpm. The supernatants were applied to a spin column by pipetting and were bound to the column by centrifuging for 1 minute. The column was washed with 0.5 mL Buffer PB and 0.75 mL Buffer PE. The cells were eluted with 50 μL Buffer EB (10 mM Tris·Cl, pH 8.5). Similar steps were used with Promega kit (for the detailed protocol see Appendix two).

2.2.15 Sequencing

Sequencing reactions were sent to the Australian National University, and their sequencing protocol was used. The protocol used was for ABI PRISM Dye Terminator Chemistry on an ABI 377 DNA Sequencer (ACRF Bimolecular Resource Facility, JCSMR, and ANU). Each DNA sample (75-105 ng) was mixed with 1 μL BDT, 1.5 μL 5x Buffer, 1 μL primer T7 (1.6 ng/μL) and 5.5 μL dH2O. The reaction conditions for dye incorporation are shown in Table 2.9. The PCR reaction was precipitated and cleared with 40 μL of 3 M NaOAc. The samples were incubated at room temperature for 15 minutes and then spun for 30 minutes at (14.000g) 14 X 1000 min-1. The samples were washed three times with 250 μL 70% ethanol and dried by air. After sequencing the samples, Sequencher 5.0 software was used to analyse the samples and compare our results to the reference sequence (Figure 2.1).

Table 2.9: Cycle instructions for sequencing. The cycle was repeated 30 times and the temperature was set at 4˚C to store the samples.

Temperature (˚C) Time Number of cycles Reaction steps

94 5 Sec 1 Initialisation 30 96 10 Sec Denaturation

50 5 Sec 30 Annealing

60 4 Min 30 Extension

4 Hold Hold Hold

2.3 Immunohistochemistry

Some immunohistochemistry (IHC) studies were conducted at The Canberra Hospital in collaboration with Dr Jane Dahlstrom (Department of Anatomical Pathology). Archival paraffin blocks of tissues from surgical pathology or post-mortem specimens were used to obtain tissue sections for immunohistochemistry. These specimens were used with the consent of the Canberra Hospital Human Research Ethics Committee HREC (ETH.6.05.396). Sections (3m) were immunostained, using ultraView™ Universal DAB detection system on a BenchMark IHC/ISH automated staining module (Ventana Medical Systems), according to the manufacturer's recommendations. Samples were briefly deparaffinized using EZPrep protease,

50 treated for 8 minutes at 37oC and then incubated with 1:100 rabbit polyclonal antiserum against purified recombinant GDAP1 (Shield et al., 2006) for 32 minutes at 37oC. The slides were then incubated with DAB chromogen (3, 3’ – diaminobenzidine tetrahydrochloride) and DAB H2O2 (0.04% hydrogen peroxide in a phosphate buffer) for 8min at 37oC, followed by counterstain using hematoxylin and bluing reagent for 4 minutes each. At least three samples of each site from three different individuals were photographed and evaluated by a pathologist. The scoring classification used was (-) for no staining; (+) for weak staining; (++) for moderate staining; and (+++) for strong staining.

2.4 Statistical analysis

The statistical analyses in our study were done using Prism 6 from GraphPad software (Prism, 2013). The different statistical tests are described below.

The T-test is an analysis that evaluates whether the means of two groups are statistically different from each other. This analysis is suitable to compare the means of two groups only (Prism6, 2013c).

ANOVA is a group of statistical models used to analyse the differences between group means, and their associated procedures. In its simplest form, ANOVA is a statistical test of whether or not the means of several groups are equal, so it generalizes the t-test of more than two groups. ANOVAs are useful in analysing three or more means (groups or variables) for statistical significance (Prism6, 2013a). t- tests and ANOVA were used to determine whether there were differences in GDAP1 or GDAP1L1 expression in the microarray data.

Post-tests were also used in the analysis of the microarray data, to determine which samples showed differences from the control group, in datasets with a significant ANOVA. The Tukey test was used to compare the average of each sample group (Prism6, 2013b).

Chapter 3. Bioinformatic analysis of GDAP1 expression.

3.1 Introduction

Mutations in the GDAP1 gene were linked with CMT disease in 2002 (Cuesta et al., 2002, Baxter et al., 2002). Since then, various groups have been trying to understand where GDAP1 is expressed, as well as its function (Pedrola et al., 2005, Niemann et al., 2005, Shield et al., 2006). The majority of these studies have been undertaken in rodent species (Cuesta et al., 2002, Pedrola et al., 2005, Niemann et al., 2005). Initial experiments using reverse transcriptase-PCR (RT-PCR) demonstrated ubiquitous expression in a limited range of human tissues, and showed that GDAP1 was expressed more highly in the central nervous tissues than in the peripheral nervous tissues (Figure 3.1, (Cuesta et al., 2002)). Cuesta et al. (2002) also showed GDAP1 expression in several mouse tissues (Figure 3.1B). From Figure 3.1, we can infer that, the level of the expression of GDAP1 appears different, between human and mouse tissues, when compared. Brain GDAP1 expression in the mouse was similar to the expression in humans. Though there was no expression of GDAP1 in the mouse liver, but there was some expression in the human liver. Relative levels of heart and skeletal muscle expression for GDAP1 were higher in humans, than in mice (Cuesta et al., 2002, Pedrola et al., 2005).

Further studies in rodent models have demonstrated, that GDAP1 is significantly expressed in different areas of the central nervous system (CNS), including the spinal cord, thalamus, olfactory bulb and cortex (Niemann et al., 2005, Niemann et al., 2006). GDAP1 has far greater expression in neurons, than in myelinating Schwann cells (Pedrola et al., 2008); although Niemann et al (2005) were able to demonstrate GDAP1 expression in the Schwann cells of rat sciatic nerves. Pedrola et al (2005) were unable to demonstrate the expression of GDAP1 in the Schwann cells of mouse dorsal root ganglion. This level of detail has not been obtained in human tissues.

To date, various studies have explored the expression of GDAP1 in rodents (Cuesta et al., 2002, Pedrola et al., 2005, Niemann et al., 2005), however limited studies have investigated the expression of GDAP1 in human tissues (Cuesta et al., 2002). The experiments described in this chapter will look more closely at the expression of GDAP1 in human tissues. This will help us to assess the usefulness in using rodent models to study GDAP1 expression. This chapter will also investigate whether the expression of GDAP1 is altered in diseases other than peripheral neuropathy.

Figure 3.1: Molecular analysis for GDAP1. RT–PCR analysis of GDAP1 and GAPDH (control) transcripts in human (A) and mouse (B) tissues. RT (–), no reverse transcriptase was added to the first- strand reaction of the brain RNA. PCR (–), no first-strand template was added to the PCR reaction (Cuesta et al., 2002).

3.2 Methodology

3.2.1 Gene Expression Omnibus (GEO) data mining 3.2.1.1 Data collection

Data were collected in two stages from the NCBI Gene Expression Omnibus (GEO) (Wheeler et al., 2003, Novoradovskaya et al., 2004). Studies analysed throughout the chapter are presented with their GEO accession number (GDS) and any relevant publication.

An initial search focussed on the expression of mouse GDAP1 and human GDAP1 in healthy tissues. Based on the human tissues which highly expressed GDAP1 (result section 3.3.1), a systematic approach was used to determine if various disease states changed the expression of GDAP1. The search terms and inclusion/ exclusion criteria used are outlined in sections 3.2.1.2 and 3.2.1.3 below. For the purposes of this analysis, “high” expression was taken to mean any tissue that had 150% of the expression of the liver. From previous studies, GDAP1 is known

54 to have high a expression in the nerve tissues and the brain, so we decide to choose any other tissue to be a reference for basal expression. Liver was chosen as a standard because it has not been included often in experimental datasets.

3.2.1.2 The search terms used

The search terms used were ‘human GDAP1’ and one of: brain, breast, mammary gland, testis, nerve, nervous tissue, spinal cord, epithelium and cell line. These search terms were chosen based on the results of ‘high’ expression of GDAP1 in human normal tissues (see section 3.3.1.2). Epithelial cells were added because they are found almost everywhere in the body, and could potentially express GDAP1 if they were from a target organ (such as the stomach and small intestine, kidney, and pancreas). The initial search was performed in December 2012 and updated in June 2013.

3.2.1.3 Criteria used in this search

Some criteria were selected for inclusion and exclusion of GEO profiles. Inclusion criteria were, that the profile must have used: 1- human tissues or cell lines, 2- a clearly defined control (with a clear description of the control samples and only one control), 3- at least one case. Both cases and controls required a minimum of two replicates. Profiles were excluded if: they investigated non-human samples, the dataset had fewer than four values (for example a control and/ or a case having less than duplicates), datasets were without clearly defined controls, and datasets for tissues were other than those defined by the search definition. GEO profiles that matched our criteria were then analysed by t-test or ANOVA (as described in section 2.4). Statistically significant datasets are described in section 3.3.

3.2.2 Oncomine data mining

Oncomine was used to search for GDAP1 expression in human cancers. GDAP1 was used as the main search term, and these data were filtered to include only mRNA microarray studies. Further criteria for inclusion were: studies comparing cancer tissue with normal tissue, a 2 fold change in GDAP1 expression between the cancer and control, a minimum p value of 0.0001and a minimum of two fold change in the expression of GDAP1. This search was performed in January 2013 and updated March 2014.

3.2.3 Immunohistochemistry studies for GDAP1

Some immunohistochemistry (IHC) study has been conducted in collaboration with The Canberra Hospital, and compared with data from the Human Protein Atlas (HPA). A comparison between our IHC results and samples from the Human Protein Atlas was done. The stains were compared in tissues found in both. The scoring classifications used are (-) for no staining; (+) for weak staining; (++) for moderate staining; and (+++) for strong staining.

3.3. Results

3.3.1 Expression of GDAP1 in normal tissues 3.3.1.1 Expression of mouse GDAP1 in normal healthy tissues

Two studies were found in this search of GDAP1 expression in mouse tissues (Table 3.1); only one of these studies contained duplicate data. For the study GDS182 (Su et al., 2004) a selection of mouse tissues were used from the strain C57BL/6L. The expression of GDAP1 has been shown in different mouse tissues. The highest levels of GDAP1 expression were seen in dorsal root gangelior and striatum and the lowest levels of GDAP1 expression were seen in the large intestine and the mammary gland (Figure 3.2).

Table 3.1: Microarray experiments showing the expression of mouse GDAP1 in healthy normal tissues.

GEO accession Reporter Array Number of Number of Reference number number tissues replicates

GDS182 GPL32, Affymetrix 45 2 Su et al. (2004) 102967 Murine Genome U74A Array

GDS3052 GPL32, Affymetrix 17 1 Unpublished 102967 Mouse Genome 430 2.0 Array

900 800 700 600 500 400 300 200

The expression of GDAP1 15 15 GDAP1 of expression The 100 0

Different normal mouse tissues

Figure 3.2: The expression of GDAP1 in different normal mouse tissues. Each column shows the average signal for duplicate samples. Data is from GDS182. (15) indicates Log2 ratio normalized (see Table 2.2).

3.3.1.2 Expression of human GDAP1 in normal healthy tissues

Six GEO profiles were found containing human GDAP1 expression data, for healthy normal human tissues as outlined in Table 3.2 below. Table 3.2 shows the arrays that were used in these studies, including the reporter, number of tissues analysed and the number of replicates included in the microarray chip. Only one of the profiles had triplicate data (GDS3113); three profiles had duplicate data while the final two studies only had single samples. When looking at the expression of GDAP1 in normal tissues, all datasets were included.

Table 3.2: Microarray experiments showing the expression of human GDAP1 in healthy normal human tissues

GEO Reporter Array Number Number Reference accession number of tissues of number replicates

GDS3113 NM_018972.1 ABI Human Genome 32 3 Dezso et al. Survey Microarray (2008) Version 2 GDS596 NM_018972 Affymetrix Human 79 2 Su et al. (2004) Genome U133A Array

GDS423 AA993206 Affymetrix Human 12 2 Yanai et al. (2005) Genome U95B Array

GDS424 N46350 Affymetrix Human 12 2 Yanai et al. (2005) Genome U95C Array

GDS3831 NM_018972 Custom microarray 42 1 Johnson et al. design based on Johnson (2003)

(2003)

GDS1096 NM_018972 Affymetrix Human 36 1 Ge et al. (2005) Genome U133A Array

Figure 3.3 compares the expression of GDAP1 in the 10 tissues found to be common in the six data sets. Each dataset has been normalised as a ratio of liver expression, hence all liver expression levels are given as one. Figure 3.4 shows the average expression for each of the 10 tissues (from Figure 3.3) compared to the liver expression. It is clear that in some datasets the expression of GDAP1 is higher in the brain, spinal cord, pancreas and skeletal muscle. When the expression of GDAP1 in the tissues is 150% of the expression of GDAP1 in the liver, we consider the expression as high. This data can be compared to the mouse data (Figure 3.2) where the expression of GDAP1 was high in nervous tissues; however as only a limited number of mouse tissues were used, it is difficult to compare expression in non-nervous system tissues.

Dezso et al. (2008) is the only study (GDS 3113) which used three replicates for each of the tissues analysed. This study was chosen as the standard, for determining expression levels for further investigation, as it could better accommodate biological variation in expression; since it has multiple samples for each tissue. Using these data, the expression of GDAP1 was defined as ‘higher’ when the expression of GDAP1 in the tissue of interest was 150% of the expression of the liver. Tissues with high expression were therefore the brain, fetal brain, spinal cord, testis, salivary gland and mammary gland (Figure 3.5). These ‘higher expression’ tissues were used as search terms for the next stage of our experiment.

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Figure 3.3: The expression of GDAP1 in normal healthy tissues for six dependent studies. Red indicates study one (GDS423; n=2), black indicates study two (GDS424; n=2), green indicates study three (GDS596; n =2), brown indicates study four (GDS831; n =1), blue indicates study five (GDS1096; n=1) and grey indicates study six (GDS3113; n=3).

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Figure 3.4: The expression of GDAP1 in normal healthy tissues. The graphs compare the expression of GDAP1 in the liver with the average of the different tissues in the six studies in figure 3.3, (GDS423; n=2), (GDS424; n=2), (GDS596; n =2), GDS831; n =1), (GDS1096; n=1) and (GDS3113; n=3).A- shows the expression in the heart, B- shows the expression in the lung, C- shows the expression in the kidney, D- shows the expression in the skeletal muscle, E- shows the expression in the thymus, F- shows the expression in the brain, G- shows the expression in the spinal cord, H- shows the expression in the prostate and I- shows the expression in the pancreas. The expression of GDAP1 indicates Log (base 2) of the ratio of the mean (see Table 2.2, the standard deviation is 1.2).

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Figure 3.5: The expression of GDAP1 in normal healthy tissues. The average expression of GDAP1and standard deviation in 32 normal healthy tissues is shown for triplicate data from GDS3113. (25) indicates RMA (see table 2.2).

3.3.1.3 In addition to studies healthy normal tissue samples

Two studies investigated normal expression of genes across a range of cell lines from different tissue origins. These profiles came from the search GDAP1 and cell line (section 3.3.2.2.5).

Novoradovskaya et al. (2004) compared expression in cell lines representing different tissues, against a Universal Reference RNA (URR) which had been prepared from pooled mRNA (from the individual cell lines). Ten human cell lines derived from the following human tissues were selected, including liver, testis, mammary gland, cervix, brain, skin, liposarcoma, macrophage, T-lymphoblast and B-lymphocyte cell lines. The data in figure 3.6 are given as a ratio of the mean expression. The GDAP1 expression was higher in the liposarcoma than the other tissues (see Figure 3.6).

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Figure 3.6: GDAP1 expression in cell lines derived from human tissues. Columns show the ratio of the mean of the expression of GDAP1 (n=1). Data is from GDS1835. (5) indicates Log (base 2) of the ratio of the median (see Table 2.2).

In the second study, Ross et al. (2000) explored the variation in gene expression among 60 cell lines used by the National Cancer Institute. They grouped cell lines according to tissue of origin; this included eight cell lines from each of melanoma and renal tumours, nine cell lines from non-small lung cancer and breast tumour, seven cell lines from CNS tumours, colon tumours and leukaemia, six cell lines from ovarian tumours and two cell lines from prostate tumour. The data in figure 3.7 is given as a ratio of the median expression for all the cell lines. The expression of GDAP1 was higher in the melanoma than the other tissues (see Figure 3.7).

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Figure 3.7: Expression of GDAP1 in 60 cell lines. Columns show the average expression levels and standard deviation for the different groups of cell lines. The black column indicates breast tumour (n=9), the second column shows CNS tumour (n=7), the white column shows colon tumour (n=7), the red column indicates leukaemia (n=7), the blue column indicates melanoma (n=8), the grey column shows non-small lung cancer (n=9), the green column shows ovarian tumour (n=6), the white column with dots shows prostate tumour(n=2), and the white check column shows renal tumour (n=8). Data is from GDS1761. (23) indicate log2 ratio of the median (see Table 2.2).

3.3.2 Expression of GDAP1 in selected tissues 3.3.2.1 Overview of systematic search results

A systematic search was used to find studies related to the search terms described in Table 3.3. These search terms were selected, based on the tissues having highest levels of GDAP1 expression compared to the expression of the liver (see Figure 3.2). The results for these search terms, with the number of profiles found, the number included for analysis and the reasons for exclusion are summarised in Table 3.3 below. The reasons for exclusion included: profiles that were in non-human samples (such as mouse or rabbit), profiles containing too few samples (less than two) for either the control or the cases, profiles with multiple controls or controls that were not clearly defined and profiles that were too complex to analyse; for example profiles looking at multiple aspects at the same time (such as multiple concentrations for several drugs over different time points). Finally if the profile was repeated with the same search or found in different searches, it has been included once. Summary data for all the studies that met inclusion criteria is discussed below. Detailed results are only discussed for studies which were statistically significant (p<0.05) by ANOVA; these results can be found in sections 3.3.2.2.1 to 3.3.2.2.5. Data for the non-significant studies can be found in Appendix three (Tables 1- 4). The data in the appendix tables are presented in the same order in which they appeared in the database searches. Data in this chapter have been rearranged according to tissue specific themes; hence the order in which the data appear in chapter 3 and appendix 3 may at times be different.

Table 3.3: Overview of systematic search for expression of GDAP1 in selected tissues. The table describes each of the search terms used, the number of profiles found, the number of profiles included for analysis and the reasons for exclusion. Further explanation for exclusion can be found in the text.

Search N N Reason for exclusion term found included [Human GDAP1 Non- less than two samples Two controls No clearly Complex Repeated and ‘---’] human for the control or the defined control studies studies case Brain 67 9 15 22 15 2 4

Breast 119 44 14 30 9 22

Mammary 7 1 2 4 Gland Cell line 326 164 17 44 15 6 5 75

Epitheliu 125 34 26 10 4 51 m Nerve 3 1 2

Nervous 4 4 4 tissues Spinal 11 11 cord Testis 14 5 4 5

3.3.2.1.1 Included studies: Human GDAP1 and brain

Thirteen GEO profiles matched the inclusion criteria for the expression of GDAP1 in the human brain. Five studies were categorized as being psychiatric or neurodegenerative disorders (2 bipolar disorders, 1 Alzheimer’s disease, 1 Downs syndrome and 1 schizophrenia), while the remaining 7 studies focused on brain cancer. Details for all of these studies can be found in Table A3.1 in Appendix three.

3.3.2.1.2 Included studies: Human GDAP1 and breast or mammary gland

In the search, ‘human GDAP1 and breast’, 119 GEO profiles where found; 44 matched the inclusion criteria. From these 44 profiles, 25 were the same studies in the cell line (Table A3.3 in Appendix three) and 19 were new and included in this search. For the search ‘human GDAP1 and mammary’, 30 profiles were found but were the same profiles found with the search term ‘breast’. Details for all of these studies can be found in Table A3.4 in Appendix three.

3.3.2.1.3 Included studies: Human GDAP1 and cell line

In this search, 326 profiles were found, and 164 profiles were included: a summary showing the tissue origin of the cell line with specific cell lines used and the number of the significant studies analysed is given in Table 3.4. Details for all of these studies can be found in Table A3.3 in Appendix three.

Table 3.4: Summary of the including cell line profiles that were found in the cell line search.

Cell line origin Number of profiles Tissues number Number of included of specific Cell significant lines were profiles represented

Breast cancer 23 16 MCF-7 4

1 MDA-MB-436

2 MDA-MB-231

3 LM2

1 BT-20

Colon cancer 6 3 HT29 1

1 RKO

Prostate cancer 5 2 LNCaP 0

1 DU145

1 DHT

Lung cancer 6 4 A549 1

1 H292

Leukemia 11 8 K562 3

1 THP-1

1 HL60

Miscellaneous 59 7 cell lines

3.3.2.1.4 Included studies: Human GDAP1 and epithelium

In this search 125 GEO profiles were found and 36 profiles were included. These are summarised in table 3.5 below. Twenty profiles were from airway epithelial cells, four profiles were found from the eye (lens epithelial, retinal pigment) and three profiles were found from each of immortalized gingival keratinocytes (HIGK) and breast cancer epithelium (this data will be discussed in section 3.3.2.2.2). One profile each was found from cervical carcinoma, primary esophageal, intestinal, ovarian, nasal and renal proximal tubule. Details of all of these studies can be found in Table A3.2 in Appendix three.

Table 3.5: Summary of the epithelium GEO profiles included. The table summarises the kind of epithelial cells and the number of profiles that were found for this cell type.

Epithelial cell type Number of profiles included

Airways 21

Cervical carcinoma 1

Primary esophageal 1

Immortalized gingival keratinocytes 3 (HIGK)

Intestinal 1

Lens 2

Retinal pigment 2

Ovarian surface (OSE) 1

Renal proximal tubule 1

Breast cancer 3

3.3.2.1.5 Included studies: Human GDAP1 and nerve or nervous tissues

Three profiles were found in the ‘nerve’ search and one matched the inclusion criteria. In the ‘nervous tissues’ search, 4 studies were found and they all matched the criteria. All five studies had previously been found in the brain search and are discussed in section 3.3.2.2.1.

3.3.2.1.6 Included studies: Human GDAP1 and spinal cord

Nineteen profiles were found in this search, six of them matched the criteria but they all compared the expression of GDAP1 for spinal cord with different normal healthy tissues. These profiles were discussed in detail in the section 3.3.1.2 on expression of GDAP1 in healthy normal tissues.

3.3.2.1.7GDAP1 expression in the testis

One profile was found in this search (GDS1835) and it matched the criteria, but it was the same study in the different cell lines (section 3.3.2.2.5).

3.3.2.2 Analysis of included studies 3.3.2.2.1 GDAP1 expression in the human brain

Of the 13 GEO profiles that matched our inclusion criteria, only three showed significant changes in GDAP1 expression by ANOVA analysis. These studies are discussed below.

Ryan et al. (2006) investigated gene expression in the brains of bipolar disorder patients. Their study, GDS2190 shows that the expression of GDAP1 was significantly lower in dorsolateral prefrontal cortex from 30 adults with bipolar disorder, compared to the control (p=0.043; Figure 3.8). They also analysed orbitofrontal cortex from ten adults with bipolar disorder (GDS2191) and observed a similar trend towards decreased GDAP1 expression in bipolar patients (Figure 3.8). The decrease in expression of GDAP1 was not significant but could be due to the smaller number of samples in the second study.

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Figure 3.8: GDAP1 expression in the brains of bipolar disorder patients. Columns show the average expression levels and standard deviation from dorsolateral prefrontal cortex (DPC) and orbitofrontal cortex (OC) of bipolar cases and their relevant controls. The white column indicates control DPC (n=31) while the red column indicates control OC (n=11). White stripy column indicates the study of dorsolateral prefrontal cortex from bipolar patients (n=30) while the red stripy column indicates orbitofrontal cortex from bipolar patients (n=10). The star indicates a significant difference (p<0.05) between the DPC case and control. (1) indicates RMA normalised signal intensity (see Table 2.2). Data is from GDS2190 and GDS2191.

Two datasets were found, which showed significant changes in the expression of GDAP1 in brain tumours. Bredel et al. (2006) looked at 50 glial brain tumours of various histogenesis (GDS1813) and found the expression of GDAP1 to be significantly altered in some tumours compared to the control samples (p=0.012; Figure 3.9). The expression of GDAP1 was higher in the oligodendroglioma than the other brain tumours. It was not significantly different from the normal brain tissue, but was significantly different from the astrocytotic tumour. Post tests showed that astrocytic tumour had significantly lower levels of GDAP1 expression compared

to normal tissues (p< 0.05).

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Figure 3.9: GDAP1 expression in glial brain tumours. Columns show the average expression levels and standard deviation for normal brain and four different brain cancer types. The black column shows normal brain (n=4), the white column shows oligodendroglioma (n=8), the red column shows anaplastic oligoastrocytoma (n=6), the blue column shows glioblastoma (n=30) and the grey column shows the astrocytic tumour (n=4). Data is from GDS1813. The star indicates a significant difference (p<0.05) in GDAP1 expression from the oligodendroglioma. (21) indicates UNF_VALUE (see Table 2.2 in chapter2).

Sun et al. (2006) also compared normal tissue with brain tumours of varied histology (GDS1962). They also found expression of GDAP1 to be decreased in tumours (ANOVA p=1.39×10-7; Figure 3.10). In particular post testing it was seen that, compared to the control, GDAP1 expression was significantly lower in the grade II astrocytomas (p=0.0003), astrocytoma grade III (p=0.0102), astrocytoma grade IV (p< 0.0001), oligodendroglioma grade II (p=0.0023) and oligodendroglioma grade III (p=0.0097).

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Figure 3.10: GDAP1 expression in gliomas of different grades. Columns show the average expression levels and standard deviation values for different grades of brain cancers. The black column shows the control (normal brain) (n=23), the white column shows astrocytoma grade II (n=7), the red column shows astrocytoma grade III (n=19), the blue column shows astrocytoma grade IV (n=81), the grey column shows oligodendroglioma grade II (n=38) and the green column shows oligodendroglioma grade III (n=12). Data is from GDS1962. The stars show p-values, * p<0.05, ** p<0.01, *** p< 0.001, **** p<0.0001 compared with the control. (7) indicates MAS5-calculated Signal intensity(see Table 2.2).

3.3.2.2.2 GDAP1 expression in breast tissue and breast cancer cell lines

The data presented in this section are the significant studies in breast tissue collated from the results of three searches using the search term ‘breast, mammary and cell lines’. One of the significant studies used breast tumour tissue, while the other five all used breast cancer cell lines.

The Richardson group (Richardson et al., 2006, Alimonti et al., 2010) investigated gene expression for 47 human breast tumour cases, including sporadic basal-like cancer (BLC), BRCA-associated breast cancer, and non-BLC tumours. The changes in expression of GDAP1 were significant by ANOVA (p=0.049), but no significant changes in the cancer samples, as

compared to the control were found (Figure 3.11).

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Figure 3.11: GDAP1 expression in human breast tumours. Columns show the average expression levels and standard deviation for normal breast tissue (n=7), sporadic basal-like cancer (n=18), BRCA1- associated breast cancer (BRCA1; n=2), and non-basal-like cancer (n=20). The black column indicates normal breast, the white column indicates non-basal-like cancer, the red column indicates BRCA1- associated breast cancer and the blue column indicates basal-like cancer. Data is from GDS2250. (9) indicates GCRMA-calculated Signal intensity, log2 transformed (see Table 2.2).

Stitziel et al. (2004) used a combined biochemical and bioinformatic approach to identify membrane-associated and secreted genes expressed by the MCF-7 breast cancer cell line. mRNA from MCF-7 cells was fractionated by sucrose density gradient centrifugation to separate RNA associated with membrane-bound polysomes and RNA associated with free polysomes. The expression of GDAP1 was higher in the cytosolic fraction than in the membrane fraction (p=0.01; Figure 3.12). This confirms what is already known about the insertion of GDAP1 into the mitochondrial membrane (Niemann et al., 2005).

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Figure 3.12: The expression of GDAP1 in fractions of the MCF-7 cell line. Columns show the average expression level and standard deviation for RNAs associated with the cytosolic fraction (black column; n=3) and the membrane fraction (white column; n=3) of MCF-7. * indicates p=0.012. Data is from GDS992. (3) indicates RMA calculated expression (see Table 2.2).

Bourdeau et al. (2008) analysed MCF-7 breast cancer cells, treated with estradiol and cycloheximide, an inhibitor of protein synthesis. The expression of GDAP1 was significantly higher in the cycloheximide treated cells and the estradiol plus cyclohexamide treated cells, compared with the control (ANOVA p<0.0001, Figure 3.13). No difference was found between the cycloheximide and cycloheximide plus estradiol treatment, suggesting that changes in

GDAP1 expression are due to the cycloheximide treatment.

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l l e o o e r i id d t d i n a m m o r i i c t x x s e e e h h o l lo c c y y c c d n a l io d a tr s e

Figure 3.13: GDAP1 expression in MCF-7 breast cancer cells treated with estradiol and cyclohexamide. Columns show the average expression levels (n=4) and standard deviation for the treatment groups. The black column shows the control (non-treated), the white column shows treatment with estradiol (25nM), the red column show treatment with cyclohexamide (10µg/mL) and the blue column indicates treatment with estradiol and cylcoheximide. **** indicates p <0.0001 compared with the control. Data is from GDS3315. (2) indicates RMA-calculated signal intensity (log2 transformed) (see Table 2.2).

Creighton et al. (2006) analysed estrogen receptor (ER) alpha positive MCF-7 breast cancer cells, overexpressing constitutively active Raf-1, constitutively active MEK, constitutively active c-erbB-2, or ligand-activatable EGFR. The control was MCF-7 that had been sham- transfected. The resulting cell lines exhibit hyperactivation of MAPK, estrogen-independent growth, and the reversible down-regulation of ERalpha expression. The expression of GDAP1 was lower in the erbB-2, MEK, Raf-1 and EGFR cell lines, compared to the control (and the cell line with long-term E2 independent growth) as seen in Figure 3.14

(P<0.0001).Transcription regulation of GDAP1 will be discussed further in chapter four.

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Figure 3.14: GDAP1 expression in MCF-7 cell lines overexpressing constitutively active Raf-1, constitutively active MEK, constitutively active c-erbB-2, or ligand-activatable EGFR. Columns show the average expression level of GDAP1 and standard deviation for the MCF-7 cell lines (n=3 each). The black column shows MCF-7 control, the white column shows MCF-7 with long-term E2 independent growth, the red column shows erbB-2 overexpressing, the blue column indicates MEK overexpressing, the grey column shows Raf-1 overexpressing and the green column shows EGFR overexpressing. * indicates p<0.05, ** indicates P < 0.01, *** indicates p<0.001 compared with the control. (2) indicates RMAExpress-calculated signal intensity (log2 transformed) ( see Table 2.2). Data is from GDS1925.

Gomez et al. (2007) investigated MCF7 breast cancer cells, over expressing X-box binding protein-1 (XBP1). XBP1 is a transcription factor that participates in the unfolded protein response (UPR). The expression of GDAP1 was significantly higher in the XBP1 over expressing cells than the control (p=0.049; Figure 3.15). Transcription regulation of GDAP1

will be discussed further in chapter four.

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Figure 3.15: The expression of GDAP1 in MCF7 cells overexpressing XBP1. Columns show the average expression levels and standard deviation for the MCF-7 control and MCF-7 cells overexpressing XBP1 (n=3). The black column indicates control, the white column indicates XBP1overexpression. * indicates p< 0.05. (5) indicates RMA (see Table 2.2). Data is from GDS2861.

In a study conducted by Hu et al. (2009) LM2 breast cancer cells were depleted for metadherin (MTDH), a cell surface protein in breast tumours that mediates lung metastasis. LM2 cells were grown alone or on a monolayer of lung microvascular endothelial cells, to identify the genes that are regulated after the metastasis gene metadherin is knocked down. The expression of GDAP1 was significantly lower in the MD culture with lung endothelial, compared to

metadherin depleted culture alone (p=0.036; Figure 3.16).

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Figure 3.16: GDAP1 expression in LM2 breast cancer cells depleted for metadherin. Columns show the average expression level and standard deviation between the values of growth protocols (n=3 each). The black column indicates control (LM2 culture alone), the white column shows LM2 control culture with lung endothelial cells, and the red column shows LM2 metadherin depleted culture alone, the blue column indicates LM2 metadherin culture grown with lung endothelial cell. * indicates p<0.05. (15) indicate log2 of Pre value (normalized ratio) (see Table 2.2). Data is from GDS3179. CLE= culture with lung endoth, MDCA= metadherin depleted culture alone, MDCLE= MD culture with lung endothelia

3.3.2.2.3 GDAP1 expression in the respiratory system

The studies described in this section were collated from two searches: the expression of ‘GDAP1 and epithelium’ and the expression of ‘GDAP1 and cell lines’. In the epithelium search, there were 20 studies described as ‘airways epithelial cells’. These studies included 10 profiles from bronchial epithelial cells, four from large airways, three from lung epithelial, one from small airway, one from buccal epithelial and one from fetal lung epithelial. In the expression of GDAP1 and cell lines, six profiles were matching the criteria, five profiles used A549 and one profile used H292. The expression of GDAP1 was significant in two profiles.

Kicic et al. (2010) examined airway epithelial cells from children with asthma and healthy controls, collected by bronchial brushing. The expression of GDAP1 was significantly higher

in the children with asthma, compared with the healthy control (p=0.01; see Figure 3.17).

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Figure 3.17: GDAP1 expression in airway epithelial cells from children with asthma. Columns show the average expression levels and standard deviation for GDAP1 in airway epithelial cells. The black column indicates the healthy controls (n=7); the white column shows children with asthma (n=9). Data is from GDS3711 * indicates p=0.01. (10) indicates GC-RMA normalized signal log2 (see Table 2.2). Asthmatic atopic = airway epithelial cells from children with asthma.

Kuner et al. (2009) compared two non-small cell lung cancer histological subtypes: adenocarcinomas and squamous cell carcinomas. Figure 3.18 shows that the expression of GDAP1 was significantly higher in the squamous cell carcinomas than in the adenocarcinomas

(p=0.002)

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Figure 3.18: The expression of GDAP1 in two non-small cell lung cancers. Columns show the average expression levels and standard deviation of the values in the diseased state. The black column shows the adenocarcinomas (n=18) and the white column indicates the squamous cell carcinomas (n=40). * indicates p=0.002. Data is from GDS3627. (12) indicates GCRMA-calculated Signal intensity (see Table 2.2).

3.3.2.2.4 GDAP1 expression in ovarian epithelial cells

Bowen et al. (2009) compared normal ovarian surface epithelia and ovarian cancer epithelial cells from 12 individuals. The expression of GDAP1 was significantly lower in the ovarian cancer than in the control (ovarian surface epithelia) (p=0.002; see Figure 3.19). Further experiments investigating changes in GDAP1 expression in cancers are discussed in section

3.3.2.

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Figure 3.19: The expression of GDAP1 in normal ovarian surface epithelia and ovarian cancer epithelial cells. Columns show the average expression levels and standard deviation in epithelial samples (n=12). The black column indicates normal ovarian surface epithelia; the white column indicates ovarian cancer epithelial cells. Data is from GDS3592. * indicates p=0.002. (20) indicates value (see Table 2.2).

3.3.2.2.5 The expression of GDAP1 in various cell lines

In this search, 164 profiles were included with 16 significant studies described. Thirteen studies are presented below; the remaining studies have been discussed in sections 3.3.1.3, 3.3.2.2.2 and 3.3.2.2.3. Section 3.3.2.1 summarising these studies and the details for all of these studies can be found in Appendix three, Table A3.

A- Leukaemia cell line studies

Six profiles investigated K562 leukaemia cells treated with 1 µM imatinib for 24 hours (Auer et al, 2004). Imatinib is a highly selective and potent growth-inhibitor of BCR/ABL1 expressing cells such as K562. Each of these six studies had three replicates for case and control. In two of the profiles (shown in Figure 3.20) the expression of GDAP1 was significantly higher in the control cells compared to the imatinib treated cells, however the remaining 4 profiles were not significant. This data is shown in Table 3.6.

Table 3.6: Summary of the 6 GEO profiles looking at K562 leukaemia cells treated with 1µM imantinib for 24 hours. The average indicates RMA expression estimate (log 2 expression estimate) (see Table 2.2).

Geo number K562 untreated K562 + 1µM imantinib

N Average Stdev N Average Stdev t-test

GDS3046 3 4 0.091 3 4 0.22 0.63

GDS3043 3 6.5 0.09 3 6.57 0.13 0.52

GDS3047 3 5.3 0.05 3 5.34 0.1 0.04

GDS3044 3 5.7 0.084 3 5 0.035 0.018

GDS3045 3 5.5 0.03 3 5.47 0.06 0.35

GDS3048 3 6 0.06 3 6 0.05 0.229

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Figure 3.20: GDAP1 expression in K562 leukaemia cells treated with 1 µM imatinib for 24 hours. Columns show the average expression levels and standard deviation for treated and untreated cells (n=3). The black column shows the K562 control and the white column shows K562 cells treated with imatinib. * indicates p<0.05. Data is from A GDS3047 or B GDS3044. (6) indicates RMA expression estimate (see Table 2.2).

B- Kidney cell line studies

Two significant studies used the kidney cell line HEK293. El Hader et al. (2005) analysed HEK293 kidney cells, overexpressing the hypertension-related calcium-regulated gene (HCaRG). HCaRG is involved in the control of renal cell proliferation and differentiation at the level of transcription factors (Solban et al., 2002). HEK293 cells were transfected with control plasmid (PCDNAI) or with the plasmid encoding HCaRG. The expression of GDAP1 was significantly higher in the HCaRG expressing cells compared with the control (p=0.03;

Figure 3.21).

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Figure 3.21: GDAP1 expression in HEK293 kidney cells expressing HCaRG. Columns show the average expression levels and standard deviation for the samples (n=4). The black column indicates the HEK293 control (transfected with pcDNAI), and the white column indicates HEK293 overexpressing the hypertension-related calcium-regulated gene (HCaRG). * indicates p=0.03. Data is from GDS2426. (18) indicates mean signal intensity (see Table 2.2).

Liu et al. (2008) analysed HEK293 kidney cells depleted for the macrophage migration inhibitory factor (MIF) using SiRNA. They were aiming to understand the molecular mechanism of cell cycle perturbation following MIF knockdown. The expression of GDAP1 was found to be significantly higher in the MIF depleted cells compared with the control (p=0.009; Figure 3.22).

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Figure 3.22: GDAP1 expressions in HEK293 kidney cells. Columns show the average expression levels and standard deviation of the sample values of HEK293 kidney cells (n=2).The black column indicates the HEK293 control, and the white column indicates HEK293 depleted for the macrophage migration inhibitory factor. * indicates p=0.009. Data is from GDS3626. (25) indicates Quantile Normalised Signal Intensity ( see Table 2.2).

C- Miscellaneous cell line studies

Whitney et al. (2006) treated the colon cancer cell line RKO for 24 hours with pronasterone A to induce expression of a full-length, transgenic Krüppel-like factor 4 (KLF4). KLF4 is an epithelially-enriched, zinc finger transcription factor. The expression of GDAP1 was significantly higher in the RKO cell line compared with non-treated RKO (p=0.013; see Figure 3.23). This implies that KLF4 may be able to up regulate expression of GDAP1. Transcription

regulation of GDAP1 will be further discussed in chapter 4. )

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Figure 3.23: GDAP1 expression in RKO colon cancer cell line. Columns show the average expression levels and standard deviation in each sample. The black column indicates the control RKO untreated cells (n=8), the white column shows the pronasterone A treated RKO cells (n=7). * indicates p<0.05. Data is from GDS1942. (7) indicates MAS5-calculated Signal intensity (see Table 2.2).

Dutta-Simmons et al. (2009) used comparative gene expression profiling in multiple myeloma to analyse MM1.S cells, depleted for beta-catenin using shRNA. Beta-catenin is a double function protein, regulating the coordination of cell–cell adhesion as well as gene transcription (James et al., 2008). The expression of GDAP1 was significantly lower in the beta-catenin

depleted cells compared with the control (p=0.04; Figure 3.24).

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Figure 3.24: GDAP1 expression in multiple myeloma MM1.S cells depleted for beta-catenin. Columns show the average expression levels and standard deviation in the treated and untreated samples (n=3). The black column indicates the control; the white column indicates MM1.S cells with beta-catenin depletion. * indicates p<0.05. Data is from GDS3578. (16) indicates normalized signal by PLIER method (see Table 2.2).

Ndolo et al. (2006) analysed Jurkat CD4+ T cells, following the induction of simian immune deficiency virus (SIV) Nef (negative regulatory factor) from an inducible promoter. Nef is a small myristoylated protein encoded by primate lentiviruses such as SIV. Nef was induced by addition of 10 µM pronasterone A and gene expression values were determined after 24 hrs. The expression of GDAP1 was significantly higher in the SIV-Nef cells than in the control (p=0.01; Figure 3.25).

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Figure 3.25: GDAP1 expression in Jurkat CD4+ T cells following induction of Nef. Columns show the average expression levels and standard deviation in the control and inducible cell lines after treatment with pronasterone A (n=3). The black column indicates the control Jurkat CD4+ T cells; the white column indicates Jurkat CD4+ T cells following induction of simian immune deficiency virus Nef. * indicates p< 0.02. Data is from GDS2164. (8) indicates GCOS-calculated Signal intensity (see Table 2.2).

Murphy et al. (2007) investigated the effect of conjugated linoleic acid (CLA) on gene expression in Caco-2 cells. They treated the Caco-2 cells (derived from epithelial colorectal adenocarcinoma) with the CLA isomers: trans-10 CLA, cis-12 CLA and cis-9 CLA for 12 days. Linoleic acid exerts isomer-specific effects on trans-epithelial calcium transport and cell growth (Pariza et al., 2001). The expression of GDAP1 was higher in the cis-12 CLA than the linoleic acid and the cis-9 trans-11 CLA (ANOVA p=0.044; Figure.3.26).

)

2 *

1 5 0

(

1 P

A 4 0

f 3 0

n o

i 2 0

e r

p 1 0

h 0 T

id A A c L L a C C 2 9 ic 1 - e - is l is c o c in l

Figure 3.26: GDAP1 expression in intestinal Caco-2 cells treated with conjugated linoleic acid. Columns show the average expression levels and standard deviation of the samples values of the different agents (n=3). The black column indicates Caco-2 cells treated with trans-10 CLA or linoleic acid (control), the white column shows Caco-2 cells treated with cis-12 CLA and the red column shows Caco-2 cells treated with cis-9CLA. * indicates p<0.05 compared to linoleic acid and cis-9 CLA. Data is from GDS3424. (12) indicates GCRMA-calculated Signal intensity (see Table 2.2).

Jaworski and Klapperich (2006) analysed IMR-90 fibroblasts cultured in a two- or a three- dimensional collagen-glycosaminoglycan (GAG) environment for 8 hours. The 3-D presentation of collagen-GAG stimulates increased fibroblast remodelling activity (Jaworski and Klapperich, 2006). The expression of GDAP1 was significantly higher in the 3- dimensional environment than in the control (p<0.05; Figure 3.27).

) 4

( 8 1

P * A

D 6

n 4

e r

p 2

h 0 T l o G G tr A A n G G o - - D D C 2 3

Figure 3.27: GDAP1 expression in IMR-90 fibroblasts in two and three dimensional collagen- glycosaminoglycan. Columns show the average expression levels and standard deviation between the values of different growth protocols (n=4). The black column indicates the control (IMR-90 Cells grown on Tissue Culture Polystyrene Dish), the white column shows IMR-90 fibroblasts in 2-dimensional GAG, and the red column shows IMR-90 fibroblasts in 3-dimensional GAG. * indicates p<0.05 compared to the control. Data is from GDS3051. (4) indicates RMA Analysed Data (see Table 2.2).

Weisschuh et al. (2007) analysed HeLa cells depleted for optineurin gene, using RNAi knockdown. Optineurin is a protein that, in humans, is encoded by the OPTN gene (Rezaie et al., 2002). The role of this gene is unclear, however mutations in the gene are associated with open-angle glaucoma (Rezaie et al., 2002). The expression of GDAP1 was significantly higher in the cells depleted of optineurin compared with the control (p=0.0009; Figure 3.28).

) 1

1 4 0 0

(

P A

D 3 0 0

n 2 0 0

s e

r 1 0 0

h 0 T l o r in t r n u o e C n ti p o n o ti le p e d

Figure 3.28: GDAP1 expression in HeLa cells depleted for optineurin using RNAi knockdown. Columns show the average expression levels and standard deviation for the HeLa cells (n=3). The black column indicates the control HeLa cells; the white column indicates HeLa cells depleted for optineurin. * indicates p=0.0009. Data is from GDS2892. (11) indicates GC-RMA normalized signal (see Table 2.2).

In a study by Philibert et al. (2007) lymphoblast cell lines were derived from six subjects with active nicotine dependence, and nine subjects who did not use tobacco. The expression of GDAP1 was significantly higher in cells from nicotine dependent patients than in the control (p=0.03; Figure 3.29).

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1 2 .0

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P A

D 1 .5

n 1 .0

s e

r 0 .5

h 0 .0 T l o e tr c n n o e c d n e p e d e n ti o ic n

Figure 3.29: GDAP1 expression in lymphoblast cell lines derived from subjects with nicotine dependence. Columns show the average expression levels and standard deviation for values of the samples. The black column indicates control (non tobacco users, n=9), the white column indicates patients with nicotine dependence (n=6). * indicates p=0.03. Data is from GDS2447. (16) indicates normalized signal by PLIER method (see Table 2.2).

Horiuchi et al. (2006) studied the effect of siRNA induced knockdown Wilms' tumour 1- associating protein (WTAP) in umbilical vein endothelial cells (HUVEC). The expression of GDAP1 was significantly higher in the WTAP knockdown cells compared to the control (p=0.003; Figure 3.30). Transcription regulation of GDAP1 will be discussed further in chapter four.

) 0

2 3 0 0

( *

D G

2 0 0

i s

s 1 0 0

h 0 T l o P tr A n T o W c

Figure 3.30: GDAP1 expression in siRNA induced knockdown Wilms' tumour 1-associating protein (WTAP). Columns show the average expression levels and standard deviation between the values of the control and the WTAP knockdown samples (n=2). The black column indicates the control and the white column shows the WTAP cell line. * indicates p=0.003. Data is from GDS2010. (20) indicates value (see Table 2.2).

3.3.2.2.6 Summary of themes found in GEO profile datasets

Two main themes emerged from this analysis of significant changes in GDAP1 expression for GEO profile datasets. The first of these themes were changes in expression in different cancers. For example, in the brain the expression of GDAP1 was lower in astrocytomas than in normal brain tissue (section 3.3.2.2.1). Other cancers showing a difference in GDAP1 expression included breast, ovary and lung. To investigate this result further, the Oncomine database was mined for supporting data and results are given in section 3.3.3. The second theme to emerge was, several studies (in various cell lines) looking at activation or repression of transcription factors. For example in HUVEC cells the expression of GDAP1 was significantly increased by the knockdown of Wilms' tumour 1-associating protein (WTAP), suggesting WTAP expression

93 may repress GDAP1 expression (section 3.3.2.2.5). Transcription regulation of GDAP1 will be further explored in chapter 4.

3.3.3 Oncomine datamining

The NCBI GEO profile searches revealed a number of studies in cancer where GDAP1 expression was altered. The Oncomine database was subsequently searched for cancer verses normal, with a minimum of two fold changes in the expression of GDAP1. mRNA was chosen for the data type, p-value 1x10-4, and the gene rank was to be in the top 10%. Positive results were found in the brain and CNS cancer, cervical cancer, colorectal cancer, liver cancer, lung cancer, lymphoma and seminoma (summarised in Table 3.7). Details of all the microarray studies that matched inclusion criteria are given in the Table A3.5A-G in Appendix 3. 23 Analyses from 59 studies matched the search criteria; 7 showed GDAP1 over expression and 4 showed GDAP1 under expression.

Table 3.7: Summary of the studies found in Oncomine database.

Type of cancer Studies matching criteria Total number of studies

Brain and CNS 15 15

Cervical 1 6

Colorectal 1 10

Liver 1 6

Lung 3 7

Lymphoma 1 5

Seminoma 1 11

3.3.3.1 GDAP1 expression in Brain and CNS cancers

From the GEO profiles data, several studies of brain cancers showed overexpression of GDAP1. Seven studies of brain cancer, which matched the inclusion criteria, were found in Oncomine and are summarised in Table 3.8. The details of these studies are discussed below. From Table 3.8 we can infer that, the glioblastomas have decreased the expression of GDAP1 in multiple studies. Five out of six studies investigating glioblastoma, show significant decreases in GDAP1 expression, compared with their controls. This supports our finding in section 3.3.2.2.1.

Table 3.8: Summary for the brain and CNS studies found in the Oncomine database.

Study Number Number of Cancer type Fold p-value of controls change samples

Sun et al. 23 19 Anaplastic -2.2 2.05x10 -8 (2006) Astrocytoma

81 Glioblastoma -3.4 2.53x10-19

50 Oligodendroglioma -2.02 1.72x10-10

Murat et 4 80 Glioblastoma -1.9 5.4x10-8 al. (2008)

Bredel et 4 27 Glioblastoma -2.450 7.02x10-4 al. (2005)

6 Anaplastic -2.8 0.112 Oligoastrocytoma

5 Oligodendroglioma -1.1 0.256

3 Anaplastic -1.04 0.394 Oligodendroglioma

The 10 5 Glioblastoma -1.6 0.006 Cancer

Genome Atlas 2012 542 Glioblastoma -2.02 1.41x10-4

Liang et al. 2 30 Glioblastoma -1.6 0.220 (2005)

3 Oligoastrocytoma -1.1 0.403

Lee et al. 3 3 22 Glioblastoma -1.7 0.006 (2006)

French et 6 4 Anaplastic -1.2 0.268 al. (2005) Oligoastrocytoma

23 Anaplastic -1.01 0.475 Oligodendroglioma

3.3.3.2 The expression of GDAP1 in lung cancer

In this search two studies out of nine matched the criteria; both of them showed significant overexpression of GDAP1in lung carcinoma (Table 3.9).

Table 3.9: Summary table for lung cancer studies found in the Oncomine database.

Study Number of Number of Cancer type Fold p-value controls change samples

Garber et 6 4 Small cell lung 6 1.73x10-5 al. (2001) carcinoma

13 Squamous Cell Lung 2.53 4.3x10-5 Carcinoma

Hou et al. 65 19 Non-small cell lung 4.3 1.85x10-6 (2010) carcinoma

3.3.3.3 GDAP1 expression in cervical cancer

Only one study out of five matched our criteria. In the Pyeon et al. (2007) multi-cancer study, a total of 84 samples were included. These were 42 head and neck cancers, 20 cervical cancers, 14 head and neck normal controls and 8 cervical normal controls. Analysis of the cervical cancers and their controls found 3.4 fold change up regulation in GDAP1 expression (p value = 1x10-6).

3.3.3.4 GDAP1 expression in colorectal cancer.

Out of nine studies found in the expression of GDAP1 in colorectal cancer, only one study matched the inclusion criteria. . Skrzypczak et al. (2010) analysed 40 micro dissected colorectal samples from eight types of epithelial or mucosal cells from tumour and normal tissues. They found 2.4 fold changes for GDAP1 up regulation between normal colon mucosa and colon adenoma (p value=5.11x10-6).

3.3.3.5 GDAP1 Expression in the Liver cancer

One study out of five matched our inclusion criteria. Chen et al. (2002) analysed 104 Hepatocellular carcinoma samples on cDNA microarrays; these samples include 76 normal liver samples, 10 metastases to liver from various primary sites and 7 in liver cancer precursor. They found a 2.3 fold change, up regulation between the hepatocellular carcinoma and the normal liver samples (p value = 7.63x10-11).

3.3.3.6 GDAP1 Expression in Lymphoma

Out of six studies only one matched our inclusion criteria. In this search, Piccaluga et al. (2007) analysed 28 unspecified peripheral T-cell lymphoma, 6 angioimmunoblastic T-Cell lymphomas, 6 anaplastic large cell lymphoma, and 20 normal T-lymphocyte (5 each of CD4- positive, CD8-positive, HLA-DR-positive, and HLA-DR-negative). They found a 2.1 fold change up regulation between the normal lymphocyte cells and the angioimmunoblastic T-Cell lymphoma (p value= 2.64x10-7).

3.3.3.7 GDAP1 expression in seminoma cells

One study out of ten atched our inclusion criteria. Korkola et al. (2006) analysed 101 adult male germ cell tumour samples and six normal testis samples. They found a 2.4 fold change down regulation between the testis and the embryonal carcinoma (p value= 1.19x10-8).

3.3.3.8 Summary of themes found in Oncomine datasets

From the results of the Oncomine datasets, it was clear that there are changes in the expression of GDAP1 and some cancer types, for example brain cancer, lung cancer, cervical cancer, colorectal cancer, liver cancer, lymphoma and seminoma cells. For many of these cancer types only one study gave a significant result with fold expression difference of greater than two.

Some studies gave significant results with less than two fold difference in the expression. Five out of six glioblastoma studies showed significant decrease in GDAP1 expression, three of these studies gave expression between 1.6 and 1.9 fold differences.

3.3.4 Immunohistochemical studies

Immunohistochemistry (IHC) studies were undertaken to determine the expression of GDAP1 at the protein level. These results were then compared with the human microarray data. A comparison between the Human Protein Atlas (HPA) and our IHC results are shown in tables 3.11 to 3.16. The immunohistochemistry (IHC) study was conducted in collaboration with The Canberra Hospital and compared with data from the Human Protein Atlas (HPA).

Table 3.10 shows results for the expression of GDAP1 by our IHC and in the HPA for different brain tissues. The normal glial cells, showed GDAP1expression as strong staining by IHC, compared to moderate staining in the HPA glial cells. Other neuronal tissue showed strong staining for GDAP1 in both datasets. Moderate ependymal staining was demonstrated in one sample by IHC.

Three different kinds of brain tumour cells were available: meningioma, glioma-astrocytoma and ependymoma. In the meningioma cells weak GDAP1 staining was observed by IHC. Ependymoma samples showed moderate to strong staining by IHC. The HPA did not have comparative samples for meningioma or ependymomna. In glioma- astrocytoma samples weak staining occurred in malignant astrocytes, compared to the samples in the Human Protein Atlas which showed inconsistent results from weak to strong staining.

Table 3.10: GDAP1 expression in the brain.

Tissue Cell type IHC HPA

N Stain N Stain

Normal brain Glial 3 +++ 3 ++

Neuron * 3 +++ 3 +++

Ependyma 1 ++ 3 nr

Brain cancers

1.Meningioma Meningioma cells 3 + nr

2.Glioma- Malignant 3 + 11 3 samples astrocytoma astrocytes +++ 5 samples ++ 1sample + 2 samples nr 3.Ependymoma Malignant cells 2 1sample ++ nr 1 sample +++

(nr) not reported, * = nissl substance.

Table 3.11 shows the results of the expression of GDAP1 in normal ovary and ovarian cancers. In normal ovary, the surface epithelium showed moderate staining for all samples. Follicles also showed moderate staining while the stroma showed weak staining. The HPA only reported weak staining in stroma for their ovarian samples. GDAP1 staining was noted in various ovarian cancers. The epithelium of mucinous cyst adenoma showed weak to moderate staining, compared to a lack of staining in the stroma. Serous cyst adenomas showed a similar pattern of staining, although one sample had weak staining in the stroma. Both fibroma/thecoma and teratoma had weak stromal staining. The HPA reported week to moderate staining for ovarian cancer.

Table 3.11: GDAP1 expression in the ovary.

Tissue Cell type IHC HPA

N Stain N Stain

Normal ovary Surface 3 ++ nr epithelium

Follicle 3 ++ nr

Stroma 3 + 3 +

Ovarian cancers Ovarian cancer*

1. Mucinous Epithelium 3 2 samples + 12 2 samples++ cyst adenoma 1 sample ++ 6 samples + 4 samples nr

Stroma 3 3 samples -

2. Serous cyst Epithelium 2 2 samples ++ nr adenoma

Stroma 2 1 sample - 1 sample +

3. Fibroma/ Stroma 2 + nr thecoma

4. Teratoma Epithelial areas 3 + nr

*=Not specified (nr) not reported

The expression of GDAP1 in the pancreas is shown in Table 3.12. By IHC, three samples showed moderate staining in the acinar epithelium and strong staining in the islets. In the HPA, dataset exocrine glandular cells did not stain and moderate staining was detected in the islets. For pancreatic cancers, the normal acinar epithelium was also found to be moderately stained,

100 compared to weaker staining in the majority of ductal adenocarcinoma samples. Pancreatic cancer samples screened by HPA also had weak to moderate staining.

Table 3.12: GDAP1 expression in the pancreas.

Tissue Cell type IHC HPA

N Stain N Stain

Pancreas Acinar epithelium 3 ++ 3 Exocrine glandular cells nr

Islets 3 +++ 3 ++

Pancreas Normal acinar 5 4samples ++ 12 Pancreas (cancer) epithelium 1 sample + cancer* 1 sample ++ 2 samples + 9 samples nr Ductal 5 3 samples + adenocarcinoma 2 samples ++

Acinar and ductal 1 ++ epithelium Mucous cystic 1 ++ tumour *=Not specified.

Table 3.13 shows the results for the expression of GDAP1 by IHC in the prostate. In the epithelium, weak to moderate staining was found; one sample with moderate staining in glandular cells was found in the Human Protein Atlas. Three samples of prostate adenocarcinoma were screened and weak staining was found in the epithelium.

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Table 3.13: GDAP1 expression in the prostate.

Tissue Cell type IHC HPA

N Stain N Stain

Prostate Epithelium 3 2 samples ++ 1 Glandular cells 1 sample + +

Muscle 3 - nr

Blood vessels 3 - nr

Prostate Epithelium 3 + nr adenocarcinom a Stroma 3 - nr

(nr) not reported

Table 3.14 shows the results for the expression of GDAP1 by IHC in the bladder. GDAP1 expression in the bladder was found to be moderate to strong in the epithelium, but was not detected in the stroma or muscle. The HPA data also showed moderate staining in urothelial cells for two samples. Samples of transitional cell carcinoma had weak to moderate staining in the epithelium by IHC. The HPA dataset did not have comparative data for bladder cancer.

Table 3.14: GDAP1 expression in the bladder.

Tissue Cell type IHC HPA

N Stain N Stain

Bladder Epithelium 3 2 samples +++ 2 Urothelial 1 sample ++ cells ++

Stroma 3 -

Muscle 3 -

Bladder - Epithelium 3 1 sample ++ nr Transitional 2 samples + cell carcinoma Stroma 3 3 samples - nr

(nr) not reported

102

Table 3.15 shows the results of the expression of GDAP1 the testis. The normal testes tissue was found in areas adjacent to areas of abnormal pathology and included four cell types: Leydig cells, Sertoli cells, epididymis and spermatogonia. For these tissues, strong staining was seen in Leydig cells, with no staining in Sertoli cells, moderate staining (two samples) in the epididymis and weak staining for spermatogonia. We also investigated testicular tissue with Leydig cell hyperplasia. For the three samples screened we found moderate staining in Leydig cells, no staining in Sertoli cells and mixed staining in epididymis. Leydig cells in the Human protein atlas also had moderate staining for GDAP1.

For the sertoli cells in the Leydig cell hyperplasia, three samples were screened in the results of IHC, 2 were found to have no staining and the results for one sample was not available, weak stains were found in the Human protein atlas. In the results of the IHC for the epididymis cells, three samples were screened, one found strong staining, one found weak staining and one sample not available.

Two types of testicular cancer were investigated, seminoma and non-seminiferous tumours. The two seminoma samples had weak staining for GDAP1 and this was the same in the Human Protein Atlas dataset. In the epithelial areas of the nonseminomatous tumours, two samples were found to have weak staining and one had no stains; no recorded data was found in the HPA.

Table 3.15: GDAP1 expression in the testis.

Tissue Cell type IHC HPA

N Stain N Stain

Adjacent Leydig cells 3 +++ nr abnormal pathology

Sertoli cells 3 - nr

Epididymis 3 2 samples ++ nr 1 sample -

Spermatogonia 3 + nr

Testicular tissues

Leydig cells 3 ++ 2 Leydig cells ++

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Leydig cell Sertoli cells 3 2 samples - 2 Cells in hyperplasia 1 sample nr seminiferus ducts ++

Epididymis 3 1 sample +++ 1 sample ++ 1 sample nr

Testis (cancer)

Seminoma 2 + 3 testis (cancer) +

Non semininous Epithelial areas 3 2 samples + tumor 1 sample -

(nr) not reported

Table 3.16 shows the results of the expression of GDAP1 in the skin. For the IHC three samples were screened, which showed strong stains in the epidermis and skin appendages and moderate staining in the connective tissue. The HPA generally found no staining in skin cell types, with the exception of weak staining in melanocytes. In the epithelium of both squamous cell carcinoma and the basal cell carcinoma, weak staining was detected. The Human Protein Atlas found weak to moderate staining in skin cancer.

Table 3.16: GDAP1 expression in the skin.

Tissue Cell type IHC HPA

N Stain N Stain

Skin Epidermis 3 +++ 2 Epidermal cells nr Skin appendages 3 2 samples +++ 3 Fibroblasts nr 1 sample ++ Connective tissue 3 ++ 3 Keratinocytes nr

3 Langerhans nr

3 Melanocytes

104

Squamous cell Epithelium 3 + 12 Skin cancer* carcinoma of 3 samples + skin 9 samples nr

Melanoma nr 11 5samples ++ 3 samples +

Basal cell Epithelium 3 + carcinoma

*=Not specified (nr) not reported.

Table 3.17 shows the results of the expression of GDAP1 in the pituitary. Fetal pituitary epithelium showed moderate to strong staining which was also seen in pituitary ademona cells. The HPA did not have pituitary data.

Table 3.17: GDAP1 expression in the pituitary.

Tissue Cell type IHC HPA

N Stain N Stain

Pituitary (fetal) Epithelium 4 (++) 3, (+++) 1 nr

Pituitary Tumor cells 3 (++) 2, (+++) 1 nr adenoma

(nr) not reported

3.3.4.1 Summary of themes found in immunohistochemical studies

From the results of the immunhistochemical studies, it was found that the GDAP1 staining in the normal brain was strong, whereas for brain cancer, staining was moderate in some tissues and weak in some other cancers, suggesting decrease in the expression of GDAP1 in cancer tissues. In the pancreas strong to moderate stains were found in the islets, but moderate stains were found in pancreatic cancer. For the prostate the stains were weak, in the bladder the stains were moderate and weak. In the testis the stains were moderate, in the skin, the stains were a mix of strong, moderate and weak. In the pituitary the stains were strong to moderate. These studies generally showed that GDAP1 expression is stronger in epithelial tissues than in stromal tissues. Strong staining was also seen in endocrine tissues like the pancreatic islets.

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3.4 Discussion

The current study investigated the expression of GDAP1 in both, normal and diseased tissues. It found that GDAP1 is expressed in most tissues with greater expression in the brain, mammary gland, testis, breast, nervous tissues, spinal cord, and epithelium. This builds on the limited information previously reported about the normal expression of GDAP1 in humans (Cuesta et al., 2002). Prior studies of GDAP1 mRNA levels in human and rodent tissues suggest that GDAP1 expression is ubiquitous in a range of tissues with higher expression in central nervous tissues than in peripheral nervous tissue. However the range of tissues investigated was small (Cuesta et al., 2002). Also, most of this information about the expression of GDAP1 was collected from rodents (Pedrola et al., 2005, Cuesta et al., 2002, Niemann et al., 2005). This study confirms ubiquitous expression of GDAP1 particularly in neural and endocrine tissues.

In toxicology, although animal experiments used to be the main technology, the future is seen in the strength of in vitro and in silico approaches based on human material (Leist et al., 2008). The results of this chapter also demonstrated that the pattern of expression for GDAP1 is different in human than the mouse. For example the relative expression of GDAP1 in human spinal cord was six times higher than in mouse spinal cord. In the skeletal muscle human GDAP1 relative expression was five times higher than the mouse GDAP1 expression. The expression of GDAP1 was high in the human mammary gland but was low in the mouse. Although, animal models for some CMT disease forms (CMT1A, CMT1B and CMTX) have been established and have allowed the elucidation of the molecular disease mechanisms for

PMP22, P0, POLG1, mitofusin 2 (MFN2), and connexin 32 genes (Martini, 2000; Meyer Zu Horste and Nave, 2006), limited literature is available describing mouse models for GDAP1 (Wagner, 2009) . Niemann et al (2014) recently showed that mice with motor neuron-specific loss of GDAP1 showed no detectable alterations in electrophysiological parameters. These findings are entirely not surprising as GDAP1-caused CMT disease is often associated with major loss of myelinated axons and axonal neuropathies have been difficult to reproduce in mice for unknown reasons (Robertson et al., 2002; Bogdanik et al., 2013). Coupled with the differences that we observed in tissue expression levels this may limit the usefulness of mouse models of GDAP1 expression in understanding the role of GDAP1 in human CMT disease.

The usefulness of animal models in biomedical research areas to understand biological mechanisms and then their correlation with human disease is not always straight forward (Leist

106 and Hartung, 2013). In the field of inflammation, mouse models appear to be mechanistically similar to human disease. However, Seok et al. (2013) analysed the biological response to injury on a molecular level by looking at the regulation of approximately 5,000 human genes related to inflammation and comparing them to their murine counterpart. They found that the correlation was very poor, and was almost absent for the main study areas of burns, trauma, and endotoxemia. When Seok et al. (2013) expanded their study to other areas, such as sepsis and infection, poor correlations of human and mouse data were also found, for example correlations between 0.54–0.79 for sepsis, and only 0.50 for infection were observed. This reflects Opal and Cross, who in their 1999 paper said ‘it has become painfully evident that animal models provide misleading and overly optimistic estimates of the survival benefit of specific antisepsis drugs when compared to clinical efficacy in actual human sepsis’.

GDAP1 has mainly been studied in the context of peripheral neuropathies based on its genetic linkage with CMT disease (Cuesta et al., 2002, Pedrola et al., 2005, Niemann et al., 2005). This study indicates that the expression of GDAP1 can be altered in some other diseases, such as bipolar disorder and cancer. The cause of this relationship could be due to the changes in the mitochondrial network resulting from altered GDAP1 expression. Mitochondrial homeostasis is important for all cells, but especially in axonal degeneration, where the mitochondrial function is crucial (Ratajewski and Pulaski, 2009). The depletion of GDAP1 can cause an elongation of mitochondrial tubules, whereas overexpression of GDAP1 protein causes mitochondrial fragmentation (Niemann et al., 2005). Nieman et al (2005) also suggested that GDAP1 is a key regulator of mitochondrial network by promoting mitochondrial fragmentation. Changes in mitochondrial morphology have previously been observed in many different diseases such as Alzheimer’s Dementia, bipolar disorder and cancer (Clay et al., 2011, Wang et al., 2014).

In the NCBI GEO datasets there was suggestion that GDAP1 expression is altered in some cancers. The most convincing data was in brain cancers, with glioblastoma having lower levels of expression for GDAP1 than normal brain tissue (Sun et al., 2006). This finding was supported by 5 Oncomine studies which showed significant decreases in GDAP1 expression; between 1.6 and 3.4 fold (Sun et al., 2006, Murat et al., 2008, Bredel et al., 2005, Liang et al., 2005, Lee et al., 2006). The immunohistochemistry studies also showed that GDAP1 expression was weaker in glioma-astrocytoma than in normal glial cells. Expression profiles also suggested that GDAP1 expression was altered during cancer progression; in the analysis

107 of grades III and IV gliomas of various histologic types, the expression of GDAP1 was found to be significantly higher in the grade III anaplastic oligodendrogliomas compared to gliomas of other grades (Freije et al., 2004). (The details and analysis of this study, GDS1975 and GDS1976 are given in Appendix three).

Glioblastoma multiform (GBM) is the most common primary brain tumour (Griguer et al., 2006). One observation is that glioma cells, which rely on glycolytic metabolism, easily adapt to bioenergetic stress by engaging their mitochondrial pathways in order to survive and grow (Griguer et al., 2006). Zhang et al (2009) proved that oxidative stress induces autophagy and apoptosis in U251 cells (glioma cell lines). Accumulation of ROS leads to changes in mitochondrial permeability with loss of mitochondrial membrane potential, and disruption of mitochondrial dynamics at a transcriptional level of fission and fusion (Zhang et al., 2009). This supports the idea that mitochondrial function plays a critical role in the biology of gliomas and brain cancer (Griguer et al., 2006). GDAP1 contains domains characteristic of glutathione- S-transferases, and a recent study has implicated GDAP1 in the control of intracellular glutathione levels and oxidative stress (Noack et al, 2012). Down-regulation of GDAP1 in brain cancers, as observed in our study, could decrease mitochondrial network by promoting mitochondrial fragmentation.These studies suggest a role for oxidative stress with GDAP1.

Our study also found some indications for changes in GDAP1 expression in other cancers. Oncomine and NCBI results both showed changes in the expression of GDAP1 in lung cancer in three studies. Altered rates of mitochondrial fission and fusion are observed in lung cancer and can influence metabolic function, proliferation and cell survival (Lennon and Salgia, 2014). From the Oncomine data, the expression of GDAP1 was changed in different type of cancers; for example, cervical, colorectal cancer, liver cancer, lung cancer, lymphoma, and seminoma. The results of our immunohistochemistry study confirmed moderate stains in different cancer tissues compared to the normal tissues;, for example ovarian cancer, prostate and bladder cancer tissues; as well as some weak stains in other type of cancer tissues such as; testis and skin cancer. Similar results were found in the Human Protein Atlas, moderate stains were found in different type of cancers like brain, ovarian, pancreatic, testicular and skin.

In some studies although ANOVA or t-tests were positive, these changes in the expression observed, were extremely small and unlikely to be biologically important. Lovell (2013) discussed the importance of differentiating between a statistically significant result in an experiment and a biologically significant one. He demonstrated that when p-value is

108 significant, it does not tell if the difference is large enough to make a biological or clinical difference, which is really the essential bottom line. The statistical test of p-value should be used only to approximate whether the null hypothesis has been rejected and not whether another hypothesis has been accepted (Lovell, 2013). This chapter used publically available data, and even with criteria for inclusion and exclusion the study designs were not under our control, which generated limitations in the datasets. For example different normalisation techniques were used in the different studies to some extent this can affect the results; and since if we want to get equal results we have to use the same analysing techniques. Another limitation of using publically data is that there are many different standard normalization method for analysing the ‘.cel’ file, the approach used in this project was an efficient way to filter the results to obtain the data that required to reach the aim of the project. Also there are many different chips that are used in the different studies. This may affect our results. As well as that each experiment is designed in a specific way for a specific reason, i.e. the main focus of every experiment is different from one study to another and the scientist designs the experiment biased on the main purpose of the study which is different from ours. Additional work needs to be undertaken to understand changes in the expression of GDAP1 in normal compared with disease tissues.

A number of expression profiles were found, involving transcription regulation. This provided some insight into regulation of the expression of GDAP1. GDAP1 expression was significantly higher in the cells expressing the transcription factors XBP1, KLF4 (Gomez et al., 2007) (GDS2861), and the expression was significantly lower in the WTAP knockdown cells (Horiuchi et al., 2006) (GDS2010). This suggests that these transcription factors play a role in the expression of GDAP1. To date, there have been limited studies looking at the transcriptional regulation of GDAP1. One study found GDAP1 to be transcriptionally regulated by YY1, which is an ubiquitously distributed transcription factor (Ratajewski and Pulaski, 2009). The transcription regulation of GDAP1 will be discussed further in chapter four.

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Chapter 4. Transcription factors and polymorphisms

4.1 Introduction

GDAP1 was originally identified as a ganglioside-differentiation induced gene using two in vitro mouse models of neural differentiation. Liu et al. (1999) observed, that GDAP1 expression in murine brain increased after birth, to reach maximal levels at adulthood. Liu et al (1999) also demonstrated up regulation of GDAP1 in the P19 cell line using retinoic acid. Retinoic acid is an important inducer of neural differentiation (Lefebvre et al., 2005). In humans, recessive forms of GDAP1-induced CMT have an early onset which is normally detected in toddlers (Cuesta et al., 2002); this lends support to the idea that mutated GDAP1 protein may be causing changes in a developmental pathway. Currently, little is known about normal GDAP1 expression in humans. To date, most studies investigating expression of GDAP1 were performed in rodent models (Pedrola et al., 2008, Niemann et al., 2005). In chapter 3 the differences in the pattern of expression of GDAP1 in human tissues from the pattern of expression of GDAP1 in mouse tissues was demonstrated. For example, the expression of GDAP1 when compared between the human and the mouse, in the spinal cord and skeletal muscle, was different, with the expression of GDAP1 in the human spinal cord six times higher than that in the mouse spinal cord. This may suggest differences in GDAP1 regulation between species.

Ratajewski and Pulaski (2009) tried to clarify the regulation of GDAP1 by studying the promoter region of GDAP1, which they found to be transcriptionally regulated by YY1. They found a consensus YY1 binding site in the GDAP1 core promoter (Ratajewski and Pulaski, 2009). YY1 is known to exert both positive and negative regulatory influences on nuclear encoded mitochondrial proteins in various cell lines, including HepG2, HeLa, HEK293, A549 and SHSY5Y (Ratajewski and Pulaski, 2009). Ratajewski and Pulaski (2009) showed that this YYI binding site was functional using both in vitro binding assays in human cell lines. RNAi- mediated knockdown YY1 in HEK293 cells led to decrease in the expression of GDAP1. On the other hand, overexpression of YY1 was shown to activate the GDAP1 promoter, using a reporter gene system, as well as increasing the level of endogenous mRNA (Ratajewski and Pulaski, 2009).

With the exception of YY1 regulation, Ratajewski and Pulaski (2009), GDAP1 expression in humans is still effectively unknown. There are some unsolved questions about the regulation

110 of the GDAP1 gene in CMT patients. For example, the same disease causing GDAP1 mutation has been shown to result in different CMT phenotypes such as axonal, intermediate or demyelinating forms of this peripheral neuropathy (Niemann et al., 2006). Some patients show demyelination phenotype, while others have a loss of myelinated axons without signs of demyelination (Baxter et al., 2002, Cuesta et al., 2002, Nelis et al., 2002). For example mutations in GDAP1 in the R12Q cause demyelination while mutations in R120W cause axonal. Several mutations may link to similar clinical symptoms, while family members with identical genotypes may have different severity or even a different subtype of disease (Berger et al., 2002). Although initially defined as a demyelinating form with symmetrical weakness and atrophy of appendages as the clinical feature, many cases of CMT with joint or even exclusive axonal presentations (with vocal cord paresis as the defining symptom) have been recorded (Ratajewski and Pulaski, 2009). The mechanism for these differences could be due to varying levels of gene expression which cause changes in transcriptional regulation due to either alteration in transcription factors or by polymorphisms in the non-coding regulatory regions of GDAP1.

Some transcription factors have been linked with CMT disease, such as SOX10 and EGR2 (Berger et al., 2002, Kamholz et al., 2000, Tanaka and Hirokawa, 2002, Jones et al., 2007). EGR2 is a zinc finger transcription factor that plays a major role in the “early growth response” of PNS development (Mirsky and Jessen, 1999). EGR2 and SOX10 have both been shown to be transcriptional regulators of myelin genes (Tanaka and Hirokawa, 2002, Jones et al., 2007). Mutations in the DNA-binding domain of EGR2 lead to a dominant form of CMT and these mutated transcription factors act negatively on myelin gene expression (Nagarajan et al., 2001). Analyses of knockout mice have shown that the EGR2/Krox20 and SOX10 transcription factors are required for Mpz expression (Jones et al., 2007). Jones et al. (2007) also confirmed that the dominant EGR2 mutations associated with human peripheral neuropathies disrupts EGR2/SOX10 synergy at specific sites. In addition, both EGR1 and EGR3 cooperate with SOX10 to activate this element, which indicates that this capacity is conserved among EGR family members (Jones et al., 2007). This chapter will investigate and compare transcription regulation for human GDAP1 and mouse GDAP1, to better understand the differences between species in the normal regulation of this gene.

Single nucleotide polymorphisms (SNPs) are single base pair variations, naturally appearing in the population (Roses, 2000). In the human genome, SNPs are the most abundant type of

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DNA sequence variation and they are found at a frequency of approximately 1 in 100bp (Marth et al., 1999, Brookes, 1999). There is a suggestion that most human phenotypes, including illnesses, are influenced by the underlying genetic variation of an individual (Buckland, 2006). It is likely that the majority of SNPs that impact on health are found outside the amino acid coding regions of genes; these regulatory SNPs (rSNPs) would be expected to affect the regulation of gene expression (Buckland, 2006). Studies suggest that about 50% of genes have one or more common rSNPs linked with them (Buckland, 2006). The role of gene promoters in disease is developing with the characterisation of regulatory polymorphisms (Stepanova et al., 2006). rSNPs in the promoter region of a gene can have profound effects on protein expression by impacting on the timing and level of protein expression (Buckland, 2006). SNPs in a regulatory region could cause complete exclusion of a natural transcription factor binding site (TFBS) or quantitative alterations in transcription factor (TF) binding efficacy (Ponomarenko et al., 2002).

The HapMap project has identified many common SNPs within the GDAP1 gene (The International HapMap, 2005), including several SNPs in the GDAP1 gene upstream of the start codon. (SNPs found in publically available databases will be described further in section 4.3.2). One region of variation has been identified as a poly-A region (Shield personal communication). The variable poly-A region in position -242 has been difficult to describe due to the degree and frequency of variation. This is mainly due to difficulties in obtaining high quality sequencing from this region, because of its length and heterozygosity in many samples. Subcloning methods would make the Poly-A region easier to sequence and provide pure sequence for reporter expression analysis. Poly-A regions have a role in gene regulation through alterations in accessibility of DNA and transcription initiation (Buckland, 2006). It is possible that SNPS in the GDAP1 promoter could cause changes in the expression of GDAP1; and this might be one of the reasons for the variations in the phenotypes found in CMT patients.

4.2 Methodology

4.2.1 Bioinformatic analysis of the GDAP1 5’ flanking region 4.2.1.1 Theoretical promoter analysis

Transcription factor binding sites (TFBS) were predicted by the MatInspector (Genomatix) software using 2000bp human and 2000bp 5’ flanking region (5’FR) mouse sequences. The specific sequences that were used are given in the section 2.1.4. As inclusion criteria, core

112 sequence similarity was chosen at 0.75 and matrix consensus sequence for a transcription factor family similarity was above 0.8. The "core sequence" of a matrix is defined as the most highly conserved nucleotide position of the matrix (Genomatix, 2014). The maximum core similarity of 1.0 is only reached when the highest conserved bases of a matrix match exactly in the query sequence. Decreasing the core similarity (while retaining the same matrix similarity) might give a few more matches in the output, but would have more mismatches in the core region of the matrix, decreasing the integrity of the putative TFBS. For the matrix, a "good" match to the matrix usually has a similarity of > 0.80. Mismatches in highly conserved positions such as the core sequence of the matrix decrease the matrix similarity more than mismatches in less conserved regions.

4.2.1.2 Algnment of mouse and human 5’FR sequences

To determine the degree of similarity between human and mouse GDAP1 5’FR, both sequences were aligned using the NCBI BLAST database. The same sequences given in section 2.1.4 were used in this alignment. BLAST is an algorithm comparing nucleotides of DNA sequences (Bergman, 2007, Mount, 2007). It enables researchers, to compare a query sequence with sequence library, and to compare two sequences together (Bergman, 2007).

4.2.1.3 Results from the Gene Expression Omnibus

Initially, the NCBI GEO database were searched for studies investigating transcription factors that have previously been linked to CMT disease including SOX, EGR, YY1, RXR and RAR. After analysing the results from the Genomatix TFBS search, specific terms were selected to investigate studies investigating families of transcription factors that were found to have multiple binding sites in the GDAP1 5’FR. Any TFBS matrix that was repeated more than 10 times in the human GDAP1 promoter was selected as a search term, for example Homeobox transcription factors.

4.2.2 Molecular biology methods

Molecular biology methods were used to identify polymorphisms in the promoter region of GDAP1 and investigate the length of the Poly-A region. The subcloning process used in this aim started with DNA samples from donors being amplified using the forward primer F-888, reverse primer R33 and Phusion Hot Start II High-Fidelity DNA polymerase. Bands of an appropriate size were excused from an agarose gel and the column purified before A-Tailing. The 1 kb fragment was then ligated into the pGEM-T Easy vector and used to transform JM109

113 competent cells. Colonies were picked from the transformation plates and used for plasmid purification. To check if the transformation was successful, restriction digest analysis using EcoRI enzyme was undertaken. Samples with an appropriately sized insert were sequenced and the resulting sequences aligned using Sequencher 5.1. This alignment was then used to identify SNPs and to assess the confidence of base assignment for any potential SNP discovered. The identified SNPs were compared to SNPs in the HapMap and dbSNPs. Further details of this process are given in section 2.2.

4.3 Results

4.3.1 Transcriptional regulation of the GDAP1 promoter 4.3.1.1 Transcription factor binding prediction

Seven hundred and five binding sites for transcription factors were predicted in the 5'FR for the human GDAP1 gene; these transcription factor binding sites are summarised in Tables A4.1, A4.2 in Appendix four. These tables were arranged as per the TF families that were most often repeated. The binding sites belonged to 138 families of transcription factors and were found to be present between 1 and 32 times in the 2000bp, analysed with an average frequency of 5.13. Figure 4.1 shows histogram for frequency of the transcription factor binding sites that were found in the human GDAP1 5’FR. The figure shows how many times the different transcription factor families were found. For example 55 transcription factor binding sites were found twice. Any transcription factor binding sites that repeated more than 10 times have been shown in Table 4.1. This number was chosen because it is approximately twice the average frequency found for the human transcription factors.

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6 0 y

c 4 0

q e

r 2 0 F

0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 1 1 1 1 1 2 2 2 2 2 3 3 N u m b e r o f tim e s T F B S o b s e rv e d

Figure 4.1: Histogram showing the frequency of transcription factor binding sites in the human GDAP1 5’FR. The vertical axes shows the number of times each transcription factor binding site was observed and the horizontal axes shows the frequency of repletion.

For the mouse GDAP1 promoter, 584 transcription factor binding sites were predicted. These belonged to 130 families of transcription factors; all transcription factors are summarised in Table six, in Appendix four. These transcription factor binding sites were found between 1 and 19 times, with an average of 4.4. Figure 4.2 shows histogram of the transcription factor binding sites found in the mouse GDAP1. The graph shows how many times the different transcription factor families were found, so 59 transcription factors binding sites were found twice.

A comparison between the frequency distributions of transcription factor binding sites in human and mouse GDAP1 5’FR is shown in Figure 4.3. The average number of sites found in the human and the mouse GDAP1 5’FR were different (5.13 compared to 4.4). This difference is partly related to the fork head domain factor. Binding sites were repeated 32 times in the human GDAP1, compared to14 times in the mouse.

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8 0

6 0

n e

u 4 0

r F 2 0

0 2 4 6 8 0 2 4 6 8 0 1 1 1 1 1 2

N u m b e r o f tim e s T F B S o b s e rv e d

Figure 4.2: Histogram showing the frequency of transcription factor binding sites in the mouse GDAP1 5’FR. The vertical axes shows the number of times each transcription factor binding site was observed

and the horizontal axes shows the frequency of repetition. s

r 4 0

o t

c 3 5

a f

3 0

n o

i 2 5

t p

i 2 0 r

c 1 5

s n

a 1 0

f 5

r 0

e n e s b a u m o m u M

u H N Figure 4.3: Frequency distribution between the mouse and the human transcription factor binding sites. The left box shows the frequency distribution of the transcription factors in the human GDAP1. The right box shows the frequency distribution of the transcription factors found in the mouse GDAP1. The line within the box indicates the average frequency.

In both the mouse and human 5’FR, a number of transcription factor binding sites were found to be present multiple times. Table 4.1 describes any transcription factor binding sites that repeated more than 10 times in the human or mouse 5’FR with the corresponding frequency found in the other species. This table was arranged as per the TF families, that were most often repeated.From the table, we can infer that, there was a difference in the frequency of many of

116 the transcription factor binding sites between the human and mouse GDAP1 gene; some transcription factors were found or repeated more often in human samples, for example the Fork head domain factors were found in the human GDAP1 gene 32 times, while they were found only 14 times in the mouse (see Table 4.1). Other transcription factors were found in the human but not in the mouse gene; for example ZF5 POZ domain zinc finger transcription factors. Likewise some transcription factors found in the mouse were not found in the human, for example the “Signal transducer and activator of transcription” (or STAT) family. These differences might mean that the pattern of expression of GDAP1 would diverge between the human and mouse.

Some transcription factors are more specific to the tissues that they express in, such as the brain, central nervous system, skeleton and spinal cord; for example the Brn POU domain factors and the Lim homeodomain factors (Table 4.1). This is consistent with the high level of expression observed in mouse and human CNS and PNS. Some transcription factors are expressed more broadly, so they have ubiquitous expression; for example Vertebrate TATA binding protein factors. This result supports the expression of GDAP1 in normal healthy tissues (section 3.3.1).

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Table 4.1: Summary of the most repeated transcription factor binding sites found in the human and mouse 5’FR. The first column shows the transcription factors families, the second column shows the repetition of TFBS for these families in human and mouse, and the last column shows the tissues that the transcription factors are typically expressed in.

Transcription factors families Repetition Tissues

Human Mouse

Fork head domain factors 32 14 Blood Cells, Breast, Central Nervous System, Digestive System, Ear, Endocrine System, Eye, Immune System, Islets of (for example FOXP1_ES.01, ILF1.01, FOXP1.02, Langerhans, Leukocytes, Liver, Lymphocytes, Muscle, Skeletal, HNF3.01, FREAC7.01, FOXJ1.01) Muscles, Nervous System, Ovary, Pancreas, Prostate, Spinal Cord, Testis, Thymus Gland, Thyroid Gland, Urogenital System.

Brn POU domain factors 22 19 Brain, Central Nervous System, Digestive System, Ear, Embryonic Structures, Endocrine System, Eye, Islets of (for example BRN2.03, TST1.01, BRN3.02, Langerhans, Nervous System, Neuroglia, Neurons, Pancreas. BRN2.03, BRN2.01)

Lim homeodomain factors 21 17 Brain, Cardiovascular System, Central Nervous System, Digestive System, Ear, Embryonic Structures, Endocrine (for example ISL2.01, ISL1.02, LMX1B.01, System, Eye, Heart, Islets of Langerhans, Kidney, Muscles, LMX1A.01, LHX1.01, LMX1B.01) Myocardium, Nervous System, Neurons, Pancreas, Pituitary Gland, Spinal Cord, Testis, Urogenital System.

Vertebrate TATA binding protein factors 21 7 ubiquitous

(for example VTATA.01, VTATA.02, LTATA.01)

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Abdominal-B type homeodomain transcription 20 10 Bone Marrow Cells, Bone, Connective Tissue, Embryonic factors Structures, Hematopoietic System, Integumentary System, Kidney, Prostate, Skeleton, Urogenital System. (for example HOXC13.01, HOXB9.01, HOXD10.01, HOXB9.01)

SOX/SRY-sex/testis determining and related HMG 19 19 Bone and Bones, Cartilage, Connective Tissue, Digestive box factors System, Ear, Embryonic Structures, Endocrine System, Islets of Langerhans, Nervous System, Neuroglia, Pancreas, Skeleton, (for example SOX5, SRY, SOX3, HMGIY, SOX9, Testis, Thyroid Gland, Urogenital System. HMGA)

Paralog hox genes 1-8 from the four hox clusters A, 19 14 Bone Marrow Cells, Bone and Bones, Connective Tissue, Ear, B, C, D Embryonic Structures,, Germ Cells, Hematopoietic System, Immune System, Lung, Respiratory System, Skeleton, Thymus (for example NANOG.01, HOXC8.01, HOXA3.02) Gland, Urogenital System.

NKX homeodomain factors 18 12 Bone and Bones, Cardiovascular System, Central Nervous System, Connective Tissue, Digestive System, Embryonic (for example NKX25.03, NKX25.05, NKX32.01, Structures, Endocrine System, Heart, Islets of Langerhans, NKX29.01) Lung, Muscles, Myocardium, Nervous System, Pancreas, Prostate, Respiratory System, Skeleton, Spinal Cord, Thyroid Gland, Urogenital System.

Octamer binding protein 18 18 Antibody-Producing Cells, Blood Cells, Immune System, Kidney, Leukocytes, Lymphocytes, Urogenital System. (for example POU3F3.01, OCT2.01, OCT1.05, OCT1.02, OCT1.03)

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Homeodomain transcription factors 17 15 Bone and Bones, Cardiovascular System, Cartilage, Central Nervous System, Connective Tissue, Digestive System, Ear, (for example HMX3.01, AREB6.04, NOBOX.01, Embryonic Structures, Endocrine System, Germ Cells, Heart, HOXC13.01) Hematopoietic System, Immune System, Islets of Langerhans, Leukocytes, Liver, Lymphocytes, Nervous System, Ovary, Pancreas, Skeleton, Spinal Cord, Thyroid Gland, Urogenital System.

Cart-1 (cartilage homeoprotein 1) 15 14 Adrenal Glands, Bone and Bones, Brain, Cardiovascular System, Central Nervous System, Connective Tissue, Digestive (for example CART1.01, ALX3.01, S8.01, System, Embryonic Structures, Endocrine System, Eye, Immune XVENT2.01) System, Islets of Langerhans, Myeloid Cells, Nervous System, Neurons, Pancreas, Phagocytes, Pituitary Gland, Skeleton, Spinal Cord.

Vertebrate caudal related homeodomain protein 14 9 Blastomeres, Digestive System, Embryonic Structures.

(for example CDX2.03, CDX1.01, CDX2.02)

AT rich interactive domain factors 14 4 Antibody-Producing Cells, Blood Cells, Cardiovascular System, Heart, Immune System, Leukocytes, Lymphocytes, Muscles, (for example MRF2.01, BRIGHT.01, JARID2.01, Myocardium. ARID5A., BRN5)

Homeobox transcription factors 14 8 Brain, Cardiovascular System, Central Nervous System, Digestive System, Ear, Embryonic Structures, Eye, Muscle, (for example GSH2.01, NKX61.01, GSH2.02, Smooth, Muscles, Nervous System, Neurons, Spinal Cord. NKX12.01)

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Human and murine ETS1 factors 12 - Antibody-Producing Cells, Blood Cells, Bone Marrow Cells, Breast, Central Nervous System, Endocrine System, Germ Cells, (for example PDEF.01, CETS1P54.01, SPI1.02, Hematopoietic System, Immune System, Leukocytes, PEA3.01) Lymphocytes, Monocytes, Myeloid Cells, Nervous System, Phagocytes, Prostate, Respiratory System, Spinal Cord, Testis, Urogenital System.

Signal transducer and activator of transcription - 12 Blood Cells, Bone Marrow Cells, Breast, Hematopoietic System, (Signal transducer and activator of transcription 5B, Immune System, Leukocytes, Lymphocytes, Myeloid Cells, STAT6, Signal transducer and activator of Phagocytes. transcription 1)

Human and murine ETS1 factors 12 12 Antibody-Producing Cells, Blood Cells, Bone Marrow Cells, Breast, Central Nervous System, Endocrine System, Germ Cells, (for example SPI1.01, ERG.02, SPDEF.01, Hematopoietic System, Immune System, Leukocytes, GABP.01) Lymphocytes, Monocytes, Myeloid Cells, Nervous System, Phagocytes, Prostate, Respiratory System, Spinal Cord, Testis, Urogenital System.

Brn-5 POU domain factors 11 6 Brain, Central Nervous System, Nervous System.

(for example BRN5.03, BRN5.01, BRN5.04)

GATA binding factors 10 10 Blood Cells, Bone Marrow Cells, Bone and Bones, Cardiovascular System, Connective Tissue, Embryonic (for example GATA3.02, GATA1.07, GATA1.02) Structures, Erythrocytes, Heart, Hematopoietic System, Immune System, Leukocytes, Lymphocytes, Muscles, Myocardium, Skeleton.

ZF5 POZ domain zinc finger 10 - Ubiquitously expressed with highest levels found in brain and ovary tissues and fibroblast cell lines. (for example ZF5.01, ZF5.02)

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EVI1-myleoid transforming protein 9 10 Adipose Tissue, Blood Cells, Bone Marrow Cells, Brain, Central Nervous System, Connective Tissue, Embryonic Structures, (for example MEL1.01, MEL1.03, EVI1.06, Hematopoietic System, Immune System, Leukocytes, EVI1.05) Lymphocytes, Nervous System, Neurons, Thymus Gland

C2H2 zinc finger transcription factors 2 - 10 Blood Cells, Bone Marrow Cells, Endocrine System, Germ Cells, Hematopoietic System, Immune System, Leukocytes, (for example ZNF202.01, ZBTB7.03, ZBP89.01, Lymphocytes, Testis, Thymus Gland, Urogenital System. ZNF219.01, ZBP89.01)

Motif composed of binding sites for pluripotency or 1 10 Embryonic Structures, Germ Cells. stem cell factors

( for example OSNT.01, OCT3_4.02)

Nuclear factor of activated T-cells 6 10 Blood Cells, Cardiovascular System, Heart, Immune System, Leukocytes, Lymphocytes, Myeloid Cells. (for example NFAT.01, NFAT5.01)

Pleomorphic adenoma gene 1 10 Brain, Central Nervous System, Digestive System, Embryonic Structures, Endocrine System, Nervous System, Neurons. ( for example PLAG1.01, PLAG1.02)

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4.3.1.2 The alignment between human and mouse GDAP1 gene

From the alignment of the human and mouse GDAP1 sequences using BLAST, eight regions of 93-100 % similarity between human and mouse GDAP1 were found. Five of these were matching in opposite directions and were matching only 12bp from the opposite end of our sequence ( the entire alignments are in Appendix A.4.4). We found one region of around 300 bp with 66% similarity between human and mouse GDAP1 and another 20bp region, 95 % similar approximately100bp downstream. This large region of homology was approximately 250pb upstream of the human ATG (Figure 4.4). Then we looked to see which transcription factors had binding sites with these aligned pieces. For the 230bp shown in Figure 4.4; 26 transcription factor binding sites were found in human GDAP1 and 46 transcription factor binding sites were found in the mouse GDAP1.

Figure 4.4: Blast alignment of the human and mouse GDAP1 5’FR. The yellow highlighted nucleotides are the transcription factor binding sites found in this region.

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4.3.1.3 The expression of GDAP1 using the NCBI data base (GEO profiles) 4.3.1.3.1 Transcription factors previously linked to CMT disease or the GDAP1 gene

To look at the effect of transcription factors on the expression of GDAP1, some search terms were chosen to look for data related to transcription factors that previously linked with either CMT disease or the GDAP1 gene. These were: SOX, EGR, YY1, RXR and RAR. Two GEO profiles were found for each of SOX and EGR. For RAR, three GEO profiles were found, and for YY1 one GEO profile was found. No GEO profiles were found for RXR. The results of these searches are discussed below. Table 4.2 defines these transcription factor matrices.

Table 4.2: Summary of the transcription factors matrices that were searched for in the NCBI GEO profiles. The first column shows the families of transcription factors, the second column shows the abbreviation of the transcription factor’s family, the third column shows some of the family members found in the 5’FR of GDAP1 in Genomatix, the fourth column defines the role of the transcription factor family and the last column shows the references. SOX, EGR, PLAG and YY are zinc fingers.

Transcription Abbreviatio Family Definition Reference factor family n members

“SRY (sex SOX SOX5, Formation of tissues and Medicine determining SOX7, organs during early (2014c) region Y)-box” SOX10, development; maintenance SOX17 of normal function in certain cells.

Early growth EGR EGR2 Formation and maintenance Medicine response of myelin, the protective (2014a) substance that covers nerve cells. Myelin is essential for the efficient transmission of nerve impulses

Pleomorphic PLAG PLAG1 Zinc finger is activated by Kas et al. adenoma the chromosomal (1997) translocations a subset of salivary gland pleomorphic adenomas

GLI-Kruppel YY YY1 Repression and activation of Ratajewsk class of zinc a diverse number of i and finger proteins YY2 promoters. May direct Pulaski histone deacetylases and (2009) histone acetyltransferases to unkown a promoter (2014)

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Retinoic acid RAR RAR Nuclear receptor activated Allenby et receptor both; all-Trans retinoic acid al. (1993) RXR and 9-cis retinoic acid. Important for the differentiation of immature white blood cells beyond a particular stage called the promyelocyte

Homeobox GSH2 Formation of many body Medicine NKX61 structures during early (2014b) embryonic development. NKX12

Figure 4.5: The human GDAP1 sequence and location of SOX, EGR and YY1 TFBS. The GDAP1 start codon is highlighted in green. SOX family TFBS are highlighted in yellow, EGR family TFBS highlighted in blue and YY1 family TFBS are highlighted in red. atccaagctt gcaggatgta tggagaaaag tgttgtgttt gagggaccag ggctttattg tcaggacttt ttcagctgca ggtgacacat ggcttatcac gagtactgga atttattcaa atatttagga tgatttagta ttaaggttgg tgcaaaagta agcgtggtgt ttgctattga aggtaatgga atagaaaact aggaatctga aggtggataa ttaaaaaatg aagttctgaa gaggaatggg agaggaggat tatatgggta gttgtaaaaa ttttgagacc tacagagaga ggatgaaatg taatataatg ccctcctaat gtcagtgaag gggcttttac acatatgttt ttatcaatgg ctacattcca ggcaataccc taggtgcttt atatatatcc tttcttgaat tcccacagcc atctgggtag gcatataatt tttatgtatc cattttttac acacaatgaa gtcaaagctc aagaaagctc aagttagggt agagctagaa tttaaacccc actctgtccg atttcaaagt tttcctcttt ccgctcttac tcttaagtgt gaactgataa aaacaatttt ggcaaggaat tcaccttttc tatagcactt gaattaactg aaggatataa aagacattat gaaatcttaa aaaattatga aagaattaca gagagtaaca aatgaagtac tgtaaaggca aggaagaacc tcatgagatt ttgtcttttc caggggccct tctctgacct gggttggctg cctgcccctc ctttctcata gtactctgtg ttcaccctaa gacagttctc atctttattg tgattgtcta tttccttggt cgtctctact gccagattat atcttgagtg ttagatatta gatttcacac atctttatat taccaggagc cagcacaatg tctggaacag tgtaggtgct caataaatat ttgccagata aattatacag gcatttattc tatggtcata tttatacata ttactgaaag agatttttaa aatcaactta ctatatgatg ccaaattttt aaagacatac aataataact agactaatat aaaattatga ttctgactat ggttataaca gaatgtttat aagacaactg aacctgtgac taagactgag tgcctacaac gtgacagggt gttctgagtg tttaacattt tattattaat ttttcacaaa aatcttatga gggaaacaat aaaattatta ttcttactta gaaaatggaa aaaccaggat atgaacttag gcagtactgt tgtagaattt ttgtgtttaa caactaggct attttatatc gccattgttt attagctgtt tatgacaaaa acagtttttt tgcatgactc catagtggta tttttttttc ttttacaaag cacagtttaa ttatcaaaat caagtactta ctgtttatac aagactattc tctaatctac agcagttagt caaatttgtg gaattagtcc actaatgtca tagtagcatc ttctaatgaa

125 agctcataga tccttttcct gttgctttgc ttgtcgaccg gaacttcctt tccaacaaag gaacaggctc caagagcaac cctcagtatc ttgggaaatt gctgctttta tacctgagca acctccaaac cgaagagtaa tttgctatca tcattcttcc ttactgccct tcataaccag ggtctcatat tttttatttt ttcttctaaa aaaaaaaaca aacccaaaaa acaaccgttc aattgcacct cccaggtgca ctcccaggct tgccaggggc tttccagtcg cagaccccgc gtgttcgcag actctgccgc cggcgaaact acatttccca gcgggccgcg cgccctcctt ccggcaggta cccctcaaaa cccggaaacg ccttgcgggg cagtgtggga gggagaagtc cagggcggac aggctgggcg cacccgtgct cgcgcacccc aagatggctga gaggcaggaa gagcagagag ggagcccatg gat

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Figure 4.6: The mouse GDAP1 sequence and location of SOX, EGR and YY1 TFBS. The GDAP1 start codon is highlighted in green. SOX family TFBS are highlighted in yellow and YY1 family TFS are highlighted in red. gcttttcttt attgtttcta cttctatttt tagatcttgg atgatttttt ttcaattcct tcacctgttt tgttgtgttt tcctgtaatt cttttgggga tttttgtgtt tcctctttaa ggacttttac atgtttagca gtgtatttct tttttttttt tccattttta ttaggtattt agctcattta catttccaat gctataccaa aagtccccca tacccaccca cccccactcc cctacccccc ccactccccc tttttggccc tggcgttccc ctgttctggg gcatataaag tttgtgtgtc caatgggcct ctctttccag tgatggccga ctaggccatc ttttgataca tatgcagcta gagtcaagag ctctggggta ctggttagtt cataatgttg ttccacctat agggttgcag atccctttag ctccttgggt actttctcta gctcctccat tgggagccct gtgatccatc cattagctga ctgtgagcat ccacttctgt gtttgctagg ccccggcata gtctcacaag agacagctac atctgggtcc tttcgataaa atcttgctag tgtatgcaat ggtatgccca tggtatttct ttaagtgagt tattaatgtc cttcttaaaa tcctctacca gaatcatgag atatgatttt acatccgaat cttgctgttt ttggtgtgta gggctatcca agacttgctg gatgaaggca cgaaggtacc ttgtccaaga aggtctgttg cttctgtggc ctgtgtgctc tcctgcatgg acctccctca gatggacccc agataaaaaa tgtcgatcac acctgaattc caagggctgg gcccttgctg taggcaagcc cttcttttgt ggggaaggta cacagaggac tgagggtcag ctcttcctac tggctgagga tgaaggccca aaatgatcct gtccaagaag ctctgttgct tctgaggccc gtgatctcct gtgcggcccc cttctgagag accccctgaa tagaaaatca agataaaaat tcttaagaag agtgctctct agttcgcatt tgcatttgcc tgacttccac atactggtca tagaacccct ttgttataga gcagctttgt catggcacta atccttggca accctgtcca cagtgagtct ttggtcagat catgtcttgg tcacatgatg ccgatgtcag tgtgaatgaa tgtcttcctc tttttcctta tatgtatatt atttccaaat cctacctgat tagtctttat gtgcatctgc actccagaac gggacgtcag acaatctgag ctgccatgtg gttgctggaa atttaactta ggacctttgg aagagctcct aaccaccaac tcatctctcc agacccataa agacactatt tttaaggcct tgtgagtagt attgccattt cgtttactgt cttatttatc tactcagttc tcacaaaaag cttacagatt aaataaaata atctctattt caaaaatgaa aacagcagga catgatctca ggtatttctg atatgaacat tcacacttaa tcaataggca cttttacatt accattgttg atttatttgt aaaatacata ctttgtattc cattgtaata tattctaatg aaaattaatt gatacttaat tttatttctt tgctctctag aactctccca cctaagtaaa aaccctgaat gtcttgtgaa actggtgctt ttatgcccaa gcaacgtcca aggcccagga gtattatttg ctgttatcat ttttccttgc cttaaaagcc agacagtcat attttagaaa atttctctac ctaaaagtca tcccattaca tctcacagat gatctcttaa gggcctcagc cgactaggca gcacagcatc cttggggaga aactacattt cccagaagcc agttcgaccg tagaggaagt cctttacgca acagggggcc ttgagaaacc aaggatgctt gctgcagcaa gtgtctgcag agagcaggcc aacccataaa gctgcctgag tcctttcatg cgaaatggct

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A- SOX and GDAP1 In our Genomatics search, SOX TFBS were found 19 times in each of the human and mouse GDAP1 5’FR (see Table 4.1). Tables 4.3 and 4.4 describe the location of the SOX TFBS in human and mouse GDAP1 5’FR respectively. In the locations of the SOX TFBS, only (+) strands are shown in Figures 4.5 and 4.6 (only (+) strands are shown in the figures, since the (–) strands were difficult to identify.

Table 4.3: Summary of the SOX transcription factor binding sites found in the human GDAP1 gene. The first column shows the start of the core sequence position, the second column shows the strand of the DNA that the transcription factor binds in, the third column shows the matrix that was found in this family, the forth column shows the matrix similarity and the fifth column shows the proposed binding sequence with the core sequence in the capital sequence. (+) means in the positive DNA strand, (-) means in the opposite DNA strand.

The SOX Strand The SOX Matrix The SOX sequence position indivedual similarity transcription factors

94 - SOX15 0.86 tcctgACAAtaaagccctggtccct

152 - HBP1 0.75 atatttgAATAaattccagtactcg

400 + SOX3 0.97 ttccaaCAAAggaacaggctccaag

580 + SOX6 0.99 cttttACAAagcacagtttaattat

759 + SOX5 0.99 gggaaaCAATaaaattattattctt

793 + HMGA 0.89 attttattattAATTtttcacaaaa

874 - SOX15 0.89 caatcACAAtaaagatgagaactgt

926 + SOX5 0.89 gacataCAATaataactagactaat

1030 + HMGA 0.90 tttgccagataAATTatacaggcat

1045 + HMGA 0.89 ggtgctcaataAATAtttgccagat

1070 + SOX9 0.96 ccagcACAAtgtctggaacagtgta

1144 - SRY 0.88 tcagaATCAtaattttatattagtc

1294 - SRY 0.77 taagaATAAtaattttattgtttcc

1307 + SOX3 0.95 gagtaaCAAAtgaagtactgtaaag

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1391 - SOX5 0.99 aataaaCAATggcgatataaaatag

1412 + SOX5 0.98 taaaaaCAATtttggcaaggaattc

1641 + SOX9 0.90 ttttatCAATggctacattccaggc

1647 + HMGA atgtttttatcAATGgctacattcc (HMGA1, HMGA2) 0.88

1741 - HBP 0.86 taaggaagAATGatgatagcaaatt

Table 4.4: Summary of the SOX transcription factor binding sites found in the mouse GDAP1 gene. The first column shows the start of core sequence position, the second column shows the strand of the DNA that the transcription factor binding in, the third column shows the matrix found in this family, the forth column shows the matrix similarity and the fifth column shows the proposed binding sequence with the core sequence in capitals sequence. (+) means in the positive DNA strand, (-) means in the opposite DNA strand.

The SOX Strand The SOX Matrix The SOX sequence position indivedual similarity transcriptio n factors 305 + SOX9 0.74 tgtgtCCAAtgggcctctctttcca

624 + HMGA 0.89 agtgagttattAATGtccttcttaa

871 - SOX3 0.97 tccccaCAAAagaagggcttgccta

1105 - SOX2 0.99 ctataACAAaggggttctatgacca

1121 - SOX6 0.97 ccatgACAAagctgctctataacaa

1214 + HMGA 0.88 atgtcagtgtgAATGaatgtcttcc

1218 + HBP1 0.98 cagtgtgAATGaatgtcttcctctt

1221 + HBP1 0.89 tgtgaatgAATGtcttcctcttttt

1344 + HMGIY 0.93 gctggaAATTtaacttaggaccttt

1568 + HMGA 0.88 cattcacacttAATCaataggcact

1589 - SOX3 0.99 aatcaACAAtggtaatgtaaaagtg

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1617 - SOX6 0.97 ggaatACAAagtatgtattttacaa

1627 - SOX9 0.95 atattACAAtggaatacaaagtatg

1637 + SRY 0.88 attccATTGtaatatattctaatga

1653 - HBP1 0.84 aagtatcAATTaattttcattagaa

1656 + HMGA 0.89 taatgaaaattAATTgatacttaat

1657 - HMGA 0.89 aattaagtatcAATTaattttcatt

1825 - HMGIY 0.82 tagagaAATTttctaaaatatgact

1834 + HMGIY 0.90 ttagaaAATTtctctacctaaaagt

Two GEO profiles were found for investigating the effect of SOX family members on GDAP1 expression; one investigated human embryonic cell lines and the other used mouse cell lines. The mouse study analysed RT4D6 Schwann cell tumor, cells depleted for SOX10 using siRNA (Lee et al., 2008). Although not significant, the expression of GDAP1 was lower in the SOX10 depleted cells than the control (p=0.26; Figure 4.7). This implies that depleting SOX10 causes a decrease in the expression of GDAP1. A specific binding site for SOX10 could not be

identified in our 2000bp mouse 5’flanking region. )

9 4 0

(

P A

D 3 0

o 2 0

r e

p 1 0

e h

T 0

C o n t r o l s o x 1 0 d e p le t io n

Figure 4.7: The expression of GDAP1 in the depletion of SOX10 in Schwann cell line. Columns show the average expression levels and standard deviation between the values of both the control and depleted cells. First column in black shows the control (n=2), left column in grey shows the cells depleted of SOX10 (n=3). Data is from GDS3480. (19) indicates Signal (see Table 2.2).

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The second study was an analysis of embryonic human stem cell (ESC) lines over-expressing the transcription factor SOX7 or SOX17 (Seguin et al., 2008). The expression of GDAP1 was significantly lower in the cells over expressing SOX17, compared with the control and the rest of the samples (ANOVA p =0.046; Figure 4.8). The expression of GDAP1 was lower in the cells overexpressing SOX7 than the SOX7 control, however this was not significant (p =0.08).

Over expressing SOX7 and SOX17 could cause decrease in the expression of GDAP1. )

9 2 0 0 0

(

P A

D 1 5 0 0

o 1 0 0 0 i

s *

r e

p 5 0 0

e h

T 0

c o n t r o ls o x 7 c o n t r o ls o x 1 7 S O X 7 O E S O X 1 7 O E

Figure 4.8: The expression of GDAP1 in the overexpression of SOX7 and SOX17 in embryonic stem cell lines. Columns show the average expression levels and standard deviation between the values of the control and the overexpressed cells. The black column shows the control of SOX 7(n=2), the grey column shows the control of SOX17 (n=2), the red column shows SOX7 over expression (n=2), and the blue column shows over expression of SOX17 (n=2). (19) indicates Signal (see Table 2.2 Chapter2). Data is from GDS3300.These two studies suggest that SOX transcription factors are important for the expression of GDAP1 although specific binding sites for SOX7, SOX10 and SOX17 were not found in our 5’flanking regions. Interestingly, depleting SOX10 in mouse cells and overexpressing both SOX7 and SOX17 in human cells, lead to a decrease in the expression of GDAP1.

B- EGR and GDAP1

When looking for EGR TFBS using Genomatix, this TFBS was found six times in mouse GDAP1 and only twice in human GDAP1 (see Tables 4.5 and 4.6 and Figures 4.5 and 4.6). In Figure 4.5, only one EGR transcription factor could be marked (marked in blue) in the human GDAP1 sequence. In Figure 4.6, in the mouse GDAP1 sequence, we could not mark any of the EGR transcription factors in the plus strand, Figure 4.5 and 4.6 only show the TFBS that are on the plus DNA strand.

Table 4.5: Summary of the EGR transcription factors found in the human GDAP1 gene. The first column shows the EGR position and the second column shows the strand of the DNA that the transcription factor binds in, the third column shows the matrix found in this family, the forth column

131 shows the matrix similarity and the fifth column shows the EGR sequence. (+) means in the positive DNA strand, (-) means in the opposite DNA strand.

The EGR Strand The EGR Matrix The EGR sequence position Matrix similarity 49 + WT1 0.93 ggcagtgTGGGagggag

47 + CKROX 0.92 cagtgtGGGAgggagaa

Table 4.6: Summary of the EGR transcription factors found in the mouse GDAP1 gene The first column shows the EGR position and the second column shows the strand of the DNA that the transcription factor binds in, the third column shows the matrix found in this family, the forth column shows the matrix similarity and the fifth column shows the EGR sequence. (+) means in the positive DNA strand, (-) means in the opposite DNA strand.

The EGR Strand The EGR Matrix The EGR sequence position Matrix similarity

221 - WT1 0.948 gtgggggTGGGtgggta

225 - EGR1 0.884 gggagtgGGGGtgggtg

227 - WT1 0.982 aggggagTGGGggtggg

242 - EGR1 0.879 gggagtgGGGGgggtag

244 - WT1 0.984 gggggagTGGGgggggt

1380 - NGFIC 0.835 agatGAGTtggtggtta

One GEO profile was found for EGR. This study looked at the expression of GDAP1 in mouse retinas on post-natal days, 30 and 42, for EGR1 deficient animals (Schippert et al., 2009). EGR plays important roles in many aspects of vertebrate development, including early retinal

132 development, especially for the amacrine cells (Close et al., 2002, Hu et al., 2004). Down regulation of EGR1 mRNA in total retinal tissue suggested that EGR1 is an important factor in controlling eye growth (Schippert et al., 2009). The expression of GDAP1 was not significantly different in the EGR1 null mice compared with the control (p value = 0.2) (Figure 4.9).

) 5 0 0

(

1 P

A 4 0 0

o 3 0 0

o i

s 2 0 0

r p

x 1 0 0

h t 0 w ild t y p e 3 0 d EG R - 1 n u ll3 0 d w ild t y p e 4 2 dEG R - 1 n u ll 4 2 d

Figure 4.9: The expression of GDAP1 in the retinas of Egr-1 deficient animals on post-natal days, 30 and 42.Columns show the average expression levels and standard deviation between the values of the control and the depleted cells. The black column shows wild type in 30 days (n=4), the grey column shows EGR1-1 null in 30 days (n=4), the red column shows the wild type in 42 days (n=4) and the blue column shows the EGR1-null in 42 days (n=4). (9) indicates GCRMA-calculated Signal intensity (see Table 2.2 Chapter2). Data is from GDS3607.

C-RAR and GDAP1

The RAR transcription factors have previously been shown to upregulate GDAP1 in the P19 cell line (Liu et al., 1999). From the promoter analysis we found one RAR binding site in the human GDAP15’FR and three times in the mouse GDAP15’FR. In the GEO profile database search, one study was found that was looking at the effect of trans retinoic acid on the retinoic

133 acid receptor α-deficient F9 teratocarcinoma cells (Laursen et al., 2012). The change of

expression in GDAP1 was not significant in this study (Figure 4.10).

) 9

( 0 .1 0

1 P

A 0 .0 5

o 0 .0 0

o i

s -0 .0 5

e r

p -0 .1 0

h -0 .1 5 T e t p u y o t k e c d o il n k W R A R

Figure 4.10: The expression of GDAP1 in RAR deficient F9 teratocarcinoma cells. Columns show the average expression levels and standard deviation between the values of the control and the RAR knockdown cells. The black column shows the control, the white column shows the RAR knockdown (n=6). Data is from GDS4294. (9) indicates GC-RMA (see Table 2.2).

D-YY1 and GDAP1

YY1 transcription factors were chosen in this search, because YY1 has been shown to influence the expression of GDAP1 (Ratajewski and Pulaski, 2009). From our Genomatix results, we found YY1 TFBS repeated six times in the human GDAP1 5’FR, and nine times in the mouse GDAP1 5’FR ( see Tables A4.1 and A4.2 in Appendix four), the location of YY1 TFBS plus strand are shown in human and mouse GDAP1 5’FR in Figures 4.5 and 4.6.

To study the effect of transcription factors YY1 and YY2, Chen et al. (2010) analysed HeLa cells using YY1 and YY2 knockdown. Compared to the control, the expression of GDAP1 was significantly lower in the YY1 knockdown HeLa cells (p=0.0004). The expression of GDAP1 was also significantly lower (p=0.03) in the combined YY1 and YY2 knockdown compared to the control. As the expression of GDAP1 was not significant in the YY2 knockdown cells, it is likely that the decreased expression in the combined YY1 and YY2 knockdown is because of YY1 knockdown (Figure 4.11). These results are consistent with Ratajewski and Pulaski (2009), where YY1 knockdown decreased the expression of GDAP1 in HEK293 cells.

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) 8

( 2 5 0

1 P

A 2 0 0

o 1 5 0

* *

o i

s 1 0 0

r p

x 5 0

h 0 T l n n n o r w w w t o o o n d d d o k k k C c c c o o o n n n k k k 1 2 2 Y Y Y Y Y -Y 1 Y Y d in b m o c

Figure 4.11: The expression of GDAP1 in HeLa cells after YY knockdown. Columns show the average expression levels and standard deviation between the values of the control and the YY1 knockdown cells. The black column shows the control, the white column shows the YY knockdown, the red column shows the YY2 knockdown, and the blue column shows the combined YY1, YY2 knockdown (n=3). Data is from GDS3788. (8) indicates GCOS MAS5 signal (see Table 2.2).

4.3.1.3.2 Over represented transcription factors

Based on the Genomatix results (section 4.3.1.1), any transcription factors that repeated more than 10 times in both human or mouse GDAP1 (Table 4.1) were chosen as search terms in the NCBI GEO database to try to determine the relationship between these families of transcription factors and GDAP1 expression. Three GEO profiles were found as a result of these searches.

A- Homeobox transcription factors and GDAP1

Two studies were found using the search term ‘homeobox transcription factor’. The first study was analysing mouse embryonic heart cells (E12.5 hearts) deficient in both of the Iroquois homeodomain transcription factors, Irx3 and Irx5 (Gaborit N, et al 2012). The expression of GDAP1 is the same in all tissues (p= 0.7; Figure 4.12).

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) 9

2 8

(

P A

D 6

n 4

s e

r 2

h 0 T Em b r Ir x 5 + /- Em b r Ir x 5 - /- Ir x 3 - /- ;Ir x 5 + /- Ir x 3 - /- ;Ir x 5 - /-

Figure 4.12: The expression of GDAP1 in E12.5 mouse hearts deficient in Irx3 and Irx5. Columns show the average expression levels and standard deviation between the values of the control and variable mouse strains. The black column shows Embryo Irx5+/- (n=3), the white column shows Embryo Irx5-/-(n=3), the red column shows Embryo Irx3-/-; Irx5+ (n=4), and the blue column shows Embryo Irx3-/-; Irx5-/-(n=4). Data is from GDS4317. (28) indicates log2 RMA signal (see Table 2.2).

The second study was looking for the expression profiling of cortex and basal ganglia, for mouse embryonic telencephalon; from mice homozygous or heterozygous, for Dlx Homeobox transcription factors (GSE number GDS1084). The expression of GDAP1 was significantly higher in the basal ganglion for homozygous Dlx mice than the rest of the samples (p =0.002, Figure 4.13).

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) 6 0 0 0

3 *

(

A D

G 4 0 0

s s

e 2 0 0

e h

T 0

s s s s u u u o u o o o g g g y y g y z z y z o z r o o o e m r t e m e o t o h h e h h n n x o x e io i t l l te r g g r o n n o c a a c g g l l a a s s a a b b

Figure 4.13: The expression of GDAP1 in the cortex and basal ganglia from embryonic telencephalon of homozygous and heterozygous Dlx1/2 mutants. Columns show the average expression levels and standard deviation between the values of the basal ganglion and the cortex. The black column shows the basal ganglion heterozygous, the white column shows the basal ganglion homozygous, the red column shows the cortex heterozygous and the blue column shows the cortex homozygous (n=4). (30) indicates the signal value calculated by MAS 5 or GCOS software (see Table 2.2 Chapter2). Data is from GDS1084.

B- Pleomorphic adenoma and GDAP1

Van Dyck et al. (2007) looked at the effect of PLAGL2 deficiency in the mouse embryonic small intestine. There was no significantly different expression of GDAP1 between the PLAGL2 wild type and PLAGL2 knockout (p=0.37, Figure 4.14).

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) 1

3 5 0

(

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A 4 0

o 3 0

o i

s 2 0

e r

p 1 0

h 0 T w ild typ e P la g L 2

Figure4.14: The expression of GDAP1 in the intestine of PLAGL2 knockout mice. Columns show the average expression levels and standard deviation between the values of the wild type and the PLAGL2 knockout. The black column shows the intestine of PLAGL2 wild type mice, the white column shows the intestine of PLAGL2 knockout mice (n=4). Data is from GDS3010.31.

4.3.2 Impact of Polymorphisms in GDAP1 regulation

4.3.2.1 Sequencing results

These experiments aimed to investigate common polymorphisms in the 5’FR of human GDAP1 to try to assess whether common rSNPs can influence GDAP1 expression.

DNA from 12 Caucasian blood donors was amplified and sub cloned into pGEM-T Easy vector for sequencing. A subcloning approach was used because obtaining high quality sequence in the Poly-A region was difficult, due to the length of the region and the occurrences of heterogeneity in some samples. Table 4.7 summarises the samples that were sequenced including the number of alleles that were sequenced and the number of legible sequences. We determined, that analysing up to four sequences from the same sample would be sufficient in establishing heterozygousity, if two unique alleles were identified in smaller number of sequences. Then we would not complete further sequencing (unless there was antigenicity in the sequences obtained). We were unable to analyse some alleles, for reasons that are summarised in Table nine of Appendix four. Sequencher was used to determine the length of the poly-A region as compared to our reference sequence (see section 2.2.15). The nucleotides found at position -832 and -510 were also determined (see Table 4.7). The results for individual alleles sequenced are given in Table eight of Appendix four.

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Table 4.7: Summary of the samples that were sequenced. The first column shows the sample ID, the second column shows the number of alleles that were sequenced and the third column shows the number of samples that were analysed.

Sample ID Number of allele sequenced Number of legible sequences

2 4 4

3 8 3

5 2 2

6 4 2

9 7 5

10 7 4

12 2 2

15 3 2

16 4 2

20 4 2

The poly-A has previously been described but was difficult to characterize (Shield personal communication). Our study confirmed that the poly-A region has a variable length ranging between 11 and 15 base pairs (see Tables 4.8, 4.9). Out of the 22 unique alleles that were sequenced, two samples (number three and nine) showed more than two alleles. However, this could be due to contamination of these samples. The most common lengths for the poly-A were 12bp and 14bp which were found in 8 and 7 alleles respectively. Length of 11bp was found in two alleles, 13bp was found in three alleles and 15bp was found in two alleles (Table 4.9). Of the 10 samples analyzed, four were confirmed to be heterozygous for the poly-A region and two were confirmed to be homozygous. One sample (sample number 5) needs further analysis to confirm homozyosity. From our data (Table 4.9), we identified 11 haplotypes. Two haplotypes were seen more frequently than the others, the haplotype G-T -14 found in five individuals, and the haplotype A-T-12 found in four individuals. We also identified a novel

139

SNP in the position -398 where G changed to A in one allele of sample. This allele also had an A at position -832, a C at position -510 and 12bp poly A region.

Table 4.8: Summary of the Poly -A region found in this study. The first column shows the Poly-A region by length in base pairs, the second column shows how many times we observed a Poly-A region of this length, and the last column shows the samples in which we observed a Poly-A region of this length.

Poly-A region by length in base Times seen Sample ID pairs (Repetition)

11 4 2,3,10

12 8 3, 6, 9, 10, 15, 16 13 3 2, 9,10

14 10 2, 5, 9, 10, 15, 20 15 2 12

Table 4.9: The sequence results for SNPs in the GDAP1 5’FR. The first column shows the individual sample number, the second the nucleotide found at -832, the third the nucleotide found at -510, the fourth shows the length of the poly-A region at which the nucleotide was found and the last column shows the number of the times that this allele was observed in this sample.

Sample ID Nucleotide Nucleotide Poly-A region Number of at -832 at -510 times observed Reference A T 12

2 G T 13 2

G T 14 2

A T 11 1

3 G C 12 1

A T 11 1

A T 12 1

5 G T 14 2

6 A T 12 1

G T 12 1

140

9 G T 14 2

A T 13 1

A T 14 1

A T 12 1

10 A T 11 2

G T 12 1

G T 14 1

12 G T 15 1

A C 15 1

15 A C 12 1

G T 14 1

16 A C 12 1

A T 13 1

20 A T 14 1

G T 14 1

As well as characterising the poly-A variability, our results confirmed two 5’FR SNPs in a Caucasian population. From the HapMap and the NCBI dbSNP databases, four SNPs were found at positions -832, -510, -242 and -157. Table 4.10 summarizes the frequency of these SNPs in a variety of ethnic populations. In our population the SNP at -832 was found in 10 out of 22 alleles (G frequency of 0.45) and the SNP at -510 was found in 4 out of 22 alleles (C frequency of 0.18). Our SNP frequencies are comparable to published data (see Table 4.10). The SNP rs4541908 in position -157 was not found in our population, however it was found in the Sub-Saharan African ethnic group, so could not be expected to be found in a predominantly Caucasian population. From Table 4.10 we can see that the frequency of the SNPs, are vary even in individuals from the same ethnic group, for example the rs7841835 allele frequency was different between three different groups of European origin CEU 0.796, CAU 0.5 and TSI 0.778.

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In the summary we identified 11 unique alleles for the GDAP1 5’FR (see Tables 4.8 and 4.9). The most commonly observed alleles were G-T-14 and A-T-12. We also identified a novel SNP at position -398, in sample number 16, when allele G changed to allele A. The intention was to subclone each unique allele into the pXPG plasmid for functional analysis. For reasons that were unresolved, the cloning efficiency for these clones was very low and we were unable to obtain subclones in pXPG for many of the alleles.

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Table 4.10: Summary of the GDAP1 5’FR SNPs identified. The first column shows the positions of the SNPs; the second column shows the allele that the SNP was found in and the allele that it changed to, the third column shows the HapMap reference number, the fourth column shows the database that these SNPs were found in, the fifth column shows the allele frequencies for different populations and the last column shows if the SNP was confirmed in this study and the frequency of the SNPs found.

SNP WT/SNP HapMap Database Allele frequency for SNP Allele frequency in our positio reference population n African European Asian American

-832 A/G rs6992221 HapMap YRI 0.14 CEU 0.458 CHB + JPT MEX 0.56 0.45 0.33

CHD 0.208

dbNCBI AAM 0.13 CAU 0.417

-510 C/T rs7841835 HapMap ASW 0.255 CEU 0. 204 JPT 0. 238 0.18

YRI 0.142 TSI 0.222 CHB 0.268

LWK 0.122 CHD 0.241

MKK 0.175 GIH 0.227

dbNCBI CAU 0.5

-242 Deletion rs5892452 dbNCBI unknown unknown unknown variable region -AAA

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-157 T/G rs4541908 HapMap 0.025 - - Not seen dbNCBI

-398 G/A Not - - - - Found in one sample found

ASW: African ancestry in Southwest USA CEU: Utah residents with Northern and Western European ancestry from the CEPH collection CHB: Han Chinese in Beijing, China CHD: Chinese in Metropolitan Denver, Colorado GIH: Gujarati Indians in Houston, Texas JPT: Japanese in Tokyo, Japan LWK: Luhya in Webuye, Kenya MEX: Mexican ancestry in Los Angeles, California MKK: Maasai in Kinyawa, Kenya TSI: Tuscan in Italy YRI: Yoruban in Ibadan, Nigeria AAM: African American CAU: European Caucasian

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We analysed the region containing the SNPs, to determine whether there were any changes in potential transcription factor binding sites. Table 4.11 shows the transcription factor binding sites found to be underlying the SNP positions. Eight transcription factors were found in regions containing SNPs. One transcription factor was found in the poly-A region (-242). One transcription factor (BLIMP1) was found at position -510 with the T allele,. When the T allele was altered to C allele, two different transcription factor binding sites, belonging to different families were found in this position (see Table 4.11). In position -832 the A allele had one transcription factor binding site for NKX25, but when we used the G allele, three transcription factors were found. The NKX25 site is still seen with the addition of binding sites for XBP1 and E4F. These changes in putative transcription factor binding could cause changes in expression.

Table 4.11: Predicted transcription factor binding sites found lying under the GDAP1 5-FR SNPs. The first column shows the SNP position, the second column shows the allele that changed in the SNP, the third column shows the transcription factor binding site predicted at that position , the fourth column shows the transcription family, the fifth column shows the transcription factors position (the start and end positions relative to the A of the GDAP1 start codon), the sixth column shows the transcription factors sequence with SNPs highlighted, the seventh column shows the matrix similarity.

SNP Allele TFBS TFBS family TFBS TFBS sequence Matrix position in position similarit TFBS y

Poly-A FOXP1 Fork head Start 243 aaaaaaaAACAaacc 0.86 region domain ca factors End 253

-510 T BLIMP Positive Start 498 agttagtCAAAtttgtg 0.82 1 regulatory ga domain I End 516 binding factor

-510 C HNF1 Hepatic Start 507 0.846 Nuclear ctacagcaGTTAgcca Factor 1 End 528 a

-510 C SRF Serum Start 496 0.58 response ttagccaaaTTTGtgg element End 514 aat binding factor

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-832 A NKX25 NKX Start 830 0.98 homeodomai aagacTGAGtgcctac n factors End 848 aac

-832 G NKX25 NKX Start 830 aagacTGAGtgcctac 0.98 homeodomai gac n factors End 848

-832 G XBP1 cAMP- Start 839 0.89 responsive tgcctacgACGTgaca element End 859 gggtg binding proteins

-832 G E4F Ubiquitous Start 823 0.88 GLI - acgACGTgacag Krueppel like End zinc finger 8834 involved in cell cycle regulation

4.5 Discussion

The aim of this chapter, was to look at transcriptional regulation for human GDAP1 and mouse GDAP1 to identify any differences in the normal regulation of this gene, between the two species. This would help us to understand the differences in the expression of GDAP1 between the human and the mouse tissues, as observed in chapter three. For example, the expression of mouse GDAP1 in normal healthy tissues was higher in the nervous tissues, while it was low in other tissues. In comparison, expression of human GDAP1 in normal tissues was high not only in the nervous tissues but also in breast, testis and various epithelium. In order to better understand the regulation of GDAP1, we analysed the transcription factor binding sites in 2000bp of human and mouse 5FR. We then looked for evidence of studies, showing changes in GDAP1 expression, due to transcription regulation.

Genomatix analysis found that transcription factors in human and mouse GDAP1 have some similarities in some of the families of transcription factors, appearing at similar frequencies in both human and mouse 5’-flanking region (5’FR). For example SOX TFBS were found 19 times in both species. In an alignment of human and mouse GDAP1 (section 4.3.1.2), we found 300bp of the human and mouse GDAP1 gene that were 66% similar (Figure 4.5). In an alignment of the human, chimpanzee, dog, mouse and rat fragments of the GDAP1 promoter

146 common binding sequences, were found for the YY1transcription factor (Ratajewski and Pulaski, 2009). On the other hand, it is clear that there are some differences in the transcription binding between human GDAP1 and mouse GDAP1; this might contribute to the differences observed in tissue expression profiles between human and mouse (Figures 3.2 and 3.3). Firstly a difference in the frequency for the same families of transcription factor binding sites was observed; for example Fork head domain factor binding sites were twice as frequent in the human (32 times)genes compared with the mouse (14 times) genes. Secondly some transcription factor families had putative binding sites in either human or mouse GDAP1, which did not appear in the other species. For example the Insulinoma associated factors were found six times in human GDAP1 but did not occur in mouse GDAP1.

The observed differences in transcription factor binding sites could contribute to differences in expression between the human and mouse GDAP1. This could lead to different results being obtained in mouse and human experiments, therefore caution should be used when generalising between mouse and human data. Fougerousse et al. (2000) demonstrated significant differences in embryonic expression patterns between human and mouse in a range of genes. The Wnt7a, a very highly conserved gene found to be important for early development, shows significant differences in spatial and temporal expression in the developing brain of humans and mice.

One aim was to determine whether some transcription factors previously linked with CMT disease, such as SOX10 and EGR2 (Niemann et al., 2006), could have an impact on GDAP1 expression. From the GEO profiles, we found that the depletion of SOX10 in a mouse cell line resulted in decreased GDAP1. Overexpressing of SOX7 and SOX17 in human cells caused decreased expression of GDAP1. Both SOX7 and SOX17 are from the SOX transcription family, but have some differences in the binding sites and the diseases associated with them (compendium, 2013b, compendium, 2013a). SOX activates the CDH5 promoter, and hence plays a role in the regulation of gene expressed in the hemogenic endothelium; it blocks further differentiation into blood precursors. SOX7 are required for the survival of both hematopoietic and endothelial precursors during specification. SOX7 binds the DNA sequence 5'-AACAAT- 3' only (compendium, 2013a). Diseases associated with SOX7 include Barrett's adenocarcinoma, and prostate adenocarcinoma (Takash et al., 2001). SOX17 binds to the sequences 5'-AACAAT-'3 or 5'-AACAAAG-3' andplays a key role in the regulation of embryonic development (compendium, 2013b, Katoh, 2002). SOX17 are required for normal looping of the embryonic heart tube and for normal development of the definitive gut

147 endoderm. Diseases associated with SOX17 include extragonadal germ cell cancer, and vesicoureteral reflux (Katoh, 2002). From the Genomatix results, the SOX family was found to have 19 TFBS in both the human and the mouse (Figure 4.2). In both species the matrix similarities were between 0.75 to 0.95; however the specific SOX family members binding those sites differed. This might indicate that SOX regulation of human and mouse GDAP1 will be different in vivo. Further cellular studies would need to be undertaken to understand SOX regulation of GDAP1.

Some transcription factor binding sites were found to be repeated more in one species than in the other species, these transcription factors were more important for that species.For example, EGR was found six times in the mouse GDAP1 and found twice only in the human GDAP1. In humans both EGR binding sites were in the positive sequence with matrix similarity of 0.92 and 0.93. We found no studies of EGR1 effect on human GDAP1. However the mouse studies did not alter GDAP1 expression when EGR1 was different, implying that EGR binding sites are different between human and mouse GDAP1. This could affect the expression of human and mouse GDAP1, and that will lead to different experiment results when studying the expression of GDAP1 in the mouse model. In contrast, we found in chapter three that the WTAP (an EGR family member) was shown to cause strong up regulation of GDAP1expression when WTAP was knocked down (Figure 3.30).

The second aim of this chapter was to investigate whether common polymorphisms alter GDAP1 expression. This study confirmed that there is a variable poly-A region in the GDAP1 gene. The length of the poly-A region varied from 11, 12, 13, 14 or 15 base pairs. The length of the poly-A region determined in the previous studies varied from 10, 11, 12, 13, 14 or 15 base pairs. Control of poly-A length can affect translation and stability of eukaryotic mRNAs (Traude and Thomas, 2007). Although well established for individual cases, it was not known to what extent this type of adjustable gene control is used to shape expression of eukaryotic transcriptomes. The number of protein encoding genes in the human genome has been found to be surprisingly low; in the region of 20–25,000, though the complexity and variation in mechanisms for the control of gene expression has been shown to be large (International Human Genome Sequencing Consortium 2004). This has led to the theory that variations in the expression of genes, rather than the expression of different genes, underlie the complexity and variation of human phenotypes; thus regulatory polymorphisms may be the main source of

148 human variation (Buckland., 2004; Knight ., 2009; Morley., 2004). This could give clues about the changes in the expression of GDAP1.

Compared to the HapMap and dbSNPs databases, sequencing results confirmed two SNPs in a Caucasian population. The G SNP found in position -832 had a frequency of 0.45 and the C SNP found in position -510 had a frequency of 0.18, the frequencies were similar to other populations demonstrated in HapMap and dbSNP. This study also characterised a variable poly-A region at position -242 which had a length of between 11 and 15 nucleotides. We also identified a novel SNP in position -398 where the G allele changes to A, this SNP was not found in either HapMap or dbSNP.

Eight predicted transcription factor binding sites were found to be lying underneath the SNPs in positions -832, -510 and -242. For the T allele at position -510 one transcription factor binding sites was found for BLIMP1. When we change the allele to be a C, two different transcription factor binding sites are revealed. These belong to different families and could cause changes in expression. For position -832 the NKX25 TFBS is seen for both alleles, however the G allele introduces additional binding sites for XBP1 and E4F. Further investigation of these rSNPs would add to our understanding of the control mechanisms for GDAP1 gene expression.

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Chapter 5. Bioinformatic analysis for GDAP1L1

5.1 Introduction

GDAP1L1, or ganglioside – induced differentiation association protein 1 like 1, is a 368 amino acid protein with a molecular weight of 42 KDa. It is located in chromosomal region 20q12- q13 and contains six exons that span 33.1 kb of DNA (Marco et al., 2004). GDAP1L1 was first described as a result of homology studies comparing GDAP1 to the glutathione transferase family (Marco et al., 2004). The key structural differences between GDAP1, GDAP1L1 and cytosolic GSTs are two extra α helices between α4 and α5 domains and the presence of putative transmembrane regions at C- terminus (Marco et al., 2004). GDAP1L1 differs from GDAP1 by the presence of two hydrophobic domains at the outermost C-terminal region of GDAP1L1 (Marco et al., 2004)

Quantitative RT-PCR analysis in a mouse model identified the expression of GDAP1L1 in the CNS within the cortex, thalamus, cerebellum, olfactory bulb and spinal cord but no expression in the PNS (Wagner, 2009). In comparison, previous studies of GDAP1 suggest that GDAP1 expression is ubiquitous with higher expression levels in central nervous tissues than in peripheral nervous tissues (Cuesta et al., 2002). Unlike GDAP1, overexpressed GDAP1L1 does not appear to localize to mitochondria but rather is dispersed throughout the cytosol (Wagner, 2009). These findings were unexpected, as the carboxy terminal region of GDAP1L1 is highly similar to the corresponding region of GDAP1, including all the features required for a tail-anchored protein(Marco et al., 2004). Some tail-anchored proteins, such as the pro- apoptotic protein Bax, are able to translocate to the nucleus under altered cellular conditions (Wagner, 2009). Niemann et al (2014) demonstrated translocation of GDAP1L1 to the mitochondria after treatment with menadione, and from these data hypothesized that GDAP1L1 might compensate for the loss of GDAP1 function in the CNS under certain cellular conditions.

GDAP1 is expressed in the peripheral and central nervous systems but mutated GDAP1 only seems to impact on peripheral nerve function in CMT (Niemann et al., 2005, Pedrola et al., 2005). This suggests that another protein compensates for GDAP1 function in the CNS (or that GDAP1 has a different function in CNS compared to PNS). Niemann et al. (2009) demonstrated that N1E-115 cells transiently overexpressing GDAP1L1 were able to rescue loss of fission activity due to GDAP1 knock-down. They concluded from this, that GDAP1L1 is

150 able to compensate for loss of GDAP1 activity under conditions of oxidative stress (Niemann et al., 2014).

Niemann et al. (2014) have recently developed a GDAP1 knockout mouse. These mice carry a functional null allele for GDAP1 which mimics mutations leading to Charcot–Marie–Tooth disease, and they develop a late onset peripheral neuropathy with mild hypomyelination and no axonal loss (Niemann et al., 2014). More GDAP1L1 was found in the spinal cord of the GDAP1 knockout mice, compared with the controls (Niemann et al., 2014) lending support to the theory that changes in the expression of GDAP1 can be compensated by GDAP1L1 in the central nervous system.

The aim of this chapter is to test whether changes in GDAP1L1 expression in response to changes in GDAP1 expression occur more generally in human tissues. This theory will be tested using a bioinformatics approach. A detailed investigation of GDAP1L1 was beyond the scope of this project, however when the Niemann et al (2014) paper was published, we decided to do a pilot study to test their theory regarding compensatory expression of GDAP1 and GDAP1L1.

5.2 Methodology

5.2.1 Gene Expression Omnibus (GEO) data mining

Data were collected in three stages from the NCBI Gene Expression Omnibus (GEO) (Edgar et al., 2002, Barrett et al., 2013). An initial search looked at the expression of human GDAP1L1 in normal healthy tissues. This search revealed that the expression for GDAP1L1 was particularly high in the brain so a search for studies involving the expression of GDAP1L1 in brain tissues was then undertaken (using the search term: human GDAP1L1 and brain). Finally to test the theory that the expression of GDAP1L1 is increased when the expression of GDAP1 is decreased or vice versa, all studies that had at least one case which was significant for changes in GDAP1 expression (as described in chapters three and four) were selected, and the expression level for GDAP1L1 in these studies determined. These searches were performed in February 2014. The same inclusion and exclusion criteria used in chapter three (section 3.2.1.3) were applied to any new studies undertaken. Studies analysed throughout the chapter are presented with their GEO accession number (GDS) and any relevant publication.

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The GEO Profile microarray data was standardised to liver expression for normal tissues, so the expression of the liver become one (1) in all studies. If the expression of GDAP1L1 in any tissues was 150% of the expression in liver than the expression was defined as high.

To compare the expression of GDAP1 and GDAP1L1, an expression ratio was generated by dividing the value of the cases by the average of the control.

5.2.2 Oncomine data mining

The Oncomine search engine was also used to investigate cancer profiles, so that we could compare the expression of GDAP1 and GDAP1L1 in different cancer tissues.

5.3 Results

5.3.1 Results for the gene expression Omnibus 5.3.1.1 The expression of human GDAP1L1 in normal healthy tissues

Six GEO profiles were found containing human GDAP1L1 expression data for healthy normal human tissues as outlined in Table 5.1. The table shows the arrays that were used in these studies, including the reporter, number of tissues analysed and the number of replicates included on the microarray chip. Only one of the profiles (GDS3113) had triplicate data; three profiles had duplicate data while the final two studies only had single samples. Studies numbered GDS 3113, GDS423, GDS596 and GDS1096 are the same studies used to determine the expression of GDAP1 in healthy normal tissues (section 3.3.1.2).

Figure 5.1 compares the expression of GDAP1L1 in the 12 tissues common to five of the data sets, the sixth datasets was not included because they were provided as ratios and it was not possible to transform these data to adapt with the rest of the data. It is clear that in some datasets the expression of GDAP1L1 is higher in the brain, spinal cord, and skeletal muscle. Dezso et al. (2008) showed triplicate data for 32 tissues and demonstrated that GDAP1L1 is also expressed at low levels in many different tissues, with higher expression in the brain, fetal brain, trachea, ovary, salivary gland, adrenal gland and spinal cord (Figure 5.2). This can be compared to GDAP1 which also has higher expression in the brain, spinal cord, and salivary gland (Figure 3.3).

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Table 5.1: Summary table of microarray experiments showing the expression of human GDAP1L1 in healthy normal human tissues.

GEO Reporter Array Number Number Reference accession number of tissues of number replicates

GDS3113 GPL2986, ABI Human Genome 32 3 Dezso et al. 177289 Survey Microarray (2008)

GDS596 GPL96, Affymetrix Human 79 2 Su et al. (2004) 219668 Genome U133A Array

GDS425 GPL94, Affymetrix Human 12 2 Yanai et al. (2005) 74480 Genome U95D Array

GDS423 GPL92, Affymetrix Human 12 2 Yanai et al. (2005) 59678 Genome U95B Array

GDS1096 GPL96, Affymetrix Human 36 1 Ge et al. (2005) 219668 Genome U133A Array

GDS3834 GPL8217, Custom microarray 42 1 She et al. (2009) 5330 design based on Johnson (2003)

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Figure 5.1: The expression of GDAP1L1 in normal healthy tissues. Red column indicates study one (GDS423; n=2), black column indicates study two (GDS596; n=2), green column indicates study three (GDS1096; n=1), the brown column indicates study four (GDS3113; n=3) and the blue column indicates study five (GDS425; n=2).

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Figure 5.2: The expression of GDAP1L1 in 32 normal healthy tissues (n=3). Data is from GDS3113.

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5.3.1.2 The expression of human GDAP1L1 in the brain

Fifteen GEO profiles matched our inclusion criteria for the search term ‘human GDAP1L1 and brain’. Details for all of these studies can be found in Tables 9 (A, B) in the Appendix five. Only two studies that found a significant difference in GDAP1L1 expression compared to the control are discussed below. Two studies that had previously been analysed for GDAP1 are discussed in section 5.3.1.3.1

Gelman et al. (2012) looked at the differences in gene expression in three regions of the brain; basal ganglia, white matter, and frontal cortex, for groups of HIV infected patients. Twenty- four human subjects in four groups were examined.These groups included: 1- Uninfected controls; 2- HIV-1 infected subjects with no substantial neurocognitive impairment (NCI); 3- Infected subjects with substantial NCI without HIV encephalitis (HIVE); 4- Infected subjects with substantial NCI and HIVE. The expression of GDAP1L1 was significantly lower in the basal ganglia and the frontal cortex of the HIV-1 infected patients with NCI, and HIV-1 infected patients with NCI and HIVE, than the rest of the samples (p< 0.0001 and p <0.002 respectively). The expression of GDAP1L1 was significantly higher in the white matter of the HIV-1 infected patients with substantial NCI without (HIVE), and HIV-1 infected patients with substantial NCI and HIVE, than the rest of the samples (Figure 5.3; p=0.001). It seems that changes in the expression of GDAP1L1 is significant with the HIV-1 patients with substantial NCI without HIVE and HIV-1 patients with substantial NCI and HIVE.

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Figure 5.3: The expression of GDAP1L1 in brain tissues of HIV infected patients. Columns show the average expression levels for cancer and controls and standard deviation (n=6). White columns indicate control, blue columns indicate HIV, red columns indicate HIV+HAD and green columns indicate HIV+HAD+HIVE. Figure 5.3 A shows the entire brain region combined in one chart. Figure 5.3 B shows only the basal ganglia, Figure 5.3 C shows only frontal cortex and chart 5.3 D shows only white matter. Data is from GDS4358. * indicates p- value< 0.0001. (26) indicates RMA signal intensity ( see Table 2.2 chapter 2). Control: Uninfected controls; HIV: HIV-1 infected subjects with no substantial neurocognitive impairment (NCI); HIV+HAD: Infected with substantial NCI without HIV encephalitis (HIVE); HIV+HAD+HIVE: Infected with substantial NCI and HIVE. HAD: HIV infected with neurocognitive impairment, HIV-associated dementia.

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Borjabad et al. (2011) studied the effect of the antiretroviral therapy on brain tissue from patients with HIV-associated neurocognitive disorders (HIV-ND) , using portmortem brain tissues from uninfected patients (as a control). The study was undertaken on brain tissue of HIV-ND untreated patents and HIV-ND patients who used antiretroviral therapy. Using Tukey’s post-test, the expression of GDAP1L1 was significantly lower in the untreated HIV-

ND compared to the control (p= 0.006; Figure 5.4).

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Figure 5.4: The expression of GDAP1L1, in portmortem brain tissues from HIV patients. Columns show the average expression levels and standard deviation for the tissue. Black columns indicate control (uninfected patients; (n=9), white columns indicates HIV-ND untreated patients (n=14) and red columns indicates HIV-ND patients who used antiretroviral therapy (n=12). Data is from GDS4231. * indicates p-value=0.006, (1) indicates RMA normalized signal intensity (see Table 2.2).

5.3.1.3 Comparison between the expression of the human GDAP1 and human GDAP1L1

In this section the same studies that showed significant changes in GDAP1 expression (from chapter 3) were used to study the expression of GDAP1L1 to try to determine whether decreases in GDAP1 expression were compensated for by an increase in GDAP1L1.

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5.3.1.3.1 Expression in the brain

When looking at the expression of GDAP1 in the brain (section 3.3.2.2.1) four GEO profiles with significant changes in expression were found. GDAP1L1 expression was only found in two of these studies and is discussed below.

Ryan et al. (2006) studied gene expression in the dorsolateral prefrontal cortex brains of bipolar disorder patients. Figure 5.5 shows ratios of expression for GDAP1 and GDAP1L1 (by dividing the value of the bipolar cases by the average of the control). The expression of GDAP1 was significantly lower in the bipolar disorder than the control (p=0.04). For GDAP1L1, the change in expression between the cases and control was not significant (p=0.7), however the relative expression of GDAP1L1 increased in cases, whereas the expression of GDAP1 decreased in

the cases, compared to the control.

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Figure 5.5: Comparison between the expression of GDA1P1 and GDAPL1 in bipolar disorder. Columns show the ratio of expression for the cases to the control. The first column shows the ratio of the control compared to the bipolar cases for GDAP1; the second column shows the ratio of the control compared to the bipolar cases for GDAP1L1. Data is from GDS2190.

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In a comparative study between normal brain tissue and brain tumours (Sun et al., 2006), GDAP1 expression was significantly lower in the cancer compared to the control (Figure 3.10). While the expression of GDAP1L1 was significantly higher in oligodendroglioma grade II than in the control, the expression of GDAP1L1 was significant lower in the glioblastoma grade IV, than in the control (p<0.001; Figure 5.6). This demonstrates in the oligodendroglioma (grade II) that the expression of GDAP1 was decreased as compared to the normal brain tissue while the

expression of GDAP1L1 was increased (Figure 5.7).

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Figure 5.6: The expression of GDAP1L1 in gliomas of different grades. Columns show the average expression levels and standard deviation values for different grades of brain cancers. The black column shows the control (normal brain n=23), the white column shows astrocytoma grade II (n=7), the red column shows astrocytoma grade III (n=19), the blue column shows astrocytoma grade IV (n=81), the grey column shows oligodendroglioma grade II (n=38) and the green column shows oligodendroglioma grade III (n=12). * indicates p< 0.0001. Data is from GDS1962. (7) indicates MAS5-calculated signal intensity (see Table 2.2).

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Figure 5.7: Comparison between the expression of GDAP1 and GDAP1L1 in oligodendroglioma Grade II. Columns show the ratio of the cases to the control. The first column shows the ratio of the control compared to the oligodendroglioma Grade II for GDAP1; the second column shows the ratio of the control compared to the oligodendroglioma Grade II for GDAP1L1. Data is from GDS1962.

5.3.1.3.2 Expression in breast

Analysis of GDAP1 expression in breast tissue in section 3.3.2.2.2 found six profiles where the expression of GDAP1 was significantly different. The expression of GDAP1L1 was found in four of these profiles and is discussed below. In three of these studies significant changes were found in GDAP1 expression, but no change was found in the expression of GDAP1L1.

Using biochemical and bioinformatics approach, Stitziel et al. (2004) identified genes expressed by the MCF-7 breast cancer cell line. The expression of GDAP1 was lower in the membrane fraction than in the cytosolic fraction (p=0.01); the expression of GDAP1L1 in the membrane fraction was slightly higher than in the cytosolic fraction, but this was not significant (p=0.95). In investigating MCF-7 breast cancer cells treated with estradiol and/ or cycloheximide (Bourdeau et al., 2008), the expression of GDAP1 was significantly higher in the cycloheximide treated cells, compared with the control (ANOVA p<0.0001, Figure 3.13). The change in expression of GDAP1L1 was not significant (p=0.3), and was almost the same in the treated cells, compared to the control. Finally, in a study looking at MCF-7 breast cancer cells overexpressing constitutively active Raf-1, constitutively active MEK, constitutively active c-erbB-2, or ligand-activatable EGFR (Creighton et al., 2006), the expression of GDAP1 161

was significantly lower in the erbB-2, MEK, Raf-1 and EGFR compared to both the control and the long-term E2 independent growth cells ( P<0.0001, Figure 3.14). The changes in the expression of GDAP1L1 was not significant and was almost the same as the control, for all cells.

Hu et al. (2009) studied LM2 breast cancer cells depleted for metadherin (MTDH). Figure 5.8 shows that the expression of GDAP1L1 decreased slightly for LM2 breast cancer cells depleted for metadherin but these was not significantly different from the control (p=0.9). Figure 5.9 compares the expressions of GDAP1and GDAP1L1; the expression of GDAP1 increased when the expression of GDAP1L1 decreased in the metadherin depleted culture alone, compared to

the control. This change was only significant for GDAP1.

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Figure 5.8: The expression of GDAP1L1 in LM2 breast cancer cells depleted for metadherin. Columns show the average expression level and standard deviation between the values of growth protocols (n=3 each). The black column indicates control (LM2 culture alone), the white column shows LM2 control culture with lung endothelial cells, and the red column shows LM2 metadherin depleted culture alone, the blue column indicates LM2 metadherin culture with lung endothelial cell. Data is from GDS3179. (15) indicate log 2 of Pre value (normalized ratio) (see Table 2.2).

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Figure 5.9: Comparison between the expression of GDA1P1 and GDAPL1 in the metadherin depleted culture alone LM2 breast cancer cells. Columns show the ratio of the cases to the control. The first column shows the ratio of the control compared to the metadherin depleted culture alone of GDAP1; the second column shows the ratio of the control compared to the metadherin depleted culture alone of GDAP1L1. Data is from GDS3179.

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5.3.1.3.3 Expression in the respiratory system

Three GEO profiles were discussed when looking at the expression of GDAP1 in the respiratory system in section 3.3.2.2.3. The expression of GDAP1L1 was found in one of these three studies.

In the analyses of airway epithelial cells from children with asthma and healthy controls (Kicic et al., 2010), the expression of GDAP1 was significantly higher in children with asthma compared with the healthy controls (p value=0.01). The expression of GDAP1L1 was significantly higher in children with asthma than in the healthy controls (p value=0.002)

(Figure 5.10)

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Figure 5.10: Comparison between the expression of GDA1P1 and GDAPL1 in the airway epithelial cells from children with asthma. Columns show the ratio of the cases to the control. The first column shows the ratio of the control compared to airway epithelial cells from children with asthma of GDAP1; the second column shows the ratio of the control compared to airway epithelial cells from children with asthma of GDAP1L1. Data is from GDS3711.

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Jorgensen et al. (2008) looked at the gene expression patterns in normal human bronchial epithelial cells exposed to cigarette smoke. Figure 5.11 shows the expression of GDAP1L1 in the normal bronchial epithelial cells exposed to cigarette smoke for up to 24 hours. Compared to the incubator control, the expression of GDAP1 was significantly lower in the two hours mock control. The expression of GDAP1L1 was higher than in the control, although this was not significant (Figure 5.12 A). In the four and eight hours reference cigarette (2R4F), the expression of GDAP1 was significantly lower than in the incubator control. However, the expression of GDAP1L1 was higher than in the control (Figure 5.12 B and C). The expression of GDAP1 was significantly lower in the eight hours light cigarette, than the incubator control, while the expression of GDAP1L1 did not change compared to the incubator control (Figure 5.12 D).

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Figure 5.11: The expression of GDAP1L1 in normal bronchial epithelial cells exposed to cigarette smoke for up to 24 hours. Columns show the average expression levels and standard deviation for exposed cell cultures (n=4). Black column shows the incubator control (IC), mock control which is (MC) is shown in white, 2R4F reference cigarette (2R4F) is shown in blue, light cigarette (LC) is shown in red. Column fill patterns indicate time of exposure with solid fill for 2 hours, check for 4 hours, dot for 8 hours and stripe for 24 hours. Data is from GDS3494. (19) indicates signal (see Table 2.2).

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Figure 5.12: Comparison between the expression of GDA1P1 and GDAPL1 in normal bronchial epithelial cells exposed to cigarette smoke. Columns show the ratio of the cases to the control. In each panel the first column shows the ratio of the treatment group to the control for GDAP1 and the second column is the ratio of treatment group to the control for GDAP1L1. Panel shows, panel A is the mock control at two hours, panel B is 2R4F treatment at four hours, panel C is the 2R4F treatment at eight hours and panel D is light cigarette treatment at eight hours.

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5.3.1.3.4 Expression in ovarian epithelial cells

One GEO profile was discussed for the expression of GDAP1 in section 3.3.2.2.4, this study also expressed GDAP1L1. When analysing normal ovarian surface epithelial cells and ovarian cancer epithelial cells (Bowen et al., 2009), the expression of GDAP1 was decreased in the ovarian cancer compared to the control (p=0.002), whereas the expression of GDAP1L1 increased (not significant p= 0.26) in the ovarian cancer compared to the control (Figure 5.13).

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Figure 5.13: Comparison between the expression of GDAP1 and GDAP1L1 in normal ovarian surface epithelia and ovarian cancer epithelial cells. Columns show the ratio of the cases to the control. The first column shows the ratio of the control compared to ovarian cancer for GDAP1; the second column shows the ratio of the control compared to ovarian cancer for GDAP1L1. Data is from GDS3592.

5.3.1.3.5 Expression in different cell lines

Eleven GEO profiles discussed in section 3.3.2.2.5 included leukaemia, kidney, colon cancer, melanoma and other cell lines; GDAP1L1 expression was found in all the 11 profiles and is discussed below.

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Two studies by Auer-Grumbach et al. (2004), investigated K562 leukaemia cells treated with 1 µM imatinib for 24 hours. In both studies the expression of GDAP1 was significantly lower in the imatinib treated cells compared to the control (p <0.04). In both studies the expression of GDAP1L1 was not significantly different in the imatinib treated cells compared to the control (p>0.3).

El Hader et al. (2005) analysed HEK293 kidney cells overexpressing HCaRG. Figure 5.14 shows the ratio of the expression of GDAP1 and GDAP1L1. The expression of GDAP1 was higher in the HCaRG overexpressing cells than in the control (p=0.03). However the expression

of GDAP1L1 was decreased (but not significantly).

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Figure 5.14: Comparison between the expression of GDAP1 and GDAP1L1 in HEK293 kidney cells expressing HCaRG. Columns show the ratio of the cases to the control. The first column shows the ratio of the control compared to HCaRG overexpression for GDAP1; the second column shows the ratio of the control compared to HCaRG overexpression for GDAP1L1. Data is from GDS2426.

Liu et al. (2008) studied HEK293 kidney cells depleted for MIF. The expression of GDAP1 was higher in the MIF depleted cells than in the control (p=0.009). However the GDAP1L1 expression did not change (p value =0.3, Figure5.15).

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Figure 5.15: Comparison between the expression of GDAP1 and GDAP1L1 in HEK293 kidney cells. Columns show the ratio of the cases to the control. The first column shows the ratio of the control compared to the MIF depleted cells for GDAP1; the second column shows the ratio of the control compared to the MIF depleted cells for GDAP1L1. Data is from GDS3626.

Four studies showed statistical difference in GDAP1 expression, but did not show any changes between GDAP1L1 and the control (GDS1942, GDS3578, GDS2164 and GDS3051). These studies are discussed below.

In the treatment of colon cancer cell line RKO with ponasterone A for 24 hours (Whitney et al., 2006), the expression of GDAP1 was significantly higher in the RKO cell line compared with the control. The expression of GDAP1L1 did not change the RKO cell line compared with the control. In comparative gene expression profiling in multiple myeloma (Dutta-Simmons et al., 2009), the expression of GDAP1 was statistically lower in the beta-catenin depleted cells compared with the control, but the expression of GDAP1L1 did not change. When studying Jurkat CD4+ T cells following the induction of simian immune deficiency virus (Ndolo et al., 2006), the expression of GDAP1 increased in the simian immune deficiency virus (SIV-Nef) compared with the control, while there was no change in the expression of GDAP1L1 when compared between SIV-Nef and the control. In the analyses of IMR-90 fibroblasts cultured in a two- or a three- dimensional collagen-glycosaminoglycan for 8 hours (Jaworski and Klapperich, 2006), the expression of GDAP1 was significantly higher in the 3-dimensional environment than in the control with p value =0.03 (Figure 3.25). However, the expression of

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GDAP1L1 was not significant p = 0.48 and did not deviate from the expression of GDAP1L1 in the control.

In the investigation of the effect of conjugated linoleic acid (CLA) on gene expression in Caco- 2 cells (Murphy et al., 2007), the expression of GDAP1 was significantly increased in the Caco- 2 cells treated with cis-12 CLA than in the control. The expression of GDAP1L1 did not change in the Caco-2 cells treated with cis-12 CLA compared to the control (Figure 5.16).

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Figure 5.16: Comparison between the expression of GDAP1 and GDAP1L1 in Caco-2 cells treated with cis-12 CLA. Columns show the ratio of the cases to the control. The first column shows the ratio of the control compared to the Caco-2 cells treated with cis-12 CLA of GDAP1; the second column shows the ratio of the control compared to the Caco-2 cells treated with cis-12 CLA of GDAP1L1. Data is from GDS3424.

When investigating HeLa cells depleted for optineurin gene using RNAi knockdown Weisschuh et al. (2007), the expression of GDAP1 was significantly higher in the cells depleted for optineurin than in the control (p =0.0009), the expression of GDAP1L1 was just slightly higher in the depletion optineurin than in the control (p =0.4, Figure 5.17).

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Figure 5.17: Comparison between the expression of GDAP1 and GDAP1L1 in HeLa cells depleted for optineurin using RNAi knockdown. Columns show the ratio of the cases to the control. The first column shows the ratio of the control compared to the cell depleted of optineurin for GDAP1; the second column shows the ratio of the control compared to the cell depleted of optineurin for GDAP1L1. Data is from GDS2892.

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Philibert et al. (2007) analysed lymphoblast cell lines derived from six subjects with active nicotine dependence. Figure 5.18 shows the ratio of the expression of GDAP1 and GDAP1L1, the expression of GDAP1 was significantly higher in the nicotine dependent subject than in the control (p=0.03). The expression of GDAP1L1 was not significant (p=0.15) but was slightly

increased in the nicotine dependent subject compared to that in the control.

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Figure 5.18: Comparison between the expression of GDAP1 and GDAP1L1 in lymphoblast cell lines derived from six subjects with active nicotine dependence. Columns show the ratio of the cases to the control. The first column shows the ratio of the control compared to the nicotine dependent subject of GDAP1; the second column shows the ratio of the control compared to the nicotine dependent subject of GDAP1L1.Data is from GDS2447.

5.3.1.3.6 Expression of transcription factors

The results in this section include studies of transcription factors described in chapters three and four. For the expression of GDAP1 in chapter three, two GEO profiles that were looking

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at transcription factors were found; one of them looking at X-box binding protein-1 (XBp1) and the other GEO profile looking at Wilms' tumour 1-associating protein (WTAP). Both of these GEO profiles were found when searching for the expression of GDAP1L1. Both of these profiles are discussed below. For the expression of GDAP1 in chapter four, eight GEO profiles looking for transcription regulation were found. These transcription factors included SOX, EGR, Homeo box and Pleomorphic adenoma. When looking for the expression of GDAP1L1, only one of the SOX GEO profiles was found and is discussed below.

In the studying of MCF7 breast cancer cells over expressing X-box binding protein-1 (Gomez et al., 2007), the expression of GDAP1 was found to be significantly higher in the XBP1 over expressing cells than in the control (p=0.04). For GDAP1L1 there were no changes in the cells expressing XBP1 compared with the control.

Horiuchi et al. (2006) investigated the effect of knockdown Wilms' tumour 1-associating protein (WTAP) in umbilical vein endothelial cells. Figure 5.19 shows that the expression of GDAP1 increased when WTAP was depleted (p=0.002). The expression of GDAP1L1 decreased in the knockdown cells, but this was not significant (p=0.93).

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Figure 5.19: Comparison between the expression of GDAP1 and GDAP1L1 in Wilms' tumor 1- associating protein (WTAP) knockdown cells. Columns show the ratio of the cases to the control. The first column shows the ratio of the control compared to the WTAP knockdown cells of GDAP1; the second column shows the ratio of the control compared to the WTAP knockdown cells of GDAP1L1. Data is from GDS2010.

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Seguin et al. (2008) analysed CA1 and CA2 embryonic stem cell lines over-expressing transcription factor SOX7 and SOX17. The expression of GDAP1 was lower for the cells over expressing SOX17 (p=0.046) or SOX7 (p=0.08) compared with their controls (see Figure 4.7). For GDAP1L1 the expression was lower than in the control for the cells over expressing SOX7, but higher expression than in the control was observed for the over expressing SOX17. Neither of these results was significant; however it is suggested that depletion of SOX17 may lead to down regulation of GDAP1 while GDAP1L1 is up regulated (Figure 5.20). In the cells over expressing SOX7, the expression of both GDAP1 and GDAP1L1 was lower than in the control,

although not significant (Figure 5.21).

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Figure 5.20: Comparison between the expression of GDAP1 and GDAP1L1 in cells overexpressing SOX17. Columns show the ratio of the cases to the control. The first column shows the ratio of the control compared to the overexpression SOX17 for GDAP1; the second column shows the ratio of the control compared to the overexpression SOX17 for GDAP1L1. Data is from GDS3300.

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Figure 5.21: Comparison between the expression of GDAP1 and GDAP1L1 in cells overexpressing SOX7. Columns show the ratio of the cases to the control. The first column shows the ratio of the control compared to the overexpression SOX7 for GDAP1; the second column shows the ratio of the control compared to the overexpression SOX7 for GDAP1L1. Data is from GDS3300.

To further investigate the results of the above transcription factors, Genomatix software was used to look at the promoter region of GDAP1L1 and compare these results with the Genomatix results of GDAP1 in section 4.3.1.1. We only investigated the transcription factors XBP1, SOX family and WTAP. XBP1 was found to have three putative TFBS in the 5’FR of GDAP1 and two putative TFBS in the promoter of GDAP1L1. WTAP TFBS was found once in 5’FR of GDAP1 and four times in the 5’FR of GDAP1L1. SOX TFBS were found 19 times in the 5’FR of GDAP1 and nine times in the 5’FR of GDAP1L1 (see Table 5.2).

Table 5.2: Comparison between XBP1, SOX and WTAP transcription factors in the GDAP1 and GDAP1L1 5’FR. The rows indicate frequencies for the transcription factor binding sites.

X-box SOX WTAP

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5.3.1.4 Summary of themes found in GEO profile datasets

Table 5.3 summarises all of the studies presented in section 5.3.1.3 which compared the expression of GDAP1 and GDAP1L1 in the various tissues and cell lines. From the 22 GEO profiles that at least have one significant case for changes in GDAP1 expression, 45 tissues or cell lines were found.

Sixteen tissues showed changes in GDAP1 and GDAP1L1 expression in an opposite direction. In three tissues the expression of GDAP1 increased while the expression of GDAP1L1 decreased. In 16 tissues the expression of GDAP1 decreased while the expression of GDAP1L1 increased. In 36% of the tissues that we investigated, the expression of GDAP1 and GDAP1L1 changed in the opposite direction. In some of these studies, the change in expression of GDAP1 or GDAP1L1 was very little and many were not significantly changed. Only seven studies showed altered expression for GDAP1 and GDAP1L1 (in opposite directions) where both datasets were significant.

Table 5.3: Summary for the expression of GDAP1 and GDAP1L1 in different tissues and cell lines. The rows show the expression of GDAP1L1 and the columns show the expression of GDAP1. The boxes contain a count describing whether a protein was seen to have increased, decreased or was not changed in expression.

The expression of GDAP1

Increased Decreased No change

Increases 3 13

GDAP1L1 Decreased 3 2

The expression of The expression No change 9 12 3

5.3.2 Oncomine data mining

From the results of oncomine data mining in chapter three, changes in the expression of GDAP1 was found in brain and CNS cancer, cervical cancer, colorectal cancer, liver cancer, lung cancer, lymphoma and seminoma. The Oncomine database was also searched for a minimum of (1x10- 2) fold changes in the expression of GDAP1L1, but no positive results were found. When a lower threshold for fold change and significance was chosen to be (1x10 -1.5),

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one study for melanoma and sarcoma was found. We were therefore unable to compare the expression of GDAP1 and the expression of GDAP1L1 in cancer using Oncomine data.

5.4 Discussion

The aim of this chapter was using bioinformatics aproach to investigate whether changes in GDAP1L1 expression occurred in response to changes in GDAP1 expression occur - in human tissues and cell lines- as suggested by Niemann et al. (2014). Firstly we looked for the expression of GDAP1L1 in normal healthy tissues. The expression of GDAP1L1 was high in the brain, fetal brain, skeletal muscle, spinal cord and kidney in the normal healthy tissues. Quantitative RT-PCR analysis in mouse model previously identified expression of GDAP1L1 in the CNS in cortex, thalamus, cerebellum, olfactory bulb and spinal cord (Wagner, 2009). Our results and Wagner’s (2009) results confirmed that GDAP1L1 is expressed more in the CNS, compared to the expression of GDAP1 which is more ubiquitous. Wagner ( 2009) has hypothesised that changes in GDAP1 are compensated for by GDAP1L1 and this is the reason for the expression of GDAP1L1 in the CNS. The expression of GDAP1 is found in tissues of the PNS and CNS (Neimann et al.,2005). However, dysfunctional GDAP1 leads to peripheral neuropathies without severe CNS phenotype. This implies that either GDAP1 function is compensated by another protein expressed in the CNS or that GDAP1 exerts different functions in the CNS compared to the PNS. GDAP1L1 is a closely related paralog of the mitochondrial fission factor GDAP1 and belongs to the same novel GDAP1 class of GST enzymes. The Wagner (2009) study aimed to determine the potential of GDAP1L1 to compensate for dysfunctional GDAP1 proteins in the CNS. Wagner et al. (2009) showed that unlike GDAP1, GDAP1L1 is expressed in the CNS but not in the PNS. Although Wagner (2009) confirmed the potential of the putative TA domain of GDAP1L1 to integrate into the mitochondrial outer membrane (MOM), under normal cellular conditions GDAP1L1 is not localized to mitochondria. However, they showed that GDAP1L1 is translocated to mitochondria upon treatment with the stressor menadione. The exact functional mechanisms, the cellular relevance and its the implications of GDAP1L1 expression on neuronal cells and GDAP1 function are not certain, they need further investigation. Using a bioinformatics approach we found that the expression of GDAP1 and GDAP1L1 in 16 tissues changed in opposite directions in case compared with controls. Seven of these are considered to be significant including grade II oligodendroglioma. These results corresponded to only 15% of the tissues and cell lines 178

investigated. From these results we can see that the expression of GDAP1L1 mostly changed when looking at studies that used tissues and cell lines from the CNS where GDAP1L1 is predominantly expressed.

Interestingly some of these tissues were investigating transcription regulation and this could give some clues about the expression of GDAP1 and GDAP1L1. For the overexpression of SOX17 the expression of GDAP1 was decreasing while the expression of GDAP1L1 increasing. This suggests that depletion of SOX17 may lead to down regulation of GDAP1 while GDAP1L1 is up regulated. This could give some clues about how the changes in GDAP1 are compensated by GDAP1L1. Further studies considering the transcription regulation of GDAP1 and GDAP1L1 could shed light on how these proteins might compensate for one another. Niemann et al. (2014) also theorised that GDAP1L1 translocates to mitochondria under stress. From our results, many studies where the expression of GDAP1L1 was found to be changed were using cancer cell lines; cancer has been found to be associated with defects in mirochondrial stress response (Zeman and Cimprich 2014).While our experiments found some evidence of GDAP1 and GDAP1L1 expression changing in opposite directions in response to disease (such as in oligodendrogliomas) this data did not strongly support that GDAP1L1 globally compensate for down regulation of GDAP1. It may be that this phenomenon is localised to brain tissues. Further work will be required to determine whether this compensatory mechanism can be more broadly applied. Also, it would be interesting to investigate potential compensatory mechanism with other fission and fusion proteins such as MFN2.

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Chapter 6. General Discussion

Charcot-Marie-Tooth (CMT) disease is a group of genetic disorders that affect the normal function of the peripheral nervous system (Berger et al., 2002). Both motor and sensory nerves are affected in CMT disease and typical features include progressive weakness and atrophy of muscles often accompanied by distal sensory deficits (Berger et al., 2002, Skre, 1974). It has been shown that mutation of different genes can cause similar CMT disease phenotypes and conversely that different mutations affecting the same gene can lead to different disease phenotypes (Niemann et al., 2006). CMT is generally divided into syndromes associated with demyelination or axonal loss (Claramunt et al., 2005).

Mutations in Ganglioside -induced Differentiation Associated Protein (GDAP1) can lead to an early onset form of CMT and are the cause of demyelinating, intermediate and/ or axonal forms of CMT (Baxter et al., 2002, Ben Othmane et al., 1993, Cuesta et al., 2002, Niemann et al., 2006). GDAP1 induced CMT is currently thought to be due to mitochondrial dysfunction (Niemann et al., 2005). RT-PCR has shown ubiquitous expression of GDAP1 in a limited selection of human and mouse tissues with predominant expression in nervous system tissues (Cuesta et al., 2002); within the cells it is localised to the mitochondria (Pedrola et al., 2005). Gene expression of GDAP1 is regulated through the course of brain development in mice with maximal gene expression in the adult stage (Liu et al., 1999). In spite of this knowledge it is unclear why mutations in GDAP1 can lead to varied CMT phenotypes. This project investigated the normal expression of GDAP1 to try to understand how the contribution of GDAP1 gene regulation and genetic mutations in phenotypes might contribute to CMT disease. These investigations were undertaken using a bioinformatics approach.

6.1 The expression of GDAP1 is ubiquitous

This study explored the expression of GDAP1 in healthy human tissues. Previous studies of GDAP1 mRNA levels in human and rodent tissues proposed that GDAP1 expression is ubiquitous in a range of tissues with higher expression in central nervous tissues than in peripheral nervous tissue (Cuesta et al., 2002). Our study confirmed ubiquitous expression of GDAP1, particularly in neural and endocrine tissues. Our study found that GDAP1 is expressed

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in most human tissues with greater expression in the brain, mammary gland, testis, breast, nervous tissues, spinal cord, and epithelium. This extends the previously published data on the expression of GDAP1 in normal human tissues (Cuesta et al., 2002) by demonstrating high expression in tissues other than neural tissue. It was interesting to find that even though GDAP1 had ubiquitous expression, particularly in nervous system tissues that GDAP1 was also significantly expressed in endocrine tissue.

6.2 The expression of GDAP1 is different in human and mouse

The spectrum of GDAP1 expression was found to be different in human compared to mouse tissues. The average expression levels of GDAP1 in specific tissues were also different between humans and mice, for example the expression of human GDAP1in the spinal cord was six times higher than in mouse spinal cord. The low correlation of animal models to human disease in some research areas has generated some doubt on the usefulness of animal data as model systems (Leist and Hartung, 2013). The classic case study is the experience of thalidomide where teratogenic effects in humans were not predicted from available animal data (Stebbings et al., 2007). Fougerousse et al. (2000) demonstrated significant differences in embryonic expression patterns between humans and mouse models for a variety of genes. Their data present two illustrative examples for Wnt7a and CAPN3. Wnt7a is a very highly conserved gene known to be important in early development; it shows significant differences in spatial and temporal expression patterns in the developing brain (midbrain, telencephalon) of man and mice. CAPN3, the locus for LGMD2A limb girdle muscular dystrophy, and its mouse orthologue differ extensively in expression in embryonic heart, lens and smooth muscle. Fougerousse et al. (2000) also showed how molecular analyses, while providing explanations for the observed differences, can be important in providing insights into mammalian evolution. This study is an example of getting different results between human and mouse when doing the same experiments. Our results showed differences in the expression of GDAP1 between human and mouse, this support our theory that we will get different results when looking to the expression of GDAP1 when comparing between human and mouse models.

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6.3 GDAP1 transcription regulation

Limited studies have investigated at the transcriptional regulation of GDAP1. Ratajewski and Pulaski (2009) found GDAP1 to be transcriptionally regulated by YY1, a broadly studied factor that seems to be involved in regulating many genes. There is a consensus YY1 binding site in the GDAP1 core promoter which Ratajewski and Pulaski (2009) showed to be functional in both in vitro binding assays and incell culture models. While YY1 is known to exert both positive and negative regulatory influences on nuclearencoded mitochondrial proteins, as well as on neurodegeneration-related genes, in all cell lines that they studied (including neuroblastoma) the effect of YY1 on GDAP1 expression is activatory (Ratajewski and Pulaski 2009).

The differences between the expression profiles of human and mouse GDAP1 may be due to differences in transcription regulation. The alignment between human and mouse GDAP1 in the 5’ FR (approximately 2000bp upstream from the respective start codons) showed only one region around 300bp to be 65% similar between human and mouse GDAP1, this is lower than normal where typically 85-90% similarity is observed (SEQanswer 2015). In the 300bp segment, 26 human and 46 mouse transcription factors binding sites (TFBS) were found. Transcription factors play important role in the expression of the genes (van Nimwegen, 2003). We confirmed differences between the transcription factors in the human GDAP1 and mouse GDAP1 5’flanking regions, for example Forkhead domain TFBS were found 32 times in human compared to 14 times in the mouse; Insulinoma associated factors were found to have six TFBS in human GDAP1 and none in the mouse.

We also found evidence for control of GDAP1 regulation by SOX, Forkhead domain and Brn POU domain factors transcription in human cells models. Mouse models suggested roles for Lim homeodomain factors, SOX and Octamer binding protein transcription factors. If the transcription regulations are different between human and mouse GDAP1 this could lead the expression of human and mouse GDAP1 to be different. Recent technological advances have made it possible to determine the genome-wide binding sites of transcription factors (TFs) (Hemberg et al., 2012). Comparisons across species have suggested a relatively low degree of evolutionary conservation of experimentally defined TF binding events (TFBEs). Using binding data for six different TFs in hepatocytes and embryonic stem cells from human and

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mouse, Hemberg et al (2012) demonstrated that evolutionary conservation of TFBEs within orthologous proximal promoters is closely linked to function, defined as expression of the target genes. They showed that firstly there is a significantly higher degree of conservation of TFBEs when the target gene is expressed in both species. Secondly there is increased conservation of binding events for groups of TFs compared to individual TFs, and thirdly conserved TFBEs have a greater impact on the expression of their target genes than non- conserved ones. These results link conservation of structural elements (TFBEs) to conservation of function (gene expression) and suggest a higher degree of functional conservation than implied by previous studies (Hemberg et al., 2012). This suggests that multi-species analyses of experimentally determined combinatorial TF binding will help identify genomic regions critical for tissue-specific gene control (Ballester et al., 2014).

6.4 Genetic variation in GDAP1 transcription

Single nucleotide polymorphisms (SNPs) are the most frequent and simplest type of DNA sequence variation between individuals. Much attention has been given to the possible phenotypic effects of SNPs that cause amino acid changes. It has been estimated that 24483 SNPs located in coding regions could produce amino acid changes, affecting a total of 9791 different genes (Sunyaev et al., 2000). However several estimates suggest that realistically only 20% of them would alter protein function (Sunyaev et al., 2000). For example, it has also been reported that polymorphisms in the gelatinase A promoter region are associated with diminished transcriptional response to estrogen (Harendza et al., 2003). When Hoogendroon et al (2003) used denaturing high performance liquid chromatography to screen the first 500 bp of the 5' flanking region of 170 opportunistically selected genes identified from the Eukaryotic Promoter Database for common polymorphisms (a large-scale screening over a set of 16 chromosomes). They found SNPs in the promoters regions of 35% of the genes that they looked at and experimental evidence showed that around one-third of promoter variants may alter gene expression to a functionally relevant extent (Hoogendroon et al., 2003).

We confirmed two SNPs at position -832 (A-G) and -510 (T-C). These were found in a Caucasion population to be at a frequency of 0.45 for the G allele and 0.18 for the C allele respectively. These results also identified a novel SNP (G to A) at position -398. This rare SNP was only found in one allele and was not listed in any online SNP databases. We predicted

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some transcription factor binding sites lying underneath the SNPs found in the human GDAP1 5’FR which were altered depending on whether the wild type or variant allele was presented. In total, eight transcription factor binding sites were found to be underlying these SNPs positions and six of the TFBS changed to binding sites for transcription factors belonging to different families. Only one SNP gives the same transcription factor binding sites when the allele changed. These changes in the predicted transcription factor binding could change the expression of GDAP1.

The regulation of the GDAP1 gene in CMT patients is not yet understood. The same disease causing mutation in GDAP1 has been shown to result in different CMT phenotypes for axonal, intermediate or demyelinating forms of this peripheral neuropathy (Niemann et al., 2006). It is possible that the mechanism for these differences is due to varying levels of gene expression caused by common SNPs in the regulatory regions of GDAP1. More studies investigating SNPs in the GDAP1 gene will help to understand the regulation of GDAP1 gene.

6.5 GDAP1 gene has variable poly-A region

The majority of GDAP1 studies have focused on mutations in the coding region of the GDAP1 gene linked to CMT disease. Our study confirmed a variable Poly-A region with a length between 11 and 15 nucleotides. Poly-A regions have a role in gene regulation through alterations in accessibility of DNA and transcription initiation (Shain et al., 1998, Buckland et al., 2006). For example, a common polymorphism in the promoter sequence of the human stromelysin-1 gene, has one allele having a run of six adenosines (6A) and another with five adenosines (5A). Ye et al (1996) investigated whether the 5A/6A promoter polymorphism plays a role in the regulation of stromelysin-1 gene expression. Using transient transfection experiments, electrophoretic mobility shift assay and DNase I footprinting, they found that the common Alterations in stromelysin-1 due to this 5A/6A polymorphism may be involved in the progression may be involved in the progression of coronary heart disease (Ye et al., 1996). A clear picture of the variability in the poly-A region may help to increase our understanding of the genetic expression of GDAP1. It is difficult to obtain high quality of sequencing from this poly-A region due to the region’s length and the presence of heterogeneity, making these experiments technically challenging.

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6.6 Evidence for changes in GDAP1 expression in cancers

While the focus to date for GDAP1 expression has been in the context of CMT (Cuesta et al., 2002, Pedrola et al., 2005, Niemann et al., 2005), we found some evidence of changes in the expression of GDAP1 in some other diseases. One example was the down regulation of GDAP1 in glioblastoma which was observed in five out of six microarray cancer studies. We also observed GDAP1 expression in glioblastoma tissue samples by immunohistochemistry study. These immunohistochemistry studies showed that GDAP1 expression was weaker in glioma- astrocytoma than in normal glial cells.

GDAP1 is thought to be a mitochondrial fission factor, and mutations in this gene affect mitochondrial dynamics in cultured cells (Niemann et al., 2005, Niemann et al., 2009, Pedrola et al., 2005). Recessive inherited GDAP1 mutations have reduced fission activity, while dominant inherited GDAP1 mutations impair mitochondrial fusion (Niemann et al., 2005, Pedrola et al., 2005, Wagner et al., 2009). Mitochondria play an important role in supplying cells with ATP and are the major source of reactive oxygen species (RSO). Mitochondrial dysfunction in metabolism and/or morphology have been frequently found in human cancer (Tokarz and Blasiak 2014). The morphological differences between cancer and normal cells include mitochondria, which appear in less numbers in cancer cells than in their normal counterparts, and show distinguishing morphological features (Arismendi-Morillo., 2009, Yu., 2011). Mitochondrial changes have been linked with mitochondrial-DNA mutations, tumoral microenvironment conditions and mitochondrial fusion–fission imbalance (Ferreira-da-Silva et al., 2015).

Recent studies revealed new molecular mechanisms underlying the involvement of mtDNA damage and mitochondrial dysfunction in cancer transformation (Li., 2012). Mitochondria have been involved in the carcinogenesis process that includes changes in the cellular metabolism and cell death pathways. The cellular function of mitochondria is reflected in their structure (Westermann., 2002). The structural mitochondrial changes in human tumors are heterogeneous and not specific for any neoplasm (Benard et al., 2007). Recent reports have described dramatic alterations in mitochondrial morphology during the early stages of apoptotic cell death, a fragmentation of the mitochondrial network and the remodeling of the cristae (Karbowski and Youle 2003). GDAP1 has been shown to influence mitochondrial

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morphology. It is possible that changes in its expression may be contributing to cancer progression.

Further study on the role of GDAP1 in cancer would help to establish whether changes in GDAP1 expression are a cause or a consequence of cancer pathophysiology. Studying the role of GDAP1 in cancer cell lines would help to understand the reasons underlying the relationship between GDAP1 and cancer. Is it due to mitochondrial dysfunction? Or is there some other reason that has not been found yet.

6.7 Compensatory role for GDAP1L1

While the expression of GDAP1 is found in tissues of the PNS and CNS (Cuesta et al., 2002, Liu et al., 1999, Niemann et al., 2005) GDAP1 induced disease does not appear to cause a severe CNS phenotype (Niemann et al., 2006). This suggests, that either GDAP1 function is somewhat compensated by another protein that is only expressed in the CNS or that GDAP1 exerts different functions in the CNS compared to the PNS (Wagner, 2009).

GDAP1L1 is a paralog of the GDAP1 and belongs to the same class of GST enzymes (Marco et al., 2004). Using mouse model, Niemann et al. (2014) found that GDAP1expression decreases in the brain that GDAP1L1 expression increase. We tested this finding for human GDAP1 using bioinformatics approach. Our results showed that the expression of human GDAP1L1 in normal healthy tissues was higher in the brain, spinal cord, and skeletal muscle. These findings are consistent with previous study on GDAP1L1 protein levels by Wagner (2009). GDAP1L1 was most highly expressed in tissues of the CNS but no expression was identified in tissues of the PNS.

This study further aimed to determine the potential of GDAP1L1 to compensate for GDAP1 protein expression in human. The results showed an inverse relationship between the expression of GDAP1 and GDAP1L1 in 15% of the profiles investigated. These results were derived from range of tissues however, many were not significant. Several of the studies investigated transcription regulation; this could give some clues about the coordinated expression of GDAP1 and GDAP1L1. The overexpression of SOX17 led to the expression of GDAP1 being decreased while the expression of GDAP1L1 was increased; our results found SOX TF repeated 19 times in human and mouse GDAP1promoter. The SOX family member

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SOX10 has been linked with CMT disease (Berger et al., 2002, Kamholz et al., 2000, Tanaka and Hirokawa, 2002, Jones et al., 2007). The X-linked form of CMT disease is directly regulated by the transcription factor SOX10 ((Berger et al., 2002, Kamholz et al., 2000) and SOX10 has been shown to be a transcriptional regulators of myelin genes (Tanaka and Hirokawa, 2002, Jones et al., 2007). More studies considering shared transcription pathways may help to explain how changes in the expression of GDAP1 can be compensated for by GDAP1L1 in the central nervous system.

6.8 Limitations and Future direction

Little is known about the regulation of human GDAP1; we need to understand more about the normal expression of GDAP1 and how changes in this gene contribute to CMT disease. In the bioinformatics analysis, we used publicity available data. Although strict inclusion and exclusion were used for the selection of these studies the experiments were not our work, so we did not have any influence over their design. As such none of these experiments were specifically designed to investigate changes in GDAP1expression, and we had no input into the relevance of the control or the number of samples. However these data provide clues about different diseases for future investigation to understand how changes in GDAP1 might impact them. For example we might consider the role of GDAP1 expression in cancer pathophysiology based on the multiple studies found where GDAP1 expression was altered in glioblastomas. Ideally we might design our own microarray and protein expression experiments to confirm these results, for example investigating changes in expression using cell culture models.

This research will be continued. For a variety of reasons we were unable to subclone the pXPG 5’FR into an appropriate expression vector; this ranged from amplification problems to a very low rate of transformation. These experiments would have allowed us to understand the normal expression of GDAP1 protein and to increase our understanding about how genetic changes in the GDAP1 gene might contribute to CMT. It is also important to understand the impact of SNPs in the 5’FR region of GDAP1. Due to the variable poly-A region at position -242 we needed to subclone alleles in order to obtain legible sequence; this was technically very time consuming. Frequency data for the SNPs identified in the GDAP1 5’FR needs to be obtained in a larger and more ethnically diverse population. Luciferase assays would help to clarify the influence of polymorphisms on the expression of the GDAP1 gene. It would also be interesting

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to determine whether any new SNPs have a possible imbalance with the poly-A region. These ideas would need to be tested in a human population by generating cases and controls to correlate important rSNPs with CMT phenotype.

Finally, our results showed some support for the increased expression of GDAP1L1 when the expression of GDAP1 was decreased, as described by Niemann et al (2014). Clearly, more in- depth studies in human models are needed to elucidate this theory. To get better results, these studies should be done using human samples (cell lines or tissues). Niemann et al. (20014) experiments were done using mice tissues. If the mechanism of this change can be understood, it might be a candidate for future drug development for CMT patients with GDAP1 induced disease. Understanding the expression of GDAP1 in PNS and CNS, without causing a severe CNS phenotype is important. The function of GDAP1 is somewhat compensated by another protein that is only expressed in the CNS. This will make a difference in drug development

Taken together, this study presents new insights into human GDAP1 expression. Evidence is provided of changes in GDAP1 expression in diseases other than CMT (such as cancers) and that common polymorphisms in the 5’ flanking region of GDAP1 may contribute to variation in GDAP1 expression. Also, the results show a difference in the expression patterns of human and mouse GDAP1 genes, supporting the need for further studies in human models to support this hypothesis. Further work will be required to fully understand the implications of changes in GDAP1 expression in CMT disease.

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Appendix

Appendix 1

Abbreviations AC Asthmatic children AD Autosomal dominant AEC Airway epithelial cells AR Autosomal recessive ARCMT Autosomal recessive CMT ATP Adenosine Tri-Phosphate BC Breast cancer CC Cervical cancer ChIP Chromatin Immunoprecipitation CLA Conjugated linoleic acid CMAP Compound muscle action potential CMT Charcot-Marie-Tooth disease CNS Central nervous system DAB Diaminobenzidine DHT Dihydrotestosterone DM Demyelinating DMSO Dimethyl sulfoxide DPC Dorsolateral prefrontal cortex EC Endothelial cells EE Eosinophilic esophagitis EGFR Enhanced green fluorescent protein EMSA Electrophoresis mobility shift assays ER Endoplasmic reticulum GAG Glycosaminoglycan GDAP1 Ganglioside-induced differentiation-associated protein 1 GDAP1L1 Ganglioside-induced Differentiation Associated Protein1 Like 1 GEO Gene Expression Omnibus GSH Glutathione GST Glutathione S-transferase HCaRG Hypertension-related calcium-regulated gene HCV Hepatitis C virus HD Hydrophobic domain HDM House dust mite HELU Hyperplastic enlarged lobular unit HIGK Gingival keratinocytes HMSN Hereditary motor and sensory neuropathy HPV Human Papilloma Virus IBC Inflammatory breast cancer IHC Immunohistochemistry ILC Invasive lobular carcinoma LDL Low density lipids

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LEC Lens epithelial cells LNCaP Androgen-sensitive human prostate adenocarcinoma cells derived from the left supraclavicular lymph node metastasis MEK Methyl Ethyl Ketone MF Myelinated fibres MFH Malignant fibrous histiocytoma MIF Migration inhibitory factor MOM Mitochondrial outer membrane MTDH Metadherin NCBI National Centre for Biotechnology Information NCV Nerve conduction velocity NEFL Neuro filament NHBE Normal Human Bronchial Epithelial Cells OB Onion bulbs OC Orbitofrontal cortex OPTN Optineurin gen OSE Ovarian surface epithelia OXPHOS Oxidative phosphorylation PCR Polymerase chain reaction PNS Peripheral nervous system PRX Periaxin PS Protein synthesis PTC Proximal tubular cells PTHrP Parathyroid hormone-related protein RNA Ribonucleic acid RS Respiratory System RT-PCR Reverse transcription polymerase chain reaction SCC Squamous cell carcinomas SI Simian immune deficiency virus STS Soft tissue sarcomas TMD Transmembrane domain UPR Unfolded protein response UV Ultra Violet YARS Tyrosyl-tRNA synthetase

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Appendix 2

Additional Material and method A2.1 Materials A2.1.1 Solutions Table A 2.1: Buffers and solution used in this study.

Solution Description Ampicillin Stock solution: 50 mg/ml in ddH2O, sterile filtered with a 0.2 μm syringe filter stored at -20°C for 1 month. Working concentration: 100 μg/ml for liquid LB medium, 50 μg/ml for LB-culture plates LB-Medium LB broth, Miller (Luria-Bertani), 25 gm of the powder to be dissolved in 1 litter of water then to be autoclaved. The brand used is Amresco, the supplier Astral Scientific Australia LB-culture plates Agar Bacteriology. 1.5 gm agar for every 100ml LB media, 15gm agar for 1 litter of LB media. The brand used is Amresco, the supplier Astral scientific Australia Loading buffer for To make 1 liter of working buffer, 20ml buffer to be added to agarose gel 980 ml ddi water. Final concentration 1x, 40Mm TRIS, 20Mm electrophoresis (1xTAE) Acetic acid and 1mM EDTA, Ph 8.3. The brand used BioRad TYM Tryptone 2%, Yeast extract 0.5%, NaCl 0.1M, MgSO4.7H2O 10Mm, ddH2O to one litter, then autoclave

A2.2 Methods

A2.2.1 Instructions for using GDAP1 gene from DNA 2.0 The DNA sample from DNA2 was absorbed on a GFC filter. To elute the DNA 100µl of 10Mm Tris-HCl, pH 7.5 was added directly to the centre of the filter and incubated for 2 minutes at room temperature. Then the filter was placed in a punctured 0.6 ml tube and placed in a 1.5 ml tube, then both the tubes were placed in a tabletop centrifuge. Then the tube was spun for 1 minute at full speed, so that the DNA containing liquid was transferred from the filter in the 0.6 ml tube to the 1.5 ml tube.

A2.2.2 Protocols used in this study

A2.2.2.1 Gel extraction protocol

A- QIAquick Gel Extraction Kit Protocol using a microcentrifuge 1. Excise the DNA fragment from the agarose gel with a clean, sharp scalpel. 2. Weigh the gel slice in a 1.5ml tube and add 3 volumes of Buffer QG to 1 volume of gel.

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3. Incubate at 50°C for 10 min (or until the gel slice has completely dissolved). Mix by vortexing the tube every 2–3 min during the incubation. 4. Add 1 gel volume of isopropanol to the sample and mix. 5. Place a QIAquick spin column in a provided 2 ml collection tube. 7. Discard flow-through and place QIAquick column back in the same collection tube. 8. To wash, add 0.75 ml of Buffer PE to QIAquick column and centrifuge for 1 min. 9. Discard the flow-through and centrifuge the QIAquick column for an additional 1 min at ≥10,000 x g (~13,000 rpm). 10. Place QIAquick column into a clean 1.5 ml microcentrifuge tube. 11. To elute DNA, add 50μl of Buffer EB (10 mM Tris•Cl, pH 8.5) or H2O to the center of the QIAquick membrane and centrifuge the column for 1 min at maximum speed. Alternatively, for increased DNA concentration, add 30 μl elution buffer to the center of the QIAquick membrane let the column stand for 1 min, and then centrifuge for 1 min.

B- Promega Wizard® SV Gel and PCR Clean-Up System

1. Following electrophoresis, excise DNA band from gel and place gel slice in a 1.5ml microcentrifuge tube. 2. Add 10μl Membrane Binding Solution per 10mg of gel slice. Vortex and incubate at 50– 65°C until gel slice is completely dissolved. 3. Insert SV Minicolumn into Collection Tube. 4. Transfer dissolved gel mixture or prepared PCR product to the Minicolumn assembly. Incubate at room temperature for 1 minute. 5. Centrifuge at 16,000 × g for 1 minute. Discard flowthrough and reinsert Minicolumn into Collection Tube. 6. Add 700μl Membrane Wash Solution (ethanol added). Centrifuge at 16,000 × g for 1 minute. Discard flowthrough and reinsert Minicolumn into Collection Tube. 7. Repeat Step 4 with 500μl Membrane Wash Solution. Centrifuge at 16,000 × g for 5 minutes. 8. Empty the Collection Tube and recentrifuge the column assembly for 1 minute with the microcentrifuge lid open (or off) to allow evaporation of any residual ethanol. Elution 9. Carefully transfer Minicolumn to a clean 1.5ml microcentrifuge tube.

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10. Add 50μl of Nuclease-Free Water to the Minicolumn. Incubate at room temperature for 1 minute. Centrifuge at 16,000 × g for 1 minute. 11. Discard Minicolumn and store DNA at 4°C or –20°C.

A2.2.2.2 DNA purification (Miniprep) protocol

Growth of bacterial cultures in tubes

1. Pick a single colony from a freshly streaked selective plate and inoculate a culture of 5 ml LB medium containing the appropriately selected antibiotic. Incubate for 12–16 h at 37°C with vigorous shaking.

2. Harvest the bacterial cells by centrifugation at > 8000 rpm (6800 x g) in a conventional, table-top microcentrifuge for 3 min, at room temperature (15–25°C). The bacterial cells can also be harvested in 15 ml centrifuge tubes, at 5400 x g for 10 min at 4°C. Remove all traces of supernatant by inverting the open centrifuge tube until all medium has been drained.

A- Plasmid DNA Purification using the QIAprep

1. Resuspend pelleted bacterial cells in 250 μl Buffer P1 and transfer to a microcentrifuge tube.

2. Add 250 μl Buffer P2 and mix thoroughly by inverting the tube 4–6 times. Mix gently by inverting the tube. Do not vortex, as this will result in shearing of genomic DNA.

3. Add 350 μl Buffer N3 and mix immediately and thoroughly by inverting the tube 4–6 times

4. Centrifuge for 10 min at 13,000 rpm (~17,900 x g) in a table-top microcentrifuge. A compact white pellet will form.

5. Apply the supernatants from step 4 to the QIAprep spin column by decanting or pipetting.

6. Centrifuge for 30–60 s. Discard the flow-through.

7. Wash the QIAprep spin column by adding 0.5 ml Buffer PB and centrifuging for 30–60 s. Discard the flow-through.

8. Wash QIAprep spin column by adding 0.75 ml Buffer PE and centrifuging for 30–60 s.

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9. Discard the flow-through, and centrifuge at full speed for an additional 1 min to remove residual wash buffer.

10. Place the QIAprep column in a clean 1.5 ml microcentrifuge tube. To elute DNA, add 50 μl Buffer EB (10 mM Tris•Cl, pH 8.5) to the centre of each QIAprep spin column, let it stand for 1 min, and centrifuge for 1 min.

B- Wizard®Plus SV Minipreps DNA Purification System 1. Pellet 1–10ml of overnight culture for 5 minutes. 2. Thoroughly resuspend pellet with 250μl of Cell Resuspension Solution. 3. Add 250μl of Cell Lysis Solution to each sample; invert 4 times to mix. 4. Add 10μl of Alkaline Protease Solution; invert 4 times to mix. Incubate 5 minutes at room temperature. 5. Add 350μl of Neutralization Solution; invert 4 times to mix. 6. Centrifuge at top speed for 10 minutes at room temperature. 7. Insert Spin Column into Collection Tube. 8. Decant cleared lysate into Spin Column. 9. Centrifuge at top speed for 1 minute at room temperature. Discard flowthrough, and reinsert Column into Collection Tube. 10. Add 750μl of Wash Solution (ethanol added). Centrifuge at top speed for 1 minute. Discard flowthrough and reinsert column into Collection Tube. 11. Repeat Step 10 with 250μl of Wash Solution. 12. Centrifuge at top speed for 2 minutes at room temperature. 13. Transfer Spin Column to a sterile 1.5ml microcentrifuge tube, being careful not to transfer any of the Column Wash Solution with the Spin Column. 14. Add 100μl of Nuclease-Free Water to the Spin Column. Centrifuge at top speed for 1 minute at room temperature. 15. Discard column, and store DNA at –20°C or below.

A2.2.2.3 PCR amplification

To amplify the GDAP1, first iProof High-Fidelity DNA Polymerase were used in different annealing temperatures and different cycles: both HF and GC buffers were used with and without DMSO. This polymerase did not give the right band size (TableA2 and Figure A1).

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This table and figure show one of the cycles that was tried in this amplification. Then the Mango Taq DNA Polymerase was used with a different cycle and with and without DMSO (Table A3). This table shows one of the cycles that was tried in this amplification. This polymerase did not give the right band size either. Finally Phusion Hot Star II High-Fidelity DNA Polymerase was used which gave the right band size. The PCR reaction took a long time to choose the right polymerase. Table A4 summaries the different polymeras used in this study. The PCR reaction started by using mix of the DNA donor samples with the forward primer F- 888. At this stage different PCR products were used with different cycles and temperatures, then run in agarose gel using gel electrophoresis; after a few months Phusion hot star high fidelity polymerase worked, and gave the right band size. Then the DNA from the donors was used for the PCR.

Table A 2.2: One of the cycles used to amplify the PCR using iProof high fidelity polymerase. The cycle was suggested by the company.

Cycle step 2 steps protocol Cycles

Temperature Time

Initial denaturation 98°C 30s 1

Denaturation 98°C 10s 35

Annealing 58°C/ 30 s

Extension 72°C 30 s

Final extension 72°C 10 min 1

Holding temperature 22°C4°C hold

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Figure A2.1: The PCR results for cycle in Table A2.3runs on the gel. The same forward primer F-888 and reverse primer R33 were used in different samples. The first column shows the DNA leader, the second column shows the blank PCR reaction (master mix only without primers), the third column shows mix of different samples, the fourth column shows sample 37, the fifth column shows sample 38, the sixth column shows sample 39.The numbers indicates the number of the blood donor).

Table A 2.3: One of the cycles used to amplify the PCR using the MangoTaq polymerase. The cycle was suggested by the company.

Cycle step 2 steps protocol Cycles

Temperature Time

Pre- denaturation 94°C 3min 1

Denaturation 94°C 30 s 30

Annealing 54°C 2 min

Extension 72°C 1 min

Final extension 72°C 5 min 1

4°C hold Holding temperature 4°C

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Table A 2.4: Summary of the number of PCR amplifications done in this study. The first column shows the name of the company used, the second column shows the type of sample used i.e. if it is collected from a single blood donor or is mix of blood samples from multiple donors or is a GDAP1 gene that was ordered from DNA2.0, the third column shows the number of PCRs done with this protocol.

The company used Type of the sample blood Number of PCR done donor or DNA2.0

iProof High fidelity DNA DNA from donors. 8 ( with different forward polymerase. primers)

MangoTaq DNA polymerase DNA from donors. 5( with different forward primers)

Phusion Hot star II high DNA mix from the donors. 1( with one forward primer) fidelity DNA polymerase.

Phusion Hot star II high DNA from the donors 20 ( with different forward fidelity DNA polymerase primers)

Phusion Hot star II high DNA from DNA2.0 4 ( with different forward fidelity DNA polymerase primers)

Figure A2.2: Agarose gel for PCR samples amplified using Phusion Hot Star II High-Fidelity DNA Polymerase. Samples were amplified with the forward primer F-888 and reverse primer R33 and were run on agarose gel. The first column from the left shows the DNA ladder, the second column shows no template control. The third to eighth columns show PCR products from four different blood donors3.

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Appendix 3

A3.1 Search result for Human GDAP1 and Brain Table A3.1: Human GDAP1 and Brain

The search terms used in this table were human GDAP1 and brain. In this search 67 profiles were found, out of which 9 were included. The table is divided into two parts A and B, depending upon the profiles found. Part A represents the neuronal mental disease studies. Part B represents all the brain cancer studies. Five studies were categorized as being psychiatric or neurodegenerative disorders (part A), while the remaining four studies focused on brain cancer (part B).

A- Neurologic and psychiatric disorders.

Control Case

GEO Reporter Array Description N Ave Stdev Description N Ave Stdev T-Test number or Anova GDS1917 GPL570, U133 Healthy control 14 189 66 Crus I/VIIa area of the 14 162 63 0.28 226271, Plus 2.0 cerebellum from N46350 Array schizophrenia patients GDS2941 NM_018972 U133A Healthy control 8 5 0.19 Postmortem brains of 8 5 0.46 0.14 individuals with Down syndrome

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GDS2190 GPL96, U133A Healthy control 31 74 27 Postmortem 30 60 22 0.04 221279 dorsolateral prefrontal GDS2190, cortex from bipolar NM_018972 disorder GDS2191 GPL96, U133A Healthy control 11 91 30 Postmortem 10 73 17 0.12 221279 dorsolateral prefrontal GDS2191, cortex from bipolar NM_018972 disorder GDS810 GPL96, U133A Healthy control 9 121 67 Incipient Alzheimer 7 55.6 42.6 0.42 221279 disease(AD) GDS810, Severe AD 7 86 69 NM_018972 Moderate AD 8 55 51

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B- Brain cancer

Control Case

GEO Reporter Array Description N Ave Stdev Description N Ave Stde T-Test or number v Anova

GDS1813 GPL1833, SHFK Normal 4 2.64 0.38 Oligodendroglioma 8 2.6 0.26 0.012 16118 GDS1813, Glioblastoma 30 1.4 0.68 H15302, Anaplastic 6 1.2 2.6 IMAGE:492 Oligoastrocytoma 04 (CLONE ID) Astrocytic tumour 4 0.25 3

GDS1975 GPL96, U133A Grade III Gliomas 8 1432 551 Grade III Gliomas 7 1012 214 3.45x10-6 221279 Anaplastic Anaplastic mixed Astrocytoma Oligoastrocytoma

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GDS1975, Grade III Gliomas 11 1602 373 NM_018972 Anaplastic Oligodendroglioma

Grade IV Gliomas 59 823 491

Glioplastoma

GDS1976 GPL97, U133B Grade III Gliomas 8 12350 6023 GradeIII Gliomas 7 11826 3311 0.0355 226269 GDS1976, Anaplastic Astrocytoma Anaplastic mixed BF002104 Oligo-Astrocytoma

N46350 GradeIII Gliomas 11 16021 5357 Anaplastic Oligodendroglioma

Grade IV Gliomas 59 10388 6063

Glioplastoma

GDS1962 GPL570, U133 Non-tumour 23 459 186 Astrocytomas 7 215 119 1.39x10-7 221279 Plus 2.0 tumour Grade II GDS1962, Array NM_018972 Astrocytomas 19 321 115 Glioblastomas 81 265 119

N46350 tumour Grade III tumour Grade IV

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BF002104 Olgodendrogliomas 38 327 118 Oligodendrogliomas 12 300 129 tumour Grade II tumour Grade III

GDS2090 GPL96, U133A Glioblastoma cells 3 64 23 Glioblastoma cells 3 33 7.6 0.09 221279 epidermal growth factor treated with GDS2090, sphingosine 1- NM_018972 phosphate (S1P)

GDS2833 GPL96, U133A SVGR2 glial cells non- 3 704 363 SVGR2 glial cells 3 648 430 0.87 221279 resistant SVG-A resistant SVG-A GDS3049, NM_018972

GDS2834 GPL97, U133B SVGR2 glial cells non- 3 187 71 SVGR2 glial cells 3 149 27 0.44 226269_at resistant SVG-A resistant SVG-A GDS2834, 002104

GPL96, 221279 GDS3049,

GDS1414 GPL96, U133A Glial cell line Hs683 3 116 52 Hs683 following 3 74 35 0.63 221279 untreated. exposure to 300 nM of candoxin (CDX) 12h

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GDS1414, CDX 24h 3 68 44 NM_018972 CDX 48h 3 142 10

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A3.2 Search result for Human GDAP1 and Epithelial cells Table A 3.2: Human GDAP1 and Epithelial cells The search terms used in this table were Human GDAP1 and epithelial. In this search 125 profiles were found out of which 36 profiles were included The study was divided into 21 airway epithelial cells, 1 cervical carcinoma cell, 1 primary esophageal epithelial cell, 3 Gingival epithelial HIGK cells, 2 epithelial cells from (HELUs), 1 intestinal epithelial cell, 2 lens epithelial cells, 2 retinal pigment, 1 ovarian surface epithelia (OSE), 1 epithelium cell in breast (moved to the breast section), 1 cell line (moved to the cell line section).

Control Case

GEO Reporte Array Description N Aver Stdev Description N Ave Stdev T-Test number r or Anova

GDS2534 GPL570 U133 Plus ME180 cervical 3 5.5 1.46 p63 depletion 3 8 2 0.14 , 226271 2.0 Array carcinoma cells.

GDS2491 GPL570 U133 Plus Large airway non- 4 610 113.4 Large airway epithelial 5 750 200 0.25 , 226269 2.0 Array smoker cells of phenotypically cigarette smokers.

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GDS2490 GPL96, U133A Large airway non- 5 94 50 Large airway epithelial 6 85 42 0.74 221279 smoker cells of phenotypically cigarette smokers

GDS2771 GPL96, U133A large airway epithelial 90 5 0.22 with cancer 97 5 0.27 0.17 221279 cells from cigarette smokers without cancer with suspect lung 5 5 0.1 cancer

GDS1348 GPL187 OMRF normal bronchial 5 2.12 x 1.89x cigarette smoke 4h 4 7.02 5.62x 0.4 2, Microarray epithelial cells 4h 10-5 10-5 x10-5 10-5 0050040 Core Facility 19019 Human Print control 24h 5 1.99x 1.52x cigarette smoke 24h 5 3.2x 1.52x -5 -5 -5 -5 9/30/03 10 10 10 10

GDS3493 GPL570 U133 Plus bronchial epithelial 3 436 164 cigarette smoke 1h 33 482 107 0.2 , 226271 2.0 Array cells control 1h

control 2h 3 287 64 cigarette smoke 2h 3 318 63

control 4h 3 398 45 cigarette smoke 4h 3 264 132

control 24h 3 239 72.68 cigarette smoke 24h 3 237 53

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GDS3494 GPL57 U133 Plus normal bronchial 4 436 25 2R4F refferance 4 459 55 0.001 0, 2.0 Array epithelial cells cigarette 2h

226269 mock control 2h

mock control 4h 4 561 51.5 2R4F refferance 4 391 38 cigarette 4h

mock control 8h 4 587 40 2R4F refferance 4 356 23 cigarette 8h

mock control 24 h 4 468 45 2R4F refferance 4 456 50 cigarette 24h

incubator control 4 562 76 light cigarette 2h 4 485 53

light cigarette 4h 4 459 28 light cigarette 8h 4 379 47

light cigarette 24h 4 505 22

206

GDS3054 GPL96, U133A buccal epithelia 5 14 9 cigarette smoking 5 14 18 0.9 221279 Nonsmoker.

GDS3309 GPL571 U133A 2.0 Nasal epithelia from 8 75 42 cigarette smoking 7 57 30 0.37 , 221279 Array non-smoker.

GDS2486 GPL570 U133 Plus Small airway epithelial 12 77 16.63 smoker 10 88 30 0.29 , 226271 2.0 Array cells of non-smoker.

GDS3711 GPL96, U133A Airway epithelial cells 7 2 0.029 asthmatic atopic 9 2.5 0.025 0.01 221279 Array (AEC) from children with asthma. (Healthy non-atopic).

GDS3223 GPL570 U133 Plus primary esophageal 3 205 28 interleukin-13 3 252 46 0.20 , 226271 2.0 Array epithelial cells from eosinophilic esophagitis (EE) patients

GDS3333 GPL96, U133A immortalized Gingival 4 151 68 P.gingivalis wild type 4 94 53 0.24 221279 keratinocytes (HIGKs) strain

Porphyromonas gingivalis SerB mutant strain

207

GDS3211 GPL96, U133A Gingival epithelial 4 101 29 HIGK infected with 4 81 32 0.99 221279 HIGK cells. Un Aggregatibacter infected actinomycetemcomitans

infected with 4 94 53 Porphyromonas gingivalis

GDS2933 GPL96, U133A Gingival epithelial 4 147 80 co -cultured with the 4 73 47.5 0.97 221279 HIGK cells. the oral commensal Streptococcus gordonii Un cultured. Fusobacterium 4 91 95 nucleatum.

GDS1327 GPL96, U133A lens epithelial cells 3 83 69 Lens cortical fibre cells. 3 46 58 0.518 221279

GDS3003 GPL570 U133 Plus H292 bronchial 3 18.7 11 House dust mite 3 16 6 0.76 , 226269 2.0 Array epithelial cells (HDM) extract.

GDS1405 GPL96, U133A Epithelial cells from 5 20 11.33 Treated with ****DCI) 5 17 15 0.78 221279 fetal lungs Untreated.

GDS2739 GPL135 U133_X3P] epithelial cells from 8 49.5 54 hyperplastic enlarged 8 112 89.5 0.11 2, Affymetrix **(HELUs) lobular units

208

g950671 Human X3P 6_3p Array

GDS2739 GPL135 [U133_X3P] Epithelial cells from 8 661 589 hyperplastic enlarged 8 466 147 0.38 2, Affymetrix (HELUs). lobular units Hs.1689 Human X3P 50.0.S1 Array _3p

GDS3524 GPL96, U133A Cultured renal proximal 3 6.2 0.09 hypoxia 3 6 0.06 0.23 221279_ tubule epithelial RPTEC cells. normoxia

GDS494 GPL97, U133B *** (PBA) at 0h in 5 291 141 (PBA) In 12h in cystic 3 252 32 0.57 226269 cystic fibrosis bronchial fibrosis bronchial epithelial model cell epithelial model cell line IB3-1. line IB3-1.

In 24 h 3 207 69

GDS493 GPL96, U133A (PBA) at 0h in cystic 4 62.3 20 In 12 h 4 69.5 20 0.88 221279 fibrosis bronchial epithelial model cell In 24 h 3 68 23 line IB3-1.

209

GDS2307 GPL570 U133 Plus Retinal pigment 3 98.6 14 ARPE-19 Treated with 3 61 39 0.179 , 226271 2.0 Array epithelial ARPE-19 LDL. cells.untrated

Treated with 3 103 18.5 oxidatively modified LDL (ox-LDL)

GDS2606 GPL571 U133A 2.0 bronchial epithelial 3 58 7 exposed to UV- 2 50 7 0.58 , 221279 Array BEAS-2B cells inactivated Staphylococcus aureus,

exposed to 3 52.5 7.5 Pseudomonas aeruginosa

exposed to respiratory 3 46 3 syncytial virus

GDS2023 GPL570 U133 Plus bronchial epithelial 4 407 135 Treated with RSV 4h. 4 379 141 0.79 , 226271 2.0 Array cells, vehicle at 4h

GDS1447 GPL96, U133A Bronchial epithelial 4 153 40 Exposure to vanadium 4 150 49 0.39 221279 cells. Un exposure. (V).

Exposure to Zinc (Zn). 4 110 50

210

GDS3393 GPL571 U133A 2.0 Bronchial epithelial 3 4 0.5 treated with interleukin 3 4 0.14 0.99 , 221279 Array cells, receptor (IL)-22

treated with interleukin 3 4 0.15 receptor IL-17

treated with Both (IL)- 3 4 0.25 22&IL-17

GDS3592 GPL57 U133 Plus normal ovarian 12 468 143 OSE cells (CEPIs). 12 236 178 0.002 0, 2.0 Array surface epithelia 226271 (OSE)

GDS1353 GPL96, U133A Array Cultured immortalized 3 6 1.4 (LEC) treated with 3 26 3 0.07 221279 lens epithelial cells Dexamethasone 4h (LEC), control 4h

control 16h 3 4.2 3 Treated with 3 5.4 6 Dexamethasone 16h

GDS3324 GPL571 U133A 2.0 Epithelium cells in 5 2 0.2 Epithelium cells in 28 2 0.2 0.096 , 221279 Array normal breast invasive breast cancer tissues.

stromal cells in normal 5 2 0.17 Stromal cells in 28 2.5 0.4 breast invasive breast cancer tissues.

211

GDS690 GPL96, U133A intestinal epithelial cell 3 5.5 0.12 Caco-2 flagellin 3 5 0.09 0.79 221279 immune response Caco-2 LTb 3 5.5 0.266 Caco-2 control Caco-2 TNFa 3 5 0.037

T84 control 5.6 0.40 T84 flagellin 3 5.5 0.04

T84 TNFa 3 5.6 0.5

T84 LTb 3 5.6 0.3

GDS2341 GPL96, U133A lung epithelial A549 351 113 A549 treated with type 320 59 0.159 221279 cells, untreated 6h IIFN 6h

A549 treated with type 4 376 64 IIIFN 6h

A549 treated with type 4 409 46 I and II IFNs 6h

A549 untreated 24h 4 400 80 A549 treated with type 4 245 91 I IFN 24h

A549 treated with type 4 412 87 II IFN 24h

A549 treated with type 4 37 76 I and II IFNs 24h

212

GDS858 GPL96, U133A lung epithelial Calu-3 4 65 37 Calu-3 infected with 4 57 41 0.89 221279 cells FRD875 non-mucoid, non-motile

Calu-3 infected with 3 99.7 42 FRD1234 non-mucoid, non-motile

Calu-3 infected with 4 94 37 FRD1 mucoid

Calu-3 infected with 4 84 34 FRD440 motile

GDS3003 GPL570 U133 Plus H292 bronchial 3 18 10 H292 bronchial 3 16. 5 0.76 , 226269 2.0 Array epithelial cells epithelial cells exposed to house dust mite (HDM) extract.

* Positive studies ** (HELUs) hyperplastic enlarged lobular units *** (PBA) phenylbutyrate

**** (DCI) dexamethasone/cyclic AMP/isobutylmethylxanthine

213

A3.3 Search result for Human GDAP1 and Cell lines Table A 3.3: Human GDAP1 and Cell lines The search in this table used the term ‘human GDAP1 and cell lines’. 326 profiles were found, out of which 175 were included. This search was divided in to 6 parts A, B, C, D, E and F. Table A is the breast cancer cell lines, Table B is the Colon cancer cell lines, Table C is the Leukaemia cell lines, Table D is the prostate cancer cell lines, Table E is the lung cancer cell lines, Table F is for the rest of the cell lines found.

A- Breast cancer cell lines Control Case

GEO number Reporter Array Description N Ave Stdev Description N Ave Stdev T-Test or Anova GDS1664 GPL96, U133A MDA-MB-231 3 198 222 MDA-MB-231 3 493 136 0.121 221279 depleted for *(PTHrP) using siRNA. GDS2864 GPL96, U133A MDA-MB-231 3 670 150 MDA-MB-231 3 354 356 0.229 221279 following with RhoGDIbeta knockdown. GDS825 GPL875, 1 cDNA MDA-MB-436. 2 -0.558 0.09 Breast cancer cell lines 2 -0.56 0.06 0.99 12754 HCC 1954. GDS2787 GPL96, U133A MCF-7 cells. 3 163 37. MCF-7 cells following 3 192 40 0.40 221279 the induction of nuclear LIM-only protein 4 (LMO4). GDS2788 GPL97, U133B MCF-7 cells 3 654 324 MCF-7 cells following 3 731 340 0.79 226271 the induction of

214

nuclear LIM-only protein 4 (LMO4). GDS2789 GPL570, U133 Plus 2.0 MCF-7 cells 3 496 185 MCF-7 cells after the 3 383 283 0.59 226271 Array (baseline). induction of LIM domains (CLIM GDS2745 GPL97, U133B MCF-7 cells 3 4063 1454 MCF-7 cells treated 3 3444 1577 0.64 226269 with 100 nM dioxin. GDS2861 GPL96, U133A MCF7 3 4.3 0.18 MCF7 cells over 3 4.6 0.13 0.049 221279 expressing**(XBP1) GDS992 GPL96, U133A MCF-7 3 6.8 0.041 MCF-7 membrane 3 6.5 0.1 0.013 221279 cytosolic fraction fraction GDS2744 GPL96, U133A cancer cells 3 828 144 MCF-7 treated with 3 822 197 0.96 221279 100 nM dioxin.

GDS2323 GPL96, U133A MCF7/BUS 3 141.7 30 Breast cancer 3 175 33 0.463 221279 cells MCF7/BUS cells starved of estrogen for 1 day. MCF7/BUS cells 3 87 57 starved of estrogen for 2 day. GDS3315 GPL570, U133 Plus 2.0 MCF-7 cells 4 6 0.42 MCF-7 treated with 4 6 0.06 9.81E-

226269_ Array estradiol (E2). 06

215

MCF-7 4 8 0.124 MCF-7 treated with 4 8 0.08 treated with estradiol and

cycloheximide. cycloheximide.

GDS3285 GPL570, U133 Plus 2.0 MCF-7 treated 3 6 0.161 MCF-7 cells treated 3 5.6 0.08 0.252 221279 Array with estrogen with estrogen for 3 for 0 hour. hour. MCF-7 breast cancer 3 6 0.17 cells treated with estrogen for 6 hour. MCF-7 breast cancer 3 6 6 cells treated with estrogen for 12 hour. GDS2324 GPL96, U133A MCF7/BUS 5 73.2 48 MCF7 treated with 17 5 114 63 0.16 221279 cells beta-estradiol Concentration.of 10 pM. At concentrations of 5 173 20 30 pM. At concentration of 60 5 175 39 pM. At concentration of 5 137 45 100 pM. GDS3283 GPL570, U133 Plus 2.0 MCF-7 cells 3 8 0.2 MCF-7 cells treated 3 8 0.1 0.15 221279 with estradiol for 3 h.

216

GDS3217 GPL570, U133 Plus 2.0 MCF7 breast 3 467 58 Estradiol 12 h. 3 638 13 0.26 226269 cancer cells Control 12h

GDS3179 GPL4133, Agil LM2 breast 3 -0.26 0.096 LM2 cells depleted 3 -0.47 0.13 0.036 40486 cancer cells for *** (MTDH) 014850 culture alone cultured alone.

4x44K LM2 control 3 -0.28 0.06 LM2 cells depleted 3 -0.07 0.19 G4112F cultured with for (MTDH) cultured lung with lung endothelial endothelial

GDS3609 GPL571, U133A 2.0 LM2 cells, 3 1 0.09 siRno collagen gell 3 1 0.09 0.1203 221279 control no collagen gell

Control 3 1 0.04 siR collagen gell 3 0.87 0.16 collagen gell

GDS3138 GPL570, U133 Plus 2.0 LM2 cells 2 143 80 LM2 cells transfected 2 117 32 0.71 226271 with a vector expressing microRNA miR-335. GDS1477 GPL96, U133A 3 4 0.58 BT-20 breast cancer 3 5 1 0.9141 221279 cells, GFP siRNA.

217

BT-20 breast BT-20 breast cancer 3 4 1 cancer cells, cells, Fox M1 siRNA Mock. GDS807 GPL1223, Arcturus 22k Microdissected 32 1 2 Cancer recurred 28 0.93 1.7 0.37 21186 human estrogen oligonucleotide positive primary breast cancer tumors disease free GDS806 GPL1223, Arcturus 22k Expression 32 0.7 1.5 Cancer recurred 28 0.54 1.35 0.66 21186 human profiling of oligonucleotide microdissected estrogen positive primary breast cancer tumours disease free *(PTHrP) parathyroid hormone-related protein ** (XBP1) spliced X-box binding protein-1 ***(MTDH)metadherin

218

B- Colon cancer cell lines Control Case

Geo Reporter Array Description N Ave Stdev Description N Ave Stde T-Test number v or Anova

GDS756 GPL96, U133A primary tumor 3 0.72 0.73 metastatic tumor 3 11 2 0.92 221279 9 GDS333 GPL570, U133 Plus 2.0 HT29 cells sensitive 3 16 3.4 HT29 resistant 3 7.5 8.5 0.17 0 226269 Array (MTX). to MTX. & GPL570, 221279 & GPL570, 226271 GDS316 GPL571, U133 Plus 2.0 HT29 derived colon 3 2 1.23 HT29 resistant 3 1 0.7 0.278 0 221279 Array cancer cells to MTX. GDS194 GPL96, U133A RKO colon cancer cell 8 65 29 RKO treated with 7 101 16 0.013 2 221279 line ponasterone A(Induced) GDS142 GPL2721, RIKILT Human HT-29 cells cultured in 5 6.5 0.06 cultured in 5 6 0.06 0.70

4 R77 - C71 Oligo Array methyl-tetrahydrofolate pteroylglutamic acid 10 ng/ml 10 ng/ml

219

NM_01897 cultured in methyl- 6 0.069 cultured in 6.4 0.087 2 tetrahydrofolate 100 6.5 pteroylglutamic acid 6 ng/ml 100 ng/ml *(MTX) Methotrexate

C- Leukaemia cell lines Control Case

Geo Reporter Array Description N Average Stdev Description N Average Stdev T- number Test or Anova

GDS2970 GPL570, U133 Plus 2.0 K562 cells 3 120 25 K562 treated 3 107 23 0.52 226271 Array with *1R-Chl.

GDS3487 GPL570, U133 Plus 2.0 K562 cells. 10 436 215 K562 cells 10 323 156 0.193 226269 Array depleted for **cAMP GDS1886 GPL96, U133A ***THP-1 7 16 10.6 THP-1 cells 7 17 17.8 0.91 221279 cellsc exposed to ****(MH) for 24 hours. GDS3046 GPL96, U133A K562 cells 3 4 0.091 Treated with 1 3 4 0.22 0.63 221279 uM imatinib for 24 hours. GDS3043 GPL96, U133A K562 cells 3 6.5 0.09 Treated with 1 3 6.57 0.13 0.52 221279 uM imatinib for 24 hours.

220

GDS3047 GPL96, U133A K562 cells 3 5.3 0.05 treated with 3 5.34 0.1 0.04 221279 1uM

imatinib for 24 hours

GDS3044 GPL96, U133A K562 cells 3 5.7 0.084 Treated with 3 5 0.035 0.018 221279 1 uM imatinib for 24 hours.

GDS3045 GPL96, U133A K562 cells 3 5.5 0.03 Treated with 1 3 5.47 0.06 0.35 221279 uM imatinib for 24 hours. GDS3048 GPL96, U133A K562 cells 3 6 0.06 Treated with 1 3 6 0.05 0.229 221279 uM imatinib for 24 hours. GDS3089 GPL96, U133A HL60 cells 3 4 1.7 Treated with 3 4.6 2.26 0.74 221279 ***** ATRA) GDS3085 GPL4274, NHICU Leukocytes 18 -0.58 0.53 Leukocytes 12 -0.44 0.63 0.43 AL110252 Human 19K from patients, with gram- v1.0 sepsis (mixed + &-).

leukocytes 25 -0.05 0.91 from patients

221

with gram- negative leukocytes 18 -0.5 0.81 from patients with gram- positive *1R-Chl polyamide-chlorambucil conjugate **cAMP Response Element Binding Protein (CREB) ***THP-1 cells acute monocytic ****MH moderate hypothermia *****ATRA tretinoin all-trans retinoic acid

D- Prostate cell line Control Case

Geo Reporter Array Description N Ave Stdev Description N Ave Stdev T- number Test or Anova

GDS2971 GPL3877,5.4.6.9& PRHU05- LNCaP 6 -0.44 0.98 LNCaP cells 6 0.17 1.7 0.54 GPL3877,3.1.14.5 S1-0006 cells treated with docetaxel. GDS3634 GPL6104, Illumina DU145 4 20 7 DU145 with 4 25 12 0.54 ILMN_1730897 humanRef- restored miR-205 8 v2.0 expression beadchip

222

GDS1699 GPL3341, 16674 SHDP Androgen 3 1.5 1 Androgen 5 1.5 2 0.98 sensitive insensitive (AI) (AS) cell prostate cancer cell lines. lines. GDS3111 GPL570, 221279 U133 Plus LNCaP 3 249 66 treated with the 3 268 55 0.99 2.0 Array prostate androgen cancer cells dihydrotestosterone for 4 h treated with the 3 244 81.6 androgen dihydrotestosterone for 16h GDS2782 GPL570, 226269 U133 Plus DHT- 3 193.9 0.23 dihydrotestoterone 3 193.8 30 0.82 2.0 Array stimulated LNCaP prostate cells untreated

bicalutamide and 3 248 96 dihydrotestoterone WGWWCW 3 145.8550 40.7 polymide and dihydrotestoterone polymide and 3 150 35.5 dihydrotestoteroe

223

E- Lung cancer cell lines Control Case

Geo Reporter Array Description N Average Stdev Description N Average Stdev T- number Test or Anova

GDS2499 GPL570, U133 A549 cells 3 141 6 Sapphyrin 3 141 4 0.69 226271 Plus 2.0 Mannitol5% 2.5uM Array Actinomycin 3 137 11 Sapphyrin 1.25 uM 3 158 11 5ug /ml GDS1204 GPL96, U133A A549 cells 3 65 26 motexafin 4h 3 70 24 0.97 221279 Control 4h

Control 12h 3 50 0 motexafin 12h 3 89 40

Cont 24h 3 59 16 Motexafin 24h 3 80 30 GDS1688 GPL96, U133A non-small cell 10 182 121 small cell cancer 9 373 188 0.061 221279 adenocarcinoma squamous cell cancer 10 156 85

GDS2777 GPL96, U133A gemcitabine 108 25 (Gem) derivative 130 44 0.56 221279 (Gem)-resistant 4 bexarotene (Gem) 4 154 32 (Gem) gemclitabine 4 174 35 derivative vehcle (Gem) Bex & Gem 4 143 18

224

GDS3101 GPL201, HG- A549 lung cells 3 22 2 cisplatin 3 22 2 0.73 221279 Focus GDS3627 GPL570, U133 Squamous cell 18 7 1 adenocarcinomas(AC) 40 6 1 0.002 226269 Plus 2.0 carcinomas

Array (SCC).

F- Miscellaneous Cell lines Control Case

Geo number Reporter Array Description N Ave Stdev Description N Ave Stdev T- Test or Anova

GDS2164 GPL570, U133 Plus Jurkat CD4+ 3 91 40 CD4+ T cells 3 211 29 0.01 226269 2.0 Array T cells following

induction of simian immune

225

deficiency virus (SIV)

GDS2164 GPL570, U133 Plus 221279 2.0 Array GDS2164 GPL570, U133 Plus 226271 2.0 Array GDS2794 GPL570, U133 Plus T-cell acute 3 99 33 following gamma- 3 103 33 0.9 226269 2.0 Array lymphoblast secretase inhibitor leukaemia (T- (GSI) ALL) GDS1580 GPL570, U133 Plus 293T cells 4 255 26 293T cells depleted 4 222 25 0.13 226271 2.0 Array for lens epithelium- derived growth factor (LEDGF/p75) GDS1580 GPL570, U133 Plus 221279 2.0 Array GDS1580 GPL570, U133 Plus 226269 2.0 Array GDS3424 GPL570, U133 Plus intestinal 3 42 5 intestinal Caco-2 3 19 3 0.044 226269 2.0 Array Caco-2 cells cells treated (CLA

intestinal intestinal Caco-2 3 28 4 Caco-2 cells cells (CLA)

226

GDS2426 GPL570, U133 Plus HEK293 4 226 96 HEK293 4 387 58 0.03 226269 2.0 Array kidney cells overexpressing

the hypertension- related calcium- regulated gene (HCaRG).

GDS2426 GPL570, U133 Plus 226271 2.0 Array

GDS2426 GPL570, U133 Plus 226269, 2.0 Array

GDS3626 GPL6102, Illumina HEK293 2 93 0.77 HEK293 depleted 2 107 1.7 0.009 ILMN_18687 human-6 kidney cells for the

97 v2.0 macrophage expression migration beadchip inhibitory factor (MIF).

GDS3524 GPL96, U133A cultured renal 3 6 0.08 hypoxia 3 6 0.06 0.23 221279 proximal

227

tubule epithelial RPTEC cells Normoxia GDS12 GPL44, 1296 FHCRC human bone 4 0.08 0.18 HS-27a 4 0.16 0.36 0.68 Human 18K marrow Array stromal cell lines HS-5. GDS3051 GPL96, U133A IMR-90 4 5 0.15 2-dimensional 4 5 0.04 0.046 221279 fibroblasts

cultured

3-dimensional 4 6 0.13

GDS3635 GPL570, U133 Plus GM5659 skin 3 51 6 GM5659 skin 3 44 10 0.44 226269 2.0 Array fibroblasts. fibroblasts treated with ascorbic acid 2-phosphate GM5659 skin 3 35 4 fibroblasts treated with ascorbic acid GDS4932 GPL96, U133A 1mM 4- 5 64 17 12 hour 3 69 24 0.99 221279 phenylbutyrate (PBA) 0 hour 24 hour 3 67 23

GDS2535 GPL571, U133A 2.0 lung 3 5 0.3 Hydrogen peroxide 3 5 0.18 1 221279 Array fibroblasts (H2O2) control 1h 1h

228

vanadium 3 5 0.28 pentoxide(V205) 1h Cont 4h 3 5 0.2 H202 4h 3 5 0.29 V205 4h 3 5 0.54 Cont 8h 3 5 0.24 H202 8h 3 5 0.09 V205 8h 3 5 0.28 Con 12h 3 5 0.3 H202 12h 3 5 0.22

V20 12h 3 5 0.31 Con 24h 5 0.1 H202 24h 3 5 0.23 3 V20 24h 3 5 0.36

GDS1504 GPL97, U133B fibroblast cell 3 92 32 HGPS AG10750 3 141 25 0.22 226269 lines Normal GM00038C Normal 3 155 23 HGPS AG11513 3 278 18 GM08398C Normal 3 220 94 HGPS AG11498 3 168 27 GM0316B

GDS1503 GPL96, U133A fibroblast cell 221279 lines GDS2054 GPL97, U133B constitutively 3 1450 133 misfolded 3 642 316 0.54 226271 express surfactant protein C misfolded surfactant protein C,

229

empty vector wild type 3 1808 441 GDS1841 GPL96, U133A constitutively 3 197 47 misfolded 3 151 8 0.99 221279 express surfactant protein misfolded surfactant protein C, empty vector wild-type 3 159 91 GDS2307 GPL570, U133 Plus retinal pigment 3 99 14 LDL 3 61 39 0.59 226271 2.0 Array epithelial ARPE-19 cells ox- LDL 3 103 18

GDS2933 GPL96, U133A Gingival 4 147 80.38 Streptococcus 4 73 47 0.80 221279_ epithelial 53 gordonii HIGK cells co- cultured Fusobacterium 4 91 95 nucleatum GDS3211 GPL96, U133A Gingival 3 101 29.54 Porphyromonas 3 94 53 0.99 221279 epithelial 87 gingivalis HIGK cells Aggregatibacter 3 81 2 actinomycetemcom itans GDS2606 GPL571, U133A 2.0 bronchial 3 58 7 Pseu 3 52 7 0.75 221279 Array epithelial Stap 3 50 7 BEAS-2B cells Resp 3 46 3 GDS3710 GPL570, U133A 2.0 A549 3 2 0.09 TGF-beta 0.5h 3 2 0.09 0.8 221279 Array epithelial cells TGF-beta 1h 3 2 0.06 TGF-bet 4h 3 2 0.02

230

TGF-beta 16h 3 2 0.027 TGF-bet 2h 3 2 0.1 TGF-beta 8h 3 2 0.006 TGF-beta 24h 3 2 0.15 TGF-beta 72h 3 2 0.048 GDS2341 GPL96, U133A lung epithelial 4 35 113 type I (IFN) 6h 4 320 58 221279 A549 cells Control 6h type II (IFN) 6h 4 376 64 typI & TyII 6h 4 409 46 Control 24h 4 400 80 type I (IFN) 24h 4 981 91

type II (IFN) 24h 4 412 87 typI & TyII 24h 4 375 76

GDS3034 GPL96, U133A HUVEC 4 9 11 HUVEC umbilical 4 8 1.8 221279 umbilical vein vein endothelial endothelial cells (EC) exposed cells (EC) to Candida albicans blastopsores GDS2892 GPL570, U133 Plus HeLa cells 3 115 21 HeLa cells depleted 3 352 41 226271 2.0 Array for optineurin using RNAi knockdown. *GDS1779 GPL570, Genome Hela normoxia 3 250 125 Astrocyte 3 682 247 226269 U133 Plus normoxia 2.0 Array Hela hypxia 3 180 138 Astrocyte hypxia 3 448 167

*GDS1779 GPL570, Genome 226271 U133 Plus 2.0 Array

231

GDS2010 GPL570, U133 Plus umbilical vein 2 47 14 HUVEC following 2 237 0.63 226269 2.0 Array endothelial siRNA knockdown cells of Wilms' tumour (HUVEC) 1-associating protein GDS2241 GPL96, U133A BeWo 4 5 0.15 JEG3 trophoblast 4 5 0.15 221279_ trophoblast cell lines. GDS2241, cell lines. GDS3413 GPL2895, GE cultured aortic 4 0.46 0.06 10 um/L 4 0.32 0.22 373058 Healthcare/ smooth muscle homocysteine GDS3413, Amersham cells AL110252.1 Biosciences 0 um/L 100 um/L 4 0.39 0.17 CodeLink homocysteine homocysteine Human Whole Genome Bioarray GDS494 GPL97, U133B 1mM 4- 5 292 141 12 h 3 252 32 226269_, phenylbutyrate 24 h 3 207 69 GDS494, (PBA) Control 0 h GDS528 GPL351, 1365 UHN colorectal 3 -0.1 1 6 h 3 -0.15 0.44 GDS528, Microarray tumour cell Centre - line (HCT116) 24 h 3 0.39 0.43 Human 19K 10 min 3 (Part B) GDS3124 GPL96, U133A nu61 3 9 3 RR Rad 3 32 39 221279_ radioresistant GDS3124, tumours RS RR Unex 3 44 61 Radi 232

RS Unex 3 24 23 GDS2852 GPL96, U133A bronchial 3 86 16 control 24 h 3 86 9 221279_ A549 cells GDS2852, o h IL-13 12h 3 103 11 NM_018972 IL-13 4h 3 79 9 IL-13 24h 3 82 29

GDS3649 GPL6884, Illumina HK2 proximal 3 139 15 C-peptide 18h 3 120 19 ILMN_172180 HumanWG- tubular cells 1 GDS3649, 6 v3.0 (PTCs) NM_018972.1 expression Control 18 h beadchip Control 48 h 3 127 7 TGF- beta1 48 h 3 93 11 C-peptide 48 h 3 134 15

TGF- beta1 and c- 3 93 11 peptide 48h GDS1094 GPL201, [HG-Focus] U2OS 6 402 227 4-OH tamoxifen 4 398 171 221279_ Affymetrix osteosarcoma estrogen receptor GDS1094, Human HG- cell line alpha NM_018972 Focus Control Target estrogen Array receptor alph Control 7 410 173 4-OH tamoxifen 4 288 126 estrogen estrogen receptor receptor beta beta Control 7 444 142 4-OH tamoxifen 4 420 145 estrogen estrogen receptor receptor alph&bet 4 alph&bet

233

GDS3432 GPL1708, Agilent- glucocorticoid 3 5 0.24 hGR-alpha A 6h 3 5 0.15 9727 012391 receptor alpha Whole (hGR-alpha) Human isoform Genome alpha A 0h Oligo hGR-alpha B 3 5 0.09 hGR-alpha B 6h 3 5 0.31 Microarray 0h G4112A (Feature hGR-alpha C 3 5 0.26 hGR-alpha C 6h 3 5 0.31 Number 0h version) hGR-alpha D 3 5. 0.4 hGR-alpha D 6h 3 4 1 0h hGR-alpha 0h 3 5 0.25 hGR-alpha 6h 3 5 0.35 hGR-alpha A 12h 3 4 0.4

12h 3 5 0.01 hGR-alpha B hGR-alpha C 12h 3 4 0.68 hGR-alpha D 12h 3 5 0.42 hGR-alpha12h 3 4 0.38 GDS3640 GPL6087, Agilent- HepG2 liver 6 0.06 0.31 HepG2 liver cells 6 -0.19 0.31 GDS3640, 012097 cells treated treated with 200 Human 1A with 100 uM uM copper sulfate Microarray copper sulfate for 4 hours (V2) for 4 hours G4110B - HepG2 liver 6 0.07 0.55 HepG2 liver cells 6 -0.28 0.39 alternative cells treated treated with 200 version with 100 uM uM copper sulfate copper sulfate for 8 hours for 8 hours 234

HepG2 liver 6 0.06 0.22 HepG2 liver cells 6 -0.06 0.46 cells treated treated with 200 with 100 uM uM copper sulfate copper sulfate for 12hours for 12 hours HepG2 liver 6 -0.05 0.28 HepG2 liver cells 6 -0.27 0.22 cells treated treated with 200 with 100 uM uM copper sulfate copper sulfate for 24 hours for 24 hours HepG2 liver cells 8 -0.08 0.21 treated with 400 uM copper sulfate for 4 hours HepG2 liver cells 6 -0.14 0.57 treated with 400 uM copper sulfate for 8 hours HepG2 liver cells 6 -0.04 0.05 treated with 400 uM copper sulfate for 12 hours HepG2 liver cells 6 -0.16 0.31 treated with 400 uM copper sulfate for 24 hours HepG2 liver cells 6 -0.09 0.2 treated with 600 uM copper sulfate for 4 hours

235

HepG2 liver cells 6 -0.06 0.28 treated with 600 uM copper sulfate for 8 hours HepG2 liver cells 6 0.06 0.29 treated with 600 uM copper sulfate for 12 hours HepG2 liver cells 6 0.15 0.14 treated with 600 uM copper sulfate for 24 hours GDS2239 GPL96, U133A HepG2 3 44 2 HepG2 hepatocytes 3 44 1.7 221279_at hepatocytes following induction (ID_REF), of hepatitis C virus GDS2239, (HCV) core protein NM_018972 expression. GDS2049 GPL96, U133A hepatoma 4 339 96 HepG2 4 407 68 221279_at HepG2 cells transduction with (ID_REF), Control 6h (GPI-PLD) for 6h GDS2049, HepG2 cells 4 556 19 HepG2 4 427 243 NM_018972 12h transduction with Control (GPI-PLD) for 12h

GDS3578 GPL570, U133 Plus multiple 3 5 0.1 MM1.S cells 3 4 0.22 221279_at 2.0 Array myeloma depleted for beta- (ID_REF), MM1.S cells catenin GDS3578, NM_018972

236

GDS2447 GPL1426, ABI lymphoblast 9 0.98 0.56 lymphoblast cell 6 1.6 0.45 0.03 202631 Human cell lines. lines derived from (ID_REF), Genome six subjects with GDS2447, Survey active nicotine NM_018972 Microarray dependence Version 1

GDS2193 GPL96, U133A adenoid cystic 3 14 0. (ACC) derived 3 9 4 0.08 221279_at carcinoma cells (ACC3) (ID_REF), (ACC) following RNAi GDS2241, knockdown of Sry- NM_018972 related high mobility group box 4 (Sox4). GDS3556 GPL570, U133 Plus H295 adrenal 3 6.5 0.55 Angiotensin ll 3 6 0.13 0.35 221279_at 2.0 Array carcinoma (ID_REF), cells solvent GDS3556, control NM_018972 GDS3537 GPL570, U133 Plus H295R 3 478 41 H295R treated with 3 697 138 0.318 226269_at 2.0 Array adrenocortical 2-OH-BDE 47 (ID_REF), carcinoma GDS3537, cells H295R treated with 3 598 33 BF002104 DMSO 2-OH-BDE85

GDS3233 GPL96, U133A cervical cancer 2 32 29 Cervical cancer 8 50 22 0.72 221279_at (CC) primary 4 Cell line

237

(ID_REF), tumours and GDS3233, cell lines. Cervical cancer 28 32.85 30 NM_018972 Normal Primary tumour GDS2494 GPL96, U133A glucocorticoid 3 93 10 following treatment 3 72 3 0.25 221279_at (GC) resistant with rapamycin for (ID_REF), T-cell 3h GDS2494, lymphoblastic following treatment 3 99 13 NM_018972 leukaemia cell with rapamycin for line CEM-c1 24 h

GDS2295 GPL571, U133 Plus diffuse large B 3 46 12 SKI-DLCL cells 3 62 8 0.13 221279_at 2.0 Array cell lymphoma treated with (ID_REF), SKI-DLCL Aplidin GDS2295, cells NM_018972 SKI-DLCL cells 3 61 6 treated with cytarabine (AraC)

SKI-DLCL cells 3 55 2 treated with both Cy & Ap

GDS2780 GPL201, [HG-Focus] liver 3 113 42 HepG2 cells 3 158 30 0.062 221279_at Affymetrix carcinoma exposed to Modche (ID_REF), Human HG- HepG2 cells Phen GDS2780, Focus HepG2 cells 3 40 7 NM_018972 Target exposed to Heavy Array metal chromium

238

HepG2 cells 3 106 32 exposed to Heavy metal arsenic HepG2 cells 3 30 30 exposed to Heavy metal antimony HepG2 cells 3 110 4 exposed to Modche 2,3 dimethox HepG2 cells 3 52 16 exposed to Modche N- nitr HepG2 cells 3 127 15 exposed to Heavy metal cadmium HepG2 cells 3 77 10 exposed to Heavy metal nickel HepG2 cells 3 100 12 exposed to Heavy metal mercury GDS2724 GPL570, U133 Plus DAOY 3 315 50 DAOY following 18 380 65 0.9 226271_at 2.0 medulloblasto RNAi knockdown (ID_REF), of Bmi-1 + mel

239

GDS2724, ma cells DAOY following -1 313 13 N46350 control RNAi knockdown 3 of BMi DAOY following 3 340 6 RNAi knockdown of AlK DAOY following 18 312 18 RNAi knockdown of mel- GDS690 GPL96, HG-U133A intestinal 3 5.5 0.12 Caco-2 flagellin 3 5 0.09 0.79 221279_at epithelial cell (ID_REF), immune GDS690, response NM_018972 Caco-2 control T84 control 3 5.6 0.40 Caco-2 LTb 3 5.5 0.266 Caco-2 TNFa 3 5 0.037 T84 flagellin 3 5.5 0.04 T84 TNFa 3 5.6 0.5 T84 LTb 3 5.6 0.3 ** (CLA) conjugated linoleic acid isomer cis-9, trans-11 CLA *(GPI-PLD) recombinant adenovirus expressing glycosylphosphatidylinositol-specific phospholipase D DAOY medulloblastoma cells following RNAi knockdown of Bmi-1, Mel-18, or both

A3.4 Search result for Human GDAP1 and Breast

Table A 3.4: Human GDAP1 and Breast The search term used in this table is human GDAP1 and breast. In this search 119 profiles where found, 41 were included, 22 of them were the same studies in the cell line and are included in Table A3; 19 were new and included in this table.

240

Control Case

Geo Reporter Array Description N Ave Stdev Description N Ave Stdev T- number Test or Anova

GDS1250 GPL96, U133A ** (ADH) 4 71 65 ADH with breast 4 38 39 0.831 221279 cancer & GDS1250, GDS3139 GPL96, U133A normal breast 15 55 35.747 breast cancer 14 52 58 0.854 221279 & epithelia GDS319, GDS3097 GPL96, U133A Invasive non-IBC. 35 5 0.166 tumour epithelia 13 5 0.21 0.5 221279 Non-inflammatory from patients with GDS307, breast cancer inflammatory breast cancer (IBC) inflammatory breast cancer GDS2618 GPL97, U133A Normal breast cell 3 528 284 non-tumourgenic 3 693 657 0.87 cancer cel 226269 GDS268, tumourgenic cancer 6 703 574 cell GDS2617 GPL96, U133A 221279 GDS267, GDS1329 GPL96, U133A apocrine tumour 6 6 0.24 basal tumor 16 6 0.1 0.58 221279 luminal tumor 27 6 0.58 GDS1329, 241

GDS2758 GPL96, U133A MCF-7 (Normoxia) 3 8 2 hypoxia 3 6 0.23 0.06 221279 dimethyloxalylglycine 3 6 0.5 GDS2758 GDS3116 GPL96, U133A breast cancer tumours 58 26 24 breast cancer 58 20 17 0.08 221279 following treatment tumours following GDS3116 with baseline treatment with letrozole GDS2635 GPL570, U133 invasive lobular 10 103 152 invasive lobular 5 352 732 0.67 226269 Plus control carcinoma (ILC) GDS2635, 2.0 cells Array ductal control 10 236 382 invasive ductal 5 160 201 carcinoma GDS2250 GPL570, U133 Normal 7 7 0.52 non-basal-like 20 7 2 0.049 226269 Plus cancer GDS2250, 2.0 BRCA-associated 2 7 2 basal-like cancer 18 6 1 Array breast cancer GDS2760 GPL570, U133 hypoxic MCF-7 3 107 13 HIF-1 alpha 3 158 27 0.079 226271 Plus depletion GDS2760, 2.0 Array HIF-2 alpha depletion 3 126 6 HIF-1 alpha HIF-2 3 144 13 GDS1925 GPL96, U133A Control 3 8 0.35 long term E2 3 8 0.47 3.75E- 221279 Array independent 05 GDS1925, growth

erbB-2 3 6 0.17 MEk 3 6 0.19

Raf-1 3 6 0.07 EGFR 3 7 0.14

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GDS2415 GPL3558, NKI- No tumour recurrence 31 -0.29 0.78 tumour recurrence 19 -0.3 0.4 0.98 10041 AVL (primary tumour) (primary tumour) GDS2415, Homo tumour recurrence 9 -0.25 0.42 H15696 sapiens (recurrent tumour) 18K cDNA GDS1326 GPL96, U133A control 3 48.63 58.17 Eralpha expresion 3 51.43 14.43 0.9918 221279 control GDS1326, control E2 3 43.93 33.29 Eralpha expresion 3 52.86 33.76 E2 GDS1761 GPL1290, NCI breast tumour 9 0.22 0.363 CNS tumour 6 -0.0001 0.13 0.0064 3758 cDNA

GDS1761

colon tumour 7 -0.14 0.53 Leukaemia 8 -0.043 0.29

melanoma 8 0.44 0.14 non-small cell lung 9 0.13 0.23 cancer(NSCLC)

ovarian tumour 6 0.17 0.32 prostate tumour 2 0.35 0.148

renal tumour 8 0.136 0.195 unknown 1 0.89

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GDS3716 GPL96, U133A reduction 18 55.07 34.95 ER-breast cancer 9 35.37 29.39 0.59 221279 mammoplasty GDS3716, prophylactic 6 40.95 40.02 ER+breast cancer 9 45.9 43.5 masectomy control GDS2367 GPL96, U133A veihcle control 2 5.36 0.08 vehicle adenovirus 3 5.408 0.17 0.71 221279 adenovirus carrying estrogen GDS2367, receptor estradiolcontrol 3 5.24 0.269 estradiol adenovirus 3 5.69 0.68 adenovirus carrying estrogen receptor tamoxifen control 3 5.26 0.046 tamoxifen 3 5.653 0.65 adenovirus adenovirus carrying estrogen receptor GDS1873 GPL96, U133A control 3 304.13 149.8 hormon treated 3 338.54 91.61 0.2264 221279 androgen GDS1873, hormon treated 3 404 130 antiestrogen 3 411 162 estogen treatment androgen and tamoxifen

aromatase inhibition 3 214.7 37.93 aromatase inhibition 3 218.6 83.9 androgen and androgen and le anastrozole trozole *(PTHrP) Parathyroid hormone-related protein

** (ADH) Atypical Ductal Hyperplasia

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A3.5 GDAP1 expression in Gliomas GDS1975and GDS1976

Two GEO profiles, GDS1975 and GDS1976 compared grades III and IV gliomas (Freije et al., 2004). These studies were excluded from our initial analysis because they did not have normal control. Shown in figure 1, both microarrays demonstrated that expression of GDAP1 was significantly higher in the grade III anaplastic oligodendrogliomas compared to gliomas of other grades (p-value = 3.45x10-06 and p=0.03 for Figure A3 (A) and A3 (B) respectively). This

suggestes that GDAP1 expression may change in different stages of cancer pathology.

) 7

( 2 5 0 0

1 P

A 2 0 0 0

o 1 5 0 0

o i

s 1 0 0 0

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x 5 0 0

h 0 T

A O O G A A M I G G A I I I I IV II I d I a e d d r a a d r r g a g r g g

A-

) 7

( 2 5 0 0 0

1 P

A 2 0 0 0 0

f 1 5 0 0 0

o i

s 1 0 0 0 0

e r

p 5 0 0 0

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Figure A3.1: Expression of GDAP1, in the analysis of grades III and IV gliomas of various histologic types for the expression of GDAP1. Black columns show grade III glioma anaplastic astrocytoma (n=8), white columns show grade III anaplastic mixed oligo-astrocytoma (n=7), red columns show grade III anaplastic oligodendroglioma (n=11), blue columns show grade IV glioma glioblastoma (n=59). Data is from GDS1975and GDS1976. (7) indicates MAS5 (Table2.2).

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A3.6 Search results for Oncomine data mining Table A 3.5: Summary of all cancer studies found in Oncomine

The following tables give the details of the studies found in Oncomine which matched the inclusion criteria.

A- GDAP1 Expression in Lung cancer

Study Numbe Numbe Cancer type Fold p-value r of r of chang controls e samples

Garber 6 4 small cell lung 6 1.73x10- Over-expressio et al. carcinoma 5 n (2001) 13 Squamous Cell 2.53 4.3x10-5 Over-expressio Lung Carcinoma n

Hou et 65 19 non-small cell 4.3 1.85x10- Over-expressio al. lung carcinoma 6 n (2010)

Weiss et 59 77 Lung 1.1 8.04x10- Over-expressio al. Adenocarcinoma 10 n (2010) 155 Squamous Cell 1.1 8.91x10- Over-expressio Lung Carcinoma 14 n

Landi et 49 58 Lung 1.1 0.002 Over-expressio al. Adenocarcinoma n (2008)

Wachi et 5 5 Squamous Cell 1.1 0.149 Over-expressio al. Lung Carcinoma n (2005)

Su et al. 30 27 Lung -1.021 0.5 Over-expressio (2007) Adenocarcinoma n

Selamat 58 58 Lung 1 0.212 Over-expressio et al. Adenocarcinoma n (2012)

247

Okayam 20 226 Lung 1.1 Over-expressio a et al. Adenocarcinoma 0.052 n (2012)

TGCA 390 9 Non-Mucinous 1.1 6.66X10 Over-expressio Bronchioloalveola -4 n r Carcinoma

10 Papillary Lung 1.1 0.018 Over-expressio Adenocarcinoma n

260 Lung 1.12 7.94X10 Over-expressio Adenocarcinoma -22 n

348 Squamous Cell 1.1 2.24X10 Over-expressio Lung Carcinoma -33 n

6 Acinar Lung 1.2 0.078 Over-expressio Adenocarcinoma n

67 Lung 1.2 1.05X10 Over-expressio Adenocarcinoma, -5 n Mixed Subtype

6 Lung Mucinous 1 0.11 Over-expressio Adenocarcinoma n

8 Squamous Cell 1 0.4 Over-expressio Lung Carcinoma, n Basaloid Variant

3 Mucinous -1.02 0.77 Bronchioloalveola Over-expressio r Carcinoma n

3 Micropapillary -1.089 0.92 Lung Over-expressio Adenocarcinoma n

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B- GDAP1 Expression in Cervical cancer Study Number Number Cancer type Fold p-value of of change controls samples Pyeon et 8 20 Cervical cancer 3.400 1.78x10- Over-expression al. (2007) 6

Scotto et 7 33 Cervical -1.016 0.52 Over-expression al. (2008) Carcinoma

79 Cervical 1.02 0.034 Over-expression Squamous Cell

Carcinoma 5 Cervical 1 0.454 Over-expression Adenocarcinoma

Zhai et al. 10 21 cervical -1.035 0.652 Over-expression (2007) squamous cell carcinoma 7 High Grade -1.064 0.727 Over-expression Cervical Squamous Biewenga 5 40 cervical -1.066 0.68 Over-expression et al. squamous cell (2008) carcinoma

TCGA 3 13 Cervical Non- 1.1 0.007 Over-expression Keratinizing Squamous Cell Carcinoma 83 Cervical 1.06 9.53x10- Over-expression Squamous Cell 5 5 CarcinomaCervical 1 0.5 Over-expression Keratinizing Squamous Cell Carcinoma

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C- GDAP1 Expression in Colorectal cancer Study Numbe Numbe Cancer type Fold p-value r of r of chang controls samples e Skrzypcza 10 5 Colon Adenoma 2.422 5.11x10 Over-expressio k et al. -6 n (2010) Kaiser et 5 41 Colon 1.412 6.97x10 Over-expressio al. (2007) Adenocarcinom -6 n a 13 Colon Mucinous 1.7 2.67x10 Over-expressio Adenocarcinom -4 n a 17 Cecum 1.8 6.59x10 Over-expressio Adenocarcinom -4 n 4 Rectala 1.8 0.006 Over-expressio Mucinous n Adenocarcinom 13 Rectosigmoida 1.6 0.01 Over-expressio Adenocarcinom n a 8 Rectal 1.8 0.1 Over-expressio Adenocarcinom n Gaspar et 22 56 Colorectala 1.127 0.05 Over-expressio al. (2008) Adenoma n Hong et al. 12 70 Colorectal 1.6 4.01x10 Over-expressio (2010) Carcinoma -6 n Zou et al. 8 9 Colon -1.546 0.9 Over-expressio (2002) Carcinoma n Skrzypcza 24 36 Colorectal 1.310 0.015 Over-expressio k et al. Carcinoma n (2010) 25 Colorectal 1.103 0.02 Over-expressio Adenocarcinom n a Sabates- 32 9 Rectal Adenoma 1.400 0.017 Over-expressio Bellver et n al. (2007) 25 Colon Adenoma 1.760 0.007 Over-expressio n Gaedcke et 65 65 Rectal 1.047 0.012 Over-expressio al. (2010) Adenocarcinom n a TCGA 19 22 Colon Mucinous 1.7 1.05x10 Over-expressio Adenocarcinom -4 n a 22 Cecum 1.396 0.002 Over-expressio Adenocarcinom n a 250

9 Rectal 1.395 0.032 Over-expressio Mucinous n Adenocarcinom 60 Rectal 1.223 0.018 Over-expressio a Adenocarcinom n 101 Colona 1.08 0.14 Over-expressio Adenocarcinom n 3 Rectosigmoida 1.11 0.37 Over-expressio Adenocarcinom n a

D- GDAP1 Expression in Liver cancer Study Number Number Cancer type Fold p-value of of change controls samples Chen et al. 76 96 Hepatocellular 2.3 7.63x10- Over-expression (2002) Carcinoma 11 Guichard 86 99 Hepatocellular 1.1 2.60x10- Over-expression et al. Carcinoma 11 (2012) 26 Hepatocellular 1.05 9.78x10- Over-expression Carcinoma 4 Roessler et 21 22 Hepatocellular 1.06 0.006 Over-expression al. (2010) Carcinoma 225 Hepatocellular 1.07 1.29x10- Over-expression Carcinoma 7 TCGA 59 97 Hepatocellular 1.2 2.93x10- Over-expression Carcinoma 11 Wurmbach 10 35 Hepatocellular 1.7 2.28x10- Over-expression et al. Carcinoma 4 (2007) 13 Cirrhosis 1.2 0.102 Over-expression

17 Liver Cell 1.06 0.421 Over-expression Dysplasia Mas et al. 19 38 Hepatocellular 1 0.476 Over-expression (2009) Carcinoma 58 Cirrhosis -1.02 0.859 Over-expression

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E- GDAP1 Expression in Lymphoma Study Numbe Numbe Cancer type Fold p-value r of r of chang control sample e s s Piccaluga 20 6 Angioimmunoblasti 2.1 2.64x1 Over-expressio et al. c T-Cell Lymphoma 0-7 n (2007) TGCA 10 18 Diffuse Large B- 1 0.135 Over-expressio Cell Lymphoma n Eckerle et 5 4 Classical Hodgkin's 1.4 0.031 Over-expressio al. (2009) Lymphoma n 7 Primary Cutaneous 1.1 0.180 Over-expressio Anaplastic Large n Cell Lymphoma 4 Anaplastic Large 1.1 0.276 Over-expressio Cell Lymphoma, n ALK-Negative 5 Anaplastic Large 1.1 0.240 Over-expressio Cell Lymphoma, n ALK-Positive Storz et 3 5 Marginal Zone B- 1.1 0.202 Over-expressio al. (2003) Cell Lymphoma n 8 Cutaneous 1 0.357 Over-expressio Follicular n Lymphoma 6 Diffuse Large B- -1.016 0.53 Over-expressio Cell Lymphoma n Brune et 5 5 Nodular 1.2 0.032 Over-expressio al. (2008) Lymphocyte n Predominant Hodgkin's Lymphoma 5 11 Diffuse Large B- 1.1 0.031 Over-expressio Cell Lymphoma n 12 Hodgkin's 1.1 0.080 Over-expressio Lymphoma n 4 T-Cell/Histiocyte- 1.08 0.26 Over-expressio Rich Large B-Cell n Lymphoma 5 Burkitt's Lymphoma 1.056 0.2 Over-expressio n 5 Follicular -1.011 0.54 Over-expressio Lymphoma n 10 38 Follicular 1.1 0.16 Over-expressio Lymphoma n

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Compagn 17 Activated B-Cell- 1.1 0.1 Over-expressio o et al. Like Diffuse Large n (2009) B-Cell Lymphoma 44 Diffuse Large B- 1.07 0.116 Over-expressio Cell Lymphoma n 9 Germinal Center B- 1.04 0.37 Over-expressio Cell-Like Diffuse n Large B-Cell Lymphoma Choi et 6 19 Chronic Adult T- -1.126 0.67 Over-expressio al. (2007) Cell n Leukemia/Lympho ma 22 Acute Adult T-Cell -1.153 0.7 Over-expressio Leukemia/Lympho n ma

F- GDAP1 Expression in Seminoma Study Number Number Cancer type Fold p-value of of change controls samples Korkola et 6 15 Embryonal -2.489 1.19x10- Under-expression al. (2006) Carcinoma, 8 NOS Sperger et 12 13 Testicular -1.315 0.005 - al. (2003) Seminoma Giordano 10 22 Adrenal -1.057 0.031 - et al. Cortex (2009) Adenoma 29 Adrenal 1.105 0.976 - Cortex Carcinoma Nindl et 6 4 Actinic -1.566 0.287 - al. (2006) (Solar) Keratosis 5 Skin -1.14 0.419 - Squamous Cell Carcinoma Gordon et 5 40 Pleural -1.107 0.39 - al. (2005) Malignant Mesothelioma Detwiller 15 9 Malignant 1.173 0.65 - et al. Fibrous (2005) Histiocytoma 253

Crabtree 27 50 Uterine -1.085 0.313 - et al. Corpus (2009) Leiomyoma Riker et 4 15 Skin Basal -1.223 0.215 - al. (2008) Cell Carcinoma 11 Skin 1.131 0.7 - Squamous Cell Carcinoma Morrison 5 8 Parathyroid 1.1 0.7 - et al. Hyperplasia (2004) 10 Non-Familial 1.189 0.91 - Multiple Gland Neoplasia 35 Parathyroid 1.31 0.97 - Gland Adenoma 3 Familial 1.3 0.97 - Parathyroid Hyperplasia Santegoets 10 9 Vulvar 1.4 0.76 - et al. Intraepithelial (2007) Neoplasia

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Appendix 4

A4.1 Transcriptional regulation of the human GDAP1 promoter Table A 4.1 Tanscription factors in human GDAP1 Summary of the transcription factors in human GDAP. The table shows the transcription factors and how many times they repeat in the promoter region and the tissues that they express in. Human GDAP1 sequence was fed into Genomatix software, using MatInspector library, Matrix Family Library Version 9.0 (August 2012). Selected groups: General Core Promoter Elements (0.75/Optimized). (core/matrix sim):Vertebrates (0.75/Optimized).

Transcription factors family repetition Tissues

Fork head domain factors 32 Blood Cells, Breast, Central Nervous System, Digestive System, Ear, Endocrine System, Eye, Immune System, Islets of Langerhans, Leukocytes, Liver, Lymphocytes, Muscle, Skeletal, Muscles, Nervous System, Ovary, Pancreas, Prostate, Spinal Cord, Testis, Thymus Gland, Thyroid Gland, Urogenital System. Brn POU domain factors 22 Brain, Central Nervous System, Digestive System, Ear, Embryonic Structures, Endocrine System, Eye, Islets of Langerhans, Nervous System, Neuroglia, Neurons, Pancreas. Lim homeodomain factors 21 Brain, Cardiovascular System, Central Nervous System, Digestive System, Ear, Embryonic Structures, Endocrine System, Eye, Heart, Islets of Langerhans, Kidney, Muscles, Myocardium, Nervous System, Neurons, Pancreas, Pituitary Gland, Spinal Cord, Testis, Urogenital System. Vertebrate TATA binding protein factor 21 Present in various tissues.

Abdominal-B type homeodomain transcription factors 20 Bone Marrow Cells, Bones, Connective Tissue, Embryonic Structures, Hematopoietic System, Integumentary System, Kidney, Prostate, Skeleton, Urogenital System.

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SOX/SRY-sex/testis determining and related HMG box 19 Bones, Cartilage, Connective Tissue, Digestive System, Ear, factors Embryonic Structures, Endocrine System, Islets of Langerhans, Nervous System, Neuroglia, Pancreas, Skeleton, Testis, Thyroid Gland, Urogenital System. Paralog hox genes 1-8 from the four hox clusters A, B, 19 Bone Marrow Cells, Bones, Connective Tissue, Ear, Embryonic C, D Structures,, Germ Cells, Hematopoietic System, Immune System, Lung, Respiratory System, Skeleton, Thymus Gland, Urogenital System. NKX homeodomain factors 18 Bones, Cardiovascular System, Central Nervous System, Connective Tissue, Digestive System, Embryonic Structures, Endocrine System, Heart, Islets of Langerhans, Lung, Muscles, Myocardium, Nervous System, Pancreas, Prostate, Respiratory System, Skeleton, Spinal Cord, Thyroid Gland, Urogenital System. Octamer binding protein 18 Antibody-Producing Cells, Blood Cells, Immune System, Kidney, Leukocytes, Lymphocytes, Urogenital System. Homeodomain transcription factors 17 Bones, Cardiovascular System, Cartilage, Central Nervous System, Connective Tissue, Digestive System, Ear, Embryonic Structures, Endocrine System, Germ Cells, Heart, Hematopoietic System, Immune System, Islets of Langerhans, Leukocytes, Liver, Lymphocytes, Nervous System, Ovary, Pancreas, Skeleton, Spinal Cord, Thyroid Gland, Urogenital System. Cart-1 (cartilage homeoprotein 1) 15 Adrenal Glands, Bones, Brain, Cardiovascular System, Central Nervous System, Connective Tissue, Digestive System, Embryonic Structures, Endocrine System, Eye, Immune System, Islets of Langerhans, Myeloid Cells, Nervous System, Neurons, Pancreas, Phagocytes, Pituitary Gland, Skeleton, Spinal Cord. Vertebrate caudal related homeodomain protein 14 Blastomeres, Digestive System, Embryonic Structures.

AT rich interactive domain factor 14 Antibody-Producing Cells, Blood Cells, Cardiovascular System, Heart, Immune System, Leukocytes, Lymphocytes, Muscles, Myocardium. 256

Homeobox transcription factors 14 Brain, Cardiovascular System, Central Nervous System, Digestive System, Ear, Embryonic Structures, Eye, Muscle, Smooth, Muscles, Nervous System, Neurons, Spinal Cord. Human and murine ETS1 factors 12 Antibody-Producing Cells, Blood Cells, Bone Marrow Cells, Breast, Central Nervous System, Endocrine System, Germ Cells, Hematopoietic System, Immune System, Leukocytes, Lymphocytes, Monocytes, Myeloid Cells, Nervous System, Phagocytes, Prostate, Respiratory System, Spinal Cord, Testis, Urogenital System. Brn-5 POU domain factors 11 Brain, Central Nervous System, Nervous System.

GATA binding factors 10 Blood Cells, Bone Marrow Cells, Bone and Bones, Cardiovascular System, Connective Tissue, Embryonic Structures, Erythrocytes, Heart, Hematopoietic System, Immune System, Leukocytes, Lymphocytes, Muscles, Myocardium, Skeleton. ZF5 POZ domain zinc finger 10 Ubiquitously expressed with highest levels found in brain and ovary tissues and fibroblast cell lines. PAR/bZIP family 9 Brain, Central Nervous System, Digestive System, Embryonic Structures, Endocrine System, Liver, Muscle, Skeletal, Muscles, Nervous System. Cellular and viral myb-like transcriptional regulators 9 Blood Cells, Bone Marrow Cells, Hematopoietic System, Immune System, Leukocytes, Lymphocytes. DM domain-containing transcription factors 9 Embryonic Structures, Endocrine System, Germ Cells, Ovary, Testis, Urogenital System. EVI1-myleoid transforming protein 9 Adipose Tissue, Blood Cells, Bone Marrow Cells, Brain, Central Nervous System, Connective Tissue, Embryonic Structures, Hematopoietic System, Immune System, Leukocytes, Lymphocytes, Nervous System, Neurons, Thymus Gland. MYT1 C2HC zinc finger protein 9 Central Nervous System, Nervous System, Neuroglia, Neurons.

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Plant TATA binding protein factor 8

RXR heterodimer binding sites 8 Bone and Bones, Connective Tissue, Digestive System, Endocrine System, Integumentary System, Liver, Parathyroid Glands, Skeleton, Thyroid Gland. Pancreatic and intestinal homeodomain transcription 8 Digestive System, Endocrine System, Islets of Langerhans, Pancreas. factor Ccaat/Enhancer Binding Protein 8 Adipose Tissue, Bone Marrow Cells, Connective Tissue, Digestive System, Hematopoietic System, Immune System, Liver, Myeloid Cells, Phagocytes. Zinc finger transcription factor RU49, zinc finger 7 Skin proliferation 1 - Zipro1 AT-binding transcription factor 7 Nervous System

Signal transducer and activator of transcription 7 Blood Cells, Bone Marrow Cells, Breast, Hematopoietic System, Immune System, Leukocytes, Lymphocytes, Myeloid Cells, Phagocytes. Distal-less homeodomain transcription factors 7 Bone and Bones, Brain, Cartilage, Central Nervous System, Connective Tissue, Digestive System, Ear, Embryonic Structures, Endocrine System, Integumentary System, Islets of Langerhans, Nervous System, Neurons, Nose, Pancreas, Respiratory System, Skeleton. PAX homeodomain binding sites 6 Brain, Central Nervous System, Digestive System, Embryonic Structures, Endocrine System, Eye, Islets of Langerhans, Muscle, Skeletal, Muscles, Nervous System, Neurons, Pancreas. Twist subfamily of class B bHLH transcription factors 6 Antibody-Producing Cells, Blood Cells, Bone Marrow Cells, Bone and Bones, Cardiovascular System, Connective Tissue, Embryonic Structures, Heart, Hematopoietic System, Immune System, Integumentary System, Leukocytes, Lymphocytes, Muscles, Myocardium, Skeleton, Thymus Gland. 258

GHF-1 pituitary specific pou domain transcription 6 Brain, Central Nervous System, Endocrine System, Nervous System, factor Pituitary Gland. Activator/repressor binding to transcription initiation 6 Embryonic Structures site (YY1) Yeast TATA binding protein factor 6

Nuclear factor of activated T-cells 6 Blood Cells, Cardiovascular System, Heart, Immune System, Leukocytes, Lymphocytes, Myeloid Cells. E2F-myc activator/cell cycle regulator 6 ubiquitous

Hepatic Nuclear Factor 1 6 Digestive System, Endocrine System, Islets of Langerhans, Kidney, Liver, Pancreas, Urogenital System. Insulinoma associated factors 6 Digestive System, Liver, Pancreas.

Special AT-rich sequence binding protein 6 Blood Cells, Brain, Central Nervous System, Embryonic Structures, Immune System, Leukocytes, Lymphocytes, Nervous System, Neurons, Thymus Gland. snRNA-activating protein complex 5 ubiquitous

NK6 homeobox transcription factors 5 Central Nervous System, Digestive System, Embryonic Structures, Endocrine System, Islets of Langerhans, Nervous System, Neurons, Pancreas, Spinal Cord. Bicoid-like homeodomain transcription factors 5 Brain, Cardiovascular System, Central Nervous System, Ear, Embryonic Structures, Endocrine System, Eye, Heart, Nervous System, Neurons, Pineal Gland, Pituitary Gland. Heat shock factors 5 ubiquitous

Serum response element binding factor 5 Cardiovascular System, Embryonic Structures, Heart, Muscle, Smooth, Muscles, Myocardium.

259

PAX-4/PAX-6 paired domain binding sites 5 Brain, Central Nervous System, Digestive System, Embryonic Structures, Endocrine System, Eye, Islets of Langerhans, Nervous System, Neurons, Pancreas. FAST-1 SMAD interacting proteins 5 Embryonic Structures

Cell cycle regulators: Cell cycle dependent element 5 ubiquitous

Nuclear factor kappa B/c-rel 5 Blood Cells, Bone Marrow Cells, Hematopoietic System, Immune System, Leukocytes, Myeloid Cells, Phagocytes. Nuclear receptor subfamily 2 factors 4 Digestive System, Endocrine System, Eye, Islets of Langerhans, Liver, Nervous System, Neurons, Pancreas, Testis, Urogenital System. E-box binding factors 4 ubiquitous

NeuroD, Beta2, HLH domain 4 Digestive System, Ear, Embryonic Structures, Endocrine System, Eye, Immune System, Islets of Langerhans, Leukocytes, Lymphocytes, Nervous System, Neuroglia, Neurons, Nose, Pancreas, Respiratory System, Spinal Cord, Thymus Gland. HOX - PBX complexes 4 Bone Marrow Cells, Bone and Bones, Brain, Central Nervous System, Connective Tissue, Ear, Embryonic Structures, Hematopoietic System, Immune System, Kidney, Lung, Nervous System, Respiratory System, Skeleton, Thymus Gland, Urogenital System. SWI/SNF related nucleophosphoproteins with a RING 4 finger DNA binding motif Interferon regulatory factors 4 Antibody-Producing Cells, Antigen-Presenting Cells, Blood Cells, Bone Marrow Cells, Hematopoietic System, Immune System, Leukocytes, Lymphocytes, Monocytes, Myeloid Cells, Phagocytes. Two-handed zinc finger homeodomain transcription 4 Embryonic Structures factors

260

LEF1/TCF 4 Adipose Tissue, Connective Tissue, Digestive System, Embryonic Structures, Endocrine System, Islets of Langerhans, Pancreas. Krueppel like transcription factors 4 Blood Cells, Bone Marrow Cells, Digestive System, Embryonic Structures, Erythrocytes, Hematopoietic System, Liver. Nuclear factor 1 4 Brain, Central Nervous System, Digestive System, Embryonic Structures, Liver,, Nervous System. RBPJ - kappa 4 Antibody-Producing Cells, Blood Cells, Immune System, Leukocytes, Lymphocytes. Activator protein 2 4 ubiquitous

C2H2 zinc finger transcription factors 3 4

Glucocorticoid responsive and related elements 4 Breast, Endocrine System, Immune System, Leydig Cells, Prostate, Testis, Urogenital System. Selenocysteine tRNA activating factor 4

Autoimmune regulatory element binding factor 3 Blood Cells, Immune System, Leukocytes, Lymphocytes, Thymus Gland. CLOX and CLOX homology (CDP) factors 3

Chorion-specific transcription factors with a GCM 3 Endocrine System, Immune System, Parathyroid Glands, Thymus DNA binding domain Gland. CAS interating zinc finger protein 3

261

TEA/ATTS DNA binding domain factors 3 Cardiovascular System, Embryonic Structures, Heart, Muscle, Skeletal, Muscles, Myocardium, Urogenital System. Cell cycle regulators: Cell cycle homo 3 logy element TALE homeodomain class recognizing TG motifs 3 Bone Marrow Cells, Brain, Central Nervous System, Embryonic Structures, Hematopoietic System, Myeloid Cells, Nervous System, Neurons. Brachyury gene, mesoderm developmental factor 3 Blood Cells, Bone and Bones, Brain, Breast, Cardiovascular System, Central Nervous System, Ear, Embryonic Structures, Endocrine System, Heart, Immune System, Leukocytes, Lymphocytes, Muscles, Myocardium, Nervous System, Pituitary Gland, Thyroid Gland. Core promoter initiator elements 3

MEF2, myocyte-specific enhancer binding factor 3 Cardiovascular System, Embryonic Structures, Heart, Muscle, Skeletal, Muscles, Myocardium. Sine oculis (SIX) homeodomain factors 3 Brain, Central Nervous System, Ear, Embryonic Structures, Endocrine System, Eye, Kidney, Muscle, Skeletal, Muscles, Nervous System, Pituitary Gland,Urogenital System. Grainyhead-like transcription factors 3 Embryonic Structures

HOX - MEIS1 heterodimers 3 Bone Marrow Cells, Bone and Bones, Brain, Central Nervous System, Connective Tissue, Embryonic Structures, Hematopoietic System, Integumentary System, Kidney, Nervous System, Neurons, Prostate, Skeleton, Urogenital System. CCAAT binding factors 3 ubiquitous

X-box binding factors 3 Antibody-Producing Cells, Blood Cells, Endocrine System, Immune System, Leukocytes, Lymphocytes, Testis, Urogenital System.

262

Onecut homeodomain factor HNF6 3 Digestive System, Embryonic Structures, Endocrine System, Islets of Langerhans, Liver, Pancreas. PAX-2 binding sites 3 Ear, Embryonic Structures, Kidney, Urogenital System.

POZ domain zinc finger expressed in B-Cells 3 Antibody-Producing Cells, Blood Cells, Germ Cells, Immune System, Leukocytes, Lymphocytes, Urogenital System. Vertebrate SMAD family of transcription factors 2 Embryonic Structures, Kidney, Urogenital System.

Cyclin D binding myb-like transcription factor 2

PBX1 - MEIS1 complexes 2 Bone Marrow Cells, Embryonic Structures, Hematopoietic System.

NK1 homeobox transcription factors 2

PAX-1 binding sites 2 Bone and Bones, Cartilage, Connective Tissue, Embryonic Structures, Immune System, Skeleton, Thymus Gland. Barbiturate-inducible element box from pro+eukaryotic 2 genes Spalt-like transcription factor 1 2 Ear, Embryonic Structures, Kidney, Urogenital System.

cAMP-responsive element binding proteins 2 Brain, Central Nervous System, Endocrine System, Germ Cells, Nervous System, Pineal Gland, Urogenital System. Ikaros zinc finger family 2 Antibody-Producing Cells, Blood Cells, Bone Marrow Cells, Hematopoietic System, Immune System, Leukocytes, Lymphocytes, Thymus Gland. Peroxisome proliferator-activated receptor 2 Adipose Tissue, Connective Tissue, Digestive System, Liver.

PAX-2/5/8 binding sites 2 Antibody-Producing Cells, Blood Cells, Bone Marrow Cells, Ear, Embryonic Structures, Endocrine System, Hematopoietic System, 263

Immune System, Kidney, Leukocytes, Lymphocytes, Thyroid Gland, Urogenital System. Vertebrate homologues of enhancer of split complex 2 Cardiovascular System, Central Nervous System, Ear, Embryonic Structures, Heart, Muscles, Myocardium, Nervous System, Neurons. OVO homolog-like transcription factors 2

Core promoter motif ten elements 2

EGR/nerve growth factor induced protein C & related 2 Brain, Central Nervous System, Endocrine System, Kidney, Nervous factors System, Testis, Urogenital System. GC-Box factors SP1/GC 2 Ubiquitous.

Nuclear respiratory factor 1 2 Adipose Tissue, Connective Tissue, Muscle, Skeletal, Muscles.

GLI zinc finger family 2 Cardiovascular System, Central Nervous System, Embryonic Structures, Eye, Heart, Nervous System, Prostate, Spinal Cord, Urogenital System. Ras-responsive element binding protein 2

MAF and AP1 related factors 1 Antibody-Producing Cells, Blood Cells, Blood Platelets, Bone Marrow Cells, Digestive System, Ear, Endocrine System, Eye, Hematopoietic System, Immune System, Islets of Langerhans, Liver, Nervous System, Neurons, Pancreas. Microphthalmia transcription factor 1 Antibody-Producing Cells, Embryonic Structures, Eye, Immune System, Integumentary System, Kidney, Myeloid Cells, Urogenital System. C2H2 zinc finger transcription factors 1 1

264

Ubiquitous GLI - Krueppel like zinc finger involved in 1 Ubiquitous cell cycle regulation THAP domain containing protein 1 Brain, Central Nervous System, Nervous System.

Lactotransferrin motif 1 Blood Cells, Bone Marrow Cells, Granulocytes, Hematopoietic System, Immune System, Leukocytes, Myeloid Cells, Phagocytes. Activator-, mediator- and TBP-dependent core 1 promoter element for RNA polymerase II transcription from TATA-less promoters C2H2 zinc finger protein PLZF 1

C2H2 zinc finger transcription factors 10 1

C2H2 zinc finger transcription factors 4 1

CTCF and BORIS gene family, transcriptional 1 Blood Cells, Embryonic Structures, Endocrine System, Erythrocytes, regulators with 11 highly conserved zinc finger Germ Cells, Testis, Urogenital System. domains Downstream Immunoglobulin Control Element, critical 1 Antibody-Producing Cells, Blood Cells, Embryonic Structures, for B cell activity and specificity Immune System, Leukocytes, Lymphocytes, Muscle, Skeletal, Muscles. GDNF-inducible zinc finger gene 1 1

265

Estrogen response elements 1 Breast, Endocrine System, Ovary, Urogenital System.

GA-boxes 1

GT box 1

Huntington's disease gene regulatory region binding 1 proteins Hypoxia inducible factor, bHLH/PAS protein family 1 Brain, Cardiovascular System, Central Nervous System, Endocrine System, Nervous System, Pineal Gland. KRAB domain zinc finger protein 57 1

MEF3 binding sites 1 Brain, Central Nervous System, Ear, Embryonic Structures, Endocrine System, Eye, Kidney, Muscle, Skeletal, Muscles, Nervous System, Pituitary Gland, Urogenital System. Members of ZIC-family, zinc finger protein of the 1 Brain, Central Nervous System, Embryonic Structures, Nervous cerebellum System, Neurons. Metal induced transcription factor 1

Motif composed of binding sites for pluripotency or 1 Embryonic Structures, Germ Cells. stem cell factors Neuron-specific olfactory factor 1 Antibody-Producing Cells, Blood Cells, Bone Marrow Cells, Hematopoietic System, Immune System, Leukocytes, Lymphocytes, Nervous System, Neurons, Nose, Respiratory System. Nucleoside diphosphate kinase 1

Olfactory associated zinc finger protein 1

266

PAX-9 binding sites 1 Bone and Bones, Connective Tissue, Embryonic Structures, Skeleton.

Pleomorphic adenoma gene 1 Brain, Central Nervous System, Digestive System, Embryonic Structures, Endocrine System, Nervous System, Neurons. Positive regulatory domain I binding factor 1 Antibody-Producing Cells, Blood Cells, Immune System, Leukocytes, Lymphocytes. PRDI-BF1 and RIZ homologous (PR) domain proteins 1 (PRDM) v-ERB and RAR-related orphan receptor alpha 1 Blood Cells, Brain, Central Nervous System, Immune System, Leukocytes, Lymphocytes, Nervous System, Neurons. Zinc finger protein ZNF35 1

267

A4.2 Transcriptional regulation of the mouse GDAP1 promoter Table A4.2: Transcription factors in mouse GDAP1 Summary of the transcription factors in mouse GDAP1. The table shows the transcription factors and how many times they repeat in the promoter region and the tissues that they express in. Mouse GDAP1 sequence was fed into Genomatix software, using MatInspector library, Matrix Family Library Version 9.0 (August 2012). Selected groups: General Core Promoter Elements (0.75/Optimized). (core/matrix sim):Vertebrates (0.75/Optimized).

Transcription factors family repetition Tissues

SOX/SRY-sex/testis determining and related HMG 19 Bone and Bones, Cartilage, Connective Tissue, Digestive System, Ear, box factors Embryonic Structures, Endocrine System, Islets of Langerhans, Nervous System, Neuroglia, Pancreas, Skeleton, Testis, Thyroid Gland, Urogenital System. Brn POU domain factors 19 Brain, Central Nervous System, Digestive System, Ear, Embryonic Structures, Endocrine System, Eye, Islets of Langerhans, Nervous System, Neuroglia, Neurons, Pancreas. Octamer binding protein 18 Antibody-Producing Cells, Blood Cells, Immune System, Kidney, Leukocytes, Lymphocytes, Urogenital System. Lim homeodomain factors 17 Brain, Cardiovascular System, Central Nervous System, Digestive System, Ear, Embryonic Structures, Endocrine System, Eye, Heart, Islets of Langerhans, Kidney, Muscles, Myocardium, Nervous System, Neurons, Pancreas, Pituitary Gland, Spinal, Cord, Testis, Urogenital System. Homeodomain transcription factors 15 Blood Cells, Bone Marrow Cells, Bone and Bones, Cardiovascular System, Cartilage, Central Nervous System, Connective Tissue, Digestive System, Ear, Embryonic, Structures, Endocrine System, Germ Cells, Heart, Hematopoietic System, Immune System, Islets of Langerhans, Leukocytes, Liver, Lymphocytes, Nervous System, Ovary, Pancreas, Skeleton, Spinal Cord, Thyroid Gland, Urogenital System.

268

Cart-1 (cartilage homeoprotein 1) 14 Adrenal Glands, Bone and Bones, Brain, Cardiovascular System, Central Nervous System, Connective Tissue, Digestive System, Embryonic Structures, Endocrine System, Eye, Immune System, Islets of Langerhans, Myeloid Cells, Nervous System, Neurons, Pancreas, Phagocytes, Pituitary Gland, Skeleton, Spinal Cord. Fork head domain factors 14 Blood Cells, Breast, Central Nervous System, Digestive System, Ear, Endocrine System, Eye, Immune System, Islets of Langerhans, Leukocytes, Liver, Lymphocytes, Muscle, Skeletal, Muscles, Nervous System, Ovary, Pancreas, Prostate, Spinal Cord, Testis, Thymus Gland, Thyroid Gland, Urogenital System. Paralog hox genes 1-8 from the four hox clusters A, 14 Bone Marrow Cells, Bone and Bones, Connective Tissue, Ear, B, C, D Embryonic Structures, Germ Cells, Hematopoietic System, Immune System, Lung, Respiratory System, Skeleton, Thymus Gland, Urogenital System. NKX homeodomain factors 12 Bone and Bones, Cardiovascular System, Central Nervous System, Connective Tissue, Digestive System, Embryonic Structures, Endocrine System, Heart, Islets of Langerhans, Lung, Muscles, Myocardium, Nervous System, Pancreas, Prostate, Respiratory System, Skeleton, Spinal Cord, Thyroid Gland, Urogenital System. Signal transducer and activator of transcription 12 Blood Cells, Bone Marrow Cells, Breast, Hematopoietic System, Immune System, Leukocytes, Lymphocytes, Myeloid Cells, Phagocytes. Human and murine ETS1 factors 12 Antibody-Producing Cells, Blood Cells, Bone Marrow Cells, Breast, Central Nervous System, Endocrine System, Germ Cells, Hematopoietic System, Immune System, Leukocytes, Lymphocytes, Monocytes, Myeloid Cells, Nervous System, Phagocytes, Prostate, Respiratory System, Spinal Cord, Testis, Urogenital System. Abdominal-B type homeodomain transcription 10 Bone Marrow Cells, Bone and Bones, Connective Tissue, Embryonic factors Structures, Hematopoietic System, Integumentary System, Kidney, Prostate, Skeleton, Urogenital System. 269

EVI1-myleoid transforming protein 10 Adipose Tissue, Blood Cells, Bone Marrow Cells, Brain, Central Nervous System, Connective Tissue, Embryonic Structures, Hematopoietic System, Immune System, Leukocytes, Lymphocytes, Nervous System, Neurons, Thymus Gland C2H2 zinc finger transcription factors 2 10 Blood Cells, Bone Marrow Cells, Endocrine System, Germ Cells, Hematopoietic System, Immune System, Leukocytes, Lymphocytes, Testis, Thymus Gland, Urogenital System. GATA binding factors 10 Blood Cells, Bone Marrow Cells, Bone and Bones, Cardiovascular System, Connective Tissue, Embryonic Structures, Erythrocytes, Heart, Hematopoietic System, Immune System, Leukocytes, Lymphocytes, Muscles, Myocardium, Skeleton. Motif composed of binding sites for pluripotency 10 Embryonic Structures, Germ Cells. or stem cell factors Nuclear factor of activated T-cells 10 Blood Cells, Cardiovascular System, Heart, Immune System, Leukocytes, Lymphocytes, Myeloid Cells. Pleomorphic adenoma gene 10 Brain, Central Nervous System, Digestive System, Embryonic Structures, Endocrine System, Nervous System, Neurons. Vertebrate caudal related homeodomain protein 9 Blastomeres, Digestive System, Embryonic Structures.

Homeobox transcription factors 8 Brain, Cardiovascular System, Central Nervous System, Digestive System, Ear, Embryonic Structures, Eye, Muscle, Smooth, Muscles, Nervous System, Neurons, Spinal Cord. Activator/repressor binding to transcription 8 Embryonic Structures. initiation site Twist subfamily of class B bHLH transcription 8 Antibody-Producing Cells, Blood Cells, Bone Marrow Cells, Bone and factors Bones, Cardiovascular System, Connective Tissue, Embryonic Structures, Heart, Hematopoietic System, Immune System, Integumentary System, Leukocytes, Lymphocytes, Muscles, Myocardium, Skeleton, Thymus Gland.

270

LEF1/TCF 8 Adipose Tissue, Connective Tissue, Digestive System, Embryonic Structures, Endocrine System, Islets of Langerhans, Pancreas. Krueppel like transcription factors 8 Blood Cells, Bone Marrow Cells, Digestive System, Embryonic Structures, Erythrocytes, Hematopoietic System, Liver. PAR/bZIP family 8 Brain, Central Nervous System, Digestive System, Embryonic Structures, Endocrine System, Liver, Muscle, Skeletal, Muscles, Nervous System. Nuclear receptor subfamily 2 factors 7 Adrenal Glands, Digestive System, Endocrine System, Eye, Islets of Langerhans, Liver, Nervous System, Neurons, Pancreas, Testis, Urogenital System. Vertebrate TATA binding protein factor 7 Present in various tissues.

Serum response element binding factor 7 Cardiovascular System, Embryonic Structures, Heart, Muscle, Smooth, Muscles, Myocardium. Brn-5 POU domain factors 6 Brain, Central Nervous System, Nervous System.

cAMP-responsive element binding proteins 6 Brain, Central Nervous System, Endocrine System, Germ Cells, Nervous System, Pineal Gland, Urogenital System. EGR/nerve growth factor induced protein C & 6 Brain, Central Nervous System, Endocrine System, Kidney, Nervous related factors System, Testis, Urogenital System. Hepatic Nuclear Factor 1 6 Digestive System, Endocrine System, Islets of Langerhans, Kidney, Liver, Pancreas, Urogenital System. Interferon regulatory factors 6 Antibody-Producing Cells, Antigen-Presenting Cells, Blood Cells, Bone Marrow Cells, Hematopoietic System, Immune System, Leukocytes, Lymphocytes, Monocytes, Myeloid Cells, Phagocytes. Mouse Krueppel like factor 6

MEF2, myocyte-specific enhancer binding factor 6 Cardiovascular System, Embryonic Structures, Heart, Muscle, Skeletal, Muscles, Myocardium.

271

NK6 homeobox transcription factors 6 Central Nervous System, Digestive System, Embryonic Structures, Endocrine System, Islets of Langerhans, Nervous System, Neurons, Pancreas, Spinal Cord. RXR heterodimer binding sites 6 Bone and Bones, Connective Tissue, Digestive System, Endocrine System, Integumentary System, Liver, Parathyroid Glands, Skeleton, Thyroid Gland. Ccaat/Enhancer Binding Protein 5 Adipose Tissue, Bone Marrow Cells, Connective Tissue, Digestive System, Hematopoietic System, Immune System, Liver, Myeloid Cells, Phagocytes. HOX - PBX complexes 5 Bone Marrow Cells, Bone and Bones, Brain, Central Nervous System, Connective Tissue, Ear, Embryonic Structures, Hematopoietic System, Immune System, Kidney, Lung, Nervous System, Respiratory System, Skeleton, Thymus Gland, Urogenital System. POZ domain zinc finger expressed in B-Cells 5 Antibody-Producing Cells, Blood Cells, Germ Cells, Immune System, Leukocytes, Lymphocytes, Urogenital System. SWI/SNF related nucleophosphoproteins with a 5 RING finger DNA binding motif X-box binding factors 5 Antibody-Producing Cells, Blood Cells, Endocrine System, Immune System, Leukocytes, Lymphocytes, Testis, Urogenital System. AT rich interactive domain factor 4 Antibody-Producing Cells, Blood Cells, Cardiovascular System, Heart, Immune System, Leukocytes, Lymphocytes, Muscles, Myocardium. CLOX and CLOX homology (CDP) factors 4

DM domain-containing transcription factors 4 Embryonic Structures, Endocrine System, Germ Cells, Ovary, Testis, Urogenital System. GC-Box factors SP1/GC 4 Ubiquitous.

GHF-1 pituitary specific pou domain transcription 4 Brain, Central Nervous System, Endocrine System, Nervous System, factor Pituitary Gland.

272

GLI zinc finger family 4 Cardiovascular System, Central Nervous System, Embryonic Structures, Eye, Heart, Nervous System, Prostate, Spinal Cord, Urogenital System. Cellular and viral myb-like transcriptional 4 Blood Cells, Bone Marrow Cells, Hematopoietic System, Immune regulators System, Leukocytes, Lymphocytes. Iroquois homeobox transcription factors 4 Brain, Cardiovascular System, Central Nervous System, Embryonic Structures, Heart, Muscles, Myocardium, Nervous System, Neurons. MAF and AP1 related factors 4 Antibody-Producing Cells, Blood Cells, Blood Platelets, Bone Marrow Cells, Digestive System, Ear, Endocrine System, Eye, Hematopoietic System, Immune System, Islets of Langerhans, Liver, Nervous System, Neurons, Pancreas. Onecut homeodomain factor HNF6 4 Digestive System, Embryonic Structures, System, Islets of Langerhans, Liver, Pancreas. MYT1 C2HC zinc finger protein 4 Central Nervous System, Nervous System, Neuroglia, Neurons.

PAX-4/PAX-6 paired domain binding sites 4 Brain, Central Nervous System, Digestive System, Embryonic Structures, Endocrine System, Eye, Islets of Langerhans, Nervous System, Neurons, Pancreas. Ras-responsive element binding protein 4

Vertebrate SMAD family of transcription factors 4 Embryonic Structures, Kidney, Urogenital System.

E-box binding factors 4 ubiquitous

Vertebrate steroidogenic factor 3 Adrenal Glands, Brain, Central Nervous System, Endocrine System, Leydig Cells, Nervous System, Ovary, Pituitary Gland, Testis, Urogenital System. Two-handed zinc finger homeodomain 3 Embryonic Structures transcription factors

273

Bicoid-like homeodomain transcription factors 3 Brain, Cardiovascular System, Central Nervous System, Ear, Embryonic Structures, Endocrine System, Eye, Heart, Nervous System, Neurons, Pineal Gland, Pituitary Gland AT-binding transcription factor 3 Nervous System

Pancreatic and intestinal homeodomain 3 Digestive System, Endocrine System, Islets of Langerhans, Pancreas. transcription factor Nuclear factor 1 3 Brain, Central Nervous System, Digestive System, Embryonic Structures, Liver, Nervous System. Nucleoside diphosphate kinase 3

PAX-2 binding sites 3 Ear, Embryonic Structures, Kidney, Urogenital System.

Myoblast determining factors 3 Antibody-Producing Cells, Blood Cells, Embryonic Structures, Germ Cells, Immune System, Leukocytes, Lymphocytes, Muscle, Skeletal, Muscles, Thymus Gland, Urogenital System. NeuroD, Beta2, HLH domain 3 Antibody-Producing Cells, Blood Cells, Brain, Central Nervous System, Digestive System, Ear, Embryonic Structures, Endocrine System, Eye, Immune System, Islets of Langerhans, Leukocytes, Lymphocytes, Nervous System, Neuroglia, Neurons, Nose, Pancreas, Respiratory System, Spinal Cord, Thymus Gland. Neuron-restrictive silencer factor 3 Brain, Central Nervous System, Nervous System, Neurons.

Positive regulatory domain I binding factor 3 Antibody-Producing Cells, Blood Cells, Immune System, Leukocytes, Lymphocytes. PRDI-BF1 and RIZ homologous (PR) domain 3 proteins (PRDM) Glucocorticoid responsive and related elements 3 Breast, Endocrine System, Immune System, Leydig Cells, Prostate, Testis, Urogenital System.

274

FAST-1 SMAD interacting proteins 3 Embryonic Structures.

Chorion-specific transcription factors with a GCM 3 Embryonic Structures, Endocrine System, Immune System, Parathyroid DNA binding domain Glands, Thymus Gland. Distal-less homeodomain transcription factors 3 Bone and Bones, Brain, Cartilage, Central Nervous System, Connective Tissue, Digestive System, Ear, Embryonic Structures, Endocrine System, Integumentary System, Islets of Langerhans, Nervous System, Neurons, Nose, Pancreas, Respiratory System, Skeleton. Human muscle-specific Mt binding site 3

Brachyury gene, mesoderm developmental factor 2 Blood Cells, Bone and Bones, Brain, Breast, Cardiovascular System, Central Nervous System, Ear, Embryonic Structures, Endocrine System, Heart, Immune System, Leukocytes, Lymphocytes, Muscles, Myocardium, Nervous System, Pituitary Gland, Thyroid Gland. CAS interating zinc finger protein 2

Krueppel-like C2H2 zinc finger factors 2 hypermethylated in cancer Myeloid zinc finger 1 factors 2 Blood Cells, Bone Marrow Cells, Hematopoietic System, Immune System, Leukocytes, Myeloid Cells. NK1 homeobox transcription factors 2

Microphthalmia transcription factor 2 Antibody-Producing Cells, Embryonic Structures, Eye, Immune System, Integumentary System, Kidney, Myeloid Cells, Urogenital System. CTCF and BORIS gene family, transcriptional 2 Blood Cells, Embryonic Structures, Endocrine System, Erythrocytes, regulators with 11 highly conserved zinc finger Germ Cells, Testis, Urogenital System. domains

275

Cyclin D binding myb-like transcription factor 2

Spalt-like transcription factor 2 2

Special AT-rich sequence binding protein 2 Blood Cells, Brain, Central Nervous System, Embryonic Structures, Immune System, Leukocytes, Lymphocytes, Nervous System, Neurons, Thymus Gland. v-ERB and RAR-related orphan receptor alpha 2 Blood Cells, Brain, Central Nervous System, Immune System, Leukocytes, Lymphocytes, Nervous System, Neurons. Testis-specific bHLH-Zip transcription factors 2

p53 tumor suppressor 2 Ubiquitous.

Peroxisome proliferator-activated receptor 2 Adipose Tissue, Connective Tissue, Digestive System, Liver.

Zinc finger transcription factor RU49, zinc finger 2 proliferation 1 - Zipro1 Heat shock factors 2 Ubiquitous.

HOX - MEIS1 heterodimers 2 Bone Marrow Cells, Bone and Bones, Brain, Central Nervous System, Connective Tissue, Embryonic Structures, Hematopoietic System, Integumentary System, Kidney, Nervous System, Neurons, Prostate, Skeleton, Urogenital System. C2H2 zinc finger transcription factors 5 2

PAX-1 binding sites 2 Bone and Bones, Cartilage, Connective Tissue, Embryonic Structures, Immune System, Skeleton, Thymus Gland. Calcium-response elements 1

276

Calsenilin, presenilin binding protein, EF hand 1 Brain, Cardiovascular System, Central Nervous System, Germ Cells, transcription factor Heart, Muscles, Myocardium, Nervous System, Neurons, Urogenital System AP1, Activating protein 1 1 Antigen-Presenting Cells, Brain, Central Nervous System, Endocrine System, Immune System, Nervous System, Neurons, Pineal Gland. Carbohydrate response elements, consist of two E 1 Adipose Tissue, Digestive System, Endocrine System, Islets of box motifs separated by 5 bp Langerhans, Liver, Pancreas. Barbiturate-inducible element box from 1 pro+eukaryotic genes CCAAT binding factors 1 ubiquitous

Cell cycle regulators: Cell cycle homology element 1

Core promoter initiator elements 1

Downstream Immunoglobulin Control Element, 1 Antibody-Producing Cells, Blood Cells, Embryonic Structures, Immune critical for B cell activity and specificity System, Leukocytes, Lymphocytes, Muscle, Skeletal, Muscles. E2F-myc activator/cell cycle regulator 1 ubiquitous

Bromodomain and PHD domain transcription 1 Brain, Central Nervous System, Nervous System. factors C2H2 zinc finger protein PLZF 1 Blood Cells, Bone Marrow Cells, Endocrine System, Germ Cells, Hematopoietic System, Immune System, Leukocytes, Lymphocytes, Testis, Thymus Gland, Urogenital System. C2H2 zinc finger transcription factors 3 1

Grainyhead-like transcription factors 1 Embryonic Structures.

277

Growth factor independence transcriptional 1 Blood Cells, Bone Marrow Cells, Ear, Granulocytes, Hematopoietic repressor System, Immune System, Leukocytes, Lymphocytes, Myeloid Cells, Phagocytes. GT box 1

Histone nuclear factor P 1

Human acute myelogenous leukemia factors 1 Bone Marrow Cells, Bone and Bones, Cartilage, Connective Tissue, Embryonic Structures, Hematopoietic System, Skeleton. Hypoxia inducible factor, bHLH/PAS protein 1 Brain, Cardiovascular System, Central Nervous System, Endocrine familyv System, Nervous System, Pineal Gland. Lactotransferrin motif 1 Blood Cells, Bone Marrow Cells, Granulocytes, Hematopoietic System, Immune System, Leukocytes, Myeloid Cells, Phagocytes. Members of ZIC-family, zinc finger protein of the 1 Brain, Central Nervous System, Embryonic Structures, Nervous cerebellum System, Neurons. Myc associated zinc fingers 1 Blood Cells, Immune System, Leukocytes.

Odd-skipped related factors 1 Embryonic Structures

Olfactory associated zinc finger protein 1

PAX homeodomain binding sites 1 Brain, Central Nervous System, Digestive System, Embryonic Structures, Endocrine System, Eye, Islets of Langerhans, Muscle, Skeletal, Muscles, Nervous System, Neurons, Pancreas. PAX-3 binding sites 1 Embryonic Structures, Muscle, Skeletal, Muscles

PBX1 - MEIS1 complexes 1 Bone Marrow Cells, Embryonic Structures, Hematopoietic System.

278

Plant TATA binding protein factor 1

RBPJ - kappa 1 Antibody-Producing Cells, Blood Cells, Immune System, Leukocytes, Lymphocytes RP58 (ZFP238) zinc finger protein 1

Selenocysteine tRNA activating factor 1

Sine oculis (SIX) homeodomain factors 1 Brain, Central Nervous System, Ear, Embryonic Structures, Endocrine System, Eye, Kidney, Muscle, Skeletal, Muscles, Nervous System, Pituitary Gland, Urogenital System. snRNA-activating protein complex 1

Spalt-like transcription factor 1 1 Ear, Embryonic Structures, Kidney, Urogenital System.

TALE homeodomain class recognizing TG motifs 1 Bone Marrow Cells, Brain, Central Nervous System, Embryonic Structures, Hematopoietic System, Myeloid Cells, Nervous System, Neurons. Yeast TATA binding protein factor 1

Zfx and Zfy - transcription factors implicated in 1 Embryonic Structures, Endocrine System, Germ Cells, Ovary, Testis, mammalian sex determination Urogenital System. Ubiquitous GLI - Krueppel like zinc finger 1 Ubiquitous involved in cell cycle regulation Vertebrate homologues of enhancer of split 1 Cardiovascular System, Central Nervous System, Ear, Embryonic complex Structures, Heart, Muscles, Myocardium, Nervous System, Neurons.

279

A.4.3 Impact of Polymorphisms in GDAP1 regulation Table A4.3: Polymorphisms

The table shows the result of the sequencing for the SNPs at position -832 and an observed SNP at position -510. The first column shows the sequencing number, the second column shows the sample ID numbers of the individual samples, the third column shows the SNP at -8832, the forth column shows the SNP at -510 and the last column shows the poly-A region. Some sequences were not included in the study. The reasons for that are mentioned in this table.

Sequencing Sample ID SNP at -832 SNP at -510 Poly-A region number REF REF A T 12

11 9 G T 14

12 10 G T 14

13 2 G T 13

14 10 A T 11

15 2 A T 11

16 2 G T 13

17 2 G T 14

18 2 G T 14

19 9 A T 13

20 9 A T 14

21 9 G T 14

22 10 Not included, failed to be sequenced

23 10 A T 11

24 10 G T 12

25 3 Not included, matched nonspecific for GDAP1

26 3 Not included, not matched GDAP1

27 3 Not included, not matched GDAP1

280

28 9 Not included, matched nonspecific for GDAP1

29 10 Not included, matched nonspecific for GDAP1

30 10 Not included, too short for poly A but GDAP1 sequence 31 9 Not included, failed to be sequenced

32 3 Not included, too short for poly A but GDAP1 sequence 33 3 Not included, failed to be sequenced

34 3 G C 12

35 5 G T 14

36 5 G T 14

37 6 A T 12

38 6 G T 12

40 12 G T 15

41 12 A C 15

42 15 Not included, failed to be sequenced

43 3 A T 11

44 3 A T 12

45 2 Not included, failed to be sequenced

46 2 Not included, not matched GDAP1

47 6 Not included, failed to be sequenced

48 6 Not included, too short for poly A but GDAP1

49 15 A C 12

50 15 G T 14

51 16 A C 12

53 9 A T 12

52 16 A T 13

281

54 20 A T 14

56 20 G T 14

A4.4 PCR reaction optimisation

To amplify the GDAP1 promoter region, we trialled several DNA polymerase, including iProof, MangoTaq and Phusion, which were all high fidelity. First iProof High-Fidelity DNA Polymerase was trialled using different annealing temperatures (58o C, 60oC) with different annealing times (between 10 and 30 seconds). The iproof protocol suggested use of two buffers, HF and GC and inclusion of DMSO, which were all then applied. Details are given in Appendix A2.2.2.3. We were unable to get a clear band of the approximate size (see Figure A4.1).

We had a similar experience with Mango Taq DNA Polymerase, tried with different temperatures (54oC, 58oC), and also with different annealing temperatures with the inclusion of DMSO. Finally Phusion Hot Start II High-Fidelity DNA Polymerase was tried with different annealing temperatures and cycles, which gave the right band size (see Figure A4.2).

Phusion Hot Star II High-Fidelity DNA Polymerase was subsequently used for the PCR with the cycle conditions given in Table A4.4. The 20 μl reaction contained the following: added to 20 μL H2O, 4 μL 5x Phusion HF Buffer, 0.4 μL 10mM dNTPs, 0.5 μL Forward primers (F-), 0.5 μL Reverse primer (R33) and 0.2 μL Phusion Hot Start II High-Fidelity DNA polymerase. As a control only the master mix was used. The Eppendorf Master Cycler ARCBS-SA PCR machine was used.

Table A4.4: PCR cycling instructions used for amplifying GDAP1 gene. (Adapted from Thermoscientific website).

Cycle step 2 steps protocol Cycles

Temperature Time

Initialisation 98°C 30s 1

Denaturation 98°C 5-10s 25–35

Annealing - -

282

Extension 72°C 15–30 s

Final extension 72°C 5–10 min 1

Hold 4°C4°C hold

Figure A4.1: Agarose gel for PCR samples amplified using Phusion Hot Star II High-Fidelity DNA Polymerase. Samples were amplified with the forward primer F-888 and reverse primer R33 and was run on agarose gel. The first column from the left shows the DNA ladder, the second column shows no template control. The third to eighth columns show PCR products from four different blood donors.

283

Figure A4.2: Agarose gel for PCR samples amplified using iProof High-Fidelity Polymerase. Samples were amplified with the forward primer F-888 and reverse primer R33 and was run on 1.5% agarose gel. The first column from the left shows the DNA ladder, the second column shows no template control. The third to sixth columns show PCR products from four different blood donors.

A.4.5 Human and mouse GDAP1 gene alignment

Blast alignment of the human and mouse GDAP1 5’FR.

Human1628 AACCCTCAGTATCTTGGGAAATTGCTGCTTTTATACCTGAGCAACCTCCAA-ACCGAAGA 1686 |||||| | | ||||| |||| || ||||||||| || |||||| ||||| || | || Mouse1721 AACCCTGAATGTCTTGTGAAACTGGTGCTTTTATGCCCAAGCAACGTCCAAGGCCCAGGA 1780

Human1687 GTA--ATTTGCTATCATCATTCTTCCTTACTGCCCTTCATAACCAGGGTCTCATAtttt 1743 ||| ||||||| | |||||| |||||| |||| | | |||| ||||||||| Mouse1781 GTATTATTTGCTGTTATCATTTTTCCTT----GCCTTAAAAGCCAGACAGTCATATTTT 1835

Human1209 TTTAT-TATTAATTTTTCACAAAAATCTTATGAGGGAAACAATAAAATTATTATTCTTAC 1267 ||||| || | | || ||||||||| |||| || |||||||| || ||| | Mouse1465 TTTATCTACTCAGTTCTCACAAAAAGCTTACA---GATTAAATAAAATAATC--TCT-AT 1518

Human1268 TTAGAAAATGGAAAAACCAGGATATGAACTTAGGCAGTACTGTTGTAGAATTTTTGTGTT 1327 || |||||| ||| | ||||| |||| || ||| | | ||| | | ||| || | Mouse1519 TTCAAAAATGAAAACAGCAGGACATGATCTCAGGTATTTCTGATAT-GAACATTCACACT 1577

Human 1328 TAACAACTAGGCTATTTTATATCGCCATTGTTTATT 1363 ||| | ||||| ||||| || |||||||| ||| Mouse 1578 TAATCAATAGGCACTTTTACATTACCATTGTTGATT 1613

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Human 1872 GGCGAAACTACATTTCCCAG 1891 || ||||||||||||||||| Mouse 1926 GGAGAAACTACATTTCCCAG 1945

Human 1529 CATAGTAGCATCTTCTAATGAAAGCTCATAGATCCTT 1565 ||| ||| || ||||||||||| | || ||| ||| Mouse 1641 CATTGTAATATATTCTAATGAAAATTAATTGATACTT 1677

Human 440 GGCATATAATTTTTATGTATCCA 462 ||||||||| ||| ||| |||| Mouse 290 GGCATATAAAGTTTGTGTGTCCA 312

Human 680 AAAGAATTACAG 691 |||||||||||| Mouse 94 AAAGAATTACAG 83

Human 1317 ATTTTTGTGTTT 1328 |||||||||||| Mouse 100 ATTTTTGTGTTT 111

Human 1339 CTATTTTATATCGCCATTGTTTATTAGCTGTTTA 1372 || |||| | | ||||| |||||||| | |||| Mouse 149 CTTTTTTTTTTTTCCATT-TTTATTAGGTATTTA 181

Human 1272 AAAATGGAAAAA 1283 |||||||||||| Mouse 168 AAAATGGAAAAA 157

Human 1243 GAAACAATAAA 1253 ||||||||||| Mouse 18 GAAACAATAAA 8

Human 449 TTTTTATGTATCCATTTTT 467 ||||| | | ||||||||| Mouse 151 TTTTTTTTTTTCCATTTTT 169

Human 573 TTAAGTGTGAA 583 ||||||||||| Mouse 1580 TTAAGTGTGAA 1570

Human 715 AAGGCAAGGAAGAA 728 ||||||||||| || Mouse 1813 AAGGCAAGGAAAAA 1800

Human 832 TCTTTATTGT 841 ||||||||||

285

Mouse 6 TCTTTATTGT 15

Human 31 TGTTGTGTTT 40 |||||||||| Mouse 71 TGTTGTGTTT 80

Human 1225 CACAAAAATC 1234 |||||||||| Mouse 108 CACAAAAATC 99

Human 551 TTTCCTCTTT 560 |||||||||| Mouse 109 TTTCCTCTTT 118

Human 654 ACATTATGAA 663 |||||||||| Mouse 408 ACATTATGAA 399

Human 167 GTGTTTGCTA 176 |||||||||| Mouse 519 GTGTTTGCTA 528

Human 1069 TTAAAGACATACAAT 1083 ||||||| |||| || Mouse 624 TTAAAGAAATACCAT 610

Human 1068 TTTAAAGACATACAATAATAACT 1090 ||| |||| ||| |||||||| Mouse 650 TTTTAAGAAGGACATTAATAACT 628

Human 189 GAATAGAAAA 198 |||||||||| Mouse 1028 GAATAGAAAA 1037

Human 546 AAAGTTTTCCTCTTT 560 || || ||||||||| Mouse 1229 AATGTCTTCCTCTTT 1243

Human 976 AGATAAATTATACAG 990 |||||||| | |||| Mouse 1471 AGATAAATAAGACAG 1457

Human 218 TAATTAAAAAATGAA 232 || || |||||||||

286

Mouse 1516 TATTTCAAAAATGAA 1530

Human 185 AATGGAATAGAAA 197 ||||||||| ||| Mouse 1644 AATGGAATACAAA 1632

Human 1248 AATAAAATTA 1257 |||||||||| Mouse 1686 AATAAAATTA 1677

287

Appendix 5 Search result for Human GDAP1L1 and Brain Table A 5.1: Human GDAP1L1 and Brain Summary table of all the studies found in the search term GDAP1L1 and brain. The search term human GDAP1L1 and brain: in this search 59 profiles were found, out of which 15 matched our criteria. The table is divided into two parts A and B, depending upon the profiles found. Part A represents the neuronal mental disease studies. Part B, on the other hand, represents all the brain cancer studies. Five studies were categorized as being psychiatric or neurodegenerative disorders ( part A), while the remaining four studies focused on brain cancer ( part B).

Control Case

Geo Reporter Array Description N Ave Stdev Description N Ave Stdev T-Test number or Anova

GDS2190 GPL96, U133A Healthy control 31 161.5 31.8 Postmortem 30 164.8 38 0.7 219668 dorsolateral prefrontal cortex from bipolar disorder GDS2191 GPL96, U133A Healthy control 11 163.8 26.3 Postmortem 10 162 32.7 0.44 219668 dorsolateral prefrontal cortex from bipolar disorder

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GDS2941 GPL96, U133A Healthy control 8 7.3 0.17 postmortem brains of 8 7 0.4 0.14 219668 individuals with Down

syndrome GDS1917 GPL570, U133 Healthy control 14 100.8 30 crus I/VIIa area of the 14 97.4 24.7 0.74 219668 Plus 2.0 cerebellum from Array schizophrenia patients GDS810 GPL96, U133A Healthy control 9 135.9 63.2 Incipient Alzheimer 7 167.18 51.2 0.17 219668 disease(AD)

Moderate AD 9 133.6 69.4

sever AD 6 89.3 51.8 GDS1414 GPL96, U133A Glial cell line Hs683 3 39.6 21 Hs683 following 3 36.4 32.2 0.63 219668 untreated. exposure to 300 nM of candoxin (CDX) 12h

(CDX) 24h 3 66.8 54.1

(CDX) 48h 3 33.7 15.5 GDS2090 GPL96, U133A glioblastoma cells 3 17.3 12.1 glioblastoma cells 3 11.1 4.5 0.47 219668 epidermal growth factor treated with sphingosine 1- phosphate (S1P)

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GDS2833 GPL96, U133A SVGR2 glial cells non- 3 141 58.6 SVGR2 glial cells 3 264 149 0.25 219668 resistant SVG-A resistant SVG-A

GDS1779 GPL570, Genome Hela normoxia 3 54 41.9 Astrocyte normoxia 3 51.1 40.4 0.5 219668 U133 Plus 2.0 Hela hypxia 3 103.9 67.8 Astrocyte hypxia 3 71.9 22.5 Array GDS4476 HuGene- Vector control cells 12 parkin-deficient 9 0.08 GPL6244, 44.7 6.9 51.1 8.98 8062796 1_0-st U87MG glioma cells GDS3383 U133A Control 10 0.19 Coronic stress 11 5.87 0.3 0.59 GPL96, 5.8 219668 Peripheral blood CD14+ leukocytes

GDS2652 GPL570, HG- control 4 472.13 197.9 ARA and low DHA 4 342.7 52.25 0.348 219668 U133_Pl 4 us_2 ARA and high DHA 4 401.5 22.29

GDS4231 GPL570, HG- uninfected control 9 5x10-7 0.374 HIV-1HAND 14 -0.413 0.2 0.006 219668 U133_Pl UNTREATED us_2 HIV-1HAND 12 0.0514 0.511 antiretroviral therapy

290

GDS4475 GPL570, HG- Cyr61 3 5.73 0.129 Un induced 3 5.75 0.147 0.838 219668 U133_Pl us_2

GDS4358 HIV basal ganglia 6 7.519 0.188 GPL570, HG- control basal ganglia 6 7.61 0.31 5.3x10- 219668 U133_Pl 12 control frontal cortix 6 8.51 0.29 HIV frontal cortix 6 8.36 0.48 us_2

control white matter 6 5.81 0.22 HIV white matter 6 5.816 0.396

HIV+ HAD basal 6 7.404 0.122 ganglia

HIV+ HAD frontal 6 8.26 0.26 cortix

HIV+ HADwhite 6 6.48 1.45 matter

HIV+ HAD+HIVE 6 6.53 0.825 basal ganglia

291

HIV+ HAD+HIVE 6 7.638 0.69 frontal cortix

HIV+ HAD+HIVE 6 7.39 0.9 white matter

292

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